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Title: The Trimmed Lasso: Sparsity and Robustness,
Abstract: Nonconvex penalty methods for sparse modeling in linear regression have been
a topic of fervent interest in recent years. Herein, we study a family of
nonconvex penalty functions that we call the trimmed Lasso and that offers
exact control over the desired level of sparsity of estimators. We analyze its
structural properties and in doing so show the following:
1) Drawing parallels between robust statistics and robust optimization, we
show that the trimmed-Lasso-regularized least squares problem can be viewed as
a generalized form of total least squares under a specific model of
uncertainty. In contrast, this same model of uncertainty, viewed instead
through a robust optimization lens, leads to the convex SLOPE (or OWL) penalty.
2) Further, in relating the trimmed Lasso to commonly used sparsity-inducing
penalty functions, we provide a succinct characterization of the connection
between trimmed-Lasso- like approaches and penalty functions that are
coordinate-wise separable, showing that the trimmed penalties subsume existing
coordinate-wise separable penalties, with strict containment in general.
3) Finally, we describe a variety of exact and heuristic algorithms, both
existing and new, for trimmed Lasso regularized estimation problems. We include
a comparison between the different approaches and an accompanying
implementation of the algorithms. | [
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Title: Mobile phone identification through the built-in magnetometers,
Abstract: Mobile phones identification through their built in components has been
demonstrated in literature for various types of sensors including the camera,
microphones and accelerometers. The identification is performed by the
exploitation of the small but significant differences in the electronic
circuits generated during the production process. Thus, these differences
become an intrinsic property of the electronic components, which can be
detected and become an unique fingerprint of the component and of the mobile
phone. In this paper, we investigate the identification of mobile phones
through their builtin magnetometers, which has not been reported in literature
yet. Magnetometers are stimulated with different waveforms using a solenoid
connected to a computer s audio board. The identification is performed
analyzing the digital output of the magnetometer through the use of statistical
features and the Support Vector Machine (SVM) machine learning algorithm. We
prove that this technique can distinguish different models and brands with very
high accuracy but it can only distinguish phones of the same model with limited
accuracy. | [
1,
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] |
Title: A Machine Learning Framework for Stock Selection,
Abstract: This paper demonstrates how to apply machine learning algorithms to
distinguish good stocks from the bad stocks. To this end, we construct 244
technical and fundamental features to characterize each stock, and label stocks
according to their ranking with respect to the return-to-volatility ratio.
Algorithms ranging from traditional statistical learning methods to recently
popular deep learning method, e.g. Logistic Regression (LR), Random Forest
(RF), Deep Neural Network (DNN), and the Stacking, are trained to solve the
classification task. Genetic Algorithm (GA) is also used to implement feature
selection. The effectiveness of the stock selection strategy is validated in
Chinese stock market in both statistical and practical aspects, showing that:
1) Stacking outperforms other models reaching an AUC score of 0.972; 2) Genetic
Algorithm picks a subset of 114 features and the prediction performances of all
models remain almost unchanged after the selection procedure, which suggests
some features are indeed redundant; 3) LR and DNN are radical models; RF is
risk-neutral model; Stacking is somewhere between DNN and RF. 4) The portfolios
constructed by our models outperform market average in back tests. | [
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] |
Title: Zampa's systems theory: a comprehensive theory of measurement in dynamic systems,
Abstract: The article outlines in memoriam Prof. Pavel Zampa's concepts of system
theory which enable to devise a measurement in dynamic systems independently of
the particular system behaviour. From the point of view of Zampa's theory,
terms like system time, system attributes, system link, system element, input,
output, subsystems, and state variables are defined. In Conclusions, Zampa's
theory is discussed together with another mathematical approaches of
qualitative dynamics known since the 19th century. In Appendices, we present
applications of Zampa's technical approach to measurement of complex dynamical
(chemical and biological) systems at the Institute of Complex Systems,
University of South Bohemia in Ceske Budejovice. | [
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] |
Title: Learning Data Manifolds with a Cutting Plane Method,
Abstract: We consider the problem of classifying data manifolds where each manifold
represents invariances that are parameterized by continuous degrees of freedom.
Conventional data augmentation methods rely upon sampling large numbers of
training examples from these manifolds; instead, we propose an iterative
algorithm called M_{CP} based upon a cutting-plane approach that efficiently
solves a quadratic semi-infinite programming problem to find the maximum margin
solution. We provide a proof of convergence as well as a polynomial bound on
the number of iterations required for a desired tolerance in the objective
function. The efficiency and performance of M_{CP} are demonstrated in
high-dimensional simulations and on image manifolds generated from the ImageNet
dataset. Our results indicate that M_{CP} is able to rapidly learn good
classifiers and shows superior generalization performance compared with
conventional maximum margin methods using data augmentation methods. | [
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] |
Title: Theory of magnetism in La$_2$NiMnO$_6$,
Abstract: The magnetism of ordered and disordered La$_2$NiMnO$_6$ is explained using a
model involving double exchange and superexchange. The concept of majority spin
hybridization in the large coupling limit is used to explain the ferromagnetism
of La$_2$NiMnO$_6$ as compared to the ferrimagnetism of Sr$_{2}$FeMoO$_{6}$.
The ferromagnetic insulating ground state in the ordered phase is explained.
The essential role played by the Ni-Mn superexchange between the Ni $e_{g}$
electron spins and the Mn $t_{2g}$ core electron spins in realizing this ground
state, is outlined. In presence of antisite disorder, the model system is found
to exhibit a tendency of becoming a spin-glass at low temperatures, while it
continues to retain a ferromagnetic transition at higher temperatures, similar
to recent experimental observations [D. Choudhury .et.al., Phys. Rev. Lett.
108, 127201 (2012)]. This reentrant spin-glass or reentrant ferromagnetic
behaviour is explained in terms of the competition of the ferromagnetic double
exchange between the Ni $e_{g}$ and the Mn $e_{g}$ electrons, and the
ferromagnetic Ni-Mn superexchange, with the antiferromagnetic antisite Mn-Mn
superexchange. | [
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] |
Title: Counterexample-guided Abstraction Refinement for POMDPs,
Abstract: Partially Observable Markov Decision Process (POMDP) is widely used to model
probabilistic behavior for complex systems. Compared with MDPs, POMDP models a
system more accurate but solving a POMDP generally takes exponential time in
the size of its state space. This makes the formal verification and synthesis
problems much more challenging for POMDPs, especially when multiple system
components are involved. As a promising technique to reduce the verification
complexity, the abstraction method tries to find an abstract system with a
smaller state space but preserves enough properties for the verification
purpose. While abstraction based verification has been explored extensively for
MDPs, in this paper, we present the first result of POMDP abstraction and its
refinement techniques. The main idea follows the counterexample-guided
abstraction refinement (CEGAR) framework. Starting with a coarse guess for the
POMDP abstraction, we iteratively use counterexamples from formal verification
to refine the abstraction until the abstract system can be used to infer the
verification result for the original POMDP. Our main contributions have two
folds: 1) we propose a novel abstract system model for POMDP and a new
simulation relation to capture the partial observability then prove the
preservation on a fragment of Probabilistic Computation Tree Logic (PCTL); 2)
to find a proper abstract system that can prove or disprove the satisfaction
relation on the concrete POMDP, we develop a novel refinement algorithm. Our
work leads to a sound and complete CEGAR framework for POMDP. | [
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] |
Title: An Overview of Multi-Task Learning in Deep Neural Networks,
Abstract: Multi-task learning (MTL) has led to successes in many applications of
machine learning, from natural language processing and speech recognition to
computer vision and drug discovery. This article aims to give a general
overview of MTL, particularly in deep neural networks. It introduces the two
most common methods for MTL in Deep Learning, gives an overview of the
literature, and discusses recent advances. In particular, it seeks to help ML
practitioners apply MTL by shedding light on how MTL works and providing
guidelines for choosing appropriate auxiliary tasks. | [
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] |
Title: Low temperature synthesis of heterostructures of transition metal dichalcogenide alloys (WxMo1-xS2) and graphene with superior catalytic performance for hydrogen evolution,
Abstract: Large-area ($\sim$cm$^2$) films of vertical heterostructures formed by
alternating graphene and transition-metal dichalcogenide(TMD) alloys are
obtained by wet chemical routes followed by a thermal treatment at low
temperature (300 $^\circ$C). In particular, we synthesized stacked graphene and
W$_x$Mo$_{1-x}$S$_2$ alloy phases that were used as hydrogen evolution
catalysts. We observed a Tafel slope of 38.7 mV dec$^{-1}$ and 96 mV onset
potential (at current density of 10 mA cm$^{-2}$) when the heterostructure
alloy is annealed at 300 $^o$C. These results indicate that heterostructure
formed by graphene and W$_{0.4}$Mo$_{0.6}$S$_2$ alloys are far more efficient
than WS$_2$ and MoS$_2$ by at least a factor of two, and it is superior than
any other reported TMD system. This strategy offers a cheap and low temperature
synthesis alternative able to replace Pt in the hydrogen evolution reaction
(HER). Furthermore, the catalytic activity of the alloy is stable over time,
i.e. the catalytic activity does not experience a significant change even after
1000 cycles. Using density functional theory calculations, we found that this
enhanced hydrogen evolution in the W$_x$Mo$_{1-x}$S$_2$ alloys is mainly due to
the lower energy barrier created by a favorable overlap of the d-orbitals from
the transition metals and the s-orbitals of H$_2$, with the lowest energy
barrier occurring for W$_{0.4}$Mo$_{0.6}$S$_2$ alloy. Thus, it is now possible
to further improve the performance of the "inert" TMD basal plane via metal
alloying, in addition to the previously reported strategies of creation of
point defects, vacancies and edges. The synthesis of
graphene/W$_{0.4}$Mo$_{0.6}$S$_2$ produced at relatively low temperatures is
scalable and could be used as an effective low cost Pt-free catalyst. | [
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] |
Title: Virtual refinements of the Vafa-Witten formula,
Abstract: We conjecture a formula for the generating function of virtual
$\chi_y$-genera of moduli spaces of rank 2 sheaves on arbitrary surfaces with
holomorphic 2-form. Specializing the conjecture to minimal surfaces of general
type and to virtual Euler characteristics, we recover (part of) a formula of C.
Vafa and E. Witten.
These virtual $\chi_y$-genera can be written in terms of descendent Donaldson
invariants. Using T. Mochizuki's formula, the latter can be expressed in terms
of Seiberg-Witten invariants and certain explicit integrals over Hilbert
schemes of points. These integrals are governed by seven universal functions,
which are determined by their values on $\mathbb{P}^2$ and $\mathbb{P}^1 \times
\mathbb{P}^1$. Using localization we calculate these functions up to some
order, which allows us to check our conjecture in many cases.
In an appendix by H. Nakajima and the first named author, the virtual Euler
characteristic specialization of our conjecture is extended to include
$\mu$-classes, thereby interpolating between Vafa-Witten's formula and Witten's
conjecture for Donaldson invariants. | [
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] |
Title: Uncertainty in Multitask Transfer Learning,
Abstract: Using variational Bayes neural networks, we develop an algorithm capable of
accumulating knowledge into a prior from multiple different tasks. The result
is a rich and meaningful prior capable of few-shot learning on new tasks. The
posterior can go beyond the mean field approximation and yields good
uncertainty on the performed experiments. Analysis on toy tasks shows that it
can learn from significantly different tasks while finding similarities among
them. Experiments of Mini-Imagenet yields the new state of the art with 74.5%
accuracy on 5 shot learning. Finally, we provide experiments showing that other
existing methods can fail to perform well in different benchmarks. | [
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] |
Title: Geared Rotationally Identical and Invariant Convolutional Neural Network Systems,
Abstract: Theorems and techniques to form different types of transformationally
invariant processing and to produce the same output quantitatively based on
either transformationally invariant operators or symmetric operations have
recently been introduced by the authors. In this study, we further propose to
compose a geared rotationally identical CNN system (GRI-CNN) with a small step
angle by connecting networks of participated processes at the first flatten
layer. Using an ordinary CNN structure as a base, requirements for constructing
a GRI-CNN include the use of either symmetric input vector or kernels with an
angle increment that can form a complete cycle as a "gearwheel". Four basic
GRI-CNN structures were studied. Each of them can produce quantitatively
identical output results when a rotation angle of the input vector is evenly
divisible by the step angle of the gear. Our study showed when an input vector
rotated with an angle does not match to a step angle, the GRI-CNN can also
produce a highly consistent result. With a design of using an ultra-fine
gear-tooth step angle (e.g., 1 degree or 0.1 degree), all four GRI-CNN systems
can be constructed virtually isotropically. | [
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] |
Title: Conditional quantum one-time pad,
Abstract: Suppose that Alice and Bob are located in distant laboratories, which are
connected by an ideal quantum channel. Suppose further that they share many
copies of a quantum state $\rho_{ABE}$, such that Alice possesses the $A$
systems and Bob the $BE$ systems. In our model, there is an identifiable part
of Bob's laboratory that is insecure: a third party named Eve has infiltrated
Bob's laboratory and gained control of the $E$ systems. Alice, knowing this,
would like use their shared state and the ideal quantum channel to communicate
a message in such a way that Bob, who has access to the whole of his laboratory
($BE$ systems), can decode it, while Eve, who has access only to a sector of
Bob's laboratory ($E$ systems) and the ideal quantum channel connecting Alice
to Bob, cannot learn anything about Alice's transmitted message. We call this
task the conditional one-time pad, and in this paper, we prove that the optimal
rate of secret communication for this task is equal to the conditional quantum
mutual information $I(A;B|E)$ of their shared state. We thus give the
conditional quantum mutual information an operational meaning that is different
from those given in prior works, via state redistribution, conditional erasure,
or state deconstruction. We also generalize the model and method in several
ways, one of which demonstrates that the negative tripartite interaction
information $-I_{3}(A;B;E) = I(A;BE)-I(A;B)-I(A;E)$ of a tripartite state
$\rho_{ABE}$ is an achievable rate for a secret-sharing task, i.e., the case in
which Alice's message should be secure from someone possessing only the $AB$ or
$AE$ systems but should be decodable by someone possessing all systems $A$,
$B$, and $E$. | [
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] |
Title: The Representation Theory of 2-Sylow Subgroups of the Symmetric Group,
Abstract: We study the Bratteli diagram of 2-Sylow subgroups of symmetric groups. We
show that it is simple, has a recursive structure, and self-similarities at all
scales. We contrast its subgraph of one-dimensional representations with the
Macdonald tree. | [
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] |
Title: Debiasing the Debiased Lasso with Bootstrap,
Abstract: In this paper, we prove that under proper conditions, bootstrap can further
debias the debiased Lasso estimator for statistical inference of
low-dimensional parameters in high-dimensional linear regression. We prove that
the required sample size for inference with bootstrapped debiased Lasso, which
involves the number of small coefficients, can be of smaller order than the
existing ones for the debiased Lasso. Therefore, our results reveal the
benefits of having strong signals. Our theory is supported by results of
simulation experiments, which compare coverage probabilities and lengths of
confidence intervals with and without bootstrap, with and without debiasing. | [
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] |
Title: Asymptotic Normality of Extensible Grid Sampling,
Abstract: Recently, He and Owen (2016) proposed the use of Hilbert's space filling
curve (HSFC) in numerical integration as a way of reducing the dimension from
$d>1$ to $d=1$. This paper studies the asymptotic normality of the HSFC-based
estimate when using scrambled van der Corput sequence as input. We show that
the estimate has an asymptotic normal distribution for functions in
$C^1([0,1]^d)$, excluding the trivial case of constant functions. The
asymptotic normality also holds for discontinuous functions under mild
conditions. It was previously known only that scrambled $(0,m,d)$-net
quadratures enjoy the asymptotic normality for smooth enough functions, whose
mixed partial gradients satisfy a Hölder condition. As a by-product, we find
lower bounds for the variance of the HSFC-based estimate. Particularly, for
nontrivial functions in $C^1([0,1]^d)$, the low bound is of order $n^{-1-2/d}$,
which matches the rate of the upper bound established in He and Owen (2016). | [
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] |
Title: Hardening Stratum, the Bitcoin Pool Mining Protocol,
Abstract: Stratum, the de-facto mining communication protocol used by blockchain based
cryptocurrency systems, enables miners to reliably and efficiently fetch jobs
from mining pool servers. In this paper we exploit Stratum's lack of encryption
to develop passive and active attacks on Bitcoin's mining protocol, with
important implications on the privacy, security and even safety of mining
equipment owners. We introduce StraTap and ISP Log attacks, that infer miner
earnings if given access to miner communications, or even their logs. We
develop BiteCoin, an active attack that hijacks shares submitted by miners, and
their associated payouts. We build BiteCoin on WireGhost, a tool we developed
to hijack and surreptitiously maintain Stratum connections. Our attacks reveal
that securing Stratum through pervasive encryption is not only undesirable (due
to large overheads), but also ineffective: an adversary can predict miner
earnings even when given access to only packet timestamps. Instead, we devise
Bedrock, a minimalistic Stratum extension that protects the privacy and
security of mining participants. We introduce and leverage the mining cookie
concept, a secret that each miner shares with the pool and includes in its
puzzle computations, and that prevents attackers from reconstructing or
hijacking the puzzles. We have implemented our attacks and collected 138MB of
Stratum protocol traffic from mining equipment in the US and Venezuela. We show
that Bedrock is resilient to active attacks even when an adversary breaks the
crypto constructs it uses. Bedrock imposes a daily overhead of 12.03s on a
single pool server that handles mining traffic from 16,000 miners. | [
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] |
Title: Representations associated to small nilpotent orbits for complex Spin groups,
Abstract: This paper provides a comparison between the $K$-structure of unipotent
representations and regular sections of bundles on nilpotent orbits for complex
groups of type $D$. Precisely, let $ G_ 0 =Spin(2n,\mathbb C)$ be the Spin
complex group viewed as a real group, and $K\cong G_0$ be the complexification
of the maximal compact subgroup of $G_0$. We compute $K$-spectra of the regular
functions on some small nilpotent orbits $\mathcal O$ transforming according to
characters $\psi$ of $C_{ K}(\mathcal O)$ trivial on the connected component of
the identity $C_{ K}(\mathcal O)^0$. We then match them with the ${K}$-types of
the genuine (i.e. representations which do not factor to $SO(2n,\mathbb C)$)
unipotent representations attached to $\mathcal O$. | [
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] |
Title: Fast Linear Transformations in Python,
Abstract: This paper introduces a new free library for the Python programming language,
which provides a collection of structured linear transforms, that are not
represented as explicit two dimensional arrays but in a more efficient way by
exploiting the structural knowledge.
This allows fast and memory savy forward and backward transformations while
also provding a clean but still flexible interface to these effcient
algorithms, thus making code more readable, scable and adaptable.
We first outline the goals of this library, then how they were achieved and
lastly we demonstrate the performance compared to current state of the art
packages available for Python.
This library is released and distributed under a free license. | [
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] |
Title: Physical description of nature from a system-internal viewpoint,
Abstract: Objectivity is often considered as an ideal for scientific description of
nature. When we describe physical phenomena, thus, we have exclusively taken an
objective viewpoint by excluding a subject. Here we consider how nature can be
described from a subjective viewpoint and how it is related to the objective
description. To this end, we introduce a viewpoint-shift operation that sets
perspective within a system, and subject the system to the laws of
thermodynamics. We consider a situation in which the activation of an active
part of the system starts to influence an objective part at t = 0, bringing the
system into non-equilibrium. We find that the perspective alters physical state
functions of the system, or leaves a viewpoint-dependent physical trace that is
detectable. In the system-internal viewpoint, a system in the heat bath
self-organizes to maximize the free energy, creating order. The active part
keeps increasing a gap from an initial equilibrium state as long as the energy
is available, forming a memory in the form of organized matter. This outcome of
a system-internal viewpoint in physics matches our intuition coming from our
daily-life experience that our subjective action leads to a change of our
environment. This suggests that this system-internal viewpoint may provide a
clue to understand a long-standing problem on the physical meaning to be
subjective. | [
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] |
Title: Training-induced inversion of spontaneous exchange bias field on La1.5Ca0.5CoMnO6,
Abstract: In this work we report the synthesis and structural, electronic and magnetic
properties of La1.5Ca0.5CoMnO6 double-perovskite. This is a re-entrant spin
cluster material which exhibits a non-negligible negative exchange bias effect
when it is cooled in zero magnetic field from an unmagnetized state down to low
temperature. X-ray powder diffraction, X-ray photoelectron spectroscopy and
magnetometry results indicate mixed valence state at Co site, leading to
competing magnetic phases and uncompensated spins at the magnetic interfaces.
We compare the results for this Ca-doped material with those reported for the
resemblant compound La1.5Sr0.5CoMnO6, and discuss the much smaller spontaneous
exchange bias effect observed for the former in terms of its structural and
magnetic particularities. For La1.5Ca0.5CoMnO6, when successive magnetization
loops are carried, the spontaneous exchange bias field inverts its sign from
negative to positive from the first to the second measurement. We discuss this
behavior based on the disorder at the magnetic interfaces, related to the
presence of a glassy phase. This compound also exhibits a large conventional
exchange bias, for which there is no sign inversion of the exchange bias field
for consecutive cycles. | [
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] |
Title: Efficient and Adaptive Linear Regression in Semi-Supervised Settings,
Abstract: We consider the linear regression problem under semi-supervised settings
wherein the available data typically consists of: (i) a small or moderate sized
'labeled' data, and (ii) a much larger sized 'unlabeled' data. Such data arises
naturally from settings where the outcome, unlike the covariates, is expensive
to obtain, a frequent scenario in modern studies involving large databases like
electronic medical records (EMR). Supervised estimators like the ordinary least
squares (OLS) estimator utilize only the labeled data. It is often of interest
to investigate if and when the unlabeled data can be exploited to improve
estimation of the regression parameter in the adopted linear model.
In this paper, we propose a class of 'Efficient and Adaptive Semi-Supervised
Estimators' (EASE) to improve estimation efficiency. The EASE are two-step
estimators adaptive to model mis-specification, leading to improved (optimal in
some cases) efficiency under model mis-specification, and equal (optimal)
efficiency under a linear model. This adaptive property, often unaddressed in
the existing literature, is crucial for advocating 'safe' use of the unlabeled
data. The construction of EASE primarily involves a flexible
'semi-non-parametric' imputation, including a smoothing step that works well
even when the number of covariates is not small; and a follow up 'refitting'
step along with a cross-validation (CV) strategy both of which have useful
practical as well as theoretical implications towards addressing two important
issues: under-smoothing and over-fitting. We establish asymptotic results
including consistency, asymptotic normality and the adaptive properties of
EASE. We also provide influence function expansions and a 'double' CV strategy
for inference. The results are further validated through extensive simulations,
followed by application to an EMR study on auto-immunity. | [
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] |
Title: Small Moving Window Calibration Models for Soft Sensing Processes with Limited History,
Abstract: Five simple soft sensor methodologies with two update conditions were
compared on two experimentally-obtained datasets and one simulated dataset. The
soft sensors investigated were moving window partial least squares regression
(and a recursive variant), moving window random forest regression, the mean
moving window of $y$, and a novel random forest partial least squares
regression ensemble (RF-PLS), all of which can be used with small sample sizes
so that they can be rapidly placed online. It was found that, on two of the
datasets studied, small window sizes led to the lowest prediction errors for
all of the moving window methods studied. On the majority of datasets studied,
the RF-PLS calibration method offered the lowest one-step-ahead prediction
errors compared to those of the other methods, and it demonstrated greater
predictive stability at larger time delays than moving window PLS alone. It was
found that both the random forest and RF-PLS methods most adequately modeled
the datasets that did not feature purely monotonic increases in property
values, but that both methods performed more poorly than moving window PLS
models on one dataset with purely monotonic property values. Other data
dependent findings are presented and discussed. | [
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] |
Title: Deep Learning the Physics of Transport Phenomena,
Abstract: We have developed a new data-driven paradigm for the rapid inference,
modeling and simulation of the physics of transport phenomena by deep learning.
Using conditional generative adversarial networks (cGAN), we train models for
the direct generation of solutions to steady state heat conduction and
incompressible fluid flow purely on observation without knowledge of the
underlying governing equations. Rather than using iterative numerical methods
to approximate the solution of the constitutive equations, cGANs learn to
directly generate the solutions to these phenomena, given arbitrary boundary
conditions and domain, with high test accuracy (MAE$<$1\%) and state-of-the-art
computational performance. The cGAN framework can be used to learn causal
models directly from experimental observations where the underlying physical
model is complex or unknown. | [
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] |
Title: A Survey on Methods and Theories of Quantized Neural Networks,
Abstract: Deep neural networks are the state-of-the-art methods for many real-world
tasks, such as computer vision, natural language processing and speech
recognition. For all its popularity, deep neural networks are also criticized
for consuming a lot of memory and draining battery life of devices during
training and inference. This makes it hard to deploy these models on mobile or
embedded devices which have tight resource constraints. Quantization is
recognized as one of the most effective approaches to satisfy the extreme
memory requirements that deep neural network models demand. Instead of adopting
32-bit floating point format to represent weights, quantized representations
store weights using more compact formats such as integers or even binary
numbers. Despite a possible degradation in predictive performance, quantization
provides a potential solution to greatly reduce the model size and the energy
consumption. In this survey, we give a thorough review of different aspects of
quantized neural networks. Current challenges and trends of quantized neural
networks are also discussed. | [
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] |
Title: Constructing confidence sets for the matrix completion problem,
Abstract: In the present note we consider the problem of constructing honest and
adaptive confidence sets for the matrix completion problem. For the Bernoulli
model with known variance of the noise we provide a realizable method for
constructing confidence sets that adapt to the unknown rank of the true matrix. | [
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] |
Title: Ring objects in the equivariant derived Satake category arising from Coulomb branches (with an appendix by Gus Lonergan),
Abstract: This is the second companion paper of arXiv:1601.03586. We consider the
morphism from the variety of triples introduced in arXiv:1601.03586 to the
affine Grassmannian. The direct image of the dualizing complex is a ring object
in the equivariant derived category on the affine Grassmannian (equivariant
derived Satake category). We show that various constructions in
arXiv:1601.03586 work for an arbitrary commutative ring object.
The second purpose of this paper is to study Coulomb branches associated with
star shaped quivers, which are expected to be conjectural Higgs branches of
$3d$ Sicilian theories in type $A$ by arXiv:1007.0992. | [
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] |
Title: Well-Posedness of a Navier-Stokes/Mean Curvature Flow system,
Abstract: We consider a two-phase flow of two incompressible, viscous and immiscible
fluids which are separated by a sharp interface in the case of a simple phase
transition. In this model the interface is no longer material and its evolution
is governed by a convective mean curvature flow equation, which is coupled to a
two-phase Navier-Stokes equation with Young-Laplace law. The problem arises as
a sharp interface limit of a diffuse interface model, which consists of a
Navier-Stokes system coupled with an Allen-Cahn equation. We prove existence of
strong solutions for sufficiently small times and regular initial data. | [
0,
0,
1,
0,
0,
0
] |
Title: Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control,
Abstract: MOBAs represent a huge segment of online gaming and are growing as both an
eSport and a casual genre. The natural starting point for AI researchers
interested in MOBAs is to develop an AI to play the game better than a human -
but MOBAs have many more challenges besides adversarial AI. In this paper we
introduce the reader to the wider context of MOBA culture, propose a range of
challenges faced by the community today, and posit concrete AI projects that
can be undertaken to begin solving them. | [
1,
0,
0,
0,
0,
0
] |
Title: Domains for Higher-Order Games,
Abstract: We study two-player inclusion games played over word-generating higher-order
recursion schemes. While inclusion checks are known to capture verification
problems, two-player games generalize this relationship to program synthesis.
In such games, non-terminals of the grammar are controlled by opposing players.
The goal of the existential player is to avoid producing a word that lies
outside of a regular language of safe words.
We contribute a new domain that provides a representation of the winning
region of such games. Our domain is based on (functions over) potentially
infinite Boolean formulas with words as atomic propositions. We develop an
abstract interpretation framework that we instantiate to abstract this domain
into a domain where the propositions are replaced by states of a finite
automaton. This second domain is therefore finite and we obtain, via standard
fixed-point techniques, a direct algorithm for the analysis of two-player
inclusion games. We show, via a second instantiation of the framework, that our
finite domain can be optimized, leading to a (k+1)EXP algorithm for order-k
recursion schemes. We give a matching lower bound, showing that our approach is
optimal. Since our approach is based on standard Kleene iteration, existing
techniques and tools for fixed-point computations can be applied. | [
1,
0,
0,
0,
0,
0
] |
Title: Improved torque formula for low and intermediate mass planetary migration,
Abstract: The migration of planets on nearly circular, non-inclined orbits in
protoplanetary discs is entirely described by the disc's torque. This torque is
a complex function of the disc parameters, and essentially amounts to the sum
of two components: the Lindblad torque and the corotation torque. Known torque
formulae do not reproduce accurately the torque actually experienced in
numerical simulations by low- and intermediate- mass planets in radiative
discs. One of the main reasons for this inaccuracy is that these formulae have
been worked out in two-dimensional analyses. Here we revisit the torque formula
and update many of its dimensionless coefficients by means of tailored, three-
dimensional numerical simulations. In particular, we derive the dependence of
the Lindblad torque on the temperature gradient, the dependence of the
corotation torque on the radial entropy gradient (and work out a suitable
expression of this gradient in a three-dimensional disc). We also work out the
dependence of the corotation torque on the radial temperature gradient,
overlooked so far. Corotation torques are known to scale very steeply with the
width of the horseshoe region. We extend the expression of this width to the
domain of intermediate mass planets, so that our updated torque formula remains
valid for planets up to typically several tens of Earth masses, provided these
relatively massive planets do not significantly deplete their coorbital region.
Our torque expression can be applied to low- and intermediate-mass planets in
optically thick protoplanetary discs, as well as protomoons embedded in
circumplanetary discs. | [
0,
1,
0,
0,
0,
0
] |
Title: The fluid running in the subnanochannel with functional surface,
Abstract: We have researched the motion of gas in the subnanochannel with functional
surface which wettability has a gradient for the fluid by using molecular
dynamics simulation. The results show that the gas is driven to flow under a
single heat source and without any other work or energy applied to the system.
The driving source is owed to the potential gradient of the functional face
which keeps the fluid running in the subnanochannel. The width of the channel
and the pressure of the reservoir has a significant influence on the flow
velocity, which, respectively, has an optimal value for the maximum velocity.
The flow velocity grows with the increasing temperature. | [
0,
1,
0,
0,
0,
0
] |
Title: Data-Driven Decentralized Optimal Power Flow,
Abstract: The implementation of optimal power flow (OPF) methods to perform voltage and
power flow regulation in electric networks is generally believed to require
communication. We consider distribution systems with multiple controllable
Distributed Energy Resources (DERs) and present a data-driven approach to learn
control policies for each DER to reconstruct and mimic the solution to a
centralized OPF problem from solely locally available information.
Collectively, all local controllers closely match the centralized OPF solution,
providing near-optimal performance and satisfaction of system constraints. A
rate distortion framework facilitates the analysis of how well the resulting
fully decentralized control policies are able to reconstruct the OPF solution.
Our methodology provides a natural extension to decide what buses a DER should
communicate with to improve the reconstruction of its individual policy. The
method is applied on both single- and three-phase test feeder networks using
data from real loads and distributed generators. It provides a framework for
Distribution System Operators to efficiently plan and operate the contributions
of DERs to active distribution networks. | [
0,
0,
0,
1,
0,
0
] |
Title: Thermal-induced stress of plasmonic magnetic nanocomposites,
Abstract: We present theoretical calculations to interpret optical and mechanical
properties of Ag@Fe3O4 nanoflowers. The microstructures and nature of optical
peaks of nanoflowers are determined by means of the Mie theory associated with
effective dielectric approximation and the experimental absorption spectrum.
Under laser illumination, the thermal strain fields inside and outside the
structure due to the absorbed optical energy are studied using continuum
mechanics approach. Our findings provide simple but comprehensive description
of the elastic behaviors of previous experiments. | [
0,
1,
0,
0,
0,
0
] |
Title: High-resolution photoelectron-spectroscopic investigation of the H$_2$O$^+$ cation in its ${\mathrm {\tilde A^+}}$ electronic state,
Abstract: The photoelectron spectrum of water has been recorded in the vicinity of the
${\mathrm {\tilde A^+}}$ $\leftarrow$ $\tilde{\mathrm{X}}$ transition between
112 000 and 116 000 cm$^{-1}$ (13.89-14.38 eV). The high-resolution allowed the
observation of the rotational structure of several bands. Rotational
assignments of the transitions involving the $\Pi(080)$, $\Sigma(070)$ and
$\Pi(060)$ vibronic states of the $\tilde{\mathrm{A}}^+$ electronic state are
deduced from previous studies of the $\tilde{\mathrm{A}}^+ -
\tilde{\mathrm{X}}^+$ band system of H$_2$O$^+$ (Lew, Can. J. Phys. 54, 2028
(1976) and Huet et al., J. Chem. Phys. 107, 5645 (1997)) and photoionization
selection rules. The transition to the $\Sigma(030)$ vibronic state is
tentatively assigned. | [
0,
1,
0,
0,
0,
0
] |
Title: Connectedness of the Balmer spectra of right bounded derived categories,
Abstract: By virtue of Balmer's celebrated theorem, the classification of thick tensor
ideals of a tensor triangulated category $\T$ is equivalent to the topological
structure of its Balmer spectrum $\spc \T$. Motivated by this theorem, we
discuss connectedness and noetherianity of the Balmer spectrum of a right
bounded derived category of finitely generated modules over a commutative ring. | [
0,
0,
1,
0,
0,
0
] |
Title: Underdamped Langevin MCMC: A non-asymptotic analysis,
Abstract: We study the underdamped Langevin diffusion when the log of the target
distribution is smooth and strongly concave. We present a MCMC algorithm based
on its discretization and show that it achieves $\varepsilon$ error (in
2-Wasserstein distance) in $\mathcal{O}(\sqrt{d}/\varepsilon)$ steps. This is a
significant improvement over the best known rate for overdamped Langevin MCMC,
which is $\mathcal{O}(d/\varepsilon^2)$ steps under the same
smoothness/concavity assumptions.
The underdamped Langevin MCMC scheme can be viewed as a version of
Hamiltonian Monte Carlo (HMC) which has been observed to outperform overdamped
Langevin MCMC methods in a number of application areas. We provide quantitative
rates that support this empirical wisdom. | [
1,
0,
0,
1,
0,
0
] |
Title: Two-dimensional compressible viscous flow around a circular cylinder,
Abstract: Direct numerical simulation is performed to study compressible, viscous flow
around a circular cylinder. The present study considers two-dimensional,
shock-free continuum flow by varying the Reynolds number between 20 and 100 and
the freestream Mach number between 0 and 0.5. The results indicate that
compressibility effects elongate the near wake for cases above and below the
critical Reynolds number for two-dimensional flow under shedding. The wake
elongation becomes more pronounced as the Reynolds number approaches this
critical value. Moreover, we determine the growth rate and frequency of linear
instability for cases above the critical Reynolds number. From the analysis, it
is observed that the frequency of the Bénard-von Kármán vortex street in
the time-periodic, fully-saturated flow increases from the dominant unstable
frequency found from the linear stability analysis as the Reynolds number
increases from its critical value, even for the low range of Reynolds numbers
considered. We also notice that the compressibility effects reduce the growth
rate and dominant frequency in the linear growth stage. Semi-empirical
functional relationships for the growth rate and the dominant frequency in
linearized flow around the cylinder in terms of the Reynolds number and
freestream Mach number are presented. | [
0,
1,
0,
0,
0,
0
] |
Title: BindsNET: A machine learning-oriented spiking neural networks library in Python,
Abstract: The development of spiking neural network simulation software is a critical
component enabling the modeling of neural systems and the development of
biologically inspired algorithms. Existing software frameworks support a wide
range of neural functionality, software abstraction levels, and hardware
devices, yet are typically not suitable for rapid prototyping or application to
problems in the domain of machine learning. In this paper, we describe a new
Python package for the simulation of spiking neural networks, specifically
geared towards machine learning and reinforcement learning. Our software,
called BindsNET, enables rapid building and simulation of spiking networks and
features user-friendly, concise syntax. BindsNET is built on top of the PyTorch
deep neural networks library, enabling fast CPU and GPU computation for large
spiking networks. The BindsNET framework can be adjusted to meet the needs of
other existing computing and hardware environments, e.g., TensorFlow. We also
provide an interface into the OpenAI gym library, allowing for training and
evaluation of spiking networks on reinforcement learning problems. We argue
that this package facilitates the use of spiking networks for large-scale
machine learning experimentation, and show some simple examples of how we
envision BindsNET can be used in practice. BindsNET code is available at
this https URL | [
0,
0,
0,
0,
1,
0
] |
Title: Monaural Audio Speaker Separation with Source Contrastive Estimation,
Abstract: We propose an algorithm to separate simultaneously speaking persons from each
other, the "cocktail party problem", using a single microphone. Our approach
involves a deep recurrent neural networks regression to a vector space that is
descriptive of independent speakers. Such a vector space can embed empirically
determined speaker characteristics and is optimized by distinguishing between
speaker masks. We call this technique source-contrastive estimation. The
methodology is inspired by negative sampling, which has seen success in natural
language processing, where an embedding is learned by correlating and
de-correlating a given input vector with output weights. Although the matrix
determined by the output weights is dependent on a set of known speakers, we
only use the input vectors during inference. Doing so will ensure that source
separation is explicitly speaker-independent. Our approach is similar to recent
deep neural network clustering and permutation-invariant training research; we
use weighted spectral features and masks to augment individual speaker
frequencies while filtering out other speakers. We avoid, however, the severe
computational burden of other approaches with our technique. Furthermore, by
training a vector space rather than combinations of different speakers or
differences thereof, we avoid the so-called permutation problem during
training. Our algorithm offers an intuitive, computationally efficient response
to the cocktail party problem, and most importantly boasts better empirical
performance than other current techniques. | [
1,
0,
0,
1,
0,
0
] |
Title: Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks,
Abstract: The quest for biologically plausible deep learning is driven, not just by the
desire to explain experimentally-observed properties of biological neural
networks, but also by the hope of discovering more efficient methods for
training artificial networks. In this paper, we propose a new algorithm named
Variational Probably Flow (VPF), an extension of minimum probability flow for
training binary Deep Boltzmann Machines (DBMs). We show that weight updates in
VPF are local, depending only on the states and firing rates of the adjacent
neurons. Unlike contrastive divergence, there is no need for Gibbs
confabulations; and unlike backpropagation, alternating feedforward and
feedback phases are not required. Moreover, the learning algorithm is effective
for training DBMs with intra-layer connections between the hidden nodes.
Experiments with MNIST and Fashion MNIST demonstrate that VPF learns reasonable
features quickly, reconstructs corrupted images more accurately, and generates
samples with a high estimated log-likelihood. Lastly, we note that,
interestingly, if an asymmetric version of VPF exists, the weight updates
directly explain experimental results in Spike-Timing-Dependent Plasticity
(STDP). | [
1,
0,
0,
1,
0,
0
] |
Title: Node classification for signed networks using diffuse interface methods,
Abstract: Signed networks are a crucial tool when modeling friend and foe
relationships. In contrast to classical undirected, weighted graphs, the edge
weights for signed graphs are positive and negative. Crucial network properties
are often derived from the study of the associated graph Laplacians. We here
study several different signed network Laplacians with a focus on the task of
classifying the nodes of the graph. We here extend a recently introduced
technique based on a partial differential equation defined on the signed
network, namely the Allen-Cahn-equation, to classify the nodes into two or more
classes. We illustrate the performance of this approach on several real-world
networks. | [
1,
0,
0,
1,
0,
0
] |
Title: Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods,
Abstract: Recently we proposed a general, ensemble-based feature engineering wrapper
(FEW) that was paired with a number of machine learning methods to solve
regression problems. Here, we adapt FEW for supervised classification and
perform a thorough analysis of fitness and survival methods within this
framework. Our tests demonstrate that two fitness metrics, one introduced as an
adaptation of the silhouette score, outperform the more commonly used Fisher
criterion. We analyze survival methods and demonstrate that $\epsilon$-lexicase
survival works best across our test problems, followed by random survival which
outperforms both tournament and deterministic crowding. We conduct a benchmark
comparison to several classification methods using a large set of problems and
show that FEW can improve the best classifier performance in several cases. We
show that FEW generates consistent, meaningful features for a biomedical
problem with different ML pairings. | [
1,
0,
0,
1,
0,
0
] |
Title: Anisotropic Fermi surface probed by the de Haas-van Alphen oscillation in proposed Dirac Semimetal TaSb$_{2}$,
Abstract: TaSb$_{2}$ has been predicted theoretically and proposed through
magnetotransport experiment to be a topological semimetal. In earlier reports,
the Shubnikov-de Haas oscillation has been analyzed to probe the Fermi surface,
with magnetic field along a particular crystallographic axis only. By employing
a sample rotator, we reveal highly anisotropic transverse magnetoresistance by
rotating the magnetic field along different crystallographic directions. To
probe the anisotropy in the Fermi surface, we have performed magnetization
measurements and detected strong de Haas-van Alphen (dHvA) oscillations for the
magnetic field applied along \textbf{b} and \textbf{c} axes as well as
perpendicular to \textbf{bc} plane of the crystals. Three Fermi pockets have
been identified by analyzing the dHvA oscillations. Hall measurement reveals
electron as the only charge carrier, i.e., all the three Fermi pockets are
electron type. With the application of magnetic field along different crystal
directions, the cross sectional areas of the Fermi pockets have been found
significantly different. Other physical parameters, such as the effective mass
of the charge carrier and Fermi velocity have also been calculated using the
Lifshitz-Kosevich formula. | [
0,
1,
0,
0,
0,
0
] |
Title: Stochastic Deconvolutional Neural Network Ensemble Training on Generative Pseudo-Adversarial Networks,
Abstract: The training of Generative Adversarial Networks is a difficult task mainly
due to the nature of the networks. One such issue is when the generator and
discriminator start oscillating, rather than converging to a fixed point.
Another case can be when one agent becomes more adept than the other which
results in the decrease of the other agent's ability to learn, reducing the
learning capacity of the system as a whole. Additionally, there exists the
problem of Mode Collapse which involves the generators output collapsing to a
single sample or a small set of similar samples. To train GANs a careful
selection of the architecture that is used along with a variety of other
methods to improve training. Even when applying these methods there is low
stability of training in relation to the parameters that are chosen. Stochastic
ensembling is suggested as a method for improving the stability while training
GANs. | [
0,
0,
0,
1,
0,
0
] |
Title: A Local Prime Factor Decomposition Algorithm for Strong Product Graphs,
Abstract: This work is concerned with the prime factor decomposition (PFD) of strong
product graphs. A new quasi-linear time algorithm for the PFD with respect to
the strong product for arbitrary, finite, connected, undirected graphs is
derived. Moreover, since most graphs are prime although they can have a
product-like structure, also known as approximate graph products, the practical
application of the well-known "classical" prime factorization algorithm is
strictly limited. This new PFD algorithm is based on a local approach that
covers a graph by small factorizable subgraphs and then utilizes this
information to derive the global factors. Therefore, we can take advantage of
this approach and derive in addition a method for the recognition of
approximate graph products. | [
1,
0,
0,
0,
0,
0
] |
Title: Dispersion for the wave equation outside a ball and counterexamples,
Abstract: The purpose of this note is to prove dispersive estimates for the wave
equation outside a ball in R^d. If d = 3, we show that the linear flow
satisfies the dispersive estimates as in R^3. In higher dimensions d $\ge$ 4 we
show that losses in dispersion do appear and this happens at the Poisson spot. | [
0,
0,
1,
0,
0,
0
] |
Title: Minimal surfaces near short geodesics in hyperbolic $3$-manifolds,
Abstract: If $M$ is a finite volume complete hyperbolic $3$-manifold, the quantity
$\mathcal A_1(M)$ is defined as the infimum of the areas of closed minimal
surfaces in $M$. In this paper we study the continuity property of the
functional $\mathcal A_1$ with respect to the geometric convergence of
hyperbolic manifolds. We prove that it is lower semi-continuous and even
continuous if $\mathcal A_1(M)$ is realized by a minimal surface satisfying
some hypotheses. Understanding the interaction between minimal surfaces and
short geodesics in $M$ is the main theme of this paper | [
0,
0,
1,
0,
0,
0
] |
Title: Softening and Yielding of Soft Glassy Materials,
Abstract: Solids deform and fluids flow, but soft glassy materials, such as emulsions,
foams, suspensions, and pastes, exhibit an intricate mix of solid and
liquid-like behavior. While much progress has been made to understand their
elastic (small strain) and flow (infinite strain) properties, such
understanding is lacking for the softening and yielding phenomena that connect
these asymptotic regimes. Here we present a comprehensive framework for
softening and yielding of soft glassy materials, based on extensive numerical
simulations of oscillatory rheological tests, and show that two distinct
scenarios unfold depending on the material's packing density. For dense
systems, there is a single, pressure-independent strain where the elastic
modulus drops and the particle motion becomes diffusive. In contrast, for
weakly jammed systems, a two-step process arises: at an intermediate softening
strain, the elastic and loss moduli both drop down and then reach a new plateau
value, whereas the particle motion becomes diffusive at the distinctly larger
yield strain. We show that softening is associated with an extensive number of
microscopic contact changes leading to a non-analytic rheological signature.
Moreover, the scaling of the softening strain with pressure suggest the
existence of a novel pressure scale above which softening and yielding
coincide, and we verify the existence of this crossover scale numerically. Our
findings thus evidence the existence of two distinct classes of soft glassy
materials -- jamming dominated and dense -- and show how these can be
distinguished by their rheological fingerprint. | [
0,
1,
0,
0,
0,
0
] |
Title: Seemless Utilization of Heterogeneous XSede Resources to Accelerate Processing of a High Value Functional Neuroimaging Dataset,
Abstract: We describe the technical effort used to process a voluminous high value
human neuroimaging dataset on the Open Science Grid with opportunistic use of
idle HPC resources to boost computing capacity more than 5-fold. With minimal
software development effort and no discernable competitive interference with
other HPC users, this effort delivered 15,000,000 core hours over 7 months. | [
0,
0,
0,
0,
1,
0
] |
Title: Nonlinear Network description for many-body quantum systems in continuous space,
Abstract: We show that the recently introduced iterative backflow renormalization can
be interpreted as a general neural network in continuum space with non-linear
functions in the hidden units. We use this wave function within Variational
Monte Carlo for liquid $^4$He in two and three dimensions, where we typically
find a tenfold increase in accuracy over currently used wave functions.
Furthermore, subsequent stages of the iteration procedure define a set of
increasingly good wave functions, each with its own variational energy and
variance of the local energy: extrapolation of these energies to zero variance
gives values in close agreement with the exact values. For two dimensional
$^4$He, we also show that the iterative backflow wave function can describe
both the liquid and the solid phase with the same functional form -a feature
shared with the Shadow Wave Function, but now joined by much higher accuracy.
We also achieve significant progress for liquid $^3$He in three dimensions,
improving previous variational and fixed-node energies for this very
challenging fermionic system. | [
0,
1,
0,
0,
0,
0
] |
Title: Winding number $m$ and $-m$ patterns acting on concordance,
Abstract: We prove that for any winding number $m>0$ pattern $P$ and winding number
$-m$ pattern $Q$, there exist knots $K$ such that the minimal genus of a
cobordism between $P(K)$ and $Q(K)$ is arbitrarily large. This answers a
question posed by Cochran-Harvey [CH17] and generalizes a result of
Kim-Livingston [KL05]. | [
0,
0,
1,
0,
0,
0
] |
Title: Averages of shifted convolution sums for $GL(3) \times GL(2)$,
Abstract: Let $A_f(1,n)$ be the normalized Fourier coefficients of a $GL(3)$ Maass cusp
form $f$ and let $a_g(n)$ be the normalized Fourier coefficients of a $GL(2)$
cusp form $g$. Let $\lambda(n)$ be either $A_f(1,n)$ or the triple divisor
function $d_3(n)$. It is proved that for any $\epsilon>0$, any integer $r\geq
1$ and $r^{5/2}X^{1/4+7\delta/2}\leq H\leq X$ with $\delta>0$, $$
\frac{1}{H}\sum_{h\geq 1}W\left(\frac{h}{H}\right) \sum_{n\geq
1}\lambda(n)a_g(rn+h)V\left(\frac{n}{X}\right)\ll X^{1-\delta+\epsilon}, $$
where $V$ and $W$ are smooth compactly supported functions, and the implied
constants depend only on the associated forms and $\epsilon$. | [
0,
0,
1,
0,
0,
0
] |
Title: Reply to Hicks et al 2017, Reply to Morrison et al 2016 Refining the relevant population in forensic voice comparison, Reply to Hicks et al 2015 The importance of distinguishing info from evidence/observations when formulating propositions,
Abstract: The present letter to the editor is one in a series of publications
discussing the formulation of hypotheses (propositions) for the evaluation of
strength of forensic evidence. In particular, the discussion focusses on the
issue of what information may be used to define the relevant population
specified as part of the different-speaker hypothesis in forensic voice
comparison. The previous publications in the series are: Hicks et al. 2015
<this http URL>; Morrison et al. (2016)
<this http URL>; Hicks et al. (2017)
<this http URL>. The latter letter to the
editor mostly resolves the apparent disagreement between the two groups of
authors. We briefly discuss one outstanding point of apparent disagreement, and
attempt to correct a misinterpretation of our earlier remarks. We believe that
at this point there is no actual disagreement, and that both groups of authors
are calling for greater collaboration in order to reduce the likelihood of
future misunderstandings. | [
0,
0,
0,
1,
0,
0
] |
Title: Fourier Multipliers on the Heisenberg groups revisited,
Abstract: In this paper, we give explicit expressions of differential-difference
operators appeared in the hypothesis of the general Fourier multiplier theorem
associated to the Heisenberg groups proved by Mauceri and De Micheal for one
dimension and C. Lin for higher dimension. We also give a much shorter proof of
the above-mentioned theorem. Then we obtain a sharp weighted estimate for
Fourier multipliers on the Heisenberg groups. | [
0,
0,
1,
0,
0,
0
] |
Title: Free Boundary Minimal Surfaces in the Unit Three-Ball via Desingularization of the Critical Catenoid and the Equatorial Disk,
Abstract: We construct a new family of high genus examples of free boundary minimal
surfaces in the Euclidean unit 3-ball by desingularizing the intersection of a
coaxial pair of a critical catenoid and an equatorial disk. The surfaces are
constructed by singular perturbation methods and have three boundary
components. They are the free boundary analogue of the Costa-Hoffman-Meeks
surfaces and the surfaces constructed by Kapouleas by desingularizing coaxial
catenoids and planes. It is plausible that the minimal surfaces we constructed
here are the same as the ones obtained recently by Ketover using the min-max
method. | [
0,
0,
1,
0,
0,
0
] |
Title: Multi-scale Transactive Control In Interconnected Bulk Power Systems Under High Renewable Energy Supply and High Demand Response Scenarios,
Abstract: This thesis presents the design, analysis, and validation of a hierarchical
transactive control system that engages demand response resources to enhance
the integration of renewable electricity generation resources. This control
system joins energy, capacity and regulation markets together in a unified
homeostatic and economically efficient electricity operation that increases
total surplus while improving reliability and decreasing carbon emissions from
fossil-based generation resources.
The work encompasses: (1) the derivation of a short-term demand response
model suitable for transactive control systems and its validation with field
demonstration data; (2) an aggregate load model that enables effective control
of large populations of thermal loads using a new type of thermostat (discrete
time with zero deadband); (3) a methodology for optimally controlling response
to frequency deviations while tracking schedule area exports in areas that have
high penetration of both intermittent renewable resources and fast-acting
demand response; and (4) the development of a system-wide (continental
interconnection) scale strategy for optimal power trajectory and resource
dispatch based on a shift from primarily energy cost-based approach to a
primarily ramping cost-based one.
The results show that multi-layer transactive control systems can be
constructed, will enhance renewable resource utilization, and will operate in a
coordinated manner with bulk power systems that include both regions with and
without organized power markets. Estimates of Western Electric Coordionating
Council (WECC) system cost savings under target renewable energy generation
levels resulting from the proposed system exceed US$150B annually by the year
2024, when compared to the existing control system. | [
0,
1,
0,
0,
0,
0
] |
Title: Constraining the contribution of active galactic nuclei to reionisation,
Abstract: Recent results have suggested that active galactic nuclei (AGN) could provide
enough photons to reionise the Universe. We assess the viability of this
scenario using a semi-numerical framework for modeling reionisation, to which
we add a quasar contribution by constructing a Quasar Halo Occupation
Distribution (QHOD) based on Giallongo et al. observations. Assuming a constant
QHOD, we find that an AGN-only model cannot simultaneously match observations
of the optical depth $\tau_e$, neutral fraction, and ionising emissivity. Such
a model predicts $\tau_e$ too low by $\sim 2\sigma$ relative to Planck
constraints, and reionises the Universe at $z\lesssim 5$. Arbitrarily
increasing the AGN emissivity to match these results yields a strong mismatch
with the observed ionising emissivity at $z\sim 5$. If we instead assume a
redshift-independent AGN luminosity function yielding an emissivity evolution
like that assumed in Madau & Haardt model, then we can match $\tau_e$ albeit
with late reionisation, however such evolution is inconsistent with
observations at $z\sim 4-6$ and poorly motivated physically. These results
arise because AGN are more biased towards massive halos than typical reionising
galaxies, resulting in stronger clustering and later formation times.
AGN-dominated models produce larger ionising bubbles that are reflected in
$\sim\times 2$ more 21cm power on all scales. A model with equal parts galaxies
and AGN contribution is still (barely) consistent with observations, but could
be distinguished using next-generation 21cm experiments HERA and SKA-low. We
conclude that, even with recent claims of more faint AGN than previously
thought, AGN are highly unlikely to dominate the ionising photon budget for
reionisation. | [
0,
1,
0,
0,
0,
0
] |
Title: Two-walks degree assortativity in graphs and networks,
Abstract: Degree ssortativity is the tendency for nodes of high degree (resp.low
degree) in a graph to be connected to high degree nodes (resp. to low degree
ones). It is sually quantified by the Pearson correlation coefficient of the
degree-degree correlation. Here we extend this concept to account for the
effect of second neighbours to a given node in a graph. That is, we consider
the two-walks degree of a node as the sum of all the degrees of its adjacent
nodes. The two-walks degree assortativity of a graph is then the Pearson
correlation coefficient of the two-walks degree-degree correlation. We found
here analytical expression for this two-walks degree assortativity index as a
function of contributing subgraphs. We then study all the 261,000 connected
graphs with 9 nodes and observe the existence of assortative-assortative and
disassortative-disassortative graphs according to degree and two-walks degree,
respectively. More surprinsingly, we observe a class of graphs which are degree
disassortative and two-walks degree assortative. We explain the existence of
some of these graphs due to the presence of certain topological features, such
as a node of low-degree connected to high-degree ones. More importantly, we
study a series of 49 real-world networks, where we observe the existence of the
disassortative-assortative class in several of them. In particular, all
biological networks studied here were in this class. We also conclude that no
graphs/networks are possible with assortative-disassortative structure. | [
1,
1,
0,
0,
0,
0
] |
Title: Variational Analysis of Constrained M-Estimators,
Abstract: We propose a unified framework for establishing existence of nonparametric
M-estimators, computing the corresponding estimates, and proving their strong
consistency when the class of functions is exceptionally rich. In particular,
the framework addresses situations where the class of functions is complex
involving information and assumptions about shape, pointwise bounds, location
of modes, height at modes, location of level-sets, values of moments, size of
subgradients, continuity, distance to a `prior' function, multivariate total
positivity, and any combination of the above. The class might be engineered to
perform well in a specific setting even in the presence of little data. The
framework views the class of functions as a subset of a particular metric space
of upper semicontinuous functions under the Attouch-Wets distance. In addition
to allowing a systematic treatment of numerous M-estimators, the framework
yields consistency of plug-in estimators of modes of densities, maximizers of
regression functions, and related quantities, and also enables computation by
means of approximating parametric classes. We establish consistency through a
one-sided law of large numbers, here extended to sieves, that relaxes
assumptions of uniform laws, while ensuring global approximations even under
model misspecification. | [
0,
0,
1,
1,
0,
0
] |
Title: Minimally-Supervised Attribute Fusion for Data Lakes,
Abstract: Aggregate analysis, such as comparing country-wise sales versus global market
share across product categories, is often complicated by the unavailability of
common join attributes, e.g., category, across diverse datasets from different
geographies or retail chains, even after disparate data is technically ingested
into a common data lake. Sometimes this is a missing data issue, while in other
cases it may be inherent, e.g., the records in different geographical databases
may actually describe different product 'SKUs', or follow different norms for
categorization. Record linkage techniques can be used to automatically map
products in different data sources to a common set of global attributes,
thereby enabling federated aggregation joins to be performed. Traditional
record-linkage techniques are typically unsupervised, relying textual
similarity features across attributes to estimate matches. In this paper, we
present an ensemble model combining minimal supervision using Bayesian network
models together with unsupervised textual matching for automating such
'attribute fusion'. We present results of our approach on a large volume of
real-life data from a market-research scenario and compare with a standard
record matching algorithm. Finally we illustrate how attribute fusion using
machine learning could be included as a data-lake management feature,
especially as our approach also provides confidence values for matches,
enabling human intervention, if required. | [
1,
0,
0,
0,
0,
0
] |
Title: Diffusivities bounds in the presence of Weyl corrections,
Abstract: In this paper, we investigate the behavior of the thermoelectric DC
conductivities in the presence of Weyl corrections with momentum dissipation in
the incoherent limit. Moreover, we compute the butterfly velocity and study the
charge and energy diffusion with broken translational symmetry. Our results
show that the Weyl coupling $\gamma$, violates the bounds on the charge and
energy diffusivity. It is also shown that the Weyl corrections violate the
bound on the DC electrical conductivity in the incoherent limit. | [
0,
1,
0,
0,
0,
0
] |
Title: Family-specific scaling laws in bacterial genomes,
Abstract: Among several quantitative invariants found in evolutionary genomics, one of
the most striking is the scaling of the overall abundance of proteins, or
protein domains, sharing a specific functional annotation across genomes of
given size. The size of these functional categories change, on average, as
power-laws in the total number of protein-coding genes. Here, we show that such
regularities are not restricted to the overall behavior of high-level
functional categories, but also exist systematically at the level of single
evolutionary families of protein domains. Specifically, the number of proteins
within each family follows family-specific scaling laws with genome size.
Functionally similar sets of families tend to follow similar scaling laws, but
this is not always the case. To understand this systematically, we provide a
comprehensive classification of families based on their scaling properties.
Additionally, we develop a quantitative score for the heterogeneity of the
scaling of families belonging to a given category or predefined group. Under
the common reasonable assumption that selection is driven solely or mainly by
biological function, these findings point to fine-tuned and interdependent
functional roles of specific protein domains, beyond our current functional
annotations. This analysis provides a deeper view on the links between
evolutionary expansion of protein families and the functional constraints
shaping the gene repertoire of bacterial genomes. | [
0,
1,
0,
0,
0,
0
] |
Title: Crowdsourcing Multiple Choice Science Questions,
Abstract: We present a novel method for obtaining high-quality, domain-targeted
multiple choice questions from crowd workers. Generating these questions can be
difficult without trading away originality, relevance or diversity in the
answer options. Our method addresses these problems by leveraging a large
corpus of domain-specific text and a small set of existing questions. It
produces model suggestions for document selection and answer distractor choice
which aid the human question generation process. With this method we have
assembled SciQ, a dataset of 13.7K multiple choice science exam questions
(Dataset available at this http URL). We demonstrate that the
method produces in-domain questions by providing an analysis of this new
dataset and by showing that humans cannot distinguish the crowdsourced
questions from original questions. When using SciQ as additional training data
to existing questions, we observe accuracy improvements on real science exams. | [
1,
0,
0,
1,
0,
0
] |
Title: Ce 3$p$ hard x-ray photoelectron spectroscopy study of the topological Kondo insulator CeRu$_4$Sn$_6$,
Abstract: Bulk sensitive hard x-ray photoelectron spectroscopy data of the Ce 3$p$ core
level of CeRu$_4$Sn$_6$ are presented. Using a combination of full multiplet
and configuration iteration model we were able to obtain an accurate lineshape
analysis of the data, thereby taking into account correlations for the strong
plasmon intensities. We conclude that CeRu$_4$Sn$_6$ is a moderately mixed
valence compound with a weight of 8% for the Ce $f^0$ configuration in the
ground state. | [
0,
1,
0,
0,
0,
0
] |
Title: A short proof of the middle levels theorem,
Abstract: Consider the graph that has as vertices all bitstrings of length $2n+1$ with
exactly $n$ or $n+1$ entries equal to 1, and an edge between any two bitstrings
that differ in exactly one bit. The well-known middle levels conjecture asserts
that this graph has a Hamilton cycle for any $n\geq 1$. In this paper we
present a new proof of this conjecture, which is much shorter and more
accessible than the original proof. | [
1,
0,
0,
0,
0,
0
] |
Title: Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification,
Abstract: After being trained, classifiers must often operate on data that has been
corrupted by noise. In this paper, we consider the impact of such noise on the
features of binary classifiers. Inspired by tools for classifier robustness, we
introduce the same classification probability (SCP) to measure the resulting
distortion on the classifier outputs. We introduce a low-complexity estimate of
the SCP based on quantization and polynomial multiplication. We also study
channel coding techniques based on replication error-correcting codes. In
contrast to the traditional channel coding approach, where error-correction is
meant to preserve the data and is agnostic to the application, our schemes
specifically aim to maximize the SCP (equivalently minimizing the distortion of
the classifier output) for the same redundancy overhead. | [
1,
0,
0,
1,
0,
0
] |
Title: Kropina change of a Finsler space with m-th root metric,
Abstract: In this paper, we find a condition under which a Finsler space with Kropina
change of mth-root metric is projectively related to a mth-root metric and also
we find a condition under which this Kropina transformed mth-root metric is
locally dually flat. Moreover we find the condition for its Projective
flatness. | [
0,
0,
1,
0,
0,
0
] |
Title: Direct Visualization of 2D Topological Insulator in Single-layer 1T'-WTe2,
Abstract: We grow nearly freestanding single-layer 1T'-WTe2 on graphitized 6H-SiC(0001)
by using molecular beam epitaxy (MBE), and characterize its electronic
structure with scanning tunneling microscopy / spectroscopy (STM/STS). We
demonstrate the existence of topological edge states at the periphery of
single-layer WTe2 islands. Surprisingly, we also find a band gap in the bulk
and the semiconducting behaviors of the single-layer WTe2 at low temperature,
which is likely resulted from an incommensurate charge density wave (CDW)
transition. The realization of two-dimensional topological insulators (2D TIs)
in single-layer transition metal dichalcogenide (TMD) thus provides a promising
platform for further exploration of the 2D TIs' physics and related
applications. | [
0,
1,
0,
0,
0,
0
] |
Title: Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields,
Abstract: This paper is concerned with structured machine learning, in a supervised
machine learning context. It discusses how to make joint structured learning on
interdependent objects of different nature, as well as how to enforce logical
con-straints when predicting labels. We explain how this need arose in a
Document Understanding task. We then discuss a general extension to Conditional
Random Field (CRF) for this purpose and present the contributed open source
implementation on top of the open source PyStruct library. We evaluate its
performance on a publicly available dataset. | [
1,
0,
0,
1,
0,
0
] |
Title: Mobile big data analysis with machine learning,
Abstract: This paper investigates to identify the requirement and the development of
machine learning-based mobile big data analysis through discussing the insights
of challenges in the mobile big data (MBD). Furthermore, it reviews the
state-of-the-art applications of data analysis in the area of MBD. Firstly, we
introduce the development of MBD. Secondly, the frequently adopted methods of
data analysis are reviewed. Three typical applications of MBD analysis, namely
wireless channel modeling, human online and offline behavior analysis, and
speech recognition in the internet of vehicles, are introduced respectively.
Finally, we summarize the main challenges and future development directions of
mobile big data analysis. | [
0,
0,
0,
1,
0,
0
] |
Title: Enhancement of Galaxy Overdensity around Quasar Pairs at z<3.6 based on the Hyper Suprime-Cam Subaru Strategic Program Survey,
Abstract: We investigate the galaxy overdensity around proto-cluster scale quasar pairs
at high (z>3) and low (z~1) redshift based on the unprecedentedly wide and deep
optical survey of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP).
Using the first-year survey data covering effectively ~121 deg^2 with the
5sigma depth of i~26.4 and the SDSS DR12Q catalog, we find two luminous pairs
at z~3.3 and 3.6 which reside in >5sigma overdense regions of g-dropout
galaxies at i<25. The projected separations of the two pairs are R_perp=1.75
and 1.04 proper Mpc, and their velocity offsets are Delta V=692 and 1448 km
s^{-1}, respectively. This result is in clear contrast to the average z~4
quasar environments as discussed in Uchiyama et al. (2017) and implies that the
quasar activities of the pair members are triggered via major mergers in
proto-clusters, unlike the vast majority of isolated quasars in general fields
that may turn on via non-merger events such as bar and disk instabilities. At
z~1, we find 37 pairs with R_perp<2 pMpc and Delta V<2300 km s^{-1} in the
current HSC-Wide coverage, including four from Hennawi et al. (2006). The
distribution of the peak overdensity significance within two arcminutes around
the pairs has a long tail toward high density (>4sigma) regions. Thanks to the
large sample size, we find a statistical evidence that this excess is unique to
the pair environments when compared to single quasar and randomly selected
galaxy environments at the same redshift range. Moreover, there are nine
small-scale (R_perp<1 pMpc) pairs, two of which are found to reside in cluster
fields. Our results demonstrate that <2 pMpc-scale quasar pairs at both
redshift range tend to occur in massive haloes, although perhaps not the most
massive ones, and that they are useful to search for rare density peaks. | [
0,
1,
0,
0,
0,
0
] |
Title: Search for cosmic dark matter by means of ultra high purity NaI(Tl) scintillator,
Abstract: The dark matter search project by means of ultra high purity NaI(Tl)
scintillator is now underdevelopment. An array of large volume NaI(Tl)
detectors whose volume is 12.7 cm$\phi\times$12.7 cm is applied to search for
dark matter signal. To remove radioactive impurities in NaI(Tl) crystal is one
of the most important task to find small number of dark matter signals. We have
developed high purity NaI(Tl) crystal which contains small amounts of
radioactive impurities, $<4$ ppb of $^{nat}$K, 0.3 ppt of Th chain, 58
$\mu$Bq/kg of $^{226}$Ra and 30 $\mu$Bq/kg of $^{210}$Pb. Future prospects to
search for dark matter by means of a large volume and high purity NaI(Tl)
scintillator is discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition,
Abstract: Recurrent neural networks have been the dominant models for many speech and
language processing tasks. However, we understand little about the behavior and
the class of functions recurrent networks can realize. Moreover, the heuristics
used during training complicate the analyses. In this paper, we study recurrent
networks' ability to learn long-term dependency in the context of speech
recognition. We consider two decoding approaches, online and batch decoding,
and show the classes of functions to which the decoding approaches correspond.
We then draw a connection between batch decoding and a popular training
approach for recurrent networks, truncated backpropagation through time.
Changing the decoding approach restricts the amount of past history recurrent
networks can use for prediction, allowing us to analyze their ability to
remember. Empirically, we utilize long-term dependency in subphonetic states,
phonemes, and words, and show how the design decisions, such as the decoding
approach, lookahead, context frames, and consecutive prediction, characterize
the behavior of recurrent networks. Finally, we draw a connection between
Markov processes and vanishing gradients. These results have implications for
studying the long-term dependency in speech data and how these properties are
learned by recurrent networks. | [
1,
0,
0,
0,
0,
0
] |
Title: Wavelength Dependence of Picosecond Laser-Induced Periodic Surface Structures on Copper,
Abstract: The physical mechanisms of the laser-induced periodic surface structures
(LIPSS) formation are studied in this paper for single-pulse irradiation
regimes. The change in the LIPSS period with wavelength of incident laser
radiation is investigated experimentally, using a picosecond laser system,
which provides 7-ps pulses in near-IR, visible, and UV spectral ranges. The
experimental results are compared with predictions made under the assumption
that the surface-scattered waves are involved in the LIPSS formation.
Considerable disagreement suggests that hydrodynamic mechanisms can be
responsible for the observed pattern periodicity. | [
0,
1,
0,
0,
0,
0
] |
Title: Time-efficient Garbage Collection in SSDs,
Abstract: SSDs are currently replacing magnetic disks in many application areas. A
challenge of the underlying flash technology is that data cannot be updated
in-place. A block consisting of many pages must be completely erased before a
single page can be rewritten. This victim block can still contain valid pages
which need to be copied to other blocks before erasure. The objective of
garbage collection strategies is to minimize write amplification induced by
copying valid pages from victim blocks while minimizing the performance
overhead of the victim selection. Victim selection strategies minimizing write
amplification, like the cost-benefit approach, have linear runtime, while the
write amplifications of time-efficient strategies, like the greedy strategy,
significantly reduce the lifetime of SSDs. In this paper, we propose two
strategies which optimize the performance of cost-benefit, while (almost)
preserving its write amplification. Trace-driven simulations for single- and
multi-channel SSDs show that the optimizations help to keep the write
amplification low while improving the runtime by up to 24-times compared to the
original cost-benefit strategy, so that the new strategies can be used in
multi-TByte SSDs. | [
1,
0,
0,
0,
0,
0
] |
Title: Annealed limit theorems for the ising model on random regular graphs,
Abstract: In a recent paper [15], Giardin{à}, Giberti, Hofstad, Prioriello have
proved a law of large number and a central limit theorem with respect to the
annealed measure for the magnetization of the Ising model on some random graphs
including the random 2-regular graph. We present a new proof of their results,
which applies to all random regular graphs. In addition, we prove the existence
of annealed pressure in the case of configuration model random graphs. | [
0,
1,
1,
0,
0,
0
] |
Title: Multi-Task Learning of Keyphrase Boundary Classification,
Abstract: Keyphrase boundary classification (KBC) is the task of detecting keyphrases
in scientific articles and labelling them with respect to predefined types.
Although important in practice, this task is so far underexplored, partly due
to the lack of labelled data. To overcome this, we explore several auxiliary
tasks, including semantic super-sense tagging and identification of multi-word
expressions, and cast the task as a multi-task learning problem with deep
recurrent neural networks. Our multi-task models perform significantly better
than previous state of the art approaches on two scientific KBC datasets,
particularly for long keyphrases. | [
1,
0,
0,
1,
0,
0
] |
Title: On the interior motive of certain Shimura varieties : the case of Picard varieties,
Abstract: The aim of this article is the construction of the interior motive of a
Picard variety. Those are Shimura varieties of PEL type. Our result is an
application of the strategy developed by Wildeshaus to construct a
Hecke-invariant motive whose realizations correspond to interior cohomology. | [
0,
0,
1,
0,
0,
0
] |
Title: SenGen: Sentence Generating Neural Variational Topic Model,
Abstract: We present a new topic model that generates documents by sampling a topic for
one whole sentence at a time, and generating the words in the sentence using an
RNN decoder that is conditioned on the topic of the sentence. We argue that
this novel formalism will help us not only visualize and model the topical
discourse structure in a document better, but also potentially lead to more
interpretable topics since we can now illustrate topics by sampling
representative sentences instead of bag of words or phrases. We present a
variational auto-encoder approach for learning in which we use a factorized
variational encoder that independently models the posterior over topical
mixture vectors of documents using a feed-forward network, and the posterior
over topic assignments to sentences using an RNN. Our preliminary experiments
on two different datasets indicate early promise, but also expose many
challenges that remain to be addressed. | [
1,
0,
0,
1,
0,
0
] |
Title: Atomistic-continuum multiscale modelling of magnetisation dynamics at non-zero temperature,
Abstract: In this article, a few problems related to multiscale modelling of magnetic
materials at finite temperatures and possible ways of solving these problems
are discussed. The discussion is mainly centred around two established
multiscale concepts: the partitioned domain and the upscaling-based
methodologies. The major challenge for both multiscale methods is to capture
the correct value of magnetisation length accurately, which is affected by a
random temperature-dependent force. Moreover, general limitations of these
multiscale techniques in application to spin systems are discussed. | [
0,
1,
1,
0,
0,
0
] |
Title: Regularly Varying Functions, Generalized contents, and the spectrum of fractal strings,
Abstract: We revisit the problem of characterizing the eigenvalue distribution of the
Dirichlet-Laplacian on bounded open sets $\Omega\subset\mathbb{R}$ with fractal
boundaries. It is well-known from the results of Lapidus and Pomerance
\cite{LapPo1} that the asymptotic second term of the eigenvalue counting
function can be described in terms of the Minkowski content of the boundary of
$\Omega$ provided it exists. He and Lapidus \cite{HeLap2} discussed a
remarkable extension of this characterization to sets $\Omega$ with boundaries
that are not necessarily Minkowski measurable. They employed so-called
generalized Minkowski contents given in terms of gauge functions more general
than the usual power functions. The class of valid gauge functions in their
theory is characterized by some technical conditions, the geometric meaning and
necessity of which is not obvious. Therefore, it is not completely clear how
general the approach is and which sets $\Omega$ are covered. Here we revisit
these results and put them in the context of regularly varying functions. Using
Karamata theory, it is possible to get rid of most of the technical conditions
and simplify the proofs given by He and Lapidus, revealing thus even more of
the beauty of their results. Further simplifications arise from
characterization results for Minkowski contents obtained in \cite{RW13}. We
hope our new point of view on these spectral problems will initiate some
further investigations of this beautiful theory. | [
0,
0,
1,
0,
0,
0
] |
Title: The Geodesic Distance between $\mathcal{G}_I^0$ Models and its Application to Region Discrimination,
Abstract: The $\mathcal{G}_I^0$ distribution is able to characterize different regions
in monopolarized SAR imagery. It is indexed by three parameters: the number of
looks (which can be estimated in the whole image), a scale parameter and a
texture parameter. This paper presents a new proposal for feature extraction
and region discrimination in SAR imagery, using the geodesic distance as a
measure of dissimilarity between $\mathcal{G}_I^0$ models. We derive geodesic
distances between models that describe several practical situations, assuming
the number of looks known, for same and different texture and for same and
different scale. We then apply this new tool to the problems of (i)~identifying
edges between regions with different texture, and (ii)~quantify the
dissimilarity between pairs of samples in actual SAR data. We analyze the
advantages of using the geodesic distance when compared to stochastic
distances. | [
1,
0,
0,
1,
0,
0
] |
Title: Search for Common Minima in Joint Optimization of Multiple Cost Functions,
Abstract: We present a novel optimization method, named the Combined Optimization
Method (COM), for the joint optimization of two or more cost functions. Unlike
the conventional joint optimization schemes, which try to find minima in a
weighted sum of cost functions, the COM explores search space for common minima
shared by all the cost functions. Given a set of multiple cost functions that
have qualitatively different distributions of local minima with each other, the
proposed method finds the common minima with a high success rate without the
help of any metaheuristics. As a demonstration, we apply the COM to the crystal
structure prediction in materials science. By introducing the concept of data
assimilation, i.e., adopting the theoretical potential energy of the crystal
and the crystallinity, which characterizes the agreement with the theoretical
and experimental X-ray diffraction patterns, as cost functions, we show that
the correct crystal structures of Si diamond, low quartz, and low cristobalite
can be predicted with significantly higher success rates than the previous
methods. | [
0,
0,
0,
1,
0,
0
] |
Title: Statistical Inferences for Polarity Identification in Natural Language,
Abstract: Information forms the basis for all human behavior, including the ubiquitous
decision-making that people constantly perform in their every day lives. It is
thus the mission of researchers to understand how humans process information to
reach decisions. In order to facilitate this task, this work proposes a novel
method of studying the reception of granular expressions in natural language.
The approach utilizes LASSO regularization as a statistical tool to extract
decisive words from textual content and draw statistical inferences based on
the correspondence between the occurrences of words and an exogenous response
variable. Accordingly, the method immediately suggests significant implications
for social sciences and Information Systems research: everyone can now identify
text segments and word choices that are statistically relevant to authors or
readers and, based on this knowledge, test hypotheses from behavioral research.
We demonstrate the contribution of our method by examining how authors
communicate subjective information through narrative materials. This allows us
to answer the question of which words to choose when communicating negative
information. On the other hand, we show that investors trade not only upon
facts in financial disclosures but are distracted by filler words and
non-informative language. Practitioners - for example those in the fields of
investor communications or marketing - can exploit our insights to enhance
their writings based on the true perception of word choice. | [
1,
0,
0,
1,
0,
0
] |
Title: Effect of Adaptive and Cooperative Adaptive Cruise Control on Throughput of Signalized Arterials,
Abstract: The paper evaluates the influence of the maximum vehicle acceleration and
variable proportions of ACC/CACC vehicles on the throughput of an intersection.
Two cases are studied: (1) free road downstream of the intersection; and (2)
red light at some distance downstream of the intersection. Simulation of a
4-mile stretch of an arterial with 13 signalized intersections is used to
evaluate the impact of (C)ACC vehicles on the mean and standard deviation of
travel time as the proportion of (C)ACC vehicles is increased. The results
suggest a very high urban mobility benefit of (C)ACC vehicles at little or no
cost in infrastructure. | [
1,
0,
0,
0,
0,
0
] |
Title: Very cost effective bipartition in Gamma(Z_n),
Abstract: Let Z_n be the finite commutative ring of residue classes modulo n and
Gamma(Z_n) be its zero-divisor graph. The nilradical graph and non-nilradical
graph of Z_n are denoted by N(Z_n) and Omega(Z_n) respectively. In 2012, Haynes
et al. [5] introduced the concept of very cost effective graph. For a graph G =
(V,E) and a set of vertices S subset of V, a vertex v in S is said to be very
cost effective if it is adjacent to more vertices in V§than in S. A
bipartition Pi = {S, V§} is called very cost effective if both S and V§are
very cost effective sets [5,6]. In this paper, we investigate the very cost
effective bipartition of Gamma(Z_n), where n = p_1 p_2 ... p_m, here all p_i's
are distinct primes. In addition, we discuss the cases in which N(Z_n) and
Omega(Z_n) graphs have very cost effective bipartition for different n.
Finally, we derive some results for very cost effective bipartition of the Line
graph and Total graph of Gamma(Z_n), denoted by L(Gamma(Z_n)) and T(Gamma(Z_n))
respectively. | [
0,
0,
1,
0,
0,
0
] |
Title: Doubly dressed bosons - exciton-polaritons in a strong terahertz field,
Abstract: We demonstrate the existence of a novel quasiparticle: an exciton in a
semiconductor doubly dressed with two photons of different wavelengths: near
infrared cavity photon and terahertz (THz) photon, with the THz coupling
strength approaching the ultra-strong coupling regime. This quasiparticle is
composed of three different bosons, being a mixture of a matter-light
quasiparticle. Our observations are confirmed by a detailed theoretical
analysis, treating quantum mechanically all three bosonic fields. The doubly
dressed quasiparticles retain the bosonic nature of their constituents, but
their internal quantum structure strongly depends on the intensity of the
applied terahertz field. | [
0,
1,
0,
0,
0,
0
] |
Title: Fast Stability Scanning for Future Grid Scenario Analysis,
Abstract: Future grid scenario analysis requires a major departure from conventional
power system planning, where only a handful of most critical conditions is
typically analyzed. To capture the inter-seasonal variations in renewable
generation of a future grid scenario necessitates the use of computationally
intensive time-series analysis. In this paper, we propose a planning framework
for fast stability scanning of future grid scenarios using a novel feature
selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm.
To achieve the computational speed-up, the stability analysis is performed only
on small number of representative cluster centroids instead of on the full set
of operating conditions. As a case study, we perform small-signal stability and
steady-state voltage stability scanning of a simplified model of the Australian
National Electricity Market with significant penetration of renewable
generation. The simulation results show the effectiveness of the proposed
approach. Compared to an exhaustive time series scanning, the proposed
framework reduced the computational burden up to ten times, with an acceptable
level of accuracy. | [
1,
0,
0,
1,
0,
0
] |
Title: Femtosecond Mega-electron-volt Electron Energy-Loss Spectroscopy,
Abstract: Pump-probe electron energy-loss spectroscopy (EELS) with femtosecond temporal
resolution will be a transformative research tool for studying non-equilibrium
chemistry and electronic dynamics of matter. In this paper, we propose a new
concept of femtosecond EELS utilizing mega-electron-volt electron beams from a
radio-frequency (rf) photocathode source. The high acceleration gradient and
high beam energy of the rf gun are critical to the generation of 10-femtosecond
electron beams, which enables improvement of the temporal resolution by more
than one order of magnitude beyond the state of the art. The major innovation
in our proposal - the `reference-beam technique', relaxes the energy stability
requirement on the rf power source by roughly two orders of magnitude.
Requirements on the electron beam quality, photocathode, spectrometer and
detector are also discussed. Supported by particle-tracking simulations, we
demonstrate the feasibility of achieving sub-electron-volt energy resolution
and ~10-femtosecond temporal resolution with existing or near-future hardware
performances. | [
0,
1,
0,
0,
0,
0
] |
Title: On the Power of Symmetric Linear Programs,
Abstract: We consider families of symmetric linear programs (LPs) that decide a
property of graphs (or other relational structures) in the sense that, for each
size of graph, there is an LP defining a polyhedral lift that separates the
integer points corresponding to graphs with the property from those
corresponding to graphs without the property. We show that this is equivalent,
with at most polynomial blow-up in size, to families of symmetric Boolean
circuits with threshold gates. In particular, when we consider polynomial-size
LPs, the model is equivalent to definability in a non-uniform version of
fixed-point logic with counting (FPC). Known upper and lower bounds for FPC
apply to the non-uniform version. In particular, this implies that the class of
graphs with perfect matchings has polynomial-size symmetric LPs while we obtain
an exponential lower bound for symmetric LPs for the class of Hamiltonian
graphs. We compare and contrast this with previous results (Yannakakis 1991)
showing that any symmetric LPs for the matching and TSP polytopes have
exponential size. As an application, we establish that for random, uniformly
distributed graphs, polynomial-size symmetric LPs are as powerful as general
Boolean circuits. We illustrate the effect of this on the well-studied
planted-clique problem. | [
1,
0,
0,
0,
0,
0
] |
Title: Pulsar science with the CHIME telescope,
Abstract: The CHIME telescope (the Canadian Hydrogen Intensity Mapping Experiment)
recently built in Penticton, Canada, is currently being commissioned.
Originally designed as a cosmology experiment, it was soon recognized that
CHIME has the potential to simultaneously serve as an incredibly useful radio
telescope for pulsar science. CHIME operates across a wide bandwidth of 400-800
MHz and will have a collecting area and sensitivity comparable to that of the
100-m class radio telescopes. CHIME has a huge field of view of ~250 square
degrees. It will be capable of observing 10 pulsars simultaneously, 24-hours
per day, every day, while still accomplishing its missions to study Baryon
Acoustic Oscillations and Fast Radio Bursts. It will carry out daily monitoring
of roughly half of all pulsars in the northern hemisphere, including all
NANOGrav pulsars employed in the Pulsar Timing Array project. It will cycle
through all pulsars in the northern hemisphere with a range of cadence of no
more than 10 days. | [
0,
1,
0,
0,
0,
0
] |
Title: Approximation by generalized Kantorovich sampling type series,
Abstract: In the present article, we analyse the behaviour of a new family of
Kantorovich type sampling operators $(K_w^{\varphi}f)_{w>0}.$ First, we give a
Voronovskaya type theorem for these Kantorovich generalized sampling series and
a corresponding quantitative version in terms of the first order of modulus of
continuity. Further, we study the order of approximation in $C({\mathbb{R}})$
(the set of all uniformly continuous and bounded functions on ${\mathbb{R}}$)
for the family $(K_w^{\varphi}f)_{w>0}.$ Finally, we give some examples of
kernels such as B-spline kernels and Blackman-Harris kernel to which the theory
can be applied. | [
0,
0,
1,
0,
0,
0
] |
Title: Mechanical Instability Leading Epithelial Cell Delamination,
Abstract: We theoretically investigate the mechanical stability of three-dimensional
(3D) foam geometry in a cell sheet and apply its understandings to epithelial
integrity and cell delamination. Analytical calculations revealed that the
monolayer integrity of cell sheet is lost to delamination by a spontaneous
symmetry breaking, inherently depending on the 3D foam geometry of cells; i.e.,
the instability spontaneously appears when the cell density in the sheet plane
increases and/or when the number of neighboring cells decreases, as observed in
vivo. The instability is also facilitated by the delaminated cell-specific
force generation upon lateral surfaces, which are driven by cell-intrinsic
genetic programs during cell invasion and apoptosis in physiology. In
principle, this instability emerges from the force balance on the lateral
boundaries among cells. Additionally, taking into account the cell-intrinsic
force generation on apical and basal sides, which are also broadly observed in
morphogenesis, homeostasis, and carcinogenesis, we found apically/basally
directed cell delaminations and pseudostratified structures, which could
universally explain mechanical regulations of epithelial geometries in both
physiology and pathophysiology. | [
0,
0,
0,
0,
1,
0
] |
Title: A two-layer shallow water model for bedload sediment transport: convergence to Saint-Venant-Exner model,
Abstract: A two-layer shallow water type model is proposed to describe bedload sediment
transport. The upper layer is filled by water and the lower one by sediment.
The key point falls on the definition of the friction laws between the two
layers, which are a generalization of those introduced in Fernández-Nieto et
al. (ESAIM: M2AN, 51:115-145, 2017). This definition allows to apply properly
the two-layer shallow water model for the case of intense and slow bedload
sediment transport. Moreover, we prove that the two-layer model converges to a
Saint-Venant-Exner system (SVE) including gravitational effects when the ratio
between the hydrodynamic and morphodynamic time scales is small. The SVE with
gravitational effects is a degenerated nonlinear parabolic system. This means
that its numerical approximation is very expensive from a computational point
of view, see for example T. Morales de Luna et al. (J. Sci. Comp., 48(1):
258-273, 2011). In this work, gravitational effects are introduced into the
two-layer system without such extra computational cost. Finally, we also
consider a generalization of the model that includes a non-hydrostatic pressure
correction for the fluid layer and the boundary condition at the sediment
surface. Numerical tests show that the model provides promising results and
behave well in low transport rate regimes as well as in many other situations. | [
0,
1,
0,
0,
0,
0
] |
Title: Chondrule Accretion with a Growing Protoplanet,
Abstract: Chondrules are primitive materials in the Solar System. They are formed in
the first about 3 Myr of the Solar System's history. This timescale is longer
than that of Mars formation, and it is conceivable that protoplanets,
planetesimals and chondrules might have existed simultaneously in the solar
nebula. Due to protoplanets perturbation on the planetesimal dynamics and
chondrule accretion on them, all the formed chondrules are unlikely to be
accreted by planetesimals. We investigate the amount of chondrules accreted by
planetesimals in such a condition. We assume that a protoplanet is in
oligarchic growth, and we perform analytical calculations of chondrule
accretion both by a protoplanet and by planetesimals. Through the oligarchic
growth stage, planetesimals accrete about half of the formed chondrules. The
smallest planetesimals get the largest amount of the chondrules, compared with
the amount accreted by more massive planetesimals. We perform a parameter study
and find that this fraction is not largely changed for a wide range of
parameter sets. | [
0,
1,
0,
0,
0,
0
] |
Title: Exemplar or Matching: Modeling DCJ Problems with Unequal Content Genome Data,
Abstract: The edit distance under the DCJ model can be computed in linear time for
genomes with equal content or with Indels. But it becomes NP-Hard in the
presence of duplications, a problem largely unsolved especially when Indels are
considered. In this paper, we compare two mainstream methods to deal with
duplications and associate them with Indels: one by deletion, namely
DCJ-Indel-Exemplar distance; versus the other by gene matching, namely
DCJ-Indel-Matching distance. We design branch-and-bound algorithms with set of
optimization methods to compute exact distances for both. Furthermore, median
problems are discussed in alignment with both of these distance methods, which
are to find a median genome that minimizes distances between itself and three
given genomes. Lin-Kernighan (LK) heuristic is leveraged and powered up by
sub-graph decomposition and search space reduction technologies to handle
median computation. A wide range of experiments are conducted on synthetic data
sets and real data sets to show pros and cons of these two distance metrics per
se, as well as putting them in the median computation scenario. | [
1,
0,
0,
0,
0,
0
] |
Title: MIHash: Online Hashing with Mutual Information,
Abstract: Learning-based hashing methods are widely used for nearest neighbor
retrieval, and recently, online hashing methods have demonstrated good
performance-complexity trade-offs by learning hash functions from streaming
data. In this paper, we first address a key challenge for online hashing: the
binary codes for indexed data must be recomputed to keep pace with updates to
the hash functions. We propose an efficient quality measure for hash functions,
based on an information-theoretic quantity, mutual information, and use it
successfully as a criterion to eliminate unnecessary hash table updates. Next,
we also show how to optimize the mutual information objective using stochastic
gradient descent. We thus develop a novel hashing method, MIHash, that can be
used in both online and batch settings. Experiments on image retrieval
benchmarks (including a 2.5M image dataset) confirm the effectiveness of our
formulation, both in reducing hash table recomputations and in learning
high-quality hash functions. | [
1,
0,
0,
0,
0,
0
] |
Title: On the Dedekind different of a Cayley-Bacharach scheme,
Abstract: Given a 0-dimensional scheme $\mathbb{X}$ in a projective space
$\mathbb{P}^n_K$ over a field $K$, we characterize the Cayley-Bacharach
property of $\mathbb{X}$ in terms of the algebraic structure of the Dedekind
different of its homogeneous coordinate ring. Moreover, we characterize
Cayley-Bacharach schemes by Dedekind's formula for the conductor and the
complementary module, we study schemes with minimal Dedekind different using
the trace of the complementary module, and we prove various results about
almost Gorenstein and nearly Gorenstein schemes. | [
0,
0,
1,
0,
0,
0
] |
Title: A note on the role of projectivity in likelihood-based inference for random graph models,
Abstract: There is widespread confusion about the role of projectivity in
likelihood-based inference for random graph models. The confusion is rooted in
claims that projectivity, a form of marginalizability, may be necessary for
likelihood-based inference and consistency of maximum likelihood estimators. We
show that likelihood-based superpopulation inference is not affected by lack of
projectivity and that projectivity is not a necessary condition for consistency
of maximum likelihood estimators. | [
0,
0,
1,
1,
0,
0
] |
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