Abstract:
  We present the first BVR photometry, period variation, and photometric
light-curve analysis of two poorly studied eclipsing binaries V1321 Cyg and CR
Tau. Observations were carried out from November 2017 to January 2020 at the
observatory of Uzhhorod National University. Period variations were studied
using all available early published as well as our minima times. We have used
newly developed ELISa code for the light curve analysis and determination of
photometric parameters of both systems. We found that V1321 Cyg is a close
detached eclipsing system with a low photometric mass ratio of $q=0.28$ which
suggests that the binary is a post mass transfer system. No significant period
changes in this system are detected. CR Tau is, on the other hand, a
semi-detached system where the secondary component almost fills its Roche lobe.
We detected a long-term period increase at a rate of $1.49 \times 10^{-7} d/y$,
which support mass transfer from lower mass secondary component to the more
massive primary.


Abstract:
  Environmental Sound Classification (ESC) is a challenging field of research
in non-speech audio processing. Most of current research in ESC focuses on
designing deep models with special architectures tailored for specific audio
datasets, which usually cannot exploit the intrinsic patterns in the data.
However recent studies have surprisingly shown that transfer learning from
models trained on ImageNet is a very effective technique in ESC. Herein, we
propose SoundCLR, a supervised contrastive learning method for effective
environment sound classification with state-of-the-art performance, which works
by learning representations that disentangle the samples of each class from
those of other classes. Our deep network models are trained by combining a
contrastive loss that contributes to a better probability output by the
classification layer with a cross-entropy loss on the output of the classifier
layer to map the samples to their respective 1-hot encoded labels. Due to the
comparatively small sizes of the available environmental sound datasets, we
propose and exploit a transfer learning and strong data augmentation pipeline
and apply the augmentations on both the sound signals and their log-mel
spectrograms before inputting them to the model. Our experiments show that our
masking based augmentation technique on the log-mel spectrograms can
significantly improve the recognition performance. Our extensive benchmark
experiments show that our hybrid deep network models trained with combined
contrastive and cross-entropy loss achieved the state-of-the-art performance on
three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of
99.75\%, 93.4\%, and 86.49\% respectively. The ensemble version of our models
also outperforms other top ensemble methods. The code is available at
https://github.com/alireza-nasiri/SoundCLR.


Abstract:
  We study the Morse index of minimal surfaces with free boundary in a
half-space. We improve previous estimates relating the Neumann index to the
Dirichlet index and use this to answer a question of Ambrozio, Buzano,
Carlotto, and Sharp concerning the non-existence of index two embedded minimal
surfaces with free boundary in a half-space. We also give a simplified proof of
a result of Chodosh and Maximo concerning lower bounds for the index of the
Costa deformation family.


Abstract:
  Deep Neural Networks (DNN) are increasingly commonly used in software
engineering and code intelligence tasks. These are powerful tools that are
capable of learning highly generalizable patterns from large datasets through
millions of parameters. At the same time, training DNNs means walking a knife's
edges, because their large capacity also renders them prone to memorizing data
points. While traditionally thought of as an aspect of over-training, recent
work suggests that the memorization risk manifests especially strongly when the
training datasets are noisy and memorization is the only recourse.
Unfortunately, most code intelligence tasks rely on rather noise-prone and
repetitive data sources, such as GitHub, which, due to their sheer size, cannot
be manually inspected and evaluated. We evaluate the memorization and
generalization tendencies in neural code intelligence models through a case
study across several benchmarks and model families by leveraging established
approaches from other fields that use DNNs, such as introducing targeted noise
into the training dataset. In addition to reinforcing prior general findings
about the extent of memorization in DNNs, our results shed light on the impact
of noisy dataset in training.


Abstract:
  We study the question which Boolean algebras have the property that for every
generating set there is an ultrafilter selecting maximal number of its
elements. We call it the ultrafilter selection property. For cardinality
aleph-one the property is equivalent to the fact that the space of ultrafilters
is not Corson compact. We also consider the pointwise topology on a Boolean
algebra, proving a result on the Lindel\"of number in the context of the
ultrafilter selection property. Finally, we discuss poset Boolean algebras,
interval algebras, and semilattices in the context of ultrafilter selection
properties.


Abstract:
  Non-intrusive load monitoring (NILM) helps disaggregate the household's main
electricity consumption to energy usages of individual appliances, thus greatly
cutting down the cost in fine-grained household load monitoring. To address the
arisen privacy concern in NILM applications, federated learning (FL) could be
leveraged for NILM model training and sharing. When applying the FL paradigm in
real-world NILM applications, however, we are faced with the challenges of edge
resource restriction, edge model personalization and edge training data
scarcity.
  In this paper we present FedNILM, a practical FL paradigm for NILM
applications at the edge client. Specifically, FedNILM is designed to deliver
privacy-preserving and personalized NILM services to large-scale edge clients,
by leveraging i) secure data aggregation through federated learning, ii)
efficient cloud model compression via filter pruning and multi-task learning,
and iii) personalized edge model building with unsupervised transfer learning.
Our experiments on real-world energy data show that, FedNILM is able to achieve
personalized energy disaggregation with the state-of-the-art accuracy, while
ensuring privacy preserving at the edge client.


Abstract:
  Let $q$ be a power of the prime number $p$, let $K={\mathbb F}_q(t)$, and let
$r\ge 2$ be an integer. For points ${\mathbf a}, {\mathbf b}\in K$ which are
$\mathbb{F}_q$-linearly independent, we show that there exist positive
constants $N_0$ and $c_0$ such that for each integer $\ell\ge N_0$ and for each
generator $\tau$ of ${\mathbb F}_{q^\ell}/{\mathbb F}_q$, we have that for all
except $N_0$ values $\lambda\in{\overline{\mathbb{F}_q}}$, the corresponding
specializations ${\mathbf a}, {\mathbf b}(\tau)$ and ${\mathbf b}(\tau)$ cannot
have orders of degrees less than $c_0\log\log\ell$ as torsion points for the
Drinfeld module $\Phi^{(\tau,\lambda)}:\mathbb{F}_q[T] {\longrightarrow}
{\mathrm{End}}_{\overline{\mathbb{F}_q}}({\mathbb G}_a)$ (where ${\mathbb G}_a$
is the additive group scheme), given by $\Phi^{(\tau,\lambda)}_T(x)=\tau
x+\lambda x^q + x^{q^r}$.


Abstract:
  We consider a system of static spin qubits embedded in a one-dimensional spin
coherent channel and develop a scheme to readout the state of one and two
qubits separately. We use unpolarized flying qubits for this purpose that
scatter off from the static qubits due to the Heisenberg exchange interaction.
Analysing the transmission coefficient as a function of density matrix elements
along with additional unitary gates we reconstruct the state of static qubits.


Abstract:
  We propose a novel and effective purification based adversarial defense
method against pre-processor blind white- and black-box attacks. Our method is
computationally efficient and trained only with self-supervised learning on
general images, without requiring any adversarial training or retraining of the
classification model. We first show an empirical analysis on the adversarial
noise, defined to be the residual between an original image and its adversarial
example, has almost zero mean, symmetric distribution. Based on this
observation, we propose a very simple iterative Gaussian Smoothing (GS) which
can effectively smooth out adversarial noise and achieve substantially high
robust accuracy. To further improve it, we propose Neural Contextual Iterative
Smoothing (NCIS), which trains a blind-spot network (BSN) in a self-supervised
manner to reconstruct the discriminative features of the original image that is
also smoothed out by GS. From our extensive experiments on the large-scale
ImageNet using four classification models, we show that our method achieves
both competitive standard accuracy and state-of-the-art robust accuracy against
most strong purifier-blind white- and black-box attacks. Also, we propose a new
benchmark for evaluating a purification method based on commercial image
classification APIs, such as AWS, Azure, Clarifai and Google. We generate
adversarial examples by ensemble transfer-based black-box attack, which can
induce complete misclassification of APIs, and demonstrate that our method can
be used to increase adversarial robustness of APIs.


Abstract:
  We present a unified approach for constructing Slepian functions - also known
as prolate spheroidal wave functions - on the sphere for arbitrary tensor ranks
including scalar, vectorial, and rank 2 tensorial Slepian functions, using
spin-weighted spherical harmonics. For the special case of spherical cap
regions, we derived commuting operators, allowing for a numerically stable and
computationally efficient construction of the spin-weighted
spherical-harmonic-based Slepian functions. Linear relationships between the
spin-weighted and the classical scalar, vectorial, tensorial, and higher-rank
spherical harmonics allow the construction of classical
spherical-harmonic-based Slepian functions from their spin-weighted
counterparts, effectively rendering the construction of spherical-cap Slepian
functions for any tensorial rank a computationally fast and numerically stable
task.


Abstract:
  A metastable cosmic-string network is a generic consequence of many grand
unified theories (GUTs) when combined with cosmic inflation. Metastable cosmic
strings are not topologically stable, but decay on cosmic time scales due to
pair production of GUT monopoles. This leads to a network consisting of
metastable long strings on superhorizon scales as well as of string loops and
segments on subhorizon scales. We compute for the first time the complete
stochastic gravitational-wave background (SGWB) arising from all these network
constituents, including several technical improvements to both the derivation
of the loop and segment contributions. We find that the gravitational waves
emitted by string loops provide the main contribution to the gravitational-wave
spectrum in the relevant parameter space. The resulting spectrum is consistent
with the tentative signal observed by the NANOGrav and Parkes pulsar timing
collaborations for a string tension of G\mu ~ 10^-11...-7 and has ample
discovery space for ground- and space-based detectors. For GUT-scale string
tensions, G\mu ~ 10^-8...-7, metastable strings predict a SGWB in the
LIGO-Virgo-KAGRA band that could be discovered in the near future.


Abstract:
  We propose a novel transformer-based styled handwritten text image generation
approach, HWT, that strives to learn both style-content entanglement as well as
global and local writing style patterns. The proposed HWT captures the long and
short range relationships within the style examples through a self-attention
mechanism, thereby encoding both global and local style patterns. Further, the
proposed transformer-based HWT comprises an encoder-decoder attention that
enables style-content entanglement by gathering the style representation of
each query character. To the best of our knowledge, we are the first to
introduce a transformer-based generative network for styled handwritten text
generation. Our proposed HWT generates realistic styled handwritten text images
and significantly outperforms the state-of-the-art demonstrated through
extensive qualitative, quantitative and human-based evaluations. The proposed
HWT can handle arbitrary length of text and any desired writing style in a
few-shot setting. Further, our HWT generalizes well to the challenging scenario
where both words and writing style are unseen during training, generating
realistic styled handwritten text images.


Abstract:
  This paper extends Bayesian mortality projection models for multiple
populations considering the stochastic structure and the effect of spatial
autocorrelation among the observations. We explain high levels of
overdispersion according to adjacent locations based on the conditional
autoregressive model. In an empirical study, we compare different hierarchical
projection models for the analysis of geographical diversity in mortality
between the Japanese counties in multiple years, according to age. By a Markov
chain Monte Carlo (MCMC) computation, results have demonstrated the flexibility
and predictive performance of our proposed model.


Abstract:
  The knowledge on attacks contained in Cyber Threat Intelligence (CTI) reports
is very important to effectively identify and quickly respond to cyber threats.
However, this knowledge is often embedded in large amounts of text, and
therefore difficult to use effectively. To address this challenge, we propose a
novel approach and tool called EXTRACTOR that allows precise automatic
extraction of concise attack behaviors from CTI reports. EXTRACTOR makes no
strong assumptions about the text and is capable of extracting attack behaviors
as provenance graphs from unstructured text. We evaluate EXTRACTOR using
real-world incident reports from various sources as well as reports of DARPA
adversarial engagements that involve several attack campaigns on various OS
platforms of Windows, Linux, and FreeBSD. Our evaluation results show that
EXTRACTOR can extract concise provenance graphs from CTI reports and show that
these graphs can successfully be used by cyber-analytics tools in
threat-hunting.


Abstract:
  The current tension between the direct and the early Universe measurements of
the Hubble Constant, $H_0$, requires detailed scrutiny of all the data and
methods used in the studies on both sides of the debate. The Cepheids in the
type Ia supernova (SNIa) host galaxy NGC 5584 played a key role in the local
measurement of $H_0$. The SH0ES project used the observations of this galaxy to
derive a relation between Cepheids' periods and ratios of their amplitudes in
different optical bands of the Hubble Space Telescope (HST), and used these
relations to analyse the light curves of the Cepheids in around half of the
current sample of local SNIa host galaxies. In this work, we present an
independent detailed analysis of the Cepheids in NGC 5584. We employ different
tools for our photometric analysis and a completely different method for our
light curve analysis, and we do not find a systematic difference between our
period and mean magnitude measurements compared to those reported by SH0ES. By
adopting a period-luminosity relation calibrated by the Cepheids in the Milky
Way, we measure a distance modulus $\mu=31.810\pm0.047$ (mag) which is in
agreement with $\mu=31.786\pm0.046$ (mag) measured by SH0ES. In addition, the
relations we find between periods and amplitude ratios of the Cepheids in NGC
5584 are significantly tighter than those of SH0ES and their potential impact
on the direct $H_0$ measurement will be investigated in future studies.


Abstract:
  A commonly cited inefficiency of neural network training using
back-propagation is the update locking problem: each layer must wait for the
signal to propagate through the full network before updating. Several
alternatives that can alleviate this issue have been proposed. In this context,
we consider a simple alternative based on minimal feedback, which we call
Decoupled Greedy Learning (DGL). It is based on a classic greedy relaxation of
the joint training objective, recently shown to be effective in the context of
Convolutional Neural Networks (CNNs) on large-scale image classification. We
consider an optimization of this objective that permits us to decouple the
layer training, allowing for layers or modules in networks to be trained with a
potentially linear parallelization. With the use of a replay buffer we show
that this approach can be extended to asynchronous settings, where modules can
operate and continue to update with possibly large communication delays. To
address bandwidth and memory issues we propose an approach based on online
vector quantization. This allows to drastically reduce the communication
bandwidth between modules and required memory for replay buffers. We show
theoretically and empirically that this approach converges and compare it to
the sequential solvers. We demonstrate the effectiveness of DGL against
alternative approaches on the CIFAR-10 dataset and on the large-scale ImageNet
dataset.


Abstract:
  Precise control over the electronic and optical properties of defect centers
in solid-state materials is necessary for their applications as quantum
sensors, transducers, memories, and emitters. In this study, we demonstrate,
from first principles, how to tune these properties via the formation of defect
polaritons. Specifically, we investigate three defect types -- CHB, CB-CB, and
CB-VN -- in monolayer hexagonal boron nitride (hBN). The lowest-lying
electronic excitation of these systems is coupled to an optical cavity where we
explore the strong light-matter coupling regime. For all defect systems, we
show that the polaritonic splitting that shifts the absorption energy of the
lower polariton is much higher than can be expected from a Jaynes-Cummings
interaction. In addition, we find that the absorption intensity of the lower
polariton increases by several orders of magnitude, suggesting a possible route
toward overcoming phonon-limited single photon emission from defect centers.
Finally, we find that initially localized electronic transition densities can
become delocalized across the entire material under strong light-matter
coupling. These findings are a result of an effective continuum of electronic
transitions near the lowest-lying electronic transition for both pristine hBN
and hBN with defect centers that dramatically enhances the strength of the
light-matter interaction. We expect our findings to spur experimental
investigations of strong light-matter coupling between defect centers and
cavity photons for applications in quantum technologies.


Abstract:
  We propose a method for the blind separation of sounds of musical instruments
in audio signals. We describe the individual tones via a parametric model,
training a dictionary to capture the relative amplitudes of the harmonics. The
model parameters are predicted via a U-Net, which is a type of deep neural
network. The network is trained without ground truth information, based on the
difference between the model prediction and the individual time frames of the
short-time Fourier transform. Since some of the model parameters do not yield a
useful backpropagation gradient, we model them stochastically and employ the
policy gradient instead. To provide phase information and account for
inaccuracies in the dictionary-based representation, we also let the network
output a direct prediction, which we then use to resynthesize the audio signals
for the individual instruments. Due to the flexibility of the neural network,
inharmonicity can be incorporated seamlessly and no preprocessing of the input
spectra is required. Our algorithm yields high-quality separation results with
particularly low interference on a variety of different audio samples, both
acoustic and synthetic, provided that the sample contains enough data for the
training and that the spectral characteristics of the musical instruments are
sufficiently stable to be approximated by the dictionary.


Abstract:
  We propose a novel approach to dimensionality reduction combining techniques
of metric geometry and distributed persistent homology, in the form of a
gradient-descent based method called DIPOLE. DIPOLE is a
dimensionality-reduction post-processing step that corrects an initial
embedding by minimizing a loss functional with both a local, metric term and a
global, topological term. By fixing an initial embedding method (we use
Isomap), DIPOLE can also be viewed as a full dimensionality-reduction pipeline.
This framework is based on the strong theoretical and computational properties
of distributed persistent homology and comes with the guarantee of almost sure
convergence. We observe that DIPOLE outperforms popular methods like UMAP,
t-SNE, and Isomap on a number of popular datasets, both visually and in terms
of precise quantitative metrics.


Abstract:
  Continual relation extraction is an important task that focuses on extracting
new facts incrementally from unstructured text. Given the sequential arrival
order of the relations, this task is prone to two serious challenges, namely
catastrophic forgetting and order-sensitivity. We propose a novel
curriculum-meta learning method to tackle the above two challenges in continual
relation extraction. We combine meta learning and curriculum learning to
quickly adapt model parameters to a new task and to reduce interference of
previously seen tasks on the current task. We design a novel relation
representation learning method through the distribution of domain and range
types of relations. Such representations are utilized to quantify the
difficulty of tasks for the construction of curricula. Moreover, we also
present novel difficulty-based metrics to quantitatively measure the extent of
order-sensitivity of a given model, suggesting new ways to evaluate model
robustness. Our comprehensive experiments on three benchmark datasets show that
our proposed method outperforms the state-of-the-art techniques. The code is
available at the anonymous GitHub repository:
https://github.com/wutong8023/AAAI_CML.


Abstract:
  A methodology to generate sparse Galerkin models of chaotic/unsteady fluid
flows containing a minimal number of active triadic interactions is proposed.
The key idea is to find an appropriate set of basis functions for the
projection representing elementary flow structures that interact minimally one
with the other and thus result in a triadic interaction coefficient tensor with
sparse structure. Interpretable and computationally efficient Galerkin models
can be thus obtained, since a reduced number of triadic interactions needs to
be computed to evaluate the right hand side of the model. To find the basis
functions, a subspace rotation technique is used, whereby a set of Proper
Orthogonal Decomposition (POD) modes is rotated into a POD subspace of larger
dimension using coordinates associated to low-energy dissipative scales to
alter energy paths and the structure of the triadic interaction coefficient
tensor. This rotation is obtained as the solution of a non-convex optimisation
problem that maximises the energy captured by the new basis, promotes sparsity
and ensures long-term temporal stability of the sparse Galerkin system. We
demonstrate the approach on two-dimensional lid-driven cavity flow at $Re = 2
\times 10^4$ where the motion is chaotic. We show that the procedure generates
Galerkin models with a reduced set of active triadic interactions, distributed
in modal space according to established knowledge of scale interactions in
two-dimensional flows. This property, however, is only observed if long-term
temporal stability is explicitly included in the formulation, indicating that a
dynamical constraint is necessary to obtain a physically consistent
sparsification.


Abstract:
  This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on
predicting language IDs from speech.
  Spoken Language Identification (LID) is an important step in a multilingual
Automated Speech Recognition (ASR) system pipeline. For many low-resource and
endangered languages, only single-speaker recordings may be available,
demanding a need for domain and speaker-invariant language ID systems. In this
memo, we show that a convolutional neural network with a Self-Attentive Pooling
layer shows promising results for the language identification task.


Abstract:
  Industrial cyber-physical systems (ICPSs) manage critical infrastructures by
controlling the processes based on the "physics" data gathered by edge sensor
networks. Recent innovations in ubiquitous computing and communication
technologies have prompted the rapid integration of highly interconnected
systems to ICPSs. Hence, the "security by obscurity" principle provided by
air-gapping is no longer followed. As the interconnectivity in ICPSs increases,
so does the attack surface. Industrial vulnerability assessment reports have
shown that a variety of new vulnerabilities have occurred due to this
transition while the most common ones are related to weak boundary protection.
Although there are existing surveys in this context, very little is mentioned
regarding these reports. This paper bridges this gap by defining and reviewing
ICPSs from a cybersecurity perspective. In particular, multi-dimensional
adaptive attack taxonomy is presented and utilized for evaluating real-life
ICPS cyber incidents. We also identify the general shortcomings and highlight
the points that cause a gap in existing literature while defining future
research directions.


Abstract:
  Policy-based reinforcement learning methods suffer from the policy collapse
problem. We find valued-based reinforcement learning methods with
{\epsilon}-greedy mechanism are capable of enjoying three characteristics,
Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off,
which help value-based methods avoid the policy collapse problem. However,
there does not exist a parallel mechanism for policy-based methods that
achieves all three characteristics. In this paper, we propose an entropy
regularization free mechanism that is designed for policy-based methods, which
achieves Closed-form Diversity, Objective-invariant Exploration and Adaptive
Trade-off. Our experiments show that our mechanism is super sample-efficient
for policy-based methods and boosts a policy-based baseline to a new
State-Of-The-Art on Arcade Learning Environment.


Abstract:
  Despite the recent successes of reinforcement learning in games and robotics,
it is yet to become broadly practical. Sample efficiency and unreliable
performance in rare but challenging scenarios are two of the major obstacles.
Drawing inspiration from the effectiveness of deliberate practice for achieving
expert-level human performance, we propose a new adversarial sampling approach
guided by a failure predictor named "CoachNet". CoachNet is trained online
along with the agent to predict the probability of failure. This probability is
then used in a stochastic sampling process to guide the agent to more
challenging episodes. This way, instead of wasting time on scenarios that the
agent has already mastered, training is focused on the agent's "weak spots". We
present the design of CoachNet, explain its underlying principles, and
empirically demonstrate its effectiveness in improving sample efficiency and
test-time robustness in common continuous control tasks.


Abstract:
  We propose the study of the inclusive hadroproduction of a heavy-flavored jet
in association with a light jet, as a probe channel of strong interactions at
high energies. We build up a hybrid factorization that encodes genuine
high-energy effects, provided by a partial next-to-leading BFKL resummation,
inside the standard collinear structure of the cross section. We present a
detailed analysis of different distributions, shaped on kinematic ranges
typical of experimental analyses at the Large Hadron Collider, and differential
in rapidity, azimuthal angle and transverse momentum. The fair stability that
these distributions exhibit under higher-order corrections motivates our
interest toward future studies. Here, the hybrid factorization could help to
deepen our understanding of heavy-flavor physics in wider kinematic ranges,
like the ones accessible at the Electron-Ion Collider.


Abstract:
  Random samples of quantum states are an important resource for various tasks
in quantum information science, and samples in accordance with a
problem-specific distribution can be indispensable ingredients. Some algorithms
generate random samples by a lottery that follows certain rules and yield
samples from the set of distributions that the lottery can access. Other
algorithms, which use random walks in the state space, can be tailored to any
distribution, at the price of autocorrelations in the sample and with
restrictions to low-dimensional systems in practical implementations. In this
paper, we present a two-step algorithm for sampling from the quantum state
space that overcomes some of these limitations.
  We first produce a CPU-cheap large proposal sample, of uncorrelated entries,
by drawing from the family of complex Wishart distributions, and then reject or
accept the entries in the proposal sample such that the accepted sample is
strictly in accordance with the target distribution. We establish the explicit
form of the induced Wishart distribution for quantum states. This enables us to
generate a proposal sample that mimics the target distribution and, therefore,
the efficiency of the algorithm, measured by the acceptance rate, can be many
orders of magnitude larger than that for a uniform sample as the proposal.
  We demonstrate that this sampling algorithm is very efficient for one-qubit
and two-qubit states, and reasonably efficient for three-qubit states, while it
suffers from the "curse of dimensionality" when sampling from structured
distributions of four-qubit states.


Abstract:
  Recently, in topological insulators (TIs) the phenomenon of planar Hall
effect (PHE) wherein a current driven in presence an in-plane magnetic field
generates a transverse voltage has been experimentally witnessed. There have
been a couple of theoretical explanations of this phenomenon. We investigate
this phenomenon based on scattering theory on a normal metal-TI-normal metal
hybrid structure and calculate the conductances in longitudinal and transverse
directions to the applied bias. The transverse conductance depends on the
spatial location between the two NM-TI junctions where it is calculated. It is
zero in the drain electrode when the chemical potentials of the top and the
bottom TI surfaces ($\mu_t$ and $\mu_b$ respectively) are equal. The
longitudinal conductance is $\pi$-periodic in $\phi$-the angle between the bias
direction and the direction of the in-plane magnetic field. The transverse
conductance is $\pi$-periodic in $\phi$ when $\mu_t=\mu_b$ whereas it is
$2\pi$-periodic in $\phi$ when $\mu_t\neq\mu_b$. As a function of the magnetic
field, the magnitude of transverse conductance increases initially and peaks.
At higher magnetic fields, it decays for angles $\phi$ closer to $0,\pi$
whereas oscillates for angles $\phi$ close to $\pi/2$. The conductances
oscillate with the length of the TI region. A finite width of the system makes
the transport separate into finitely many channels. The features of the
conductances are similar to those in the limit of infinitely wide system except
when the width is so small that only one channel participates in the transport.
When only one channel participates in transport, the transverse conductance in
the region $0<x<L$ is zero for $\mu_t=\mu_b$ and the transverse conductance in
the region $x>L$ is zero even for the case $\mu_t\neq\mu_b$. We understand the
features in the obtained results.


Abstract:
  In this paper we establish the generalized Beukers integral
$I_{m}(a_{1},...,a_{n})$ with some methods of partial fraction decomposition.
Thus one obtains an explicit expression of the generalized Beukers integral.
Further, we estimate the rational denominator of $I$ and. In the second section
of this paper, we provide some estimates of the upper and lower bound of the
value $J_{3}$, which involves the generalized Beukers integral and is related
to $\zeta(5)$.


Abstract:
  Discoveries of ordered quantum states of matter are of great fundamental
interest, and often lead to unique applications. The most well known example --
superconductivity -- is caused by the formation and condensation of pairs of
electrons. A key property of superconductors is diamagnetism: magnetic fields
are screened by dissipationless currents. Fundamentally, what distinguishes
superconducting states from normal states is a spontaneously broken symmetry
corresponding to long-range coherence of fermion pairs. Here we report a set of
experimental observations in hole doped Ba$_{1-x}$K$_x$Fe$_2$As$_2$ which are
not consistent with conventional superconducting behavior. Our specific-heat
measurements indicate the formation of fermionic bound states when the
temperature is lowered from the normal state. However, for $x \sim 0.8$,
instead of the standard for superconductors, zero resistance and diamagnetic
screening, for a range of temperatures, we observe the opposite effect: the
generation of self-induced magnetic fields measured by spontaneous Nernst
effect and muon spin rotation experiments. The finite resistance and the lack
of any detectable diamagnetic screening in this state exclude the spontaneously
broken symmetry associated with superconducting two-fermion correlations.
Instead, combined evidence from transport and thermodynamic measurements
indicates that the formation of fermionic bound states leads to spontaneous
breaking of time-reversal symmetry above the superconducting transition
temperature. These results demonstrate the existence of a
broken-time-reversal-symmetry bosonic metal state. In the framework of a
multiband theory, such a state is characterized by quartic correlations: the
long-range order exists only for {\it pairs} of fermion pairs.


Abstract:
  Deep Neural Networks (NNs) have been widely utilized in contact-rich
manipulation tasks to model the complicated contact dynamics. However, NN-based
models are often difficult to decipher which can lead to seemingly inexplicable
behaviors and unidentifiable failure cases. In this work, we address the
interpretability of NN-based models by introducing the kinodynamic images. We
propose a methodology that creates images from the kinematic and dynamic data
of a contact-rich manipulation task. Our formulation visually reflects the
task's state by encoding its kinodynamic variations and temporal evolution. By
using images as the state representation, we enable the application of
interpretability modules that were previously limited to vision-based tasks. We
use this representation to train Convolution-based Networks and we extract
interpretations of the model's decisions with Grad-CAM, a technique that
produces visual explanations. Our method is versatile and can be applied to any
classification problem using synchronous features in manipulation to visually
interpret which parts of the input drive the model's decisions and distinguish
its failure modes. We evaluate this approach on two examples of real-world
contact-rich manipulation: pushing and cutting, with known and unknown objects.
Finally, we demonstrate that our method enables both detailed visual
inspections of sequences in a task, as well as high-level evaluations of a
model's behavior and tendencies. Data and code for this work are available at
https://github.com/imitsioni/interpretable_manipulation.


Abstract:
  The goal of Question Answering over Knowledge Graphs (KGQA) is to find
answers for natural language questions over a knowledge graph. Recent KGQA
approaches adopt a neural machine translation (NMT) approach, where the natural
language question is translated into a structured query language. However, NMT
suffers from the out-of-vocabulary problem, where terms in a question may not
have been seen during training, impeding their translation. This issue is
particularly problematic for the millions of entities that large knowledge
graphs describe. We rather propose a KGQA approach that delegates the
processing of entities to entity linking (EL) systems. NMT is then used to
create a query template with placeholders that are filled by entities
identified in an EL phase. Slot filling is used to decide which entity fills
which placeholder. Experiments for QA over Wikidata show that our approach
outperforms pure NMT: while there remains a strong dependence on having seen
similar query templates during training, errors relating to entities are
greatly reduced.


Abstract:
  Real world applications such as economics and policy making often involve
solving multi-agent games with two unique features: (1) The agents are
inherently asymmetric and partitioned into leaders and followers; (2) The
agents have different reward functions, thus the game is general-sum. The
majority of existing results in this field focuses on either symmetric solution
concepts (e.g. Nash equilibrium) or zero-sum games. It remains open how to
learn the Stackelberg equilibrium -- an asymmetric analog of the Nash
equilibrium -- in general-sum games efficiently from noisy samples.
  This paper initiates the theoretical study of sample-efficient learning of
the Stackelberg equilibrium, in the bandit feedback setting where we only
observe noisy samples of the reward. We consider three representative
two-player general-sum games: bandit games, bandit-reinforcement learning
(bandit-RL) games, and linear bandit games. In all these games, we identify a
fundamental gap between the exact value of the Stackelberg equilibrium and its
estimated version using finitely many noisy samples, which can not be closed
information-theoretically regardless of the algorithm. We then establish sharp
positive results on sample-efficient learning of Stackelberg equilibrium with
value optimal up to the gap identified above, with matching lower bounds in the
dependency on the gap, error tolerance, and the size of the action spaces.
Overall, our results unveil unique challenges in learning Stackelberg
equilibria under noisy bandit feedback, which we hope could shed light on
future research on this topic.


Abstract:
  With increasing data and model complexities, the time required to train
neural networks has become prohibitively large. To address the exponential rise
in training time, users are turning to data parallel neural networks (DPNN) to
utilize large-scale distributed resources on computer clusters. Current DPNN
approaches implement the network parameter updates by synchronizing and
averaging gradients across all processes with blocking communication
operations. This synchronization is the central algorithmic bottleneck. To
combat this, we introduce the Distributed Asynchronous and Selective
Optimization (DASO) method which leverages multi-GPU compute node architectures
to accelerate network training. DASO uses a hierarchical and asynchronous
communication scheme comprised of node-local and global networks while
adjusting the global synchronization rate during the learning process. We show
that DASO yields a reduction in training time of up to 34% on classical and
state-of-the-art networks, as compared to other existing data parallel training
methods.


Abstract:
  We introduce GEM, a living benchmark for natural language Generation (NLG),
its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly
evolving ecosystem of automated metrics, datasets, and human evaluation
standards. Due to this moving target, new models often still evaluate on
divergent anglo-centric corpora with well-established, but flawed, metrics.
This disconnect makes it challenging to identify the limitations of current
models and opportunities for progress. Addressing this limitation, GEM provides
an environment in which models can easily be applied to a wide set of tasks and
in which evaluation strategies can be tested. Regular updates to the benchmark
will help NLG research become more multilingual and evolve the challenge
alongside models. This paper serves as the description of the data for which we
are organizing a shared task at our ACL 2021 Workshop and to which we invite
the entire NLG community to participate.


Abstract:
  We present an improved scheme for absorption imaging of alkali atoms at
moderate magnetic fields, where the excited state is well in the Paschen-Back
regime but the ground state hyperfine manifold is not. It utilizes four atomic
levels to obtain an approximately closed optical cycle. With the resulting
absorption of the corresponding two laser frequencies we extract the atomic
column density of a $^{39}$K Bose-Einstein condensate. The scheme can be
readily applied to all other alkali-like species.


Abstract:
  Aims: We present the first measurements of the solar-wind angular-momentum
(AM) flux recorded by the Solar Orbiter spacecraft. Our aim is the validation
of these measurements to support future studies of the Sun's AM loss. Methods:
We combine 60-minute averages of the proton bulk moments and the magnetic field
measured by the Solar Wind Analyser (SWA) and the magnetometer (MAG) onboard
Solar Orbiter. We calculate the AM flux per solid-angle element using data from
the first orbit of the mission's cruise phase during 2020. We separate the
contributions from protons and from magnetic stresses to the total AM flux.
Results: The AM flux varies significantly over time. The particle contribution
typically dominates over the magnetic-field contribution during our measurement
interval. The total AM flux shows the largest variation and is typically
anti-correlated with the radial solar-wind speed. We identify a compression
region, potentially associated with a co-rotating interaction region or a
coronal mass ejection, that leads to a significant localised increase in the AM
flux, yet without a significant increase in the AM per unit mass. We repeat our
analysis using the density estimate from the Radio and Plasma Waves (RPW)
instrument. Using this independent method, we find a decrease in the peaks of
positive AM flux but otherwise consistent results. Conclusions: Our results
largely agree with previous measurements of the solar-wind AM flux in terms of
amplitude, variability, and dependence on radial solar-wind bulk speed. Our
analysis highlights the potential for future, more detailed, studies of the
solar wind's AM and its other large-scale properties with data from Solar
Orbiter. We emphasise the need to study the radial evolution and latitudinal
dependence of the AM flux in combination with data from Parker Solar Probe and
assets at heliocentric distances of 1 au and beyond.


Abstract:
  In this paper, we present a semi-supervised training technique using
pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has
shown promising performance compared with traditional clustering-based methods,
especially in the case of overlapping speech. However, to get a well-tuned
model, EEND requires labeled data for all the joint speech activities of every
speaker at each time frame in a recording. In this paper, we explore a
pseudo-labeling approach that employs unlabeled data. First, we propose an
iterative pseudo-label method for EEND, which trains the model using unlabeled
data of a target condition. Then, we also propose a committee-based training
method to improve the performance of EEND. To evaluate our proposed method, we
conduct the experiments of model adaptation using labeled and unlabeled data.
Experimental results on the CALLHOME dataset show that our proposed
pseudo-label achieved a 37.4% relative diarization error rate reduction
compared to a seed model. Moreover, we analyzed the results of semi-supervised
adaptation with pseudo-labeling. We also show the effectiveness of our approach
on the third DIHARD dataset.


Abstract:
  We investigate Nagaoka ferromagnetism in the two-dimensional Hubbard model
with one hole using the spin-adapted ($SU(2)$ conserving) full configuration
interaction quantum Monte Carlo method. This methodology gives us access to the
ground state energies of all possible spin states $S$ of finite Hubbard
lattices, here obtained for lattices up to 24 sites, for various interaction
strengths ($U$). The critical interaction strength, $U_c$, at which the Nagaoka
transition occurs is determined for each lattice and is found to be
proportional to the lattice size for the larger lattices. Below $U_c$ the
overall ground states are found to favour the minimal total spin ($S=\frac 1
2$), and no intermediate spin state is found to be the overall ground state on
lattices larger than 16 sites. However, at $U_c$, the energies of all the spin
states are found to be nearly degenerate, implying that large fluctuations in
total spin can be expected in the vicinity of the Nagaoka transition.


Abstract:
  Lepton flavor violated process are strongly suppressed in the Standard Model
due to very small neutrino mass, but can be sizable in some extended models.
The current experimental bounds on decay modes $\ell^{'\pm} \to a \ell^{\pm} $
are much weaker than other flavor violated processes because of the huge
irreducible backgrounds $\ell' \to \ell \bar{\nu}_{\ell} \nu_{\ell'}$. In this
paper, we give the full helicity density matrix of both the signal and
backgrounds, and then study polarization effects. Particularly, we treat
inclusively the two missing neutrinos in the background, and we find that both
longitudinal and transverse polarization effects survives even the relative
kinematical degrees of freedom of the two neutrinos are integrated out.
Furthermore, we have show that signal and backgrounds have distinctive
polarization effects which can be measured by using energy fractions of the
charged decay products. This is particularly useful because kinematical
reconstruction is not required. In case of that the decaying lepton is not
polarized, we show that correlations between polarizations of lepton pair
generated at colliders are still useful to search for the signals. More
interestingly, polarization correlations depends on product of scalar and
pseudo-scalar ALP couplings, and hence are sensitive to their relative sign. We
demonstrate that how the polarization correlation effects can be used to
investigate flavor violating decays $\tau^{\pm} \to a \ell^{\pm} $ at the
BelleII experiment.


Abstract:
  We show that the resource theory of contextuality does not admit catalysts,
i.e., there are no correlations that can enable an otherwise impossible
resource conversion and still be recovered afterward. As a corollary, we
observe that the same holds for nonlocality. As entanglement allows for
catalysts, this adds a further example to the list of "anomalies of
entanglement," showing that nonlocality and entanglement behave differently as
resources. We also show that catalysis remains impossible even if, instead of
classical randomness, we allow some more powerful behaviors to be used freely
in the free transformations of the resource theory.


Abstract:
  Off-policy sampling and experience replay are key for improving sample
efficiency and scaling model-free temporal difference learning methods. When
combined with function approximation, such as neural networks, this combination
is known as the deadly triad and is potentially unstable. Recently, it has been
shown that stability and good performance at scale can be achieved by combining
emphatic weightings and multi-step updates. This approach, however, is
generally limited to sampling complete trajectories in order, to compute the
required emphatic weighting. In this paper we investigate how to combine
emphatic weightings with non-sequential, off-line data sampled from a replay
buffer. We develop a multi-step emphatic weighting that can be combined with
replay, and a time-reversed $n$-step TD learning algorithm to learn the
required emphatic weighting. We show that these state weightings reduce
variance compared with prior approaches, while providing convergence
guarantees. We tested the approach at scale on Atari 2600 video games, and
observed that the new X-ETD($n$) agent improved over baseline agents,
highlighting both the scalability and broad applicability of our approach.


Abstract:
  We establish a platform to transfer $L_p$-completely bounded maps on tensor
products of von Neumann algebras to $L_p$-completely bounded maps on the
corresponding amalgamated free products. As a consequence, we obtain a
H\"ormander-Mikhlin multiplier theory for free products of groups. Let
$\mathbb{F}_\infty$ be a free group on infinite generators $\{g_1,
g_2,\cdots\}$. Given $d\ge1$ and a bounded symbol $m$ on $\mathbb{Z}^d$
satisfying the classical H\"ormander-Mikhlin condition, the linear map
$M_m:\mathbb{C}[\mathbb{F}_\infty]\to \mathbb{C}[\mathbb{F}_\infty]$ defined by
$\lambda(g)\mapsto m(k_1,\cdots, k_d)\lambda(g)$ for $g=g_{i_1}^{k_1}\cdots
g_{i_n}^{k_n}\in\mathbb{F}_\infty$ in reduced form (with $k_l=0$ in
$m(k_1,\cdots, k_d)$ for $l>n$), extends to a complete bounded map on
$L_p(\widehat{\mathbb{F}}_\infty)$ for all $1<p<\infty$, where
$\widehat{\mathbb{F}}_\infty$ is the group von Neumann algebra of
$\mathbb{F}_\infty$. A similar result holds for any free product of discrete
groups.


Abstract:
  [Context & motivation] Driven by the need for faster time-to-market and
reduced development lead-time, large-scale systems engineering companies are
adopting agile methods in their organizations. This agile transformation is
challenging and it is common that adoption starts bottom-up with agile software
teams within the context of traditional company structures.
  [Question/Problem] This creates the challenge of agile teams working within a
document-centric and plan-driven (or waterfall) environment. While it may be
desirable to take the best of both worlds, it is not clear how that can be
achieved especially with respect to managing requirements in large-scale
systems.
  [Principal ideas/Results] This paper presents an exploratory case study at an
automotive company, focusing on two departments of a large-scale systems
company that is in the process of company-wide agile adoption.
  [Contribution] We present challenges related to requirements engineering that
agile teams face while working within a larger plan-driven context and propose
potential strategies to mitigate the challenges. Challenges relate to, e.g.,
development teams not being aware of the high-level requirement and dealing
with flexibility of writing user stories. We found that strategies for
overcoming most of these challenges are still lacking and thus call for more
research.


Abstract:
  One of the critical challenges facing imaging studies of the 21-cm signal at
the Epoch of Reionization (EoR) is the separation of astrophysical foreground
contamination. These foregrounds are known to lie in a wedge-shaped region of
$(k_{\perp},k_{\parallel})$ Fourier space. Removing these Fourier modes excises
the foregrounds at grave expense to image fidelity, since the cosmological
information at these modes is also removed by the wedge filter. However, the
21-cm EoR signal is non-Gaussian, meaning that the lost wedge modes are
correlated to the surviving modes by some covariance matrix. We have developed
a machine learning-based method which exploits this information to identify
ionized regions within a wedge-filtered image. Our method reliably identifies
the largest ionized regions and can reconstruct their shape, size, and location
within an image. We further demonstrate that our method remains viable when
instrumental effects are accounted for, using the Hydrogen Epoch of
Reionization Array and the Square Kilometre Array as fiducial instruments. The
ability to recover spatial information from wedge-filtered images unlocks the
potential for imaging studies using current- and next-generation instruments
without relying on detailed models of the astrophysical foregrounds themselves.


Abstract:
  We consider a cognitive radio based Internet of Things (CR-IoT) system where
the secondary IoT device (SD) accesses the licensed channel during the
transmission vacancies of the primary IoT device (PD). We focus on the impact
of the IoT devices' heterogeneous traffic pattern on the energy efficiency and
on the age of information (AoI) performance of the SD. We first derive
closed-form expressions of the energy efficiency and the average AoI, and
subsequently explore their convexity and monotonicity to the transmit power.
Following these characterizations, an optimal transmit power optimization
algorithm (TPOA) is proposed for the SD to maximize the energy efficiency while
maintaining the average AoI under a predefined threshold. Numerical results
verify the different preferences of the SD toward different PD traffic
patterns, and provides insights into the tradeoff between the energy efficiency
and the average AoI.


Abstract:
  We consider the decoupling theory of a broad class of $C^5$ surfaces
$\mathbb{M} \subset \mathbb{R}^3$ lacking planar points. In particular, our
approach also applies to surfaces which are not graphed by mixed homogeneous
polynomials. The study of $\mathbb{M}$ furnishes opportunity to recast
iterative linear decoupling in a more general form. Here, Taylor-based analysis
is combined with efforts to build a library of canonical surfaces
(non-cylindrical in general) by which $\mathbb{M}$ may be approximated for
decoupling purposes. The work presented may be generalized to the consideration
of other surfaces not addressed.


Abstract:
  Reconstructing interactions from observational data is a critical need for
investigating natural biological networks, wherein network dimensionality (i.e.
number of interacting components) is usually high and interactions are
time-varying. These pose a challenge to existing methods that can quantify only
small interaction networks or assume static interactions under steady state.
Here, we proposed a novel approach to reconstruct high-dimensional,
time-varying interaction networks using empirical time series. This method,
named "multiview distance regularized S-map", generalized the state space
reconstruction to accommodate high dimensionality and overcome difficulties in
quantifying massive interactions with limited data. When we evaluated this
method using the time series generated from a large theoretical model involving
hundreds of interacting species, estimated interaction strengths were in good
agreement with theoretical expectations. As a result, reconstructed networks
preserved important topological properties, such as centrality, strength
distribution and derived stability measures. Moreover, our method effectively
forecasted the dynamic behavior of network nodes. Applying this method to a
natural bacterial community helped identify keystone species from the
interaction network and revealed the mechanisms governing the dynamical
stability of bacterial community. Our method overcame the challenge of high
dimensionality and disentangled complex time-varying interactions in large
natural dynamical systems.


Abstract:
  A central goal of synthetic biology is the design of molecular controllers
that can manipulate the dynamics of intracellular networks in a stable and
accurate manner. To address the fact that detailed knowledge about
intracellular networks is unavailable, integral-feedback controllers (IFCs)
have been put forward for controlling molecular abundances. These controllers
can maintain accuracy in spite of the uncertainties in the controlled networks.
However, this desirable feature is achieved only if stability is also
maintained. In this paper, we show that molecular IFCs can suffer from a
hazardous instability called negative-equilibrium catastrophe (NEC), whereby
all nonnegative equilibria vanish under the action of the controllers, and some
of the molecular abundances blow up. We show that unimolecular IFCs do not
exist due to a NEC. We then derive a family of bimolecular IFCs that are
safeguarded against NECs when uncertain unimolecular networks, with any number
of molecular species, are controlled. However, when IFCs are applied on
uncertain bimolecular (and hence most intracellular) networks, we show that
preventing NECs generally becomes an intractable problem as the number of
interacting molecular species increases.


Abstract:
  The Dirac Equation is solved approximately for relativistic generalized
Woods-Saxon potential including Coulomb-like tensor potential in exact
pseudospin and spin symmetry limits. The bound states energy eigenvalues are
found by using wavefunction boundary conditions, and corresponding radial
wavefunctions are obtained in terms of hypergeometric function. Some numerical
examples are given for the dependence of bound states energy eigenvalues on
quantum numbers and potential parameters.


Abstract:
  We introduce a new framework for solving an important class of computational
problems involving finite permutation groups, which includes calculating set
stabilisers, intersections of subgroups, and isomorphisms of combinatorial
structures. Our techniques are inspired by and generalise 'partition
backtrack', which is the current state-of-the-art algorithm introduced by
Jeffrey Leon in 1991. But, instead of ordered partitions, we use labelled
directed graphs to organise our backtrack search algorithms, which allows for a
richer representation of many problems while often resulting in smaller search
spaces. In this article we present the theory underpinning our framework, we
describe our algorithms, and we show the results of some experiments. An
implementation of our algorithms is available as free software in the
GraphBacktracking package for GAP.


Abstract:
  The early development of a zygote can be mathematically described by a
developmental tree. To compare developmental trees of different species, we
need to define distances on trees. If children cells after a division are not
distinguishable, developmental trees are represented by the space of rooted
trees with possibly repeated labels, where all vertices are unordered. On this
space, we define two metrics: the best-match metric and the left-regular
metric, which show some advantages over existing methods. If children cells
after a division are partially distinguishable, developmental trees are
represented by the space of rooted trees with possibly repeated labels, where
vertices can be ordered or unordered. This space cannot have a metric. Instead,
we define a semimetric, which is a variant of the best-match metric. To compute
the best-match distance between two trees, the expected time complexity and
worst-case time complexity are both $\mathcal{O}(n^2)$, where $n$ is the tree
size. To compute the left-regular distance between two trees, the expected time
complexity is $\mathcal{O}(n)$, and the worst-case time complexity is
$\mathcal{O}(n\log n)$.


Abstract:
  It has been recognized for some time that even for perfect conductors, the
interaction Casimir entropy, due to quantum/thermal fluctuations, can be
negative. This result was not considered problematic because it was thought
that the self-entropies of the bodies would cancel this negative interaction
entropy, yielding a total entropy that was positive. In fact, this cancellation
seems not to occur. The positive self-entropy of a perfectly conducting sphere
does indeed just cancel the negative interaction entropy of a system consisting
of a perfectly conducting sphere and plate, but a model with weaker coupling in
general possesses a regime where negative self-entropy appears. The physical
meaning of this surprising result remains obscure. In this paper we re-examine
these issues, using improved physical and mathematical techniques, partly based
on the Abel-Plana formula, and present numerical results for arbitrary
temperatures and couplings, which exhibit the same remarkable features.


Abstract:
  A software architect uses quality requirements to design the architecture of
a system. However, it is essential to ensure that the system's final
architectural design achieves the standard quality requirements. The existing
architectural evaluation frameworks require basic skills and experience for
practical usage, which novice software architects lack.
  We propose a framework that enables novice software architects to infer the
system's quality requirements and tactics using the software architectural
block-line diagram. The framework takes an image as input, extracts various
components and connections, and maps them to viable architectural patterns,
followed by identifying the system's corresponding quality attributes (QAs) and
tactics. The framework includes a specifically trained machine learning model
based on image processing and semantic similarity methods to assist software
architects in evaluating a given design by a) evaluating an input architectural
design based on the architectural patterns present in it, b) lists out the
strengths and weaknesses of the design in terms of QAs, c) recommends the
necessary architectural tactics that can be embedded in the design to achieve
the lacking QAs.
  To train our framework, we developed a dataset of 2,035 architectural images
from fourteen architectural patterns such as Client-Server, Microservices, and
Model View Controller, available at
https://www.doi.org/10.6084/m9.figshare.14156408. The framework achieves a
Correct Recognition Rate of 98.71% in identifying the architectural patterns.
We evaluated the proposed framework's effectiveness and usefulness by using
controlled and experimental groups, in which the experimental group performed
approximately 150% better than the controlled group. The experiments were
performed as a part of the Masters of Computer Science course in an Engineering
Institution.


Abstract:
  Hyperproperties are system properties that require quantification over
multiple execution traces of a system. Hyperproperties can express several
specifications of interest for cyber-physical systems--such as opacity,
robustness, and noninterference--which cannot be expressed using linear-time
properties. This paper presents for the first time a discretization-free
approach for the formal verification of discrete-time uncertain dynamical
systems against hyperproperties. The proposed approach involves decomposition
of complex hyperproperties into several verification conditions by exploiting
the automata-based structures corresponding to the complements of the original
specifications. These verification conditions are then discharged by
synthesizing so-called augmented barrier certificates, which provide certain
safety guarantees for the underlying system. For systems with polynomial-type
dynamics, we present a sound procedure to synthesize polynomial-type augmented
barrier certificates by reducing the problem to sum-of-squares optimizations.
We demonstrate the effectiveness of our proposed approaches on two physical
case studies against two important hyperproperties: initial-state opacity and
initial-state robustness.


Abstract:
  Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO)
systems have been considered as one of the primary candidates for the fifth
generation (5G) and beyond 5G wireless communication networks to satisfy the
ever-increasing capacity demands. Full-duplex technology can further enhance
the advantages of mmWave massive MIMO systems. However, strong
self-interference (SI) is the major limiting factor in full-duplex technology.
Hence, this paper proposes a novel angular-based joint hybrid
precoding/combining (AB-JHPC) technique for the full-duplex mmWave massive-MIMO
systems. Our primary goals are listed as: (i) improving the self-interference
cancellation (SIC), (ii) increasing the intended signal power, (iii) decreasing
the channel estimation overhead, (iv) designing the massive MIMO systems with a
low number of RF chains. First, the RF-stage of AB-JHPC is developed via slow
time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information. A
joint transmit/receive RF beamformer design is proposed for covering
(excluding) the AoD/AoA support of intended (SI) channel. Second, the BB-stage
of AB-JHPC is constructed via the reduced-size effective intended channel.
After using the well-known singular value decomposition(SVD) approach at the
BB-stage, we also propose a new semi-blind minimum mean square error (S-MMSE)
technique to further suppress the residual SI power by using AoD/AoA
parameters. The numerical results demonstrate that the SI signal is remarkably
canceled via the proposed AB-JHPC technique. It is shown that AB-JHPC achieves
85.7 dB SIC and the total amount of SIC almost linearly increases via antenna
isolation techniques. We observe that the proposed full-duplex mmWave massive
MIMO systems double the achievable rate capacity compared to its half-duplex
counterpart as the antenna array size increases and the transmit/receive
antenna isolation improves.


Abstract:
  We discuss the spectral decomposition of the hypergeometric differential
operators on the line $\mathrm{Re}\, z=1/2$. Such operators arise in the
problem of decomposition of tensor products of unitary representations of the
universal covering of the group $SL(2\,{\mathbb R}$. Our main purpose is a
search of natural bases in generalized eigenspaces and variants of the
inversion formula.


Abstract:
  In this article we prove the existence of a new family of periodic solutions
for discrete, nonlinear Schrodinger equations subject to spatially localized
driving and damping and we show numerically that they provide a more accurate
approximation to metastable states in these systems than previous proposals. We
also study the stability properties of these solutions and show that they fit
well with a previously proposed mechanism for the emergence and persistence of
metastable behavior.


Abstract:
  Serverless computing has emerged as a new paradigm for running short-lived
computations in the cloud. Due to its ability to handle IoT workloads, there
has been considerable interest in running serverless functions at the edge.
However, the constrained nature of the edge and the latency sensitive nature of
workloads result in many challenges for serverless platforms. In this paper, we
present LaSS, a platform that uses model-driven approaches for running
latency-sensitive serverless computations on edge resources. LaSS uses
principled queuing-based methods to determine an appropriate allocation for
each hosted function and auto-scales the allocated resources in response to
workload dynamics. LaSS uses a fair-share allocation approach to guarantee a
minimum of allocated resources to each function in the presence of overload. In
addition, it utilizes resource reclamation methods based on container deflation
and termination to reassign resources from over-provisioned functions to
under-provisioned ones. We implement a prototype of our approach on an
OpenWhisk serverless edge cluster and conduct a detailed experimental
evaluation. Our results show that LaSS can accurately predict the resources
needed for serverless functions in the presence of highly dynamic workloads,
and reprovision container capacity within hundreds of milliseconds while
maintaining fair share allocation guarantees.


Abstract:
  In this paper, we propose a simple enhancement for the passkey entry protocol
in the authentication stage 1 of Secure Simple Pairing using preexisting
cryptographic hash functions and random integer generation present in the
protocol. The new protocol is more secure and efficient than previous known
protocols. Our research mainly focuses on strengthening the passkey entry
protocol and protecting the devices against passive eavesdropping and active
Man-in-the-middle (MITM) attacks in both Bluetooth Basic Rate/Enhanced Data
Rate (BR/EDR) and Bluetooth Low Energy (Bluetooth LE). This method can be used
for any device which uses the passkey entry protocol.


Abstract:
  Optimal mechanical impact absorbers are reusable and exhibit high specific
energy absorption. The forced intrusion of liquid water in hydrophobic
nanoporous materials, such as zeolitic imidazolate frameworks (ZIFs), presents
an attractive pathway to engineer such systems. However, to harness their full
potential, it is crucial to understand the underlying water intrusion and
ex-trusion mechanisms under realistic, high-rate deformation conditions.
Herein, we report a critical increase of the energy absorption capacity of
confined water-ZIF systems at elevated strain rates. Starting from ZIF-8 as
proof-of-concept, we demonstrate that this attractive rate depend-ence is
generally applicable to cage-type ZIFs but disappears for channel-containing
zeolites. Molecular simulations reveal that this phenomenon originates from the
intrinsic nanosecond timescale needed for critical-sized water clusters to
nucleate inside the nanocages, expediting water transport through the
framework. Harnessing this fundamental understanding, design rules are
formulated to construct effective, tailorable, and reusable impact energy
absorbers for challenging new applications.


Abstract:
  Monitoring and controlling the state of polarization of electromagnetic waves
is of significant interest for various basic and practical applications such as
linear position sensing and medical imaging. Here, we propose the first
conformal digital metamaterial absorber to detect the polarization state of THz
incident waves. The proposed polarimeter is capable of characterizing four
independent polarization states of (TE, TM, $\pm {45^\circ}$ linear, and
RCP/LCP) by observing the reflectivity of the structure with respect to the x-
and y-direction. Besides, the proposed structure displays a strong absorptivity
above 90\% up to the incidence angle of $50^{\circ}$ for oblique incident waves
with different polarizations. By mere changing the bias voltage of two
orthogonal VO2 microwires via two independent computer-programmed multichannel
DC network, distinct conditions for reflected waves occurs under excitations of
different polarizations, whereby the polarization state of the incident wave
may readily be estimated. We believe that the proposed metasurface-based
polarimeter can pave the way for polarization detection applications on curved
surfaces.


Abstract:
  For reductive groups $G$ over a number field we discuss automorphic liftings
from cuspidal irreducible automorphic representations $\pi$ of $G(\mathbb{A})$
to cuspidal irreducible automorphic representations on $H(\mathbb{A})$ for the
quasi-split inner form $H$ of $G$. We show the existence of cohomological
nontrivial weak global liftings in many cases. A priori these weak liftings do
not give a description of the precise nature of the corresponding local
liftings at the ramified places and in particular do not characterize the image
of the lift. For inner forms of the group $H=\mathrm{GSp}(4)$ however we
address these finer details. Especially, we prove the recent conjectures of
Ibukiyama and Kitayama on paramodular newforms of squarefree level.


Abstract:
  In this work, we investigate gravitational baryogenesis in the framework of
$f(P)$ gravity to understand the applicability of this class of modified
gravity in addressing the baryon asymmetry of the Universe. For the analysis,
we set $f(P) = \alpha P$ where $\alpha$ is the model parameter. We found that
in $f(P)$ gravity, the CP-violating interaction acquires a modification through
the addition of the nontopological cubic term $P$ in addition to the Ricci
scalar $R$ and the mathematical expression of the baryon-to-entropy ratio
depends not only on the time derivative of $R$ but also the time derivative of
$P$. Additionally, we also investigate the consequences of a more complete and
generalized CP-violating interaction proportional to $f(P)$ instead of $P$ in
addressing the baryon asymmetry of the Universe. For this type of interaction,
we report that the baryon-to-entropy ratio is proportional to $\dot{R}$,
$\dot{P}$ and $f^{'}(P)$. We report that for both of these cases, rational
values of $\alpha$ and $\chi$ generate acceptable baryon-to-entropy ratios
compatible with observations.


Abstract:
  We study the dark matter phenomenology of Standard Model extensions
addressing the reported anomaly in the $R_K$ observable at one-loop. The
article covers the case of fermionic singlet DM coupling leptophilically,
quarkphilically or amphiphilically to the SM. The setup utilizes a large
coupling of the new particle content to the second lepton generation to explain
the $R_K$ anomaly, which in return tends to diminish the dark matter relic
density. Further, dark matter direct detection experiments provide stringent
bounds even in cases where the dark matter candidate only contributes a small
fraction of the observed dark matter energy density. In fact, direct detection
rules out all considered models as an explanation for the $R_K$ anomaly in the
case of Dirac dark matter. Conversely, for Majorana dark matter, the $R_K$
anomaly can be addressed in agreement with direct detection in coannihilation
scenarios. For leptophilic dark matter this region only exists for $M_\text{DM}
\lesssim 1000 \, \mathrm{GeV}$ and dark matter is underabundant. Quarkphilic
and amphiphilic scenarios even provide narrow regions of parameter space where
the observed relic density can be reproduced while offering an explanation to
$R_K$ in agreement with direct detection experiments.


Abstract:
  This paper introduces new algorithm for line extraction from laser range data
including methodology for efficient computation. The task is cast to series of
one dimensional problems in various spaces. A fast and simple specialization of
DBSCAN algorithm is proposed to solve one dimensional subproblems. Experiments
suggest that the method is suitable for real-time applications, handles noise
well and may be useful in practice.


Abstract:
  We introduce a new sparse sliced inverse regression estimator called Cholesky
matrix penalization and its adaptive version for achieving sparsity in
estimating the dimensions of the central subspace. The new estimators use the
Cholesky decomposition of the covariance matrix of the covariates and include a
regularization term in the objective function to achieve sparsity in a
computationally efficient manner. We establish the theoretical values of the
tuning parameters that achieve estimation and variable selection consistency
for the central subspace. Furthermore, we propose a new projection information
criterion to select the tuning parameter for our proposed estimators and prove
that the new criterion facilitates selection consistency. The Cholesky matrix
penalization estimator inherits the strength of the Matrix Lasso and the Lasso
sliced inverse regression estimator; it has superior performance in numerical
studies and can be adapted to other sufficient dimension methods in the
literature.


Abstract:
  Let $1<p<\infty$ and let $n\geq 1$. It is proved that a function $f:{\mathbb
R}\to {\mathbb C}$ is $n$-times Fr\'echet differentiable on ${\mathcal S}^p$ at
every self-adjoint operator if and only if $f$ is $n$-times differentiable,
$f',f'',\ldots,f^{(n)}$ are bounded and $f^{(n)}$ is uniformly continuous.


Abstract:
  The knowledge of distribution grid models, including topologies and line
impedances, is essential to grid monitoring, control and protection. However,
this information is often unavailable, incomplete or outdated. The increasing
deployment of smart meters (SMs) provides a unique opportunity to address this
issue. This paper proposes a two-stage data-driven framework for distribution
grid modeling using SM data. In the first stage, we propose to identify the
topology via reconstructing a weighted Laplacian matrix of distribution
networks, which is mathematically proven to be robust against moderately
heterogeneous R/X profiles. In the second stage, we develop nonlinear least
absolute deviations (LAD) and least squares (LS) regression models to estimate
line impedances of single branches based on a nonlinear inverse power flow,
which is then embedded within a bottom-up sweep algorithm to achieve the
identification across the network in a branch-wise manner. Because the
estimation models are inherently non-convex programs and NP-hard, we specially
address their tractable convex relaxations and verify the exactness. In
addition, we design a conductor library to significantly narrow down the
solution space. Numerical results on the modified IEEE 13-bus, 37-bus and
69-bus test feeders validate the effectiveness of the proposed methods.


Abstract:
  The early and robust detection of anomalies occurring in discrete
manufacturing processes allows operators to prevent harm, e.g. defects in
production machinery or products. While current approaches for data-driven
anomaly detection provide good results on the exact processes they were trained
on, they often lack the ability to flexibly adapt to changes, e.g. in products.
Continual learning promises such flexibility, allowing for an automatic
adaption of previously learnt knowledge to new tasks. Therefore, this article
discusses different continual learning approaches from the group of
regularization strategies, which are implemented, evaluated and compared based
on a real industrial metal forming dataset.


Abstract:
  The possible symmetries of the superconducting pair amplitude is a
consequence of the fermionic nature of the Cooper pairs. For spin-$1/2$ systems
this leads to the $\mathcal{SPOT}=-1$ classification of superconductivity,
where $\mathcal{S}$, $\mathcal{P}$, $\mathcal{O}$, and $\mathcal{T}$ refer to
the exchange operators for spin, parity, orbital, and time between the paired
electrons. However, this classification no longer holds for higher spin
fermions, where each electron also possesses a finite orbital angular momentum
strongly coupled with the spin degree of freedom, giving instead a conserved
total angular moment. For such systems, we here instead introduce the
$\mathcal{JPT}=-1$ classification, where $\mathcal{J}$ is the exchange operator
for the $z$-component of the total angular momentum quantum numbers. We then
specifically focus on spin-$3/2$ fermion systems and several superconducting
cubic half-Heusler compounds that have recently been proposed to be spin-$3/2$
superconductors. By using a generic Hamiltonian suitable for these compounds we
calculate the superconducting pair amplitudes and find finite pair amplitudes
for all possible symmetries obeying the $\mathcal{JPT}=-1$ classification,
including all possible odd-frequency (odd-$\omega$) combinations. Moreover, one
of the very interesting properties of spin-$3/2$ superconductors is the
possibility of them hosting a Bogoliubov Fermi surface (BFS), where the
superconducting energy gap is closed across a finite area. We show that a
spin-$3/2$ superconductor with a pair potential satisfying an odd-gap
time-reversal product and being non-commuting with the normal-state Hamiltonian
hosts both a BFS and has finite odd-$\omega$ pair amplitudes. We then reduce
the full spin-$3/2$ Hamiltonian to an effective two-band model and show that
odd-$\omega$ pairing is inevitably present in superconductors with a BFS and
vice versa.


Abstract:
  Utilization of Machine Learning (ML) algorithms, especially Deep Neural
Network (DNN) models, becomes a widely accepted standard in many domains more
particularly IoT-based systems. DNN models reach impressive performances in
several sensitive fields such as medical diagnosis, smart transport or security
threat detection, and represent a valuable piece of Intellectual Property. Over
the last few years, a major trend is the large-scale deployment of models in a
wide variety of devices. However, this migration to embedded systems is slowed
down because of the broad spectrum of attacks threatening the integrity,
confidentiality and availability of embedded models. In this review, we cover
the landscape of attacks targeting the confidentiality of embedded DNN models
that may have a major impact on critical IoT systems, with a particular focus
on model extraction and data leakage. We highlight the fact that Side-Channel
Analysis (SCA) is a relatively unexplored bias by which model's confidentiality
can be compromised. Input data, architecture or parameters of a model can be
extracted from power or electromagnetic observations, testifying a real need
from a security point of view.


Abstract:
  This paper studies the nature of fractional linear transformations in a
general relativity context as well as in a quantum theoretical framework. Two
features are found to deserve special attention: the first is the possibility
of separating the limit-point condition at infinity into loxodromic,
hyperbolic, parabolic and elliptic cases. This is useful in a context in which
one wants to look for a correspondence between essentially self-adjoint
spherically symmetric Hamiltonians of quantum physics and the theory of
Bondi-Metzner-Sachs transformations in general relativity. The analogy
therefore arising, suggests that further investigations might be performed for
a theory in which the role of fractional linear maps is viewed as a bridge
between the quantum theory and general relativity. The second aspect to point
out is the possibility of interpreting the limit-point condition at both ends
of the positive real line, for a second-order singular differential operator,
which occurs frequently in applied quantum mechanics, as the limiting procedure
arising from a very particular Kleinian group which is the hyperbolic cyclic
group. In this framework, this work finds that a consistent system of equations
can be derived and studied. Hence one is led to consider the entire
transcendental functions, from which it is possible to construct a fundamental
system of solutions of a second-order differential equation with singular
behavior at both ends of the positive real line, which in turn satisfy the
limit-point conditions.


Abstract:
  We examine how introduction of Shared Connected and Automated vehicles
(SCAVs) as a new mobility mode could affect travel demand, welfare, as well as
traffic congestion in the network. To do so, we adapt an agent-based day-to-day
adjustment process and develop a central dispatching system, which is
implemented on an in-house traffic microsimulator. We consider a two-sided
market in which demand and SCAV fleet size change endogenously. For dispatching
SCAV fleet size, we take changing traffic conditions into account. There are
two available transport modes: private Connected Automated Vehicles (CAVs) and
SCAVs. The designed system is applied on downtown Toronto network using real
data. The results show that demand of SCAVs goes up by 43 per cent over seven
study days from 670 trips on the first day to 959 trips on the seventh day.
Whereas, there is a 10 per cent reduction in private CAV demand from 2807 trips
to 2518 trips during the same duration. Moreover, total travel time of the
network goes down by seven per cent indicating that traffic congestion was
reduced in the network.


Abstract:
  The following paper proposes a new approach to determine whether a logical
(CNF) formula is satisfiable or not using probability theory methods.
Furthermore, we will introduce an algorithm that speeds up the standard
solution for (CNF-SAT) in some cases. It is known that any (CNF) formula is
solved with a time complexity of $2^n$ where n is the number of different
literals in the (CNF) formula. In our approach, we will follow an enhanced
method from a probabilistic point of view that does not always increase
exponentially with the number of different literals. This will enhance the
chance of determining whether a large formula is satisfiable or not in many
cases. Additionally, we will point out at some promising properties that follow
from applying probability theory concepts and axioms to logic, which might
originate more insights about the satisfiability of logical formulas.


Abstract:
  We show that $\Theta$-positive Anosov representations
$\rho:\Gamma\to\mathsf{PO}(p,q)$ of a surface group $\Gamma$ satisfy root vs
weight collar lemmas for all the Anosov roots, and are positively ratioed with
respect to all such roots. From this we deduce that $\Theta$-positive Anosov
representations $\rho:\Gamma\to\mathsf{PO}(p,q)$ form connected components of
character varieties.


Abstract:
  In recent years, biometric authentication technology for smartphones has
become widespread, with the mainstream methods being fingerprint authentication
and face recognition. However, fingerprint authentication cannot be used when
hands are wet, and face recognition cannot be used when a person is wearing a
mask. Therefore, we examine a personal authentication system using the pinna as
a new approach for biometric authentication on smartphones. Authentication
systems based on the acoustic transfer function of the pinna (PRTF: Pinna
Related Transfer Function) have been investigated. However, the authentication
accuracy decreases due to the positional fluctuation across each measurement.
In this paper, we propose multimodal personal authentication on smartphones
using PRTF. The pinna image and positional sensor information are used with the
PRTF, and the effectiveness of the authentication method is examined. We
demonstrate that the proposed authentication system can compensate for the
positional changes in each measurement and improve robustness.


Abstract:
  We propose a Concentrated Document Topic Model(CDTM) for unsupervised text
classification, which is able to produce a concentrated and sparse document
topic distribution. In particular, an exponential entropy penalty is imposed on
the document topic distribution. Documents that have diverse topic
distributions are penalized more, while those having concentrated topics are
penalized less. We apply the model to the benchmark NIPS dataset and observe
more coherent topics and more concentrated and sparse document-topic
distributions than Latent Dirichlet Allocation(LDA).


Abstract:
  We show that neural networks with absolute value activation function and with
the path norm, the depth, the width and the network weights having logarithmic
dependence on $1/\varepsilon$ can $\varepsilon$-approximate functions that are
analytic on certain regions of $\mathbb{C}^d$.


Abstract:
  We consider a bilevel attacker-defender problem to find the worst-case attack
on the relays that control the transmission grid. The attacker maximizes load
shed by infiltrating a number of relays and rendering the components connected
to them inoperable. The defender responds by minimizing the load shed,
re-dispatching using a DC optimal power flow (DCOPF) problem on the remaining
network. Though worst-case interdiction problems on the transmission grid are
well-studied, there remains a need for exact and scalable methods. Methods
based on using duality on the inner problem rely on the bounds of the dual
variables of the defender problem in order to reformulate the bilevel problem
as a mixed integer linear problem. Valid dual bounds tend to be large,
resulting in weak linear programming relaxations and making the problem
difficult to solve at scale. Often smaller heuristic bounds are used, resulting
in a lower bound. In this work we also consider a lower bound, where instead of
bounding the dual variables, we drop the constraints corresponding to Ohm's
law, relaxing DCOPF to capacitated network flow. We present theoretical results
showing that, for uncongested networks, approximating DCOPF with network flow
yields the same set of injections, which suggests that this restriction likely
gives a high-quality lower bound in the uncongested case. Furthermore, we show
that in the network flow relaxation of the defender problem, the duals are
bounded by 1, so we can solve our restriction exactly. Last, we see empirically
that this formulation scales well computationally. Through experiments on 16
networks with up to 6468 buses, we find that this bound is almost always as
tight as we can get from guessing the dual bounds, even for congested networks.
In addition, calculating the bound is approximately 150 times faster than
achieving the same bound with the reformulation guessing the dual bounds.


Abstract:
  A variety is a class of algebraic structures axiomatized by a set of
equations. An equation is linear if there is at most one occurrence of an
operation symbol on each side. We show that a variety axiomatized by linear
equations has the strong amalgamation property.
  Suppose further that the language has no constant symbol and, for each
equation, either one side is operation-free, or exactly the same variables
appear on both sides. Then also the joint embedding property holds.
  Examples include most varieties defining classical Maltsev conditions. In a
few special cases, the above properties are preserved when further unary
operations appear in the equations.


Abstract:
  Let $\mathcal{L}=(L,[\cdot\,,\cdot],\delta)$ be an algebraic Lie algebroid
over a smooth projective curve of genus $g\geq 2$ such that $L$ is a line
bundle whose degree is less than $2-2g$. Let $r$ and $d$ be coprime numbers. We
prove that the motivic class (in the Grothendieck ring of varieties) of the
moduli space of $\mathcal{L}$-connections of rank $r$ and degree $d$ over $X$
does not depend on the Lie algebroid structure $[\cdot\,,\cdot]$ and $\delta$
of $\mathcal{L}$ and neither on the line bundle $L$ itself, but only the degree
of $L$ (and of course on $r,d,g$ and $X$). In particular it is equal to the
motivic class of the moduli space of $K_X(D)$-twisted Higgs bundles of rank $r$
and degree $d$, for $D$ any divisor of positive degree. As a consequence,
similar results (actually a little stronger) are obtained for the corresponding
$E$-polynomials. Some applications of these results are then deduced.


Abstract:
  The main two families of real hypersurfaces in complex space forms are Hopf
and ruled. However, very little is known about real hypersurfaces in the
indefinite complex projective space $\mathbb{C}P^n_p$. In a previous work,
Kimura and the second author introduced Hopf real hypersurfaces in
$\mathbb{C}P^n_p$. In this paper, ruled real hypersurfaces in the indefinite
complex projective space are introduced, as those whose maximal holomorphic
distribution is integrable, and such that the leaves are totally geodesic
holomorphic hyperplanes. A detailed description of the shape operator is
computed, obtaining two main different families. A method of construction is
exhibited, by gluing in a suitable way totally geodesic holomorphic hyperplanes
along a non-null curve. Next, the classification of all minimal ruled real
hypersurfaces is obtained, in terms of three main families of curves, namely
geodesics, totally real circles and a third case which is not a Frenet curve,
but can be explicitly computed. Four examples are described.


Abstract:
  A remarkable result at the intersection of number theory and group theory
states that the order of a finite group $G$ (denoted $|G|$) is divisible by the
dimension $d_R$ of any irreducible complex representation of $G$. We show that
the integer ratios ${ |G|^2 / d_R^2 } $ are combinatorially constructible using
finite algorithms which take as input the amplitudes of combinatoric
topological strings ($G$-CTST) of finite groups based on 2D Dijkgraaf-Witten
topological field theories ($G$-TQFT2). The ratios are also shown to be
eigenvalues of handle creation operators in $G$-TQFT2/$G$-CTST. These strings
have recently been discussed as toy models of wormholes and baby universes by
Marolf and Maxfield, and Gardiner and Megas. Boundary amplitudes of the
$G$-TQFT2/$G$-CTST provide algorithms for combinatoric constructions of
normalized characters. Stringy S-duality for closed $G$-CTST gives a dual
expansion generated by disconnected entangled surfaces. There are universal
relations between $G$-TQFT2 amplitudes due to the finiteness of the number $K $
of conjugacy classes. These relations can be labelled by Young diagrams and are
captured by null states in an inner product constructed by coupling the
$G$-TQFT2 to a universal TQFT2 based on symmetric group algebras. We discuss
the scenario of a 3D holographic dual for this coupled theory and the
implications of the scenario for the factorization puzzle of 2D/3D holography
raised by wormholes in 3D.


Abstract:
  Exploration of new superconductors has always been one of the research
directions in condensed matter physics. We report here a new layered
heterostructure of [(Fe,Al)(OH)2][FeSe]1.2, which is synthesized by the
hydrothermal ion-exchange technique. The structure is suggested by a
combination of X-ray powder diffraction and the electron diffraction (ED).
[(Fe,Al)(OH)2][FeSe]1.2 is composed of the alternating stacking of tetragonal
FeSe layer and hexagonal (Fe,Al)(OH)2 layer. In [(Fe,Al)(OH)2][FeSe]1.2, there
exists mismatch between the FeSe sub-layer and (Fe,Al)(OH)2 sub-layer, and the
lattice of the layered heterostructure is quasi-commensurate. The
as-synthesized [(Fe,Al)(OH)2][FeSe]1.2 is non-superconducting due to the Fe
vacancies in the FeSe layer. The superconductivity with a Tc of 40 K can be
achieved after a lithiation process, which is due to the elimination of the Fe
vacancies in the FeSe layer. The Tc is nearly the same as that of (Li,Fe)OHFeSe
although the structure of [(Fe,Al)(OH)2][FeSe]1.2 is quite different from that
of (Li,Fe)OHFeSe. The new layered heterostructure of [(Fe,Al)(OH)2][FeSe]1.2
contains an iron selenium tetragonal lattice interleaved with a hexagonal metal
hydroxide lattice. These results indicate that the superconductivity is very
robust for FeSe-based superconductors. It opens a path for exploring
super-conductivity in iron-base superconductors.


Abstract:
  We present Graph Neural Diffusion (GRAND) that approaches deep learning on
graphs as a continuous diffusion process and treats Graph Neural Networks
(GNNs) as discretisations of an underlying PDE. In our model, the layer
structure and topology correspond to the discretisation choices of temporal and
spatial operators. Our approach allows a principled development of a broad new
class of GNNs that are able to address the common plights of graph learning
models such as depth, oversmoothing, and bottlenecks. Key to the success of our
models are stability with respect to perturbations in the data and this is
addressed for both implicit and explicit discretisation schemes. We develop
linear and nonlinear versions of GRAND, which achieve competitive results on
many standard graph benchmarks.


Abstract:
  We introduce the notion of soficity for locally compact groups and list a
number of open problems.


Abstract:
  In this study, an order by disorder mechanism has been proposed in a
two-dimensional PXP model, where the extensive degeneracy of the classical
ground-state manifold is due to strict occupation constraints instead of
geometrical frustrations. By performing an unbias large-scale quantum monte
carlos simulation, we find that local quantum fluctuations, which usually work
against long-range ordering, lift the macroscopic classical degeneracy and give
rise to a compressible ground state with charge-density-wave long-range order.
A simple trial wavefunction has been proposed to capture the essence of the
ground-state of the two-dimensional PXP model. The finite temperature
properties of this model have also been studied, and we find a thermal phase
transition with an universality class of two-dimensional Ising model.


Abstract:
  Theoretical models of galaxy-AGN co-evolution ascribe an important role for
the feedback process to a short, luminous, obscured, and dust-enshrouded phase
during which the accretion rate of the SMBH is expected to be at its maximum
and the associated AGN-driven winds are also predicted to be maximally
developed. To test this scenario, we have isolated a text-book candidate from
the eROSITA Final Equatorial-Depth Survey (eFEDS) obtained within the
Performance and Verification program of the eROSITA telescope on board Spectrum
Roentgen Gamma. From an initial catalog of 246 hard X-ray selected sources
matched with the photometric and spectroscopic information available within the
eROSITA and Hyper Suprime-Cam consortia, three candidates Quasars in the
feedback phase have been isolated applying the diagnostic proposed in Brusa et
al. (2015). Only one source (eFEDSU J091157.5+014327) has a spectrum already
available (from SDSS-DR16, z=0.603) and it unambiguously shows the presence of
a broad component (FWHM~1650 km/s) in the [OIII]5007 line. The associated
observed L_[OIII] is ~2.6x10^{42} erg/s, one to two orders of magnitude larger
than that observed in local Seyferts and comparable to those observed in a
sample of z~0.5 Type 1 Quasars. From the multiwavelength data available we
derive an Eddington Ratio (L_bol/L_Edd) of ~0.25, and a bolometric correction
in the hard X-ray of k_bol~10, lower than those observed for objects at similar
bolometric luminosity. The presence of an outflow, the high X-ray luminosity
and moderate X-ray obscuration (L_X~10^44.8 erg/s, N_H~2.7x10^22 cm^-2) and the
red optical color, all match the prediction of quasars in the feedback phase
from merger driven models. Forecasting to the full eROSITA all-sky survey with
its spectroscopic follow-up, we predict that by the end of 2024 we will have a
sample of few hundreds such objects at z=0.5-2.


Abstract:
  The simultaneous rise of machine learning as a service and concerns over user
privacy have increasingly motivated the need for private inference (PI). While
recent work demonstrates PI is possible using cryptographic primitives, the
computational overheads render it impractical. The community is largely
unprepared to address these overheads, as the source of slowdown in PI stems
from the ReLU operator whereas optimizations for plaintext inference focus on
optimizing FLOPs. In this paper we re-think the ReLU computation and propose
optimizations for PI tailored to properties of neural networks. Specifically,
we reformulate ReLU as an approximate sign test and introduce a novel
truncation method for the sign test that significantly reduces the cost per
ReLU. These optimizations result in a specific type of stochastic ReLU. The key
observation is that the stochastic fault behavior is well suited for the
fault-tolerant properties of neural network inference. Thus, we provide
significant savings without impacting accuracy. We collectively call the
optimizations Circa and demonstrate improvements of up to 4.7x storage and 3x
runtime over baseline implementations; we further show that Circa can be used
on top of recent PI optimizations to obtain 1.8x additional speedup.