title: Hephaestus: A large scale multitask dataset towards InSAR understanding

abstract:
  Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products
in particular, are one of the largest sources of Earth Observation data. InSAR
provides unique information on diverse geophysical processes and geology, and
on the geotechnical properties of man-made structures. However, there are only
a limited number of applications that exploit the abundance of InSAR data and
deep learning methods to extract such knowledge. The main barrier has been the
lack of a large curated and annotated InSAR dataset, which would be costly to
create and would require an interdisciplinary team of experts experienced on
InSAR data interpretation. In this work, we put the effort to create and make
available the first of its kind, manually annotated dataset that consists of
19,919 individual Sentinel-1 interferograms acquired over 44 different
volcanoes globally, which are split into 216,106 InSAR patches. The annotated
dataset is designed to address different computer vision problems, including
volcano state classification, semantic segmentation of ground deformation,
detection and classification of atmospheric signals in InSAR imagery,
interferogram captioning, text to InSAR generation, and InSAR image quality
assessment.


title: Learning Data-Driven PCHD Models for Control Engineering Applications

abstract:
  The design of control engineering applications usually requires a model that
accurately represents the dynamics of the real system. In addition to classical
physical modeling, powerful data-driven approaches are increasingly used.
However, the resulting models are not necessarily in a form that is
advantageous for controller design. In the control engineering domain, it is
highly beneficial if the system dynamics is given in PCHD form (Port-Controlled
Hamiltonian Systems with Dissipation) because globally stable control laws can
be easily realized while physical interpretability is guaranteed. In this work,
we exploit the advantages of both strategies and present a new framework to
obtain nonlinear high accurate system models in a data-driven way that are
directly in PCHD form. We demonstrate the success of our method by model-based
application on an academic example, as well as experimentally on a test bed.


title: Search-based Methods for Multi-Cloud Configuration

abstract:
  Multi-cloud computing has become increasingly popular with enterprises
looking to avoid vendor lock-in. While most cloud providers offer similar
functionality, they may differ significantly in terms of performance and/or
cost. A customer looking to benefit from such differences will naturally want
to solve the multi-cloud configuration problem: given a workload, which cloud
provider should be chosen and how should its nodes be configured in order to
minimize runtime or cost? In this work, we consider solutions to this
optimization problem. We develop and evaluate possible adaptations of
state-of-the-art cloud configuration solutions to the multi-cloud domain.
Furthermore, we identify an analogy between multi-cloud configuration and the
selection-configuration problems commonly studied in the automated machine
learning (AutoML) field. Inspired by this connection, we utilize popular
optimizers from AutoML to solve multi-cloud configuration. Finally, we propose
a new algorithm for solving multi-cloud configuration, CloudBandit (CB). It
treats the outer problem of cloud provider selection as a best-arm
identification problem, in which each arm pull corresponds to running an
arbitrary black-box optimizer on the inner problem of node configuration. Our
experiments indicate that (a) many state-of-the-art cloud configuration
solutions can be adapted to multi-cloud, with best results obtained for
adaptations which utilize the hierarchical structure of the multi-cloud
configuration domain, (b) hierarchical methods from AutoML can be used for the
multi-cloud configuration task and can outperform state-of-the-art cloud
configuration solutions and (c) CB achieves competitive or lower regret
relative to other tested algorithms, whilst also identifying configurations
that have 65% lower median cost and 20% lower median time in production,
compared to choosing a random provider and configuration.


title: A Corpus for Understanding and Generating Moral Stories

abstract:
  Teaching morals is one of the most important purposes of storytelling. An
essential ability for understanding and writing moral stories is bridging story
plots and implied morals. Its challenges mainly lie in: (1) grasping knowledge
about abstract concepts in morals, (2) capturing inter-event discourse
relations in stories, and (3) aligning value preferences of stories and morals
concerning good or bad behavior. In this paper, we propose two understanding
tasks and two generation tasks to assess these abilities of machines. We
present STORAL, a new dataset of Chinese and English human-written moral
stories. We show the difficulty of the proposed tasks by testing various models
with automatic and manual evaluation on STORAL. Furthermore, we present a
retrieval-augmented algorithm that effectively exploits related concepts or
events in training sets as additional guidance to improve performance on these
tasks.


title: Classical algorithms for many-body quantum systems at finite energies

abstract:
  We investigate quantum inspired algorithms to compute physical observables of
quantum many-body systems at finite energies. They are based on the quantum
algorithms proposed in [Lu et al. PRX Quantum 2, 020321 (2021)], which use the
quantum simulation of the dynamics of such systems, as well as classical
filtering and sampling techniques. Here, we replace the quantum simulation by
standard classical methods based on matrix product states and operators. As a
result, we can address significantly larger systems than those reachable by
exact diagonalization or by other algorithms. We demonstrate the performance
with spin chains up to 80 sites.


title:
On the potential of bright, young pulsars to power ultra-high gamma-ray
  sources

abstract:
  The recent discovery of a new population of ultra-high-energy gamma-ray
sources with spectra extending beyond 100 TeV revealed the presence of Galactic
PeVatrons - cosmic-ray factories accelerating particles to PeV energies. These
sources, except for the one associated with the Crab Nebula, are not yet
identified. With an extension of 1 degree or more, most of them contain several
potential counterparts, including Supernova Remnants, young stellar clusters
and Pulsar Wind Nebulae (PWNe), which can perform as PeVatrons and thus power
the surrounding diffuse ultra-high energy gamma-ray structures. In the case of
PWNe, gamma rays are produced by electrons, accelerated at the pulsar wind
termination shock, through the inverse Compton scattering of 2.7 K CMB
radiation. The high conversion efficiency of pulsar rotational power to
relativistic electrons, combined with the short cooling timescales, allow
gamma-ray luminosities up to the level of $L_\gamma \sim 0.1 \dot{E}$. The
pulsar spin-down luminosity, $\dot E$, also determines the absolute maximum
energy of individual photons: $E_{\rm \gamma~\rm max}\approx 0.9 \dot
E_{36}^{0.65}~~\rm{PeV}$. This fundamental constraint dominates over the
condition set by synchrotron energy losses of electrons for young PWNe with
typical magnetic field of $\approx$100$\mu$G with $\dot{E} \lesssim 10^{37}\
\rm erg/s$. We discuss the implications of $E_{\rm \gamma~\rm max}$ by
comparing it with the highest energy photons reported by LHAASO from a dozen of
ultra-high-energy sources. Whenever a PWN origin of the emission is possible,
we use the LHAASO measurements to set upper limits on the nebular magnetic
field.


title: K-theory of real Grassmann manifolds

abstract:
  Let $G_{n,k}$ denote the real Grassmann manifold of $k$-dimensional vector
subspaces of $\mathbb R^n$. Using the Hodgkin spectral sequence, we compute the
complex $K$-ring of $G_{n,k}$, up to a small indeterminacy, for all values of
$n,k$ where $2\le k\le n-2$. When $n\equiv 0\!\!\mod 4, k\equiv 1\!\!\mod 2$
our result is complete.


title:
DAM-GAN : Image Inpainting using Dynamic Attention Map based on Fake
  Texture Detection

abstract:
  Deep neural advancements have recently brought remarkable image synthesis
performance to the field of image inpainting. The adaptation of generative
adversarial networks (GAN) in particular has accelerated significant progress
in high-quality image reconstruction. However, although many notable GAN-based
networks have been proposed for image inpainting, still pixel artifacts or
color inconsistency occur in synthesized images during the generation process,
which are usually called fake textures. To reduce pixel inconsistency disorder
resulted from fake textures, we introduce a GAN-based model using dynamic
attention map (DAM-GAN). Our proposed DAM-GAN concentrates on detecting fake
texture and products dynamic attention maps to diminish pixel inconsistency
from the feature maps in the generator. Evaluation results on CelebA-HQ and
Places2 datasets with other image inpainting approaches show the superiority of
our network.


title: GIMO: Gaze-Informed Human Motion Prediction in Context

abstract:
  Predicting human motion is critical for assistive robots and AR/VR
applications, where the interaction with humans needs to be safe and
comfortable. Meanwhile, an accurate prediction depends on understanding both
the scene context and human intentions. Even though many works study
scene-aware human motion prediction, the latter is largely underexplored due to
the lack of ego-centric views that disclose human intent and the limited
diversity in motion and scenes. To reduce the gap, we propose a large-scale
human motion dataset that delivers high-quality body pose sequences, scene
scans, as well as ego-centric views with eye gaze that serves as a surrogate
for inferring human intent. By employing inertial sensors for motion capture,
our data collection is not tied to specific scenes, which further boosts the
motion dynamics observed from our subjects. We perform an extensive study of
the benefits of leveraging eye gaze for ego-centric human motion prediction
with various state-of-the-art architectures. Moreover, to realize the full
potential of gaze, we propose a novel network architecture that enables
bidirectional communication between the gaze and motion branches. Our network
achieves the top performance in human motion prediction on the proposed
dataset, thanks to the intent information from the gaze and the denoised gaze
feature modulated by the motion. The proposed dataset and our network
implementation will be publicly available.


title: Effective models of non-singular quantum black holes

abstract:
  We investigate how the resolution of the singularity problem for the
Schwarzschild BH could be related to the presence of quantum gravity effects at
horizon scales. Motivated by the analogy with the cosmological Schwarzschild-dS
solution, we construct a class of non-singular, static, asymptotically-flat BH
solutions with a dS core, sourced by an anisotropic fluid, which encodes the
quantum corrections. The latter are parametrized by a single length-scale
$\ell$, which has a dual interpretation as an effective "quantum hair" and as
the length-scale resolving the classical singularity. Depending on the value of
$\ell$, these solutions can have two horizons, be extremal (when the two
horizons merge) or be horizonless exotic stars. We also investigate the
thermodynamic behavior of our BH solutions and propose a generalization of the
area law in order to account for their entropy. We find a second-order phase
transition near extremality, when $\ell$ is of order of the classical
Schwarzschild radius $R_{\rm S}$. BHs with $\ell\sim R_{\rm S}$ are
thermodynamically preferred with respect to those with $\ell\ll R_{\rm S}$,
supporting the relevance of quantum corrections at horizon scales. We also find
that the extremal configuration is a zero-temperature, zero-entropy state with
its near-horizon geometry factorizing as AdS$_2\times$ S$^2$, signalizing the
possible relevance of these models for the information paradox. We show that
the presence of quantum corrections with $\ell\sim R_{\rm S}$ have observable
phenomenological signatures in the photon orbits and in the QNMs spectrum. In
particular, in the near-extremal regime, the imaginary part of the spectrum
scales with the temperature as $c_1/\ell+c_2\ell T_\text{H}^2$, while it goes
to zero linearly in the near-horizon limit. Our general findings are confirmed
by revisiting two already-known models, namely the Hayward and gaussian-core
BHs.


title: Electrical breakdown in Thick-GEM based WELL detectors

abstract:
  The occurrence of electrical breakdown (i.e., discharges) in gas detectors
restricts their dynamic range and degrades their performance. Among the
different methods developed to mitigate these effects, the use of resistive
materials in the detector assembly has been found to be very effective. In this
paper, we studied and characterized electrical discharges in Thick-GEM based
WELL-type detectors with and without resistive elements. We present a new
method to measure discharges in the resistive configurations, which allows
demonstrating for the first time the occurrence of discharges in the Resistive
Plate WELL detector as well. It also provides a direct measurement of the
Raether limit.


title:
A theoretical framework for the Hamiltonian of angular momentum
  optomechanical system

abstract:
  Photon carries linear momentum and angular momentum simultaneously. Within
the light-matter interaction process, exchange of linear momentum results in
optical forces, whereas exchange of angular momentum leads to optical torques.
Use of optical forces (light pressure or damping) have been long and wide in
quantum optomechanics, however, those of optical torque and optical angular
momentum are not. Here we propose a theoretical framework based on optical
angular momentum and optical torques to derive the Hamiltonians of cavity
orbital and spin angular momentum optomechanical systems, respectively.
Moreover, based on the method, we successfully obtain the Hamiltonian of the
complex angular momentum optomechanical systems consisting of micro-cavity and
several torsional oscillators, whose reflection coefficients are non-unit. Our
results indicate the general applicability of our theoretical framework for the
Hamiltonian of angular momentum optomechanical systems and extend the research
scope of quantum optomechanics.


title:
Effective Goal-oriented 6G Communications: the Energy-aware Edge
  Inferencing Case

abstract:
  Currently, the world experiences an unprecedentedly increasing generation of
application data, from sensor measurements to video streams, thanks to the
extreme connectivity capability provided by 5G networks. Going beyond 5G
technology, such data aim to be ingested by Artificial Intelligence (AI)
functions instantiated in the network to facilitate informed decisions,
essential for the operation of applications, such as automated driving and
factory automation. Nonetheless, while computing platforms hosting Machine
Learning (ML) models are ever powerful, their energy footprint is a key
impeding factor towards realizing a wireless network as a sustainable
intelligent platform. Focusing on a beyond 5G wireless network, overlaid by a
Multi-access Edge Computing (MEC) infrastructure with inferencing capabilities,
our paper tackles the problem of energy-aware dependable inference by
considering inference effectiveness as value of a goal that needs to be
accomplished by paying the minimum price in energy consumption. Both
MEC-assisted standalone and ensemble inference options are evaluated. It is
shown that, for some system scenarios, goal effectiveness above 84% is achieved
and sustained even by relaxing communication reliability requirements by one
decimal digit, while enjoying a device radio energy consumption reduction of
almost 23% at the same time. Also, ensemble inference is shown to improve
system-wide energy efficiency and even achieve higher goal effectiveness, as
compared to the standalone case for some system parameterizations.


title:
Isogeometric Analysis of Acoustic Scattering with Perfectly Matched
  Layers (IGAPML)

abstract:
  The perfectly matched layer (PML) formulation is a prominent way of handling
radiation problems in unbounded domain and has gained interest due to its
simple implementation in finite element codes. However, its simplicity can be
advanced further using the isogeometric framework. This work presents a spline
based PML formulation which avoids additional coordinate transformation as the
formulation is based on the same space in which the numerical solution is
sought. The procedure can be automated for any convex artificial boundary. This
removes restrictions on the domain construction using PML and can therefore
reduce computational cost and improve mesh quality. The usage of spline basis
functions with higher continuity also improves the accuracy of the numerical
solution.


title:
Reversible parabolic diffeomorphisms of $(\mathbb{C}^2,0)$ and
  exceptional hyperbolic CR-singularities

abstract:
  The aim of this article is twofold: First we study holomorphic germs of
parabolic diffeomorphisms of $(\mathbb{C}^2,0)$ that are reversed by a
holomorphic reflection and posses an analytic first integral with
non-degenerate critical point at the origin. We find a canonical formal normal
form and provide a complete analytic classification (in formal generic cases)
in terms of a collection of functional invariants. Their restriction to an
irreductible component of the zero locus of the first integral reduces to the
Birkhoff--\'Ecalle--Voronin modulus of the 1-dimensional restricted parabolic
germ.
  We then generalize this classification also to germs of anti-holomorphic
diffeomorphisms of $(\mathbb{C}^2,0)$ whose square iterate is of the above
form.
  Related to it, we solve the problem of both formal and analytic
classification of germs of real analytic surfaces in $\mathbb{C}^2$ with
non-degenerate CR singularities of exceptional hyperbolic type, under the
assumption that the surface is holomorphically flat, i.e. that it can be
locally holomorphically embedded in a real hyperplane of $\mathbb{C}^2$.


title: Who Benefits from Political Connections in Brazilian Municipalities

abstract:
  A main issue in improving public sector efficiency is to understand to what
extent public appointments are based on worker capability, instead of being
used to reward political supporters (patronage). I contribute to a recent
literature documenting patronage in public sector employment by establishing
what type of workers benefit the most from political connections. Under the
(empirically supported) assumption that in close elections the result of the
election is as good as random, I estimate a causal forest to identify
heterogeneity in the conditional average treatment effect of being affiliated
to the party of the winning mayor. Contrary to previous literature, for most
positions we find positive selection on education, but a negative selection on
(estimated) ability. Overall, unemployed workers or low tenure employees that
are newly affiliated to the winning candidate's party benefit the most from
political connections, suggesting that those are used for patronage.


title:
Mesoscopic Quantum Thermo-mechanics: a new frontier of experimental
  physics

abstract:
  Within the last decade, experimentalists have demonstrated their impressive
ability to control mechanical modes within mesoscopic objects down to the
quantum level: it is now possible to create mechanical Fock states, to entangle
mechanical modes from distinct objects, store quantum information or transfer
it from one quantum bit to another, among the many possibilities found in
today's literature. Indeed mechanics is quantum, very much like spins or
electromagnetic degrees of freedom. And all of this is in particular referred
to as a new engineering resource for quantum technologies. But there is also
much more beyond this utilitarian aspect: invoking the original discussions of
Braginsky and Caves where a quantum oscillator is thought of as a quantum
detector for a classical field, namely a gravitational wave, it is also a
unique sensing capability for quantum fields. The subject of study is then the
baths to which the mechanical mode is coupled to, let them be known or unknown
in nature. This Letter is about this new potentiality, that addresses
stochastic thermodynamics, potentially down to its quantum version, the search
for a fundamental underlying (random) field postulated in recent theories that
can be affiliated to the class of the Wave-function Collapse models, and more
generally open questions of Condensed Matter like the actual nature of the
elusive (and ubiquitous) Two-Level Systems present within all mechanical
objects. But such research turns out to be much more demanding than the usage
of a few quantum mechanical modes: all the known baths have to be identified,
experiments have to be conducted in-equilibrium, and the word "mechanics" needs
to be justified by a real ability to move substantially the centre-of-mass when
a proper drive tone is applied to the system.


title: A note on dyadic approximation in Cantor's set

abstract:
  We consider the convergence theory for dyadic approximation in the
middle-third Cantor set, $K$, for approximation functions of the form
$\psi_{\tau}(n) = n^{-\tau}$ ($\tau \ge 0$). In particular, we show that for
values of $\tau$ beyond a certain threshold we have that almost no point in $K$
is dyadically $\psi_{\tau}$-well approximable with respect to the natural
probability measure on $K$. This refines a previous result in this direction
obtained by the first, third, and fourth named authors (arXiv, 2020).


title: Event Transition Planning for Open-ended Text Generation

abstract:
  Open-ended text generation tasks, such as dialogue generation and story
completion, require models to generate a coherent continuation given limited
preceding context. The open-ended nature of these tasks brings new challenges
to the neural auto-regressive text generators nowadays. Despite these neural
models are good at producing human-like text, it is difficult for them to
arrange causalities and relations between given facts and possible ensuing
events. To bridge this gap, we propose a novel two-stage method which
explicitly arranges the ensuing events in open-ended text generation. Our
approach can be understood as a specially-trained coarse-to-fine algorithm,
where an event transition planner provides a "coarse" plot skeleton and a text
generator in the second stage refines the skeleton. Experiments on two
open-ended text generation tasks demonstrate that our proposed method
effectively improves the quality of the generated text, especially in coherence
and diversity. The code is available at:
\url{https://github.com/qtli/EventPlanforTextGen}.


title: Dynamical quantum phase transitions from quantum optics perspective

abstract:
  It is shown that dynamical quantum phase transitions observed as
singularities in the Loschmidt rate singularities bear close resemblance to
standard Rabi oscillations known from dynamics of two-level systems. For some
many-body systems this analogy may go even further and the behaviour observed
for e.g. transverse Ising chain may be directly mapped to such simple dynamics.
It is explained why A simple link between Loschmidt echo singularities and
quantum scars is suggested.


title: Simplicial Attention Networks

abstract:
  Graph representation learning methods have mostly been limited to the
modelling of node-wise interactions. Recently, there has been an increased
interest in understanding how higher-order structures can be utilised to
further enhance the learning abilities of graph neural networks (GNNs) in
combinatorial spaces. Simplicial Neural Networks (SNNs) naturally model these
interactions by performing message passing on simplicial complexes,
higher-dimensional generalisations of graphs. Nonetheless, the computations
performed by most existent SNNs are strictly tied to the combinatorial
structure of the complex. Leveraging the success of attention mechanisms in
structured domains, we propose Simplicial Attention Networks (SAT), a new type
of simplicial network that dynamically weighs the interactions between
neighbouring simplicies and can readily adapt to novel structures.
Additionally, we propose a signed attention mechanism that makes SAT
orientation equivariant, a desirable property for models operating on (co)chain
complexes. We demonstrate that SAT outperforms existent convolutional SNNs and
GNNs in two image and trajectory classification tasks.


title: STAU: A SpatioTemporal-Aware Unit for Video Prediction and Beyond

abstract:
  Video prediction aims to predict future frames by modeling the complex
spatiotemporal dynamics in videos. However, most of the existing methods only
model the temporal information and the spatial information for videos in an
independent manner but haven't fully explored the correlations between both
terms. In this paper, we propose a SpatioTemporal-Aware Unit (STAU) for video
prediction and beyond by exploring the significant spatiotemporal correlations
in videos. On the one hand, the motion-aware attention weights are learned from
the spatial states to help aggregate the temporal states in the temporal
domain. On the other hand, the appearance-aware attention weights are learned
from the temporal states to help aggregate the spatial states in the spatial
domain. In this way, the temporal information and the spatial information can
be greatly aware of each other in both domains, during which, the
spatiotemporal receptive field can also be greatly broadened for more reliable
spatiotemporal modeling. Experiments are not only conducted on traditional
video prediction tasks but also other tasks beyond video prediction, including
the early action recognition and object detection tasks. Experimental results
show that our STAU can outperform other methods on all tasks in terms of
performance and computation efficiency.


title: On the relative asymptotic expressivity of inference frameworks

abstract:
  Let $\sigma$ be a first-order signature and let $\mathbf{W}_n$ be the set of
all $\sigma$-structures with domain $[n] = \{1, \ldots, n\}$. We can think of
each structure in $\mathbf{W}_n$ as representing a "possible (state of the)
world". By an inference framework we mean a class $\mathbf{F}$ of pairs
$(\mathbb{P}, L)$, where $\mathbb{P} = (\mathbb{P}_n : n = 1, 2, 3, \ldots)$
and each $\mathbb{P}_n$ is a probability distribution on $\mathbb{W}_n$, and
$L$ is a logic with truth values in the unit interval $[0, 1]$.
  From the point of view of probabilistic and logical expressivity one may
consider an inference framework as optimal if it allows any pair $(\mathbb{P},
L)$ where $\mathbb{P} = (\mathbb{P}_n : n = 1, 2, 3, \ldots)$ is a sequence of
probability distributions on $\mathbb{W}_n$ and $L$ is a logic. But from the
point of view of using a pair $(\mathbb{P}, L)$ from such an inference
framework for making inferences on $\mathbb{W}_n$ when $n$ is large we face the
problem of computational complexity. This motivates looking for an "optimal"
trade-off (in a given context) between expressivity and computational
efficiency.
  We define a notion that an inference framework is "asymptotically at least as
expressive" as another inference framework. This relation is a preorder and we
describe a (strict) partial order on the equivalence classes of some inference
frameworks that in our opinion are natural in the context of machine learning
and artificial intelligence. The results have bearing on issues concerning
efficient learning and probabilistic inference, but are also new instances of
results in finite model theory about "almost sure elimination" of extra
syntactic features (e.g quantifiers) beyond the connectives. Often such a
result has a logical convergence law as a corollary.


title: Circular orderability and quandles

abstract:
  In this paper, we introduce the notion of circular orderability for quandles.
We show that the set all right (respectively left) circular orderings of a
quandle is a compact topological space. We also show that the space of right
(respectively left) orderings of a quandle embeds in its space of right
(respectively left) circular orderings. Examples of quandles that are not left
circularly orderable and examples of quandles that are neither left nor right
circularly orderable are given.


title:
Same-hemisphere three-gluon-emission contribution to the zero-jettiness
  soft function at N3LO QCD

abstract:
  We complete the calculation of the three-gluon-emission contribution to the
same-hemisphere part of the zero-jettiness soft function at
next-to-next-to-next-to-leading order in perturbative QCD.


title:
The moduli stack of enriched structures and a logarithmic
  compactification

abstract:
  Enriched curves have been studied over algebraically closed fields by Main\`o
([Mai98]) and recently over general base schemes in [BH19]. In this paper, we
study enriched curves from a logarithmic viewpoint: we give a succinct
definition of the stack of rich log curves, which is an open substack of the
stack of log curves, and define an enriched curve to be a curve with a minimal
rich log structure on it. This logarithmic view point turns out to be a natural
language for enriched structures, leading naturally to a simple modular
compactification. This modular compactification is a smooth log blowup of the
stack of log curves, answering affirmatively two questions from [BH19]. We also
generalise the concept of rich curves to $r$-rich curves, and show similar
results. We include a chapter phrasing some of the key definitions solely in
the language of real tropical geometry.


title: Noise mitigation strategies in physical feedforward neural networks

abstract:
  Physical neural networks are promising candidates for next generation
artificial intelligence hardware. In such architectures, neurons and
connections are physically realized and do not leverage digital, i.e.
practically infinite signal-to-noise ratio digital concepts. They therefore are
prone to noise, and base don analytical derivations we here introduce
connectivity topologies, ghost neurons as well as pooling as noise mitigation
strategies. Finally, we demonstrate the effectiveness of the combined methods
based on a fully trained neural network classifying the MNIST handwritten
digits.


title:
Quantity vs Quality: Investigating the Trade-Off between Sample Size and
  Label Reliability

abstract:
  In this paper, we study learning in probabilistic domains where the learner
may receive incorrect labels but can improve the reliability of labels by
repeatedly sampling them. In such a setting, one faces the problem of whether
the fixed budget for obtaining training examples should rather be used for
obtaining all different examples or for improving the label quality of a
smaller number of examples by re-sampling their labels. We motivate this
problem in an application to compare the strength of poker hands where the
training signal depends on the hidden community cards, and then study it in
depth in an artificial setting where we insert controlled noise levels into the
MNIST database. Our results show that with increasing levels of noise,
resampling previous examples becomes increasingly more important than obtaining
new examples, as classifier performance deteriorates when the number of
incorrect labels is too high. In addition, we propose two different validation
strategies; switching from lower to higher validations over the course of
training and using chi-square statistics to approximate the confidence in
obtained labels.


title: Bounding suprema of canonical processes via convex hull

abstract:
  We discuss the method of bounding suprema of canonical processes based on the
inclusion of their index set into a convex hull of a well-controlled set of
points. While the upper bound is immediate, the reverse estimate was
established to date only for a narrow class of regular stochastic processes. We
show that for specific index sets, including arbitrary ellipsoids, regularity
assumptions may be substantially weakened.


title: Instability of Universal Terms in the Entanglement Entropy

abstract:
  The role of symmetries in what concerns entanglement entropy has been
extensively explored in the last years and revealed a profound connection with
the quantum field theory's algebraic structure. Recently, it was found that
some universal contributions to the entanglement entropy and mutual information
may be non uniquely defined in theories with generalized symmetries. Here, we
study this issue in detail in the particular case of the entanglement entropy
of the Maxwell theory in $(2+1)$ dimensions for rotationally symmetric regions.
In this setup, the problem can be dimensionally reduced to a half-line. We find
that the only difference between the reduced problem for the Maxwell field and
the reduced scalar free field stems from the Fourier angular $n=0$ mode. This
simplification allows us to check explicitly the many issues that characterize
models with broken global symmetries. Namely, we manifestly show that the
additive algebras break Haag duality, and single out the non-local operators
which are responsible for the failure of this property. More interestingly, we
present concrete lattice realizations that confirm that the logarithmic
"universal" term of the Maxwell entanglement entropy for disks depends on the
details of the algebra assignation. This ambiguity hinders the identification
of possible topological contributions characteristic of models with generalized
symmetries and tarnishes its universal character. We further calculate the
Maxwell mutual information for two nearly complementary concentric disks. We
obtain the expected universal contribution with a log-log dependence and check
that, unlike entropy, this is stable. Accordingly, this supports mutual
information as the appropriate probe to sense additivity-duality breaking and
the consequent universal topological contributions.


title:
SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via
  Siamese Heterogeneous Graph Attention Network

abstract:
  Unlike IPv4 addresses, which are typically masked by a NAT, IPv6 addresses
could easily be correlated with user activity, endangering their privacy.
Mitigations to address this privacy concern have been deployed, making existing
approaches for address-to-user correlation unreliable. This work demonstrates
that an adversary could still correlate IPv6 addresses with users accurately,
even with these protection mechanisms. To do this, we propose an IPv6 address
correlation model - SiamHAN. The model uses a Siamese Heterogeneous Graph
Attention Network to measure whether two IPv6 client addresses belong to the
same user even if the user's traffic is protected by TLS encryption. Using a
large real-world dataset, we show that, for the tasks of tracking target users
and discovering unique users, the state-of-the-art techniques could achieve
only 85% and 60% accuracy, respectively. However, SiamHAN exhibits 99% and 88%
accuracy.


title: A Maxwell demon that can work at macroscopic scales

abstract:
  Maxwell's demons work by rectifying thermal fluctuations. They are not
expected to function at macroscopic scales where fluctuations become negligible
and dynamics become deterministic. We propose an electronic implementation of
an autonomous Maxwell's demon that indeed stops working in the regular
macroscopic limit as the dynamics becomes deterministic. However, we find that
if the power supplied to the demon is scaled up appropriately, the
deterministic limit is avoided and the demon continues to work. The price to
pay is a decreasing thermodynamic efficiency. Our work suggests that novel
strategies may be found in nonequilibrium settings to bring to the macroscale
non-trivial effects so far only observed at microscopic scales.


title:
Decentralized Nash Equilibria Learning for Online Game with Bandit
  Feedback

abstract:
  This paper studies distributed online bandit learning of generalized Nash
equilibria for online game, where cost functions of all players and coupled
constraints are time-varying. The values rather than full information of cost
and local constraint functions are revealed to local players gradually. The
goal of each player is to selfishly minimize its own cost function with no
future information subject to a strategy set constraint and time-varying
coupled inequality constraints. To this end, a distributed online algorithm
based on mirror descent and one-point bandit feedback is designed for seeking
generalized Nash equilibria of the online game. It is shown that the devised
online algorithm achieves sublinear expected regrets and accumulated constraint
violation if the path variation of the generalized Nash equilibrium sequence is
sublinear. Furthermore, the proposed algorithm is extended to the scenario of
delayed bandit feedback, that is, the values of cost and constraint functions
are disclosed to local players with time delays. It is also demonstrated that
the online algorithm with delayed bandit feedback still has sublinear expected
regrets and accumulated constraint violation under some conditions on the path
variation and delay. Simulations are presented to illustrate the efficiency of
theoretical results.


title: THORN: Temporal Human-Object Relation Network for Action Recognition

abstract:
  Most action recognition models treat human activities as unitary events.
However, human activities often follow a certain hierarchy. In fact, many human
activities are compositional. Also, these actions are mostly human-object
interactions. In this paper we propose to recognize human action by leveraging
the set of interactions that define an action. In this work, we present an
end-to-end network: THORN, that can leverage important human-object and
object-object interactions to predict actions. This model is built on top of a
3D backbone network. The key components of our model are: 1) An object
representation filter for modeling object. 2) An object relation reasoning
module to capture object relations. 3) A classification layer to predict the
action labels. To show the robustness of THORN, we evaluate it on
EPIC-Kitchen55 and EGTEA Gaze+, two of the largest and most challenging
first-person and human-object interaction datasets. THORN achieves
state-of-the-art performance on both datasets.


title: Localizing narrow Fe K$\alpha$ emission within bright AGN

abstract:
  The 6.4 keV Fe Ka emission line is a ubiquitous feature in X-ray spectra of
AGN, and its properties track the interaction between the variable primary
X-ray continuum and the surrounding structure from which it arises. We clarify
the nature and origin of the narrow Fe Ka emission using X-ray spectral,
timing, and imaging constraints, plus possible correlations to AGN and host
galaxy properties, for 38 bright nearby AGN ($z<0.5$) from the BAT AGN
Spectroscopic Survey. Modeling Chandra and XMM-Newton spectra, we computed line
full-width half-maxima (FWHMs) and constructed Fe Ka line and 2-10 keV
continuum light curves. The FWHM provides one estimate of the Fe Ka emitting
region size, RFeKa, assuming virial motion. A second estimate comes from
comparing the degree of correlation between the variability of the continuum
and line-only light curves, compared to simulated light curves. Finally, we
extracted Chandra radial profiles to place upper limits on RFeKa. We found that
for 90% (21/24) of AGN with FWHM measurements, RFeKa is smaller than the
fiducial dust sublimation radius, Rsub. Despite a wide range of variability
properties, the constraints on the Fe Ka photon reprocessor size independently
confirm that RFeKa is smaller than Rsub in 83% of AGN. Finally, the imaging
analysis yields loose upper limits for all but two sources; notably, the
Circinus Galaxy and NGC 1068 show significant but subdominant extended Fe Ka
emission out to $\sim$100 and $\sim$800 pc, respectively. Based on independent
constraints, we conclude that the majority of the narrow Fe Ka emission in
typical AGN predominantly arises from regions smaller than and presumably
inside Rsub, and thus it is associated either with the outer broad line region
or outer accretion disk. However, the large diversity of continuum and narrow
Fe Ka variability properties are not easily accommodated by a universal
scenario.


title:
Single SiGe Quantum Dot Emission Deterministically Enhanced in a High-Q
  Photonic Crystal Resonator

abstract:
  We report the resonantly enhanced radiative emission from a single SiGe
quantum dot (QD), which is deterministically embedded into a bichromatic
photonic crystal resonator (PhCR) at the position of its largest modal electric
field by a scalable method. By optimizing our molecular beam epitaxy (MBE)
growth technique, we were able to reduce the amount of Ge within the whole
resonator to obtain an absolute minimum of exactly one QD, accurately
positioned by lithographic methods relative to the PhCR, and an otherwise flat,
a few monolayer thin, Ge wetting layer (WL). With this method, record quality
(Q) factors for QD-loaded PhCRs up to $Q\sim 10^5$ are achieved. A comparison
with control PhCRs on samples containing a WL but no QDs is presented, as well
as a detailed analysis of the dependence of the resonator-coupled emission on
temperature, excitation intensity, and emission decay after pulsed excitation.
Our findings undoubtedly confirm a single QD in the center of the resonator as
a potentially novel photon source in the telecom spectral range.


title:
Mathematical artificial intelligence design of mutation-proof COVID-19
  monoclonal antibodies

abstract:
  Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
variants have compromised existing vaccines and posed a grand challenge to
coronavirus disease 2019 (COVID-19) prevention, control, and global economic
recovery. For COVID-19 patients, one of the most effective COVID-19 medications
is monoclonal antibody (mAb) therapies. The United States Food and Drug
Administration (U.S. FDA) has given the emergency use authorization (EUA) to a
few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also
undermined by SARS-CoV-2 mutations. It is imperative to develop effective
mutation-proof mAbs for treating COVID-19 patients infected by all emerging
variants and/or the original SARS-CoV-2. We carry out a deep mutational
scanning to present the blueprint of such mAbs using algebraic topology and
artificial intelligence (AI). To reduce the risk of clinical trial-related
failure, we select five mAbs either with FDA EUA or in clinical trials as our
starting point. We demonstrate that topological AI-designed mAbs are effective
to variants of concerns and variants of interest designated by the World Health
Organization (WHO), as well as the original SARS-CoV-2. Our topological AI
methodologies have been validated by tens of thousands of deep mutational data
and their predictions have been confirmed by results from tens of experimental
laboratories and population-level statistics of genome isolates from hundreds
of thousands of patients.


title:
Modeling and Executing Production Processes with Capabilities and Skills
  using Ontologies and BPMN

abstract:
  Current challenges of the manufacturing industry require modular and
changeable manufacturing systems that can be adapted to variable conditions
with little effort. At the same time, production recipes typically represent
important company know-how that should not be directly tied to changing plant
configurations. Thus, there is a need to model general production recipes
independent of specific plant layouts. For execution of such a recipe however,
a binding to then available production resources needs to be made. In this
contribution, select a suitable modeling language to model and execute such
recipes. Furthermore, we present an approach to solve the issue of recipe
modeling and execution in modular plants using semantically modeled
capabilities and skills as well as BPMN. We make use of BPMN to model
\emph{capability processes}, i.e. production processes referencing abstract
descriptions of resource functions. These capability processes are not bound to
a certain plant layout, as there can be multiple resources fulfilling the same
capability. For execution, every capability in a capability process is replaced
by a skill realizing it, effectively creating a \emph{skill process} consisting
of various skill invocations. The presented solution is capable of
orchestrating and executing complex processes that integrate production steps
with typical IT functionalities such as error handling, user interactions and
notifications. Benefits of the approach are demonstrated using a flexible
manufacturing system.


title:
Linear kinetic effects of core plasma on low frequency Alfven and
  acoustic eigenmodes in tokamaks

abstract:
  The resonant and non-resonant effects of core plasma on the excitation of low
frequency modes with $\omega < \omega_{BAE}$, such as Beta-induced Alfv\'en
Acoustic Eigenmodes (BAAEs) and Kinetic Ballooning Modes (KBMs) are examined in
the framework of the generalized fishbone-like dispersion relation. The
formalism of the fishbone-like equation contains all the necessary ingredients
to describe the features of these low frequency fluctuations, and explain
experimental findings. Core plasma properties (diamagnetic frequency and
precession resonance with trapped ions) strongly affect the excitation of the
modes, and in the case of BAAEs more effectively than the energetic particles.
The diamagnetic frequency of the core plasma also contributes to the coupling
of the BAAEs with the KBMs, thus affecting the excitation and polarization of
both modes. Energetic particles can still provide a non-resonant drive to some
of the low frequency modes.


title: Crossed products of $4$-algebras. Applications

abstract:
  A $4$-algebra is a commutative algebra $A$ over a field $k$ such that
$(a^2)^2 = 0$, for all $a \in A$. We have proved recently \cite{Mil} that
$4$-algebras play a prominent role in the classification of finite dimensional
Bernstein algebras. Let $A$ be a $4$-algebra, $E$ a vector space and $\pi : E
\to A$ a surjective linear map with $V = {\rm Ker} (\pi)$. All $4$-algebra
structures on $E$ such that $\pi : E \to A$ is an algebra map are described and
classified by a global cohomological object ${\mathbb G} {\mathbb H}^{2} \, (A,
\, V)$. Any such $4$-algebra is isomorphic to a crossed product $V \# A$ and
${\mathbb G} {\mathbb H}^{2} \, (A, \, V)$ is a coproduct, over all
$4$-algebras structures $\cdot_V$ on $V$, of all non-abelian cohomologies
${\mathbb H}^{2}_{\rm nab} \, \bigl(A, \, (V, \, \cdot_{V} )\bigl)$, which are
the classifying objects for all extensions of $A$ by $V$. Several applications
and examples are provided: in particular, ${\mathbb G} {\mathbb H}^{2} \, (A,
\, k)$ and ${\mathbb G} {\mathbb H}^{2} \, (k, \, V)$ are explicitly computed
and the Galois group ${\rm Gal} \, (V \# A/ V )$ of the extension $V
\hookrightarrow V \# A$ is described.


title:
ADMX-Orpheus First Search for 70 $\mu$eV Dark Photon Dark Matter:
  Detailed Design, Operations, and Analysis

abstract:
  Dark matter makes up 85% of the matter in the universe and 27% of its energy
density, but we don't know what comprises dark matter. It is possible that dark
matter may be composed of either axions or dark photons, both of which can be
detected using an ultra-sensitive microwave cavity known as a haloscope. The
haloscope employed by ADMX consists of a cylindrical cavity operating at the
TM$_{010}$ mode and is sensitive to the QCD axion with masses of few $\mu$eV.
However, this haloscope design becomes challenging to implement for higher
masses. This is because higher masses require smaller-diameter cavities,
consequently reducing the detection volume which diminishes the detected signal
power. ADMX-Orpheus mitigates this issue by operating a tunable,
dielectrically-loaded cavity at a higher-order mode, allowing the detection
volume to remain large. This paper describes the design, operation, analysis,
and results of the inaugural ADMX-Orpheus dark photon search between 65.5
$\mu$eV (15.8 GHz) and 69.3 $\mu$eV (16.8 GHz), as well as future directions
for axion searches and for exploring more parameter space.


title:
Generation of Attosecond Pulses with Controllable Carrier-Envelope Phase
  via High-order Frequency Mixing

abstract:
  Advancing table-top attosecond sources in brightness and pulse duration is of
immense interest and importance for an expanding sphere of applications. Recent
theoretical studies [New J. Phys., 22 093030 (2020)] have found that high-order
frequency mixing (HFM) in a two-color laser field can be much more efficient
than high-order harmonic generation (HHG). Here we study the attosecond
properties of the coherent XUV generated via HFM analytically and numerically,
focusing on the practically important case when one of the fields has much
lower frequency and much lower intensity than the other one. We derive simple
analytical equations describing intensities and phase locking of the HFM
spectral components. We show that the duration of attosecond pulses generated
via HFM, while being very similar to that obtained via HHG in the plateau, is
shortened for the cut-off region. Moreover, our study demonstrates that the
carrier-envelope phase of the attopulses produced via HFM, in contrast to HHG,
can be easily controlled by the phases of the generating fields.


title: Interpreting the $W$ mass anomaly in the vectorlike quark models

abstract:
  The new measurement of $W$-boson mass from the CDF collaboration depicts a
remarkable $7\sigma$ disagreement with the Standard Model (SM) prediction. This
highly implies that there exist new particles or fields beyond the SM. In this
work, we explore the possibility of explaining the $W$ mass anomaly in the
simple extension of the SM with the vector-like quarks. Confronting with the
current LHC data and the electroweak precision measurements, we find that the
vector-like quark models can reconcile SM theory with the $W$ mass anomaly.


title:
Regularity Of The Semi-group Of Regular Probability Measures On Compact
  Hausdorff Topological Groups

abstract:
  There are many deep results on the structure of REGULAR probability measures
$P(G)$ on compact/locally compact, Hausdorff topological groups G. See, for
instance, the classic monographs by KR Parthasarathy, Ulf Grenander,
A.Mukherjea and Nicolas A.Tserpes. It is known that the set P(G) forms a
semi-group under convolution. Wendel in his remarkable paper, proved a basic
result regarding support of convolution of two probability measures.
Consequently, he established that the semi-group P(G) is not a group. In this
short paper, it is proved that for a compact topological group G, the
semi-group P(G) of probability measures is not algebraically regular. However,
there are concrete regular semi-groups in which P(G) can be embedded.


title: Negative temperature is cool for cooling

abstract:
  In this work, we study an autonomous refrigerator composed of three qubits
[Phys. Rev. Lett. 105, 130401 (2010)] operating with one of the reservoirs at
negative temperatures, which has the purpose of cooling one of the qubits. We
find the values of the lowest possible temperature that the qubit of interest
reaches when fixing the relevant parameters, and we also study the limit for
cooling the qubit arbitrarily close to absolute zero. We thus proceed to a
comparative study showing that reservoirs at effective negative temperatures
are more powerful than those at positive temperatures for cooling the qubit of
interest.


title: GazeOnce: Real-Time Multi-Person Gaze Estimation

abstract:
  Appearance-based gaze estimation aims to predict the 3D eye gaze direction
from a single image. While recent deep learning-based approaches have
demonstrated excellent performance, they usually assume one calibrated face in
each input image and cannot output multi-person gaze in real time. However,
simultaneous gaze estimation for multiple people in the wild is necessary for
real-world applications. In this paper, we propose the first one-stage
end-to-end gaze estimation method, GazeOnce, which is capable of simultaneously
predicting gaze directions for multiple faces (>10) in an image. In addition,
we design a sophisticated data generation pipeline and propose a new dataset,
MPSGaze, which contains full images of multiple people with 3D gaze ground
truth. Experimental results demonstrate that our unified framework not only
offers a faster speed, but also provides a lower gaze estimation error compared
with state-of-the-art methods. This technique can be useful in real-time
applications with multiple users.


title:
Unsupervised Ranking and Aggregation of Label Descriptions for Zero-Shot
  Classifiers

abstract:
  Zero-shot text classifiers based on label descriptions embed an input text
and a set of labels into the same space: measures such as cosine similarity can
then be used to select the most similar label description to the input text as
the predicted label. In a true zero-shot setup, designing good label
descriptions is challenging because no development set is available. Inspired
by the literature on Learning with Disagreements, we look at how probabilistic
models of repeated rating analysis can be used for selecting the best label
descriptions in an unsupervised fashion. We evaluate our method on a set of
diverse datasets and tasks (sentiment, topic and stance). Furthermore, we show
that multiple, noisy label descriptions can be aggregated to boost the
performance.


title:
A Generalisable Data Fusion Framework to Infer Mode of Transport Using
  Mobile Phone Data

abstract:
  Cities often lack up-to-date data analytics to evaluate and implement
transport planning interventions to achieve sustainability goals, as
traditional data sources are expensive, infrequent, and suffer from data
latency. Mobile phone data provide an inexpensive source of geospatial
information to capture human mobility at unprecedented geographic and temporal
granularity. This paper proposes a method to estimate updated mode of
transportation usage in a city, with novel usage of mobile phone application
traces to infer previously hard to detect modes, such as bikes and
ride-hailing/taxi. By using data fusion and matrix factorisation, we integrate
socioeconomic and demographic attributes of the local resident population into
the model. We tested the method in a case study of Santiago (Chile), and found
that changes from 2012 to 2020 in mode of transportation inferred by the method
are coherent with expectations from domain knowledge and the literature, such
as ride-hailing trips replacing mass transport.


title:
Per-run Algorithm Selection with Warm-starting using Trajectory-based
  Features

abstract:
  Per-instance algorithm selection seeks to recommend, for a given problem
instance and a given performance criterion, one or several suitable algorithms
that are expected to perform well for the particular setting. The selection is
classically done offline, using openly available information about the problem
instance or features that are extracted from the instance during a dedicated
feature extraction step. This ignores valuable information that the algorithms
accumulate during the optimization process.
  In this work, we propose an alternative, online algorithm selection scheme
which we coin per-run algorithm selection. In our approach, we start the
optimization with a default algorithm, and, after a certain number of
iterations, extract instance features from the observed trajectory of this
initial optimizer to determine whether to switch to another optimizer. We test
this approach using the CMA-ES as the default solver, and a portfolio of six
different optimizers as potential algorithms to switch to. In contrast to other
recent work on online per-run algorithm selection, we warm-start the second
optimizer using information accumulated during the first optimization phase. We
show that our approach outperforms static per-instance algorithm selection. We
also compare two different feature extraction principles, based on exploratory
landscape analysis and time series analysis of the internal state variables of
the CMA-ES, respectively. We show that a combination of both feature sets
provides the most accurate recommendations for our test cases, taken from the
BBOB function suite from the COCO platform and the YABBOB suite from the
Nevergrad platform.


title:
Generalizing to the Future: Mitigating Entity Bias in Fake News
  Detection

abstract:
  The wide dissemination of fake news is increasingly threatening both
individuals and society. Fake news detection aims to train a model on the past
news and detect fake news of the future. Though great efforts have been made,
existing fake news detection methods overlooked the unintended entity bias in
the real-world data, which seriously influences models' generalization ability
to future data. For example, 97\% of news pieces in 2010-2017 containing the
entity `Donald Trump' are real in our data, but the percentage falls down to
merely 33\% in 2018. This would lead the model trained on the former set to
hardly generalize to the latter, as it tends to predict news pieces about
`Donald Trump' as real for lower training loss. In this paper, we propose an
entity debiasing framework (\textbf{ENDEF}) which generalizes fake news
detection models to the future data by mitigating entity bias from a
cause-effect perspective. Based on the causal graph among entities, news
contents, and news veracity, we separately model the contribution of each cause
(entities and contents) during training. In the inference stage, we remove the
direct effect of the entities to mitigate entity bias. Extensive offline
experiments on the English and Chinese datasets demonstrate that the proposed
framework can largely improve the performance of base fake news detectors, and
online tests verify its superiority in practice. To the best of our knowledge,
this is the first work to explicitly improve the generalization ability of fake
news detection models to the future data. The code has been released at
https://github.com/ICTMCG/ENDEF-SIGIR2022.


title: Quantum Random Access Code in Noisy Channels

abstract:
  Random access code (RAC) communication protocol particularly useful when the
communication between parties is restricted. In this work we built upon works
that have previously proven quantum random access code (QRAC), in the absence
of noise, to be more advantageous than classical random access code (CRAC),
investigate the effects of noisy channel on QRAC performance and how the losses
can be mitigated by using the see-saw method optimized by semi-definite
programming when the noisy channel is known.


title:
Graph neural networks and attention-based CNN-LSTM for protein
  classification

abstract:
  This paper focuses on three critical problems on protein classification.
Firstly, Carbohydrate-active enzyme (CAZyme) classification can help people to
understand the properties of enzymes. However, one CAZyme may belong to several
classes. This leads to Multi-label CAZyme classification. Secondly, to capture
information from the secondary structure of protein, protein classification is
modeled as graph classification problem. Thirdly, compound-protein interactions
prediction employs graph learning for compound with sequential embedding for
protein. This can be seen as classification task for compound-protein pairs.
This paper proposes three models for protein classification. Firstly, this
paper proposes a Multi-label CAZyme classification model using CNN-LSTM with
Attention mechanism. Secondly, this paper proposes a variational graph
autoencoder based subspace learning model for protein graph classification.
Thirdly, this paper proposes graph isomorphism networks (GIN) and
Attention-based CNN-LSTM for compound-protein interactions prediction, as well
as comparing GIN with graph convolution networks (GCN) and graph attention
networks (GAT) in this task. The proposed models are effective for protein
classification. Source code and data are available at
https://github.com/zshicode/GNN-AttCL-protein. Besides, this repository
collects and collates the benchmark datasets with respect to above problems,
including CAZyme classification, enzyme protein graph classification,
compound-protein interactions prediction, drug-target affinities prediction and
drug-drug interactions prediction. Hence, the usage for evaluation by benchmark
datasets can be more conveniently.


title: Extra boson mix with Z boson explaining the mass of W boson

abstract:
  We explore the possibility of explaining the W mass with an extra gauge boson
mixing with the Z boson at tree level. Extra boson mixing with Z boson will
change the expression of Z boson mass, thus altering the W boson mass. We
explore two models in this work. We find that in the Derivative Portal Dark
Matter model, there are parameters space which can give the observed W boson
mass. And in the U(1) extension model, the kinetic mixing between extra boson
and B boson can also predict the observed W boson mass. Both model indicate an
extra vector boson with best fit mass around 120 GeV.


title: Optical conductivity and orbital magnetization of Floquet vortex states

abstract:
  Motivated by recent experimental demonstrations of Floquet topological
insulators, there have been several theoretical proposals for using structured
light, either spatial or spectral, to create other properties such as flat band
and vortex states. In particular, the generation of vortex states in a massive
Dirac fermion insulator irradiated by light carrying nonzero orbital angular
momentum (OAM) has been proposed [Kim et al. Phys. Rev. B 105, L081301(2022)].
Here, we evaluate the orbital magnetization and optical conductivity as
physical observables for such a system. We show that the OAM of light induces
nonzero orbital magnetization and current density. The orbital magnetization
density increases linearly as a function of OAM degree, and non-linearly as a
function of the frequency of the light field. In certain regimes, we find that
orbital magnetization density is independent of the system size, width, and
Rabi frequency of light. Furthermore, we study the optical conductivity for
various types of electron transitions between different states such as vortex,
edge, and bulk that are present in the system. Based on conductance frequency
peaks, a scheme for the detection of vortex states is proposed.


title: Search for pulsars in the summed power spectra at a frequency of 111 MHz

abstract:
  The search of pulsars in monitoring observations being carried out for 5
years using LPA LPI radio telescope was done in 96 spatial beams covering daily
17,000 square degrees. Five new pulsars were detected. Candidates into pulsars
were selected in the summed power spectra. The use of a noise generator allowed
to renormalize the data and to perform correct summing up of the power spectra
for individual directions in the sky. Herewith, the sensitivity increased more
than 20 times in comparison with individual sessions of observations. For
pulsars with pulse durations greater than 100 ms at declinations +30o<dec.<+40o
it is equal to 1.2 mJy and 0.4 mJy in/out of the Galaxy plane.


title: Geometrical Origin of Growth of Matter Perturbations

abstract:
  The density perturbation of the universe has been considered in the framework
of a Finslerian cosmological model in which the background spacetime of the
universe is taken as the spatially flat FLRW spacetime with a Finslerian
perturbation. The inhomogeneity in the matter(energy) density arises naturally
in this consideration of gravity in the $( \alpha, \beta)$ - Finsler space for
the background spacetime of the universe. The resulting inhomogeneous matter
density indicates matter perturbation departing from the uniform density, and
it is caused by the Finslerian perturbation in the Riemannian spacetime, thus
ascertaining the geometrical origin of the growth of matter perturbation.


title:
Hyperbolic angles in Lorentzian length spaces and timelike curvature
  bounds

abstract:
  Within the synthetic-geometric framework of Lorentzian (pre-)length spaces
developed in Kunzinger and S\"amann (Ann. Glob. Anal. Geom. 54(3):399--447,
2018) we introduce a notion of a hyperbolic angle, an angle between timelike
curves and related concepts like timelike tangent cone and exponential map.
This provides valuable technical tools for the further development of the
theory and paves the way for the main result of the article, which is the
characterization of timelike curvature bounds (defined via triangle comparison)
with an angle monotonicity condition. Further, we improve on a geodesic
non-branching result for spaces with timelike curvature bounded below.


title: Simple fluid with broken time reversal invariance

abstract:
  We characterize a system of hard spheres with a simple collision rule that
breaks time reversal symmetry, but conserves energy. The collisions lead to an
a-chiral, isotropic, and homogeneous stationary state, whose properties are
determined in simulations and compared to an approximate theory originally
developed for elastic hard spheres. In the nonequilibrium fluid state,
velocities are correlated, a phenomenon known from other nonequilibrium
stationary states. The correlations are long-ranged decaying like $1/r^d$ in
$d$ dimensions. Such correlations are expected on general grounds far from
equilibrium and had previously been observed in driven or non-stationary
systems.


title: Approximate Sampling of Graphs with Near-$P$-stable Degree Intervals

abstract:
  The approximate uniform sampling of graph realizations with a given degree
sequence is an everyday task in several social science, computer science,
engineering etc. projects. One approach is using Markov chains. The best
available current result about the well-studied switch Markov chain is that it
is rapidly mixing on P-stable degree sequences (see
DOI:10.1016/j.ejc.2021.103421). The switch Markov chain does not change any
degree sequence. However, there are cases where degree intervals are specified
rather than a single degree sequence. (A natural scenario where this problem
arises is in hypothesis testing on social networks that are only partially
observed.) Rechner, Strowick, and M\"uller-Hannemann introduced in 2018 the
notion of degree interval Markov chain which uses three (separately
well-studied) local operations (switch, hinge-flip and toggle), and employing
on degree sequence realizations where any two sequences under scrutiny have
very small coordinate-wise distance. Recently Amanatidis and Kleer published a
beautiful paper (arXiv:2110.09068), showing that the degree interval Markov
chain is rapidly mixing if the sequences are coming from a system of very thin
intervals which are centered not far from a regular degree sequence. In this
paper we extend substantially their result, showing that the degree interval
Markov chain is rapidly mixing if the intervals are centred at P-stable degree
sequences.


title:
Numerical study of non-adiabatic quantum thermodynamics of the driven
  resonant level model: Non-equilibrium entropy production and higher order
  corrections

abstract:
  We present our numerical study on quantum thermodynamics of the resonant
level model subjected to non-equilibrium condition as well as external driving.
Following our previous work on non-equilibrium quantum thermodynamics (Phys.
Rev. B 101, 184304 [2020]), we expand the density operator into a series of
power in the driving speed, where we can determine the non-adiabatic
thermodynamic quantities. Particularly, we calculate the non-equilibrium
entropy production rate as well as higher order non-adiabatic corrections to
the energy and/or population. In the limit of weak system-bath coupling, our
results reduce to the one from the quantum master equation.


title: Identifying organizations receiving personal data in Android Apps

abstract:
  Many studies have demonstrated that mobile applications are common means to
collect massive amounts of personal data. This goes unnoticed by most users,
who are also unaware that many different organizations are receiving this data,
even from multiple apps in parallel. This paper assesses different techniques
to identify the organizations that are receiving personal data flows in the
Android ecosystem, namely the WHOIS service, SSL certificates inspection, and
privacy policy textual analysis. Based on our findings, we propose a fully
automated method that combines the most successful techniques, achieving a
94.73% precision score in identifying the recipient organization. We further
demonstrate our method by evaluating 1,000 Android apps and exposing the
corporations that collect the users' personal data.


title:
Non-flat and non-Extensive Thermodynamic Effects of M{\o}ller tetradic
  theory of gravitation on cosmology

abstract:
  We derive non-flat cosmological models for two cases (i.e., dust and
radiation) in the context of M{\o}ller's tetradic theory (MTT) of gravitation
using the tetrad that creates the non-flat Friedmann-Robertson-Walker (FRW)
metric. These two models are affected by the free dimensional parameter,
$\lambda$, that characterized MTT, which approaches zero in the flat case for
both models. Using standard definitions of thermodynamics, we calculate the
radius horizon, Hawking temperature, and entropy of our non-flat models in the
framework of cosmology and show the effect of $\lambda$ on open and closed
universes. We then use the first law of thermodynamics to construct non-flat
cosmological models via the non-extensive thermodynamic approach. The resulting
models are affected by $\lambda$ and the extensive parameter, $\delta$, which
quantifies the effect of non-extensive thermodynamics. When we set, $\lambda=0$
and $\delta=1$, we return to Einstein's general relativity models. We study the
evolution of our models in the presence of collisionless non-relativistic
matter and describe precise forms of the dark energy density and
equation-of-state parameter constraining the non-extensive thermodynamic
parameter. We show that insertion of the non-extensive thermodynamic parameter
affects the non-flat FRW universe in a manner that noticeably differs from that
observed under normal thermodynamics. We also show that the deceleration of the
open universe behaves as dark energy in a future epoch, i.e., when the redshift
approaches -1, i.e., $z\approx$-1.


title:
On analysis of the exponential map of volume-preserving diffeomorphism
  group on closed orientable surfaces through the vorticity

abstract:
  We study the exponential map of group of volume-preserving diffeomorphisms on
closed orientable surfaces via the vorticity formulation of the incompressible
Euler equation. We present an alternative, fluid dynamical proof of the theorem
of Ebin--Misio\l{}ek--Preston: the exponential is a nonlinear Fredholm mapping
of index zero. We extend Shnirelman's rigidity result for the exponential map
from 2-dimensional flat torus to arbitrary orientable closed surfaces. That is,
we prove that the exponential map is Fredholm quasiregular.


title: Backdooring Explainable Machine Learning

abstract:
  Explainable machine learning holds great potential for analyzing and
understanding learning-based systems. These methods can, however, be
manipulated to present unfaithful explanations, giving rise to powerful and
stealthy adversaries. In this paper, we demonstrate blinding attacks that can
fully disguise an ongoing attack against the machine learning model. Similar to
neural backdoors, we modify the model's prediction upon trigger presence but
simultaneously also fool the provided explanation. This enables an adversary to
hide the presence of the trigger or point the explanation to entirely different
portions of the input, throwing a red herring. We analyze different
manifestations of such attacks for different explanation types in the image
domain, before we resume to conduct a red-herring attack against malware
classification.


title:
On the (dis)similarities between stationary imprecise and non-stationary
  precise uncertainty models in algorithmic randomness

abstract:
  The field of algorithmic randomness studies what it means for infinite binary
sequences to be random for some given uncertainty model. Classically,
martingale-theoretic notions of such randomness involve precise uncertainty
models, and it is only recently that imprecision has been introduced into this
context. As a consequence, the investigation into how imprecision alters our
view on martingale-theoretic random sequences has only just begun. In this
contribution, where we allow for non-computable uncertainty models, we
establish a close and surprising connection between precise and imprecise
uncertainty models in this randomness context. In particular, we show that
there are stationary imprecise models and non-computable non-stationary precise
models that have the exact same set of random sequences. We also give a
preliminary discussion of the possible implications of our result for a
statistics based on imprecise probabilities, and shed some light on the
practical (ir)relevance of both imprecise and non-computable precise
uncertainty models in that context.


title:
A Reinforcement Learning-based Volt-VAR Control Dataset and Testing
  Environment

abstract:
  To facilitate the development of reinforcement learning (RL) based power
distribution system Volt-VAR control (VVC), this paper introduces a suite of
open-source datasets for RL-based VVC algorithm research that is sample
efficient, safe, and robust. The dataset consists of two components: 1. a
Gym-like VVC testing environment for the IEEE-13, 123, and 8500-bus test
feeders and 2. a historical operational dataset for each of the feeders.
Potential users of the dataset and testing environment could first train an
sample-efficient off-line (batch) RL algorithm on the historical dataset and
then evaluate the performance of the trained RL agent on the testing
environments. This dataset serves as a useful testbed to conduct RL-based VVC
research mimicking the real-world operational challenges faced by electric
utilities. Meanwhile, it allows researchers to conduct fair performance
comparisons between different algorithms.


title:
An advanced spatio-temporal convolutional recurrent neural network for
  storm surge predictions

abstract:
  In this research paper, we study the capability of artificial neural network
models to emulate storm surge based on the storm track/size/intensity history,
leveraging a database of synthetic storm simulations. Traditionally,
Computational Fluid Dynamics solvers are employed to numerically solve the
storm surge governing equations that are Partial Differential Equations and are
generally very costly to simulate. This study presents a neural network model
that can predict storm surge, informed by a database of synthetic storm
simulations. This model can serve as a fast and affordable emulator for the
very expensive CFD solvers. The neural network model is trained with the storm
track parameters used to drive the CFD solvers, and the output of the model is
the time-series evolution of the predicted storm surge across multiple nodes
within the spatial domain of interest. Once the model is trained, it can be
deployed for further predictions based on new storm track inputs. The developed
neural network model is a time-series model, a Long short-term memory, a
variation of Recurrent Neural Network, which is enriched with Convolutional
Neural Networks. The convolutional neural network is employed to capture the
correlation of data spatially. Therefore, the temporal and spatial correlations
of data are captured by the combination of the mentioned models, the ConvLSTM
model. As the problem is a sequence to sequence time-series problem, an
encoder-decoder ConvLSTM model is designed. Some other techniques in the
process of model training are also employed to enrich the model performance.
The results show the proposed convolutional recurrent neural network
outperforms the Gaussian Process implementation for the examined synthetic
storm database.


title:
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of
  Perturbed Samples

abstract:
  A rising number of botnet families have been successfully detected using deep
learning architectures. While the variety of attacks increases, these
architectures should become more robust against attacks. They have been proven
to be very sensitive to small but well constructed perturbations in the input.
Botnet detection requires extremely low false-positive rates (FPR), which are
not commonly attainable in contemporary deep learning. Attackers try to
increase the FPRs by making poisoned samples. The majority of recent research
has focused on the use of model loss functions to build adversarial examples
and robust models. In this paper, two LSTM-based classification algorithms for
botnet classification with an accuracy higher than 98% are presented. Then, the
adversarial attack is proposed, which reduces the accuracy to about 30%. Then,
by examining the methods for computing the uncertainty, the defense method is
proposed to increase the accuracy to about 70%. By using the deep ensemble and
stochastic weight averaging quantification methods it has been investigated the
uncertainty of the accuracy in the proposed methods.


title:
Magnetic Field Tuning of Parallel Spin Stripe Order and Fluctuations
  near the Pseudogap Quantum Critical Point in
  La$_{1.36}$Nd$_{0.4}$Sr$_{0.24}$CuO$_4$

abstract:
  A quantum critical point in the single layer, hole-doped cuprate system
La$_{1.6-x}$Nd$_{0.4}$Sr$_x$CuO$_4$ (Nd-LSCO), near $x$ = 0.23 has been
proposed as an organizing principle for understanding high temperature
superconductivity. Our earlier neutron diffraction work on Nd-LSCO at optimal
and high doping revealed static parallel spin stripes to exist out to the QCP
and slightly beyond, at $x$ = 0.24 and 0.26. We examine more closely the
parallel spin stripe order parameter in Nd-LSCO in both zero magnetic field and
fields up to 8 T for H // c in these single crystals. In contrast to earlier
studies at lower doping, we observe that H //c in excess of $\sim$ 2.5 T
eliminates the incommensurate quasi-Bragg peaks associated with parallel spin
stripes. But this elastic scattering is not destroyed by the field; rather it
is transferred to commensurate {\textbf{Q} = 0} Bragg positions, implying that
the spins participating in the spin stripes have been polarized. Inelastic
neutron scattering measurements at high fields show an increase in the low
energy, parallel spin stripe fluctuations and evidence for a spin gap,
$\Delta_{spin}$= 3 $\pm$ 0.5 meV for Nd-LSCO with $x$ = 0.24. This is shown to
be consistent with spin gap measurements as a function of superconducting T$_C$
over five different families of cuprate superconductors, which follow the
approximate linear relation, $\Delta_{spin}$ = 3.5 k$_B$T$_C$.


title:
L2C2: Last-Level Compressed-Cache NVM and a Procedure to Forecast
  Performance and Lifetime

abstract:
  Several emerging non-volatile memory (NVM) technologies are rising as
interesting alternatives to build the Last-Level Cache (LLC). Their advantages,
compared to SRAM memory, are higher density and lower static power, but each
write operation slightly wears out the bitcell, to the point of losing its
storage capacity. In this context, this paper proposes a new LLC organization
designed to extend the lifetime of the NVM data array and a methodological
proposal to forecast in detail the capacity and performance of NVM caches over
its lifetime. Data compression is one of the techniques dealing with the
degradation of a NV-LLC, as it decreases the write bandwidth delivered to the
cache. The proposed NV-LLC organization takes advantage of compression, but
with a relevant contribution: as capacity is reduced by write wear, degraded
cache frames can allocate blocks whose compressed size is adequate. From a
methodological point of view, although different approaches are used in the
literature to analyze the degradation of a NV-LLC, none of them allows to study
in detail its temporal evolution. In this sense, this work proposes a forecast
procedure that combines detailed simulation and prediction, allowing an
accurate analysis of the impact of different cache control policies and
mechanisms (replacement, wear leveling, compression, etc.) on the temporal
evolution of the indices of interest, such as the effective capacity of the
NV-LLC or the system IPC. The proposed NV-LLC organization has a small added
cost compared to that of a baseline NV-LLC without compression in terms of
area, latency and energy consumption, and increases up to 6-36 times the time
required to reach 50% effective capacity in an STT-RAM-based NV-LLC.


title: Isogeometric Analysis of Acoustic Scattering using Infinite Elements

abstract:
  Isogeometric analysis (IGA) has proven to be an improvement on the classical
finite element method (FEM) in several fields, including structural mechanics
and fluid dynamics. In this paper, the performance of IGA coupled with the
infinite element method (IEM) for some acoustic scattering problems is
investigated. In particular, the simple problem of acoustic scattering by a
rigid sphere, and the scattering of acoustic waves by an elastic spherical
shell with fluid domains both inside and outside, representing a full
acoustic-structure interaction (ASI) problem. Finally, a mock shell and a
simplified submarine benchmark are investigated. The numerical examples include
comparisons between IGA and the FEM. Our main finding is that the usage of IGA
significantly increases the accuracy compared to the usage of $C^0$ FEM due to
increased inter-element continuity of the spline basis functions.


title:
Weak solvability of nonlinear elliptic equations involving variable
  exponents

abstract:
  We are concerned with the study of the existence and multiplicity of
solutions for Dirichlet boundary value problems, involving the $( p( m ), \, q(
m ) )-$ equation and the nonlinearity is superlinear but does not fulfil the
Ambrosetti-Rabinowitz condition in the framework of Sobolev spaces with
variable exponents in a complete manifold. The main results are proved using
the mountain pass theorem and Fountain theorem with Cerami sequences. Moreover,
an example of a $( p( m ), \, q( m ) )$ equation that highlights the
applicability of our theoretical results is also provided.


title:
Bounds to the first eigenvalues of weighted p-Steklov and
  (p,q)-Laplacian Steklov problems

abstract:
  We consider the Steklov problem associated with the weighted p-Laplace
operator and $(p,q)$-Laplacian on submanifolds with the boundary of Euclidean
spaces and prove Reilly-type upper bounds for their first eigenvalues.


title:
BSAL: A Framework of Bi-component Structure and Attribute Learning for
  Link Prediction

abstract:
  Given the ubiquitous existence of graph-structured data, learning the
representations of nodes for the downstream tasks ranging from node
classification, link prediction to graph classification is of crucial
importance. Regarding missing link inference of diverse networks, we revisit
the link prediction techniques and identify the importance of both the
structural and attribute information. However, the available techniques either
heavily count on the network topology which is spurious in practice or cannot
integrate graph topology and features properly. To bridge the gap, we propose a
bicomponent structural and attribute learning framework (BSAL) that is designed
to adaptively leverage information from topology and feature spaces.
Specifically, BSAL constructs a semantic topology via the node attributes and
then gets the embeddings regarding the semantic view, which provides a flexible
and easy-to-implement solution to adaptively incorporate the information
carried by the node attributes. Then the semantic embedding together with
topology embedding is fused together using an attention mechanism for the final
prediction. Extensive experiments show the superior performance of our proposal
and it significantly outperforms baselines on diverse research benchmarks.


title:
Exact SDP relaxations for quadratic programs with bipartite graph
  structures

abstract:
  For nonconvex quadratically constrained quadratic programs (QCQPs), we first
show that, under certain feasibility conditions, the standard semidefinite
(SDP) relaxation is exact for QCQPs with bipartite graph structures. The exact
optimal solutions are obtained by examining the dual SDP relaxation and the
rank of the optimal solution of this dual SDP relaxation under strong duality.
Our results on the QCQPs generalize the results on QCQP with sign-definite
bipartite graph structures, QCQPs with forest structures, and QCQPs with
nonpositive off-diagonal data elements. Second, we propose a conversion method
from QCQPs with no particular structure to the ones with bipartite graph
structures. As a result, we demonstrate that a wider class of QCQPs can be
exactly solved by the SDP relaxation. Numerical instances are presented for
illustration.


title:
All-optical Compton photon source generation via modulating pulse
  profile by convex focusing plasma mirror

abstract:
  A relatively efficient scheme to generate energetic $\gamma$-ray is
all-optical Compton scattering based on wakefield acceleration and plasma
mirror where planar or concave solid target is usually employed to reflect
driving pulse. For flat plasma mirror case, the colliding pulse radius is far
larger than the radius of accelerated electron beam, which leads to smaller
laser utilization efficiency. The concave plasma mirror can efficiently focus
reflected laser to match electron beam radius. But the produced photon beam
collimation is worse due to intensive transverse field and larger transverse
wave vector of colliding laser. Here, we propose a new mechanism to achieve
Compton scattering via employing convex plasma mirror. The convex plasma mirror
improves reflected laser longitudinal length in addition to focusing effect,
which ensures longer colliding time and larger laser utilization efficiency.
Importantly, it leads transverse wave vector of colliding laser to be smaller,
which guarantees better photon bunch collimation.


title:
Performance Measurement of Security Academic Information System using
  Maturity Level

abstract:
  This study aims to information security in academic information systems to
provide recommendations for improvements in information security management by
the expected maturity level based on ISO/IEC 27002:2013. By using a qualitative
descriptive approach, data collection and validation techniques with
triangulation techniques are interviews, observation, and documentation. The
data were analyzed by using gap analysis and to measure the maturity level
determined 15 objective control and 45 security controls scattered in 5
clauses, the result of the research found that the performance of academic
information system maturity level at level 2. That is, the current level of
maturity is below the expected maturity level, so it needs to be increased to
the expected level.


title: On $\mathbf{K}$-reflections of Scott spaces

abstract:
  In this paper, for a full subcategory $\mathbf{K}$ of the category of all
$T_0$ spaces with continuous mappings, we investigate the questions under what
conditions the $\mathbf{K}$-reflection of a Scott space is still a Scott space
and under what conditions the Scott $\mathbf{K}$-completion of a poset exists.
Some necessary and sufficient conditions for the $\mathbf{K}$-reflection of a
Scott space to be a Scott space and for the existence of Scott
$\mathbf{K}$-completion of a poset are established, respectively. It is shown
that neither the sobrification nor the well-filtered reflection of the
Johnstone space is a Scott space. The $\mathbf{K}$-reflections of Alexandroff
spaces and the $\mathbf{K}$-completions of posets are also discussed.


title:
Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet
  Printing Dynamics

abstract:
  Calibration of highly dynamic multi-physics manufacturing processes such as
electro-hydrodynamics-based additive manufacturing (AM) technologies (E-jet
printing) is still performed by labor-intensive trial-and-error practices.
These practices have hindered the broad adoption of these technologies,
demanding a new paradigm of self-calibrating E-jet printing machines. To
address this need, we developed GPJet, an end-to-end physics-informed Bayesian
learning framework, and tested it on a virtual E-jet printing machine with
in-process jet monitoring capabilities. GPJet consists of three modules: a) the
Machine Vision module, b) the Physics-Based Modeling Module, and c) the Machine
Learning (ML) module. We demonstrate that the Machine Vision module can extract
high-fidelity jet features in real-time from video data using an automated
parallelized computer vision workflow. In addition, we show that the Machine
Vision module, combined with the Physics-based modeling module, can act as
closed-loop sensory feedback to the Machine Learning module of high- and
low-fidelity data. Powered by our data-centric approach, we demonstrate that
the online ML planner can actively learn the jet process dynamics using video
and physics with minimum experimental cost. GPJet brings us one step closer to
realizing the vision of intelligent AM machines that can efficiently search
complex process-structure-property landscapes and create optimized material
solutions for a wide range of applications at a fraction of the cost and speed.


title:
Special Session: Towards an Agile Design Methodology for Efficient,
  Reliable, and Secure ML Systems

abstract:
  The real-world use cases of Machine Learning (ML) have exploded over the past
few years. However, the current computing infrastructure is insufficient to
support all real-world applications and scenarios. Apart from high efficiency
requirements, modern ML systems are expected to be highly reliable against
hardware failures as well as secure against adversarial and IP stealing
attacks. Privacy concerns are also becoming a first-order issue. This article
summarizes the main challenges in agile development of efficient, reliable and
secure ML systems, and then presents an outline of an agile design methodology
to generate efficient, reliable and secure ML systems based on user-defined
constraints and objectives.


title:
Multiplier with Reduced Activities and Minimized Interconnect for Inner
  Product Arrays

abstract:
  We present a pipelined multiplier with reduced activities and minimized
interconnect based on online digit-serial arithmetic. The working precision has
been truncated such that $p<n$ bits are used to compute $n$ bits product,
resulting in significant savings in area and power. The digit slices follow
variable precision according to input, increasing upto $p$ and then decreases
according to the error profile. Pipelining has been done to achieve high
throughput and low latency which is desirable for compute intensive inner
products. Synthesis results of the proposed designs have been presented and
compared with the non-pipelined online multiplier, pipelined online multiplier
with full working precision and conventional serial-parallel and array
multipliers. For $8, 16, 24$ and $32$ bit precision, the proposed low power
pipelined design show upto $38\%$ and $44\%$ reduction in power and area
respectively compared to the pipelined online multiplier without working
precision truncation.


title:
From Laser Speckle to Particle Size Distribution in drying powders: A
  Physics-Enhanced AutoCorrelation-based Estimator (PEACE)

abstract:
  Extracting quantitative information about highly scattering surfaces from an
imaging system is challenging because the phase of the scattered light
undergoes multiple folds upon propagation, resulting in complex speckle
patterns. One specific application is the drying of wet powders in the
pharmaceutical industry, where quantifying the particle size distribution (PSD)
is of particular interest. A non-invasive and real-time monitoring probe in the
drying process is required, but there is no suitable candidate for this
purpose. In this report, we develop a theoretical relationship from the PSD to
the speckle image and describe a physics-enhanced autocorrelation-based
estimator (PEACE) machine learning algorithm for speckle analysis to measure
the PSD of a powder surface. This method solves both the forward and inverse
problems together and enjoys increased interpretability, since the machine
learning approximator is regularized by the physical law.


title: Entropy-based Stability-Plasticity for Lifelong Learning

abstract:
  The ability to continuously learn remains elusive for deep learning models.
Unlike humans, models cannot accumulate knowledge in their weights when
learning new tasks, mainly due to an excess of plasticity and the low incentive
to reuse weights when training a new task. To address the stability-plasticity
dilemma in neural networks, we propose a novel method called Entropy-based
Stability-Plasticity (ESP). Our approach can decide dynamically how much each
model layer should be modified via a plasticity factor. We incorporate branch
layers and an entropy-based criterion into the model to find such factor. Our
experiments in the domains of natural language and vision show the
effectiveness of our approach in leveraging prior knowledge by reducing
interference. Also, in some cases, it is possible to freeze layers during
training leading to speed up in training.


title: Simulation of machine learning-based 6G systems in virtual worlds

abstract:
  Digital representations of the real world are being used in many
applications, such as augmented reality. 6G systems will not only support use
cases that rely on virtual worlds but also benefit from their rich contextual
information to improve performance and reduce communication overhead. This
paper focuses on the simulation of 6G systems that rely on a 3D representation
of the environment, as captured by cameras and other sensors. We present new
strategies for obtaining paired MIMO channels and multimodal data. We also
discuss trade-offs between speed and accuracy when generating channels via ray
tracing. We finally provide beam selection simulation results to assess the
proposed methodology.


title:
A Survey on Neural Abstractive Summarization Methods and Factual
  Consistency of Summarization

abstract:
  Automatic summarization is the process of shortening a set of textual data
computationally, to create a subset (a summary) that represents the most
important pieces of information in the original text. Existing summarization
methods can be roughly divided into two types: extractive and abstractive. An
extractive summarizer explicitly selects text snippets (words, phrases,
sentences, etc.) from the source document, while an abstractive summarizer
generates novel text snippets to convey the most salient concepts prevalent in
the source.


title:
Taxonomy of Cohesion Coefficients for Weighted and Directed Multilayer
  Networks

abstract:
  Clustering and closure coefficients are among the most widely applied
indicators in the description of the topological structure of a network. Many
distinct definitions have been proposed over time, particularly in the case of
weighted networks, where the choice of the weight attributed to the triangles
is a crucial aspect. In the present work, in the framework of weighted directed
multilayer networks, we extend the classical clustering and closure
coefficients through the introduction of the clumping coefficient, which
generalizes them to incomplete triangles of any type. We then organize the
class of these coefficients in a systematic taxonomy in the more general
context of weighted directed multilayer networks. Such cohesion coefficients
have also been adapted to the different scales that characterize a multilayer
network, in order to grasp their structure from different perspectives. We also
show how the tensor formalism allows incorporating the new definitions, as well
as all those existing in the literature, in a single unified writing, in such a
way that a suitable choice of the involved adjacency tensors allows obtaining
each of them. Finally, through some applications to simulated networks, we show
the effectiveness of the proposed coefficients in capturing different
peculiarities of the network structure on different scales.


title:
A Data-Driven Method for Automated Data Superposition with Applications
  in Soft Matter Science

abstract:
  The superposition of data sets with internal parametric self-similarity is a
longstanding and widespread technique for the analysis of many types of
experimental data across the physical sciences. Typically, this superposition
is performed manually, or recently by one of a few automated algorithms.
However, these methods are often heuristic in nature, are prone to user bias
via manual data shifting or parameterization, and lack a native framework for
handling uncertainty in both the data and the resulting model of the superposed
data. In this work, we develop a data-driven, non-parametric method for
superposing experimental data with arbitrary coordinate transformations, which
employs Gaussian process regression to learn statistical models that describe
the data, and then uses maximum a posteriori estimation to optimally superpose
the data sets. This statistical framework is robust to experimental noise, and
automatically produces uncertainty estimates for the learned coordinate
transformations. Moreover, it is distinguished from black-box machine learning
in its interpretability -- specifically, it produces a model that may itself be
interrogated to gain insight into the system under study. We demonstrate these
salient features of our method through its application to four representative
data sets characterizing the mechanics of soft materials. In every case, our
method replicates results obtained using other approaches, but with reduced
bias and the addition of uncertainty estimates. This method enables a
standardized, statistical treatment of self-similar data across many fields,
producing interpretable data-driven models that may inform applications such as
materials classification, design, and discovery.

categories: physics.data-an cond-mat.soft cs.LG physics.comp-ph