Abstract:
  This paper proposes a new Jacobian-based inverse kinematics (IK) explicitly
considering box-constrained joint space. To control humanoid robots, the
reference pose of end effector(s) is planned in task space, then mapped into
the reference joints by IK. Due to the limited analytical solutions for IK,
iterative numerical IK solvers based on Jacobian between task and joint spaces
have become popular. However, the conventional Jacobian-based IK does not
explicitly consider the joint constraints, and therefore, they usually clamp
the obtained joints during iteration according to the constraints in practice.
The problem in clamping operation has been pointed out that it causes numerical
instability due to non-smoothed objective function. To alleviate the clamping
problem, this study explicitly considers the joint constraints, especially the
box constraints in this paper, inside the new IK solver. Specifically, instead
of clamping, a mirror descent (MD) method with box-constrained real joint space
and no-constrained mirror space is integrated with the conventional
Jacobian-based IK methods, so-called MD-IK. In addition, to escape local optima
nearly on the boundaries of constraints, a heuristic technique, called
$\epsilon$-clamping, is implemented as margin in software level. As a result,
MD-IK achieved more stable and enough fast i) regulation on the random
reference poses and ii) tracking to the random trajectories compared to the
conventional IK solvers.


Abstract:
  The HGR is a quite challenging task as its performance is influenced by
various aspects such as illumination variations, cluttered backgrounds,
spontaneous capture, etc. The conventional CNN networks for HGR are following
two stage pipeline to deal with the various challenges: complex signs,
illumination variations, complex and cluttered backgrounds. The existing
approaches needs expert expertise as well as auxiliary computation at stage 1
to remove the complexities from the input images. Therefore, in this paper, we
proposes an novel end-to-end compact CNN framework: fine grained feature
attentive network for hand gesture recognition (Fit-Hand) to solve the
challenges as discussed above. The pipeline of the proposed architecture
consists of two main units: FineFeat module and dilated convolutional (Conv)
layer. The FineFeat module extracts fine grained feature maps by employing
attention mechanism over multiscale receptive fields. The attention mechanism
is introduced to capture effective features by enlarging the average behaviour
of multi-scale responses. Moreover, dilated convolution provides global
features of hand gestures through a larger receptive field. In addition,
integrated layer is also utilized to combine the features of FineFeat module
and dilated layer which enhances the discriminability of the network by
capturing complementary context information of hand postures. The effectiveness
of Fit- Hand is evaluated by using subject dependent (SD) and subject
independent (SI) validation setup over seven benchmark datasets: MUGD-I,
MUGD-II, MUGD-III, MUGD-IV, MUGD-V, Finger Spelling and OUHANDS, respectively.
Furthermore, to investigate the deep insights of the proposed Fit-Hand
framework, we performed ten ablation study.


Abstract:
  The residue cocycle associated to a suitable spectral triple is the key
component of the Connes-Moscovici local index theorem in noncommutative
geometry. We review the relationship between the residue cocycle and heat
kernel asymptotics. We use a modified version of the Getzler calculus to
compute the cocycle for a class of Dirac-type operators introduced by Bismut,
obtained by deforming a Dirac operator by a closed 3-form B. We also compute
the cocycle in low-dimensions when the 3-form B is not closed.


Abstract:
  We propose the adversarially robust kernel smoothing (ARKS) algorithm,
combining kernel smoothing, robust optimization, and adversarial training for
robust learning. Our methods are motivated by the convex analysis perspective
of distributionally robust optimization based on probability metrics, such as
the Wasserstein distance and the maximum mean discrepancy. We adapt the
integral operator using supremal convolution in convex analysis to form a novel
function majorant used for enforcing robustness. Our method is simple in form
and applies to general loss functions and machine learning models. Furthermore,
we report experiments with general machine learning models, such as deep neural
networks, to demonstrate that ARKS performs competitively with the
state-of-the-art methods based on the Wasserstein distance.


Abstract:
  The dynamics and radiation of ultrarelativistic electrons in strong
counterpropagating laser beams are investigated. Assuming that the particle
energy is the dominant scale in the problem, an approximate solution of
classical equations of motion is derived and the characteristic features of the
motion are examined. A specific regime is found with comparable strong field
quantum parameters of the beams, when the electron trajectory exhibits
ultrashort spike-like features, which bears great significance to the
corresponding radiation properties. An analytical expression for the spectral
distribution of spontaneous radiation is derived in the framework of the
Baier-Katkov semiclassical approximation based on the classical trajectory. All
the analytical results are further validated by exact numerical calculations.
We consider a non-resonant regime of interaction, when the laser frequencies in
the electron rest frame are far from each other, avoiding stimulated emission.
Special attention is devoted to settings when the description of radiation via
the local constant field approximation fails and to corresponding spectral
features. Periodic and non-periodic regimes are considered, when lab
frequencies of the laser waves are always commensurate. The sensitivity of
spectra with respect to the electron beam spread, focusing and finite duration
of the laser beams is explored.


Abstract:
  Interactive driving scenarios, such as lane changes, merges and unprotected
turns, are some of the most challenging situations for autonomous driving.
Planning in interactive scenarios requires accurately modeling the reactions of
other agents to different future actions of the ego agent. We develop
end-to-end models for conditional behavior prediction (CBP) that take as an
input a query future trajectory for an ego-agent, and predict distributions
over future trajectories for other agents conditioned on the query. Leveraging
such a model, we develop a general-purpose agent interactivity score derived
from probabilistic first principles. The interactivity score allows us to find
interesting interactive scenarios for training and evaluating behavior
prediction models. We further demonstrate that the proposed score is effective
for agent prioritization under computational budget constraints.


Abstract:
  The performance of fine-tuning pre-trained language models largely depends on
the hyperparameter configuration. In this paper, we investigate the performance
of modern hyperparameter optimization methods (HPO) on fine-tuning pre-trained
language models. First, we study and report three HPO algorithms' performances
on fine-tuning two state-of-the-art language models on the GLUE dataset. We
find that using the same time budget, HPO often fails to outperform grid search
due to two reasons: insufficient time budget and overfitting. We propose two
general strategies and an experimental procedure to systematically troubleshoot
HPO's failure cases. By applying the procedure, we observe that HPO can succeed
with more appropriate settings in the search space and time budget; however, in
certain cases overfitting remains. Finally, we make suggestions for future
work. Our implementation can be found in
https://github.com/microsoft/FLAML/tree/main/flaml/nlp/.


Abstract:
  The relative abundance of alpha particles with respect to proton, usually
expressed as $A_{He}$ = ($n_\alpha/n_p$)*100, is known to respond to solar
activity although changes in its behaviour in the last four solar cycles are
not known. In this letter, by systematically analysing inter-calibrated
$A_{He}$ data obtained from the first Lagrangian point of the Sun-Earth system,
we show that $A_{He}$ variations are distinctively different in solar cycle 24
as compared to the last three cycles. The frequency of $A_{He}$ = 2-3% events
is significantly higher in slow/intermediate solar winds in cycle 24 as opposed
to the dominance of the typical $A_{He}$ = 4-5% events in the previous three
cycles. Further, the occurrence of $A_{He}$ $\geq$ 10% events is significantly
reduced in cycle 24. Not only that, the changes in delay of $A_{He}$ with
respect to peak sunspot numbers are less sensitive to changes in solar wind
velocity in cycle 24. The investigation suggests that the coronal magnetic
field configuration started undergoing systematic changes starting from cycle
23 and this altered magnetic field configuration affected the way helium got
processed and depleted in the solar atmosphere.


Abstract:
  Large-area crop classification using multi-spectral imagery is a widely
studied problem for several decades and is generally addressed using classical
Random Forest classifier. Recently, deep convolutional neural networks (DCNN)
have been proposed. However, these methods only achieved results comparable
with Random Forest. In this work, we present a novel CNN based architecture for
large-area crop classification. Our methodology combines both spatio-temporal
analysis via 3D CNN as well as temporal analysis via 1D CNN. We evaluated the
efficacy of our approach on Yolo and Imperial county benchmark datasets. Our
combined strategy outperforms both classical as well as recent DCNN based
methods in terms of classification accuracy by 2% while maintaining a minimum
number of parameters and the lowest inference time.


Abstract:
  Man-in-The-Middle (MiTM) attacks present numerous threats to a smart grid. In
a MiTM attack, an intruder embeds itself within a conversation between two
devices to either eavesdrop or impersonate one of the devices, making it appear
to be a normal exchange of information. Thus, the intruder can perform false
data injection (FDI) and false command injection (FCI) attacks that can
compromise power system operations, such as state estimation, economic
dispatch, and automatic generation control (AGC). Very few researchers have
focused on MiTM methods that are difficult to detect within a smart grid. To
address this, we are designing and implementing multi-stage MiTM intrusions in
an emulation-based cyber-physical power system testbed against a large-scale
synthetic grid model to demonstrate how such attacks can cause physical
contingencies such as misguided operation and false measurements. MiTM
intrusions create FCI, FDI, and replay attacks in this synthetic power grid.
This work enables stakeholders to defend against these stealthy attacks, and we
present detection mechanisms that are developed using multiple alerts from
intrusion detection systems and network monitoring tools. Our contribution will
enable other smart grid security researchers and industry to develop further
detection mechanisms for inconspicuous MiTM attacks.


Abstract:
  In this paper, we present a provably correct controller synthesis approach
for switched stochastic control systems with metric temporal logic (MTL)
specifications with provable probabilistic guarantees. We first present the
stochastic control bisimulation function for switched stochastic control
systems, which bounds the trajectory divergence between the switched stochastic
control system and its nominal deterministic control system in a probabilistic
fashion. We then develop a method to compute optimal control inputs by solving
an optimization problem for the nominal trajectory of the deterministic control
system with robustness against initial state variations and stochastic
uncertainties. We implement our robust stochastic controller synthesis approach
on both a four-bus power system and a nine-bus power system under generation
loss disturbances, with MTL specifications expressing requirements for the grid
frequency deviations, wind turbine generator rotor speed variations and the
power flow constraints at different power lines.


Abstract:
  It is often said that asymmetric dark matter is light compared to typical
weakly interacting massive particles. Here we point out a simple scheme with a
neutrino portal and $\mathcal{O}(60 \text{ GeV})$ asymmetric dark matter which
may be ''added'' to any standard baryogenesis scenario. The dark sector
contains a copy of the Standard Model gauge group, as well as (at least) one
matter family, Higgs, and right-handed neutrino. After baryogenesis, some
lepton asymmetry is transferred to the dark sector through the neutrino portal
where dark sphalerons convert it into a dark baryon asymmetry. Dark hadrons
form asymmetric dark matter and may be directly detected due to the vector
portal. Surprisingly, even dark anti-neutrons may be directly detected if they
have a sizeable electric dipole moment. The dark photons visibly decay at
current and future experiments which probe complementary parameter space to
dark matter direct detection searches. Exotic Higgs decays are excellent
signals at future $e^+ e^-$ Higgs factories.


Abstract:
  A high statistics $\Sigma p$ scattering experiment has been performed at the
J-PARC Hadron Experimental Facility. Data for momentum-tagged $\Sigma^{-}$
running in a liquid hydrogen target were accumulated by detecting the $\pi^{-}p
\to K^{+}\Sigma^{-}$ reaction with a high intensity $\pi^{-}$ beam of 20
M/spill. Differential cross sections of the $\Sigma^{-}p$ elastic scattering
were derived with a drastically improved accuracy by identifying the largest
statistics of about 4,500 events from 1.72 $\times$ $10^{7}$ $\Sigma^{-}$. The
derived differential cross section shows a clear forward-peaking angular
distribution for a $\Sigma^{-}$ momentum range from 470 to 850 MeV/$c$. The
accurate data will impose a strong constraint on the theoretical models of the
baryon-baryon interactions.


Abstract:
  Bound states in the continuum (BICs) are non-radiating solutions of the wave
equation with a spectrum embedded in the continuum of propagating waves of the
surrounding space. The complete decoupling of BICs from the radiation continuum
makes their excitation impossible from the far-field. Here, we develop a
general theory of parametric excitation of BICs in nonlinear systems with
Kerr-type nonlinearity via spontaneous symmetry breaking, which results in a
coupling of a BIC and a bright mode of the system. Using the temporal
coupled-mode theory and perturbation analysis, we found the threshold intensity
for excitation of a BIC and study the possible stable and unstable solutions
depending on the pump intensity and frequency detuning between the pump and
BIC. We revealed that at some parameters of the pump beam, there are no stable
solutions and the BIC can be used for frequency comb generation. Our findings
can be very promising for use in nonlinear photonic devices and all-optical
networks.


Abstract:
  Six significant new methodological developments of the previously-presented
"metastimuli architecture" for human learning through machine learning of
spatially correlated structural position within a user's personal information
management system (PIMS), providing the basis for haptic metastimuli, are
presented. These include architectural innovation, recurrent (RNN) artificial
neural network (ANN) application, a variety of atom embedding techniques
(including a novel technique we call "nabla" embedding inspired by
linguistics), ANN hyper-parameter (one that affects the network but is not
trained, e.g. the learning rate) optimization, and meta-parameter (one that
determines the system performance but is not trained and not a hyper-parameter,
e.g. the atom embedding technique) optimization for exploring the large design
space. A technique for using the system for automatic atom categorization in a
user's PIMS is outlined. ANN training and hyper- and meta-parameter
optimization results are presented and discussed in service of methodological
recommendations.


Abstract:
  We present a full analysis of a broadband spectral line survey of Sagittarius
B2 (Main), one of the most chemically rich regions in the Galaxy located within
the giant molecular cloud complex Sgr B2 in the Central Molecular Zone. Our
goal is to derive the molecular abundances and temperatures of the high-mass
star-forming region Sgr B2(M) and thus its physical and astrochemical
conditions. Sgr B2(M) was observed using the Heterodyne Instrument for the
Far-Infrared (HIFI) on board the Herschel Space Observatory in a spectral line
survey from 480 to 1907 GHz at a spectral resolution of 1.1 MHz, which provides
one of the largest spectral coverages ever obtained toward this high-mass
star-forming region in the submillimeter with high spectral resolution and
includes frequencies > 1 THz unobservable from the ground. We model the
molecular emission from the submillimeter to the far-IR using the XCLASS
program. For each molecule, a quantitative description was determined taking
all emission and absorption features of that species across the entire spectral
range into account. Additionally, we derive velocity resolved ortho / para
ratios for those molecules for which ortho and para resolved molecular
parameters are available. Finally, the temperature and velocity distributions
are analyzed and the derived abundances are compared with those obtained for
Sgr B2(N) from a similar HIFI survey. A total of 92 isotopologues were
identified, arising from 49 different molecules, ranging from free ions to
complex organic compounds and originating from a variety of environments from
the cold envelope to hot and dense gas within the cores. Sulfur dioxide,
methanol, and water are the dominant contributors. For the ortho / para ratios
we find deviations from the high temperature values between 13 and 27 %. In
total 14 % of all lines remain unidentified.


Abstract:
  This paper concerns the a priori generalization analysis of the Deep Ritz
Method (DRM) [W. E and B. Yu, 2017], a popular neural-network-based method for
solving high dimensional partial differential equations. We derive the
generalization error bounds of two-layer neural networks in the framework of
the DRM for solving two prototype elliptic PDEs: Poisson equation and static
Schr\"odinger equation on the $d$-dimensional unit hypercube. Specifically, we
prove that the convergence rates of generalization errors are independent of
the dimension $d$, under the a priori assumption that the exact solutions of
the PDEs lie in a suitable low-complexity space called spectral Barron space.
Moreover, we give sufficient conditions on the forcing term and the potential
function which guarantee that the solutions are spectral Barron functions. We
achieve this by developing a new solution theory for the PDEs on the spectral
Barron space, which can be viewed as an analog of the classical Sobolev
regularity theory for PDEs.


Abstract:
  In this paper, we study the problem of fair sparse regression on a biased
dataset where bias depends upon a hidden binary attribute. The presence of a
hidden attribute adds an extra layer of complexity to the problem by combining
sparse regression and clustering with unknown binary labels. The corresponding
optimization problem is combinatorial, but we propose a novel relaxation of it
as an \emph{invex} optimization problem. To the best of our knowledge, this is
the first invex relaxation for a combinatorial problem. We show that the
inclusion of the debiasing/fairness constraint in our model has no adverse
effect on the performance. Rather, it enables the recovery of the hidden
attribute. The support of our recovered regression parameter vector matches
exactly with the true parameter vector. Moreover, we simultaneously solve the
clustering problem by recovering the exact value of the hidden attribute for
each sample. Our method uses carefully constructed primal dual witnesses to
provide theoretical guarantees for the combinatorial problem. To that end, we
show that the sample complexity of our method is logarithmic in terms of the
dimension of the regression parameter vector.


Abstract:
  Neutral atom arrays are promising for large-scale quantum computing
especially because it is possible to prepare large-scale qubit arrays. An
unsolved issue is how to selectively excite one qubit deep in a 3D atomic array
to Rydberg states. In this work, we show two methods for this purpose. The
first method relies on a well-known result: in a dipole transition between two
quantum states driven by two off-resonant fields of equal strength but opposite
detunings $\pm\Delta$, the transition is characterized by two counter-rotating
Rabi frequencies $\Omega e^{\pm i\Delta t}$~[or $\pm\Omega e^{\pm i\Delta t}$
if the two fields have a $\pi$-phase difference]. This pair of detuned fields
lead to a time-dependent Rabi frequency $2\Omega \cos(\Delta t)$~[or $2i\Omega
\sin(\Delta t)$], so that a full transition between the two levels is
recovered. We show that when the two detuned fields are sent in different
directions, one atom in a 3D optical lattice can be selectively addressed for
Rydberg excitation, and when its state is restored, the state of any nontarget
atoms irradiated in the light path is also restored. Moreover, we find that the
Rydberg excitation by this method can significantly suppress the fundamental
blockade error of a Rydberg gate, paving the way for a high-fidelity entangling
gate with commonly used quasi-rectangular pulse that is easily obtained by
pulse pickers. Along the way, we find a second method for single-site Rydberg
addressing in 3D, where a selected target atom can be excited to Rydberg state
while preserving the state of any nontarget atom due to a spin echo sequence.
The capability to selectively address a target atom in 3D atomic arrays for
Rydberg excitation makes it possible to design large-scale neutral-atom
information processor based on Rydberg blockade.


Abstract:
  The $r$-index (Gagie et al., JACM 2020) represented a breakthrough in
compressed indexing of repetitive text collections, outperforming its
alternatives by orders of magnitude. Its space usage, $\mathcal{O}(r)$ where
$r$ is the number of runs in the Burrows-Wheeler Transform of the text, is
however larger than Lempel-Ziv and grammar-based indexes, and makes it
uninteresting in various real-life scenarios of milder repetitiveness. In this
paper we introduce the $sr$-index, a variant that limits the space to
$\mathcal{O}(\min(r,n/s))$ for a text of length $n$ and a given parameter $s$,
at the expense of multiplying by $s$ the time per occurrence reported. The
$sr$-index is obtained by carefully subsampling the text positions indexed by
the $r$-index, in a way that we prove is still able to support pattern matching
with guaranteed performance. Our experiments demonstrate that the $sr$-index
sharply outperforms virtually every other compressed index on repetitive texts,
both in time and space, even matching the performance of the $r$-index while
using 1.5--3.0 times less space. Only some Lempel-Ziv-based indexes achieve
better compression than the $sr$-index, using about half the space, but they
are an order of magnitude slower.


Abstract:
  We present a subsampling strategy for the offline stage of the Reduced Basis
Method. The approach is aimed at bringing down the considerable offline costs
associated with using a finely-sampled training set. The proposed algorithm
exploits the potential of the pivoted QR decomposition and the discrete
empirical interpolation method to identify important parameter samples. It
consists of two stages. In the first stage, we construct a low-fidelity
approximation to the solution manifold over a fine training set. Then, for the
available low-fidelity snapshots of the output variable, we apply the pivoted
QR decomposition or the discrete empirical interpolation method to identify a
set of sparse sampling locations in the parameter domain. These points reveal
the structure of the parametric dependence of the output variable. The second
stage proceeds with a subsampled training set containing a by far smaller
number of parameters than the initial training set. Different subsampling
strategies inspired from recent variants of the empirical interpolation method
are also considered. Tests on benchmark examples justify the new approach and
show its potential to substantially speed up the offline stage of the Reduced
Basis Method, while generating reliable reduced-order models.


Abstract:
  We address the detection of material defects, which are inside a layered
material structure using compressive sensing based multiple-input and
multiple-output (MIMO) wireless radar. Here, the strong clutter due to the
reflection of the layered structure's surface often makes the detection of the
defects challenging. Thus, sophisticated signal separation methods are required
for improved defect detection. In many scenarios, the number of defects that we
are interested in is limited and the signaling response of the layered
structure can be modeled as a low-rank structure. Therefore, we propose joint
rank and sparsity minimization for defect detection. In particular, we propose
a non-convex approach based on the iteratively reweighted nuclear and
$\ell_1-$norm (a double-reweighted approach) to obtain a higher accuracy
compared to the conventional nuclear norm and $\ell_1-$norm minimization. To
this end, an iterative algorithm is designed to estimate the low-rank and
sparse contributions. Further, we propose deep learning to learn the parameters
of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the
speed of convergence of the algorithm. Our numerical results show that the
proposed approach outperforms the conventional approaches in terms of mean
square errors of the recovered low-rank and sparse components and the speed of
convergence.


Abstract:
  A two-dimensional string is simply a two-dimensional array. We continue the
study of the combinatorial properties of repetitions in such strings over the
binary alphabet, namely the number of distinct tandems, distinct quartics, and
runs. First, we construct an infinite family of $n\times n$ 2D strings with
$\Omega(n^{3})$ distinct tandems. Second, we construct an infinite family of
$n\times n$ 2D strings with $\Omega(n^{2}\log n)$ distinct quartics. Third, we
construct an infinite family of $n\times n$ 2D strings with $\Omega(n^{2}\log
n)$ runs. This resolves an open question of Charalampopoulos, Radoszewski,
Rytter, Wale\'n, and Zuba [ESA 2020], who asked if the number of distinct
quartics and runs in an $n\times n$ 2D string is $\mathcal{O}(n^{2})$.


Abstract:
  Bit depth adaptation, where the bit depth of a video sequence is reduced
before transmission and up-sampled during display, can potentially reduce data
rates with limited impact on perceptual quality. In this context, we conducted
a subjective study on a UHD video database, BVI-BD, to explore the relationship
between bit depth and visual quality. In this work, three bit depth adaptation
methods are investigated, including linear scaling, error diffusion, and a
novel adaptive Gaussian filtering approach. The results from a subjective
experiment indicate that above a critical bit depth, bit depth adaptation has
no significant impact on perceptual quality, while reducing the amount
information that is required to be transmitted. Below the critical bit depth,
advanced adaptation methods can be used to retain `good' visual quality (on
average) down to around 2 bits per color channel for the outlined experimental
setup - a large reduction compared to the typically used 8 bits per color
channel. A selection of image quality metrics were subsequently bench-marked on
the subjective data, and analysis indicates that a bespoke quality metric is
required for bit depth adaptation.


Abstract:
  Trialities of $\mathcal{W}$-algebras are certain nontrivial isomorphisms
between the affine cosets of three different $\mathcal{W}$-(super)algebras, and
were first conjectured in the physics literature by Gaiotto and Rap\v{c}\'ak.
In this paper we prove trialities among eight families of
$\mathcal{W}$-(super)algebras of types $B$, $C$, and $D$. The key idea is to
identify the affine cosets of these algebras with one-parameter quotients of
the universal two-parameter even spin $\mathcal{W}_{\infty}$-algebra which was
recently constructed by Kanade and the second author. Our result is a vast
generalization of both Feigin-Frenkel duality in types $B$, $C$, and $D$, and
the coset realization of principal $\mathcal{W}$-algebras of type $D$ due to
Arakawa and us. It also provides a new coset realization of principal
$\mathcal{W}$-algebras of types $B$ and $C$. As an application, we prove the
rationality of the affine vertex superalgebra $L_k(\mathfrak{osp}_{1|2n})$, the
minimal $\mathcal{W}$-algebra $\mathcal{W}_{k-1/2}(\mathfrak{sp}_{2n+2},
f_{\text{min}})$, and the coset $\text{Com}(L_k(\mathfrak{sp}_{2m}),
L_k(\mathfrak{sp}_{2n}))$, for all integers $k,n,m \geq 1$ with $m<n$. We also
prove the rationality of some families of principal $\mathcal{W}$-superalgebras
of $\mathfrak{osp}_{1|2n}$ and $\mathfrak{osp}_{2|2n}$, and subregular
$\mathcal{W}$-algebras of $\mathfrak{so}_{2n+1}$


Abstract:
  Algorithmic stock trading has become a staple in today's financial market,
the majority of trades being now fully automated. Deep Reinforcement Learning
(DRL) agents proved to be to a force to be reckon with in many complex games
like Chess and Go. We can look at the stock market historical price series and
movements as a complex imperfect information environment in which we try to
maximize return - profit and minimize risk. This paper reviews the progress
made so far with deep reinforcement learning in the subdomain of AI in finance,
more precisely, automated low-frequency quantitative stock trading. Many of the
reviewed studies had only proof-of-concept ideals with experiments conducted in
unrealistic settings and no real-time trading applications. For the majority of
the works, despite all showing statistically significant improvements in
performance compared to established baseline strategies, no decent
profitability level was obtained. Furthermore, there is a lack of experimental
testing in real-time, online trading platforms and a lack of meaningful
comparisons between agents built on different types of DRL or human traders. We
conclude that DRL in stock trading has showed huge applicability potential
rivalling professional traders under strong assumptions, but the research is
still in the very early stages of development.


Abstract:
  The fractional quantum Hall effect (FQHE) is extensively studied, but the
explanation for Hall plateau widths and excitation energy gaps remains elusive.
We study the effective theory of FQHE built upon experimental inputs of Hall
current distribution, edge dynamics, and many-body correlations. We argue that
correlated composites of integer spin, comprising electrons and their images,
localized at the edge of the incompressible strip are the basic transport
entity. We show in the lowest Landau level that Zeeman interactions of these
composites produce all odd denominator plateaus and effective fractional
charges. Utilizing field-dependent chemical potential and effective g-factor,
we fully explain the observed Hall resistivity curve and excitation energy gaps
of the half-filling family. The plateau heights are systematically generated by
multi-particle correlations, whereas the plateau widths and excitation energy
gaps are determined by the correlation strengths. We explicitly show that the
Drude-like behavior at half-filling follows from equal strength of
multi-particle correlations.


Abstract:
  The cross-integration of spin-wave and superconducting technologies is a
promising method for creating novel hybrid devices for future information
processing technologies to store, manipulate, or convert data in both classical
and quantum regimes. Hybrid magnon-polariton systems have been widely studied
using bulk Yttrium Iron Garnet (Y$_{3}$Fe$_{5}$O$_{12}$, YIG) and
three-dimensional microwave photon cavities. However, limitations in YIG growth
have thus far prevented its incorporation into CMOS compatible technology such
as high quality factor superconducting quantum technology. To overcome this
impediment, we have used Plasma Focused Ion Beam (PFIB) technology -- taking
advantage of precision placement down to the micron-scale -- to integrate YIG
with superconducting microwave devices. Ferromagnetic resonance has been
measured at millikelvin temperatures on PFIB-processed YIG samples using planar
microwave circuits. Furthermore, we demonstrate strong coupling between
superconducting resonator and YIG ferromagnetic resonance modes by maintaining
reasonably low loss while reducing the system down to the micron scale. This
achievement of strong coupling on-chip is a crucial step toward fabrication of
functional hybrid quantum devices that advantage from spin-wave and
superconducting components.


Abstract:
  Vehicle Re-identification (re-id) over surveillance camera network with
non-overlapping field of view is an exciting and challenging task in
intelligent transportation systems (ITS). Due to its versatile applicability in
metropolitan cities, it gained significant attention. Vehicle re-id matches
targeted vehicle over non-overlapping views in multiple camera network.
However, it becomes more difficult due to inter-class similarity, intra-class
variability, viewpoint changes, and spatio-temporal uncertainty. In order to
draw a detailed picture of vehicle re-id research, this paper gives a
comprehensive description of the various vehicle re-id technologies,
applicability, datasets, and a brief comparison of different methodologies. Our
paper specifically focuses on vision-based vehicle re-id approaches, including
vehicle appearance, license plate, and spatio-temporal characteristics. In
addition, we explore the main challenges as well as a variety of applications
in different domains. Lastly, a detailed comparison of current state-of-the-art
methods performances over VeRi-776 and VehicleID datasets is summarized with
future directions. We aim to facilitate future research by reviewing the work
being done on vehicle re-id till to date.


Abstract:
  More than 2 billion mobile users worldwide type in multiple languages in the
soft keyboard. On a monolingual keyboard, 38% of falsely auto-corrected words
are valid in another language. This can be easily avoided by detecting the
language of typed words and then validating it in its respective language.
Language detection is a well-known problem in natural language processing. In
this paper, we present a fast, light-weight and accurate Language Detection
Engine (LDE) for multilingual typing that dynamically adapts to user intended
language in real-time. We propose a novel approach where the fusion of
character N-gram model and logistic regression based selector model is used to
identify the language. Additionally, we present a unique method of reducing the
inference time significantly by parameter reduction technique. We also discuss
various optimizations fabricated across LDE to resolve ambiguity in input text
among the languages with the same character pattern. Our method demonstrates an
average accuracy of 94.5% for Indian languages in Latin script and that of 98%
for European languages on the code-switched data. This model outperforms
fastText by 60.39% and ML-Kit by 23.67% in F1 score for European languages. LDE
is faster on mobile device with an average inference time of 25.91
microseconds.


Abstract:
  Collisions between interstellar gas clouds are potentially an important
mechanism for triggering star formation. This is because they are able to
rapidly generate large masses of dense gas. Observationally, cloud collisions
are often identified in position-velocity (PV) space through bridging features
between intensity peaks, usually of CO emission. Using a combination of
hydrodynamical simulations, time-dependent chemistry, and radiative transfer,
we produce synthetic molecular line observations of overlapping clouds that are
genuinely colliding, and overlapping clouds that are just chance
superpositions. Molecules tracing denser material than CO, such as NH$_3$ and
HCN, reach peak intensity ratios of $0.5$ and $0.2$ with respect to CO in the
`bridging feature' region of PV space for genuinely colliding clouds. For
overlapping clouds that are just chance superpositions, the peak NH$_3$ and HCN
intensities are co-located with the CO intensity peaks. This represents a way
of confirming cloud collisions observationally, and distinguishing them from
chance alignments of unrelated material.


Abstract:
  Decay rates in quantum field theory (QFT) are typically calculated assuming
the particles are represented by momentum eigenstates (i.e. plane waves).
However, strictly speaking, localized free particles should be represented by
wave packets. This yields width corrections to the decay rate and to the clock
behaviour under Lorentz boosts. We calculate the decay rate of a particle of
mass $M$ modeled as a Gaussian wavepacket of width $a$ and centered at zero
momentum. We find the decay rate to be $\Gamma_0 \big[1- \frac{3 a^2}{4 M^2}
+\mathcal{O}\big(\tfrac{a^4}{M^4}\big)\big]$ where $\Gamma_0$ is the decay rate
of the particle at rest treated as a plane wave. The leading correction is then
of order $\tfrac{a^2}{M^2}$. We then perform a Lorentz boost of velocity $v$ on
the above Gaussian and find that its decay rate does not decrease
\textit{exactly} by the Lorentz factor $\sqrt{1-v^2}$. There is a correction of
order $\tfrac{a^2v^2}{M^2}$. Therefore, the decaying wave packet does not act
exactly like a typical clock under Lorentz boosts and we refer to it is a "WP
clock" (wave packet clock). A WP clock does not move with a single velocity
relative to an observer but has a spread in velocities (more specifically, a
spread in momenta). Nonetheless, it is best viewed as a single clock as the
wave packet represents a one-particle state in QFT. WP clocks do not violate
Lorentz symmetry and are not based on new physics: they are a consequence of
the combined requirements of special relativity, quantum mechanics and
\textit{localized} free particles.


Abstract:
  While the longitudinal field that dominates photospheric network regions has
been studied extensively, small scale transverse fields have recently been
found to be ubiquitous in the quiet internetwork photosphere. Few observations
have captured how this field evolves. We aim to statistically characterise the
magnetic properties and observe the temporal evolution of small scale magnetic
features. We present two high spatial/temporal resolution observations that
reveal the dynamics of two disk centre internetwork regions taken by the new
GRIS/IFU (GREGOR Infrared Spectrograph Integral Field Unit) with the highly
magnetically sensitive Fe I line pair at 15648.52 {\AA} and 15652.87 {\AA}.
With the SIR code, we consider two inversion schemes: scheme 1 (S1), where a
magnetic atmosphere is embedded in a field free medium, and scheme 2 (S2), with
two magnetic models and a fixed stray light component. S1 inversions returned a
median magnetic field strength of 200 and 240 G for the two datasets,
respectively. We consider the median transverse (horizontal) component, among
pixels with Stokes Q or U, and the median unsigned longitudinal (vertical)
component, among pixels with Stokes V, above a noise threshold. We determined
the former to be 263 G and 267 G, and the latter to be 131 G and 145 G, for the
two datasets, respectively. We present three regions of interest (ROIs),
tracking the dynamics of small scale magnetic features. We apply S1 and S2
inversions to specific profiles, and find S2 produces better approximations
when there is evidence of mixed polarities. We find patches of linear
polarization with magnetic flux density between 130 and 150 G, appearing
preferentially at granule/intergranular lane (IGL) boundaries. The weak hG
magnetic field appears to be organised in terms of complex loop structures,
with transverse fields often flanked by opposite polarity longitudinal fields.


Abstract:
  We consider the following variant of contextual linear bandits motivated by
routing applications in navigational engines and recommendation systems. We
wish to learn a hidden $d$-dimensional value $w^*$. Every round, we are
presented with a subset $\mathcal{X}_t \subseteq \mathbb{R}^d$ of possible
actions. If we choose (i.e. recommend to the user) action $x_t$, we obtain
utility $\langle x_t, w^* \rangle$ but only learn the identity of the best
action $\arg\max_{x \in \mathcal{X}_t} \langle x, w^* \rangle$. We design
algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d
\log d))$. To accomplish this, we design novel cutting-plane algorithms with
low "regret" -- the total distance between the true point $w^*$ and the
hyperplanes the separation oracle returns. We also consider the variant where
we are allowed to provide a list of several recommendations. In this variant,
we give an algorithm with $O(d^2 \log d)$ regret and list size
$\mathrm{poly}(d)$. Finally, we construct nearly tight algorithms for a weaker
variant of this problem where the learner only learns the identity of an action
that is better than the recommendation. Our results rely on new algorithmic
techniques in convex geometry (including a variant of Steiner's formula for the
centroid of a convex set) which may be of independent interest.


Abstract:
  We present a catalogue of white dwarf candidates selected from Gaia early
data release three (EDR3). We applied several selection criteria in absolute
magnitude, colour, and Gaia quality flags to remove objects with unreliable
measurements while preserving most stars compatible with the white dwarf locus
in the Hertzsprung-Russell diagram. We then used a sample of over 30 000
spectroscopically confirmed white dwarfs and contaminants from the Sloan
Digital Sky Survey (SDSS) to map the distribution of these objects in the Gaia
absolute magnitude-colour space. Finally, we adopt the same method presented in
our previous Gaia DR2 work to calculate a probability of being a white dwarf
(Pwd) for $\simeq$1.3 million sources which passed our quality selection. The
Pwd values can be used to select a sample of $\simeq$359 000 high-confidence
white dwarf candidates in the magnitude range 8< G <21. We calculated stellar
parameters (effective temperature, surface gravity, and mass) for all these
stars by fitting Gaia astrometry and photometry with synthetic models. We
estimate an upper limit of 93 per cent for the overall completeness of our
catalogue for white dwarfs with G $\leq$20 mag and effective temperature
(Teff)>7000K, at high Galactic latitudes (|b|>20{\deg}). Alongside the main
catalogue we include a reduced-proper-motion extension containing $\simeq$10
200 white dwarf candidates with unreliable parallax measurements which could,
however be identified on the basis of their proper motion. We also performed a
cross-match of our catalogues with SDSS DR16 spectroscopy and provide spectral
classification based on visual inspection for all resulting matches.


Abstract:
  Zero-shot cross-domain dialogue state tracking (DST) enables us to handle
task-oriented dialogue in unseen domains without the expense of collecting
in-domain data. In this paper, we propose a slot description enhanced
generative approach for zero-shot cross-domain DST. Specifically, our model
first encodes dialogue context and slots with a pre-trained self-attentive
encoder, and generates slot values in an auto-regressive manner. In addition,
we incorporate Slot Type Informed Descriptions that capture the shared
information across slots to facilitate cross-domain knowledge transfer.
Experimental results on the MultiWOZ dataset show that our proposed method
significantly improves existing state-of-the-art results in the zero-shot
cross-domain setting.


Abstract:
  Let $G$ be a connected semisimple real Lie group with finite center, and
$\mu$ a probability measure on $G$ whose support generates a Zariski-dense
subgroup of $G$. We consider the right $\mu$-random walk on $G$ and show that
each random trajectory spends most of its time at bounded distance of a
well-chosen Weyl chamber. We infer that if $G$ has rank one, and $\mu$ has a
finite first moment, then for any discrete subgroup $\Lambda \subseteq G$, the
$\mu$-walk and the geodesic flow on $\Lambda \backslash G$ are either both
transient, or both recurrent and ergodic, thus extending a well known theorem
due to Hopf-Tsuji-Sullivan-Kaimanovich dealing with the Brownian motion.


Abstract:
  Brillouin scattering has been widely exploited for advanced photonics
functionalities such as microwave photonics, signal processing, sensing,
lasing, and more recently in micro- and nano-photonic waveguides. So far, all
the works have focused on the opto-acoustic interaction driven from the core
region of micro- and nano-waveguides. Here we observe, for the first time, an
efficient Brillouin scattering generated by an evanescent field nearby a
sub-wavelength waveguide embedded in a pressurised gas cell, with a maximum
gain coefficient of $18.90 \pm 0.17$ m$^{-1}$W$^{-1}$. This gain is 11 times
larger than the highest Brillouin gain obtained in a hollow-core fibre and 79
times larger than in a standard single-mode fibre. The realisation of strong
free-space Brillouin scattering from a waveguide benefits from the flexibility
of confined light while providing a direct access to the opto-acoustic
interaction, as required in free-space optoacoustics such as Brillouin
spectroscopy and microscopy. Therefore, our work creates an important bridge
between Brillouin scattering in waveguides, Brillouin spectroscopy and
microscopy, and opens new avenues in light-sound interactions, optomechanics,
sensing, lasing and imaging.


Abstract:
  The primate heteromodal cortex presents an evident functional modularity at a
mesoscopic level, with physiological and anatomical evidence pointing to it as
likely substrate of long-term memory. In order to investigate some of its
properties, a model of multimodular autoassociator is studied. Each of the many
modules represents a neocortical functional ensemble of recurrently connected
neurons and operates as a Hebbian autoassociator, storing a number of local
features which it can recall upon cue. The global memory patterns are made of
combinations of features sparsely distributed across the modules. Intermodular
connections are modelled as a finite-connectivity random graph. Any pair of
features in any respective pair of modules is allowed to be involved in several
memory patterns; the coarse-grained modular network dynamics is defined in such
a way as to overcome the consequent ambiguity of associations. Effects of
long-range homeostatic synaptic scaling on network performance are also
assessed.
  The dynamical process of cued retrieval almost saturates a natural upper
bound while producing negligible spurious activation. The extent of finite-size
effects on storage capacity is quantitatively evaluated. In the limit of
infinite size, the functional relationship between storage capacity and number
of features per module reduces to that which other authors found by methods
from equilibrium statistical mechanics, which suggests that the origin of the
functional form is of a combinatorial nature. In contrast with its apparent
inevitability at intramodular level, long-range synaptic scaling results to be
of minor relevance to both retrieval and storage capacity, casting doubt on its
existence in the neocortex. A conjecture is also posited about how statistical
fluctuation of connectivity across the network may underpin spontaneous
emergence of semantic hierarchies through learning.


Abstract:
  We consider the problem of bi-directional controlled quantum teleportation of
information encoded in phase opposite coherent state among two distant partners
Alice and Bob, with the consent of controller, Charlie. We use five-mode
cluster-type entangled coherent state as the quantum resource to achieve this
task. The scheme uses linear optical devices such as beam splitter, phase
shifters, and photon counters. We have shown that for moderately large coherent
amplitude, near-perfect bi-directional controlled teleportation can be obtained
in terms of the average fidelity of teleportation.


Abstract:
  Deep learning approaches may help radiologists in the early diagnosis and
timely treatment of cerebrovascular diseases. Accurate cerebral vessel
segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) is an
essential step in this process. This study investigates deep learning
approaches for automatic, fast and accurate cerebrovascular segmentation for
TOF-MRAs. The performance of several data augmentation and selection methods
for training a 2D and 3D U-Net for vessel segmentation was investigated in five
experiments: a) without augmentation, b) Gaussian blur, c) rotation and
flipping, d) Gaussian blur, rotation and flipping and e) different input patch
sizes. All experiments were performed by patch-training both a 2D and 3D U-Net
and predicted on a test set of MRAs. Ground truth was manually defined using an
interactive threshold and region growing method. The performance was evaluated
using the Dice Similarity Coefficient (DSC), Modified Hausdorff Distance and
Volumetric Similarity, between the predicted images and the interactively
defined ground truth. The segmentation performance of all trained networks on
the test set was found to be good, with DSC scores ranging from 0.72 to 0.83.
Both the 2D and 3D U-Net had the best segmentation performance with Gaussian
blur, rotation and flipping compared to other experiments without augmentation
or only one of those augmentation techniques. Additionally, training on larger
patches or slices gave optimal segmentation results. In conclusion, vessel
segmentation can be optimally performed on TOF-MRAs using a trained 3D U-Net on
larger patches, where data augmentation including Gaussian blur, rotation and
flipping was performed on the training data.


Abstract:
  Traffic monitoring cameras are powerful tools for traffic management and
essential components of intelligent road infrastructure systems. In this paper,
we present a vehicle localization and traffic scene reconstruction framework
using these cameras, dubbed as CAROM, i.e., "CARs On the Map". CAROM processes
traffic monitoring videos and converts them to anonymous data structures of
vehicle type, 3D shape, position, and velocity for traffic scene reconstruction
and replay. Through collaborating with a local department of transportation in
the United States, we constructed a benchmarking dataset containing GPS data,
roadside camera videos, and drone videos to validate the vehicle tracking
results. On average, the localization error is approximately 0.8 m and 1.7 m
within the range of 50 m and 120 m from the cameras, respectively.


Abstract:
  As technology becomes more widely available, millions of users worldwide have
installed some form of smart device in their homes or workplaces. These devices
are often off-the-shelf commodity systems, such as Google Home or Samsung
SmartThings, that are installed by end-users looking to automate a small
deployment. In contrast to these "plug-and-play" systems, purpose-built
Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI,
Savant offer a smart solution for more sophisticated applications (e.g.,
complete lighting control, A/V management, security). In contrast to commodity
systems, E-IoT systems are usually closed source, costly, require certified
installers, and are overall more robust for their use cases. Due to this, E-IoT
systems are often found in expensive smart homes, government and academic
conference rooms, yachts, and smart private offices. However, while there has
been plenty of research on the topic of commodity systems, no current study
exists that provides a complete picture of E-IoT systems, their components, and
relevant threats. As such, lack of knowledge of E-IoT system threats, coupled
with the cost of E-IoT systems has led many to assume that E-IoT systems are
secure. To address this research gap, raise awareness on E-IoT security, and
motivate further research, this work emphasizes E-IoT system components, E-IoT
vulnerabilities, solutions, and their security implications. In order to
systematically analyze the security of E-IoT systems, we divide E-IoT systems
into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and
Applications Layer, and Business Layer. We survey attacks and defense
mechanisms, considering the E-IoT components at each layer and the associated
threats. In addition, we present key observations in state-of-the-art E-IoT
security and provide a list of open research problems that need further
research.


Abstract:
  We show that the bicategory of finite groupoids and right-free permutation
bimodules is a quotient of the bicategory of Mackey 2-motives introduced in
arXiv:1808.04902, obtained by modding out the so-called cohomological
relations. This categorifies Yoshida's Theorem for ordinary cohomological
Mackey functors, and provides a direct connection between Mackey 2-motives and
the usual blocks of representation theory.


Abstract:
  In this paper, we initiate the study of wave propagation in a recently
proposed mathematical model for stretch-limited elastic strings. We consider
the longitudinal motion of a simple class of uniform, semi-infinite,
stretch-limited strings under no external force with finite end held fixed and
prescribed tension at the infinite end. We study a class of motions such that
the string has one inextensible segment, where the local stretch is maximized,
and one extensible segment. The equations of motion reduce to a simple and
novel shock front problem in one spatial variable for which we prove existence
and uniqueness of local-in-time solutions for appropriate initial data. We then
prove the orbital asymptotic stability of an explicit two-parameter family of
piece-wise constant stretched motions. If the prescribed tension at the
infinite end is increasing in time, our results provide an open set of initial
data launching motions resulting in the string becoming fully inextensible and
tension blowing up in finite time.


Abstract:
  In this paper, we study the spontaneous scalarization of an extended,
self-gravitating system which is static, cylindrically symmetric and possesses
electromagnetic fields. We demonstrate that a real massive scalar field
condenses on this Melvin magnetic universe solution when introducing a
non-minimal coupling between the scalar field and (a) the magnetic field and
(b) the curvature of the space-time, respectively. We find that in both cases,
the solutions exist on a finite interval of the coupling constant and that
solutions with a number of nodes $k$ in the scalar field exist. For case (a) we
observe that the intervals of existence are mutually exclusive for different
$k$.


Abstract:
  Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here
we propose an enhanced ASR-TTS (EAT) model that incorporates two main features:
1) The ASR$\rightarrow$TTS direction is equipped with a language model reward
to penalize the ASR hypotheses before forwarding it to TTS. 2) In the
TTS$\rightarrow$ASR direction, a hyper-parameter is introduced to scale the
attention context from synthesized speech before sending it to ASR to handle
out-of-domain data. Training strategies and the effectiveness of the EAT model
are explored under out-of-domain data conditions. The results show that EAT
reduces the performance gap between supervised and self-supervised training
significantly by absolute 2.6\% and 2.7\% on Librispeech and BABEL
respectively.


Abstract:
  We present the Deception Detection and Physiological Monitoring (DDPM)
dataset and initial baseline results on this dataset. Our application context
is an interview scenario in which the interviewee attempts to deceive the
interviewer on selected responses. The interviewee is recorded in RGB,
near-infrared, and long-wave infrared, along with cardiac pulse, blood
oxygenation, and audio. After collection, data were annotated for
interviewer/interviewee, curated, ground-truthed, and organized into train /
test parts for a set of canonical deception detection experiments. Baseline
experiments found random accuracy for micro-expressions as an indicator of
deception, but that saccades can give a statistically significant response. We
also estimated subject heart rates from face videos (remotely) with a mean
absolute error as low as 3.16 bpm. The database contains almost 13 hours of
recordings of 70 subjects, and over 8 million visible-light, near-infrared, and
thermal video frames, along with appropriate meta, audio and pulse oximeter
data. To our knowledge, this is the only collection offering recordings of five
modalities in an interview scenario that can be used in both deception
detection and remote photoplethysmography research.


Abstract:
  Let $E$ be an elliptic curve defined over a number field $K$ and let $v$ be a
finite place of $K$. Write $K^{tv}$ the maximal extension of $K$ in which $v$
is totally split and $L$ the field generated over $K^{tv}$ by all torsion
points of $E$. Under some conditions, we will show that the absolute
logarithmic Weil height (resp. N\'eron-Tate height) of any element of $L^*$
(resp. $E(L)$) is either $0$ or bounded from below by a positive constant
depending only on $E, K$ and $v$. This lower bound will be explicit in the
toric case when $K=\mathbb{Q}$.


Abstract:
  We consider integer programs (IP) defined by equations and box constraints,
where it is known that determinants in the constraint matrix are one measure of
complexity. For example, Artmann et al. showed that an IP can be solved in
strongly polynomial time if the constraint matrix is bimodular, that is, the
determinants are bounded in absolute value by two. Determinants are also used
to bound the $\ell_1$-distance between IP solutions and solutions of its linear
relaxation. One of the first works to quantify the complexity of IPs with
bounded determinants was that of Heller, who identified the maximum number of
differing columns in a totally unimodular constraint matrix. So far, each
extension of Heller's bound to general determinants has been exponential in the
determinants or the number of equations. We provide the first column bound that
is polynomial in both values. As a corollary, we give the first
$\ell_1$-distance bound that is polynomial in the determinants and the number
of equations. We also show a tight bound on the number of differing columns in
a bimodular constraint matrix; this is the first tight bound since Heller's
result. Our analysis reveals combinatorial properties of bimodular IPs that may
be of independent interest, in particular in recognition algorithms for IPs
with bounded determinants.


Abstract:
  In 1939 Weyl showed that the volume of spherical tubes around compact
submanifolds M of Euclidean space depends solely on the induced Riemannian
metric on M. Can this intrinsic nature of the tube volume be preserved for
tubes with more general cross sections D than the round ball? Under
sufficiently strong symmetry conditions on D the answer turns out to be yes.


Abstract:
  A variety of new physics scenarios allow for neutrinos to up-scatter into a
heavy neutral lepton state. For a range of couplings and neutrino energies, the
heavy neutrino may travel some distance before decaying to visible final
states. When both the up-scattering and decay occur within the detector volume,
these "double bang" events produce distinctive phenomenology with very low
background. In this work, we first consider the current sensitivity at
Super-Kamiokande via the atmospheric neutrino flux, and find current data may
already provide new constraints. We then examine projected future sensitivity
at DUNE and Hyper-Kamiokande, including both atmospheric and beam flux
contributions to double-bang signals.


Abstract:
  We propose a simple yet reliable bottom-up approach with a good trade-off
between accuracy and efficiency for the problem of multi-person pose
estimation. Given an image, we employ an Hourglass Network to infer all the
keypoints from different persons indiscriminately as well as the guiding
offsets connecting the adjacent keypoints belonging to the same persons. Then,
we greedily group the candidate keypoints into multiple human poses (if any),
utilizing the predicted guiding offsets. And we refer to this process as greedy
offset-guided keypoint grouping (GOG). Moreover, we revisit the
encoding-decoding method for the multi-person keypoint coordinates and reveal
some important facts affecting accuracy. Experiments have demonstrated the
obvious performance improvements brought by the introduced components. Our
approach is comparable to the state of the art on the challenging COCO dataset
under fair conditions. The source code and our pre-trained model are publicly
available online.


Abstract:
  We present a system for localizing sound sources in a room with several
ad-hoc microphone arrays. Each circular array performs direction of arrival
(DOA) estimation independently using commercial software. The DOAs are fed to a
fusion center, concatenated, and used to perform the localization based on two
proposed methods, which require only few labeled source locations (anchor
points) for training. The first proposed method is based on principal component
analysis (PCA) of the observed DOA and does not require any knowledge of anchor
points. The array cluster can then perform localization on a manifold defined
by the PCA of concatenated DOAs over time. The second proposed method performs
localization using an affine transformation between the DOA vectors and the
room manifold. The PCA has fewer requirements on the training sequence, but is
less robust to missing DOAs from one of the arrays. The methods are
demonstrated with five IoT 8-microphone circular arrays, placed at unspecified
fixed locations in an office. Both the PCA and the affine method can easily map
out a rectangle based on a few anchor points with similar accuracy. The
proposed methods provide a step towards monitoring activities in a smart home
and require little installation effort as the array locations are not needed.


Abstract:
  We consider $\rm R$-matrix realization of the quantum deformations of the
loop algebras $\tilde{\mathfrak{g}}$ corresponding to non-exceptional affine
Lie algebras of type $\widehat{\mathfrak{g}}=A^{(1)}_{N-1}$, $B^{(1)}_n$,
$C^{(1)}_n$, $D^{(1)}_n$, $A^{(2)}_{N-1}$. For each $U_q(\tilde{\mathfrak{g}})$
we investigate the commutation relations between Gauss coordinates of the
fundamental $\mathbb{L}$-operators using embedding of the smaller algebra into
bigger one. The new realization of these algebras in terms of the currents is
given. The relations between all off-diagonal Gauss coordinates and certain
projections from the ordered products of the currents are presented. These
relations are important in applications to the quantum integrable models.


Abstract:
  Quantum logic gates for photonic qubits can be implemented using the quantum
Zeno effect based on strong two-photon absorption. The fidelity of quantum Zeno
gates of this kind may be substantially reduced by photon loss. Heralding on
those outcomes in which both of the logical qubits emerge from the Zeno gate
can increase the fidelity at the expense of a limited success rate (P.M. Leung
et al., Phys. Rev. A 74, 062325 (2006)). We analyze the performance of heralded
quantum Zeno gates by solving Schrodinger's equation for a system of photons
coupled to three-level atoms. This approach identifies several potential error
sources that are not described by earlier models that assumed a fixed rate of
single-photon loss and two-photon absorption.


Abstract:
  In this work we address the task of observing the performance of a semantic
segmentation deep neural network (DNN) during online operation, i.e., during
inference, which is of high importance in safety-critical applications such as
autonomous driving. Here, many high-level decisions rely on such DNNs, which
are usually evaluated offline, while their performance in online operation
remains unknown. To solve this problem, we propose an improved online
performance prediction scheme, building on a recently proposed concept of
predicting the primary semantic segmentation task's performance. This can be
achieved by evaluating the auxiliary task of monocular depth estimation with a
measurement supplied by a LiDAR sensor and a subsequent regression to the
semantic segmentation performance. In particular, we propose (i) sequential
training methods for both tasks in a multi-task training setup, (ii) to share
the encoder as well as parts of the decoder between both task's networks for
improved efficiency, and (iii) a temporal statistics aggregation method, which
significantly reduces the performance prediction error at the cost of a small
algorithmic latency. Evaluation on the KITTI dataset shows that all three
aspects improve the performance prediction compared to previous approaches.


Abstract:
  We develop a new approach to $L^{\infty}$-a priori estimates for degenerate
complex Monge-Amp\`ere equations on complex manifolds. It only relies on
compactness and envelopes properties of quasi-plurisubharmonic functions. In a
prequel \cite{GL21a} we have shown how this method allows one to obtain new and
efficient proofs of several fundamental results in K\"ahler geometry. In
\cite{GL21b} we have studied the behavior of Monge-Amp\`ere volumes on
hermitian manifolds. We extend here the techniques of \cite{GL21a} to the
hermitian setting and use the bounds established in \cite{GL21b}, producing new
relative a priori estimates, as well as several existence results for
degenerate complex Monge-Amp\`ere equations on compact hermitian manifolds.


Abstract:
  Though deep neural network models exhibit outstanding performance for various
applications, their large model size and extensive floating-point operations
render deployment on mobile computing platforms a major challenge, and, in
particular, on Internet of Things devices. One appealing solution is model
quantization that reduces the model size and uses integer operations commonly
supported by microcontrollers . To this end, a 1-bit quantized DNN model or
deep binary neural network maximizes the memory efficiency, where each
parameter in a BNN model has only 1-bit. In this paper, we propose a
reconfigurable BNN (RBNN) to further amplify the memory efficiency for
resource-constrained IoT devices. Generally, the RBNN can be reconfigured on
demand to achieve any one of M (M>1) distinct tasks with the same parameter
set, thus only a single task determines the memory requirements. In other
words, the memory utilization is improved by times M. Our extensive experiments
corroborate that up to seven commonly used tasks can co-exist (the value of M
can be larger). These tasks with a varying number of classes have no or
negligible accuracy drop-off on three binarized popular DNN architectures
including VGG, ResNet, and ReActNet. The tasks span across different domains,
e.g., computer vision and audio domains validated herein, with the prerequisite
that the model architecture can serve those cross-domain tasks. To protect the
intellectual property of an RBNN model, the reconfiguration can be controlled
by both a user key and a device-unique root key generated by the intrinsic
hardware fingerprint. By doing so, an RBNN model can only be used per paid user
per authorized device, thus benefiting both the user and the model provider.


Abstract:
  In this paper, we propose a Collaboration of Experts (CoE) framework to pool
together the expertise of multiple networks towards a common aim. Each expert
is an individual network with expertise on a unique portion of the dataset,
which enhances the collective capacity. Given a sample, an expert is selected
by the delegator, which simultaneously outputs a rough prediction to support
early termination. To fulfill this framework, we propose three modules to impel
each model to play its role, namely weight generation module (WGM), label
generation module (LGM) and variance calculation module (VCM). Our method
achieves the state-of-the-art performance on ImageNet, 80.7% top-1 accuracy
with 194M FLOPs. Combined with PWLU activation function and CondConv, CoE
further achieves the accuracy of 80.0% with only 100M FLOPs for the first time.
More importantly, our method is hardware friendly and achieves a 3-6x speedup
compared with some existing conditional computation approaches.


Abstract:
  We consider excited states in a CFT, obtained by applying a weak unitary
perturbation to the vacuum. The perturbation is generated by the integral of a
local operator $J^{(n)}$ of modular weight $n$ over a spacelike surface passing
through $x = 0$. For $\vert n \vert \geq 2$ the modular Hamiltonian associated
with a division of space at $x = 0$ picks up an endpoint contribution,
sensitive to the details of the perturbation (including the shape of the
spacelike surface) at $x = 0$. The endpoint contribution is a sum of light-ray
moments of the perturbing operator $J^{(n)}$ and its descendants. For
perturbations on null planes only moments of $J^{(n)}$ itself contribute.


Abstract:
  The analysis of longitudinal travel data enables investigating how mobility
patterns vary across the population and identify the spatial properties
thereof. The objective of this study is to identify the extent to which users
explore different parts of the network as well as identify distinctive user
groups in terms of the spatial extent of their mobility patterns. To this end,
we propose two means for representing spatial mobility profiles and clustering
travellers accordingly. We represent users patterns in terms of zonal visiting
frequency profiles and grid-cells spatial extent heatmaps. We apply the
proposed analysis to a large-scale multi-modal mobility data set from the
public transport system in Stockholm, Sweden. We unravel three clusters -
locals, commuters and explorers - that best describe the zonal visiting
frequency and show that their composition varies considerably across users'
place of residence and related demographics. We also identify 18 clusters of
visiting spatial extent which form four groups that follow similar shapes of
travel extent yet oriented in different directions. The approach proposed and
applied in this study could be applied for any longitudinal individual travel
demand data.


Abstract:
  We study the radio and optical properties of the brightest group galaxies
(BGGs) in a sample of galaxy groups from the SDSS DR7. The luminosity
difference between the BGG and the second ranked galaxy in the group (known as
the luminosity, or magnitude, gap) has been used as a probe for the level of
galaxy interaction for the BGG within the group. We study the properties of
BGGs with magnitude gaps in the range 0-2.7 magnitudes, in order to investigate
any relation between luminosity gap and the radio properties of the BGG. In
order to eliminate selection biases, we ensure that all variations in stellar
mass are accounted for. We then confirm that, at fixed stellar mass, there are
no significant variations in the optical properties of the BGGs over the full
range of luminosity gaps studied. We compare these optical results with the
EAGLE hydrodynamical simulations and find broad consistency with the
observational data. Using EAGLE we also confirm that no trends begin to arise
in the simulated data at luminosity gaps beyond our observational limits.
Finally, we find that, at fixed stellar mass, the fraction of BGGs that are
radio-loud also shows no trends as a function of luminosity gap. We examine how
the BGG offset from the center of group may affect the radio results and find
no significant trend for the fraction of radio-loud BGGs with magnitude gap in
either the BGG samples with greater or less than 100kpc offset from the center
of group.


Abstract:
  This paper studies M-estimators with gradient-Lipschitz loss function
regularized with convex penalty in linear models with Gaussian design matrix
and arbitrary noise distribution. A practical example is the robust M-estimator
constructed with the Huber loss and the Elastic-Net penalty and the noise
distribution has heavy-tails. Our main contributions are three-fold. (i) We
provide general formulae for the derivatives of regularized M-estimators
$\hat\beta(y,X)$ where differentiation is taken with respect to both $y$ and
$X$; this reveals a simple differentiability structure shared by all convex
regularized M-estimators. (ii) Using these derivatives, we characterize the
distribution of the residual $r_i = y_i-x_i^\top\hat\beta$ in the intermediate
high-dimensional regime where dimension and sample size are of the same order.
(iii) Motivated by the distribution of the residuals, we propose a novel
adaptive criterion to select tuning parameters of regularized M-estimators. The
criterion approximates the out-of-sample error up to an additive constant
independent of the estimator, so that minimizing the criterion provides a proxy
for minimizing the out-of-sample error. The proposed adaptive criterion does
not require the knowledge of the noise distribution or of the covariance of the
design. Simulated data confirms the theoretical findings, regarding both the
distribution of the residuals and the success of the criterion as a proxy of
the out-of-sample error. Finally our results reveal new relationships between
the derivatives of $\hat\beta(y,X)$ and the effective degrees of freedom of the
M-estimator, which are of independent interest.


Abstract:
  In this article, we get classification theorems for a Ricci soliton on the
pseudo-Riemannian hypersurface of the Minkowski space $\mathbb{E}^4_1$ taking
the potential vector field as the tangent component of the position vector of
the pseudo-Riemannian hypersurface, denoted by $(M,g,{\bf x}^T,\lambda)$ in
both Riemannian and Lorentzian settings. First, we obtain the necessary and
sufficient condition that a pseudo-Riemannian hypersurface $(M,g)$ in
$\mathbb{E}^4_1$ admits a Ricci soliton $(M,g,{\bf x}^T,\lambda)$. In each of
the form of the shape operator of a pseudo-Riemannian hypersurface, we obtain
characterization a Ricci soliton on a pseudo-Riemannian hypersurface. More
precisely, we show that totally umbilical hypersurfaces, hyperbolic and a
pseudo-spherical cylinder in $\mathbb{E}^4_1$ is a shrinking Ricci soliton
whose the potential vector field is the tangent part of the position vector.
Furthermore, we conclude that there exists only a shrinking Ricci soliton on a
Lorentzian isoparametric hypersurface in $\mathbb{E}^4_1$ with
nondiagonalizable shape operator whose the minimal polynomial has double real
roots.


Abstract:
  Characteristics of Oscillating Flow in a Straight Tube with varying thickness
of porous layer at the wall is studied. Results for two different porous layer
is discussed and compared in terms of Velocity Profile, phase differences and
radial position of maximum velocity over the cross-section. Present problem is
modeled using Navier-Stokes Equation in clear medium at the core of tube while
Darcy-BrinkmannForcheimmer Model is used for the outer porous layer. Solutions
for these equations have been obtained using Finite Volume Method based 3D
unstructured solver. Velocity profile obtained for tube with high permeability
porous layer is the main highlight of results.


Abstract:
  We report polymer composite films containing fillers comprised of
quasi-one-dimensional (1D) van der Waals materials, specifically transition
metal trichalcogenides containing 1D structural motifs that enable their
exfoliation into bundles of atomic threads. These nanostructures are
characterized by extremely large aspect ratios of up to 10^6. The polymer
composites with low loadings of quasi-1D TaSe3 fillers (below 3 vol. %)
revealed excellent electromagnetic interference shielding in the X-band GHz and
EHF sub-THz frequency ranges, while remaining DC electrically insulating. The
unique electromagnetic shielding characteristics of these films are attributed
to effective coupling of the electromagnetic waves to the high-aspect-ratio
electrically-conductive TaSe3 atomic-thread bundles even when the filler
concentration is below the electrical percolation threshold. These novel films
are promising for high-frequency communication technologies, which require
electromagnetic shielding films that are flexible, lightweight, corrosion
resistant, electrically insulating and inexpensive.


Abstract:
  We present Affect2MM, a learning method for time-series emotion prediction
for multimedia content. Our goal is to automatically capture the varying
emotions depicted by characters in real-life human-centric situations and
behaviors. We use the ideas from emotion causation theories to computationally
model and determine the emotional state evoked in clips of movies. Affect2MM
explicitly models the temporal causality using attention-based methods and
Granger causality. We use a variety of components like facial features of
actors involved, scene understanding, visual aesthetics, action/situation
description, and movie script to obtain an affective-rich representation to
understand and perceive the scene. We use an LSTM-based learning model for
emotion perception. To evaluate our method, we analyze and compare our
performance on three datasets, SENDv1, MovieGraphs, and the LIRIS-ACCEDE
dataset, and observe an average of 10-15% increase in the performance over SOTA
methods for all three datasets.


Abstract:
  With increasingly more data and computation involved in their training,
machine learning models constitute valuable intellectual property. This has
spurred interest in model stealing, which is made more practical by advances in
learning with partial, little, or no supervision. Existing defenses focus on
inserting unique watermarks in a model's decision surface, but this is
insufficient: the watermarks are not sampled from the training distribution and
thus are not always preserved during model stealing. In this paper, we make the
key observation that knowledge contained in the stolen model's training set is
what is common to all stolen copies. The adversary's goal, irrespective of the
attack employed, is always to extract this knowledge or its by-products. This
gives the original model's owner a strong advantage over the adversary: model
owners have access to the original training data. We thus introduce $dataset$
$inference$, the process of identifying whether a suspected model copy has
private knowledge from the original model's dataset, as a defense against model
stealing. We develop an approach for dataset inference that combines
statistical testing with the ability to estimate the distance of multiple data
points to the decision boundary. Our experiments on CIFAR10, SVHN, CIFAR100 and
ImageNet show that model owners can claim with confidence greater than 99% that
their model (or dataset as a matter of fact) was stolen, despite only exposing
50 of the stolen model's training points. Dataset inference defends against
state-of-the-art attacks even when the adversary is adaptive. Unlike prior
work, it does not require retraining or overfitting the defended model.


Abstract:
  Operations in many essential industries including finance and banking are
often characterized by the need to perform repetitive sequential tasks. Despite
their criticality to the business, workflows are rarely fully automated or even
formally specified, though there may exist a number of natural language
documents describing these procedures for the employees of the company. Plan
extraction methods provide us with the possibility of extracting structure
plans from such natural language descriptions of the plans/workflows, which
could then be leveraged by an automated system. In this paper, we investigate
the utility of generalized language models in performing such extractions
directly from such texts. Such models have already been shown to be quite
effective in multiple translation tasks, and our initial results seem to point
to their effectiveness also in the context of plan extractions. Particularly,
we show that GPT-3 is able to generate plan extraction results that are
comparable to many of the current state of the art plan extraction methods.


Abstract:
  We consider a class of time-fractional phase field models including the
Allen-Cahn and Cahn-Hilliard equations. We establish several weighted
positivity results for functionals driven by the Caputo time-fractional
derivative. Several novel criterions are examined for showing the
positive-definiteness of the associated kernel functions. We deduce strict
energy-dissipation for a number of non-local energy functionals, thereby
proving fractional energy dissipation laws.


Abstract:
  The cooperation mechanism of indirect reciprocity has been studied by making
multiple variations of its parts. This research proposes a new variant of Nowak
and Sigmund model, focused on agents' attitude; it is called Individualistic
Indirect Reciprocity. In our model, an agent reinforces its strategy to the
extent to which it makes a profit. We also include conditions related to the
environment, visibility of agents, cooperation demand, and the attitude of an
agent to maintain his cooperation strategy. Using Agent-Based Model and a Data
Science method, we show on simulation results that the discriminatory stance of
the agents prevails in most cases. In general, cooperators only appear in
conditions with low visibility of reputation and a high degree of cooperation
demand. The results also show that when the reputation of others is unknown,
with a high obstinacy and high cooperation demand, a heterogeneous society is
obtained. The simulations show a wide diversity of scenarios, centralized,
polarized, and mixed societies.


Abstract:
  Ultra-precise astrometry from the Gaia mission is expected to lead to
astrometric detections of more than 20,000 exoplanets in our Galaxy. One of the
factors that could hamper such detections is the astrometric jitter caused by
the magnetic activity of the planet host stars. In our previous study, we
modeled astrometric jitter for the Sun observed equator-on. In this work, we
generalize our model and calculate the photocenter jitter as it would be
measured by the Gaia and Small-JASMINE missions for stars with solar rotation
rate and effective temperature, but with various values of the inclination
angle of the stellar rotation axis. In addition, we consider the effect of
metallicity and of nesting of active regions (i.e. the tendency of active
regions to emerge in the vicinity of each other). We find that, while the
jitter of stars observed equator-on does not have any long-term trends and can
be easily filtered out, the photocenters of stars observed out of their
equatorial planes experience systematic shifts over the course of the activity
cycle. Such trends allow the jitter to be detected with continuous
measurements, in which case it can interfere with planet detectability. An
increase in the metallicity is found to increase the jitter caused by stellar
activity. Active-region nesting can further enhance the peak-to-peak amplitude
of the photocenter jitter to a level that could be detected by Gaia.


Abstract:
  As mathematical computing becomes more democratized in high-level languages,
high-performance symbolic-numeric systems are necessary for domain scientists
and engineers to get the best performance out of their machine without deep
knowledge of code optimization. Naturally, users need different term types
either to have different algebraic properties for them, or to use efficient
data structures. To this end, we developed Symbolics.jl, an extendable symbolic
system which uses dynamic multiple dispatch to change behavior depending on the
domain needs. In this work we detail an underlying abstract term interface
which allows for speed without sacrificing generality. We show that by
formalizing a generic API on actions independent of implementation, we can
retroactively add optimized data structures to our system without changing the
pre-existing term rewriters. We showcase how this can be used to optimize term
construction and give a 113x acceleration on general symbolic transformations.
Further, we show that such a generic API allows for complementary
term-rewriting implementations. We demonstrate the ability to swap between
classical term-rewriting simplifiers and e-graph-based term-rewriting
simplifiers. We showcase an e-graph ruleset which minimizes the number of CPU
cycles during expression evaluation, and demonstrate how it simplifies a
real-world reaction-network simulation to halve the runtime. Additionally, we
show a reaction-diffusion partial differential equation solver which is able to
be automatically converted into symbolic expressions via multiple dispatch
tracing, which is subsequently accelerated and parallelized to give a 157x
simulation speedup. Together, this presents Symbolics.jl as a next-generation
symbolic-numeric computing environment geared towards modeling and simulation.


Abstract:
  We demonstrate that a long-propagating plasma bubble executing undulatory
motion can be produced in the wake of two co-propagating laser pulses: a
near-single-cycle injector and a multi-cycle driver. When the undulation
amplitude exceeds the analytically derived threshold, highly-localized
injections of plasma electrons into the bubble are followed by their
long-distance acceleration. While the locations of the injection regions are
controlled by the carrier-envelope phase (CEP) of the injector pulse, the
mono-energetic spectrum of the accelerated sub-femtosecond high-charge electron
bunches is shown to be nearly CEP-independent


Abstract:
  What if deep neural networks can learn from sparsity-inducing priors? When
the networks are designed by combining layer modules (CNN, RNN, etc), engineers
less exploit the inductive bias, i.e., existing well-known rules or prior
knowledge, other than annotated training data sets. We focus on employing
sparsity-inducing priors in deep learning to encourage the network to concisely
capture the nature of high-dimensional data in an unsupervised way. In order to
use non-differentiable sparsity-inducing norms as loss functions, we plug their
proximal mappings into the automatic differentiation framework. We demonstrate
unsupervised learning of U-Net for background subtraction using low-rank and
sparse priors. The U-Net can learn moving objects in a training sequence
without any annotation, and successfully detect the foreground objects in test
sequences.


Abstract:
  We report full vector mapping of local magnetization in CeAlSi, a Weyl
semimetal in which both inversion and time-reversal symmetries are broken. The
vector maps reveal unanticipated features both within domains and at their
boundaries. Boundaries between domains form two kinds of walls with distinct
topology and therefore different interactions with Weyl fermions. Domain walls
aligned along the tetragonal axes, e.g. (100), exhibit emergent chirality
forbidden by the bulk space group, while diagonal walls are non-chiral. Within
the domains, we observe that the previously reported set of four easy axes
aligned along the in-plane diagonals of the tetragonal structure actually split
to form an octet with decreasing temperature below the magnetic transition. All
the above phenomena are ultimately traced to the noncollinear magnetic
structure of CeAlSi.


Abstract:
  This paper presents a multiple learner algorithm called the 'Three Ensemble
Clustering 3EC' algorithm that classifies unlabeled data into quality clusters
as a part of unsupervised learning. It offers the flexibility to explore the
context of new clusters formed by an ensemble of algorithms based on internal
validation indices.
  It is worth mentioning that the input data set is considered to be a cluster
of clusters. An anomaly can possibly manifest as a cluster as well. Each
partitioned cluster is considered to be a new data set and is a candidate to
explore the most optimal algorithm and its number of partition splits until a
predefined stopping criteria is met. The algorithms independently partition the
data set into clusters and the quality of the partitioning is assessed by an
ensemble of internal cluster validation indices. The 3EC algorithm presents the
validation index scores from a choice of algorithms and its configuration of
partitions and it is called the Tau Grid. 3EC chooses the most optimal score.
The 3EC algorithm owes its name to the two input ensembles of algorithms and
internal validation indices and an output ensemble of final clusters.
  Quality plays an important role in this clustering approach and it also acts
as a stopping criteria from further partitioning. Quality is determined based
on the quality of the clusters provided by an algorithm and its optimal number
of splits. The 3EC algorithm determines this from the score of the ensemble of
validation indices. The user can configure the stopping criteria by providing
quality thresholds for the score range of each of the validation indices and
the optimal size of the output cluster. The users can experiment with different
sets of stopping criteria and choose the most 'sensible group' of quality
clusters


Abstract:
  We study scattering of itinerant electrons off a magnetic hopfion in a
three-dimensional metallic magnet described by a magnetization vector $\mathbf
S(\mathbf r)$. A hopfion is a confined topological soliton of $\mathbf
S(\mathbf r)$ characterized by an {\it emergent} magnetic field
$B_\gamma(\mathbf r) \equiv \epsilon_{\alpha\beta\gamma} \,\mathbf
S\cdot(\nabla_\alpha \mathbf S\times \nabla_\beta \mathbf S)/4 \neq 0$ with
vanishing average value $\langle \mathbf B(\mathbf r)\rangle = 0$. We evaluate
the scattering amplitude in the opposite limits of large and small hopfion
radius $R$ using the eikonal and Born approximations, respectively. In both
limits, we find that the scattering cross-section contains a skew-scattering
component giving rise to the Hall effect within a hopfion plane. That
conclusion contests the popular notion that the topological Hall effect in
non-collinear magnetic structures necessarily implies $\langle \mathbf
B(\mathbf r)\rangle \neq 0$. In the limit of small hopfion radius $pR \ll 1$,
we expand the Born series in powers of momentum $p$ and identify different
expansion terms corresponding to the hopfion anisotropy, toroidal moment, and
skew-scattering.


Abstract:
  We develop an approximate quasi-static theory describing the low-frequency
plasmonic resonances of slender nanometallic rings and configurations thereof.
In particular, we use asymptotic arguments to reduce the plasmonic eigenvalue
problem governing the geometric (material- and frequency-independent) modes of
a given ring structure to a 1D-periodic integro-differential problem in which
the eigenfunctions are represented by azimuthal voltage and polarization-charge
profiles associated with each ring. We obtain closed-form solutions to the
reduced eigenvalue problem for azimuthally invariant rings (including
torus-shaped rings but also allowing for non-circular cross-sectional shapes),
as well as coaxial dimers and chains of such rings. For more general
geometries, involving azimuthally non-uniform rings and non-coaxial structures,
the reduced eigenvalue problem is solved using a semi-analytical scheme based
on Fourier expansions of the reduced eigenfunctions. In conjunction with the
quasi-static spectral theory of plasmonic resonance, the geometric modes of a
given nanometallic structure explicitly determine its response to external
radiation. Here, we employ the asymptotically approximated modes described
above to obtain comparable approximations for the frequency response of a wide
range of nanometallic slender-ring structures under plane-wave illumination.
These examples demonstrate how the theory can be employed to interpret and
geometrically tune the plasmonic properties of highly nontrivial 3D
nanometallic structures.


Abstract:
  We investigate the solution to the logistic equation involving non-local
operators in time. In the linear case such operators lead to the well-known
theory of time changes. We provide the probabilistic representation for the
non-linear logistic equation with non-local operators in time. The so-called
fractional logistic equation has been investigated by many researchers, the
problem to find the explicit representation of the solution on the whole real
line is still open. In our recent work the solution on compact sets has been
written in terms of Euler's numbers.


Abstract:
  A graph $\Ga=(V,E)$ is called a Cayley graph of some group $T$ if the
automorphism group $\Aut(\Ga)$ contains a subgroup $T$ which acts on regularly
on $V$. If the subgroup $T$ is normal in $\Aut(\Ga)$ then $\Ga$ is called a
normal Cayley graph of $T$. Let $r$ be an odd prime. Fang et al. \cite{FMW}
proved that, with a finite number of exceptions for finite simple group $T$,
every connected symmetric Cayley graph of $T$ of valency $r$ is normal. In this
paper, employing maximal factorizations of finite almost simple groups, we work
out a possible list of those exceptions for $T$.


Abstract:
  We examine some basic physics surrounding regenerative braking and air drag.
First, we analyze under what conditions it becomes energetically favorable to
use aggressive regenerative braking to reach a lower speed over ``coasting''
where one relies solely on air drag to slow down. We then proceed to
reformulate the question as an optimization problem to find the
velocity-profile that maximizes battery charge. Making a simplifying assumption
on battery-charging efficiency, we formulate the recovered energy as an
integral quantity and solve the associated Euler-Lagrange equation to find the
optimal braking curve. Using Lagrange-multipliers, we also explore the effect
of adding a fixed-displacement constraint.


Abstract:
  The physical mechanism for triggering the changing-look phenomenon in active
galactic nuclei (AGNs) is still unclear. We explore this issue based on the
multi-wavelength spectral and flux variations for a changing-look AGN Mrk~1018
with long-term observations in the X-ray, optical/ultraviolet(UV), and radio
bands. Both the optical and the X-ray emission experience rapid decay in
changing-look phase during 2010--2015, where a re-flare appears in the
optical/UV and X-ray bands. We find a time lag of $\sim 20 $ days of optical/UV
behind X-ray variations in type 1.9 phase. The 5 GHz radio flux decreases by
$\sim 20$\% in type 1.9 phase during 2016--2017. We find both X-ray photon
index ($\Gamma$) and the optical-to-X-ray spectral index (\alphaox\,) are
anti-correlated with the Eddington scaled 2--10~keV X-ray luminosity
($L_\mathrm{X}/L_\mathrm{Edd}$) in the type 1.9 phase. However, the type 1
phase deviates from these two anti-correlations, which suggests that the change
of broad emission lines might be regulated by the evolution of accretion disk
(e.g., disappearing of the inner cold disk in the type 1.9 phase).


Abstract:
  This study on public-private research collaboration measures the variation
over time of the propensity of academics to collaborate with colleagues from
private companies. It also investigates the change in weights of the main
drivers underlying the academics' propensity to collaborate, and whether the
type profile of the collaborating academics changes. To do this, the study
applies an inferential model on a dataset of professors working in Italian
universities in consecutive periods, 2010-2013 and 2014-2017. The results,
obtained at overall and field levels, support the formulation of policies aimed
at fostering public-private research collaborations, and should be taken into
account in post-assessment of their effectiveness.


Abstract:
  In recent years the use of convolutional layers to encode an inductive bias
(translational equivariance) in neural networks has proven to be a very
fruitful idea. The successes of this approach have motivated a line of research
into incorporating other symmetries into deep learning methods, in the form of
group equivariant convolutional neural networks. Much of this work has been
focused on roto-translational symmetry of $\mathbf R^d$, but other examples are
the scaling symmetry of $\mathbf R^d$ and rotational symmetry of the sphere. In
this work, we demonstrate that group equivariant convolutional operations can
naturally be incorporated into learned reconstruction methods for inverse
problems that are motivated by the variational regularisation approach. Indeed,
if the regularisation functional is invariant under a group symmetry, the
corresponding proximal operator will satisfy an equivariance property with
respect to the same group symmetry. As a result of this observation, we design
learned iterative methods in which the proximal operators are modelled as group
equivariant convolutional neural networks. We use roto-translationally
equivariant operations in the proposed methodology and apply it to the problems
of low-dose computerised tomography reconstruction and subsampled magnetic
resonance imaging reconstruction. The proposed methodology is demonstrated to
improve the reconstruction quality of a learned reconstruction method with a
little extra computational cost at training time but without any extra cost at
test time.