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Mathematical and numerical validation of the simplified spherical harmonics approach for time-dependent anisotropic-scattering transport problems in homogeneous media | In this work, we extend the solid harmonics derivation, which was used by
Ackroyd et al to derive the steady-state SP$_N$ equations, to transient
problems. The derivation expands the angular flux in ordinary surface harmonics
but uses harmonic polynomials to generate additional surface spherical harmonic
terms to be used in Galerkin projection. The derivation shows the equivalence
between the SP$_N$ and the P$_N$ approximation. Also, we use the line source
problem and McClarren's "box" problem to demonstrate such equivalence
numerically. Both problems were initially proposed for isotropic scattering,
but here we add higher-order scattering moments to them. Results show that the
difference between the SP$_N$ and P$_N$ scalar flux solution is at the roundoff
level.
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L1-norm Principal-Component Analysis of Complex Data | L1-norm Principal-Component Analysis (L1-PCA) of real-valued data has
attracted significant research interest over the past decade. However, L1-PCA
of complex-valued data remains to date unexplored despite the many possible
applications (e.g., in communication systems). In this work, we establish
theoretical and algorithmic foundations of L1-PCA of complex-valued data
matrices. Specifically, we first show that, in contrast to the real-valued case
for which an optimal polynomial-cost algorithm was recently reported by
Markopoulos et al., complex L1-PCA is formally NP-hard in the number of data
points. Then, casting complex L1-PCA as a unimodular optimization problem, we
present the first two suboptimal algorithms in the literature for its solution.
Our experimental studies illustrate the sturdy resistance of complex L1-PCA
against faulty measurements/outliers in the processed data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Viscous dissipation of surface waves and its relevance to analogue gravity experiments | We consider dissipation of surface waves on fluids, with a view to its
effects on analogue gravity experiments. We begin by reviewing some general
properties of wave dissipation, before restricting our attention to surface
waves and the dissipative role played by viscosity there. Finally, with
particular focus on water, we consider several experimental setups inspired by
analogue gravity: the analogue Hawking effect, the black hole laser, the
analogue wormhole, and double bouncing at the wormhole entrance. Dissipative
effects are considered in each, and we give estimates for their optimized
experimental parameters.
| 0 | 1 | 0 | 0 | 0 | 0 |
Neural Optimizer Search with Reinforcement Learning | We present an approach to automate the process of discovering optimization
methods, with a focus on deep learning architectures. We train a Recurrent
Neural Network controller to generate a string in a domain specific language
that describes a mathematical update equation based on a list of primitive
functions, such as the gradient, running average of the gradient, etc. The
controller is trained with Reinforcement Learning to maximize the performance
of a model after a few epochs. On CIFAR-10, our method discovers several update
rules that are better than many commonly used optimizers, such as Adam,
RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two
new optimizers, named PowerSign and AddSign, which we show transfer well and
improve training on a variety of different tasks and architectures, including
ImageNet classification and Google's neural machine translation system.
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Hybrid Sterility Can Only be Primary When Acting as a Reproductive Barrier for Sympatric Speciation, | Parental gametes unite to form a zygote that develops into an adult with
gonads that, in turn, produce gametes. Interruption of this germinal cycle by
prezygotic or postzygotic reproductive barriers can result in two independent
cycles, each with the potential to evolve into a new species. When the
speciation process is complete, members of each species are fully
reproductively isolated from those of the other. During speciation a primary
barrier may be supported and eventually superceded by a later appearing
secondary barrier. For those holding certain cases of prezygotic isolation to
be primary (e.g. elephant cannot copulate with mouse), the onus is to show that
they had not been preceded over evolutionary time by periods of postzygotic
hybrid inviability (genically determined) or sterility (genically or
chromosomally determined). Likewise, the onus is upon those holding cases of
hybrid inviability to be primary (e.g. Dobzhansky-Muller epistatic
incompatibilities), to show that they had not been preceded by periods, however
brief, of hybrid sterility. The latter, when acting as a sympatric barrier
causing reproductive isolation, can only be primary. In many cases, hybrid
sterility may result from incompatibilities between parental chromosomes that
attempt to pair during meiosis in the gonad of their offspring
(Winge-Crowther-Bateson incompatibilities). While WCB incompatibilities have
long been observed on a microscopic scale, there is growing evidence for a role
of dispersed finer DNA sequence differences.
| 0 | 0 | 0 | 0 | 1 | 0 |
Point-Cloud-Based Aerial Fragmentation Analysis for Application in the Minerals Industry | This work investigates the application of Unmanned Aerial Vehicle (UAV)
technology for measurement of rock fragmentation without placement of scale
objects in the scene to determine image scale. Commonly practiced image-based
rock fragmentation analysis requires a technician to walk to a rock pile, place
a scale object of known size in the area of interest, and capture individual 2D
images. Our previous work has used UAV technology for the first time to acquire
real-time rock fragmentation data and has shown comparable quality of results;
however, it still required the (potentially dangerous) placement of scale
objects, and continued to make the assumption that the rock pile surface is
planar and that the scale objects lie on the surface plane. This work improves
our UAV-based approach to enable rock fragmentation measurement without
placement of scale objects and without the assumption of planarity. This is
achieved by first generating a point cloud of the rock pile from 2D images,
taking into account intrinsic and extrinsic camera parameters, and then taking
2D images for fragmentation analysis. This work represents an important step
towards automating post-blast rock fragmentation analysis. In experiments, a
rock pile with known size distribution was photographed by the UAV with and
without using scale objects. For fragmentation analysis without scale objects,
a point cloud of the rock pile was generated and used to compute image scale.
Comparison of the rock size distributions show that this point-cloud-based
method enables producing measurements with better or comparable accuracy
(within 10% of the ground truth) to the manual method with scale objects.
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DyNet: The Dynamic Neural Network Toolkit | We describe DyNet, a toolkit for implementing neural network models based on
dynamic declaration of network structure. In the static declaration strategy
that is used in toolkits like Theano, CNTK, and TensorFlow, the user first
defines a computation graph (a symbolic representation of the computation), and
then examples are fed into an engine that executes this computation and
computes its derivatives. In DyNet's dynamic declaration strategy, computation
graph construction is mostly transparent, being implicitly constructed by
executing procedural code that computes the network outputs, and the user is
free to use different network structures for each input. Dynamic declaration
thus facilitates the implementation of more complicated network architectures,
and DyNet is specifically designed to allow users to implement their models in
a way that is idiomatic in their preferred programming language (C++ or
Python). One challenge with dynamic declaration is that because the symbolic
computation graph is defined anew for every training example, its construction
must have low overhead. To achieve this, DyNet has an optimized C++ backend and
lightweight graph representation. Experiments show that DyNet's speeds are
faster than or comparable with static declaration toolkits, and significantly
faster than Chainer, another dynamic declaration toolkit. DyNet is released
open-source under the Apache 2.0 license and available at
this http URL.
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Polyteam Semantics | Team semantics is the mathematical framework of modern logics of dependence
and independence in which formulae are interpreted by sets of assignments
(teams) instead of single assignments as in first-order logic. In order to
deepen the fruitful interplay between team semantics and database dependency
theory, we define "Polyteam Semantics" in which formulae are evaluated over a
family of teams. We begin by defining a novel polyteam variant of dependence
atoms and give a finite axiomatisation for the associated implication problem.
We also characterise the expressive power of poly-dependence logic by
properties of polyteams that are downward closed and definable in existential
second-order logic (ESO). The analogous result is shown to hold for
poly-independence logic and all ESO-definable properties.
| 1 | 0 | 0 | 0 | 0 | 0 |
Where Classification Fails, Interpretation Rises | An intriguing property of deep neural networks is their inherent
vulnerability to adversarial inputs, which significantly hinders their
application in security-critical domains. Most existing detection methods
attempt to use carefully engineered patterns to distinguish adversarial inputs
from their genuine counterparts, which however can often be circumvented by
adaptive adversaries. In this work, we take a completely different route by
leveraging the definition of adversarial inputs: while deceiving for deep
neural networks, they are barely discernible for human visions. Building upon
recent advances in interpretable models, we construct a new detection framework
that contrasts an input's interpretation against its classification. We
validate the efficacy of this framework through extensive experiments using
benchmark datasets and attacks. We believe that this work opens a new direction
for designing adversarial input detection methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling | Latent Dirichlet Allocation (LDA) models trained without stopword removal
often produce topics with high posterior probabilities on uninformative words,
obscuring the underlying corpus content. Even when canonical stopwords are
manually removed, uninformative words common in that corpus will still dominate
the most probable words in a topic. In this work, we first show how the
standard topic quality measures of coherence and pointwise mutual information
act counter-intuitively in the presence of common but irrelevant words, making
it difficult to even quantitatively identify situations in which topics may be
dominated by stopwords. We propose an additional topic quality metric that
targets the stopword problem, and show that it, unlike the standard measures,
correctly correlates with human judgements of quality. We also propose a
simple-to-implement strategy for generating topics that are evaluated to be of
much higher quality by both human assessment and our new metric. This approach,
a collection of informative priors easily introduced into most LDA-style
inference methods, automatically promotes terms with domain relevance and
demotes domain-specific stop words. We demonstrate this approach's
effectiveness in three very different domains: Department of Labor accident
reports, online health forum posts, and NIPS abstracts. Overall we find that
current practices thought to solve this problem do not do so adequately, and
that our proposal offers a substantial improvement for those interested in
interpreting their topics as objects in their own right.
| 1 | 0 | 0 | 0 | 0 | 0 |
Buckling in Armored Droplets | The issue of the buckling mechanism in droplets stabilized by solid particles
(armored droplets) is tackled at a mesoscopic level using dissipative particle
dynamics simulations. We consider spherical water droplet in a decane solvent
coated with nanoparticle monolayers of two different types: Janus and
homogeneous. The chosen particles yield comparable initial three-phase contact
angles, chosen to maximize the adsorption energy at the interface. We study the
interplay between the evolution of droplet shape, layering of the particles,
and their distribution at the interface when the volume of the droplets is
reduced. We show that Janus particles affect strongly the shape of the droplet
with the formation of a crater-like depression. This evolution is actively
controlled by a close-packed particle monolayer at the curved interface. On the
contrary, homogeneous particles follow passively the volume reduction of the
droplet, whose shape does not deviate too much from spherical, even when a
nanoparticle monolayer/bilayer transition is detected at the interface. We
discuss how these buckled armored droplets might be of relevance in various
applications including potential drug delivery systems and biomimetic design of
functional surfaces.
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Density of Analytic Polynomials in Abstract Hardy Spaces | Let $X$ be a separable Banach function space on the unit circle $\mathbb{T}$
and $H[X]$ be the abstract Hardy space built upon $X$. We show that the set of
analytic polynomials is dense in $H[X]$ if the Hardy-Littlewood maximal
operator is bounded on the associate space $X'$. This result is specified to
the case of variable Lebesgue spaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
On non-Abelian Lie Bracket of Generalized Covariant Hamilton Systems | This is a theoretical paper, which is a continuation of [arXiv:1710.10597],
it considers the non-abelian Lie algebra $\mathcal{G}$ of Lie groups for
$\left[ {{X}_{i}},{{X}_{j}} \right]=c_{ij}^{k}{{X}_{k}}\in \mathcal{G}$ on the
foundation of the GCHS, where $c_{ij}^{k}\in {{C}^{\infty }}\left( U,R \right)$
are the structure constants. The GPWB [arXiv:1710.10597] is nonlinear bracket
applying to the non-Euclidean space, the second order (2,0) form antisymmetric
curvature tensor ${{F}_{ij}}=c_{ij}^{k}{{D}_{k}}$, and Qsu quantity
${{q}_{i}}=w_{i}^{k}{{D}_{k}}$ are accordingly obtained by using the
non-abelian Lie bracket. The GCHS $\left\{ H,f \right\}\in {{C}^{\infty
}}\left( M,\mathbb{R} \right)$ holds for the non-symplectic vector field
$X_{H}^{M}\in \mathcal{G}$ and $f\in {{C}^{\infty }}\left( M,\mathbb{R}
\right)$ that implies the covariant evolution equation consists of two parts,
NGHS and W dynamics along with the second order invariant operator
$\frac{{\mathcal{D}^{2}}}{d{{t}^{2}}}=\frac{{{d}^{2}}}{d{{t}^{2}}}+2w\frac{d}{dt}+\beta$.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Topology of Statistical Verifiability | Topological models of empirical and formal inquiry are increasingly
prevalent. They have emerged in such diverse fields as domain theory [1, 16],
formal learning theory [18], epistemology and philosophy of science [10, 15, 8,
9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open
sets are typically interpreted as hypotheses deductively verifiable by true
propositional information that rules out relevant possibilities. However, in
statistical data analysis, one routinely receives random samples logically
compatible with every statistical hypothesis. We bridge the gap between
propositional and statistical data by solving for the unique topology on
probability measures in which the open sets are exactly the statistically
verifiable hypotheses. Furthermore, we extend that result to a topological
characterization of learnability in the limit from statistical data.
| 1 | 0 | 0 | 0 | 0 | 0 |
A spectral approach to the linking number in the 3-torus | Given a closed Riemannian manifold and a pair of multi-curves in it, we give
a formula relating the linking number of the later to the spectral theory of
the Laplace operator acting on differential one forms. As an application, we
compute the linking number of any two multi-geodesics of the flat torus of
dimension 3, generalising a result of P. Dehornoy.
| 0 | 0 | 1 | 0 | 0 | 0 |
Volkov-Pankratov states in topological heterojunctions | We show that a smooth interface between two insulators of opposite
topological Z2 indices possesses multiple surface states, both massless and
massive. While the massless surface state is non-degenerate, chiral and
insensitive to the interface potential, the massive surface states only appear
for a sufficiently smooth heterojunction. The surface states are particle-hole
symmetric and a voltage drop reveals their intrinsic relativistic nature,
similarly to Landau bands of Dirac electrons in a magnetic field. We discuss
the relevance of the massive Dirac surface states in recent ARPES and transport
experiments.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sampling-based Estimation of In-degree Distribution with Applications to Directed Complex Networks | The focus of this work is on estimation of the in-degree distribution in
directed networks from sampling network nodes or edges. A number of sampling
schemes are considered, including random sampling with and without replacement,
and several approaches based on random walks with possible jumps. When sampling
nodes, it is assumed that only the out-edges of that node are visible, that is,
the in-degree of that node is not observed. The suggested estimation of the
in-degree distribution is based on two approaches. The inversion approach
exploits the relation between the original and sample in-degree distributions,
and can estimate the bulk of the in-degree distribution, but not the tail of
the distribution. The tail of the in-degree distribution is estimated through
an asymptotic approach, which itself has two versions: one assuming a power-law
tail and the other for a tail of general form. The two estimation approaches
are examined on synthetic and real networks, with good performance results,
especially striking for the asymptotic approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Phase I results with the Large Angle Beamstrahlung Monitor (LABM) with SuperKEKB beams | We report on the SuperKEKB Phase I operations of the Large Angle
Beamstrhalung Monitor (LABM). The detector is described and its performance
characterized using the synchrotron radiation backgrounds from the last Beam
Line magnets. The backgrounds are also used to determine the expected position
of the Interaction Point (IP), and the expected background rates during Phase
II.
| 0 | 1 | 0 | 0 | 0 | 0 |
Network flow of mobile agents enhances the evolution of cooperation | We study the effect of contingent movement on the persistence of cooperation
on complex networks with empty nodes. Each agent plays Prisoner's Dilemma game
with its neighbors and then it either updates the strategy depending on the
payoff difference with neighbors or it moves to another empty node if not
satisfied with its own payoff. If no neighboring node is empty, each agent
stays at the same site. By extensive evolutionary simulations, we show that the
medium density of agents enhances cooperation where the network flow of mobile
agents is also medium. Moreover, if the movements of agents are more frequent
than the strategy updating, cooperation is further promoted. In scale-free
networks, the optimal density for cooperation is lower than other networks
because agents get stuck at hubs. Our study suggests that keeping a smooth
network flow is significant for the persistence of cooperation in ever-changing
societies.
| 0 | 0 | 0 | 0 | 1 | 0 |
Neural Face Editing with Intrinsic Image Disentangling | Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
Data Reduction and Image Reconstruction Techniques for Non-Redundant Masking | The technique of non-redundant masking (NRM) transforms a conventional
telescope into an interferometric array. In practice, this provides a much
better constrained point spread function than a filled aperture and thus higher
resolution than traditional imaging methods. Here we describe an NRM data
reduction pipeline. We discuss strategies for NRM observations regarding
dithering patterns and calibrator selection. We describe relevant image
calibrations and use example Large Binocular Telescope datasets to show their
effects on the scatter in the Fourier measurements. We also describe the
various ways to calculate Fourier quantities, and discuss different calibration
strategies. We present the results of image reconstructions from simulated
observations where we adjust prior images, weighting schemes, and error bar
estimation. We compare two imaging algorithms and discuss implications for
reconstructing images from real observations. Finally, we explore how the
current state of the art compares to next generation Extremely Large
Telescopes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dynamic transport in a quantum wire driven by spin-orbit interaction | We consider a gated one-dimensional (1D) quantum wire disturbed in a
contactless manner by an alternating electric field produced by a tip of a
scanning probe microscope. In this schematic 1D electrons are driven not by a
pulling electric field but rather by a non-stationary spin-orbit interaction
(SOI) created by the tip. We show that a charge current appears in the wire in
the presence of the Rashba SOI produced by the gate net charge and image
charges of 1D electrons induced on the gate (iSOI). The iSOI contributes to the
charge susceptibility by breaking the spin-charge separation between the
charge- and spin collective excitations, generated by the probe. The velocity
of the excitations is strongly renormalized by SOI, which opens a way to
fine-tune the charge and spin response of 1D electrons by changing the gate
potential. One of the modes softens upon increasing the gate potential to
enhance the current response as well as the power dissipated in the system.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Nonlinear Kernel Support Matrix Machine for Matrix Learning | In many problems of supervised tensor learning (STL), real world data such as
face images or MRI scans are naturally represented as matrices, which are also
called as second order tensors. Most existing classifiers based on tensor
representation, such as support tensor machine (STM) need to solve iteratively
which occupy much time and may suffer from local minima. In this paper, we
present a kernel support matrix machine (KSMM) to perform supervised learning
when data are represented as matrices. KSMM is a general framework for the
construction of matrix-based hyperplane to exploit structural information. We
analyze a unifying optimization problem for which we propose an asymptotically
convergent algorithm. Theoretical analysis for the generalization bounds is
derived based on Rademacher complexity with respect to a probability
distribution. We demonstrate the merits of the proposed method by exhaustive
experiments on both simulation study and a number of real-word datasets from a
variety of application domains.
| 1 | 0 | 0 | 1 | 0 | 0 |
Noisy Networks for Exploration | We introduce NoisyNet, a deep reinforcement learning agent with parametric
noise added to its weights, and show that the induced stochasticity of the
agent's policy can be used to aid efficient exploration. The parameters of the
noise are learned with gradient descent along with the remaining network
weights. NoisyNet is straightforward to implement and adds little computational
overhead. We find that replacing the conventional exploration heuristics for
A3C, DQN and dueling agents (entropy reward and $\epsilon$-greedy respectively)
with NoisyNet yields substantially higher scores for a wide range of Atari
games, in some cases advancing the agent from sub to super-human performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
Global stability of the Rate Control Protocol (RCP) and some implications for protocol design | The Rate Control Protocol (RCP) is a congestion control protocol that relies
on explicit feedback from routers. RCP estimates the flow rate using two forms
of feedback: rate mismatch and queue size. However, it remains an open design
question whether queue size feedback in RCP is useful, given the presence of
rate mismatch. The model we consider has RCP flows operating over a single
bottleneck, with heterogeneous time delays. We first derive a sufficient
condition for global stability, and then highlight how this condition favors
the design choice of having only rate mismatch in the protocol definition.
| 1 | 0 | 0 | 0 | 0 | 0 |
Performance Evaluation of Channel Decoding With Deep Neural Networks | With the demand of high data rate and low latency in fifth generation (5G),
deep neural network decoder (NND) has become a promising candidate due to its
capability of one-shot decoding and parallel computing. In this paper, three
types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
(CNN) and recurrent neural network (RNN), are proposed with the same parameter
magnitude. The performance of these deep neural networks are evaluated through
extensive simulation. Numerical results show that RNN has the best decoding
performance, yet at the price of the highest computational overhead. Moreover,
we find there exists a saturation length for each type of neural network, which
is caused by their restricted learning abilities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Trading Bounds for Memory in Games with Counters | We study two-player games with counters, where the objective of the first
player is that the counter values remain bounded. We investigate the existence
of a trade-off between the size of the memory and the bound achieved on the
counters, which has been conjectured by Colcombet and Loeding.
We show that unfortunately this conjecture does not hold: there is no
trade-off between bounds and memory, even for finite arenas. On the positive
side, we prove the existence of a trade-off for the special case of thin tree
arenas. This allows to extend the theory of regular cost functions over thin
trees, and obtain as a corollary the decidability of cost monadic second-order
logic over thin trees.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Tropical Discs Counting on Elliptic K3 Surfaces with General Singular Fibres | Using Lagrangian Floer theory, we study the tropical geometry of K3 surfaces
with general singular fibres. In particular, we give the local models for the
type $I_n$, $II$, $III$ and $IV$ singular fibres in the Kodaira's
classification and generalize the correspondence theorem between open
Gromov-Witten invariants/tropical discs counting to these cases.
| 0 | 0 | 1 | 0 | 0 | 0 |
The Frobenius problem for four numerical semigroups | The greatest integer that does not belong to a numerical semigroup $S$ is
called the Frobenius number of $S$ and finding the Frobenius number is called
the Frobenius problem. In this paper, we introduce the Frobenius problem for
numerical semigroups generated by Thabit number base b and Thabit number of the
second kind base b which are motivated by the Frobenius problem for Thabit
numerical semigroups. Also, we introduce the Frobenius problem for numerical
semigroups generated by Cunningham number and Fermat number base $b$
| 0 | 0 | 1 | 0 | 0 | 0 |
Coherence and its Role in Excitation Energy Transfer in Fenna-Mathews-Olson Complex | We show that the coherence between different bacteriochlorophyll-a (BChla)
sites in the Fenna-Mathews-Olson complex is an essential ingredient for
excitation energy transfer between various sites. The coherence delocalizes the
excitation energy, which results in the redistribution of excitation among all
the BChla sites in the steady state. We further show that the system remains
partially coherent at the steady state. In our numerical simulation of the
non-Markovian density matrix equation, we consider both the inhomogeneity of
the protein environment and the effect of active vibronic modes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Optimal Caching and Scheduling for Cache-enabled D2D Communications | To maximize offloading gain of cache-enabled device-to-device (D2D)
communications, content placement and delivery should be jointly designed. In
this letter, we jointly optimize caching and scheduling policies to maximize
successful offloading probability, defined as the probability that a user can
obtain desired file in local cache or via D2D link with data rate larger than a
given threshold. We obtain the optimal scheduling factor for a random
scheduling policy that can control interference in a distributed manner, and a
low complexity solution to compute caching distribution. We show that the
offloading gain can be remarkably improved by the joint optimization.
| 1 | 0 | 0 | 0 | 0 | 0 |
Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model | Many interesting natural phenomena are sparsely distributed and discrete.
Locating the hotspots of such sparsely distributed phenomena is often difficult
because their density gradient is likely to be very noisy. We present a novel
approach to this search problem, where we model the co-occurrence relations
between a robot's observations with a Bayesian nonparametric topic model. This
approach makes it possible to produce a robust estimate of the spatial
distribution of the target, even in the absence of direct target observations.
We apply the proposed approach to the problem of finding the spatial locations
of the hotspots of a specific phytoplankton taxon in the ocean. We use
classified image data from Imaging FlowCytobot (IFCB), which automatically
measures individual microscopic cells and colonies of cells. Given these
individual taxon-specific observations, we learn a phytoplankton community
model that characterizes the co-occurrence relations between taxa. We present
experiments with simulated robot missions drawn from real observation data
collected during a research cruise traversing the US Atlantic coast. Our
results show that the proposed approach outperforms nearest neighbor and
k-means based methods for predicting the spatial distribution of hotspots from
in-situ observations.
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Effective field theory for dissipative fluids (II): classical limit, dynamical KMS symmetry and entropy current | In this paper we further develop the fluctuating hydrodynamics proposed in
arXiv:1511.03646 in a number of ways. We first work out in detail the classical
limit of the hydrodynamical action, which exhibits many simplifications. In
particular, this enables a transparent formulation of the action in physical
spacetime in the presence of arbitrary external fields. It also helps to
clarify issues related to field redefinitions and frame choices. We then
propose that the action is invariant under a $Z_2$ symmetry to which we refer
as the dynamical KMS symmetry. The dynamical KMS symmetry is physically
equivalent to the previously proposed local KMS condition in the classical
limit, but is more convenient to implement and more general. It is applicable
to any states in local equilibrium rather than just thermal density matrix
perturbed by external background fields. Finally we elaborate the formulation
for a conformal fluid, which contains some new features, and work out the
explicit form of the entropy current to second order in derivatives for a
neutral conformal fluid.
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Quantum non demolition measurements: parameter estimation for mixtures of multinomials | In Quantum Non Demolition measurements, the sequence of observations is
distributed as a mixture of multinomial random variables. Parameters of the
dynamics are naturally encoded into this family of distributions. We show the
local asymptotic mixed normality of the underlying statistical model and the
consistency of the maximum likelihood estimator. Furthermore, we prove the
asymptotic optimality of this estimator as it saturates the usual Cramér Rao
bound.
| 0 | 0 | 1 | 1 | 0 | 0 |
Why We Need New Evaluation Metrics for NLG | The majority of NLG evaluation relies on automatic metrics, such as BLEU . In
this paper, we motivate the need for novel, system- and data-independent
automatic evaluation methods: We investigate a wide range of metrics, including
state-of-the-art word-based and novel grammar-based ones, and demonstrate that
they only weakly reflect human judgements of system outputs as generated by
data-driven, end-to-end NLG. We also show that metric performance is data- and
system-specific. Nevertheless, our results also suggest that automatic metrics
perform reliably at system-level and can support system development by finding
cases where a system performs poorly.
| 1 | 0 | 0 | 0 | 0 | 0 |
First constraints on fuzzy dark matter from Lyman-$α$ forest data and hydrodynamical simulations | We present constraints on the masses of extremely light bosons dubbed fuzzy
dark matter from Lyman-$\alpha$ forest data. Extremely light bosons with a De
Broglie wavelength of $\sim 1$ kpc have been suggested as dark matter
candidates that may resolve some of the current small scale problems of the
cold dark matter model. For the first time we use hydrodynamical simulations to
model the Lyman-$\alpha$ flux power spectrum in these models and compare with
the observed flux power spectrum from two different data sets: the XQ-100 and
HIRES/MIKE quasar spectra samples. After marginalization over nuisance and
physical parameters and with conservative assumptions for the thermal history
of the IGM that allow for jumps in the temperature of up to $5000\rm\,K$,
XQ-100 provides a lower limit of 7.1$\times 10^{-22}$ eV, HIRES/MIKE returns a
stronger limit of 14.3$\times 10^{-22}$ eV, while the combination of both data
sets results in a limit of 20 $\times 10^{-22}$ eV (2$\sigma$ C.L.). The limits
for the analysis of the combined data sets increases to 37.5$\times 10^{-22}$
eV (2$\sigma$ C.L.) when a smoother thermal history is assumed where the
temperature of the IGM evolves as a power-law in redshift. Light boson masses
in the range $1-10 \times10^{-22}$ eV are ruled out at high significance by our
analysis, casting strong doubts that FDM helps solve the "small scale crisis"
of the cold dark matter models.
| 0 | 1 | 0 | 0 | 0 | 0 |
On functionals involving the torsional rigidity related to some classes of nonlinear operators | In this paper we study optimal estimates for two functionals involving the
anisotropic $p$-torsional rigidity $T_p(\Omega)$, $1<p<+\infty$. More
precisely, we study $\Phi(\Omega)=\frac{T_p(\Omega)}{|\Omega|M(\Omega)}$ and
$\Psi(\Omega)=\frac{T_p(\Omega)}{|\Omega|[R_{F}(\Omega)]^{\frac{p}{p-1}}}$,
where $M(\Omega)$ is the maximum of the torsion function $u_{\Omega}$ and
$R_F(\Omega)$ is the anisotropic inradius of $\Omega$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Quasars Probing Quasars IX. The Kinematics of the Circumgalactic Medium Surrounding z ~ 2 Quasars | We examine the kinematics of the gas in the environments of galaxies hosting
quasars at $z\sim2$. We employ 148 projected quasar pairs to study the
circumgalactic gas of the foreground quasars in absorption. The sample selects
foreground quasars with precise redshift measurements, using emission-lines
with precision $\lesssim300\,{\rm km\,s^{-1}}$ and average offsets from the
systemic redshift $\lesssim|100\,{\rm km\,s^{-1}}|$. We stack the background
quasar spectra at the foreground quasar's systemic redshift to study the mean
absorption in \ion{C}{2}, \ion{C}{4}, and \ion{Mg}{2}. We find that the mean
absorptions exhibit large velocity widths $\sigma_v\approx300\,{\rm
km\,s^{-1}}$. Further, the mean absorptions appear to be asymmetric about the
systemic redshifts. The mean absorption centroids exhibit small redshift
relative to the systemic $\delta v\approx+200\,{\rm km\,s^{-1}}$, with large
intrinsic scatter in the centroid velocities of the individual absorption
systems. We find the observed widths are consistent with gas in gravitational
motion and Hubble flow. However, while the observation of large widths alone
does not require galactic-scale outflows, the observed offsets suggest that the
gas is on average outflowing from the galaxy. The observed offsets also suggest
that the ionizing radiation from the foreground quasars is anisotropic and/or
intermittent.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification | The computer-aided analysis of medical scans is a longstanding goal in the
medical imaging field. Currently, deep learning has became a dominant
methodology for supporting pathologists and radiologist. Deep learning
algorithms have been successfully applied to digital pathology and radiology,
nevertheless, there are still practical issues that prevent these tools to be
widely used in practice. The main obstacles are low number of available cases
and large size of images (a.k.a. the small n, large p problem in machine
learning), and a very limited access to annotation at a pixel level that can
lead to severe overfitting and large computational requirements. We propose to
handle these issues by introducing a framework that processes a medical image
as a collection of small patches using a single, shared neural network. The
final diagnosis is provided by combining scores of individual patches using a
permutation-invariant operator (combination). In machine learning community
such approach is called a multi-instance learning (MIL).
| 1 | 0 | 0 | 1 | 0 | 0 |
Reeb dynamics inspired by Katok's example in Finsler geometry | Inspired by Katok's examples of Finsler metrics with a small number of closed
geodesics, we present two results on Reeb flows with finitely many periodic
orbits. The first result is concerned with a contact-geometric description of
magnetic flows on the 2-sphere found recently by Benedetti. We give a simple
interpretation of that work in terms of a quaternionic symmetry. In the second
part, we use Hamiltonian circle actions on symplectic manifolds to produce
compact, connected contact manifolds in dimension at least five with
arbitrarily large numbers of periodic Reeb orbits. This contrasts sharply with
recent work by Cristofaro-Gardiner, Hutchings and Pomerleano on Reeb flows in
dimension three. With the help of Hamiltonian plugs and a surgery construction
due to Laudenbach we reprove a result of Cieliebak: one can produce Hamiltonian
flows in dimension at least five with any number of periodic orbits; in
dimension three, with any number greater than one.
| 0 | 0 | 1 | 0 | 0 | 0 |
Pressure-induced magnetic collapse and metallization of $\mathrm{TlF}{\mathrm{e}}_{1.6}\mathrm{S}{\mathrm{e}}_{2}$ | The crystal structure, magnetic ordering, and electrical resistivity of
TlFe1.6Se2 were studied at high pressures. Below ~7 GPa, TlFe1.6Se2 is an
antiferromagnetically ordered semiconductor with a ThCr2Si2-type structure. The
insulator-to-metal transformation observed at a pressure of ~ 7 GPa is
accompanied by a loss of magnetic ordering and an isostructural phase
transition. In the pressure range ~ 7.5 - 11 GPa a remarkable downturn in
resistivity, which resembles a superconducting transition, is observed below 15
K. We discuss this feature as the possible onset of superconductivity
originating from a phase separation in a small fraction of the sample in the
vicinity of the magnetic transition.
| 0 | 1 | 0 | 0 | 0 | 0 |
Real-Time Adaptive Image Compression | We present a machine learning-based approach to lossy image compression which
outperforms all existing codecs, while running in real-time.
Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG
2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of
generic images across all quality levels. At the same time, our codec is
designed to be lightweight and deployable: for example, it can encode or decode
the Kodak dataset in around 10ms per image on GPU.
Our architecture is an autoencoder featuring pyramidal analysis, an adaptive
coding module, and regularization of the expected codelength. We also
supplement our approach with adversarial training specialized towards use in a
compression setting: this enables us to produce visually pleasing
reconstructions for very low bitrates.
| 1 | 0 | 0 | 1 | 0 | 0 |
Probabilistic Graphical Modeling approach to dynamic PET direct parametric map estimation and image reconstruction | In the context of dynamic emission tomography, the conventional processing
pipeline consists of independent image reconstruction of single time frames,
followed by the application of a suitable kinetic model to time activity curves
(TACs) at the voxel or region-of-interest level. The relatively new field of 4D
PET direct reconstruction, by contrast, seeks to move beyond this scheme and
incorporate information from multiple time frames within the reconstruction
task. Existing 4D direct models are based on a deterministic description of
voxels' TACs, captured by the chosen kinetic model, considering the photon
counting process the only source of uncertainty. In this work, we introduce a
new probabilistic modeling strategy based on the key assumption that activity
time course would be subject to uncertainty even if the parameters of the
underlying dynamic process were known. This leads to a hierarchical Bayesian
model, which we formulate using the formalism of Probabilistic Graphical
Modeling (PGM). The inference of the joint probability density function arising
from PGM is addressed using a new gradient-based iterative algorithm, which
presents several advantages compared to existing direct methods: it is flexible
to an arbitrary choice of linear and nonlinear kinetic model; it enables the
inclusion of arbitrary (sub)differentiable priors for parametric maps; it is
simpler to implement and suitable to integration in computing frameworks for
machine learning. Computer simulations and an application to real patient scan
showed how the proposed approach allows us to weight the importance of the
kinetic model, providing a bridge between indirect and deterministic direct
methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Perturbative Thermodynamic Geometry of Nonextensive Ideal Classical, Bose and Fermi Gases | We investigate perturbative thermodynamic geometry of nonextensive ideal
Classical, Bose and Fermi gases.We show that the intrinsic statistical
interaction of nonextensive Bose (Fermi) gas is attractive (repulsive) similar
to the extensive case but the value of thermodynamic curvature is changed by
nonextensive parameter. In contrary to the extensive ideal classical gas, the
nonextensive one may be divided to two different regimes. According to
deviation parameter of the system to the nonextensive case, one can find a
special value of fugacity, $z^{*}$, where the sign of thermodynamic curvature
is changed. Therefore, we argue that the nonextensive parameter induces an
attractive (repulsive) statistical interaction for $z<z^{*}$ ($z>z^{*}$) for an
ideal classical gas. Also, according to the singular point of thermodynamic
curvature, we consider the condensation of nonextensive Boson gas.
| 0 | 1 | 0 | 0 | 0 | 0 |
On some mellin transforms for the Riemann zeta function in the critical strip | We offer two new Mellin transform evaluations for the Riemann zeta function
in the region $0<\Re(s)<1.$ Some discussion is offered in the way of evaluating
some further Fourier integrals involving the Riemann xi function.
| 0 | 0 | 1 | 0 | 0 | 0 |
Timelike surfaces in Minkowski space with a canonical null direction | Given a constant vector field $Z$ in Minkowski space, a timelike surface is
said to have a canonical null direction with respect to $Z$ if the projection
of $Z$ on the tangent space of the surface gives a lightlike vector field. In
this paper we describe these surfaces in the ruled case. For example when the
Minkowski space has three dimensions then a surface with a canonical null
direction is minimal and flat. On the other hand, we describe several
properties in the non ruled case and we partially describe these surfaces in
four-dimensional Minkowski space. We give different ways for building these
surfaces in four-dimensional Minkowski space and we finally use the Gauss map
for describe another properties of these surfaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
The existence and global exponential stability of almost periodic solutions for neutral type CNNs on time scales | In this paper, a class of neutral type competitive neural networks with mixed
time-varying delays and leakage delays on time scales is proposed. Based on the
exponential dichotomy of linear dynamic equations on time scales, Banach's
fixed point theorem and the theory of calculus on time scales, some sufficient
conditions that are independent of the backwards graininess function of the
time scale are obtained for the existence and global exponential stability of
almost periodic solutions for this class of neural networks. The obtained
results are completely new and indicate that both the continuous time and the
discrete time cases of the networks share the same dynamical behavior. Finally,
an examples is given to show the effectiveness of the obtained results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fluid-Structure Interaction for the Classroom: Interpolation, Hearts, and Swimming! | While students may find spline interpolation easily digestible, based on
their familiarity with continuity of a function and its derivatives, some of
its inherent value may be missed when students only see it applied to standard
data interpolation exercises. In this paper, we offer alternatives where
students can qualitatively and quantitatively witness the resulting dynamical
differences when objects are driven through a fluid using different spline
interpolation methods. They say, seeing is believing; here we showcase the
differences between linear and cubic spline interpolation using examples from
fluid pumping and aquatic locomotion. Moreover, students can define their own
interpolation functions and visualize the dynamics unfold. To solve the
fluid-structure interaction system, the open source software IB2d is used. In
that vein, all simulation codes, analysis scripts, and movies are provided for
streamlined use.
| 0 | 0 | 0 | 0 | 1 | 0 |
Modelling the evaporation of nanoparticle suspensions from heterogeneous surfaces | We present a Monte Carlo (MC) grid-based model for the drying of drops of a
nanoparticle suspension upon a heterogeneous surface. The model consists of a
generalised lattice-gas in which the interaction parameters in the Hamiltonian
can be varied to model different properties of the materials involved. We show
how to choose correctly the interactions, to minimise the effects of the
underlying grid so that hemispherical droplets form. We also include the
effects of surface roughness to examine the effects of contact-line pinning on
the dynamics. When there is a `lid' above the system, which prevents
evaporation, equilibrium drops form on the surface, which we use to determine
the contact angle and how it varies as the parameters of the model are changed.
This enables us to relate the interaction parameters to the materials used in
applications. The model has also been applied to drying on heterogeneous
surfaces, in particular to the case where the suspension is deposited on a
surface consisting of a pair of hydrophilic conducting metal surfaces that are
either side of a band of hydrophobic insulating polymer. This situation occurs
when using inkjet printing to manufacture electrical connections between the
metallic parts of the surface. The process is not always without problems,
since the liquid can dewet from the hydrophobic part of the surface, breaking
the bridge before the drying process is complete. The MC model reproduces the
observed dewetting, allowing the parameters to be varied so that the conditions
for the best connection can be established. We show that if the hydrophobic
portion of the surface is located at a step below the height of the
neighbouring metal, the chance of dewetting of the liquid during the drying
process is significantly reduced.
| 0 | 1 | 0 | 0 | 0 | 0 |
Image retargeting via Beltrami representation | Image retargeting aims to resize an image to one with a prescribed aspect
ratio. Simple scaling inevitably introduces unnatural geometric distortions on
the important content of the image. In this paper, we propose a simple and yet
effective method to resize an image, which preserves the geometry of the
important content, using the Beltrami representation. Our algorithm allows
users to interactively label content regions as well as line structures. Image
resizing can then be achieved by warping the image by an orientation-preserving
bijective warping map with controlled distortion. The warping map is
represented by its Beltrami representation, which captures the local geometric
distortion of the map. By carefully prescribing the values of the Beltrami
representation, images with different complexity can be effectively resized.
Our method does not require solving any optimization problems and tuning
parameters throughout the process. This results in a simple and efficient
algorithm to solve the image retargeting problem. Extensive experiments have
been carried out, which demonstrate the efficacy of our proposed method.
| 1 | 0 | 0 | 0 | 0 | 0 |
Coupling Load-Following Control with OPF | In this paper, the optimal power flow (OPF) problem is augmented to account
for the costs associated with the load-following control of a power network.
Load-following control costs are expressed through the linear quadratic
regulator (LQR). The power network is described by a set of nonlinear
differential algebraic equations (DAEs). By linearizing the DAEs around a known
equilibrium, a linearized OPF that accounts for steady-state operational
constraints is formulated first. This linearized OPF is then augmented by a set
of linear matrix inequalities that are algebraically equivalent to the
implementation of an LQR controller. The resulting formulation, termed LQR-OPF,
is a semidefinite program which furnishes optimal steady-state setpoints and an
optimal feedback law to steer the system to the new steady state with minimum
load-following control costs. Numerical tests demonstrate that the setpoints
computed by LQR-OPF result in lower overall costs and frequency deviations
compared to the setpoints of a scheme where OPF and load-following control are
considered separately.
| 1 | 0 | 1 | 0 | 0 | 0 |
A cellular algebra with specific decomposition of the unity | Let $ \mathbb{A}$ be a cellular algebra over a field $\mathbb{F}$ with a
decomposition of the identity $ 1_{\mathbb{A}} $ into orthogonal idempotents $
e_i$, $i \in I$ (for some finite set $I$) satisfying some properties. We
describe the entire Loewy structure of cell modules of the algebra $ \mathbb{A}
$ by using the representation theory of the algebra $ e_i \mathbb{A} e_i $ for
each $ i $. Moreover, we also study the block theory of $\mathbb{A}$ by using
this decomposition.
| 0 | 0 | 1 | 0 | 0 | 0 |
On relation between discrete Frenet frames and the bi-Hamiltonian structure of the discrete nonlinear Schrödinger equation | The discrete Frenet equation entails a local framing of a discrete, piecewise
linear polygonal chain in terms of its bond and torsion angles. In particular,
the tangent vector of a segment is akin the classical O(3) spin variable. Thus
there is a relation to the lattice Heisenberg model, that can be used to model
physical properties of the chain. On the other hand, the Heisenberg model is
closely related to the discrete nonlinear Schrödinger (DNLS) equation. Here
we apply these interrelations to develop a perspective on discrete chains
dynamics: We employ the properties of a discrete chain in terms of a spinorial
representation of the discrete Frenet equation, to introduce a bi-hamiltonian
structure for the discrete nonlinear Schrödinger equation (DNLSE), which we
then use to produce integrable chain dynamics.
| 0 | 1 | 1 | 0 | 0 | 0 |
A stronger version of a question proposed by K. Mahler | In 1902, P. Stäckel proved the existence of a transcendental function
$f(z)$, analytic in a neighbourhood of the origin, and with the property that
both $f(z)$ and its inverse function assume, in this neighbourhood, algebraic
values at all algebraic points. Based on this result, in 1976, K. Mahler raised
the question of the existence of such functions which are analytic in
$\mathbb{C}$. Recently, the authors answered positively this question. In this
paper, we prove a much stronger version of this result by considering other
subsets of $\mathbb{C}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models | We consider classifiers for high-dimensional data under the strongly spiked
eigenvalue (SSE) model. We first show that high-dimensional data often have the
SSE model. We consider a distance-based classifier using eigenstructures for
the SSE model. We apply the noise reduction methodology to estimation of the
eigenvalues and eigenvectors in the SSE model. We create a new distance-based
classifier by transforming data from the SSE model to the non-SSE model. We
give simulation studies and discuss the performance of the new classifier.
Finally, we demonstrate the new classifier by using microarray data sets.
| 0 | 0 | 0 | 1 | 0 | 0 |
Group Field theory and Tensor Networks: towards a Ryu-Takayanagi formula in full quantum gravity | We establish a dictionary between group field theory (thus, spin networks and
random tensors) states and generalized random tensor networks. Then, we use
this dictionary to compute the Rényi entropy of such states and recover the
Ryu-Takayanagi formula, in two different cases corresponding to two different
truncations/approximations, suggested by the established correspondence.
| 0 | 1 | 0 | 0 | 0 | 0 |
Behavior of Accelerated Gradient Methods Near Critical Points of Nonconvex Functions | We examine the behavior of accelerated gradient methods in smooth nonconvex
unconstrained optimization, focusing in particular on their behavior near
strict saddle points. Accelerated methods are iterative methods that typically
step along a direction that is a linear combination of the previous step and
the gradient of the function evaluated at a point at or near the current
iterate. (The previous step encodes gradient information from earlier stages in
the iterative process.) We show by means of the stable manifold theorem that
the heavy-ball method method is unlikely to converge to strict saddle points,
which are points at which the gradient of the objective is zero but the Hessian
has at least one negative eigenvalue. We then examine the behavior of the
heavy-ball method and other accelerated gradient methods in the vicinity of a
strict saddle point of a nonconvex quadratic function, showing that both
methods can diverge from this point more rapidly than the steepest-descent
method.
| 0 | 0 | 1 | 0 | 0 | 0 |
Distinguishing differential susceptibility, diathesis-stress and vantage sensitivity: beyond the single gene and environment model | Currently, two main approaches exist to distinguish differential
susceptibility from diathesis-stress and vantage sensitivity in genotype x
environment interaction (GxE) research: Regions of significance (RoS) and
competitive-confirmatory approaches. Each is limited by their
single-gene/single-environment foci given that most phenotypes are the product
of multiple interacting genetic and environmental factors. We thus addressed
these two concerns in a recently developed R package (LEGIT) for constructing
GxE interaction models with latent genetic and environmental scores using
alternating optimization. Herein we test, by means of computer simulation,
diverse GxE models in the context of both single and multiple genes and
environments. Results indicate that the RoS and competitive-confirmatory
approaches were highly accurate when the sample size was large, whereas the
latter performed better in small samples and for small effect sizes. The
confirmatory approach generally had good accuracy (a) when effect size was
moderate and N >= 500 and (b) when effect size was large and N >= 250, whereas
RoS performed poorly. Computational tools to determine the type of GxE of
multiple genes and environments are provided as extensions in our LEGIT R
package.
| 0 | 0 | 0 | 1 | 0 | 0 |
A weak type estimate for rough singular integrals | We obtain a weak type $(1,1)$ estimate for a maximal operator associated with
the classical rough homogeneous singular integrals $T_{\Omega}$. In particular,
this provides a different approach to a sparse domination for $T_{\Omega}$
obtained recently by Conde-Alonso, Culiuc, Di Plinio and Ou.
| 0 | 0 | 1 | 0 | 0 | 0 |
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System | The Internet of things (IoT) is still in its infancy and has attracted much
interest in many industrial sectors including medical fields, logistics
tracking, smart cities and automobiles. However as a paradigm, it is
susceptible to a range of significant intrusion threats. This paper presents a
threat analysis of the IoT and uses an Artificial Neural Network (ANN) to
combat these threats. A multi-level perceptron, a type of supervised ANN, is
trained using internet packet traces, then is assessed on its ability to thwart
Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the
classification of normal and threat patterns on an IoT Network. The ANN
procedure is validated against a simulated IoT network. The experimental
results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS
attacks.
| 1 | 0 | 0 | 0 | 0 | 0 |
An experimental study of Bitcoin fluctuation using machine learning methods | In this paper, we study the ability to make the short-term prediction of the
exchange price fluctuations towards the United States dollar for the Bitcoin
market. We use the data of realized volatility collected from one of the
largest Bitcoin digital trading offices in 2016 and 2017 as well as order
information. Experiments are performed to evaluate a variety of statistical and
machine learning approaches.
| 0 | 0 | 0 | 1 | 0 | 0 |
A unified theory for exact stochastic modelling of univariate and multivariate processes with continuous, mixed type, or discrete marginal distributions and any correlation structure | Hydroclimatic processes are characterized by heterogeneous spatiotemporal
correlation structures and marginal distributions that can be continuous,
mixed-type, discrete or even binary. Simulating exactly such processes can
greatly improve hydrological analysis and design. Yet this challenging task is
accomplished often by ad hoc and approximate methodologies that are devised for
specific variables and purposes. In this study, a single framework is proposed
allowing the exact simulation of processes with any marginal and any
correlation structure. We unify, extent, and improve of a general-purpose
modelling strategy based on the assumption that any process can emerge by
transforming a parent Gaussian process with a specific correlation structure. A
novel mathematical representation of the parent-Gaussian scheme provides a
consistent and fully general description that supersedes previous specific
parameterizations, resulting in a simple, fast and efficient simulation
procedure for every spatiotemporal process. In particular, introducing a simple
but flexible procedure we obtain a parametric expression of the correlation
transformation function, allowing to assess the correlation structure of the
parent-Gaussian process that yields the prescribed correlation of the target
process after marginal back transformation. The same framework is also
applicable for cyclostationary and multivariate modelling. The simulation of a
variety of hydroclimatic variables with very different correlation structures
and marginals, such as precipitation, stream flow, wind speed, humidity,
extreme events per year, etc., as well as a multivariate application,
highlights the flexibility, advantages, and complete generality of the proposed
methodology.
| 0 | 0 | 1 | 1 | 0 | 0 |
The Guiding Influence of Stanley Mandelstam, from S-Matrix Theory to String Theory | The guiding influence of some of Stanley Mandelstam's key contributions to
the development of theoretical high energy physics is discussed, from the
motivation for the study of the analytic properties of the scattering matrix
through to dual resonance models and their evolution into string theory.
| 0 | 1 | 0 | 0 | 0 | 0 |
Radiation-driven turbulent accretion onto massive black holes | Accretion of gas and interaction of matter and radiation are at the heart of
many questions pertaining to black hole (BH) growth and coevolution of massive
BHs and their host galaxies. To answer them it is critical to quantify how the
ionizing radiation that emanates from the innermost regions of the BH accretion
flow couples to the surrounding medium and how it regulates the BH fueling. In
this work we use high resolution 3-dimensional (3D) radiation-hydrodynamic
simulations with the code Enzo, equipped with adaptive ray tracing module
Moray, to investigate radiation-regulated BH accretion of cold gas. Our
simulations reproduce findings from an earlier generation of 1D/2D simulations:
the accretion powered UV and X-ray radiation forms a highly ionized bubble,
which leads to suppression of BH accretion rate characterized by quasi-periodic
outbursts. A new feature revealed by the 3D simulations is the highly turbulent
nature of the gas flow in vicinity of the ionization front. During quiescent
periods between accretion outbursts, the ionized bubble shrinks in size and the
gas density that precedes the ionization front increases. Consequently, the 3D
simulations show oscillations in the accretion rate of only ~2-3 orders of
magnitude, significantly smaller than 1D/2D models. We calculate the energy
budget of the gas flow and find that turbulence is the main contributor to the
kinetic energy of the gas but corresponds to less than 10% of its thermal
energy and thus does not contribute significantly to the pressure support of
the gas.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bayesian Inference of Log Determinants | The log-determinant of a kernel matrix appears in a variety of machine
learning problems, ranging from determinantal point processes and generalized
Markov random fields, through to the training of Gaussian processes. Exact
calculation of this term is often intractable when the size of the kernel
matrix exceeds a few thousand. In the spirit of probabilistic numerics, we
reinterpret the problem of computing the log-determinant as a Bayesian
inference problem. In particular, we combine prior knowledge in the form of
bounds from matrix theory and evidence derived from stochastic trace estimation
to obtain probabilistic estimates for the log-determinant and its associated
uncertainty within a given computational budget. Beyond its novelty and
theoretic appeal, the performance of our proposal is competitive with
state-of-the-art approaches to approximating the log-determinant, while also
quantifying the uncertainty due to budget-constrained evidence.
| 1 | 0 | 0 | 1 | 0 | 0 |
Investigating prescriptions for artificial resistivity in smoothed particle magnetohydrodynamics | In numerical simulations, artificial terms are applied to the evolution
equations for stability. To prove their validity, these terms are thoroughly
tested in test problems where the results are well known. However, they are
seldom tested in production-quality simulations at high resolution where they
interact with a plethora of physical and numerical algorithms. We test three
artificial resistivities in both the Orszag-Tang vortex and in a star formation
simulation. From the Orszag-Tang vortex, the Price et. al. (2017) artificial
resistivity is the least dissipative thus captures the density and magnetic
features; in the star formation algorithm, each artificial resistivity
algorithm interacts differently with the sink particle to produce various
results, including gas bubbles, dense discs, and migrating sink particles. The
star formation simulations suggest that it is important to rely upon physical
resistivity rather than artificial resistivity for convergence.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Planning and Control Framework for Humanoid Systems: Robust, Optimal, and Real-time Performance | Humanoid robots are increasingly demanded to operate in interactive and
human-surrounded environments while achieving sophisticated locomotion and
manipulation tasks. To accomplish these tasks, roboticists unremittingly seek
for advanced methods that generate whole-body coordination behaviors and
meanwhile fulfill various planning and control objectives. Undoubtedly, these
goals pose fundamental challenges to the robotics and control community. To
take an incremental step towards reducing the performance gap between
theoretical foundations and real implementations, we present a planning and
control framework for the humanoid, especially legged robots, for achieving
high performance and generating agile motions. A particular concentration is on
the robust, optimal and real-time performance. This framework constitutes three
hierarchical layers: First, we present a robust optimal phase-space planning
framework for dynamic legged locomotion over rough terrain. This framework is a
hybrid motion planner incorporating a series of pivotal components. Second, we
take a step toward formally synthesizing high-level reactive planners for
whole-body locomotion in constrained environments. We formulate a two-player
temporal logic game between the contact planner and its possibly-adversarial
environment. Third, we propose a distributed control architecture for the
latency-prone humanoid robotic systems. A central experimental phenomenon is
observed that the stability of high impedance distributed controllers is highly
sensitive to damping feedback delay but much less to stiffness feedback delay.
We pursue a detailed analysis of the distributed controllers where damping
feedback effort is executed in proximity to the control plant, and stiffness
feedback effort is implemented in a latency-prone centralized control process.
| 1 | 0 | 1 | 0 | 0 | 0 |
Attention-based Wav2Text with Feature Transfer Learning | Conventional automatic speech recognition (ASR) typically performs
multi-level pattern recognition tasks that map the acoustic speech waveform
into a hierarchy of speech units. But, it is widely known that information loss
in the earlier stage can propagate through the later stages. After the
resurgence of deep learning, interest has emerged in the possibility of
developing a purely end-to-end ASR system from the raw waveform to the
transcription without any predefined alignments and hand-engineered models.
However, the successful attempts in end-to-end architecture still used
spectral-based features, while the successful attempts in using raw waveform
were still based on the hybrid deep neural network - Hidden Markov model
(DNN-HMM) framework. In this paper, we construct the first end-to-end
attention-based encoder-decoder model to process directly from raw speech
waveform to the text transcription. We called the model as "Attention-based
Wav2Text". To assist the training process of the end-to-end model, we propose
to utilize a feature transfer learning. Experimental results also reveal that
the proposed Attention-based Wav2Text model directly with raw waveform could
achieve a better result in comparison with the attentional encoder-decoder
model trained on standard front-end filterbank features.
| 1 | 0 | 0 | 0 | 0 | 0 |
Amplitude death and resurgence of oscillation in network of mobile oscillators | The phenomenon of amplitude death has been explored using a variety of
different coupling strategies in the last two decades. In most of the work, the
basic coupling arrangement is considered to be static over time, although many
realistic systems exhibit significant changes in the interaction pattern as
time varies. In this article, we study the emergence of amplitude death in a
dynamical network composed of time-varying interaction amidst a collection of
random walkers in a finite region of three dimensional space. We consider an
oscillator for each walker and demonstrate that depending upon the network
parameters and hence the interaction between them, global oscillation in the
network gets suppressed. In this framework, vision range of each oscillator
decides the number of oscillators with which it interacts. In addition, with
the use of an appropriate feedback parameter in the coupling strategy, we
articulate how the suppressed oscillation can be resurrected in the systems'
parameter space. The phenomenon of amplitude death and the resurgence of
oscillation is investigated taking limit cycle and chaotic oscillators for
broad ranges of parameters, like interaction strength k between the entities,
vision range r and the speed of movement v.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quintessential Inflation with $α$-attractors | A novel approach to quintessential inflation model building is studied,
within the framework of $\alpha$-attractors, motivated by supergravity
theories. Inflationary observables are in excellent agreement with the latest
CMB observations, while quintessence explains the dark energy observations
without any fine-tuning. The model is kept intentionally minimal, avoiding the
introduction of many degrees of freedom, couplings and mass scales. In stark
contrast to $\Lambda$CDM, for natural values of the parameters, the model
attains transient accelerated expansion, which avoids the future horizon
problem, while it maintains the field displacement mildly sub-Planckian such
that the flatness of the quintessential tail is not lifted by radiative
corrections and violations of the equivalence principle (fifth force) are under
control. In particular, the required value of the cosmological constant is near
the eletroweak scale. Attention is paid to the reheating of the Universe, which
avoids gravitino overproduction and respects nucleosynthesis constraints.
Kination is treated in a model independent way. A spike in gravitational waves,
due to kination, is found not to disturb nucleosynthesis as well.
| 0 | 1 | 0 | 0 | 0 | 0 |
Degenerate and chiral states in the extended Heisenberg model in the kagome lattice | We present a study of the low temperature phases of the antiferromagnetic
extended classical Heisenberg model in the kagome lattice, up to third nearest
neighbors. First, we focus on the degenerate lines in the boundaries of the
well-known staggered chiral phases. These boundaries have either semi-extensive
or extensive degeneracy, and we discuss the partial selection of states by
thermal fluctuations. Then, we study the model under an external magnetic field
on these lines and in the staggered chiral phases. We pay particular attention
to the highly frustrated point, where the three exchange couplings are equal.
We show that this point can me mapped to a model with spin liquid behavior and
non-zero chirality. Finally, we explore the effect of Dzyaloshinskii-Moriya
(DM) interactions in two ways: an homogeneous and a staggered DM interaction.
In both cases, there is a rich low temperature phase diagram, with different
spontaneously broken symmetries and non trivial chiral phases.
| 0 | 1 | 0 | 0 | 0 | 0 |
Enhancing Blood Glucose Prediction with Meal Absorption and Physical Exercise Information | Objective: Numerous glucose prediction algorithm have been proposed to
empower type 1 diabetes (T1D) management. Most of these algorithms only account
for input such as glucose, insulin and carbohydrate, which limits their
performance. Here, we present a novel glucose prediction algorithm which, in
addition to standard inputs, accounts for meal absorption and physical exercise
information to enhance prediction accuracy. Methods: a compartmental model of
glucose-insulin dynamics combined with a deconvolution technique for state
estimation is employed for glucose prediction. In silico data corresponding
from the 10 adult subjects of UVa-Padova simulator, and clinical data from 10
adults with T1D were used. Finally, a comparison against a validated glucose
prediction algorithm based on a latent variable with exogenous input (LVX)
model is provided. Results: For a prediction horizon of 60 minutes, accounting
for meal absorption and physical exercise improved glucose forecasting
accuracy. In particular, root mean square error (mg/dL) went from 26.68 to
23.89, p<0.001 (in silico data); and from 37.02 to 35.96, p<0.001 (clinical
data - only meal information). Such improvement in accuracy was translated into
significant improvements on hypoglycaemia and hyperglycaemia prediction.
Finally, the performance of the proposed algorithm is statistically superior to
that of the LVX algorithm (26.68 vs. 32.80, p<0.001 (in silico data); 37.02 vs.
49.17, p<0.01 (clinical data). Conclusion: Taking into account meal absorption
and physical exercise information improves glucose prediction accuracy.
| 1 | 0 | 0 | 0 | 1 | 0 |
The fraction of cool-core clusters in X-ray vs. SZ samples using Chandra observations | We derive and compare the fractions of cool-core clusters in the {\em Planck}
Early Sunyaev-Zel'dovich sample of 164 clusters with $z \leq 0.35$ and in a
flux-limited X-ray sample of 100 clusters with $z \leq 0.30$, using {\em
Chandra} observations. We use four metrics to identify cool-core clusters: 1)
the concentration parameter: the ratio of the integrated emissivity profile
within 0.15 $r_{500}$ to that within $r_{500}$, and 2) the ratio of the
integrated emissivity profile within 40 kpc to that within 400 kpc, 3) the
cuspiness of the gas density profile: the negative of the logarithmic
derivative of the gas density with respect to the radius, measured at 0.04
$r_{500}$, and 4) the central gas density, measured at 0.01 $r_{500}$. We find
that the sample of X-ray selected clusters, as characterized by each of these
metrics, contains a significantly larger fraction of cool-core clusters
compared to the sample of SZ selected clusters (44$\pm$7\% vs. 28$\pm$4\% using
the concentration parameter in the 0.15--1.0 $r_{500}$ range, 61$\pm$8\% vs.
36$\pm$5\% using the concentration parameter in the 40--400 kpc range,
64$\pm$8\% vs. 38$\pm$5\% using the cuspiness, and 53$\pm$7\% vs. 39$\pm$5\%
using the central gas density). Qualitatively, cool-core clusters are more
X-ray luminous at fixed mass. Hence, our X-ray flux-limited sample, compared to
the approximately mass-limited SZ sample, is over-represented with cool-core
clusters. We describe a simple quantitative model that uses the excess
luminosity of cool-core clusters compared to non-cool-core clusters at fixed
mass to successfully predict the observed fraction of cool-core clusters in
X-ray selected samples.
| 0 | 1 | 0 | 0 | 0 | 0 |
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers | Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune algorithm parameters. However, the commonly used
supervised learning approaches require the labeled data (e.g., bounding boxes),
which is expensive for videos. Also, the TBD framework is usually suboptimal
since it is not end-to-end, i.e., it considers the task as detection and
tracking, but not jointly. To achieve both label-free and end-to-end learning
of MOT, we propose a Tracking-by-Animation framework, where a differentiable
neural model first tracks objects from input frames and then animates these
objects into reconstructed frames. Learning is then driven by the
reconstruction error through backpropagation. We further propose a
Reprioritized Attentive Tracking to improve the robustness of data association.
Experiments conducted on both synthetic and real video datasets show the
potential of the proposed model.
| 0 | 0 | 0 | 1 | 0 | 0 |
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | Generating novel graph structures that optimize given objectives while
obeying some given underlying rules is fundamental for chemistry, biology and
social science research. This is especially important in the task of molecular
graph generation, whose goal is to discover novel molecules with desired
properties such as drug-likeness and synthetic accessibility, while obeying
physical laws such as chemical valency. However, designing models to find
molecules that optimize desired properties while incorporating highly complex
and non-differentiable rules remains to be a challenging task. Here we propose
Graph Convolutional Policy Network (GCPN), a general graph convolutional
network based model for goal-directed graph generation through reinforcement
learning. The model is trained to optimize domain-specific rewards and
adversarial loss through policy gradient, and acts in an environment that
incorporates domain-specific rules. Experimental results show that GCPN can
achieve 61% improvement on chemical property optimization over state-of-the-art
baselines while resembling known molecules, and achieve 184% improvement on the
constrained property optimization task.
| 0 | 0 | 0 | 1 | 0 | 0 |
Impact of the positive ion current on large size neutrino detectors and delayed photon emission | Given their small mobility coefficient in liquid argon with respect to the
electrons, the ions spend a considerably longer time in the active volume. We
studied the effects of the positive ion current in a liquid argon time
projection chamber, in the context of massive argon experiments for neutrino
physics. The constant recombination between free ions and electrons produces a
quenching of the charge signal and a constant emission of photons, uncorrelated
in time and space to the physical interactions. The predictions evidence some
potential concerns for multi-ton argon detectors, particularly when operated on
surface
| 0 | 1 | 0 | 0 | 0 | 0 |
On Multilevel Coding Schemes Based on Non-Binary LDPC Codes | We address the problem of constructing of coding schemes for the channels
with high-order modulations. It is known, that non-binary LDPC codes are
especially good for such channels and significantly outperform their binary
counterparts. Unfortunately, their decoding complexity is still large. In order
to reduce the decoding complexity we consider multilevel coding schemes based
on non-binary LDPC codes (NB-LDPC-MLC schemes) over smaller fields. The use of
such schemes gives us a reasonable gain in complexity. At the same time the
performance of NB-LDPC-MLC schemes is practically the same as the performance
of LDPC codes over the field matching the modulation order. In particular by
means of simulations we showed that the performance of NB-LDPC-MLC schemes over
GF(16) is the same as the performance of non-binary LDPC codes over GF(64) and
GF(256) in AWGN channel with QAM64 and QAM256 accordingly. We also perform a
comparison with binary LDPC codes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Tunable coupling-induced resonance splitting in self-coupled Silicon ring cavity with robust spectral characteristics | We propose and demonstrate a self-coupled microring resonator for resonance
splitting by mutual mode coupling of cavity mode and counter-propagating mode
in Silicon-on-Insulator platform The resonator is constructed with a
self-coupling region that can excite counter-propagating mode. We
experimentally study the effect of self-coupling on the resonance splitting,
resonance extinction, and quality-factor evolution and stability. Based on the
coupling, we achieve 72% of FSR splitting for a cavity with FSR 2.1 nm with <
5% variation in the cavity quality factor. The self-coupled resonance splitting
shows highly robust spectral characteristic that can be exploited for sensing
and optical signal processing.
| 0 | 1 | 0 | 0 | 0 | 0 |
High-precision measurements and theoretical calculations of indium excited-state polarizabilities | We report measurements of the $^{115}$In $7p_{1/2}$ and $7p_{3/2}$ scalar and
tensor polarizabilities using two-step diode laser spectroscopy in an atomic
beam. The scalar polarizabilities are one to two orders of magnitude larger
than for lower lying indium states due to the close proximity of the $7p$ and
$6d$ states. For the scalar polarizabilities, we find values (in atomic units)
of $1.811(4) \times 10^5$ $a_0^3$ and $2.876(6) \times 10^5$ $a_0^3$ for the
$7p_{1/2}$ and $7p_{3/2}$ states respectively. We estimate the smaller tensor
polarizability component of the $7p_{3/2}$ state to be $-1.43(18) \times 10^4$
$a_0^3$. These measurements represent the first high-precision benchmarks of
transition properties of such high excited states of trivalent atomic systems.
We also present new ab initio calculations of these quantities and other In
polarizabilities using two high-precision relativistic methods to make a global
comparison of the accuracies of the two approaches. The precision of the
experiment is sufficient to differentiate between the two theoretical methods
as well as to allow precise determination of the indium $7p-6d$ matrix
elements. The results obtained in this work are applicable to other heavier and
more complicated systems, and provide much needed guidance for the development
of even more precise theoretical approaches.
| 0 | 1 | 0 | 0 | 0 | 0 |
2D reductions of the equation $u_{yy} = u_{tx} + u_yu_{xx} - u_xu_{xy}$ and their nonlocal symmetries | We consider the 3D equation $u_{yy} = u_{tx} + u_yu_{xx} - u_xu_{xy}$ and its
2D reductions: (1) $u_{yy} = (u_y+y)u_{xx}-u_xu_{xy}-2$ (which is equivalent to
the Gibbons-Tsarev equation) and (2) $u_{yy} = (u_y+2x)u_{xx} + (y-u_x)u_{xy}
-u_x$. Using reduction of the known Lax pair for the 3D equation, we describe
nonlocal symmetries of~(1) and~(2) and show that the Lie algebras of these
symmetries are isomorphic to the Witt algebra.
| 0 | 1 | 0 | 0 | 0 | 0 |
An improved Belief Propagation algorithm finds many Bethe states in the random field Ising model on random graphs | We first present an empirical study of the Belief Propagation (BP) algorithm,
when run on the random field Ising model defined on random regular graphs in
the zero temperature limit. We introduce the notion of maximal solutions for
the BP equations and we use them to fix a fraction of spins in their ground
state configuration. At the phase transition point the fraction of
unconstrained spins percolates and their number diverges with the system size.
This in turn makes the associated optimization problem highly non trivial in
the critical region. Using the bounds on the BP messages provided by the
maximal solutions we design a new and very easy to implement BP scheme which is
able to output a large number of stable fixed points. On one side this new
algorithm is able to provide the minimum energy configuration with high
probability in a competitive time. On the other side we found that the number
of fixed points of the BP algorithm grows with the system size in the critical
region. This unexpected feature poses new relevant questions on the physics of
this class of models.
| 1 | 1 | 0 | 0 | 0 | 0 |
Large-scale diversity estimation through surname origin inference | The study of surnames as both linguistic and geographical markers of the past
has proven valuable in several research fields spanning from biology and
genetics to demography and social mobility. This article builds upon the
existing literature to conceive and develop a surname origin classifier based
on a data-driven typology. This enables us to explore a methodology to describe
large-scale estimates of the relative diversity of social groups, especially
when such data is scarcely available. We subsequently analyze the
representativeness of surname origins for 15 socio-professional groups in
France.
| 0 | 0 | 0 | 1 | 0 | 0 |
Schur $Q$-functions and the Capelli eigenvalue problem for the Lie superalgebra $\mathfrak q(n)$ | Let $\mathfrak l:= \mathfrak q(n)\times\mathfrak q(n)$, where $\mathfrak
q(n)$ denotes the queer Lie superalgebra. The associative superalgebra $V$ of
type $Q(n)$ has a left and right action of $\mathfrak q(n)$, and hence is
equipped with a canonical $\mathfrak l$-module structure. We consider a
distinguished basis $\{D_\lambda\}$ of the algebra of $\mathfrak l$-invariant
super-polynomial differential operators on $V$, which is indexed by strict
partitions of length at most $n$. We show that the spectrum of the operator
$D_\lambda$, when it acts on the algebra $\mathscr P(V)$ of super-polynomials
on $V$, is given by the factorial Schur $Q$-function of Okounkov and Ivanov.
This constitutes a refinement and a new proof of a result of Nazarov, who
computed the top-degree homogeneous part of the Harish-Chandra image of
$D_\lambda$. As a further application, we show that the radial projections of
the spherical super-polynomials corresponding to the diagonal symmetric pair
$(\mathfrak l,\mathfrak m)$, where $\mathfrak m:=\mathfrak q(n)$, of
irreducible $\mathfrak l$-submodules of $\mathscr P(V)$ are the classical Schur
$Q$-functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Dynamics of cosmological perturbations in modified Brans-Dicke cosmology with matter-scalar field interaction | In this work we focus on a novel completion of the well-known Brans-Dicke
theory that introduces an interaction between the dark energy and dark matter
sectors, known as complete Brans-Dicke (CBD) theory. We obtain viable
cosmological accelerating solutions that fit Supernovae observations with great
precision without any scalar potential $V(\phi)$. We use these solutions to
explore the impact of the CBD theory on the large scale structure by studying
the dynamics of its linear perturbations. We observe a growing behavior of the
lensing potential $\Phi_{+}$ at late-times, while the growth rate is actually
suppressed relatively to $\Lambda$CDM, which allows the CBD theory to provide a
competitive fit to current RSD measurements of $f\sigma_{8}$. However, we also
observe that the theory exhibits a pathological change of sign in the effective
gravitational constant concerning the perturbations on sub-horizon scales that
could pose a challenge to its validity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech | Speech emotion recognition is an important and challenging task in the realm
of human-computer interaction. Prior work proposed a variety of models and
feature sets for training a system. In this work, we conduct extensive
experiments using an attentive convolutional neural network with multi-view
learning objective function. We compare system performance using different
lengths of the input signal, different types of acoustic features and different
types of emotion speech (improvised/scripted). Our experimental results on the
Interactive Emotional Motion Capture (IEMOCAP) database reveal that the
recognition performance strongly depends on the type of speech data independent
of the choice of input features. Furthermore, we achieved state-of-the-art
results on the improvised speech data of IEMOCAP.
| 1 | 0 | 0 | 0 | 0 | 0 |
Men Are from Mars, Women Are from Venus: Evaluation and Modelling of Verbal Associations | We present a quantitative analysis of human word association pairs and study
the types of relations presented in the associations. We put our main focus on
the correlation between response types and respondent characteristics such as
occupation and gender by contrasting syntagmatic and paradigmatic associations.
Finally, we propose a personalised distributed word association model and show
the importance of incorporating demographic factors into the models commonly
used in natural language processing.
| 1 | 0 | 0 | 0 | 0 | 0 |
Galaxies as High-Resolution Telescopes | Recent observations show a population of active galaxies with milliarcseconds
offsets between optical and radio emission. Such offsets can be an indication
of extreme phenomena associated with supermassive black holes including
relativistic jets, binary supermassive black holes, or even recoiling
supermassive black holes. However, the multi-wavelength structure of active
galaxies at a few milliarcseconds cannot be fathomed with direct observations.
We propose using strong gravitational lensing to elucidate the multi-wavelength
structure of sources. When sources are located close to the caustic of lensing
galaxy, even small offset in the position of the sources results in a drastic
difference in the position and magnification of mirage images. We show that the
angular offset in the position of the sources can be amplified more than 50
times in the observed position of mirage images. We find that at least 8% of
the observed gravitationally lensed quasars will be in the caustic
configuration. The synergy between SKA and Euclid will provide an ideal set of
observations for thousands of gravitationally lensed sources in the caustic
configuration, which will allow us to elucidate the multi-wavelength structure
for a large ensemble of sources, and study the physical origin of radio
emissions, their connection to supermassive black holes, and their cosmic
evolution.
| 0 | 1 | 0 | 0 | 0 | 0 |
Continuous CM-regularity of semihomogeneous vector bundles | We show that if $X$ is an abelian variety of dimension $g \geq 1$ and
${\mathcal E}$ is an M-regular coherent sheaf on $X$, the Castelnuovo-Mumford
regularity of ${\mathcal E}$ with respect to an ample and globally generated
line bundle ${\mathcal O}(1)$ on $X$ is at most $g$, and that equality is
obtained when ${\mathcal E}^{\vee}(1)$ is continuously globally generated. As
an application, we give a numerical characterization of ample semihomogeneous
vector bundles for which this bound is attained.
| 0 | 0 | 1 | 0 | 0 | 0 |
Factorisation of the product of Dirichlet series of completely multiplicative functions | In the first chapter, we will present a computation of the square value of
the module of L functions associated to a Dirichlet character. This computation
suggests to ask if a certain ring of arithmetic multiplicative functions exists
and if it is unique. This search has led to the construction of that ring in
chapter two. Finally, in the third chapter, we will present some propositions
associated with this ring. The result below is one of the main results of this
work :
For F and G two completely multiplicative functions, $ s $ a complex number
such as the dirichlet series $ D(F,s) $ and $ D(G,s) $ converge :
$ \forall F,G \in \mathbb{M}_{c} : D(F,s) \times D(G,s) = D(F \times G,2s)
\times D(F \square G,s) $
where the operation $ \square $ is defined in chapter two as the sum of the
previously mentioned ring. Here are some similar versions, with $ s = x+iy $ :
$ \forall F, G \in \mathbb{M}_{c} : ~ D(F,s) \times D(G,\overline{s}) = D(F
\times G,2x) \times D(\frac{F}{\text{Id}_{e}^{iy}} \square
\frac{G}{\text{Id}_{e}^{-iy}}, x) $
$ \forall F, G \in \mathbb{M}_{c} : ~ |D(F,s)|^{2} = D(|F|^{2},2x) \times
D(\frac{F}{\text{Id}_{e}^{iy}} \square \overline{\frac{F}{\text{Id}_{e}^{iy}}},
x) $
| 0 | 0 | 1 | 0 | 0 | 0 |
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables | Many inverse problems involve two or more sets of variables that represent
different physical quantities but are tightly coupled with each other. For
example, image super-resolution requires joint estimation of the image and
motion parameters from noisy measurements. Exploiting this structure is key for
efficiently solving these large-scale optimization problems, which are often
ill-conditioned.
In this paper, we present a new method called Linearize And Project (LAP)
that offers a flexible framework for solving inverse problems with coupled
variables. LAP is most promising for cases when the subproblem corresponding to
one of the variables is considerably easier to solve than the other. LAP is
based on a Gauss-Newton method, and thus after linearizing the residual, it
eliminates one block of variables through projection. Due to the linearization,
this block can be chosen freely. Further, LAP supports direct, iterative, and
hybrid regularization as well as constraints. Therefore LAP is attractive,
e.g., for ill-posed imaging problems. These traits differentiate LAP from
common alternatives for this type of problem such as variable projection
(VarPro) and block coordinate descent (BCD). Our numerical experiments compare
the performance of LAP to BCD and VarPro using three coupled problems whose
forward operators are linear with respect to one block and nonlinear for the
other set of variables.
| 1 | 0 | 1 | 0 | 0 | 0 |
Obtaining the Current-Flux Relations of the Saturated PMSM by Signal Injection | This paper proposes a method based on signal injection to obtain the
saturated current-flux relations of a PMSM from locked-rotor experiments. With
respect to the classical method based on time integration, it has the main
advantage of being completely independent of the stator resistance; moreover,
it is less sensitive to voltage biases due to the power inverter, as the
injected signal may be fairly large.
| 1 | 0 | 0 | 0 | 0 | 0 |
Approximation properties of (p,q)-Meyer-Konig-Zeller Durrmeyer operators | In this paper, we introduce Durrmeyer type modification of Meyer-Konig-Zeller
operators based on (p,q)-integers. Rate of convergence of these operators are
explored with the help of Korovkin type theorems. We establish some direct
results for proposed operators. We also obtain statistical approximation
properties of operators. In last section, we show rate of convergence of
(p,q)-Meyer-Konig-Zeller Durrmeyer operators for some functions by means of
Matlab programming.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep Graph Infomax | We present Deep Graph Infomax (DGI), a general approach for learning node
representations within graph-structured data in an unsupervised manner. DGI
relies on maximizing mutual information between patch representations and
corresponding high-level summaries of graphs---both derived using established
graph convolutional network architectures. The learnt patch representations
summarize subgraphs centered around nodes of interest, and can thus be reused
for downstream node-wise learning tasks. In contrast to most prior approaches
to unsupervised learning with GCNs, DGI does not rely on random walk
objectives, and is readily applicable to both transductive and inductive
learning setups. We demonstrate competitive performance on a variety of node
classification benchmarks, which at times even exceeds the performance of
supervised learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
Solitons and breathers for nonisospectral mKdV equation with Darboux transformation | Under investigation in this paper is the nonisospectral and variable
coefficients modified Kortweg-de Vries (vc-mKdV) equation, which manifests in
diverse areas of physics such as fluid dynamics, ion acoustic solitons and
plasma mechanics. With the degrees of restriction reduced, a simplified
constraint is introduced, under which the vc-mKdV equation is an integrable
system and the spectral flow is time-varying. The Darboux transformation for
such equation is constructed, which gives rise to the generation of variable
kinds of solutions including the double-breather coherent structure, periodical
soliton-breather and localized solitons and breathers. In addition, the effect
of variable coefficients and initial phases is discussed in terms of the
soliton amplitude, polarity, velocity and width, which might provide feasible
soliton management with certain conditions taken into account.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sequential detection of low-rank changes using extreme eigenvalues | We study the problem of detecting an abrupt change to the signal covariance
matrix. In particular, the covariance changes from a "white" identity matrix to
an unknown spiked or low-rank matrix. Two sequential change-point detection
procedures are presented, based on the largest and the smallest eigenvalues of
the sample covariance matrix. To control false-alarm-rate, we present an
accurate theoretical approximation to the average-run-length (ARL) and expected
detection delay (EDD) of the detection, leveraging the extreme eigenvalue
distributions from random matrix theory and by capturing a non-negligible
temporal correlation in the sequence of scan statistics due to the sliding
window approach. Real data examples demonstrate the good performance of our
method for detecting behavior change of a swarm.
| 0 | 0 | 1 | 1 | 0 | 0 |
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework | In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.
| 1 | 0 | 0 | 0 | 0 | 0 |
Does a growing static length scale control the glass transition? | Several theories of the glass transition propose that the structural
relaxation time {\tau}{\alpha} is controlled by a growing static length scale
{\xi} that is determined by the free energy landscape but not by the local
dynamical rules governing its exploration. We argue, based on recent
simulations using particle-radius-swap dynamics, that only a modest factor in
the increase in {\tau}{\alpha} on approach to the glass transition may stem
from the growth of a static length, with a vastly larger contribution
attributable instead to a slowdown of local dynamics. This reinforces arguments
that we base on the observed strong coupling of particle diffusion and density
fluctuations in real glasses
| 0 | 1 | 0 | 0 | 0 | 0 |
Canonical models of arithmetic $(1; \infty)$ curves | In 1983 Takeuchi showed that up to conjugation there are exactly 4 arithmetic
subgroups of $\textrm{PSL}_2 (\mathbb{R})$ with signature $(1; \infty)$.
Shinichi Mochizuki gave a purely geometric characterization of the
corresponding arithmetic $(1; \infty)$-curves, which also arise naturally in
the context of his recent work on inter-universal Teichmüller theory.
Using Bely\u{\i} maps, we explicitly determine the canonical models of these
curves. We also study their arithmetic properties and modular interpretations.
| 0 | 0 | 1 | 0 | 0 | 0 |
State observation and sensor selection for nonlinear networks | A large variety of dynamical systems, such as chemical and biomolecular
systems, can be seen as networks of nonlinear entities. Prediction, control,
and identification of such nonlinear networks require knowledge of the state of
the system. However, network states are usually unknown, and only a fraction of
the state variables are directly measurable. The observability problem concerns
reconstructing the network state from this limited information. Here, we
propose a general optimization-based approach for observing the states of
nonlinear networks and for optimally selecting the observed variables. Our
results reveal several fundamental limitations in network observability, such
as the trade-off between the fraction of observed variables and the observation
length on one side, and the estimation error on the other side. We also show
that owing to the crucial role played by the dynamics, purely graph- theoretic
observability approaches cannot provide conclusions about one's practical
ability to estimate the states. We demonstrate the effectiveness of our methods
by finding the key components in biological and combustion reaction networks
from which we determine the full system state. Our results can lead to the
design of novel sensing principles that can greatly advance prediction and
control of the dynamics of such networks.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Faster Implementation of Online Run-Length Burrows-Wheeler Transform | Run-length encoding Burrows-Wheeler Transformed strings, resulting in
Run-Length BWT (RLBWT), is a powerful tool for processing highly repetitive
strings. We propose a new algorithm for online RLBWT working in run-compressed
space, which runs in $O(n\lg r)$ time and $O(r\lg n)$ bits of space, where $n$
is the length of input string $S$ received so far and $r$ is the number of runs
in the BWT of the reversed $S$. We improve the state-of-the-art algorithm for
online RLBWT in terms of empirical construction time. Adopting the dynamic list
for maintaining a total order, we can replace rank queries in a dynamic wavelet
tree on a run-length compressed string by the direct comparison of labels in a
dynamic list. The empirical result for various benchmarks show the efficiency
of our algorithm, especially for highly repetitive strings.
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