Abstract:
  Video object segmentation, aiming to segment the foreground objects given the
annotation of the first frame, has been attracting increasing attentions. Many
state-of-the-art approaches have achieved great performance by relying on
online model updating or mask-propagation techniques. However, most online
models require high computational cost due to model fine-tuning during
inference. Most mask-propagation based models are faster but with relatively
low performance due to failure to adapt to object appearance variation. In this
paper, we are aiming to design a new model to make a good balance between speed
and performance. We propose a model, called NPMCA-net, which directly localizes
foreground objects based on mask-propagation and non-local technique by
matching pixels in reference and target frames. Since we bring in information
of both first and previous frames, our network is robust to large object
appearance variation, and can better adapt to occlusions. Extensive experiments
show that our approach can achieve a new state-of-the-art performance with a
fast speed at the same time (86.5% IoU on DAVIS-2016 and 72.2% IoU on
DAVIS-2017, with speed of 0.11s per frame) under the same level comparison.
Source code is available at https://github.com/siyueyu/NPMCA-net.


Abstract:
  Turbulent mixing layers in nature are often characterized by the presence of
a mean shear and an unstable buoyancy gradient between two streams of different
velocity. Depending on the relative strength of shear versus buoyancy, either
the former or the latter may dominate the turbulence and mixing between the two
streams. In this paper, we present a phenomenological theory that leads to the
identification of two distinct turbulent regimes: an early regime, dominated by
the mean shear, and a later regime dominated by the buoyancy. The main
theoretical result consists of the identification of a cross-over time-scale
that discerns between the shear- and the buoyancy-dominated turbulence. This
cross-over time depends on three large-scale constants of the flow, namely the
buoyancy difference, the velocity difference between the two streams, and the
gravitational acceleration. We validate our theory against direct numerical
simulations (DNSs) of a temporal turbulent mixing layer compounded with an
unstable stratification. We observe that the cross-over time correctly predicts
the transition from shear to buoyancy driven turbulence, in terms of turbulent
kinetic energy production, energy spectra scaling and mixing layer thickness.


Abstract:
  We use a superconducting microresonator as a cavity to sense absorption of
microwaves by a superconducting quantum point contact defined by surface gates
over a proximitized two-dimensional electron gas. Renormalization of the cavity
frequency with phase difference across the point contact is consistent with
adiabatic coupling to Andreev bound states. Near $\pi$ phase difference, we
observe random fluctuations in absorption with gate voltage, related to quantum
interference-induced modulations in the electron transmission. We identify
features consistent with the presence of single Andreev bound states and
describe the Andreev-cavity interaction using a dispersive Jaynes-Cummings
model. By fitting the weak Andreev-cavity coupling, we extract ~GHz decoherence
consistent with charge noise and the transmission dispersion associated with a
localized state.


Abstract:
  Product descriptions on e-commerce websites often suffer from missing
important aspects. Clarification question generation (CQGen) can be a promising
approach to help alleviate the problem. Unlike traditional QGen assuming the
existence of answers in the context and generating questions accordingly, CQGen
mimics user behaviors of asking for unstated information. The generated CQs can
serve as a sanity check or proofreading to help e-commerce merchant to identify
potential missing information before advertising their product, and improve
consumer experience consequently. Due to the variety of possible user
backgrounds and use cases, the information need can be quite diverse but also
specific to a detailed topic, while previous works assume generating one CQ per
context and the results tend to be generic. We thus propose the task of Diverse
CQGen and also tackle the challenge of specificity. We propose a new model
named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to
deal with the tasks. Automatic and human evaluation on 2 datasets (Home &
Kitchen, Office) showed that KPCNet can generate more specific questions and
promote better group-level diversity than several competing baselines.


Abstract:
  We consider the $(1+1)$-dimensional quasilinear wave equation
$g(x)w_{tt}-w_{xx}+h(x) (w_t^3)_t=0$ on $\mathbb{R}\times\mathbb{R}$ which
arises in the study of localized electromagnetic waves modeled by
Kerr-nonlinear Maxwell equations. We are interested in time-periodic, spatially
localized solutions. Here $g\in L^{\infty}(\mathbb{R})$ is even with
$g\not\equiv 0$ and $h(x)=\gamma\,\delta_0(x)$ with
$\gamma\in{\mathbb{R}}\backslash\{0\}$ and $\delta_0$ the delta-distribution
supported in $0$. We assume that $0$ lies in a spectral gap of the operators
$L_k=-\frac{d^2}{dx^2}-k^2\omega^2g$ on $L^2(\mathbb{R})$ for all $k\in
2\mathbb{Z}+1$ together with additional properties of the fundamental set of
solutions of $L_k$. By expanding $w$ into a Fourier series in time we transfer
the problem of finding a suitably defined weak solution to finding a minimizer
of a functional on a sequence space. The solutions that we have found are
exponentially localized in space. Moreover, we show that they can be well
approximated by truncating the Fourier series in time. The guiding examples,
where all assumptions are fulfilled, are explicitly given step potentials and
periodic step potentials $g$. In these examples we even find infinitely many
distinct breathers.


Abstract:
  Detection of higher order modes of gravitational waves in third-generation
(3G) ground-based detectors such as Cosmic Explorer and Einstein Telescope is
explored. Using the astrophysical population of binary black holes based on
events reported in the second gravitational wave catalog by Laser
Interferometer Gravitational Wave Observatory (LIGO) and Virgo (GWTC-2), in
conjunction with the Madau-Dickinson model for redshift evolution of the binary
black hole mergers, we assess the detectability of these higher order modes
using a network consisting of three third-generation detectors. We find that
the two subleading modes [(3,3) and (4,4)] can be detected in approximately 30%
of the population with a network signal-to-noise ratio of 3 or more, and for
nearly 10% of the sources, the five leading modes will be detectable. Besides,
a study concerning the effect of binary's mass ratio and its orbital
inclination with the observer's line-of-sight in detecting various modes is
presented. For a few selected events of the LIGO-Virgo catalog, we identify the
modes that would have been detected if a third-generation detector was
operational when these events were recorded. We also compute the detectability
of higher modes by Voyager and find that only $\sim$ 6 and 2% of the detectable
population will have an associated detection of (3,3) and (4,4) modes,
respectively. Observing these higher order modes in the 3G era would have a
huge impact on the science possible with these detectors ranging from
astrophysics and cosmology to testing strong-field gravity.


Abstract:
  Malicious applications (especially in the Android platform) are a serious
threat to developers and end-users. Many research efforts have hence been
devoted to developing effective approaches to defend Android malware. However,
with the explosive growth of Android malware and the continuous advancement of
malicious evasion technologies like obfuscation and reflection, android malware
defenses based on manual rules or traditional machine learning may not be
effective due to limited apriori knowledge. In recent years, a dominant
research field of deep learning (DL) with the powerful feature abstraction
ability has demonstrated a compelling and promising performance in various
fields, like Nature Language processing and image processing. To this end,
employing deep learning techniques to thwart the attack of Android malware has
recently gained considerable research attention. Yet, there exists no
systematic literature review that focuses on deep learning approaches for
Android Malware defenses. In this paper, we conducted a systematic literature
review to search and analyze how deep learning approaches have been applied in
the context of malware defenses in the Android environment. As a result, a
total of 104 studies were identified over the period 2014-2020. The results of
our investigation show that even though most of these studies still mainly
consider DL-based on Android malware detection, 35 primary studies (33.7\%)
design the defenses approaches based on other scenarios. This review also
describes research trends, research focuses, challenges, and future research
directions in DL-based Android malware defenses.


Abstract:
  We obtain a complete characterization for doubly commuting mixed invariant
subspaces of the Hardy space over the unit polydisc. We say a closed subspace
$\mathcal{Q}$ of $H^2(\mathbb{D}^n)$ is mixed invariant if
$M_{z_{j}}(\mathcal{Q}) \subseteq \mathcal{Q}$ for $1 \leq j \leq k$ and
$M_{z_{j}}^*(\mathcal{Q}) \subseteq \mathcal{Q}$, $k+1 \leq j \leq n$ for some
integer $k \in \{1, 2, \ldots, n-1 \}$. We prove that a mixed invariant
subspace $\mathcal{Q}$ of $H^2(\mathbb{D}^n)$ is doubly commuting if and only
if \[ \mathcal{Q} = \Theta H^2(\mathbb{D}^k) \otimes \mathcal{Q}_{\theta_1}
\otimes \cdots \otimes \mathcal{Q}_{\theta_{n-k}}, \] where $\Theta \in
H^{\infty}(\mathbb{D}^k)$ is some inner function and $\mathcal{Q}_{\theta_j}$
is either a Jordan block $H^2(\mathbb{D})\ominus \theta_j H^2(\mathbb{D})$ for
some inner function $\theta_j$ or the Hardy space $H^2(\mathbb{D})$.
Furthermore, an explicit representation for the commutant of an $n$-tuple of
doubly commuting shifts as well as a representation for the commutant of a
doubly commuting tuple of shifts and co-shifts are obtained. Finally, we
discuss some concrete examples of mixed invariant subspaces.


Abstract:
  This paper concerns Saez and Stantcheva's (2016) generalized social marginal
welfare weights (GSMWW), which aggregate losses and gains due to tax policies,
while incorporating non-utilitarian ethical considerations. The approach
evaluates local tax changes without a global social objective. I argue that
local tax policy comparisons implicitly entail global comparisons. Moreover,
whenever welfare weights do not have a utilitarian structure, these implied
global comparisons are inconsistent. One motivation for GSMWW is that it
preserves the Pareto principle. I argue that the approach's problems should
spark a reconsideration of Pareto if one wants to represent broader values in
formal policy analysis.


Abstract:
  We study the effect of two metallic slabs on the collective dynamics of
electrons in graphene positioned between the two slabs. We show that if the
slabs are perfect conductors the plasmons of graphene display a linear
dispersion relation. The velocity of these acoustic plasmons crucially depends
on the distance between the two metal gates and the graphene sheet. In the case
of generic slabs, the dispersion relation of graphene plasmons is much more
complicated but we find that acoustic plasmons can still be obtained under
specific conditions.


Abstract:
  We compute thermal 2-point correlation functions in the black brane $AdS_5$
background dual to 4d CFT's at finite temperature for operators of large
scaling dimension. We find a formula that matches the expected structure of the
OPE. It exhibits an exponentiation property, whose origin we explain. We also
compute the first correction to the two-point function due to graviton
emission, which encodes the proper time from the event horizon to the black
hole singularity.


Abstract:
  This paper introduces a logic with a class of social network models that is
based on standard Linear Temporal Logic (LTL), leveraging the power of existing
model checkers for the analysis of social networks. We provide a short
literature overview, and then define our logic and its axiomatization, present
some simple motivational examples of both models and formulas, and show its
soundness and completeness via a translation into propositional formulas.
Lastly, we briefly discuss model checking and time complexity analysis.


Abstract:
  We investigate the correlations that can arise between Alice and Bob in
prepare-and-measure communication scenarios where the source (Alice) and the
measurement device (Bob) can share prior entanglement. The paradigmatic example
of such a scenario is the quantum dense coding protocol, where the
communication capacity of a qudit can be doubled if a two-qudit entangled state
is shared between Alice and Bob. We provide examples of correlations that
actually require more general protocols based on higher-dimensional entangled
states. This motivates us to investigate the set of correlations that can be
obtained from communicating either a classical or a quantum $d$-dimensional
system in the presence of an unlimited amount of entanglement. We show how such
correlations can be characterized by a hierarchy of semidefinite programming
relaxations by reducing the problem to a non-commutative polynomial
optimization problem. We also introduce an alternative relaxation hierarchy
based on the notion of informationally-restricted quantum correlations, which,
though it represents a strict (non-converging) relaxation scheme, is less
computationally demanding. As an application, we introduce device-independent
tests of the dimension of classical and quantum systems that, in contrast to
previous results, do not make the implicit assumption that Alice and Bob share
no entanglement. We also establish several relations between communication with
and without entanglement as resources for creating correlations.


Abstract:
  RFID tags see a widespread use in modern security systems, including home
intercoms, access control cards, etc. Here we focus on access control systems.
Currently they have either high cost or low security guarantees. Hence, the
developments focusing on improving access control security while lowering the
cost is a rapidly developing field. The purpose of this work is to create an
alternative access control scheme, where card scanners are replaced with
passive RFID tags, and all the communications are done via user's smartphone
Wi-Fi. Based on the analysis of existing approaches, it was concluded that use
of mobile systems is the most promising due to their expandability and presence
of a large number of sensors, such as NFC, camera etc. In the proposed model
RFID tags are mounted near a turnstile or a smart door. Tag reading and
programming is done via NFC chip directly on an Android or iOS mobile device,
which allows for a significant price cut for such a system implementation. A
detailed description of a tag writing procedure with the data required to
perform it is provided. To enhance security, together with smartphone-based
authorization we require the user to provide his photograph while entering a
secure gate. The photograph is then displayed on a monitoring dashboard
side-by-side with his registration picture, so that the two can then be matched
against each other. The developed client-server application offers
administrative system used to configure gate access policies and monitor
entrances with filters by access time, user and gate. Also, we propose a mobile
application that allows gate registration and serves as a door unlock key. The
suggested access control model reduces installation costs required, as it is
fully wireless and uses cheap autonomous RFID-tags as its main component.


Abstract:
  The goal of this paper is to investigate an approach for derivative-free
optimization that has not received sufficient attention in the literature and
is yet one of the simplest to implement and parallelize. It consists of
computing gradients of a smoothed approximation of the objective function (and
constraints), and employing them within established codes. These gradient
approximations are calculated by finite differences, with a differencing
interval determined by the noise level in the functions and a bound on the
second or third derivatives. It is assumed that noise level is known or can be
estimated by means of difference tables or sampling. The use of finite
differences has been largely dismissed in the derivative-free optimization
literature as too expensive in terms of function evaluations and/or as
impractical when the objective function contains noise. The test results
presented in this paper suggest that such views should be re-examined and that
the finite-difference approach has much to be recommended. The tests compared
NEWUOA, DFO-LS and COBYLA against the finite-difference approach on three
classes of problems: general unconstrained problems, nonlinear least squares,
and general nonlinear programs with equality constraints.


Abstract:
  A four-wave-mixing, frequency-comb-based, hyperspectral imaging technique
that is spectrally precise, potentially rapid, and can in principle be applied
to any material, is demonstrated in a near-diffraction-limited microscopy
application.


Abstract:
  Molecular simulations are playing an ever increasing role, finding
applications in fields as varied as physics, chemistry, biology and material
science. However, many phenomena of interest take place on time scales that are
out of reach of standard molecular simulations. This is known as the sampling
problem and over the years several enhanced sampling methods have been
developed to mitigate this issue. We propose a unified approach that puts on
the same footing the two most popular families of enhanced sampling methods,
and paves the way for novel combined approaches. The on-the-fly probability
enhanced sampling method provides an efficient implementation of such
generalized approach, while also focusing on simplicity and robustness.


Abstract:
  Elastic $K^+ n\to K^+ n$ and charge exchange $K^+n\to K^0p$ reactions at low
momenta are investigated using the partial wave analysis. Isospin relation
gives constrains for the partial $s$- and $p$-waves among elastic $K^+p$,
$K^+n$ and charge exchange $K^+n\to K^0p$ amplitudes. Two sets of the phase
shift for these partial waves in the isoscalar channel are obtained from the
fit of experimental data on total and differential cross sections. Polarization
observable leads to a good criterion to decide which set is valid. Differential
cross section data near $P_{\rm Lab}=434$ MeV/$c$ suggests a possibility of a
resonance, e.g., the exotic $\Theta^+(1540)$ baryon around $\sqrt{s}\simeq$
1.54GeV.


Abstract:
  The ability to distinguish between the self and the background is of
paramount importance for robotic tasks. The particular case of hands, as the
end effectors of a robotic system that more often enter into contact with other
elements of the environment, must be perceived and tracked with precision to
execute the intended tasks with dexterity and without colliding with obstacles.
They are fundamental for several applications, from Human-Robot Interaction
tasks to object manipulation. Modern humanoid robots are characterized by high
number of degrees of freedom which makes their forward kinematics models very
sensitive to uncertainty. Thus, resorting to vision sensing can be the only
solution to endow these robots with a good perception of the self, being able
to localize their body parts with precision. In this paper, we propose the use
of a Convolution Neural Network (CNN) to segment the robot hand from an image
in an egocentric view. It is known that CNNs require a huge amount of data to
be trained. To overcome the challenge of labeling real-world images, we propose
the use of simulated datasets exploiting domain randomization techniques. We
fine-tuned the Mask-RCNN network for the specific task of segmenting the hand
of the humanoid robot Vizzy. We focus our attention on developing a methodology
that requires low amounts of data to achieve reasonable performance while
giving detailed insight on how to properly generate variability in the training
dataset. Moreover, we analyze the fine-tuning process within the complex model
of Mask-RCNN, understanding which weights should be transferred to the new task
of segmenting robot hands. Our final model was trained solely on synthetic
images and achieves an average IoU of 82% on synthetic validation data and
56.3% on real test data. These results were achieved with only 1000 training
images and 3 hours of training time using a single GPU.


Abstract:
  The aim of the present work is to investigate the effects of strong magnetic
fields on the hadron-quark phase transition point at zero temperature. To
describe the hadronic phase, a relativistic mean field (RMF) model is used and
to describe the quark phase a density dependent quark mass model (DDQM) is
employed. As compared with the results obtained with non-magnetised matter, we
observe a shift of the transition point towards higher pressures and, generally
also towards higher chemical potentials. An investigation of the phase
transitions that could sustain hybrid stars is also performed.


Abstract:
  In this paper we introduce a space-dependent multiscale model to describe the
spatial spread of an infectious disease under uncertain data with particular
interest in simulating the onset of the COVID-19 epidemic in Italy. While virus
transmission is ruled by a SEIAR type compartmental model, within our approach
the population is given by a sum of commuters moving on a extra-urban scale and
non commuters interacting only on the smaller urban scale. A transport dynamic
of the commuter population at large spatial scales, based on kinetic equations,
is coupled with a diffusion model for non commuters at the urban scale. Thanks
to a suitable scaling limit, the kinetic transport model used to describe the
dynamics of commuters, within a given urban area coincides with the diffusion
equations that characterize the movement of non-commuting individuals. Because
of the high uncertainty in the data reported in the early phase of the
epidemic, the presence of random inputs in both the initial data and the
epidemic parameters is included in the model. A robust numerical method is
designed to deal with the presence of multiple scales and the uncertainty
quantification process. In our simulations, we considered a realistic
geographical domain, describing the Lombardy region, in which the size of the
cities, the number of infected individuals, the average number of daily
commuters moving from one city to another, and the epidemic aspects are taken
into account through a calibration of the model parameters based on the actual
available data. The results show that the model is able to describe correctly
the main features of the spatial expansion of the first wave of COVID-19 in
northern Italy.


Abstract:
  Adopting shared data resources requires scientists to place trust in the
originators of the data. When shared data is later used in the development of
artificial intelligence (AI) systems or machine learning (ML) models, the trust
lineage extends to the users of the system, typically practitioners in fields
such as healthcare and finance. Practitioners rely on AI developers to have
used relevant, trustworthy data, but may have limited insight and recourse.
This paper introduces a software architecture and implementation of a system
based on design patterns from the field of self-sovereign identity. Scientists
can issue signed credentials attesting to qualities of their data resources.
Data contributions to ML models are recorded in a bill of materials (BOM),
which is stored with the model as a verifiable credential. The BOM provides a
traceable record of the supply chain for an AI system, which facilitates
on-going scrutiny of the qualities of the contributing components. The verified
BOM, and its linkage to certified data qualities, is used in the AI Scrutineer,
a web-based tool designed to offer practitioners insight into ML model
constituents and highlight any problems with adopted datasets, should they be
found to have biased data or be otherwise discredited.


Abstract:
  We present a comparative study of the accuracy and precision of correlation
function methods and full-field inference in cosmological data analysis. To do
so, we examine a Bayesian hierarchical model that predicts log-normal fields
and their two-point correlation function. Although a simplified analytic model,
the log-normal model produces fields that share many of the essential
characteristics of the present-day non-Gaussian cosmological density fields. We
use three different statistical techniques: (i) a standard likelihood-based
analysis of the two-point correlation function; (ii) a likelihood-free
(simulation-based) analysis of the two-point correlation function; (iii) a
field-level analysis, made possible by the more sophisticated data assimilation
technique. We find that (a) standard assumptions made to write down a
likelihood for correlation functions can cause significant biases, a problem
that is alleviated with simulation-based inference; and (b) analysing the
entire field offers considerable advantages over correlation functions, through
higher accuracy, higher precision, or both. The gains depend on the degree of
non-Gaussianity, but in all cases, including for weak non-Gaussianity, the
advantage of analysing the full field is substantial.


Abstract:
  In this work, we propose a novel strategy of adaptive sparse array beamformer
design, referred to as regularized complementary antenna switching (RCAS), to
swiftly adapt both array configuration and excitation weights in accordance to
the dynamic environment for enhancing interference suppression. In order to
achieve an implementable design of array reconfiguration, the RCAS is conducted
in the framework of regularized antenna switching, whereby the full array
aperture is collectively divided into separate groups and only one antenna in
each group is switched on to connect with the processing channel. A set of
deterministic complementary sparse arrays with good quiescent beampatterns is
first designed by RCAS and full array data is collected by switching among them
while maintaining resilient interference suppression. Subsequently, adaptive
sparse array tailored for the specific environment is calculated and
reconfigured based on the information extracted from the full array data. The
RCAS is devised as an exclusive cardinality-constrained optimization, which is
reformulated by introducing an auxiliary variable combined with a piece-wise
linear function to approximate the $l_0$-norm function. A regularization
formulation is proposed to solve the problem iteratively and eliminate the
requirement of feasible initial search point. A rigorous theoretical analysis
is conducted, which proves that the proposed algorithm is essentially an
equivalent transformation of the original cardinality-constrained optimization.
Simulation results validate the effectiveness of the proposed RCAS strategy.


Abstract:
  We conduct an empirical study of neural machine translation (NMT) for truly
low-resource languages, and propose a training curriculum fit for cases when
both parallel training data and compute resource are lacking, reflecting the
reality of most of the world's languages and the researchers working on these
languages. Previously, unsupervised NMT, which employs back-translation (BT)
and auto-encoding (AE) tasks has been shown barren for low-resource languages.
We demonstrate that leveraging comparable data and code-switching as weak
supervision, combined with BT and AE objectives, result in remarkable
improvements for low-resource languages even when using only modest compute
resources. The training curriculum proposed in this work achieves BLEU scores
that improve over supervised NMT trained on the same backbone architecture by
+12.2 BLEU for English to Gujarati and +3.7 BLEU for English to Kazakh,
showcasing the potential of weakly-supervised NMT for the low-resource
languages. When trained on supervised data, our training curriculum achieves a
new state-of-the-art result on the Somali dataset (BLEU of 29.3 for Somali to
English). We also observe that adding more time and GPUs to training can
further improve performance, which underscores the importance of reporting
compute resource usage in MT research.


Abstract:
  In a combinatorial exchange setting, players place sell (resp. buy) bids on
combinations of traded goods. Besides the question of finding an optimal
selection of winning bids, the question of how to share the obtained profit is
of high importance. The egalitarian allocation is a well-known solution concept
of profit sharing games which tries to distribute profit among players in a
most equal way while respecting individual contributions to the obtained
profit. Given a set of winning bids, we construct a special network graph and
show that every flow in said graph corresponds to a core payment. Furthermore,
we show that the egalitarian allocation can be characterized as an almost equal
maximum flow which is a maximum flow with the additional property that the
difference of flow value on given edge sets is bounded by a constant. With
this, we are able to compute the egalitarian allocation in polynomial time.


Abstract:
  Two exact lens equations have been recently shown to be equivalent to each
other, being consistent with the gravitational deflection angle of light from a
source to an observer, both of which can be within a finite distance from a
lens object [Phys. Rev. D 102, 064060 (2020)]. We examine methods for iterative
solutions of the gravitational lens equations in the strong deflection limit.
It has been so far unclear whether a convergent series expansion can be
provided by the gravitational lens approach based on the geometrical optics for
obtaining approximate solutions in the strong deflection limit in terms of a
small offset angle. By using the ratio of the lens mass to the lens distance,
we discuss a slightly different method for iterative solutions and the behavior
of the convergence. Finite distance effects begin at the third order in the
iterative method. The iterative solutions in the strong deflection limit are
estimated for Sgr $A^{*}$ and M87. These results suggest that only the linear
order solution can be relevant with current observations, while the finite
distance effects at the third order may be negligible in the Schwarzschild lens
model for these astronomical objects.


Abstract:
  Language Identification in textual documents is the process of automatically
detecting the language contained in a document based on its content. The
present Language Identification techniques presume that a document contains
text in one of the fixed set of languages, however, this presumption is
incorrect when dealing with multilingual document which includes content in
more than one possible language. Due to the unavailability of large standard
corpora for Hindi-English mixed lingual language processing tasks we propose
the language lexicons, a novel kind of lexical database that supports several
multilingual language processing tasks. These lexicons are built by learning
classifiers over transliterated Hindi and English vocabulary. The designed
lexicons possess richer quantitative characteristic than its primary source of
collection which is revealed using the visualization techniques.


Abstract:
  In this paper, we propose a new greedy algorithm for sparse approximation,
called SLS for Single L_1 Selection. SLS essentially consists of a greedy
forward strategy, where the selection rule of a new component at each iteration
is based on solving a least-squares optimization problem, penalized by the L_1
norm of the remaining variables. Then, the component with maximum amplitude is
selected. Simulation results on difficult sparse deconvolution problems
involving a highly correlated dictionary reveal the efficiency of the method,
which outperforms popular greedy algorithms and Basis Pursuit Denoising when
the solution is sparse.


Abstract:
  Power allocation in spectrum sharing systems is challenging due to excessive
interference that the secondary system could impose on the primary system.
Therefore, an interference threshold constraint is considered to regulate the
secondary system's activity. However, the primary receivers should measure the
interference and inform the secondary users accordingly. These cause design
complexities, e.g., due to transceiver's hardware impairments, and impose a
substantial signaling overhead. We set our main goal to mitigate these
requirements in order to make the spectrum sharing systems practically
feasible. To cope with the lack of a model we develop a coexisting deep
reinforcement learning approach for continuous power allocation in both
systems. Importantly, via our solution, the two systems allocate power merely
based on geographical location of their users. Moreover, the inter-system
signaling requirement is reduced to exchanging only the number of primary users
that their QoS requirements are violated. We observe that compared to a
centralized agent that allocates power based on full (accurate) channel
information, our solution is more robust and strictly guarantees QoS
requirements of the primary users. This implies that both systems can operate
simultaneously with almost-zero inter-system signaling overhead.


Abstract:
  Nowadays, neural network models achieve state-of-the-art results in many
areas as computer vision or speech processing. For sequential data, especially
for Natural Language Processing (NLP) tasks, Recurrent Neural Networks (RNNs)
and their extensions, the Long Short Term Memory (LSTM) network and the Gated
Recurrent Unit (GRU), are among the most used models, having a "term-to-term"
sequence processing. However, if many works create extensions and improvements
of the RNN, few have focused on developing other ways for sequential data
processing with neural networks in a "term-to-term" way. This paper proposes
the original Hidden Neural Markov Chain (HNMC) framework, a new family of
sequential neural models. They are not based on the RNN but on the Hidden
Markov Model (HMM), a probabilistic graphical model. This neural extension is
possible thanks to the recent Entropic Forward-Backward algorithm for HMM
restoration. We propose three different models: the classic HNMC, the HNMC2,
and the HNMC-CN. After describing our models' whole construction, we compare
them with classic RNN and Bidirectional RNN (BiRNN) models for some sequence
labeling tasks: Chunking, Part-Of-Speech Tagging, and Named Entity Recognition.
For every experiment, whatever the architecture or the embedding method used,
one of our proposed models has the best results. It shows this new neural
sequential framework's potential, which can open the way to new models, and
might eventually compete with the prevalent BiLSTM and BiGRU.


Abstract:
  The Navier-Stokes equations generate an infinite set of generalized Lyapunov
exponents defined by different ways of measuring the distance between
exponentially diverging perturbed and unperturbed solutions. This set is
demonstrated to be similar, yet different, from the generalized Lyapunov
exponent that provides moments of distance between two fluid particles below
the Kolmogorov scale. We derive rigorous upper bounds on dimensionless Lyapunov
exponent of the fluid particles that demonstrate the exponent's decay with
Reynolds number $Re$ in accord with previous studies. In contrast, terms of
cumulant series for exponents of the moments have power-law growth with $Re$.
We demonstrate as an application that the growth of small fluctuations of
magnetic field in ideal conducting turbulence is hyper-intermittent, being
exponential in both time and Reynolds number. We resolve the existing
contradiction between the theory, that predicts slow decrease of dimensionless
Lyapunov exponent of turbulence with $Re$, and observations exhibiting quite
fast growth. We demonstrate that it is highly plausible that a pointwise limit
for the growth of small perturbations of the Navier-Stokes equations exists.


Abstract:
  Forecasting could be negatively impacted due to anonymization requirements in
data protection legislation. To measure the potential severity of this problem,
we derive theoretical bounds for the loss to forecasts from additive
exponential smoothing models using protected data. Following the guidelines of
anonymization from the General Data Protection Regulation (GDPR) and California
Consumer Privacy Act (CCPA), we develop the $k$-nearest Time Series ($k$-nTS)
Swapping and $k$-means Time Series ($k$-mTS) Shuffling methods to create
protected time series data that minimizes the loss to forecasts while
preventing a data intruder from detecting privacy issues. For efficient and
effective decision making, we formally model an integer programming problem for
a perfect matching for simultaneous data swapping in each cluster. We call it a
two-party data privacy framework since our optimization model includes the
utilities of a data provider and data intruder. We apply our data protection
methods to thousands of time series and find that it maintains the forecasts
and patterns (level, trend, and seasonality) of time series well compared to
standard data protection methods suggested in legislation. Substantively, our
paper addresses the challenge of protecting time series data when used for
forecasting. Our findings suggest the managerial importance of incorporating
the concerns of forecasters into the data protection itself.


Abstract:
  We consider a linear mixed-effects model with a clustered structure, where
the parameters are estimated using maximum likelihood (ML) based on possibly
unbalanced data. Inference with this model is typically done based on
asymptotic theory, assuming that the number of clusters tends to infinity with
the sample size. However, when the number of clusters is fixed, classical
asymptotic theory developed under a divergent number of clusters is no longer
valid and can lead to erroneous conclusions. In this paper, we establish the
asymptotic properties of the ML estimators of random-effects parameters under a
general setting, which can be applied to conduct valid statistical inference
with fixed numbers of clusters. Our asymptotic theorems allow both fixed
effects and random effects to be misspecified, and the dimensions of both
effects to go to infinity with the sample size.


Abstract:
  We report on multi-band observations of the transient source Swift
J0840.7-3516, which was detected in outburst in 2020 February by the Neil
Gehrels Swift Observatory. The outburst episode lasted just ~5 days, during
which the X-ray luminosity quickly decreased from ~3E37 erg/s at peak down to
~5E33 erg/s in quiescence (0.3-10 keV; at 10 kpc). Such a marked and rapid
decrease in the flux was also registered at UV and optical wavelengths. In
outburst, the source showed considerable aperiodic variability in the X-rays on
timescales as short as a few seconds. The spectrum of the source in the energy
range 0.3-20 keV was well described by a thermal, blackbody-like, component
plus a non-thermal, power law-like, component and it softened considerably as
the source returned to quiescence. The spectrum of the optical counterpart in
quiescence showed broad emission features associated mainly with ionised carbon
and oxygen, superposed on a blue continuum. No evidence for bright continuum
radio emission was found in quiescence. We discuss possible scenarios for the
nature of this source, and show that the observed phenomenology points to a
transient ultra-compact X-ray binary system.


Abstract:
  We introduce the effectual topological complexity (ETC) of a $G$-space $X$.
This is a $G$-equivariant homotopy invariant sitting in between the effective
topological complexity of the pair $(X,G)$ and the (regular) topological
complexity of the orbit space $X/G$. We study ETC for spheres and surfaces with
antipodal involution, obtaining a full computation in the case of the torus.
This allows us to prove the vanishing of twice the non-trivial obstruction
responsible for the fact that the topological complexity of the Klein bottle is
4. In addition, this gives a counterexample to the possibility -- suggested in
Pave\v{s}i\'c's work on the topological complexity of a map -- that ETC of
$(X,G)$ would agree with Farber's $TC(X)$ whenever the projection map $X\to
X/G$ is finitely sheeted. We conjecture that ETC of spheres with antipodal
action recasts the Hopf invariant one problem, and describe (conjecturally
optimal) effectual motion planners.


Abstract:
  A pre-requisite for the design of wireless systems is the understanding of
the propagation channel. While a wealth of propagation knowledge exists for
bands below 6 GHz, the same can not be said for bands approaching
millimeter-wave frequencies. In this paper, we present the design,
implementation and measurement-based verification of a re-configurable
27.5-29.5 GHz channel sounder for measuring dynamic directional channels. Based
on the switched array principle, our design is capable of characterizing
128$\times$256 dual-polarized channels with snapshot times of around 600 ms.
This is in sharp contrast to measurement times on the order of tens-of-minutes
with rotating horn antenna sounders. Our design lends itself to high angular
resolution at both link ends with calibrated antenna arrays sampled at
2$^\circ$ and 5$^\circ$ intervals in the azimuth and elevation domains. This is
complemented with a bandwidth of up to 2 GHz, enabling nanosecond-level delay
resolution. The short measurement times and stable radio frequency design
facilitates real-time processing and averaging of the received wavefronts to
gain measurement signal-to-noise ratio and dynamic range. After disclosing the
sounder design and implementation, we demonstrate its capabilities by
presenting dynamic and static measurements at 28 GHz over a 1 GHz bandwidth in
an office corridor environment.


Abstract:
  We propose a numerical approximation method for the Cahn-Hilliard equations
that incorporates continuous data assimilation in order to achieve long time
accuracy. The method uses a C$^0$ interior penalty spatial discretization of
the fourth order Cahn-Hilliard equations, together with a backward Euler
temporal discretization. We prove the method is long time stable and long time
accurate, for arbitrarily inaccurate initial conditions, provided enough data
measurements are incorporated into the simulation. Numerical experiments
illustrate the effectiveness of the method on a benchmark test problem.


Abstract:
  Teleparallel gravity has significantly increased in popularity in recent
decades, bringing attention to Einstein's other theory of gravity. In this
Review, we relate this form of geometry to the broader metric-affine approach
to forming gravitational theories where we describe a systematic way of
constructing consistent teleparallel theories that respect certain physical
conditions such as local Lorentz invariance. We first use teleparallel gravity
to formulate a teleparallel equivalent of general relativity which is
dynamically equivalent to general relativity but which may have different
behaviors for other scenarios, such as quantum gravity. After setting this
foundation, we describe the plethora of modified teleparallel theories of
gravity that have been proposed in the literature. In the second part of the
Review, we first survey works in teleparallel astrophysics literature where we
focus on the open questions in this regime of physics. We then discuss the
cosmological consequences for the various formulations of teleparallel gravity.
We do this at background level by exploring works using various approaches
ranging from dynamical systems to Noether symmetries, and more. Naturally, we
then discuss perturbation theory, firstly by giving a concise approach in which
this can be applied in teleparallel gravity theories and then apply it to a
number of important theories in the literature. Finally, we examine works in
observational and precision cosmology across the plethora of proposal theories.
This is done using some of the latest observations and is used to tackle
cosmological tensions which may be alleviated in teleparallel cosmology. We
also introduce a number of recent works in the application of machine learning
to gravity, we do this through deep learning and Gaussian processes, together
with discussions about other approaches in the literature.


Abstract:
  We revisit the task of quantum state redistribution in the one-shot setting,
and design a protocol for this task with communication cost in terms of a
measure of distance from quantum Markov chains. More precisely, the distance is
defined in terms of quantum max-relative entropy and quantum hypothesis testing
entropy.
  Our result is the first to operationally connect quantum state redistribution
and quantum Markov chains, and can be interpreted as an operational
interpretation for a possible one-shot analogue of quantum conditional mutual
information. The communication cost of our protocol is lower than all
previously known ones and asymptotically achieves the well-known rate of
quantum conditional mutual information. Thus, our work takes a step towards the
important open question of near-optimal characterization of the one-shot
quantum state redistribution.


Abstract:
  Motivated by the possible non-spin-singlet superconductivity in the
magic-angle twisted trilayer graphene experiment, we investigate the
triplet-pairing superconductivity arising from a correlation-induced
spin-fermion model on a honeycomb lattice. We find that the $f$-wave pairing is
favored due to the valley-sublattice structure, and the superconducting state
is time-reversal symmetric, fully gapped, and non-topological. With a small
in-plane magnetic field, the superconducting state becomes partially polarized,
and the transition temperature can be slightly enhanced. Our results apply
qualitatively for the triplet-pairing superconductivity in graphene-based
moir\'e systems, which is fundamentally distinct from triplet superconductivity
in $^3$He and ferromagnetic superconductors.


Abstract:
  Redshift-space distortions (RSD) generically affect any spatially-dependent
observable that is mapped using redshift information. The effect on the
observed clustering of galaxies is the primary example of this. This paper is
devoted to another example: the effect of RSD on the apparent peculiar motions
of tracers as inferred from their positions in redshift space (i.e. the
observed distance). Our theoretical study is motivated by practical
considerations, mainly, the direct estimation of the velocity power spectrum,
which is preferably carried out using the tracer's redshift-space position (so
as to avoid uncertainties in distance measurements). We formulate the
redshift-space velocity field and show that RSD enters as a higher-order
effect. Physically, this effect may be interpreted as a dissipative correction
to the usual perfect-fluid description of dark matter. We show that the effect
on the power spectrum is a damping on relatively large, quasilinear scales
($k>0.01\,h\,{\rm Mpc}^{-1}$), as was observed, though unexplained, in $N$-body
simulations elsewhere. This paper presents two power spectrum models for the
peculiar velocity field in redshift space, both of which can be considered
velocity analogues of existing clustering models. In particular, we show that
the "Finger-of-God" effect, while also present in the velocity field, cannot be
entirely blamed for the observed damping in simulations. Our work provides some
of the missing modelling ingredients required for a density--velocity
multi-tracer analysis, which has been proposed for upcoming redshift surveys.


Abstract:
  We consider a multi-source status update system in which status updates are
transmitted as packets containing the measured value of the monitored process
and a time stamp representing the time when the sample was generated. The
packets of each source are generated according to the Poisson process and the
packets are served according to an exponentially distributed service time. We
assume that the received status update packets needs further processing before
being used (hence, computation-intensive). This is mathematically modeled by
introducing an additional server at the sink node. The sink server serves the
packets according to an exponentially distributed service time. We introduce
two packet management policies, namely, i) a preemptive policy and ii) a
blocking policy and derive the moment generating function (MGF) of the AoI of
each source under both policies. In the preemptive policy, a new arriving
packet preempts any possible packet that is currently under service regardless
of the packet's source index. In the blocking policy, when a server is busy at
the arrival instant of a packet the arriving packet is blocked and cleared. We
assume that the same preemptive/blocking policy is employed in both transmitter
and sink servers. Numerical results are provided to assess the results.


Abstract:
  Phase separation and transitions among different molecular states are
ubiquitous in living cells. Such transitions can be governed by local
equilibrium thermodynamics or by active processes controlled by biological
fuel. It remains largely unexplored how the behavior of phase-separating
systems with molecular transitions differs between thermodynamic equilibrium
and cases where detailed balance of the molecular transition rates is broken
due to the presence of fuel. Here, we present a model of a phase-separating
ternary mixture where two components can convert into each other. At
thermodynamic equilibrium, we find that molecular transitions can give rise to
a lower dissolution temperature and thus reentrant phase behavior. Moreover, we
find a discontinuous thermodynamic phase transition in the composition of the
droplet phase if both converting molecules attract themselves with similar
interaction strength. Breaking detailed-balance of the molecular transition
leads to quasi-discontinuous changes in droplet composition by varying the fuel
amount for a larger range of inter-molecular interactions. Our findings
showcase that phase separation with molecular transitions provides a versatile
mechanism to control properties of intra-cellular and synthetic condensates via
discontinuous switches in droplet composition.


Abstract:
  We implement a Laplace method for the renormalised solution to the
generalised 2D Parabolic Anderson Model (gPAM) driven by a small spatial white
noise. Our work rests upon Hairer's theory of regularity structures which
allows to generalise classical ideas of Azencott and Ben Arous on path space as
well as Aida and Inahama and Kawabi on rough path space to the space of models.
The technical cornerstone of our argument is a Taylor expansion of the solution
in the noise intensity parameter: We prove precise bounds for its terms and the
remainder and use them to estimate asymptotically irrevelant terms to arbitrary
order. While most of our arguments are not specific to gPAM, we also outline
how to adapt those that are.


Abstract:
  This paper describes the NTNU ASR system participating in the Formosa Speech
Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the
Wild project (FSW). FSR-2020 aims at fostering the development of Taiwanese
speech recognition. Apart from the issues on tonal and dialectical variations
of the Taiwanese language, speech artificially contaminated with different
types of real-world noise also has to be dealt with in the final test stage;
all of these make FSR-2020 much more challenging than before. To work around
the under-resourced issue, the main technical aspects of our ASR system include
various deep learning techniques, such as transfer learning, semi-supervised
learning, front-end speech enhancement and model ensemble, as well as data
cleansing and data augmentation conducted on the training data. With the best
configuration, our system obtains 13.1 % syllable error rate (SER) on the
final-test set, achieving the first place among all participating systems on
Track 3.


Abstract:
  Characterizing the structural properties of neural networks is crucial yet
poorly understood, and there are no well-established similarity measures
between networks. In this work, we observe that neural networks can be
represented as abstract simplicial complex and analyzed using their topological
'fingerprints' via Persistent Homology (PH). We then describe a PH-based
representation proposed for characterizing and measuring similarity of neural
networks. We empirically show the effectiveness of this representation as a
descriptor of different architectures in several datasets. This approach based
on Topological Data Analysis is a step towards better understanding neural
networks and serves as a useful similarity measure.


Abstract:
  Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle
(UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new
transmission framework is proposed, where multiple UAV-mounted base stations
employ NOMA to serve multiple groups of ground users with the aid of an IRS.
The three-dimensional (3D) placement and transmit power of UAVs, the reflection
matrix of the IRS, and the NOMA decoding orders among users are jointly
optimized for maximization of the sum rate of considered networks. To tackle
the formulated mixed-integer non-convex optimization problem with coupled
variables, a block coordinate descent (BCD)-based iterative algorithm is
developed. Specifically, the original problem is decomposed into three
subproblems, which are alternatingly solved by exploiting the penalty method
and the successive convex approximation technique. The proposed BCD-based
algorithm is demonstrated to be able to obtain a stationary point of the
original problem with polynomial time complexity. Numerical results show that:
1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum
rate compared to the benchmark schemes, i.e., orthogonal multiple access
(OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing
performance gain for multi-UAV networks by both enhancing channel qualities of
UAVs to their served users and mitigating the inter-UAV interference; and 3)
optimizing the UAV placement can make the sum rate gain brought by NOMA more
distinct due to the flexible decoding order design.


Abstract:
  Event suffix and remaining time prediction are sequence to sequence learning
tasks. They have wide applications in different areas such as economics,
digital health, business process management and IT infrastructure monitoring.
Timestamped event sequences contain ordered events which carry at least two
attributes: the event's label and its timestamp. Suffix and remaining time
prediction are about obtaining the most likely continuation of event labels and
the remaining time until the sequence finishes, respectively. Recent deep
learning-based works for such predictions are prone to potentially large
prediction errors because of closed-loop training (i.e., the next event is
conditioned on the ground truth of previous events) and open-loop inference
(i.e., the next event is conditioned on previously predicted events). In this
work, we propose an encoder-decoder architecture for open-loop training to
advance the suffix and remaining time prediction of event sequences. To capture
the joint temporal dynamics of events, we harness the power of adversarial
learning techniques to boost prediction performance. We consider four real-life
datasets and three baselines in our experiments. The results show improvements
up to four times compared to the state of the art in suffix and remaining time
prediction of event sequences, specifically in the realm of business process
executions. We also show that the obtained improvements of adversarial training
are superior compared to standard training under the same experimental setup.


Abstract:
  Blotto Games are a popular model of multi-dimensional strategic resource
allocation. Two players allocate resources in different battlefields in an
auction setting. While competition with equal budgets is well understood,
little is known about strategic behavior under asymmetry of resources. We
introduce a genetic algorithm, a search heuristic inspired from biological
evolution, interpreted as social learning, to solve this problem. Most
performant strategies are combined to create more performant strategies.
Mutations allow the algorithm to efficiently scan the space of possible
strategies, and consider a wide diversity of deviations. We show that our
genetic algorithm converges to the analytical Nash equilibrium of the symmetric
Blotto game. We present the solution concept it provides for asymmetrical
Blotto games. It notably sees the emergence of "guerilla warfare" strategies,
consistent with empirical and experimental findings. The player with less
resources learns to concentrate its resources to compensate for the asymmetry
of competition. When players value battlefields heterogeneously, counter
strategies and bidding focus is obtained in equilibrium. These features are
consistent with empirical and experimental findings, and provide a learning
foundation for their existence.


Abstract:
  Recently proposed orthogonal time frequency space (OTFS) modulation has been
considered as a promising candidate for accommodating various emerging
communication and sensing applications in high-mobility environments. In this
paper, we propose a novel cross domain iterative detection algorithm to enhance
the error performance of OTFS modulation. Different from conventional OTFS
detection methods, the proposed algorithm applies basic estimation/detection
approaches to both the time domain and delay-Doppler (DD) domain and
iteratively updates the extrinsic information from two domains with the unitary
transformation. In doing so, the proposed algorithm exploits the time domain
channel sparsity and the DD domain symbol constellation constraints. We
evaluate the estimation/detection error variance in each domain for each
iteration and derive the state evolution to investigate the detection error
performance. We show that the performance gain due to iterations comes from the
non-Gaussian constellation constraint in the DD domain. More importantly, we
prove the proposed algorithm can indeed converge and, in the convergence, the
proposed algorithm can achieve almost the same error performance as the
maximum-likelihood sequence detection even in the presence of fractional
Doppler shifts. Furthermore, the computational complexity associated with the
domain transformation is low, thanks to the structure of the discrete Fourier
transform (DFT) kernel. Simulation results are consistent with our analysis and
demonstrate a significant performance improvement compared to conventional OTFS
detection methods.


Abstract:
  We propose MultiOpEd, an open-domain news editorial corpus that supports
various tasks pertaining to the argumentation structure in news editorials,
focusing on automatic perspective discovery. News editorial is a genre of
persuasive text, where the argumentation structure is usually implicit.
However, the arguments presented in an editorial typically center around a
concise, focused thesis, which we refer to as their perspective. MultiOpEd aims
at supporting the study of multiple tasks relevant to automatic perspective
discovery, where a system is expected to produce a single-sentence thesis
statement summarizing the arguments presented. We argue that identifying and
abstracting such natural language perspectives from editorials is a crucial
step toward studying the implicit argumentation structure in news editorials.
We first discuss the challenges and define a few conceptual tasks towards our
goal. To demonstrate the utility of MultiOpEd and the induced tasks, we study
the problem of perspective summarization in a multi-task learning setting, as a
case study. We show that, with the induced tasks as auxiliary tasks, we can
improve the quality of the perspective summary generated. We hope that
MultiOpEd will be a useful resource for future studies on argumentation in the
news editorial domain.


Abstract:
  We generalize Conway-Sloane's constructions of the Leech lattice from
Niemeier lattices using Lorentzian lattice to holomorphic vertex operator
algebras (VOA) of central charge 24. It provides a tool for analyzing the
structures and relations among holomorphic VOAs related by orbifold
construction. In particular, we are able to get some useful information about
certain lattice subVOAs associated with the Cartan subalgebra of the weight one
Lie algebra. We also obtain a relatively elementary proof that any strongly
regular holomorphic VOA of central charge $24$ with a non-trivial weight one
subspace can be constructed directly by a single orbifold construction from the
Leech lattice VOA without using a dimension formula.


Abstract:
  Observations reveal a relatively small but statistically significant
North-South (NS) asymmetry in sunspot activity varying on a time scale of
several solar cycles. This paper proposes a dynamo model for the phenomenon of
long-term NS asymmetry. The model separates dynamo equations for magnetic
fields of dipolar and quadrupolar equatorial parity. The NS asymmetry results
from the superposition of dipolar and quadrupolar fields. Model computations
confirm the formerly proposed excitation of the quadrupolar dynamo mode by a
dominant dipolar mode mediated by the equator-symmetric fluctuations in the
$\alpha$-effect as a mechanism for the long-term NS asymmetry. An analytically
solvable example of oscillations excited by short-term random forcing is given
to justify the numerical result of NS asymmetry coherent on a time scale of
several (about 6 in the present model) solar cycles resulting from random
variations in the $\alpha$-effect on a time scale of one solar rotation. The
model computations show the phase locking phenomenon of dipolar and quadrupolar
fields oscillating predominantly in phase (northern type asymmetry) or in
antiphase (southern type asymmetry) with relatively short irregular transitions
between these two states. Large asymmetry in the simulated Grand minima is
found and explained by weak magnetic quenching of the $\alpha$-effect during
the minima. The possibility of polar field asymmetry in activity minima as a
precursor of sunspot asymmetry in the following activity cycles is discussed
based on the dynamo model and observations.


Abstract:
  This paper describes the simulation framework of the Extreme Energy Events
(EEE) experiment. EEE is a network of cosmic muon trackers, each made of three
Multi-gap Resistive Plate Chambers (MRPC), able to precisely measure the
absolute muon crossing time and the muon integrated angular flux at the ground
level. The response of a single MRPC and the combination of three chambers have
been implemented in a GEANT4-based framework (GEMC) to study the telescope
response. The detector geometry, as well as details about the surrounding
materials and the location of the telescopes have been included in the
simulations in order to realistically reproduce the experimental set-up of each
telescope. A model based on the latest parametrization of the cosmic muon flux
has been used to generate single muon events. After validating the framework by
comparing simulations to selected EEE telescope data, it has been used to
determine detector parameters not accessible by analysing experimental data
only, such as detection efficiency, angular and spatial resolution.


Abstract:
  Micro-expressions (MEs) are involuntary facial movements revealing people's
hidden feelings in high-stake situations and have practical importance in
medical treatment, national security, interrogations and many human-computer
interaction systems. Early methods for MER mainly based on traditional
appearance and geometry features. Recently, with the success of deep learning
(DL) in various fields, neural networks have received increasing interests in
MER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid
facial movements, leading to difficult data collection, thus have small-scale
datasets. DL based MER becomes challenging due to above ME characters. To date,
various DL approaches have been proposed to solve the ME issues and improve MER
performance. In this survey, we provide a comprehensive review of deep
micro-expression recognition (MER), including datasets, deep MER pipeline, and
the bench-marking of most influential methods. This survey defines a new
taxonomy for the field, encompassing all aspects of MER based on DL. For each
aspect, the basic approaches and advanced developments are summarized and
discussed. In addition, we conclude the remaining challenges and and potential
directions for the design of robust deep MER systems. To the best of our
knowledge, this is the first survey of deep MER methods, and this survey can
serve as a reference point for future MER research.


Abstract:
  Multi-Target Multi-Camera Tracking has a wide range of applications and is
the basis for many advanced inferences and predictions. This paper describes
our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City
Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle
tracking framework guided by the crossroad zones. The framework includes: (1)
Use mature detection and vehicle re-identification models to extract targets
and appearance features. (2) Use modified JDETracker (without detection module)
to track single-camera vehicles and generate single-camera tracklets. (3)
According to the characteristics of the crossroad, the Tracklet Filter Strategy
and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering
in Adjacent Cameras for multi-camera tracklets matching. Through the above
techniques, our method obtained an IDF1 score of 0.8095, ranking first on the
leaderboard. The code have released: https://github.com/LCFractal/AIC21-MTMC.


Abstract:
  We present the first application of a hierarchical Markov Chain Monte Carlo
(MCMC) follow-up on continuous gravitational-wave candidates from real-data
searches. The follow-up uses an MCMC sampler to draw parameter-space points
from the posterior distribution, constructed using the matched-filter as a
log-likelihood. As outliers are narrowed down, coherence time increases,
imposing more restrictive phase-evolution templates. We introduce a novel Bayes
factor to compare results from different stages: The signal hypothesis is
derived from first principles, while the noise hypothesis uses extreme value
theory to derive a background model. The effectiveness of our proposal is
evaluated on fake Gaussian data and applied to a set of 30 outliers produced by
different continuous wave searches on O2 Advanced LIGO data. The results of our
analysis suggest all but five outliers are inconsistent with an astrophysical
origin under the standard continuous wave signal model. We successfully ascribe
four of the surviving outliers to instrumental artifacts and a strong hardware
injection present in the data. The behavior of the fifth outlier suggests an
instrumental origin as well, but we could not relate it to any known
instrumental cause.


Abstract:
  We propose a scheme of lattice twisted-mass fermion regularization which is
particularly convenient for application to isospin breaking (IB) QCD and QED
calculations, based in particular on the so called RM123 approach, in which the
IB terms of the action are treated as a perturbation. The main, practical
advantage of this scheme is that it allows the calculation of IB effects on
some mesonic observables, like e.g. the pi+ - pi0 mass splitting, using lattice
correlation functions in which the quark and antiquark fields in the meson are
regularized with opposite values of the Wilson parameter r. These correlation
functions are found to be affected by much smaller statistical fluctuations,
with respect to the analogous functions in which quark and antiquark fields are
regularized with the same value of r. Two numerical application of this scheme,
that we call "rotated twisted-mass", within pure QCD and QCD+QED respectively,
are also provided for illustration.


Abstract:
  We construct a dynamical quantization for contact manifolds in terms of a
flat connection acting on a Hilbert tractor bundle. We show that this contact
quantization, which is independent of the choice of contact form, can be
obtained by quantizing the Reeb dynamics of an ambient strict contact manifold
equivariantly with respect to an R+-action. The contact quantization further
determines a certain contact tractor connection whose parallel sections
determine a distinguished choice of Reeb dynamics and their quantization. This
relationship relies on tractor constructions from parabolic geometries and
mirrors the tight relationship between Einstein metrics and conformal
geometries. Finally, we construct in detail the dynamical quantization of the
unique tight contact structure on the 3-sphere, where the Holstein-Primakoff
transformation makes a surprising appearance.


Abstract:
  In this paper we prove that every $2$-generator finite $p$-group $G$ has a
non-inner automorphism of order $p$ leaving $G^p\gamma_4(G)$ elementwise fixed
($p\ge 5$). Moreover, we prove a $2$-generator finite $3$-group satisfying
$|\Omega_1(Z_2(G))|=p^2$ has a non-inner automorphism of order $p$ leaving
$G^p\gamma_3(G)$ elementwise fixed. As a consequence we prove the non-inner
automorphism conjecture for every finite $p$-group of coclass $4$ ($p\ge 3$),
and coclass $5$ ($p\ge 5$).


Abstract:
  A pair of Banach spaces $(E, F)$ is said to have the weak maximizing property
(WMP, for short) if for every bounded linear operator $T$ from $E$ into $F$,
the existence of a non-weakly null maximizing sequence for $T$ implies that $T$
attains its norm. This property was recently introduced in an article by R.
Aron, D. Garc\'ia, D. Pelegrino and E. Teixeira, raising several open
questions. The aim of the present paper is to contribute to the better
knowledge of the WMP and its limitations. Namely, we provide sufficient
conditions for a pair of Banach spaces to fail the WMP and study the behaviour
of this property with respect to quotients, subspaces, and direct sums, which
open the gate to present several consequences. For instance, we deal with pairs
of the form $(L_p[0,1], L_q[0,1])$, proving that these pairs fail the WMP
whenever $p>2$ or $q<2$. We also show that, under certain conditions on $E$,
the assumption that $(E, F)$ has the WMP for every Banach space $F$ implies
that $E$ must be finite dimensional. On the other hand, we show that $(E, F)$
has the WMP for every reflexive space $E$ if and only if $F$ has the Schur
property. We also give a complete characterization for the pairs $(\ell_s
\oplus_p \ell_p, \ell_s \oplus_q \ell_q)$ to have the WMP by calculating the
moduli of asymptotic uniform convexity of $\ell_s \oplus_p \ell_p$ and of
asymptotic uniform smoothness of $\ell_s \oplus_q \ell_q$ when $1 < p \leq s
\leq q < \infty$. We conclude the paper by discussing some variants of the WMP
and presenting a list of open problems on the topic of the paper.


Abstract:
  Code-Switching (CSW) is a common phenomenon that occurs in multilingual
geographic or social contexts, which raises challenging problems for natural
language processing tools. We focus here on Machine Translation (MT) of CSW
texts, where we aim to simultaneously disentangle and translate the two mixed
languages. Due to the lack of actual translated CSW data, we generate
artificial training data from regular parallel texts. Experiments show this
training strategy yields MT systems that surpass multilingual systems for
code-switched texts. These results are confirmed in an alternative task aimed
at providing contextual translations for a L2 writing assistant.


Abstract:
  We present heyoka, a new, modern and general-purpose implementation of
Taylor's integration method for the numerical solution of ordinary differential
equations. Detailed numerical tests focused on difficult high-precision
gravitational problems in astrodynamics and celestial mechanics show how our
general-purpose integrator is competitive with and often superior to
state-of-the-art specialised symplectic and non-symplectic integrators in both
speed and accuracy. In particular, we show how Taylor methods are capable of
satisfying Brouwer's law for the conservation of energy in long-term
integrations of planetary systems over billions of dynamical timescales. We
also show how close encounters are modelled accurately during simulations of
the formation of the Kirkwood gaps and of Apophis' 2029 close encounter with
the Earth (where heyoka surpasses the speed and accuracy of domain-specific
methods). heyoka can be used from both C++ and Python, and it is publicly
available as an open-source project.


Abstract:
  The problem of recognizing various types of tissues present in
multi-gigapixel histology images is an important fundamental pre-requisite for
downstream analysis of the tumor microenvironment in a bottom-up analysis
paradigm for computational pathology. In this paper, we propose a deep
dictionary learning approach to solve the problem of tissue phenotyping in
histology images. We propose deep Multi-Resolution Dictionary Learning
(deepMRDL) in order to benefit from deep texture descriptors at multiple
different spatial resolutions. We show the efficacy of the proposed approach
through extensive experiments on four benchmark histology image datasets from
different organs (colorectal cancer, breast cancer and breast lymphnodes) and
tasks (namely, cancer grading, tissue phenotyping, tumor detection and tissue
type classification). We also show that the proposed framework can employ most
off-the-shelf CNNs models to generate effective deep texture descriptors.


Abstract:
  Unsupervised pretraining is an integral part of many natural language
processing systems, and transfer learning with language models has achieved
remarkable results in many downstream tasks. In the clinical application of
medical code assignment, diagnosis and procedure codes are inferred from
lengthy clinical notes such as hospital discharge summaries. However, it is not
clear if pretrained models are useful for medical code prediction without
further architecture engineering. This paper conducts a comprehensive
quantitative analysis of various contextualized language models' performance,
pretrained in different domains, for medical code assignment from clinical
notes. We propose a hierarchical fine-tuning architecture to capture
interactions between distant words and adopt label-wise attention to exploit
label information. Contrary to current trends, we demonstrate that a carefully
trained classical CNN outperforms attention-based models on a MIMIC-III subset
with frequent codes. Our empirical findings suggest directions for improving
the medical code assignment application.


Abstract:
  The search of new physics~(NP) beyond the Standard Model is one of the most
important tasks of high energy physics. A common characteristic of the NP
signals is that they are usually few and kinematically different. We use a
model independent strategy to study the phenomenology of NP by directly picking
out and studying the kinematically unusual events. For this purpose, the
isolation forest~(IF) algorithm is applied, which is found to be efficient in
identifying the signal events of the anomalous quartic gauge couplings~(aQGCs).
The IF algorithm can also be used to constraint the coefficients of aQGCs. As a
machine learning algorithm, the IF algorithm shows a good prospect in the
future studies of NP.


Abstract:
  We prove a generalized version of Renault's theorem for Cartan subalgebras.
We show that the original assumptions of second countability and separability
are not needed. This weakens the assumption of topological principality of the
underlying groupoid to effectiveness.


Abstract:
  We consider chance-constrained binary knapsack problems, where the weights of
items are independent random variables with the means and standard deviations
known. The chance constraint can be reformulated as a second-order cone
constraint under some assumptions for the probability distribution of the
weights. The problem becomes a second-order cone-constrained binary knapsack
problem, which is equivalent to a robust binary knapsack problem with an
ellipsoidal uncertainty set.
  We demonstrate that optimal solutions to robust binary knapsack problems with
inner and outer polyhedral approximations of the ellipsoidal uncertainty set
can provide both upper and lower bounds on the optimal value of the
second-order cone-constrained binary knapsack problem, and they can be obtained
by solving ordinary binary knapsack problems repeatedly. Moreover, we prove
that the solution providing the upper bound converges to the optimal solution
to the second-order cone-constrained binary knapsack problem as the
approximation of the uncertainty set becomes more accurate. Based on this, a
pseudo-polynomial time algorithm is obtained under the assumption that the
coefficients of the problem are integer-valued. We also propose an exact
algorithm, which iteratively improves the accuracy of the approximation until
an exact optimal solution is obtained. The exact algorithm also runs in
pseudo-polynomial time. Computational results are presented to show the quality
of the upper and lower bounds and efficiency of the exact algorithm compared to
CPLEX and a previous study.


Abstract:
  In recent years, the use of multilevel inverter has become popular due to its
many advantages. Due to the popularity of multilevel inverters in industries
and in applications that require a wide range of voltages, there have been many
challenges to achieve high-quality voltage. Many pieces of research have been
done to solve the problem of annoying harmonics in multilevel inverters. In
this study, pulse width modulation (PWM) has been proposed as a switching
method. Using the Selective Harmonic Elimination (SHE) technique, the inverter
switching can be carried out in low frequency, and also, this method can reduce
the annoying harmonics significantly. However, in the lower modulation index,
eliminating such harmonics is challenging, resulting in a considerable increase
in output voltage distortion. This study suggests a way to solve the problem.
In this research, eliminating selected harmonics in a multilevel inverter with
variable DC links is proposed. The DC-link variable method in this study is to
use a high-frequency isolated DC-DC converter. The proposed method is verified
on a 5-level Cascaded H-Bridge (CHB) inverter using the SHE-PWM method solved
by particle swarm optimization (PSO) algorithm.


Abstract:
  A model with complete supersymmetric unification of the Standard Model matter
content, interactions and families replication into a single $E_8$ gauge
superfield in ten dimensions is presented. The gauge and extended Poincar\'e
symmetries are broken through compactification of the
$\mathbb{T}^6/(\mathbb{Z}_3\times \mathbb{Z}_3)$ orbifold with Wilson lines,
which reduces the original symmetry and matter content into those of the
Standard Model. Proton decay is strongly suppressed automatically, while the
unification scale is required to be as low as $10^6-10^9\ {\rm GeV}$ so that
the corresponding physics may be potentially accessible and testable by future
collider measurements. Such a low-scale Grand Unification concept yields a
highly-constrained and predictive New Physics framework in a minimal and
phenomenologically testable and consistent way.


Abstract:
  Almost paracontact Riemannian manifolds of the lowest dimension are studied,
whose paracontact distributions are equipped with an almost paracomplex
structure. These manifolds are constructed as a product of a real line and a
2-dimensional Riemannian space-form. Their metric is obtained in two ways: as a
cone metric and as a hyperbolic extension of the metric of the underlying
paracomplex 2-manifold. The resulting manifolds are studied and characterized
in terms of the classification used and their curvature properties.


Abstract:
  Entanglement is an indispensable quantum resource for quantum information
technology. In continuous-variable quantum optics, photon subtraction can
increase the entanglement between Gaussian states of light, but for mixed
states the extent of this entanglement increase is poorly understood. In this
work, we use an entanglement measure based the R\'enyi-2 entropy to prove that
single-photon subtraction increases bipartite entanglement by no more than
log2. This value coincides with the maximal amount of bipartite entanglement
that can be achieved with one photon.


Abstract:
  We approach the important challenge of code autocompletion as an open-domain
task, in which a sequence-to-sequence code generator model is enhanced with the
ability to attend to reference code snippets supplied by a semantic code search
engine. In this work, we present a novel framework to precisely retrieve
template functions as well as intent-snippet pairs and effectively train such a
retrieval-guided code generator. To demonstrate the effectiveness of our model
designs, we perform extensive experiments with CodeSearchNet which contains
template functions and CoNaLa which contains Stack Overflow intent-snippet
pairs. We also investigate different retrieval models, including Elasticsearch,
DPR, and our fusion representation search model, which currently holds the
number one spot on the CodeSearchNet leaderboard. We observe improvements by
leveraging multiple database elements and further gain from retrieving diverse
data points by using Maximal Marginal Relevance. Overall, we see a 4%
improvement to cross-entropy loss, a 15% improvement to edit distance, and a
44% improvement to BLEU score when retrieving template functions. We see
subtler improvements of 2%, 11%, and 6% respectively when retrieving Stack
Overflow intent-snippet pairs. We also create a novel Stack Overflow-Function
Alignment dataset, which consists of 150K tuples of functions and Stack
Overflow intent-snippet pairs that are of help in writing the associated
function, of which 1.7K are manually curated.


Abstract:
  We extend Sharkovskii's theorem to the cases of $N$-dimensional maps which
are close to 1D maps, with an attracting $n$-periodic orbit. We prove that,
with relatively weak topological assumptions, there exist also $m$-periodic
orbits for all $m\triangleright n$ in Sharkovskii's order, in the nearby.
  We also show, as an example of application, how to obtain such a result for
the R\"ossler system with an attracting periodic orbit, for four sets of
parameter values. The proofs are computer-assisted.


Abstract:
  We show that the attraction-repulsion chemotaxis system \begin{equation*}
\begin{cases} u_t = \Delta u - \chi\nabla\cdot(u\nabla v_1) +
\xi\nabla\cdot(u\nabla v_2)\\
  \partial_t v_1 = \Delta v_1 - \beta v_1 + \alpha u \\
  \partial_t v_2 = \Delta v_2 - \delta v_2 + \gamma u, \end{cases}
\end{equation*} posed with homogeneous Neumann boundary conditions in bounded
domains $\Omega=B_R \subset \mathbb{R}^3$, $R>0$, admits radially symmetric
solutions which blow-up in finite time if it is attraction-dominated in the
sense that $\chi\alpha-\xi\gamma>0$.


Abstract:
  Friedel oscillation is a well-known wave phenomenon, which represents the
oscillatory response of electron waves to imperfection. By utilizing the
pseudospin-momentum locking in gapless graphene, two recent experiments
demonstrate the measurement of the topological Berry phase by corresponding to
the unique number of wavefront dislocations in Friedel oscillations. Here, we
study the Friedel oscillations in gapped graphene, in which the
pseudospin-momentum locking is broken. Unusually, the wavefront dislocations do
occur as that in gapless graphene, which expects the immediate verification in
the current experimental condition. The number of wavefront dislocations is
ascribed to the invariant pseudospin winding number in gaped and gapless
graphene. This study deepens the understanding of correspondence between
topological quantity and wavefront dislocations in Friedel oscillations, and
implies the possibility to observe the wavefront dislocations of Friedel
oscillations in intrinsic gapped two-dimensional materials, e.g., transition
metal dichalcogenides.


Abstract:
  The scattering-adapted flexible inner region ensemble separator (SAFIRES) is
a partitioning scheme designed to divide a simulation cell into two regions to
be treated with different computational methodologies. SAFIRES prevents
particles from crossing between regions and resolves boundary events through
elastic collisions of the particles mediated by the boundary, conserving energy
and momenta. A multiple-time-step propagation algorithm is introduced where the
time step is scaled automatically to identify the moment a collision occurs. If
the length of the time step is kept constant, the new propagator reduces to a
regular algorithm for Langevin dynamics, and to the velocity Verlet algorithm
for classical dynamics if the friction coefficient is set to zero. SAFIRES
constitutes the exact limit of the premise behind boundary-based methods such
as FIRES, BEST, and BCC which take advantage of the indistinguishability of
molecules on opposite sides of the separator. It gives correct average ensemble
statistics despite the introduction of an ensemble separator. SAFIRES is tested
in simulations where the molecules on the two sides are treated in the same
way, for a Lennard-Jones (LJ) liquid and a LJ liquid in contact with a surface,
as well as for liquid modelling simulations using the TIP4P force field.
Simulations using SAFIRES are shown to reproduce the unconstrained reference
simulations without significant deviations.


Abstract:
  Energy-based models (EBMs) are generative models that are usually trained via
maximum likelihood estimation. This approach becomes challenging in generic
situations where the trained energy is nonconvex, due to the need to sample the
Gibbs distribution associated with this energy. Using general Fenchel duality
results, we derive variational principles dual to maximum likelihood EBMs with
shallow overparametrized neural network energies, both in the active (aka
feature-learning) and lazy regimes. In the active regime, this dual formulation
leads to a training algorithm in which one updates concurrently the particles
in the sample space and the neurons in the parameter space of the energy. We
also consider a variant of this algorithm in which the particles are sometimes
restarted at random samples drawn from the data set, and show that performing
these restarts at every iteration step corresponds to score matching training.
Using intermediate parameter setups in our dual algorithm thereby gives a way
to interpolate between maximum likelihood and score matching training. These
results are illustrated in simple numerical experiments.


Abstract:
  Models of asymmetric dark matter (ADM) seek to explain the apparent
coincidence between the present-day mass densities of visible and dark matter,
$\Omega_{\mathrm{DM}} \simeq 5\Omega_{\mathrm{VM}}$. However, most ADM models
only relate the number densities of visible and dark matter without motivating
the similar particle masses. We expand upon a recent work that obtained a
natural mass relationship in a mirror matter ADM model with two Higgs doublets
in each sector, by looking to implement dark electroweak baryogenesis as the
means of asymmetry generation. We explore two aspects of the mechanism: the
nature of the dark electroweak phase transition, and the transfer of particle
asymmetries between the sectors by the use of portal interactions. We find that
both aspects can be implemented successfully for various regions of the
parameter space. We also analyse one portal interaction -- the neutron portal
-- in greater detail, in order to satisfy the observational constraints on dark
radiation.


Abstract:
  We report on the electronic properties of an artificial system obtained by
the intercalation of equiatomic FeCo layers under graphene grown on Ir(111).
Upon intercalation, the FeCo film grows epitaxially on Ir(111), resulting in a
lattice-mismatched system. By performing Density Functional Theory
calculations, we show that the intercalated FeCo layer leads to a pronounced
corrugation of the graphene film. At the same time, the FeCo intercalated
layers induce a clear transition from a nearly undisturbed to a strongly
hybridized graphene {\pi}-band, as measured by angle-resolved photoemission
spectroscopy. A comparison of experimental results with the computed band
structure and the projected density of states unveils a spin-selective
hybridization between the {\pi} band of graphene and FeCo-3d states. Our
results demonstrate that the reduced dimensionality, as well as the
hybridization within the FeCo layers, induce a narrowing and a clear splitting
of Fe 3d-up and Fe 3d-down spin bands of the confined FeCo layers with respect
to bulk Fe and Co.


Abstract:
  Estimating three-dimensional human poses from the positions of
two-dimensional joints has shown promising results.However, using
two-dimensional joint coordinates as input loses more information than
image-based approaches and results in ambiguity.In order to overcome this
problem, we combine bone length and camera parameters with two-dimensional
joint coordinates for input.This combination is more discriminative than the
two-dimensional joint coordinates in that it can improve the accuracy of the
model's prediction depth and alleviate the ambiguity that comes from projecting
three-dimensional coordinates into two-dimensional space. Furthermore, we
introduce direction constraints which can better measure the difference between
the ground truth and the output of the proposed model. The experimental results
on the H36M show that the method performed better than other state-of-the-art
three-dimensional human pose estimation approaches.


Abstract:
  In this paper, we proposed a multi-document summarization system using
semantic role labeling (SRL) and semantic graph for Indonesian news articles.
In order to improve existing summarizer, our system modified summarizer that
employed subject, predicate, object, and adverbial (SVOA) extraction for
predicate argument structure (PAS) extraction. SVOA extraction is replaced with
SRL model for Indonesian. We also replace the genetic algorithm to identify
important PAS with the decision tree classifier since the summarizer without
genetic algorithm gave better performance. The decision tree model is employed
to identify important PAS. The decision tree model with 10 features achieved
better performance than decision tree with 4 sentence features. Experiments and
evaluations are conducted to generate 100 words summary and 200 words summary.
The evaluation shows the proposed model get 0.313 average ROUGE-2 recall in 100
words summary and 0.394 average ROUGE-2 recall in 200 words summary.


Abstract:
  The task of binary quantum hypothesis testing is to determine the state of a
quantum system via measurements on it, given the side information that it is in
one of two possible states, say $\rho$ and $\sigma$. This task is generally
studied in either the symmetric setting, in which the two possible errors
incurred in the task (the so-called type I and type II errors) are treated on
an equal footing, or the asymmetric setting in which one minimizes the type II
error probability under the constraint that the corresponding type I error
probability is below a given threshold. Here we define a one-parameter family
of binary quantum hypothesis testing tasks, which we call $s$-hypothesis
testing, and in which the relative significance of the two errors are weighted
by a parameter $s$. In particular, $s$-hypothesis testing interpolates
continuously between the regimes of symmetric and asymmetric hypothesis
testing. Moreover, if arbitrarily many identical copies of the system are
assumed to be available, then the minimal error probability of $s$-hypothesis
testing is shown to decay exponentially in the number of copies, with a decay
rate given by a quantum divergence which we denote as $\xi_s(\rho\|\sigma)$,
and which satisfies a host of interesting properties. Moreover, this
one-parameter family of divergences interpolates continuously between the
corresponding decay rates for symmetric hypothesis testing (the quantum
Chernoff divergence) for $s = 1$, and asymmetric hypothesis testing (the
Umegaki relative entropy) for $s = 0$.


Abstract:
  Recommendation systems often use online collaborative filtering (CF)
algorithms to identify items a given user likes over time, based on ratings
that this user and a large number of other users have provided in the past.
This problem has been studied extensively when users' preferences do not change
over time (static case); an assumption that is often violated in practical
settings. In this paper, we introduce a novel model for online non-stationary
recommendation systems which allows for temporal uncertainties in the users'
preferences. For this model, we propose a user-based CF algorithm, and provide
a theoretical analysis of its achievable reward. Compared to related
non-stationary multi-armed bandit literature, the main fundamental difficulty
in our model lies in the fact that variations in the preferences of a certain
user may affect the recommendations for other users severely. We also test our
algorithm over real-world datasets, showing its effectiveness in real-world
applications. One of the main surprising observations in our experiments is the
fact our algorithm outperforms other static algorithms even when preferences do
not change over time. This hints toward the general conclusion that in
practice, dynamic algorithms, such as the one we propose, might be beneficial
even in stationary environments.


Abstract:
  Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA)
have been regarded as promising technologies to improve computation capability
and offloading efficiency of the mobile devices in the sixth generation (6G)
mobile system. This paper mainly focuses on the hybrid NOMA-MEC system, where
multiple users are first grouped into pairs, and users in each pair offload
their tasks simultaneously by NOMA, and then a dedicated time duration is
scheduled to the more delay-tolerable user for uploading the remaining data by
orthogonal multiple access (OMA). For the conventional NOMA uplink
transmission, successive interference cancellation (SIC) is applied to decode
the superposed signals successively according to the channel state information
(CSI) or the quality of service (QoS) requirement. In this work, we integrate
the hybrid SIC scheme which dynamically adapts the SIC decoding order among all
NOMA groups. To solve the user grouping problem, a deep reinforcement learning
(DRL) based algorithm is proposed to obtain a close-to-optimal user grouping
policy. Moreover, we optimally minimize the offloading energy consumption by
obtaining the closed-form solution to the resource allocation problem.
Simulation results show that the proposed algorithm converges fast, and the
NOMA-MEC scheme outperforms the existing orthogonal multiple access (OMA)
scheme.


Abstract:
  We consider the problem of computing shortest paths in weighted unit-disk
graphs in constant dimension $d$. Although the single-source and all-pairs
variants of this problem are well-studied in the plane case, no non-trivial
exact distance oracles for unit-disk graphs have been known to date, even for
$d=2$.
  The classical result of Sedgewick and Vitter [Algorithmica '86] shows that
for weighted unit-disk graphs in the plane the $A^*$ search has average-case
performance superior to that of a standard shortest path algorithm, e.g.,
Dijkstra's algorithm. Specifically, if the $n$ corresponding points of a
weighted unit-disk graph $G$ are picked from a unit square uniformly at random,
and the connectivity radius is $r\in (0,1)$, $A^*$ finds a shortest path in $G$
in $O(n)$ expected time when $r=\Omega(\sqrt{\log n/n})$, even though $G$ has
$\Theta((nr)^2)$ edges in expectation. In other words, the work done by the
algorithm is in expectation proportional to the number of vertices and not the
number of edges.
  In this paper, we break this natural barrier and show even stronger sublinear
time results. We propose a new heuristic approach to computing point-to-point
exact shortest paths in unit-disk graphs. We analyze the average-case behavior
of our heuristic using the same random graph model as used by Sedgewick and
Vitter and prove it superior to $A^*$. Specifically, we show that, if we are
able to report the set of all $k$ points of $G$ from an arbitrary rectangular
region of the plane in $O(k + t(n))$ time, then a shortest path between
arbitrary two points of such a random graph on the plane can be found in
$O(1/r^2 + t(n))$ expected time. In particular, the state-of-the-art range
reporting data structures imply a sublinear expected bound for all
$r=\Omega(\sqrt{\log n/n})$ and $O(\sqrt{n})$ expected bound for
$r=\Omega(n^{-1/4})$ after only near-linear preprocessing of the point set.


Abstract:
  After the 2017 TuSimple Lane Detection Challenge, its evaluation based on
accuracy and F1 score has become the de facto standard to measure the
performance of lane detection methods. In this work, we conduct the first
large-scale empirical study to evaluate the robustness of state-of-the-art lane
detection methods under physical-world adversarial attacks in autonomous
driving. We evaluate 4 major types of lane detection approaches with the
conventional evaluation and end-to-end evaluation in autonomous driving
scenarios and then discuss the security proprieties of each lane detection
model. We demonstrate that the conventional evaluation fails to reflect the
robustness in end-to-end autonomous driving scenarios. Our results show that
the most robust model on the conventional metrics is the least robust in the
end-to-end evaluation. Although the competition dataset and its metrics have
played a substantial role in developing performant lane detection methods along
with the rapid development of deep neural networks, the conventional evaluation
is becoming obsolete and the gap between the metrics and practicality is
critical. We hope that our study will help the community make further progress
in building a more comprehensive framework to evaluate lane detection models.