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Title: A Hybrid Approach to Video Source Identification,
Abstract: Multimedia Forensics allows to determine whether videos or images have been
captured with the same device, and thus, eventually, by the same person.
Currently, the most promising technology to achieve this task, exploits the
unique traces left by the camera sensor into the visual content. Anyway, image
and video source identification are still treated separately from one another.
This approach is limited and anachronistic if we consider that most of the
visual media are today acquired using smartphones, that capture both images and
videos. In this paper we overcome this limitation by exploring a new approach
that allows to synergistically exploit images and videos to study the device
from which they both come. Indeed, we prove it is possible to identify the
source of a digital video by exploiting a reference sensor pattern noise
generated from still images taken by the same device of the query video. The
proposed method provides comparable or even better performance, when compared
to the current video identification strategies, where a reference pattern is
estimated from video frames. We also show how this strategy can be effective
even in case of in-camera digitally stabilized videos, where a non-stabilized
reference is not available, by solving some state-of-the-art limitations. We
explore a possible direct application of this result, that is social media
profile linking, i.e. discovering relationships between two or more social
media profiles by comparing the visual contents - images or videos - shared
therein. | [
1,
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] |
Title: Boosting the Actor with Dual Critic,
Abstract: This paper proposes a new actor-critic-style algorithm called Dual
Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian
dual form of the Bellman optimality equation, which can be viewed as a
two-player game between the actor and a critic-like function, which is named as
dual critic. Compared to its actor-critic relatives, Dual-AC has the desired
property that the actor and dual critic are updated cooperatively to optimize
the same objective function, providing a more transparent way for learning the
critic that is directly related to the objective function of the actor. We then
provide a concrete algorithm that can effectively solve the minimax
optimization problem, using techniques of multi-step bootstrapping, path
regularization, and stochastic dual ascent algorithm. We demonstrate that the
proposed algorithm achieves the state-of-the-art performances across several
benchmarks. | [
1,
0,
0,
0,
0,
0
] |
Title: Counting Dominating Sets of Graphs,
Abstract: Counting dominating sets in a graph $G$ is closely related to the
neighborhood complex of $G$. We exploit this relation to prove that the number
of dominating sets $d(G)$ of a graph is determined by the number of complete
bipartite subgraphs of its complement. More precisely, we state the following.
Let $G$ be a simple graph of order $n$ such that its complement has exactly
$a(G)$ subgraphs isomorphic to $K_{2p,2q}$ and exactly $b(G)$ subgraphs
isomorphic to $K_{2p+1,2q+1}$. Then $d(G) = 2^n -1 + 2[a(G)-b(G)]$. We also
show some new relations between the domination polynomial and the neighborhood
polynomial of a graph. | [
0,
0,
1,
0,
0,
0
] |
Title: High SNR Consistent Compressive Sensing,
Abstract: High signal to noise ratio (SNR) consistency of model selection criteria in
linear regression models has attracted a lot of attention recently. However,
most of the existing literature on high SNR consistency deals with model order
selection. Further, the limited literature available on the high SNR
consistency of subset selection procedures (SSPs) is applicable to linear
regression with full rank measurement matrices only. Hence, the performance of
SSPs used in underdetermined linear models (a.k.a compressive sensing (CS)
algorithms) at high SNR is largely unknown. This paper fills this gap by
deriving necessary and sufficient conditions for the high SNR consistency of
popular CS algorithms like $l_0$-minimization, basis pursuit de-noising or
LASSO, orthogonal matching pursuit and Dantzig selector. Necessary conditions
analytically establish the high SNR inconsistency of CS algorithms when used
with the tuning parameters discussed in literature. Novel tuning parameters
with SNR adaptations are developed using the sufficient conditions and the
choice of SNR adaptations are discussed analytically using convergence rate
analysis. CS algorithms with the proposed tuning parameters are numerically
shown to be high SNR consistent and outperform existing tuning parameters in
the moderate to high SNR regime. | [
1,
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1,
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] |
Title: Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement,
Abstract: In this paper, we focus on fully automatic traffic surveillance camera
calibration, which we use for speed measurement of passing vehicles. We improve
over a recent state-of-the-art camera calibration method for traffic
surveillance based on two detected vanishing points. More importantly, we
propose a novel automatic scene scale inference method. The method is based on
matching bounding boxes of rendered 3D models of vehicles with detected
bounding boxes in the image. The proposed method can be used from arbitrary
viewpoints, since it has no constraints on camera placement. We evaluate our
method on the recent comprehensive dataset for speed measurement BrnoCompSpeed.
Experiments show that our automatic camera calibration method by detection of
two vanishing points reduces error by 50% (mean distance ratio error reduced
from 0.18 to 0.09) compared to the previous state-of-the-art method. We also
show that our scene scale inference method is more precise, outperforming both
state-of-the-art automatic calibration method for speed measurement (error
reduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error
reduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results
of the proposed automatic camera calibration method on video sequences obtained
from real surveillance cameras in various places, and under different lighting
conditions (night, dawn, day). | [
1,
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0,
0,
0
] |
Title: Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting,
Abstract: The success of autonomous systems will depend upon their ability to safely
navigate human-centric environments. This motivates the need for a real-time,
probabilistic forecasting algorithm for pedestrians, cyclists, and other agents
since these predictions will form a necessary step in assessing the risk of any
action. This paper presents a novel approach to probabilistic forecasting for
pedestrians based on weighted sums of ordinary differential equations that are
learned from historical trajectory information within a fixed scene. The
resulting algorithm is embarrassingly parallel and is able to work at real-time
speeds using a naive Python implementation. The quality of predicted locations
of agents generated by the proposed algorithm is validated on a variety of
examples and considerably higher than existing state of the art approaches over
long time horizons. | [
1,
0,
1,
0,
0,
0
] |
Title: DSBGK Method to Incorporate the CLL Reflection Model and to Simulate Gas Mixtures,
Abstract: Molecular reflections on usual wall surfaces can be statistically described
by the Maxwell diffuse reflection model, which has been successfully applied in
the DSBGK simulations. We develop the DSBGK algorithm to implement the
Cercignani-Lampis-Lord (CLL) reflection model, which is widely applied to
polished surfaces and used particularly in modeling space shuttles to predict
the heat and force loads exerted by the high-speed flows around the surfaces.
We also extend the DSBGK method to simulate gas mixtures and high contrast of
number densities of different components can be handled at a cost of memory
usage much lower than that needed by the DSMC simulations because the average
numbers of simulated molecules of different components per cell can be equal in
the DSBGK simulations. | [
0,
1,
0,
0,
0,
0
] |
Title: Efficient sampling of conditioned Markov jump processes,
Abstract: We consider the task of generating draws from a Markov jump process (MJP)
between two time points at which the process is known. Resulting draws are
typically termed bridges and the generation of such bridges plays a key role in
simulation-based inference algorithms for MJPs. The problem is challenging due
to the intractability of the conditioned process, necessitating the use of
computationally intensive methods such as weighted resampling or Markov chain
Monte Carlo. An efficient implementation of such schemes requires an
approximation of the intractable conditioned hazard/propensity function that is
both cheap and accurate. In this paper, we review some existing approaches to
this problem before outlining our novel contribution. Essentially, we leverage
the tractability of a Gaussian approximation of the MJP and suggest a
computationally efficient implementation of the resulting conditioned hazard
approximation. We compare and contrast our approach with existing methods using
three examples. | [
0,
0,
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1,
0,
0
] |
Title: An enthalpy-based multiple-relaxation-time lattice Boltzmann method for solid-liquid phase change heat transfer in metal foams,
Abstract: In this paper, an enthalpy-based multiple-relaxation-time (MRT) lattice
Boltzmann (LB) method is developed for solid-liquid phase change heat transfer
in metal foams under local thermal non-equilibrium (LTNE) condition. The
enthalpy-based MRT-LB method consists of three different MRT-LB models: one for
flow field based on the generalized non-Darcy model, and the other two for
phase change material (PCM) and metal foam temperature fields described by the
LTNE model. The moving solid-liquid phase interface is implicitly tracked
through the liquid fraction, which is simultaneously obtained when the energy
equations of PCM and metal foam are solved. The present method has several
distinctive features. First, as compared with previous studies, the present
method avoids the iteration procedure, thus it retains the inherent merits of
the standard LB method and is superior over the iteration method in terms of
accuracy and computational efficiency. Second, a volumetric LB scheme instead
of the bounce-back scheme is employed to realize the no-slip velocity condition
in the interface and solid phase regions, which is consistent with the actual
situation. Last but not least, the MRT collision model is employed, and with
additional degrees of freedom, it has the ability to reduce the numerical
diffusion across phase interface induced by solid-liquid phase change.
Numerical tests demonstrate that the present method can be served as an
accurate and efficient numerical tool for studying metal foam enhanced
solid-liquid phase change heat transfer in latent heat storage. Finally,
comparisons and discussions are made to offer useful information for practical
applications of the present method. | [
0,
1,
0,
0,
0,
0
] |
Title: Failure of Smooth Pasting Principle and Nonexistence of Equilibrium Stopping Rules under Time-Inconsistency,
Abstract: This paper considers a time-inconsistent stopping problem in which the
inconsistency arises from non-constant time preference rates. We show that the
smooth pasting principle, the main approach that has been used to construct
explicit solutions for conventional time-consistent optimal stopping problems,
may fail under time-inconsistency. Specifically, we prove that the smooth
pasting principle solves a time-inconsistent problem within the intra-personal
game theoretic framework if and only if a certain inequality on the model
primitives is satisfied. We show that the violation of this inequality can
happen even for very simple non-exponential discount functions. Moreover, we
demonstrate that the stopping problem does not admit any intra-personal
equilibrium whenever the smooth pasting principle fails. The "negative" results
in this paper caution blindly extending the classical approaches for
time-consistent stopping problems to their time-inconsistent counterparts. | [
0,
0,
0,
0,
0,
1
] |
Title: Perils of Zero-Interaction Security in the Internet of Things,
Abstract: The Internet of Things (IoT) demands authentication systems which can provide
both security and usability. Recent research utilizes the rich sensing
capabilities of smart devices to build security schemes operating without human
interaction, such as zero-interaction pairing (ZIP) and zero-interaction
authentication (ZIA). Prior work proposed a number of ZIP and ZIA schemes and
reported promising results. However, those schemes were often evaluated under
conditions which do not reflect realistic IoT scenarios. In addition, drawing
any comparison among the existing schemes is impossible due to the lack of a
common public dataset and unavailability of scheme implementations.
In this paper, we address these challenges by conducting the first
large-scale comparative study of ZIP and ZIA schemes, carried out under
realistic conditions. We collect and release the most comprehensive dataset in
the domain to date, containing over 4250 hours of audio recordings and 1
billion sensor readings from three different scenarios, and evaluate five
state-of-the-art schemes based on these data. Our study reveals that the
effectiveness of the existing proposals is highly dependent on the scenario
they are used in. In particular, we show that these schemes are subject to
error rates between 0.6% and 52.8%. | [
1,
0,
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] |
Title: Coarse-grained simulation of auxetic, two-dimensional crystal dynamics,
Abstract: The increasing number of protein-based metamaterials demands reliable and
efficient methods to study the physicochemical properties they may display. In
this regard, we develop a simulation strategy based on Molecular Dynamics (MD)
that addresses the geometric degrees of freedom of an auxetic two-dimensional
protein crystal. This model consists of a network of impenetrable rigid squares
linked through massless rigid rods, thus featuring a large number of both
holonomic and nonholonomic constraints. Our MD methodology is optimized to
study highly constrained systems and allows for the simulation of long-time
dynamics with reasonably large timesteps. The data extracted from the
simulations shows a persistent motional interdependence among the protein
subunits in the crystal. We characterize the dynamical correlations featured by
these subunits and identify two regimes characterized by their locality or
nonlocality, depending on the geometric parameters of the crystal. From the
same data, we also calculate the Poisson\rq{}s (longitudinal to axial strain)
ratio of the crystal, and learn that, due to holonomic constraints (rigidness
of the rod links), the crystal remains auxetic even after significant changes
in the original geometry. The nonholonomic ones (collisions between subunits)
increase the number of inhomogeneous deformations of the crystal, thus driving
it away from an isotropic response. Our work provides the first simulation of
the dynamics of protein crystals and offers insights into promising mechanical
properties afforded by these materials. | [
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1,
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] |
Title: Predicting wind pressures around circular cylinders using machine learning techniques,
Abstract: Numerous studies have been carried out to measure wind pressures around
circular cylinders since the early 20th century due to its engineering
significance. Consequently, a large amount of wind pressure data sets have
accumulated, which presents an excellent opportunity for using machine learning
(ML) techniques to train models to predict wind pressures around circular
cylinders. Wind pressures around smooth circular cylinders are a function of
mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident
wind, and circumferential angle of the cylinder. Considering these three
parameters as the inputs, this study trained two ML models to predict mean and
fluctuating pressures respectively. Three machine learning algorithms including
decision tree regressor, random forest, and gradient boosting regression trees
(GBRT) were tested. The GBRT models exhibited the best performance for
predicting both mean and fluctuating pressures, and they are capable of making
accurate predictions for Re ranging from 10^4 to 10^6 and Ti ranging from 0% to
15%. It is believed that the GBRT models provide very efficient and economical
alternative to traditional wind tunnel tests and computational fluid dynamic
simulations for determining wind pressures around smooth circular cylinders
within the studied Re and Ti range. | [
1,
0,
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1,
0,
0
] |
Title: Randomly coloring simple hypergraphs with fewer colors,
Abstract: We study the problem of constructing a (near) uniform random proper
$q$-coloring of a simple $k$-uniform hypergraph with $n$ vertices and maximum
degree $\Delta$. (Proper in that no edge is mono-colored and simple in that two
edges have maximum intersection of size one). We show that if $q\geq
\max\{C_k\log n,500k^3\Delta^{1/(k-1)}\}$ then the Glauber Dynamics will become
close to uniform in $O(n\log n)$ time, given a random (improper) start. This
improves on the results in Frieze and Melsted [5]. | [
1,
0,
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0
] |
Title: Contributed Discussion to Uncertainty Quantification for the Horseshoe by Stéphanie van der Pas, Botond Szabó and Aad van der Vaart,
Abstract: We begin by introducing the main ideas of the paper under discussion. We
discuss some interesting issues regarding adaptive component-wise credible
intervals. We then briefly touch upon the concepts of self-similarity and
excessive bias restriction. This is then followed by some comments on the
extensive simulation study carried out in the paper. | [
0,
0,
1,
1,
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0
] |
Title: Learning Models from Data with Measurement Error: Tackling Underreporting,
Abstract: Measurement error in observational datasets can lead to systematic bias in
inferences based on these datasets. As studies based on observational data are
increasingly used to inform decisions with real-world impact, it is critical
that we develop a robust set of techniques for analyzing and adjusting for
these biases. In this paper we present a method for estimating the distribution
of an outcome given a binary exposure that is subject to underreporting. Our
method is based on a missing data view of the measurement error problem, where
the true exposure is treated as a latent variable that is marginalized out of a
joint model. We prove three different conditions under which the outcome
distribution can still be identified from data containing only error-prone
observations of the exposure. We demonstrate this method on synthetic data and
analyze its sensitivity to near violations of the identifiability conditions.
Finally, we use this method to estimate the effects of maternal smoking and
opioid use during pregnancy on childhood obesity, two import problems from
public health. Using the proposed method, we estimate these effects using only
subject-reported drug use data and substantially refine the range of estimates
generated by a sensitivity analysis-based approach. Further, the estimates
produced by our method are consistent with existing literature on both the
effects of maternal smoking and the rate at which subjects underreport smoking. | [
1,
0,
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] |
Title: A simple introduction to Karmarkar's Algorithm for Linear Programming,
Abstract: An extremely simple, description of Karmarkar's algorithm with very few
technical terms is given. | [
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0,
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0
] |
Title: Asymptotics of the bound state induced by $δ$-interaction supported on a weakly deformed plane,
Abstract: In this paper we consider the three-dimensional Schrödinger operator with
a $\delta$-interaction of strength $\alpha > 0$ supported on an unbounded
surface parametrized by the mapping $\mathbb{R}^2\ni x\mapsto (x,\beta f(x))$,
where $\beta \in [0,\infty)$ and $f\colon \mathbb{R}^2\rightarrow\mathbb{R}$,
$f\not\equiv 0$, is a $C^2$-smooth, compactly supported function. The surface
supporting the interaction can be viewed as a local deformation of the plane.
It is known that the essential spectrum of this Schrödinger operator
coincides with $[-\frac14\alpha^2,+\infty)$. We prove that for all sufficiently
small $\beta > 0$ its discrete spectrum is non-empty and consists of a unique
simple eigenvalue. Moreover, we obtain an asymptotic expansion of this
eigenvalue in the limit $\beta \rightarrow 0+$. In particular, this eigenvalue
tends to $-\frac14\alpha^2$ exponentially fast as $\beta\rightarrow 0+$. | [
0,
0,
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0,
0,
0
] |
Title: Análise comparativa de pesquisas de origens e destinos: uma abordagem baseada em Redes Complexas,
Abstract: In this paper, a comparative study was conducted between complex networks
representing origin and destination survey data. Similarities were found
between the characteristics of the networks of Brazilian cities with networks
of foreign cities. Power laws were found in the distributions of edge weights
and this scale - free behavior can occur due to the economic characteristics of
the cities. | [
1,
0,
0,
0,
0,
0
] |
Title: Development of probabilistic dam breach model using Bayesian inference,
Abstract: Dam breach models are commonly used to predict outflow hydrographs of
potentially failing dams and are key ingredients for evaluating flood risk. In
this paper a new dam breach modeling framework is introduced that shall improve
the reliability of hydrograph predictions of homogeneous earthen embankment
dams. Striving for a small number of parameters, the simplified physics-based
model describes the processes of failing embankment dams by breach enlargement,
driven by progressive surface erosion. Therein the erosion rate of dam material
is modeled by empirical sediment transport formulations. Embedding the model
into a Bayesian multilevel framework allows for quantitative analysis of
different categories of uncertainties. To this end, data available in
literature of observed peak discharge and final breach width of historical dam
failures was used to perform model inversion by applying Markov Chain Monte
Carlo simulation. Prior knowledge is mainly based on non-informative
distribution functions. The resulting posterior distribution shows that the
main source of uncertainty is a correlated subset of parameters, consisting of
the residual error term and the epistemic term quantifying the breach erosion
rate. The prediction intervals of peak discharge and final breach width are
congruent with values known from literature. To finally predict the outflow
hydrograph for real case applications, an alternative residual model was
formulated that assumes perfect data and a perfect model. The fully
probabilistic fashion of hydrograph prediction has the potential to improve the
adequate risk management of downstream flooding. | [
0,
0,
0,
1,
0,
0
] |
Title: One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network,
Abstract: There is an inherent need for autonomous cars, drones, and other robots to
have a notion of how their environment behaves and to anticipate changes in the
near future. In this work, we focus on anticipating future appearance given the
current frame of a video. Existing work focuses on either predicting the future
appearance as the next frame of a video, or predicting future motion as optical
flow or motion trajectories starting from a single video frame. This work
stretches the ability of CNNs (Convolutional Neural Networks) to predict an
anticipation of appearance at an arbitrarily given future time, not necessarily
the next video frame. We condition our predicted future appearance on a
continuous time variable that allows us to anticipate future frames at a given
temporal distance, directly from the input video frame. We show that CNNs can
learn an intrinsic representation of typical appearance changes over time and
successfully generate realistic predictions at a deliberate time difference in
the near future. | [
1,
0,
0,
0,
0,
0
] |
Title: Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming,
Abstract: We design a new myopic strategy for a wide class of sequential design of
experiment (DOE) problems, where the goal is to collect data in order to to
fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling
(MPS), is inspired by the classical posterior (Thompson) sampling algorithm for
multi-armed bandits and leverages the flexibility of probabilistic programming
and approximate Bayesian inference to address a broad set of problems.
Empirically, this general-purpose strategy is competitive with more specialised
methods in a wide array of DOE tasks, and more importantly, enables addressing
complex DOE goals where no existing method seems applicable. On the theoretical
side, we leverage ideas from adaptive submodularity and reinforcement learning
to derive conditions under which MPS achieves sublinear regret against natural
benchmark policies. | [
0,
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1,
0,
0
] |
Title: Data-Driven Sparse Structure Selection for Deep Neural Networks,
Abstract: Deep convolutional neural networks have liberated its extraordinary power on
various tasks. However, it is still very challenging to deploy state-of-the-art
models into real-world applications due to their high computational complexity.
How can we design a compact and effective network without massive experiments
and expert knowledge? In this paper, we propose a simple and effective
framework to learn and prune deep models in an end-to-end manner. In our
framework, a new type of parameter -- scaling factor is first introduced to
scale the outputs of specific structures, such as neurons, groups or residual
blocks. Then we add sparsity regularizations on these factors, and solve this
optimization problem by a modified stochastic Accelerated Proximal Gradient
(APG) method. By forcing some of the factors to zero, we can safely remove the
corresponding structures, thus prune the unimportant parts of a CNN. Comparing
with other structure selection methods that may need thousands of trials or
iterative fine-tuning, our method is trained fully end-to-end in one training
pass without bells and whistles. We evaluate our method, Sparse Structure
Selection with several state-of-the-art CNNs, and demonstrate very promising
results with adaptive depth and width selection. | [
1,
0,
0,
0,
0,
0
] |
Title: Scaling laws and bounds for the turbulent G.O. Roberts dynamo,
Abstract: Numerical simulations of the G.O. Roberts dynamo are presented. Dynamos both
with and without a significant mean field are obtained. Exact bounds are
derived for the total energy which conform with the Kolmogorov phenomenology of
turbulence. Best fits to numerical data show the same functional dependences as
the inequalities obtained from optimum theory. | [
0,
1,
0,
0,
0,
0
] |
Title: On Popov's formula involving the Von Mangoldt function,
Abstract: We offer a generalization of a formula of Popov involving the Von Mangoldt
function. Some commentary on its relation to other results in analytic number
theory is mentioned as well as an analogue involving the m$\ddot{o}$bius
function. | [
0,
0,
1,
0,
0,
0
] |
Title: Multi-hop assortativities for networks classification,
Abstract: Several social, medical, engineering and biological challenges rely on
discovering the functionality of networks from their structure and node
metadata, when it is available. For example, in chemoinformatics one might want
to detect whether a molecule is toxic based on structure and atomic types, or
discover the research field of a scientific collaboration network. Existing
techniques rely on counting or measuring structural patterns that are known to
show large variations from network to network, such as the number of triangles,
or the assortativity of node metadata. We introduce the concept of multi-hop
assortativity, that captures the similarity of the nodes situated at the
extremities of a randomly selected path of a given length. We show that
multi-hop assortativity unifies various existing concepts and offers a
versatile family of 'fingerprints' to characterize networks. These fingerprints
allow in turn to recover the functionalities of a network, with the help of the
machine learning toolbox. Our method is evaluated empirically on established
social and chemoinformatic network benchmarks. Results reveal that our
assortativity based features are competitive providing highly accurate results
often outperforming state of the art methods for the network classification
task. | [
1,
0,
0,
1,
0,
0
] |
Title: Rotating Rayleigh-Taylor turbulence,
Abstract: The turbulent Rayleigh--Taylor system in a rotating reference frame is
investigated by direct numerical simulations within the Oberbeck-Boussinesq
approximation. On the basis of theoretical arguments, supported by our
simulations, we show that the Rossby number decreases in time, and therefore
the Coriolis force becomes more important as the system evolves and produces
many effects on Rayleigh--Taylor turbulence. We find that rotation reduces the
intensity of turbulent velocity fluctuations and therefore the growth rate of
the temperature mixing layer. Moreover, in presence of rotation the conversion
of potential energy into turbulent kinetic energy is found to be less effective
and the efficiency of the heat transfer is reduced. Finally, during the
evolution of the mixing layer we observe the development of a
cyclone-anticyclone asymmetry. | [
0,
1,
0,
0,
0,
0
] |
Title: A global model for predicting the arrival of imported dengue infections,
Abstract: With approximately half of the world's population at risk of contracting
dengue, this mosquito-borne disease is of global concern. International
travellers significantly contribute to dengue's rapid and large-scale spread by
importing the disease from endemic into non-endemic countries. To prevent
future outbreaks and dengue from establishing in non-endemic countries,
knowledge about the arrival time and location of infected travellers is
crucial. We propose a network model that predicts the monthly number of dengue
infected air passengers arriving at any given airport. We consider
international air travel volumes, monthly dengue incidence rates and temporal
infection dynamics. Our findings shed light onto dengue importation routes and
reveal country-specific reporting rates that have been until now largely
unknown. | [
1,
0,
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0,
1,
0
] |
Title: Observation of surface plasmon polaritons in 2D electron gas of surface electron accumulation in InN nanostructures,
Abstract: Recently, heavily doped semiconductors are emerging as an alternate for low
loss plasmonic materials. InN, belonging to the group III nitrides, possesses
the unique property of surface electron accumulation (SEA) which provides two
dimensional electron gas (2DEG) system. In this report, we demonstrated the
surface plasmon properties of InN nanoparticles originating from SEA using the
real space mapping of the surface plasmon fields for the first time. The SEA is
confirmed by Raman studies which are further corroborated by photoluminescence
and photoemission spectroscopic studies. The frequency of 2DEG corresponding to
SEA is found to be in the THz region. The periodic fringes are observed in the
near-field scanning optical microscopic images of InN nanostructures. The
observed fringes are attributed to the interference of propagated and back
reflected surface plasmon polaritons (SPPs). The observation of SPPs is solely
attributed to the 2DEG corresponding to the SEA of InN. In addition, resonance
kind of behavior with the enhancement of the near-field intensity is observed
in the near-field images of InN nanostructures. Observation of SPPs indicates
that InN with SEA can be a promising THz plasmonic material for the light
confinement. | [
0,
1,
0,
0,
0,
0
] |
Title: Partial Information Stochastic Differential Games for Backward Stochastic Systems Driven By Lévy Processes,
Abstract: In this paper, we consider a partial information two-person zero-sum
stochastic differential game problem where the system is governed by a backward
stochastic differential equation driven by Teugels martingales associated with
a Lévy process and an independent Brownian motion. One sufficient (a
verification theorem) and one necessary conditions for the existence of optimal
controls are proved. To illustrate the general results, a linear quadratic
stochastic differential game problem is discussed. | [
0,
0,
1,
0,
0,
0
] |
Title: Inter-Session Modeling for Session-Based Recommendation,
Abstract: In recent years, research has been done on applying Recurrent Neural Networks
(RNNs) as recommender systems. Results have been promising, especially in the
session-based setting where RNNs have been shown to outperform state-of-the-art
models. In many of these experiments, the RNN could potentially improve the
recommendations by utilizing information about the user's past sessions, in
addition to its own interactions in the current session. A problem for
session-based recommendation, is how to produce accurate recommendations at the
start of a session, before the system has learned much about the user's current
interests. We propose a novel approach that extends a RNN recommender to be
able to process the user's recent sessions, in order to improve
recommendations. This is done by using a second RNN to learn from recent
sessions, and predict the user's interest in the current session. By feeding
this information to the original RNN, it is able to improve its
recommendations. Our experiments on two different datasets show that the
proposed approach can significantly improve recommendations throughout the
sessions, compared to a single RNN working only on the current session. The
proposed model especially improves recommendations at the start of sessions,
and is therefore able to deal with the cold start problem within sessions. | [
1,
0,
0,
0,
0,
0
] |
Title: Multiscale Modeling of Shock Wave Localization in Porous Energetic Material,
Abstract: Shock wave interactions with defects, such as pores, are known to play a key
role in the chemical initiation of energetic materials. The shock response of
hexanitrostilbene is studied through a combination of large scale reactive
molecular dynamics and mesoscale hydrodynamic simulations. In order to extend
our simulation capability at the mesoscale to include weak shock conditions (<
6 GPa), atomistic simulations of pore collapse are used to define a strain rate
dependent strength model. Comparing these simulation methods allows us to
impose physically-reasonable constraints on the mesoscale model parameters. In
doing so, we have been able to study shock waves interacting with pores as a
function of this viscoplastic material response. We find that the pore collapse
behavior of weak shocks is characteristically different to that of strong
shocks. | [
0,
1,
0,
0,
0,
0
] |
Title: The Diverse Club: The Integrative Core of Complex Networks,
Abstract: A complex system can be represented and analyzed as a network, where nodes
represent the units of the network and edges represent connections between
those units. For example, a brain network represents neurons as nodes and axons
between neurons as edges. In many networks, some nodes have a
disproportionately high number of edges. These nodes also have many edges
between each other, and are referred to as the rich club. In many different
networks, the nodes of this club are assumed to support global network
integration. However, another set of nodes potentially exhibits a connectivity
structure that is more advantageous to global network integration. Here, in a
myriad of different biological and man-made networks, we discover the diverse
club--a set of nodes that have edges diversely distributed across the network.
The diverse club exhibits, to a greater extent than the rich club, properties
consistent with an integrative network function--these nodes are more highly
interconnected and their edges are more critical for efficient global
integration. Moreover, we present a generative evolutionary network model that
produces networks with a diverse club but not a rich club, thus demonstrating
that these two clubs potentially evolved via distinct selection pressures.
Given the variety of different networks that we analyzed--the c. elegans, the
macaque brain, the human brain, the United States power grid, and global air
traffic--the diverse club appears to be ubiquitous in complex networks. These
results warrant the distinction and analysis of two critical clubs of nodes in
all complex systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Bayesian Semisupervised Learning with Deep Generative Models,
Abstract: Neural network based generative models with discriminative components are a
powerful approach for semi-supervised learning. However, these techniques a)
cannot account for model uncertainty in the estimation of the model's
discriminative component and b) lack flexibility to capture complex stochastic
patterns in the label generation process. To avoid these problems, we first
propose to use a discriminative component with stochastic inputs for increased
noise flexibility. We show how an efficient Gibbs sampling procedure can
marginalize the stochastic inputs when inferring missing labels in this model.
Following this, we extend the discriminative component to be fully Bayesian and
produce estimates of uncertainty in its parameter values. This opens the door
for semi-supervised Bayesian active learning. | [
0,
0,
0,
1,
0,
0
] |
Title: Stability of Valuations: Higher Rational Rank,
Abstract: Given a klt singularity $x\in (X, D)$, we show that a quasi-monomial
valuation $v$ with a finitely generated associated graded ring is the minimizer
of the normalized volume function $\widehat{\rm vol}_{(X,D),x}$, if and only if
$v$ induces a degeneration to a K-semistable log Fano cone singularity.
Moreover, such a minimizer is unique among all quasi-monomial valuations up to
rescaling. As a consequence, we prove that for a klt singularity $x\in X$ on
the Gromov-Hausdorff limit of Kähler-Einstein Fano manifolds, the
intermediate K-semistable cone associated to its metric tangent cone is
uniquely determined by the algebraic structure of $x\in X$, hence confirming a
conjecture by Donaldson-Sun. | [
0,
0,
1,
0,
0,
0
] |
Title: Higgs mode and its decay in a two dimensional antiferromagnet,
Abstract: Condensed-matter analogs of the Higgs boson in particle physics allow
insights into its behavior in different symmetries and dimensionalities.
Evidence for the Higgs mode has been reported in a number of different
settings, including ultracold atomic gases, disordered superconductors, and
dimerized quantum magnets. However, decay processes of the Higgs mode (which
are eminently important in particle physics) have not yet been studied in
condensed matter due to the lack of a suitable material system coupled to a
direct experimental probe. A quantitative understanding of these processes is
particularly important for low-dimensional systems where the Higgs mode decays
rapidly and has remained elusive to most experimental probes. Here, we discover
and study the Higgs mode in a two-dimensional antiferromagnet using
spin-polarized inelastic neutron scattering. Our spin-wave spectra of
Ca$_2$RuO$_4$ directly reveal a well-defined, dispersive Higgs mode, which
quickly decays into transverse Goldstone modes at the antiferromagnetic
ordering wavevector. Through a complete mapping of the transverse modes in the
reciprocal space, we uniquely specify the minimal model Hamiltonian and
describe the decay process. We thus establish a novel condensed matter platform
for research on the dynamics of the Higgs mode. | [
0,
1,
0,
0,
0,
0
] |
Title: Robust and Efficient Boosting Method using the Conditional Risk,
Abstract: Well-known for its simplicity and effectiveness in classification, AdaBoost,
however, suffers from overfitting when class-conditional distributions have
significant overlap. Moreover, it is very sensitive to noise that appears in
the labels. This article tackles the above limitations simultaneously via
optimizing a modified loss function (i.e., the conditional risk). The proposed
approach has the following two advantages. (1) It is able to directly take into
account label uncertainty with an associated label confidence. (2) It
introduces a "trustworthiness" measure on training samples via the Bayesian
risk rule, and hence the resulting classifier tends to have finite sample
performance that is superior to that of the original AdaBoost when there is a
large overlap between class conditional distributions. Theoretical properties
of the proposed method are investigated. Extensive experimental results using
synthetic data and real-world data sets from UCI machine learning repository
are provided. The empirical study shows the high competitiveness of the
proposed method in predication accuracy and robustness when compared with the
original AdaBoost and several existing robust AdaBoost algorithms. | [
0,
0,
0,
1,
0,
0
] |
Title: High Dimensional Robust Estimation of Sparse Models via Trimmed Hard Thresholding,
Abstract: We study the problem of sparsity constrained $M$-estimation with arbitrary
corruptions to both {\em explanatory and response} variables in the
high-dimensional regime, where the number of variables $d$ is larger than the
sample size $n$. Our main contribution is a highly efficient gradient-based
optimization algorithm that we call Trimmed Hard Thresholding -- a robust
variant of Iterative Hard Thresholding (IHT) by using trimmed mean in gradient
computations. Our algorithm can deal with a wide class of sparsity constrained
$M$-estimation problems, and we can tolerate a nearly dimension independent
fraction of arbitrarily corrupted samples. More specifically, when the
corrupted fraction satisfies $\epsilon \lesssim {1} /\left({\sqrt{k} \log
(nd)}\right)$, where $k$ is the sparsity of the parameter, we obtain accurate
estimation and model selection guarantees with optimal sample complexity.
Furthermore, we extend our algorithm to sparse Gaussian graphical model
(precision matrix) estimation via a neighborhood selection approach. We
demonstrate the effectiveness of robust estimation in sparse linear, logistic
regression, and sparse precision matrix estimation on synthetic and real-world
US equities data. | [
1,
0,
1,
1,
0,
0
] |
Title: An efficient data structure for counting all linear extensions of a poset, calculating its jump number, and the likes,
Abstract: Achieving the goals in the title (and others) relies on a cardinality-wise
scanning of the ideals of the poset. Specifically, the relevant numbers
attached to the k+1 element ideals are inferred from the corresponding numbers
of the k-element (order) ideals. Crucial in all of this is a compressed
representation (using wildcards) of the ideal lattice. The whole scheme invites
distributed computation. | [
1,
0,
0,
0,
0,
0
] |
Title: Perception-in-the-Loop Adversarial Examples,
Abstract: We present a scalable, black box, perception-in-the-loop technique to find
adversarial examples for deep neural network classifiers. Black box means that
our procedure only has input-output access to the classifier, and not to the
internal structure, parameters, or intermediate confidence values.
Perception-in-the-loop means that the notion of proximity between inputs can be
directly queried from human participants rather than an arbitrarily chosen
metric. Our technique is based on covariance matrix adaptation evolution
strategy (CMA-ES), a black box optimization approach. CMA-ES explores the
search space iteratively in a black box manner, by generating populations of
candidates according to a distribution, choosing the best candidates according
to a cost function, and updating the posterior distribution to favor the best
candidates. We run CMA-ES using human participants to provide the fitness
function, using the insight that the choice of best candidates in CMA-ES can be
naturally modeled as a perception task: pick the top $k$ inputs perceptually
closest to a fixed input. We empirically demonstrate that finding adversarial
examples is feasible using small populations and few iterations. We compare the
performance of CMA-ES on the MNIST benchmark with other black-box approaches
using $L_p$ norms as a cost function, and show that it performs favorably both
in terms of success in finding adversarial examples and in minimizing the
distance between the original and the adversarial input. In experiments on the
MNIST, CIFAR10, and GTSRB benchmarks, we demonstrate that CMA-ES can find
perceptually similar adversarial inputs with a small number of iterations and
small population sizes when using perception-in-the-loop. Finally, we show that
networks trained specifically to be robust against $L_\infty$ norm can still be
susceptible to perceptually similar adversarial examples. | [
1,
0,
0,
1,
0,
0
] |
Title: Deep Fluids: A Generative Network for Parameterized Fluid Simulations,
Abstract: This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than traditional CPU solvers, while achieving
compression rates of over 1300x. | [
0,
0,
0,
1,
0,
0
] |
Title: Mean squared displacement and sinuosity of three-dimensional random search movements,
Abstract: Correlated random walks (CRW) have been used for a long time as a null model
for animal's random search movement in two dimensions (2D). An increasing
number of studies focus on animals' movement in three dimensions (3D), but the
key properties of CRW, such as the way the mean squared displacement is related
to the path length, are well known only in 1D and 2D. In this paper I derive
such properties for 3D CRW, in a consistent way with the expression of these
properties in 2D. This should allow 3D CRW to act as a null model when
analyzing actual 3D movements similarly to what is done in 2D | [
0,
0,
0,
0,
1,
0
] |
Title: Local Algorithms for Hierarchical Dense Subgraph Discovery,
Abstract: Finding the dense regions of a graph and relations among them is a
fundamental problem in network analysis. Core and truss decompositions reveal
dense subgraphs with hierarchical relations. The incremental nature of
algorithms for computing these decompositions and the need for global
information at each step of the algorithm hinders scalable parallelization and
approximations since the densest regions are not revealed until the end. In a
previous work, Lu et al. proposed to iteratively compute the $h$-indices of
neighbor vertex degrees to obtain the core numbers and prove that the
convergence is obtained after a finite number of iterations. This work
generalizes the iterative $h$-index computation for truss decomposition as well
as nucleus decomposition which leverages higher-order structures to generalize
core and truss decompositions. In addition, we prove convergence bounds on the
number of iterations. We present a framework of local algorithms to obtain the
core, truss, and nucleus decompositions. Our algorithms are local, parallel,
offer high scalability, and enable approximations to explore time and quality
trade-offs. Our shared-memory implementation verifies the efficiency,
scalability, and effectiveness of our local algorithms on real-world networks. | [
1,
0,
0,
0,
0,
0
] |
Title: Analysis and mitigation of interface losses in trenched superconducting coplanar waveguide resonators,
Abstract: Improving the performance of superconducting qubits and resonators generally
results from a combination of materials and fabrication process improvements
and design modifications that reduce device sensitivity to residual losses. One
instance of this approach is to use trenching into the device substrate in
combination with superconductors and dielectrics with low intrinsic losses to
improve quality factors and coherence times. Here we demonstrate titanium
nitride coplanar waveguide resonators with mean quality factors exceeding two
million and controlled trenching reaching 2.2 $\mu$m into the silicon
substrate. Additionally, we measure sets of resonators with a range of sizes
and trench depths and compare these results with finite-element simulations to
demonstrate quantitative agreement with a model of interface dielectric loss.
We then apply this analysis to determine the extent to which trenching can
improve resonator performance. | [
0,
1,
0,
0,
0,
0
] |
Title: A Ball Breaking Away from a Fluid,
Abstract: We consider the withdrawal of a ball from a fluid reservoir to understand the
longevity of the connection between that ball and the fluid it breaks away
from, at intermediate Reynolds numbers. Scaling arguments based on the
processes observed as the ball interacts with the fluid surface were applied to
the `pinch-off time', when the ball breaks its connection with the fluid from
which it has been withdrawn, measured experimentally. At the lowest Reynolds
numbers tested, pinch-off occurs in a `surface seal' close to the reservoir
surface, where at larger Reynolds numbers pinch-off occurs in an `ejecta seal'
close to the ball. Our scaling analysis shows that the connection between ball
and fluid is controlled by the fluid film draining from the ball as it
continues to be winched away from the fluid reservoir. The draining flow itself
depends on the amount of fluid coating the ball on exit from the reservoir. We
consider the possibilities that this coating was created through: a surface
tension driven Landau Levitch Derjaguin wetting of the surface; a
visco-inertial quick coating; or alternatively through the inertia of the fluid
moving with the ball through the reservoir. We show that although the pinch-off
mechanism is controlled by viscosity, the coating mechanism is governed by a
different length and timescale, dictated by the inertial added mass of the ball
when submersed. | [
0,
1,
0,
0,
0,
0
] |
Title: A parallel orbital-updating based plane-wave basis method for electronic structure calculations,
Abstract: Motivated by the recently proposed parallel orbital-updating approach in real
space method, we propose a parallel orbital-updating based plane-wave basis
method for electronic structure calculations, for solving the corresponding
eigenvalue problems. In addition, we propose two new modified parallel
orbital-updating methods. Compared to the traditional plane-wave methods, our
methods allow for two-level parallelization, which is particularly interesting
for large scale parallelization. Numerical experiments show that these new
methods are more reliable and efficient for large scale calculations on modern
supercomputers | [
0,
1,
1,
0,
0,
0
] |
Title: Dynamics of the multi-soliton waves in the sine-Gordon model with two identical point impurities,
Abstract: The particular type of four-kink multi-solitons (or quadrons) adiabatic
dynamics of the sine-Gordon equation in a model with two identical point
attracting impurities has been studied. This model can be used for describing
magnetization localized waves in multilayer ferromagnet. The quadrons structure
and properties has been numerically investigated. The cases of both large and
small distances between impurities has been viewed. The dependence of the
localized in impurity region nonlinear high-amplitude waves frequencies on the
distance between the impurities has been found. For an analytical description
of two bound localized on impurities nonlinear waves dynamics, using
perturbation theory, the system of differential equations for harmonic
oscillators with elastic link has been found. The analytical model
qualitatively describes the results of the sine-Gordon equation numerical
simulation. | [
0,
1,
0,
0,
0,
0
] |
Title: Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access,
Abstract: A multi-user multi-armed bandit (MAB) framework is used to develop algorithms
for uncoordinated spectrum access. The number of users is assumed to be unknown
to each user. A stochastic setting is first considered, where the rewards on a
channel are the same for each user. In contrast to prior work, it is assumed
that the number of users can possibly exceed the number of channels, and that
rewards can be non-zero even under collisions. The proposed algorithm consists
of an estimation phase and an allocation phase. It is shown that if every user
adopts the algorithm, the system wide regret is constant with time with high
probability. The regret guarantees hold for any number of users and channels,
in particular, even when the number of users is less than the number of
channels. Next, an adversarial multi-user MAB framework is considered, where
the rewards on the channels are user-dependent. It is assumed that the number
of users is less than the number of channels, and that the users receive zero
reward on collision. The proposed algorithm combines the Exp3.P algorithm
developed in prior work for single user adversarial bandits with a collision
resolution mechanism to achieve sub-linear regret. It is shown that if every
user employs the proposed algorithm, the system wide regret is of the order
$O(T^\frac{3}{4})$ over a horizon of time $T$. The algorithms in both
stochastic and adversarial scenarios are extended to the dynamic case where the
number of users in the system evolves over time and are shown to lead to
sub-linear regret. | [
0,
0,
0,
1,
0,
0
] |
Title: A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation,
Abstract: In this paper, we analyze the effects of contact models on contact-implicit
trajectory optimization for manipulation. We consider three different
approaches: (1) a contact model that is based on complementarity constraints,
(2) a smooth contact model, and our proposed method (3) a variable smooth
contact model. We compare these models in simulation in terms of physical
accuracy, quality of motions, and computation time. In each case, the
optimization process is initialized by setting all torque variables to zero,
namely, without a meaningful initial guess. For simulations, we consider a
pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our
results demonstrate that the optimization based on the proposed variable smooth
contact model provides a good trade-off between the physical fidelity and
quality of motions at the cost of increased computation time. | [
1,
0,
0,
0,
0,
0
] |
Title: Is It Safe to Uplift This Patch? An Empirical Study on Mozilla Firefox,
Abstract: In rapid release development processes, patches that fix critical issues, or
implement high-value features are often promoted directly from the development
channel to a stabilization channel, potentially skipping one or more
stabilization channels. This practice is called patch uplift. Patch uplift is
risky, because patches that are rushed through the stabilization phase can end
up introducing regressions in the code. This paper examines patch uplift
operations at Mozilla, with the aim to identify the characteristics of uplifted
patches that introduce regressions. Through statistical and manual analyses, we
quantitatively and qualitatively investigate the reasons behind patch uplift
decisions and the characteristics of uplifted patches that introduced
regressions. Additionally, we interviewed three Mozilla release managers to
understand organizational factors that affect patch uplift decisions and
outcomes. Results show that most patches are uplifted because of a wrong
functionality or a crash. Uplifted patches that lead to faults tend to have
larger patch size, and most of the faults are due to semantic or memory errors
in the patches. Also, release managers are more inclined to accept patch uplift
requests that concern certain specific components, and-or that are submitted by
certain specific developers. | [
1,
0,
0,
0,
0,
0
] |
Title: Common change point estimation in panel data from the least squares and maximum likelihood viewpoints,
Abstract: We establish the convergence rates and asymptotic distributions of the common
break change-point estimators, obtained by least squares and maximum likelihood
in panel data models and compare their asymptotic variances. Our model
assumptions accommodate a variety of commonly encountered probability
distributions and, in particular, models of particular interest in econometrics
beyond the commonly analyzed Gaussian model, including the zero-inflated
Poisson model for count data, and the probit and tobit models. We also provide
novel results for time dependent data in the signal-plus-noise model, with
emphasis on a wide array of noise processes, including Gaussian process,
MA$(\infty)$ and $m$-dependent processes. The obtained results show that
maximum likelihood estimation requires a stronger signal-to-noise model
identifiability condition compared to its least squares counterpart. Finally,
since there are three different asymptotic regimes that depend on the behavior
of the norm difference of the model parameters before and after the change
point, which cannot be realistically assumed to be known, we develop a novel
data driven adaptive procedure that provides valid confidence intervals for the
common break, without requiring a priori knowledge of the asymptotic regime the
problem falls in. | [
0,
0,
1,
1,
0,
0
] |
Title: Outage analysis in two-way communication with RF energy harvesting relay and co-channel interference,
Abstract: The study of relays with the scope of energy-harvesting (EH) looks
interesting as a means of enabling sustainable, wireless communication without
the need to recharge or replace the battery driving the relays. However,
reliability of such communication systems becomes an important design challenge
when such relays scavenge energy from the information bearing RF signals
received from the source, using the technique of simultaneous wireless
information and power transfer (SWIPT). To this aim, this work studies
bidirectional communication in a decode-and-forward (DF) relay assisted
cooperative wireless network in presence of co-channel interference (CCI). In
order to quantify the reliability of the bidirectional communication systems, a
closed form expression for the outage probability of the system is derived for
both power splitting (PS) and time switching (TS) mode of operation of the
relay. Simulation results are used to validate the accuracy of our analytical
results and illustrate the dependence of the outage probability on various
system parameters, like PS factor, TS factor, and distance of the relay from
both the users. Results of performance comparison between PS relaying (PSR) and
TS relaying (TSR) schemes are also presented. Besides, simulation results are
also used to illustrate the spectral-efficiency and the energy-efficiency of
the proposed system. The results show that, both in terms of spectral
efficiency and the energy-efficiency, the two-way communication system in
presence of moderate CCI power, performs better than the similar system without
CCI. Additionally, it is also found that PSR is superior to TSR protocol in
terms of peak energy-efficiency. | [
1,
0,
0,
0,
0,
0
] |
Title: KGAN: How to Break The Minimax Game in GAN,
Abstract: Generative Adversarial Networks (GANs) were intuitively and attractively
explained under the perspective of game theory, wherein two involving parties
are a discriminator and a generator. In this game, the task of the
discriminator is to discriminate the real and generated (i.e., fake) data,
whilst the task of the generator is to generate the fake data that maximally
confuses the discriminator. In this paper, we propose a new viewpoint for GANs,
which is termed as the minimizing general loss viewpoint. This viewpoint shows
a connection between the general loss of a classification problem regarding a
convex loss function and a f-divergence between the true and fake data
distributions. Mathematically, we proposed a setting for the classification
problem of the true and fake data, wherein we can prove that the general loss
of this classification problem is exactly the negative f-divergence for a
certain convex function f. This allows us to interpret the problem of learning
the generator for dismissing the f-divergence between the true and fake data
distributions as that of maximizing the general loss which is equivalent to the
min-max problem in GAN if the Logistic loss is used in the classification
problem. However, this viewpoint strengthens GANs in two ways. First, it allows
us to employ any convex loss function for the discriminator. Second, it
suggests that rather than limiting ourselves in NN-based discriminators, we can
alternatively utilize other powerful families. Bearing this viewpoint, we then
propose using the kernel-based family for discriminators. This family has two
appealing features: i) a powerful capacity in classifying non-linear nature
data and ii) being convex in the feature space. Using the convexity of this
family, we can further develop Fenchel duality to equivalently transform the
max-min problem to the max-max dual problem. | [
1,
0,
0,
1,
0,
0
] |
Title: The solitary g-mode frequencies in early B-type stars,
Abstract: We present possible explanations of pulsations in early B-type main sequence
stars which arise purely from the excitation of gravity modes. There are three
stars with this type of oscillations detected from the BRITE light curves:
$\kappa$ Cen, a Car, $\kappa$ Vel. We show that by changing metallicity or the
opacity profile it is possible in some models to dump pressure modes keeping
gravity modes unstable. Other possible scenario involves pulsations of a lower
mass companion. | [
0,
1,
0,
0,
0,
0
] |
Title: The microarchitecture of a multi-threaded RISC-V compliant processing core family for IoT end-nodes,
Abstract: Internet-of-Things end-nodes demand low power processing platforms
characterized by heterogeneous dedicated units, controlled by a processor core
running concurrent control threads. Such architecture scheme fits one of the
main target application domain of the RISC-V instruction set. We present an
open-source processing core compliant with RISC-V on the software side and with
the popular Pulpino processor platform on the hardware side, while supporting
interleaved multi-threading for IoT applications. The latter feature is a novel
contribution in this application domain. We report details about the
microarchitecture design along with performance data. | [
1,
0,
0,
0,
0,
0
] |
Title: Optimal Input Placement in Lattice Graphs,
Abstract: The control of dynamical, networked systems continues to receive much
attention across the engineering and scientific research fields. Of particular
interest is the proper way to determine which nodes of the network should
receive external control inputs in order to effectively and efficiently control
portions of the network. Published methods to accomplish this task either find
a minimal set of driver nodes to guarantee controllability or a larger set of
driver nodes which optimizes some control metric. Here, we investigate the
control of lattice systems which provides analytical insight into the
relationship between network structure and controllability. First we derive a
closed form expression for the individual elements of the controllability
Gramian of infinite lattice systems. Second, we focus on nearest neighbor
lattices for which the distance between nodes appears in the expression for the
controllability Gramian. We show that common control energy metrics scale
exponentially with respect to the maximum distance between a driver node and a
target node. | [
1,
0,
0,
0,
0,
0
] |
Title: Construction of constant mean curvature n-noids using the DPW method,
Abstract: We construct constant mean curvature surfaces in euclidean space with genus
zero and n ends asymptotic to Delaunay surfaces using the DPW method. | [
0,
0,
1,
0,
0,
0
] |
Title: Information Retrieval and Recommendation System for Astronomical Observatories,
Abstract: We present a machine learning based information retrieval system for
astronomical observatories that tries to address user defined queries related
to an instrument. In the modern instrumentation scenario where heterogeneous
systems and talents are simultaneously at work, the ability to supply with the
right information helps speeding up the detector maintenance operations.
Enhancing the detector uptime leads to increased coincidence observation and
improves the likelihood for the detection of astrophysical signals. Besides,
such efforts will efficiently disseminate technical knowledge to a wider
audience and will help the ongoing efforts to build upcoming detectors like the
LIGO-India etc even at the design phase to foresee possible challenges. The
proposed method analyses existing documented efforts at the site to
intelligently group together related information to a query and to present it
on-line to the user. The user in response can further go into interesting links
and find already developed solutions or probable ways to address the present
situation optimally. A web application that incorporates the above idea has
been implemented and tested for LIGO Livingston, LIGO Hanford and Virgo
observatories. | [
0,
1,
0,
0,
0,
0
] |
Title: Database Learning: Toward a Database that Becomes Smarter Every Time,
Abstract: In today's databases, previous query answers rarely benefit answering future
queries. For the first time, to the best of our knowledge, we change this
paradigm in an approximate query processing (AQP) context. We make the
following observation: the answer to each query reveals some degree of
knowledge about the answer to another query because their answers stem from the
same underlying distribution that has produced the entire dataset. Exploiting
and refining this knowledge should allow us to answer queries more
analytically, rather than by reading enormous amounts of raw data. Also,
processing more queries should continuously enhance our knowledge of the
underlying distribution, and hence lead to increasingly faster response times
for future queries.
We call this novel idea---learning from past query answers---Database
Learning. We exploit the principle of maximum entropy to produce answers, which
are in expectation guaranteed to be more accurate than existing sample-based
approximations. Empowered by this idea, we build a query engine on top of Spark
SQL, called Verdict. We conduct extensive experiments on real-world query
traces from a large customer of a major database vendor. Our results
demonstrate that Verdict supports 73.7% of these queries, speeding them up by
up to 23.0x for the same accuracy level compared to existing AQP systems. | [
1,
0,
0,
0,
0,
0
] |
Title: Graph Theoretical Models of Closed n-Dimensional Manifolds: Digital Models of a Moebius Strip, a Torus, a Projective Plane a Klein Bottle and n-Dimensional Spheres,
Abstract: In this paper, we show how to construct graph theoretical models of
n-dimensional continuous objects and manifolds. These models retain topological
properties of their continuous counterparts. An LCL collection of n-cells in
Euclidean space is introduced and investigated. If an LCL collection of n-cells
is a cover of a continuous n-dimensional manifold then the intersection graph
of this cover is a digital closed n-dimensional manifold with the same topology
as its continuous counterpart. As an example, we prove that the digital model
of a continuous n-dimensional sphere is a digital n-sphere with at least 2n+2
points, the digital model of a continuous projective plane is a digital
projective plane with at least eleven points, the digital model of a continuous
Klein bottle is the digital Klein bottle with at least sixteen points, the
digital model of a continuous torus is the digital torus with at least sixteen
points and the digital model of a continuous Moebius band is the digital
Moebius band with at least twelve points. | [
1,
0,
1,
0,
0,
0
] |
Title: Completely $p$-primitive binary quadratic forms,
Abstract: Let $f(x,y)=ax^2+bxy+cy^2$ be a binary quadratic form with integer
coefficients. For a prime $p$ not dividing the discriminant of $f$, we say $f$
is completely $p$-primitive if for any non-zero integer $N$, the diophantine
equation $f(x,y)=N$ has always an integer solution $(x,y)=(m,n)$ with
$(m,n,p)=1$ whenever it has an integer solution. In this article, we study
various properties of completely $p$-primitive binary quadratic forms. In
particular, we give a necessary and sufficient condition for a definite binary
quadratic form $f$ to be completely $p$-primitive. | [
0,
0,
1,
0,
0,
0
] |
Title: Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM,
Abstract: The numerical availability of statistical inference methods for a modern and
robust analysis of longitudinal- and multivariate data in factorial experiments
is an essential element in research and education. While existing approaches
that rely on specific distributional assumptions of the data (multivariate
normality and/or characteristic covariance matrices) are implemented in
statistical software packages, there is a need for user-friendly software that
can be used for the analysis of data that do not fulfill the aforementioned
assumptions and provide accurate p-value and confidence interval estimates.
Therefore, newly developed statistical methods for the analysis of repeated
measures designs and multivariate data that neither assume multivariate
normality nor specific covariance matrices have been implemented in the freely
available R-package MANOVA.RM. The package is equipped with a graphical user
interface for plausible applications in academia and other educational purpose.
Several motivating examples illustrate the application of the methods. | [
0,
0,
0,
1,
0,
0
] |
Title: Multiple Illumination Phaseless Super-Resolution (MIPS) with Applications To Phaseless DOA Estimation and Diffraction Imaging,
Abstract: Phaseless super-resolution is the problem of recovering an unknown signal
from measurements of the magnitudes of the low frequency Fourier transform of
the signal. This problem arises in applications where measuring the phase, and
making high-frequency measurements, are either too costly or altogether
infeasible. The problem is especially challenging because it combines the
difficult problems of phase retrieval and classical super-resolution | [
1,
0,
1,
0,
0,
0
] |
Title: Precise measurement of hyperfine structure in the $ \rm {3\,S_{1/2}} $ state of $ \rm{^7Li} $,
Abstract: We report a precise measurement of hyperfine structure in the $ \rm
{3\,S_{1/2}} $ state of the odd isotope of Li, namely $ \rm {^7Li} $. The state
is excited from the ground $ \rm {2\,S_{1/2}} $ state (which has the same
parity) using two single-photon transitions via the intermediate $ \rm
{2\,P_{3/2}} $ state. The value of the hyperfine constant we measure is $ A =
93.095(52)$ MHz, which resolves two discrepant values reported in the
literature measured using other techniques. Our value is also consistent with
theoretical calculations. | [
0,
1,
0,
0,
0,
0
] |
Title: Algebraic models of the Euclidean plane,
Abstract: We introduce a new invariant, the real (logarithmic)-Kodaira dimension, that
allows to distinguish smooth real algebraic surfaces up to birational
diffeomorphism. As an application, we construct infinite families of smooth
rational real algebraic surfaces with trivial homology groups, whose real loci
are diffeomorphic to $\mathbb{R}^2$, but which are pairwise not birationally
diffeomorphic. There are thus infinitely many non-trivial models of the
euclidean plane, contrary to the compact case. | [
0,
0,
1,
0,
0,
0
] |
Title: Decentralization of Multiagent Policies by Learning What to Communicate,
Abstract: Effective communication is required for teams of robots to solve
sophisticated collaborative tasks. In practice it is typical for both the
encoding and semantics of communication to be manually defined by an expert;
this is true regardless of whether the behaviors themselves are bespoke,
optimization based, or learned. We present an agent architecture and training
methodology using neural networks to learn task-oriented communication
semantics based on the example of a communication-unaware expert policy. A
perimeter defense game illustrates the system's ability to handle dynamically
changing numbers of agents and its graceful degradation in performance as
communication constraints are tightened or the expert's observability
assumptions are broken. | [
1,
0,
0,
0,
0,
0
] |
Title: A Theory of Complex Stochastic Systems with Two Types of Counteracting Entities,
Abstract: Many complex systems share two characteristics: 1) they are stochastic in
nature, and 2) they are characterized by a large number of factors. At the same
time, various natural complex systems appear to have two types of intertwined
constituents that exhibit counteracting effects on their equilibrium. In this
study, we employ these few characteristics to lay the groundwork for analyzing
such complex systems. The equilibrium point of these systems is generally
studied either through the kinetic notion of equilibrium or its energetic
notion, but not both. We postulate that these systems attempt to regulate the
state vector of their constituents such that both the kinetic and the energetic
notions of equilibrium are met. Based on this postulate, we prove: 1) the
existence of a point such that the kinetic notion of equilibrium is met for the
less abundant constituents and, at the same time, the state vector of more
abundant entities is regulated to minimize the energetic notion of equilibrium;
2) the effect of unboundedly increasing less (more) abundant constituents
stabilizes (destabilizes) the system; and 3) the (unrestricted) equilibrium of
the system is the point at which the number of stabilizing and destabilizing
entities increase unboundedly with the same rate. | [
0,
1,
0,
0,
0,
0
] |
Title: Stability of the Poincaré bundle,
Abstract: Let X be an irreducible smooth projective curve, of genus at least two, over
an algebraically closed field k. Let $\mathcal{M}^d_G$ denote the moduli stack
of principal G-bundles over X of fixed topological type $d \in \pi_1(G)$, where
G is any almost simple affine algebraic group over k. We prove that the
universal bundle over $X \times \mathcal{M}^d_G$ is stable with respect to any
polarization on $X \times \mathcal{M}^d_G$. A similar result is proved for the
Poincaré adjoint bundle over $X \times M_G^{d, rs}$, where $M_G^{d, rs}$ is
the coarse moduli space of regularly stable principal G-bundles over X of fixed
topological type d. | [
0,
0,
1,
0,
0,
0
] |
Title: Morse geodesics in torsion groups,
Abstract: In this paper we exhibit Morse geodesics, often called "hyperbolic
directions", in infinite unbounded torsion groups. The groups studied are
lacunary hyperbolic groups and constructed using graded small cancellation
conditions. In all previously known examples, Morse geodesics were found in
groups which also contained Morse elements, infinite order elements whose
cyclic subgroup gives a Morse quasi-geodesic. Our result presents the first
example of a group which contains Morse geodesics but no Morse elements. In
fact, we show that there is an isometrically embedded $7$-regular tree inside
such groups where every infinite, simple path is a Morse geodesic. | [
0,
0,
1,
0,
0,
0
] |
Title: Global and local thermometry schemes in coupled quantum systems,
Abstract: We study the ultimate bounds on the estimation of temperature for an
interacting quantum system. We consider two coupled bosonic modes that are
assumed to be thermal and using quantum estimation theory establish the role
the Hamiltonian parameters play in thermometry. We show that in the case of a
conserved particle number the interaction between the modes leads to a decrease
in the overall sensitivity to temperature, while interestingly, if particle
exchange is allowed with the thermal bath the converse is true. We explain this
dichotomy by examining the energy spectra. Finally, we devise experimentally
implementable thermometry schemes that rely only on locally accessible
information from the total system, showing that almost Heisenberg limited
precision can still be achieved, and we address the (im)possibility for
multiparameter estimation in the system. | [
0,
1,
0,
0,
0,
0
] |
Title: The statistical significance filter leads to overconfident expectations of replicability,
Abstract: We show that publishing results using the statistical significance
filter---publishing only when the p-value is less than 0.05---leads to a
vicious cycle of overoptimistic expectation of the replicability of results.
First, we show analytically that when true statistical power is relatively low,
computing power based on statistically significant results will lead to
overestimates of power. Then, we present a case study using 10 experimental
comparisons drawn from a recently published meta-analysis in psycholinguistics
(Jäger et al., 2017). We show that the statistically significant results
yield an illusion of replicability. This illusion holds even if the researcher
doesn't conduct any formal power analysis but just uses statistical
significance to informally assess robustness (i.e., replicability) of results. | [
0,
0,
1,
1,
0,
0
] |
Title: Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences,
Abstract: We introduce new techniques to the analysis of neural spatiotemporal dynamics
via applying $\epsilon$-machine reconstruction to electroencephalography (EEG)
microstate sequences. Microstates are short duration quasi-stable states of the
dynamically changing electrical field topographies recorded via an array of
electrodes from the human scalp, and cluster into four canonical classes. The
sequence of microstates observed under particular conditions can be considered
an information source with unknown underlying structure. $\epsilon$-machines
are discrete dynamical system automata with state-dependent probabilities on
different future observations (in this case the next measured EEG microstate).
They artificially reproduce underlying structure in an optimally predictive
manner as generative models exhibiting dynamics emulating the behaviour of the
source. Here we present experiments using both simulations and empirical data
supporting the value of associating these discrete dynamical systems with
mental states (e.g. mind-wandering, focused attention, etc.) and with clinical
populations. The neurodynamics of mental states and clinical populations can
then be further characterized by properties of these dynamical systems,
including: i) statistical complexity (determined by the number of states of the
corresponding $\epsilon$-automaton); ii) entropy rate; iii) characteristic
sequence patterning (syntax, probabilistic grammars); iv) duration, persistence
and stability of dynamical patterns; and v) algebraic measures such as
Krohn-Rhodes complexity or holonomy length of the decompositions of these. The
potential applications include the characterization of mental states in
neurodynamic terms for mental health diagnostics, well-being interventions,
human-machine interface, and others on both subject-specific and
group/population-level. | [
1,
1,
0,
0,
0,
0
] |
Title: The LCES HIRES/Keck Precision Radial Velocity Exoplanet Survey,
Abstract: We describe a 20-year survey carried out by the Lick-Carnegie Exoplanet
Survey Team (LCES), using precision radial velocities from HIRES on the Keck-I
telescope to find and characterize extrasolar planetary systems orbiting nearby
F, G, K, and M dwarf stars. We provide here 60,949 precision radial velocities
for 1,624 stars contained in that survey. We tabulate a list of 357 significant
periodic signals that are of constant period and phase, and not coincident in
period and/or phase with stellar activity indices. These signals are thus
strongly suggestive of barycentric reflex motion of the star induced by one or
more candidate exoplanets in Keplerian motion about the host star. Of these
signals, 225 have already been published as planet claims, 60 are classified as
significant unpublished planet candidates that await photometric follow-up to
rule out activity-related causes, and 54 are also unpublished, but are
classified as "significant" signals that require confirmation by additional
data before rising to classification as planet candidates. Of particular
interest is our detection of a candidate planet with a minimum mass of 3.9
Earth masses and an orbital period of 9.9 days orbiting Lalande 21185, the
fourth-closest main sequence star to the Sun. For each of our exoplanetary
candidate signals, we provide the period and semi-amplitude of the Keplerian
orbital fit, and a likelihood ratio estimate of its statistical significance.
We also tabulate 18 Keplerian-like signals that we classify as likely arising
from stellar activity. | [
0,
1,
0,
0,
0,
0
] |
Title: Artificial Intelligence Based Malware Analysis,
Abstract: Artificial intelligence methods have often been applied to perform specific
functions or tasks in the cyber-defense realm. However, as adversary methods
become more complex and difficult to divine, piecemeal efforts to understand
cyber-attacks, and malware-based attacks in particular, are not providing
sufficient means for malware analysts to understand the past, present and
future characteristics of malware.
In this paper, we present the Malware Analysis and Attributed using Genetic
Information (MAAGI) system. The underlying idea behind the MAAGI system is that
there are strong similarities between malware behavior and biological organism
behavior, and applying biologically inspired methods to corpora of malware can
help analysts better understand the ecosystem of malware attacks. Due to the
sophistication of the malware and the analysis, the MAAGI system relies heavily
on artificial intelligence techniques to provide this capability. It has
already yielded promising results over its development life, and will hopefully
inspire more integration between the artificial intelligence and cyber--defense
communities. | [
1,
0,
0,
0,
0,
0
] |
Title: A New Achievable Rate Region for Multiple-Access Channel with States,
Abstract: The problem of reliable communication over the multiple-access channel (MAC)
with states is investigated. We propose a new coding scheme for this problem
which uses quasi-group codes (QGC). We derive a new computable single-letter
characterization of the achievable rate region. As an example, we investigate
the problem of doubly-dirty MAC with modulo-$4$ addition. It is shown that the
sum-rate $R_1+R_2=1$ bits per channel use is achievable using the new scheme.
Whereas, the natural extension of the Gel'fand-Pinsker scheme, sum-rates
greater than $0.32$ are not achievable. | [
1,
0,
0,
0,
0,
0
] |
Title: Analytic heating rate of neutron star merger ejecta derived from Fermi's theory of beta decay,
Abstract: Macronovae (kilonovae) that arise in binary neutron star mergers are powered
by radioactive beta decay of hundreds of $r$-process nuclides. We derive, using
Fermi's theory of beta decay, an analytic estimate of the nuclear heating rate.
We show that the heating rate evolves as a power law ranging between $t^{-6/5}$
to $t^{-4/3}$. The overall magnitude of the heating rate is determined by the
mean values of nuclear quantities, e.g., the nuclear matrix elements of beta
decay. These values are specified by using nuclear experimental data. We
discuss the role of higher order beta transitions and the robustness of the
power law. The robust and simple form of the heating rate suggests that
observations of the late-time bolometric light curve $\propto t^{-\frac{4}{3}}$
would be a direct evidence of a $r$-process driven macronova. Such observations
could also enable us to estimate the total amount of $r$-process nuclei
produced in the merger. | [
0,
1,
0,
0,
0,
0
] |
Title: On well-posedness of a velocity-vorticity formulation of the Navier-Stokes equations with no-slip boundary conditions,
Abstract: We study well-posedness of a velocity-vorticity formulation of the
Navier--Stokes equations, supplemented with no-slip velocity boundary
conditions, a no-penetration vorticity boundary condition, along with a natural
vorticity boundary condition depending on a pressure functional. In the
stationary case we prove existence and uniqueness of a suitable weak solution
to the system under a small data condition. The topic of the paper is driven by
recent developments of vorticity based numerical methods for the Navier--Stokes
equations. | [
0,
0,
1,
0,
0,
0
] |
Title: Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories,
Abstract: Generative Adversarial Networks (GANs) represent a promising class of
generative networks that combine neural networks with game theory. From
generating realistic images and videos to assisting musical creation, GANs are
transforming many fields of arts and sciences. However, their application to
healthcare has not been fully realized, more specifically in generating
electronic health records (EHR) data. In this paper, we propose a framework for
exploring the value of GANs in the context of continuous laboratory time series
data. We devise an unsupervised evaluation method that measures the predictive
power of synthetic laboratory test time series. Further, we show that when it
comes to predicting the impact of drug exposure on laboratory test data,
incorporating representation learning of the training cohorts prior to training
GAN models is beneficial. | [
1,
0,
0,
1,
0,
0
] |
Title: Properties of Kinetic Transition Networks for Atomic Clusters and Glassy Solids,
Abstract: A database of minima and transition states corresponds to a network where the
minima represent nodes and the transition states correspond to edges between
the pairs of minima they connect via steepest-descent paths. Here we construct
networks for small clusters bound by the Morse potential for a selection of
physically relevant parameters, in two and three dimensions. The properties of
these unweighted and undirected networks are analysed to examine two features:
whether they are small-world, where the shortest path between nodes involves
only a small number or edges; and whether they are scale-free, having a degree
distribution that follows a power law. Small-world character is present, but
statistical tests show that a power law is not a good fit, so the networks are
not scale-free. These results for clusters are compared with the corresponding
properties for the molecular and atomic structural glass formers
ortho-terphenyl and binary Lennard-Jones. These glassy systems do not show
small-world properties, suggesting that such behaviour is linked to the
structure-seeking landscapes of the Morse clusters. | [
0,
1,
0,
1,
0,
0
] |
Title: Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos,
Abstract: Deep learning has been demonstrated to achieve excellent results for image
classification and object detection. However, the impact of deep learning on
video analysis (e.g. action detection and recognition) has been limited due to
complexity of video data and lack of annotations. Previous convolutional neural
networks (CNN) based video action detection approaches usually consist of two
major steps: frame-level action proposal detection and association of proposals
across frames. Also, these methods employ two-stream CNN framework to handle
spatial and temporal feature separately. In this paper, we propose an
end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for
action detection in videos. The proposed architecture is a unified network that
is able to recognize and localize action based on 3D convolution features. A
video is first divided into equal length clips and for each clip a set of tube
proposals are generated next based on 3D Convolutional Network (ConvNet)
features. Finally, the tube proposals of different clips are linked together
employing network flow and spatio-temporal action detection is performed using
these linked video proposals. Extensive experiments on several video datasets
demonstrate the superior performance of T-CNN for classifying and localizing
actions in both trimmed and untrimmed videos compared to state-of-the-arts. | [
1,
0,
0,
0,
0,
0
] |
Title: Expropriations, Property Confiscations and New Offshore Entities: Evidence from the Panama Papers,
Abstract: Using the Panama Papers, we show that the beginning of media reporting on
expropriations and property confiscations in a country increases the
probability that offshore entities are incorporated by agents from the same
country in the same month. This result is robust to the use of country-year
fixed effects and the exclusion of tax havens. Further analysis shows that the
effect is driven by countries with non-corrupt and effective governments, which
supports the notion that offshore entities are incorporated when reasonably
well-intended and well-functioning governments become more serious about
fighting organized crime by confiscating proceeds of crime. | [
0,
0,
0,
0,
0,
1
] |
Title: Concave Flow on Small Depth Directed Networks,
Abstract: Small depth networks arise in a variety of network related applications,
often in the form of maximum flow and maximum weighted matching. Recent works
have generalized such methods to include costs arising from concave functions.
In this paper we give an algorithm that takes a depth $D$ network and strictly
increasing concave weight functions of flows on the edges and computes a $(1 -
\epsilon)$-approximation to the maximum weight flow in time $mD \epsilon^{-1}$
times an overhead that is logarithmic in the various numerical parameters
related to the magnitudes of gradients and capacities.
Our approach is based on extending the scaling algorithm for approximate
maximum weighted matchings by [Duan-Pettie JACM`14] to the setting of small
depth networks, and then generalizing it to concave functions. In this more
restricted setting of linear weights in the range $[w_{\min}, w_{\max}]$, it
produces a $(1 - \epsilon)$-approximation in time $O(mD \epsilon^{-1} \log(
w_{\max} /w_{\min}))$. The algorithm combines a variety of tools and provides a
unified approach towards several problems involving small depth networks. | [
1,
0,
0,
0,
0,
0
] |
Title: A supernova at 50 pc: Effects on the Earth's atmosphere and biota,
Abstract: Recent 60Fe results have suggested that the estimated distances of supernovae
in the last few million years should be reduced from 100 pc to 50 pc. Two
events or series of events are suggested, one about 2.7 million years to 1.7
million years ago, and another may at 6.5 to 8.7 million years ago. We ask what
effects such supernovae are expected to have on the terrestrial atmosphere and
biota. Assuming that the Local Bubble was formed before the event being
considered, and that the supernova and the Earth were both inside a weak,
disordered magnetic field at that time, TeV-PeV cosmic rays at Earth will
increase by a factor of a few hundred. Tropospheric ionization will increase
proportionately, and the overall muon radiation load on terrestrial organisms
will increase by a factor of 150. All return to pre-burst levels within 10kyr.
In the case of an ordered magnetic field, effects depend strongly on the field
orientation. The upper bound in this case is with a largely coherent field
aligned along the line of sight to the supernova, in which case TeV-PeV cosmic
ray flux increases are 10^4; in the case of a transverse field they are below
current levels. We suggest a substantial increase in the extended effects of
supernovae on Earth and in the lethal distance estimate; more work is
needed.This paper is an explicit followup to Thomas et al. (2016). We also here
provide more detail on the computational procedures used in both works. | [
0,
1,
0,
0,
0,
0
] |
Title: Plugo: a VLC Systematic Perspective of Large-scale Indoor Localization,
Abstract: Indoor localization based on Visible Light Communication (VLC) has been in
favor with both the academia and industry for years. In this paper, we present
a prototyping photodiode-based VLC system towards large-scale localization.
Specially, we give in-depth analysis of the design constraints and
considerations for large-scale indoor localization research. After that we
identify the key enablers for such systems: 1) distributed architecture, 2)
one-way communication, and 3) random multiple access. Accordingly, we propose
Plugo -- a photodiode-based VLC system conforming to the aforementioned
criteria. We present a compact design of the VLC-compatible LED bulbs featuring
plug-and-go use-cases. The basic framed slotted Additive Links On-line Hawaii
Area (ALOHA) is exploited to achieve random multiple access over the shared
optical medium. We show its effectiveness in beacon broadcasting by
experiments, and further demonstrate its scalability to large-scale scenarios
through simulations. Finally, preliminary localization experiments are
conducted using fingerprinting-based methods in a customized testbed, achieving
an average accuracy of 0.14 m along with a 90-percentile accuracy of 0.33 m. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Convolutional Networks as shallow Gaussian Processes,
Abstract: We show that the output of a (residual) convolutional neural network (CNN)
with an appropriate prior over the weights and biases is a Gaussian process
(GP) in the limit of infinitely many convolutional filters, extending similar
results for dense networks. For a CNN, the equivalent kernel can be computed
exactly and, unlike "deep kernels", has very few parameters: only the
hyperparameters of the original CNN. Further, we show that this kernel has two
properties that allow it to be computed efficiently; the cost of evaluating the
kernel for a pair of images is similar to a single forward pass through the
original CNN with only one filter per layer. The kernel equivalent to a
32-layer ResNet obtains 0.84% classification error on MNIST, a new record for
GPs with a comparable number of parameters. | [
0,
0,
0,
1,
0,
0
] |
Title: "i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter,
Abstract: Social media users often make explicit predictions about upcoming events.
Such statements vary in the degree of certainty the author expresses toward the
outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win"
or "No way Leonardo wins!". Can popular beliefs on social media predict who
will win? To answer this question, we build a corpus of tweets annotated for
veridicality on which we train a log-linear classifier that detects positive
veridicality with high precision. We then forecast uncertain outcomes using the
wisdom of crowds, by aggregating users' explicit predictions. Our method for
forecasting winners is fully automated, relying only on a set of contenders as
input. It requires no training data of past outcomes and outperforms sentiment
and tweet volume baselines on a broad range of contest prediction tasks. We
further demonstrate how our approach can be used to measure the reliability of
individual accounts' predictions and retrospectively identify surprise
outcomes. | [
1,
0,
0,
0,
0,
0
] |
Title: Low energy bands and transport properties of chromium arsenide,
Abstract: We apply a method that combines the tight-binding approximation and the
Lowdin down-folding procedure to evaluate the electronic band structure of the
newly discovered pressure-induced superconductor CrAs. By integrating out all
low-lying arsenic degrees of freedom, we derive an effective Hamiltonian model
describing the Cr d bands near the Fermi level. We calculate and make
predictions for the energy spectra, the Fermi surface, the density of states
and transport and magnetic properties of this compound. Our results are
consistent with local-density approximation calculations as well as they show
good agreement with available experimental data for resistivity and Cr magnetic
moment. | [
0,
1,
0,
0,
0,
0
] |
Title: High Dynamic Range Imaging Technology,
Abstract: In this lecture note, we describe high dynamic range (HDR) imaging systems;
such systems are able to represent luminances of much larger brightness and,
typically, also a larger range of colors than conventional standard dynamic
range (SDR) imaging systems. The larger luminance range greatly improve the
overall quality of visual content, making it appears much more realistic and
appealing to observers. HDR is one of the key technologies of the future
imaging pipeline, which will change the way the digital visual content is
represented and manipulated today. | [
1,
0,
0,
0,
0,
0
] |
Title: Classification of pro-$p$ PD$^2$ pairs and the pro-$p$ curve complex,
Abstract: We classify pro-$p$ Poincaré duality pairs in dimension two. We then use
this classification to build a pro-$p$ analogue of the curve complex and
establish its basic properties. We conclude with some statements concerning
separability properties of the mapping class group. | [
0,
0,
1,
0,
0,
0
] |
Title: Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure,
Abstract: In kernel methods, temporal information on the data is commonly included by
using time-delayed embeddings as inputs. Recently, an alternative formulation
was proposed by defining a gamma-filter explicitly in a reproducing kernel
Hilbert space, giving rise to a complex model where multiple kernels operate on
different temporal combinations of the input signal. In the original
formulation, the kernels are then simply combined to obtain a single kernel
matrix (for instance by averaging), which provides computational benefits but
discards important information on the temporal structure of the signal.
Inspired by works on multiple kernel learning, we overcome this drawback by
considering the different kernels separately. We propose an efficient strategy
to adaptively combine and select these kernels during the training phase. The
resulting batch and online algorithms automatically learn to process highly
nonlinear temporal information extracted from the input signal, which is
implicitly encoded in the kernel values. We evaluate our proposal on several
artificial and real tasks, showing that it can outperform classical approaches
both in batch and online settings. | [
1,
0,
0,
1,
0,
0
] |
Title: On bifibrations of model categories,
Abstract: In this article, we develop a notion of Quillen bifibration which combines
the two notions of Grothendieck bifibration and of Quillen model structure. In
particular, given a bifibration $p:\mathcal E\to\mathcal B$, we describe when a
family of model structures on the fibers $\mathcal E_A$ and on the basis
category $\mathcal B$ combines into a model structure on the total category
$\mathcal E$, such that the functor $p$ preserves cofibrations, fibrations and
weak equivalences. Using this Grothendieck construction for model structures,
we revisit the traditional definition of Reedy model structures, and possible
generalizations, and exhibit their bifibrational nature. | [
1,
0,
1,
0,
0,
0
] |
Title: Deep Learning Sparse Ternary Projections for Compressed Sensing of Images,
Abstract: Compressed sensing (CS) is a sampling theory that allows reconstruction of
sparse (or compressible) signals from an incomplete number of measurements,
using of a sensing mechanism implemented by an appropriate projection matrix.
The CS theory is based on random Gaussian projection matrices, which satisfy
recovery guarantees with high probability; however, sparse ternary {0, -1, +1}
projections are more suitable for hardware implementation. In this paper, we
present a deep learning approach to obtain very sparse ternary projections for
compressed sensing. Our deep learning architecture jointly learns a pair of a
projection matrix and a reconstruction operator in an end-to-end fashion. The
experimental results on real images demonstrate the effectiveness of the
proposed approach compared to state-of-the-art methods, with significant
advantage in terms of complexity. | [
1,
0,
0,
1,
0,
0
] |
Title: Learning a Predictive Model for Music Using PULSE,
Abstract: Predictive models for music are studied by researchers of algorithmic
composition, the cognitive sciences and machine learning. They serve as base
models for composition, can simulate human prediction and provide a
multidisciplinary application domain for learning algorithms. A particularly
well established and constantly advanced subtask is the prediction of
monophonic melodies. As melodies typically involve non-Markovian dependencies
their prediction requires a capable learning algorithm. In this thesis, I apply
the recent feature discovery and learning method PULSE to the realm of symbolic
music modeling. PULSE is comprised of a feature generating operation and
L1-regularized optimization. These are used to iteratively expand and cull the
feature set, effectively exploring feature spaces that are too large for common
feature selection approaches. I design a general Python framework for PULSE,
propose task-optimized feature generating operations and various
music-theoretically motivated features that are evaluated on a standard corpus
of monophonic folk and chorale melodies. The proposed method significantly
outperforms comparable state-of-the-art models. I further discuss the free
parameters of the learning algorithm and analyze the feature composition of the
learned models. The models learned by PULSE afford an easy inspection and are
musicologically interpreted for the first time. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM,
Abstract: Many real-world data sets, especially in biology, are produced by highly
multivariate and nonlinear complex dynamical systems. In this paper, we focus
on brain imaging data, including both calcium imaging and functional MRI data.
Standard vector-autoregressive models are limited by their linearity
assumptions, while nonlinear general-purpose, large-scale temporal models, such
as LSTM networks, typically require large amounts of training data, not always
readily available in biological applications; furthermore, such models have
limited interpretability. We introduce here a novel approach for learning a
nonlinear differential equation model aimed at capturing brain dynamics.
Specifically, we propose a variable-projection optimization approach to
estimate the parameters of the multivariate (coupled) van der Pol oscillator,
and demonstrate that such a model can accurately represent nonlinear dynamics
of the brain data. Furthermore, in order to improve the predictive accuracy
when forecasting future brain-activity time series, we use this analytical
model as an unlimited source of simulated data for pretraining LSTM; such
model-specific data augmentation approach consistently improves LSTM
performance on both calcium and fMRI imaging data. | [
0,
0,
0,
1,
1,
0
] |
Title: Abstract Interpretation with Unfoldings,
Abstract: We present and evaluate a technique for computing path-sensitive interference
conditions during abstract interpretation of concurrent programs. In lieu of
fixed point computation, we use prime event structures to compactly represent
causal dependence and interference between sequences of transformers. Our main
contribution is an unfolding algorithm that uses a new notion of independence
to avoid redundant transformer application, thread-local fixed points to reduce
the size of the unfolding, and a novel cutoff criterion based on subsumption to
guarantee termination of the analysis. Our experiments show that the abstract
unfolding produces an order of magnitude fewer false alarms than a mature
abstract interpreter, while being several orders of magnitude faster than
solver-based tools that have the same precision. | [
1,
0,
0,
0,
0,
0
] |
Title: Perturbation theory for cosmologies with non-linear structure,
Abstract: The next generation of cosmological surveys will operate over unprecedented
scales, and will therefore provide exciting new opportunities for testing
general relativity. The standard method for modelling the structures that these
surveys will observe is to use cosmological perturbation theory for linear
structures on horizon-sized scales, and Newtonian gravity for non-linear
structures on much smaller scales. We propose a two-parameter formalism that
generalizes this approach, thereby allowing interactions between large and
small scales to be studied in a self-consistent and well-defined way. This uses
both post-Newtonian gravity and cosmological perturbation theory, and can be
used to model realistic cosmological scenarios including matter, radiation and
a cosmological constant. We find that the resulting field equations can be
written as a hierarchical set of perturbation equations. At leading-order,
these equations allow us to recover a standard set of Friedmann equations, as
well as a Newton-Poisson equation for the inhomogeneous part of the Newtonian
energy density in an expanding background. For the perturbations in the
large-scale cosmology, however, we find that the field equations are sourced by
both non-linear and mode-mixing terms, due to the existence of small-scale
structures. These extra terms should be expected to give rise to new
gravitational effects, through the mixing of gravitational modes on small and
large scales - effects that are beyond the scope of standard linear
cosmological perturbation theory. We expect our formalism to be useful for
accurately modelling gravitational physics in universes that contain non-linear
structures, and for investigating the effects of non-linear gravity in the era
of ultra-large-scale surveys. | [
0,
1,
0,
0,
0,
0
] |
Title: Phase Synchronization on Spacially Embeded Duplex Networks with Total Cost Constraint,
Abstract: Synchronization on multiplex networks have attracted increasing attention in
the past few years. We investigate collective behaviors of Kuramoto oscillators
on single layer and duplex spacial networks with total cost restriction, which
was introduced by Li et. al [Li G., Reis S. D., Moreira A. A., Havlin S.,
Stanley H. E. and Jr A. J., {\it Phys. Rev. Lett.} 104, 018701 (2010)] and
termed as the Li network afterwards. In the Li network model, with the increase
of its spacial exponent, the network's structure will vary from the random type
to the small-world one, and finally to the regular lattice.We first explore how
the spacial exponent influences the synchronizability of Kuramoto oscillators
on single layer Li networks and find that the closer the Li network is to a
regular lattice, the more difficult for it to evolve into synchronization. Then
we investigate synchronizability of duplex Li networks and find that the
existence of inter-layer interaction can greatly enhance inter-layer and global
synchronizability. When the inter-layer coupling strength is larger than a
certain critical value, whatever the intra-layer coupling strength is, the
inter-layer synchronization will always occur. Furthermore, on single layer Li
networks, nodes with larger degrees more easily reach global synchronization,
while on duplex Li networks, this phenomenon becomes much less obvious.
Finally, we study the impact of inter-link density on global synchronization
and obtain that sparse inter-links can lead to the emergence of global
synchronization for duplex Li networks just as dense inter-links do. In a word,
inter-layer interaction plays a vital role in determining synchronizability for
duplex spacial networks with total cost constraint. | [
0,
1,
0,
0,
0,
0
] |
Title: Neutral Carbon Emission in luminous infrared galaxies The \CI\ Lines as Total Molecular Gas Tracers,
Abstract: We present a statistical study on the [C I]($^{3} \rm P_{1} \rightarrow {\rm
^3 P}_{0}$), [C I] ($^{3} \rm P_{2} \rightarrow {\rm ^3 P}_{1}$) lines
(hereafter [C I] (1$-$0) and [C I] (2$-$1), respectively) and the CO (1$-$0)
line for a sample of (ultra)luminous infrared galaxies [(U)LIRGs]. We explore
the correlations between the luminosities of CO (1$-$0) and [C I] lines, and
find that $L'_\mathrm{CO(1-0)}$ correlates almost linearly with both $L'_
\mathrm{[CI](1-0)}$ and $L'_\mathrm{[CI](2-1)}$, suggesting that [C I] lines
can trace total molecular gas mass at least for (U)LIRGs. We also investigate
the dependence of $L'_\mathrm{[CI](1-0)}$/$L'_\mathrm{CO(1-0)}$,
$L'_\mathrm{[CI](2-1)}$/$L'_\mathrm{CO(1-0)}$ and
$L'_\mathrm{[CI](2-1)}$/$L'_\mathrm{[CI](1-0)}$ on the far-infrared color of
60-to-100 $\mu$m, and find non-correlation, a weak correlation and a modest
correlation, respectively. Under the assumption that these two carbon
transitions are optically thin, we further calculate the [C I] line excitation
temperatures, atomic carbon masses, and the mean [C I] line flux-to-H$_2$ mass
conversion factors for our sample. The resulting $\mathrm{H_2}$ masses using
these [C I]-based conversion factors roughly agree with those derived from
$L'_\mathrm{CO(1-0)}$ and CO-to-H$_2$ conversion factor. | [
0,
1,
0,
0,
0,
0
] |
Title: Composition Factors of Tensor Products of Symmetric Powers,
Abstract: We determine the composition factors of the tensor product $S(E)\otimes S(E)$
of two copies of the symmetric algebra of the natural module $E$ of a general
linear group over an algebraically closed field of positive characteristic. Our
main result may be regarded as a substantial generalisation of the tensor
product theorem of Krop and Sullivan, on composition factors of $S(E)$. We
earlier answered the question of which polynomially injective modules are
infinitesimally injective in terms of the "divisibility index". We are now able
to give an explicit description of the divisibility index for polynomial
modules for general linear groups of degree at most $3$. | [
0,
0,
1,
0,
0,
0
] |
Title: Attention-Based Guided Structured Sparsity of Deep Neural Networks,
Abstract: Network pruning is aimed at imposing sparsity in a neural network
architecture by increasing the portion of zero-valued weights for reducing its
size regarding energy-efficiency consideration and increasing evaluation speed.
In most of the conducted research efforts, the sparsity is enforced for network
pruning without any attention to the internal network characteristics such as
unbalanced outputs of the neurons or more specifically the distribution of the
weights and outputs of the neurons. That may cause severe accuracy drop due to
uncontrolled sparsity. In this work, we propose an attention mechanism that
simultaneously controls the sparsity intensity and supervised network pruning
by keeping important information bottlenecks of the network to be active. On
CIFAR-10, the proposed method outperforms the best baseline method by 6% and
reduced the accuracy drop by 2.6x at the same level of sparsity. | [
0,
0,
0,
1,
0,
0
] |
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