title: Modular $A_n(V)$ theory

abstract:
  A series of associative algebras $A_n(V)$ for a vertex operator algebra $V$
over an arbitrary algebraically closed field and nonnegative integers $n$ are
constructed such that there is a one to one correspondence between irreducible
$A_n(V)$-modules which are not $A_{n-1}(V)$ modules and irreducible
$V$-modules. Moreover, $V$ is rational if and only if $A_n(V)$ is semisimple
for all $n.$ In particular, the homogeneous subspaces of any irreducible
$V$-module are finite dimensional for rational vertex operator algebra $V.$


title:
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic
  Segmentation

abstract:
  Recently, very deep convolutional neural networks (CNNs) have shown
outstanding performance in object recognition and have also been the first
choice for dense classification problems such as semantic segmentation.
However, repeated subsampling operations like pooling or convolution striding
in deep CNNs lead to a significant decrease in the initial image resolution.
Here, we present RefineNet, a generic multi-path refinement network that
explicitly exploits all the information available along the down-sampling
process to enable high-resolution prediction using long-range residual
connections. In this way, the deeper layers that capture high-level semantic
features can be directly refined using fine-grained features from earlier
convolutions. The individual components of RefineNet employ residual
connections following the identity mapping mindset, which allows for effective
end-to-end training. Further, we introduce chained residual pooling, which
captures rich background context in an efficient manner. We carry out
comprehensive experiments and set new state-of-the-art results on seven public
datasets. In particular, we achieve an intersection-over-union score of 83.4 on
the challenging PASCAL VOC 2012 dataset, which is the best reported result to
date.


title: Exotic-Hadron Signature by Constituent-Counting Rule in Perturbative QCD

abstract:
  We explain a method to find internal quark configurations of exotic hadron
candidates by using the constituent counting rule. The counting rule was
theoretically predicted in perturbative QCD for hard exclusive hadron
reactions, and it has been tested in experiments for stable hadrons including
compound systems of hadrons such as the deuteron, $^3$H, and $^3$He. It
indicates that the cross section scales as $d\sigma /dt \sim 1/s^{n-2}$, where
$s$ is the center-of-mass energy squared and $n$ is the total number of
constituents. We apply this method for finding internal configurations of
exotic hadron candidates, especially $\Lambda (1405)$. There is a possibility
that $\Lambda (1405)$ could be five-quark state or a $\bar K N$ molecule, and
scaling properties should be different between the ordinary three-quark state
or five-quark one. We predict such a difference in $\pi^- + p \to K^0 + \Lambda
(1405)$, and it could be experimentally tested, for example, at J-PARC. On the
other hand, there are already measurements for $\gamma + p \to K^+ + \Lambda
(1405)$ as well as the ground $\Lambda$ in photoproduction reactions. Analyzing
such data, we found an interesting indication that $\Lambda (1405)$ looks like
a five-quark state at medium energies and a three-quark one at high energies.
However, accurate higher-energy measurements are necessary for drawing a solid
conclusion, and it should be done at JLab by using the updated 12 GeV electron
beam. Furthermore, we discuss studies of exotic hadron candidates, such as $f_0
(980)$ and $a_0 (980)$, in electron-positron annihilation by using generalized
distribution amplitudes and the counting rule. These studies should be possible
as a KEKB experiment.


title: Large Deviation implies First and Second Laws of Thermodynamics

abstract:
  To reconstruct thermodynamics based on the microscopic laws is one of the
most important unfulfilled goals of statistical physics. Here, we show that the
first law and the second law for adiabatic processes are derived from an
assumption that "probability distributions of energy in Gibbs states satisfy
large deviation", which is widely accepted as a property of thermodynamic
equilibrium states. We define an adiabatic transformation as a randomized
energy-preserving unitary transformations on the many-body systems and the work
storage. As the second law, we show that an adiabatic transformation from a set
of Gibbs states to another set of Gibbs states is possible if and only if the
regularized von Neumann entropy becomes large. As the first law, we show that
the energy loss of the thermodynamic systems during the adiabatic
transformation is stored in the work storage as "work," in the following
meaning; (i) the energy of the work storage takes certain values
macroscopically, in the initial state and the final state. (ii) the entropy of
the work storage in the final state is macroscopically equal to the entropy of
the initial state. As corollaries, our results give the principle of maximam
work and the first law for the isothermal processes.


title:
FURL: Fixed-memory and Uncertainty Reducing Local Triangle Counting for
  Graph Streams

abstract:
  How can we accurately estimate local triangles for all nodes in simple and
multigraph streams? Local triangle counting in a graph stream is one of the
most fundamental tasks in graph mining with important applications including
anomaly detection and social network analysis. Although there have been several
local triangle counting methods in a graph stream, their estimation has a large
variance which results in low accuracy, and they do not consider multigraph
streams which have duplicate edges. In this paper, we propose FURL, an accurate
local triangle counting method for simple and multigraph streams. FURL improves
the accuracy by reducing a variance through biased estimation and handles
duplicate edges for multigraph streams. Also, FURL handles a stream of any size
by using a fixed amount of memory. Experimental results show that FURL
outperforms the state-of-the-art method in accuracy and performs well in
multigraph streams. In addition, we report interesting patterns discovered from
real graphs by FURL, which include unusual local structures in a user
communication network and a core-periphery structure in the Web.


title:
Collisionless electrostatic shock formation and ion acceleration in
  intense laser interactions with near critical density plasmas

abstract:
  Laser-driven collisonless electrostatic shock formation and the subsequent
ion acceleration have been studied in near critical density plasmas.
Particle-in-cell simulations show that both the speed of laser-driven
collisionless electrostatic shock and the energies of shock-accelerated ions
can be greatly enhanced due to fast laser propagation in near critical density
plasmas. However, a response time longer than tens of laser wave cycles is
required before the shock formation in a near critical density plasma, in
contrast to the quick shock formation in a highly overdense target. More
important, we find that some ions can be reflected by the collisionless shock
even if the electrostatic potential jump across the shock is smaller than the
ion kinetic energy in the shock frame, which seems against the conventional
ion-reflection condition. These anomalous ion reflections are attributed to the
strongly time-oscillating electric field accompanying laser-driven
collisionless shock in a near critical density plasma.


title: Kodaira fibrations and beyond: methods for moduli theory

abstract:
  Kodaira fibred surfaces are a remarkable example of projective classifying
spaces, and there are still many intriguing open questions concerning them,
especially the slope question. The topological characterization of Kodaira
fibrations is emblematic of the use of topological methods in the study of
moduli spaces of surfaces and higher dimensional complex algebraic varieties,
and their compactifications. The paper contains some new results but is mostly
a survey paper, dealing with fibrations, questions on monodromy and
factorizations in the mapping class group, old and new results on Variation of
Hodge Structures, especially a recent answer given (in joint work with
Dettweiler) to a long standing question posed by Fujita. In the landscape of
our tour, Galois coverings, deformations and rigid manifolds (new results
obtained with Ingrid Bauer) projective classifying spaces, the action of the
absolute Galois group on moduli spaces, stand also in the forefront. These
questions lead to interesting algebraic surfaces, for instance the BCDH
surfaces, hypersurfaces in Bagnera-de Franchis varieties, Inoue-type surfaces.


title:
3D Reconstruction of Heterogeneous Virus Particles with Statistical
  Geometric Symmetry

abstract:
  In 3-D reconstruction problems, the image data obtained from cryo electron
microscopy is the projection of many heterogeneous instances of the object
under study (e.g., a virus). When the object is heterogeneous but has an
overall symmetry, it is natural to describe the object as stochastic with
symmetrical statistics. This paper presents a maximum likelihood reconstruction
approach which allows each object to lack symmetry while constraining the {\it
statistics} of the ensemble of objects to have symmetry. This algorithm is
demonstrated on bacteriophage HK97 and is contrasted with an existing algorithm
in which each object, while still heterogeneous, has the symmetry.
Reconstruction results show that the proposed algorithm eliminates
long-standing distortions in previous heterogeneity calculations associated
with symmetry axes, and provides estimates that make more biologically sense
than the estimates of existing algorithms.


title:
Temperatures of renormalizable quantum field theories in curved
  spacetime

abstract:
  In this paper we compute the temperature registered by an Unruh-DeWitt
detector coupled to a Hadamard renormalizable quantum field in an arbitrary
state, moving along an accelerated trajectory in a curved spacetime. For a
massless and conformally invariant field, the generalized expression for the
temperature is given by the quadratic sum of the 4-acceleration, Raychaudhuri
scalar, and renormalized field polarization. We can further find a novel
constraint on the renormalized quantum field polarization in relativistic
systems that are in global thermal equilibrium.


title:
A Business Zone Recommender System Based on Facebook and Urban Planning
  Data

abstract:
  We present ZoneRec---a zone recommendation system for physical businesses in
an urban city, which uses both public business data from Facebook and urban
planning data. The system consists of machine learning algorithms that take in
a business' metadata and outputs a list of recommended zones to establish the
business in. We evaluate our system using data of food businesses in Singapore
and assess the contribution of different feature groups to the recommendation
quality.


title: Real-analytic AbC constructions on the torus

abstract:
  In this article we demonstrate a way to extend the AbC (approximation by
conjugation) method invented by Anosov and Katok from the smooth category to
the category of real-analytic diffeomorphisms on the torus. We present a
general framework for such constructions and prove several results. In
particular, we construct minimal but not uniquely ergodic diffeomorphisms and
nonstandard real-analytic realizations of toral translations.


title:
Analysis of the R-symmetric supersymmetric models including quantum
  corrections

abstract:
  We study the Minimal R-symmetric Supersymmetric Standard Model (MRSSM) at the
quantum level. The thesis consists of two parts. First one treats about the
electroweak sector of the model. Among others, it identifies the parameter
region allowed by the electroweak precision observables. Since the MRSSM
contains an $SU(2)_L$-triplet with a non-zero vacuum expectation value the
emphasis is put on the calculation of the $W$ boson mass. To that end, a full
one-loop calculation of $m_W$ augmented with the leading two-loop SM result is
presented. The region is then checked against the measurement of the Higgs
boson mass. For this, the full one-loop and leading two-loop corrections to the
Higgs boson mass in the MRSSM are calculated. Devised benchmark points,
consistent with both of these observables, are shown to fulfill also a number
of additional experimental constraints like properties of the Higgs boson(s),
$b$-physics observables and vacuum stability. Correlating all of these
observables allows to put bounds on the parameters of the model. Second part of
the thesis treats about the strongly interacting scalar sector. First, NLO QCD
corrections to the production of scalar gluon (sgluon) pair at the LHC are
calculated. A set of $K$-factors for a selected sgluon masses at 13 and 14 TeV
LHC is presented. This calculation is applied to constrain the sgluon mass
using 2015 data set from Run 2 of the LHC. To that end, a same-sign lepton
search by ATLAS is recasted for the case of the production of the sgluon pair
decaying to $t\bar{t}$ pairs. The analysis is reproduced with the help of
shower Monte Carlo softwares and the program performing a fast detector
response simulation. The analysis shows that already using 3.2/fb of integrated
luminosity the exclusion limits from Run 2 are competitive with the 8 TeV ones.


title:
Influence of Nambu-Goldstone mode on energy-weighted sum of excitation
  strengths in random-phase approximation

abstract:
  Influence of the Nambu-Goldstone (NG) mode on the energy-weighted sum (EWS)
of the excitation strengths is analyzed, within the random-phase approximation
(RPA). When a certain symmetry is broken at the mean-field level, a NG mode
emerges in the RPA, which can be represented by canonical variables forming a
two-dimensional Jordan block. A general formula is rederived which separates
out the NG-mode contribution to the EWS, via the projection on the subspace
directed by the NG mode. As examples, the formula is applied to the $E1$
excitation and the rotational excitations in nuclei, further confirming
theoretical consistency of the RPA.


title: Temporal Generative Adversarial Nets with Singular Value Clipping

abstract:
  In this paper, we propose a generative model, Temporal Generative Adversarial
Nets (TGAN), which can learn a semantic representation of unlabeled videos, and
is capable of generating videos. Unlike existing Generative Adversarial Nets
(GAN)-based methods that generate videos with a single generator consisting of
3D deconvolutional layers, our model exploits two different types of
generators: a temporal generator and an image generator. The temporal generator
takes a single latent variable as input and outputs a set of latent variables,
each of which corresponds to an image frame in a video. The image generator
transforms a set of such latent variables into a video. To deal with
instability in training of GAN with such advanced networks, we adopt a recently
proposed model, Wasserstein GAN, and propose a novel method to train it stably
in an end-to-end manner. The experimental results demonstrate the effectiveness
of our methods.


title: Modeling Review Spam Using Temporal Patterns and Co-bursting Behaviors

abstract:
  Online reviews play a crucial role in helping consumers evaluate and compare
products and services. However, review hosting sites are often targeted by
opinion spamming. In recent years, many such sites have put a great deal of
effort in building effective review filtering systems to detect fake reviews
and to block malicious accounts. Thus, fraudsters or spammers now turn to
compromise, purchase or even raise reputable accounts to write fake reviews.
Based on the analysis of a real-life dataset from a review hosting site
(dianping.com), we discovered that reviewers' posting rates are bimodal and the
transitions between different states can be utilized to differentiate spammers
from genuine reviewers. Inspired by these findings, we propose a two-mode
Labeled Hidden Markov Model to detect spammers. Experimental results show that
our model significantly outperforms supervised learning using linguistic and
behavioral features in identifying spammers. Furthermore, we found that when a
product has a burst of reviews, many spammers are likely to be actively
involved in writing reviews to the product as well as to many other products.
We then propose a novel co-bursting network for detecting spammer groups. The
co-bursting network enables us to produce more accurate spammer groups than the
current state-of-the-art reviewer-product (co-reviewing) network.


title:
On the notions of facets, weak facets, and extreme functions of the
  Gomory-Johnson infinite group problem

abstract:
  We investigate three competing notions that generalize the notion of a facet
of finite-dimensional polyhedra to the infinite-dimensional Gomory-Johnson
model. These notions were known to coincide for continuous piecewise linear
functions with rational breakpoints. We show that two of the notions, extreme
functions and facets, coincide for the case of continuous piecewise linear
functions, removing the hypothesis regarding rational breakpoints. We then
separate the three notions using discontinuous examples.


title:
State splitting, strong shift equivalence and stable isomorphism of
  Cuntz-Krieger algebras

abstract:
  We prove that if two nonnegative matrices are strong shift equivalent, the
associated stable Cuntz-Krieger algebras with generalized gauge actions are
conjugate. The proof is done by a purely functional analytic method and based
on constructing imprimitivity bimodule from bipartite directed graphs through
strong shift equivalent matrices, so that we may clarify K-theoretic behavior
of the stable conjugacy between the associated stable Cuntz-Krieger algebras.
We also examine our machinery for the matrices obtained by state splitting
graphs, so that topological conjugacy of the topological Markov shifts is
described in terms of some equivalence relation of the Cuntz-Krieger algebras
with canonical masas and the gauge actions without stabilization.


title: $PGL_2(\mathbb{F}_\ell)$ number fields with rational companion forms

abstract:
  We give a list of $PGL_2(\mathbb{F}_\ell)$ number fields for $\ell \geq 11$
which have rational companion forms. Our list has fifty-three fields and seems
likely to be complete. Some of the fields on our list are very lightly ramified
for their Galois group.


title: Extremal hypergraphs for matching number and domination number

abstract:
  A matching in a hypergraph $\mathcal{H}$ is a set of pairwise disjoint
hyperedges. The matching number $\nu(\mathcal{H})$ of $\mathcal{H}$ is the size
of a maximum matching in $\mathcal{H}$. A subset $D$ of vertices of
$\mathcal{H}$ is a dominating set of $\mathcal{H}$ if for every $v\in
V\setminus D$ there exists $u\in D$ such that $u$ and $v$ lie in an hyperedge
of $\mathcal{H}$. The cardinality of a minimum dominating set of $\mathcal{H}$
is the domination number of $\mathcal{H}$, denoted by $\gamma(\mathcal{H})$. It
was proved that $\gamma(\mathcal{H})\leq (r-1)\nu(\mathcal{H})$ for $r$-uniform
hypergraphs and the 2-uniform hypergraphs (graphs) achieving equality
$\gamma(\mathcal{H})=\nu(\mathcal{H})$ have been characterized. In this paper
we generalize the inequality $\gamma(\mathcal{H})\leq (r-1)\nu(\mathcal{H})$ to
arbitrary hypergraph of rank $r$ and we completely characterize the extremal
hypergraphs $\mathcal{H}$ of rank $3$ achieving equality
$\gamma(\mathcal{H})=(r-1)\nu(\mathcal{H})$.


title: Dual Ramanujan-Fourier series

abstract:
  Let $c_q(n)$ be the Ramanujan sums. Many results concerning Ramanujan-Fourier
series $f(n)=\sum_{q=1}^\infty a_q c_q (n)$ are obtained by many
mathematicians. In this paper we study series of the form
$f(q)=\sum_{n=1}^\infty a_n c_q (n)$, which we call dual Ramanujan-Fourier
series. We extend Lucht's theorem and Delange's theorem to this case and obtain
some results.


title: On Selecting a Conjunction Operation in Probabilistic Soft Logic

abstract:
  Probabilistic Soft Logic has been proposed and used in several applications
as an efficient way to deal with inconsistency, uncertainty and relational
representation. In several applications, this approach has led to an adequate
description of the corresponding human reasoning. In this paper, we provide a
theoretical explanation for one of the semi-heuristic choices made in this
approach: namely, we explain the choice of the corresponding conjunction
operations. Our explanation leads to a more general family of operations which
may be used in future applications of probabilistic soft logic.


title: A Note on Amortized Branching Program Complexity

abstract:
  In this paper, we show that while almost all functions require exponential
size branching programs to compute, for all functions $f$ there is a branching
program computing a doubly exponential number of copies of $f$ which has linear
size per copy of $f$. This result disproves a conjecture about non-uniform
catalytic computation, rules out a certain type of bottleneck argument for
proving non-monotone space lower bounds, and can be thought of as a
constructive analogue of Razborov's result that submodular complexity measures
have maximum value $O(n)$.


title: Two direct solvers for a system of linear equations

abstract:
  A system of linear equations is normally understood as a linear mapping
between two vector spaces. However, most direct solutions (e.g., QR, LU, ...)
rely on the inelegant approach of back-substitution: a significant departure
from such a characterization. In this paper, two new methods are introduced
which respect the underlying vector space throughout, whether it be the row or
column space of the coefficient matrix. Both solutions produce a generalized
inverse as well as a projection operator for the null space. They also have the
unique feature of admitting an online solution, which allows a solution to be
computed as the data becomes available.


title:
Fabrication and Characterization of PbIn-Au-PbIn Superconducting
  Junctions

abstract:
  We report on the fabrication and measurement results of the electrical
transport properties of superconductor-normal metal-superconductor (SNS) weak
links, made of PbIn superconductor and Au metal. The maximum supercurrent
reaches up to ~ 6 \mu A at T = 2.3 K and the supercurrent persists even at
higher temperature of T = 4.7 K. Magnetic field dependence of the critical
current is consistent with a theoretical fit using the narrow junction model.
The superconducting quantum interference device (SQUID) was also fabricated
using two PbIn-Au-PbIn junctions connected in parallel. Under perpendicular
magnetic field, we clearly observed periodic oscillations of dV/dI with a
period of magnetic flux quantum threading into the supercurrent loop of the
SQUID. Our fabrication methods would provide an easy and simple way to explore
the superconducting proximity effects without ultra-low-temperature cryostats.


title: Bilinear Forms with Exponential Sums with Binomials

abstract:
  We obtain several estimates for bilinear form with exponential sums with
binomials $mx^k + nx^\ell$. In particular we show the existence of nontrivial
cancellations between such sums when the coefficients $m$ and $n$ vary over
rather sparse sets of general nature


title:
Electronic and magnetic properties of FeSe$_{0.5}$Te$_{0.5}$ : A
  first-principles study

abstract:
  The atomic structures, electronic band structures and magnetic properties of
monolayer FeSe and FeSe$_{0.5}$Te$_{0.5}$ of different configurations have been
systematically investigated via first-principles calculations with the
inclusion of spin-orbit coupling (SOC). Three different antiferromagnetic (AFM)
orders, including checkerboard order, collinear order and pair-checkerboard
order, as well as paramagnetic state have been explored. In monolayer FeSe,
collinear AFM order is found to be the most stable order, in accordance with
previous investigations. Substituting half Se atoms with Te atoms, the
pair-checkerboard AFM order is the ground-state magnetic order in
FeSe$_{0.5}$Te$_{0.5}$. Both AFM-ordered FeSe and FeSe$_{0.5}$Te$_{0.5}$ have
Dirac-cone-like band structures. SOC has a great influence on the band
structures at the Dirac cone. The direction of the magnetic moments (in-plane
or out-of-plane) directly determines whether the Dirac cone could be opened by
SOC, and the gap values also relate to the specific magnetic structure.
Although SOC is stronger in FeSe$_{0.5}$Te$_{0.5}$, the SOC-induced band gaps
are either only slightly enlarged or even much shrunk compared with those gaps
in FeSe. Due to the symmetry breaking brought by Te-substitution, the band
structures of FeSe$_{0.5}$Te$_{0.5}$ have a new feature of combined
Rashba-Dresselhaus splitting. Our results have provided a comprehensive study
on the magnetic property of FeSe$_{0.5}$Te$_{0.5}$, which may help to
understand the relation between magnetism and the superconductivity in the
high-Tc monolayer superconductor.


title:
Surface Plasmonic Lattice Solitons in Semi-infinite Graphene Sheet
  Arrays

abstract:
  We investigate the surface plasmonic lattice solitons (PLSs) in semi-infinite
graphene sheet arrays. The surface soliton is formed as the SPPs tunneling is
inhibited by the graphene nonlinearity, and meanwhile the incident power should
be above a threshold value. Thanks to the strong confinement of surface plasmon
polaritons (SPPs) on graphene, the effective width of surface PLSs can be
squeezed into deep-subwavelength scale of ~ 0.001{\lambda}. Based on the stable
propagation of surface PLSs, we find that the light propagation can be switched
from the array boundary to the inner graphene sheets by reducing the incident
power or increasing the chemical potential of graphene. The study may find
promising application in optical switches on deep-subwavelength scale.


title:
Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral
  Hallucination and Low-rank Embedding

abstract:
  Surveillance cameras today often capture NIR (near infrared) images in
low-light environments. However, most face datasets accessible for training and
verification are only collected in the VIS (visible light) spectrum. It remains
a challenging problem to match NIR to VIS face images due to the different
light spectrum. Recently, breakthroughs have been made for VIS face recognition
by applying deep learning on a huge amount of labeled VIS face samples. The
same deep learning approach cannot be simply applied to NIR face recognition
for two main reasons: First, much limited NIR face images are available for
training compared to the VIS spectrum. Second, face galleries to be matched are
mostly available only in the VIS spectrum. In this paper, we propose an
approach to extend the deep learning breakthrough for VIS face recognition to
the NIR spectrum, without retraining the underlying deep models that see only
VIS faces. Our approach consists of two core components, cross-spectral
hallucination and low-rank embedding, to optimize respectively input and output
of a VIS deep model for cross-spectral face recognition. Cross-spectral
hallucination produces VIS faces from NIR images through a deep learning
approach. Low-rank embedding restores a low-rank structure for faces deep
features across both NIR and VIS spectrum. We observe that it is often equally
effective to perform hallucination to input NIR images or low-rank embedding to
output deep features for a VIS deep model for cross-spectral recognition. When
hallucination and low-rank embedding are deployed together, we observe
significant further improvement; we obtain state-of-the-art accuracy on the
CASIA NIR-VIS v2.0 benchmark, without the need at all to re-train the
recognition system.


title:
Text Classification Improved by Integrating Bidirectional LSTM with
  Two-dimensional Max Pooling

abstract:
  Recurrent Neural Network (RNN) is one of the most popular architectures used
in Natural Language Processsing (NLP) tasks because its recurrent structure is
very suitable to process variable-length text. RNN can utilize distributed
representations of words by first converting the tokens comprising each text
into vectors, which form a matrix. And this matrix includes two dimensions: the
time-step dimension and the feature vector dimension. Then most existing models
usually utilize one-dimensional (1D) max pooling operation or attention-based
operation only on the time-step dimension to obtain a fixed-length vector.
However, the features on the feature vector dimension are not mutually
independent, and simply applying 1D pooling operation over the time-step
dimension independently may destroy the structure of the feature
representation. On the other hand, applying two-dimensional (2D) pooling
operation over the two dimensions may sample more meaningful features for
sequence modeling tasks. To integrate the features on both dimensions of the
matrix, this paper explores applying 2D max pooling operation to obtain a
fixed-length representation of the text. This paper also utilizes 2D
convolution to sample more meaningful information of the matrix. Experiments
are conducted on six text classification tasks, including sentiment analysis,
question classification, subjectivity classification and newsgroup
classification. Compared with the state-of-the-art models, the proposed models
achieve excellent performance on 4 out of 6 tasks. Specifically, one of the
proposed models achieves highest accuracy on Stanford Sentiment Treebank binary
classification and fine-grained classification tasks.


title:
Combinatorics and Representation Theory for Generalized Permutohedra I:
  Simplicial Plates

abstract:
  In this paper, we announce results from our thesis, which studies for the
first time the categorification of the theory of generalized permutohedra. The
vector spaces in the categorification are tightly constrained by certain
continuity relations which appeared in physics in the mid 20th century. We
describe here the action of the symmetric group on the vector spaces in this
categorification. Generalized permutohedra are replaced by vector spaces of
characteristic functions of polyhedral cones about faces of permutohedra,
called plates, due to A. Ocneanu. The symmetric group acts on plates by
coordinate permutation.
  In combinatorics, the Eulerian numbers count the number of permutations with
a given number of ascent and descents. The classical Worpitzky identity expands
a power $r^p$ as a sum of Eulerian numbers, with binomial coefficients. In our
thesis, for the main result we generalize the classical Worpitzky identity to
an isomorphism of symmetric group modules, corresponding geometrically to the
tiling of a scaled simplex by unit hypersimplices. In the categorification, the
volume of a hypersimplex is replaced by the complex-linear dimension of a
vector space associated to it. The main technical aspect of the proof of the
character formula for the simplex involves a partition of unity of a
commutative algebra of translations on a discrete torus, and a certain modular
Diophantine equation.
  A detailed paper is in preparation.


title:
Phrase Localization and Visual Relationship Detection with Comprehensive
  Image-Language Cues

abstract:
  This paper presents a framework for localization or grounding of phrases in
images using a large collection of linguistic and visual cues. We model the
appearance, size, and position of entity bounding boxes, adjectives that
contain attribute information, and spatial relationships between pairs of
entities connected by verbs or prepositions. Special attention is given to
relationships between people and clothing or body part mentions, as they are
useful for distinguishing individuals. We automatically learn weights for
combining these cues and at test time, perform joint inference over all phrases
in a caption. The resulting system produces state of the art performance on
phrase localization on the Flickr30k Entities dataset and visual relationship
detection on the Stanford VRD dataset.


title: Cascaded Face Alignment via Intimacy Definition Feature

abstract:
  In this paper, we present a random-forest based fast cascaded regression
model for face alignment, via a novel local feature. Our proposed local
lightweight feature, namely intimacy definition feature (IDF), is more
discriminative than landmark pose-indexed feature, more efficient than
histogram of oriented gradients (HOG) feature and scale-invariant feature
transform (SIFT) feature, and more compact than the local binary feature (LBF).
Experimental results show that our approach achieves state-of-the-art
performance when tested on the most challenging datasets. Compared with an
LBF-based algorithm, our method can achieve about two times the speed-up and
more than 20% improvement, in terms of alignment accuracy measurement, and save
an order of magnitude of memory requirement.


title: Generalized Lam\'e equation with finite monodromy

abstract:
  In this paper, we study the algebraic form of the symmetric generalized
Lam\'e equations which have finite projective monodromy groups. In particular,
we consider equations with $3$ regular singular points on a flat torus $T$
which takes the form \begin{equation*} \begin{split} \frac{d^2
y}{dz^2}-\left[n_1(n_1+1)(\wp(z+a) +\wp(z-a))\right.\left. +A_1(\zeta(z+a) -
\zeta(z-a))+n_0(n_0+1) \wp(z)+B\right]y=0, \end{split} \end{equation*} where
$n_1, n_0 \in \Bbb R$, $A_1,B \in \Bbb C$, and $\wp$ is the Weierstrass
elliptic function.
  We give a complete list of all the group types that occur as the finite
projective monodromy groups on the algebraic form and give the corresponding
parameters $n_0$ and $n_1$. For equations with only $1$ or $2$ regular singular
points, we further determine their monodromy group types.
  The main tool used is the Grothendieck correspondence which gives a bijection
between Belyi pairs and dessin d'enfants. By Klein's theorem, we may regard
generalized Lam\'e equations with finite monodromy as pullbacks of the
hypergeometric equations. In this paper, we will restrict our cases with the
assumption that the pullback maps are Belyi functions. Under this setup, our
main results consist of a systematic construction on the required dessin. In
particular a gluing procedure of dessin will be developed to enable inductive
constructions of the dessin.


title:
Galactic Bulge Preferred Over Dark Matter for the Galactic Center
  Gamma-Ray Excess

abstract:
  An anomalous gamma-ray excess emission has been found in Fermi Large Area
Telescope data covering the centre of the Galaxy. Several theories have been
proposed for this `Galactic Centre Excess'. They include self-annihilation of
dark matter particles, an unresolved population of millisecond pulsars, an
unresolved population of young pulsars, or a series of burst events. Here we
report on a new analysis that exploits hydrodynamical modelling to register the
position of interstellar gas associated with diffuse Galactic gamma-ray
emission. We find evidence that the Galactic Centre Excess gamma rays are
statistically better described by the stellar over-density in the Galactic
bulge and the nuclear stellar bulge, rather than a spherical excess. Given its
non-spherical nature, we argue that the Galactic Centre Excess is not a dark
matter phenomenon but rather associated with the stellar population of the
Galactic bulge and nuclear bulge.

categories: astro-ph.HE astro-ph.CO astro-ph.GA hep-ph


title:
A Unified Framework for Community Detection and Network Representation
  Learning

abstract:
  Network representation learning (NRL) aims to learn low-dimensional vectors
for vertices in a network. Most existing NRL methods focus on learning
representations from local context of vertices (such as their neighbors).
Nevertheless, vertices in many complex networks also exhibit significant global
patterns widely known as communities. It's intuitive that vertices in the same
community tend to connect densely and share common attributes. These patterns
are expected to improve NRL and benefit relevant evaluation tasks, such as link
prediction and vertex classification. Inspired by the analogy between network
representation learning and text modeling, we propose a unified NRL framework
by introducing community information of vertices, named as Community-enhanced
Network Representation Learning (CNRL). CNRL simultaneously detects community
distribution of each vertex and learns embeddings of both vertices and
communities. Moreover, the proposed community enhancement mechanism can be
applied to various existing NRL models. In experiments, we evaluate our model
on vertex classification, link prediction, and community detection using
several real-world datasets. The results demonstrate that CNRL significantly
and consistently outperforms other state-of-the-art methods while verifying our
assumptions on the correlations between vertices and communities.


title: Self-Supervised Video Representation Learning With Odd-One-Out Networks

abstract:
  We propose a new self-supervised CNN pre-training technique based on a novel
auxiliary task called "odd-one-out learning". In this task, the machine is
asked to identify the unrelated or odd element from a set of otherwise related
elements. We apply this technique to self-supervised video representation
learning where we sample subsequences from videos and ask the network to learn
to predict the odd video subsequence. The odd video subsequence is sampled such
that it has wrong temporal order of frames while the even ones have the correct
temporal order. Therefore, to generate a odd-one-out question no manual
annotation is required. Our learning machine is implemented as multi-stream
convolutional neural network, which is learned end-to-end. Using odd-one-out
networks, we learn temporal representations for videos that generalizes to
other related tasks such as action recognition.
  On action classification, our method obtains 60.3\% on the UCF101 dataset
using only UCF101 data for training which is approximately 10% better than
current state-of-the-art self-supervised learning methods. Similarly, on HMDB51
dataset we outperform self-supervised state-of-the art methods by 12.7% on
action classification task.


title:
ATCA 16 cm Observation of CIZA J1358.9-4750: Implication of Merger Stage
  and Constraint on Non-Thermal Properties

abstract:
  We report the Australia Telescope Compact Array 16 cm observation of CIZA
J1358.9-4750. Recent X-ray studies imply that this galaxy cluster is composed
of merging, binary clusters. Using the EW367 configuration, we found no
significant diffuse radio emission in and around the cluster. An upper limit of
the total radio power at 1.4 GHz is $\sim 1.1 \times 10^{22}$ Watt/Hz in $30$
square arcminutes which is a typical size of radio relics. It is known that an
empirical relation holds between the total radio power and X-ray luminosity of
the host cluster. The upper limit is about one order of magnitude lower than
the power expected from the relation. Very young ($\sim 70$ Myr) shocks with
low Mach numbers ($\sim 1.3$), which are often seen at an early stage of merger
simulations, are suggested by the previous X-ray observation. The shocks may
generate cosmic-ray electrons with a steep energy spectrum, which is consistent
with non-detection of bright ($>10^{23}$ Watt/Hz) relic in this 16 cm band
observation. Based on the assumption of energy equipartition, the upper limit
gives the magnetic-field strength below $0.68f(D_{\rm los}/{\rm
1~Mpc})^{-1}(\gamma_{\rm min}/200)^{-1}$ $\mu$G, where $f$ is the cosmic-ray
total energy density over the cosmic-ray electron energy density, $D_{\rm los}$
is the depth of the shock wave along the sightline and $\gamma_{\rm min}$ is
the lower cutoff Lorentz factor of the cosmic-ray electron energy spectrum.


title:
Observation of Transverse $\Lambda/\bar{\Lambda}$ Hyperon Polarization
  in $e^+e^-$ Annihilation at Belle

abstract:
  We report the first observation of the polarization of
$\Lambda/\bar{\Lambda}$ hyperons transverse to its production plane in $e^+e^-$
annihilation. We observe a significant polarization that rises with the
fractional energy carried by the hyperon as well as its transverse momentum. To
define the production plane, we use the direction of the hyperon momentum
together with either the thrust axis in the event or the momentum vector of a
hadron in the opposite hemisphere. Furthermore, we investigate the
contributions to the hyperon polarization from the feed-down from
$\Sigma^0/\bar{\Sigma}^0$ and $\Lambda_c^{\pm}$ decays. This measurement uses a
dataset of 800.4\~fb$^{-1}$ collected by the Belle experiment at or near a
center-of-mass energy of 10.58 GeV.


title:
High-Dimensional Bayesian Regularised Regression with the BayesReg
  Package

abstract:
  Bayesian penalized regression techniques, such as the Bayesian lasso and the
Bayesian horseshoe estimator, have recently received a significant amount of
attention in the statistics literature. However, software implementing
state-of-the-art Bayesian penalized regression, outside of general purpose
Markov chain Monte Carlo platforms such as STAN, is relatively rare. This paper
introduces bayesreg, a new toolbox for fitting Bayesian penalized regression
models with continuous shrinkage prior densities. The toolbox features Bayesian
linear regression with Gaussian or heavy-tailed error models and Bayesian
logistic regression with ridge, lasso, horseshoe and horseshoe$+$ estimators.
The toolbox is free, open-source and available for use with the MATLAB and R
numerical platforms.


title: Trading information complexity for error

abstract:
  We consider the standard two-party communication model. The central problem
studied in this article is how much one can save in information complexity by
allowing an error of $\epsilon$.
  For arbitrary functions, we obtain lower bounds and upper bounds indicating a
gain that is of order $\Omega(h(\epsilon))$ and $O(h(\sqrt{\epsilon}))$. Here
$h$ denotes the binary entropy function. We analyze the case of the two-bit AND
function in detail to show that for this function the gain is
$\Theta(h(\epsilon))$. This answers a question of [M. Braverman, A. Garg, D.
Pankratov, and O. Weinstein, From information to exact communication (extended
abstract), STOC'13].
  We obtain sharp bounds for the set disjointness function of order $n$. For
the case of the distributional error, we introduce a new protocol that achieves
a gain of $\Theta(\sqrt{h(\epsilon)})$ provided that $n$ is sufficiently large.
We apply these results to answer another of question of Braverman et al.
regarding the randomized communication complexity of the set disjointness
function.
  Answering a question of [Mark Braverman, Interactive information complexity,
STOC'12], we apply our analysis of the set disjointness function to establish a
gap between the two different notions of the prior-free information cost. This
implies that amortized randomized communication complexity is not necessarily
equal to the amortized distributional communication complexity with respect to
the hardest distribution.


title: Deep Learning for the Classification of Lung Nodules

abstract:
  Deep learning, as a promising new area of machine learning, has attracted a
rapidly increasing attention in the field of medical imaging. Compared to the
conventional machine learning methods, deep learning requires no hand-tuned
feature extractor, and has shown a superior performance in many visual object
recognition applications. In this study, we develop a deep convolutional neural
network (CNN) and apply it to thoracic CT images for the classification of lung
nodules. We present the CNN architecture and classification accuracy for the
original images of lung nodules. In order to understand the features of lung
nodules, we further construct new datasets, based on the combination of
artificial geometric nodules and some transformations of the original images,
as well as a stochastic nodule shape model. It is found that simplistic
geometric nodules cannot capture the important features of lung nodules.


title: Scalable Adaptive Stochastic Optimization Using Random Projections

abstract:
  Adaptive stochastic gradient methods such as AdaGrad have gained popularity
in particular for training deep neural networks. The most commonly used and
studied variant maintains a diagonal matrix approximation to second order
information by accumulating past gradients which are used to tune the step size
adaptively. In certain situations the full-matrix variant of AdaGrad is
expected to attain better performance, however in high dimensions it is
computationally impractical. We present Ada-LR and RadaGrad two computationally
efficient approximations to full-matrix AdaGrad based on randomized
dimensionality reduction. They are able to capture dependencies between
features and achieve similar performance to full-matrix AdaGrad but at a much
smaller computational cost. We show that the regret of Ada-LR is close to the
regret of full-matrix AdaGrad which can have an up-to exponentially smaller
dependence on the dimension than the diagonal variant. Empirically, we show
that Ada-LR and RadaGrad perform similarly to full-matrix AdaGrad. On the task
of training convolutional neural networks as well as recurrent neural networks,
RadaGrad achieves faster convergence than diagonal AdaGrad.


title: SIMEX estimation for single-index model with covariate measurement error

abstract:
  In this paper, we consider the single-index measurement error model with
mismeasured covariates in the nonparametric part. To solve the problem, we
develop a simulation-extrapolation (SIMEX) algorithm based on the local linear
smoother and the estimating equation. For the proposed SIMEX estimation, it is
not needed to assume the distribution of the unobserved covariate. We transform
the boundary of a unit ball in $\mathbb{R}^p$ to the interior of a unit ball in
$\mathbb{R}^{p-1}$ by using the constraint $\|\beta\|=1$. The proposed SIMEX
estimator of the index parameter is shown to be asymptotically normal under
some regularity conditions. We also derive the asymptotic bias and variance of
the estimator of the unknown link function. Finally, the performance of the
proposed method is examined by simulation studies and is illustrated by a real
data example.


title:
Constraint on dust evolution processes in normal galaxies at $z>6$
  detected by ALMA

abstract:
  Recent ALMA observations of high-redshift normal galaxies have been providing
a great opportunity to clarify the general origin of dust in the Universe, not
biased to very bright special objects even at $z>6$. To clarify what constraint
we can get for the dust enrichment in normal galaxies detected by ALMA, we use
a theoretical model that includes major processes driving dust evolution in a
galaxy; that is, dust condensation in stellar ejecta, dust growth by the
accretion of gas-phase metals, and supernova destruction. Using the dust
emission fluxes detected in two normal galaxies at $z>6$ by ALMA as a
constraint, we can get the range of the time-scales (or efficiencies) of the
above mentioned processes. We find that if we assume extremely high
condensation efficiency in stellar ejecta ($f_{\mathrm{in}} \ga 0.5$), rapid
dust enrichment by stellar sources in the early phase may be enough to explain
the observed ALMA flux, unless dust destruction by supernovae in those galaxies
is stronger than that in nearby galaxies. If we assume a condensation
efficiency expected from theoretical calculations ($f_{\mathrm{in}} \la 0.1$),
strong dust growth (even stronger than assumed for nearby galaxies if they are
metal-poor galaxies) is required. These results indicate that the normal
galaxies detected by ALMA at $z>6$ are biased to objects (i) with high dust
condensation efficiency in stellar ejecta, (ii) with strong dust growth in very
dense molecular clouds, or (iii) with efficient dust growth because of fast
metal enrichment up to solar metallicity. A measurement of metallicity is
crucial to distinguish among these possibilities.


title: Sparse Sliced Inverse Regression Via Lasso

abstract:
  For multiple index models, it has recently been shown that the sliced inverse
regression (SIR) is consistent for estimating the sufficient dimension
reduction (SDR) space if and only if $\rho=\lim\frac{p}{n}=0$, where $p$ is the
dimension and $n$ is the sample size. Thus, when $p$ is of the same or a higher
order of $n$, additional assumptions such as sparsity must be imposed in order
to ensure consistency for SIR. By constructing artificial response variables
made up from top eigenvectors of the estimated conditional covariance matrix,
we introduce a simple Lasso regression method to obtain an estimate of the SDR
space. The resulting algorithm, Lasso-SIR, is shown to be consistent and
achieve the optimal convergence rate under certain sparsity conditions when $p$
is of order $o(n^2\lambda^2)$, where $\lambda$ is the generalized
signal-to-noise ratio. We also demonstrate the superior performance of
Lasso-SIR compared with existing approaches via extensive numerical studies and
several real data examples.


title: ResFeats: Residual Network Based Features for Image Classification

abstract:
  Deep residual networks have recently emerged as the state-of-the-art
architecture in image segmentation and object detection. In this paper, we
propose new image features (called ResFeats) extracted from the last
convolutional layer of deep residual networks pre-trained on ImageNet. We
propose to use ResFeats for diverse image classification tasks namely, object
classification, scene classification and coral classification and show that
ResFeats consistently perform better than their CNN counterparts on these
classification tasks. Since the ResFeats are large feature vectors, we propose
to use PCA for dimensionality reduction. Experimental results are provided to
show the effectiveness of ResFeats with state-of-the-art classification
accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant
performance improvement on MIT-67 dataset compared to the widely used CNN
features.


title: Trembling electrons cause conductance fluctuation

abstract:
  The highly successful Dirac equation can predict peculiar effects such as
Klein tunneling and the "Zitterbewegung" (German for "trembling motion") of
electrons. From the time it was first identified by Erwin Schrodinger,
Zitterbewegung (ZB) has been considered a key to understanding relativistic
quantum mechanics. However, observing the original ZB of electrons is too
difficult, and instead various emulations using entity models have been
proposed, producing several successes. Expectations are high regarding charge
transports in semiconductors and graphene; however, very few reports have
appeared on them. Here, we report that ZB has a surprisingly large effect on
charge transports when we play "flat pinball" with such trembling electrons in
a semiconductor nanostructure. The stage here is a narrow strip of InAs
two-dimensional electron gas with a strong Rashba spin-orbit coupling. Six
quantum point contacts (QPCs) are attached to the strip as pinball pockets. The
ZB appeared as a large reproducible conductance fluctuation versus in-plane
magnetic fields in the transport between two QPCs. Numerical simulations
successfully reproduced our experimental observations, certifying that ZB
causes a new type of conductance fluctuation.


title: Revisiting the quadrisection problem of Jacob Bernoulli

abstract:
  Two perpendicular segments which divide a given triangle into 4 regions of
equal area is called a quadrisection of the triangle. Leonhard Euler proved in
1779 that every scalene triangle has a quadrisection with its triangular part
on the middle leg. We provide a complete description of the quadrisections of a
triangle. For example, there is only one isosceles triangle which has exactly
two quadrisections.


title: The Importance of being consistent

abstract:
  We review the role of self-consistency in density functional theory. We apply
a recent analysis to both Kohn-Sham and orbital-free DFT, as well as to
Partition-DFT, which generalizes all aspects of standard DFT. In each case, the
analysis distinguishes between errors in approximate functionals versus errors
in the self-consistent density. This yields insights into the origins of many
errors in DFT calculations, especially those often attributed to
self-interaction or delocalization error. In many classes of problems, errors
can be substantially reduced by using `better' densities. We review the history
of these approaches, many of their applications, and give simple pedagogical
examples.


title: Structure of 311 Service Requests as a Signature of Urban Location

abstract:
  While urban systems demonstrate high spatial heterogeneity, many urban
planning, economic and political decisions heavily rely on a deep understanding
of local neighborhood contexts. We show that the structure of 311 Service
Requests enables one possible way of building a unique signature of the local
urban context, thus being able to serve as a low-cost decision support tool for
urban stakeholders. Considering examples of New York City, Boston and Chicago,
we demonstrate how 311 Service Requests recorded and categorized by type in
each neighborhood can be utilized to generate a meaningful classification of
locations across the city, based on distinctive socioeconomic profiles.
Moreover, the 311-based classification of urban neighborhoods can present
sufficient information to model various socioeconomic features. Finally, we
show that these characteristics are capable of predicting future trends in
comparative local real estate prices. We demonstrate 311 Service Requests data
can be used to monitor and predict socioeconomic performance of urban
neighborhoods, allowing urban stakeholders to quantify the impacts of their
interventions.


title: Gland Instance Segmentation Using Deep Multichannel Neural Networks

abstract:
  Objective: A new image instance segmentation method is proposed to segment
individual glands (instances) in colon histology images. This process is
challenging since the glands not only need to be segmented from a complex
background, they must also be individually identified. Methods: We leverage the
idea of image-to-image prediction in recent deep learning by designing an
algorithm that automatically exploits and fuses complex multichannel
information - regional, location, and boundary cues - in gland histology
images. Our proposed algorithm, a deep multichannel framework, alleviates heavy
feature design due to the use of convolutional neural networks and is able to
meet multifarious requirements by altering channels. Results: Compared with
methods reported in the 2015 MICCAI Gland Segmentation Challenge and other
currently prevalent instance segmentation methods, we observe state-of-the-art
results based on the evaluation metrics. Conclusion: The proposed deep
multichannel algorithm is an effective method for gland instance segmentation.
Significance: The generalization ability of our model not only enable the
algorithm to solve gland instance segmentation problems, but the channel is
also alternative that can be replaced for a specific task.


title: Lattice topology dictates photon statistics

abstract:
  Propagation of coherent light through a disordered network is accompanied by
randomization and possible conversion into thermal light. Here, we show that
network topology plays a decisive role in determining the statistics of the
emerging field if the underlying lattice satisfies chiral symmetry. By
examining one-dimensional arrays of randomly coupled waveguides arranged on
linear and ring topologies, we are led to a remarkable prediction: the field
circularity and the photon statistics in ring lattices are dictated by its
parity -- whether the number of sites is even or odd, while the same quantities
are insensitive to the parity of a linear lattice. Adding or subtracting a
single lattice site can switch the photon statistics from super-thermal to
sub-thermal, or vice versa. This behavior is understood by examining the real
and imaginary fields on a chiral-symmetric lattice, which form two strands that
interleave along the lattice sites. These strands can be fully braided around
an even-sited ring lattice thereby producing super-thermal photon statistics,
while an odd-sited lattice is incommensurate with such an arrangement and the
statistics become sub-thermal. Such effects are suppressed in the limit of high
lattice disorder, whereupon Anderson localization arrests the spread of light
around the lattice and eliminates topology-dependent phenomena.


title:
Energy spectrum of a 2D Dirac oscillator in the presence of a constant
  magnetic field and an antidot potential

abstract:
  We investigate the energy spectrum and the corresponding eigenfunctions of a
2D Dirac oscillator confined by an antidot potential in the presence of a
magnetic field and Aharonov-Bohm flux field. Analytical solutions are obtained
and compared with the results of the Schr\"odinger equation found in the
literature. Further, the dependence of the spectrum on the magnetic quantum
number and on the repulsive potential is discussed.


title:
Ergodic theory, Dynamic Mode Decomposition and Computation of Spectral
  Properties of the Koopman operator

abstract:
  We establish the convergence of a class of numerical algorithms, known as
Dynamic Mode Decomposition (DMD), for computation of the eigenvalues and
eigenfunctions of the infinite-dimensional Koopman operator. The algorithms act
on data coming from observables on a state space, arranged in Hankel-type
matrices. The proofs utilize the assumption that the underlying dynamical
system is ergodic. This includes the classical measure-preserving systems, as
well as systems whose attractors support a physical measure. Our approach
relies on the observation that vector projections in DMD can be used to
approximate the function projections by the virtue of Birkhoff's ergodic
theorem. Using this fact, we show that applying DMD to Hankel data matrices in
the limit of infinite-time observations yields the true Koopman eigenfunctions
and eigenvalues. We also show that the Singular Value Decomposition, which is
the central part of most DMD algorithms, converges to the Proper Orthogonal
Decomposition of observables. We use this result to obtain a representation of
the dynamics of systems with continuous spectrum based on the lifting of the
coordinates to the space of observables. The numerical application of these
methods is demonstrated using well-known dynamical systems and examples from
computational fluid dynamics.


title:
A New System of Global Fractional-order Interval Implicit Projection
  Neural Networks

abstract:
  The purpose of this paper is to introduce and investigate a new system of
global fractional-order interval implicit projection neural networks. An
existence and uniqueness theorem of the equilibrium point for such kind of
global fractional-order interval implicit projection neural networks is
obtained under some suitable assumptions. Moreover, Mittag-Leffler stability of
the global fractional-order interval implicit projection neural networks is
also proved. Finally, two numerical examples are given to illustrate the
validity of our results.


title:
Quantifying immediate price impact of trades based on the $k$-shell
  decomposition of stock trading networks

abstract:
  Traders in a stock market exchange stock shares and form a stock trading
network. Trades at different positions of the stock trading network may contain
different information. We construct stock trading networks based on the limit
order book data and classify traders into $k$ classes using the $k$-shell
decomposition method. We investigate the influences of trading behaviors on the
price impact by comparing a closed national market (A-shares) with an
international market (B-shares), individuals and institutions, partially filled
and filled trades, buyer-initiated and seller-initiated trades, and trades at
different positions of a trading network. Institutional traders professionally
use some trading strategies to reduce the price impact and individuals at the
same positions in the trading network have a higher price impact than
institutions. We also find that trades in the core have higher price impacts
than those in the peripheral shell.


title: Two weight estimates with matrix measures for well localized operators

abstract:
  In this paper, we give necessary and sufficient conditions for weighted $L^2$
estimates with matrix-valued measures of well localized operators. Namely, we
seek estimates of the form: \[ \| T(\mathbf{W} f)\|_{L^2(\mathbf{V})} \le
C\|f\|_{L^2(\mathbf{W})} \] where $T$ is formally an integral operator with
additional structure, $\mathbf{W}, \mathbf{V}$ are matrix measures, and the
underlying measure space possesses a filtration. The characterization we obtain
is of Sawyer-type; in particular we show that certain natural testing
conditions obtained by studying the operator and its adjoint on indicator
functions suffice to determine boundedness. Working in both the matrix weighted
setting and the setting of measure spaces with arbitrary filtrations requires
novel modifications of a T1 proof strategy; a particular benefit of this level
of generality is that we obtain polynomial estimates on the complexity of
certain Haar shift operators.


title:
MV-RNN: A Multi-View Recurrent Neural Network for Sequential
  Recommendation

abstract:
  Sequential recommendation is a fundamental task for network applications, and
it usually suffers from the item cold start problem due to the insufficiency of
user feedbacks. There are currently three kinds of popular approaches which are
respectively based on matrix factorization (MF) of collaborative filtering,
Markov chain (MC), and recurrent neural network (RNN). Although widely used,
they have some limitations. MF based methods could not capture dynamic user's
interest. The strong Markov assumption greatly limits the performance of MC
based methods. RNN based methods are still in the early stage of incorporating
additional information. Based on these basic models, many methods with
additional information only validate incorporating one modality in a separate
way. In this work, to make the sequential recommendation and deal with the item
cold start problem, we propose a Multi-View Recurrent Neural Network (MV-RNN})
model. Given the latent feature, MV-RNN can alleviate the item cold start
problem by incorporating visual and textual information. First, At the input of
MV-RNN, three different combinations of multi-view features are studied, like
concatenation, fusion by addition and fusion by reconstructing the original
multi-modal data. MV-RNN applies the recurrent structure to dynamically capture
the user's interest. Second, we design a separate structure and a united
structure on the hidden state of MV-RNN to explore a more effective way to
handle multi-view features. Experiments on two real-world datasets show that
MV-RNN can effectively generate the personalized ranking list, tackle the
missing modalities problem and significantly alleviate the item cold start
problem.


title:
Spectral functions in functional renormalization group approach --
  analysis of the collective soft modes at the QCD critical point --

abstract:
  We first review the method to calculate the spectral functions in the
functional renormalization group (FRG) approach, which has been recently
developed. We also provide the numerical stability conditions given by the
present authors for a generic nonlinear evolution equation that are necessary
for obtaining the accurate effective potential from the flow equation in the
FRG. As an interesting example, we report the recent calculation of the
spectral functions of the mesonic and particle-hole excitations using a chiral
effective model of Quantum Chromodynamics (QCD); we extract the dispersion
relations from them and try to reveal the nature of the soft modes at the QCD
critical point (CP) where the phase transition is second order. Our result
shows that a clear development and the softening of the phonon mode in the
space-like region as the system approaches the CP; furthermore it turns out
that the sigma mesonic mode once in the time-like region gets to merge with the
phonon mode in the close vicinity of the CP, implying a novel possibility about
the nature of the soft mode of the QCD CP.


title:
Error analysis of regularized least-square regression with Fredholm
  kernel

abstract:
  Learning with Fredholm kernel has attracted increasing attention recently
since it can effectively utilize the data information to improve the prediction
performance. Despite rapid progress on theoretical and experimental
evaluations, its generalization analysis has not been explored in learning
theory literature. In this paper, we establish the generalization bound of
least square regularized regression with Fredholm kernel, which implies that
the fast learning rate O(l^{-1}) can be reached under mild capacity conditions.
Simulated examples show that this Fredholm regression algorithm can achieve the
satisfactory prediction performance.


title: Ontology Driven Disease Incidence Detection on Twitter

abstract:
  In this work we address the issue of generic automated disease incidence
monitoring on twitter. We employ an ontology of disease related concepts and
use it to obtain a conceptual representation of tweets. Unlike previous key
word based systems and topic modeling approaches, our ontological approach
allows us to apply more stringent criteria for determining which messages are
relevant such as spatial and temporal characteristics whilst giving a stronger
guarantee that the resulting models will perform well on new data that may be
lexically divergent. We achieve this by training learners on concepts rather
than individual words. For training we use a dataset containing mentions of
influenza and Listeria and use the learned models to classify datasets
containing mentions of an arbitrary selection of other diseases. We show that
our ontological approach achieves good performance on this task using a variety
of Natural Language Processing Techniques. We also show that word vectors can
be learned directly from our concepts to achieve even better results.


title: Systemic Risk and Interbank Lending

abstract:
  We propose a simple model of the banking system incorporating a game feature
where the evolution of monetary reserve is modeled as a system of coupled
Feller diffusions. The Markov Nash equilibrium generated through minimizing the
linear quadratic cost subject to Cox-Ingersoll-Ross type processes creates
liquidity and deposit rate. The adding liquidity leads to a flocking effect but
the deposit rate diminishes the growth rate of the total monetary reserve
causing a large number of bank defaults. In addition, the corresponding Mean
Field Game and the infinite time horizon stochastic game with the discount
factor are also discussed.


title: On the List-Decodability of Random Self-Orthogonal Codes

abstract:
  In 2011, Guruswami-H{\aa}stad-Kopparty \cite{Gru} showed that the
list-decodability of random linear codes is as good as that of general random
codes. In the present paper, we further strengthen the result by showing that
the list-decodability of random {\it Euclidean self-orthogonal} codes is as
good as that of general random codes as well, i.e., achieves the classical
Gilbert-Varshamov bound. Specifically, we show that, for any fixed finite field
$\F_q$, error fraction $\delta\in (0,1-1/q)$ satisfying $1-H_q(\delta)\le
\frac12$ and small $\epsilon>0$, with high probability a random Euclidean
self-orthogonal code over $\F_q$ of rate $1-H_q(\delta)-\epsilon$ is $(\delta,
O(1/\epsilon))$-list-decodable. This generalizes the result of linear codes to
Euclidean self-orthogonal codes. In addition, we extend the result to list
decoding {\it symplectic dual-containing} codes by showing that the
list-decodability of random symplectic dual-containing codes achieves the
quantum Gilbert-Varshamov bound as well. This implies that list-decodability of
quantum stabilizer codes can achieve the quantum Gilbert-Varshamov bound.
  The counting argument on self-orthogonal codes is an important ingredient to
prove our result.


title:
Estimation of respiratory pattern from video using selective ensemble
  aggregation

abstract:
  Non-contact estimation of respiratory pattern (RP) and respiration rate (RR)
has multiple applications. Existing methods for RP and RR measurement fall into
one of the three categories - (i) estimation through nasal air flow
measurement, (ii) estimation from video-based remote photoplethysmography, and
(iii) estimation by measurement of motion induced by respiration using motion
detectors. These methods, however, require specialized sensors, are
computationally expensive and/or critically depend on selection of a region of
interest (ROI) for processing. In this paper a general framework is described
for estimating a periodic signal driving noisy LTI channels connected in
parallel with unknown dynamics. The method is then applied to derive a
computationally inexpensive method for estimating RP using 2D cameras that does
not critically depend on ROI. Specifically, RP is estimated by imaging the
changes in the reflected light caused by respiration-induced motion. Each
spatial location in the field of view of the camera is modeled as a
noise-corrupted linear time-invariant (LTI) measurement channel with unknown
system dynamics, driven by a single generating respiratory signal. Estimation
of RP is cast as a blind deconvolution problem and is solved through a method
comprising subspace projection and statistical aggregation. Experiments are
carried out on 31 healthy human subjects by generating multiple RPs and
comparing the proposed estimates with simultaneously acquired ground truth from
an impedance pneumograph device. The proposed estimator agrees well with the
ground truth device in terms of correlation measures, despite variability in
clothing pattern, angle of view and ROI.


title:
Existence of solution to parabolic equations with mixed boundary
  condition on non-cylindrical domain

abstract:
  In this paper we are concerned with the initial boundary value problems of
linear and semi-linear parabolic equations with mixed boundary conditions on
non-cylindrical domains in spatial-temporal space. We obtain the existence of a
weak solution to the problem. In the case of the linear equation the parts for
every type of boundary condition are any open subsets of the boundary being
nonempty the part for Dirichlet condition at any time. Due to this it is
difficult to reduce the problem to one on a cylindrical domain by
diffeomorphism of the domain. By a transformation of unknown function and
penalty method we connect the problem to a monotone operator equation for
functions defined on the non-cylindrical domain. In this way a semilinear
problem is considered when the part of boundary for Dirichlet condition is
cylindrical.


title:
The lifetime of the $B_c^-$ meson and the anomalies in $B\to
  D^{(*)}\tau\nu$

abstract:
  We investigate a new constraint on new-physics interpretations of the
anomalies observed in $B\to D^{(*)}\tau\nu$ decays making use of the lifetime
of the $B_c^-$ meson. A constraint is obtained by demanding that the rate for
$B_c^-\to\tau^-\bar\nu$ does not exceed the fraction of the total width that is
allowed by the calculation of the lifetime in the standard model. This leads to
a very strong bound on new-physics scenarios involving scalar operators since
they lift the slight, but not negligible, chiral suppression of the
$B_c^-\to\tau^-\bar\nu$ amplitude in the standard model. The new constraint
renders a scalar interpretation of the enhancement measured in $R_{D^*}$
implausible, including explanations implementing extra Higgs doublets or
certain classes of leptoquarks. We also discuss the complementarity of
$R_{D^{(*)}}$ and a measurement of the longitudinal polarization of the $\tau$
in the $B\to D^*\tau\nu$ decay in light of our findings.


title:
Hybridization oscillation in the one-dimensional Kondo-Heisenberg model
  with Kondo holes

abstract:
  We use the density matrix renormalization group method to study the
properties of the one-dimensional Kondo-Heisenberg model doped with Kondo
holes. We find that the perturbation of the Kondo holes to the local
hybridization exhibits spatial oscillation pattern and its amplitude decays
exponentially with distance away from the Kondo hole sites. The hybridization
oscillation is correlated with both the charge density oscillation of the
conduction electrons and the oscillation in the correlation function of the
Heisenberg spins. In particular, we find that the oscillation wavelength for
intermediate Kondo couplings is given by the Fermi wavevector of the large
Fermi surface even before it is formed. This suggests that heavy electrons
responsible for the oscillation are already present in this regime and start to
accumulate around the to-be-formed large Fermi surface in the Brillouin zone.


title: Deep Temporal Linear Encoding Networks

abstract:
  The CNN-encoding of features from entire videos for the representation of
human actions has rarely been addressed. Instead, CNN work has focused on
approaches to fuse spatial and temporal networks, but these were typically
limited to processing shorter sequences. We present a new video representation,
called temporal linear encoding (TLE) and embedded inside of CNNs as a new
layer, which captures the appearance and motion throughout entire videos. It
encodes this aggregated information into a robust video feature representation,
via end-to-end learning. Advantages of TLEs are: (a) they encode the entire
video into a compact feature representation, learning the semantics and a
discriminative feature space; (b) they are applicable to all kinds of networks
like 2D and 3D CNNs for video classification; and (c) they model feature
interactions in a more expressive way and without loss of information. We
conduct experiments on two challenging human action datasets: HMDB51 and
UCF101. The experiments show that TLE outperforms current state-of-the-art
methods on both datasets.


title:
Hyper-Kaehler compactification of the intermediate Jacobian fibration of
  a cubic fourfold : the twisted case

abstract:
  The starting point of this note is our recent paper with Laza and Sacc\`a on
the construction of deformations of O'Grady's $10$-dimensional manifolds as
compactifications of intermediate Jacobian fibrations associated to cubic
fourfolds. The note provides a complement to that paper consisting in the
analogous construction in the twisted case, leading to isogenous but presumably
not isomorphic or birational hyper-K\"ahler manifolds.


title:
Reconstructions of the dark-energy equation of state and the
  inflationary potential

abstract:
  We use a mathematical approach based on the constraints systems in order to
reconstruct the equation of state and the inflationary potential for the
inflaton field from the observed spectral indices for the density perturbations
$n_{s}$ and the tensor to scalar ratio $r$. From the astronomical data, we can
observe that the measured values of these two indices lie on a two-dimensional
surface. We express these indices in terms of the Hubble slow-roll parameters
and we assume that $n_{s}-1=h\left( r\right) $. For the function $h\left(
r\right) $, we consider three cases, where $h\left( r\right) $ is constant,
linear and quadratic, respectively. From this, we derive second-order equations
whose solutions provide us with the explicit forms for the expansion
scale-factor, the scalar-field potential, and the effective equation of state
for the scalar field. Finally, we show that for there exist mappings which
transform one cosmological solution to another and allow new solutions to be
generated from existing ones.


title:
Aging and linear response in the H\'ebraud-Lequeux model for amorphous
  rheology

abstract:
  We analyse the aging dynamics of the H\'ebraud-Lequeux model, a
self-consistent stochastic model for the evolution of local stress in an
amorphous material. We show that the model exhibits initial-condition dependent
freezing: the stress diffusion constant decays with time as $D\sim 1/t^2$
during aging so that the cumulative amount of memory that can be erased, which
is given by the time integral of $D(t)$, is finite. Accordingly the shear
stress relaxation function, which we determine in the long-time regime, only
decays to a plateau and becomes progressively elastic as the system ages. The
frequency-dependent shear modulus exhibits a corresponding overall decay of the
dissipative part with system age, while the characteristic relaxation times
scale linearly with age as expected.


title:
Significance of Mobility on Received Signal Strength: An Experimental
  Investigation

abstract:
  In this paper, estimation of mobility using received signal strength is
presented. In contrast to standard methods, speed can be inferred without the
use of any additional hardware like accelerometer, gyroscope or position
estimator. The strength of Wi-Fi signal is considered herein to compute the
time-domain features such as mean, minimum, maximum, and autocorrelation. The
experiments are carried out in different environments like academic area,
residential area and in open space. The complexity of the algorithm in training
and testing phase are quadratic and linear with the number of Wi-Fi samples
respectively. The experimental results indicate that the average error in the
estimated speed is 12 % when the maximum signal strength features are taken
into account. The proposed method is cost-effective and having a low complexity
with reasonable accuracy in a Wi-Fi or cellular environment. Additionally, the
proposed method is scalable that is the performance is not affected in a
multi-smartphones scenario.


title:
Covariate conscious approach for Gait recognition based upon Zernike
  moment invariants

abstract:
  Gait recognition i.e. identification of an individual from his/her walking
pattern is an emerging field. While existing gait recognition techniques
perform satisfactorily in normal walking conditions, there performance tend to
suffer drastically with variations in clothing and carrying conditions. In this
work, we propose a novel covariate cognizant framework to deal with the
presence of such covariates. We describe gait motion by forming a single 2D
spatio-temporal template from video sequence, called Average Energy Silhouette
image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the
parts of AESI infected with covariates. Following this, features are extracted
from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of
Directional Pixels (MDPs) methods. The obtained features are fused together to
form the final well-endowed feature set. Experimental evaluation of the
proposed framework on three publicly available datasets i.e. CASIA dataset B,
OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently
published gait recognition approaches, prove its superior performance.


title: Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring

abstract:
  We present a new notion of probabilistic duality for random variables
involving mixture distributions. Using this notion, we show how to implement a
highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise
graphical models with strictly positive factors that requires almost no
preprocessing and is easy to implement. Moreover, we show how our method can be
combined with blocking to improve mixing. Even though our method leads to
inferior mixing times compared to a sequential Gibbs sampler, we argue that our
method is still very useful for large dynamic networks, where factors are added
and removed on a continuous basis, as it is hard to maintain a graph coloring
in this setup. Similarly, our method is useful for parallelizing Gibbs sampling
in graphical models that do not allow for graph colorings with a small number
of colors such as densely connected graphs.


title: Path integrals for awkward actions

abstract:
  Time derivatives of scalar fields occur quadratically in textbook actions. A
simple Legendre transformation turns the lagrangian into a hamiltonian that is
quadratic in the momenta. The path integral over the momenta is gaussian. Mean
values of operators are euclidian path integrals of their classical
counterparts with positive weight functions. Monte Carlo simulations can
estimate such mean values.
  This familiar framework falls apart when the time derivatives do not occur
quadratically. The Legendre transformation becomes difficult or so intractable
that one can't find the hamiltonian. Even if one finds the hamiltonian, it
usually is so complicated that one can't path-integrate over the momenta and
get a euclidian path integral with a positive weight function. Monte Carlo
simulations don't work when the weight function assumes negative or complex
values.
  This paper solves both problems. It shows how to make path integrals without
knowing the hamiltonian. It also shows how to estimate complex path integrals
by combining the Monte Carlo method with parallel numerical integration and a
look-up table. This "Atlantic City method" lets one estimate the energy
densities of theories that, unlike those with quadratic time derivatives, may
have finite energy densities. It may lead to a theory of dark energy.
  The approximation of multiple integrals over weight functions that assume
negative or complex values is the long-standing sign problem. The Atlantic City
method solves it for problems in which numerical integration leads to a
positive weight function.


title: Scalable Approximations for Generalized Linear Problems

abstract:
  In stochastic optimization, the population risk is generally approximated by
the empirical risk. However, in the large-scale setting, minimization of the
empirical risk may be computationally restrictive. In this paper, we design an
efficient algorithm to approximate the population risk minimizer in generalized
linear problems such as binary classification with surrogate losses and
generalized linear regression models. We focus on large-scale problems, where
the iterative minimization of the empirical risk is computationally
intractable, i.e., the number of observations $n$ is much larger than the
dimension of the parameter $p$, i.e. $n \gg p \gg 1$. We show that under random
sub-Gaussian design, the true minimizer of the population risk is approximately
proportional to the corresponding ordinary least squares (OLS) estimator. Using
this relation, we design an algorithm that achieves the same accuracy as the
empirical risk minimizer through iterations that attain up to a cubic
convergence rate, and that are cheaper than any batch optimization algorithm by
at least a factor of $\mathcal{O}(p)$. We provide theoretical guarantees for
our algorithm, and analyze the convergence behavior in terms of data
dimensions. Finally, we demonstrate the performance of our algorithm on
well-known classification and regression problems, through extensive numerical
studies on large-scale datasets, and show that it achieves the highest
performance compared to several other widely used and specialized optimization
algorithms.


title: Fourier-Mukai partners for very general special cubic fourfolds

abstract:
  We exhibit explicit examples of very general special cubic fourfolds with
discriminant $d$ admitting an associated (twisted) K3 surface, which have
non-isomorphic Fourier-Mukai partners. In particular, in the untwisted setting,
we show that the number of Fourier-Mukai partners for a very general special
cubic fourfold with discriminant $d$ and having an associated K3 surface, is
equal to the number $m$ of Fourier-Mukai partners of its associated K3 surface,
if $d \equiv 2 (\text{mod}\,6)$; else, if $d \equiv 0 (\text{mod}\,6)$, the
cubic fourfold has $\lceil m/2 \rceil$ Fourier-Mukai partners.


title: Adaptive modulation in Ni2Mn1.4In0.6 magnetic shape memory Heusler alloy

abstract:
  The origin of incommensurate structural modulation in Ni-Mn based Heusler
type magnetic shape memory alloys (MSMAs) is still an unresolved issue inspite
of intense focus on this due to its role in the magnetic field induced
ultra-high strains. In the archetypal MSMA Ni2MnGa, the observation of
non-uniform displacement of atoms from their mean positions in the modulated
martensite phase, premartensite phase and charge density wave as well as the
presence of phason broadening of satellite peaks have been taken in support of
the electronic instability model linked with a soft acoustic phonon. We present
here results of a combined high resolution synchrotron x-ray powder diffraction
(SXRPD) and neutron powder diffraction (NPD) study on Ni2Mn1.4In0.6 using
(3+1)D superspace group approach, which reveal not only uniform atomic
displacements in the modulated structure of the martensite phase with
physically acceptable ordered magnetic moments in the antiferromagnetic phase
at low temperatures but also the absence of any premartensite phase and phason
broadening of the satellite peaks. Our HRTEM studies and first principles
calculations of the ground state also support uniform atomic displacements
predicted by powder diffraction studies. All these observations suggest that
the structural modulation in the martensite phase of Ni2Mn1.4In0.6 MSMA can be
explained in terms of the adaptive phase model. The present study underlines
the importance of superspace group analysis using complimentary SXRPD and NPD
in understanding the physics of the origin of modulation as well as the
magnetic and the modulated ground states of the Heusler type MSMAs. Our work
also highlights the fact that the mechanism responsible for the origin of
modulated structure in different Ni-Mn based MSMAs may not be universal and it
must be investigated thoroughly in different alloy compositions.


title:
Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for
  Isolated Gesture Recognition

abstract:
  Recently, the popularity of depth-sensors such as Kinect has made depth
videos easily available while its advantages have not been fully exploited.
This paper investigates, for gesture recognition, to explore the spatial and
temporal information complementarily embedded in RGB and depth sequences. We
propose a convolutional twostream consensus voting network (2SCVN) which
explicitly models both the short-term and long-term structure of the RGB
sequences. To alleviate distractions from background, a 3d depth-saliency
ConvNet stream (3DDSN) is aggregated in parallel to identify subtle motion
characteristics. These two components in an unified framework significantly
improve the recognition accuracy. On the challenging Chalearn IsoGD benchmark,
our proposed method outperforms the first place on the leader-board by a large
margin (10.29%) while also achieving the best result on RGBD-HuDaAct dataset
(96.74%). Both quantitative experiments and qualitative analysis shows the
effectiveness of our proposed framework and codes will be released to
facilitate future research.


title:
Limit distributions for KPZ growth models with spatially homogeneous
  random initial conditions

abstract:
  For stationary KPZ growth in 1+1 dimensions the height fluctuations are
governed by the Baik-Rains distribution. Using the totally asymmetric single
step growth model, alias TASEP, we investigate height fluctuations for a
general class of spatially homogeneous random initial conditions. We prove that
for TASEP there is a one-parameter family of limit distributions, labeled by
the diffusion coefficient of the initial conditions. The distributions are
defined through a variational formula. We use Monte Carlo simulations to obtain
their numerical plots. Also discussed is the connection to the six-vertex model
at its conical point.


title: Coherence and stiffness of spin waves in diluted ferromagnets

abstract:
  We present results of a numerical analysis of magnon spectra in supercells
simulating two-dimensional and bulk random diluted ferromagnets with
long-ranged pair exchange interactions. We show that low-energy spectral
regions for these strongly disordered systems contain a coherent component
leading to interference phenomena manifested by a pronounced sensitivity of the
lowest excitation energies to the adopted boundary conditions. The dependence
of configuration averages of these excitation energies on the supercell size
can be used for an efficient determination of the spin-wave stiffness D. The
developed formalism is applied to the ferromagnetic Mn-doped GaAs semiconductor
with optional incorporation of phosphorus; the obtained concentration trends of
D are found in reasonable agreement with recent experiments. Moreover, a
relation of the spin stiffness to the Curie temperature Tc has been studied for
Mn-doped GaAs and GaN semiconductors. It is found that the ratio Tc/D exhibits
qualitatively the same dependence on Mn concentration in both systems.


title: Control of nonlinear switched systems based on validated simulation

abstract:
  We present an algorithm of control synthesis for nonlinear switched systems,
based on an existing procedure of state-space bisection and made available for
nonlinear systems with the help of validated simulation. The use of validated
simulation also permits to take bounded perturbations and varying parameters
into account. It is particularly interesting for safety critical applications,
such as in aeronautical, military or medical fields. The whole approach is
entirely guaranteed and the induced controllers are correct-by-design.


title: Microscale Flow Dynamics of Ribbons and Sheets

abstract:
  Numerical study of the hydrodynamics of thin sheets and ribbons presents
difficulties associated with resolving multiple length scales. To circumvent
these difficulties, asymptotic methods have been developed to describe the
dynamics of slender fibres and ribbons. However, such theories entail
restrictions on the shapes that can be studied, and often break down in regions
where standard boundary element methods are still impractical. In this paper we
develop a regularised stokeslet method for ribbons and sheets in order to
bridge the gap between asymptotic and boundary element methods. The method is
validated against the analytical solution for plate ellipsoids, as well as the
dynamics of ribbon helices and an experimental microswimmer. We then
demonstrate the versatility of this method by calculating the flow around a
double helix, and the swimming dynamics of a microscale "magic carpet".


title: Training Sparse Neural Networks

abstract:
  Deep neural networks with lots of parameters are typically used for
large-scale computer vision tasks such as image classification. This is a
result of using dense matrix multiplications and convolutions. However, sparse
computations are known to be much more efficient. In this work, we train and
build neural networks which implicitly use sparse computations. We introduce
additional gate variables to perform parameter selection and show that this is
equivalent to using a spike-and-slab prior. We experimentally validate our
method on both small and large networks and achieve state-of-the-art
compression results for sparse neural network models.


title: Scaling Properties of Multilayer Random Networks

abstract:
  Multilayer networks are widespread in natural and manmade systems. Key
properties of these networks are their spectral and eigenfunction
characteristics, as they determine the critical properties of many dynamics
occurring on top of them. In this paper, we numerically demonstrate that the
normalized localization length $\beta$ of the eigenfunctions of multilayer
random networks follows a simple scaling law given by $\beta=x^*/(1+x^*)$, with
$x^*=\gamma(b_{\text{eff}}^2/L)^\delta$, $\gamma,\delta\sim 1$ and
$b_{\text{eff}}$ being the effective bandwidth of the adjacency matrix of the
network, whose size is $L=M\times N$. The reported scaling law for $\beta$
might help to better understand criticality in multilayer networks as well as
to predict the eigenfunction localization properties of them.


title: Zonal structure of unbounded external-flow and aerodynamics

abstract:
  This paper starts from the far-field behaviours of velocity field in
externally-unbounded flow. We find that the well-known algebraic decay of
disturbance velocity as derived kinematically is too conservative. Once the
kinetics is taken into account by working on the fundamental solutions of
far-field linearized Navier-Stokes equations, it is proven that the furthest
far-field zone adjacent to the uniform fluid at infinity must be unsteady,
viscous and compressible, where all disturbances degenerate to sound waves that
decay exponentially. But this optimal rate does not exist in some commonly used
simplified flow models, such as steady flow, incompressible flow and inviscid
flow, because they actually work in true subspaces of the unbounded free space,
which are surrounded by further far fields of different nature. This finding
naturally leads to a zonal structure of externally-unbounded flow field. The
significance of the zonal structure is demonstrated by its close relevance to
existing theories of aerodynamic force and moment in external flows, including
the removal of the difficulties or paradoxes inherent in the simplified models.


title:
Pontential energies and potential-energy tensors for subsystems: general
  properties

abstract:
  With regard to generic two-component systems, the theory of first variations
of global quantities is reviewed and explicit expressions are inferred for
subsystem potential energies and potential-energy tensors. Performing a
conceptual experiment, a physical interpretation of subsystem potential
energies and potential-energy tensors is discussed. Subsystem tidal radii are
defined by requiring an unbound component in absence of the other one. To this
respect, a few guidance examples are presented as: (i) an embedding and an
embedded homogeneous sphere; (ii) an embedding and an embedded truncated,
singular isothermal sphere where related centres are sufficiently distant;
(iii) a homogeneous sphere and a Roche system i.e. a mass point surrounded by a
vanishing atmosphere. The results are discussed and compared with the findings
of earlier investigations.


title: Dynamical Stationarity as a Result of Sustained Random Growth

abstract:
  In sustained growth with random dynamics stationary distributions can exist
without detailed balance. This suggests thermodynamical behavior in fast
growing complex systems. In order to model such phenomena we apply both a
discrete and a continuous master equation. The derivation of elementary rates
from known stationary distributions is a generalization of the
fluctuation--dissipation theorem. Entropic distance evolution is given for such
systems. We reconstruct distributions obtained for growing networks, particle
production, scientific citations and income distribution.


title:
On the number of eigenvalues of modified permutation matrices in
  mesoscopic intervals

abstract:
  We are interested in two random matrix ensembles related to permutations: the
ensemble of permutation matrices following Ewens' distribution of a given
parameter $\theta >0$, and its modification where entries equal to $1$ in the
matrices are replaced by independent random variables uniformly distributed on
the unit circle. For the elements of each ensemble, we focus on the random
numbers of eigenvalues lying in some specified arcs of the unit circle. We show
that for a finite number of fixed arcs, the fluctuation of the numbers of
eigenvalues belonging to them is asymptotically Gaussian. Moreover, for a
single arc, we extend this result to the case where the length goes to zero
sufficiently slowly when the size of the matrix goes to infinity. Finally, we
investigate the behaviour of the largest and smallest spacing between two
distinct consecutive eigenvalues.


title:
Center of the quantum affine vertex algebra associated with
  trigonometric R-matrix

abstract:
  We consider the quantum vertex algebra associated with the trigonometric
R-matrix in type A as defined by Etingof and Kazhdan. We show that its center
is a commutative associative algebra and construct an algebraically independent
family of topological generators of the center at the critical level.


title: Thermogeometric phase transition in a unified framework

abstract:
  Using geomterothermodynamics (GTD), we investigate the phase transition of
black hole in a metric independent way. We show that for any black hole,
curvature scalar (of equilibrium state space geometry) is singular at the point
where specific heat diverges. Previously such a result could only be shown by
taking specific examples on a case by case basis. A different type of phase
transition, where inverse specific heat diverges, is also studied within this
framework. We show that in the latter case, metric (of equilibrium state space
geometry) is singular instead of curvature scalar. Since a metric singularity
may be a coordinate artifact, we propose that GTD indicates that it is the
singularity of specific heat and not inverse specific heat which indicates a
phase transition of black holes.