Abstract:
  During the early stages of developing Case-Based Reasoning (CBR) systems the
definition of similarity measures is challenging since this task requires
transferring implicit knowledge of domain experts into knowledge
representations. While an entire CBR system is very explanatory, the similarity
measure determines the ranking but do not necessarily show which features
contribute to high (or low) rankings. In this paper we present our work on
opening the knowledge engineering process for similarity modelling. This work
present is a result of an interdisciplinary research collaboration between AI
and public health researchers developing e-Health applications. During this
work explainability and transparency of the development process is crucial to
allow in-depth quality assurance of the by the domain experts.


Abstract:
  Offline Chinese handwriting text recognition is a long-standing research
topic in the field of pattern recognition. In previous studies, text detection
and recognition are separated, which leads to the fact that text recognition is
highly dependent on the detection results. In this paper, we propose a robust
end-to-end Chinese text page spotter framework. It unifies text detection and
text recognition with text kernel that integrates global text feature
information to optimize the recognition from multiple scales, which reduces the
dependence of detection and improves the robustness of the system. Our method
achieves state-of-the-art results on the CASIA-HWDB2.0-2.2 dataset and
ICDAR-2013 competition dataset. Without any language model, the correct rates
are 99.12% and 94.27% for line-level recognition, and 99.03% and 94.20% for
page-level recognition, respectively.


Abstract:
  Graph neural networks (GNNs) have been widely used in deep learning on
graphs. They can learn effective node representations that achieve superior
performances in graph analysis tasks such as node classification and node
clustering. However, most methods ignore the heterogeneity in real-world
graphs. Methods designed for heterogeneous graphs, on the other hand, fail to
learn complex semantic representations because they only use meta-paths instead
of meta-graphs. Furthermore, they cannot fully capture the content-based
correlations between nodes, as they either do not use the self-attention
mechanism or only use it to consider the immediate neighbors of each node,
ignoring the higher-order neighbors. We propose a novel Higher-order
Attribute-Enhancing (HAE) framework that enhances node embedding in a
layer-by-layer manner. Under the HAE framework, we propose a Higher-order
Attribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network
representation learning. HAEGNN simultaneously incorporates meta-paths and
meta-graphs for rich, heterogeneous semantics, and leverages the self-attention
mechanism to explore content-based nodes interactions. The unique higher-order
architecture of HAEGNN allows examining the first-order as well as higher-order
neighborhoods. Moreover, HAEGNN shows good explainability as it learns the
importances of different meta-paths and meta-graphs. HAEGNN is also
memory-efficient, for it avoids per meta-path based matrix calculation.
Experimental results not only show HAEGNN superior performance against the
state-of-the-art methods in node classification, node clustering, and
visualization, but also demonstrate its superiorities in terms of memory
efficiency and explainability.


Abstract:
  Recently a number of studies demonstrated impressive performance on diverse
vision-language multi-modal tasks such as image captioning and visual question
answering by extending the BERT architecture with multi-modal pre-training
objectives. In this work we explore a broad set of multi-modal representation
learning tasks in the medical domain, specifically using radiology images and
the unstructured report. We propose Medical Vision Language Learner (MedViLL)
which adopts a Transformer-based architecture combined with a novel multimodal
attention masking scheme to maximize generalization performance for both
vision-language understanding tasks (image-report retrieval, disease
classification, medical visual question answering) and vision-language
generation task (report generation). By rigorously evaluating the proposed
model on four downstream tasks with two chest X-ray image datasets (MIMIC-CXR
and Open-I), we empirically demonstrate the superior downstream task
performance of MedViLL against various baselines including task-specific
architectures.


Abstract:
  The cross section for W or Z boson production in association with two photons
is measured in proton-proton collisions at a centre-of-mass energy of 13 TeV.
The data set corresponds to an integrated luminosity of 137 fb$^{-1}$ collected
by the CMS experiment at the LHC. The W $\to$ $\ell\nu$ and Z $\to$ $\ell\ell$
decay modes (where $\ell =$ e, $\mu$) are used to extract the W$\gamma\gamma$
and Z$\gamma\gamma$ cross sections in a phase space defined by electron (muon)
with transverse momentum larger than 30 GeV and photon transverse momentum
larger than 20 GeV. All leptons and photons are required to have absolute
pseudorapidity smaller than 2.5. The measured cross sections in this phase
space are $\sigma$(W$\gamma\gamma$) = 13.6 $^{+1.9}_{-1.9}$ (stat)
${}^{+4.0}_{-4.0}$ (syst) $\pm$ 0.08 (PDF + scale) fb and
$\sigma$(Z$\gamma\gamma$) = 5.41 $^{+0.58}_{-0.55}$ (stat) ${}^{+0.64}_{-0.70}$
(syst) $\pm$ 0.06 (PDF + scale) fb. Limits on anomalous quartic gauge couplings
are set in the framework of an effective field theory with dimension-8
operators.


Abstract:
  Automatic hate speech detection in online social networks is an important
open problem in Natural Language Processing (NLP). Hate speech is a
multidimensional issue, strongly dependant on language and cultural factors.
Despite its relevance, research on this topic has been almost exclusively
devoted to English. Most supervised learning resources, such as labeled
datasets and NLP tools, have been created for this same language. Considering
that a large portion of users worldwide speak in languages other than English,
there is an important need for creating efficient approaches for multilingual
hate speech detection. In this work we propose to address the problem of
multilingual hate speech detection from the perspective of transfer learning.
Our goal is to determine if knowledge from one particular language can be used
to classify other language, and to determine effective ways to achieve this. We
propose a hate specific data representation and evaluate its effectiveness
against general-purpose universal representations most of which, unlike our
proposed model, have been trained on massive amounts of data. We focus on a
cross-lingual setting, in which one needs to classify hate speech in one
language without having access to any labeled data for that language. We show
that the use of our simple yet specific multilingual hate representations
improves classification results. We explain this with a qualitative analysis
showing that our specific representation is able to capture some common
patterns in how hate speech presents itself in different languages.
  Our proposal constitutes, to the best of our knowledge, the first attempt for
constructing multilingual specific-task representations. Despite its
simplicity, our model outperformed the previous approaches for most of the
experimental setups. Our findings can orient future solutions toward the use of
domain-specific representations.


Abstract:
  Spin-orbit coupling of planetary systems plays an important role in the
dynamics and habitability of planets. However, symplectic integrators that can
accurately simulate not only how orbit affects spin but also how spin affects
orbit have not been constructed for general systems. Thus, we develop
symplectic Lie-group integrators to simulate systems consisting gravitationally
interacting rigid bodies. A user friendly package (GRIT
\url{https://github.com/GRIT-RBSim/GRIT}) is provided and external forcings
such as tidal interactions are also included. As a demonstration, this package
is applied to Trappist-I. It shows that the differences in transit timing
variations due to spin-orbit coupling could reach a few min in ten year
measurements, and strong planetary perturbations can push Trappist-I f, g and h
out of the synchronized states.


Abstract:
  This paper studies a multi-armed bandit (MAB) version of the range-searching
problem. In its basic form, range searching considers as input a set of points
(on the real line) and a collection of (real) intervals. Here, with each
specified point, we have an associated weight, and the problem objective is to
find a maximum-weight point within every given interval.
  The current work addresses range searching with stochastic weights: each
point corresponds to an arm (that admits sample access) and the point's weight
is the (unknown) mean of the underlying distribution. In this MAB setup, we
develop sample-efficient algorithms that find, with high probability,
near-optimal arms within the given intervals, i.e., we obtain PAC (probably
approximately correct) guarantees. We also provide an algorithm for a
generalization wherein the weight of each point is a multi-dimensional vector.
The sample complexities of our algorithms depend, in particular, on the size of
the optimal hitting set of the given intervals.
  Finally, we establish lower bounds proving that the obtained sample
complexities are essentially tight. Our results highlight the significance of
geometric constructs -- specifically, hitting sets -- in our MAB setting.


Abstract:
  Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines.


Abstract:
  We study a model of two-dimensional classical dimers on the square lattice
with strong geometric constraints (there is exactly one bond with the nearest
point for every point in the lattice). This model corresponds to the quantum
dimer model suggested by D.S. Rokhsar and S.A. Kivelson (1988). We use the
directed-loop algorithm to show the system undergoes a Berezinskii-Kostelitz
Thousless transition (BKT transition) in finite temperatures. After that, if we
destroy the geometric constraint of dimers, the topological transition will
transfer to a quasi one-order transition. For the dimer updates, we also
introduce a new cluster updating algorithm called the edged cluster algorithm.
By this method, we succeed in rapidly traversing the winding (topological)
sections uniformly and widening the effective matrical ensemble to include more
topological sections.


Abstract:
  We investigate inverse proximity effects in a spin-triplet superconductor
(TSC) interfaced with a ferromagnet (FM), assuming different types of magnetic
profiles and chiral or helical pairings. The region of the coexistence of
spin-triplet superconductivity and magnetism is significantly influenced by the
orientation and spatial extension of the magnetization with respect to the spin
configuration of the Cooper pairs, resulting into clearcut anisotropy
signatures. A characteristic mark of the inverse proximity effect arises in the
induced spin-polarization at the TSC interface. This is unexpectedly stronger
when the magnetic proximity is weaker, thus unveiling immediate detection
signatures for spin-triplet pairs. We show that an anomalous magnetic proximity
can occur at the interface between the itinerant ferromagnet, SrRuO$_3$, and
the unconventional superconductor Sr$_2$RuO$_4$. Such scenario indicates the
potential to design characteristic inverse proximity effects in experimentally
available SrRuO$_3$-Sr$_2$RuO$_4$ heterostructures and to assess the occurrence
of spin-triplet pairs in the highly debated superconducting phase of
Sr$_2$RuO$_4$.


Abstract:
  Neural Radiance Fields (NeRF) are able to reconstruct scenes with
unprecedented fidelity, and various recent works have extended NeRF to handle
dynamic scenes. A common approach to reconstruct such non-rigid scenes is
through the use of a learned deformation field mapping from coordinates in each
input image into a canonical template coordinate space. However, these
deformation-based approaches struggle to model changes in topology, as
topological changes require a discontinuity in the deformation field, but these
deformation fields are necessarily continuous. We address this limitation by
lifting NeRFs into a higher dimensional space, and by representing the 5D
radiance field corresponding to each individual input image as a slice through
this "hyper-space". Our method is inspired by level set methods, which model
the evolution of surfaces as slices through a higher dimensional surface. We
evaluate our method on two tasks: (i) interpolating smoothly between "moments",
i.e., configurations of the scene, seen in the input images while maintaining
visual plausibility, and (ii) novel-view synthesis at fixed moments. We show
that our method, which we dub HyperNeRF, outperforms existing methods on both
tasks. Compared to Nerfies, HyperNeRF reduces average error rates by 4.1% for
interpolation and 8.6% for novel-view synthesis, as measured by LPIPS.
Additional videos, results, and visualizations are available at
https://hypernerf.github.io.


Abstract:
  Reconfigurable intelligent surface (RIS) emerges as a promising technology
for the next generation networks. In this paper, we utilize the tools from
stochastic geometry to study the performance of a RIS-assisted millimeter wave
(mmWave) cellular network. Specifically, the locations of the base stations
(BSs) and the midpoints of the blockages are modeled as two independent Poisson
point processes (PPP) where the blockages are modeled by line boolean model and
a fraction of the blockages are coated with RISs. The distinguish
characteristics of mmWave communications, i.e., the directional beamforming and
different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS),
are incorporated into the analysis. We derive the expressions of the coverage
probability and the area spectral efficiency. The coverage probability under
the special case where the blockage parameter is sufficiently small is also
derived. Numerical results demonstrate that better coverage performance and
higher energy efficiency can be achieved by the large-scale deployment of RISs.
In addition, the tradeoff between the BS and RIS densities is investigated and
the results show that the RISs are excellent supplementary for the traditional
networks to improve the coverage probability with limited power consumption.


Abstract:
  Method: In this study, a new method is introduced for distinguishing
noise-free segments of ECG from noisy segments that use sample amplitude
dispersion with an adoptive threshold for variance of samples amplitude and a
method which uses compatibility of detected beats in ECG and some of the other
signals which are related to the heart activity such as BP, arterial pressure
(ART) and pulmonary artery pressure (PAP). A prioritization is applied in other
pulsatile signals based on the amplitude and clarity of the peaks on them, and
a fusion strategy is employed for segments on which ECG is noisy and other
available signals in the data, which contain peaks corresponding to R peak of
the ECG, are scored in three steps scoring function.


Abstract:
  With the proliferation of data movement across the Internet, global data
traffic per year has already exceeded the Zettabyte scale. The network
infrastructure and end-systems facilitating the vast data movement consume an
extensive amount of electricity, measured in terawatt-hours per year. This
massive energy footprint costs the world economy billions of dollars partially
due to energy consumed at the network end-systems. Although extensive research
has been done on managing power consumption within the core networking
infrastructure, there is little research on reducing the power consumption at
the end-systems during active data transfers. This paper presents a novel
cross-layer optimization framework, called Cross-LayerHLA, to minimize energy
consumption at the end-systems by applying machine learning techniques to
historical transfer logs and extracting the hidden relationships between
different parameters affecting both the performance and resource utilization.
It utilizes offline analysis to improve online learning and dynamic tuning of
application-level and kernel-level parameters with minimal overhead. This
approach minimizes end-system energy consumption and maximizes data transfer
throughput. Our experimental results show that Cross-LayerHLA outperforms other
state-of-the-art solutions in this area.


Abstract:
  Motivated by numerical schemes for large scale geophysical flow, we consider
the rotating shallow water and Boussinesq equations on the whole space with
horizontal kinetic energy backscatter source terms built from negative
viscosity and stabilising hyperviscosity with constant parameters. We study the
impact of this energy input through various explicit flows, which are
simultaneously solving the nonlinear equations and the linear equations that
arise upon dropping the transport nonlinearity, i.e. the linearisation in the
zero state. These include barotropic, parallel and Kolmogorov flows as well as
monochromatic inertia gravity waves. With focus on stable stratification we
find that the backscatter generates numerous solutions of this type that grow
exponentially and unboundedly, also with vertical structure. This signifies the
possibility of undesired energy concentration into specific modes due to the
backscatter. Families of steady state flows of this type arise as well and
superposition principles in the nonlinear equations provide explicit sufficient
conditions for instability of some of these. For certain steady barotropic
flows of this type we provide numerical evidence of eigenmodes whose growth
rates are proportional to the amplitude factor of the flow. For all other
arising steady solutions we prove this is not possible.


Abstract:
  In single particle analysis (SPA), the task is to recover the scattering
potential of a macromolecular structure from cryo-electron microscope images of
many copies of the structure in unknown orientations. The idealized, noise-free
SPA inverse problem has been shown to be uniquely solvable - up to hand - when
the forward model is based on the ray transform. More accurate forward models
take the non-zero curvature of the Ewald sphere into account. We analyze an
Ewald sphere corrected forward model for SPA and use the diffraction slice
theorem to prove that the corresponding inverse problem is uniquely solvable,
including the hand of the structure.


Abstract:
  Polycrystalline solids can exhibit material properties that differ
significantly from those of equivalent single-crystal samples, in part, because
of a spontaneous redistribution of mobile point defects into so-called
space-charge regions adjacent to grain boundaries. The general analytical form
of these space-charge regions is known only in the dilute limit, where
defect-defect correlations can be neglected. Using kinetic Monte Carlo
simulations of a three-dimensional Coulomb lattice gas, we show that
grain-boundary space-charge regions in non-dilute solid electrolytes exhibit
overscreening -- damped oscillatory space-charge profiles -- and underscreening
-- decay lengths that are longer than the corresponding Debye length and that
increase with increasing defect-defect interaction strength. Overscreening and
underscreening are known phenomena in concentrated liquid electrolytes, and the
observation of functionally analogous behaviour in solid electrolyte
space-charge regions suggests that the same underlying physics drives behaviour
in both classes of systems. We therefore expect theoretical approaches
developed to study non-dilute liquid electrolytes to be equally applicable to
future studies of solid electrolytes.


Abstract:
  We introduce the notion of $q$-analogs of strongly regular graphs and give
some first examples of such structures. We prove a necessary condition on the
parameters and show the connection to designs over finite fields.


Abstract:
  Partial control is a technique used in systems with transient chaos. The aim
of this control method is to avoid the escape of the orbits from a region Q of
the phase space where the transient chaotic dynamics takes place. This
technique is based on finding a special subset of Q called the safe set. The
chaotic orbit can be sustained in the safe set with a minimum amount of
control. In this work we develop a control strategy to gradually lead any
chaotic orbit in Q to the safe set by using the safety function. With the
technique proposed here, the safe set can be converted into a global attractor
of Q.


Abstract:
  After affecting the world in unexpected ways, COVID-19 has started mutating
which is evident with the insurgence of its new variants. The governments,
hospitals, schools, industries, and humans, in general, are looking for a
potential solution in the vaccine which will eventually be available but its
timeline for eradicating the virus is yet unknown. Several researchers have
encouraged and recommended the use of good practices such as physical
healthcare monitoring, immunity-boosting, personal hygiene, mental healthcare,
and contact tracing for slowing down the spread of the virus. In this article,
we propose the use of wearable/mobile sensors integrated with the Internet of
Everything to cover the spectrum of good practices in an automated manner. We
present hypothetical frameworks for each of the good practice modules and
propose the COvid-19 Resistance Framework using the Internet of Everything
(CORFIE) to tie all the individual modules in a unified architecture. We
envision that CORFIE would be influential in assisting people with the new
normal for current and future pandemics as well as instrumental in halting the
economic losses, respectively. We also provide potential challenges and their
probable solutions in compliance with the proposed CORFIE.


Abstract:
  We study the homological algebra in the category $\mathcal{P}_p$ of strict
polynomial functors of degree $p$ over a field of positive characteristic $p$.
We determine the decomposition matrix of our category and we calculate the
Ext-groups between functors important from the point of view of representation
theory. Our results include computations of the Ext-algebras for simple
functors and Schur functors and the Ext-groups between Schur and Weyl functors.
We observe that the category $\mathcal{P}_p$ has a Kazhdan-Lusztig theory and
we show that the dg algebras computing the Ext-algebras for simple functors and
Schur functors are formal. These last results allow one to describe the bounded
derived category of $\mathcal{P}_p$ as derived categories of certain explicitly
described graded algebras. We also generalize our results to all blocks of
$p$-weight 1 in $\mathcal{P}_e$ for $e > p$.


Abstract:
  Path planning for multiple robots (MRPP) represents a task of finding
non-colliding paths for robots through which they can navigate from their
initial positions to specified goal positions. The problem is usually modeled
using undirected graphs where robots move between vertices across edges.
Contemporary optimal solving algorithms include dedicated search-based methods,
that solve the problem directly, and compilation-based algorithms that reduce
MRPP to a different formalism for which an efficient solver exists, such as
constraint programming (CP), mixed integer programming (MIP), or Boolean
satisfiability (SAT). In this paper, we enhance existing SAT-based algorithm
for MRPP via spartification of the set of candidate paths for each robot from
which target Boolean encoding is derived. Suggested sparsification of the set
of paths led to smaller target Boolean formulae that can be constructed and
solved faster while optimality guarantees of the approach have been kept.


Abstract:
  We define a stratification of the moduli stack of coherent sheaves on an
elliptic curve which allows us (1) to give an explicit description of the
irreducible components of the global nilpotent cone of elliptic curves, (2) to
establish an explicit bijection between the simple objects of the category of
perverse sheaves defined by Schiffmann to categorify the elliptic Hall algebra
(the so-called spherical Eisenstein sheaves) and the irreducible components of
the global nilpotent cone and (3) to give an explicit description and
parametrization of the perverse sheaves on the moduli stack of coherent sheaves
on an elliptic curve having nilpotent singular support. Along the way, we find
a combinatorial parametrization of the irreducible components of the semistable
locus of the elliptic global nilpotent cone.


Abstract:
  Traditionally, it was accepted that a relational database can be normalized
step-by-step, from a set of un-normalized tables to tables in $1NF$, then to
$2NF$, then to $3NF$, then (possibly) to $BCNF$. The rule applied to a table in
$1NF$ in order to transform it to a set of tables in $2NF$ seems to be too
straightforward to pose any difficulty.
  However, we show that, depending on the set of functional dependencies, it is
impossible to reach $2NF$ and stop there; one must, in these cases, perform the
normalization from $1NF$ to $3NF$ as an indecomposable move. The minimal setup
to exhibit the phenomena requires a single composite key, and two partially
overlapping chains of transitive dependencies.


Abstract:
  BL Lacertae (BL Lac) is categorized as TeV blazar and considered as a
possible source of astrophysical neutrinos. In 2020, the brightest X-ray flare
ever detected from it. A detailed study can answer many puzzling questions
related to multiband emissions and fast-flux variability often seen in this
kind of source. We have performed the temporal and spectral analysis of the
brightest flare. The variability is characterized by the fractional variability
amplitude and the variability time. We found that the source has crossed all
its previous limits of flux and reached the maximum ever seen from it in
optical and X-rays. It is highly variable in X-rays with fractional variability
above 100$\%$ (1.8397$\pm$0.0181) and the fastest variability time of 11.28
hours within a day. The broadband light curves correlation with X-ray suggest a
time lag of one day. A broadband SED modeling is pursued to understand the
possible physical mechanisms responsible for broadband emission. Modeling
requires two emission regions located at two different sites to explain the low
and high flux states. A significant spectral change is observed in the
optical-UV and X-ray spectrum during the high state, which eventually leads to
shifts in the location of the synchrotron peak towards higher energy,
suggesting an emergence of a new HBL component.


Abstract:
  A reliable, remote, and continuous real-time respiratory sound monitor with
automated respiratory sound analysis ability is urgently required in many
clinical scenarios-such as in monitoring disease progression of coronavirus
disease 2019-to replace conventional auscultation with a handheld stethoscope.
However, a robust computerized respiratory sound analysis algorithm has not yet
been validated in practical applications. In this study, we developed a lung
sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds
(duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels,
13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze
labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous
adventitious sound labels (all crackles). We conducted benchmark tests for long
short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM
(BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM,
CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and
adventitious sound detection. We also conducted a performance comparison
between the LSTM-based and GRU-based models, between unidirectional and
bidirectional models, and between models with and without a CNN. The results
revealed that these models exhibited adequate performance in lung sound
analysis. The GRU-based models outperformed, in terms of F1 scores and areas
under the receiver operating characteristic curves, the LSTM-based models in
most of the defined tasks. Furthermore, all bidirectional models outperformed
their unidirectional counterparts. Finally, the addition of a CNN improved the
accuracy of lung sound analysis, especially in the CAS detection tasks.


Abstract:
  In a wireless network, gathering information at the base station about mobile
users based only on uplink channel measurements is an interesting challenge.
Indeed, accessing the users locations and predicting their downlink channels
would be particularly useful in order to optimize the network efficiency. In
this paper, a supervised machine learning approach addressing these tasks in an
unified way is proposed. It relies on a labeled database that can be acquired
in a simple way by the base station while operating. The proposed regression
method can be seen as a computationally efficient two layers neural network
initialized with a non-parametric estimator. It is illustrated on realistic
channel data, both for the positioning and channel mapping tasks, achieving
better results than previously proposed approaches, at a lower cost.


Abstract:
  Reconfigurable intelligent surfaces (RISs) have emerged as a cost- and
energy-efficient technology that can customize and program the physical
propagation environment by reflecting radio waves in preferred directions.
However, the purely passive reflection of RISs not only limits the end-to-end
channel beamforming gains, but also hinders the acquisition of accurate channel
state information for the phase control at RISs. In this paper, we provide an
overview of a hybrid relay-reflecting intelligent surface (HR-RIS)
architecture, in which only a few elements are active and connected to power
amplifiers and radio frequency chains. The introduction of a small number of
active elements enables a remarkable system performance improvement which can
also compensate for losses due to hardware impairments such as the deployment
of limited-resolution phase shifters. Particularly, the active processing
facilitates efficient channel estimation and localization at HR-RISs. We
present two practical architectures for HR-RISs, namely, fixed and dynamic
HR-RISs, and discuss their applications to beamforming, channel estimation, and
localization. The benefits, key challenges, and future research directions for
HR-RIS-aided communications are also highlighted. Numerical results for an
exemplary deployment scenario show that HR-RISs with only four active elements
can attain up to 42.8 percent and 41.8 percent improvement in spectral
efficiency and energy efficiency, respectively, compared with conventional
RISs.


Abstract:
  The standard way to demonstrate the relevance of chiral symmetry for the NN
interaction is to consider higher partial waves of NN scattering which are
controlled entirely by chiral pion-exchanges (since contacts vanish). However,
in applications of NN-potentials to nuclear structure and reactions, the lower
partial waves are the important ones, making the largest contributions. Lower
partial waves are sensitive to the short-range potential, and so, when the
short-range contacts were to dominate over the chiral pion-contributions in
lower partial waves, then the predictions from "chiral potentials" would have
little to do with chiral symmetry. To address this issue, we investigate
systematically the role of the (chiral) one- and two-pion exchanges, on the one
hand, and the effect of the contacts, on the other hand, in the lower partial
waves of NN scattering. We are able to clearly identify the signature of chiral
symmetry in lower partial waves. Our study has also a pedagogical spin-off as
it demonstrates in detail how the reproduction of the lower partial-wave phase
shifts comes about from the various ingredients of the theory.


Abstract:
  Multilayer position-sensitive 10B-RPC thermal neutron detectors offer an
attractive combination of sub-millimeter spatial resolution and high (>50%)
detection efficiency. Here we describe a new position reconstruction method
based on a statistical approach. Using experimental data, we compare the
performance of this method with that of the centroid reconstruction. Both
methods result in a similar image linearity/uniformity and spatial resolution.
However, the statistical method allows to improve the image quality at the
detector periphery, offers more flexible event filtering and allows to develop
automatic quality monitoring procedures for early detection of situations when
a change in the detector operation conditions starts to affect reconstruction
quality.


Abstract:
  The advent of increasingly large and complex datasets has fundamentally
altered the way that scientists conduct astronomy research. The need to work
closely to the data has motivated the creation of online science platforms,
which include a suite of software tools and services, therefore going beyond
data storage and data access. We present two example applications of Jupyter as
a part of astrophysical science platforms for professional researchers and
students. First, the Astro Data Lab is developed and operated by NOIRLab with a
mission to serve the astronomy community with now over 1500 registered users.
Second, the Dark Energy Spectroscopic Instrument science platform serves its
geographically distributed team comprising about 900 collaborators from over 90
institutions. We describe the main uses of Jupyter and the interfaces that
needed to be created to embed it within science platform ecosystems. We use
these examples to illustrate the broader concept of empowering researchers and
providing them with access to not only large datasets but also cutting-edge
software, tools, and data services without requiring any local installation,
which can be relevant for a wide range of disciplines. Future advances may
involve science platform networks, and tools for simultaneously developing
Jupyter notebooks to facilitate collaborations.


Abstract:
  Generally, people behave in social dilemmas such as proself and prosocial.
However, inside social groups, people have a tendency to choose prosocial
alternatives due to in-group favoritism. The bioelectrical activity of the
human brain shows the differences between proself and prosocial exist even out
of a socialized group. Moreover, a group socialization strengthens these
differences. We used EEG System, "Neuron-Spectrum-4/EPM" (16 channels), to
track the brain bioelectrical activity during decision making in laboratory
experiments with the Prisoner's dilemma game and the short-term socialization
stage. We compared the spatial distribution of the spectral density during the
different experimental parts. The noncooperative decision was characterized by
the increased values of spectral the beta rhythm in the orbital regions of
prefrontal cortex. The cooperative choice, on the contrary, was accompanied by
the theta-rhythm activation in the central cortex regions in both hemispheres
and the high-frequency alpha rhythm in the medial regions of the prefrontal
cortex. People who increased the cooperation level after the socialization
stage was initially different from the ones who decreased the cooperation in
terms of the bioelectrical activity. Well-socialized participants differed by
increased values of spectral density of theta-diapason and decreased values of
spectral density of beta-diapason in the middle part of frontal lobe. People
who decreased the cooperation level after the socialization stage was
characterized by decreased values of spectral density of alpha rhythm in the
middle and posterior convex regions of both hemispheres.


Abstract:
  Let G be a reductive group and L a Levi subgroup. Parabolic induction and
restriction are a pair of adjoint functors between Ad-equivariant derived
categories of either constructible sheaves or (not necessarily holonomic)
D-modules on G and L, respectively. Bezrukavnikov and Yom Din proved,
generalizing a classic result of Lusztig, that these functors are exact. In
this paper, we consider a special case where L=T is a maximal torus. We give
explicit formulas for parabolic induction and restriction in terms of the
Harish-Chandra D-module on G x T. We show that this module is flat over D(T),
which easily implies that parabolic induction and restriction are exact
functors between the corresponding abelian categories of D-modules.


Abstract:
  In this paper we investigate the $(g-2)_\mu$ discrepancy in the context of
the R-parity conserving next-to-minimal supersymmetric Standard Model plus
right-handed neutrinos superfields. The model has the ability to reproduce
neutrino physics data and includes the interesting possibility to have the
right-handed sneutrino as the lightest supersymmetric particle and a viable
dark matter candidate. Since right-handed sneutrinos are singlets, no new
contributions for $\delta a_{\mu}$ with respect to the MSSM and NMSSM are
present. However, the possibility to have the right-handed sneutrino as the
lightest supersymmetric particle opens new ways to escape Large Hadron Collider
and direct detection constraints. In particular, we find that dark matter
masses within $10 \lesssim m_{\tilde{\nu}_{R}} \lesssim 600$ GeV are fully
compatible with current experimental constraints. Remarkably, not only spectra
with light sleptons are needed, but we obtain solutions with $m_{\tilde{\mu}}
\gtrsim 600$ GeV in the entire dark matter mass range that could be probed by
new $(g-2)_\mu$ data in the near future. In addition, dark matter direct
detection experiments will be able to explore a sizable portion of the allowed
parameter space with $m_{\tilde{\nu}_{R}} \lesssim 300$ GeV, while indirect
detection experiments will be able to probe a much smaller fraction within $200
\lesssim m_{\tilde{\nu}_{R}} \lesssim 350$ GeV.


Abstract:
  We analyze the early stage of evolution of a universe with two scale factors
proposed in [1] when matter is present. The scale factors describe two causally
disconnected patches of the universe interacting trough a non-trivial Poisson
bracket structure in the momentum sector characterized by one parameter \kappa.
We studied two scenarios in which one of the patches is always filled with
relativistic matter while the other contains relativistic matter in one case,
and non-relativistic matter in the second case. By solving numerically the set
of equations governing the dynamics, we found that the energy content of one
sector drains to the other and from here it is possible to constraint the
deformation parameter \kappa by imposing that the decay of the energy density
happens, at most, at the Big Bang Nucleosynthesis temperature in order to
return to the usual behavior of radiation. The relation with Non Standard
Cosmologies is also addressed.


Abstract:
  Activity and self-generated motion are fundamental features observed in many
living and non-living systems. Given that inter-particle adhesive forces are
known to regulate particle dynamics, we investigate how adhesion strength
controls the boundary growth and roughness in an active particle aggregate.
Using particle based simulations incorporating both activity (birth, death and
growth) and systematic physical interactions (elasticity and adhesion), we
establish that inter-particle adhesion strength ($f^{ad}$) controls the surface
roughness of a densely packed three-dimensional(3D) active particle aggregate
expanding into a highly viscous medium. We discover that the surface roughness
of a 3D active particle aggregate increases in proportion to the inter-particle
adhesion strength, $f^{ad}$. We show that asymmetry in the radial and
tangential active particle mean squared displacement (MSD) suppresses 3D
surface roughness at lower adhesion strengths. By analyzing the statistical
properties of particle displacements at the aggregate periphery, we determine
that the 3D surface roughness is driven by the movement of active particle
towards the core at high inter-particle adhesion strengths. Our results
elucidate the physics controlling the expansion of adhesive 3D active particle
collectives into a highly viscous medium, with implications into understanding
stochastic interface growth in active matter systems characterized by self
generated particle flux.


Abstract:
  Group IV and V monolayers are the promising state-of-the-art 2D materials
owing to their high carrier mobility, tunable bandgaps, and optical linear
dichroism along with outstanding electronic and thermoelectric properties.
Furthermore, recent studies reveal the stability of free-standing 2D monolayers
by hydrogenation. Inspired by this, we systematically predict and investigate
the structure and properties of various hydrogen saturated silicon phosphide
(H-Si-P) monolayers, based on first-principles calculations. According to the
results, H-Si-P monolayers belong to indirect bandgap semiconductors with a
highly stable structure. Their bandgaps and band edge positions assessed using
accurate hybrid functional are shown to be effectively adjusted by applying a
biaxial strain. Furthermore, the absorption spectra of these monolayers,
simulated in the context of time-dependent density functional theory, exhibit
their excellent potential for solar energy conversion and visible-light-driven
photocatalytic water splitting. In this respect, this work provides valuable
guidance for finding more 2D semiconductors and nanostructures for
nanoelectronic and optoelectronic applications, as well as for photocatalytic
water splitting.


Abstract:
  Antiferromagnetism (AF) such as Neel ordering is often closely related to
Coulomb interactions such as Hubbard repulsion in two-dimensional (2D) systems.
Whether Neel AF ordering in 2D can be dominantly induced by electron-phonon
couplings (EPC) has not been completely understood. Here, by employing
numerically-exact sign-problem-free quantum Monte Carlo (QMC) simulations, we
show that optical Su-Schrieffer-Heeger (SSH) phonons with frequency $\omega$
and EPC constant $\lambda$ can induce AF ordering for a wide range of phonon
frequency $\omega>\omega_c$. For $\omega<\omega_c$, a valence-bond-solid (VBS)
order appears and there is a direct quantum phase transition between VBS and AF
phases at $\omega_c$. The phonon mechanism of the AF ordering is related to the
fact that SSH phonons directly couple to electron hopping whose second-order
process can induce an effective AF spin exchange. Our results shall shed new
lights to understanding AF ordering in correlated quantum materials.


Abstract:
  This study aimed to investigate the antitumor effect of ultrasmall gold
nanoparticles (AuNPs), around 3 nm, stabilized by the anionic polysaccharide
gum arabic (GA-AuNPs). The focus was downregulation of cancer hallmarks of
aggressive tumors, using a highly metastatic model of melanoma. We first
demonstrated that GA-AuNPs showed excellent stability under biological
environment. Non-cytotoxic concentrations to seven different cell lines,
including tumorigenic and non-tumorigenic cells, were determined by standard 2D
in vitro assays. Gold concentrations below 2.4 mg L-1 were non-cytotoxic and
therefore chosen for further analyses. Cells exposed to GA-AuNPs were uptaken
by melanoma cells through endocytic processes. Next, we described remarkable
biological properties using non-cytotoxic concentrations of this nanomaterial.
Invasion through an extracellular matrix barrier as well as 3D growth capacity
(anchorage-independent colony formation and spheroids growth) were negatively
affected by 2.4 mg L-1 GA-AuNPs. Additionally, exposed spheroids showed
morphological changes, suggesting that GA-AuNPs could penetrate into the
preformed tumor and affect its integrity. All together these results
demonstrate that side effects, such as cytotoxicity, can be avoided by choosing
the right concentration, nevertheless, preserving desirable effects such as
modulation of key tumor cell malignancy features.


Abstract:
  In this paper we present results of determination of the statistical
precision of Higgs branching fraction measurement, for Higgs decay to ZZ$^\ast$
pairs at 3 TeV and 350 GeV CLIC. Measurements are simulated with the CLIC_ILD
detector model, taking into consideration all relevant physics and beam-induced
background processes. It is shown that the product of the branching fraction
BR(${H\rightarrow\thinspace ZZ^\ast}$) and the Higgs production cross-section
can be measured with a relative statistical uncertainty of 3% (18%) at 3 TeV
(350 GeV) center-of-mass energy, using semileptonic final states and assuming
integrated luminosity of 5 (1) ab$^{-1}$.


Abstract:
  The interaction of strong pulsed femtosecond laser field with atoms having
three equivalent electrons in the outer shell ($p^3$ configuration, e.g.
nitrogen) is studied via numerical integration of a time-dependent
Schr\"{o}dinger equation on a grid approach. Single, double and triple
ionization yields originating from a completely antisymmetric wave function are
calculated and extracted using a restricted-geometry model with the soft-core
potential and three active electrons. The direct triple ionization channel is
found to produce a larger yield than the channel connected with single and then
direct double ionization. Compared against earlier results investigating the $n
s^ 2 n p^1$ configuration, we propose that the differences found here might in
fact be accessible through electron's momentum distribution.


Abstract:
  We propose nonvolatile binary stable states based on microelectromechanical
systems (MEMS). They may be used as MEMS memory, where the bistable memory is
the buckling direction of a plate of a capacitor. The binary memory is switched
by applying a voltage to the MEMS. Furthermore, we propose an Ising model by
making a series of buckled MEMS. It is a great merit of this mechanism that the
annealing process is automatically carried out due to the damping effect of the
spring. We find a duality relation between the parallel-plate MEMS under
constant-voltage condition and the comb-teeth MEMS under constant-charge
condition.


Abstract:
  New carbon forms exhibiting extraordinary physico-chemical properties can be
generated from nanostructured precursors under extreme pressure. Nevertheless,
synthesis of such fascinating materials is often not well understood that
results, as is the case of C60 precursor, in irreproducibility of the results
and impeding further progress in the materials design. Here the semiconducting
amorphous carbon having bandgaps of 0.1-0.3 eV and the advantages of isotropic
superhardness and superior toughness over single-crystal diamond and inorganic
glasses are produced from transformation of fullerene at high pressure and
moderate temperatures. A systematic investigation of the structure and bonding
evolution was carried out by using rich arsenal of complimentary
characterization methods, which helps to build a model of the transformation
that can be used in further high p,T synthesis of novel nanocarbon systems for
advanced applications. The produced amorphous carbon materials have the
potential of demanding optoelectronic applications that diamond and graphene
cannot achieve


Abstract:
  Abolition is the process of destroying and then rebuilding the structures
that impede liberation. This paper addresses the particular case of Black folk
in the United States, but is relevant to the global decolonization movement.
Using notions of abolition and infrastructures of feeling developed by Ruth
Wilson Gilmore, I view Historically Black Colleges and Universities ( HBCUs )
as a particular kind of abolitionist project, created for the explicit purpose
of nurturing and sustaining Black excellence particularly within the sciences.
I then examine how artificial intelligence (AI) in particular and computing in
general have contributed to racial oppression and the further confinement and
diminishing of Black existence. I conclude by examining how the space held by
HBCUs in computing might contribute to a re-imagining of AI as a technology
that enhances the possibility and actualization of Black life.


Abstract:
  Face recognition (FR) systems are fast becoming ubiquitous. However,
differential performance among certain demographics was identified in several
widely used FR models. The skin tone of the subject is an important factor in
addressing the differential performance. Previous work has used modeling
methods to propose skin tone measures of subjects across different
illuminations or utilized subjective labels of skin color and demographic
information. However, such models heavily rely on consistent background and
lighting for calibration, or utilize labeled datasets, which are time-consuming
to generate or are unavailable. In this work, we have developed a novel and
data-driven skin color measure capable of accurately representing subjects'
skin tone from a single image, without requiring a consistent background or
illumination. Our measure leverages the dichromatic reflection model in RGB
space to decompose skin patches into diffuse and specular bases.


Abstract:
  The interplay between structural and electronic degrees of freedom in complex
materials is the subject of extensive debate in physics and materials science.
Particularly interesting questions pertain to the nature and extent of
pre-transitional short-range order in diverse systems ranging from shape-memory
alloys to unconventional superconductors, and how this microstructure affects
macroscopic properties. Here we use neutron and X-ray scattering to uncover
universal structural fluctuations in La$_{2-x}$Sr$_x$CuO$_4$ and
Tl$_2$Ba$_2$CuO$_{6+{\delta}}$, two cuprate superconductors with distinct point
disorder effects and optimal superconducting transition temperatures. The
fluctuations are present in wide doping and temperature ranges, including
compositions that maintain high average structural symmetry, and they exhibit
unusual, yet simple scaling laws. We relate this behavior to pre-transitional
phenomena in a broad class of systems with martensitic transitions, and argue
that it can be understood as a rare-region effect caused by intrinsic, doping-
and compound-independent nanoscale inhomogeneity. We also uncover remarkable
parallels with superconducting fluctuations, which indicates that the
underlying inhomogeneity plays a pivotal role in cuprate physics.


Abstract:
  In this paper, we investigate when system identification is statistically
easy or hard, in the finite sample regime. Statistically easy to learn linear
system classes have sample complexity that is polynomial with the system
dimension. Most prior research in the finite sample regime falls in this
category, focusing on systems that are directly excited by process noise.
Statistically hard to learn linear system classes have worst-case sample
complexity that is at least exponential with the system dimension, regardless
of the identification algorithm. Using tools from minimax theory, we show that
classes of linear systems can be hard to learn. Such classes include, for
example, under-actuated or under-excited systems with weak coupling among the
states. Having classified some systems as easy or hard to learn, a natural
question arises as to what system properties fundamentally affect the hardness
of system identifiability. Towards this direction, we characterize how the
controllability index of linear systems affects the sample complexity of
identification. More specifically, we show that the sample complexity of
robustly controllable linear systems is upper bounded by an exponential
function of the controllability index. This implies that identification is easy
for classes of linear systems with small controllability index and potentially
hard if the controllability index is large. Our analysis is based on recent
statistical tools for finite sample analysis of system identification as well
as a novel lower bound that relates controllability index with the least
singular value of the controllability Gramian.


Abstract:
  Mathematical models are becoming increasingly important in magnetic resonance
imaging (MRI), as they provide a mechanistic approach for making a link between
tissue microstructure and signals acquired using the medical imaging
instrument. The Bloch equations, which describes spin and relaxation in a
magnetic field, is a set of integer order differential equations with a
solution exhibiting mono-exponential behaviour in time. Parameters of the model
may be estimated using a non-linear solver, or by creating a dictionary of
model parameters from which MRI signals are simulated and then matched with
experiment. We have previously shown the potential efficacy of a magnetic
resonance fingerprinting (MRF) approach, i.e. dictionary matching based on the
classical Bloch equations, for parcellating the human cerebral cortex. However,
this classical model is unable to describe in full the mm-scale MRI signal
generated based on an heterogenous and complex tissue micro-environment. The
time-fractional order Bloch equations has been shown to provide, as a function
of time, a good fit of brain MRI signals. We replaced the integer order Bloch
equations with the previously reported time-fractional counterpart within the
MRF framework and performed experiments to parcellate human gray matter, which
is cortical brain tissue with different cyto-architecture at different spatial
locations. Our findings suggest that the time-fractional order parameters,
{\alpha} and {\beta}, potentially associate with the effect of interareal
architectonic variability, hypothetically leading to more accurate cortical
parcellation.


Abstract:
  The iron-chalcogenide high temperature superconductor Fe(Se,Te) (FST) has
been reported to exhibit complex magnetic ordering and nontrivial band topology
which may lead to novel superconducting phenomena. However, the recent studies
have so far been largely concentrated on its band and spin structures while its
mesoscopic electronic and magnetic response, crucial for future device
applications, has not been explored experimentally. Here, we used scanning
superconducting quantum interference device microscopy for its sensitivity to
both local diamagnetic susceptibility and current distribution in order to
image the superfluid density and supercurrent in FST. We found that in FST with
10% interstitial Fe, whose magnetic structure was heavily disrupted, bulk
superconductivity was significantly suppressed whereas edge still preserved
strong superconducting diamagnetism. The edge dominantly carried supercurrent
despite of a very long magnetic penetration depth. The temperature dependence
of the superfluid density and supercurrent distribution were distinctively
different between the edge and the bulk. Our Heisenberg modeling showed that
magnetic dopants stabilize anti-ferromagnetic spin correlation along the edge,
which may contribute towards its robust superconductivity. Our observations
hold implication for FST as potential platforms for topological quantum
computation and superconducting spintronics.


Abstract:
  This is the second lecture note on the error analysis of interpolation on
simplicial elements without the shape regularity assumption (the previous one
is arXiv:1908.03894). In this manuscript, we explain the error analysis of
Lagrange interpolation on (possibly anisotropic) tetrahedrons. The manuscript
is not intended to be a research paper. We hope that, in the future, it will be
merged into a textbook on the mathematical theory of the finite element
methods.


Abstract:
  Inertial-sensor-based attitude estimation is a crucial technology in various
applications, from human motion tracking to autonomous aerial and ground
vehicles. Application scenarios differ in characteristics of the performed
motion, presence of disturbances, and environmental conditions. Since
state-of-the-art attitude estimators do not generalize well over these
characteristics, their parameters must be tuned for the individual motion
characteristics and circumstances. We propose RIANN, a ready-to-use, neural
network-based, parameter-free, real-time-capable inertial attitude estimator,
which generalizes well across different motion dynamics, environments, and
sampling rates, without the need for application-specific adaptations. We
gather six publicly available datasets of which we exploit two datasets for the
method development and the training, and we use four datasets for evaluation of
the trained estimator in three different test scenarios with varying practical
relevance. Results show that RIANN outperforms state-of-the-art attitude
estimation filters in the sense that it generalizes much better across a
variety of motions and conditions in different applications, with different
sensor hardware and different sampling frequencies. This is true even if the
filters are tuned on each individual test dataset, whereas RIANN was trained on
completely separate data and has never seen any of these test datasets. RIANN
can be applied directly without adaptations or training and is therefore
expected to enable plug-and-play solutions in numerous applications, especially
when accuracy is crucial but no ground-truth data is available for tuning or
when motion and disturbance characteristics are uncertain. We made RIANN
publicly available.


Abstract:
  In this paper, we introduce the Kaizen framework that uses a continuously
improving teacher to generate pseudo-labels for semi-supervised speech
recognition (ASR). The proposed approach uses a teacher model which is updated
as the exponential moving average (EMA) of the student model parameters. We
demonstrate that it is critical for EMA to be accumulated with full-precision
floating point. The Kaizen framework can be seen as a continuous version of the
iterative pseudo-labeling approach for semi-supervised training. It is
applicable for different training criteria, and in this paper we demonstrate
its effectiveness for frame-level hybrid hidden Markov model-deep neural
network (HMM-DNN) systems as well as sequence-level Connectionist Temporal
Classification (CTC) based models.
  For large scale real-world unsupervised public videos in UK English and
Italian languages the proposed approach i) shows more than 10% relative word
error rate (WER) reduction over standard teacher-student training; ii) using
just 10 hours of supervised data and a large amount of unsupervised data closes
the gap to the upper-bound supervised ASR system that uses 650h or 2700h
respectively.


Abstract:
  In logic-based knowledge representation, query answering has essentially
replaced mere satisfiability checking as the inferencing problem of primary
interest. For knowledge bases in the basic description logic ALC, the
computational complexity of conjunctive query (CQ) answering is well known to
be ExpTime-complete and hence not harder than satisfiability. This does not
change when the logic is extended by certain features (such as counting or role
hierarchies), whereas adding others (inverses, nominals or transitivity
together with role-hierarchies) turns CQ answering exponentially harder. We
contribute to this line of results by showing the surprising fact that even
extending ALC by just the Self operator - which proved innocuous in many other
contexts - increases the complexity of CQ entailment to 2ExpTime. As common for
this type of problem, our proof establishes a reduction from alternating Turing
machines running in exponential space, but several novel ideas and encoding
tricks are required to make the approach work in that specific, restricted
setting.


Abstract:
  The Aharonov-Bohm (A-B) effect showed that the phase of electron wave pattern
could be changed by the excluded electromagnetic field, the region where
electromagnetic field is zero. This apparent non-local effect has been
explained by mainly two salient interpretations called "the interpretation of
electromagnetic potentials" and "the interpretation of interaction energy". In
this paper the author reviews and investigates both the interpretations and
analysis their differences. Then the author clarifies in details and extend the
approach of interaction energy interpretation and argue that as quantum
mechanics involved energy of quantum systems and electromagnetic energy
contained in electromagnetic field, the natural and more physically acceptable
interpretation of the effect should be "the interpretation of interaction
energy".


Abstract:
  Stellar tidal streams are sensitive tracers of the properties of the
gravitational potential in which they orbit and detailed observations of their
density structure can be used to place stringent constraints on fluctuations in
the potential caused by, e.g., the expected populations of dark matter subhalos
in the standard cold dark matter paradigm (CDM). Simulations of the evolution
of stellar streams in live $N$-body halos without low-mass dark-matter
subhalos, however, indicate that streams exhibit significant perturbations on
small scales even in the absence of substructure. Here we demonstrate, using
high-resolution $N$-body simulations combined with sophisticated semi-analytic
and simple analytic models, that the mass resolutions of
$10^4$--$10^5\,\rm{M}_{\odot}$ commonly used to perform such simulations cause
spurious stream density variations with a similar magnitude on large scales as
those expected from a CDM-like subhalo population and an order of magnitude
larger on small, yet observable, scales. We estimate that mass resolutions of
$\approx100\,\rm{M}_{\odot}$ ($\approx1\,\rm{M}_{\odot}$) are necessary for
spurious, numerical density variations to be well below the CDM subhalo
expectation on large (small) scales. That streams are sensitive to a
simulation's particle mass down to such small masses indicates that streams are
sensitive to dark matter clustering down to these low masses if a significant
fraction of the dark matter is clustered or concentrated in this way, for
example, in MACHO models with masses of $10$--$100\,\rm{M}_{\odot}$.


Abstract:
  The relic gravitational wave background due to tensor linear perturbations
generated during Higgs inflation is computed. Both the Standard Model and a
well-motivated phenomenological completion (that accounts for all the
experimentally confirmed evidence of new physics) are considered. We focus on
critical Higgs inflation, which improves on the non-critical version and
features an amplification of the tensor fluctuations. The latter property
allows us to establish that future space-borne interferometers, such as DECIGO,
BBO and ALIA, may detect the corresponding primordial gravitational waves.


Abstract:
  Building footprint segmentations for high resolution images are increasingly
demanded for many remote sensing applications. By the emerging deep learning
approaches, segmentation networks have made significant advances in the
semantic segmentation of objects. However, these advances and the increased
access to satellite images require the generation of accurate object boundaries
in satellite images. In the current paper, we propose a novel network-based on
Pix2Pix methodology to solve the problem of inaccurate boundaries obtained by
converting satellite images into maps using segmentation networks in order to
segment building footprints. To define the new network named G2G, our framework
includes two generators where the first generator extracts localization
features in order to merge them with the boundary features extracted from the
second generator to segment all detailed building edges. Moreover, different
strategies are implemented to enhance the quality of the proposed networks'
results, implying that the proposed network outperforms state-of-the-art
networks in segmentation accuracy with a large margin for all evaluation
metrics. The implementation is available at
https://github.com/A2Amir/A-Novel-Adaptive-Deep-Network-for-Building-Footprint-Segmentation.


Abstract:
  We present a constrained density functional perturbation theory scheme for
the calculation of structural and harmonic vibrational properties of insulators
in the presence of an excited and thermalized electron-hole plasma. The method
is ideal to tame ultrafast light induced structural transitions in the regime
where the photocarriers thermalize faster than the lattice, the electron-hole
recombination time is longer than the phonon period and the photocarrier
concentration is large enough to be approximated by an electron-hole plasma.
The complete derivation presented here includes total energy, forces and stress
tensor, variable cell structural optimization, harmonic vibrational properties
and the electron-phonon interaction. We discuss in detail the case of zone
center optical phonons not conserving the number of electrons and inducing a
Fermi shift in the photo-electron and hole distributions. We validate our
implementation by comparing with finite differences in Te and VSe2. By
calculating the evolution of the phonon spectrum of Te, Si and GaAs as a
function of the fluence of the incoming laser light, we demonstrate that even
at low fluences, corresponding to approximately 0.1 photocarriers per cell, the
phonon spectrum is substantially modified with respect to the ground state one
with new Kohn anomalies appearing and a substantial softening of zone center
optical phonons. Our implementation can be efficiently used to detect
reversible transient phases and irreversible structural transition induced by
ultrafast light absorption.


Abstract:
  The absolute/relative debate on the nature of space and time is ongoing for
thousands of years. Here we attempt to investigate space and time from the
information theoretic point of view to understand spatial and temporal
correlations under the relative assumption. Correlations, as a measure of
relationship between two quantities, do not distinguish space and time in
classical probability theory; quantum correlations in space are well-studied
but temporal correlations are not well understood. The thesis investigates
quantum correlations in space-time, by treating temporal correlations equally
in form as spatial correlations and unifying quantum correlations in space and
time. In particular, we follow the pseudo-density matrix formalism in which
quantum states in spacetime are properly defined by correlations from
measurements. We first review classical correlations, quantum correlations in
space and time, to motivate the pseudo-density matrix formalism in finite
dimensions. Next we generalise the pseudo-density matrix formulation to the
Gaussian case, general continuous variables via Wigner representations, and
general measurement processes like weak measurements. Then we compare the
pseudo-density matrix formalism with other spacetime formulations: indefinite
causal structures, consistent histories, generalised non-local games,
out-of-time-order correlation functions, and path integrals. We argue that in
non-relativistic quantum mechanics, different spacetime formulations are
closely related via quantum correlations, except path integrals. Finally, we
apply the pseudo-density matrix formulation to time crystals. By defining time
crystals as long-range order in time, we analyse continuous and discrete time
translation symmetry as well as discuss the existence of time crystals from an
algebraic point of view. Finally, we summarise our work and provide the outlook
for future directions.


Abstract:
  Building on the embedding of an $n$-abelian category $\mathscr{M}$ into an
abelian category $\mathcal{A}$ as an $n$-cluster-tilting subcategory of
$\mathcal{A}$, in this paper we relate the $n$-torsion classes of $\mathscr{M}$
with the torsion classes of $\mathcal{A}$. Indeed, we show that every
$n$-torsion class in $\mathscr{M}$ is given by the intersection of a torsion
class in $\mathcal{A}$ with $\mathscr{M}$. Moreover, we show that every chain
of $n$-torsion classes in the $n$-abelian category $\mathscr{M}$ induces a
Harder-Narasimhan filtration for every object of $\mathscr{M}$. We use the
relation between $\mathscr{M}$ and $\mathcal{A}$ to show that every
Harder-Narasimhan filtration induced by a chain of $n$-torsion classes in
$\mathscr{M}$ can be induced by a chain of torsion classes in $\mathcal{A}$.
Furthermore, we show that $n$-torsion classes are preserved by Galois covering
functors, thus we provide a way to systematically construct new (chains of)
$n$-torsion classes.


Abstract:
  We introduce LAM, a subsystem of IMALL2 with restricted additive rules able
to manage duplication linearly, called linear additive rules. LAM is presented
as the type assignment system for a calculus endowed with copy constructors,
which deal with substitution in a linear fashion. As opposed to the standard
additive rules, the linear additive rules do not affect the complexity of term
reduction: typable terms of LAM enjoy linear strong normalization. Moreover, a
mildly weakened version of cut-elimination for this system is proven which
takes a cubic number of steps. Finally, we define a sound translation from
proofs of LAM into linear lambda terms of IMLL2, and we study its complexity.


Abstract:
  In this paper we present results from a radio-optical study of the galaxy
populations of the galaxy clusters Abell 1300 and MACS J1931.8$-$2634, a merger
and a relaxed system respectively both located at $z \sim 0.3$, aimed at
finding evidence of merger-induced radio emission. Radio observations are taken
at 1.28 GHz with the MeerKAT interferometer during its early-stage
commissioning phase, and combined with archive optical data. We generated
catalogues containing 107 and 162 radio sources in the A$~$1300 and MACS
J1931.8--2634 cluster fields respectively, above a 0.2 mJy threshold and within
a 30~arcmin radius from the cluster centre (corresponding to 8.1 and 8.8 Mpc
respectively). By cross-correlating the radio and optical catalogues, and
including spectroscopic information, 9 and 6 sources were found to be cluster
members and used to construct the radio luminosity functions respectively for
both clusters. The comparison of the radio source catalogues between the two
cluster fields leads to a marginal difference, with a $2\sigma$ statistical
significance. We derived the radio luminosity function at 1.28 GHz in both
clusters, in the power range $22.81 < \rm {log~P_{1.28~GHz}~(W/Hz)} < 25.95$,
and obtained that in A 1300 the radio luminosity function averaged over the
full radio power interval is only $3.3 \pm 1.9$ times higher than the MACS
J1931.8--2634 one, suggesting no statistical difference in their probability to
host nuclear radio emission. We conclude that, at least for the two clusters
studied here, the role of cluster mergers in affecting the statistical
properties of the radio galaxy population is negligible.


Abstract:
  We introduce the notion of Mixed Symmetry Quantum Phase Transition (MSQPT) as
singularities in the transformation of the lowest-energy state properties of a
system of identical particles inside each permutation symmetry sector $\mu$,
when some Hamiltonian control parameters $\lambda$ are varied. We use a
three-level Lipkin-Meshkov-Glick (LMG) model, with $U(3)$ dynamical symmetry,
to exemplify our construction. After reviewing the construction of $U(3)$
unirreps using Young tableaux and Gelfand basis, we firstly study the case of a
finite number $N$ of three-level atoms, showing that some precursors
(fidelity-susceptibility, level population, etc.) of MSQPTs appear in all
permutation symmetry sectors. Using coherent (quasi-classical) states of $U(3)$
as variational states, we compute the lowest-energy density for each sector
$\mu$ in the thermodynamic $N\to\infty$ limit. Extending the control parameter
space by $\mu$, the phase diagram exhibits four distinct quantum phases in the
$\lambda$-$\mu$ plane that coexist at a quadruple point. The ground state of
the whole system belongs to the fully symmetric sector $\mu=1$ and shows a
four-fold degeneracy, due to the spontaneous breakdown of the parity symmetry
of the Hamiltonian. The restoration of this discrete symmetry leads to the
formation of four-component Schr\"odinger cat states.


Abstract:
  Pan-omics, pan-cancer analysis has advanced our understanding of the
molecular heterogeneity of cancer, expanding what was known from single-cancer
or single-omics studies. However, pan-cancer, pan-omics analyses have been
limited in their ability to use information from multiple sources of data
(e.g., omics platforms) and multiple sample sets (e.g., cancer types) to
predict important clinical outcomes, like overall survival. We address the
issue of prediction across multiple high-dimensional sources of data and
multiple sample sets by using exploratory results from BIDIFAC+, a method for
integrative dimension reduction of bidimensionally-linked matrices, in a
predictive model. We apply a Bayesian hierarchical model that performs variable
selection using spike-and-slab priors which are modified to allow for the
borrowing of information across clustered data. This method is used to predict
overall patient survival from the Cancer Genome Atlas (TCGA) using data from 29
cancer types and 4 omics sources. Our model selected patterns of variation
identified by BIDIFAC+ that differentiate clinical tumor subtypes with markedly
different survival outcomes. We also use simulations to evaluate the
performance of the modified spike-and-slab prior in terms of its variable
selection accuracy and prediction accuracy under different underlying
data-generating frameworks. Software and code used for our analysis can be
found at https://github.com/sarahsamorodnitsky/HierarchicalSS_PanCanPanOmics/ .


Abstract:
  While convolutional neural networks (CNNs) trained by back-propagation have
seen unprecedented success at semantic segmentation tasks, they are known to
struggle on out-of-distribution data. Markov random fields (MRFs) on the other
hand, encode simpler distributions over labels that, although less flexible
than UNets, are less prone to over-fitting. In this paper, we propose to fuse
both strategies by computing the product of distributions of a UNet and an MRF.
As this product is intractable, we solve for an approximate distribution using
an iterative mean-field approach. The resulting MRF-UNet is trained jointly by
back-propagation. Compared to other works using conditional random fields
(CRFs), the MRF has no dependency on the imaging data, which should allow for
less over-fitting. We show on 3D neuroimaging data that this novel network
improves generalisation to out-of-distribution samples. Furthermore, it allows
the overall number of parameters to be reduced while preserving high accuracy.
These results suggest that a classic MRF smoothness prior can allow for less
over-fitting when principally integrated into a CNN model. Our implementation
is available at https://github.com/balbasty/nitorch.


Abstract:
  By definition, the $p$-terminal dc-Josephson current is sensitive to the
superconducting phase variables of $p$ terminals. In the paper, we establish
protocol for direct detection of the $p$-terminal dc-Josephson effect with
$p\ge 3$ in a device containing $N$ superconducting leads
$S_1,\,S_2,\,...,\,S_N$ having the phase variables
$\varphi_1,\,\varphi_2,\,...,\,\varphi_N$. The calculated signal ${\chi}^{(N)}$
can be probed in microwave experiments, and it corresponds to the higher-order
nonlocal inverse inductance obtained from differentiating the current $I_1$
through $S_1$ with respect to the remaining $N-2$ independent phase differences
$\varphi_2-\varphi_N,\,\varphi_3-\varphi_N,\, ...,\varphi_{N-1}-\varphi_N$. We
find that the values $p\le N-2$ do not contribute to ${\chi}^{(N)}$, and that
${\chi}^{(N)}\ne 0$ implies evidence for the $p=N-1$ or the $p=N$-terminal
dc-Josephson currents. For $N=4$ superconducting leads, we demonstrate that
${\chi}^{(4)}\ne 0$ implies evidence for the $p=3$ or $p=4$ dc-Josephson
effect, irrespective of the $p=2$-terminal dc-Josephson current. Thus, we
provide a way to demonstrate the dc-Josephson effect with more than three
terminals (i.e. with $p\ge 3$) in a device containing more than four
superconducting leads (i.e. with $N\ge 4$). The predicted ${\chi}^{(4)}$ is
"yes or no" answer to the $p\ge 3$ dc-Josephson effect, i.e. for $N=4$,
nonvanishingly small $\chi^{(4)}\ne 0$ implies the $p=3$ or $p=4$-terminal
dc-Josephson effect and vanishingly small $\chi^{(4)}=0$ implies absence of the
$p=3$ and $p=4$-terminal dc-Josephson effect. The paper can be viewed as
generalizing the recently considered $\varphi$-junctions in Andreev molecules
to arbitrary number $N$ of the superconducting leads, and it relies on basic
properties of the dc-Josephson effect that are not directly related to
nontrivial topology and Weyl point singularities.


Abstract:
  We provide a dynamical proof of the van der Corput inequality for sequences
in Hilbert spaces that is based on the Furstenberg correspondence principle.
This is done by reducing the inequality to the mean ergodic theorem for
contractions on Hilbert spaces. The key difficulty therein is that the
Furstenberg correspondence principle is, a priori, limited to scalar-valued
sequences. We therefore discuss how interpreting the Furstenberg correspondence
principle via the Gelfand--Naimark--Segal construction for C*-algebras allows
to study not just scalar but general Hilbert space-valued sequences in terms of
unitary operators. This yields a proof of the van der Corput inequality in the
spirit of the Furstenberg correspondence principle and the flexibility of this
method is discussed via new proofs for different variants of the inequality.


Abstract:
  The elliptic interface problems with discontinuous and high-contrast
coefficients appear in many applications and often lead to huge condition
numbers of the corresponding linear systems. Thus, it is highly desired to
construct high order schemes to solve the elliptic interface problems with
discontinuous and high-contrast coefficients. Let $\Gamma$ be a smooth curve
inside a rectangular region $\Omega$. In this paper, we consider the elliptic
interface problem $-\nabla\cdot (a \nabla u)=f$ in $\Omega\setminus \Gamma$
with Dirichlet boundary conditions, where the coefficient $a$ and the source
term $f$ are smooth in $\Omega\setminus \Gamma$ and the two nonzero jump
condition functions $[u]$ and $[a\nabla u\cdot \vec{n}]$ across $\Gamma$ are
smooth along $\Gamma$. To solve such elliptic interface problems, we propose a
high order compact finite difference scheme for numerically computing both the
solution $u$ and the gradient $\nabla u$ on uniform Cartesian grids without
changing coordinates into local coordinates. Our numerical experiments confirm
the fourth order accuracy for computing the solution $u$, the gradient $\nabla
u$ and the velocity $a \nabla u$ of the proposed compact finite difference
scheme on uniform meshes for the elliptic interface problems with discontinuous
and high-contrast coefficients.


Abstract:
  Organ-at-risk contouring is still a bottleneck in radiotherapy, with many
deep learning methods falling short of promised results when evaluated on
clinical data. We investigate the accuracy and time-savings resulting from the
use of an interactive-machine-learning method for an organ-at-risk contouring
task. We compare the method to the Eclipse contouring software and find strong
agreement with manual delineations, with a dice score of 0.95. The annotations
created using corrective-annotation also take less time to create as more
images are annotated, resulting in substantial time savings compared to manual
methods, with hearts that take 2 minutes and 2 seconds to delineate on average,
after 923 images have been delineated, compared to 7 minutes and 1 seconds when
delineating manually. Our experiment demonstrates that
interactive-machine-learning with corrective-annotation provides a fast and
accessible way for non computer-scientists to train deep-learning models to
segment their own structures of interest as part of routine clinical workflows.
  Source code is available at
\href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}.


Abstract:
  We revisit the Kitaev model for fault tolerant quantum computing on a square
lattice with underlying quantum double $D(G)$ symmetry, where $G$ is a finite
group. We provide projection operators for its quasiparticles content as
irreducible representations of $D(G)$ and combine this with $D(G)$-bimodule
properties of open ribbon excitation spaces $L(s_0,s_1)$ to show how open
ribbons can be used to teleport information between their endpoints $s_0,s_1$.
We give a self-contained account that builds on earlier work but emphasises
applications to quantum computing as surface code theory, including gates on
$D(S_3)$. We show how the theory reduces to a simpler theory for toric codes in
the case of $D( \Bbb Z_n)\cong \Bbb C\Bbb Z_n^2$, including toric ribbon
operators and their braiding. In the other direction, we show how our
constructions generalise to $D(H)$ models based on a finite-dimensional Hopf
algebra $H$, including site actions of $D(H)$ and partial results on ribbon
equivariance even when the Hopf algebra is not semisimple.


Abstract:
  In 2010, Hrushovski--Loeser showed that the Berkovich analytification of a
quasi-projective variety over a non-Archimedean valued field admits a
deformation retraction onto a finite simplicial complex. In this article, we
adapt the tools and methods developed by Hrushovski--Loeser to study if such
deformation retractions can be obtained to be compatible with respect to a
given morphism. Amongst other results, we show that compatible deformation
retractions exist over a constructible partition of the base and prove the
general statement in the case of a morphism of relative dimension 1 where the
target is a smooth connected curve.


Abstract:
  We present LaLaLoc to localise in environments without the need for prior
visitation, and in a manner that is robust to large changes in scene
appearance, such as a full rearrangement of furniture. Specifically, LaLaLoc
performs localisation through latent representations of room layout. LaLaLoc
learns a rich embedding space shared between RGB panoramas and layouts inferred
from a known floor plan that encodes the structural similarity between
locations. Further, LaLaLoc introduces direct, cross-modal pose optimisation in
its latent space. Thus, LaLaLoc enables fine-grained pose estimation in a scene
without the need for prior visitation, as well as being robust to dynamics,
such as a change in furniture configuration. We show that in a domestic
environment LaLaLoc is able to accurately localise a single RGB panorama image
to within 8.3cm, given only a floor plan as a prior.


Abstract:
  While the Heisenberg model for magnetic Mott insulators on planar lattice
structures is comparatively well understood in the case of transition metal
ions, the intrinsic spin-orbit entanglement of 4f magnetic ions on such
lattices shows fascinating new physics largely due to corresponding strong
anisotropies both in their single-ion and their exchange properties. We show
here that the Yb delafossites, containing perfect magnetic Yb$^{3+}$ triangular
lattice planes with pseudospin $s=1/2$ at low temperatures, are an ideal
platform to study these new phenomena. Competing frustrated interactions may
lead to an absence of magnetic order associated to a gapless spin liquid ground
state with a huge linear specific heat exceeding that of many heavy fermions,
whereas the application of a magnetic field induces anisotropic magnetic order
with successive transitions into different long ranged ordered structures. In
this comparative study, we discuss our experimental findings in terms of a
unified crystal-field and exchange model. We combine electron paramagnetic
resonance (EPR) experiments and results from neutron scattering with
measurements of the magnetic susceptibility, isothermal magnetization up to
full polarization, and specific heat to determine the relevant model
parameters. The impact of the crystal field is discussed as well as the
symmetry-compatible form of the exchange tensor, and we give explicit
expressions for the anisotropic g factor, the temperature dependence of the
susceptibility, the exchange-narrowed EPR linewidth and the saturation field.


Abstract:
  As a fire erupts, the first few minutes can be critical, and first
respondents must race to the scene to analyze the situation and act fast before
it gets out of hand. Factors such as road traffic condition and distance may
not allow quick rescue operation using traditional means and methods, leading
to unmanageable spreading of fire, injuries or even deaths that can be avoided.
FireFly drone-based rescue consists of a squad of highly equipped drones that
will be the first responders to the fire site. Their intervention will make the
task of the fire rescue team much more effective and will contribute to reduce
the overall damage. As soon as the fire is detected by in-building implanted
sensors, the fire department would deploy a set of FireFly drones that would
fly to the site, scan the building, and send live fire status information to
the Fire fighter team. The drones would have the ability to identify trapped
humans using AI based pattern recognition tools (using sensors and thermal
cameras) and then drop them rescue kits as appropriate. The drones will also be
equipped with fire detection and recognition capabilities and be able to drop
fire extinguishing balls as first attempts to put off seeds of fires before
they evolve. The integration of drones with firefighting will allow for ease of
access and control of fire outbreaks. Drones will also result in increased
response time, prevention of further damage, and allow relaying of vital
information to out of reach places regarding the characteristics of the fire
scene.


Abstract:
  The signal resulting from magnetic resonance spectroscopy is occupied by
noises and irregularities so in the further analysis preprocessing techniques
have to be introduced. The main idea of the paper is to develop a model of a
signal as a sum of harmonics and to find its parameters. Such an approach is
based on singular value decomposition applied to the data arranged in the
Hankel matrix (HSVD) and can be used in each step of preprocessing techniques.
For that purpose a method has was tested on real phantom data.


Abstract:
  This paper revisits feature pyramids networks (FPN) for one-stage detectors
and points out that the success of FPN is due to its divide-and-conquer
solution to the optimization problem in object detection rather than
multi-scale feature fusion. From the perspective of optimization, we introduce
an alternative way to address the problem instead of adopting the complex
feature pyramids - {\em utilizing only one-level feature for detection}. Based
on the simple and efficient solution, we present You Only Look One-level
Feature (YOLOF). In our method, two key components, Dilated Encoder and Uniform
Matching, are proposed and bring considerable improvements. Extensive
experiments on the COCO benchmark prove the effectiveness of the proposed
model. Our YOLOF achieves comparable results with its feature pyramids
counterpart RetinaNet while being $2.5\times$ faster. Without transformer
layers, YOLOF can match the performance of DETR in a single-level feature
manner with $7\times$ less training epochs. With an image size of
$608\times608$, YOLOF achieves 44.3 mAP running at 60 fps on 2080Ti, which is
$13\%$ faster than YOLOv4. Code is available at
\url{https://github.com/megvii-model/YOLOF}.


Abstract:
  We study the transient phenomena appearing in a subgap region of the double
quantum dot coupled in series between the superconducting and normal metallic
leads, focusing on the development of the superconducting proximity effect. For
the uncorrelated nanostructure we derive explicit expressions of the
time-dependent occupancies in both quantum dots, charge currents, and electron
pairing induced on individual dots and between them. We show that the initial
configurations substantially affect the dynamical processes, in which the
in-gap bound states emerge upon coupling the double quantum dot to
superconducting reservoir. In particular, the superconducting proximity effect
would be temporarily blocked whenever the quantum dots are initially singly
occupied. Such {\it triplet}/{\it Andreev blockade} has been recently reported
experimentally for double quantum dots embedded in the Josephson [D. Bouman et
al., Phys. Rev. B 102, 220505 (2020)] and Andreev [P. Zhang et al.,
arXiv:2102.03283 (2021)] junctions. We also address the role of correlation
effects within the lowest-order decoupling scheme and by the time-dependent
numerical renormalization group calculations. Competition of the repulsive
Coulomb interactions with the superconducting proximity effect leads to
renormalization of the in-gap quasiparticles, speeding up the quantum
oscillations and narrowing a region of transient phenomena, whereas the
dynamical Andreev blockade is well pronounced in the weak inter-dot coupling
limit. We propose feasible methods for detecting the characteristic time-scales
that could be observable by the Andreev spectroscopy.


Abstract:
  The abundance of interstellar ice constituents is usually expressed with
respect to the water ice because, in denser regions, a significant portion of
the interstellar grain surface would be covered by water ice. The binding
energy (BE), or adsorption energy of the interstellar species regulates the
chemical complexity of the interstellar grain mantle. Due to the high abundance
of water ice, the BE of surface species with the water is usually provided and
widely used in astrochemical modeling. However, the hydrogen molecules would
cover some part of the grain mantle in the denser and colder part of the
interstellar medium. Even at around ~ 10K, few atoms and simple molecules with
lower adsorption energies can migrate through the surface. The BE of the
surface species with H2 substrate would be very different from that of a water
substrate. However, adequate information regarding these differences is
lacking. Here, we employ the quantum chemical calculation to provide the BE of
95 interstellar species with H2 substrate. These are representative of the BEs
of species to a H2 overlayer on a grain surface. On average, we notice that the
BE with the H2 monomer substrate is almost ten times lower than the BE of these
species reported earlier with the H2 O c-tetramer configuration. The encounter
desorption of H and H2 was introduced (with ED (H, H2 ) =45 K and ED (H2 , H2 )
=23 K) to have a realistic estimation of the abundances of the surface species
in the colder and denser region. Our quantum chemical calculations yield higher
adsorption energy of H2 than that of H (ED (H, H2 ) = 23 - 25 K and ED (H2, H2
) =67 - 79 K). We further implement an astrochemical model to study the effect
of encounter desorption with the resent realistic estimation. The encounter
desorption of the N atom (calculations yield ED (N, H2 ) =83 K) is introduced
to study the differences with its inclusion.


Abstract:
  The effective incorporation of cross-utterance information has the potential
to improve language models (LMs) for automatic speech recognition (ASR). To
extract more powerful and robust cross-utterance representations for the
Transformer LM (TLM), this paper proposes the R-TLM which uses hidden states in
a long short-term memory (LSTM) LM. To encode the cross-utterance information,
the R-TLM incorporates an LSTM module together with a segment-wise recurrence
in some of the Transformer blocks. In addition to the LSTM module output, a
shortcut connection using a fusion layer that bypasses the LSTM module is also
investigated. The proposed system was evaluated on the AMI meeting corpus, the
Eval2000 and the RT03 telephone conversation evaluation sets. The best R-TLM
achieved 0.9%, 0.6%, and 0.8% absolute WER reductions over the single-utterance
TLM baseline, and 0.5%, 0.3%, 0.2% absolute WER reductions over a strong
cross-utterance TLM baseline on the AMI evaluation set, Eval2000 and RT03
respectively. Improvements on Eval2000 and RT03 were further supported by
significance tests. R-TLMs were found to have better LM scores on words where
recognition errors are more likely to occur. The R-TLM WER can be further
reduced by interpolation with an LSTM-LM.


Abstract:
  The Roadmap is performed in the context of a Ph.D. research in collaboration
between the Experimental Software Engineering Group, from the Systems
Engineering and Computing Program of the Federal University of Rio de Janeiro
(COPPE/UFRJ) and the Laboratory of Industrial and Human Automation Control,
Mechanical engineering and Computer Science (LAMIH UMR CNRS 8201) in the
Universit\`e Polytechnique Hauts-de-France (UPHF).
  The Roadmap resulted from an investigation on the particularities of IoT
applications. It is the concrete organization of the concepts and evidence
gathered from different experimental studies. It comes to support the
definition of IoT software systems, with specific items for the project team to
discuss and define the essential aspects related to the specifying, designing,
and implementing an IoT application.


Abstract:
  Magnetic beads attract each other forming chains. We pushed such chains into
an inclined Hele-Shaw cell and discovered that they spontaneously form
self-similar patterns. Depending on the angle of inclination of the cell, two
completely different situations emerge, namely, above the static friction angle
the patterns resemble the stacking of a rope and below they look similar to a
fortress from above. Moreover, locally the first pattern forms a square
lattice, while the second pattern exhibits triangular symmetry. For both
patterns, the size distributions of enclosed areas follow power laws. We
characterize the morphological transition between the two patterns
experimentally and numerically and explain the change in polarization as a
competition between friction-induced buckling and gravity.


Abstract:
  We consider the $0$-Extension problem, where we are given an undirected graph
$\mathcal{G}=(V,E)$ equipped with non-negative edge weights $w:E\rightarrow
\mathbb{R}^+$, a collection $ T=\{ t_1,\ldots,t_k\}\subseteq V$ of $k$ special
vertices called terminals, and a semi-metric $D$ over $T$. The goal is to
assign every non-terminal vertex to a terminal while minimizing the sum over
all edges of the weight of the edge multiplied by the distance in $D$ between
the terminals to which the endpoints of the edge are assigned. $0$-Extension
admits two known algorithms, achieving approximations of $O(\log{k})$
[C{\u{a}}linescu-Karloff-Rabani SICOMP '05] and $O(\log{k}/\log{\log{k}})$
[Fakcharoenphol-Harrelson-Rao-Talwar SODA '03]. Both known algorithms are based
on rounding a natural linear programming relaxation called the metric
relaxation, in which $D$ is extended from $T$ to the entire of $V$. The current
best known integrality gap for the metric relaxation is $\Omega
(\sqrt{\log{k}})$. In this work we present an improved integrality gap of
$\Omega(\log^{\frac{2}{3}}k)$ for the metric relaxation. Our construction is
based on the randomized extension of one graph by another, a notion that
captures lifts of graphs as a special case and might be of independent
interest. Inspired by algebraic topology, our analysis of the gap instance is
based on proving no continuous section (in the topological sense) exists in the
randomized extension.


Abstract:
  The Anti-Extradition Law Amendment Bill protests in Hong Kong present a rich
context for exploring information security practices among protesters due to
their large-scale urban setting and highly digitalised nature. We conducted
in-depth, semi-structured interviews with 11 participants of these protests.
Research findings reveal how protesters favoured Telegram and relied on its
security for internal communication and organisation of on-the-ground
collective action; were organised in small private groups and large public
groups to enable collective action; adopted tactics and technologies that
enable pseudonymity; and developed a variety of strategies to detect
compromises and to achieve forms of forward secrecy and post-compromise
security when group members were (presumed) arrested. We further show how group
administrators had assumed the roles of leaders in these 'leaderless' protests
and were critical to collective protest efforts.


Abstract:
  The finding of a new formulation of the magnetization vector of a quantum
system interacting with a static uniform magnetic field\cite{Selenu1} is
reported. There a gauge invariant form of its divergence is shown being
expressed as a function of the electronic current per state coupled with the
Berry curvature of the quantum system. A Fourier analysis of the magnetization
vector and magnetization density is reported as an application of the presented
formula it could be applied in the context of computational
modelling\cite{Martin} of quantum matter.