Abstract:
  In this paper, the ground state Wigner function of a many-body system is
explored theoretically and numerically. First, an eigenvalue problem for Wigner
function is derived based on the energy operator of the system. The validity of
finding the ground state through solving this eigenvalue problem is obtained by
building a correspondence between its solution and the solution of stationary
Schr\"odinger equation. Then, a numerical method is designed for solving
proposed eigenvalue problem in one dimensional case, which can be briefly
described by i) a simplified model is derived based on a quantum hydrodynamic
model [Z. Cai et al, J. Math. Chem., 2013] to reduce the dimension of the
problem, ii) an imaginary time propagation method is designed for solving the
model, and numerical techniques such as solution reconstruction are proposed
for the feasibility of the method. Results of several numerical experiments
verify our method, in which the potential application of the method for large
scale system is demonstrated by examples with density functional theory.


Abstract:
  We study Density Functional Theory models for systems which are
translationally invariant in some directions, such as a homogeneous 2-d slab in
the 3-d space. We show how the different terms of the energy are modified and
we derive reduced equations in the remaining directions. In the Thomas-Fermi
model, we prove that there is perfect screening, and provide decay estimates
for the electronic density away from the slab. In Kohn-Sham models, we prove
that the Pauli principle is replaced by a penalization term in the energy. In
the reduced Hartree-Fock model in particular, we prove that the resulting model
is well-posed, and provide some properties for the minimizer.


Abstract:
  Quantum information processing tasks require exotic quantum states as a
prerequisite. They are usually prepared with many different methods tailored to
the specific resource state. Here we provide a versatile unified state
preparation scheme based on a driven quantum network composed of
randomly-coupled fermionic nodes. The output of such a system is then
superposed with the help of linear mixing where weights and phases are trained
in order to obtain desired output quantum states. We explicitly show that our
method is robust and can be utilized to create almost perfect maximally
entangled, NOON, W, cluster, and discorded states. Furthermore, the treatment
includes energy decay in the system as well as dephasing and depolarization.
Under these noisy conditions we show that the target states are achieved with
high fidelity by tuning controllable parameters and providing sufficient
strength to the driving of the quantum network. Finally, in very noisy systems,
where noise is comparable to the driving strength, we show how to concentrate
entanglement by mixing more states in a larger network.


Abstract:
  Deep neural networks (DNNs) have been shown to be vulnerable against
adversarial examples (AEs), which are maliciously designed to cause dramatic
model output errors. In this work, we reveal that normal examples (NEs) are
insensitive to the fluctuations occurring at the highly-curved region of the
decision boundary, while AEs typically designed over one single domain (mostly
spatial domain) exhibit exorbitant sensitivity on such fluctuations. This
phenomenon motivates us to design another classifier (called dual classifier)
with transformed decision boundary, which can be collaboratively used with the
original classifier (called primal classifier) to detect AEs, by virtue of the
sensitivity inconsistency. When comparing with the state-of-the-art algorithms
based on Local Intrinsic Dimensionality (LID), Mahalanobis Distance (MD), and
Feature Squeezing (FS), our proposed Sensitivity Inconsistency Detector (SID)
achieves improved AE detection performance and superior generalization
capabilities, especially in the challenging cases where the adversarial
perturbation levels are small. Intensive experimental results on ResNet and VGG
validate the superiority of the proposed SID.


Abstract:
  In this paper, some generalized metric properties in strongly topological
gyrogroups are studied.


Abstract:
  A magnon Bose-Einstein condensate in superfluid $^3$He is a fine instrument
for studying the surrounding macroscopic quantum system. At zero temperature,
the BEC is subject to a few, distinct forms of decay into other collective
excitations, owing to momentum and energy conservation in a quantum vacuum. We
study the vortex-Higgs mechanism: the vortices relax the requirement for
momentum conservation, allowing the optical magnons of the BEC to transform
into light Higgs quasiparticles. This observation expands the spectrum of
possible interactions between magnetic quasiparticles in $^3$He-B, opens
pathways for hunting down elusive phenomena such as the Kelvin wave cascade or
bound Majorana fermions, and lays groundwork for building magnon-based quantum
devices.


Abstract:
  Natural language-based vehicle retrieval is a task to find a target vehicle
within a given image based on a natural language description as a query. This
technology can be applied to various areas including police searching for a
suspect vehicle. However, it is challenging due to the ambiguity of language
descriptions and the difficulty of processing multi-modal data. To tackle this
problem, we propose a deep neural network called SBNet that performs natural
language-based segmentation for vehicle retrieval. We also propose two
task-specific modules to improve performance: a substitution module that helps
features from different domains to be embedded in the same space and a future
prediction module that learns temporal information. SBnet has been trained
using the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three
unique natural language descriptions each and tested 530 unique vehicle tracks
and their corresponding query sets. SBNet achieved a significant improvement
over the baseline in the natural language-based vehicle tracking track in the
AI City Challenge 2021.


Abstract:
  Let $n \ge 2$ be an integer and consider the defining ideal of the Fermat
configuration of points in $\mathbb{P}^2$:
$I_n=(x(y^n-z^n),y(z^n-x^n),z(x^n-y^n)) \subset R=\mathbb{C}[x,y,z]$. In this
paper, we compute explicitly the least degree of generators of its symbolic
powers in all unknown cases. As direct applications, we easily verify
Chudnovsky's Conjecture, Demailly's Conjecture and Harbourne-Huneke Containment
problem as well as calculating explicitly the Waldschmidt constant and
(asymptotic) resurgence number.


Abstract:
  In a classical plasma the momentum distribution, $n(k)$, decays
exponentially, for large $k$, and the same is observed for an ideal Fermi gas.
However, when quantum and correlation effects are relevant simultaneously, an
algebraic decay, $n_\infty(k)\sim k^{-8}$ has been predicted. This is of
relevance for cross sections and threshold processes in dense plasmas that
depend on the number of energetic particles. Here we present extensive
\textit{ab initio} results for the momentum distribution of the nonideal
uniform electron gas at warm dense matter conditions. Our results are based on
first principle fermionic path integral Monte Carlo (CPIMC) simulations and
clearly confirm the $k^{-8}$ asymptotic. This asymptotic behavior is directly
linked to short-range correlations which are analyzed via the on-top pair
distribution function (on-top PDF), i.e. the PDF of electrons with opposite
spin. We present extensive results for the density and temperature dependence
of the on-top PDF and for the momentum distribution in the entire momentum
range.


Abstract:
  Natural Language Processing offers new insights into language data across
almost all disciplines and domains, and allows us to corroborate and/or
challenge existing knowledge. The primary hurdles to widening participation in
and use of these new research tools are, first, a lack of coding skills in
students across K-16, and in the population at large, and second, a lack of
knowledge of how NLP-methods can be used to answer questions of disciplinary
interest outside of linguistics and/or computer science. To broaden
participation in NLP and improve NLP-literacy, we introduced a new tool
web-based tool called Natural Language Processing 4 All (NLP4All). The intended
purpose of NLP4All is to help teachers facilitate learning with and about NLP,
by providing easy-to-use interfaces to NLP-methods, data, and analyses, making
it possible for non- and novice-programmers to learn NLP concepts
interactively.


Abstract:
  In this paper, we propose and analyze a diffuse interface model for
inductionless magnetohydrodynamic fluids. The model couples a convective
Cahn-Hilliard equation for the evolution of the interface, the Navier-Stokes
system for fluid flow and the possion quation for electrostatics. The model is
derived from Onsager's variational principle and conservation laws
systematically. We perform formally matched asymptotic expansions and develop
several sharp interface models in the limit when the interfacial thickness
tends to zero. It is shown that the sharp interface limit of the models are the
standard incompressible inductionless magnetohydrodynamic equations coupled
with several different interface conditions for different choice of the
mobilities. Numerical results verify the convergence of the diffuse interface
model with different mobilitiess.


Abstract:
  We consider the concatenation of a convolutional code (CC) with an optimized
cyclic redundancy check (CRC) code as a promising paradigm for good short
blocklength codes. The resulting CRC-aided convolutional code naturally permits
the use of serial list Viterbi decoding (SLVD) to achieve maximum-likelihood
decoding. The convolutional encoder of interest is of rate-$1/\omega$ and the
convolutional code is either zero-terminated (ZT) or tail-biting (TB). The
resulting CRC-aided convolutional code is called a CRC-ZTCC or a CRC-TBCC. To
design a good CRC-aided convolutional code, we propose the distance-spectrum
optimal (DSO) CRC polynomial. A DSO CRC search algorithm for the TBCC is
provided. Our analysis reveals that the complexity of SLVD is governed by the
expected list rank which converges to $1$ at high SNR. This allows a good
performance to be achieved with a small increase in complexity. In this paper,
we focus on transmitting $64$ information bits with a rate-$1/2$ convolutional
encoder. For a target error probability $10^{-4}$, simulations show that the
best CRC-ZTCC approaches the random-coding union (RCU) bound within $0.4$ dB.
Several CRC-TBCCs outperform the RCU bound at moderate SNR values.


Abstract:
  In this paper, we obtain some results on precise large deviations for
non-random and random sums of widely dependent random variables with common
dominatedly varying tail distribution or consistently varying tail distribution
on $(-\infty,\infty)$. Then we apply the results to reinsurance and insurance
and give some asymptotic estimates on proportional reinsurance, random-time
ruin probability and the finite-time ruin probability.


Abstract:
  The energy range between about 100 keV and 1 GeV is of interest for a vast
class of astrophysical topics. In particular, (1) it is the missing ingredient
for understanding extreme processes in the multi-messenger era; (2) it allows
localizing cosmic-ray interactions with background material and radiation in
the Universe, and spotting the reprocessing of these particles; (3) last but
not least, gamma-ray emission lines trace the formation of elements in the
Galaxy and beyond. In addition, studying the still largely unexplored MeV
domain of astronomy would provide for a rich observatory science, including the
study of compact objects, solar- and Earth-science, as well as fundamental
physics. The technological development of silicon microstrip detectors makes it
possible now to detect MeV photons in space with high efficiency and low
background. During the last decade, a concept of detector ("ASTROGAM") has been
proposed to fulfil these goals, based on a silicon hodoscope, a 3D
position-sensitive calorimeter, and an anticoincidence detector. In this paper
we stress the importance of a medium size (M-class) space mission, dubbed
"ASTROMEV", to fulfil these objectives.


Abstract:
  In network science complex systems are represented as a mathematical graphs
consisting of a set of nodes representing the components and a set of edges
representing their interactions. The framework of networks has led to
significant advances in the understanding of the structure, formation and
function of complex systems. Social and biological processes such as the
dynamics of epidemics, the diffusion of information in social media, the
interactions between species in ecosystems or the communication between neurons
in our brains are all actively studied using dynamical models on complex
networks. In all of these systems, the patterns of connections at the
individual level play a fundamental role on the global dynamics and finding the
most important nodes allows one to better understand and predict their
behaviors. An important research effort in network science has therefore been
dedicated to the development of methods allowing to find the most important
nodes in networks. In this short entry, we describe network centrality measures
based on the notions of network traversal they rely on. This entry aims at
being an introduction to this extremely vast topic, with many contributions
from several fields, and is by no means an exhaustive review of all the
literature about network centralities.


Abstract:
  We propose a new method to define anomaly scores and apply this to particle
physics collider events. Anomalies can be either rare, meaning that these
events are a minority in the normal dataset, or different, meaning they have
values that are not inside the dataset. We quantify these two properties using
an ensemble of One-Class Deep Support Vector Data Description models, which
quantifies differentness, and an autoregressive flow model, which quantifies
rareness. These two parameters are then combined into a single anomaly score
using different combination algorithms. We train the models using a dataset
containing only simulated collisions from the Standard Model of particle
physics and test it using various hypothetical signals in four different
channels and a secret dataset where the signals are unknown to us. The anomaly
detection method described here has been evaluated in a summary paper [1] where
it performed very well compared to a large number of other methods. The method
is simple to implement and is applicable to other datasets in other fields as
well.


Abstract:
  As a fundamental structure in real-world networks, communities can be
reflected by abundant node attributes with the fusion of graph topology. In
attribute-aware community detection, probabilistic generative models (PGMs)
have become the mainstream fusion method due to their principled
characterization and interpretation. Here, we propose a novel PGM without
imposing any distributional assumptions on attributes, which is superior to
existing PGMs that require attributes to be categorical or Gaussian
distributed. Based on the famous block model of graph structure, our model
fuses the attribute by describing its effect on node popularity using an
additional term. To characterize the effect quantitatively, we analyze the
detectability of communities for the proposed model and then establish the
requirements of the attribute-popularity term, which leads to a new scheme for
the model selection problem in attribute-aware community detection. With the
model determined, an efficient algorithm is developed to estimate the
parameters and to infer the communities. The proposed method is validated from
two aspects. First, the effectiveness of our algorithm is theoretically
guaranteed by the detectability condition, whose correctness is verified by
numerical experiments on artificial graphs. Second, extensive experiments show
that our method outperforms the competing approaches on a variety of real-world
networks.


Abstract:
  The link relation on simple subcoalgebras is used for decompositions of
coalgebras. In this paper, we provide more sufficient conditions for this link
relation, and prove a formula on the products between link-indecomposable
components of Hopf algebras with the dual Chevalley property. Furthermore, we
show that each of its component is generated by a simple subcoalgebra, as a
faithfully flat module (in fact, a projective generator) over a Hopf subalgebra
which is the component containing the unit element. Our conclusions generalize
some relevant results on pointed Hopf algebras, which were established by
Montgomery in 1995.


Abstract:
  Mycelium networks are promising substrates for designing unconventional
computing devices providing rich topologies and geometries where signals
propagate and interact. Fulfilling our long-term objectives of prototyping
electrical analog computers from living mycelium networks, including networks
hybridised with nanoparticles, we explore the possibility of implementing
Boolean logical gates based on electrical properties of fungal colonies. We
converted a 3D image-data stack of \emph{Aspergillus niger} fungal colony to an
Euclidean graph and modelled the colony as resistive and capacitive (RC)
networks, where electrical parameters of edges were functions of the edges'
lengths. We found that {\sc and}, {\sc or} and {\sc and-not} gates are
implementable in RC networks derived from the geometrical structure of the real
fungal colony.


Abstract:
  We report a comprehensive investigation of Ln2NiIrO6 (Ln = La, Pr, Nd) using
thermodynamic and transport properties, neutron powder diffraction, resonant
inelastic x-ray scattering, and density functional theory (DFT) calculations to
investigate the role of A-site cations on the magnetic interactions in this
family of hybrid 3d-5d-4f compositions. Magnetic structure determination using
neutron diffraction reveals antiferromagnetism for La2NiIrO6, a collinear
ferrimagnetic Ni/Ir state that is driven to long range antiferromagnetism upon
the onset of Nd ordering in Nd2NiIrO6, and a non-collinear ferrimagnetic Ni/Ir
sublattice interpenetrated by a ferromagnetic Pr lattice for Pr2NiIrO6. For
Pr2NiIrO6 heat capacity results reveal the presence of two independent magnetic
sublattices and transport resistivity indicates insulating behavior and a
conduction pathway that is thermally mediated. First principles DFT calculation
elucidates the existence of the two independent magnetic sublattices within
Pr2NiIrO6 and offers insight into the behavior in La2NiIrO6 and Nd2NiIrO6.
Resonant inelastic x-ray scattering is consistent with spin-orbit coupling
splitting the t2g manifold of octahedral Ir4+ into a Jeff = 1/2 and Jeff = 3/2
state for all members of the series considered.


Abstract:
  Constraint satisfaction problems (CSP's) and data stream models are two
powerful abstractions to capture a wide variety of problems arising in
different domains of computer science. Developments in the two communities have
mostly occurred independently and with little interaction between them. In this
work, we seek to investigate whether bridging the seeming communication gap
between the two communities may pave the way to richer fundamental insights. To
this end, we focus on two foundational problems: model counting for CSP's and
computation of zeroth frequency moments ($F_0$) for data streams.
  Our investigations lead us to observe striking similarity in the core
techniques employed in the algorithmic frameworks that have evolved separately
for model counting and $F_0$ computation. We design a recipe for translation of
algorithms developed for $F_0$ estimation to that of model counting, resulting
in new algorithms for model counting. We then observe that algorithms in the
context of distributed streaming can be transformed to distributed algorithms
for model counting. We next turn our attention to viewing streaming from the
lens of counting and show that framing $F_0$ estimation as a special case of
#DNF counting allows us to obtain a general recipe for a rich class of
streaming problems, which had been subjected to case-specific analysis in prior
works. In particular, our view yields a state-of-the art algorithm for
multidimensional range efficient $F_0$ estimation with a simpler analysis.


Abstract:
  Quantum chemistry calculations such as the prediction of molecular properties
and modeling of chemical reactions are a few of the critical areas where
near-term quantum computers can showcase quantum advantage. We present a method
to calculate energy derivatives for both ground state and excited state
energies with respect to the parameters of a chemical system based on the
framework of the variational quantum eigensolver (VQE). A low-depth
implementation of quantum circuits within the hybrid variational paradigm is
designed, and their computational costs are analyzed. We showcase the
effectiveness of our method by incorporating it in some key quantum chemistry
applications of energy derivatives, such as to perform minimum energy
configuration search and estimate molecular response properties estimation of
H$_2$ molecule, and also to find the transition state of H$_2$ + H
$\leftrightarrow$ H + H$_2$ reaction. The obtained results are shown to be in
complete agreement with their respective full configuration interaction (FCI)
values.


Abstract:
  We introduce shadow structures for singular knot theory. Precisely, we define
\emph{two} invariants of singular knots and links. First, we introduce a notion
of action of a singquandle on a set to define a shadow counting invariant of
singular links which generalize the classical shadow colorings of knots by
quandles. We then define a shadow polynomial invariant for shadow structures.
Lastly, we enhance the shadow counting invariant by combining both the shadow
counting invariant and the shadow polynomial invariant. Explicit examples of
computations are given.


Abstract:
  Due to their simplicity and versatility of design, straight strip or
rectangular pad anode structures are frequently employed with micro-pattern gas
detectors to reconstruct high precision space points for various tracking
applications. The particle impact point is typically determined by
interpolating the charge collected by several neighboring pads. However, to
effectively extract the inherent positional information, the lateral spacing of
the straight pads must be significantly smaller than the extent of the charge
cloud. In contrast, highly interleaved anode patterns, such as zigzags, can
adequately sample the charge with a pitch comparable to the size of the charge
cloud or even larger. This has the considerable advantage of providing the same
performance while requiring far fewer instrumented channels. Additionally, the
geometric parameters defining such zigzag structures may be tuned to provide a
uniform detector response without the need for so-called pad response
functions, while simultaneously maintaining excellent position resolution. We
have measured the position resolution of a variety of zigzag shaped anode
patterns optimized for various MPGDs, including GEM, Micromegas, and
micro-RWELL and compared this performance to the same detectors equipped with
straight pads of varying pitch. We report on the performance results of each
readout structure, evaluated under identical conditions in a test beam.


Abstract:
  Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but
it requires expertise and experience to identify abnormalities. It is thus
crucial to develop automated models for the detection of abnormalities in EEGs
related to epilepsy. This paper describes the development of a novel class of
compact convolutional neural networks (CNNs) for detecting abnormal patterns
and electrodes in EEGs for epilepsy. The designed model is inspired by a CNN
developed for brain-computer interfacing called multichannel EEGNet (mEEGNet).
Unlike the EEGNet, the proposed model, mEEGNet, has the same number of
electrode inputs and outputs to detect abnormal patterns. The mEEGNet was
evaluated with a clinical dataset consisting of 29 cases of juvenile and
childhood absence epilepsy labeled by a clinical expert. The labels were given
to paroxysmal discharges visually observed in both ictal (seizure) and
interictal (nonseizure) durations. Results showed that the mEEGNet detected
abnormalities with the area under the curve, F1-values, and sensitivity
equivalent to or higher than those of existing CNNs. Moreover, the number of
parameters is much smaller than other CNN models. To our knowledge, the dataset
of absence epilepsy validated with machine learning through this research is
the largest in the literature.


Abstract:
  Single-cycle pulses with deterministic carrier-envelope phase enable the
study and control of light-matter interactions at the sub-cycle timescale, as
well as the efficient generation of low-noise multi-octave frequency combs.
However, current single-cycle light sources are difficult to implement and
operate, hindering their application and accessibility in a wider range of
research. In this paper, we present a single-cycle 100 MHz frequency comb in a
compact, turn-key, and reliable all-silica-fiber format. This is achieved by
amplifying 2 $\mu$m seed pulses in heavily-doped Tm:fiber, followed by cascaded
self-compression to yield 6.8 fs pulses with 215 kW peak power and 374 mW
average power. The corresponding spectrum covers more than two octaves, from
below 700 nm up to 3500 nm. Driven by this single-cycle pump, supercontinuum
with 180 mW of integrated power and a smooth spectral amplitude between 2100
and 2700 nm is generated directly in silica fibers. To broaden
applications,few-cycle pulses extending from 6 $\mu$m to beyond 22 $\mu$m with
long-term stable carrier-envelope phase are created using intra-pulse
difference frequency, and electro-optic sampling yields comb-tooth-resolved
spectra. Our work demonstrates the first all-fiber configuration that generates
single-cycle pulses, and provides a practical source to study nonlinear optics
on the same timescale.


Abstract:
  We propose a novel generic reputation bootstrapping framework for composite
services. Multiple reputation-related indicators are considered in a
layer-based framework to implicitly reflect the reputation of the component
services. The importance of an indicator on the future performance of a
component service is learned using a modified Random Forest algorithm. We
propose a topology-aware Forest Deep Neural Network (fDNN) to find the
correlations between the reputation of a composite service and reputation
indicators of component services. The trained fDNN model predicts the
reputation of a new composite service with the confidence value. Experimental
results with real-world dataset prove the efficiency of the proposed approach.


Abstract:
  Face recognition technology related to recognizing identities is widely
adopted in intelligence gathering, law enforcement, surveillance, and consumer
applications. Recently, this technology has been ported to smartphones and
body-worn cameras (BWC). Face recognition technology in body-worn cameras is
used for surveillance, situational awareness, and keeping the officer safe.
Only a handful of academic studies exist in face recognition using the
body-worn camera. A recent study has assembled BWCFace facial image dataset
acquired using a body-worn camera and evaluated the ResNet-50 model for face
identification. However, for real-time inference in resource constraint
body-worn cameras and privacy concerns involving facial images, on-device face
recognition is required. To this end, this study evaluates lightweight
MobileNet-V2, EfficientNet-B0, LightCNN-9 and LightCNN-29 models for face
identification using body-worn camera. Experiments are performed on a publicly
available BWCface dataset. The real-time inference is evaluated on three mobile
devices. The comparative analysis is done with heavy-weight VGG-16 and
ResNet-50 models along with six hand-crafted features to evaluate the trade-off
between the performance and model size. Experimental results suggest the
difference in maximum rank-1 accuracy of lightweight LightCNN-29 over
best-performing ResNet-50 is \textbf{1.85\%} and the reduction in model
parameters is \textbf{23.49M}. Most of the deep models obtained similar
performances at rank-5 and rank-10. The inference time of LightCNNs is 2.1x
faster than other models on mobile devices. The least performance difference of
\textbf{14\%} is noted between LightCNN-29 and Local Phase Quantization (LPQ)
descriptor at rank-1. In most of the experimental settings, lightweight
LightCNN models offered the best trade-off between accuracy and the model size
in comparison to most of the models.


Abstract:
  In this paper we first show that each Kummer quartic surface (a quartic
surface $X$ with 16 singular points) is, in canonical coordinates, equal to its
dual surface, and that the Gauss map induces a fixpoint free involution
$\gamma$ on the minimal resolution $S$ of $X$. Then we study the corresponding
Enriques surfaces $S/ \gamma$. We also describe in detail the remarkable
properties of the most symmetric Kummer quartic, which we call the Cefal\'u
quartic. We also investigate the Kummer quartic surfaces whose associated
Abelian surface is isogenous to a product of elliptic curves through an isogeny
with kernel $(\mathbb{Z}/2)^2$, and show the existence of polarized nodal K3
surfaces $X$ of any degree $d=2k$ with the maximal number of nodes, such that
$X$ and its nodes are defined over $\mathbb{R}$. We take then as parameter
space for Kummer quartics an open set in $\mathbb{P}^3$, parametrizing
nondegenerate $(16_6, 16_6)$-configurations, and compare with other parameter
spaces.
  We also extend to positive characteristic some results which were previously
known over $\mathbb{C}$.
  We end with a section devoted to remarks on normal cubic surfaces, and
providing some other examples of strictly selfdual hypersurfaces.


Abstract:
  In this paper we focus on the problem of learning online an optimal policy
for Active Visual Search (AVS) of objects in unknown indoor environments. We
propose POMP++, a planning strategy that introduces a novel formulation on top
of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to
allow training-free online policy learning in unknown environments. We present
a new belief reinvigoration strategy which allows to use POMCP with a
dynamically growing state space to address the online generation of the floor
map. We evaluate our method on two public benchmark datasets, AVD that is
acquired by real robotic platforms and Habitat ObjectNav that is rendered from
real 3D scene scans, achieving the best success rate with an improvement of
>10% over the state-of-the-art methods.


Abstract:
  An $r^{\an}$-center of a compact body $\Om$ in an $n$ dimensional Euclidean
space is a point that gives an extremal value of the regularized Riesz
potential, which is the (Hadamard regularization of) integration on $\Om$ of
the distance from the point to the power $\an$. We show that for any real
number $\la$ if a compact body is sufficiently close to a ball in the sense of
asphericity then the $r^{\an}$-center is unique. We also study the regularized
potentials of a unit ball.


Abstract:
  This paper is the second of a series that tackles the properties of molecular
gas in galaxies residing in clusters and their related large-scale structures.
Out of 21 targeted fields, 19 galaxies were detected in CO(3-2) with the
Atacama Large Millimeter Array (ALMA), including two detections within a single
field. These galaxies are either bona fide members of the CL1301.7$-$1139
cluster ($z=0.4828$, $\sigma_{cl}=681$ km s$^{-1}$), or located within $\sim 7
\times R_{200}$, its virial radius. They have been selected to sample the range
of photometric local densities around CL1301.7$-$1139, with stellar masses
above log($M_{\rm star}$) = 10, and to be located in the blue clump of
star-forming galaxies derived from the $u$, $g$, and $i$ photometric bands.
Unlike previous works, our sample selection does not impose a minimum star
formation rate or detection in the far-infrared. As such and as much as
possible, it delivers an unbiased view of the gas content of normal
star-forming galaxies at $z \sim 0.5$. Our study highlights the variety of
paths to star formation quenching, and most likely the variety of physical
properties (i.e. temperature, density) of the corresponding galaxy's cold
molecular gas. Just as in the case of CL1411.1$-$1148, although to a smaller
extent, we identify a number of galaxies with lower gas fraction than
classically found in other surveys. These galaxies can still be on the
star-forming main sequence. When these galaxies are not inside the cluster
virialised region, we provide hints that they are linked to their infall
regions within $\sim 4 \times R_{200}$.


Abstract:
  In this paper, we establish quantitative estimates for nonlinear sampling
Kantorovich operators in terms of the modulus of continuity in the setting of
Orlicz spaces. This general frame allows us to directly deduce some
quantitative estimates of approximation in $L^{p}$-spaces, $1\leq p<\infty $,
and in other well-known instances of Orlicz spaces, such as the Zygmung and the
exponential spaces. Further, the qualitative order of approximation has been
obtained assuming $f$ in suitable Lipschitz classes. The above estimates
achieved in the general setting of Orlicz spaces, have been also improved in
the $L^p$-case, using a direct approach suitable to this context. At the end,
we consider the particular cases of the nonlinear sampling Kantorovich
operators constructed by using some special kernels.


Abstract:
  Securing the communication between a web server and a browser is a
fundamental task of securing the World Wide Web. Websites today rely heavily on
HTTPS to set up secure connections. In recent years, several incidents
undermined this trust and therefore the security of the HTTPS system. In this
paper we introduce an approach allowing to secure JavaScript files in case a
HTTPS connection between web server and browser is compromised. Our paper
presents a solution to safeguard the user's browser so that it only processes
content (e.g., JavaScript or HTML) that was genuinely provided by the web
application service providers themselves. Our solution makes use of service
workers, a recently proposed W3C Candidate Recommendation enabling applications
to take advantage of persistent background processing, including hooks to
enable bootstrapping of web applications while offline. It demonstrates how
service workers are able to validate the integrity of JavaScript files within
the client's browser and how service workers are used to detect and mitigate
malicious JavaScript files.


Abstract:
  Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning
have gained popularity because of their ability to represent hierarchical data.
We propose that it is possible to take advantage of the hierarchical
characteristic present in the images by using hyperbolic neural networks in a
GAN architecture. In this study, different configurations using fully connected
hyperbolic layers in the GAN, CGAN, and WGAN are tested, in what we call the
HGAN, HCGAN, and HWGAN, respectively. The results are measured using the
Inception Score (IS) and the Fr\'echet Inception Distance (FID) on the MNIST
dataset. Depending on the configuration and space curvature, better results are
achieved for each proposed hyperbolic versions than their euclidean
counterpart.


Abstract:
  Ambipolar transport is a commonly occurring theme in semimetals and
semiconductors. Here we present an analytical formulation of the conductivity
for a two-band system. Electron and hole carrier densities and their respective
conductivities are mapped into a two-dimensional unit-less phase space.
Provided that one of the carrier densities is known, the dimensionless phase
space can be probed through magnetoresistance measurements. This formulation of
the two-band model for conductivity is applied to magnetoresistance experiments
on Ca$_3$Ru$_2$O$_7$. While previous such measurements focused on the
low-temperature limit, we cover a broad temperature range and find negative
magnetoresistance in an intermediate interval below the electronic transition
at 48 K. The low-temperature magnetoresistance in Ca$_3$Ru$_2$O$_7$ is
consistent with a two-band structure. However, the model fails to describe the
full temperature and magnetic field dependence. Negative magnetoresistance
found in an intermediate temperature range is, for example, not captured by
this model. We thus conclude that the electronic and magnetic structure in this
intermediate temperature range render the system beyond the most simple
two-band model.


Abstract:
  In this work we show that the cosmological lithium problem and the $H_0$
tension could be eased at the same time by allowing variations in the
fundamental constants. We compute the primordial abundances of light elements
resulting from Big Bang Nucleosynthesis considering the fine structure
constant, the Higgs' vacuum expectation value and Newton's constant as free
parameters. Using the observational data for abundances, we set constraints on
the variations of the fundamental constants. An interpretation of the results
in terms of the number of effective relativistic species gives $N_{\rm
eff}=4.04\pm0.12$. If one extrapolates the fit of {\sffamily Planck}
considering this $N_{\rm eff}$, the value of the inferred Hubble's constant
shifts to $H_0=(71.85\pm0.77)\,\text{km}\,\text{s}^{-1} \text{Mpc}^{-1}$,
compatible with current direct determinations.


Abstract:
  We report the study of sudden transitions or tipping in a collection of
systems induced due to multiplexing with another network of systems. The
emergent dynamics of oscillators on one layer can undergo a sudden transition
to steady state due to indirect coupling with a shared environment, mean field
couplings and conjugate couplings among them. In all these cases, when
multiplexed with another set of similar systems, the tipping phenomena are
induced on the second layer also with a similar pattern of behaviour. We
consider van der Pol oscillator as nodal dynamics with various network
topologies like scale free and regular networks with local and nonlocal
couplings. We also report how the coupling topology influences the nature of
transitions on both layers, under multiplexing.


Abstract:
  In this paper, we introduce central vertex corona, central edge corona, and
central edge neighborhood corona of graphs using central graph. Also, we
determine their adjacency spectrum, Laplacian spectrum and signless Laplacian
spectrum. From our results, it is possible to obtain infinitely many pairs of
adjacency (respectively, Laplacian and signless Laplacian) cospectral graphs.
As an application, we calculate the number of spanning trees and the Kirchhoff
index of the resulting graphs.


Abstract:
  We investigated the mechanism of magnetic noise due to both surface and bulk
impurities. For surface noise, we apply the Langevin method to spin fluctuation
theory to calculate the noise for paramagnetic surface impurities absorbed in a
thin layer of water. We find that the mechanisms generating noise are spin flip
and spin precession which depend on impurity spin relaxation and spin
precession time. For the bulk noise, we consider carbon-13 and nitrogen as
impurities and employ the correlated-cluster expansion to calculate noise.
Carbon-13 noise is a few orders of magnitude larger than nitrogen due to the
higher impurity density in the typical NV center diamond system. We also find
that the noise in the secular approximation underestimates noise under low
applied magnetic field. Overall, the major source of magnetic field noise is
spin precession noise, which is more than five orders of magnitude larger than
the spin flip noise.


Abstract:
  Working in a variant of the intersection type assignment system of Coppo,
Dezani-Ciancaglini and Veneri [1981], we prove several facts about sets of
terms having a given intersection type. Our main result is that every strongly
normalizing term $M$ admits a *uniqueness typing*, which is a pair $(\Gamma,A)$
such that
  1) $\Gamma \vdash M : A$
  2) $\Gamma \vdash N : A \Longrightarrow M =_{\beta\eta} N$
  We also discuss several presentations of intersection type algebras, and the
corresponding choices of type assignment rules.


Abstract:
  The Cadmium Zinc Telluride Imager (CZTI) on AstroSat is a hard X-ray
coded-aperture mask instrument with a primary field of view of 4.6 x 4.6
degrees (FWHM). The instrument collimators become increasingly transparent at
energies above $\sim$100 keV, making CZTI sensitive to radiation from the
entire sky. While this has enabled CZTI to detect a large number of off-axis
transient sources, calculating the source flux or spectrum requires knowledge
of the direction and energy dependent attenuation of the radiation incident
upon the detector. Here, we present a GEANT4-based mass model of CZTI and
AstroSat that can be used to simulate the satellite response to the incident
radiation, and to calculate an effective "response file" for converting the
source counts into fluxes and spectra. We provide details of the geometry and
interaction physics, and validate the model by comparing the simulations of
imaging and flux studies with observations. Spectroscopic validation of the
mass model is discussed in a companion paper, Chattopadhyay 2021.


Abstract:
  Purpose: The development of metaheuristic algorithms has increased by
researchers to use them extensively in the field of business, science, and
engineering. One of the common metaheuristic optimization algorithms is called
Grey Wolf Optimization (GWO). The algorithm works based on imitation of the
wolves' searching and the process of attacking grey wolves. The main purpose of
this paper to overcome the GWO problem which is trapping into local optima.
  Design or Methodology or Approach: In this paper, the K-means clustering
algorithm is used to enhance the performance of the original Grey Wolf
Optimization by dividing the population into different parts. The proposed
algorithm is called K-means clustering Grey Wolf Optimization (KMGWO).
  Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To
evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019
benchmark test functions. Results prove that KMGWO is better compared to GWO.
KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization
Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank
in terms of performance. Statistical results proved that KMGWO achieved a
higher significant value compared to the compared algorithms. Also, the KMGWO
is used to solve a pressure vessel design problem and it has outperformed
results.
  Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also
compared to cat swarm optimization (CSO), whale optimization algorithm-bat
algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of
performance. Also, the KMGWO is used to solve a classical engineering problem
and it is superior


Abstract:
  Primal heuristics play a crucial role in exact solvers for Mixed Integer
Programming (MIP). While solvers are guaranteed to find optimal solutions given
sufficient time, real-world applications typically require finding good
solutions early on in the search to enable fast decision-making. While much of
MIP research focuses on designing effective heuristics, the question of how to
manage multiple MIP heuristics in a solver has not received equal attention.
Generally, solvers follow hard-coded rules derived from empirical testing on
broad sets of instances. Since the performance of heuristics is
instance-dependent, using these general rules for a particular problem might
not yield the best performance. In this work, we propose the first data-driven
framework for scheduling heuristics in an exact MIP solver. By learning from
data describing the performance of primal heuristics, we obtain a
problem-specific schedule of heuristics that collectively find many solutions
at minimal cost. We provide a formal description of the problem and propose an
efficient algorithm for computing such a schedule. Compared to the default
settings of a state-of-the-art academic MIP solver, we are able to reduce the
average primal integral by up to 49% on a class of challenging instances.


Abstract:
  Functional Time Series are sequences of dependent random elements taking
values on some functional space. Most of the research on this domain is focused
on producing a predictor able to forecast the value of the next function having
observed a part of the sequence. For this, the Autoregresive Hilbertian process
is a suitable framework. We address here the problem of constructing
simultaneous predictive confidence bands for a stationary functional time
series. The method is based on an entropy measure for stochastic processes, in
particular functional time series. To construct predictive bands we use a
functional bootstrap procedure that allow us to estimate the prediction law
through the use of pseudo-predictions. Each pseudo-realisation is then
projected into a space of finite dimension, associated to a functional basis.
We use Reproducing Kernel Hilbert Spaces (RKHS) to represent the functions,
considering then the basis associated to the reproducing kernel. Using a simple
decision rule, we classify the points on the projected space among those
belonging to the minimum entropy set and those that do not. We push back the
minimum entropy set to the functional space and construct a band using the
regularity property of the RKHS. The proposed methodology is illustrated
through artificial and real-world data sets.


Abstract:
  In this article, we develop a graphical calculus for multi-qudit computations
with generalized Clifford algebras, using the algebraic framework we developed
in our previous paper. We build our graphical calculus out of a fixed set of
graphical primitives defined by algebraic expressions constructed out of
elements of a given generalized Clifford algebra, a graphical primitive
corresponding to the ground state, and also graphical primitives corresponding
to projections onto the ground state of each qudit. We establish many
"superpower"-like properties of the graphical calculus using purely algebraic
methods, including a novel algebraic proof of a Yang-Baxter-like equation. We
also derive several new identities for the braid elements, which are key to our
proofs. In terms of physics, we connect these braid identities to physics by
showing the presence of a conserved charge. Furthermore, we demonstrate that in
many cases, the verification of involved vector identities can be reduced to
the combinatorial application of two basic vector identities. Finally, we show
how to explicitly compute various vector states in an efficient manner using
algebraic methods.


Abstract:
  How deep neural networks (DNNs) learn from noisy labels has been studied
extensively in image classification but much less in image segmentation. So
far, our understanding of the learning behavior of DNNs trained by noisy
segmentation labels remains limited. In this study, we address this deficiency
in both binary segmentation of biological microscopy images and multi-class
segmentation of natural images. We generate extremely noisy labels by randomly
sampling a small fraction (e.g., 10%) or flipping a large fraction (e.g., 90%)
of the ground truth labels. When trained with these noisy labels, DNNs provide
largely the same segmentation performance as trained by the original ground
truth. This indicates that DNNs learn structures hidden in labels rather than
pixel-level labels per se in their supervised training for semantic
segmentation. We refer to these hidden structures in labels as meta-structures.
When DNNs are trained by labels with different perturbations to the
meta-structure, we find consistent degradation in their segmentation
performance. In contrast, incorporation of meta-structure information
substantially improves performance of an unsupervised segmentation model
developed for binary semantic segmentation. We define meta-structures
mathematically as spatial density distributions and show both theoretically and
experimentally how this formulation explains key observed learning behavior of
DNNs.


Abstract:
  We survey the inclusion of interferometric elements in nonlinear spectroscopy
performed with quantum light. Controlled interference of electromagnetic fields
coupled to matter can induce constructive or destructive contributions of
microscopic coupling sequences (histories) of matter. Since quantum fields do
not commute, quantum light signals are sensitive to the order of light-matter
coupling sequence. Matter correlation functions are thus imprinted by different
field factors, which depend on that order. We identify the associated quantum
information obtained by controlling the weights of different contributing
pathways, and offer several experimental schemes for recovering it. Nonlinear
quantum response functions include out-of-time-ordering matter correlators
(OTOC) which reveal how perturbations spread throughout a quantum system
(information scrambling). Their effect becomes most notable when using
ultrafast pulse sequences with respect to the path difference induced by the
interferometer. OTOC appear in quantum-informatics studies in other fields,
including black holes, high energy, and condensed matter physics.


Abstract:
  As an extension of the weak perturbation theory obtained in recent analysis
on infinite-derivative non-local non-Abelian gauge theories motivated from
p-adic string field theory, and postulated as direction of UV-completion in 4-D
Quantum Field Theory (QFT), here we investigate the confinement conditions and
$\beta-$function in the strong coupling regime. We extend the confinement
criterion, previously obtained by Kugo and Ojima for the local theory based on
the Becchi-Rouet-Stora-Tyutin (BRST) invariance, to the non-local theory, by
using a set of exact solutions of the corresponding local theory. We show that
the infinite-derivatives which are active in the UV provides finite
contributions also in the infrared (IR) limit and provide a proof of
confinement, granted by the absence of the Landau pole. The main difference
with the local case is that the IR fixed point is moved to infinity. We also
show that in the limit of the energy scale of non-locality $M \rightarrow
\infty$ we reproduce the local theory results and see how asymptotic freedom is
properly recovered.


Abstract:
  The low temperature dependence of the nuclear magnetic resonance frequency
and spin-lattice relaxation rate measured in the chiral magnet MnSi by Yasuoka
and coworkers [J. Phys. Soc. Jpn. 85, 073701 (2016)] is interpreted in terms of
helimagnon excitations. The theoretically predicted gapless and anisotropic
dispersion relation which is probed at extremely small energy is experimentally
confirmed. Whenever comparison is possible, the results are found
quantitatively consistent with those of the inelastic neutron scattering and
muon spin rotation and relaxation techniques. Further studies are suggested.


Abstract:
  Convolutional Neural Networks have become the standard for image
classification tasks, however, these architectures are not invariant to
translations of the input image. This lack of invariance is attributed to the
use of stride which ignores the sampling theorem, and fully connected layers
which lack spatial reasoning. We show that stride can greatly benefit
translation invariance given that it is combined with sufficient similarity
between neighbouring pixels, a characteristic which we refer to as local
homogeneity. We also observe that this characteristic is dataset-specific and
dictates the relationship between pooling kernel size and stride required for
translation invariance. Furthermore we find that a trade-off exists between
generalization and translation invariance in the case of pooling kernel size,
as larger kernel sizes lead to better invariance but poorer generalization.
Finally we explore the efficacy of other solutions proposed, namely global
average pooling, anti-aliasing, and data augmentation, both empirically and
through the lens of local homogeneity.


Abstract:
  A thermoelectric effect of the noncentrosymmetric electron-phonon coupled
system is proposed, in which a temperature difference between electrons and
phonons, instead of a spatial temperature gradient, induces an electric
current. This is a realization of Seebeck ratchet in solids with the asymmetric
energy dispersion of electrons, and the dynamical phonons act as the
fluctuation of the scalar potential and induce the dc current. Possible
realizations of this mechanism are also discussed.


Abstract:
  Smart electricity meters typically upload readings a few times a day. Utility
providers aim to increase the upload frequency in order to access consumption
information in near real time, but the legacy compressors fail to provide
sufficient savings on the low-bandwidth, high-cost data connection. We propose
a new compression method and data format for DLMS smart meter readings, which
is significantly better with frequent uploads and enable reporting every
reading in near real time with the same or lower data sizes than the currently
available compressors in the DLMS protocol.


Abstract:
  Population control is an essential component of any projector Monte Carlo
algorithm. This control mechanism usually introduces a bias in the sampled
quantities that is inversely proportional to the population size. In this
paper, we investigate the population control bias in the full configuration
interaction quantum Monte Carlo method. We identify the precise origin of this
bias and quantify it in general. We show that it has different effects on
different estimators and that the shift estimator is particularly susceptible.
We derive a re-weighting technique, similar to the one used in diffusion Monte
Carlo, for correcting this bias and apply it to the shift estimator. We also
show that by using importance sampling, the bias can be reduced substantially.
We demonstrate the necessity and the effectiveness of applying these techniques
for sign-problem-free systems where this bias is especially notable.
Specifically, we show results for large one-dimensional Hubbard models and the
two-dimensional Heisenberg model, where corrected FCIQMC results are comparable
to the other high-accuracy results.


Abstract:
  A multiscale asymptotic homogenization method for periodic microstructured
materials in presence of thermoelasticity with periodic spatially dependent one
relaxation time is introduced. The asymptotic expansions of the
micro-displacement and the micro-temperature fields are rewritten on the
transformed Laplace space and expressed as power series of the microstructural
length scale, leading to a set of recursive differential problems over the
periodic unit cell. The solution of such cell problems leads to the
perturbation functions. Up-scaling and down-scaling relations are then defined,
and the latter allow expressing the microscopic fields in terms of the
macroscopic ones and their gradients. Average field equations of infinite order
are also derived. The efficiency of the proposed technique was tested in
relation to a bi dimensional orthotropic layered body with orthotropy axis
parallel to the direction of the layers, where the mechanical and temperature
constitutive properties were well stabilised. The dispersion curves of the
homogenized medium, truncated at the first order are compared with the
dispersion curves of the heterogeneous continuum obtained by the Floquet-Bloch
theory. The results obtained with the two different approaches show a very good
agreement.


Abstract:
  As a prevailing task in video surveillance and forensics field, person
re-identification (re-ID) aims to match person images captured from
non-overlapped cameras. In unconstrained scenarios, person images often suffer
from the resolution mismatch problem, i.e., \emph{Cross-Resolution Person
Re-ID}. To overcome this problem, most existing methods restore low resolution
(LR) images to high resolution (HR) by super-resolution (SR). However, they
only focus on the HR feature extraction and ignore the valid information from
original LR images. In this work, we explore the influence of resolutions on
feature extraction and develop a novel method for cross-resolution person re-ID
called \emph{\textbf{M}ulti-Resolution \textbf{R}epresentations \textbf{J}oint
\textbf{L}earning} (\textbf{MRJL}). Our method consists of a Resolution
Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN
uses an input image to construct a HR version and a LR version with an encoder
and two decoders, while the DFFN adopts a dual-branch structure to generate
person representations from multi-resolution images. Comprehensive experiments
on five benchmarks verify the superiority of the proposed MRJL over the
relevent state-of-the-art methods.


Abstract:
  DRAM is the dominant main memory technology used in modern computing systems.
Computing systems implement a memory controller that interfaces with DRAM via
DRAM commands. DRAM executes the given commands using internal components
(e.g., access transistors, sense amplifiers) that are orchestrated by DRAM
internal timings, which are fixed foreach DRAM command. Unfortunately, the use
of fixed internal timings limits the types of operations that DRAM can perform
and hinders the implementation of new functionalities and custom mechanisms
that improve DRAM reliability, performance and energy. To overcome these
limitations, we propose enabling programmable DRAM internal timings for
controlling in-DRAM components. To this end, we design CODIC, a new low-cost
DRAM substrate that enables fine-grained control over four previously fixed
internal DRAM timings that are key to many DRAM operations. We implement CODIC
with only minimal changes to the DRAM chip and the DDRx interface. To
demonstrate the potential of CODIC, we propose two new CODIC-based security
mechanisms that outperform state-of-the-art mechanisms in several ways: (1) a
new DRAM Physical Unclonable Function (PUF) that is more robust and has
significantly higher throughput than state-of-the-art DRAM PUFs, and (2) the
first cold boot attack prevention mechanism that does not introduce any
performance or energy overheads at runtime.


Abstract:
  Seismic full-waveform inversion (FWI) techniques aim to find a
high-resolution subsurface geophysical model provided with waveform data. Some
recent effort in data-driven FWI has shown some encouraging results in
obtaining 2D velocity maps. However, due to high computational complexity and
large memory consumption, the reconstruction of 3D high-resolution velocity
maps via deep networks is still a great challenge. In this paper, we present
InversionNet3D, an efficient and scalable encoder-decoder network for 3D FWI.
The proposed method employs group convolution in the encoder to establish an
effective hierarchy for learning information from multiple sources while
cutting down unnecessary parameters and operations at the same time. The
introduction of invertible layers further reduces the memory consumption of
intermediate features during training and thus enables the development of
deeper networks with more layers and higher capacity as required by different
application scenarios. Experiments on the 3D Kimberlina dataset demonstrate
that InversionNet3D achieves state-of-the-art reconstruction performance with
lower computational cost and lower memory footprint compared to the baseline.


Abstract:
  Many natural systems exhibit chaotic behaviour such as the weather,
hydrology, neuroscience and population dynamics. Although many chaotic systems
can be described by relatively simple dynamical equations, characterizing these
systems can be challenging, due to sensitivity to initial conditions and
difficulties in differentiating chaotic behavior from noise. Ideally, one
wishes to find a parsimonious set of equations that describe a dynamical
system. However, model selection is more challenging when only a subset of the
variables are experimentally accessible. Manifold learning methods using
time-delay embeddings can successfully reconstruct the underlying structure of
the system from data with hidden variables, but not the equations. Recent work
in sparse-optimization based model selection has enabled model discovery given
a library of possible terms, but regression-based methods require measurements
of all state variables. We present a method combining variational annealing --
a technique previously used for parameter estimation in chaotic systems with
hidden variables -- with sparse optimization methods to perform model
identification for chaotic systems with unmeasured variables. We applied the
method to experimental data from an electrical circuit with Lorenz-system like
behavior to successfully recover the circuit equations with two measured and
one hidden variable. We discuss the robustness of our method to varying noise
and manifold sampling using ground-truth time-series simulated from the classic
Lorenz system.


Abstract:
  A result of Farahat and Higman shows that there is a ``universal'' algebra,
$\mathrm{FH}$, interpolating the centres of symmetric group algebras,
$Z(\mathbb{Z}S_n)$. We explain that this algebra is isomorphic to $\mathcal{R}
\otimes \Lambda$, where $\mathcal{R}$ is the ring of integer-valued polynomials
and $\Lambda$ is the ring of symmetric functions. Moreover, the isomorphism is
via ``evaluation at Jucys-Murphy elements'', which leads to character formulae
for symmetric groups. Then, we generalise this result to wreath products
$\Gamma \wr S_n$ of a fixed finite group $\Gamma$. This involves constructing
wreath-product versions $\mathcal{R}_\Gamma$ and $\Lambda(\Gamma_*)$ of
$\mathcal{R}$ and $\Lambda$, respectively, which are interesting in their own
right (for example, both are Hopf algebras). We show that the universal algebra
for wreath products, $\mathrm{FH}_\Gamma$, is isomorphic to $\mathcal{R}_\Gamma
\otimes \Lambda(\Gamma_*)$ and use this to compute the $p$-blocks of wreath
products.


Abstract:
  Bias evaluation in machine-learning based services (MLS) based on traditional
algorithmic fairness notions that rely on comparative principles is practically
difficult, making it necessary to rely on human auditor feedback. However, in
spite of taking rigorous training on various comparative fairness notions,
human auditors are known to disagree on various aspects of fairness notions in
practice, making it difficult to collect reliable feedback. This paper offers a
paradigm shift to the domain of algorithmic fairness via proposing a new
fairness notion based on the principle of non-comparative justice. In contrary
to traditional fairness notions where the outcomes of two individuals/groups
are compared, our proposed notion compares the MLS' outcome with a desired
outcome for each input. This desired outcome naturally describes a human
auditor's expectation, and can be easily used to evaluate MLS on crowd-auditing
platforms. We show that any MLS can be deemed fair from the perspective of
comparative fairness (be it in terms of individual fairness, statistical
parity, equal opportunity or calibration) if it is non-comparatively fair with
respect to a fair auditor. We also show that the converse holds true in the
context of individual fairness. Given that such an evaluation relies on the
trustworthiness of the auditor, we also present an approach to identify fair
and reliable auditors by estimating their biases with respect to a given set of
sensitive attributes, as well as quantify the uncertainty in the estimation of
biases within a given MLS. Furthermore, all of the above results are also
validated on COMPAS, German credit and Adult Census Income datasets.


Abstract:
  Copper oxide (CuO) thin films have been deposited on glass substrates by a
facile sol-gel dip-coating technique with varying withdrawal speeds from 0.73
to 4.17 mm/s. The variation of film thickness manifested by dip-coating
withdrawal speeds was investigated in detail to investigate its effect on the
structural, morphological, optoelectrical, and wettability properties of CuO
thin films for carbon dioxide (CO2) gas-sensing applications. The
crystallinity, as well as phase purity of dip-coated CuO, were confirmed by
both X-ray diffraction (XRD) and Raman spectral analyses. The surface
morphology of the films characterized by the scanning electron microscopy (SEM)
revealed that pore density decreases with the increase of withdrawal speeds and
grain size is found to increase with the increase of film thickness
corroborating the XRD results. The optical bandgap of dip-coated CuO films was
estimated in the range of 1.47 - 1.52 eV from the UV-VIS-NIR transmission data
and it is found to decrease with the increment of Urbach tail states
accompanied by the increase of film thickness. The ratio of the electrical and
optical conductivity of CuO films is found to decrease with increasing
withdrawal speeds due to the variation of carrier concentration. Among all the
studied films, the sample deposited to a 0.73 mm/s withdrawal speed exhibited
the highest crystallinity, porous morphology, highest pore density,
optoelectrical conductivity as well as water contact angle, and therefore
maximum gas sensing response of CO2 vapor in the air recorded at room
temperature.


Abstract:
  We demonstrate that it is possible to efficiently control ultracold chemical
reactions of alkali-metal atoms colliding with open-shell alkali-metal dimers
in their metastable triplet states by choosing the internal hyperfine and
rovibrational states of the reactants as well as by inducing magnetic Feshbach
resonances with an external magnetic field. We base these conclusions on
coupled-channel statistical calculations that include the effects of hyperfine
contact and magnetic-field-induced Zeeman interactions on ultracold chemical
reactions of hyperfine-resolved ground-state Na and the triplet
NaLi(a$^3\Sigma^+$) producing singlet Na$_2$($^1\Sigma^+_g$) and a Li atom. We
find that the reaction rates are sensitive to the initial hyperfine states of
the reactants. The chemical reaction of fully spin-polarized, high-spin states
of rotationless NaLi(a$^3\Sigma^+, v = 0, N = 0$) molecules with fully
spin-polarized Na is suppressed by a factor of 10-100 compared to that of
unpolarized reactants. We interpret these findings within the adiabatic state
model, which treats the reaction as a sequence of nonadiabatic transitions
between the initial non-reactive high-spin state and the final low-spin states
of the reaction complex. In addition, we show that magnetic Feshbach resonances
can similarly change reaction rate coefficients by several orders of magnitude.
Some of these resonances are due to resonant trimer bound states dissociating
to the $N=2$ rotational state of NaLi(a$^3\Sigma^+, v = 0$) and would thus
exist in systems without hyperfine interactions.


Abstract:
  A key desiderata for inclusive and accessible speech recognition technology
is ensuring its robust performance to children's speech. Notably, this includes
the rapidly advancing neural network based end-to-end speech recognition
systems. Children speech recognition is more challenging due to the larger
intra-inter speaker variability in terms of acoustic and linguistic
characteristics compared to adult speech. Furthermore, the lack of adequate and
appropriate children speech resources adds to the challenge of designing robust
end-to-end neural architectures. This study provides a critical assessment of
automatic children speech recognition through an empirical study of
contemporary state-of-the-art end-to-end speech recognition systems. Insights
are provided on the aspects of training data requirements, adaptation on
children data, and the effect of children age, utterance lengths, different
architectures and loss functions for end-to-end systems and role of language
models on the speech recognition performance.


Abstract:
  In 1878, Jordan proved that if a finite group $G$ has a faithful
representation of dimension $n$ over $\mathbb{C}$, then $G$ has a normal
abelian subgroup with index bounded above by a function of $n$. The same result
fails if one replaces $\mathbb{C}$ by a field of positive characteristic, due
to the presence of large unipotent and/or Lie type subgroups. For this reason,
a long-standing problem in group and representation theory has been to find the
"correct analogue" of Jordan's theorem in characteristic $p>0$. Progress has
been made in a number of different directions, most notably by Brauer and Feit
in 1966; by Collins in 2008; and by Larsen and Pink in 2011. With a 1968
theorem of Steinberg in mind (which shows that a significant proportion of
elements in a simple group of Lie type are unipotent), we prove in this paper
that if a finite group $G$ has a faithful representation over a field of
characteristic $p$, then a significant proportion of the elements of $G$ must
have $p$-power order. We prove similar results for permutation groups, and
present a general method for counting $p$-elements in finite groups. All of our
results are best possible.


Abstract:
  We study robust testing and estimation of discrete distributions in the
strong contamination model. We consider both the "centralized setting" and the
"distributed setting with information constraints" including communication and
local privacy (LDP) constraints. Our technique relates the strength of
manipulation attacks to the earth-mover distance using Hamming distance as the
metric between messages(samples) from the users. In the centralized setting, we
provide optimal error bounds for both learning and testing. Our lower bounds
under local information constraints build on the recent lower bound methods in
distributed inference. In the communication constrained setting, we develop
novel algorithms based on random hashing and an $\ell_1/\ell_1$ isometry.


Abstract:
  Limerick generation exemplifies some of the most difficult challenges faced
in poetry generation, as the poems must tell a story in only five lines, with
constraints on rhyme, stress, and meter. To address these challenges, we
introduce LimGen, a novel and fully automated system for limerick generation
that outperforms state-of-the-art neural network-based poetry models, as well
as prior rule-based poetry models. LimGen consists of three important pieces:
the Adaptive Multi-Templated Constraint algorithm that constrains our search to
the space of realistic poems, the Multi-Templated Beam Search algorithm which
searches efficiently through the space, and the probabilistic Storyline
algorithm that provides coherent storylines related to a user-provided prompt
word. The resulting limericks satisfy poetic constraints and have thematically
coherent storylines, which are sometimes even funny (when we are lucky).


Abstract:
  This paper proposes a general multi-modal data learning method, which
includes Global Homogeneous Transformation, Local Homogeneous Transformation
and their combination. During ReID model training, on the one hand, it randomly
selected a rectangular area in the RGB image and replace its color with the
same rectangular area in corresponding homogeneous image, thus it generate a
training image with different homogeneous areas; On the other hand, it convert
an image into a homogeneous image. These two methods help the model to directly
learn the relationship between different modalities in the Special ReID task.
In single-modal ReID tasks, it can be used as an effective data augmentation.
The experimental results show that our method achieves a performance
improvement of up to 3.3% in single modal ReID task, and performance
improvement in the Sketch Re-identification more than 8%. In addition, our
experiments also show that this method is also very useful in adversarial
training for adversarial defense. It can help the model learn faster and better
from adversarial examples.


Abstract:
  In this paper, we consider the long time asymptotic behavior for the initial
value problem of the Novikov equation \begin{align} &u_{t}-u_{txx}+4
u_{x}=3uu_xu_{xx}+u^2u_{xxx}, \nonumber &u(x, 0)=u_{0}(x),\nonumber \end{align}
where $u_0(x)$ is assumed to decay to a nonzero constant: $u(x)\to\kappa>0$,
$x\to\pm\infty$. The study makes crucial use of the inverse scattering
transform as well as of the $\overline\partial$ generalization of Deift-Zhou
steepest descent method for oscillatory Riemann-Hilbert (RH) problems. Based on
the spectral analysis of the Lax pair associated with the Novikov equation and
scattering matrix, the solution of the Cauchy problem is characterized via the
solution of a RH problem. In different space-time solitonic region of $x/t$, we
further compute the different long time asymptotic expansion of the solution
$u(x,t)$, which implies soliton resolution conjecture and can be characterized
with an $N(\Lambda)$-soliton whose parameters are modulated by a sum of
localized soliton-soliton interactions as one moves through the region. And it
has diverse residual error order from $\overline\partial$ equation:
$\mathcal{O}(t^{-1+2\rho})$ for
$\xi=\frac{y}{t}\in(-\infty,-0.125)\cup(1,+\infty)$ and $\mathcal{O}(t^{-3/4})$
for $\xi=\frac{y}{t}\in(-0.125,1)$.


Abstract:
  Politically sensitive topics are still a challenge for open-domain chatbots.
However, dealing with politically sensitive content in a responsible,
non-partisan, and safe behavior way is integral for these chatbots. Currently,
the main approach to handling political sensitivity is by simply changing such
a topic when it is detected. This is safe but evasive and results in a chatbot
that is less engaging. In this work, as a first step towards a politically safe
chatbot, we propose a group of metrics for assessing their political prudence.
We then conduct political prudence analysis of various chatbots and discuss
their behavior from multiple angles through our automatic metric and human
evaluation metrics. The testsets and codebase are released to promote research
in this area.


Abstract:
  We investigate the radio properties of a sample of 53 sources selected at 850
$\mu$m from the SCUBA-2 Cosmology Legacy Survey using new deep, low-frequency
radio imaging of the Lockman Hole field from the Low Frequency Array. Combining
these data with additional radio observations from the GMRT and the JVLA, we
find a variety of radio spectral shapes and luminosities within our sample
despite their similarly bright submillimetre flux densities. We characterise
their spectral shapes in terms of multi-band radio spectral indices. Finding
strong spectral flattening at low frequencies in ~20% of sources, we
investigate the differences between sources with extremely flat low-frequency
spectra and those with `normal' radio spectral indices. As there are no other
statistically significant differences between the two subgroups of our sample
as split by the radio spectral index, we suggest that any differences are
undetectable in galaxy-averaged properties that we can observe with our
unresolved images, and likely relate to galaxy properties that we cannot
resolve, on scales $\lesssim$ 1 kpc. We attribute the observed spectral
flattening in the radio to free-free absorption, proposing that those sources
with significant low-frequency spectral flattening have a clumpy distribution
of star-forming gas. We estimate an average spatial extent of absorbing
material of at most several hundred parsecs to produce the levels of absorption
observed in the radio spectra. This estimate is consistent with the
highest-resolution observations of submillimetre galaxies in the literature,
which find examples of non-uniform dust distributions on scales of ~100 pc,
with evidence for clumps and knots in the interstellar medium. Additionally, we
find two bright (> 6 mJy) submm sources undetected at all other wavelengths. We
speculate that these objects may be very high redshift sources, likely residing
at z > 4.


Abstract:
  The nascent computational paradigm of quantum reservoir computing presents an
attractive use of near-term, noisy-intermediate-scale quantum processors. To
understand the potential power and use cases of quantum reservoir computing, it
is necessary to define a conceptual framework to separate its constituent
components and determine their impacts on performance. In this manuscript, we
utilize such a framework to isolate the input encoding component of
contemporary quantum reservoir computing schemes. We find that across the
majority of schemes the input encoding implements a nonlinear transformation on
the input data. As nonlinearity is known to be a key computational resource in
reservoir computing, this calls into question the necessity and function of
further, post-input, processing. Our findings will impact the design of future
quantum reservoirs, as well as the interpretation of results and fair
comparison between proposed designs.


Abstract:
  The design of attacks for cyber physical systems is critical to assess CPS
resilience at design time and run-time, and to generate rich datasets from
testbeds for research. Attacks against cyber physical systems distinguish
themselves from IT attacks in that the main objective is to harm the physical
system. Therefore, both cyber and physical system knowledge are needed to
design such attacks. The current practice to generate attacks either focuses on
the cyber part of the system using IT cyber security existing body of
knowledge, or uses heuristics to inject attacks that could potentially harm the
physical process. In this paper, we present a systematic approach to
automatically generate integrity attacks from the CPS safety and control
specifications, without knowledge of the physical system or its dynamics. The
generated attacks violate the system operational and safety requirements, hence
present a genuine test for system resilience. We present an algorithm to
automate the malware payload development. Several examples are given throughout
the paper to illustrate the proposed approach.


Abstract:
  We provide new ionization correction factors (ICFs) for carbon, nitrogen,
neon, sulfur, chlorine, and argon in giant H II regions. The ICFs were computed
using the most representative photoionization models from a large initial grid.
The models were selected using an observational sample of 985 giant H II
regions (GHR) in spiral galaxies and blue compact galaxies (BCG). The
observational sample was also used to assign a weight to each model describing
how well it agrees with observations in the [O III]/Hbeta versus [N II]/Halpha
diagram. In addition to the ICFs we provide, for the first time, analytical
expressions for their formal uncertainties. We use our ICFs to compute the
abundances of nitrogen, neon, sulfur, and argon in our samples. Our abundances
are robust within the adopted framework, but may require revision in the case
of important changes in atomic data or in the spectral energy distribution of
the ionizing radiation in H II regions. Considering the abundance patterns we
obtained for the BCG sample (abundances for the GHR sample are less reliable)
we find that oxygen is depleted into dust grains at a rate increasing with
metallicity and reaching 0.12 dex at solar abundances. The discussion of
possible depletion of sulfur and argon requires considering recent Type Ia
Supernova yields, which are still uncertain.


Abstract:
  By exploiting an innovative total-variation compressive sensing (TV-CS)
formulation, a new method for the synthesis of physically contiguous clustered
linear arrays is presented. The computation of the feed network excitations is
recast as the maximization of the gradient sparsity of the excitation vector
subject to matching a user-defined pattern. The arising TV-CS functional is
then optimized by means of a deterministic alternating direction algorithm. A
selected set of representative numerical results, drawn from a wide validation,
is reported to illustrate the potentialities and the limitations of the
proposed approach when clustering arrays of both ideal and realistic antenna
elements. Comparisons with some competitive state-of-the-art subarraying
techniques are performed as well.


Abstract:
  Crystallization has long been the subject of research as one of the basic
ways in which solid materials are constructed. In particular, the nucleation
stage has not been isolated, thus has been predicted through many calculations
and achieved theoretical completion through the nucleation rate(J). Si nce most
of these results were obtained through isotropic building blocks in
three-dimensional space, it was difficult to interpret nuclei formed by
anisotropic building block in 2D or 1D structure. Recently, a lot of studies
related to amyloid fibril have shown nucleation of anisotropic building block.
However, due to the complexity of the amyloid fibrils, there is no unified
explanation of the thermodynamic method of classical nucleation theory which is
the energy loss from surface and energy gain from volume. We have
experimentally demonstrated the isolation of nuclei of the orthorhombic phase
of HYLION-12 which is a Dirac metamaterial and provide the effect of anisotropy
of the molecules on nucleation The thermal behavior of nuclei of Dirac
metamaterial through DSC has demonstrated that it can be crystallized to a
Dirac metamaterial through the first order phase transition. The growth process
is verified at low temperature where no phase transition occurs. The
calculation of surface and bulk energy of the Dirac metamaterial was conducted.
It could explain the isolation of nuclei of the Dirac metamaterial by enlarging
the thermodynamic classical nucleation theory.


Abstract:
  Motivated by the probabilistic methods for nonlinear differential equations
introduced by McKean (1975) for the Kolmogorov-Petrovski-Piskunov (KPP)
equation, and by Le Jan and Sznitman (1997) for the incompressible
Navier-Stokes equations, we identify a new class of stochastic cascade models,
referred to as {\it doubly stochastic Yule cascades}. We establish
non-explosion criteria under the assumption that the randomization of Yule
intensities from generation to generation is by an ergodic time-reversible
Markov chain. In addition to the cascade models that arise in the analysis of
certain deterministic nonlinear differential equations, this model includes the
multiplicative branching random walks, the branching Markov processes, and the
stochastic generalizations of the percolation and/or cell aging models
introduced by Aldous and Shields (1988) and independently by Athreya (1985).


Abstract:
  Multiple views of data, both naturally acquired (e.g., image and audio) and
artificially produced (e.g., via adding different noise to data samples), have
proven useful in enhancing representation learning. Natural views are often
handled by multiview analysis tools, e.g., (deep) canonical correlation
analysis [(D)CCA], while the artificial ones are frequently used in
self-supervised learning (SSL) paradigms, e.g., SimCLR and Barlow Twins. Both
types of approaches often involve learning neural feature extractors such that
the embeddings of data exhibit high cross-view correlations. Although
intuitive, the effectiveness of correlation-based neural embedding is only
empirically validated. This work puts forth a theory-backed framework for
unsupervised multiview learning. Our development starts with proposing a
multiview model, where each view is a nonlinear mixture of shared and private
components. Consequently, the learning problem boils down to shared/private
component identification and disentanglement. Under this model, latent
correlation maximization is shown to guarantee the extraction of the shared
components across views (up to certain ambiguities). In addition, the private
information in each view can be provably disentangled from the shared using
proper regularization design. The method is tested on a series of tasks, e.g.,
downstream clustering, which all show promising performance. Our development
also provides a unifying perspective for understanding various DCCA and SSL
schemes.


Abstract:
  Interleaved texts, where posts belonging to different threads occur in a
sequence, commonly occur in online chat posts, so that it can be time-consuming
to quickly obtain an overview of the discussions. Existing systems first
disentangle the posts by threads and then extract summaries from those threads.
A major issue with such systems is error propagation from the disentanglement
component. While end-to-end trainable summarization system could obviate
explicit disentanglement, such systems require a large amount of labeled data.
To address this, we propose to pretrain an end-to-end trainable hierarchical
encoder-decoder system using synthetic interleaved texts. We show that by
fine-tuning on a real-world meeting dataset (AMI), such a system out-performs a
traditional two-step system by 22%. We also compare against transformer models
and observed that pretraining with synthetic data both the encoder and decoder
outperforms the BertSumExtAbs transformer model which pretrains only the
encoder on a large dataset.


Abstract:
  As the use of Internet of Things (IoT) devices for monitoring purposes
becomes ubiquitous, the efficiency of sensor communication is a major issue for
the modern Internet. Channel coding is less efficient for extremely short
packets, and traditional techniques that rely on source compression require
extensive signaling or pre-existing knowledge of the source dynamics. In this
work, we propose an encoding and decoding scheme that learns source dynamics
online using a Hidden Markov Model (HMM), puncturing a short packet code to
outperform existing compression-based approaches. Our approach shows
significant performance improvements for sources that are highly correlated in
time, with no additional complexity on the sender side.


Abstract:
  Many machine learning and data science tasks require solving non-convex
optimization problems. When the loss function is a sum of multiple terms, a
popular method is the stochastic gradient descent. Viewed as a process for
sampling the loss function landscape, the stochastic gradient descent is known
to prefer flat minima. Though this is desired for certain optimization problems
such as in deep learning, it causes issues when the goal is to find the global
minimum, especially if the global minimum resides in a sharp valley.
  Illustrated with a simple motivating example, we show that the fundamental
reason is that the difference in the Lipschitz constants of multiple terms in
the loss function causes stochastic gradient descent to experience different
variances at different minima. In order to mitigate this effect and perform
faithful optimization, we propose a combined resampling-reweighting scheme to
balance the variance at local minima and extend to general loss functions. We
explain from the numerical stability perspective how the proposed scheme is
more likely to select the true global minimum, and the local convergence
analysis perspective how it converges to a minimum faster when compared with
the vanilla stochastic gradient descent. Experiments from robust statistics and
computational chemistry are provided to demonstrate the theoretical findings.


Abstract:
  In this work, we introduce the concept of the fractional quantum heat engine.
We examine the space-fractional quantum Szilard heat engine as an example to
show that the space-fractional quantum heat engines can produce higher
efficiency than the conventional quantum heat engines.


Abstract:
  Computed tomography (CT) imaging is a promising approach to diagnosing the
COVID-19. Machine learning methods can be employed to train models from labeled
CT images and predict whether a case is positive or negative. However, there
exists no publicly-available and large-scale CT data to train accurate models.
In this work, we propose a multi-stage attentive transfer learning framework
for improving COVID-19 diagnosis. Our proposed framework consists of three
stages to train accurate diagnosis models through learning knowledge from
multiple source tasks and data of different domains. Importantly, we propose a
novel self-supervised learning method to learn multi-scale representations for
lung CT images. Our method captures semantic information from the whole lung
and highlights the functionality of each lung region for better representation
learning. The method is then integrated to the last stage of the proposed
transfer learning framework to reuse the complex patterns learned from the same
CT images. We use a base model integrating self-attention (ATTNs) and
convolutional operations. Experimental results show that networks with ATTNs
induce greater performance improvement through transfer learning than networks
without ATTNs. This indicates attention exhibits higher transferability than
convolution. Our results also show that the proposed self-supervised learning
method outperforms several baseline methods.


Abstract:
  Large-scale pre-trained models like BERT, have obtained a great success in
various Natural Language Processing (NLP) tasks, while it is still a challenge
to adapt them to the math-related tasks. Current pre-trained models neglect the
structural features and the semantic correspondence between formula and its
context. To address these issues, we propose a novel pre-trained model, namely
\textbf{MathBERT}, which is jointly trained with mathematical formulas and
their corresponding contexts. In addition, in order to further capture the
semantic-level structural features of formulas, a new pre-training task is
designed to predict the masked formula substructures extracted from the
Operator Tree (OPT), which is the semantic structural representation of
formulas. We conduct various experiments on three downstream tasks to evaluate
the performance of MathBERT, including mathematical information retrieval,
formula topic classification and formula headline generation. Experimental
results demonstrate that MathBERT significantly outperforms existing methods on
all those three tasks. Moreover, we qualitatively show that this pre-trained
model effectively captures the semantic-level structural information of
formulas. To the best of our knowledge, MathBERT is the first pre-trained model
for mathematical formula understanding.


Abstract:
  Both brain science and the deep learning communities have the problem of
interpreting neural activity. For deep learning, even though we can access all
neurons' activity data, interpretation of how the deep network solves the task
is still challenging. Although a large amount of effort has been devoted to
interpreting a deep network, there is still no consensus of what interpretation
is. This paper tries to push the discussion in this direction and proposes an
information-theoretic progressive framework to synthesize interpretation.
Firstly, we discuss intuitions of interpretation: interpretation is
meta-information; interpretation should be at the right level; inducing
independence is helpful to interpretation; interpretation is naturally
progressive; interpretation doesn't have to involve a human. Then, we build the
framework with an information map splitting idea and implement it with the
variational information bottleneck technique. After that, we test the framework
with the CLEVR dataset. The framework is shown to be able to split information
maps and synthesize interpretation in the form of meta-information.


Abstract:
  In $2020$, Korea Disease Control and Prevention Agency reported three rounds
of surveys on seroprevalence of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) antibodies in South Korea. We analyze the seroprevalence surveys
using a Bayesian method with an informative prior distribution on the
seroprevalence parameter, and the sensitivity and specificity of the diagnostic
test. We construct the informative prior using the posterior distribution
obtained from the clinical evaluation data based on the plaque reduction
neutralization test. The constraint of the seroprevalence parameter induced
from the known confirmed coronavirus 2019 cases can be imposed naturally in the
proposed Bayesian model. We also prove that the confidence interval of the
seroprevalence parameter based on the Rao's test can be the empty set, while
the Bayesian method renders a reasonable interval estimator. As of the $30$th
of October $2020$, the $95\%$ credible interval of the estimated SARS-CoV-2
positive population does not exceed $307,448$, approximately $0.6\%$ of the
Korean population.


Abstract:
  The use of the Internet has become an urgent necessity in various fields and
activities. One of such important fields could be electronic business
(E-Business). E-business includes all operations and activities related to
Internet commerce, including e-commerce, e-marketing, eservices, e-payment, and
electronic transfer. This proposal focuses on two perspectives that are under
e-commerce umbrella where many e-payment process models suffer from the lack of
clarity in the special procedures of the verification of the quality of
services. This proposal, therefore, proposes a new E-payment process model to
enhance the quality of service through maintaining availability. The proposed
model focuses on the concepts of electronic payment model dimensions, which
includes integrity, non-repudiation, authenticity, confidentiality, privacy,
and availability. Accordingly, the proposed model implements the availability
dimension in order to improve the quality of services that are provided to the
consumer.


Abstract:
  Agile attitude maneuvering maximizes the utility of remote sensing satellite
constellations. By taking into account a satellite's physical properties and
its actuator specifications, we may leverage the full performance potential of
the attitude control system to conduct agile remote sensing beyond conventional
slew-and-stabilize maneuvers. Employing a constellation of agile satellites,
coordinated by an autonomous and responsive scheduler, can significantly
increase overall response rate, revisit time and global coverage for the
mission. In this paper, we use recent advances in sequential convex programming
based trajectory optimization to enable rapid-target acquisition, pointing and
tracking capabilities for a scheduler-based constellation. We present two
problem formulations. The Minimum-Time Slew Optimal Control Problem determines
the minimum time, required energy, and optimal trajectory to slew between any
two orientations given nonlinear quaternion kinematics, gyrostat and actuator
dynamics, and state/input constraints. By gridding the space of 3D rotations
and efficiently solving this problem on the grid, we produce lookup tables or
parametric fits off-line that can then be used on-line by a scheduler to
compute accurate estimates of minimum-time and maneuver energy for real-time
constellation scheduling. The Minimum-Effort Multi-Target Pointing Optimal
Control Problem is used on-line by each satellite to produce continuous
attitude-state and control-input trajectories that realize a given schedule
while minimizing attitude error and control effort. The optimal trajectory may
then be achieved by a low-level tracking controller. We demonstrate our
approach with an example of a reference satellite in Sun-synchronous orbit
passing over globally-distributed, Earth-observation targets.


Abstract:
  We present the Python package ana_cont for the analytic continuation of
fermionic and bosonic many-body Green's functions by means of either the Pade
approximants or the maximum entropy method. The determination of
hyperparameters and the implementation are described in detail. The code is
publicly available on GitHub, where also documentation and learning resources
are provided.


Abstract:
  Non-Hermitian non-reciprocal systems are known to be extremely sensitive to
boundary conditions, exhibiting diverse localizing behaviors and spectrum
structures when translational invariance is locally broken, either by tuning
the boundary coupling strength, or by introducing an effective boundary using
impurities or defects. In this work, we consider the single impurity problem in
the Hatano-Nelson model and the Su-Schreieffer-Heeger model, which can be
exactly solved with the single impurity being treated as an effective boundary
of the system. From our exact solutions for finite-size systems, we unveil that
increasing the impurity strength can lead to a transition of the bulk states
from non-skin states to skin states, accompanied by the change of the spectrum
structure from an ellipse in the complex plane to a segment along the real
axis. These exact results indicate that the critical value of impurity strength
is size-dependent, and increases exponentially with the lattice size when the
impurity is strong or the system is large enough. OUr exact solutions are also
useful for determining the point-gap topological transition in the concerned
models.


Abstract:
  We report on temporal and spectral study of a flat spectrum radio quasar, PKS
B1222+216, in flare state to get insight into acceleration and emission
mechanisms inside the jet. This is one of the brightest and highly active
blazar in the MeV-GeV regime. Long term multi-waveband light curves of this
object showed a flaring activity in 2014 with two distinct flares. Work
presented here includes the study of flux-index variation, flare fitting,
hardness ratio and spectral modelling of both X-ray and $\gamma-$ray data. The
flux-index correlation we have found in MeV-GeV regime indicates a 'softer when
brighter' feature. Modelling of $\gamma-$ray light curves suggests that low
energy particles initiate both the flares followed by the injection of high
energy particles. The short rise time indicates the presence of Fermi first
order acceleration. Multi-waveband spectral energy distributions (SEDs)
generated for flares are fitted with a single-zone leptonic model. This SED
modelling shows the inverse Compton scattering of photon field reprocessed from
Broad Line Region (BLR) primarily accounts for GeV emission. We have also
report a shift in break-energy in the soft X-ray regime during the flaring
activity which is the consequence of a rapid change in injection spectra.