Abstract:
  Multiple-point geostatistics plays an important role in characterizing
complex subsurface aquifer systems such as channelized structures. However,
only a few studies have paid attention to how to choose an applicable training
image. In this paper, a TI selection method based on Recurrent Neural Networks
is proposed. A synthetic case is tested using two channelized training images
given the hydraulic head time series. Three different RNNs architectures are
tested for the selection performance. Various scenarios of the model input are
also tested including the number of observation wells, the observation time
steps, the influence of observation noise, and the training dataset size. In
this TI selection task, the GRU has the best performance among all three
architectures and can reach to a 97.63% accuracy.


Abstract:
  Let $p$ be a prime and $n$ a positive integer such that $\sqrt{\frac p2} + 1
\leq n \leq \sqrt{p}$. For any arithmetic progression $A$ of length $n$ in
$\mathbb{F}_p$, we establish an asymptotic formula for the number of directions
determined by $A \times A \subset \mathbb{F}_p^2$. The key idea is to reduce
the problem to counting the number of solutions to the bilinear Diophantine
equation $ad+bc=p$ in variables $1\le a,b,c,d\le n$; our asymptotic formula for
the number of solutions is of independent interest.


Abstract:
  Galaxy clusters host the largest particle accelerators in the Universe: Shock
waves in the intracluster medium (ICM), a hot and ionised plasma, that
accelerate particles to high energies. Radio observations pick up synchrotron
emission in the ICM, proving the existence of accelerated cosmic-ray electrons.
However, a sign of cosmic-ray protons, in form of $\gamma$-rays. remains
undetected. This is know as the missing $\gamma$-ray problem and it directly
challenges the shock acceleration mechanism at work in the ICM. Over the last
decade, theoretical and numerical studies focused on improving our knowledge on
the microphysics that govern the shock acceleration process in the ICM. These
new models are able to predict a $\gamma$-ray signal, produced by shock
accelerated cosmic-ray protons, below the detection limits set modern
$\gamma$-ray observatories. In this review, we summarise the latest advances in
solving the missing $\gamma$-ray problem.


Abstract:
  The atomically clean surface of the iron-based superconductor FeTe0.55Se0.45
is investigated by low-temperature STM with different tip apex states. By
manipulating a single Fe atom onto the tip apex, signatures of the orbital
nature of the subsurface Fe layer of FeTe0.55Se0.45 can be identified. By
preparing a charged tip state, the intrinsic spatial inhomogeneity of the
chemical potential of FeTe0.55Se0.45 can be revealed. As a result, three
different types of vortex bound states originating from locally varying
topological properties of the FeTe0.55Se0.45 surface are observed by scanning
tunneling spectroscopy.


Abstract:
  We show that constraints on scalar field potentials and towers of light
massive states in asymptotic limits of scalar field space (as posited by the de
Sitter Conjecture and the Swampland Distance Conjecture, respectively) are
correlated with the prospects for defining asymptotic observables in expanding
FRW cosmologies. The observations of a "census taker" in an eternally inflating
cosmology are further related to the question of whether certain domain walls
satisfy a version of the Weak Gravity Conjecture. This suggests that answers to
fundamental questions about asymptotic observables in cosmology could help shed
light on the Swampland program, and vice versa.


Abstract:
  We present a general approach to justify the random phase approximation for
the homogeneous Fermi gas in three-dimensions in the mean-field scaling regime.
We consider a system of $N$ fermions on a torus, interacting via a two-body
repulsive potential proportional to $N^{-\frac{1}{3}}$. In the limit
$N\rightarrow\infty$, we derive the exact leading order of the correlation
energy of the system, which coincides with the prediction of Gell-Mann and
Brueckner in 1957.


Abstract:
  By tracing the orbital and star formation histories of galaxies within the
Coma cluster, we seek to understand the role of the high-density cluster
environment in quenching these galaxies. We combine star formation histories
extracted from high-signal-to-noise spectra of 11 early-type galaxies around
the center of the Coma cluster with probability distributions for their orbital
parameters obtained using an N-body simulation to connect their orbital and
star formation histories. We find that all 11 galaxies likely quenched near
their first pericentric approach. Higher stellar mass galaxies
($\log(M_\star/\mathrm{M}_\odot)>10$) had formed a higher fraction of their
stellar mass (more than $\sim90\%$) than their lower mass counterparts ($\sim
80$-$90\%$) by the time they fell into the cluster (when they cross
$2.5r_\mathrm{vir}$). We find that the expected infall occurred around
$z\sim0.6$, followed by the first pericentric passage $\sim4\,\mathrm{Gyr}$
later. Galaxies in our sample formed a significant fraction of their stellar
mass, up to $15\%$, between infall and first pericenter, and had assembled more
than $\sim98\%$ of their cumulative stellar mass by first pericenter. Unlike
previous low-redshift studies that suggest that star formation continues until
about first apocenter or later, the high percentage of stellar mass already
formed by first pericenter in our sample galaxies points to star formation
ceasing within a gigayear after the first pericentric passage. We consider the
possible physical mechanisms driving quenching and find that our results
resemble the situation in clusters at $z\sim1$, where active stripping of gas
(ram-pressure or tidally driven) seems to be required to quench satellites by
their first pericentric passage. However, a larger sample will be required to
conclusively account for the unknown fraction of preprocessed satellites in the
Coma cluster.


Abstract:
  We propose a minimal off-lattice model of living organisms where just a very
few dynamical rules of growth are assumed. The stable coexistence of many
clusters is detected when we replace the global restriction rule by a locally
applied one. A rich variety of evolving patterns is revealed where players
movement has a decisive role on the evolutionary outcome. For example,
intensive individual mobility may jeopardize the survival of the population,
but if we increase players movement further then it can save the population.
Notably, the collective drive of population members is capable to compensate
the negative consequence of intensive movement and keeps the system alive. When
the drive becomes biased then the resulting unidirectional flow alters the
stable pattern and produce a stripe-like state instead of the previously
observed hexagonal arrangement of clusters. Interestingly, the rotation of
stripes can be flipped if the individual movement exceeds a threshold value.


Abstract:
  We present an overview of multisoliton chains arising in various
non-integrable field theories, and discuss different mechanisms, which may lead
to the occurrence of such axially-symmetric classical solutions. We explain the
pattern of interactions between different solitons, in particular Q-balls,
Skyrmions and monopoles and show how chains of interacting non-BPS solitons may
form in a dynamic equilibrium between repulsive and attractive forces.


Abstract:
  We show that for a closed hyperbolic 3-manifold the size of the first
eigenvalue of the Hodge Laplacian acting on coexact 1-forms is comparable to an
isoperimetric ratio relating geodesic length and stable commutator length with
comparison constants that depend polynomially on the volume and on a lower
bound on injectivity radius, refining estimates of Lipnowski and Stern. We use
this estimate to show that there exist sequences of closed hyperbolic
3-manifolds with injectivity radius bounded below and volume going to infinity
for which the 1-form Laplacian has spectral gap vanishing exponentially fast in
the volume.


Abstract:
  We present a methodology for computing the flexibility index when uncertainty
is characterized using multivariate Gaussian random variables. Our approach
computes the flexibility index by solving a mixed-integer conic program (MICP).
This methodology directly characterizes ellipsoidal sets to capture
correlations in contrast to previous methodologies that employ approximations.
We also show that, under a Gaussian representation, the flexibility index can
be used to obtain a lower bound for the so-called stochastic flexibility index
(i.e., the probability of having feasible operation). Our results also show
that the methodology can be generalized to capture different types of
uncertainty sets.


Abstract:
  We consider a situation where an $N$-level system (NLS) is coupled to a heat
bath without being necessarily thermalized. For this situation we derive
general Jarzinski-type equations and conclude that heat and entropy is flowing
from the hot bath to the cold NLS and, vice versa, from the hot NLS to the cold
bath. The Clausius relation between increase of entropy and transfer of heat
divided by a suitable temperature assumes the form of two inequalities which
have already been considered in the literature. Our approach is illustrated by
an analytical example.


Abstract:
  We derive the Hydrodynamics for a system of N active, spherical, underdamped
particles, interacting through conservative forces. At the microscopic level,
we represent the evolution of the particles in terms of the Kramers equation
for the probability density distribution of their positions, velocities, and
orientations, while at a mesoscopic level we switch to a coarse-grained
description introducing an appropriate set of hydrodynamic fields given by the
lower-order moments of the distribution. In addition to the usual density and
polarization fields, the Hydrodynamics developed in this paper takes into
account the velocity and kinetic temperature fields, which are crucial to
understanding new aspects of the behavior of active liquids. By imposing a
suitable closure of the hydrodynamic moment equations and truncation of the
Born-Bogolubov-Green-Kirkwood-Yvon hierarchy, we obtain a closed set of
mesoscopic balance equations. At this stage, we focus our interest on the small
deviations of the hydrodynamic fields from their averages and apply the methods
of the theory of linear hydrodynamic fluctuations. Our treatment sheds light on
the peculiar properties of isotropic active liquids and their emergent
dynamical collective phenomena, such as the spontaneous alignment of the
particle velocities. We predict the existence within the liquid phase of
spatial equal-time Ornstein-Zernike-like velocity correlations both for the
longitudinal and the transverse modes. At variance with active solids, in
active liquids, the correlation length of the transverse velocity fluctuations
is sensibly shorter than the length of the longitudinal fluctuations. In
particular, the latter depends on the sound speed and increases with the
persistence time, while the former displays a weaker dependence on these
parameters. Finally, within the same framework, we derive the dynamical
structure factors ...


Abstract:
  Collective motion in animal groups, such as swarms of insects, flocks of
birds, and schools of fish, are some of the most visually striking examples of
emergent behavior. Empirical analysis of these behaviors in experiment or
computational simulation primarily involves the use of "swarm-averaged" metrics
or order parameters such as velocity alignment and angular momentum. Recently,
tools from computational topology have been applied to the analysis of swarms
to further understand and automate the detection of fundamentally different
swarm structures evolving in space and time. Here, we show how the field of
graph signal processing can be used to fuse these two approaches by
collectively analyzing swarm properties using graph Fourier harmonics that
respect the topological structure of the swarm. This graph Fourier analysis
reveals hidden structure in a number of common swarming states and forms the
basis of a flexible analysis framework for collective motion.


Abstract:
  Over 1000 students over four semesters were given the option of taking an
introductory statistics class either by in-person attendance in lectures,
augmented by online recorded lectures, or by taking the same class without the
in-person lectures. The all-online students did slightly better on
computer-graded exams. The causal effect of choosing only online lectures was
estimated by adjusting for potential confounders using four methods. The four
nearly identical point estimates remained positive but were small and not
statistically significant. No statistically significant differences were found
in preliminary comparisons of effects on females/males, U.S./non-U.S. citizens,
freshmen/non-freshman, and lower-scoring/higher-scoring math ACT groups.


Abstract:
  Transformers have shown outstanding results for natural language
understanding and, more recently, for image classification. We here extend this
work and propose a transformer-based approach for image retrieval: we adopt
vision transformers for generating image descriptors and train the resulting
model with a metric learning objective, which combines a contrastive loss with
a differential entropy regularizer. Our results show consistent and significant
improvements of transformers over convolution-based approaches. In particular,
our method outperforms the state of the art on several public benchmarks for
category-level retrieval, namely Stanford Online Product, In-Shop and CUB-200.
Furthermore, our experiments on ROxford and RParis also show that, in
comparable settings, transformers are competitive for particular object
retrieval, especially in the regime of short vector representations and
low-resolution images.


Abstract:
  We study the charge of the 4D-Einstein-Gauss-Bonnet black hole by a negative
charge and a positive charge of a particle-antiparticle pair on the horizons r-
and r+, respectively. We show that there are two types of the Schwarzschild
black hole. We show also that the Einstein-Gauss-Bonnet black hole charge has
quantified values. We obtain the Hawking-Bekenstein formula with two
logarithmic corrections, the second correction depends on the cosmological
constant and the black hole charge. Finally, we study the thermodynamics of the
EGB-AdS black hole.


Abstract:
  An introduction to the On-Line Encyclopedia of Integer Sequences (or OEIS,
https://oeis.org) for graduate students in mathematics


Abstract:
  Almost 25 years ago, Jarzynski published a paper in which it was asserted:
the work done, W, in driving a system from state A to state B, characterized by
the Helmholtz free energies FA and FB, satisfies an equality in which an
average over an ensemble of measurements for W determines the difference in
Free energy. Several features of this result require more detailed description,
to be given in the text. The equality is significant and unexpected. So is the
statement that the equality is independent of the rate of change from state A
to state B. A few years ago, I had presented three papers in which the
contraction of the description from full phase space to coordinate space only
was made. This was motivated by the large difference in time scales for momenta
relaxation and coordinate relaxation. The Jarzynski equality (JE) will be shown
here to be correct only in the limit of extremely slow processes. The proposed
counterexample is for a macromolecular harmonic oscillator with a Hooke's
spring constant, k, that during the time interval linearly changes from k1 to
k2. When performed very slowly, we obtain JE in a form in which the free energy
is related to the ratio of the k's. However, when the transition is performed
more rapidly the equality is lost. Jarzynski has suggested to me that Hummer
and Szabo have shown that JE follows directly from the Feynman-Kac formula. We
show here that F-K does not reproduce the direct calculation executed in this
paper and that it has been misapplied in earlier papers.


Abstract:
  Average partial effects (APEs) are generally not point-identified in binary
response panel models with unrestricted unobserved heterogeneity. We show their
point-identification under an index sufficiency assumption on the unobserved
heterogeneity, even when the error distribution is unspecified. This assumption
does not impose parametric restrictions on the unobserved heterogeneity. We
then construct a three-step semiparametric estimator for the APE. In the first
step, we estimate the common parameters using either a conditional logit or
smoothed maximum score estimator. In the second step, we estimate the
conditional expectation of the outcomes given the indices and a generated
regressor that depends on first-step estimates. In the third step, we average
derivatives of this conditional expectation to obtain a partial mean that
estimates the APE. We show that this proposed three-step APE estimator is
consistent and asymptotically normal. We evaluate its finite-sample properties
in Monte Carlo simulations. We then illustrate our estimator in a study of
determinants of married women's labor supply.


Abstract:
  We first revisit impact-parameter dependent collisions of ultra-relativistic
particles in quantum field theory. Two conditions sufficient for defining an
impact-parameter dependent cross section are given, which could be violated in
proton-proton collisions. By imposing these conditions, a general formula for
the impact-parameter dependent cross section is derived. Then, using
soft-collinear effective theory, we derive a factorization formula for the
impact-parameter dependent cross section for inclusive hard processes with only
colorless final-state products in hadron and nuclear collisions. It entails
defining thickness beam functions, which are Fourier transforms of transverse
phase-space parton distribution functions. By modelling non-perturbative modes
in thickness beam functions of large nuclei in heavy-ion collisions, the
factorization formula confirms the cross section in the Glauber model for hard
processes. Besides, the factorization formula is verified up to one loop in
perturbative QCD for the inclusive Drell-Yan process in quark-antiquark
collisions at a finite impact parameter.


Abstract:
  The sensitivity to angular rotation of the top class Sagnac gyroscope
GINGERINO is carefully investigated with standard statistical means, using 103
days of continuous operation and the available geodesic measurements of the
Earth angular rotation rate. All features of the Earth rotation rate are
correctly reproduced. The sensitivity of fractions of frad/s is attained for
long term runs. This excellent sensitivity and stability put Sagnac gyroscopes
at the forefront for fundamental physics, in particular for tests of general
relativity and Lorentz violation, where the sensitivity plays the key role to
provide reliable data for deeper theoretical investigations. The achieved
sensitivity overcomes the conventionally expected one for Sagnac ring laser
gyroscopes.


Abstract:
  A phonoriton is an elementary excitation that is predicted to emerge from
hybridization between exciton, phonon and photon. Besides the intriguing
many-particle structure, phonoritons are of interest as they could serve as
functional nodes in devices that utilize electronic, phononic and photonic
elements for energy conversion and thermal transport applications. Although
phonoritons are predicted to emerge in an excitonic medium under intense
electromagnetic wave irradiation, the stringent condition for their existence
has eluded direct observation in solids. In particular, on-resonance, intense
pumping scheme has been proposed but excessive photoexcitation of carriers
prevents optical detection. Here we theoretically predict the appearance of
phonoritonic features in monolayer hexagonal boron nitride (hBN) embedded in an
optical cavity. The coherent superposition nature of phonoriton states is
evidenced by the hybridization of exciton-polariton branches with phonon
replicas that is tunable by the cavity-matter coupling strength. This finding
simultaneously provides an experimental pathway to observe the predicted
phonoritons and opens a new avenue for tuning materials properties.


Abstract:
  Direct hyperlogarithmic integration offers a strong alternative to
differential equation methods for Feynman integration, particularly for
multi-particle diagrams. We review a variety of results by the authors in which
this method, employed with some care, can compute diagrams of up to eight
particles and four loops. We also highlight situations in which this method
fails due to an algebraic obstruction. In a large number of cases the
obstruction can be associated with a Calabi-Yau manifold.


Abstract:
  In precision crop protection, (target-orientated) object detection in image
processing can help navigate Unmanned Aerial Vehicles (UAV, crop protection
drones) to the right place to apply the pesticide. Unnecessary application of
non-target areas could be avoided. Deep learning algorithms dominantly use in
modern computer vision tasks which require high computing time, memory
footprint, and power consumption. Based on the Edge Artificial Intelligence, we
investigate the main three paths that lead to dealing with this problem,
including hardware accelerators, efficient algorithms, and model compression.
Finally, we integrate them and propose a solution based on a light deep neural
network (DNN), called Ag-YOLO, which can make the crop protection UAV have the
ability to target detection and autonomous operation. This solution is
restricted in size, cost, flexible, fast, and energy-effective. The hardware is
only 18 grams in weight and 1.5 watts in energy consumption, and the developed
DNN model needs only 838 kilobytes of disc space. We tested the developed
hardware and software in comparison to the tiny version of the state-of-art
YOLOv3 framework, known as YOLOv3-Tiny to detect individual palm in a
plantation. An average F1 score of 0.9205 at the speed of 36.5 frames per
second (in comparison to similar accuracy at 18 frames per second and 8.66
megabytes of the YOLOv3-Tiny algorithm) was reached. This developed detection
system is easily plugged into any machines already purchased as long as the
machines have USB ports and run Linux Operating System.


Abstract:
  We introduce a computational method and a user-friendly code with a
terminal-based graphical user interface (GUI), named ElATools, developed to
analyze mechanical and anisotropic elastic properties. ElATools enables facile
analysis of the second-order elastic stiffness tensor of two-dimensional (2D)
and three-dimensional (3D) crystal systems. It computes and displays the main
mechanical properties including the bulk modulus, Young's modulus, shear
modulus, hardness, p-wave modulus, universal anisotropy index, Chung-Buessem
anisotropy index, log-Euclidean anisotropy parameter, Cauchy pressures,
Poisson's ratio, and Pugh's ratio, using three averaging schemes of Voigt,
Reuss, and Hill. It includes an online and offline database from the Materials
Project with more than 13,000 elastic stiffness constants for 3D materials. The
program supports output files of the well-known computational codes IRelast,
IRelast2D, ElaStic, and AELAS. Four types of plotting and visualization tools
are integrated to conveniently interface with GNUPLOT, XMGRACE, view3dscene,
and plotly libraries, offering immediate post-processing of the results. It can
efficiently identify anomalous mechanical properties, such as negative linear
compressibility, negative Poisson's ratio, and highly-anisotropic elastic
modulus in 2D and 3D materials, which are central properties to design and
develop high-performance nanoscale electromechanical devices. Six case studies
on selected material systems, namely, ZnAu$_2$ (CN)$_4$, CrB$_2$,
$\delta$-phosphorene, Pd$_2$O$_6$Se$_2$ monolayer, and GaAs, and a hypothetical
set of systems with cubic symmetry are presented to demonstrate the descriptive
and predictive capabilities of ElATools.


Abstract:
  I want to combine two hitherto largely independent research projects,
scientific understanding and mechanistic explanations. Understanding is not
only achieved by answering why-questions, that is, by providing scientific
explanations, but also by answering what-questions, that is, by providing what
I call scientific descriptions. Based on this distinction, I develop three
forms of understanding: understanding-what, understanding-why, and
understanding-how. I argue that understanding-how is a particularly deep form
of understanding, because it is based on mechanistic explanations, which answer
why something happens in virtue of what it is made of. I apply the three forms
of understanding to two case studies: first, to the historical development of
thermodynamics and, second, to the differences between the Clausius and the
Boltzmann entropy in explaining thermodynamic processes.


Abstract:
  We show that the singular set $\Sigma$ in the classical obstacle problem can
be locally covered by a $C^\infty$ hypersurface, up to an "exceptional" set
$E$, which has Hausdorff dimension at most $n-2$ (countable, in the $n=2$
case). Outside this exceptional set, the solution admits a polynomial expansion
of arbitrarily large order. We also prove that $\Sigma\setminus E$ is extremely
unstable with respect to monotone perturbations of the boundary datum. We apply
this result to the planar Hele-Shaw flow, showing that the free boundary can
have singular points for at most countable many times.


Abstract:
  Control systems often must satisfy strict safety requirements over an
extended operating lifetime. Control Barrier Functions (CBFs) are a promising
recent approach to constructing simple and safe control policies. This paper
proposes a framework for verifying that a CBF guarantees safety for all time
and synthesizing CBFs with verifiable safety in polynomial control systems. Our
approach is to show that safety of CBFs is equivalent to the non-existence of
solutions to a family of polynomial equations, and then prove that this
nonexistence is equivalent to a pair of sum-of-squares constraints via the
Positivstellensatz of algebraic geometry. We develop this Positivstellensatz to
verify CBFs, as well as generalization to high-degree systems and multiple CBF
constraints. We then propose a set of heuristics for CBF synthesis, including a
general alternating-descent heuristic, a specialized approach for compact safe
regions, and an approach for convex unsafe regions. Our approach is illustrated
on two numerical examples.


Abstract:
  In this paper, we present two sharp upper bounds for the spectral radius of
(bipartite) graphs with forbidden a star forest and characterize all extremal
graphs. Moreover, the minimum least eigenvalue of the adjacency matrix of graph
with forbidden a star forest and all extremal graphs for graphs are obtained.


Abstract:
  We present independent determinations of cosmological parameters using the
distance estimator based on the established correlation between the Balmer line
luminosity, L(H$\beta$), and the velocity dispersion ($\sigma$) for HII
galaxies (HIIG). These results are based on new VLT-KMOS high spectral
resolution observations of 41 high-z ($1.3 \leq$ z $\leq 2.6$) HIIG combined
with published data for 45 high-z and 107 z $\leq 0.15$ HIIG, while the
cosmological analysis is based on the MultiNest MCMC procedure not considering
systematic uncertainties. Using only HIIG to constrain the matter density
parameter ($\Omega_m$), we find $\Omega_m = 0.244^{+0.040}_{-0.049}$ (stat), an
improvement over our best previous cosmological parameter constraints, as
indicated by a 37% increase of the FoM. The marginalised best-fit parameter
values for the plane $\{\Omega_m; w_0\}$ = $\{0.249^{+0.11}_{-0.065};
-1.18^{+0.45}_{-0.41}\}$ (stat) show an improvement of the cosmological
parameters constraints by 40%. Combining the HIIG Hubble diagram, the cosmic
microwave background (CMB) and the baryon acoustic oscillation (BAO) probes
yields $\Omega_m=0.298 \pm 0.012$ and $w_0=-1.005 \pm 0.051$, which are
certainly compatible -- although less constraining -- than the solution based
on the joint analysis of SNIa/CMB/BAO. An attempt to constrain the evolution of
the dark energy with time (CPL model), using a joint analysis of the HIIG, CMB
and BAO measurements, shows a degenerate 1$\sigma$ contour of the parameters in
the $\{w_0,w_a\}$ plane.


Abstract:
  We generalize the Bogomolov-Gieseker inequality for semistable coherent
sheaves on smooth projective surfaces to smooth Deligne-Mumford surfaces. We
work over positive characteristic $p>0$ and generalize Langer's method to
smooth Deligne-Mumford stacks. As applications we obtain the Bogomolov
inequality for semistable coherent sheaves on a Deligne-Mumford surface in
characteristic zero, and the Bogomolov inequality for semistable sheaves on a
root stack over a smooth surface which is equivalent to the Bogomolov
inequality for the rational parabolic sheaves on a smooth surface $S$. In a
joint appendix with Hao Max Sun, we generalize the Bogomolov inequality formula
to Simpson Higgs sheaves on tame Deligne-Mumford stacks.


Abstract:
  Fraud has led to a huge addition of expenses in health insurance sector in
India. The work is aimed to provide methods applied to health insurance fraud
detection. The work presents two approaches - a markov model and an improved
markov model using gradient boosting method in health insurance claims. The
dataset 382,587 claims of which 38,082 claims are fraudulent. The markov based
model gave the accuracy of 94.07% with F1-score at 0.6683. However, the
improved markov model performed much better in comparison with the accuracy of
97.10% and F1-score of 0.8546. It was observed that the improved markov model
gave much lower false positives compared to markov model.


Abstract:
  We propose an efficient framework with compatibility between normal printing
and printing with special color inks in this paper. Special color inks can be
used for printing to represent some particular colors and specific optical
properties, which are difficult to express using only CMYK inks. Special color
layers are required in addition to the general color layer for printing with
special color inks. We introduce a reversible data hiding (RDH) method to embed
the special color layers into the general color layer without visible
artifacts. The proposed method can realize both normal printing and printing
with special color inks by using a single layer. Our experimental results show
that the quality of the marked image is virtually identical to that of the
original image, i.e., the general color layer.


Abstract:
  Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively
with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy)
samples that do not conform to the expected normal patterns. UAD has two main
advantages over its fully supervised counterpart. Firstly, it is able to
directly leverage large datasets available from health screening programs that
contain mostly normal image samples, avoiding the costly manual labelling of
abnormal samples and the subsequent issues involved in training with extremely
class-imbalanced data. Further, UAD approaches can potentially detect and
localise any type of lesions that deviate from the normal patterns. One
significant challenge faced by UAD methods is how to learn effective
low-dimensional image representations to detect and localise subtle
abnormalities, generally consisting of small lesions. To address this
challenge, we propose a novel self-supervised representation learning method,
called Constrained Contrastive Distribution learning for anomaly detection
(CCD), which learns fine-grained feature representations by simultaneously
predicting the distribution of augmented data and image contexts using
contrastive learning with pretext constraints. The learned representations can
be leveraged to train more anomaly-sensitive detection models. Extensive
experiment results show that our method outperforms current state-of-the-art
UAD approaches on three different colonoscopy and fundus screening datasets.
Our code is available at https://github.com/tianyu0207/CCD.


Abstract:
  On the unit tangent bundle of a nonflat compact nonpositively curved surface,
we prove that there is a unique probability Borel measure invariant by the
horocyclic flow which gives full measure to the set of rank 1 vectors recurrent
by the geodesic flow. If we assume in addition that the surface has no flat
strips, we show that the horocyclic flow is uniquely ergodic. These results are
valid for any parametrization of the horocyclic flow.


Abstract:
  The map of half-BPS line defects under mirror symmetry has previously been
worked out for 3d $\mathcal{N}=4$ linear quivers with unitary gauge groups,
where these defects have a clear realization in terms of a brane picture in
Type IIB String Theory. In this work, we initiate the study of line defects and
the associated mirror maps for more general 3d $\mathcal{N}=4$ quiver gauge
theories from a QFT approach, using the $S$-type operations introduced in
\cite{Dey:2020hfe}. In particular, our construction does not rely on any String
Theory realization of the quiver gauge theories and the defects. After
discussing the general framework for the construction of these line defects and
their mirror maps, we focus on quiver gauge theories of the $D$-type and the
affine $D$-type with unitary gauge groups, as a concrete set of examples. Some
of the line defects we study admit a Hanany-Witten description and we show that
the associated mirror maps predicted by the Type IIB construction in these
cases agree with the QFT computation. In addition, we study an example
involving defects in an affine $D$-type theory, for which the dual theory is
not directly realized by the Type IIB description. In a companion paper, we
will discuss defects in infinite families of non-ADE quivers using the general
construction developed in this paper.


Abstract:
  This paper introduces the Class-wise Principal Component Analysis, a
supervised feature extraction method for hyperspectral data. Hyperspectral
Imaging (HSI) has appeared in various fields in recent years, including Remote
Sensing. Realizing that information extraction tasks for hyperspectral images
are burdened by data-specific issues, we identify and address two major
problems. Those are the Curse of Dimensionality which occurs due to the
high-volume of the data cube and the class imbalance problem which is common in
hyperspectral datasets. Dimensionality reduction is an essential preprocessing
step to complement a hyperspectral image classification task. Therefore, we
propose a feature extraction algorithm for dimensionality reduction, based on
Principal Component Analysis (PCA). Evaluations are carried out on the Indian
Pines dataset to demonstrate that significant improvements are achieved when
using the reduced data in a classification task.


Abstract:
  We describe a system being developed for measuring the shapes of the mirrors
of the Fred Young Submillimeter Telescope (FYST), now under construction for
the CCAT Observatory. "Holographic" antenna-measuring techniques are an
efficient and accurate way of measuring the surfaces of large millimeter-wave
telescopes and they have the advantage of measuring the wave-front errors of
the whole system under operational conditions, e.g. at night on an exposed
site. Applying this to FYST, however, presents significant challenges because
of the high accuracy needed, the fact that the telescope consists of two large
off-axis mirrors, and a requirement that measurements can be made without
personnel present. We use a high-frequency (~300GHz) source which is relatively
close to the telescope aperture (<1/100th of the Fresnel distance) to minimize
atmospheric effects. The main receiver is in the receiver cabin and can be
moved under remote control to different positions, so that the wave-front
errors in different parts of the focal plane can be measured. A second receiver
placed on the yoke provides a phase reference. The signals are combined in a
digital cross-correlation spectrometer. Scanning the telescope provides a map
of the complex beam pattern. The surface errors are found by inference, i.e. we
make models of the reflectors with errors and calculate the patterns expected,
and then iterate to find the best match to the data. To do this we have
developed a fast and accurate method for calculating the patterns using the
Kirchhoff-Fresnel formulation. This paper presents details of the design and
outlines the results from simulations of the measurement and inference process.
These indicate that a measurement accuracy of ~3 microns rms is achievable.


Abstract:
  Understanding the production mechanism of light (anti-)nuclei in high-energy
nuclear collisions and cosmic rays has been a long-standing problem in nuclear
physics. In the present study, we develop a stochastic method to solve the
relativistic kinetic equations for light nuclei production from many-body
reactions with the inclusion of their finite sizes. The present approach gives
an excellent description of the deuteron and helium-3 data from central Au+Au
(Pb+Pb) collisions at $\sqrt{s_{\rm NN}}$ $=$ $200~\rm GeV$ ($2.76~\rm TeV$).
It can also naturally explain their suppressed production in $pp$ collisions at
7 TeV as a result of their finite sizes.


Abstract:
  Autonomous vehicles interacting with other traffic participants heavily rely
on the perception and prediction of other agents' behaviors to plan safe
trajectories. However, as occlusions limit the vehicle's perception ability,
reasoning about potential hazards beyond the field of view is one of the most
challenging issues in developing autonomous driving systems. This paper
introduces a novel analytical approach that poses safe trajectory planning
under occlusions as a hybrid zero-sum dynamic game between the autonomous
vehicle (evader) and an initially hidden traffic participant (pursuer). Due to
occlusions, the pursuer's state is initially unknown to the evader and may
later be discovered by the vehicle's sensors. The analysis yields optimal
strategies for both players as well as the set of initial conditions from which
the autonomous vehicle is guaranteed to avoid collisions. We leverage this
theoretical result to develop a novel trajectory planning framework for
autonomous driving that provides worst-case safety guarantees while minimizing
conservativeness by accounting for the vehicle's ability to actively avoid
other road users as soon as they are detected in future observations. Our
framework is agnostic to the driving environment and suitable for various
motion planners. We demonstrate our algorithm on challenging urban and highway
driving scenarios using the open-source CARLA simulator.


Abstract:
  The interface between a solid and vacuum can become electronically distinct
from the bulk. This feature, encountered in the case of quantum Hall effect,
has a manifestation in insulators with topologically protected metallic surface
states. Non-trivial Berry curvature of the Bloch waves or periodically driven
perturbation are known to generate it. Here, by studying the angle-dependent
magnetoresistance in prismatic bismuth crystals of different shapes, we detect
a robust surface contribution to electric conductivity when the magnetic field
is aligned parallel to a two-dimensional boundary between the three-dimensional
crystal and vacuum. The effect is absent in antimony, which has an identical
crystal symmetry, a similar Fermi surface structure and equally ballistic
carriers, but an inverted band symmetry and a topological invariant of opposite
sign. Our observation confirms that the boundary interrupting the cyclotron
orbits remains metallic in bismuth, which is in agreement with what was
predicted by Azbel decades ago. However, the absence of the effect in antimony
indicates an intimate link between band symmetry and this boundary conductance.


Abstract:
  Transport networks are typically optimized, either by evolutionary pressures
in biological systems, or by human design, in engineered structures. In the
case of systems such as the animal vasculature, the transport of fluids is not
only hindered by the inherent resistance to flow but also kept in a dynamic
state by the pulsatile nature of the heart and elastic properties of the vessel
walls. While this imparted pulsatility necessarily increases the dissipation of
energy caused by the resistance, the vessel elasticity helps to reduce overall
dissipation by attenuating the amplitude of the pulsatile components of the
flow. However, we find that this reduction in energy loss comes at the price of
increasing the time required to respond to changes in the flow boundary
conditions for vessels longer than a critical size. In this regime, dissipation
and response time are found to follow a simple power law scaling relation in
single vessels as well as hierarchically structured networks. Applying
biologically relevant parameters to the model suggests that animal vascular
networks may have evolved to maintain a minimal response time over lesser
dissipation.


Abstract:
  In this paper, we introduce Super Luminal ("xLumi"), a new payment channel
protocol for blockchain systems. xLumi is a simple unidirectional payment
channel that can be extended to a bidirectional payment channel or a complete
network. xLumi guarantees the security of the payment channel's funds by using
a simple set of mathematical rules that can be easily implemented on any
blockchain with the necessary infrastructure. We also give the detailed
implementation methods of this idea using V Systems contract systems in this
paper.


Abstract:
  Many neural network quantization techniques have been developed to decrease
the computational and memory footprint of deep learning. However, these methods
are evaluated subject to confounding tradeoffs that may affect inference
acceleration or resource complexity in exchange for higher accuracy. In this
work, we articulate a variety of tradeoffs whose impact is often overlooked and
empirically analyze their impact on uniform and mixed-precision post-training
quantization, finding that these confounding tradeoffs may have a larger impact
on quantized network accuracy than the actual quantization methods themselves.
Because these tradeoffs constrain the attainable hardware acceleration for
different use-cases, we encourage researchers to explicitly report these design
choices through the structure of "quantization cards." We expect quantization
cards to help researchers compare methods more effectively and engineers
determine the applicability of quantization techniques for their hardware.


Abstract:
  The present work reports on the flow physics of turbulent supersonic flow
over backward facing step (BFS) at Mach 2 using LES methodology where the
dynamic Smagorinsky model is used for SGS modeling, while POD is invoked to
identify the coherent structures present in the flow. The mean data obtained
through the computations is in good agreement with the experimental
measurements, while the iso-surfaces of Q-criterion at different time instants
show the complex flow structures. The presence of counter rotating vortex pair
in the shear layer along with the complex shock wave/boundary layer interaction
leading to the separation of boundary layer is also evident from the contours
of both Q and the modulus of vorticity. Further, the POD analysis reveals the
presence of coherent structures, where the first and second modes confirm the
vortical structures near the step as well as along the shear layer in the
downstream region; while the second, third and fourth modes confirm the
presence of vortices along the shear layer due to Kelvin-Helmholtz (K-H)
instability. Moreover, POD as well as frequency analysis is extended at
different planes to extract the detailed flow features.


Abstract:
  We study the generalized Forchheimer flows of slightly compressible fluids in
rotating porous media. In the problem's model, the varying density in the
Coriolis force is fully accounted for without any simplifications. It results
in a doubly nonlinear parabolic equation for the density. We derive a priori
estimates for the solutions in terms of the initial, boundary data and physical
parameters, emphasizing on the case of unbounded data. Weighted
Poincar\'e-Sobolev inequalities suitable to the equation's nonlinearity,
adapted Moser's iteration and maximum principle are used and combined to obtain
different types of estimates.


Abstract:
  Let a group $A$ act on the group $G$ coprimely. Suppose that the order of the
fixed point subgroup $C_G(A)$ is not divisible by an arbitrary but fixed prime
$p$. In the present paper we determine bounds for the $p$-length of the group
$G$ in terms of the order of $A$, and investigate how some $A$-invariant
$p$-subgroups are embedded in $G$ under various additional assumptions.


Abstract:
  In this work, we propose two algorithms that can be applied in the context of
a networked virtual environment to efficiently handle the interpolation of
displacement data for hand-based VR HMDs. Our algorithms, based on the use of
dual-quaternions and multivectors respectively, impact the network consumption
rate and are highly effective in scenarios involving multiple users. We
illustrate convincing results in a modern game engine and a medical VR
collaborative training scenario.


Abstract:
  The collection $\mathcal{M}$ of all isometry classes of compact metric spaces
endowed with the Gromov-Hausdorff distance $d_\mathcal{GH}$ is known to be a
geodesic space. However, there is no known structural characterization of
geodesics in $\mathcal{M}$. In this paper we provide two such
characterizations. We first prove that every Gromov-Hausdorff geodesic is in
fact a geodesic in the Hausdorff hyperspace of some compact metric space, which
we call a Hausdorff geodesic. Inspired by this characterization, we further
elucidate a structural connection between Hausdorff geodesics and Wasserstein
geodesics: every Hausdorff geodesic is equivalent to a so-called Hausdorff
displacement interpolation. This equivalence allows us to establish that every
Gromov-Hausdorff geodesic is dynamic, a notion which we develop in analogy with
dynamic optimal couplings in the theory of optimal transport.
  Besides geodesics in $\mathcal{M}$, we also study geodesics on the collection
$\mathcal{M}^w$ of isomorphism classes of compact metric measure spaces. Sturm
constructed a family of Gromov-type distances on $\mathcal{M}^w$, which we
denote $d_{\mathcal{GW},{p}}^\mathrm{S}$ (for $p\in[1,\infty)$), and proved
that $(\mathcal{M}^w,d_{\mathcal{GW},{p}}^\mathrm{S})$ is also a geodesic
space. We are interested in $d_{\mathcal{GW},{p}}^\mathrm{S}$ geodesics which
are (essentially) Wasserstein geodesics. We prove the set of such geodesics is
dense in the set of all $d_{\mathcal{GW},{p}}^\mathrm{S}$ geodesics and
identify a rich class of such geodesics.


Abstract:
  A few years ago, the first CNN surpassed human performance on ImageNet.
However, it soon became clear that machines lack robustness on more challenging
test cases, a major obstacle towards deploying machines "in the wild" and
towards obtaining better computational models of human visual perception. Here
we ask: Are we making progress in closing the gap between human and machine
vision? To answer this question, we tested human observers on a broad range of
out-of-distribution (OOD) datasets, recording 85,120 psychophysical trials
across 90 participants. We then investigated a range of promising machine
learning developments that crucially deviate from standard supervised CNNs
along three axes: objective function (self-supervised, adversarially trained,
CLIP language-image training), architecture (e.g. vision transformers), and
dataset size (ranging from 1M to 1B).
  Our findings are threefold. (1.) The longstanding distortion robustness gap
between humans and CNNs is closing, with the best models now exceeding human
feedforward performance on most of the investigated OOD datasets. (2.) There is
still a substantial image-level consistency gap, meaning that humans make
different errors than models. In contrast, most models systematically agree in
their categorisation errors, even substantially different ones like contrastive
self-supervised vs. standard supervised models. (3.) In many cases,
human-to-model consistency improves when training dataset size is increased by
one to three orders of magnitude. Our results give reason for cautious
optimism: While there is still much room for improvement, the behavioural
difference between human and machine vision is narrowing. In order to measure
future progress, 17 OOD datasets with image-level human behavioural data and
evaluation code are provided as a toolbox and benchmark at:
https://github.com/bethgelab/model-vs-human/


Abstract:
  Recent years have seen many breakthroughs in natural language processing
(NLP), transitioning it from a mostly theoretical field to one with many
real-world applications. Noting the rising number of applications of other
machine learning and AI techniques with pervasive societal impact, we
anticipate the rising importance of developing NLP technologies for social
good. Inspired by theories in moral philosophy and global priorities research,
we aim to promote a guideline for social good in the context of NLP. We lay the
foundations via the moral philosophy definition of social good, propose a
framework to evaluate the direct and indirect real-world impact of NLP tasks,
and adopt the methodology of global priorities research to identify priority
causes for NLP research. Finally, we use our theoretical framework to provide
some practical guidelines for future NLP research for social good. Our data and
code are available at http://github.com/zhijing-jin/nlp4sg_acl2021. In
addition, we curate a list of papers and resources on NLP for social good at
https://github.com/zhijing-jin/NLP4SocialGood_Papers.


Abstract:
  Consensus of autonomous agents is a benchmark problem in multi-agent control.
In this paper, we consider continuous-time averaging consensus policies (or
Laplacian flows) and their discrete-time counterparts over time-varying graphs
in presence of unknown but bounded communication delays. It is known that
consensus is established (no matter how large the delays are) if the graph is
periodically, or uniformly quasi-strongly connected (UQSC). The UQSC condition
is often believed to be the weakest sufficient condition under which consensus
can be proved. We show that the UQSC condition can actually be substantially
relaxed and replaced by a condition that we call aperiodic quasi-strong
connectivity (AQSC), which, in some sense, proves to be very close to the
necessary condition of integral connectivity. Furthermore, in some special
situations such as undirected or type-symmetric graph, we find a necessary and
sufficient condition for consensus in presence of bounded delay; the relevant
results have been previously proved only in the undelayed case. The consensus
criteria established in this paper generalize a number of results known in the
literature.


Abstract:
  We discuss the fundamental noise limitations of a ferromagnetic torque sensor
based on a levitated magnet in the tipping regime. We evaluate the optimal
magnetic field resolution taking into account the thermomechanical noise and
the mechanical detection noise at the standard quantum limit (SQL). We find
that the Energy Resolution Limit (ERL), pointed out in recent literature as a
relevant benchmark for most classes of magnetometers, can be surpassed by many
orders of magnitude. Moreover, similarly to the case of a ferromagnetic
gyroscope, it is also possible to surpass the standard quantum limit for
magnetometry with independent spins, arising from spin-projection noise. Our
finding indicates that magnetomechanical systems optimized for magnetometry can
achieve a magnetic field resolution per unit volume several orders of magnitude
better than any conventional magnetometer. We discuss possible implications,
focusing on fundamental physics problems such as the search for exotic
interactions beyond the standard model.


Abstract:
  An analogous construction of simplicial homotopy group for Kan complex can be
applied to saturated complicial sets to give monoids. In this paper, we
investigate how the construction of loop spaces of Kan complexes lifts to the
complicial setting and relates to such monoids.


Abstract:
  Although Natural Language Processing (NLP) is at the core of many tools young
people use in their everyday life, high school curricula (in Italy) do not
include any computational linguistics education. This lack of exposure makes
the use of such tools less responsible than it could be and makes choosing
computational linguistics as a university degree unlikely. To raise awareness,
curiosity, and longer-term interest in young people, we have developed an
interactive workshop designed to illustrate the basic principles of NLP and
computational linguistics to high school Italian students aged between 13 and
18 years. The workshop takes the form of a game in which participants play the
role of machines needing to solve some of the most common problems a computer
faces in understanding language: from voice recognition to Markov chains to
syntactic parsing. Participants are guided through the workshop with the help
of instructors, who present the activities and explain core concepts from
computational linguistics. The workshop was presented at numerous outlets in
Italy between 2019 and 2021, both face-to-face and online.


Abstract:
  We consider the quantum difference equation of the Hilbert scheme of points
in $\mathbb{C}^2$. This equation is the K-theoretic generalization of the
quantum differential equation discovered by A. Okounkov and R. Pandharipande.
We obtain two explicit descriptions for the monodromy of these equations -
representation-theoretic and algebro-geometric. In the representation-theoretic
description, the monodromy acts via certain explicit elements in the quantum
toroidal algebra $\frak{gl}_1$. In the algebro-geometric description, the
monodromy features as transition matrices between the stable envelope bases in
the equivariant K-theory and elliptic cohomology. Using the second approach we
identify the monodromy matrices for the differential equation with the
K-theoretic $R$-matrices of cyclic quiver varieties, which appear as
subvarieties in the $3D$-mirror Hilbert scheme. Most of the results in the
paper are illustrated by explicit examples for cases $n=2$ and $n=3$ in the
Appendix.


Abstract:
  Despite the success of convolutional neural networks (CNNs) in many computer
vision and image analysis tasks, they remain vulnerable against so-called
adversarial attacks: Small, crafted perturbations in the input images can lead
to false predictions. A possible defense is to detect adversarial examples. In
this work, we show how analysis in the Fourier domain of input images and
feature maps can be used to distinguish benign test samples from adversarial
images. We propose two novel detection methods: Our first method employs the
magnitude spectrum of the input images to detect an adversarial attack. This
simple and robust classifier can successfully detect adversarial perturbations
of three commonly used attack methods. The second method builds upon the first
and additionally extracts the phase of Fourier coefficients of feature-maps at
different layers of the network. With this extension, we are able to improve
adversarial detection rates compared to state-of-the-art detectors on five
different attack methods.


Abstract:
  The vast majority of existing studies that estimate the average unexplained
gender pay gap use unnecessarily restrictive linear versions of the
Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7
million employees in Switzerland, we investigate how the methodological
improvements made possible by such big data affect estimates of the unexplained
gender pay gap. We study the sensitivity of the estimates with regard to i) the
availability of observationally comparable men and women, ii) model flexibility
when controlling for wage determinants, and iii) the choice of different
parametric and semi-parametric estimators, including variants that make use of
machine learning methods. We find that these three factors matter greatly.
Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39%
when we enforce comparability between men and women and use a more flexible
specification of the wage equation. Semi-parametric matching yields estimates
that when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and
also less sensitive to the way wage determinants are included.


Abstract:
  Python has become the de facto language for scientific computing. Programming
in Python is highly productive, mainly due to its rich science-oriented
software ecosystem built around the NumPy module. As a result, the demand for
Python support in High Performance Computing (HPC) has skyrocketed. However,
the Python language itself does not necessarily offer high performance. In this
work, we present a workflow that retains Python's high productivity while
achieving portable performance across different architectures. The workflow's
key features are HPC-oriented language extensions and a set of automatic
optimizations powered by a data-centric intermediate representation. We show
performance results and scaling across CPU, GPU, FPGA, and the Piz Daint
supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over
previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated
Python, and up to 93.16% scaling efficiency on 512 nodes.


Abstract:
  The objective of this study is improving the location estimate of a mobile
robot capable of motion on a plane and mounted with a conventional 2D LIDAR
sensor, given an initial guess for its location on a 2D map of its
surroundings. Documented herein is the theoretical reasoning behind solving a
matching problem between two homoriented 2D scans, one derived from the robot's
physical sensor and one derived by simulating its operation within the map, in
a manner that does not require the establishing of correspondences between
their constituting rays. Two results are proved and subsequently shown through
experiments. The first is that the true position of the sensor can be recovered
with arbitrary precision when the physical sensor reports faultless
measurements and there is no discrepancy between the environment the robot
operates in and its perception of it by the robot. The second is that when
either is affected by disturbance, the location estimate is bound in a
neighbourhood of the true location whose radius is proportional to the
affecting disturbance.


Abstract:
  Complex nonlinear turbulent dynamical systems are ubiquitous in many areas.
Recovering unobserved state variables is an important topic for the data
assimilation of turbulent systems. In this article, an efficient continuous in
time data assimilation scheme is developed, which exploits closed analytic
formulae for updating the unobserved state variables. Therefore, it is
computationally efficient and accurate. The new data assimilation scheme is
combined with a simple reduced order modeling technique that involves a cheap
closure approximation and a noise inflation. In such a way, many complicated
turbulent dynamical systems can satisfy the requirements of the mathematical
structures for the proposed efficient data assimilation scheme. The new data
assimilation scheme is then applied to the Sabra shell model, which is a
conceptual model for nonlinear turbulence. The goal is to recover the
unobserved shell velocities across different spatial scales. It has been shown
that the new data assimilation scheme is skillful in capturing the nonlinear
features of turbulence including the intermittency and extreme events in both
the chaotic and the turbulent dynamical regimes. It has also been shown that
the new data assimilation scheme is more accurate and computationally cheaper
than the standard ensemble Kalman filter and nudging data assimilation schemes
for assimilating the Sabra shell model.


Abstract:
  Face authentication on mobile end has been widely applied in various
scenarios. Despite the increasing reliability of cutting-edge face
authentication/verification systems to variations like blinking eye and subtle
facial expression, anti-spoofing against high-resolution rendering replay of
paper photos or digital videos retains as an open problem. In this paper, we
propose a simple yet effective face anti-spoofing system, termed Aurora Guard
(AG). Our system firstly extracts the normal cues via light reflection
analysis, and then adopts an end-to-end trainable multi-task Convolutional
Neural Network (CNN) to accurately recover subjects' intrinsic depth and
material map to assist liveness classification, along with the light CAPTCHA
checking mechanism in the regression branch to further improve the system
reliability. Experiments on public Replay-Attack and CASIA datasets demonstrate
the merits of our proposed method over the state-of-the-arts. We also conduct
extensive experiments on a large-scale dataset containing 12,000 live and
diverse spoofing samples, which further validates the generalization ability of
our method in the wild.


Abstract:
  Reconfigurable intelligent surface (RIS) is an excellent use case for
line-of-sight (LOS) based technologies such as free-space optical (FSO)
communications. In this paper, we analyze the performance of RIS-empowered FSO
(RISE-FSO) systems by unifying Fisher-Snedecor (F), Gamma-Gamma (GG), and
Malaga (M) distributions for atmospheric turbulence with zero-boresight
pointing errors over deterministic as well as random path-loss in foggy
conditions with heterodyne detection (HD) and intensity modulation/direct
detection (IM/DD) methods. By deriving the probability density function (PDF)
and cumulative distribution function (CDF) of the direct-link (DL) with the
statistical effect of atmospheric turbulence, pointing errors, and random fog,
we develop exact expressions of PDF and CDF of the resultant channel for the
RISE-FSO system. Using the derived statistical results, we present exact
expressions of outage probability, average bit-error-rate (BER), ergodic
capacity, and moments of signal-to-noise ratio (SNR) for both DL-FSO and
RISE-FSO systems. We also develop an asymptotic analysis of the outage
probability and average BER and derive the diversity order of the considered
systems. We validate the analytical expressions using Monte-Carlo simulations
and demonstrate the performance scaling of the FSO system with the number of
RIS elements for various turbulence channels, detection techniques, and weather
conditions.


Abstract:
  We introduce a framework for learning from unlabeled video what is
predictable in the future. Instead of committing up front to features to
predict, our approach learns from data which features are predictable. Based on
the observation that hyperbolic geometry naturally and compactly encodes
hierarchical structure, we propose a predictive model in hyperbolic space. When
the model is most confident, it will predict at a concrete level of the
hierarchy, but when the model is not confident, it learns to automatically
select a higher level of abstraction. Experiments on two established datasets
show the key role of hierarchical representations for action prediction.
Although our representation is trained with unlabeled video, visualizations
show that action hierarchies emerge in the representation.


Abstract:
  We present a novel conditional Generative Adversarial Network (cGAN)
architecture that is capable of generating 3D Computed Tomography scans in
voxels from noisy and/or pixelated approximations and with the potential to
generate full synthetic 3D scan volumes. We believe conditional cGAN to be a
tractable approach to generate 3D CT volumes, even though the problem of
generating full resolution deep fakes is presently impractical due to GPU
memory limitations. We present results for autoencoder, denoising, and
depixelating tasks which are trained and tested on two novel COVID19 CT
datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from
12.53 - 46.46 dB, and the Structural Similarity index ( SSIM) range from 0.89
to 1.


Abstract:
  We propose a new way for sympathetic cooling of ions in an electromagnetic
Paul trap: it implies the use for this purpose of cold buffer atoms in the
region of atom-ion confinement-induced resonance (CIR). The problem is that the
unavoidable micromotion of the ion and the long-range nature of its interaction
with the environment of colder atoms in a hybrid atomic-ion trap prevent its
sympathetic cooling. We show that the destructive effect of ion micromotion on
its sympathetic cooling can however be suppressed in the vicinity of the
atom-ion CIR. The origin of this is the "fermionization" of the atom-ion wave
function near CIR, where the atom-ion pair behaves as a pair of noninteracting
identical fermions. This prevents the complete approach of the atom with the
ion near resonance and does not enhance the ion micromotion, which interferes
with its sympathetic cooling. We investigate the effect of sympathetic cooling
around CIRs in atom-ion and atom-atom confined collisions within the
qusiclassical-quantum approach using the Li-Yb$^+$ and Li-Yb confined systems
as an example. In this approach, the Schr\"odinger equation for a cold light
atom is integrated simultaneously with the classical Hamilton equations for a
hotter heavy ion or atom during collision. We have found the region near the
atom-ion CIR where the sympathetic cooling of the ion by cold atoms is possible
in a hybrid atom-ion trap. We also show that it is possible to improve the
efficiency of sympathetic cooling in atomic traps by using atomic CIRs.


Abstract:
  Adversarial learning is used to test the robustness of machine learning
algorithms under attack and create attacks that deceive the anomaly detection
methods in Industrial Control System (ICS). Given that security assessment of
an ICS demands that an exhaustive set of possible attack patterns is studied,
in this work, we propose an association rule mining-based attack generation
technique. The technique has been implemented using data from a secure Water
Treatment plant. The proposed technique was able to generate more than 300,000
attack patterns constituting a vast majority of new attack vectors which were
not seen before. Automatically generated attacks improve our understanding of
the potential attacks and enable the design of robust attack detection
techniques.


Abstract:
  We study the response of a thermal state of the Hubbard-like system to either
global or local non-Hermitian perturbation, which coalesces the degenerate
ground state within the $U(1)$ symmetry breaking phase. We show that the
dynamical response of the system is strongly sensitive to the underlying
quantum phase transition (QPT) from a Mott insulator to a superfluid state. The
Uhlmann fidelity in the superfluid phase decays to a steady value determined by
the order of the exceptional point (EP) within the subspace spanned by the
degenerate ground states but remains almost unchanged in the Mott insulating
phase. It demonstrates that the phase diagram at zero temperature is preserved
even though a local probing field is applied. Specifically, two celebrated
models including the Bose-Hubbard model and the Jaynes-Cummings-Hubbard model
are employed to demonstrate this property in the finite-size system, wherein
fluctuations of the boson and polariton number are observed based on EP
dynamics. This work presents an alternative approach to probe the
superfluid-insulator QPT at non-zero temperature.


Abstract:
  The huge size of the widely used BERT family models has led to recent efforts
about model distillation. The main goal of distillation is to create a
task-agnostic pre-trained model that can be fine-tuned on downstream tasks
without fine-tuning its full-sized version. Despite the progress of
distillation, to what degree and for what reason a task-agnostic model can be
created from distillation has not been well studied. Also, the mechanisms
behind transfer learning of those BERT models are not well investigated either.
Therefore, this work focuses on analyzing the acceptable deduction when
distillation for guiding the future distillation procedure. Specifically, we
first inspect the prunability of the Transformer heads in RoBERTa and ALBERT
using their head importance estimation proposed by Michel et al. (2019), and
then check the coherence of the important heads between the pre-trained task
and downstream tasks. Hence, the acceptable deduction of performance on the
pre-trained task when distilling a model can be derived from the results, and
we further compare the behavior of the pruned model before and after
fine-tuning. Our studies provide guidance for future directions about BERT
family model distillation.


Abstract:
  We give a new construction of sheaves on a relative site associated to a
product $X\times S$ where $S$ plays the role of a parameter space, expanding
the previous construction by the same authors, where the subanalytic structure
on $S$ was required. Here we let this last condition fall. In this way the
construction becomes much easier to apply when dimension of $S$ is bigger than
one. We also study the functorial properties of base change with respect to the
parameter space.


Abstract:
  We discuss the extension of the empirical equation: $\left\langle
s_{N}^{2}\right\rangle_{0}\propto g\,l^{2}$, where the subscript 0 denotes the
ideal value with no excluded volume and $g$ the generation number from the root
to the youngest (outermost) generation. By analogy with the linear chain
problem, we introduce the assumption that the scaling relation, $\left\langle
s_{N}^{2}\right\rangle_{0}\propto g^{2\lambda}\,l^{2}$, exists for arbitrary
polymeric architectures, where $\lambda$ is an exponent for the backbone
structure. Then, making use of the relationship between $g$ and $N$ (monomer
number), we can deduce the exponent, $\nu$, for polymers with various
architectures. The theory of the excluded volume effects impose the severe
restriction on the quantities: $\nu_{0}$, $\nu$, and $\lambda$; for instance,
the inequality, $\nu_{0}\ge\frac{1}{d+1}$, must be satisfied for isolated
polymers in good solvents. An intriguing question is whether or not there
exists an actual molecule that violates this inequality. We take up two
examples having $\nu_{0}=1/4$ for $d=2$ and $\nu_{0}=1/6$ for $d=3$, and
discuss this question.


Abstract:
  In this work, we propose FastDPM, a unified framework for fast sampling in
diffusion probabilistic models. FastDPM generalizes previous methods and gives
rise to new algorithms with improved sample quality. We systematically
investigate the fast sampling methods under this framework across different
domains, on different datasets, and with different amount of conditional
information provided for generation. We find the performance of a particular
method depends on data domains (e.g., image or audio), the trade-off between
sampling speed and sample quality, and the amount of conditional information.
We further provide insights and recipes on the choice of methods for
practitioners.


Abstract:
  Since its discovery as a Kondo insulator 50 years ago, SmB6 recently received
a revival of interest due to detection of unexpected quantum oscillations in
the insulating state, discovery of disorder-immune bulk transport, and
proposals of correlation-driven topological physics. While recent transport
results attribute the anomalous low temperature conduction to two-dimensional
surface states, important alternatives, such as conduction channel residing in
one-dimensional dislocation lines, have not been adequately explored. Here we
study SmB6 with scanning microwave impedance microscopy and uncover evidence
for conducting one-dimensional states terminating at surface step edges. These
states remain conducting up to room temperature, indicating unusual robustness
against scattering and an unconventional origin. Our results bring to light a
heretofore undetected conduction route in SmB6 that contributes to the low
temperature transport. The unique scenario of intrinsic one-dimensional
conducting channels in a highly insulating correlated bulk offers a
one-dimensional platform that may host exotic physics.


Abstract:
  This study offers a step-by-step practical procedure from the analysis of the
current status of the spare parts inventory system to advanced service-level
analysis by virtue of simulation-optimization technique for a real-world case
study associated with a seaport. The remarkable variety and immense diversity
on one hand, and extreme complexities not only in consumption patterns but in
the supply of spare parts in an international port with technically advance
port operator machinery, on the other hand, have convinced the managers to deal
with this issue in a structural framework. The huge available data require
cleaning and classification to properly process them and derive reorder point
(ROP) estimation, reorder quantity (ROQ) estimation, and associated service
level analysis. Finally, from 247000 items used in 9 years long, 1416 inventory
items are elected as a result of ABC analysis integrating with the Analytic
Hierarchy Process (AHP), which led to the main items that need to be kept under
strict inventory control. The ROPs and the pertinent quantities are simulated
by Arena software for all the main items, each of which took approximately 30
minutes run-time on a personal computer to determine near-optimal estimations.


Abstract:
  In this paper, we study the optimal control of the mean and variance of the
network state vector. We develop an algorithm to optimize the control input
placement subject to constraints on the state, which must be achieved at a
given time threshold; seeking an input placement which moves the moment at
minimum cost. First, we solve the state-selection problem for a number of
variants of the first and second moment, and find solutions related to the
eigenvalues of the systems' Gramian matrices. Our algorithm then uses this
information to find a locally optimal input placement. This is a Generalization
of the Projected Gradient Method (GPGM). We solve the problem for some common
versions of these moments, including the mean state and versions of the second
moment which induce discord, repel from a certain state, or encourage
convergence. We then perform simulations, and discuss a measure of centrality
based on the system flux -- a measure which describes what nodes are most
important to optimal control of the average state.


Abstract:
  The precise simulation of particle transport through detectors remains a key
element for the successful interpretation of high energy physics results.
However, Monte Carlo based simulation is extremely demanding in terms of
computing resources. This challenge motivates investigations of faster,
alternative approaches for replacing the standard Monte Carlo approach.
  We apply Generative Adversarial Networks (GANs), a deep learning technique,
to replace the calorimeter detector simulations and speeding up the simulation
time by orders of magnitude. We follow a previous approach which used
three-dimensional convolutional neural networks and develop new two-dimensional
convolutional networks to solve the same 3D image generation problem faster.
Additionally, we increased the number of parameters and the neural networks
representational power, obtaining a higher accuracy. We compare our best
convolutional 2D neural network architecture and evaluate it versus the
previous 3D architecture and Geant4 data. Our results demonstrate a high
physics accuracy and further consolidate the use of GANs for fast detector
simulations.


Abstract:
  Tactile Internet (TI) enables the omnipresence and exchange of tactile
experiences across the global via the ultra-reliable and ultra-responsive
connectivity. This article argues for coordinated multi-point (CoMP) enabled
radio access network (RAN) slicing as an efficient solution that satisfies the
stringent reliable and responsive connectivity requirements for supporting
tactile applications. This article presents the emerging challenges when
accommodating CoMP-enabled RAN slicing in the TI ecosystem and expounds the
functional split of CoMP-enabled RAN. Besides, this article elaborates on the
implementation prototype of CoMP-enabled RAN slicing for TI with the
coexistence of diverse vertical applications. Finally, this article studies a
use case of enabling TI-included application multiplexing as an example of
CoMP-enabled RAN slicing for TI.


Abstract:
  The connectivity between vehicles, infrastructure, and other traffic
participants brings a new dimension to automotive safety applications. Soon all
the newly produced cars will have Vehicle to Everything (V2X) communication
modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is
essential to identify the different sensor measurements for the same targets
(Data Association) to use connectivity reliably as a safety feature alongside
the standard ADAS functionality. Considering the camera is the most common
sensor available for ADAS systems, in this paper, we present an experimental
implementation of a Mahalanobis distance-based data association algorithm
between the camera and the Vehicle to Vehicle (V2V) communication sensors. The
implemented algorithm has low computational complexity and the capability of
running in real-time. One can use the presented algorithm for sensor fusion
algorithms or higher-level decision-making applications in ADAS modules.


Abstract:
  The availability of labelled data is one of the main limitations in machine
learning. We can alleviate this using weak supervision: a framework that uses
expert-defined rules $\boldsymbol{\lambda}$ to estimate probabilistic labels
$p(y|\boldsymbol{\lambda})$ for the entire data set. These rules, however, are
dependent on what experts know about the problem, and hence may be inaccurate
or may fail to capture important parts of the problem-space. To mitigate this,
we propose Active WeaSuL: an approach that incorporates active learning into
weak supervision. In Active WeaSuL, experts do not only define rules, but they
also iteratively provide the true label for a small set of points where the
weak supervision model is most likely to be mistaken, which are then used to
better estimate the probabilistic labels. In this way, the weak labels provide
a warm start, which active learning then improves upon. We make two
contributions: 1) a modification of the weak supervision loss function, such
that the expert-labelled data inform and improve the combination of weak
labels; and 2) the maxKL divergence sampling strategy, which determines for
which data points expert labelling is most beneficial. Our experiments show
that when the budget for labelling data is limited (e.g. $\leq 60$ data
points), Active WeaSuL outperforms weak supervision, active learning, and
competing strategies, with only a handful of labelled data points. This makes
Active WeaSuL ideal for situations where obtaining labelled data is difficult.


Abstract:
  The half-life of $^{212}$Po was measured with the highest up-to-date accuracy
as $T_{1/2}=295.1(4)$ ns by using thorium-loaded liquid scintillator.


Abstract:
  This paper presents a data-driven localization framework with high precision
in time-varying complex multipath environments, such as dense urban areas and
indoors, where GPS and model-based localization techniques come short. We
consider the angle-delay profile (ADP), a linear transformation of channel
state information (CSI), in massive MIMO systems and show that ADPs preserve
users' motion when stacked temporally. We discuss that given a static
environment, future frames of ADP time-series are predictable employing a video
frame prediction algorithm. We express that a deep convolutional neural network
(DCNN) can be employed to learn the background static scattering environment.
To detect foreground changes in the environment, corresponding to path blockage
or addition, we introduce an algorithm taking advantage of the trained DCNN.
Furthermore, we present DyLoc, a data-driven framework to recover distorted
ADPs due to foreground changes and to obtain precise location estimations. We
evaluate the performance of DyLoc in several dynamic scenarios employing
DeepMIMO dataset to generate geo-tagged CSI datasets for indoor and outdoor
environments. We show that previous DCNN-based techniques fail to perform with
desirable accuracy in dynamic environments, while DyLoc pursues localization
precisely. Moreover, simulations show that as the environment gets richer in
terms of the number of multipath, DyLoc gets more robust to foreground changes.


Abstract:
  In exploratory data analysis of known classes of high dimensional data, a
central question is how distinct are the classes? The Direction Projection
Permutation (DiProPerm) hypothesis test provides an answer to this that is
directly connected to a visual analysis of the data. In this paper, we propose
an improved DiProPerm test that solves 3 major challenges of the original
version. First, we implement only balanced permutations to increase the test
power for data with strong signals. Second, our mathematical analysis leads to
an adjustment to correct the null behavior of both balanced and the
conventional all permutations. Third, new confidence intervals (reflecting
permutation variation) for test significance are also proposed for comparison
of results across different contexts. This improvement of DiProPerm inference
is illustrated in the context of comparing cancer types in examples from The
Cancer Genome Atlas.


Abstract:
  We reply to the Comment by Svhaika, Ruocco, Schirmacher and collaborators.
There were two accidental mistakes in our original paper (Phys. Rev. Lett. 112,
145501 (2019)), which have been now corrected. All the physical conclusions and
results of the original paper, including the prediction of boson peak due to
anharmonicity, remain valid in the corrected version.


Abstract:
  Intrinsically broken symmetries in the bulk of topological insulators (TIs)
are manifested in their surface states. In spite of particle-hole asymmetry in
TIs, it has often been assumed that their surface states are characterized by a
particle-hole symmetric Dirac energy dispersion. In this work we demonstrate
that the effect of particle-hole asymmetry is essential to correctly describe
the energy spectrum and the magneto-optical response in TIs thin-films. In
thin-films of TIs with a substantial degree of particle-hole symmetry breaking,
such as Sb$_2$Te$_3$, the longitudinal optical conductivity displays absorption
peaks arising from optical transitions between bulk and surface Landau levels
for low photon energies. The transition energies between the bulk and surface
Landau levels exhibit clearly discernable signatures from those between surface
Landau levels due to their distinct magnetic field dependence. Bulk
contributions to the magneto-optical conductivity in a TI thin-film are
enhanced via one type of doping while being suppressed by the other. This
asymmetric dependence on type of doping aids in revealing the particle-hole
asymmetry in TI thin-films.


Abstract:
  According to the embodied cognition perspective, linguistic negation may
block the motor simulations induced by language processing. Transcranial
magnetic stimulation (TMS) was applied to the left primary motor cortex (hand
area) of monolingual Italian and German healthy participants during a rapid
serial visual presentation of sentences from their own language. In these
languages, the negative particle is located at the beginning and at the end of
the sentence, respectively. The study investigated whether the interruption of
the motor simulation processes, accounted for by reduced motor evoked
potentials (MEPs), takes place similarly in two languages differing on the
position of the negative marker. Different levels of sentence concreteness were
also manipulated to investigate if negation exerts generalized effects or if it
is affected by the semantic features of the sentence. Our findings indicate
that negation acts as a block on motor representations, but independently from
the language and words concreteness level.


Abstract:
  We prove that if $f$ is a polynomial over a number field $K$ with a finite
superattracting periodic point and a non-archimedean place of bad reduction,
then there is an $\epsilon>0$ such that only finitely many $P\in K^{\text{ab}}$
have canonical height less than $\epsilon$ with respect to $f$. The key
ingredient is the geometry of the filled Julia set at a place of bad reduction.
We also prove a conditional uniform boundedness result for the $K$-rational
preperiodic points of such polynomials, as well as a uniform lower bound on the
canonical height of non-preperiodic points in $K$. We further prove
unconditional analogues of these results in the function field setting.


Abstract:
  We revisit the proposal that the ensemble average over free boson CFTs in two
dimensions - parameterized by Narain's moduli space - is dual to an exotic
theory of gravity in three dimensions dubbed $U(1)$ gravity. We consider
flavored partition functions, where the usual genus $g$ partition function is
weighted by Wilson lines coupled to the conserved $U(1)$ currents of these
theories. These flavored partition functions obey a heat equation which relates
deformations of the Riemann surface moduli to those of the chemical potentials
which measure these $U(1)$ charges. This allows us to derive a Siegel-Weil
formula which computes the average of these flavored partition functions. The
result takes the form of a "sum over geometries," albeit with modifications
relative to the unflavored case.


Abstract:
  Modern trends in data collection are bringing current mainstream techniques
for database query processing to their limits. Consequently, various novel
approaches for efficient query processing are being actively studied. One such
approach is based on hypertree decompositions (HDs), which have been shown to
carry great potential to process complex queries more efficiently and with
stronger theoretical guarantees. However, using HDs for query execution relies
on the difficult task of computing decompositions of the query structure, which
guides the efficient execution of the query. From theoretical results we know
that the performance of purely sequential methods is inherently limited, yet
the problem is susceptible to parallelisation.
  In this paper we propose the first algorithm for computing hypertree
decompositions that is well-suited for parallelisation. The proposed algorithm
log-k-decomp requires only a logarithmic number of recursion levels and
additionally allows for highly parallelised pruning of the search space by
restriction to balanced separators. We provide detailed experimental evaluation
over the HyperBench benchmark and demonstrate that our approach is highly
effective especially for complex queries.


Abstract:
  Few-shot intent detection is a challenging task due to the scare annotation
problem. In this paper, we propose a Pseudo Siamese Network (PSN) to generate
labeled data for few-shot intents and alleviate this problem. PSN consists of
two identical subnetworks with the same structure but different weights: an
action network and an object network. Each subnetwork is a transformer-based
variational autoencoder that tries to model the latent distribution of
different components in the sentence. The action network is learned to
understand action tokens and the object network focuses on object-related
expressions. It provides an interpretable framework for generating an utterance
with an action and an object existing in a given intent. Experiments on two
real-world datasets show that PSN achieves state-of-the-art performance for the
generalized few shot intent detection task.


Abstract:
  A double-phase Ar detector can efficiently identify particles and reconstruct
their positions. However, the properties of electroluminescence (EL) for
secondary light emission in the gas phase are not fully understood. Earlier
studies have explained the EL process using an ordinary EL mechanism because of
an Ar excimer; however, this mechanism does not predict the emission of visible
light (VL). Recent measurements have demonstrated VL components in Ar gas EL,
to explain which a new mechanism called neutral bremsstrahlung (NBrS) was
proposed. In this study, we investigated gaseous Ar EL in the VL region from
300 to 600 nm at room temperature and normal pressure using a gaseous time
projection chamber (TPC). The secondary emission light from the TPC
luminescence region was dispersed using a spectrometer. The observed spectrum
was interpreted using the ordinary EL and NBrS models, and the effect of
nitrogen impurities is discussed herein.


Abstract:
  A remarkable theorem of Joris states that a function $f$ is $C^\infty$ if two
relatively prime powers of $f$ are $C^\infty$. Recently, Thilliez showed that
an analogous theorem holds in Denjoy--Carleman classes of Roumieu type. We
prove that a division property, equivalent to Joris's result, is valid in a
wide variety of ultradifferentiable classes. Generally speaking, it holds in
all dimensions for non-quasianalytic classes. In the quasianalytic case we have
general validity in dimension one, but we also get validity in all dimensions
for certain quasianalytic classes.


Abstract:
  We present High Sensitivity Array (HSA) and enhanced Multi Element Remotely
Linked Interferometer Network (eMERLIN) observations of the radio-loud
broad-lined type Ic supernova PTF11qcj obtained $\sim7.5$ years after the
explosion. Previous observations of this supernova at 5.5 yrs since explosion
showed a double-peaked radio light curve accompanied by a detection in the
X-rays, but no evidence for broad H$\alpha$ spectral features. The Very Long
Baseline Interferometry (VLBI) observations presented here show that the
PTF11qcj GHz radio ejecta remains marginally resolved at the sub-milliarcsecond
level at $\approx 7.5$ yrs after the explosion, pointing toward a
non-relativistic expansion. Our VLBI observations thus favor a scenario in
which the second peak of the PTF11qcj radio light curve is related to strong
interaction of the supernova ejecta with a circumstellar medium of variable
density, rather than to the emergence of an off-axis jet. Continued VLBI
monitoring of PTF11qcj in the radio may strengthen further this conclusion.


Abstract:
  In self-supervised representation learning, a common idea behind most of the
state-of-the-art approaches is to enforce the robustness of the representations
to predefined augmentations. A potential issue of this idea is the existence of
completely collapsed solutions (i.e., constant features), which are typically
avoided implicitly by carefully chosen implementation details. In this work, we
study a relatively concise framework containing the most common components from
recent approaches. We verify the existence of complete collapse and discover
another reachable collapse pattern that is usually overlooked, namely
dimensional collapse. We connect dimensional collapse with strong correlations
between axes and consider such connection as a strong motivation for feature
decorrelation (i.e., standardizing the covariance matrix). The gains from
feature decorrelation are verified empirically to highlight the importance and
the potential of this insight.


Abstract:
  The study of topological magnetic excitations has attracted widespread
attention in the past few years. In this thesis, I have studied some examples
of novel topological magnonic phases/phenomena in low-dimensional quantum
magnets. The first chapter motivates the research based on the research gap in
this field of study. The second chapter is written to make the thesis
self-sufficient and the concepts are explained through examples. In the second
chapter, the following formalisms and physical observables are described:
Holstein-Primakoff, bond operator, Schwinger boson, Bogoliubov-Valatin, Group
theory, Berry-phase, Berry-curvature, Chern number, thermal Hall conductance,
Nernst conductivity, dynamical spin structure factor, edge-current. The main
results of the thesis are shown in the third, fourth, and fifth chapters. In
the third chapter, I have shown that anti-chiral edge states (co-propagating
edge states) arise in the ferromagnetic Heisenberg model on the honeycomb
lattice with Dzyaloshinskii-Moriya (DM) interactions. My results suggest that
such anti-chiral edge states may be induced in certain realistic models of
quantum magnets. In the fourth chapter, I have found the emergence of many
magnon band-topological phases in the flux state of the Shastry-Sutherland
model. I have derived a simple analytical form of the temperature dependence of
derivative of thermal Hall conductivity near the band topological transition
point which I propose to be experimentally useful. In the fifth chapter, I have
investigated the emergence of Weyl triplons due to inter-layer DM-interaction
in a microscopic model of SrCu2(BO3)2, a widely studied frustrated quantum
magnet. I have shown that the thermal magnon Hall conductivity has a
quasi-linear dependence as a function of the magnetic field in a Weyl-triplon
region.


Abstract:
  Large pre-trained language models achieve state-of-the-art results when
fine-tuned on downstream NLP tasks. However, they almost exclusively focus on
text-only representation, while neglecting cell-level layout information that
is important for form image understanding. In this paper, we propose a new
pre-training approach, StructuralLM, to jointly leverage cell and layout
information from scanned documents. Specifically, we pre-train StructuralLM
with two new designs to make the most of the interactions of cell and layout
information: 1) each cell as a semantic unit; 2) classification of cell
positions. The pre-trained StructuralLM achieves new state-of-the-art results
in different types of downstream tasks, including form understanding (from
78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and
document image classification (from 94.43 to 96.08).


Abstract:
  Obtaining viewer responses from videos can be useful for creators and
streaming platforms to analyze the video performance and improve the future
user experience. In this report, we present our method for 2021 Evoked
Expression from Videos Challenge. In particular, our model utilizes both audio
and image modalities as inputs to predict emotion changes of viewers. To model
long-range emotion changes, we use a GRU-based model to predict one sparse
signal with 1Hz. We observe that the emotion changes are smooth. Therefore, the
final dense prediction is obtained via linear interpolating the signal, which
is robust to the prediction fluctuation. Albeit simple, the proposed method has
achieved pearson's correlation score of 0.04430 on the final private test set.


Abstract:
  We often find illegal immigrants moving from one country to another. By means
of land these illegal immigrants move over the fence cut the border wires and
moves to the other part of the land. What do you think our soldiers are not
doing their job? It is not that actually. It is very difficult to watch
consistently over the border. Soldiers have a limited vision to check around
the whole land mass. So for them we have come up with a solution that we have
built a device that will sense the presence of an intruder (illegal immigrant).
This device will be installed over the fences. When an intruder passes over the
fence this device will transmit the signal to the soldiers Smartphone app
(BLYNK app). The soldier will be notified with the signal and after receiving
the signal, the soldier can switch on the alarm and the emergency lights via
the app. By this the soldiers in the camp will be alerted and can take their
respective positions and arrest the intruder which was passing the border
illegally. By this device we can alert the soldiers in the border to take more
safety precautions to keep our country safe. Index Terms-Node MCU ESP32,
infrared sensor, Blynk app.


Abstract:
  We report a planar 1D photonic bandgap waveguide exhibiting four second-order
exceptional points (EP2s). The interactions between the selective pairs of
supported quasi-guided TE modes are modulated by spatial distribution of
transverse in-homogeneous gain-loss profile.


Abstract:
  This paper investigates the security enhancement of an intelligent reflecting
surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a
distributed IRS enabled NOMA transmission framework is proposed to serve users
securely in the presence of a passive eavesdropper. Considering that
eavesdropper's instantaneous channel state information (CSI) is challenging to
acquire in practice, we utilize secrecy outage probability (SOP) as the
security metric. A problem of maximizing the minimum secrecy rate among users,
subject to the successive interference cancellation (SIC) decoding constraints
and SOP constraints, by jointly optimizing transmit beamforming at the BS and
phase shifts of IRSs, is formulated. For special case with a single-antenna BS,
we derive the exact closed-form SOP expressions and propose a novel
ring-penalty based successive convex approximation (SCA) algorithm to design
power allocation and phase shifts jointly. While for the more general and
challenging case with a multi-antenna BS, we adopt the Bernstein-type
inequality to approximate the SOP constraints by a deterministic convex form.
To proceed, an efficient alternating optimization (AO) algorithm is developed
to solve the considered problem. Numerical results validate the advantages of
the proposed algorithms over the baseline schemes. Particularly, two
interesting phenomena on distributed IRS deployment are revealed: 1) the
secrecy rate peak is achieved only when distributed IRSs share the reflecting
elements equally; and 2) the distributed IRS deployment does not always
outperform the centralized IRS deployment, due to the tradeoff between the
number of IRSs and the reflecting elements equipped at each IRS.