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Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting | Tensor factorization with hard and/or soft constraints has played an
important role in signal processing and data analysis. However, existing
algorithms for constrained tensor factorization have two drawbacks: (i) they
require matrix-inversion; and (ii) they cannot (or at least is very difficult
to) handle structured regularizations. We propose a new tensor factorization
algorithm that circumvents these drawbacks. The proposed method is built upon
alternating optimization, and each subproblem is solved by a primal-dual
splitting algorithm, yielding an efficient and flexible algorithmic framework
to constrained tensor factorization. The advantages of the proposed method over
a state-of-the-art constrained tensor factorization algorithm, called AO-ADMM,
are demonstrated on regularized nonnegative tensor factorization.
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Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes | In this paper, we propose a replay attack spoofing detection system for
automatic speaker verification using multitask learning of noise classes. We
define the noise that is caused by the replay attack as replay noise. We
explore the effectiveness of training a deep neural network simultaneously for
replay attack spoofing detection and replay noise classification. The
multi-task learning includes classifying the noise of playback devices,
recording environments, and recording devices as well as the spoofing
detection. Each of the three types of the noise classes also includes a genuine
class. The experiment results on the ASVspoof2017 datasets demonstrate that the
performance of our proposed system is improved by 30% relatively on the
evaluation set.
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On One Property of Tikhonov Regularization Algorithm | For linear inverse problem with Gaussian random noise we show that Tikhonov
regularization algorithm is minimax in the class of linear estimators and is
asymptotically minimax in the sense of sharp asymptotic in the class of all
estimators. The results are valid if some a priori information on a Fourier
coefficients of solution is provided. For trigonometric basis this a priori
information implies that the solution belongs to a ball in Besov space
$B^r_{2\infty}$.
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Observation of Extra Photon Recoil in a Distorted Optical Field | Light carries momentum which induces on atoms a recoil for each photon
absorbed. In vacuum, for a monochromatic beam of frequency $\nu$, the global
momentum per photon is bounded by general principles and is smaller than $h
\nu/c$ leading to a limit on the recoil. However, locally this limit can be
broken. In this paper, we give a general formula to calculate the recoil in
vacuum. We show that in a laser beam with a distorted optical field, there are
regions where the recoil can be higher than this limit. Using atoms placed in
those regions we are able to measure directly the extra recoil.
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Weak Poincaré Inequalities for Convergence Rate of Degenerate Diffusion Processes | For a contraction $C_0$-semigroup on a separable Hilbert space, the decay
rate is estimated by using the weak Poincaré inequalities for the symmetric
and anti-symmetric part of the generator. As applications, non-exponential
convergence rate is characterized for a class of degenerate diffusion
processes, so that the study of hypocoercivity is extended. Concrete examples
are presented.
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rTraceroute: Réunion Traceroute Visualisation | Traceroute is the main tools to explore Internet path. It provides limited
information about each node along the path. However, Traceroute cannot go
further in statistics analysis, or \emph{Man-Machine Interface (MMI)}.
Indeed, there are no graphical tool that is able to draw all paths used by IP
routes. We present a new tool that can handle more than 1,000 Traceroute
results, map them, identify graphically MPLS links, get information of usage of
all routes (in percent) to improve the knowledge between countries' links.
rTraceroute want to go deeper in usage of atomic traces. In this paper, we will
discuss the concept of rTraceroute and present some example of usage.
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Effects of heterogeneity in site-site couplings for tight-binding models on scale-invariant structures | We studied the thermodynamic behaviors of non-interacting bosons and fermions
trapped by a scale-invariant branching structure of adjustable degree of
heterogeneity. The full energy spectrum in tight-binding approximation was
analytically solved . We found that the log-periodic oscillation of the
specific heat for Fermi gas depended on the heterogeneity of hopping. Also, low
dimensional Bose-Einstein condensation occurred only for non-homogeneous setup.
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Are generative deep models for novelty detection truly better? | Many deep models have been recently proposed for anomaly detection. This
paper presents comparison of selected generative deep models and classical
anomaly detection methods on an extensive number of non--image benchmark
datasets. We provide statistical comparison of the selected models, in many
configurations, architectures and hyperparamaters. We arrive to conclusion that
performance of the generative models is determined by the process of selection
of their hyperparameters. Specifically, performance of the deep generative
models deteriorates with decreasing amount of anomalous samples used in
hyperparameter selection. In practical scenarios of anomaly detection, none of
the deep generative models systematically outperforms the kNN.
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Minimizing the waiting time for a one-way shuttle service | Consider a terminal in which users arrive continuously over a finite period
of time at a variable rate known in advance. A fleet of shuttles has to carry
the users over a fixed trip. What is the shuttle schedule that minimizes their
waiting time? This is the question addressed in the present paper. We propose
efficient algorithms for several variations of this question with proven
performance guarantees. The techniques used are of various types (convex
optimization, shortest paths,...). The paper ends with numerical experiments
showing that most of our algorithms behave also well in practice.
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Two weight bump conditions for matrix weights | In this paper we extend the theory of two weight, $A_p$ bump conditions to
the setting of matrix weights. We prove two matrix weight inequalities for
fractional maximal operators, fractional and singular integrals, sparse
operators and averaging operators. As applications we prove quantitative, one
weight estimates, in terms of the matrix $A_p$ constant, for singular
integrals, and prove a Poincaré inequality related to those that appear in
the study of degenerate elliptic PDEs.
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Legendrian ribbons and strongly quasipositive links in an open book | We show that a link in an open book can be realized as a strongly
quasipositive braid if and only if it bounds a Legendrian ribbon with respect
to the associated contact structure. This generalizes a result due to Baader
and Ishikawa for links in the three-sphere. We highlight some related
techniques for determining whether or not a link is strongly quasipositive,
emphasizing applications to fibered links and satellites.
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Δ-cumulants in terms of moments | The \Delta-convolution of real probability measures, introduced by Bożejko,
generalizes both free and boolean convolutions. It is linearized by the
\Delta-cumulants, and Yoshida gave a combinatorial formula for moments in terms
of \Delta-cumulants, that implicitly defines the latter. It relies on the
definition of an appropriate weight on noncrossing partitions. We give here two
different expressions for the \Delta-cumulants: the first one is a simple
variant of Lagrange inversion formula, and the second one is a combinatorial
inversion of Yoshida's formula involving Schröder trees.
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A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds | When classifying point clouds, a large amount of time is devoted to the
process of engineering a reliable set of features which are then passed to a
classifier of choice. Generally, such features - usually derived from the
3D-covariance matrix - are computed using the surrounding neighborhood of
points. While these features capture local information, the process is usually
time-consuming, and requires the application at multiple scales combined with
contextual methods in order to adequately describe the diversity of objects
within a scene. In this paper we present a 1D-fully convolutional network that
consumes terrain-normalized points directly with the corresponding spectral
data,if available, to generate point-wise labeling while implicitly learning
contextual features in an end-to-end fashion. Our method uses only the
3D-coordinates and three corresponding spectral features for each point.
Spectral features may either be extracted from 2D-georeferenced images, as
shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted
directly for passive-derived point clouds,i.e. from muliple-view imagery. We
train our network by splitting the data into square regions, and use a pooling
layer that respects the permutation-invariance of the input points. Evaluated
using the ISPRS 3D Semantic Labeling Contest, our method scored second place
with an overall accuracy of 81.6%. We ranked third place with a mean F1-score
of 63.32%, surpassing the F1-score of the method with highest accuracy by
1.69%. In addition to labeling 3D-point clouds, we also show that our method
can be easily extended to 2D-semantic segmentation tasks, with promising
initial results.
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Boundedness properties in function spaces | Some boundedness properties of function spaces (considered as topological
groups) are studied.
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Linear-size CDAWG: new repetition-aware indexing and grammar compression | In this paper, we propose a novel approach to combine \emph{compact directed
acyclic word graphs} (CDAWGs) and grammar-based compression. This leads us to
an efficient self-index, called Linear-size CDAWGs (L-CDAWGs), which can be
represented with $O(\tilde e_T \log n)$ bits of space allowing for $O(\log
n)$-time random and $O(1)$-time sequential accesses to edge labels, and $O(m
\log \sigma + occ)$-time pattern matching. Here, $\tilde e_T$ is the number of
all extensions of maximal repeats in $T$, $n$ and $m$ are respectively the
lengths of the text $T$ and a given pattern, $\sigma$ is the alphabet size, and
$occ$ is the number of occurrences of the pattern in $T$. The repetitiveness
measure $\tilde e_T$ is known to be much smaller than the text length $n$ for
highly repetitive text. For constant alphabets, our L-CDAWGs achieve $O(m +
occ)$ pattern matching time with $O(e_T^r \log n)$ bits of space, which
improves the pattern matching time of Belazzougui et al.'s run-length
BWT-CDAWGs by a factor of $\log \log n$, with the same space complexity. Here,
$e_T^r$ is the number of right extensions of maximal repeats in $T$. As a
byproduct, our result gives a way of constructing an SLP of size $O(\tilde
e_T)$ for a given text $T$ in $O(n + \tilde e_T \log \sigma)$ time.
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Topic Lifecycle on Social Networks: Analyzing the Effects of Semantic Continuity and Social Communities | Topic lifecycle analysis on Twitter, a branch of study that investigates
Twitter topics from their birth through lifecycle to death, has gained immense
mainstream research popularity. In the literature, topics are often treated as
one of (a) hashtags (independent from other hashtags), (b) a burst of keywords
in a short time span or (c) a latent concept space captured by advanced text
analysis methodologies, such as Latent Dirichlet Allocation (LDA). The first
two approaches are not capable of recognizing topics where different users use
different hashtags to express the same concept (semantically related), while
the third approach misses out the user's explicit intent expressed via
hashtags. In our work, we use a word embedding based approach to cluster
different hashtags together, and the temporal concurrency of the hashtag
usages, thus forming topics (a semantically and temporally related group of
hashtags).We present a novel analysis of topic lifecycles with respect to
communities. We characterize the participation of social communities in the
topic clusters, and analyze the lifecycle of topic clusters with respect to
such participation. We derive first-of-its-kind novel insights with respect to
the complex evolution of topics over communities and time: temporal morphing of
topics over hashtags within communities, how the hashtags die in some
communities but morph into some other hashtags in some other communities (that,
it is a community-level phenomenon), and how specific communities adopt to
specific hashtags. Our work is fundamental in the space of topic lifecycle
modeling and understanding in communities: it redefines our understanding of
topic lifecycles and shows that the social boundaries of topic lifecycles are
deeply ingrained with community behavior.
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Unstable Footwear as a Speed-Dependent Noise-Based Training Gear to Exercise Inverted Pendulum Motion During Walking | Previous research on unstable footwear has suggested that it may induce
plantar mechanical noise during walking. The purpose of this study was to
explore whether unstable footwear could be considered as a noise-based training
gear to exercise body center of mass (CoM) motion during walking or not. Ground
reaction forces were collected among 24 healthy young women walking at speeds
between 3 and 6 km h-1 with control running shoes and unstable rocker-bottom
shoes. The external mechanical work, the recovery of mechanical energy of the
CoM during and within the step cycles, and the phase shift between potential
and kinetic energy curves of the CoM were computed. Our findings support the
idea that unstable rocker-bottom footwear could serve as a speed-dependent
noise- based training gear to exercise CoM motion during walking. At slow
speed, it acts as a stochastic resonance or facilitator, whereas at brisk speed
it acts as a constraint.
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Gaussian autoregressive process with dependent innovations. Some asymptotic results | In this paper we introduce a modified version of a gaussian standard
first-order autoregressive process where we allow for a dependence structure
between the state variable $Y_{t-1}$ and the next innovation $\xi_t$. We call
this model dependent innovations gaussian AR(1) process (DIG-AR(1)). We analyze
the moment and temporal dependence properties of the new model. After proving
that the OLS estimator does not consistently estimate the autoregressive
parameter, we introduce an infeasible estimator and we provide its
$\sqrt{T}$-asymptotic normality.
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Structured Peer Learning Program - An Innovative Approach to Computer Science Education | Structured Peer Learning (SPL) is a form of peer-based supplemental
instruction that focuses on mentoring, guidance, and development of technical,
communication, and social skills in both the students receiving assistance and
the students in teaching roles. This paper explores the methodology, efficacy,
and reasoning behind the practical realization of a SPL program designed to
increase student knowledge and success in undergraduate Computer Science
courses. Students expressed an increased level of comfort when asking for help
from student teachers versus traditional educational resources, historically
showed an increased average grade in lower-level courses, and felt that the
program positively impacted their desire to continue in or switch to a Computer
major. Additionally, results indicated that advances in programming, analytical
thinking, and abstract analysis skills were evident in not only the students
but also the student teachers, suggesting a strong bidirectional flow of
knowledge.
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Analysis of the possibility for time-optimal control of the scanning system of the GREEN-WAKE's project lidar | The monograph represents analysis of the possibility for time-optimal control
of a prototype of an aircraft-mounted scanning-imaging system of a Light
Detection and Ranging based wake vortex detection system. The study is a part
of the research project "Demonstration of Light Detection and Ranging based
wake vortex detection system incorporating an atmospheric hazard map" or
GREEN-WAKE (Project ID 213254) of the European Union Seventh Framework Program
for Research and Technological Development. The scanning system comprises two
light mirror actuators. The study is decomposed into several group of problems.
The first and second groups consider the mathematical models of the scanning
system and the mirror actuators. The third group of problems deals with the
design of closed loop tracking control systems of both the large and small
mirror actuators. The control of each one system is synthesized as a near
time-optimal control of the precise linear model of the mirror actuator. The
control algorithms realize the state of the art method for synthesis of
time-optimal control of any order for a class of linear time-optimal control
problems developed in the author's dissertation. The last discovers and
theoretically proves new properties of a class of linear time-optimal control
problems. From the point of view of the control synthesis algorithms the main
advantage is that the time-optimal control is produced by a multistage
procedure within the class of problems but without the need of describing the
hyper-surfaces of switching over. The fourth group of problems considers
modeling the real scan picture but with inclusion also of the Coulomb's
friction model. The fifth group of problems investigates the ways of
improvement of the real scan picture. As a result an excellent repetition and
clearness of the scanning alongside with symmetry and matching the scan pattern
are seen.
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Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples | Recently, researchers have discovered that the state-of-the-art object
classifiers can be fooled easily by small perturbations in the input
unnoticeable to human eyes. It is also known that an attacker can generate
strong adversarial examples if she knows the classifier parameters. Conversely,
a defender can robustify the classifier by retraining if she has access to the
adversarial examples. We explain and formulate this adversarial example problem
as a two-player continuous zero-sum game, and demonstrate the fallacy of
evaluating a defense or an attack as a static problem. To find the best
worst-case defense against whitebox attacks, we propose a continuous minimax
optimization algorithm. We demonstrate the minimax defense with two types of
attack classes -- gradient-based and neural network-based attacks. Experiments
with the MNIST and the CIFAR-10 datasets demonstrate that the defense found by
numerical minimax optimization is indeed more robust than non-minimax defenses.
We discuss directions for improving the result toward achieving robustness
against multiple types of attack classes.
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From Attention to Participation: Reviewing and Modelling Engagement with Computers | Over the last decades, the Internet and mobile technology have consolidated
the digital as a public sphere of life. Designers are asked to create engaging
digital experiences. However, in some cases engagement is seen as a
psychological state, while in others it emphasizes a participative vein. In
this paper, I review and discuss both and propose a new definition to clarify
the concept engagement with computers. Thus, engagement is a quality of an
active connection between a user and a computing product, either a website or a
mobile phone app. Studying it requires understanding a set of aspects like the
user's affect, motivation and attention, as well as the product's design,
content and composition. Finally, I propose explaining these concepts aligned
with engagement and integrate them into a preliminary model to measure the
manifestations.
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Circularizing Planet Nine through dynamical friction with an extended, cold planetesimal belt | Unexpected clustering in the orbital elements of minor bodies beyond the
Kuiper belt has led to speculations that our solar system actually hosts nine
planets, the eight established plus a hypothetical "Planet Nine". Several
recent studies have shown that a planet with a mass of about 10 Earth masses on
a distant eccentric orbit with perihelion far beyond the Kuiper belt could
create and maintain this clustering. The evolutionary path resulting in an
orbit such as the one suggested for Planet Nine is nevertheless not easily
explained. Here we investigate whether a planet scattered away from the
giant-planet region could be lifted to an orbit similar to the one suggested
for Planet Nine through dynamical friction with a cold, distant planetesimal
belt. Recent simulations of planetesimal formation via the streaming
instability suggest that planetesimals can readily form beyond 100au. We
explore this circularisation by dynamical friction with a set of numerical
simulations. We find that a planet that is scattered from the region close to
Neptune onto an eccentric orbit has a 20-30% chance of obtaining an orbit
similar to that of Planet Nine after 4.6Gyr. Our simulations also result in
strong or partial clustering of the planetesimals; however, whether or not this
clustering is observable depends on the location of the inner edge of the
planetesimal belt. If the inner edge is located at 200au the degree of
clustering amongst observable objects is significant.
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Tactics of Adversarial Attack on Deep Reinforcement Learning Agents | We introduce two tactics to attack agents trained by deep reinforcement
learning algorithms using adversarial examples, namely the strategically-timed
attack and the enchanting attack. In the strategically-timed attack, the
adversary aims at minimizing the agent's reward by only attacking the agent at
a small subset of time steps in an episode. Limiting the attack activity to
this subset helps prevent detection of the attack by the agent. We propose a
novel method to determine when an adversarial example should be crafted and
applied. In the enchanting attack, the adversary aims at luring the agent to a
designated target state. This is achieved by combining a generative model and a
planning algorithm: while the generative model predicts the future states, the
planning algorithm generates a preferred sequence of actions for luring the
agent. A sequence of adversarial examples is then crafted to lure the agent to
take the preferred sequence of actions. We apply the two tactics to the agents
trained by the state-of-the-art deep reinforcement learning algorithm including
DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much
reward as the uniform attack (i.e., attacking at every time step) does by
attacking the agent 4 times less often. Our enchanting attack lures the agent
toward designated target states with a more than 70% success rate. Videos are
available at this http URL
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Differentially Private Distributed Learning for Language Modeling Tasks | One of the big challenges in machine learning applications is that training
data can be different from the real-world data faced by the algorithm. In
language modeling, users' language (e.g. in private messaging) could change in
a year and be completely different from what we observe in publicly available
data. At the same time, public data can be used for obtaining general knowledge
(i.e. general model of English). We study approaches to distributed fine-tuning
of a general model on user private data with the additional requirements of
maintaining the quality on the general data and minimization of communication
costs. We propose a novel technique that significantly improves prediction
quality on users' language compared to a general model and outperforms gradient
compression methods in terms of communication efficiency. The proposed
procedure is fast and leads to an almost 70% perplexity reduction and 8.7
percentage point improvement in keystroke saving rate on informal English
texts. We also show that the range of tasks our approach is applicable to is
not limited by language modeling only. Finally, we propose an experimental
framework for evaluating differential privacy of distributed training of
language models and show that our approach has good privacy guarantees.
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Tensor Train Neighborhood Preserving Embedding | In this paper, we propose a Tensor Train Neighborhood Preserving Embedding
(TTNPE) to embed multi-dimensional tensor data into low dimensional tensor
subspace. Novel approaches to solve the optimization problem in TTNPE are
proposed. For this embedding, we evaluate novel trade-off gain among
classification, computation, and dimensionality reduction (storage) for
supervised learning. It is shown that compared to the state-of-the-arts tensor
embedding methods, TTNPE achieves superior trade-off in classification,
computation, and dimensionality reduction in MNIST handwritten digits and
Weizmann face datasets.
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Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach | Three dimensional (3D) topology optimization problems always involve huge
numbers of Degrees of Freedom (DOFs) in finite element analysis (FEA) and
design variables in numerical optimization, respectively. This will inevitably
lead to large computational efforts in the solution process. In the present
paper, an efficient and explicit topology optimization approach which can
reduce not only the number of design variables but also the number of degrees
of freedom in FEA is proposed based on the Moving Morphable Voids (MMVs)
solution framework. This is achieved by introducing a set of geometry
parameters (e.g., control points of B-spline surfaces) to describe the boundary
of a structure explicitly and removing the unnecessary DOFs from the FE model
at every step of numerical optimization. Numerical examples demonstrate that
the proposed approach does can overcome the bottleneck problems associated with
a 3D topology optimization problem in a straightforward way and enhance the
solution efficiency significantly.
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The index of compact simple Lie groups | Let M be an irreducible Riemannian symmetric space. The index i(M) of M is
the minimal codimension of a (non-trivial) totally geodesic submanifold of M.
The purpose of this note is to determine the index i(M) for all irreducible
Riemannian symmetric spaces M of type (II) and (IV).
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Cell Tracking via Proposal Generation and Selection | Microscopy imaging plays a vital role in understanding many biological
processes in development and disease. The recent advances in automation of
microscopes and development of methods and markers for live cell imaging has
led to rapid growth in the amount of image data being captured. To efficiently
and reliably extract useful insights from these captured sequences, automated
cell tracking is essential. This is a challenging problem due to large
variation in the appearance and shapes of cells depending on many factors
including imaging methodology, biological characteristics of cells, cell matrix
composition, labeling methodology, etc. Often cell tracking methods require a
sequence-specific segmentation method and manual tuning of many tracking
parameters, which limits their applicability to sequences other than those they
are designed for. In this paper, we propose 1) a deep learning based cell
proposal method, which proposes candidates for cells along with their scores,
and 2) a cell tracking method, which links proposals in adjacent frames in a
graphical model using edges representing different cellular events and poses
joint cell detection and tracking as the selection of a subset of cell and edge
proposals. Our method is completely automated and given enough training data
can be applied to a wide variety of microscopy sequences. We evaluate our
method on multiple fluorescence and phase contrast microscopy sequences
containing cells of various shapes and appearances from ISBI cell tracking
challenge, and show that our method outperforms existing cell tracking methods.
Code is available at: this https URL
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Geometric Properties of Isostables and Basins of Attraction of Monotone Systems | In this paper, we study geometric properties of basins of attraction of
monotone systems. Our results are based on a combination of monotone systems
theory and spectral operator theory. We exploit the framework of the Koopman
operator, which provides a linear infinite-dimensional description of nonlinear
dynamical systems and spectral operator-theoretic notions such as eigenvalues
and eigenfunctions. The sublevel sets of the dominant eigenfunction form a
family of nested forward-invariant sets and the basin of attraction is the
largest of these sets. The boundaries of these sets, called isostables, allow
studying temporal properties of the system. Our first observation is that the
dominant eigenfunction is increasing in every variable in the case of monotone
systems. This is a strong geometric property which simplifies the computation
of isostables. We also show how variations in basins of attraction can be
bounded under parametric uncertainty in the vector field of monotone systems.
Finally, we study the properties of the parameter set for which a monotone
system is multistable. Our results are illustrated on several systems of two to
four dimensions.
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Elongation and shape changes in organisms with cell walls: a dialogue between experiments and models | The generation of anisotropic shapes occurs during morphogenesis of almost
all organisms. With the recent renewal of the interest in mechanical aspects of
morphogenesis, it has become clear that mechanics contributes to anisotropic
forms in a subtle interaction with various molecular actors. Here, we consider
plants, fungi, oomycetes, and bacteria, and we review the mechanisms by which
elongated shapes are generated and maintained. We focus on theoretical models
of the interplay between growth and mechanics, in relation with experimental
data, and discuss how models may help us improve our understanding of the
underlying biological mechanisms.
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Localic completion of uniform spaces | We extend the notion of localic completion of generalised metric spaces by
Steven Vickers to the setting of generalised uniform spaces. A generalised
uniform space (gus) is a set X equipped with a family of generalised metrics on
X, where a generalised metric on X is a map from the product of X to the upper
reals satisfying zero self-distance law and triangle inequality.
For a symmetric generalised uniform space, the localic completion lifts its
generalised uniform structure to a point-free generalised uniform structure.
This point-free structure induces a complete generalised uniform structure on
the set of formal points of the localic completion that gives the standard
completion of the original gus with Cauchy filters.
We extend the localic completion to a full and faithful functor from the
category of locally compact uniform spaces into that of overt locally compact
completely regular formal topologies. Moreover, we give an elementary
characterisation of the cover of the localic completion of a locally compact
uniform space that simplifies the existing characterisation for metric spaces.
These results generalise the corresponding results for metric spaces by Erik
Palmgren.
Furthermore, we show that the localic completion of a symmetric gus is
equivalent to the point-free completion of the uniform formal topology
associated with the gus.
We work in Aczel's constructive set theory CZF with the Regular Extension
Axiom. Some of our results also require Countable Choice.
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Kinetic inhibition of MHD-shocks in the vicinity of a parallel magnetic field | According to magnetohydrodynamics (MHD), the encounter of two collisional
magnetized plasmas at high velocity gives rise to shock waves. Investigations
conducted so far have found that the same conclusion still holds in the case of
collisionless plasmas. For the case of a flow-aligned field, MHD stipulates
that the field and the fluid are disconnected, so that the shock produced is
independent of the field. We present a violation of this MHD prediction when
considering the encounter of two cold pair plasmas along a flow-aligned
magnetic field. As the guiding magnetic field grows, isotropization is
progressively suppressed, resulting in a strong influence of the field on the
resulting structure. A micro-physics analysis allows to understand the
mechanisms at work. Particle-in-cell simulations also support our conclusions
and show that the results are not restricted to a strictly parallel field.
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Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things | In this paper, we propose Squeezed Convolutional Variational AutoEncoder
(SCVAE) for anomaly detection in time series data for Edge Computing in
Industrial Internet of Things (IIoT). The proposed model is applied to labeled
time series data from UCI datasets for exact performance evaluation, and
applied to real world data for indirect model performance comparison. In
addition, by comparing the models before and after applying Fire Modules from
SqueezeNet, we show that model size and inference times are reduced while
similar levels of performance is maintained.
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Global Weisfeiler-Lehman Graph Kernels | Most state-of-the-art graph kernels only take local graph properties into
account, i.e., the kernel is computed with regard to properties of the
neighborhood of vertices or other small substructures. On the other hand,
kernels that do take global graph propertiesinto account may not scale well to
large graph databases. Here we propose to start exploring the space between
local and global graph kernels, striking the balance between both worlds.
Specifically, we introduce a novel graph kernel based on the $k$-dimensional
Weisfeiler-Lehman algorithm. Unfortunately, the $k$-dimensional
Weisfeiler-Lehman algorithm scales exponentially in $k$. Consequently, we
devise a stochastic version of the kernel with provable approximation
guarantees using conditional Rademacher averages. On bounded-degree graphs, it
can even be computed in constant time. We support our theoretical results with
experiments on several graph classification benchmarks, showing that our
kernels often outperform the state-of-the-art in terms of classification
accuracies.
| 1 | 0 | 0 | 1 | 0 | 0 |
An analytical Model which Determines the Apparent T1 for Modified Look-Locker Inversion Recovery (MOLLI) -- Analysis of the Longitudinal Relaxation under the Influence of Discontinuous Balanced and Spoiled Gradient Echo Readouts | Quantitative nuclear magnetic resonance imaging (MRI) shifts more and more
into the focus of clinical research. Especially determination of relaxation
times without/and with contrast agents becomes the foundation of tissue
characterization, e.g. in cardiac MRI for myocardial fibrosis. Techniques which
assess longitudinal relaxation times rely on repetitive application of readout
modules, which are interrupted by free relaxation periods, e.g. the Modified
Look-Locker Inversion Recovery = MOLLI sequence. These discontinuous sequences
reveal an apparent relaxation time, and, by techniques extrapolated from
continuous readout sequences, the real T1 is determined. What is missing is a
rigorous analysis of the dependence of the apparent relaxation time on its real
partner, readout sequence parameters and biological parameters as heart rate.
This is provided in this paper for the discontinuous balanced steady state free
precession (bSSFP) and spoiled gradient echo readouts. It turns out that the
apparente longitudinal relaxation rate is the time average of the relaxation
rates during the readout module, and free relaxation period. Knowing the heart
rate our results vice versa allow to determine the real T1 from its measured
apparent partner.
| 0 | 1 | 0 | 0 | 0 | 0 |
Updated Constraints on the Dark Matter Interpretation of CDMS-II-Si Data | We present an updated halo-dependent and halo-independent analysis of viable
light WIMP dark matter candidates which could account for the excess observed
in CDMS-II-Si. We include recent constraints from LUX, PandaX-II, and PICO-60,
as well as projected sensitivities for XENON1T, SuperCDMS SNOLAB, LZ, DARWIN,
DarkSide-20k, and PICO-250, on candidates with spin-independent isospin
conserving and isospin-violating interactions, and either elastic or exothermic
scattering. We show that there exist dark matter candidates which can explain
the CDMS-II-Si data and remain very marginally consistent with the null results
of all current experiments, however such models are highly tuned, making a dark
matter interpretation of CDMS-II-Si very unlikely. We find that these models
can only be ruled out in the future by an experiment comparable to LZ or
PICO-250.
| 0 | 1 | 0 | 0 | 0 | 0 |
Emulating Simulations of Cosmic Dawn for 21cm Power Spectrum Constraints on Cosmology, Reionization, and X-ray Heating | Current and upcoming radio interferometric experiments are aiming to make a
statistical characterization of the high-redshift 21cm fluctuation signal
spanning the hydrogen reionization and X-ray heating epochs of the universe.
However, connecting 21cm statistics to underlying physical parameters is
complicated by the theoretical challenge of modeling the relevant physics at
computational speeds quick enough to enable exploration of the high dimensional
and weakly constrained parameter space. In this work, we use machine learning
algorithms to build a fast emulator that mimics expensive simulations of the
21cm signal across a wide parameter space to high precision. We embed our
emulator within a Markov-Chain Monte Carlo framework, enabling it to explore
the posterior distribution over a large number of model parameters, including
those that govern the Epoch of Reionization, the Epoch of X-ray Heating, and
cosmology. As a worked example, we use our emulator to present an updated
parameter constraint forecast for the Hydrogen Epoch of Reionization Array
experiment, showing that its characterization of a fiducial 21cm power spectrum
will considerably narrow the allowed parameter space of reionization and
heating parameters, and could help strengthen Planck's constraints on
$\sigma_8$. We provide both our generalized emulator code and its
implementation specifically for 21cm parameter constraints as publicly
available software.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fluctuations and Noise Signatures of Driven Magnetic Skyrmions | Magnetic skyrmions are particle-like objects with topologically-protected
stability which can be set into motion with an applied current. Using a
particle-based model we simulate current-driven magnetic skyrmions interacting
with random quenched disorder and examine the skyrmion velocity fluctuations
parallel and perpendicular to the direction of motion as a function of
increasing drive. We show that the Magnus force contribution to skyrmion
dynamics combined with the random pinning produces an isotropic effective
shaking temperature. As a result, the skyrmions form a moving crystal at large
drives instead of the moving smectic state observed in systems with a
negligible Magnus force where the effective shaking temperature is anisotropic.
We demonstrate that spectral analysis of the velocity noise fluctuations can be
used to identify dynamical phase transitions and to extract information about
the different dynamic phases, and show how the velocity noise fluctuations are
correlated with changes in the skyrmion Hall angle, transport features, and
skyrmion lattice structure.
| 0 | 1 | 0 | 0 | 0 | 0 |
Metallic maps between metallic Riemannian manifolds and constancy of certain maps | In this paper, we introduce metallic maps between metallic Riemannian
manifolds, provide an example and obtain certain conditions for such maps to be
totally geodesic. We also give a sufficient condition for a map between
metallic Riemannian manifolds to be harmonic map. Then we investigate the
constancy of certain maps between metallic Riemannian manifolds and various
manifolds by imposing the holomorphic-like condition. Moreover, we check the
reverse case and show that some such maps are constant if there is a condition
for this.
| 0 | 0 | 1 | 0 | 0 | 0 |
Hashing as Tie-Aware Learning to Rank | Hashing, or learning binary embeddings of data, is frequently used in nearest
neighbor retrieval. In this paper, we develop learning to rank formulations for
hashing, aimed at directly optimizing ranking-based evaluation metrics such as
Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
first observe that the integer-valued Hamming distance often leads to tied
rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
their continuous relaxations, and perform gradient-based optimization with deep
neural networks. Our results establish the new state-of-the-art for image
retrieval by Hamming ranking in common benchmarks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Models of compact stars on paraboloidal spacetime satisfying Karmarkar condition | A new exact solution of Einstein's field equations on the background of
paraboloidal spacetime using Karmarkar condition is reported. The physical
acceptability conditions of the model are investigated and found that the model
is compatible with a number of compact star candidates like Her X-1, LMC X-4,
EXO 1785-248, PSR J1903+327, Vela X-1 and PSR J1614-2230. A noteworthy feature
of the model is that it is geometrically significant and simple in form.
| 0 | 1 | 0 | 0 | 0 | 0 |
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification | Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.
| 1 | 0 | 0 | 1 | 0 | 0 |
An Input Reconstruction Approach for Command Following in Linear MIMO Systems | The idea of posing a command following or tracking control problem as an
input reconstruction problem is explored in the paper. For a class of square
MIMO systems with known dynamics, by pretending that reference commands are
actual outputs of the system, input reconstruction methods can be used to
determine control action that will result in a system following desired
reference commands. A feedback controller which is a combination of an unbiased
state estimator and an input reconstructor that ensures unbiased tracking of
reference commands is proposed. Simulations and real-time implementation are
presented to demonstrate utility of the proposed idea. Conditions under which
proposed controller may be used for non-square systems are also discussed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module | Wireless communication plays a vital role in the promising performance of
connected and automated vehicle (CAV) technology. This paper proposes a
Vissim-based microscopic traffic simulation framework with an analytical
dedicated short-range communication (DSRC) module for packet reception. Being
derived from ns-2, a packet-level network simulator, the DSRC probability
module takes into account the imperfect wireless communication that occurs in
real-world deployment. Four managed lane deployment strategies are evaluated
using the proposed framework. While the average packet reception rate is above
93\% among all tested scenarios, the results reveal that the reliability of the
vehicle-to-vehicle (V2V) communication can be influenced by the deployment
strategies. Additionally, the proposed framework exhibits desirable scalability
for traffic simulation and it is able to evaluate transportation-network-level
deployment strategies in the near future for CAV technologies.
| 1 | 0 | 0 | 0 | 0 | 0 |
Synchronization of Kuramoto oscillators in a bidirectional frequency-dependent tree network | This paper studies the synchronization of a finite number of Kuramoto
oscillators in a frequency-dependent bidirectional tree network. We assume that
the coupling strength of each link in each direction is equal to the product of
a common coefficient and the exogenous frequency of its corresponding head
oscillator. We derive a sufficient condition for the common coupling strength
in order to guarantee frequency synchronization in tree networks. Moreover, we
discuss the dependency of the obtained bound on both the graph structure and
the way that exogenous frequencies are distributed. Further, we present an
application of the obtained result by means of an event-triggered algorithm for
achieving frequency synchronization in a star network assuming that the common
coupling coefficient is given.
| 1 | 0 | 0 | 0 | 0 | 0 |
On Exchangeability in Network Models | We derive representation theorems for exchangeable distributions on finite
and infinite graphs using elementary arguments based on geometric and
graph-theoretic concepts. Our results elucidate some of the key differences,
and their implications, between statistical network models that are finitely
exchangeable and models that define a consistent sequence of probability
distributions on graphs of increasing size.
| 0 | 0 | 1 | 1 | 0 | 0 |
Replicator equation on networks with degree regular communities | The replicator equation is one of the fundamental tools to study evolutionary
dynamics in well-mixed populations. This paper contributes to the literature on
evolutionary graph theory, providing a version of the replicator equation for a
family of connected networks with communities, where nodes in the same
community have the same degree. This replicator equation is applied to the
study of different classes of games, exploring the impact of the graph
structure on the equilibria of the evolutionary dynamics.
| 0 | 0 | 0 | 0 | 1 | 0 |
Go game formal revealing by Ising model | Go gaming is a struggle for territory control between rival, black and white,
stones on a board. We model the Go dynamics in a game by means of the Ising
model whose interaction coefficients reflect essential rules and tactics
employed in Go to build long-term strategies. At any step of the game, the
energy functional of the model provides the control degree (strength) of a
player over the board. A close fit between predictions of the model with actual
games is obtained.
| 1 | 0 | 0 | 0 | 0 | 0 |
High resolution structural characterisation of laser-induced defect clusters inside diamond | Laser writing with ultrashort pulses provides a potential route for the
manufacture of three-dimensional wires, waveguides and defects within diamond.
We present a transmission electron microscopy (TEM) study of the intrinsic
structure of the laser modifications and reveal a complex distribution of
defects. Electron energy loss spectroscopy (EELS) indicates that the majority
of the irradiated region remains as $sp^3$ bonded diamond.
Electrically-conductive paths are attributed to the formation of multiple
nano-scale, $sp^2$-bonded graphitic wires and a network of strain-relieving
micro-cracks.
| 0 | 1 | 0 | 0 | 0 | 0 |
Neural networks for post-processing ensemble weather forecasts | Ensemble weather predictions require statistical post-processing of
systematic errors to obtain reliable and accurate probabilistic forecasts.
Traditionally, this is accomplished with distributional regression models in
which the parameters of a predictive distribution are estimated from a training
period. We propose a flexible alternative based on neural networks that can
incorporate nonlinear relationships between arbitrary predictor variables and
forecast distribution parameters that are automatically learned in a
data-driven way rather than requiring pre-specified link functions. In a case
study of 2-meter temperature forecasts at surface stations in Germany, the
neural network approach significantly outperforms benchmark post-processing
methods while being computationally more affordable. Key components to this
improvement are the use of auxiliary predictor variables and station-specific
information with the help of embeddings. Furthermore, the trained neural
network can be used to gain insight into the importance of meteorological
variables thereby challenging the notion of neural networks as uninterpretable
black boxes. Our approach can easily be extended to other statistical
post-processing and forecasting problems. We anticipate that recent advances in
deep learning combined with the ever-increasing amounts of model and
observation data will transform the post-processing of numerical weather
forecasts in the coming decade.
| 0 | 0 | 0 | 1 | 0 | 0 |
Power maps in finite groups | In recent work, Pomerance and Shparlinski have obtained results on the number
of cycles in the functional graph of the map $x \mapsto x^a$ in
$\mathbb{F}_p^*$. We prove similar results for other families of finite groups.
In particular, we obtain estimates for the number of cycles for cyclic groups,
symmetric groups, dihedral groups and $SL_2(\mathbb{F}_q)$. We also show that
the cyclic group of order $n$ minimizes the number of cycles among all
nilpotent groups of order $n$ for a fixed exponent. Finally, we pose several
problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Permutation polynomials, fractional polynomials, and algebraic curves | In this note we prove a conjecture by Li, Qu, Li, and Fu on permutation
trinomials over $\mathbb{F}_3^{2k}$. In addition, new examples and
generalizations of some families of permutation polynomials of
$\mathbb{F}_{3^k}$ and $\mathbb{F}_{5^k}$ are given. We also study permutation
quadrinomials of type $Ax^{q(q-1)+1} + Bx^{2(q-1)+1} + Cx^{q} + x$. Our method
is based on the investigation of an algebraic curve associated with a
{fractional polynomial} over a finite field.
| 0 | 0 | 1 | 0 | 0 | 0 |
Recursive Whitening Transformation for Speaker Recognition on Language Mismatched Condition | Recently in speaker recognition, performance degradation due to the channel
domain mismatched condition has been actively addressed. However, the
mismatches arising from language is yet to be sufficiently addressed. This
paper proposes an approach which employs recursive whitening transformation to
mitigate the language mismatched condition. The proposed method is based on the
multiple whitening transformation, which is intended to remove un-whitened
residual components in the dataset associated with i-vector length
normalization. The experiments were conducted on the Speaker Recognition
Evaluation 2016 trials of which the task is non-English speaker recognition
using development dataset consist of both a large scale out-of-domain (English)
dataset and an extremely low-quantity in-domain (non-English) dataset. For
performance comparison, we develop a state-of- the-art system using deep neural
network and bottleneck feature, which is based on a phonetically aware model.
From the experimental results, along with other prior studies, effectiveness of
the proposed method on language mismatched condition is validated.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning Representations for Soft Skill Matching | Employers actively look for talents having not only specific hard skills but
also various soft skills. To analyze the soft skill demands on the job market,
it is important to be able to detect soft skill phrases from job advertisements
automatically. However, a naive matching of soft skill phrases can lead to
false positive matches when a soft skill phrase, such as friendly, is used to
describe a company, a team, or another entity, rather than a desired candidate.
In this paper, we propose a phrase-matching-based approach which
differentiates between soft skill phrases referring to a candidate vs.
something else. The disambiguation is formulated as a binary text
classification problem where the prediction is made for the potential soft
skill based on the context where it occurs. To inform the model about the soft
skill for which the prediction is made, we develop several approaches,
including soft skill masking and soft skill tagging.
We compare several neural network based approaches, including CNN, LSTM and
Hierarchical Attention Model. The proposed tagging-based input representation
using LSTM achieved the highest recall of 83.92% on the job dataset when fixing
a precision to 95%.
| 0 | 0 | 0 | 1 | 0 | 0 |
Time-reversal and spatial reflection symmetry localization anomalies in (2+1)D topological phases of matter | We study a class of anomalies associated with time-reversal and spatial
reflection symmetry in (2+1)D topological phases of matter. In these systems,
the topological quantum numbers of the quasiparticles, such as the fusion rules
and braiding statistics, possess a $\mathbb{Z}_2$ symmetry which can be
associated with either time-reversal (denoted $\mathbb{Z}_2^{\bf T})$ or
spatial reflections. Under this symmetry, correlation functions of all Wilson
loop operators in the low energy topological quantum field theory (TQFT) are
invariant. However, the theories that we study possess a severe anomaly
associated with the failure to consistently localize the symmetry action to the
quasiparticles, precluding even defining a notion of symmetry
fractionalization. We present simple sufficient conditions which determine when
$\mathbb{Z}_2^{\bf T}$ symmetry localization anomalies exist. We present an
infinite series of TQFTs with such anomalies, some examples of which include
USp$(4)_2$ and SO$(4)_4$ Chern-Simons (CS) theory. The theories that we find
with these $\mathbb{Z}_2^{\bf T}$ anomalies can be obtained by gauging the
unitary $\mathbb{Z}_2$ subgroup of a different TQFT with a $\mathbb{Z}_4^{\bf
T}$ symmetry. We show that the anomaly can be resolved in several ways: (1) the
true symmetry of the theory is $\mathbb{Z}_4^{\bf T}$, or (2) the theory can be
considered to be a theory of fermions, with ${\bf T}^2 = (-1)^{N_f}$
corresponding to fermion parity. Finally, we demonstrate that theories with the
$\mathbb{Z}_2^{\bf T}$ localization anomaly can be compatible with
$\mathbb{Z}_2^{\bf T}$ if they are "pseudo-realized" at the surface of a (3+1)D
symmetry-enriched topological phase. The "pseudo-realization" refers to the
fact that the bulk (3+1)D system is described by a dynamical $\mathbb{Z}_2$
gauge theory and thus only a subset of the quasiparticles are confined to the
surface.
| 0 | 1 | 0 | 0 | 0 | 0 |
Extending the modeling of the anisotropic galaxy power spectrum to $k = 0.4 \ h\mathrm{Mpc}^{-1}$ | We present a new model for the redshift-space power spectrum of galaxies and
demonstrate its accuracy in modeling the monopole, quadrupole, and hexadecapole
of the galaxy density field down to scales of $k = 0.4 \ h\mathrm{Mpc}^{-1}$.
The model describes the clustering of galaxies in the context of a halo model
and the clustering of the underlying halos in redshift space using a
combination of Eulerian perturbation theory and $N$-body simulations. The
modeling of redshift-space distortions is done using the so-called distribution
function approach. The final model has 13 free parameters, and each parameter
is physically motivated rather than a nuisance parameter, which allows the use
of well-motivated priors. We account for the Finger-of-God effect from centrals
and both isolated and non-isolated satellites rather than using a single
velocity dispersion to describe the combined effect. We test and validate the
accuracy of the model on several sets of high-fidelity $N$-body simulations, as
well as realistic mock catalogs designed to simulate the BOSS DR12 CMASS data
set. The suite of simulations covers a range of cosmologies and galaxy bias
models, providing a rigorous test of the level of theoretical systematics
present in the model. The level of bias in the recovered values of $f \sigma_8$
is found to be small. When including scales to $k = 0.4 \ h\mathrm{Mpc}^{-1}$,
we find 15-30\% gains in the statistical precision of $f \sigma_8$ relative to
$k = 0.2 \ h\mathrm{Mpc}^{-1}$ and a roughly 10-15\% improvement for the
perpendicular Alcock-Paczynski parameter $\alpha_\perp$. Using the BOSS DR12
CMASS mocks as a benchmark for comparison, we estimate an uncertainty on $f
\sigma_8$ that is $\sim$10-20\% larger than other similar Fourier-space RSD
models in the literature that use $k \leq 0.2 \ h\mathrm{Mpc}^{-1}$, suggesting
that these models likely have a too-limited parametrization.
| 0 | 1 | 0 | 0 | 0 | 0 |
Finite rank and pseudofinite groups | It is proven that an infinite finitely generated group cannot be elementarily
equivalent to an ultraproduct of finite groups of a given Prüfer rank.
Furthermore, it is shown that an infinite finitely generated group of finite
Prüfer rank is not pseudofinite.
| 0 | 0 | 1 | 0 | 0 | 0 |
Thermographic measurements of the spin Peltier effect in metal/yttrium-iron-garnet junction systems | The spin Peltier effect (SPE), heat-current generation due to spin-current
injection, in various metal (Pt, W, and Au single layers and Pt/Cu
bilayer)/ferrimagnetic insulator (yttrium iron garnet: YIG) junction systems
has been investigated by means of a lock-in thermography (LIT) method. The SPE
is excited by a spin current across the metal/YIG interface, which is generated
by applying a charge current to the metallic layer via the spin Hall effect.
The LIT method enables the thermal imaging of the SPE free from the
Joule-heating contribution. Importantly, we observed spin-current-induced
temperature modulation not only in the Pt/YIG and W/YIG systems but also in the
Au/YIG and Pt/Cu/YIG systems, excluding the possible contamination by anomalous
Ettingshausen effects due to proximity-induced ferromagnetism near the
metal/YIG interface. As demonstrated in our previous study, the SPE signals are
confined only in the vicinity of the metal/YIG interface; we buttress this
conclusion by reducing a spatial blur due to thermal diffusion in an infrared
emission layer on the sample surface used for the LIT measurements. We also
found that the YIG-thickness dependence of the SPE is similar to that of the
spin Seebeck effect measured in the same Pt/YIG sample, implying the reciprocal
relation between them.
| 0 | 1 | 0 | 0 | 0 | 0 |
Number-theoretic aspects of 1D localization: "popcorn function" with Lifshitz tails and its continuous approximation by the Dedekind $η$ | We discuss the number-theoretic properties of distributions appearing in
physical systems when an observable is a quotient of two independent
exponentially weighted integers. The spectral density of ensemble of linear
polymer chains distributed with the law $\sim f^L$ ($0<f<1$), where $L$ is the
chain length, serves as a particular example. At $f\to 1$, the spectral density
can be expressed through the discontinuous at all rational points, Thomae
("popcorn") function. We suggest a continuous approximation of the popcorn
function, based on the Dedekind $\eta$-function near the real axis. Moreover,
we provide simple arguments, based on the "Euclid orchard" construction, that
demonstrate the presence of Lifshitz tails, typical for the 1D Anderson
localization, at the spectral edges. We emphasize that the ultrametric
structure of the spectral density is ultimately connected with number-theoretic
relations on asymptotic modular functions. We also pay attention to connection
of the Dedekind $\eta$-function near the real axis to invariant measures of
some continued fractions studied by Borwein and Borwein in 1993.
| 0 | 1 | 1 | 0 | 0 | 0 |
Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates | In this paper, we describe a phenomenon, which we named "super-convergence",
where neural networks can be trained an order of magnitude faster than with
standard training methods. The existence of super-convergence is relevant to
understanding why deep networks generalize well. One of the key elements of
super-convergence is training with one learning rate cycle and a large maximum
learning rate. A primary insight that allows super-convergence training is that
large learning rates regularize the training, hence requiring a reduction of
all other forms of regularization in order to preserve an optimal
regularization balance. We also derive a simplification of the Hessian Free
optimization method to compute an estimate of the optimal learning rate.
Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet
datasets, and resnet, wide-resnet, densenet, and inception architectures. In
addition, we show that super-convergence provides a greater boost in
performance relative to standard training when the amount of labeled training
data is limited. The architectures and code to replicate the figures in this
paper are available at github.com/lnsmith54/super-convergence. See
this http URL for an application of
super-convergence to win the DAWNBench challenge (see
this https URL).
| 1 | 0 | 0 | 1 | 0 | 0 |
Space of initial conditions for a cubic Hamiltonian system | In this paper we perform the analysis that leads to the space of initial
conditions for the Hamiltonian system $q' = p^2 + zq + \alpha$, $p' = -q^2 - zp
- \beta$, studied by the author in an earlier article. By compactifying the
phase space of the system from $\mathbb{C}^2$ to $\mathbb{CP}^2$ three base
points arise in the standard coordinate charts covering the complex projective
space. Each of these is removed by a sequence of three blow-ups, a construction
to regularise the system at these points. The resulting space, where the
exceptional curves introduced after the first and second blow-up are removed,
is the so-called Okamoto's space of initial conditions for this system which,
at every point, defines a regular initial value problem in some coordinate
chart of the space.
| 0 | 1 | 1 | 0 | 0 | 0 |
Electronic properties of WS$_2$ on epitaxial graphene on SiC(0001) | This work reports an electronic and micro-structural study of an appealing
system for optoelectronics: tungsten disulphide WS$_2$ on epitaxial graphene
(EG) on SiC(0001). The WS$_2$ is grown via chemical vapor deposition (CVD) onto
the EG. Low-energy electron diffraction (LEED) measurements assign the
zero-degree orientation as the preferential azimuthal alignment for WS$_2$/EG.
The valence-band (VB) structure emerging from this alignment is investigated by
means of photoelectron spectroscopy measurements, with both high space and
energy resolution. We find that the spin-orbit splitting of monolayer WS$_2$ on
graphene is of 462 meV, larger than what is reported to date for other
substrates. We determine the value of the work function for the WS$_2$/EG to be
4.5$\pm$0.1 eV. A large shift of the WS$_2$ VB maximum is observed as well ,
due to the lowering of the WS$_2$ work function caused by the donor-like
interfacial states of EG. Density functional theory (DFT) calculations carried
out on a coincidence supercell confirm the experimental band structure to an
excellent degree. X-ray photoemission electron microscopy (XPEEM) measurements
performed on single WS$_2$ crystals confirm the van der Waals nature of the
interface coupling between the two layers. In virtue of its band alignment and
large spin-orbit splitting, this system gains strong appeal for optical
spin-injection experiments and opto-spintronic applications in general.
| 0 | 1 | 0 | 0 | 0 | 0 |
Electrical transport and optical band gap of NiFe$_\textrm{2}$O$_\textrm{x}$ thin films | We fabricated NiFe$_\textrm{2}$O$_\textrm{x}$ thin films on
MgAl$_2$O$_4$(001) substrates by reactive dc magnetron co-sputtering varying
the oxygen partial pressure during deposition. The fabrication of a variable
material with oxygen deficiency leads to controllable electrical and optical
properties which would be beneficial for the investigations of the transport
phenomena and would, therefore, promote the use of such materials in spintronic
and spin caloritronic applications. We used several characterization techniques
in order to investigate the film properties, focusing on their structural,
magnetic, electrical, and optical properties. From the electrical resistivity
measurements we obtained the conduction mechanisms that govern the systems in
high and low temperature regimes, extracting low thermal activation energies
which unveil extrinsic transport mechanisms. The thermal activation energy
decreases in the less oxidized samples revealing the pronounced contribution of
a large amount of electronic states localized in the band gap to the electrical
conductivity. Hall effect measurements showed the mixed-type semiconducting
character of our films. The optical band gaps were determined via
ultraviolet-visible spectroscopy. They follow a similar trend as the thermal
activation energy, with lower band gap values in the less oxidized samples.
| 0 | 1 | 0 | 0 | 0 | 0 |
Towards parallelizable sampling-based Nonlinear Model Predictive Control | This paper proposes a new sampling-based nonlinear model predictive control
(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
N and linear in the number of samples. The idea of the proposed algorithm is to
use the sequence of predicted inputs from the previous time step as a warm
start, and to iteratively update this sequence by changing its elements one by
one, starting from the last predicted input and ending with the first predicted
input. This strategy, which resembles the dynamic programming principle, allows
for parallelization up to a certain level and yields a suboptimal nonlinear MPC
algorithm with guaranteed recursive feasibility, stability and improved cost
function at every iteration, which is suitable for real-time implementation.
The complexity of the algorithm per each time step in the prediction horizon
depends only on the horizon, the number of samples and parallel threads, and it
is independent of the measured system state. Comparisons with the fmincon
nonlinear optimization solver on benchmark examples indicate that as the
simulation time progresses, the proposed algorithm converges rapidly to the
"optimal" solution, even when using a small number of samples.
| 1 | 0 | 0 | 0 | 0 | 0 |
Quantum decoherence from entanglement during inflation | We study the primary entanglement effect on the decoherence of fields reduced
density matrix which are in interaction with another fields or independent mode
functions. We show that the primary entanglement has a significant role in
decoherence of the system quantum state. We find that the existence of
entanglement could couple dynamical equations coming from Schrödinger
equation. We show if one wants to see no effect of the entanglement parameter
in decoherence then interaction terms in Hamiltonian can not be independent
from each other. Generally, including the primary entanglement destroys the
independence of the interaction terms. Our results could be generalized to
every scalar quantum field theory with a well defined quantization in a given
curved space time.
| 0 | 1 | 0 | 0 | 0 | 0 |
Breast density classification with deep convolutional neural networks | Breast density classification is an essential part of breast cancer
screening. Although a lot of prior work considered this problem as a task for
learning algorithms, to our knowledge, all of them used small and not
clinically realistic data both for training and evaluation of their models. In
this work, we explore the limits of this task with a data set coming from over
200,000 breast cancer screening exams. We use this data to train and evaluate a
strong convolutional neural network classifier. In a reader study, we find that
our model can perform this task comparably to a human expert.
| 1 | 0 | 0 | 1 | 0 | 0 |
Spatial Integration by a Dielectric Slab Waveguide and its Planar Graphene-based Counterpart | Motivated by the recent progress in analog computing [Science 343, 160
(2014)], a new approach to perform spatial integration is presented using a
dielectric slab waveguide. Our approach is indeed based on the fact that the
transmission coefficient of a simple dielectric slab waveguide at its mode
excitation angle matches the Green's function of first order integration.
Inspired by the mentioned dielectric-based integrator, we further demonstrate
its graphene-based counterpart. The latter is not only reconfigurable but also
highly miniaturized in contrast to the previously reported designs [Opt.
Commun. 338, 457 (2015)]. Such integrators have the potential to be used in
ultrafast analog computation and signal processing.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders | A crucial challenge in image-based modeling of biomedical data is to identify
trends and features that separate normality and pathology. In many cases, the
morphology of the imaged object exhibits continuous change as it deviates from
normality, and thus a generative model can be trained to model this
morphological continuum. Moreover, given side information that correlates to
certain trend in morphological change, a latent variable model can be
regularized such that its latent representation reflects this side information.
In this work, we use the Wasserstein Auto-encoder to model this pathology
continuum, and apply the Hilbert-Schmitt Independence Criterion (HSIC) to
enforce dependency between certain latent features and the provided side
information. We experimentally show that the model can provide disentangled and
interpretable latent representations and also generate a continuum of
morphological changes that corresponds to change in the side information.
| 1 | 0 | 0 | 1 | 0 | 0 |
Control Problems with Vanishing Lie Bracket Arising from Complete Odd Circulant Evolutionary Games | We study an optimal control problem arising from a generalization of
rock-paper-scissors in which the number of strategies may be selected from any
positive odd number greater than 1 and in which the payoff to the winner is
controlled by a control variable $\gamma$. Using the replicator dynamics as the
equations of motion, we show that a quasi-linearization of the problem admits a
special optimal control form in which explicit dynamics for the controller can
be identified. We show that all optimal controls must satisfy a specific second
order differential equation parameterized by the number of strategies in the
game. We show that as the number of strategies increases, a limiting case
admits a closed form for the open-loop optimal control. In performing our
analysis we show necessary conditions on an optimal control problem that allow
this analytic approach to function.
| 1 | 0 | 0 | 0 | 0 | 0 |
Teaching DevOps in Corporate Environments: An experience report | This paper describes our experience of training a team of developers of an
East-European phone service provider. The training experience was structured in
two sessions of two days each conducted in different weeks with a gap of about
fifteen days. The first session was dedicated to the Continuous Integration
Delivery Pipeline, and the second on Agile methods. We summarize the activity,
its preparation and delivery and draw some conclusions out of it on our
mistakes and how future session should be addressed.
| 1 | 0 | 0 | 0 | 0 | 0 |
WKB solutions of difference equations and reconstruction by the topological recursion | The purpose of this article is to analyze the connection between
Eynard-Orantin topological recursion and formal WKB solutions of a
$\hbar$-difference equation: $\Psi(x+\hbar)=\left(e^{\hbar\frac{d}{dx}}\right)
\Psi(x)=L(x;\hbar)\Psi(x)$ with $L(x;\hbar)\in GL_2( (\mathbb{C}(x))[\hbar])$.
In particular, we extend the notion of determinantal formulas and topological
type property proposed for formal WKB solutions of $\hbar$-differential systems
to this setting. We apply our results to a specific $\hbar$-difference system
associated to the quantum curve of the Gromov-Witten invariants of
$\mathbb{P}^1$ for which we are able to prove that the correlation functions
are reconstructed from the Eynard-Orantin differentials computed from the
topological recursion applied to the spectral curve $y=\cosh^{-1}\frac{x}{2}$.
Finally, identifying the large $x$ expansion of the correlation functions,
proves a recent conjecture made by B. Dubrovin and D. Yang regarding a new
generating series for Gromov-Witten invariants of $\mathbb{P}^1$.
| 0 | 1 | 1 | 0 | 0 | 0 |
Diagrammatic Exciton Basis Theory of the Photophysics of Pentacene Dimers | Covalently linked acene dimers are of interest as candidates for
intramolecular singlet fission. We report many-electron calculations of the
energies and wavefunctions of the optical singlets, the lowest triplet exciton
and the triplet-triplet biexciton, as well as the final states of excited state
absorptions from these states in a family of phenyl-linked pentacene dimers.
While it is difficult to distinguish between the triplet and the
triplet-triplet from their transient absorptions in the 500-600 nm region, by
comparing theoretical transient absorption spectra against published and
unpublished experimental transient absorptions in the near and mid infrared we
conclude that the end product of photoexcitation in these particular
bipentacenes is the bound triplet-triplet and not free triplets. We predict
additional transient absorptions at even longer wavelengths, beyond 1500 nm, to
the equivalent of the classic 2$^1$A$_g^-$ in linear polyenes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Developing and evaluating an interactive tutorial on degenerate perturbation theory | We discuss an investigation of student difficulties with degenerate
perturbation theory (DPT) carried out in advanced quantum mechanics courses by
administering free-response and multiple-choice questions and conducting
individual interviews with students. We find that students share many common
difficulties related to this topic. We used the difficulties found via research
as resources to develop and evaluate a Quantum Interactive Learning Tutorial
(QuILT) which strives to help students develop a functional understanding of
DPT. We discuss the development of the DPT QuILT and its preliminary evaluation
in the undergraduate and graduate courses.
| 0 | 1 | 0 | 0 | 0 | 0 |
Time optimal sampled-data controls for heat equations | In this paper, we first design a time optimal control problem for the heat
equation with sampled-data controls, and then use it to approximate a time
optimal control problem for the heat equation with distributed controls. Our
design is reasonable from perspective of sampled-data controls. And it might
provide a right way for the numerical approach of a time optimal distributed
control problem, via the corresponding semi-discretized (in time variable) time
optimal control problem.
The study of such a time optimal sampled-data control problem is not easy,
because it may have infinitely many optimal controls. We find connections among
this problem, a minimal norm sampled-data control problem and a minimization
problem. And obtain some properties on these problems. Based on these, we not
only build up error estimates for optimal time and optimal controls between the
time optimal sampled-data control problem and the time optimal distributed
control problem, in terms of the sampling period, but also prove that such
estimates are optimal in some sense.
| 0 | 0 | 1 | 0 | 0 | 0 |
Malware distributions and graph structure of the Web | Knowledge about the graph structure of the Web is important for understanding
this complex socio-technical system and for devising proper policies supporting
its future development. Knowledge about the differences between clean and
malicious parts of the Web is important for understanding potential treats to
its users and for devising protection mechanisms. In this study, we conduct
data science methods on a large crawl of surface and deep Web pages with the
aim to increase such knowledge. To accomplish this, we answer the following
questions. Which theoretical distributions explain important local
characteristics and network properties of websites? How are these
characteristics and properties different between clean and malicious
(malware-affected) websites? What is the prediction power of local
characteristics and network properties to classify malware websites? To the
best of our knowledge, this is the first large-scale study describing the
differences in global properties between malicious and clean parts of the Web.
In other words, our work is building on and bridging the gap between
\textit{Web science} that tackles large-scale graph representations and
\textit{Web cyber security} that is concerned with malicious activities on the
Web. The results presented herein can also help antivirus vendors in devising
approaches to improve their detection algorithms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Depth creates no more spurious local minima | We show that for any convex differentiable loss function, a deep linear
network has no spurious local minima as long as it is true for the two layer
case. When applied to the quadratic loss, our result immediately implies the
powerful result in [Kawaguchi 2016] that there is no spurious local minima in
deep linear networks. Further, with the recent work [Zhou and Liang 2018], we
can remove all the assumptions in [Kawaguchi 2016]. Our proof is short and
elementary. It builds on the recent work of [Laurent and von Brecht 2018] and
uses a new rank one perturbation argument.
| 1 | 0 | 0 | 1 | 0 | 0 |
On the Application of ISO 26262 in Control Design for Automated Vehicles | Research on automated vehicles has experienced an explosive growth over the
past decade. A main obstacle to their practical realization, however, is a
convincing safety concept. This question becomes ever more important as more
sophisticated algorithms are used and the vehicle automation level increases.
The field of functional safety offers a systematic approach to identify
possible sources of risk and to improve the safety of a vehicle. It is based on
practical experience across the aerospace, process and other industries over
multiple decades. This experience is compiled in the functional safety standard
for the automotive domain, ISO 26262, which is widely adopted throughout the
automotive industry. However, its applicability and relevance for highly
automated vehicles is subject to a controversial debate. This paper takes a
critical look at the discussion and summarizes the main steps of ISO 26262 for
a safe control design for automated vehicles.
| 1 | 0 | 0 | 0 | 0 | 0 |
Semi-wavefront solutions in models of collective movements with density-dependent diffusivity | This paper deals with a nonhomogeneous scalar parabolic equation with
possibly degenerate diffusion term; the process has only one stationary state.
The equation can be interpreted as modeling collective movements (crowd
dynamics, for instance). We first prove the existence of semi-wavefront
solutions for every wave speed; their properties are investigated. Then, a
family of travelling wave solutions is constructed by a suitable combination of
the previous semi-wavefront solutions. Proofs exploit comparison-type
techniques and are carried out in the case of one spatial variable; the
extension to the general case is straightforward.
| 0 | 0 | 1 | 0 | 0 | 0 |
Self-Regulated Transport in Photonic Crystals with Phase-Changing Defects | Phase changing materials (PCM) are widely used for optical data recording,
sensing, all-optical switching, and optical limiting. Our focus here is on the
case when the change in the transmission characteristics of the optical
material is caused by the input light itself. Specifically, the light-induced
heating triggers the phase transition in the PCM. In this paper, using a
numerical example, we demonstrate that incorporating the PCM in a photonic
structure can lead to a dramatic modification of the effects of light-induced
phase transition, as compared to a stand-alone sample of the same PCM. Our
focus is on short pulses. We discuss some possible applications of such
phase-changing photonic structures for optical sensing and limiting.
| 0 | 1 | 0 | 0 | 0 | 0 |
Assessing randomness in case assignment: the case study of the Brazilian Supreme Court | Sortition, i.e., random appointment for public duty, has been employed by
societies throughout the years, especially for duties related to the judicial
system, as a firewall designated to prevent illegitimate interference between
parties in a legal case and agents of the legal system. In judicial systems of
modern western countries, random procedures are mainly employed to select the
jury, the court and/or the judge in charge of judging a legal case, so that
they have a significant role in the course of a case. Therefore, these random
procedures must comply with some principles, as statistical soundness; complete
auditability; open-source programming; and procedural, cryptographical and
computational security. Nevertheless, some of these principles are neglected by
some random procedures in judicial systems, that are, in some cases, performed
in secrecy and are not auditable by the involved parts. The assignment of cases
in the Brazilian Supreme Court (Supremo Tribunal Federal) is an example of such
procedures, for it is performed by a closed-source algorithm, unknown to the
public and to the parts involved in the judicial cases, that allegedly assign
the cases randomly to the justice chairs based on their caseload.
In this context, this article presents a review of how sortition has been
employed historically by societies, and discusses how Mathematical Statistics
may be applied to random procedures of the judicial system, as it has been
applied for almost a century on clinical trials, for example. Based on this
discussion, a statistical model for assessing randomness in case assignment is
proposed and applied to the Brazilian Supreme Court in order to shed light on
how this assignment process is performed by the closed-source algorithm.
Guidelines for random procedures are outlined and topics for further researches
presented.
| 0 | 0 | 0 | 1 | 0 | 0 |
Particle picture representation of the non-symmetric Rosenblatt process and Hermite processes of any order | We provide a particle picture representation for the non-symmetric Rosenblatt
process and for Hermite processes of any order, extending the result of
Bojdecki, Gorostiza and Talarczyk in~\cite{FILT}. We show that these processes
can be obtained as limits in the sense of finite-dimensional distributions of
certain functionals of a system of particles evolving according to symmetric
stable Lévy motions. In the case of $k$-Hermite processes the corresponding
functional involves $k$-intersection local time of symmetric stable Lévy
processes
| 0 | 0 | 1 | 0 | 0 | 0 |
Houdini: Fooling Deep Structured Prediction Models | Generating adversarial examples is a critical step for evaluating and
improving the robustness of learning machines. So far, most existing methods
only work for classification and are not designed to alter the true performance
measure of the problem at hand. We introduce a novel flexible approach named
Houdini for generating adversarial examples specifically tailored for the final
performance measure of the task considered, be it combinatorial and
non-decomposable. We successfully apply Houdini to a range of applications such
as speech recognition, pose estimation and semantic segmentation. In all cases,
the attacks based on Houdini achieve higher success rate than those based on
the traditional surrogates used to train the models while using a less
perceptible adversarial perturbation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Don't relax: early stopping for convex regularization | We consider the problem of designing efficient regularization algorithms when
regularization is encoded by a (strongly) convex functional. Unlike classical
penalization methods based on a relaxation approach, we propose an iterative
method where regularization is achieved via early stopping. Our results show
that the proposed procedure achieves the same recovery accuracy as penalization
methods, while naturally integrating computational considerations. An empirical
analysis on a number of problems provides promising results with respect to the
state of the art.
| 1 | 0 | 1 | 0 | 0 | 0 |
Interplay between social influence and competitive strategical games in multiplex networks | We present a model that takes into account the coupling between evolutionary
game dynamics and social influence. Importantly, social influence and game
dynamics take place in different domains, which we model as different layers of
a multiplex network. We show that the coupling between these dynamical
processes can lead to cooperation in scenarios where the pure game dynamics
predicts defection. In addition, we show that the structure of the network
layers and the relation between them can further increase cooperation.
Remarkably, if the layers are related in a certain way, the system can reach a
polarized metastable state.These findings could explain the prevalence of
polarization observed in many social dilemmas.
| 1 | 1 | 0 | 0 | 0 | 0 |
Data Augmentation for Low-Resource Neural Machine Translation | The quality of a Neural Machine Translation system depends substantially on
the availability of sizable parallel corpora. For low-resource language pairs
this is not the case, resulting in poor translation quality. Inspired by work
in computer vision, we propose a novel data augmentation approach that targets
low-frequency words by generating new sentence pairs containing rare words in
new, synthetically created contexts. Experimental results on simulated
low-resource settings show that our method improves translation quality by up
to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Lagrangian approach to modeling heat flux driven close-contact melting | Close-contact melting refers to the process of a heat source melting its way
into a phase-change material. Of special interest is the close-contact melting
velocity, or more specifically the relative velocity between the heat source
and the phase-change material. In this work, we present a novel numerical
approach to simulate quasi-steady, heat flux driven close-contact melting. It
extends existing approaches found in the literature, and, for the first time,
allows to study the impact of a spatially varying heat flux distribution. We
will start by deriving the governing equations in a Lagrangian reference frame
fixed to the heat source. Exploiting the narrowness of the melt film enables us
to reduce the momentum balance to the Reynolds equation, which is coupled to
the energy balance via the velocity field. We particularize our derivation for
two simple, yet technically relevant geometries, namely a 3d circular disc and
a 2d planar heat source. An iterative solution procedure for the coupled system
is described in detail and discussed on the basis of a convergence study.
Furthermore, we present an extension to allow for rotational melting modes.
Various test cases demonstrate the proficiency of our method. In particular, we
will utilize the method to assess the efficiency of the close-contact melting
process and to quantify the model error introduced if convective losses are
neglected. Finally, we will draw conclusions and present an outlook to future
work.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spectral Graph Analysis: A Unified Explanation and Modern Perspectives | Complex networks or graphs are ubiquitous in sciences and engineering:
biological networks, brain networks, transportation networks, social networks,
and the World Wide Web, to name a few. Spectral graph theory provides a set of
useful techniques and models for understanding `patterns of interconnectedness'
in a graph. Our prime focus in this paper is on the following question: Is
there a unified explanation and description of the fundamental spectral graph
methods? There are at least two reasons to be interested in this question.
Firstly, to gain a much deeper and refined understanding of the basic
foundational principles, and secondly, to derive rich consequences with
practical significance for algorithm design. However, despite half a century of
research, this question remains one of the most formidable open issues, if not
the core problem in modern network science. The achievement of this paper is to
take a step towards answering this question by discovering a simple, yet
universal statistical logic of spectral graph analysis. The prescribed
viewpoint appears to be good enough to accommodate almost all existing spectral
graph techniques as a consequence of just one single formalism and algorithm.
| 0 | 0 | 1 | 1 | 0 | 0 |
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting | Accurate demand forecasts can help on-line retail organizations better plan
their supply-chain processes. The challenge, however, is the large number of
associative factors that result in large, non-stationary shifts in demand,
which traditional time series and regression approaches fail to model. In this
paper, we propose a Neural Network architecture called AR-MDN, that
simultaneously models associative factors, time-series trends and the variance
in the demand. We first identify several causal features and use a combination
of feature embeddings, MLP and LSTM to represent them. We then model the output
density as a learned mixture of Gaussian distributions. The AR-MDN can be
trained end-to-end without the need for additional supervision. We experiment
on a dataset of an year's worth of data over tens-of-thousands of products from
Flipkart. The proposed architecture yields a significant improvement in
forecasting accuracy when compared with existing alternatives.
| 0 | 0 | 0 | 1 | 0 | 0 |
Extreme Dimension Reduction for Handling Covariate Shift | In the covariate shift learning scenario, the training and test covariate
distributions differ, so that a predictor's average loss over the training and
test distributions also differ. In this work, we explore the potential of
extreme dimension reduction, i.e. to very low dimensions, in improving the
performance of importance weighting methods for handling covariate shift, which
fail in high dimensions due to potentially high train/test covariate divergence
and the inability to accurately estimate the requisite density ratios. We first
formulate and solve a problem optimizing over linear subspaces a combination of
their predictive utility and train/test divergence within. Applying it to
simulated and real data, we show extreme dimension reduction helps sometimes
but not always, due to a bias introduced by dimension reduction.
| 1 | 0 | 0 | 1 | 0 | 0 |
Landau-Zener transitions for Majorana fermions | One-dimensional systems obtained as low-energy limits of hybrid
superconductor-topological insulator devices provide means of production,
transport, and destruction of Majorana bound states (MBSs) by variations of the
magnetic flux. When two or more pairs of MBSs are present in the intermediate
state, there is a possibility of a Landau-Zener transition, wherein even a slow
variation of the flux leads to production of a quasiparticle pair. We study
numerically a version of this process, with four MBSs produced and subsequently
destroyed, and find that, quite universally, the probability of quasiparticle
production in it is 50%. This implies that the effect may be a limiting factor
in applications requiring a high degree of quantum coherence.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Hyperspherical Learning | Convolution as inner product has been the founding basis of convolutional
neural networks (CNNs) and the key to end-to-end visual representation
learning. Benefiting from deeper architectures, recent CNNs have demonstrated
increasingly strong representation abilities. Despite such improvement, the
increased depth and larger parameter space have also led to challenges in
properly training a network. In light of such challenges, we propose
hyperspherical convolution (SphereConv), a novel learning framework that gives
angular representations on hyperspheres. We introduce SphereNet, deep
hyperspherical convolution networks that are distinct from conventional inner
product based convolutional networks. In particular, SphereNet adopts
SphereConv as its basic convolution operator and is supervised by generalized
angular softmax loss - a natural loss formulation under SphereConv. We show
that SphereNet can effectively encode discriminative representation and
alleviate training difficulty, leading to easier optimization, faster
convergence and comparable (even better) classification accuracy over
convolutional counterparts. We also provide some theoretical insights for the
advantages of learning on hyperspheres. In addition, we introduce the learnable
SphereConv, i.e., a natural improvement over prefixed SphereConv, and
SphereNorm, i.e., hyperspherical learning as a normalization method.
Experiments have verified our conclusions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching | This paper presents a robotic pick-and-place system that is capable of
grasping and recognizing both known and novel objects in cluttered
environments. The key new feature of the system is that it handles a wide range
of object categories without needing any task-specific training data for novel
objects. To achieve this, it first uses a category-agnostic affordance
prediction algorithm to select and execute among four different grasping
primitive behaviors. It then recognizes picked objects with a cross-domain
image classification framework that matches observed images to product images.
Since product images are readily available for a wide range of objects (e.g.,
from the web), the system works out-of-the-box for novel objects without
requiring any additional training data. Exhaustive experimental results
demonstrate that our multi-affordance grasping achieves high success rates for
a wide variety of objects in clutter, and our recognition algorithm achieves
high accuracy for both known and novel grasped objects. The approach was part
of the MIT-Princeton Team system that took 1st place in the stowing task at the
2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
available online at this http URL
| 1 | 0 | 0 | 0 | 0 | 0 |
Toward an enumerative geometry with quadratic forms | We develop various aspects of classical enumerative geometry, including Euler
characteristics and formulas for counting degenerate fibres in a pencil, with
the classical numerical formulas being replaced by identitites in the
Grothendieck-Witt group of quadratic forms with coefficients in the base-field.
| 0 | 0 | 1 | 0 | 0 | 0 |
Robust reputation-based ranking on multipartite rating networks | The spread of online reviews, ratings and opinions and its growing influence
on people's behavior and decisions boosted the interest to extract meaningful
information from this data deluge. Hence, crowdsourced ratings of products and
services gained a critical role in business, governments, and others. We
propose a new reputation-based ranking system utilizing multipartite rating
subnetworks, that clusters users by their similarities, using Kolmogorov
complexity. Our system is novel in that it reflects a diversity of
opinions/preferences by assigning possibly distinct rankings, for the same
item, for different groups of users. We prove the convergence and efficiency of
the system and show that it copes better with spamming/spurious users, and it
is more robust to attacks than state-of-the-art approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
Duality and upper bounds in optimal stochastic control governed by partial differential equations | A dual control problem is presented for the optimal stochastic control of a
system governed by partial differential equations. Relationships between the
optimal values of the original and the dual problems are investigated and two
duality theorems are proved. The dual problem serves to provide upper bounds
for the optimal and maximum value of the original one or even to give the
optimal value.
| 0 | 0 | 1 | 0 | 0 | 0 |
On Sampling from Massive Graph Streams | We propose Graph Priority Sampling (GPS), a new paradigm for order-based
reservoir sampling from massive streams of graph edges. GPS provides a general
way to weight edge sampling according to auxiliary and/or size variables so as
to accomplish various estimation goals of graph properties. In the context of
subgraph counting, we show how edge sampling weights can be chosen so as to
minimize the estimation variance of counts of specified sets of subgraphs. In
distinction with many prior graph sampling schemes, GPS separates the functions
of edge sampling and subgraph estimation. We propose two estimation frameworks:
(1) Post-Stream estimation, to allow GPS to construct a reference sample of
edges to support retrospective graph queries, and (2) In-Stream estimation, to
allow GPS to obtain lower variance estimates by incrementally updating the
subgraph count estimates during stream processing. Unbiasedness of subgraph
estimators is established through a new Martingale formulation of graph stream
order sampling, which shows that subgraph estimators, written as a product of
constituent edge estimators are unbiased, even when computed at different
points in the stream. The separation of estimation and sampling enables
significant resource savings relative to previous work. We illustrate our
framework with applications to triangle and wedge counting. We perform a
large-scale experimental study on real-world graphs from various domains and
types. GPS achieves high accuracy with less than 1% error for triangle and
wedge counting, while storing a small fraction of the graph with average update
times of a few microseconds per edge. Notably, for a large Twitter graph with
more than 260M edges, GPS accurately estimates triangle counts with less than
1% error, while storing only 40K edges.
| 1 | 0 | 1 | 1 | 0 | 0 |
Optimal Investment, Demand and Arbitrage under Price Impact | This paper studies the optimal investment problem with random endowment in an
inventory-based price impact model with competitive market makers. Our goal is
to analyze how price impact affects optimal policies, as well as both pricing
rules and demand schedules for contingent claims. For exponential market makers
preferences, we establish two effects due to price impact: constrained trading,
and non-linear hedging costs. To the former, wealth processes in the impact
model are identified with those in a model without impact, but with constrained
trading, where the (random) constraint set is generically neither closed nor
convex. Regarding hedging, non-linear hedging costs motivate the study of
arbitrage free prices for the claim. We provide three such notions, which
coincide in the frictionless case, but which dramatically differ in the
presence of price impact. Additionally, we show arbitrage opportunities, should
they arise from claim prices, can be exploited only for limited position sizes,
and may be ignored if outweighed by hedging considerations. We also show that
arbitrage inducing prices may arise endogenously in equilibrium, and that
equilibrium positions are inversely proportional to the market makers'
representative risk aversion. Therefore, large positions endogenously arise in
the limit of either market maker risk neutrality, or a large number of market
makers.
| 0 | 0 | 0 | 0 | 0 | 1 |
Amplitude Mediated Chimera States with Active and Inactive Oscillators | The emergence and nature of amplitude mediated chimera states,
spatio-temporal patterns of co-existing coherent and incoherent regions, are
investigated for a globally coupled system of active and inactive
Ginzburg-Landau oscillators. The existence domain of such states is found to
shrink and shift in parametric space as the fraction of inactive oscillators is
increased. The role of inactive oscillators is found to be two fold - they get
activated to form a separate region of coherent oscillations and in addition
decrease the common collective frequency of the coherent regions by their
presence. The dynamical origin of these effects is delineated through a
detailed bifurcation analysis of a reduced model equation that is based on a
mean field approximation. Our results may have practical implications for the
robustness of such states in biological or physical systems where age related
deterioration in the functionality of components can occur.
| 0 | 1 | 0 | 0 | 0 | 0 |
Astronomical random numbers for quantum foundations experiments | Photons from distant astronomical sources can be used as a classical source
of randomness to improve fundamental tests of quantum nonlocality,
wave-particle duality, and local realism through Bell's inequality and
delayed-choice quantum eraser tests inspired by Wheeler's cosmic-scale
Mach-Zehnder interferometer gedankenexperiment. Such sources of random numbers
may also be useful for information-theoretic applications such as key
distribution for quantum cryptography. Building on the design of an
"astronomical random-number generator" developed for the recent "cosmic Bell"
experiment [Handsteiner et al., Phys. Rev. Lett. 118, 060401 (2017)], in this
paper we report on the design and characterization of a device that, with
20-nanosecond latency, outputs a bit based on whether the wavelength of an
incoming photon is greater than or less than 700 nm. Using the one-meter
telescope at the Jet Propulsion Laboratory (JPL) Table Mountain Observatory, we
generated random bits from astronomical photons in both color channels from 50
stars of varying color and magnitude, and from 12 quasars with redshifts up to
$z = 3.9$. With stars, we achieved bit rates of $\sim 1 \times 10^6$ Hz /
m$^2$, limited by saturation for our single-photon detectors, and with quasars
of magnitudes between 12.9 and 16, we achieved rates between $\sim 10^2$ and $2
\times 10^3$ Hz /m$^2$. For bright quasars, the resulting bitstreams exhibit
sufficiently low amounts of statistical predictability as quantified by the
mutual information. In addition, a sufficiently high fraction of bits generated
are of true astronomical origin in order to address both the locality and
freedom-of-choice loopholes when used to set the measurement settings in a test
of the Bell-CHSH inequality.
| 0 | 1 | 0 | 0 | 0 | 0 |
Subsets and Splits