Abstract:
  To assess generalization, machine learning scientists typically either (i)
bound the generalization gap and then (after training) plug in the empirical
risk to obtain a bound on the true risk; or (ii) validate empirically on
holdout data. However, (i) typically yields vacuous guarantees for
overparameterized models. Furthermore, (ii) shrinks the training set and its
guarantee erodes with each re-use of the holdout set. In this paper, we
introduce a method that leverages unlabeled data to produce generalization
bounds. After augmenting our (labeled) training set with randomly labeled fresh
examples, we train in the standard fashion. Whenever classifiers achieve low
error on clean data and high error on noisy data, our bound provides a tight
upper bound on the true risk. We prove that our bound is valid for 0-1
empirical risk minimization and with linear classifiers trained by gradient
descent. Our approach is especially useful in conjunction with deep learning
due to the early learning phenomenon whereby networks fit true labels before
noisy labels but requires one intuitive assumption. Empirically, on canonical
computer vision and NLP tasks, our bound provides non-vacuous generalization
guarantees that track actual performance closely. This work provides
practitioners with an option for certifying the generalization of deep nets
even when unseen labeled data is unavailable and provides theoretical insights
into the relationship between random label noise and generalization.


Abstract:
  The increasing digitization and interconnection of legacy Industrial Control
Systems (ICSs) open new vulnerability surfaces, exposing such systems to
malicious attackers. Furthermore, since ICSs are often employed in critical
infrastructures (e.g., nuclear plants) and manufacturing companies (e.g.,
chemical industries), attacks can lead to devastating physical damages. In
dealing with this security requirement, the research community focuses on
developing new security mechanisms such as Intrusion Detection Systems (IDSs),
facilitated by leveraging modern machine learning techniques. However, these
algorithms require a testing platform and a considerable amount of data to be
trained and tested accurately. To satisfy this prerequisite, Academia,
Industry, and Government are increasingly proposing testbed (i.e., scaled-down
versions of ICSs or simulations) to test the performances of the IDSs.
Furthermore, to enable researchers to cross-validate security systems (e.g.,
security-by-design concepts or anomaly detectors), several datasets have been
collected from testbeds and shared with the community. In this paper, we
provide a deep and comprehensive overview of ICSs, presenting the architecture
design, the employed devices, and the security protocols implemented. We then
collect, compare, and describe testbeds and datasets in the literature,
highlighting key challenges and design guidelines to keep in mind in the design
phases. Furthermore, we enrich our work by reporting the best performing IDS
algorithms tested on every dataset to create a baseline in state of the art for
this field. Finally, driven by knowledge accumulated during this survey's
development, we report advice and good practices on the development, the
choice, and the utilization of testbeds, datasets, and IDSs.


Abstract:
  Strong gravitational lensing of gravitational wave sources offers a novel
probe of both the lens galaxy and the binary source population. In particular,
the strong lensing event rate and the time delay distribution of
multiply-imaged gravitational-wave binary coalescence events can be used to
constrain the mass distribution of the lenses as well as the intrinsic
properties of the source population. We calculate the strong lensing event rate
for a range of second (2G) and third generation (3G) detectors, including
Advanced LIGO/Virgo, A+, Einstein Telescope (ET), and Cosmic Explorer (CE). For
3G detectors, we find that {$\sim0.1\%$} of observed events are expected to be
strongly lensed. We predict detections of {$\sim 1$} lensing pair per year with
A+, and {$\sim 50$} pairs {per year} with ET/CE. These rates are highly
sensitive to the characteristic galaxy velocity dispersion, $\sigma_*$,
implying that observations of the rates will be a sensitive probe of lens
properties. We explore using the time delay distribution between
multiply-imaged gravitational-wave sources to constrain properties of the
lenses. We find that 3G detectors would constrain $\sigma_*$ to {$\sim21\%$
after 5 years}. Finally, we show that the presence or absence of strong lensing
{within the detected population} provides useful insights into the source
redshift and mass distribution out to redshifts beyond the peak of the star
formation rate, which can be used to constrain formation channels and their
relation to the star formation rate and delay time distributions for these
systems.


Abstract:
  Two new classes of skew codes over a finite field $\F$ are proposed, called
skew convolutional codes and skew trellis codes. These two classes are defined
by, respectively, left or right sub-modules over the skew fields of fractions
of skew polynomials over $\F$. The skew convolutional codes can be represented
as periodic time-varying ordinary convolutional codes. The skew trellis codes
are in general nonlinear over $\F$. Every code from both classes has a code
trellis and can be decoded by Viterbi or BCJR algorithms.


Abstract:
  A key challenge for abstractive summarization is ensuring factual consistency
of the generated summary with respect to the original document. For example,
state-of-the-art models trained on existing datasets exhibit entity
hallucination, generating names of entities that are not present in the source
document. We propose a set of new metrics to quantify the entity-level factual
consistency of generated summaries and we show that the entity hallucination
problem can be alleviated by simply filtering the training data. In addition,
we propose a summary-worthy entity classification task to the training process
as well as a joint entity and summary generation approach, which yield further
improvements in entity level metrics.


Abstract:
  This paper considers distributed estimation of linear systems when the state
observations are corrupted with Gaussian noise of unbounded support and under
possible random adversarial attacks. We consider sensors equipped with single
time-scale estimators and local chi-square ($\chi^2$) detectors to
simultaneously opserve the states, share information, fuse the
noise/attack-corrupted data locally, and detect possible anomalies in their own
observations. While this scheme is applicable to a wide variety of systems
associated with full-rank (invertible) matrices, we discuss it within the
context of distributed inference in social networks. The proposed technique
outperforms existing results in the sense that: (i) we consider Gaussian noise
with no simplifying upper-bound assumption on the support; (ii) all existing
$\chi^2$-based techniques are centralized while our proposed technique is
distributed, where the sensors \textit{locally} detect attacks, with no central
coordinator, using specific probabilistic thresholds; and (iii) no
local-observability assumption at a sensor is made, which makes our method
feasible for large-scale social networks. Moreover, we consider a Linear Matrix
Inequalities (LMI) approach to design block-diagonal gain (estimator) matrices
under appropriate constraints for isolating the attacks.


Abstract:
  A double-phase argon Time Projection Chamber (TPC), with an active mass of
185 g, has been designed and constructed for the Recoil Directionality (ReD)
experiment. The aim of the ReD project is to investigate the directional
sensitivity of argon-based TPCs via columnar recombination to nuclear recoils
in the energy range of interest (20-200 keV$_{nr}$) for direct dark matter
searches. The key novel feature of the ReD TPC is a readout system based on
cryogenic Silicon Photomultipliers, which are employed and operated
continuously for the first time in an argon TPC. Over the course of six months,
the ReD TPC was commissioned and characterised under various operating
conditions using $\gamma$-ray and neutron sources, demonstrating remarkable
stability of the optical sensors and reproducibility of the results. The
scintillation gain and ionisation amplification of the TPC were measured to be
$g_1 = (0.194 \pm 0.013)$ PE/photon and $g_2 = (20.0 \pm 0.9)$ PE/electron,
respectively. The ratio of the ionisation to scintillation signals (S2/S1),
instrumental for the positive identification of a candidate directional signal
induced by WIMPs, has been investigated for both nuclear and electron recoils.
At a drift field of 183 V/cm, an S2/S1 dispersion of 12% was measured for
nuclear recoils of approximately 60-90 keV$_{nr}$, as compared to 18% for
electron recoils depositing 60 keV of energy. The detector performance reported
here meets the requirements needed to achieve the principal scientific goals of
the ReD experiment in the search for a directional effect due to columnar
recombination. A phenomenological parameterisation of the recombination
probability in LAr is presented and employed for modeling the dependence of
scintillation quenching and charge yield on the drift field for electron
recoils between 50-500 keV and fields up to 1000 V/cm.


Abstract:
  Although distributed machine learning has opened up numerous frontiers of
research, the separation of large models across different devices, nodes, and
sites can invite significant communication overhead, making reliable training
difficult.
  The focus on gradients as the primary shared statistic during training has
led to a number of intuitive algorithms for distributed deep learning; however,
gradient-based algorithms for training large deep neural networks (DNNs) are
communication-heavy, often requiring additional modifications via sparsity
constraints, compression, quantization, and other similar approaches, to lower
bandwidth.
  We introduce a surprisingly simple statistic for training distributed DNNs
that is more communication-friendly than the gradient. The error
backpropagation process can be modified to share these smaller intermediate
values instead of the gradient, reducing communication overhead with no impact
on accuracy. The process provides the flexibility of averaging gradients during
backpropagation, enabling novel flexible training schemas while leaving room
for further bandwidth reduction via existing gradient compression methods.
Finally, consideration of the matrices used to compute the gradient inspires a
new approach to compression via structured power iterations, which can not only
reduce bandwidth but also enable introspection into distributed training
dynamics, without significant performance loss.


Abstract:
  Field observations form the basis of many scientific studies, especially in
ecological and social sciences. Despite efforts to conduct such surveys in a
standardized way, observations can be prone to systematic measurement errors.
The removal of systematic variability introduced by the observation process, if
possible, can greatly increase the value of this data. Existing non-parametric
techniques for correcting such errors assume linear additive noise models. This
leads to biased estimates when applied to generalized linear models (GLM). We
present an approach based on residual functions to address this limitation. We
then demonstrate its effectiveness on synthetic data and show it reduces
systematic detection variability in moth surveys.


Abstract:
  We present an approach based on density-functional theory for the calculation
of fundamental gaps of both finite and periodic two-dimensional (2D) electronic
systems. The computational cost of our approach is comparable to that of total
energy calculations performed via standard semi-local forms. We achieve this by
replacing the 2D local density approximation with a more sophisticated -- yet
computationally simple -- orbital-dependent modeling of the exchange potential
within the procedure by Guandalini et al. [Phys. Rev. B 99, 125140 (2019)]. We
showcase promising results for semiconductor 2D quantum dots and artificial
graphene systems, where the band structure can be tuned through, e.g., Kekul\'e
distortion.


Abstract:
  Many weakly supervised classification methods employ a noise transition
matrix to capture the class-conditional label corruption. To estimate the
transition matrix from noisy data, existing methods often need to estimate the
noisy class-posterior, which could be unreliable due to the overconfidence of
neural networks. In this work, we propose a theoretically grounded method that
can estimate the noise transition matrix and learn a classifier simultaneously,
without relying on the error-prone noisy class-posterior estimation.
Concretely, inspired by the characteristics of the stochastic label corruption
process, we propose total variation regularization, which encourages the
predicted probabilities to be more distinguishable from each other. Under mild
assumptions, the proposed method yields a consistent estimator of the
transition matrix. We show the effectiveness of the proposed method through
experiments on benchmark and real-world datasets.


Abstract:
  I suggest a novel solution of the inflation and reheating problems of the
very early universe. My start point is directly to solve the evolution equation
system of the slow-roll parameters rather than build an inflaton potential. My
model can completely calculate the time evolutions of the inflation and
reheating processes provided a few boundary values. The numerical results of
the model not only clearly show the slow-roll characteristic of the inflation
and the unconventional mechanism of the inflaton mass generation, but also
perfectly reproduce all of the measured data of the inflation. In addition, the
model establishes the relationships among the inflation, the reheating and the
particle physics, in particular, it predicts that the reheating duration is
$\approx1.1$ times the inflaton lifetime, $r_{0.002}$ is one to two order of
magnitude smaller than its current upper bound, and so on. Finally, it is very
possible to test the model in the near future.


Abstract:
  A popular way to create detailed yet easily controllable 3D shapes is via
procedural modeling, i.e. generating geometry using programs. Such programs
consist of a series of instructions along with their associated parameter
values. To fully realize the benefits of this representation, a shape program
should be compact and only expose degrees of freedom that allow for meaningful
manipulation of output geometry. One way to achieve this goal is to design
higher-level macro operators that, when executed, expand into a series of
commands from the base shape modeling language. However, manually authoring
such macros, much like shape programs themselves, is difficult and largely
restricted to domain experts. In this paper, we present ShapeMOD, an algorithm
for automatically discovering macros that are useful across large datasets of
3D shape programs. ShapeMOD operates on shape programs expressed in an
imperative, statement-based language. It is designed to discover macros that
make programs more compact by minimizing the number of function calls and free
parameters required to represent an input shape collection. We run ShapeMOD on
multiple collections of programs expressed in a domain-specific language for 3D
shape structures. We show that it automatically discovers a concise set of
macros that abstract out common structural and parametric patterns that
generalize over large shape collections. We also demonstrate that the macros
found by ShapeMOD improve performance on downstream tasks including shape
generative modeling and inferring programs from point clouds. Finally, we
conduct a user study that indicates that ShapeMOD's discovered macros make
interactive shape editing more efficient.


Abstract:
  In some scientific fields, it is common to have certain variables of interest
that are of particular importance and for which there are many studies
indicating a relationship with a different explanatory variable. In such cases,
particularly those where no relationships are known among explanatory
variables, it is worth asking under what conditions it is possible for all such
claimed effects to exist simultaneously. This paper addresses this question by
reviewing some theorems from multivariate analysis that show, unless the
explanatory variables also have sizable effects on each other, it is impossible
to have many such large effects. We also discuss implications for the
replication crisis in social science.


Abstract:
  Convolutions are the core operation of deep learning applications based on
Convolutional Neural Networks (CNNs). Current GPU architectures are highly
efficient for training and deploying deep CNNs, and hence, these are largely
used in production for this purpose. State-of-the-art implementations, however,
present a lack of efficiency for some commonly used network configurations.
  In this paper we propose a GPU-based implementation of the convolution
operation for CNN inference that favors coalesced accesses, without requiring
prior data transformations. Our experiments demonstrate that our proposal
yields notable performance improvements in a range of common CNN forward
propagation convolution configurations, with speedups of up to 2.29x with
respect to the best implementation of convolution in cuDNN, hence covering a
relevant region in currently existing approaches.


Abstract:
  In this paper we prove two results pertaining to the (unramified and global)
geometric Langlands program. The first result is an analogue of the Ramanujan
conjecture: any cuspidal D-module on Bun_G is tempered. We actually prove a
more general statement: any D-module that is *-extended from a quasi-compact
open substack of Bun_G is tempered. Then the assertion about cuspidal objects
is an immediate consequence of a theorem of Drinfeld-Gaitsgory. Building up on
this, we prove our second main result, the automorphic gluing theorem for the
group SL_2: it states that any D-module on Bun_{SL_2} is determined by its
tempered part and its constant term. This theorem (vaguely speaking, an
analogue of Langlands' classification for the group SL_2(R)) corresponds under
geometric Langlands to the spectral gluing theorem of Arinkin-Gaitsgory and the
author.


Abstract:
  We find all solutions to the parametrized family of norm-form equations
$x^3-(t^3-1)y^3+3(t^3-1)xy+(t^3-1)^2 = \pm 1$ studied by Amoroso, Masser and
Zannier. Our proof relies upon an appeal to lower bounds for linear forms in
logarithms and various elementary arguments.


Abstract:
  In this paper, we have designed and employed a suspended-wall silo to remove
Janssen effect in order to explore directly the local pressure dependence of
Granular Orifice Flow (GOF) systematically. We find that as Janssen effect is
removed, the flow rate Q changes linearly with the external pressure. The slope
{\alpha} of the linear change decays exponentially with the ratio of the silo
size and the size of the orifice {\Phi}/D, which suggests the existence of a
characteristic ratio {\lambda} (~2.4). When {\Phi}/D > {\lambda}, {\alpha}
gradually decays to zero, and the effect of external pressure on the GOF
becomes negligible, where the Beverloo law retrieves. Our results show that
Janssen effect is not a determining factor of the constant rate of GOF,
although it may contribute to shield the top load. The key parameter in GOF is
{\Phi}/D. In small {\Phi}/D, the flow rate of GOF can be directly adjusted by
the external pressure via our suspended-wall setup, which may be useful to the
transportation of granules in microgravity environment where the gravity-driven
Beverloo law is disabled.


Abstract:
  Recent experiments in quantum simulators have provided evidence for the
Many-Body Localized (MBL) phase in 1D and 2D bosonic quantum matter. The
theoretical study of such bosonic MBL, however, is a daunting task due to the
unbounded nature of its Hilbert space. In this work, we introduce a method to
compute the long-time real-time evolution of 1D and 2D bosonic systems in an
MBL phase at strong disorder and weak interactions. We focus on local dynamical
indicators that are able to distinguish an MBL phase from an Anderson localized
one. In particular, we consider the temporal fluctuations of local observables,
the spatiotemporal behavior of two-time correlators and Out-Of-Time-Correlators
(OTOCs). We show that these few-body observables can be computed with a
computational effort that depends only polynomially on system size but is
independent of the target time, by extending a recently proposed numerical
method [Phys. Rev. B 99, 241114 (2019)] to mixed states and bosons. Our method
also allows us to surrogate our numerical study with analytical considerations
of the time-dependent behavior of the studied quantities.


Abstract:
  Benford's Law (BL) or the Significant Digit Law defines the probability
distribution of the first digit of numerical values in a data sample. This Law
is observed in many naturally occurring datasets. It can be seen as a measure
of naturalness of a given distribution and finds its application in areas like
anomaly and fraud detection. In this work, we address the following question:
Is the distribution of the Neural Network parameters related to the network's
generalization capability? To that end, we first define a metric, MLH (Model
Enthalpy), that measures the closeness of a set of numbers to Benford's Law and
we show empirically that it is a strong predictor of Validation Accuracy.
Second, we use MLH as an alternative to Validation Accuracy for Early Stopping,
removing the need for a Validation set. We provide experimental evidence that
even if the optimal size of the validation set is known before-hand, the peak
test accuracy attained is lower than not using a validation set at all.
Finally, we investigate the connection of BL to Free Energy Principle and First
Law of Thermodynamics, showing that MLH is a component of the internal energy
of the learning system and optimization as an analogy to minimizing the total
energy to attain equilibrium.


Abstract:
  Predicting the densest random disc packing fraction is an unsolved paradigm
problem relevant to a number of disciplines and technologies. One difficulty is
that it is ill-defined without setting a criterion for the disorder. Another is
that the density depends on the packing protocol and the multitude of possible
protocol parameters has so far hindered a general solution. A new approach is
proposed here. After formulating a well-posed form of the general
protocol-independent problem for planar packings of discs, a systematic
criterion is proposed to avoid crystalline hexagonal order as well as further
topological order. The highest possible random packing fraction is then derived
exactly: $\phi_{RCP}=0.852525...$. The solution is based on the cell order
distribution that is shown to: (i) yield directly the packing fraction; (ii)
parameterise all possible packing protocols; (iii) make it possible to define
and limit all topological disorder. The method is further useful for predicting
the highest packing fraction in specific protocols, which is illustrated for a
family of simply-sheared packings that generate maximum-entropy cell order
distributions.


Abstract:
  Quantum state tomography (QST) is a crucial ingredient for almost all aspects
of experimental quantum information processing. As an analog of the "imaging"
technique in the quantum settings, QST is born to be a data science problem,
where machine learning techniques, noticeably neural networks, have been
applied extensively. In this work, we build an integrated all-optical setup for
neural network QST, based on an all-optical neural network (AONN). Our AONN is
equipped with built-in nonlinear activation function, which is based on
electromagnetically induced transparency. Experiment results demonstrate the
validity and efficiency of the all-optical setup, indicating that AONN can
mitigate the state-preparation-and-measurement error and predict the phase
parameter in the quantum state accurately. Given that optical setups are highly
desired for future quantum networks, our all-optical setup of integrated
AONN-QST may shed light on replenishing the all-optical quantum network with
the last brick.


Abstract:
  Recent advances in training deep learning models have demonstrated the
potential to provide accurate chest X-ray interpretation and increase access to
radiology expertise. However, poor generalization due to data distribution
shifts in clinical settings is a key barrier to implementation. In this study,
we measured the diagnostic performance for 8 different chest X-ray models when
applied to (1) smartphone photos of chest X-rays and (2) external datasets
without any finetuning. All models were developed by different groups and
submitted to the CheXpert challenge, and re-applied to test datasets without
further tuning. We found that (1) on photos of chest X-rays, all 8 models
experienced a statistically significant drop in task performance, but only 3
performed significantly worse than radiologists on average, and (2) on the
external set, none of the models performed statistically significantly worse
than radiologists, and five models performed statistically significantly better
than radiologists. Our results demonstrate that some chest X-ray models, under
clinically relevant distribution shifts, were comparable to radiologists while
other models were not. Future work should investigate aspects of model training
procedures and dataset collection that influence generalization in the presence
of data distribution shifts.


Abstract:
  In this work we characterise the properties of the object SDSS
J020536.84-081424.7, an extended nebular region with projected extension of $14
\times 14$ kpc$^{2}$ in the line of sight of the ETG Mrk 1172, using
unprecedented spectroscopic data from MUSE. We perform a spatially resolved
stellar population synthesis and estimate the stellar mass for both Mrk 1172
($1 \times 10^{11} M_{\odot}$) and our object of study ($3 \times 10^{9}
M_{\odot}$). While the stellar content of Mrk 1172 is dominated by an old
($\sim 10$ Gyr) stellar population, the extended nebular emission has its light
dominated by young to intermediate age populations (from $\sim 100$ Myr to
$\sim 1$ Gyr) and presents strong emission lines such as: H${\beta}$, [O III]
${\lambda}{\lambda}$4959,5007, H${\alpha}$, [N II]
${\lambda}{\lambda}$6549,6585 and [S II] ${\lambda}{\lambda}$6717,6732. Using
these emission lines we find that it is metal-poor (with $Z \sim$ 1/3
$Z_{\odot}$, comparable to the LMC) and is actively forming stars ($0.70$
M$_{\odot}$ yr$^{-1}$), especially in a few bright clumpy knots that are
readily visible in H${\alpha}$. The object has an ionised gas mass $\geq 3.8
\times 10^{5}$ M$_{\odot}$. Moreover, the motion of the gas is well described
by a gas in circular orbit in the plane of a disk and is being affected by
interaction with Mrk 1172. We conclude that SDSS J020536.84-081424.7 is most
likely a dwarf irregular galaxy (dIGal).


Abstract:
  We construct new examples of exceptional Hahn and Jacobi polynomials.
Exceptional polynomials are orthogonal polynomials with respect to a measure
which are also eigenfunctions of a second order difference or differential
operator. The most apparent difference between classical or classical discrete
orthogonal polynomials and their exceptional counterparts is that the
exceptional families have gaps in their degrees, in the sense that not all
degrees are present in the sequence of polynomials. The new examples have the
novelty that they depend on an arbitrary number of continuous parameters.


Abstract:
  Weak instruments present a major setback to empirical work. This paper
introduces an estimator that admits weak, uncorrelated, or mean-independent
instruments that are non-independent of endogenous covariates. Relative to
conventional instrumental variable methods, the proposed estimator weakens the
relevance condition considerably without imposing a stronger exclusion
restriction. Identification mainly rests on (1) a weak conditional median
exclusion restriction imposed on pairwise differences in disturbances and (2)
non-independence between covariates and instruments. Under mild conditions, the
estimator is consistent and asymptotically normal. Monte Carlo experiments
showcase an excellent performance of the estimator, and two empirical examples
illustrate its practical utility.


Abstract:
  We propose a novel thermal production mechanism for dark matter based on the
idea that dark matter particles $\chi$ can transform (`infect') heat bath
particles $\psi$: $\chi \psi \rightarrow \chi \chi$. For a small initial
abundance of $\chi$ this induces an exponential growth in the dark matter
number density, closely resembling the epidemic curves of a spreading pathogen
after an initial outbreak. To quantify this relation we present a sharp duality
between the Boltzmann equation for the dark matter number density and
epidemiological models for the spread of infectious diseases. Finally we
demonstrate that the exponential growth naturally stops before $\chi$
thermalizes with the heat bath, corresponding to a triumphant `flattening of
the curve' that matches the observed dark matter abundance.


Abstract:
  We analyze the optimal information design in a click-through auction with
fixed valuations per click, but stochastic click-through rates. While the
auctioneer takes as given the auction rule of the click-through auction, namely
the generalized second-price auction, the auctioneer can design the information
flow regarding the click-through rates among the bidders. A natural requirement
in this context is to ask for the information structure to be calibrated in the
learning sense. With this constraint, the auction needs to rank the ads by a
product of the bid and an unbiased estimator of the click-through rates, and
the task of designing an optimal information structure is thus reduced to the
task of designing an optimal unbiased estimator.
  We show that in a symmetric setting with uncertainty about the click-through
rates, the optimal information structure attains both social efficiency and
surplus extraction. The optimal information structure requires private (rather
than public) signals to the bidders. It also requires correlated (rather than
independent) signals, even when the underlying uncertainty regarding the
click-through rates is independent. Beyond symmetric settings, we show that the
optimal information structure requires partial information disclosure.


Abstract:
  Abstract We study the analytic expression for four flavor neutrino
oscillation in the presence of matter. We calculate the time evolution operator
on flavor and mass basis. We find the matter dependent mass square difference
and neutrino transition probabilities for (3+1) four flavor neutrino
oscillation.


Abstract:
  We study dynanics of $SU(N-4)$ gauge theories with fermions in rank-2
symmetric tensor and $N$ anti-fundamental representations, by perturbing
supersymmetric theories with anomaly-mediated supersymmetry breaking. We find
the $SU(N)\times U(1)$ global symmetry is dynamically broken to $SO(N)$ for
$N\geq 17$, a different result from conjectures in the literature. For $N<17$,
theories flow to infrared fixed points.


Abstract:
  In this paper we discuss contrastive explanations for formal argumentation -
the question why a certain argument (the fact) can be accepted, whilst another
argument (the foil) cannot be accepted under various extension-based semantics.
The recent work on explanations for argumentation-based conclusions has mostly
focused on providing minimal explanations for the (non-)acceptance of
arguments. What is still lacking, however, is a proper argumentation-based
interpretation of contrastive explanations. We show under which conditions
contrastive explanations in abstract and structured argumentation are
meaningful, and how argumentation allows us to make implicit foils explicit.


Abstract:
  Deep learning has demonstrated its strengths in numerous binary analysis
tasks, including function boundary detection, binary code search, function
prototype inference, value set analysis, etc. When applying deep learning to
binary analysis tasks, we need to decide what input should be fed into the
neural network model. More specifically, we need to answer how to represent an
instruction in a fixed-length vector. The idea of automatically learning
instruction representations is intriguing, however the existing schemes fail to
capture the unique characteristics of disassembly. These schemes ignore the
complex intra-instruction structures and mainly rely on control flow in which
the contextual information is noisy and can be influenced by compiler
optimizations.
  In this paper, we propose to pre-train an assembly language model called
PalmTree for generating general-purpose instruction embeddings by conducting
self-supervised training on large-scale unlabeled binary corpora. PalmTree
utilizes three pre-training tasks to capture various characteristics of
assembly language. These training tasks overcome the problems in existing
schemes, thus can help to generate high-quality representations. We conduct
both intrinsic and extrinsic evaluations, and compare PalmTree with other
instruction embedding schemes. PalmTree has the best performance for intrinsic
metrics, and outperforms the other instruction embedding schemes for all
downstream tasks.


Abstract:
  Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on
dialog systems and found that improvement on individual components (e.g., NLU,
policy) in prior work may not necessarily bring benefit to pipeline systems in
system-wise evaluation. To improve the system-wise performance, in this paper,
we propose new joint system-wise optimization techniques for the pipeline
dialog system. First, we propose a new data augmentation approach which
automates the labeling process for NLU training. Second, we propose a novel
stochastic policy parameterization with Poisson distribution that enables
better exploration and offers a principled way to compute policy gradient.
Third, we propose a reward bonus to help policy explore successful dialogs. Our
approaches outperform the competitive pipeline systems from Takanobu et al.
(2020) by big margins of 12% success rate in automatic system-wise evaluation
and of 16% success rate in human evaluation on the standard multi-domain
benchmark dataset MultiWOZ 2.1, and also outperform the recent state-of-the-art
end-to-end trained model from DSTC9.


Abstract:
  Orthogonal frequency division multiplexing (OFDM) is one of the dominant
waveforms in wireless communication systems due to its efficient
implementation. However, it suffers from a loss of spectral efficiency as it
requires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) and
pilots to estimate the channel. We propose in this work to address these
drawbacks by learning a neural network (NN)-based receiver jointly with a
constellation geometry and bit labeling at the transmitter, that allows CP-less
and pilotless communication on top of OFDM without a significant loss in bit
error rate (BER). Our approach enables at least 18% throughput gains compared
to a pilot and CP-based baseline, and at least 4% gains compared to a system
that uses a neural receiver with pilots but no CP.


Abstract:
  Theory of choreographic languages typically includes a number of complex
results that are proved by structural induction. The high number of cases and
the subtle details in some of them lead to long reviewing processes, and
occasionally to errors being found in published proofs. In this work, we take a
published proof of Turing completeness of a choreographic language and
formalise it in Coq. Our development includes formalising the choreographic
language and its basic properties, Kleene's theory of partial recursive
functions, the encoding of these functions as choreographies, and proving this
encoding correct.
  With this effort, we show that theorem proving can be a very useful tool in
the field of choreographic languages: besides the added degree of confidence
that we get from a mechanised proof, the formalisation process led us to a
significant simplification of the underlying theory. Our results offer a
foundation for the future formal development of choreographic languages.


Abstract:
  In this paper, we propose a novel fault attack termed as Single Event
Transient Fault Analysis (SETFA) attack, which is well suited for hardware
implementations. The proposed approach pinpoints hotspots in the cypher's Sbox
combinational logic circuit that significantly reduce the key entropy when
subjected to faults. ELEPHANT is a parallel authenticated encryption and
associated data (AEAD) scheme targeted to hardware implementations, a finalist
in the Lightweight cryptography (LWC) competition launched by NIST. In this
work, we investigate vulnerabilities of ELEPHANT against fault analysis. We
observe that the use of 128-bit random nonce makes it resistant against many
cryptanalysis techniques like differential, linear, etc., and their variants.
However, the relaxed nature of Statistical Fault Analysis (SFA) methods makes
them widely applicable in restrictive environments. We propose a SETFA-based
key recovery attack on Elephant. We performed Single experiments with random
plaintexts and keys, on Dumbo, a Sponge-based instance of the Elephant-AEAD
scheme. Our proposed approach could recover the secret key in 85-250
ciphertexts. In essence, this work investigates new vulnerabilities towards
fault analysis that may require to be addressed to ensure secure computations
and communications in IoT scenarios.


Abstract:
  This paper proposes a robust beamforming (BF) scheme to enhance physical
layer security (PLS) of the downlink of a multibeam satellite system in the
presence of either uncoordinated or coordinated eavesdroppers (Eves).
Specifically, with knowing only the approximate locations of the Eves, we aim
at maximizing the worst-case achievable secrecy rate (ASR) of the legitimate
user (LU), subject to the constraints of per-antenna transmit power and quality
of service (QoS) requirement of the LU. Since the optimization problem is
non-convex, we first adopt the discretization method to deal with the unknown
regions of the Eves and then exploit the log-sum-exp function to approximate
the objective function. Afterwards, a BF method joint alternating direction
method of multipliers (ADMM) with Dinkelbach iteration is presented to solve
this non-convex problem. Finally, simulation results verify that our robust BF
algorithm can effectively improve the security of multibeam satellite systems.


Abstract:
  We investigate the role of the Higgs \emph{doublet} in the thermal decoupling
of multi-TeV dark matter coupled to the Weak interactions of the Standard Model
and the Higgs. The Higgs doublet can mediate a long-range force that affects
the annihilation processes and binds dark matter into bound states. More
importantly, the emission of a Higgs doublet by a pair of dark matter particles
can give rise to extremely rapid monopole bound-state formation processes and
bound-to-bound transitions. We compute these effects in the unbroken
electroweak phase. To this end, we consider the simplest renormalisable
fermionic model, consisting of a singlet and a doublet under $SU_{L}(2)$ that
are stabilised by a $\mathbb{Z}_2$ symmetry, in the regime where the two
multiplets coannihilate. In a companion paper, we use the results to show that
the formation of metastable bound states via Higgs-doublet emission and their
decay decrease the relic density very significantly.


Abstract:
  The proton radiography diagnostic is widely used in laser-plasma experiments
to make magnetic field measurements. Recent developments in analysis have
enabled quantitative reconstruction of path-integrated magnetic field values,
but making conclusions about the three-dimensional structure of the fields
remains challenging. In this Letter we propose and demonstrate in kinetic
simulations a novel target geometry which makes possible the production of
multiple proton beams from a single laser pulse, enabling the application of
tomographic methods to proton radiography.


Abstract:
  In the current work, we consider diversion of childbirth patients who arrive
seeking emergency admission to public primary health centers (PHCs). PHCs are
the first point of contact for an Indian patient with formal medical care, and
offer medical care on an outpatient basis, and limited inpatient and childbirth
care. In this context, real-time prediction of the wait time of the arriving
patient becomes important in order to determine whether the patient must be
diverted to another PHC or not. We study this problem using a discrete event
simulation that we develop of medical care operations in two PHCs in India. We
approximate the labour room service at each PHC as an M/G/1 queueing system and
show how the accuracy of real-time delay predictors impacts the extent of the
change in operational outcomes at each PHC. We simulate patient diversion using
actual delays as well as the delay estimates generated by various delay
predictors based on the state of the system such as queue-length, elapsed
service time, and observed delay histories. The simulation of the diversion
process also incorporates travel time between the PHCs. We also propose a new
delay predictor that incorporates information regarding the system state as
well as the service time distribution. We compare the operational outcomes at
both PHCs without diversion and with diversion using the above delay
predictors. We show numerically that more accurate delay predictors lead to
more equitable distribution of resources involved in provision of childbirth
care across both PHCs.


Abstract:
  We study eight different gamma-ray burst (GRB) data sets to examine whether
current GRB measurements -- that probe a largely unexplored part of
cosmological redshift ($z$) space -- can be used to reliably constrain
cosmological model parameters. We use three Amati-correlation samples and five
Combo-correlation samples to simultaneously derive correlation and cosmological
model parameter constraints. The intrinsic dispersion of each GRB data set is
taken as a goodness measurement. We examine the consistency between the
cosmological bounds from GRBs with those determined from better-established
cosmological probes, such as baryonic acoustic oscillation (BAO) and Hubble
parameter $H(z)$ measurements. We use the Markov chain Monte Carlo method
implemented in \textsc{MontePython} to find best-fit correlation and
cosmological parameters, in six different cosmological models, for the eight
GRB samples, alone or in conjunction with BAO and $H(z)$ data. For the Amati
correlation case, we compile a data set of 118 bursts, the A118 sample, which
is the largest -- about half of the total Amati-correlation GRBs -- current
collection of GRBs suitable for constraining cosmological parameters. This
updated GRB compilation has the smallest intrinsic dispersion of the three
Amati-correlation GRB data sets we examined. We are unable to define a
collection of reliable bursts for current Combo-correlation GRB data.
Cosmological constraints determined from the A118 sample are consistent with --
but significantly weaker than -- those from BAO and $H(z)$ data. They also are
consistent with the spatially-flat $\Lambda$CDM model as well as with dynamical
dark energy models and non-spatially-flat models. Since GRBs probe a largely
unexplored region of $z$, it is well worth acquiring more and better-quality
burst data which will give a more definitive answer to the question of the
title.


Abstract:
  Interactive technologies are getting closer to our bodies and permeate the
infrastructure of our homes. While such technologies offer many benefits, they
can also cause an initial feeling of unease in users. It is important for
Human-Computer Interaction to manage first impressions and avoid designing
technologies that appear creepy. To that end, we developed the Perceived
Creepiness of Technology Scale (PCTS), which measures how creepy a technology
appears to a user in an initial encounter with a new artefact. The scale was
developed based on past work on creepiness and a set of ten focus groups
conducted with users from diverse backgrounds. We followed a structured process
of analytically developing and validating the scale. The PCTS is designed to
enable designers and researchers to quickly compare interactive technologies
and ensure that they do not design technologies that produce initial feelings
of creepiness in users.


Abstract:
  Understanding player strategies is a key question when analyzing player
behavior both for academic researchers and industry practitioners. For game
designers and game user researchers, it is important to gauge the distance
between intended strategies and emergent strategies; this comparison allows
identification of glitches or undesirable behaviors. For academic researchers
using games for serious purposes such as education, the strategies adopted by
players are indicative of their cognitive progress in relation to serious
goals, such as learning process. Current techniques and systems created to
address these needs present a few drawbacks. Qualitative methods are difficult
to scale upwards to include large number of players and are prone to subjective
biases. Other approaches such as visualization and analytical tools are either
designed to provide an aggregated overview of the data, losing the nuances of
individual player behaviors, or, in the attempt of accounting for individual
behavior, are not specifically designed to reduce the visual cognitive load. In
this work, we propose a novel visualization technique that specifically
addresses the tasks of comparing behavior sequences in order to capture an
overview of the strategies enacted by players and at the same time examine
individual player behaviors to identify differences and outliers. This approach
allows users to form hypotheses about player strategies and verify them. We
demonstrate the effectiveness of the technique through a case study: utilizing
a prototype system to investigate data collected from a commercial educational
puzzle game. While the prototype's usability can be improved, initial testing
results show that core features of the system proved useful to our potential
users for understanding player strategies.


Abstract:
  In this analysis, we work with the data set that was compiled by Darren
Linvill and Patrick Warren, along with a representative sample of Facebook ads
that were released by the House Intelligence Committee Minority. The goal of
this analysis is to use the categories defined by Linvill and Warren in the
Twitter data and investigate if these categories exist in Facebook ads. This
begin to give us insights to the tactics used between the two social media
services. Further, we try to replicate Linvill and Warren's original
categorization of the Twitter data. Lastly, we investigate what categories may
exist in the Facebook data.


Abstract:
  This paper presents a novel hierarchical motion planning approach based on
Rapidly-Exploring Random Trees (RRT) for global planning and Model Predictive
Control (MPC) for local planning. The approach targets a three-wheeled cycle
rickshaw (trishaw) used for autonomous urban transportation in shared spaces.
Due to the nature of the vehicle, the algorithms had to be adapted in order to
adhere to non-holonomic kinematic constraints using the Kinematic Single-Track
Model.
  The vehicle is designed to offer transportation for people and goods in
shared environments such as roads, sidewalks, bicycle lanes but also open
spaces that are often occupied by other traffic participants. Therefore, the
algorithm presented in this paper needs to anticipate and avoid dynamic
obstacles, such as pedestrians or bicycles, but also be fast enough in order to
work in real-time so that it can adapt to changes in the environment. Our
approach uses an RRT variant for global planning that has been modified for
single-track kinematics and improved by exploiting dead-end nodes. This allows
us to compute global paths in unstructured environments very fast. In a second
step, our MPC-based local planner makes use of the global path to compute the
vehicle's trajectory while incorporating dynamic obstacles such as pedestrians
and other road users.
  Our approach has shown to work both in simulation as well as first real-life
tests and can be easily extended for more sophisticated behaviors.


Abstract:
  AlphaZero has achieved impressive performance in deep reinforcement learning
by utilizing an architecture that combines search and training of a neural
network in self-play. Many researchers are looking for ways to reproduce and
improve results for other games/tasks. However, the architecture is designed to
learn from scratch, tabula rasa, accepting a cold-start problem in self-play.
Recently, a warm-start enhancement method for Monte Carlo Tree Search was
proposed to improve the self-play starting phase. It employs a fixed parameter
$I^\prime$ to control the warm-start length. Improved performance was reported
in small board games. In this paper we present results with an adaptive switch
method. Experiments show that our approach works better than the fixed
$I^\prime$, especially for "deep," tactical, games (Othello and Connect Four).
We conjecture that the adaptive value for $I^\prime$ is also influenced by the
size of the game, and that on average $I^\prime$ will increase with game size.
We conclude that AlphaZero-like deep reinforcement learning benefits from
adaptive rollout based warm-start, as Rapid Action Value Estimate did for
rollout-based reinforcement learning 15 years ago.


Abstract:
  For $n\geq s> r\geq 1$ and $k\geq 2$, write $n \rightarrow (s)_{k}^r$ if
every hyperedge colouring with $k$ colours of the complete $r$-uniform
hypergraph on $n$ vertices has a monochromatic subset of size $s$. Improving
upon previous results by \textcite{AGLM14} and \textcite{EHMR84} we show that
\[ \text{if } r \geq 3 \text{ and } n \nrightarrow (s)_k^r \text{ then } 2^n
\nrightarrow (s+1)_{k+3}^{r+1}. \] This yields an improvement for some of the
known lower bounds on multicolour hypergraph Ramsey numbers.
  Given a hypergraph $H=(V,E)$, we consider the Ramsey-like problem of
colouring all $r$-subsets of $V$ such that no hyperedge of size $\geq r+1$ is
monochromatic. We provide upper and lower bounds on the number of colours
necessary in terms of the chromatic number $\chi(H)$. In particular we show
that this number is $O(\log^{(r-1)} (r \chi(H)) + r)$.


Abstract:
  Reflecting our experiences in areas, like Algebraic Specifications, Abstract
Model Theory, Graph Transformations, and Model Driven Software Engineering
(MDSE), we present a general, category independent approach to Logics of
First-Order Constraints (LFOC). Traditional First-Order Logic, Description
Logic and the sketch framework are discussed as examples. We use the concept of
institution [Diaconescu08,GoguenBurstall92] as a guideline to describe LFOC's.
The main result states that any choice of the six parameters, we are going to
describe, gives us a corresponding "institution of constraints" at hand. The
"presentations" for an institution of constraints can be characterized as
"first-order sketches". As a corresponding variant of the "sketch-entailments"
in [Makkai97], we finally introduce "sketch rules" to equip LFOC's with the
necessary expressive power.


Abstract:
  Podcasts are spoken documents across a wide-range of genres and styles, with
growing listenership across the world, and a rapidly lowering barrier to entry
for both listeners and creators. The great strides in search and recommendation
in research and industry have yet to see impact in the podcast space, where
recommendations are still largely driven by word of mouth. In this perspective
paper, we highlight the many differences between podcasts and other media, and
discuss our perspective on challenges and future research directions in the
domain of podcast information access.


Abstract:
  Pairwise comparison matrices are increasingly used in settings where some
pairs are missing. However, there exist few inconsistency indices for similar
incomplete data sets and no reasonable measure has an associated threshold.
This paper generalises the famous rule of thumb for the acceptable level of
inconsistency, proposed by Saaty, to incomplete pairwise comparison matrices.
The extension is based on choosing the missing elements such that the maximal
eigenvalue of the incomplete matrix is minimised. Consequently, the
well-established values of the random index cannot be adopted: the
inconsistency of random matrices is found to be the function of matrix size and
the number of missing elements, with a nearly linear dependence in the case of
the latter variable. Our results can be directly built into decision-making
software and used by practitioners as a statistical criterion for accepting or
rejecting an incomplete pairwise comparison matrix.


Abstract:
  The recent discovery of AV$_3$Sb$_5$ (A=K,Rb,Cs) has uncovered an intriguing
arena for exotic Fermi surface instabilities in a kagome metal. Among them,
superconductivity is found in the vicinity of multiple van Hove singularities,
exhibiting indications of unconventional pairing. We show that the sublattice
interference mechanism is central to understanding the formation of
superconductivity in a kagome metal. Starting from an appropriately chosen
minimal tight-binding model with multiple with multiple van Hove singularities
close to the Fermi level for AV$_3$Sb$_5$, we provide a random phase
approximation analysis of superconducting instabilities. Non-local Coulomb
repulsion, the sublattice profile of the van Hove bands, and the bare
interaction strength turn out to be the crucial parameters to determine the
preferred pairing symmetry. Implications for potentially topological surface
states are discussed, along with a proposal for additional measurements to pin
down the nature of superconductivity in AV$_3$Sb$_5$.


Abstract:
  Silicon can be isotopically enriched, allowing for the fabrication of highly
coherent semiconductor spin qubits. However, the conduction band of bulk Si
exhibits a six-fold valley degeneracy, which may adversely impact the
performance of silicon quantum devices. To date, the spatial characterization
of valley states in Si remains limited. Moreover, techniques for probing valley
states in functional electronic devices are needed. We describe here a
cryogen-free scanning gate microscope for the characterization of
Si/Si$_{0.7}$Ge$_{0.3}$ quantum devices at mK temperatures. The microscope is
based on the Pan-walker design, with coarse positioning piezo stacks and a fine
scanning piezo tube. A tungsten microscope tip is attached to a tuning fork for
active control of the tip-to-sample distance. To reduce vibration noise from
the pulse tube cooler, we utilize both active and passive vibration isolation
mechanisms, and achieve a root-mean-square noise in $z$ of $\sim$ 2 nm. Our
microscope is designed to characterize fully functioning
Si/Si$_{0.7}$Ge$_{0.3}$ quantum devices. As a proof of concept, we use the
microscope to manipulate the charge occupation of a Si quantum dot, opening up
a range of possibilities for the exploration of quantum devices and materials.


Abstract:
  We develop a theory of the spin battery effect in
superconductor/ferromagnetic insulator (SC/FI) systems taking into account the
magnetic proximity effect. We demonstrate that the spin-energy mixing enabled
by the superconductivity leads to the enhancement of spin accumulation by
several orders of magnitude relative to the normal state. This finding can
explain the recently observed giant inverse spin Hall effect generated by
thermal magnons in the SC/FI system. We suggest a non-local electrical
detection scheme which can directly probe the spin accumulation driven by the
magnetization dynamics. We predict a giant Seebeck effect converting the magnon
temperature bias into the non-local voltage signal. We also show how this can
be used to enhance the sensitivity of magnon detection even up to the
single-magnon level.


Abstract:
  Deep neural networks (DNNs) have been widely used for medical image analysis.
However, the lack of access a to large-scale annotated dataset poses a great
challenge, especially in the case of rare diseases, or new domains for the
research society. Transfer of pre-trained features, from the relatively large
dataset is a considerable solution. In this paper, we have explored supervised
segmentation using domain adaptation for optic nerve and orbital tumor, when
only small sampled CT images are given. Even the lung image database consortium
image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed
domain adaptation method improved the performance of attention U-Net for the
segmentation in public optic nerve dataset and our clinical orbital tumor
dataset. The code and dataset are available at https://github.com/cmcbigdata.


Abstract:
  The increasing amount of distributed energy resources including renewable
energy systems and electric vehicles is expected to change electric power grids
significantly, where conventional consumers are transformed to prosumers since
they can produce electricity as well. In such an ecosystem, prosumers can start
offering their excess energy to supply demands of the other customers on the
grids behind the meter without interference of distribution system operators
(DSO). Besides, DSOs require more accurate and more frequent data form
prosumers' net demand to be able to operate their network efficiently. The main
challenge in these new distribution grids is the amount of data that needs to
be collected in this platform is unbelievably high, and more immortally,
prosumers will likely refuse to share their information with DSOs due to their
potential privacy and economic concerns. Blockchain technology as an efficient
distributed solution for management of data and financial transactions, has
been considered to solve this trust issue. With blockchain-based solutions,
data and financial transactions between all parties will take placed through
distributed ledgers without any interference from an intermediary. In this
paper, impacts of blockchain technologies on electric power industry is
studied. The paper specifically focuses on LO3 Energy -- one of startups
applying blockchain to electric power grids -- their blockchain-based solution
called Exergy, and their use cases to implement such solutions.


Abstract:
  Mean dimension is a topological invariant of dynamical systems, which
originates with Mikhail Gromov in 1999 and which was studied with deep
applications around 2000 by Elon Lindenstrauss and Benjamin Weiss within the
framework of amenable group actions. Let a countable discrete amenable group
$G$ act continuously on compact metrizable spaces $X$ and $Y$. Consider the
product action of $G$ on the product space $X\times Y$. The product inequality
for mean dimension is well known: $\mathrm{mdim}(X\times
Y,G)\le\mathrm{mdim}(X,G)+\mathrm{mdim}(Y,G)$, while it was unknown for a long
time if the product inequality could be an equality. In 2019, Masaki Tsukamoto
constructed the first example of two different continuous actions of $G$ on
compact metrizable spaces $X$ and $Y$, respectively, such that the product
inequality becomes strict. However, there is still one longstanding problem
which remains open in this direction, asking if there exists a continuous
action of $G$ on some compact metrizable space $X$ such that
$\mathrm{mdim}(X\times X,G)<2\cdot\mathrm{mdim}(X,G)$. We solve this problem.
Somewhat surprisingly, we prove, in contrast to (topological) dimension theory,
a rather satisfactory theorem: If an infinite (countable discrete) amenable
group $G$ acts continuously on a compact metrizable space $X$, then we have
$\mathrm{mdim}(X^n,G)=n\cdot\mathrm{mdim}(X,G)$, for any positive integer $n$.
Our product formula for mean dimension, together with the example and
inequality (stated previously), eventually allows mean dimension of product
actions to be fully understood.


Abstract:
  We study the homogenous quenching processes in a holographic s+p model with
reentrant phase transitions. We first realize the reentrant phase transition in
the holographic model in probe limit and draw the phase diagram. Next, we
compare the time evolution of the two condensates in two groups of numerical
quenching experiments across the reentrant region, with different quenching
speed as well as different width of the reentrant region, respectively. We also
study the dynamical competition between the two orders in quenching processes
from the normal phase to the superconductor phase.


Abstract:
  We propose and discuss sensitivity metrics for reliability analysis, which
are based on the value of information. These metrics are easier to interpret
than other existing sensitivity metrics in the context of a specific decision
and they are applicable to any type of reliability assessment, including those
with dependent inputs. We develop computational strategies that enable
efficient evaluation of these metrics, in some scenarios without additional
runs of the deterministic model. The metrics are investigated by application to
numerical examples.


Abstract:
  Synonymous keyword retrieval has become an important problem for sponsored
search ever since major search engines relax the exact match product's matching
requirement to a synonymous level. Since the synonymous relations between
queries and keywords are quite scarce, the traditional information retrieval
framework is inefficient in this scenario. In this paper, we propose a novel
quotient space-based retrieval framework to address this problem. Considering
the synonymy among keywords as a mathematical equivalence relation, we can
compress the synonymous keywords into one representative, and the corresponding
quotient space would greatly reduce the size of the keyword repository. Then an
embedding-based retrieval is directly conducted between queries and the keyword
representatives. To mitigate the semantic gap of the quotient space-based
retrieval, a single semantic siamese model is utilized to detect both the
keyword--keyword and query-keyword synonymous relations. The experiments show
that with our quotient space-based retrieval method, the synonymous keyword
retrieving performance can be greatly improved in terms of memory cost and
recall efficiency. This method has been successfully implemented in Baidu's
online sponsored search system and has yielded a significant improvement in
revenue.


Abstract:
  We compute the motivic Euler characteristic of Ayoub's nearby cycles by
strata of a semi-stable reduction, for a degeneration to multiple isolated
quasi-homogeneous singularities resolved by a single weighted blow-up. This
allows to compare the local picture at the singularities with the global
conductor formula for hypersurfaces developed by Levine, Pepin Lehalleur and
Srinivas, revealing that the formula is local in nature, thus extending it to
the more general setting considered in this paper. This gives a quadratic
refinement for the classical Milnor number formula with multiple singularities
of a certain type.


Abstract:
  We propose a robust in-time predictor for in-hospital COVID-19 patient's
probability of requiring mechanical ventilation. A challenge in the risk
prediction for COVID-19 patients lies in the great variability and irregular
sampling of patient's vitals and labs observed in the clinical setting.
Existing methods have strong limitations in handling time-dependent features'
complex dynamics, either oversimplifying temporal data with summary statistics
that lose information or over-engineering features that lead to less robust
outcomes. We propose a novel in-time risk trajectory predictive model to handle
the irregular sampling rate in the data, which follows the dynamics of risk of
performing mechanical ventilation for individual patients. The model
incorporates the Multi-task Gaussian Process using observed values to learn the
posterior joint multi-variant conditional probability and infer the missing
values on a unified time grid. The temporal imputed data is fed into a
multi-objective self-attention network for the prediction task. A novel
positional encoding layer is proposed and added to the network for producing
in-time predictions. The positional layer outputs a risk score at each
user-defined time point during the entire hospital stay of an inpatient. We
frame the prediction task into a multi-objective learning framework, and the
risk scores at all time points are optimized altogether, which adds robustness
and consistency to the risk score trajectory prediction. Our experimental
evaluation on a large database with nationwide in-hospital patients with
COVID-19 also demonstrates that it improved the state-of-the-art performance in
terms of AUC (Area Under the receiver operating characteristic Curve) and AUPRC
(Area Under the Precision-Recall Curve) performance metrics, especially at
early times after hospital admission.


Abstract:
  Interference is the cornerstone of Huygens source design for reshaping and
controlling scattering patterns. The conventional underpinning principle, such
as for the Kerker effect, is the interference of electric and magnetic dipole
and quadrupole modes. Here a route to realize transverse Kerker scattering
through employing only the interference between the electric dipole and
magnetic quadrupole is demonstrated. The proposed approach is numerically
validated in an ultra-thin Silicon square nanoplate metasurface, and is further
verified by multipole decomposition. The metasurface is shown to be invisible
fornear-infrared wavelengths and with an enhanced electric field in the region
of the nanoparticle. Additionally, we develop further the proposed approach
with practical implementation for invisibility applications by exploring the
effects of the aspect ratio of the square plate nanoresonator, the
inter-particle separation, and the presence of a substrate. Further it is
demonstrated that invisibility can be observed at oblique incidence up to
60{\deg} for a transverse magnetic plane wave. The results are relevant for
Huygens metasurface design for perfect reflectors, invisibility and devices for
harmonic generation manipulation.


Abstract:
  We study a one dimensional quantum XY spin chain driven by a local noisy spin
impurity with finite correlation time, along the transverse field direction. We
recover the celebrated Zeno crossover and we show that entanglement can be used
as a proxy for the heating and strong-measurement regimes. We compute the
entanglement entropy of a block of spins and we observe that its velocity
spreading decreases at strong dissipation, as a result of the Zeno effect. Upon
increasing the correlation time of the noise, the location of the Zeno
crossover shifts at stronger dissipation rates opening up a broader heating
phase. We offer insight on the mechanisms underlying the dynamics of the
entanglement entropy by monitoring different time traces of the local
transverse magnetisation profile. Our results aim at starting a complementary
viewpoint on the field of dissipative quantum impurities, based on a
theoretical quantum information perspective.


Abstract:
  In this short article we show a particular version of the Hedberg inequality
which can be used to derive, in a very simple manner, functional inequalities
involving Sobolev and Besov spaces in the general setting of Lebesgue spaces of
variable exponents and in the framework of Orlicz spaces.


Abstract:
  Recently, two-dimensional monolayer MoSi2N4 with hexagonal structure was
successfully synthesized in experiment (Hong et al 2020 Science 369, 670). The
fabricated monolayer MoSi2N4 is predicted to have excellent mechanical
properties. Motived by the experiment, we perform first-principles calculations
to investigate the mechanical properties of monolayer MoSi2N4, including its
ideal tensile strengths, critical strains, and failure mechanisms. Our results
demonstrate that monolayer MoSi2N4 can withstand stresses up to 51.6 and 49.2
GPa along zigzag and armchair directions, respectively. The corresponding
critical strains are 26.5% and 17.5%, respectively. For biaxial strain, the
ideal tensile strength is 50.6 GPa with a critical strain of 19.5%. Compared
with monolayer MoS2, monolayer MoSi2N4 possesses much higher elastic moduli and
ideal tensile strengths for both uniaxial and biaxial strains. Interestingly,
the critical strain and failure mechanism of zigzag direction in MoSi2N4 are
almost the same as those of armchair direction in MoS2, while the critical
strain and failure mechanism of armchair direction for MoSi2N4 are similar to
the ones of zigzag direction for MoS2. Our work reveals the remarkable
mechanical characteristics of monolayer MoSi2N4.


Abstract:
  We present a new learning-based method for identifying safe and navigable
regions in off-road terrains and unstructured environments from RGB images. Our
approach consists of classifying groups of terrains based on their navigability
levels using coarse-grained semantic segmentation. We propose a bottleneck
transformer-based deep neural network architecture that uses a novel group-wise
attention mechanism to distinguish between navigability levels of different
terrains. Our group-wise attention heads enable the network to explicitly focus
on the different groups and improve the accuracy. We show through extensive
evaluations on the RUGD and RELLIS-3D datasets that our learning algorithm
improves visual perception accuracy in off-road terrains for navigation. We
compare our approach with prior work on these datasets and achieve an
improvement over the state-of-the-art mIoU by 6.74-39.1% on RUGD and
3.82-10.64% on RELLIS-3D. In addition, we deploy our method on a Clearpath
Jackal robot. Our approach improves the performance of the navigation algorithm
in terms of average progress towards the goal by 54.73% and the false positives
in terms of forbidden region by 29.96%.


Abstract:
  This paper introduces the \emph{Simultaneous Assignment Problem}. Here, we
are given an assignment problem on some of the subgraphs of a given graph, and
we are looking for a heaviest assignment which is feasible when restricted to
any of the assignment problems. More precisely, we are given a graph with a
weight- and a capacity function on its edges and a set of its subgraphs
$H_1,\dots,H_k$ along with a degree upper bound function for each of them. In
addition, we are also given a laminar system on the node set with an upper
bound on the degree-sum of the nodes in each set in the system. We want to
assign each edge a non-negative integer below its capacity such that the total
weight is maximized, the degrees in each subgraph are below the degree upper
bound associated with the subgraph, and the degree-sum bound is respected in
each set of the laminar system.
  The problem is shown to be APX-hard in the unweighted case even if the graph
is a forest and $k=2$. This also implies that the Distance matching problem is
APX-hard in the weighted case and that the Cyclic distance matching problem is
APX-hard in the unweighted case. We identify multiple special cases when the
problem can be solved in strongly polynomial time. One of these cases, the
so-called locally laminar case, is a common generalization of the Hierarchical
b-matching problem and the Laminar matchoid problem, and it implies that both
of these problems can be solved efficiently in the weighted, capacitated case
-- improving upon the most general polynomial-time algorithms for these
problems. The problem can be constant approximated when $k$ is a constant, and
we show that the approximation factor matches the integrality gap of a
strengthened LP-relaxation for small $k$. We give improved approximation
algorithms for special cases, for example, when the degree bounds are uniform
or the graph is sparse.


Abstract:
  Let $V$ be an $n$-dimensional vector space over a finite field
$\mathbb{F}_q$, where $q$ is a prime power. Define the \emph{generalized
$q$-Kneser graph} $K_q(n,k,t)$ to be the graph whose vertices are the
$k$-dimensional subspaces of $V$ and two vertices $F_1$ and $F_2$ are adjacent
if $\dim(F_1\cap F_2)<t$. Then $K_q(n,k,1)$ is the well-known $q$-Kneser graph.
In this paper, we determine the treewidth of $K_q(n,k,t)$ for $n\geq
2t(k-t+1)+k+1$ and $t\ge 1$ exactly. Note that $K_q(n,k,k-1)$ is the complement
of the Grassmann graph $G_q(n,k)$. We give a more precise result for the
treewidth of $\overline{G_q(n,k)}$ for any possible $n$, $k$ and $q$.


Abstract:
  The creation of an electron-positron pair in the collision of two real
photons, namely the linear Breit-Wheeler process, has never been detected
directly in the laboratory since its prediction in 1934 despite its fundamental
importance in quantum electrodynamics and astrophysics. In the last few years,
several experimental setup have been proposed to observe this process in the
laboratory, relying either on thermal radiation, Bremsstrahlung, linear or
multiphoton inverse Compton scattering photons sources created by lasers or by
the mean of a lepton collider coupled with lasers. In these propositions, the
influence of the photons' energy distribution on the total number of produced
pairs has been taken into account with an analytical model only for two of
these cases. We hereafter develop a general and original, semi-analytical model
to estimate the influence of the photons energy distribution on the total
number of pairs produced by the collision of two such photon beams, and give
optimum energy parameters for some of the proposed experimental configurations.
Our results shows that the production of optimum Bremsstrahlung and linear
inverse Compton sources are, only from energy distribution considerations,
already reachable in today's facilities. Despite its less interesting energy
distribution features for the LBW pair production, the photon sources generated
via multiphoton inverse Compton scattering by the propagation of a laser in a
micro-channel can also be interesting, thank to the high collision luminosity
that could eventually be reached by such configurations. These results then
gives important insights for the design of experiments intended to detect
linear Breit-Wheeler produced positrons in the laboratory for the first time.


Abstract:
  The quasiparticle formalism invented by Lev Landau for description of
conventional Fermi liquids is generalized to exotic superconductivity
attributed to Cooper pairing, whose measured properties defy explanation within
the standard BCS-Fermi Liquid description. We demonstrate that in such systems
the quasiparticle number remains equal to particle number, just as in common
Fermi liquids. We are then able to explain the puzzling relationship between
the variation with doping $x$ of two key properties of the family
La$_{2-x}$Sr$_x$Cu0$_4$ of exotic superconductors, namely the $T=0$ superfluid
density $\rho_{s0}(x)$ and the coefficient $A_1(x)$ in the linear-in-$T$
component of the normal-state low-$T$ resistivity $\rho(T)=\rho_0+A_1T+A_2T^2$,
in terms of the presence of interaction-induced flat bands in the ground states
of these metals.


Abstract:
  CTB 80 (G69.0+2.7) is a relatively old (50--80 kyr) supernova remnant (SNR)
with a complex radio morphology showing three extended radio arms and a radio
and X-ray nebula near the location of the pulsar PSR B1951+32. We report on a
study of the GeV emission in the region of CTB 80 with \emph{Fermi}-LAT data.
An extended source with a size of 1.3$^\circ$, matching the size of the
infrared shell associated to the SNR, was discovered. The GeV emission,
detected up to an energy of $\sim 20$ GeV, is more significant at the location
of the northern radio arm where previous observations imply that the SNR shock
is interacting with ambient material. Both hadronic and leptonic scenarios can
reproduce the multiwavelength data reasonably well. The hadronic cosmic ray
energy density required is considerably larger than the local Galactic value
and the gamma-ray leptonic emission is mainly due to bremsstrahlung
interactions. We conclude that GeV particles are still trapped or accelerated
by the SNR producing the observed high-energy emission when interacting with
ambient material.


Abstract:
  We prove unique weak solvability and Feller property for stochastic
differential equations with drift in a large class of time-dependent vector
fields. This class contains, in particular, the critical
Ladyzhenskaya-Prodi-Serrin class, the weak $L^d$ class as well as some vector
fields that are not even in $L^{2+\varepsilon}_{\rm loc}$, $\varepsilon>0$.


Abstract:
  For a digraph $G$ and $v \in V(G)$, let $\delta^+(v)$ be the number of
out-neighbors of $v$ in $G$. The Caccetta-H\"{a}ggkvist conjecture states that
for all $k \ge 1$, if $G$ is a digraph with $n = |V(G)|$ such that $\delta^+(v)
\ge k$ for all $v \in V(G)$, then $G$ contains a directed cycle of length at
most $\lceil n/k \rceil$. Aharoni proposed a generalization of this conjecture,
that a simple edge-colored graph on $n$ vertices with $n$ color classes, each
of size $k$, has a rainbow cycle of length at most $\lceil n/k \rceil$. With
Pelik\'anov\'a and Pokorn\'a, we showed that this conjecture is true if each
color class has size ${\Omega}(k\log k)$. In this paper, we present a proof of
the conjecture if each color class has size ${\Omega}(k)$, which improved the
previous result and is only a constant factor away from Aharoni's conjecture.
We also consider what happens when the condition on the number of colors is
relaxed.


Abstract:
  In this work we present a dual-mode mid-infrared workflow [6], for detecting
sub-superficial mural damages in frescoes artworks. Due to the large nature of
frescoes, multiple thermal images are recorded. Thus, the experimental setup
may introduce measurements errors, seen as inter-frame changes in the image
contrast, after mosaicking. An approach to lowering errors is to post-process
the mosaic [10] via osmosis partial differential equation (PDE) [12, 13], which
preserves details, mass and balance the lights: efficient numerical study for
osmosis on large images is proposed [2, 11], based on operator splitting [8].
Our range of Cultural Heritage applications include the detection of
sub-superficial voids in Monocromo (L. Da Vinci, Castello Sforzesco, Milan)
[5], the light-balance for multi-spectral imaging and the data integration on
the Archimedes Palimpsest [10].


Abstract:
  Machine learning technologies using deep neural networks (DNNs), especially
convolutional neural networks (CNNs), have made automated, accurate, and fast
medical image analysis a reality for many applications, and some DNN-based
medical image analysis systems have even been FDA-cleared. Despite the
progress, challenges remain to build DNNs as reliable as human expert doctors.
It is known that DNN classifiers may not be robust to noises: by adding a small
amount of noise to an input image, a DNN classifier may make a wrong
classification of the noisy image (i.e., in-distribution adversarial sample),
whereas it makes the right classification of the clean image. Another issue is
caused by out-of-distribution samples that are not similar to any sample in the
training set. Given such a sample as input, the output of a DNN will become
meaningless. In this study, we investigated the in-distribution (IND) and
out-of-distribution (OOD) adversarial robustness of a representative CNN for
lumbar disk shape reconstruction from spine MR images. To study the
relationship between dataset size and robustness to IND adversarial attacks, we
used a data augmentation method to create training sets with different levels
of shape variations. We utilized the PGD-based algorithm for IND adversarial
attacks and extended it for OOD adversarial attacks to generate OOD adversarial
samples for model testing. The results show that IND adversarial training can
improve the CNN robustness to IND adversarial attacks, and larger training
datasets may lead to higher IND robustness. However, it is still a challenge to
defend against OOD adversarial attacks.


Abstract:
  The Posner-Robinson Theorem states that for any reals $Z$ and $A$ such that
$Z \oplus 0' \leq_\mathrm{T} A$ and $0 <_\mathrm{T} Z$, there exists $B$ such
that $A \equiv_\mathrm{T} B' \equiv_\mathrm{T} B \oplus Z \equiv_\mathrm{T} B
\oplus 0'$. Consequently, any nonzero Turing degree
$\operatorname{deg}_\mathrm{T}(Z)$ is a Turing jump relative to some $B$. Here
we prove the hyperarithmetical analog, based on an unpublished proof of Slaman,
namely that for any reals $Z$ and $A$ such that $Z \oplus \mathcal{O}
\leq_\mathrm{T} A$ and $0 <_\mathrm{HYP} Z$, there exists $B$ such that $A
\equiv_\mathrm{T} \mathcal{O}^B \equiv_\mathrm{T} B \oplus Z \equiv_\mathrm{T}
B \oplus \mathcal{O}$. As an analogous consequence, any nonhyperarithmetical
Turing degree $\operatorname{deg}_\mathrm{T}(Z)$ is a hyperjump relative to
some $B$.


Abstract:
  We provide a sharp lower bound on the $p$-norm of a sum of independent
uniform random variables in terms of its variance when $0 < p < 1$. We address
an analogous question for $p$-R\'enyi entropy for $p$ in the same range.


Abstract:
  Understanding turbulence is the key to our comprehension of many natural and
technological flow processes. At the heart of this phenomenon lies its
intricate multi-scale nature, describing the coupling between different-sized
eddies in space and time. Here we introduce a new paradigm for analyzing the
structure of turbulent flows by quantifying correlations between different
length scales using methods inspired from quantum many-body physics. We present
results for interscale correlations of two paradigmatic flow examples, and use
these insights along with tensor network theory to design a structure-resolving
algorithm for simulating turbulent flows. With this algorithm, we find that the
incompressible Navier-Stokes equations can be accurately solved within a
computational space reduced by over an order of magnitude compared to direct
numerical simulation. Our quantum-inspired approach provides a pathway towards
conducting computational fluid dynamics on quantum computers.


Abstract:
  We investigate one-dimensional three-body systems composed of two identical
bosons and one imbalanced atom (impurity) with two-body and three-body
zero-range interactions. For the case in the absence of three-body interaction,
we give a complete phase diagram of the number of three-body bound states in
the whole region of mass ratio via the direct calculation of the
Skornyakov-Ter-Martirosyan equations. We demonstrate that other low-lying
three-body bound states emerge when the mass of the impurity particle is not
equal to another two identical particles. We can obtain not only the binding
energies but also the corresponding wave functions. When the mass of impurity
atom is vary large, there are at most three three-body bound states. We then
study the effect of three-body zero-range interaction and unveil that it can
induces one more three-body bound state at a certain region of coupling
strength ratio under a fixed mass ratio.


Abstract:
  We consider an input-to-response (ItR) system characterized by (1)
parameterized input with a known probability distribution and (2) stochastic
ItR function with heteroscedastic randomness. Our purpose is to efficiently
quantify the extreme response probability when the ItR function is expensive to
evaluate. The problem setup arises often in physics and engineering problems,
with randomness in ItR coming from either intrinsic uncertainties (say, as a
solution to a stochastic equation) or additional (critical) uncertainties that
are not incorporated in a low-dimensional input parameter space (as a result of
dimension reduction applied to the original high-dimensional input space). To
reduce the required sampling numbers, we develop a sequential Bayesian
experimental design method leveraging the variational heteroscedastic Gaussian
process regression (VHGPR) to account for the stochastic ItR, along with a new
criterion to select the next-best samples sequentially. The validity of our new
method is first tested in two synthetic problems with the stochastic ItR
functions defined artificially. Finally, we demonstrate the application of our
method to an engineering problem of estimating the extreme ship motion
probability in irregular waves, where the uncertainty in ItR naturally
originates from standard wave group parameterization, which reduces the
original high-dimensional wave field into a two-dimensional parameter space.


Abstract:
  It is a long-standing conjecture that any CFT with a large central charge and
a large gap $\Delta_{\text{gap}}$ in the spectrum of higher-spin single-trace
operators must be dual to a local effective field theory in AdS. We prove a
sharp form of this conjecture by deriving numerical bounds on bulk Wilson
coefficients in terms of $\Delta_{\text{gap}}$ using the conformal bootstrap.
Our bounds exhibit the scaling in $\Delta_{\text{gap}}$ expected from
dimensional analysis in the bulk. Our main tools are dispersive sum rules that
provide a dictionary between CFT dispersion relations and S-matrix dispersion
relations in appropriate limits. This dictionary allows us to apply
recently-developed flat-space methods to construct positive CFT functionals. We
show how AdS$_{4}$ naturally resolves the infrared divergences present in 4D
flat-space bounds. Our results imply the validity of twice-subtracted
dispersion relations for any S-matrix arising from the flat-space limit of
AdS/CFT.


Abstract:
  We describe a new addition to the WebVectors toolkit which is used to serve
word embedding models over the Web. The new ELMoViz module adds support for
contextualized embedding architectures, in particular for ELMo models. The
provided visualizations follow the metaphor of `two-dimensional text' by
showing lexical substitutes: words which are most semantically similar in
context to the words of the input sentence. The system allows the user to
change the ELMo layers from which token embeddings are inferred. It also
conveys corpus information about the query words and their lexical substitutes
(namely their frequency tiers and parts of speech). The module is well
integrated into the rest of the WebVectors toolkit, providing lexical
hyperlinks to word representations in static embedding models. Two web services
have already implemented the new functionality with pre-trained ELMo models for
Russian, Norwegian and English.


Abstract:
  We present a novel binding mechanism where a neutral Rydberg atom and an
atomic ion form a molecular bound state at large internuclear distance. The
binding mechanism is based on Stark shifts and level crossings which are
induced in the Rydberg atom due to the electric field of the ion. At particular
internuclear distances between Rydberg atom and ion, potential wells occur
which can hold atom-ion molecular bound states. Apart from the binding
mechanism we describe important properties of the long-range atom-ion Rydberg
molecule, such as its lifetime and decay paths, its vibrational and rotational
structure, and its large dipole moment. Furthermore, we discuss methods how to
produce and detect it. The unusual properties of the long-range atom-ion
Rydberg molecule give rise to interesting prospects for studies of wave packet
dynamics in engineered potential energy landscapes.


Abstract:
  In a previous study, we presented VT-Lane, a three-step framework for
real-time vehicle detection, tracking, and turn movement classification at
urban intersections. In this study, we present a case study incorporating the
highly accurate trajectories and movement classification obtained via VT-Lane
for the purpose of speed estimation and driver behavior calibration for traffic
at urban intersections. First, we use a highly instrumented vehicle to verify
the estimated speeds obtained from video inference. The results of the speed
validation show that our method can estimate the average travel speed of
detected vehicles in real-time with an error of 0.19 m/sec, which is equivalent
to 2% of the average observed travel speeds in the intersection of the study.
Instantaneous speeds (at the resolution of 30 Hz) were found to be estimated
with an average error of 0.21 m/sec and 0.86 m/sec respectively for
free-flowing and congested traffic conditions. We then use the estimated speeds
to calibrate the parameters of a driver behavior model for the vehicles in the
area of study. The results show that the calibrated model replicates the
driving behavior with an average error of 0.45 m/sec, indicating the high
potential for using this framework for automated, large-scale calibration of
car-following models from roadside traffic video data, which can lead to
substantial improvements in traffic modeling via microscopic simulation.


Abstract:
  Visual attention mechanisms are a key component of neural network models for
computer vision. By focusing on a discrete set of objects or image regions,
these mechanisms identify the most relevant features and use them to build more
powerful representations. Recently, continuous-domain alternatives to discrete
attention models have been proposed, which exploit the continuity of images.
These approaches model attention as simple unimodal densities (e.g. a
Gaussian), making them less suitable to deal with images whose region of
interest has a complex shape or is composed of multiple non-contiguous patches.
In this paper, we introduce a new continuous attention mechanism that produces
multimodal densities, in the form of mixtures of Gaussians. We use the EM
algorithm to obtain a clustering of relevant regions in the image, and a
description length penalty to select the number of components in the mixture.
Our densities decompose as a linear combination of unimodal attention
mechanisms, enabling closed-form Jacobians for the backpropagation step.
Experiments on visual question answering in the VQA-v2 dataset show competitive
accuracies and a selection of regions that mimics human attention more closely
in VQA-HAT. We present several examples that suggest how multimodal attention
maps are naturally more interpretable than their unimodal counterparts, showing
the ability of our model to automatically segregate objects from ground in
complex scenes.


Abstract:
  Given a dynamic network, where edges appear and disappear over time, we are
interested in finding sets of edges that have similar temporal behavior and
form a dense subgraph. Formally, we define the problem as the enumeration of
the maximal subgraphs that satisfy specific density and similarity thresholds.
To measure the similarity of the temporal behavior, we use the correlation
between the binary time series that represent the activity of the edges. For
the density, we study two variants based on the average degree. For these
problem variants we enumerate the maximal subgraphs and compute a compact
subset of subgraphs that have limited overlap. We propose an approximate
algorithm that scales well with the size of the network, while achieving a high
accuracy. We evaluate our framework on both real and synthetic datasets. The
results of the synthetic data demonstrate the high accuracy of the
approximation and show the scalability of the framework.


Abstract:
  Fisher-KPP equation is proved to be the scaling limit of a system of Brownian
particles with local interaction. Particles proliferate and die depending on
the local concentration of other particles. Opposite to discrete models,
controlling concentration of particles is a major difficulty in Brownian
particle interaction; local interactions instead of mean field or moderate ones
makes it more difficult to implement the law of large numbers properties. The
approach taken here to overcome these difficulties is largely inspired by A.
Hammond and F. Rezakhanlou [10] implemented there in the mean free path case
instead of the local interaction regime.


Abstract:
  We extend previous works by considering two additional radio frequencies (K
band and X/Ka band) with the aim to study the frequency dependence of the
source positions and its potential connection with the physical properties of
the underlying AGN. We compared the absolute source positions measured at four
different wavelengths, that is, the optical position from the Gaia Early Data
Release 3 (EDR3) and the radio positions at the dual S/X, X/Ka combinations and
at K band, as available from the third realization of the International
Celestial Reference Frame (ICRF3), for 512 common sources. We first aligned the
three ICRF3 individual catalogs onto the Gaia EDR3 frame and compare the
optical-to-radio offsets before and after the alignment. Then we studied the
correlation of optical-to-radio offsets with the observing (radio) frequency,
source morphology, magnitude, redshift, and source type. The deviation among
optical-to-radio offsets determined in the different radio bands is less than
0.5 mas, but there is statistical evidence that the optical-to-radio offset is
smaller at K band compared to S/X band for sources showing extended structures.
The optical-to-radio offset was found to statistically correlate with the
structure index. Large optical-to-radio offsets appear to favor faint sources
but are well explained by positional uncertainty, which is also larger for
these sources. We did not detect any statistically significant correlation
between the optical-to-radio offset and the redshift. The radio source
structure might also be a major cause for the radio-to-optical offset. For the
alignment of with the Gaia celestial reference frame, the S/X band frame
remains the preferred choice at present.


Abstract:
  Robot manipulation of unknown objects in unstructured environments is a
challenging problem due to the variety of shapes, materials, arrangements and
lighting conditions. Even with large-scale real-world data collection, robust
perception and manipulation of transparent and reflective objects across
various lighting conditions remain challenging. To address these challenges we
propose an approach to performing sim-to-real transfer of robotic perception.
The underlying model, SimNet, is trained as a single multi-headed neural
network using simulated stereo data as input and simulated object segmentation
masks, 3D oriented bounding boxes (OBBs), object keypoints, and disparity as
output. A key component of SimNet is the incorporation of a learned stereo
sub-network that predicts disparity. SimNet is evaluated on 2D car detection,
unknown object detection, and deformable object keypoint detection and
significantly outperforms a baseline that uses a structured light RGB-D sensor.
By inferring grasp positions using the OBB and keypoint predictions, SimNet can
be used to perform end-to-end manipulation of unknown objects in both easy and
hard scenarios using our fleet of Toyota HSR robots in four home environments.
In unknown object grasping experiments, the predictions from the baseline RGB-D
network and SimNet enable successful grasps of most of the easy objects.
However, the RGB-D baseline only grasps 35% of the hard (e.g., transparent)
objects, while SimNet grasps 95%, suggesting that SimNet can enable robust
manipulation of unknown objects, including transparent objects, in unknown
environments.


Abstract:
  Internet of Things (IoT) is being considered as the growth engine for
industrial revolution 4.0. The combination of IoT, cloud computing and
healthcare can contribute in ensuring well-being of people. One important
challenge of IoT network is maintaining privacy and to overcome security
threats. This paper provides a systematic review of the security aspects of
IoT. Firstly, the application of IoT in industrial and medical service
scenarios are described, and the security threats are discussed for the
different layers of IoT healthcare architecture. Secondly, different types of
existing malware including spyware, viruses, worms, keyloggers, and trojan
horses are described in the context of IoT. Thirdly, some of the recent malware
attacks such as Mirai, echobot and reaper are discussed. Next, a comparative
discussion is presented on the effectiveness of different machine learning
algorithms in mitigating the security threats. It is found that the k-nearest
neighbor (kNN) machine learning algorithm exhibits excellent accuracy in
detecting malware. This paper also reviews different tools for ransomware
detection, classification and analysis. Finally, a discussion is presented on
the existing security issues, open challenges and possible future scopes in
ensuring IoT security.


Abstract:
  Let $\pi$ be a set of primes such that $|\pi|\geqslant 2$ and $\pi$ differs
from the set of all primes. Denote by $r$ the smallest prime which does not
belong to $\pi$ and set $m=r$ if $r=2,3$ and $m=r-1$ if $r\geqslant 5$. We
study the following conjecture: a conjugacy class $D$ of a finite group $G$ is
contained in $O\pi(G)$ if and only if every $m$ elements of $D$ generate a
$\pi$-subgroup. We confirm this conjecture for each group $G$ whose nonabelian
composition factors are isomorphic to alternating, linear and unitary simple
groups.