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An upper bound on the distinguishing index of graphs with minimum degree at least two | The distinguishing index of a simple graph $G$, denoted by $D'(G)$, is the
least number of labels in an edge labeling of $G$ not preserved by any
non-trivial automorphism. It was conjectured by Pilśniak (2015) that for any
2-connected graph $D'(G) \leq \lceil \sqrt{\Delta (G)}\rceil +1$. We prove a
more general result for the distinguishing index of graphs with minimum degree
at least two from which the conjecture follows. Also we present graphs $G$ for
which $D'(G)\leq \lceil \sqrt{\Delta }\rceil$.
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Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization | Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy'
image from a pathological one, could be helpful in tasks such as anomaly
detection, understanding changes induced by pathology and disease or even as
data augmentation. We treat this task as a factor decomposition problem: we aim
to separate what appears to be healthy and where disease is (as a map). The two
factors are then recombined (by a network) to reconstruct the input disease
image. We train our models in an adversarial way using either paired or
unpaired settings, where we pair disease images and maps (as segmentation
masks) when available. We quantitatively evaluate the quality of pseudo healthy
images. We show in a series of experiments, performed in ISLES and BraTS
datasets, that our method is better than conditional GAN and CycleGAN,
highlighting challenges in using adversarial methods in the image translation
task of pseudo healthy image generation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Closure operators, frames, and neatest representations | Given a poset $P$ and a standard closure operator $\Gamma:\wp(P)\to\wp(P)$ we
give a necessary and sufficient condition for the lattice of $\Gamma$-closed
sets of $\wp(P)$ to be a frame in terms of the recursive construction of the
$\Gamma$-closure of sets. We use this condition to show that given a set
$\mathcal{U}$ of distinguished joins from $P$, the lattice of
$\mathcal{U}$-ideals of $P$ fails to be a frame if and only if it fails to be
$\sigma$-distributive, with $\sigma$ depending on the cardinalities of sets in
$\mathcal{U}$. From this we deduce that if a poset has the property that
whenever $a\wedge(b\vee c)$ is defined for $a,b,c\in P$ it is necessarily equal
to $(a\wedge b)\vee (a\wedge c)$, then it has an $(\omega,3)$-representation.
This answers a question from the literature.
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The structure of rationally factorized Lax type flows and their analytical integrability | The work is devoted to constructing a wide class of differential-functional
dynamical systems, whose rich algebraic structure makes their integrability
analytically effective. In particular, there is analyzed in detail the operator
Lax type equations for factorized seed elements, there is proved an important
theorem about their operator factorization and the related analytical solution
scheme to the corresponding nonlinear differential-functional dynamical
systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Policy Representations in Multiagent Systems | Modeling agent behavior is central to understanding the emergence of complex
phenomena in multiagent systems. Prior work in agent modeling has largely been
task-specific and driven by hand-engineering domain-specific prior knowledge.
We propose a general learning framework for modeling agent behavior in any
multiagent system using only a handful of interaction data. Our framework casts
agent modeling as a representation learning problem. Consequently, we construct
a novel objective inspired by imitation learning and agent identification and
design an algorithm for unsupervised learning of representations of agent
policies. We demonstrate empirically the utility of the proposed framework in
(i) a challenging high-dimensional competitive environment for continuous
control and (ii) a cooperative environment for communication, on supervised
predictive tasks, unsupervised clustering, and policy optimization using deep
reinforcement learning.
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Jamming-Resistant Receivers for the Massive MIMO Uplink | We design a jamming-resistant receiver scheme to enhance the robustness of a
massive MIMO uplink system against jamming. We assume that a jammer attacks the
system both in the pilot and data transmission phases. The key feature of the
proposed scheme is that, in the pilot phase, we estimate not only the
legitimate channel, but also the jamming channel by exploiting a purposely
unused pilot sequence. The jamming channel estimate is used to constructed
linear receive filters that reject the impact of the jamming signal. The
performance of the proposed scheme is analytically evaluated using asymptotic
properties of massive MIMO. The optimal regularized zero-forcing receiver and
the optimal power allocation are also studied. Numerical results are provided
to verify our analysis and show that the proposed scheme greatly improves the
achievable rates, as compared to conventional receivers. Interestingly, the
proposed scheme works particularly well under strong jamming attacks, since the
improved estimate of the jamming channel outweighs the extra jamming power.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multiplex core-periphery organization of the human connectome | The behavior of many complex systems is determined by a core of densely
interconnected units. While many methods are available to identify the core of
a network when connections between nodes are all of the same type, a principled
approach to define the core when multiple types of connectivity are allowed is
still lacking. Here we introduce a general framework to define and extract the
core-periphery structure of multi-layer networks by explicitly taking into
account the connectivity of the nodes at each layer. We show how our method
works on synthetic networks with different size, density, and overlap between
the cores at the different layers. We then apply the method to multiplex brain
networks whose layers encode information both on the anatomical and the
functional connectivity among regions of the human cortex. Results confirm the
presence of the main known hubs, but also suggest the existence of novel brain
core regions that have been discarded by previous analysis which focused
exclusively on the structural layer. Our work is a step forward in the
identification of the core of the human connectome, and contributes to shed
light to a fundamental question in modern neuroscience.
| 1 | 0 | 0 | 0 | 1 | 0 |
Towards Learned Clauses Database Reduction Strategies Based on Dominance Relationship | Clause Learning is one of the most important components of a conflict driven
clause learning (CDCL) SAT solver that is effective on industrial instances.
Since the number of learned clauses is proved to be exponential in the worse
case, it is necessary to identify the most relevant clauses to maintain and
delete the irrelevant ones. As reported in the literature, several learned
clauses deletion strategies have been proposed. However the diversity in both
the number of clauses to be removed at each step of reduction and the results
obtained with each strategy creates confusion to determine which criterion is
better. Thus, the problem to select which learned clauses are to be removed
during the search step remains very challenging. In this paper, we propose a
novel approach to identify the most relevant learned clauses without favoring
or excluding any of the proposed measures, but by adopting the notion of
dominance relationship among those measures. Our approach bypasses the problem
of the diversity of results and reaches a compromise between the assessments of
these measures. Furthermore, the proposed approach also avoids another
non-trivial problem which is the amount of clauses to be deleted at each
reduction of the learned clause database.
| 1 | 0 | 0 | 0 | 0 | 0 |
Integrable structure of products of finite complex Ginibre random matrices | We consider the squared singular values of the product of $M$ standard
complex Gaussian matrices. Since the squared singular values form a
determinantal point process with a particular Meijer G-function kernel, the gap
probabilities are given by a Fredholm determinant based on this kernel. It was
shown by Strahov \cite{St14} that a hard edge scaling limit of the gap
probabilities is described by Hamiltonian differential equations which can be
formulated as an isomonodromic deformation system similar to the theory of the
Kyoto school. We generalize this result to the case of finite matrices by first
finding a representation of the finite kernel in integrable form. As a result
we obtain the Hamiltonian structure for a finite size matrices and formulate it
in terms of a $(M+1) \times (M+1)$ matrix Schlesinger system. The case $M=1$
reproduces the Tracy and Widom theory which results in the Painlevé V
equation for the $(0,s)$ gap probability. Some integrals of motion for $M = 2$
are identified, and a coupled system of differential equations in two unknowns
is presented which uniquely determines the corresponding $(0,s)$ gap
probability.
| 0 | 1 | 1 | 0 | 0 | 0 |
Global well-posedness of the 3D primitive equations with horizontal viscosity and vertical diffusivity | In this paper, we consider the 3D primitive equations of oceanic and
atmospheric dynamics with only horizontal eddy viscosities in the horizontal
momentum equations and only vertical diffusivity in the temperature equation.
Global well-posedness of strong solutions is established for any initial data
such that the initial horizontal velocity $v_0\in H^2(\Omega)$ and the initial
temperature $T_0\in H^1(\Omega)\cap L^\infty(\Omega)$ with $\nabla_HT_0\in
L^q(\Omega)$, for some $q\in(2,\infty)$. Moreover, the strong solutions enjoy
correspondingly more regularities if the initial temperature belongs to
$H^2(\Omega)$. The main difficulties are the absence of the vertical viscosity
and the lack of the horizontal diffusivity, which, interact with each other,
thus causing the "\,mismatching\," of regularities between the horizontal
momentum and temperature equations. To handle this "mismatching" of
regularities, we introduce several auxiliary functions, i.e., $\eta, \theta,
\varphi,$ and $\psi$ in the paper, which are the horizontal curls or some
appropriate combinations of the temperature with the horizontal divergences of
the horizontal velocity $v$ or its vertical derivative $\partial_zv$. To
overcome the difficulties caused by the absence of the horizontal diffusivity,
which leads to the requirement of some $L^1_t(W^{1,\infty}_\textbf{x})$-type a
priori estimates on $v$, we decompose the velocity into the
"temperature-independent" and temperature-dependent parts and deal with them in
different ways, by using the logarithmic Sobolev inequalities of the
Brézis-Gallouet-Wainger and Beale-Kato-Majda types, respectively.
Specifically, a logarithmic Sobolev inequality of the limiting type, introduced
in our previous work [12], is used, and a new logarithmic type Gronwall
inequality is exploited.
| 0 | 1 | 1 | 0 | 0 | 0 |
Superintegrable systems on 3-dimensional curved spaces: Eisenhart formalism and separability | The Eisenhart geometric formalism, which transforms an Euclidean natural
Hamiltonian $H=T+V$ into a geodesic Hamiltonian ${\cal T}$ with one additional
degree of freedom, is applied to the four families of quadratically
superintegrable systems with multiple separability in the Euclidean plane.
Firstly, the separability and superintegrability of such four geodesic
Hamiltonians ${\cal T}_r$ ($r=a,b,c,d$) in a three-dimensional curved space are
studied and then these four systems are modified with the addition of a
potential ${\cal U}_r$ leading to ${\cal H}_r={\cal T}_r +{\cal U}_r$.
Secondly, we study the superintegrability of the four Hamiltonians
$\widetilde{\cal H}_r= {\cal H}_r/ \mu_r$, where $\mu_r$ is a certain
position-dependent mass, that enjoys the same separability as the original
system ${\cal H}_r$. All the Hamiltonians here studied describe superintegrable
systems on non-Euclidean three-dimensional manifolds with a broken spherically
symmetry.
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An Incentive-Based Online Optimization Framework for Distribution Grids | This paper formulates a time-varying social-welfare maximization problem for
distribution grids with distributed energy resources (DERs) and develops online
distributed algorithms to identify (and track) its solutions. In the considered
setting, network operator and DER-owners pursue given operational and economic
objectives, while concurrently ensuring that voltages are within prescribed
limits. The proposed algorithm affords an online implementation to enable
tracking of the solutions in the presence of time-varying operational
conditions and changing optimization objectives. It involves a strategy where
the network operator collects voltage measurements throughout the feeder to
build incentive signals for the DER-owners in real time; DERs then adjust the
generated/consumed powers in order to avoid the violation of the voltage
constraints while maximizing given objectives. The stability of the proposed
schemes is analytically established and numerically corroborated.
| 1 | 0 | 1 | 0 | 0 | 0 |
Ricci solitons on Ricci pseudosymmetric $(LCS)_n$-manifolds | The object of the present paper is to study some types of Ricci
pseudosymmetric $(LCS)_n$-manifolds whose metric is Ricci soliton. We found the
conditions when Ricci soliton on concircular Ricci pseudosymmetric, projective
Ricci pseudosymmetric, $W_{3}$-Ricci pseudosymmetric, conharmonic Ricci
pseudosymmetric, conformal Ricci pseudosymmetric $(LCS)_n$-manifolds to be
shrinking, steady and expanding. We also construct an example of concircular
Ricci pseudosymmetric $(LCS)_3$-manifold whose metric is Ricci soliton.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optimal Gossip Algorithms for Exact and Approximate Quantile Computations | This paper gives drastically faster gossip algorithms to compute exact and
approximate quantiles.
Gossip algorithms, which allow each node to contact a uniformly random other
node in each round, have been intensely studied and been adopted in many
applications due to their fast convergence and their robustness to failures.
Kempe et al. [FOCS'03] gave gossip algorithms to compute important aggregate
statistics if every node is given a value. In particular, they gave a beautiful
$O(\log n + \log \frac{1}{\epsilon})$ round algorithm to $\epsilon$-approximate
the sum of all values and an $O(\log^2 n)$ round algorithm to compute the exact
$\phi$-quantile, i.e., the the $\lceil \phi n \rceil$ smallest value.
We give an quadratically faster and in fact optimal gossip algorithm for the
exact $\phi$-quantile problem which runs in $O(\log n)$ rounds. We furthermore
show that one can achieve an exponential speedup if one allows for an
$\epsilon$-approximation. We give an $O(\log \log n + \log \frac{1}{\epsilon})$
round gossip algorithm which computes a value of rank between $\phi n$ and
$(\phi+\epsilon)n$ at every node.% for any $0 \leq \phi \leq 1$ and $0 <
\epsilon < 1$. Our algorithms are extremely simple and very robust - they can
be operated with the same running times even if every transmission fails with
a, potentially different, constant probability. We also give a matching
$\Omega(\log \log n + \log \frac{1}{\epsilon})$ lower bound which shows that
our algorithm is optimal for all values of $\epsilon$.
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Influence of Heat Treatment on the Corrosion Behavior of Purified Magnesium and AZ31 Alloy | Magnesium and its alloys are ideal for biodegradable implants due to their
biocompatibility and their low-stress shielding. However, they can corrode too
rapidly in the biological environment. The objective of this research was to
develop heat treatments to slow the corrosion of high purified magnesium and
AZ31 alloy in simulated body fluid at 37°C. Heat treatments were performed
at different temperatures and times. Hydrogen evolution, weight loss, PDP, and
EIS methods were used to measure the corrosion rates. Results show that heat
treating can increase the corrosion resistance of HP-Mg by 2x and AZ31 by 10x.
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Towards a scientific blockchain framework for reproducible data analysis | Publishing reproducible analyses is a long-standing and widespread challenge
for the scientific community, funding bodies and publishers. Although a
definitive solution is still elusive, the problem is recognized to affect all
disciplines and lead to a critical system inefficiency. Here, we propose a
blockchain-based approach to enhance scientific reproducibility, with a focus
on life science studies and precision medicine. While the interest of encoding
permanently into an immutable ledger all the study key information-including
endpoints, data and metadata, protocols, analytical methods and all
findings-has been already highlighted, here we apply the blockchain approach to
solve the issue of rewarding time and expertise of scientists that commit to
verify reproducibility. Our mechanism builds a trustless ecosystem of
researchers, funding bodies and publishers cooperating to guarantee digital and
permanent access to information and reproducible results. As a natural
byproduct, a procedure to quantify scientists' and institutions' reputation for
ranking purposes is obtained.
| 1 | 0 | 0 | 0 | 0 | 0 |
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data | Google uses continuous streams of data from industry partners in order to
deliver accurate results to users. Unexpected drops in traffic can be an
indication of an underlying issue and may be an early warning that remedial
action may be necessary. Detecting such drops is non-trivial because streams
are variable and noisy, with roughly regular spikes (in many different shapes)
in traffic data. We investigated the question of whether or not we can predict
anomalies in these data streams. Our goal is to utilize Machine Learning and
statistical approaches to classify anomalous drops in periodic, but noisy,
traffic patterns. Since we do not have a large body of labeled examples to
directly apply supervised learning for anomaly classification, we approached
the problem in two parts. First we used TensorFlow to train our various models
including DNNs, RNNs, and LSTMs to perform regression and predict the expected
value in the time series. Secondly we created anomaly detection rules that
compared the actual values to predicted values. Since the problem requires
finding sustained anomalies, rather than just short delays or momentary
inactivity in the data, our two detection methods focused on continuous
sections of activity rather than just single points. We tried multiple
combinations of our models and rules and found that using the intersection of
our two anomaly detection methods proved to be an effective method of detecting
anomalies on almost all of our models. In the process we also found that not
all data fell within our experimental assumptions, as one data stream had no
periodicity, and therefore no time based model could predict it.
| 1 | 0 | 0 | 1 | 0 | 0 |
A parallel approach to bi-objective integer programming | To obtain a better understanding of the trade-offs between various
objectives, Bi-Objective Integer Programming (BOIP) algorithms calculate the
set of all non-dominated vectors and present these as the solution to a BOIP
problem. Historically, these algorithms have been compared in terms of the
number of single-objective IPs solved and total CPU time taken to produce the
solution to a problem. This is equitable, as researchers can often have access
to widely differing amounts of computing power. However, the real world has
recently seen a large uptake of multi-core processors in computers, laptops,
tablets and even mobile phones. With this in mind, we look at how to best
utilise parallel processing to improve the elapsed time of optimisation
algorithms. We present two methods of parallelising the recursive algorithm
presented by Ozlen, Burton and MacRae. Both new methods utilise two threads and
improve running times. One of the new methods, the Meeting algorithm, halves
running time to achieve near-perfect parallelisation. The results are compared
with the efficiency of parallelisation within the commercial IP solver IBM ILOG
CPLEX, and the new methods are both shown to perform better.
| 1 | 0 | 1 | 0 | 0 | 0 |
The adaptive zero-error capacity for a class of channels with noisy feedback | The adaptive zero-error capacity of discrete memoryless channels (DMC) with
noiseless feedback has been shown to be positive whenever there exists at least
one channel output "disprover", i.e. a channel output that cannot be reached
from at least one of the inputs. Furthermore, whenever there exists a
disprover, the adaptive zero-error capacity attains the Shannon (small-error)
capacity. Here, we study the zero-error capacity of a DMC when the channel
feedback is noisy rather than perfect. We show that the adaptive zero-error
capacity with noisy feedback is lower bounded by the forward channel's
zero-undetected error capacity, and show that under certain conditions this is
tight.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comparison of Self-Aware and Organic Computing Systems | With increasing complexity and heterogeneity of computing devices, it has
become crucial for system to be autonomous, adaptive to dynamic environment,
robust, flexible, and having so called self-*properties. These autonomous
systems are called organic computing(OC) systems. OC system was proposed as a
solution to tackle complex systems. Design time decisions have been shifted to
run time in highly complex and interconnected systems as it is very hard to
consider all scenarios and their appropriate actions in advance. Consequently,
Self-awareness becomes crucial for these adaptive autonomous systems. To cope
with evolving environment and changing user needs, system need to have
knowledge about itself and its surroundings. Literature review shows that for
autonomous and intelligent systems, researchers are concerned about knowledge
acquisition, representation and learning which is necessary for a system to
adapt. This paper is written to compare self-awareness and organic computing by
discussing their definitions, properties and architecture.
| 1 | 0 | 0 | 0 | 0 | 0 |
First Order Methods beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems | We focus on nonconvex and nonsmooth minimization problems with a composite
objective, where the differentiable part of the objective is freed from the
usual and restrictive global Lipschitz gradient continuity assumption. This
longstanding smoothness restriction is pervasive in first order methods (FOM),
and was recently circumvent for convex composite optimization by Bauschke,
Bolte and Teboulle, through a simple and elegant framework which captures, all
at once, the geometry of the function and of the feasible set. Building on this
work, we tackle genuine nonconvex problems. We first complement and extend
their approach to derive a full extended descent lemma by introducing the
notion of smooth adaptable functions. We then consider a Bregman-based proximal
gradient methods for the nonconvex composite model with smooth adaptable
functions, which is proven to globally converge to a critical point under
natural assumptions on the problem's data. To illustrate the power and
potential of our general framework and results, we consider a broad class of
quadratic inverse problems with sparsity constraints which arises in many
fundamental applications, and we apply our approach to derive new globally
convergent schemes for this class.
| 1 | 0 | 1 | 0 | 0 | 0 |
An Army of Me: Sockpuppets in Online Discussion Communities | In online discussion communities, users can interact and share information
and opinions on a wide variety of topics. However, some users may create
multiple identities, or sockpuppets, and engage in undesired behavior by
deceiving others or manipulating discussions. In this work, we study
sockpuppetry across nine discussion communities, and show that sockpuppets
differ from ordinary users in terms of their posting behavior, linguistic
traits, as well as social network structure. Sockpuppets tend to start fewer
discussions, write shorter posts, use more personal pronouns such as "I", and
have more clustered ego-networks. Further, pairs of sockpuppets controlled by
the same individual are more likely to interact on the same discussion at the
same time than pairs of ordinary users. Our analysis suggests a taxonomy of
deceptive behavior in discussion communities. Pairs of sockpuppets can vary in
their deceptiveness, i.e., whether they pretend to be different users, or their
supportiveness, i.e., if they support arguments of other sockpuppets controlled
by the same user. We apply these findings to a series of prediction tasks,
notably, to identify whether a pair of accounts belongs to the same underlying
user or not. Altogether, this work presents a data-driven view of deception in
online discussion communities and paves the way towards the automatic detection
of sockpuppets.
| 1 | 1 | 0 | 1 | 0 | 0 |
Robust Bayesian Optimization with Student-t Likelihood | Bayesian optimization has recently attracted the attention of the automatic
machine learning community for its excellent results in hyperparameter tuning.
BO is characterized by the sample efficiency with which it can optimize
expensive black-box functions. The efficiency is achieved in a similar fashion
to the learning to learn methods: surrogate models (typically in the form of
Gaussian processes) learn the target function and perform intelligent sampling.
This surrogate model can be applied even in the presence of noise; however, as
with most regression methods, it is very sensitive to outlier data. This can
result in erroneous predictions and, in the case of BO, biased and inefficient
exploration. In this work, we present a GP model that is robust to outliers
which uses a Student-t likelihood to segregate outliers and robustly conduct
Bayesian optimization. We present numerical results evaluating the proposed
method in both artificial functions and real problems.
| 1 | 0 | 0 | 1 | 0 | 0 |
Vehicle Localization and Control on Roads with Prior Grade Map | We propose a map-aided vehicle localization method for GPS-denied
environments. This approach exploits prior knowledge of the road grade map and
vehicle on-board sensor measurements to accurately estimate the longitudinal
position of the vehicle. Real-time localization is crucial to systems that
utilize position-dependent information for planning and control. We validate
the effectiveness of the localization method on a hierarchical control system.
The higher level planner optimizes the vehicle velocity to minimize the energy
consumption for a given route by employing traffic condition and road grade
data. The lower level is a cruise control system that tracks the
position-dependent optimal reference velocity. Performance of the proposed
localization algorithm is evaluated using both simulations and experiments.
| 1 | 0 | 0 | 0 | 0 | 0 |
Estimating Tactile Data for Adaptive Grasping of Novel Objects | We present an adaptive grasping method that finds stable grasps on novel
objects. The main contributions of this paper is in the computation of the
probability of success of grasps in the vicinity of an already applied grasp.
Our method performs grasp adaptions by simulating tactile data for grasps in
the vicinity of the current grasp. The simulated data is used to evaluate
hypothetical grasps and thereby guide us toward better grasps. We demonstrate
the applicability of our method by constructing a system that can plan, apply
and adapt grasps on novel objects. Experiments are conducted on objects from
the YCB object set and the success rate of our method is 88%. Our experiments
show that the application of our grasp adaption method improves grasp stability
significantly.
| 1 | 0 | 0 | 0 | 0 | 0 |
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints | Algorithm-dependent generalization error bounds are central to statistical
learning theory. A learning algorithm may use a large hypothesis space, but the
limited number of iterations controls its model capacity and generalization
error. The impacts of stochastic gradient methods on generalization error for
non-convex learning problems not only have important theoretical consequences,
but are also critical to generalization errors of deep learning.
In this paper, we study the generalization errors of Stochastic Gradient
Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed
with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian
results respectively. The stability-based theory obtains a bound of
$O\left(\frac{1}{n}L\sqrt{\beta T_k}\right)$, where $L$ is uniform Lipschitz
parameter, $\beta$ is inverse temperature, and $T_k$ is aggregated step sizes.
For PAC-Bayesian theory, though the bound has a slower $O(1/\sqrt{n})$ rate,
the contribution of each step is shown with an exponentially decaying factor by
imposing $\ell^2$ regularization, and the uniform Lipschitz constant is also
replaced by actual norms of gradients along trajectory. Our bounds have no
implicit dependence on dimensions, norms or other capacity measures of
parameter, which elegantly characterizes the phenomenon of "Fast Training
Guarantees Generalization" in non-convex settings. This is the first
algorithm-dependent result with reasonable dependence on aggregated step sizes
for non-convex learning, and has important implications to statistical learning
aspects of stochastic gradient methods in complicated models such as deep
learning.
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Near-UV OH Prompt Emission in the Innermost Coma of 103P/Hartley 2 | The Deep Impact spacecraft fly-by of comet 103P/Hartley 2 occurred on 2010
November 4, one week after perihelion with a closest approach (CA) distance of
about 700 km. We used narrowband images obtained by the Medium Resolution
Imager (MRI) onboard the spacecraft to study the gas and dust in the innermost
coma. We derived an overall dust reddening of 15\%/100 nm between 345 and 749
nm and identified a blue enhancement in the dust coma in the sunward direction
within 5 km from the nucleus, which we interpret as a localized enrichment in
water ice. OH column density maps show an anti-sunward enhancement throughout
the encounter except for the highest resolution images, acquired at CA, where a
radial jet becomes visible in the innermost coma, extending up to 12 km from
the nucleus. The OH distribution in the inner coma is very different from that
expected for a fragment species. Instead, it correlates well with the water
vapor map derived by the HRI-IR instrument onboard Deep Impact
\citep{AHearn2011}. Radial profiles of the OH column density and derived water
production rates show an excess of OH emission during CA that cannot be
explained with pure fluorescence. We attribute this excess to a prompt emission
process where photodissociation of H$_2$O directly produces excited
OH*($A^2\it{\Sigma}^+$) radicals. Our observations provide the first direct
imaging of Near-UV prompt emission of OH. We therefore suggest the use of a
dedicated filter centered at 318.8 nm to directly trace the water in the coma
of comets.
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Effective gravity and effective quantum equations in a system inspired by walking droplets experiments | In this paper we suggest a macroscopic toy system in which a potential-like
energy is generated by a non-uniform pulsation of the medium (i.e. pulsation of
transverse standing oscillations that the elastic medium of the system tends to
support at each point). This system is inspired by walking droplets experiments
with submerged barriers. We first show that a Poincaré-Lorentz covariant
formalization of the system causes inconsistency and contradiction. The
contradiction is solved by using a general covariant formulation and by
assuming a relation between the metric associated with the elastic medium and
the pulsation of the medium. (Calculations are performed in a Newtonian-like
metric, constant in time). We find ($i$) an effective Schrödinger equation
with external potential, ($ii$) an effective de Broglie-Bohm guidance formula
and ($iii$) an energy of the `particle' which has a direct counterpart in
general relativity as well as in quantum mechanics. We analyze the wave and the
`particle' in an effective free fall and with a harmonic potential. This
potential-like energy is an effective gravitational potential, rooted in the
pulsation of the medium at each point. The latter, also conceivable as a
natural clock, makes easy to understand why proper time varies from place to
place.
| 0 | 1 | 0 | 0 | 0 | 0 |
A gradient estimate for nonlocal minimal graphs | We consider the class of measurable functions defined in all of
$\mathbb{R}^n$ that give rise to a nonlocal minimal graph over a ball of
$\mathbb{R}^n$. We establish that the gradient of any such function is bounded
in the interior of the ball by a power of its oscillation. This estimate,
together with previously known results, leads to the $C^\infty$ regularity of
the function in the ball. While the smoothness of nonlocal minimal graphs was
known for $n = 1, 2$ (but without a quantitative bound), in higher dimensions
only their continuity had been established.
To prove the gradient bound, we show that the normal to a nonlocal minimal
graph is a supersolution of a truncated fractional Jacobi operator, for which
we prove a weak Harnack inequality. To this end, we establish a new universal
fractional Sobolev inequality on nonlocal minimal surfaces.
Our estimate provides an extension to the fractional setting of the
celebrated gradient bounds of Finn and of Bombieri, De Giorgi & Miranda for
solutions of the classical mean curvature equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
The GAPS Programme with HARPS-N@TNG XIV. Investigating giant planet migration history via improved eccentricity and mass determination for 231 transiting planets | We carried out a Bayesian homogeneous determination of the orbital parameters
of 231 transiting giant planets (TGPs) that are alone or have distant
companions; we employed DE-MCMC methods to analyse radial-velocity (RV) data
from the literature and 782 new high-accuracy RVs obtained with the HARPS-N
spectrograph for 45 systems over 3 years. Our work yields the largest sample of
systems with a transiting giant exoplanet and coherently determined orbital,
planetary, and stellar parameters. We found that the orbital parameters of TGPs
in non-compact planetary systems are clearly shaped by tides raised by their
host stars. Indeed, the most eccentric planets have relatively large orbital
separations and/or high mass ratios, as expected from the equilibrium tide
theory. This feature would be the outcome of high-eccentricity migration (HEM).
The distribution of $\alpha=a/a_R$, where $a$ and $a_R$ are the semi-major axis
and the Roche limit, for well-determined circular orbits peaks at 2.5; this
also agrees with expectations from the HEM. The few planets of our sample with
circular orbits and $\alpha >5$ values may have migrated through disc-planet
interactions instead of HEM. By comparing circularisation times with stellar
ages, we found that hot Jupiters with $a < 0.05$ au have modified tidal quality
factors $10^{5} < Q'_p < 10^{9}$, and that stellar $Q'_s > 10^{6}-10^{7}$ are
required to explain the presence of eccentric planets at the same orbital
distance. As a by-product of our analysis, we detected a non-zero eccentricity
for HAT-P-29; we determined that five planets that were previously regarded to
have hints of non-zero eccentricity have circular orbits or undetermined
eccentricities; we unveiled curvatures caused by distant companions in the RV
time series of HAT-P-2, HAT-P-22, and HAT-P-29; and we revised the planetary
parameters of CoRoT-1b.
| 0 | 1 | 0 | 0 | 0 | 0 |
Injective stabilization of additive functors. I. Preliminaries | This paper is the first one in a series of three dealing with the concept of
injective stabilization of the tensor product and its applications. Its primary
goal is to collect known facts and establish a basic operational calculus that
will be used in the subsequent parts. This is done in greater generality than
is necessary for the stated goal. Several results of independent interest are
also established. They include, among other things, connections with
satellites, an explicit construction of the stabilization of a finitely
presented functor, various exactness properties of the injectively stable
functors, a construction, from a functor and a short exact sequence, of a
doubly-infinite exact sequence by splicing the injective stabilization of the
functor and its derived functors. When specialized to the tensor product with a
finitely presented module, the injective stabilization with coefficients in the
ring is isomorphic to the 1-torsion functor. The Auslander-Reiten formula is
extended to a more general formula, which holds for arbitrary (i.e., not
necessarily finite) modules over arbitrary associative rings with identity.
Weakening of the assumptions in the theorems of Eilenberg and Watts leads to
characterizations of the requisite zeroth derived functors.
The subsequent papers, provide applications of the developed techniques.
Part~II deals with new notions of torsion module and cotorsion module of a
module. This is done for arbitrary modules over arbitrary rings. Part~III
introduces a new concept, called the asymptotic stabilization of the tensor
product. The result is closely related to different variants of stable homology
(these are generalizations of Tate homology to arbitrary rings). A comparison
transformation from Vogel homology to the asymptotic stabilization of the
tensor product is constructed and shown to be epic.
| 0 | 0 | 1 | 0 | 0 | 0 |
Three hypergraph eigenvector centralities | Eigenvector centrality is a standard network analysis tool for determining
the importance of (or ranking of) entities in a connected system that is
represented by a graph. However, many complex systems and datasets have natural
multi-way interactions that are more faithfully modeled by a hypergraph. Here
we extend the notion of graph eigenvector centrality to uniform hypergraphs.
Traditional graph eigenvector centralities are given by a positive eigenvector
of the adjacency matrix, which is guaranteed to exist by the Perron-Frobenius
theorem under some mild conditions. The natural representation of a hypergraph
is a hypermatrix (colloquially, a tensor). Using recently established
Perron-Frobenius theory for tensors, we develop three tensor eigenvectors
centralities for hypergraphs, each with different interpretations. We show that
these centralities can reveal different information on real-world data by
analyzing hypergraphs constructed from n-gram frequencies, co-tagging on stack
exchange, and drug combinations observed in patient emergency room visits.
| 1 | 0 | 0 | 0 | 0 | 0 |
Reinforcement Learning using Augmented Neural Networks | Neural networks allow Q-learning reinforcement learning agents such as deep
Q-networks (DQN) to approximate complex mappings from state spaces to value
functions. However, this also brings drawbacks when compared to other function
approximators such as tile coding or their generalisations, radial basis
functions (RBF) because they introduce instability due to the side effect of
globalised updates present in neural networks. This instability does not even
vanish in neural networks that do not have any hidden layers. In this paper, we
show that simple modifications to the structure of the neural network can
improve stability of DQN learning when a multi-layer perceptron is used for
function approximation.
| 0 | 0 | 0 | 1 | 0 | 0 |
Instantons and Fluctuations in a Lagrangian Model of Turbulence | We perform a detailed analytical study of the Recent Fluid Deformation (RFD)
model for the onset of Lagrangian intermittency, within the context of the
Martin-Siggia-Rose-Janssen-de Dominicis (MSRJD) path integral formalism. The
model is based, as a key point, upon local closures for the pressure Hessian
and the viscous dissipation terms in the stochastic dynamical equations for the
velocity gradient tensor. We carry out a power counting hierarchical
classification of the several perturbative contributions associated to
fluctuations around the instanton-evaluated MSRJD action, along the lines of
the cumulant expansion. The most relevant Feynman diagrams are then integrated
out into the renormalized effective action, for the computation of velocity
gradient probability distribution functions (vgPDFs). While the subleading
perturbative corrections do not affect the global shape of the vgPDFs in an
appreciable qualitative way, it turns out that they have a significant role in
the accurate description of their non-Gaussian cores.
| 0 | 1 | 0 | 0 | 0 | 0 |
The heavy path approach to Galton-Watson trees with an application to Apollonian networks | We study the heavy path decomposition of conditional Galton-Watson trees. In
a standard Galton-Watson tree conditional on its size $n$, we order all
children by their subtree sizes, from large (heavy) to small. A node is marked
if it is among the $k$ heaviest nodes among its siblings. Unmarked nodes and
their subtrees are removed, leaving only a tree of marked nodes, which we call
the $k$-heavy tree. We study various properties of these trees, including their
size and the maximal distance from any original node to the $k$-heavy tree. In
particular, under some moment condition, the $2$-heavy tree is with high
probability larger than $cn$ for some constant $c > 0$, and the maximal
distance from the $k$-heavy tree is $O(n^{1/(k+1)})$ in probability. As a
consequence, for uniformly random Apollonian networks of size $n$, the expected
size of the longest simple path is $\Omega(n)$.
| 1 | 0 | 1 | 0 | 0 | 0 |
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models | We consider a firm that sells a large number of products to its customers in
an online fashion. Each product is described by a high dimensional feature
vector, and the market value of a product is assumed to be linear in the values
of its features. Parameters of the valuation model are unknown and can change
over time. The firm sequentially observes a product's features and can use the
historical sales data (binary sale/no sale feedbacks) to set the price of
current product, with the objective of maximizing the collected revenue. We
measure the performance of a dynamic pricing policy via regret, which is the
expected revenue loss compared to a clairvoyant that knows the sequence of
model parameters in advance.
We propose a pricing policy based on projected stochastic gradient descent
(PSGD) and characterize its regret in terms of time $T$, features dimension
$d$, and the temporal variability in the model parameters, $\delta_t$. We
consider two settings. In the first one, feature vectors are chosen
antagonistically by nature and we prove that the regret of PSGD pricing policy
is of order $O(\sqrt{T} + \sum_{t=1}^T \sqrt{t}\delta_t)$. In the second
setting (referred to as stochastic features model), the feature vectors are
drawn independently from an unknown distribution. We show that in this case,
the regret of PSGD pricing policy is of order $O(d^2 \log T + \sum_{t=1}^T
t\delta_t/d)$.
| 1 | 0 | 0 | 1 | 0 | 0 |
A fast algorithm for maximal propensity score matching | We present a new algorithm which detects the maximal possible number of
matched disjoint pairs satisfying a given caliper when a bipartite matching is
done with respect to a scalar index (e.g., propensity score), and constructs a
corresponding matching. Variable width calipers are compatible with the
technique, provided that the width of the caliper is a Lipschitz function of
the index. If the observations are ordered with respect to the index then the
matching needs $O(N)$ operations, where $N$ is the total number of subjects to
be matched. The case of 1-to-$n$ matching is also considered.
We offer also a new fast algorithm for optimal complete one-to-one matching
on a scalar index when the treatment and control groups are of the same size.
This allows us to improve greedy nearest neighbor matching on a scalar index.
Keywords: propensity score matching, nearest neighbor matching, matching with
caliper, variable width caliper.
| 1 | 0 | 0 | 1 | 0 | 0 |
Fractional quantum Hall systems near nematicity: bimetric theory, composite fermions, and Dirac brackets | We perform a detailed comparison of the Dirac composite fermion and the
recently proposed bimetric theory for a quantum Hall Jain states near half
filling. By tuning the composite Fermi liquid to the vicinity of a nematic
phase transition, we find that the two theories are equivalent to each other.
We verify that the single mode approximation for the response functions and the
static structure factor becomes reliable near the phase transition. We show
that the dispersion relation of the nematic mode near the phase transition can
be obtained from the Dirac brackets between the components of the nematic order
parameter. The dispersion is quadratic at low momenta and has a magnetoroton
minimum at a finite momentum, which is not related to any nearby inhomogeneous
phase.
| 0 | 1 | 0 | 0 | 0 | 0 |
Recognizing Objects In-the-wild: Where Do We Stand? | The ability to recognize objects is an essential skill for a robotic system
acting in human-populated environments. Despite decades of effort from the
robotic and vision research communities, robots are still missing good visual
perceptual systems, preventing the use of autonomous agents for real-world
applications. The progress is slowed down by the lack of a testbed able to
accurately represent the world perceived by the robot in-the-wild. In order to
fill this gap, we introduce a large-scale, multi-view object dataset collected
with an RGB-D camera mounted on a mobile robot. The dataset embeds the
challenges faced by a robot in a real-life application and provides a useful
tool for validating object recognition algorithms. Besides describing the
characteristics of the dataset, the paper evaluates the performance of a
collection of well-established deep convolutional networks on the new dataset
and analyzes the transferability of deep representations from Web images to
robotic data. Despite the promising results obtained with such representations,
the experiments demonstrate that object classification with real-life robotic
data is far from being solved. Finally, we provide a comparative study to
analyze and highlight the open challenges in robot vision, explaining the
discrepancies in the performance.
| 1 | 0 | 0 | 0 | 0 | 0 |
Generalizing Geometric Brownian Motion | To convert standard Brownian motion $Z$ into a positive process, Geometric
Brownian motion (GBM) $e^{\beta Z_t}, \beta >0$ is widely used. We generalize
this positive process by introducing an asymmetry parameter $ \alpha \geq 0$
which describes the instantaneous volatility whenever the process reaches a new
low. For our new process, $\beta$ is the instantaneous volatility as prices
become arbitrarily high. Our generalization preserves the positivity, constant
proportional drift, and tractability of GBM, while expressing the instantaneous
volatility as a randomly weighted $L^2$ mean of $\alpha$ and $\beta$. The
running minimum and relative drawup of this process are also analytically
tractable. Letting $\alpha = \beta$, our positive process reduces to Geometric
Brownian motion. By adding a jump to default to the new process, we introduce a
non-negative martingale with the same tractabilities. Assuming a security's
dynamics are driven by these processes in risk neutral measure, we price
several derivatives including vanilla, barrier and lookback options.
| 0 | 0 | 0 | 0 | 0 | 1 |
Challenges testing the no-hair theorem with gravitational waves | General relativity's no-hair theorem states that isolated astrophysical black
holes are described by only two numbers: mass and spin. As a consequence, there
are strict relationships between the frequency and damping time of the
different modes of a perturbed Kerr black hole. Testing the no-hair theorem has
been a longstanding goal of gravitational-wave astronomy. The recent detection
of gravitational waves from black hole mergers would seem to make such tests
imminent. We investigate how constraints on black hole ringdown parameters
scale with the loudness of the ringdown signal---subject to the constraint that
the post-merger remnant must be allowed to settle into a perturbative,
Kerr-like state. In particular, we require that---for a given detector---the
gravitational waveform predicted by numerical relativity is indistinguishable
from an exponentially damped sine after time $t^\text{cut}$. By requiring the
post-merger remnant to settle into such a perturbative state, we find that
confidence intervals for ringdown parameters do not necessarily shrink with
louder signals. In at least some cases, more sensitive measurements probe later
times without necessarily providing tighter constraints on ringdown frequencies
and damping times. Preliminary investigations are unable to explain this result
in terms of a numerical relativity artifact.
| 0 | 1 | 0 | 0 | 0 | 0 |
Speculation On a Source of Dark Matter | By drawing an analogy with superfluid 4He vortices we suggest that dark
matter may consist of irreducibly small remnants of cosmic strings.
| 0 | 1 | 0 | 0 | 0 | 0 |
Analyzing Cloud Optical Properties Using Sky Cameras | Clouds play a significant role in the fluctuation of solar radiation received
by the earth's surface. It is important to study the various cloud properties,
as it impacts the total solar irradiance falling on the earth's surface. One of
such important optical properties of the cloud is the Cloud Optical Thickness
(COT). It is defined with the amount of light that can pass through the clouds.
The COT values are generally obtained from satellite images. However, satellite
images have a low temporal- and spatial- resolutions; and are not suitable for
study in applications as solar energy generation and forecasting. Therefore,
ground-based sky cameras are now getting popular in such fields. In this paper,
we analyze the cloud optical thickness value, from the ground-based sky
cameras, and provide future research directions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Response Formulae for $n$-point Correlations in Statistical Mechanical Systems and Application to a Problem of Coarse Graining | Predicting the response of a system to perturbations is a key challenge in
mathematical and natural sciences. Under suitable conditions on the nature of
the system, of the perturbation, and of the observables of interest, response
theories allow to construct operators describing the smooth change of the
invariant measure of the system of interest as a function of the small
parameter controlling the intensity of the perturbation. In particular,
response theories can be developed both for stochastic and chaotic
deterministic dynamical systems, where in the latter case stricter conditions
imposing some degree of structural stability are required. In this paper we
extend previous findings and derive general response formulae describing how
n-point correlations are affected by perturbations to the vector flow. We also
show how to compute the response of the spectral properties of the system to
perturbations. We then apply our results to the seemingly unrelated problem of
coarse graining in multiscale systems: we find explicit formulae describing the
change in the terms describing parameterisation of the neglected degrees of
freedom resulting from applying perturbations to the full system. All the terms
envisioned by the Mori-Zwanzig theory - the deterministic, stochastic, and
non-Markovian terms - are affected at 1st order in the perturbation. The
obtained results provide a more comprehesive understanding of the response of
statistical mechanical systems to perturbations and contribute to the goal of
constructing accurate and robust parameterisations and are of potential
relevance for fields like molecular dynamics, condensed matter, and geophysical
fluid dynamics. We envision possible applications of our general results to the
study of the response of climate variability to anthropogenic and natural
forcing and to the study of the equivalence of thermostatted statistical
mechanical systems.
| 0 | 1 | 1 | 0 | 0 | 0 |
The Australian PCEHR system: Ensuring Privacy and Security through an Improved Access Control Mechanism | An Electronic Health Record (EHR) is designed to store diverse data
accurately from a range of health care providers and to capture the status of a
patient by a range of health care providers across time. Realising the numerous
benefits of the system, EHR adoption is growing globally and many countries
invest heavily in electronic health systems. In Australia, the Government
invested $467 million to build key components of the Personally Controlled
Electronic Health Record (PCEHR) system in July 2012. However, in the last
three years, the uptake from individuals and health care providers has not been
satisfactory. Unauthorised access of the PCEHR was one of the major barriers.
We propose an improved access control model for the PCEHR system to resolve the
unauthorised access issue. We discuss the unauthorised access issue with real
examples and present a potential solution to overcome the issue to make the
PCEHR system a success in Australia.
| 1 | 0 | 0 | 0 | 0 | 0 |
Randomized Kernel Methods for Least-Squares Support Vector Machines | The least-squares support vector machine is a frequently used kernel method
for non-linear regression and classification tasks. Here we discuss several
approximation algorithms for the least-squares support vector machine
classifier. The proposed methods are based on randomized block kernel matrices,
and we show that they provide good accuracy and reliable scaling for
multi-class classification problems with relatively large data sets. Also, we
present several numerical experiments that illustrate the practical
applicability of the proposed methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Optimal Transport: Fast Probabilistic Approximation with Exact Solvers | We propose a simple subsampling scheme for fast randomized approximate
computation of optimal transport distances. This scheme operates on a random
subset of the full data and can use any exact algorithm as a black-box
back-end, including state-of-the-art solvers and entropically penalized
versions. It is based on averaging the exact distances between empirical
measures generated from independent samples from the original measures and can
easily be tuned towards higher accuracy or shorter computation times. To this
end, we give non-asymptotic deviation bounds for its accuracy in the case of
discrete optimal transport problems. In particular, we show that in many
important instances, including images (2D-histograms), the approximation error
is independent of the size of the full problem. We present numerical
experiments that demonstrate that a very good approximation in typical
applications can be obtained in a computation time that is several orders of
magnitude smaller than what is required for exact computation of the full
problem.
| 0 | 0 | 0 | 1 | 0 | 0 |
Reliable Clustering of Bernoulli Mixture Models | A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors
with independent Bernoulli dimensions. The problem of clustering BMM data
arises in a variety of real-world applications, ranging from population
genetics to activity analysis in social networks. In this paper, we have
analyzed the information-theoretic PAC-learnability of BMMs, when the number of
clusters is unknown. In particular, we stipulate certain conditions on both
sample complexity and the dimension of the model in order to guarantee the
Probably Approximately Correct (PAC)-clusterability of a given dataset. To the
best of our knowledge, these findings are the first non-asymptotic (PAC) bounds
on the sample complexity of learning BMMs.
| 1 | 0 | 0 | 1 | 0 | 0 |
Short-time behavior of the heat kernel and Weyl's law on $RCD^*(K, N)$-spaces | In this paper, we prove pointwise convergence of heat kernels for
mGH-convergent sequences of $RCD^*(K,N)$-spaces. We obtain as a corollary
results on the short-time behavior of the heat kernel in $RCD^*(K,N)$-spaces.
We use then these results to initiate the study of Weyl's law in the $RCD$
setting
| 0 | 0 | 1 | 0 | 0 | 0 |
Football and Beer - a Social Media Analysis on Twitter in Context of the FIFA Football World Cup 2018 | In many societies alcohol is a legal and common recreational substance and
socially accepted. Alcohol consumption often comes along with social events as
it helps people to increase their sociability and to overcome their
inhibitions. On the other hand we know that increased alcohol consumption can
lead to serious health issues, such as cancer, cardiovascular diseases and
diseases of the digestive system, to mention a few. This work examines alcohol
consumption during the FIFA Football World Cup 2018, particularly the usage of
alcohol related information on Twitter. For this we analyse the tweeting
behaviour and show that the tournament strongly increases the interest in beer.
Furthermore we show that countries who had to leave the tournament at early
stage might have done something good to their fans as the interest in beer
decreased again.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cross-stream migration of a surfactant-laden deformable droplet in a Poiseuille flow | The motion of a viscous deformable droplet suspended in an unbounded
Poiseuille flow in the presence of bulk-insoluble surfactants is studied
analytically. Assuming the convective transport of fluid and heat to be
negligible, we perform a small-deformation perturbation analysis to obtain the
droplet migration velocity. The droplet dynamics strongly depends on the
distribution of surfactants along the droplet interface, which is governed by
the relative strength of convective transport of surfactants as compared with
the diffusive transport of surfactants. The present study is focused on the
following two limits: (i) when the surfactant transport is dominated by surface
diffusion, and (ii) when the surfactant transport is dominated by surface
convection. In the first limiting case, it is seen that the axial velocity of
the droplet decreases with increase in the advection of the surfactants along
the surface. The variation of cross-stream migration velocity, on the other
hand, is analyzed over three different regimes based on the ratio of the
viscosity of the droplet phase to that of the carrier phase. In the first
regime the migration velocity decreases with increase in surface advection of
the surfactants although there is no change in direction of droplet migration.
For the second regime, the direction of the cross-stream migration of the
droplet changes depending on different parameters. In the third regime, the
migration velocity is merely affected by any change in the surfactant
distribution. For the other limit of higher surface advection in comparison to
surface diffusion of the surfactants, the axial velocity of the droplet is
found to be independent of the surfactant distribution. However, the
cross-stream velocity is found to decrease with increase in non-uniformity in
surfactant distribution.
| 0 | 1 | 0 | 0 | 0 | 0 |
PCA in Data-Dependent Noise (Correlated-PCA): Nearly Optimal Finite Sample Guarantees | We study Principal Component Analysis (PCA) in a setting where a part of the
corrupting noise is data-dependent and, as a result, the noise and the true
data are correlated. Under a bounded-ness assumption on the true data and the
noise, and a simple assumption on data-noise correlation, we obtain a nearly
optimal sample complexity bound for the most commonly used PCA solution,
singular value decomposition (SVD). This bound is a significant improvement
over the bound obtained by Vaswani and Guo in recent work (NIPS 2016) where
this "correlated-PCA" problem was first studied; and it holds under a
significantly weaker data-noise correlation assumption than the one used for
this earlier result.
| 1 | 0 | 0 | 1 | 0 | 0 |
Using a Predator-Prey Model to Explain Variations of Cloud Spot Price | The spot pricing scheme has been considered to be resource-efficient for
providers and cost-effective for consumers in the Cloud market. Nevertheless,
unlike the static and straightforward strategies of trading on-demand and
reserved Cloud services, the market-driven mechanism for trading spot service
would be complicated for both implementation and understanding. The largely
invisible market activities and their complex interactions could especially
make Cloud consumers hesitate to enter the spot market. To reduce the
complexity in understanding the Cloud spot market, we decided to reveal the
backend information behind spot price variations. Inspired by the methodology
of reverse engineering, we developed a Predator-Prey model that can simulate
the interactions between demand and resource based on the visible spot price
traces. The simulation results have shown some basic regular patterns of market
activities with respect to Amazon's spot instance type m3.large. Although the
findings of this study need further validation by using practical data, our
work essentially suggests a promising approach (i.e.~using a Predator-Prey
model) to investigate spot market activities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Separatrix crossing in rotation of a body with changing geometry of masses | We consider free rotation of a body whose parts move slowly with respect to
each other under the action of internal forces. This problem can be considered
as a perturbation of the Euler-Poinsot problem. The dynamics has an approximate
conservation law - an adiabatic invariant. This allows to describe the
evolution of rotation in the adiabatic approximation. The evolution leads to an
overturn in the rotation of the body: the vector of angular velocity crosses
the separatrix of the Euler-Poinsot problem. This crossing leads to a
quasi-random scattering in body's dynamics. We obtain formulas for
probabilities of capture into different domains in the phase space at
separatrix crossings.
| 0 | 1 | 0 | 0 | 0 | 0 |
SING: Symbol-to-Instrument Neural Generator | Recent progress in deep learning for audio synthesis opens the way to models
that directly produce the waveform, shifting away from the traditional paradigm
of relying on vocoders or MIDI synthesizers for speech or music generation.
Despite their successes, current state-of-the-art neural audio synthesizers
such as WaveNet and SampleRNN suffer from prohibitive training and inference
times because they are based on autoregressive models that generate audio
samples one at a time at a rate of 16kHz. In this work, we study the more
computationally efficient alternative of generating the waveform frame-by-frame
with large strides. We present SING, a lightweight neural audio synthesizer for
the original task of generating musical notes given desired instrument, pitch
and velocity. Our model is trained end-to-end to generate notes from nearly
1000 instruments with a single decoder, thanks to a new loss function that
minimizes the distances between the log spectrograms of the generated and
target waveforms. On the generalization task of synthesizing notes for pairs of
pitch and instrument not seen during training, SING produces audio with
significantly improved perceptual quality compared to a state-of-the-art
autoencoder based on WaveNet as measured by a Mean Opinion Score (MOS), and is
about 32 times faster for training and 2, 500 times faster for inference.
| 1 | 0 | 0 | 0 | 0 | 0 |
Path-by-path regularization by noise for scalar conservation laws | We prove a path-by-path regularization by noise result for scalar
conservation laws. In particular, this proves regularizing properties for
scalar conservation laws driven by fractional Brownian motion and generalizes
the respective results obtained in [Gess, Souganidis; Comm. Pure Appl. Math.
(2017)]. In addition, we introduce a new path-by-path scaling property which is
shown to be sufficient to imply regularizing effects.
| 0 | 0 | 1 | 0 | 0 | 0 |
An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog | We present a novel end-to-end trainable neural network model for
task-oriented dialog systems. The model is able to track dialog state, issue
API calls to knowledge base (KB), and incorporate structured KB query results
into system responses to successfully complete task-oriented dialogs. The
proposed model produces well-structured system responses by jointly learning
belief tracking and KB result processing conditioning on the dialog history. We
evaluate the model in a restaurant search domain using a dataset that is
converted from the second Dialog State Tracking Challenge (DSTC2) corpus.
Experiment results show that the proposed model can robustly track dialog state
given the dialog history. Moreover, our model demonstrates promising results in
producing appropriate system responses, outperforming prior end-to-end
trainable neural network models using per-response accuracy evaluation metrics.
| 1 | 0 | 0 | 0 | 0 | 0 |
Neural IR Meets Graph Embedding: A Ranking Model for Product Search | Recently, neural models for information retrieval are becoming increasingly
popular. They provide effective approaches for product search due to their
competitive advantages in semantic matching. However, it is challenging to use
graph-based features, though proved very useful in IR literature, in these
neural approaches. In this paper, we leverage the recent advances in graph
embedding techniques to enable neural retrieval models to exploit
graph-structured data for automatic feature extraction. The proposed approach
can not only help to overcome the long-tail problem of click-through data, but
also incorporate external heterogeneous information to improve search results.
Extensive experiments on a real-world e-commerce dataset demonstrate
significant improvement achieved by our proposed approach over multiple strong
baselines both as an individual retrieval model and as a feature used in
learning-to-rank frameworks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Scaling up the software development process, a case study highlighting the complexities of large team software development | Diamond Light Source is the UK's National Synchrotron Facility and as such
provides access to world class experimental services for UK and international
researchers. As a user facility, that is one that focuses on providing a good
user experience to our varied visitors, Diamond invests heavily in software
infrastructure and staff. Over 100 members of the 600 strong workforce consider
software development as a significant tool to help them achieve their primary
role. These staff work on a diverse number of different software packages,
providing support for installation and configuration, maintenance and bug
fixing, as well as additional research and development of software when
required.
This talk focuses on one of the software projects undertaken to unify and
improve the user experience of several experiments. The "mapping project" is a
large 2 year, multi group project targeting the collection and processing
experiments which involve scanning an X-ray beam over a sample and building up
an image of that sample, similar to the way that google maps bring together
small pieces of information to produce a full map of the world. The project
itself is divided into several work packages, ranging from teams of one to 5 or
6 in size, with varying levels of time commitment to the project. This paper
aims to explore one of these work packages as a case study, highlighting the
experiences of the project team, the methodologies employed, their outcomes,
and the lessons learnt from the experience.
| 1 | 0 | 0 | 0 | 0 | 0 |
Lyapunov exponents for products of matrices | Let ${\bf M}=(M_1,\ldots, M_k)$ be a tuple of real $d\times d$ matrices.
Under certain irreducibility assumptions, we give checkable criteria for
deciding whether ${\bf M}$ possesses the following property: there exist two
constants $\lambda\in {\Bbb R}$ and $C>0$ such that for any $n\in {\Bbb N}$ and
any $i_1, \ldots, i_n \in \{1,\ldots, k\}$, either $M_{i_1} \cdots M_{i_n}={\bf
0}$ or $C^{-1} e^{\lambda n} \leq \| M_{i_1} \cdots M_{i_n} \| \leq C
e^{\lambda n}$, where $\|\cdot\|$ is a matrix norm. The proof is based on
symbolic dynamics and the thermodynamic formalism for matrix products. As
applications, we are able to check the absolute continuity of a class of
overlapping self-similar measures on ${\Bbb R}$, the absolute continuity of
certain self-affine measures in ${\Bbb R}^d$ and the dimensional regularity of
a class of sofic affine-invariant sets in the plane.
| 0 | 0 | 1 | 0 | 0 | 0 |
A definitive improvement of a game-theoretic bound and the long tightness game | The main goal of the paper is the full proof of a cardinal inequality for a
space with points $G_\delta $, obtained with the help of a long version of the
Menger game. This result, which improves a similar one of Scheepers and Tall,
was already established by the authors under the Continuum Hypothesis. The
paper is completed by few remarks on a long version of the tightness game.
| 0 | 0 | 1 | 0 | 0 | 0 |
Group-Server Queues | By analyzing energy-efficient management of data centers, this paper proposes
and develops a class of interesting {\it Group-Server Queues}, and establishes
two representative group-server queues through loss networks and impatient
customers, respectively. Furthermore, such two group-server queues are given
model descriptions and necessary interpretation. Also, simple mathematical
discussion is provided, and simulations are made to study the expected queue
lengths, the expected sojourn times and the expected virtual service times. In
addition, this paper also shows that this class of group-server queues are
often encountered in many other practical areas including communication
networks, manufacturing systems, transportation networks, financial networks
and healthcare systems. Note that the group-server queues are always used to
design effectively dynamic control mechanisms through regrouping and
recombining such many servers in a large-scale service system by means of, for
example, bilateral threshold control, and customers transfer to the buffer or
server groups. This leads to the large-scale service system that is divided
into several adaptive and self-organizing subsystems through scheduling of
batch customers and regrouping of service resources, which make the middle
layer of this service system more effectively managed and strengthened under a
dynamic, real-time and even reward optimal framework. Based on this,
performance of such a large-scale service system may be improved greatly in
terms of introducing and analyzing such group-server queues. Therefore, not
only analysis of group-server queues is regarded as a new interesting research
direction, but there also exists many theoretical challenges, basic
difficulties and open problems in the area of queueing networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
MC$^2$: Multi-wavelength and dynamical analysis of the merging galaxy cluster ZwCl 0008.8+5215: An older and less massive Bullet Cluster | We analyze a rich dataset including Subaru/SuprimeCam, HST/ACS and WFC3,
Keck/DEIMOS, Chandra/ACIS-I, and JVLA/C and D array for the merging galaxy
cluster ZwCl 0008.8+5215. With a joint Subaru/HST weak gravitational lensing
analysis, we identify two dominant subclusters and estimate the masses to be
M$_{200}=\text{5.7}^{+\text{2.8}}_{-\text{1.8}}\times\text{10}^{\text{14}}\,\text{M}_{\odot}$
and 1.2$^{+\text{1.4}}_{-\text{0.6}}\times10^{14}$ M$_{\odot}$. We estimate the
projected separation between the two subclusters to be
924$^{+\text{243}}_{-\text{206}}$ kpc. We perform a clustering analysis on
confirmed cluster member galaxies and estimate the line of sight velocity
difference between the two subclusters to be 92$\pm$164 km s$^{-\text{1}}$. We
further motivate, discuss, and analyze the merger scenario through an analysis
of the 42 ks of Chandra/ACIS-I and JVLA/C and D polarization data. The X-ray
surface brightness profile reveals a remnant core reminiscent of the Bullet
Cluster. The X-ray luminosity in the 0.5-7.0 keV band is
1.7$\pm$0.1$\times$10$^{\text{44}}$ erg s$^{-\text{1}}$ and the X-ray
temperature is 4.90$\pm$0.13 keV. The radio relics are polarized up to 40$\%$.
We implement a Monte Carlo dynamical analysis and estimate the merger velocity
at pericenter to be 1800$^{+\text{400}}_{-\text{300}}$ km s$^{-\text{1}}$. ZwCl
0008.8+5215 is a low-mass version of the Bullet Cluster and therefore may prove
useful in testing alternative models of dark matter. We do not find significant
offsets between dark matter and galaxies, as the uncertainties are large with
the current lensing data. Furthermore, in the east, the BCG is offset from
other luminous cluster galaxies, which poses a puzzle for defining dark matter
-- galaxy offsets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints | Adaptive designs for multi-armed clinical trials have become increasingly
popular recently in many areas of medical research because of their potential
to shorten development times and to increase patient response. However,
developing response-adaptive trial designs that offer patient benefit while
ensuring the resulting trial avoids bias and provides a statistically rigorous
comparison of the different treatments included is highly challenging. In this
paper, the theory of Multi-Armed Bandit Problems is used to define a family of
near optimal adaptive designs in the context of a clinical trial with a
normally distributed endpoint with known variance. Through simulation studies
based on an ongoing trial as a motivation we report the operating
characteristics (type I error, power, bias) and patient benefit of these
approaches and compare them to traditional and existing alternative designs.
These results are then compared to those recently published in the context of
Bernoulli endpoints. Many limitations and advantages are similar in both cases
but there are also important differences, specially with respect to type I
error control. This paper proposes a simulation-based testing procedure to
correct for the observed type I error inflation that bandit-based and adaptive
rules can induce. Results presented extend recent work by considering a
normally distributed endpoint, a very common case in clinical practice yet
mostly ignored in the response-adaptive theoretical literature, and illustrate
the potential advantages of using these methods in a rare disease context. We
also recommend a suitable modified implementation of the bandit-based adaptive
designs for the case of common diseases.
| 0 | 0 | 0 | 1 | 0 | 0 |
Convexification of Queueing Formulas by Mixed-Integer Second-Order Cone Programming: An Application to a Discrete Location Problem with Congestion | Mixed-Integer Second-Order Cone Programs (MISOCPs) form a nice class of
mixed-inter convex programs, which can be solved very efficiently due to the
recent advances in optimization solvers. Our paper bridges the gap between
modeling a class of optimization problems and using MISOCP solvers. It is shown
how various performance metrics of M/G/1 queues can be molded by different
MISOCPs. To motivate our method practically, it is first applied to a
challenging stochastic location problem with congestion, which is broadly used
to design socially optimal service networks. Four different MISOCPs are
developed and compared on sets of benchmark test problems. The new formulations
efficiently solve large-size test problems, which cannot be solved by the best
existing method. Then, the general applicability of our method is shown for
similar optimization problems that use queue-theoretic performance measures to
address customer satisfaction and service quality.
| 1 | 0 | 0 | 0 | 0 | 0 |
Discriminative Metric Learning with Deep Forest | A Discriminative Deep Forest (DisDF) as a metric learning algorithm is
proposed in the paper. It is based on the Deep Forest or gcForest proposed by
Zhou and Feng and can be viewed as a gcForest modification. The case of the
fully supervised learning is studied when the class labels of individual
training examples are known. The main idea underlying the algorithm is to
assign weights to decision trees in random forest in order to reduce distances
between objects from the same class and to increase them between objects from
different classes. The weights are training parameters. A specific objective
function which combines Euclidean and Manhattan distances and simplifies the
optimization problem for training the DisDF is proposed. The numerical
experiments illustrate the proposed distance metric algorithm.
| 1 | 0 | 0 | 1 | 0 | 0 |
An accurate and robust genuinely multidimensional Riemann solver for Euler equations based on TV flux splitting | A simple robust genuinely multidimensional convective pressure split (CPS) ,
contact preserving, shock stable Riemann solver (GM-K-CUSP-X) for Euler
equations of gas dynamics is developed. The convective and pressure components
of the Euler system are separated following the Toro-Vazquez type PDE flux
splitting [Toro et al, 2012]. Upwind discretization of these components are
achieved using the framework of Mandal et al [Mandal et al, 2015]. The
robustness of the scheme is studied on a few two dimensional test problems. The
results demonstrate the efficacy of the scheme over the corresponding
conventional two state version of the solver. Results from two classic strong
shock test cases associated with the infamous Carbuncle phenomenon, indicate
that the present solver is completely free of any such numerical instabilities
albeit possessing contact resolution abilities.Such a finding emphasizes the
pre-existing notion about the positive effects that multidimensional flow
modelling may have towards curing of shock instabilities.
| 0 | 1 | 1 | 0 | 0 | 0 |
Strong Convergence Rate of Splitting Schemes for Stochastic Nonlinear Schrödinger Equations | We prove the optimal strong convergence rate of a fully discrete scheme,
based on a splitting approach, for a stochastic nonlinear Schrödinger (NLS)
equation. The main novelty of our method lies on the uniform a priori estimate
and exponential integrability of a sequence of splitting processes which are
used to approximate the solution of the stochastic NLS equation. We show that
the splitting processes converge to the solution with strong order $1/2$. Then
we use the Crank--Nicolson scheme to temporally discretize the splitting
process and get the temporal splitting scheme which also possesses strong order
$1/2$. To obtain a full discretization, we apply this splitting Crank--Nicolson
scheme to the spatially discrete equation which is achieved through the
spectral Galerkin approximation. Furthermore, we establish the convergence of
this fully discrete scheme with optimal strong convergence rate
$\mathcal{O}(N^{-2}+\tau^\frac12)$, where $N$ denotes the dimension of the
approximate space and $\tau$ denotes the time step size. To the best of our
knowledge, this is the first result about strong convergence rates of
temporally numerical approximations and fully discrete schemes for stochastic
NLS equations, or even for stochastic partial differential equations (SPDEs)
with non-monotone coefficients. Numerical experiments verify our theoretical
result.
| 0 | 0 | 1 | 0 | 0 | 0 |
Controllability of Conjunctive Boolean Networks with Application to Gene Regulation | A Boolean network is a finite state discrete time dynamical system. At each
step, each variable takes a value from a binary set. The value update rule for
each variable is a local function which depends only on a selected subset of
variables. Boolean networks have been used in modeling gene regulatory
networks. We focus in this paper on a special class of Boolean networks, namely
the conjunctive Boolean networks (CBNs), whose value update rule is comprised
of only logic AND operations. It is known that any trajectory of a Boolean
network will enter a periodic orbit. Periodic orbits of a CBN have been
completely understood. In this paper, we investigate the orbit-controllability
and state-controllability of a CBN: We ask the question of how one can steer a
CBN to enter any periodic orbit or to reach any final state, from any initial
state. We establish necessary and sufficient conditions for a CBN to be
orbit-controllable and state-controllable. Furthermore, explicit control laws
are presented along the analysis.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fast-neutron and gamma-ray imaging with a capillary liquid xenon converter coupled to a gaseous photomultiplier | Gamma-ray and fast-neutron imaging was performed with a novel liquid xenon
(LXe) scintillation detector read out by a Gaseous Photomultiplier (GPM). The
100 mm diameter detector prototype comprised a capillary-filled LXe
converter/scintillator, coupled to a triple-THGEM imaging-GPM, with its first
electrode coated by a CsI UV-photocathode, operated in Ne/5%CH4 cryogenic
temperatures. Radiation localization in 2D was derived from
scintillation-induced photoelectron avalanches, measured on the GPM's segmented
anode. The localization properties of Co-60 gamma-rays and a mixed
fast-neutron/gamma-ray field from an AmBe neutron source were derived from
irradiation of a Pb edge absorber. Spatial resolutions of 12+/-2 mm and 10+/-2
mm (FWHM) were reached with Co-60 and AmBe sources, respectively. The
experimental results are in good agreement with GEANT4 simulations. The
calculated ultimate expected resolutions for our application-relevant 4.4 and
15.1 MeV gamma-rays and 1-15 MeV neutrons are 2-4 mm and ~2 mm (FWHM),
respectively. These results indicate the potential applicability of the new
detector concept to Fast-Neutron Resonance Radiography (FNRR) and
Dual-Discrete-Energy Gamma Radiography (DDEGR) of large objects.
| 0 | 1 | 0 | 0 | 0 | 0 |
Is Task Board Customization Beneficial? - An Eye Tracking Study | The task board is an essential artifact in many agile development approaches.
It provides a good overview of the project status. Teams often customize their
task boards according to the team members' needs. They modify the structure of
boards, define colored codings for different purposes, and introduce different
card sizes. Although the customizations are intended to improve the task
board's usability and effectiveness, they may also complicate its comprehension
and use. The increased effort impedes the work of both the team and team
externals. Hence, task board customization is in conflict with the agile
practice of fast and easy overview for everyone. In an eye tracking study with
30 participants, we compared an original task board design with three
customized ones to investigate which design shortened the required time to
identify a particular story card. Our findings yield that only the customized
task board design with modified structures reduces the required time. The
original task board design is more beneficial than individual colored codings
and changed card sizes. According to our findings, agile teams should rethink
their current task board design. They may be better served by focusing on the
original task board design and by applying only carefully selected adjustments.
In case of customization, a task board's structure should be adjusted since
this is the only beneficial kind of customization, that additionally complies
more precisely with the concept of fast and easy project overview.
| 1 | 0 | 0 | 0 | 0 | 0 |
Characterizing the impact of model error in hydrogeologic time series recovery inverse problems | Hydrogeologic models are commonly over-smoothed relative to reality, owing to
the difficulty of obtaining accurate high-resolution information about the
subsurface. When used in an inversion context, such models may introduce
systematic biases which cannot be encapsulated by an unbiased "observation
noise" term of the type assumed by standard regularization theory and typical
Bayesian formulations. Despite its importance, model error is difficult to
encapsulate systematically and is often neglected. Here, model error is
considered for a hydrogeologically important class of inverse problems that
includes interpretation of hydraulic transients and contaminant source history
inference: reconstruction of a time series that has been convolved against a
transfer function (i.e., impulse response) that is only approximately known.
Using established harmonic theory along with two results established here
regarding triangular Toeplitz matrices, upper and lower error bounds are
derived for the effect of systematic model error on time series recovery for
both well-determined and over-determined inverse problems. A Monte Carlo study
of a realistic hydraulic reconstruction problem is presented, and the lower
error bound is seen informative about expected behavior. A possible diagnostic
criterion for blind transfer function characterization is also uncovered.
| 0 | 0 | 1 | 0 | 0 | 0 |
Suszko's Problem: Mixed Consequence and Compositionality | Suszko's problem is the problem of finding the minimal number of truth values
needed to semantically characterize a syntactic consequence relation. Suszko
proved that every Tarskian consequence relation can be characterized using only
two truth values. Malinowski showed that this number can equal three if some of
Tarski's structural constraints are relaxed. By so doing, Malinowski introduced
a case of so-called mixed consequence, allowing the notion of a designated
value to vary between the premises and the conclusions of an argument. In this
paper we give a more systematic perspective on Suszko's problem and on mixed
consequence. First, we prove general representation theorems relating
structural properties of a consequence relation to their semantic
interpretation, uncovering the semantic counterpart of substitution-invariance,
and establishing that (intersective) mixed consequence is fundamentally the
semantic counterpart of the structural property of monotonicity. We use those
to derive maximum-rank results proved recently in a different setting by French
and Ripley, as well as by Blasio, Marcos and Wansing, for logics with various
structural properties (reflexivity, transitivity, none, or both). We strengthen
these results into exact rank results for non-permeable logics (roughly, those
which distinguish the role of premises and conclusions). We discuss the
underlying notion of rank, and the associated reduction proposed independently
by Scott and Suszko. As emphasized by Suszko, that reduction fails to preserve
compositionality in general, meaning that the resulting semantics is no longer
truth-functional. We propose a modification of that notion of reduction,
allowing us to prove that over compact logics with what we call regular
connectives, rank results are maintained even if we request the preservation of
truth-functionality and additional semantic properties.
| 1 | 0 | 1 | 0 | 0 | 0 |
Optimization of distributions differences for classification | In this paper we introduce a new classification algorithm called Optimization
of Distributions Differences (ODD). The algorithm aims to find a transformation
from the feature space to a new space where the instances in the same class are
as close as possible to one another while the gravity centers of these classes
are as far as possible from one another. This aim is formulated as a
multiobjective optimization problem that is solved by a hybrid of an
evolutionary strategy and the Quasi-Newton method. The choice of the
transformation function is flexible and could be any continuous space function.
We experiment with a linear and a non-linear transformation in this paper. We
show that the algorithm can outperform 6 other state-of-the-art classification
methods, namely naive Bayes, support vector machines, linear discriminant
analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in
12 standard classification datasets. Our results show that the method is less
sensitive to the imbalanced number of instances comparing to these methods. We
also show that ODD maintains its performance better than other classification
methods in these datasets, hence, offers a better generalization ability.
| 1 | 0 | 0 | 1 | 0 | 0 |
Galois descent of semi-affinoid spaces | We study the Galois descent of semi-affinoid non-archimedean analytic spaces.
These are the non-archimedean analytic spaces which admit an affine special
formal scheme as model over a complete discrete valuation ring, such as for
example open or closed polydiscs or polyannuli. Using Weil restrictions and
Galois fixed loci for semi-affinoid spaces and their formal models, we describe
a formal model of a $K$-analytic space $X$, provided that $X\otimes_KL$ is
semi-affinoid for some finite tamely ramified extension $L$ of $K$. As an
application, we study the forms of analytic annuli that are trivialized by a
wide class of Galois extensions that includes totally tamely ramified
extensions. In order to do so, we first establish a Weierstrass preparation
result for analytic functions on annuli, and use it to linearize finite order
automorphisms of annuli. Finally, we explain how from these results one can
deduce a non-archimedean analytic proof of the existence of resolutions of
singularities of surfaces in characteristic zero.
| 0 | 0 | 1 | 0 | 0 | 0 |
Synthesis, Crystal Structure, and Physical Properties of New Layered Oxychalcogenide La2O2Bi3AgS6 | We have synthesized a new layered oxychalcogenide La2O2Bi3AgS6. From
synchrotron X-ray diffraction and Rietveld refinement, the crystal structure of
La2O2Bi3AgS6 was refined using a model of the P4/nmm space group with a =
4.0644(1) {\AA} and c = 19.412(1) {\AA}, which is similar to the related
compound LaOBiPbS3, while the interlayer bonds (M2-S1 bonds) are apparently
shorter in La2O2Bi3AgS6. The tunneling electron microscopy (TEM) image
confirmed the lattice constant derived from Rietveld refinement (c ~ 20 {\AA}).
The electrical resistivity and Seebeck coefficient suggested that the
electronic states of La2O2Bi3AgS6 are more metallic than those of LaOBiS2 and
LaOBiPbS3. The insertion of a rock-salt-type chalcogenide into the van der
Waals gap of BiS2-based layered compounds, such as LaOBiS2, will be a useful
strategy for designing new layered functional materials in the layered
chalcogenide family.
| 0 | 1 | 0 | 0 | 0 | 0 |
Planar Graph Perfect Matching is in NC | Is perfect matching in NC? That is, is there a deterministic fast parallel
algorithm for it? This has been an outstanding open question in theoretical
computer science for over three decades, ever since the discovery of RNC
matching algorithms. Within this question, the case of planar graphs has
remained an enigma: On the one hand, counting the number of perfect matchings
is far harder than finding one (the former is #P-complete and the latter is in
P), and on the other, for planar graphs, counting has long been known to be in
NC whereas finding one has resisted a solution.
In this paper, we give an NC algorithm for finding a perfect matching in a
planar graph. Our algorithm uses the above-stated fact about counting matchings
in a crucial way. Our main new idea is an NC algorithm for finding a face of
the perfect matching polytope at which $\Omega(n)$ new conditions, involving
constraints of the polytope, are simultaneously satisfied. Several other ideas
are also needed, such as finding a point in the interior of the minimum weight
face of this polytope and finding a balanced tight odd set in NC.
| 1 | 0 | 0 | 0 | 0 | 0 |
Recommendation under Capacity Constraints | In this paper, we investigate the common scenario where every candidate item
for recommendation is characterized by a maximum capacity, i.e., number of
seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the
prevalence of the task of recommending items under capacity constraints in a
variety of settings, to the best of our knowledge, none of the known
recommender methods is designed to respect capacity constraints. To close this
gap, we extend three state-of-the art latent factor recommendation approaches:
probabilistic matrix factorization (PMF), geographical matrix factorization
(GeoMF), and bayesian personalized ranking (BPR), to optimize for both
recommendation accuracy and expected item usage that respects the capacity
constraints. We introduce the useful concepts of user propensity to listen and
item capacity. Our experimental results in real-world datasets, both for the
domain of item recommendation and POI recommendation, highlight the benefit of
our method for the setting of recommendation under capacity constraints.
| 1 | 0 | 0 | 1 | 0 | 0 |
Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles | Using deep reinforcement learning, we train control policies for autonomous
vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library
for deep reinforcement learning in micro-simulators, we train two policies, one
policy with noise injected into the state and action space and one without any
injected noise. In simulation, the autonomous vehicle learns an emergent
metering behavior for both policies in which it slows to allow for smoother
merging. We then directly transfer this policy without any tuning to the
University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for
connected and automated vehicles. We characterize the performance of both
policies on the scaled city. We show that the noise-free policy winds up
crashing and only occasionally metering. However, the noise-injected policy
consistently performs the metering behavior and remains collision-free,
suggesting that the noise helps with the zero-shot policy transfer.
Additionally, the transferred, noise-injected policy leads to a 5% reduction of
average travel time and a reduction of 22% in maximum travel time in the UDSSC.
Videos of the controllers can be found at
this https URL.
| 1 | 0 | 0 | 0 | 0 | 0 |
Relative Singularity Categories | We study the following generalization of singularity categories. Let X be a
quasi-projective Gorenstein scheme with isolated singularities and A a
non-commutative resolution of singularities of X in the sense of Van den Bergh.
We introduce the relative singularity category as the Verdier quotient of the
bounded derived category of coherent sheaves on A modulo the category of
perfect complexes on X. We view it as a measure for the difference between X
and A. The main results of this thesis are the following.
(i) We prove an analogue of Orlov's localization result in our setup. If X
has isolated singularities, then this reduces the study of the relative
singularity categories to the affine case.
(ii) We prove Hom-finiteness and idempotent completeness of the relative
singularity categories in the complete local situation and determine its
Grothendieck group.
(iii) We give a complete and explicit description of the relative singularity
categories when X has only nodal singularities and the resolution is given by a
sheaf of Auslander algebras.
(iv) We study relations between relative singularity categories and classical
singularity categories. For a simple hypersurface singularity and its Auslander
resolution, we show that these categories determine each other.
(v) The developed technique leads to the following `purely commutative'
application: a description of Iyama & Wemyss triangulated category for rational
surface singularities in terms of the singularity category of the rational
double point resolution.
(vi) We give a description of singularity categories of gentle algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
Challenges to Keeping the Computer Industry Centered in the US | It is undeniable that the worldwide computer industry's center is the US,
specifically in Silicon Valley. Much of the reason for the success of Silicon
Valley had to do with Moore's Law: the observation by Intel co-founder Gordon
Moore that the number of transistors on a microchip doubled at a rate of
approximately every two years. According to the International Technology
Roadmap for Semiconductors, Moore's Law will end in 2021. How can we rethink
computing technology to restart the historic explosive performance growth?
Since 2012, the IEEE Rebooting Computing Initiative (IEEE RCI) has been working
with industry and the US government to find new computing approaches to answer
this question. In parallel, the CCC has held a number of workshops addressing
similar questions. This whitepaper summarizes some of the IEEE RCI and CCC
findings. The challenge for the US is to lead this new era of computing. Our
international competitors are not sitting still: China has invested
significantly in a variety of approaches such as neuromorphic computing, chip
fabrication facilities, computer architecture, and high-performance simulation
and data analytics computing, for example. We must act now, otherwise, the
center of the computer industry will move from Silicon Valley and likely move
off shore entirely.
| 1 | 0 | 0 | 0 | 0 | 0 |
Contiguous Relations, Laplace's Methods and Continued Fractions for 3F2(1) | Using contiguous relations we construct an infinite number of continued
fraction expansions for ratios of generalized hypergeometric series 3F2(1). We
establish exact error term estimates for their approximants and prove their
rapid convergences. To do so we develop a discrete version of Laplace's method
for hypergeometric series in addition to the use of ordinary (continuous)
Laplace's method for Euler's hypergeometric integrals.
| 0 | 0 | 1 | 0 | 0 | 0 |
Arimoto-Rényi Conditional Entropy and Bayesian $M$-ary Hypothesis Testing | This paper gives upper and lower bounds on the minimum error probability of
Bayesian $M$-ary hypothesis testing in terms of the Arimoto-Rényi conditional
entropy of an arbitrary order $\alpha$. The improved tightness of these bounds
over their specialized versions with the Shannon conditional entropy
($\alpha=1$) is demonstrated. In particular, in the case where $M$ is finite,
we show how to generalize Fano's inequality under both the conventional and
list-decision settings. As a counterpart to the generalized Fano's inequality,
allowing $M$ to be infinite, a lower bound on the Arimoto-Rényi conditional
entropy is derived as a function of the minimum error probability. Explicit
upper and lower bounds on the minimum error probability are obtained as a
function of the Arimoto-Rényi conditional entropy for both positive and
negative $\alpha$. Furthermore, we give upper bounds on the minimum error
probability as functions of the Rényi divergence. In the setup of discrete
memoryless channels, we analyze the exponentially vanishing decay of the
Arimoto-Rényi conditional entropy of the transmitted codeword given the
channel output when averaged over a random coding ensemble.
| 1 | 0 | 1 | 1 | 0 | 0 |
A note on the violation of Bell's inequality | With Bell's inequalities one has a formal expression to show how essentially
all local theories of natural phenomena that are formulated within the
framework of realism may be tested using a simple experimental arrangement. For
the case of entangled pairs of spin-1/2 particles we propose an alternative
measurement setup which is consistent to the necessary assumptions
corresponding to the derivation of the Bell inequalities. We find that the Bell
inequalities are never violated with respect to our suggested measurement
process.
| 0 | 1 | 0 | 0 | 0 | 0 |
Real-Time Model Predictive Control for Energy Management in Autonomous Underwater Vehicle | Improving endurance is crucial for extending the spatial and temporal
operation range of autonomous underwater vehicles (AUVs). Considering the
hardware constraints and the performance requirements, an intelligent energy
management system is required to extend the operation range of AUVs. This paper
presents a novel model predictive control (MPC) framework for energy-optimal
point-to-point motion control of an AUV. In this scheme, the energy management
problem of an AUV is reformulated as a surge motion optimization problem in two
stages. First, a system-level energy minimization problem is solved by managing
the trade-off between the energies required for overcoming the positive
buoyancy and surge drag force in static optimization. Next, an MPC with a
special cost function formulation is proposed to deal with transients and
system dynamics. A switching logic for handling the transition between the
static and dynamic stages is incorporated to reduce the computational efforts.
Simulation results show that the proposed method is able to achieve
near-optimal energy consumption with considerable lower computational
complexity.
| 1 | 0 | 0 | 0 | 0 | 0 |
Surjective H-Colouring over Reflexive Digraphs | The Surjective H-Colouring problem is to test if a given graph allows a
vertex-surjective homomorphism to a fixed graph H. The complexity of this
problem has been well studied for undirected (partially) reflexive graphs. We
introduce endo-triviality, the property of a structure that all of its
endomorphisms that do not have range of size 1 are automorphisms, as a means to
obtain complexity-theoretic classifications of Surjective H-Colouring in the
case of reflexive digraphs.
Chen [2014] proved, in the setting of constraint satisfaction problems, that
Surjective H-Colouring is NP-complete if H has the property that all of its
polymorphisms are essentially unary. We give the first concrete application of
his result by showing that every endo-trivial reflexive digraph H has this
property. We then use the concept of endo-triviality to prove, as our main
result, a dichotomy for Surjective H-Colouring when H is a reflexive
tournament: if H is transitive, then Surjective H-Colouring is in NL, otherwise
it is NP-complete.
By combining this result with some known and new results we obtain a
complexity classification for Surjective H-Colouring when H is a partially
reflexive digraph of size at most 3.
| 1 | 0 | 0 | 0 | 0 | 0 |
A modal typing system for self-referential programs and specifications | This paper proposes a modal typing system that enables us to handle
self-referential formulae, including ones with negative self-references, which
on one hand, would introduce a logical contradiction, namely Russell's paradox,
in the conventional setting, while on the other hand, are necessary to capture
a certain class of programs such as fixed-point combinators and objects with
so-called binary methods in object-oriented programming. The proposed system
provides a basis for axiomatic semantics of such a wider range of programs and
a new framework for natural construction of recursive programs in the
proofs-as-programs paradigm.
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Scalable Realistic Recommendation Datasets through Fractal Expansions | Recommender System research suffers currently from a disconnect between the
size of academic data sets and the scale of industrial production systems. In
order to bridge that gap we propose to generate more massive user/item
interaction data sets by expanding pre-existing public data sets. User/item
incidence matrices record interactions between users and items on a given
platform as a large sparse matrix whose rows correspond to users and whose
columns correspond to items. Our technique expands such matrices to larger
numbers of rows (users), columns (items) and non zero values (interactions)
while preserving key higher order statistical properties. We adapt the
Kronecker Graph Theory to user/item incidence matrices and show that the
corresponding fractal expansions preserve the fat-tailed distributions of user
engagements, item popularity and singular value spectra of user/item
interaction matrices. Preserving such properties is key to building large
realistic synthetic data sets which in turn can be employed reliably to
benchmark Recommender Systems and the systems employed to train them. We
provide algorithms to produce such expansions and apply them to the MovieLens
20 million data set comprising 20 million ratings of 27K movies by 138K users.
The resulting expanded data set has 10 billion ratings, 2 million items and
864K users in its smaller version and can be scaled up or down. A larger
version features 655 billion ratings, 7 million items and 17 million users.
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Implementation of a Distributed Coherent Quantum Observer | This paper considers the problem of implementing a previously proposed
distributed direct coupling quantum observer for a closed linear quantum
system. By modifying the form of the previously proposed observer, the paper
proposes a possible experimental implementation of the observer plant system
using a non-degenerate parametric amplifier and a chain of optical cavities
which are coupled together via optical interconnections. It is shown that the
distributed observer converges to a consensus in a time averaged sense in which
an output of each element of the observer estimates the specified output of the
quantum plant.
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Stacking and stability | Stacking is a general approach for combining multiple models toward greater
predictive accuracy. It has found various application across different domains,
ensuing from its meta-learning nature. Our understanding, nevertheless, on how
and why stacking works remains intuitive and lacking in theoretical insight. In
this paper, we use the stability of learning algorithms as an elemental
analysis framework suitable for addressing the issue. To this end, we analyze
the hypothesis stability of stacking, bag-stacking, and dag-stacking and
establish a connection between bag-stacking and weighted bagging. We show that
the hypothesis stability of stacking is a product of the hypothesis stability
of each of the base models and the combiner. Moreover, in bag-stacking and
dag-stacking, the hypothesis stability depends on the sampling strategy used to
generate the training set replicates. Our findings suggest that 1) subsampling
and bootstrap sampling improve the stability of stacking, and 2) stacking
improves the stability of both subbagging and bagging.
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Accelerating Discrete Wavelet Transforms on GPUs | The two-dimensional discrete wavelet transform has a huge number of
applications in image-processing techniques. Until now, several papers compared
the performance of such transform on graphics processing units (GPUs). However,
all of them only dealt with lifting and convolution computation schemes. In
this paper, we show that corresponding horizontal and vertical lifting parts of
the lifting scheme can be merged into non-separable lifting units, which halves
the number of steps. We also discuss an optimization strategy leading to a
reduction in the number of arithmetic operations. The schemes were assessed
using the OpenCL and pixel shaders. The proposed non-separable lifting scheme
outperforms the existing schemes in many cases, irrespective of its higher
complexity.
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Accelerated Consensus via Min-Sum Splitting | We apply the Min-Sum message-passing protocol to solve the consensus problem
in distributed optimization. We show that while the ordinary Min-Sum algorithm
does not converge, a modified version of it known as Splitting yields
convergence to the problem solution. We prove that a proper choice of the
tuning parameters allows Min-Sum Splitting to yield subdiffusive accelerated
convergence rates, matching the rates obtained by shift-register methods. The
acceleration scheme embodied by Min-Sum Splitting for the consensus problem
bears similarities with lifted Markov chains techniques and with multi-step
first order methods in convex optimization.
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Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models | In the last few years, we have seen the transformative impact of deep
learning in many applications, particularly in speech recognition and computer
vision. Inspired by Google's Inception-ResNet deep convolutional neural network
(CNN) for image classification, we have developed "Chemception", a deep CNN for
the prediction of chemical properties, using just the images of 2D drawings of
molecules. We develop Chemception without providing any additional explicit
chemistry knowledge, such as basic concepts like periodicity, or advanced
features like molecular descriptors and fingerprints. We then show how
Chemception can serve as a general-purpose neural network architecture for
predicting toxicity, activity, and solvation properties when trained on a
modest database of 600 to 40,000 compounds. When compared to multi-layer
perceptron (MLP) deep neural networks trained with ECFP fingerprints,
Chemception slightly outperforms in activity and solvation prediction and
slightly underperforms in toxicity prediction. Having matched the performance
of expert-developed QSAR/QSPR deep learning models, our work demonstrates the
plausibility of using deep neural networks to assist in computational chemistry
research, where the feature engineering process is performed primarily by a
deep learning algorithm.
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Constraining the Milky Way assembly history with Galactic Archaeology. Ludwig Biermann Award Lecture 2015 | The aim of Galactic Archaeology is to recover the evolutionary history of the
Milky Way from its present day kinematical and chemical state. Because stars
move away from their birth sites, the current dynamical information alone is
not sufficient for this task. The chemical composition of stellar atmospheres,
on the other hand, is largely preserved over the stellar lifetime and, together
with accurate ages, can be used to recover the birthplaces of stars currently
found at the same Galactic radius. In addition to the availability of large
stellar samples with accurate 6D kinematics and chemical abundance
measurements, this requires detailed modeling with both dynamical and chemical
evolution taken into account. An important first step is to understand the
variety of dynamical processes that can take place in the Milky Way, including
the perturbative effects of both internal (bar and spiral structure) and
external (infalling satellites) agents. We discuss here (1) how to constrain
the Galactic bar, spiral structure, and merging satellites by their effect on
the local and global disc phase-space, (2) the effect of multiple patterns on
the disc dynamics, and (3) the importance of radial migration and merger
perturbations for the formation of the Galactic thick disc. Finally, we discuss
the construction of Milky Way chemo-dynamical models and relate to
observations.
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A unified view of entropy-regularized Markov decision processes | We propose a general framework for entropy-regularized average-reward
reinforcement learning in Markov decision processes (MDPs). Our approach is
based on extending the linear-programming formulation of policy optimization in
MDPs to accommodate convex regularization functions. Our key result is showing
that using the conditional entropy of the joint state-action distributions as
regularization yields a dual optimization problem closely resembling the
Bellman optimality equations. This result enables us to formalize a number of
state-of-the-art entropy-regularized reinforcement learning algorithms as
approximate variants of Mirror Descent or Dual Averaging, and thus to argue
about the convergence properties of these methods. In particular, we show that
the exact version of the TRPO algorithm of Schulman et al. (2015) actually
converges to the optimal policy, while the entropy-regularized policy gradient
methods of Mnih et al. (2016) may fail to converge to a fixed point. Finally,
we illustrate empirically the effects of using various regularization
techniques on learning performance in a simple reinforcement learning setup.
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Arithmetic Circuits for Multilevel Qudits Based on Quantum Fourier Transform | We present some basic integer arithmetic quantum circuits, such as adders and
multipliers-accumulators of various forms, as well as diagonal operators, which
operate on multilevel qudits. The integers to be processed are represented in
an alternative basis after they have been Fourier transformed. Several
arithmetic circuits operating on Fourier transformed integers have appeared in
the literature for two level qubits. Here we extend these techniques on
multilevel qudits, as they may offer some advantages relative to qubits
implementations. The arithmetic circuits presented can be used as basic
building blocks for higher level algorithms such as quantum phase estimation,
quantum simulation, quantum optimization etc., but they can also be used in the
implementation of a quantum fractional Fourier transform as it is shown in a
companion work presented separately.
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Quantifying the distribution of editorial power and manuscript decision bias at the mega-journal PLOS ONE | We analyzed the longitudinal activity of nearly 7,000 editors at the
mega-journal PLOS ONE over the 10-year period 2006-2015. Using the
article-editor associations, we develop editor-specific measures of power,
activity, article acceptance time, citation impact, and editorial renumeration
(an analogue to self-citation). We observe remarkably high levels of power
inequality among the PLOS ONE editors, with the top-10 editors responsible for
3,366 articles -- corresponding to 2.4% of the 141,986 articles we analyzed.
Such high inequality levels suggest the presence of unintended incentives,
which may reinforce unethical behavior in the form of decision-level biases at
the editorial level. Our results indicate that editors may become apathetic in
judging the quality of articles and susceptible to modes of power-driven
misconduct. We used the longitudinal dimension of editor activity to develop
two panel regression models which test and verify the presence of editor-level
bias. In the first model we analyzed the citation impact of articles, and in
the second model we modeled the decision time between an article being
submitted and ultimately accepted by the editor. We focused on two variables
that represent social factors that capture potential conflicts-of-interest: (i)
we accounted for the social ties between editors and authors by developing a
measure of repeat authorship among an editor's article set, and (ii) we
accounted for the rate of citations directed towards the editor's own
publications in the reference list of each article he/she oversaw. Our results
indicate that these two factors play a significant role in the editorial
decision process. Moreover, these two effects appear to increase with editor
age, which is consistent with behavioral studies concerning the evolution of
misbehavior and response to temptation in power-driven environments.
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Threshold Constraints with Guarantees for Parity Objectives in Markov Decision Processes | The beyond worst-case synthesis problem was introduced recently by Bruyère
et al. [BFRR14]: it aims at building system controllers that provide strict
worst-case performance guarantees against an antagonistic environment while
ensuring higher expected performance against a stochastic model of the
environment. Our work extends the framework of [BFRR14] and follow-up papers,
which focused on quantitative objectives, by addressing the case of
$\omega$-regular conditions encoded as parity objectives, a natural way to
represent functional requirements of systems.
We build strategies that satisfy a main parity objective on all plays, while
ensuring a secondary one with sufficient probability. This setting raises new
challenges in comparison to quantitative objectives, as one cannot easily mix
different strategies without endangering the functional properties of the
system. We establish that, for all variants of this problem, deciding the
existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity
class as classical parity games. Hence, our framework provides additional
modeling power while staying in the same complexity class.
[BFRR14] Véronique Bruyère, Emmanuel Filiot, Mickael Randour, and
Jean-François Raskin. Meet your expectations with guarantees: Beyond
worst-case synthesis in quantitative games. In Ernst W. Mayr and Natacha
Portier, editors, 31st International Symposium on Theoretical Aspects of
Computer Science, STACS 2014, March 5-8, 2014, Lyon, France, volume 25 of
LIPIcs, pages 199-213. Schloss Dagstuhl - Leibniz - Zentrum fuer Informatik,
2014.
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GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures | VAEs (Variational AutoEncoders) have proved to be powerful in the context of
density modeling and have been used in a variety of contexts for creative
purposes. In many settings, the data we model possesses continuous attributes
that we would like to take into account at generation time. We propose in this
paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational
AutoEncoder architecture and its generalizations which allows a fine control on
the embedding of the data into the latent space. When augmenting the VAE loss
with this regularization, changes in the learned latent space reflects changes
of the attributes of the data. This deeper understanding of the VAE latent
space structure offers the possibility to modulate the attributes of the
generated data in a continuous way. We demonstrate its efficiency on a
monophonic music generation task where we manage to generate variations of
discrete sequences in an intended and playful way.
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Timing Solution and Single-pulse Properties for Eight Rotating Radio Transients | Rotating radio transients (RRATs), loosely defined as objects that are
discovered through only their single pulses, are sporadic pulsars that have a
wide range of emission properties. For many of them, we must measure their
periods and determine timing solutions relying on the timing of their
individual pulses, while some of the less sporadic RRATs can be timed by using
folding techniques as we do for other pulsars. Here, based on Parkes and Green
Bank Telescope (GBT) observations, we introduce our results on eight RRATs
including their timing-derived rotation parameters, positions, and dispersion
measures (DMs), along with a comparison of the spin-down properties of RRATs
and normal pulsars. Using data for 24 RRATs, we find that their period
derivatives are generally larger than those of normal pulsars, independent of
any intrinsic correlation with period, indicating that RRATs' highly sporadic
emission may be associated with intrinsically larger magnetic fields. We carry
out Lomb$-$Scargle tests to search for periodicities in RRATs' pulse detection
times with long timescales. Periodicities are detected for all targets, with
significant candidates of roughly 3.4 hr for PSR J1623$-$0841 and 0.7 hr for
PSR J1839$-$0141. We also analyze their single-pulse amplitude distributions,
finding that log-normal distributions provide the best fits, as is the case for
most pulsars. However, several RRATs exhibit power-law tails, as seen for
pulsars emitting giant pulses. This, along with consideration of the selection
effects against the detection of weak pulses, imply that RRAT pulses generally
represent the tail of a normal intensity distribution.
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