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On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications | In this paper we analyse the benefits of incorporating interval-valued fuzzy
sets into the Bousi-Prolog system. A syntax, declarative semantics and im-
plementation for this extension is presented and formalised. We show, by using
potential applications, that fuzzy logic programming frameworks enhanced with
them can correctly work together with lexical resources and ontologies in order
to improve their capabilities for knowledge representation and reasoning.
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Characterization of Thermal Neutron Beam Monitors | Neutron beam monitors with high efficiency, low gamma sensitivity, high time
and space resolution are required in neutron beam experiments to continuously
diagnose the delivered beam. In this work, commercially available neutron beam
monitors have been characterized using the R2D2 beamline at IFE (Norway) and
using a Be-based neutron source. For the gamma sensitivity measurements
different gamma sources have been used. The evaluation of the monitors
includes, the study of their efficiency, attenuation, scattering and
sensitivity to gamma. In this work we report the results of this
characterization.
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Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III | Previous studies have shown the filamentary structures in the cosmic web
influence the alignments of nearby galaxies. We study this effect in the LOWZ
sample of the Sloan Digital Sky Survey using the "Cosmic Web Reconstruction"
filament catalogue of Chen et al. (2016). We find that LOWZ galaxies exhibit a
small but statistically significant alignment in the direction parallel to the
orientation of nearby filaments. This effect is detectable even in the absence
of nearby galaxy clusters, which suggests it is an effect from the matter
distribution in the filament. A nonparametric regression model suggests that
the alignment effect with filaments extends over separations of $30-40$ Mpc. We
find that galaxies that are bright and early-forming align more strongly with
the directions of nearby filaments than those that are faint and late-forming;
however, trends with stellar mass are less statistically significant, within
the narrow range of stellar mass of this sample.
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Pandeia: A Multi-mission Exposure Time Calculator for JWST and WFIRST | Pandeia is the exposure time calculator (ETC) system developed for the James
Webb Space Telescope (JWST) that will be used for creating JWST proposals. It
includes a simulation-hybrid Python engine that calculates the two-dimensional
pixel-by-pixel signal and noise properties of the JWST instruments. This allows
for appropriate handling of realistic point spread functions, MULTIACCUM
detector readouts, correlated detector readnoise, and multiple photometric and
spectral extraction strategies. Pandeia includes support for all the JWST
observing modes, including imaging, slitted/slitless spectroscopy, integral
field spectroscopy, and coronagraphy. Its highly modular, data-driven design
makes it easily adaptable to other observatories. An implementation for use
with WFIRST is also available.
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The numbers of edges of 5-polytopes with a given number of vertices | A basic combinatorial invariant of a convex polytope $P$ is its $f$-vector
$f(P)=(f_0,f_1,\dots,f_{\dim P-1})$, where $f_i$ is the number of
$i$-dimensional faces of $P$. Steinitz characterized all possible $f$-vectors
of $3$-polytopes and Grünbaum characterized the pairs given by the first two
entries of the $f$-vectors of $4$-polytopes. In this paper, we characterize the
pairs given by the first two entries of the $f$-vectors of $5$-polytopes. The
same result was also proved by Pineda-Villavicencio, Ugon and Yost
independently.
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MapExif: an image scanning and mapping tool for investigators | Recently, the integration of geographical coordinates into a picture has
become more and more popular. Indeed almost all smartphones and many cameras
today have a built-in GPS receiver that stores the location information in the
Exif header when a picture is taken. Although the automatic embedding of
geotags in pictures is often ignored by smart phone users as it can lead to
endless discussions about privacy implications, these geotags could be really
useful for investigators in analysing criminal activity. Currently, there are
many free tools as well as commercial tools available in the market that can
help computer forensics investigators to cover a wide range of geographic
information related to criminal scenes or activities. However, there are not
specific forensic tools available to deal with the geolocation of pictures
taken by smart phones or cameras. In this paper, we propose and develop an
image scanning and mapping tool for investigators. This tool scans all the
files in a given directory and then displays particular photos based on
optional filters (date, time, device, localisation) on Google Map. The file
scanning process is not based on the file extension but its header. This tool
can also show efficiently to users if there is more than one image on the map
with the same GPS coordinates, or even if there are images with no GPS
coordinates taken by the same device in the same timeline. Moreover, this new
tool is portable; investigators can run it on any operating system without any
installation. Another useful feature is to be able to work in a read-only
environment, so that forensic results will not be modified. We also present and
evaluate this tool real world application in this paper.
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Fitch-Style Modal Lambda Calculi | Fitch-style modal deduction, in which modalities are eliminated by opening a
subordinate proof, and introduced by shutting one, were investigated in the
1990s as a basis for lambda calculi. We show that such calculi have good
computational properties for a variety of intuitionistic modal logics.
Semantics are given in cartesian closed categories equipped with an adjunction
of endofunctors, with the necessity modality interpreted by the right adjoint.
Where this functor is an idempotent comonad, a coherence result on the
semantics allows us to present a calculus for intuitionistic S4 that is simpler
than others in the literature. We show the calculi can be extended à la
tense logic with the left adjoint of necessity, and are then complete for the
categorical semantics.
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Hot Stuff for One Year (HSOY) - A 583 million star proper motion catalogue derived from Gaia DR1 and PPMXL | Recently, the first installment of data from ESA's Gaia astrometric satellite
mission (Gaia-DR1) was released, containing positions of more than 1 billion
stars with unprecedented precision, as well as only proper motions and
parallaxes, however only for a subset of 2 million objects. The second release,
due in late 2017 or early 2018, will include those quantities for most objects.
In order to provide a dataset that bridges the time gap between the Gaia-DR1
and Gaia-DR2 releases and partly remedies the lack of proper motions in the
former, HSOY ("Hot Stuff for One Year") was created as a hybrid catalogue
between Gaia-DR1 and ground-based astrometry, featuring proper motions (but no
parallaxes) for a large fraction of the DR1 objects. While not attempting to
compete with future Gaia releases in terms of data quality or number of
objects, the aim of HSOY is to provide improved proper motions partly based on
Gaia data, allowing some studies to be carried out just now or as pilot studies
for later larger projects requiring higher-precision data. The HSOY catalogue
was compiled using the positions taken from Gaia-DR1 combined with the input
data from the PPMXL catalogue, employing the same weighted least-squares
technique that was used to assemble the PPMXL catalogue itself. Results. This
effort resulted in a four-parameter astrometric catalogue containing
583,000,000 objects, with Gaia-DR1 quality positions and proper motions with
precisions from significantly less than 1 mas/yr to 5 mas/yr, depending on the
object's brightness and location on the sky.
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Asymptotic behaviour of ground states for mixtures of ferromagnetic and antiferromagnetic interactions in a dilute regime | We consider randomly distributed mixtures of bonds of ferromagnetic and
antiferromagnetic type in a two-dimensional square lattice with probability
$1-p$ and $p$, respectively, according to an i.i.d. random variable. We study
minimizers of the corresponding nearest-neighbour spin energy on large domains
in ${\mathbb Z}^2$. We prove that there exists $p_0$ such that for $p\le p_0$
such minimizers are characterized by a majority phase; i.e., they take
identically the value $1$ or $-1$ except for small disconnected sets. A
deterministic analogue is also proved.
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Extended Reduced-Form Framework for Non-Life Insurance | In this paper we propose a general framework for modeling an insurance
claims' information flow in continuous time, by generalizing the reduced-form
framework for credit risk and life insurance. In particular, we assume a
nontrivial dependence structure between the reference filtration and the
insurance internal filtration. We apply these results for pricing non-life
insurance liabilities in hybrid financial and insurance markets, while taking
into account the role of inflation under the benchmark approach. This framework
offers at the same time a general and flexible structure, and explicit and
treatable pricing formula.
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Overview of Recent Studies and Design Changes for the FNAL Magnetron Ion Source | This paper will cover several studies and design changes that will eventually
be implemented to the Fermi National Accelerator Laboratory (FNAL) magnetron
ion source. The topics include tungsten cathode insert, solenoid gas valves,
current controlled arc pulser, cesium boiler redesign, gas mixtures of hydrogen
and nitrogen, and duty factor reduction. The studies were performed on the FNAL
test stand, with the aim to improve source lifetime, stability, and reducing
the amount of tuning needed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Complete algebraic solution of multidimensional optimization problems in tropical semifield | We consider multidimensional optimization problems that are formulated in the
framework of tropical mathematics to minimize functions defined on vectors over
a tropical semifield (a semiring with idempotent addition and invertible
multiplication). The functions, given by a matrix and calculated through
multiplicative conjugate transposition, are nonlinear in the tropical
mathematics sense. We start with known results on the solution of the problems
with irreducible matrices. To solve the problems in the case of arbitrary
(reducible) matrices, we first derive the minimum value of the objective
function, and find a set of solutions. We show that all solutions of the
problem satisfy a system of vector inequalities, and then use these
inequalities to establish characteristic properties of the solution set.
Furthermore, all solutions of the problem are represented as a family of
subsets, each defined by a matrix that is obtained by using a matrix
sparsification technique. We describe a backtracking procedure that allows one
to reduce the brute-force generation of sparsified matrices by skipping those,
which cannot provide solutions, and thus offers an economical way to obtain all
subsets in the family. Finally, the characteristic properties of the solution
set are used to provide complete solutions in a closed form. We illustrate the
results obtained with simple numerical examples.
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Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back | A graph is a powerful concept for representation of relations between pairs
of entities. Data with underlying graph structure can be found across many
disciplines and there is a natural desire for understanding such data better.
Deep learning (DL) has achieved significant breakthroughs in a variety of
machine learning tasks in recent years, especially where data is structured on
a grid, such as in text, speech, or image understanding. However, surprisingly
little has been done to explore the applicability of DL on arbitrary
graph-structured data directly.
The goal of this thesis is to investigate architectures for DL on graphs and
study how to transfer, adapt or generalize concepts that work well on
sequential and image data to this domain. We concentrate on two important
primitives: embedding graphs or their nodes into a continuous vector space
representation (encoding) and, conversely, generating graphs from such vectors
back (decoding). To that end, we make the following contributions.
First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like
operation on graphs performed in the spatial domain where filters are
dynamically generated based on edge attributes. The method is used to encode
graphs with arbitrary and varying structure.
Second, we propose SuperPoint Graph, an intermediate point cloud
representation with rich edge attributes encoding the contextual relationship
between object parts. Based on this representation, ECC is employed to segment
large-scale point clouds without major sacrifice in fine details.
Third, we present GraphVAE, a graph generator allowing us to decode graphs
with variable but upper-bounded number of nodes making use of approximate graph
matching for aligning the predictions of an autoencoder with its inputs. The
method is applied to the task of molecule generation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Supermodular Optimization for Redundant Robot Assignment under Travel-Time Uncertainty | This paper considers the assignment of multiple mobile robots to goal
locations under uncertain travel time estimates. Our aim is to produce optimal
assignments, such that the average waiting time at destinations is minimized.
Our premise is that time is the most valuable asset in the system. Hence, we
make use of redundant robots to counter the effect of uncertainty. Since
solving the redundant assignment problem is strongly NP-hard, we exploit
structural properties of our problem to propose a polynomial-time, near-optimal
solution. We demonstrate that our problem can be reduced to minimizing a
supermodular cost function subject to a matroid constraint. This allows us to
develop a greedy algorithm, for which we derive sub-optimality bounds. A
comparison with the baseline non-redundant assignment shows that redundant
assignment reduces the waiting time at goals, and that this performance gap
increases as noise increases. Finally, we evaluate our method on a mobility
data set (specifying vehicle availability and passenger requests), recorded in
the area of Manhattan, New York. Our algorithm performs in real-time, and
reduces passenger waiting times when travel times are uncertain.
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Several classes of optimal ternary cyclic codes | Cyclic codes have efficient encoding and decoding algorithms over finite
fields, so that they have practical applications in communication systems,
consumer electronics and data storage systems. The objective of this paper is
to give eight new classes of optimal ternary cyclic codes with parameters
$[3^m-1,3^m-1-2m,4]$, according to a result on the non-existence of solutions
to a certain equation over $F_{3^m}$. It is worth noticing that some recent
conclusions on such optimal ternary cyclic codes are some special cases of our
work. More importantly, three of the nine open problems proposed by Ding and
Helleseth in [8] are solved completely. In addition, another one among the nine
open problems is also promoted.
| 1 | 0 | 1 | 0 | 0 | 0 |
$Σ$-pure-injective modules for string algebras and linear relations | We prove that indecomposable $\Sigma$-pure-injective modules for a string
algebra are string or band modules. The key step in our proof is a splitting
result for infinite-dimensional linear relations.
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Continuous Optimization of Adaptive Quadtree Structures | We present a novel continuous optimization method to the discrete problem of
quadtree optimization. The optimization aims at achieving a quadtree structure
with the highest mechanical stiffness, where the edges in the quadtree are
interpreted as structural elements carrying mechanical loads. We formulate
quadtree optimization as a continuous material distribution problem. The
discrete design variables (i.e., to refine or not to refine) are replaced by
continuous variables on multiple levels in the quadtree hierarchy. In discrete
quadtree optimization, a cell is only eligible for refinement if its parent
cell has been refined. We propose a continuous analogue to this dependency for
continuous multi-level design variables, and integrate it in the iterative
optimization process. Our results show that the continuously optimized quadtree
structures perform much stiffer than uniform patterns and the heuristically
optimized counterparts. We demonstrate the use of adaptive structures as
lightweight infill for 3D printed parts, where uniform geometric patterns have
been typically used in practice.
| 1 | 0 | 0 | 0 | 0 | 0 |
Machine Learning Meets Microeconomics: The Case of Decision Trees and Discrete Choice | We provide a microeconomic framework for decision trees: a popular machine
learning method. Specifically, we show how decision trees represent a
non-compensatory decision protocol known as disjunctions-of-conjunctions and
how this protocol generalizes many of the non-compensatory rules used in the
discrete choice literature so far. Additionally, we show how existing decision
tree variants address many economic concerns that choice modelers might have.
Beyond theoretical interpretations, we contribute to the existing literature of
two-stage, semi-compensatory modeling and to the existing decision tree
literature. In particular, we formulate the first bayesian model tree, thereby
allowing for uncertainty in the estimated non-compensatory rules as well as for
context-dependent preference heterogeneity in one's second-stage choice model.
Using an application of bicycle mode choice in the San Francisco Bay Area, we
estimate our bayesian model tree, and we find that it is over 1,000 times more
likely to be closer to the true data-generating process than a multinomial
logit model (MNL). Qualitatively, our bayesian model tree automatically finds
the effect of bicycle infrastructure investment to be moderated by travel
distance, socio-demographics and topography, and our model identifies
diminishing returns from bike lane investments. These qualitative differences
lead to bayesian model tree forecasts that directly align with the observed
bicycle mode shares in regions with abundant bicycle infrastructure such as
Davis, CA and the Netherlands. In comparison, MNL's forecasts are overly
optimistic.
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Structured Control Nets for Deep Reinforcement Learning | In recent years, Deep Reinforcement Learning has made impressive advances in
solving several important benchmark problems for sequential decision making.
Many control applications use a generic multilayer perceptron (MLP) for
non-vision parts of the policy network. In this work, we propose a new neural
network architecture for the policy network representation that is simple yet
effective. The proposed Structured Control Net (SCN) splits the generic MLP
into two separate sub-modules: a nonlinear control module and a linear control
module. Intuitively, the nonlinear control is for forward-looking and global
control, while the linear control stabilizes the local dynamics around the
residual of global control. We hypothesize that this will bring together the
benefits of both linear and nonlinear policies: improve training sample
efficiency, final episodic reward, and generalization of learned policy, while
requiring a smaller network and being generally applicable to different
training methods. We validated our hypothesis with competitive results on
simulations from OpenAI MuJoCo, Roboschool, Atari, and a custom 2D urban
driving environment, with various ablation and generalization tests, trained
with multiple black-box and policy gradient training methods. The proposed
architecture has the potential to improve upon broader control tasks by
incorporating problem specific priors into the architecture. As a case study,
we demonstrate much improved performance for locomotion tasks by emulating the
biological central pattern generators (CPGs) as the nonlinear part of the
architecture.
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PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits | We consider the problem of identifying any $k$ out of the best $m$ arms in an
$n$-armed stochastic multi-armed bandit. Framed in the PAC setting, this
particular problem generalises both the problem of `best subset selection' and
that of selecting `one out of the best m' arms [arcsk 2017]. In applications
such as crowd-sourcing and drug-designing, identifying a single good solution
is often not sufficient. Moreover, finding the best subset might be hard due to
the presence of many indistinguishably close solutions. Our generalisation of
identifying exactly $k$ arms out of the best $m$, where $1 \leq k \leq m$,
serves as a more effective alternative. We present a lower bound on the
worst-case sample complexity for general $k$, and a fully sequential PAC
algorithm, \GLUCB, which is more sample-efficient on easy instances. Also,
extending our analysis to infinite-armed bandits, we present a PAC algorithm
that is independent of $n$, which identifies an arm from the best $\rho$
fraction of arms using at most an additive poly-log number of samples than
compared to the lower bound, thereby improving over [arcsk 2017] and
[Aziz+AKA:2018]. The problem of identifying $k > 1$ distinct arms from the best
$\rho$ fraction is not always well-defined; for a special class of this
problem, we present lower and upper bounds. Finally, through a reduction, we
establish a relation between upper bounds for the `one out of the best $\rho$'
problem for infinite instances and the `one out of the best $m$' problem for
finite instances. We conjecture that it is more efficient to solve `small'
finite instances using the latter formulation, rather than going through the
former.
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Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data | Recommender systems (RS) help users navigate large sets of items in the
search for "interesting" ones. One approach to RS is Collaborative Filtering
(CF), which is based on the idea that similar users are interested in similar
items. Most model-based approaches to CF seek to train a
machine-learning/data-mining model based on sparse data; the model is then used
to provide recommendations. While most of the proposed approaches are effective
for small-size situations, the combinatorial nature of the problem makes it
impractical for medium-to-large instances. In this work we present a novel
approach to CF that works by training a Denoising Auto-Encoder (DAE) on
corrupted baskets, i.e., baskets from which one or more items have been
removed. The DAE is then forced to learn to reconstruct the original basket
given its corrupted input. Due to recent advancements in optimization and other
technologies for training neural-network models (such as DAE), the proposed
method results in a scalable and practical approach to CF. The contribution of
this work is twofold: (1) to identify missing items in observed baskets and,
thus, directly providing a CF model; and, (2) to construct a generative model
of baskets which may be used, for instance, in simulation analysis or as part
of a more complex analytical method.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stein's Method for Stationary Distributions of Markov Chains and Application to Ising Models | We develop a new technique, based on Stein's method, for comparing two
stationary distributions of irreducible Markov Chains whose update rules are
`close enough'. We apply this technique to compare Ising models on $d$-regular
expander graphs to the Curie-Weiss model (complete graph) in terms of pairwise
correlations and more generally $k$th order moments. Concretely, we show that
$d$-regular Ramanujan graphs approximate the $k$th order moments of the
Curie-Weiss model to within average error $k/\sqrt{d}$ (averaged over the size
$k$ subsets). The result applies even in the low-temperature regime; we also
derive some simpler approximation results for functionals of Ising models that
hold only at high enough temperatures.
| 0 | 0 | 1 | 0 | 0 | 0 |
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages | In this paper, we propose a novel and elegant solution to "Multi-Source
Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way
multilingual corpus without modifying the Neural Machine Translation (NMT)
architecture or training procedure. We simply concatenate the source sentences
to form a single long multi-source input sentence while keeping the target side
sentence as it is and train an NMT system using this preprocessed corpus. We
evaluate our method in resource poor as well as resource rich settings and show
its effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using
5 source languages) by comparing against existing methods for MSNMT. We also
provide some insights on how the NMT system leverages multilingual information
in such a scenario by visualizing attention.
| 1 | 0 | 0 | 0 | 0 | 0 |
Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods | The literature on Inverse Reinforcement Learning (IRL) typically assumes that
humans take actions in order to minimize the expected value of a cost function,
i.e., that humans are risk neutral. Yet, in practice, humans are often far from
being risk neutral. To fill this gap, the objective of this paper is to devise
a framework for risk-sensitive IRL in order to explicitly account for a human's
risk sensitivity. To this end, we propose a flexible class of models based on
coherent risk measures, which allow us to capture an entire spectrum of risk
preferences from risk-neutral to worst-case. We propose efficient
non-parametric algorithms based on linear programming and semi-parametric
algorithms based on maximum likelihood for inferring a human's underlying risk
measure and cost function for a rich class of static and dynamic
decision-making settings. The resulting approach is demonstrated on a simulated
driving game with ten human participants. Our method is able to infer and mimic
a wide range of qualitatively different driving styles from highly risk-averse
to risk-neutral in a data-efficient manner. Moreover, comparisons of the
Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL
framework more accurately captures observed participant behavior both
qualitatively and quantitatively, especially in scenarios where catastrophic
outcomes such as collisions can occur.
| 1 | 0 | 0 | 0 | 0 | 0 |
QRT maps and related Laurent systems | In recent work it was shown how recursive factorisation of certain QRT maps
leads to Somos-4 and Somos-5 recurrences with periodic coefficients, and to a
fifth-order recurrence with the Laurent property. Here we recursively factorise
the 12-parameter symmetric QRT map, given by a second-order recurrence, to
obtain a system of three coupled recurrences which possesses the Laurent
property. As degenerate special cases, we first derive systems of two coupled
recurrences corresponding to the 5-parameter multiplicative and additive
symmetric QRT maps. In all cases, the Laurent property is established using a
generalisation of a result due to Hickerson, and exact formulae for degree
growth are found from ultradiscrete (tropical) analogues of the recurrences.
For the general 18-parameter QRT map it is shown that the components of the
iterates can be written as a ratio of quantities that satisfy the same Somos-7
recurrence.
| 0 | 1 | 1 | 0 | 0 | 0 |
Model-based reinforcement learning in differential graphical games | This paper seeks to combine differential game theory with the
actor-critic-identifier architecture to determine forward-in-time, approximate
optimal controllers for formation tracking in multi-agent systems, where the
agents have uncertain heterogeneous nonlinear dynamics. A continuous control
strategy is proposed, using communication feedback from extended neighbors on a
communication topology that has a spanning tree. A model-based reinforcement
learning technique is developed to cooperatively control a group of agents to
track a trajectory in a desired formation. Simulation results are presented to
demonstrate the performance of the developed technique.
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The depth of a finite simple group | We introduce the notion of the depth of a finite group $G$, defined as the
minimal length of an unrefinable chain of subgroups from $G$ to the trivial
subgroup. In this paper we investigate the depth of (non-abelian) finite simple
groups. We determine the simple groups of minimal depth, and show, somewhat
surprisingly, that alternating groups have bounded depth. We also establish
general upper bounds on the depth of simple groups of Lie type, and study the
relation between the depth and the much studied notion of the length of simple
groups. The proofs of our main theorems depend (among other tools) on a deep
number-theoretic result, namely, Helfgott's recent solution of the ternary
Goldbach conjecture.
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TRAGALDABAS. First results on cosmic ray studies and their relation with the solar activity, the Earth magnetic field and the atmospheric properties | Cosmic rays originating from extraterrestrial sources are permanently
arriving at Earth atmosphere, where they produce up to billions of secondary
particles. The analysis of the secondary particles reaching to the surface of
the Earth may provide a very valuable information about the Sun activity,
changes in the geomagnetic field and the atmosphere, among others. In this
article, we present the first preliminary results of the analysis of the cosmic
rays measured with a high resolution tracking detector, TRAGALDABAS, located at
the Univ. of Santiago de Compostela, in Spain.
| 0 | 1 | 0 | 0 | 0 | 0 |
Approximation of solutions of SDEs driven by a fractional Brownian motion, under pathwise uniqueness | Our aim in this paper is to establish some strong stability properties of a
solution of a stochastic differential equation driven by a fractional Brownian
motion for which the pathwise uniqueness holds. The results are obtained using
Skorokhod's selection theorem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Smoothed GMM for quantile models | This paper develops theory for feasible estimators of finite-dimensional
parameters identified by general conditional quantile restrictions, under much
weaker assumptions than previously seen in the literature. This includes
instrumental variables nonlinear quantile regression as a special case. More
specifically, we consider a set of unconditional moments implied by the
conditional quantile restrictions, providing conditions for local
identification. Since estimators based on the sample moments are generally
impossible to compute numerically in practice, we study feasible estimators
based on smoothed sample moments. We propose a method of moments estimator for
exactly identified models, as well as a generalized method of moments estimator
for over-identified models. We establish consistency and asymptotic normality
of both estimators under general conditions that allow for weakly dependent
data and nonlinear structural models. Simulations illustrate the finite-sample
properties of the methods. Our in-depth empirical application concerns the
consumption Euler equation derived from quantile utility maximization.
Advantages of the quantile Euler equation include robustness to fat tails,
decoupling of risk attitude from the elasticity of intertemporal substitution,
and log-linearization without any approximation error. For the four countries
we examine, the quantile estimates of discount factor and elasticity of
intertemporal substitution are economically reasonable for a range of quantiles
above the median, even when two-stage least squares estimates are not
reasonable.
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Multi-district preference modelling | Generating realistic artificial preference distributions is an important part
of any simulation analysis of electoral systems. While this has been discussed
in some detail in the context of a single electoral district, many electoral
systems of interest are based on multiple districts. Neither treating
preferences between districts as independent nor ignoring the district
structure yields satisfactory results. We present a model based on an extension
of the classic Eggenberger-Pólya urn, in which each district is represented
by an urn and there is correlation between urns. We show in detail that this
procedure has a small number of tunable parameters, is computationally
efficient, and produces "realistic-looking" distributions. We intend to use it
in further studies of electoral systems.
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Orientability of the moduli space of Spin(7)-instantons | Let $(M,\Omega)$ be a closed $8$-dimensional manifold equipped with a
generically non-integrable $\mathrm{Spin}(7)$-structure $\Omega$. We prove that
if $\mathrm{Hom}(H^{3}(M,\mathbb{Z}), \mathbb{Z}_{2}) = 0$ then the moduli
space of irreducible $\mathrm{Spin}(7)$-instantons on $(M,\Omega)$ with gauge
group $\mathrm{SU}(r)$, $r\geq 2$, is orientable.
| 0 | 0 | 1 | 0 | 0 | 0 |
Neural Text Generation: A Practical Guide | Deep learning methods have recently achieved great empirical success on
machine translation, dialogue response generation, summarization, and other
text generation tasks. At a high level, the technique has been to train
end-to-end neural network models consisting of an encoder model to produce a
hidden representation of the source text, followed by a decoder model to
generate the target. While such models have significantly fewer pieces than
earlier systems, significant tuning is still required to achieve good
performance. For text generation models in particular, the decoder can behave
in undesired ways, such as by generating truncated or repetitive outputs,
outputting bland and generic responses, or in some cases producing
ungrammatical gibberish. This paper is intended as a practical guide for
resolving such undesired behavior in text generation models, with the aim of
helping enable real-world applications.
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Context Prediction for Unsupervised Deep Learning on Point Clouds | Point clouds provide a flexible and natural representation usable in
countless applications such as robotics or self-driving cars. Recently, deep
neural networks operating on raw point cloud data have shown promising results
on supervised learning tasks such as object classification and semantic
segmentation. While massive point cloud datasets can be captured using modern
scanning technology, manually labelling such large 3D point clouds for
supervised learning tasks is a cumbersome process. This necessitates effective
unsupervised learning methods that can produce representations such that
downstream tasks require significantly fewer annotated samples. We propose a
novel method for unsupervised learning on raw point cloud data in which a
neural network is trained to predict the spatial relationship between two point
cloud segments. While solving this task, representations that capture semantic
properties of the point cloud are learned. Our method outperforms previous
unsupervised learning approaches in downstream object classification and
segmentation tasks and performs on par with fully supervised methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
A core-set approach for distributed quadratic programming in big-data classification | A new challenge for learning algorithms in cyber-physical network systems is
the distributed solution of big-data classification problems, i.e., problems in
which both the number of training samples and their dimension is high.
Motivated by several problem set-ups in Machine Learning, in this paper we
consider a special class of quadratic optimization problems involving a "large"
number of input data, whose dimension is "big". To solve these quadratic
optimization problems over peer-to-peer networks, we propose an asynchronous,
distributed algorithm that scales with both the number and the dimension of the
input data (training samples in the classification problem). The proposed
distributed optimization algorithm relies on the notion of "core-set" which is
used in geometric optimization to approximate the value function associated to
a given set of points with a smaller subset of points. By computing local
core-sets on a smaller version of the global problem and exchanging them with
neighbors, the nodes reach consensus on a set of active constraints
representing an approximate solution for the global quadratic program.
| 1 | 0 | 1 | 0 | 0 | 0 |
On the Erasure Robustness Property of Random Matrices | The study of the restricted isometry property (RIP) for corrupted random
matrices is particularly important in the field of compressed sensing (CS) with
corruptions. If a matrix still satisfy RIP after a certain portion of rows are
erased, then we say that the matrix has the strong restricted isometry property
(SRIP. In the field of compressed sensing, random matrices satisfies certain
moment conditions are of particular interest. Among these matrices, those with
entries generated from i.i.d Gaussian or i.i.d $\pm1$ random variables are
often typically considered. Recent studies have shown that a matrix generated
from i.i.d Gaussian random variables satisfies the strong restricted isometry
property under arbitrary erasure of rows. In the first part of this paper we
will work on $\pm 1$ random matrices. We study the erasure robustness of $\pm
1$ random matrices show that with overwhelming probability the SRIP will still
hold. Moreover the analysis will also lead to the robust version of the
Johnson-Lindenstrauss Lemma for $\pm 1$ matrices. Then in the second part of
this paper we work on finite frames. The study of the stability of finite
frames under corruptions shares a lot of similarity to CS with corruption. We
will focus on the Gaussian finite frames as a starter. We will improve existing
results and confirm that a Gaussian random frame is numerically stable under
arbitrary erasure of rows.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Weighted Kendall and High-order Kernels for Permutations | We propose new positive definite kernels for permutations. First we introduce
a weighted version of the Kendall kernel, which allows to weight unequally the
contributions of different item pairs in the permutations depending on their
ranks. Like the Kendall kernel, we show that the weighted version is invariant
to relabeling of items and can be computed efficiently in $O(n \ln(n))$
operations, where $n$ is the number of items in the permutation. Second, we
propose a supervised approach to learn the weights by jointly optimizing them
with the function estimated by a kernel machine. Third, while the Kendall
kernel considers pairwise comparison between items, we extend it by considering
higher-order comparisons among tuples of items and show that the supervised
approach of learning the weights can be systematically generalized to
higher-order permutation kernels.
| 0 | 0 | 0 | 1 | 0 | 0 |
Synchronizing automata and the language of minimal reset words | We study a connection between synchronizing automata and its set $M$ of
minimal reset words, i.e., such that no proper factor is a reset word. We first
show that any synchronizing automaton having the set of minimal reset words
whose set of factors does not contain a word of length at most
$\frac{1}{4}\min\{|u|: u\in I\}+\frac{1}{16}$ has a reset word of length at
most $(n-\frac{1}{2})^{2}$ In the last part of the paper we focus on the
existence of synchronizing automata with a given ideal $I$ that serves as the
set of reset words. To this end, we introduce the notion of the tail structure
of the (not necessarily regular) ideal $I=\Sigma^{*}M\Sigma^{*}$. With this
tool, we first show the existence of an infinite strongly connected
synchronizing automaton $\mathcal{A}$ having $I$ as the set of reset words and
such that every other strongly connected synchronizing automaton having $I$ as
the set of reset words is an homomorphic image of $\mathcal{A}$. Finally, we
show that for any non-unary regular ideal $I$ there is a strongly connected
synchronizing automaton having $I$ as the set of reset words with at most
$(km^{k})2^{km^{k}n}$ states, where $k=|\Sigma|$, $m$ is the length of a
shortest word in $M$, and $n$ is the dimension of the smallest automaton
recognizing $M$ (state complexity of $M$). This automaton is computable and we
show an algorithm to compute it in time $\mathcal{O}((k^{2}m^{k})2^{km^{k}n})$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Peephole: Predicting Network Performance Before Training | The quest for performant networks has been a significant force that drives
the advancements of deep learning in recent years. While rewarding, improving
network design has never been an easy journey. The large design space combined
with the tremendous cost required for network training poses a major obstacle
to this endeavor. In this work, we propose a new approach to this problem,
namely, predicting the performance of a network before training, based on its
architecture. Specifically, we develop a unified way to encode individual
layers into vectors and bring them together to form an integrated description
via LSTM. Taking advantage of the recurrent network's strong expressive power,
this method can reliably predict the performances of various network
architectures. Our empirical studies showed that it not only achieved accurate
predictions but also produced consistent rankings across datasets -- a key
desideratum in performance prediction.
| 1 | 0 | 0 | 1 | 0 | 0 |
Optimization Landscape and Expressivity of Deep CNNs | We analyze the loss landscape and expressiveness of practical deep
convolutional neural networks (CNNs) with shared weights and max pooling
layers. We show that such CNNs produce linearly independent features at a
"wide" layer which has more neurons than the number of training samples. This
condition holds e.g. for the VGG network. Furthermore, we provide for such wide
CNNs necessary and sufficient conditions for global minima with zero training
error. For the case where the wide layer is followed by a fully connected layer
we show that almost every critical point of the empirical loss is a global
minimum with zero training error. Our analysis suggests that both depth and
width are very important in deep learning. While depth brings more
representational power and allows the network to learn high level features,
width smoothes the optimization landscape of the loss function in the sense
that a sufficiently wide network has a well-behaved loss surface with almost no
bad local minima.
| 1 | 0 | 0 | 1 | 0 | 0 |
A new cosine series antialiasing function and its application to aliasing-free glottal source models for speech and singing synthesis | We formulated and implemented a procedure to generate aliasing-free
excitation source signals. It uses a new antialiasing filter in the continuous
time domain followed by an IIR digital filter for response equalization. We
introduced a cosine-series-based general design procedure for the new
antialiasing function. We applied this new procedure to implement the
antialiased Fujisaki-Ljungqvist model. We also applied it to revise our
previous implementation of the antialiased Fant-Liljencrants model. A
combination of these signals and a lattice implementation of the time varying
vocal tract model provides a reliable and flexible basis to test fo extractors
and source aperiodicity analysis methods. MATLAB implementations of these
antialiased excitation source models are available as part of our open source
tools for speech science.
| 1 | 0 | 0 | 0 | 0 | 0 |
Defining Equations of Nilpotent Orbits for Borel Subgroups of Modality Zero in Type $A_{n}$ | Let $G$ be a quasi-simple algebraic group defined over an algebraically
closed field $k$ and $B$ a Borel subgroup of $G$ acting on the nilradical
$\mathfrak{n}$ of its Lie algebra $\mathfrak{b}$ via the Adjoint
representation. It is known that $B$ has only finitely many orbits in only five
cases: when $G$ is of type $A_{n}$ for $n \leq 4$, and when $G$ is type
$B_{2}$. In this paper, we elaborate on this work in the case when $G =SL_{n
+1}(k)$ (type $A_{n})$, for $n \leq 4$, by finding the polynomial defining
equations of each orbit. Consequences of these equations include the dimension
of the orbits and the closure ordering on the set of orbits, although these
facts are already known. The other case, when $G$ is type $B_{2}$, can be
approached the same way and is treated in a separate paper, where we believe
the determination of the closure order is new.
| 0 | 0 | 1 | 0 | 0 | 0 |
Quadratically Tight Relations for Randomized Query Complexity | Let $f:\{0,1\}^n \rightarrow \{0,1\}$ be a Boolean function. The certificate
complexity $C(f)$ is a complexity measure that is quadratically tight for the
zero-error randomized query complexity $R_0(f)$: $C(f) \leq R_0(f) \leq
C(f)^2$. In this paper we study a new complexity measure that we call
expectational certificate complexity $EC(f)$, which is also a quadratically
tight bound on $R_0(f)$: $EC(f) \leq R_0(f) = O(EC(f)^2)$. We prove that $EC(f)
\leq C(f) \leq EC(f)^2$ and show that there is a quadratic separation between
the two, thus $EC(f)$ gives a tighter upper bound for $R_0(f)$. The measure is
also related to the fractional certificate complexity $FC(f)$ as follows:
$FC(f) \leq EC(f) = O(FC(f)^{3/2})$. This also connects to an open question by
Aaronson whether $FC(f)$ is a quadratically tight bound for $R_0(f)$, as
$EC(f)$ is in fact a relaxation of $FC(f)$.
In the second part of the work, we upper bound the distributed query
complexity $D^\mu_\epsilon(f)$ for product distributions $\mu$ by the square of
the query corruption bound ($\mathrm{corr}_\epsilon(f)$) which improves upon a
result of Harsha, Jain and Radhakrishnan [2015]. A similar statement for
communication complexity is open.
| 1 | 0 | 0 | 0 | 0 | 0 |
Parabolic subgroup orbits on finite root systems | Oshima's Lemma describes the orbits of parabolic subgroups of irreducible
finite Weyl groups on crystallographic root systems. This note generalises that
result to all root systems of finite Coxeter groups, and provides a self
contained proof, independent of the representation theory of semisimple complex
Lie algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction | In human-in-the-loop machine learning, the user provides information beyond
that in the training data. Many algorithms and user interfaces have been
designed to optimize and facilitate this human--machine interaction; however,
fewer studies have addressed the potential defects the designs can cause.
Effective interaction often requires exposing the user to the training data or
its statistics. The design of the system is then critical, as this can lead to
double use of data and overfitting, if the user reinforces noisy patterns in
the data. We propose a user modelling methodology, by assuming simple rational
behaviour, to correct the problem. We show, in a user study with 48
participants, that the method improves predictive performance in a sparse
linear regression sentiment analysis task, where graded user knowledge on
feature relevance is elicited. We believe that the key idea of inferring user
knowledge with probabilistic user models has general applicability in guarding
against overfitting and improving interactive machine learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
A note on the asymptotics of the modified Bessel functions on the Stokes lines | We employ the exponentially improved asymptotic expansions of the confluent
hypergeometric functions on the Stokes lines discussed by the author [Appl.
Math. Sci. {\bf 7} (2013) 6601--6609] to give the analogous expansions of the
modified Bessel functions $I_\nu(z)$ and $K_\nu(z)$ for large $z$ and finite
$\nu$ on $\arg\,z=\pm\pi$ (and, in the case of $I_\nu(z)$, also on
$\arg\,z=0$). Numerical results are presented to illustrate the accuracy of
these expansions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fair k-Center Clustering for Data Summarization | In data summarization we want to choose k prototypes in order to summarize a
data set. We study a setting where the data set comprises several demographic
groups and we are restricted to choose k_i prototypes belonging to group i. A
common approach to the problem without the fairness constraint is to optimize a
centroid-based clustering objective such as k-center. A natural extension then
is to incorporate the fairness constraint into the clustering objective.
Existing algorithms for doing so run in time super-quadratic in the size of the
data set. This is in contrast to the standard k-center objective that can be
approximately optimized in linear time. In this paper, we resolve this gap by
providing a simple approximation algorithm for the k-center problem under the
fairness constraint with running time linear in the size of the data set and k.
If the number of demographic groups is small, the approximation guarantee of
our algorithm only incurs a constant-factor overhead. We demonstrate the
applicability of our algorithm on both synthetic and real data sets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Advanced Bayesian Multilevel Modeling with the R Package brms | The brms package allows R users to easily specify a wide range of Bayesian
single-level and multilevel models, which are fitted with the probabilistic
programming language Stan behind the scenes. Several response distributions are
supported, of which all parameters (e.g., location, scale, and shape) can be
predicted at the same time thus allowing for distributional regression.
Non-linear relationships may be specified using non-linear predictor terms or
semi-parametric approaches such as splines or Gaussian processes. To make all
of these modeling options possible in a multilevel framework, brms provides an
intuitive and powerful formula syntax, which extends the well known formula
syntax of lme4. The purpose of the present paper is to introduce this syntax in
detail and to demonstrate its usefulness with four examples, each showing other
relevant aspects of the syntax.
| 0 | 0 | 0 | 1 | 0 | 0 |
$W$-entropy, super Perelman Ricci flows and $(K, m)$-Ricci solitons | In this paper, we prove the characterization of the $(K, \infty)$-super
Perelman Ricci flows by various functional inequalities and gradient estimate
for the heat semigroup generated by the Witten Laplacian on manifolds equipped
with time dependent metrics and potentials. As a byproduct, we derive the
Hamilton type dimension free Harnack inequality on manifolds with $(K,
\infty)$-super Perelman Ricci flows. Based on a new second order differential
inequality on the Boltzmann-Shannon entropy for the heat equation of the Witten
Laplacian, we introduce a new $W$-entropy quantity and prove its monotonicity
for the heat equation of the Witten Laplacian on complete Riemannian manifolds
with the $CD(K, \infty)$-condition and on compact manifolds with $(K,
\infty)$-super Perelman Ricci flows. Our results characterize the $(K,
\infty)$-Ricci solitons and the $(K, \infty)$-Perelman Ricci flows. We also
prove a second order differential entropy inequality on $(K, m)$-super Ricci
flows, which can be used to characterize the $(K, m)$-Ricci solitons and the
$(K, m)$-Ricci flows. Finally, we give a probabilistic interpretation of the
$W$-entropy for the heat equation of the Witten Laplacian on manifolds with the
$CD(K, m)$-condition.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stellar energetic particle ionization in protoplanetary disks around T Tauri stars | Anomalies in the abundance measurements of short lived radionuclides in
meteorites indicate that the protosolar nebulae was irradiated by a high amount
of energetic particles (E$\gtrsim$10 MeV). The particle flux of the
contemporary Sun cannot explain these anomalies. However, similar to T Tauri
stars the young Sun was more active and probably produced enough high energy
particles to explain those anomalies. We want to study the interaction of
stellar energetic particles with the gas component of the disk and identify
possible observational tracers of this interaction. We use a 2D radiation
thermo-chemical protoplanetary disk code to model a disk representative for T
Tauri stars. We use a particle energy distribution derived from solar flare
observations and an enhanced stellar particle flux proposed for T Tauri stars.
For this particle spectrum we calculate the stellar particle ionization rate
throughout the disk with an accurate particle transport model. We study the
impact of stellar particles for models with varying X-ray and cosmic-ray
ionization rates. We find that stellar particle ionization has a significant
impact on the abundances of the common disk ionization tracers HCO$^+$ and
N$_2$H$^+$, especially in models with low cosmic-ray ionization rates. In
contrast to cosmic rays and X-rays, stellar particles cannot reach the midplane
of the disk. Therefore molecular ions residing in the disk surface layers are
more affected by stellar particle ionization than molecular ions tracing the
cold layers/midplane of the disk. Spatially resolved observations of molecular
ions tracing different vertical layers of the disk allow to disentangle the
contribution of stellar particle ionization from other competing ionization
sources. Modeling such observations with a model like the one presented here
allows to constrain the stellar particle flux in disks around T Tauri stars.
| 0 | 1 | 0 | 0 | 0 | 0 |
Extended Trust-Region Problems with One or Two Balls: Exact Copositive and Lagrangian Relaxations | We establish a geometric condition guaranteeing exact copositive relaxation
for the nonconvex quadratic optimization problem under two quadratic and
several linear constraints, and present sufficient conditions for global
optimality in terms of generalized Karush-Kuhn-Tucker multipliers. The
copositive relaxation is tighter than the usual Lagrangian relaxation. We
illustrate this by providing a whole class of quadratic optimization problems
that enjoys exactness of copositive relaxation while the usual Lagrangian
duality gap is infinite. Finally, we also provide verifiable conditions under
which both the usual Lagrangian relaxation and the copositive relaxation are
exact for an extended CDT (two-ball trust-region) problem. Importantly, the
sufficient conditions can be verified by solving linear optimization problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Weighted batch means estimators in Markov chain Monte Carlo | This paper proposes a family of weighted batch means variance estimators,
which are computationally efficient and can be conveniently applied in
practice. The focus is on Markov chain Monte Carlo simulations and estimation
of the asymptotic covariance matrix in the Markov chain central limit theorem,
where conditions ensuring strong consistency are provided. Finite sample
performance is evaluated through auto-regressive, Bayesian spatial-temporal,
and Bayesian logistic regression examples, where the new estimators show
significant computational gains with a minor sacrifice in variance compared
with existing methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation | Gradient-based optimization is the foundation of deep learning and
reinforcement learning. Even when the mechanism being optimized is unknown or
not differentiable, optimization using high-variance or biased gradient
estimates is still often the best strategy. We introduce a general framework
for learning low-variance, unbiased gradient estimators for black-box functions
of random variables. Our method uses gradients of a neural network trained
jointly with model parameters or policies, and is applicable in both discrete
and continuous settings. We demonstrate this framework for training discrete
latent-variable models. We also give an unbiased, action-conditional extension
of the advantage actor-critic reinforcement learning algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Truncation-free Hybrid Inference for DPMM | Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian
non-parametrics. While these models free from choosing the number of components
a-priori, computationally attractive variational inference often reintroduces
the need to do so, via a truncation on the variational distribution. In this
paper we present a truncation-free hybrid inference for DPMM, combining the
advantages of sampling-based MCMC and variational methods. The proposed
hybridization enables more efficient variational updates, while increasing
model complexity only if needed. We evaluate the properties of the hybrid
updates and their empirical performance in single- as well as mixed-membership
models. Our method is easy to implement and performs favorably compared to
existing schemas.
| 1 | 0 | 0 | 1 | 0 | 0 |
Functors and morphisms determined by subcategories | We study the existence and uniqueness of minimal right determiners in various
categories. Particularly in a Hom-finite hereditary abelian category with
enough projectives, we prove that the Auslander-Reiten-Smal{\o}-Ringel formula
of the minimal right determiner still holds. As an application, we give a
formula of minimal right determiners in the category of finitely presented
representations of strongly locally finite quivers.
| 0 | 0 | 1 | 0 | 0 | 0 |
Collective Effects in Nanolasers Explained by Generalized Rate Equations | We study the stationary photon output and statistics of small lasers. Our
closed-form expressions clarify the contribution of collective effects due to
the interaction between quantum emitters. We generalize laser rate equations
and explain photon trapping: a decrease of the photon number output below the
lasing threshold, derive an expression for the stationary cavity mode
autocorrelation function $g_2$, which implies that collective effects may
strongly influence the photon statistics. We identify conditions for coherent,
thermal and superthermal radiation, the latter being a unique fingerprint for
collective emission in lasers. These generic analytical results agree with
recent experiments, complement numerical results, and provide insight into and
design rules for nanolasers.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Risk of Machine Learning | Many applied settings in empirical economics involve simultaneous estimation
of a large number of parameters. In particular, applied economists are often
interested in estimating the effects of many-valued treatments (like teacher
effects or location effects), treatment effects for many groups, and prediction
models with many regressors. In these settings, machine learning methods that
combine regularized estimation and data-driven choices of regularization
parameters are useful to avoid over-fitting. In this article, we analyze the
performance of a class of machine learning estimators that includes ridge,
lasso and pretest in contexts that require simultaneous estimation of many
parameters. Our analysis aims to provide guidance to applied researchers on (i)
the choice between regularized estimators in practice and (ii) data-driven
selection of regularization parameters. To address (i), we characterize the
risk (mean squared error) of regularized estimators and derive their relative
performance as a function of simple features of the data generating process. To
address (ii), we show that data-driven choices of regularization parameters,
based on Stein's unbiased risk estimate or on cross-validation, yield
estimators with risk uniformly close to the risk attained under the optimal
(unfeasible) choice of regularization parameters. We use data from recent
examples in the empirical economics literature to illustrate the practical
applicability of our results.
| 0 | 0 | 0 | 1 | 0 | 0 |
Agatha: disentangling periodic signals from correlated noise in a periodogram framework | Periodograms are used as a key significance assessment and visualisation tool
to display the significant periodicities in unevenly sampled time series. We
introduce a framework of periodograms, called "Agatha", to disentangle periodic
signals from correlated noise and to solve the 2-dimensional model selection
problem: signal dimension and noise model dimension. These periodograms are
calculated by applying likelihood maximization and marginalization and combined
in a self-consistent way. We compare Agatha with other periodograms for the
detection of Keplerian signals in synthetic radial velocity data produced for
the Radial Velocity Challenge as well as in radial velocity datasets of several
Sun-like stars. In our tests we find Agatha is able to recover signals to the
adopted detection limit of the radial velocity challenge. Applied to real
radial velocity, we use Agatha to confirm previous analysis of CoRoT-7 and to
find two new planet candidates with minimum masses of 15.1 $M_\oplus$ and 7.08
$M_\oplus$ orbiting HD177565 and HD41248, with periods of 44.5 d and 13.4 d,
respectively. We find that Agatha outperforms other periodograms in terms of
removing correlated noise and assessing the significances of signals with more
robust metrics. Moreover, it can be used to select the optimal noise model and
to test the consistency of signals in time. Agatha is intended to be flexible
enough to be applied to time series analyses in other astronomical and
scientific disciplines. Agatha is available at this http URL.
| 0 | 1 | 0 | 1 | 0 | 0 |
A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome | In this work, we consider the problem of predicting the course of a
progressive disease, such as cancer or Alzheimer's. Progressive diseases often
start with mild symptoms that might precede a diagnosis, and each patient
follows their own trajectory. Patient trajectories exhibit wild variability,
which can be associated with many factors such as genotype, age, or sex. An
additional layer of complexity is that, in real life, the amount and type of
data available for each patient can differ significantly. For example, for one
patient we might have no prior history, whereas for another patient we might
have detailed clinical assessments obtained at multiple prior time-points. This
paper presents a probabilistic model that can handle multiple modalities
(including images and clinical assessments) and variable patient histories with
irregular timings and missing entries, to predict clinical scores at future
time-points. We use a sigmoidal function to model latent disease progression,
which gives rise to clinical observations in our generative model. We
implemented an approximate Bayesian inference strategy on the proposed model to
estimate the parameters on data from a large population of subjects.
Furthermore, the Bayesian framework enables the model to automatically
fine-tune its predictions based on historical observations that might be
available on the test subject. We applied our method to a longitudinal
Alzheimer's disease dataset with more than 3000 subjects [23] and present a
detailed empirical analysis of prediction performance under different
scenarios, with comparisons against several benchmarks. We also demonstrate how
the proposed model can be interrogated to glean insights about temporal
dynamics in Alzheimer's disease.
| 0 | 0 | 0 | 1 | 0 | 0 |
On the generation of the quarks through spontaneous symmetry breaking | In this paper we present the state of the art about the quarks: group SU(3),
Lie algebra, the electric charge and mass. The quarks masses are generated in
the same way as the lepton masses. It is constructed a term in the Lagrangian
that couples the Higgs doublet to the fermion fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
Distributed Holistic Clustering on Linked Data | Link discovery is an active field of research to support data integration in
the Web of Data. Due to the huge size and number of available data sources,
efficient and effective link discovery is a very challenging task. Common
pairwise link discovery approaches do not scale to many sources with very large
entity sets. We here propose a distributed holistic approach to link many data
sources based on a clustering of entities that represent the same real-world
object. Our clustering approach provides a compact and fused representation of
entities, and can identify errors in existing links as well as many new links.
We support a distributed execution of the clustering approach to achieve faster
execution times and scalability for large real-world data sets. We provide a
novel gold standard for multi-source clustering, and evaluate our methods with
respect to effectiveness and efficiency for large data sets from the geographic
and music domains.
| 1 | 0 | 0 | 0 | 0 | 0 |
A branch-and-bound algorithm for the minimum radius $k$-enclosing ball problem | The minimum $k$-enclosing ball problem seeks the ball with smallest radius
that contains at least~$k$ of~$m$ given points in a general $n$-dimensional
Euclidean space. This problem is NP-hard. We present a branch-and-bound
algorithm on the tree of the subsets of~$k$ points to solve this problem. The
nodes on the tree are ordered in a suitable way, which, complemented with a
last-in-first-out search strategy, allows for only a small fraction of nodes to
be explored. Additionally, an efficient dual algorithm to solve the subproblems
at each node is employed.
| 1 | 0 | 1 | 0 | 0 | 0 |
Dependency Parsing with Dilated Iterated Graph CNNs | Dependency parses are an effective way to inject linguistic knowledge into
many downstream tasks, and many practitioners wish to efficiently parse
sentences at scale. Recent advances in GPU hardware have enabled neural
networks to achieve significant gains over the previous best models, these
models still fail to leverage GPUs' capability for massive parallelism due to
their requirement of sequential processing of the sentence. In response, we
propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for
graph-based dependency parsing, a graph convolutional architecture that allows
for efficient end-to-end GPU parsing. In experiments on the English Penn
TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best
neural network parsers.
| 1 | 0 | 0 | 0 | 0 | 0 |
Motion Planning in Irreducible Path Spaces | The motion of a mechanical system can be defined as a path through its
configuration space. Computing such a path has a computational complexity
scaling exponentially with the dimensionality of the configuration space. We
propose to reduce the dimensionality of the configuration space by introducing
the irreducible path --- a path having a minimal swept volume. The paper
consists of three parts: In part I, we define the space of all irreducible
paths and show that planning a path in the irreducible path space preserves
completeness of any motion planning algorithm. In part II, we construct an
approximation to the irreducible path space of a serial kinematic chain under
certain assumptions. In part III, we conduct motion planning using the
irreducible path space for a mechanical snake in a turbine environment, for a
mechanical octopus with eight arms in a pipe system and for the sideways motion
of a humanoid robot moving through a room with doors and through a hole in a
wall. We demonstrate that the concept of an irreducible path can be applied to
any motion planning algorithm taking curvature constraints into account.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time | We give a polynomial-time algorithm for learning neural networks with one
layer of sigmoids feeding into any Lipschitz, monotone activation function
(e.g., sigmoid or ReLU). We make no assumptions on the structure of the
network, and the algorithm succeeds with respect to {\em any} distribution on
the unit ball in $n$ dimensions (hidden weight vectors also have unit norm).
This is the first assumption-free, provably efficient algorithm for learning
neural networks with two nonlinear layers.
Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that
combines isotonic regression with kernel methods. It outputs a hypothesis that
yields efficient oracle access to interpretable features. It also suggests a
new approach to Boolean learning problems via real-valued conditional-mean
functions, sidestepping traditional hardness results from computational
learning theory.
Along these lines, we subsume and improve many longstanding results for PAC
learning Boolean functions to the more general, real-valued setting of {\em
probabilistic concepts}, a model that (unlike PAC learning) requires non-i.i.d.
noise-tolerance.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sample-Derived Disjunctive Rules for Secure Power System Operation | Machine learning techniques have been used in the past using Monte Carlo
samples to construct predictors of the dynamic stability of power systems. In
this paper we move beyond the task of prediction and propose a comprehensive
approach to use predictors, such as Decision Trees (DT), within a standard
optimization framework for pre- and post-fault control purposes. In particular,
we present a generalizable method for embedding rules derived from DTs in an
operation decision-making model. We begin by pointing out the specific
challenges entailed when moving from a prediction to a control framework. We
proceed with introducing the solution strategy based on generalized disjunctive
programming (GDP) as well as a two-step search method for identifying optimal
hyper-parameters for balancing cost and control accuracy. We showcase how the
proposed approach constructs security proxies that cover multiple contingencies
while facing high-dimensional uncertainty with respect to operating conditions
with the use of a case study on the IEEE 39-bus system. The method is shown to
achieve efficient system control at a marginal increase in system price
compared to an oracle model.
| 0 | 0 | 0 | 1 | 0 | 0 |
Satellite altimetry reveals spatial patterns of variations in the Baltic Sea wave climate | The main properties of the climate of waves in the seasonally ice-covered
Baltic Sea and its decadal changes since 1990 are estimated from satellite
altimetry data. The data set of significant wave heights (SWH) from all
existing nine satellites, cleaned and cross-validated against in situ
measurements, shows overall a very consistent picture. A comparison with visual
observations shows a good correspondence with correlation coefficients of
0.6-0.8. The annual mean SWH reveals a tentative increase of 0.005 m yr-1, but
higher quantiles behave in a cyclic manner with a timescale of 10-15 yr.
Changes in the basin-wide average SWH have a strong meridional pattern: an
increase in the central and western parts of the sea and decrease in the east.
This pattern is likely caused by a rotation of wind directions rather than by
an increase in the wind speed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Estimation of Covariance Matrices for Portfolio Optimization using Gaussian Processes | Estimating covariances between financial assets plays an important role in
risk management and optimal portfolio allocation. In practice, when the sample
size is small compared to the number of variables, i.e. when considering a wide
universe of assets over just a few years, this poses considerable challenges
and the empirical estimate is known to be very unstable.
Here, we propose a novel covariance estimator based on the Gaussian Process
Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear
extension of standard factor models with readily interpretable parameters
reminiscent of market betas. Furthermore, our Bayesian treatment naturally
shrinks the sample covariance matrix towards a more structured matrix given by
the prior and thereby systematically reduces estimation errors.
| 0 | 0 | 0 | 0 | 0 | 1 |
Zeeman interaction and Jahn-Teller effect in $Γ_8$ multiplet | We present a thorough analysis of the interplay of magnetic moment and the
Jahn-Teller effect in the $\Gamma_8$ cubic multiplet. We find that in the
presence of dynamical Jahn-Teller effect, the Zeeman interaction remains
isotropic, whereas the $g$ and $G$ factors can change their signs. The static
Jahn-Teller distortion also can change the sign of these $g$ factors as well as
the nature of the magnetic anisotropy. Combining the theory with
state-of-the-art {\it ab initio} calculations, we analyzed the magnetic
properties of Np$^{4+}$ and Ir$^{4+}$ impurity ions in cubic environment. The
calculated $g$ factors of Np$^{4+}$ impurity agree well with experimental data.
The {\it ab initio} calculation predicts strong Jahn-Teller effect in Ir$^{4+}$
ion in cubic environment and the strong vibronic reduction of $g$ and $G$
factors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measuring the Declared SDK Versions and Their Consistency with API Calls in Android Apps | Android has been the most popular smartphone system, with multiple platform
versions (e.g., KITKAT and Lollipop) active in the market. To manage the
application's compatibility with one or more platform versions, Android allows
apps to declare the supported platform SDK versions in their manifest files. In
this paper, we make a first effort to study this modern software mechanism. Our
objective is to measure the current practice of the declared SDK versions
(which we term as DSDK versions afterwards) in real apps, and the consistency
between the DSDK versions and their app API calls. To this end, we perform a
three-dimensional analysis. First, we parse Android documents to obtain a
mapping between each API and their corresponding platform versions. We then
analyze the DSDK-API consistency for over 24K apps, among which we pre-exclude
1.3K apps that provide different app binaries for different Android versions
through Google Play analysis. Besides shedding light on the current DSDK
practice, our study quantitatively measures the two side effects of
inappropriate DSDK versions: (i) around 1.8K apps have API calls that do not
exist in some declared SDK versions, which causes runtime crash bugs on those
platform versions; (ii) over 400 apps, due to claiming the outdated targeted
DSDK versions, are potentially exploitable by remote code execution. These
results indicate the importance and difficulty of declaring correct DSDK, and
our work can help developers fulfill this goal.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Design and Implementation of Modern Online Programming Competitions | This paper presents a framework for the implementation of online programming
competitions, including a set of principles for the design of the multiplayer
game and a practical framework for the construction of the competition
environment. The paper presents a successful example competition, the 2016-17
Halite challenge, and briefly mentions a second competition, the Halite II
challenge, which launched in October 2017.
| 1 | 0 | 0 | 0 | 0 | 0 |
Emergence of a spectral gap in a class of random matrices associated with split graphs | Motivated by the intriguing behavior displayed in a dynamic network that
models a population of extreme introverts and extroverts (XIE), we consider the
spectral properties of ensembles of random split graph adjacency matrices. We
discover that, in general, a gap emerges in the bulk spectrum between -1 and 0
that contains a single eigenvalue. An analytic expression for the bulk
distribution is derived and verified with numerical analysis. We also examine
their relation to chiral ensembles, which are associated with bipartite graphs.
| 0 | 1 | 1 | 0 | 0 | 0 |
Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines | We present a software tool that employs state-of-the-art natural language
processing (NLP) and machine learning techniques to help newspaper editors
compose effective headlines for online publication. The system identifies the
most salient keywords in a news article and ranks them based on both their
overall popularity and their direct relevance to the article. The system also
uses a supervised regression model to identify headlines that are likely to be
widely shared on social media. The user interface is designed to simplify and
speed the editor's decision process on the composition of the headline. As
such, the tool provides an efficient way to combine the benefits of automated
predictors of engagement and search-engine optimization (SEO) with human
judgments of overall headline quality.
| 1 | 0 | 0 | 0 | 0 | 0 |
ParaGraphE: A Library for Parallel Knowledge Graph Embedding | Knowledge graph embedding aims at translating the knowledge graph into
numerical representations by transforming the entities and relations into
continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have
been proposed to deal with this problem, but existing single-thread
implementations of them are time-consuming for large-scale knowledge graphs.
Here, we design a unified parallel framework to parallelize these methods,
which achieves a significant time reduction without influencing the accuracy.
We name our framework as ParaGraphE, which provides a library for parallel
knowledge graph embedding. The source code can be downloaded from
this https URL .
| 1 | 0 | 0 | 0 | 0 | 0 |
The Globular Cluster - Dark Matter Halo Connection | I present a simple phenomenological model for the observed linear scaling of
the stellar mass in old globular clusters (GCs) with $z=0$ halo mass in which
the stellar mass in GCs scales linearly with progenitor halo mass at $z=6$
above a minimum halo mass for GC formation. This model reproduces the observed
$M_{\rm GCs}-M_{\rm halo}$ relation at $z=0$ and results in a prediction for
the minimum halo mass at $z=6$ required for hosting one GC: $M_{\rm
min}(z=6)=1.07 \times 10^9\,M_{\odot}$. Translated to $z=0$, the mean threshold
mass is $M_{\rm halo}(z=0) \approx 2\times 10^{10}\,M_{\odot}$. I explore the
observability of GCs in the reionization era and their contribution to cosmic
reionization, both of which depend sensitively on the (unknown) ratio of GC
birth mass to present-day stellar mass, $\xi$. Based on current detections of
$z \gtrsim 6$ objects with $M_{1500} < -17$, values of $\xi > 10$ are strongly
disfavored; this, in turn, has potentially important implications for GC
formation scenarios. Even for low values of $\xi$, some observed high-$z$
galaxies may actually be GCs, complicating estimates of reionization-era galaxy
ultraviolet luminosity functions and constraints on dark matter models. GCs are
likely important reionization sources if $5 \lesssim \xi \lesssim 10$. I also
explore predictions for the fraction of accreted versus in situ GCs in the
local Universe and for descendants of systems at the halo mass threshold of GC
formation (dwarf galaxies). An appealing feature of the model presented here is
the ability to make predictions for GC properties based solely on dark matter
halo merger trees.
| 0 | 1 | 0 | 0 | 0 | 0 |
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps | Convolutional neural networks (CNNs) have become the dominant neural network
architecture for solving many state-of-the-art (SOA) visual processing tasks.
Even though Graphical Processing Units (GPUs) are most often used in training
and deploying CNNs, their power efficiency is less than 10 GOp/s/W for
single-frame runtime inference. We propose a flexible and efficient CNN
accelerator architecture called NullHop that implements SOA CNNs useful for
low-power and low-latency application scenarios. NullHop exploits the sparsity
of neuron activations in CNNs to accelerate the computation and reduce memory
requirements. The flexible architecture allows high utilization of available
computing resources across kernel sizes ranging from 1x1 to 7x7. NullHop can
process up to 128 input and 128 output feature maps per layer in a single pass.
We implemented the proposed architecture on a Xilinx Zynq FPGA platform and
present results showing how our implementation reduces external memory
transfers and compute time in five different CNNs ranging from small ones up to
the widely known large VGG16 and VGG19 CNNs. Post-synthesis simulations using
Mentor Modelsim in a 28nm process with a clock frequency of 500 MHz show that
the VGG19 network achieves over 450 GOp/s. By exploiting sparsity, NullHop
achieves an efficiency of 368%, maintains over 98% utilization of the MAC
units, and achieves a power efficiency of over 3TOp/s/W in a core area of
6.3mm$^2$. As further proof of NullHop's usability, we interfaced its FPGA
implementation with a neuromorphic event camera for real time interactive
demonstrations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Platooning in the Presence of a Speed Drop: A Generalized Control Model | The positive impacts of platooning on travel time reliability, congestion,
emissions, and energy consumption have been shown for homogeneous roadway
segments. However, speed limit changes frequently throughout the transportation
network, due to either safety-related considerations (e.g., workzone
operations) or congestion management schemes (e.g., speed harmonization
systems). These abrupt changes in speed limit can result in shock- wave
formation and cause travel time unreliability. Therefore, designing a
platooning strategy for tracking a reference velocity profile is critical to
enabling end-to-end platooning. Accordingly, this study introduces a
generalized control model to track a desired velocity profile, while ensuring
safety in the platoon of autonomous vehicles. We define appropriate natural
error terms and the target curve in the state space of the control system,
which is the set of points where all error terms vanish and corresponds to the
case when all vehicles move with the desired velocities and in the minimum safe
distance between them. In this way, we change the tracking velocity profile
problem into a state- feedback stabilization problem with respect to the target
curve. Under certain mild assumptions on the Lipschitz constant of the speed
drop profile, we show that the stabilizing feedback can be obtained via
introducing a natural dynamics for the maximum of the error terms for each
vehicle. Moreover, we show that with this stabilizing feedback collisions will
not occur if the initial state of the system of vehicles is sufficiently close
to the target curve. We also show that the error terms remain bounded
throughout the time and space. Two scenarios were simulated, with and without
initial perturbations, and results confirmed the effectiveness of the proposed
control model in tracking the speed drop while ensuring safety and string
stability.
| 1 | 0 | 0 | 0 | 0 | 0 |
Koszul duality for Lie algebroids | This paper studies the role of dg-Lie algebroids in derived deformation
theory. More precisely, we provide an equivalence between the homotopy theories
of formal moduli problems and dg-Lie algebroids over a commutative dg-algebra
of characteristic zero. At the level of linear objects, we show that the
category of representations of a dg-Lie algebroid is an extension of the
category of quasi-coherent sheaves on the corresponding formal moduli problem.
We describe this extension geometrically in terms of pro-coherent sheaves.
| 0 | 0 | 1 | 0 | 0 | 0 |
Semi-Semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments | This paper presents a novel method to reduce the scale drift for indoor
monocular simultaneous localization and mapping (SLAM). We leverage the prior
knowledge that in the indoor environment, the line segments form tight
clusters, e.g. many door frames in a straight corridor are of the same shape,
size and orientation, so the same edges of these door frames form a tight line
segment cluster. We implement our method in the popular ORB-SLAM2, which also
serves as our baseline. In the front end we detect the line segments in each
frame and incrementally cluster them in the 3D space. In the back end, we
optimize the map imposing the constraint that the line segments of the same
cluster should be the same. Experimental results show that our proposed method
successfully reduces the scale drift for indoor monocular SLAM.
| 1 | 0 | 0 | 0 | 0 | 0 |
Direct visualization of vortex ice in a nanostructured superconductor | Artificial ice systems have unique physical properties promising for
potential applications. One of the most challenging issues in this field is to
find novel ice systems that allows a precise control over the geometries and
many-body interactions. Superconducting vortex matter has been proposed as a
very suitable candidate to study artificial ice, mainly due to availability of
tunable vortex-vortex interactions and the possibility to fabricate a variety
of nanoscale pinning potential geometries. So far, a detailed imaging of the
local configurations in a vortex-based artificial ice system is still lacking.
Here we present a direct visualization of the vortex ice state in a
nanostructured superconductor. By using the scanning Hall probe microscopy, a
large area with the vortex ice ground state configuration has been detected,
which confirms the recent theoretical predictions for this new ice system.
Besides the defects analogous to artificial spin ice systems, other types of
defects have been visualized and identified. We also demonstrate the
possibility to realize different types of defects by varying the magnetic
field.
| 0 | 1 | 0 | 0 | 0 | 0 |
Statistical Mechanics of Node-perturbation Learning with Noisy Baseline | Node-perturbation learning is a type of statistical gradient descent
algorithm that can be applied to problems where the objective function is not
explicitly formulated, including reinforcement learning. It estimates the
gradient of an objective function by using the change in the object function in
response to the perturbation. The value of the objective function for an
unperturbed output is called a baseline. Cho et al. proposed node-perturbation
learning with a noisy baseline. In this paper, we report on building the
statistical mechanics of Cho's model and on deriving coupled differential
equations of order parameters that depict learning dynamics. We also show how
to derive the generalization error by solving the differential equations of
order parameters. On the basis of the results, we show that Cho's results are
also apply in general cases and show some general performances of Cho's model.
| 1 | 0 | 0 | 1 | 0 | 0 |
New Generalized Fixed Point Results on $S_{b}$-Metric Spaces | Recently $S_{b}$-metric spaces have been introduced as the generalizations of
metric and $S$-metric spaces. In this paper we investigate some basic
properties of this new space. We generalize the classical Banach's contraction
principle using the theory of a complete $S_{b}$-metric space. Also we give an
application to linear equation systems using the $S_{b}$-metric which is
generated by a metric.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Decentralized Optimization Framework for Energy Harvesting Devices | Designing decentralized policies for wireless communication networks is a
crucial problem, which has only been partially solved in the literature so far.
In this paper, we propose the Decentralized Markov Decision Process (Dec-MDP)
framework to analyze a wireless sensor network with multiple users which access
a common wireless channel. We consider devices with energy harvesting
capabilities, so that they aim at balancing the energy arrivals with the data
departures and with the probability of colliding with other nodes. Randomly
over time, an access point triggers a SYNC slot, wherein it recomputes the
optimal transmission parameters of the whole network, and distributes this
information. Every node receives its own policy, which specifies how it should
access the channel in the future, and, thereafter, proceeds in a fully
decentralized fashion, without interacting with other entities in the network.
We propose a multi-layer Markov model, where an external MDP manages the jumps
between SYNC slots, and an internal Dec-MDP computes the optimal policy in the
near future. We numerically show that, because of the harvesting, a fully
orthogonal scheme (e.g., TDMA-like) is suboptimal in energy harvesting
scenarios, and the optimal trade-off lies between an orthogonal and a random
access system.
| 1 | 0 | 1 | 0 | 0 | 0 |
Inf-sup stable finite-element methods for the Landau--Lifshitz--Gilbert and harmonic map heat flow equation | In this paper we propose and analyze a finite element method for both the
harmonic map heat and Landau--Lifshitz--Gilbert equation, the time variable
remaining continuous. Our starting point is to set out a unified saddle point
approach for both problems in order to impose the unit sphere constraint at the
nodes since the only polynomial function satisfying the unit sphere constraint
everywhere are constants. A proper inf-sup condition is proved for the Lagrange
multiplier leading to the well-posedness of the unified formulation. \emph{A
priori} energy estimates are shown for the proposed method.
When time integrations are combined with the saddle point finite element
approximation some extra elaborations are required in order to ensure both
\emph{a priori} energy estimates for the director or magnetization vector
depending on the model and an inf-sup condition for the Lagrange multiplier.
This is due to the fact that the unit length at the nodes is not satisfied in
general when a time integration is performed. We will carry out a linear Euler
time-stepping method and a non-linear Crank--Nicolson method. The latter is
solved by using the former as a non-linear solver.
| 0 | 0 | 1 | 0 | 0 | 0 |
Power Control and Relay Selection in Full-Duplex Cognitive Relay Networks: Coherent versus Non-coherent Scenarios | This paper investigates power control and relay selection in Full Duplex
Cognitive Relay Networks (FDCRNs), where the secondary-user (SU) relays can
simultaneously receive and forward the signal from the SU source. We study both
non-coherent and coherent scenarios. In the non-coherent case, the SU relay
forwards the signal from the SU source without regulating the phase, while in
the coherent scenario, the SU relay regulates the phase when forwarding the
signal to minimize the interference at the primary-user (PU) receiver. We
consider the problem of maximizing the transmission rate from the SU source to
the SU destination subject to the interference constraint at the PU receiver
and power constraints at both the SU source and SU relay. We develop
low-complexity and high-performance joint power control and relay selection
algorithms. The superior performance of the proposed algorithms are confirmed
using extensive numerical evaluation. In particular, we demonstrate the
significant gain of phase regulation at the SU relay (i.e., the gain of the
coherent mechanism over the noncoherent mechanism).
| 1 | 0 | 1 | 1 | 0 | 0 |
Ternary and $n$-ary $f$-distributive Structures | We introduce and study ternary $f$-distributive structures, Ternary
$f$-quandles and more generally their higher $n$-ary analogues. A
classification of ternary $f$-quandles is provided in low dimensions. Moreover,
we study extension theory and introduce a cohomology theory for ternary, and
more generally $n$-ary, $f$-quandles. Furthermore, we give some computational
examples.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deformations of infinite-dimensional Lie algebras, exotic cohomology, and integrable nonlinear partial differential equations | The important unsolved problem in theory of integrable systems is to find
conditions guaranteeing existence of a Lax representation for a given PDE. The
use of the exotic cohomology of the symmetry algebras opens a way to formulate
such conditions in internal terms of the PDEs under the study. In this paper we
consider certain examples of infinite-dimensional Lie algebras with nontrivial
second exotic cohomology groups and show that the Maurer-Cartan forms of the
associated extensions of these Lie algebras generate Lax representations for
integrable systems, both known and new ones.
| 0 | 1 | 0 | 0 | 0 | 0 |
Tunable high-harmonic generation by chromatic focusing of few-cycle laser pulses | In this work we study the impact of chromatic focusing of few-cycle laser
pulses on high-order harmonic generation (HHG) through analysis of the emitted
extreme ultraviolet (XUV) radiation. Chromatic focusing is usually avoided in
the few-cycle regime, as the pulse spatio-temporal structure may be highly
distorted by the spatiotemporal aberrations. Here, however, we demonstrate it
as an additional control parameter to modify the generated XUV radiation. We
present experiments where few-cycle pulses are focused by a singlet lens in a
Kr gas jet. The chromatic distribution of focal lengths allows us to tune HHG
spectra by changing the relative singlet-target distance. Interestingly, we
also show that the degree of chromatic aberration needed to this control does
not degrade substantially the harmonic conversion efficiency, still allowing
for the generation of supercontinua with the chirped-pulse scheme, demonstrated
previously for achromatic focussing. We back up our experiments with
theoretical simulations reproducing the experimental HHG results depending on
diverse parameters (input pulse spectral phase, pulse duration, focus position)
and proving that, under the considered parameters, the attosecond pulse train
remains very similar to the achromatic case, even showing cases of isolated
attosecond pulse generation for near single-cycle driving pulses.
| 0 | 1 | 0 | 0 | 0 | 0 |
Gate Switchable Transport and Optical Anisotropy in 90° Twisted Bilayer Black Phosphorus | Anisotropy describes the directional dependence of a material's properties
such as transport and optical response. In conventional bulk materials,
anisotropy is intrinsically related to the crystal structure, and thus not
tunable by the gating techniques used in modern electronics. Here we show that,
in bilayer black phosphorus with an interlayer twist angle of 90°, the
anisotropy of its electronic structure and optical transitions is tunable by
gating. Using first-principles calculations, we predict that a
laboratory-accessible gate voltage can induce a hole effective mass that is 30
times larger along one Cartesian axis than along the other axis, and the two
axes can be exchanged by flipping the sign of the gate voltage. This
gate-controllable band structure also leads to a switchable optical linear
dichroism, where the polarization of the lowest-energy optical transitions
(absorption or luminescence) is tunable by gating. Thus, anisotropy is a
tunable degree of freedom in twisted bilayer black phosphorus.
| 0 | 1 | 0 | 0 | 0 | 0 |
MM Algorithms for Variance Component Estimation and Selection in Logistic Linear Mixed Model | Logistic linear mixed model is widely used in experimental designs and
genetic analysis with binary traits. Motivated by modern applications, we
consider the case with many groups of random effects and each group corresponds
to a variance component. When the number of variance components is large,
fitting the logistic linear mixed model is challenging. We develop two
efficient and stable minorization-maximization (MM) algorithms for the
estimation of variance components based on the Laplace approximation of the
logistic model. One of them leads to a simple iterative soft-thresholding
algorithm for variance component selection using maximum penalized approximated
likelihood. We demonstrate the variance component estimation and selection
performance of our algorithms by simulation studies and a real data analysis.
| 0 | 0 | 0 | 1 | 0 | 0 |
Settling the query complexity of non-adaptive junta testing | We prove that any non-adaptive algorithm that tests whether an unknown
Boolean function $f: \{0, 1\}^n\to \{0, 1\}$ is a $k$-junta or $\epsilon$-far
from every $k$-junta must make $\widetilde{\Omega}(k^{3/2} / \epsilon)$ many
queries for a wide range of parameters $k$ and $\epsilon$. Our result
dramatically improves previous lower bounds from [BGSMdW13, STW15], and is
essentially optimal given Blais's non-adaptive junta tester from [Blais08],
which makes $\widetilde{O}(k^{3/2})/\epsilon$ queries. Combined with the
adaptive tester of [Blais09] which makes $O(k\log k + k /\epsilon)$ queries,
our result shows that adaptivity enables polynomial savings in query complexity
for junta testing.
| 1 | 0 | 0 | 0 | 0 | 0 |
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems | Language understanding is a key component in a spoken dialogue system. In
this paper, we investigate how the language understanding module influences the
dialogue system performance by conducting a series of systematic experiments on
a task-oriented neural dialogue system in a reinforcement learning based
setting. The empirical study shows that among different types of language
understanding errors, slot-level errors can have more impact on the overall
performance of a dialogue system compared to intent-level errors. In addition,
our experiments demonstrate that the reinforcement learning based dialogue
system is able to learn when and what to confirm in order to achieve better
performance and greater robustness.
| 1 | 0 | 0 | 0 | 0 | 0 |
String stability and a delay-based spacing policy for vehicle platoons subject to disturbances | A novel delay-based spacing policy for the control of vehicle platoons is
introduced together with a notion of disturbance string stability. The
delay-based spacing policy specifies the desired inter-vehicular distance
between vehicles and guarantees that all vehicles track the same spatially
varying reference velocity profile, as is for example required for heavy-duty
vehicles driving over hilly terrain. Disturbance string stability is a notion
of string stability of vehicle platoons subject to external disturbances on all
vehicles that guarantees that perturbations do not grow unbounded as they
propagate through the platoon. Specifically, a control design approach in the
spatial domain is presented that achieves tracking of the desired spacing
policy and guarantees disturbance string stability with respect to a spatially
varying reference velocity. The results are illustrated by means of
simulations.
| 1 | 0 | 1 | 0 | 0 | 0 |
Reidemeister spectra for solvmanifolds in low dimensions | The Reidemeister number of an endomorphism of a group is the number of
twisted conjugacy classes determined by that endomorphism. The collection of
all Reidemeister numbers of all automorphisms of a group $G$ is called the
Reidemeister spectrum of $G$. In this paper, we determine the Reidemeister
spectra of all fundamental groups of solvmanifolds up to Hirsch length 4.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cross-layer Optimization for Ultra-reliable and Low-latency Radio Access Networks | In this paper, we propose a framework for cross-layer optimization to ensure
ultra-high reliability and ultra-low latency in radio access networks, where
both transmission delay and queueing delay are considered. With short
transmission time, the blocklength of channel codes is finite, and the Shannon
Capacity cannot be used to characterize the maximal achievable rate with given
transmission error probability. With randomly arrived packets, some packets may
violate the queueing delay. Moreover, since the queueing delay is shorter than
the channel coherence time in typical scenarios, the required transmit power to
guarantee the queueing delay and transmission error probability will become
unbounded even with spatial diversity. To ensure the required
quality-of-service (QoS) with finite transmit power, a proactive packet
dropping mechanism is introduced. Then, the overall packet loss probability
includes transmission error probability, queueing delay violation probability,
and packet dropping probability. We optimize the packet dropping policy, power
allocation policy, and bandwidth allocation policy to minimize the transmit
power under the QoS constraint. The optimal solution is obtained, which depends
on both channel and queue state information. Simulation and numerical results
validate our analysis, and show that setting packet loss probabilities equal is
a near optimal solution.
| 1 | 0 | 0 | 0 | 0 | 0 |
Network Inference via the Time-Varying Graphical Lasso | Many important problems can be modeled as a system of interconnected
entities, where each entity is recording time-dependent observations or
measurements. In order to spot trends, detect anomalies, and interpret the
temporal dynamics of such data, it is essential to understand the relationships
between the different entities and how these relationships evolve over time. In
this paper, we introduce the time-varying graphical lasso (TVGL), a method of
inferring time-varying networks from raw time series data. We cast the problem
in terms of estimating a sparse time-varying inverse covariance matrix, which
reveals a dynamic network of interdependencies between the entities. Since
dynamic network inference is a computationally expensive task, we derive a
scalable message-passing algorithm based on the Alternating Direction Method of
Multipliers (ADMM) to solve this problem in an efficient way. We also discuss
several extensions, including a streaming algorithm to update the model and
incorporate new observations in real time. Finally, we evaluate our TVGL
algorithm on both real and synthetic datasets, obtaining interpretable results
and outperforming state-of-the-art baselines in terms of both accuracy and
scalability.
| 1 | 0 | 1 | 0 | 0 | 0 |
Construction of dynamical semigroups by a functional regularisation à la Kato | A functional version of the Kato one-parametric regularisation for the
construction of a dynamical semigroup generator of a relative bound one
perturbation is introduced. It does not require that the minus generator of the
unperturbed semigroup is a positivity preserving operator. The regularisation
is illustrated by an example of a boson-number cut-off regularisation.
| 0 | 0 | 1 | 0 | 0 | 0 |
Visual Reasoning with Multi-hop Feature Modulation | Recent breakthroughs in computer vision and natural language processing have
spurred interest in challenging multi-modal tasks such as visual
question-answering and visual dialogue. For such tasks, one successful approach
is to condition image-based convolutional network computation on language via
Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and
shifting. We propose to generate the parameters of FiLM layers going up the
hierarchy of a convolutional network in a multi-hop fashion rather than all at
once, as in prior work. By alternating between attending to the language input
and generating FiLM layer parameters, this approach is better able to scale to
settings with longer input sequences such as dialogue. We demonstrate that
multi-hop FiLM generation achieves state-of-the-art for the short input
sequence task ReferIt --- on-par with single-hop FiLM generation --- while also
significantly outperforming prior state-of-the-art and single-hop FiLM
generation on the GuessWhat?! visual dialogue task.
| 0 | 0 | 0 | 1 | 0 | 0 |
Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data | Objective: To evaluate unsupervised clustering methods for identifying
individual-level behavioral-clinical phenotypes that relate personal biomarkers
and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials
and Methods: We used hierarchical clustering (HC) to identify groups of meals
with similar nutrition and glycemic impact for 6 individuals with T2DM who
collected self-monitoring data. We evaluated clusters on: 1) correspondence to
gold standards generated by certified diabetes educators (CDEs) for 3
participants; 2) face validity, rated by CDEs, and 3) impact on CDEs' ability
to identify patterns for another 3 participants. Results: Gold standard (GS)
included 9 patterns across 3 participants. Of these, all 9 were re-discovered
using HC: 4 GS patterns were consistent with patterns identified by HC (over
50% of meals in a cluster followed the pattern); another 5 were included as
sub-groups in broader clusers. 50% (9/18) of clusters were rated over 3 on
5-point Likert scale for validity, significance, and being actionable. After
reviewing clusters, CDEs identified patterns that were more consistent with
data (70% reduction in contradictions between patterns and participants'
records). Discussion: Hierarchical clustering of blood glucose and
macronutrient consumption appears suitable for discovering behavioral-clinical
phenotypes in T2DM. Most clusters corresponded to gold standard and were rated
positively by CDEs for face validity. Cluster visualizations helped CDEs
identify more robust patterns in nutrition and glycemic impact, creating new
possibilities for visual analytic solutions. Conclusion: Machine learning
methods can use diabetes self-monitoring data to create personalized
behavioral-clinical phenotypes, which may prove useful for delivering
personalized medicine.
| 0 | 0 | 0 | 1 | 0 | 0 |
Ultrahigh capacitive energy storage in highly oriented BaZr(x)Ti(1-x)O3 thin films prepared by pulsed laser deposition | We report structural, optical, temperature and frequency dependent
dielectric, and energy storage properties of pulsed laser deposited (100)
highly textured BaZr(x)Ti(1-x)O3 (x = 0.3, 0.4 and 0.5) relaxor ferroelectric
thin films on La0.7Sr0.3MnO3/MgO substrates which make this compound as a
potential lead-free capacitive energy storage material for scalable electronic
devices. A high dielectric constant of ~1400 - 3500 and a low dielectric loss
of <0.025 were achieved at 10 kHz for all three compositions at ambient
conditions. Ultrahigh stored and recoverable electrostatic energy densities as
high as 214 +/- 1 and 156 +/- 1 J/cm3, respectively, were demonstrated at a
sustained high electric field of ~3 MV/cm with an efficiency of 72.8 +/- 0.6 %
in optimum 30% Zr substituted BaTiO3 composition.
| 0 | 1 | 0 | 0 | 0 | 0 |
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