Abstract:
  Recent work by Jenkins and Sakellariadou claims that cusps on cosmic strings
lead to black hole production. To derive this conclusion they use the hoop
conjecture in the rest frame of the string loop, rather than in the rest frame
of the proposed black hole. Most of the energy they include is the bulk motion
of the string near the cusp. We redo the analysis taking this into account and
find that cusps on cosmic strings with realistic energy scale do not produce
black holes, unless the cusp parameters are extremely fine-tuned.


Abstract:
  Radio-frequency (14.6 MHz) AC magnetic susceptibility, $\chi^{\prime}_{AC}$,
of \dytio\ was measured using a self-oscillating tunnel-diode resonator.
Measurements were made with the excitation AC field parallel to the
superimposed DC magnetic field up 5 T in a wide temperature range from 50 mK to
100 K. At 14.6 MHz a known broad peak of $\chi^{\prime}_{AC}(T)$ from kHz -
range audio-frequency measurements around 15~K for both [111] and [110]
directions shifts to 45~K, continuing the Arrhenius activated behavior with the
same activation energy barrier of $E_a \approx 230$~K. Magnetic field
dependence of $\chi^{\prime}_{AC}$ along [111] reproduces previously reported
low-temperature two-in-two-out to three-in-one-out spin configuration
transition at about 1~T, and an intermediate phase between 1 and 1.5~T. The
boundaries of the intermediate phase show reasonable overlap with the
literature data and connect at a critical endpoint of the first-order
transition line, suggesting that these low-temperature features are frequency
independent. An unusual upturn of magnetic susceptibility at $T \to 0$ was
observed in magnetic fields between 1.5~T and 2~T for both magnetic field
directions, before fully polarized configuration sets in above 2~T.


Abstract:
  Powder-based additive manufacturing techniques provide tools to construct
intricate structures that are difficult to manufacture using conventional
methods. In Laser Powder Bed Fusion, components are built by selectively
melting specific areas of the powder bed, to form the two-dimensional
cross-section of the specific part. However, the high occurrence of defects
impacts the adoption of this method for precision applications. Therefore, a
control policy for dynamically altering process parameters to avoid phenomena
that lead to defect occurrences is necessary. A Deep Reinforcement Learning
(DRL) framework that derives a versatile control strategy for minimizing the
likelihood of these defects is presented. The generated control policy alters
the velocity of the laser during the melting process to ensure the consistency
of the melt pool and reduce overheating in the generated product. The control
policy is trained and validated on efficient simulations of the continuum
temperature distribution of the powder bed layer under various laser
trajectories.


Abstract:
  Canonical Correlation Analysis (CCA) and its regularised versions have been
widely used in the neuroimaging community to uncover multivariate associations
between two data modalities (e.g., brain imaging and behaviour). However, these
methods have inherent limitations: (1) statistical inferences about the
associations are often not robust; (2) the associations within each data
modality are not modelled; (3) missing values need to be imputed or removed.
Group Factor Analysis (GFA) is a hierarchical model that addresses the first
two limitations by providing Bayesian inference and modelling modality-specific
associations. Here, we propose an extension of GFA that handles missing data,
and highlight that GFA can be used as a predictive model. We applied GFA to
synthetic and real data consisting of brain connectivity and non-imaging
measures from the Human Connectome Project (HCP). In synthetic data, GFA
uncovered the underlying shared and specific factors and predicted correctly
the non-observed data modalities in complete and incomplete data sets. In the
HCP data, we identified four relevant shared factors, capturing associations
between mood, alcohol and drug use, cognition, demographics and
psychopathological measures and the default mode, frontoparietal control,
dorsal and ventral networks and insula, as well as two factors describing
associations within brain connectivity. In addition, GFA predicted a set of
non-imaging measures from brain connectivity. These findings were consistent in
complete and incomplete data sets, and replicated previous findings in the
literature. GFA is a promising tool that can be used to uncover associations
between and within multiple data modalities in benchmark datasets (such as,
HCP), and easily extended to more complex models to solve more challenging
tasks.


Abstract:
  A general framework for obtaining exact transition rate matrices for
stochastic systems on networks is presented and applied to many well-known
compartmental models of epidemiology. The state of the population is described
as a vector in the tensor product space of $N$ individual probability vector
spaces, whose dimension equals the number of compartments of the
epidemiological model $n_c$. The transition rate matrix for the
$n_c^N$-dimensional Markov chain is obtained by taking suitable linear
combinations of tensor products of $n_c$-dimensional matrices. The resulting
transition rate matrix is a sum over bilocal linear operators, which gives
insight in the microscopic dynamics of the system. The more familiar and
non-linear node-based mean-field approximations are recovered by restricting
the exact models to uncorrelated (separable) states. We show how the exact
transition rate matrix for the susceptible-infected (SI) model can be used to
find analytic solutions for SI outbreaks on trees and the cycle graph for
finite $N$.


Abstract:
  Uncovering how inequality emerges from human interaction is imperative for
just societies. Here we show that the way social groups interact in
face-to-face situations can enable the emergence of degree inequality. We
present a mechanism that integrates group mixing dynamics with individual
preferences, which reproduces group degree inequality found in six empirical
data sets of face-to-face interactions. We uncover the impact of group-size
imbalance on degree inequality, revealing a critical minority group size that
changes social gatherings qualitatively. If the minority group is larger than
this 'critical mass' size, it can be a well-connected, cohesive group; if it is
smaller, minority cohesion widens degree inequality. Finally, we expose the
under-representation of social groups in degree rankings due to mixing dynamics
and propose a way to reduce such biases.


Abstract:
  We study the Randic index for cactus graphs. It is conjectured to be bounded
below by radius (for other than an even path), and it is known to obey several
bounds based on diameter. We study radius and diameter for cacti then verify
the radius bound and strengthen two diameter bounds for cacti. Along the way,
we produce several other bounds for the Randic index in terms of graph size,
order, and valency for several special classes of graphs, including chemical
nontrivial cacti and cacti with starlike BC-trees.


Abstract:
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a
variety of Internet of Things (IoT) applications and services, spanning from
recommendation systems to robotics control and military surveillance. This is
driven by the easier access to sensory data and the enormous scale of
pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data
streams. Designing accurate models using such data streams, to predict future
insights and revolutionize the decision-taking process, inaugurates pervasive
systems as a worthy paradigm for a better quality-of-life. The confluence of
pervasive computing and artificial intelligence, Pervasive AI, expanded the
role of ubiquitous IoT systems from mainly data collection to executing
distributed computations with a promising alternative to centralized learning,
presenting various challenges. In this context, a wise cooperation and resource
scheduling should be envisaged among IoT devices (e.g., smartphones, smart
vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid
communication and computation overheads and ensure maximum performance. In this
paper, we conduct a comprehensive survey of the recent techniques developed to
overcome these resource challenges in pervasive AI systems. Specifically, we
first present an overview of the pervasive computing, its architecture, and its
intersection with artificial intelligence. We then review the background,
applications and performance metrics of AI, particularly Deep Learning (DL) and
online learning, running in a ubiquitous system. Next, we provide a deep
literature review of communication-efficient techniques, from both algorithmic
and system perspectives, of distributed inference, training and online learning
tasks across the combination of IoT devices, edge devices and cloud servers.
Finally, we discuss our future vision and research challenges.


Abstract:
  In this paper, we disprove EMSO(FO$^2$) convergence law for the binomial
random graph $G(n,p)$ for any constant probability $p$. More specifically, we
prove that there exists an existential monadic second order sentence with 2
first order variables such that, for every $p\in(0,1)$, the probability that it
is true on $G(n,p)$ does not converge.


Abstract:
  The distributed optimization problem is set up in a collection of nodes
interconnected via a communication network. The goal is to find the minimizer
of a global objective function formed by the addition of partial functions
locally known at each node. A number of methods are available for addressing
this problem, having different advantages. The goal of this work is to achieve
the maximum possible convergence rate. As the first step towards this end, we
propose a new method which we show converges faster than other available
options. As with most distributed optimization methods, convergence rate
depends on a step size parameter. As the second step towards our goal we
complement the proposed method with a fully distributed method for estimating
the optimal step size that maximizes convergence speed. We provide theoretical
guarantees for the convergence of the resulting method in a neighborhood of the
solution. Also, for the case in which the global objective function has a
single local minimum, we provide a different step size selection criterion
together with theoretical guarantees for convergence. We present numerical
experiments showing that, when using the same step size, our method converges
significantly faster than its rivals. Experiments also show that the
distributed step size estimation method achieves an asymptotic convergence rate
very close to the theoretical maximum.


Abstract:
  We investigate the phenomenology of light GeV-scale fermionic dark matter in
$U(1)_{L_\mu - L_{\tau}}$ gauge extension of the Standard Model. Heavy neutral
fermions alongside with a $S_1(\overline{3}$,$1$,$1/3$) scalar leptoquark and
an inert scalar doublet are added to address the flavor anomalies and light
neutrino mass respectively. The light gauge boson associated with
$U(1)_{L_\mu-L_\tau}$ gauge group mediates dark to visible sector and helps to
obtain the correct relic density. Aided with a colored scalar, we constrain the
new model parameters by using the branching ratios of various $b \to sll$ and
$b \to s \gamma$ decay processes as well as the lepton flavour non-universality
observables $R_{K^{(*)}}$ and then show the implication on the branching ratios
of some rare semileptonic $B \to (K^{(*)}, \phi)+$ missing energy, processes.


Abstract:
  In recent years, the implications of the generalized (GUP) and extended (EUP)
uncertainty prin-ciples on Maxwell-Boltzmann distribution have been widely
investigated. However, at high energy regimes, the validity of
Maxwell-Boltzmann statistics is under debate and instead, the J\"{u}ttner
distribution is proposed as the distribution function in relativistic limit.
Motivated by these considerations, in the present work, our aim is to study the
effects of GUP and EUP on a system that obeys the J\"{u}ttner distribution. To
achieve this goal, we address a method to get the distribution function by
starting from the partition function and its relation with thermal energy which
finally helps us in finding the corresponding energy density states.


Abstract:
  Supervised machine learning has several drawbacks that make it difficult to
use in many situations. Drawbacks include: heavy reliance on massive training
data, limited generalizability and poor expressiveness of high-level semantics.
Low-shot Learning attempts to address these drawbacks. Low-shot learning allows
the model to obtain good predictive power with very little or no training data,
where structured knowledge plays a key role as a high-level semantic
representation of human. This article will review the fundamental factors of
low-shot learning technologies, with a focus on the operation of structured
knowledge under different low-shot conditions. We also introduce other
techniques relevant to low-shot learning. Finally, we point out the limitations
of low-shot learning, the prospects and gaps of industrial applications, and
future research directions.


Abstract:
  Relation extraction is a type of information extraction task that recognizes
semantic relationships between entities in a sentence. Many previous studies
have focused on extracting only one semantic relation between two entities in a
single sentence. However, multiple entities in a sentence are associated
through various relations. To address this issue, we propose a relation
extraction model based on a dual pointer network with a multi-head attention
mechanism. The proposed model finds n-to-1 subject-object relations using a
forward object decoder. Then, it finds 1-to-n subject-object relations using a
backward subject decoder. Our experiments confirmed that the proposed model
outperformed previous models, with an F1-score of 80.8% for the ACE-2005 corpus
and an F1-score of 78.3% for the NYT corpus.


Abstract:
  Actor-critic style two-time-scale algorithms are very popular in
reinforcement learning, and have seen great empirical success. However, their
performance is not completely understood theoretically. In this paper, we
characterize the global convergence of an online natural actor-critic algorithm
in the tabular setting using a single trajectory. Our analysis applies to very
general settings, as we only assume that the underlying Markov chain is ergodic
under all policies (the so-called Recurrence assumption). We employ
$\epsilon$-greedy sampling in order to ensure enough exploration.
  For a fixed exploration parameter $\epsilon$, we show that the natural actor
critic algorithm is $\mathcal{O}(\frac{1}{\epsilon T^{1/4}}+\epsilon)$ close to
the global optimum after $T$ iterations of the algorithm.
  By carefully diminishing the exploration parameter $\epsilon$ as the
iterations proceed, we also show convergence to the global optimum at a rate of
$\mathcal{O}(1/T^{1/6})$.


Abstract:
  Human machine interaction systems are those of much needed in the emerging
technology to make the user aware of what is happening around. It is huge
domain in which the smart material enables the factor of convergence. One such
is the piezoelectric crystals, is a class of smart material and this has an
incredible property of self-sensing actuation (SSA). This property of SSA has
added an indescribable advantage to the robotic field by having the advantages
of exhibiting both the functionality of sensing and actuating characteristics
with reduced devices, space and power. This paper focuses on integrating the
SSA to drive an unmanned ground vehicle with wireless radio control system
which will be of great use in all the automation field. The piezo electric
plate will be used as an input device to send the signal to move the UGV in
certain direction and then, the same piezo-electric plate will be used as an
actuator for haptic feedback with the help of drive circuit if obstacles or
danger is experienced by UGV.


Abstract:
  The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that
the use of computing and communication technologies provides significant aid in
preventing, controlling, and combating infectious diseases. With the ongoing
research in next-generation networking (NGN), the use of secure and reliable
communication and networking is of utmost importance when dealing with users'
health records and other sensitive information. Through the adaptation of
Artificial Intelligence (AI)-enabled NGN, the shape of healthcare systems can
be altered to achieve smart and secure healthcare capable of coping with
epidemics that may emerge at any given moment. In this article, we envision a
cooperative and distributed healthcare framework that relies on
state-of-the-art computing, communication, and intelligence capabilities,
namely, Federated Learning (FL), mobile edge computing (MEC), and Blockchain,
to enable epidemic (or suspicious infectious disease) discovery, remote
monitoring, and fast health-authority response. The introduced framework can
also enable secure medical data exchange at the edge and between different
health entities. Such a technique, coupled with the low latency and high
bandwidth functionality of 5G and beyond networks, would enable mass
surveillance, monitoring and analysis to occur at the edge. Challenges, issues,
and design guidelines are also discussed in this article with highlights on
some trending solutions.


Abstract:
  Let $\mathcal{P}$ be a finite set of points in the plane in general position.
For any spanning tree $T$ on $\mathcal{P}$, we denote by $|T|$ the Euclidean
length of $T$. Let $T_{\text{OPT}}$ be a plane (that is, noncrossing) spanning
tree of maximum length for $\mathcal{P}$. It is not known whether such a tree
can be found in polynomial time. Past research has focused on designing
polynomial time approximation algorithms, using low diameter trees. In this
work we initiate a systematic study of the interplay between the approximation
factor and the diameter of the candidate trees. Specifically, we show three
results. First, we construct a plane tree $T_{\text{ALG}}$ with diameter at
most four that satisfies $|T_{\text{ALG}}|\ge \delta\cdot |T_{\text{OPT}}|$ for
$\delta>0.546$, thereby substantially improving the currently best known
approximation factor. Second, we show that the longest plane tree among those
with diameter at most three can be found in polynomial time. Third, for any
$d\ge 3$ we provide upper bounds on the approximation factor achieved by a
longest plane tree with diameter at most $d$ (compared to a longest general
plane tree).


Abstract:
  Jerky flow in solids results from collective dynamics of dislocations which
gives rise to serrated deformation curves and a complex evolution of the strain
heterogeneity. A rich example of this phenomenon is the Portevin-Le Chatelier
effect in alloys. The corresponding spatiotemporal patterns showed some
universal features which provided a basis for a well-known phenomenological
classification. Recent studies revealed peculiar features in both the stress
serration sequences and the kinematics of deformation bands in Al-based alloys
containing fine microstructure elements, such as nanosize precipitates and (or)
submicron grains. In the present work, jerky flow of an AlMgScZr alloy is
studied using statistical analysis of stress serrations and the accompanying
acoustic emission. As in the case of coarse-grained binary AlMg alloys, the
amplitude distributions of acoustic events obey a power-law scaling which is
usually considered as evidence of avalanchelike dynamics. However, the scaling
exponents display specific dependences on the strain and strain rate for the
investigated materials. The observed effects bear evidence to a competition
between the phenomena of synchronization and randomization of dislocation
avalanches, which may shed light on the mechanisms leading to a high variety of
jerky flow patterns observed in applied alloys.


Abstract:
  Considering a common case where measurements are obtained from independent
sensors, we present a novel outlier-robust filter for nonlinear dynamical
systems in this work. The proposed method is devised by modifying the
measurement model and subsequently using the theory of Variational Bayes and
general Gaussian filtering. We treat the measurement outliers independently for
independent observations leading to selective rejection of the corrupted data
during inference. By carrying out simulations for variable number of sensors we
verify that an implementation of the proposed filter is computationally more
efficient as compared to the proposed modifications of similar baseline methods
still yielding similar estimation quality. In addition, experimentation results
for various real-time indoor localization scenarios using Ultra-wide Band (UWB)
sensors demonstrate the practical utility of the proposed method.


Abstract:
  We present Magic Layouts; a method for parsing screenshots or hand-drawn
sketches of user interface (UI) layouts. Our core contribution is to extend
existing detectors to exploit a learned structural prior for UI designs,
enabling robust detection of UI components; buttons, text boxes and similar.
Specifically we learn a prior over mobile UI layouts, encoding common spatial
co-occurrence relationships between different UI components. Conditioning
region proposals using this prior leads to performance gains on UI layout
parsing for both hand-drawn UIs and app screenshots, which we demonstrate
within the context an interactive application for rapidly acquiring digital
prototypes of user experience (UX) designs.


Abstract:
  The synthesis of 3 and 4 abstract polymer chains divided into two sexes is
considered, where the degree of kinship of the chains is determined by their
overlap. It is shown that the use of some types of entangled bi-photon in
one-way control gives a difference in the degree of kinship between the legal
and nonlegal pairs that is unattainable with classical control. This example
demonstrates the quantum superiority in distributed computing, coming from the
violation of the Bell inequality. It may be of interest for revealing the
quantum mechanisms of synthesis of real biopolymers with directional
properties.


Abstract:
  Autonomous multi-robot optical inspection systems are increasingly applied
for obtaining inline measurements in process monitoring and quality control.
Numerous methods for path planning and robotic coordination have been developed
for static and dynamic environments and applied to different fields. However,
these approaches may not work for the autonomous multi-robot optical inspection
system due to fast computation requirements of inline optimization, unique
characteristics on robotic end-effector orientations, and complex large-scale
free-form product surfaces. This paper proposes a novel task allocation
methodology for coordinated motion planning of multi-robot inspection.
Specifically, (1) a local robust inspection task allocation is proposed to
achieve efficient and well-balanced measurement assignment among robots; (2)
collision-free path planning and coordinated motion planning are developed via
dynamic searching in robotic coordinate space and perturbation of probe poses
or local paths in the conflicting robots. A case study shows that the proposed
approach can mitigate the risk of collisions between robots and environments,
resolve conflicts among robots, and reduce the inspection cycle time
significantly and consistently.


Abstract:
  We report on high power operation of Er:Y2O3 ceramic laser at ~1.6 {\mu}m
using low scattering loss, 0.25 at.% Er3+ doped ceramic sample fabricated
in-house via co-precipitation process. The laser is in-band pumped by an Er, Yb
fiber laser at 1535.6 nm and generates 10.2 W of continuous-wave (CW) output
power at 1640.4 nm with a slope efficiency of 25% with respect to the absorbed
pump power. To the best of our knowledge, this is the first demonstration of
~1.6 {\mu}m Er:Y2O3 laser at room temperature. The prospects for further
scaling in output power and lasing efficiency via low Er3+ doping and reduced
energy-transfer upconversion are discussed.


Abstract:
  In this work, the thermodynamic properties of the organic superconductor
$\lambda$-(BETS)$_2$GaCl$_4$ are investigated to study a high-field
superconducting state known as the putative Fulde-Ferrell-Larkin-Ovchinnikov
(FFLO) phase. We observed a small thermodynamic anomaly in the field $H_{\rm
FFLO}$ $\sim$ 10~T, which corresponds to the Pauli limiting field $H_{\rm P}$.
This anomaly probably originates from a transition from a uniform
superconducting state to the FFLO state. $H_{\rm FFLO}$ does not show a strong
field-angular dependence due to a quasi-isotropic paramagnetic effect in
$\lambda$-(BETS)$_2$GaCl$_4$. The thermodynamic anomaly at $H_{\rm FFLO}$ is
smeared out and low-temperature upper critical field $H_{\rm c2}$ changes
significantly if fields are not parallel to the conducting plane even for a
deviation of $\sim$0.5$^{\circ}$. This behavior indicates that the high-field
state is very unstable, as it is influenced by the strongly anisotropic orbital
effect. Our results are consistent with the theoretical predictions on the FFLO
state, and show that the high-field superconductivity is probably an FFLO state
in $\lambda$-(BETS)$_2$GaCl$_4$ from a thermodynamic point of view.


Abstract:
  Given the recent development of rotating black-bounce-Kerr spacetimes, for
both theoretical and observational purposes it becomes interesting to see
whether it might be possible to construct black-bounce variants of the entire
Kerr-Newman family. Specifically, herein we shall consider
black-bounce-Reissner-Nordstr\"om and black-bounce-Kerr-Newman spacetimes as
particularly simple and clean everywhere-regular black hole "mimickers" that
deviate from the Kerr-Newman family in a precisely controlled and minimal
manner, and smoothly interpolate between regular black holes and traversable
wormholes. While observationally the electric charges on astrophysical black
holes are likely to be extremely low, $|Q|/m \ll 1$, introducing any non-zero
electric charge has a significant theoretical impact. In particular, we verify
the existence of a Killing tensor (and associated Carter-like constant) but
without the full Killing tower of principal tensor and Killing-Yano tensor,
also we discuss how, assuming general relativity, the black-bounce-Kerr-Newman
solution requires an interesting, non-trivial matter/energy content.


Abstract:
  In this manuscript we prove the Bernstein inequality and develop the theory
of holonomic D-modules for rings of invariants of finite groups in
characteristic zero, and for strongly F-regular finitely generated graded
algebras with FFRT in prime characteristic. In each of these cases, the ring
itself, its localizations, and its local cohomology modules are holonomic. We
also show that holonomic D-modules, in this context, have finite length. We
obtain these results using a more general version of Bernstein filtrations.


Abstract:
  Photoelectron circular dichroism (PECD) is a fascinating phenomenon both from
a fundamental science aspect but also due to its emerging role as a highly
sensitive analytic tool for chiral recognition in the gas phase. PECD has been
studied with single-photon as well as multi-photon ionization. The latter has
been investigated in the short pulse limit with femtosecond laser pulses, where
ionization can be thought of as an instantaneous process.
  In this contribution, we demonstrate that multiphoton PECD still can be
observed when using an ultra-violet nanosecond pulse to ionize chiral showcase
fenchone molecules. Compared to femtosecond ionization, the magnitude of PECD
is similar, but the lifetime of intermediate molecular states imprints itself
in the photoelectron spectra. Being able to use an industrial nanosecond laser
to investigate PECD furthermore reduces the technical requirements to apply
PECD in analytical chemistry.


Abstract:
  Inter-beat interval (IBI) measurement enables estimation of heart-rate
variability (HRV) which, in turns, can provide early indication of potential
cardiovascular diseases. However, extracting IBIs from noisy signals is
challenging since the morphology of the signal is distorted in the presence of
the noise. Electrocardiogram (ECG) of a person in heavy motion is highly
corrupted with noise, known as motion-artifact, and IBI extracted from it is
inaccurate. As a part of remote health monitoring and wearable system
development, denoising ECG signals and estimating IBIs correctly from them have
become an emerging topic among signal-processing researchers. Apart from
conventional methods, deep-learning techniques have been successfully used in
signal denoising recently, and diagnosis process has become easier, leading to
accuracy levels that were previously unachievable. We propose a deep-learning
approach leveraging tiramisu autoencoder model to suppress motion-artifact
noise and make the R-peaks of the ECG signal prominent even in the presence of
high-intensity motion. After denoising, IBIs are estimated more accurately
expediting diagnosis tasks. Results illustrate that our method enables IBI
estimation from noisy ECG signals with SNR up to -30dB with average root mean
square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level,
our error percentage remains below 8% and outperforms other state of the art
techniques.


Abstract:
  Recommender Systems (RS) have employed knowledge distillation which is a
model compression technique training a compact student model with the knowledge
transferred from a pre-trained large teacher model. Recent work has shown that
transferring knowledge from the teacher's intermediate layer significantly
improves the recommendation quality of the student. However, they transfer the
knowledge of individual representation point-wise and thus have a limitation in
that primary information of RS lies in the relations in the representation
space. This paper proposes a new topology distillation approach that guides the
student by transferring the topological structure built upon the relations in
the teacher space. We first observe that simply making the student learn the
whole topological structure is not always effective and even degrades the
student's performance. We demonstrate that because the capacity of the student
is highly limited compared to that of the teacher, learning the whole
topological structure is daunting for the student. To address this issue, we
propose a novel method named Hierarchical Topology Distillation (HTD) which
distills the topology hierarchically to cope with the large capacity gap. Our
extensive experiments on real-world datasets show that the proposed method
significantly outperforms the state-of-the-art competitors. We also provide
in-depth analyses to ascertain the benefit of distilling the topology for RS.


Abstract:
  Evidence-based fact checking aims to verify the truthfulness of a claim
against evidence extracted from textual sources. Learning a representation that
effectively captures relations between a claim and evidence can be challenging.
Recent state-of-the-art approaches have developed increasingly sophisticated
models based on graph structures. We present a simple model that can be trained
on sequence structures. Our model enables inter-sentence attentions at
different levels and can benefit from joint training. Results on a large-scale
dataset for Fact Extraction and VERification (FEVER) show that our model
outperforms the graph-based approaches and yields 1.09% and 1.42% improvements
in label accuracy and FEVER score, respectively, over the best published model.


Abstract:
  A simple vibrational model of heat transfer in two-dimensional (2D) fluids
relates the heat conductivity coefficient to the longitudinal and transverse
sound velocities, specific heat, and the mean interatomic separation. This
model is demonstrated not to contradict the available experimental and
numerical data on heat transfer in 2D complex plasma layers. Additionally, the
heat conductivity coefficient of a 2D one-component plasma with a logarithmic
interaction is evaluated.


Abstract:
  Neural Architecture Search (NAS) has enabled the possibility of automated
machine learning by streamlining the manual development of deep neural network
architectures defining a search space, search strategy, and performance
estimation strategy. To solve the need for multi-platform deployment of
Convolutional Neural Network (CNN) models, Once-For-All (OFA) proposed to
decouple Training and Search to deliver a one-shot model of sub-networks that
are constrained to various accuracy-latency tradeoffs. We find that the
performance estimation strategy for OFA's search severely lacks
generalizability of different hardware deployment platforms due to single
hardware latency lookup tables that require significant amount of time and
manual effort to build beforehand. In this work, we demonstrate the framework
for building latency predictors for neural network architectures to address the
need for heterogeneous hardware support and reduce the overhead of lookup
tables altogether. We introduce two generalizability strategies which include
fine-tuning using a base model trained on a specific hardware and NAS search
space, and GPU-generalization which trains a model on GPU hardware parameters
such as Number of Cores, RAM Size, and Memory Bandwidth. With this, we provide
a family of latency prediction models that achieve over 50% lower RMSE loss as
compared to with ProxylessNAS. We also show that the use of these latency
predictors match the NAS performance of the lookup table baseline approach if
not exceeding it in certain cases.


Abstract:
  We give explicit formulas for the geodesic growth series of a Right Angled
Coxeter Group (RACG) based on a link-regular graph that is 4-clique free, i.e.
without tetrahedrons


Abstract:
  We consider some supervised binary classification tasks and a regression
task, whereas SVM and Deep Learning, at present, exhibit the best
generalization performances. We extend the work [3] on a generalized quadratic
loss for learning problems that examines pattern correlations in order to
concentrate the learning problem into input space regions where patterns are
more densely distributed. From a shallow methods point of view (e.g.: SVM),
since the following mathematical derivation of problem (9) in [3] is incorrect,
we restart from problem (8) in [3] and we try to solve it with one procedure
that iterates over the dual variables until the primal and dual objective
functions converge. In addition we propose another algorithm that tries to
solve the classification problem directly from the primal problem formulation.
We make also use of Multiple Kernel Learning to improve generalization
performances. Moreover, we introduce for the first time a custom loss that
takes in consideration pattern correlation for a shallow and a Deep Learning
task. We propose some pattern selection criteria and the results on 4 UCI
data-sets for the SVM method. We also report the results on a larger binary
classification data-set based on Twitter, again drawn from UCI, combined with
shallow Learning Neural Networks, with and without the generalized quadratic
loss. At last, we test our loss with a Deep Neural Network within a larger
regression task taken from UCI. We compare the results of our optimizers with
the well known solver SVMlight and with Keras Multi-Layers Neural Networks with
standard losses and with a parameterized generalized quadratic loss, and we
obtain comparable results.


Abstract:
  We show that the symmetric radial decreasing rearrangement can increase the
fractional Gagliardo semi-norm in domains.


Abstract:
  We consider two degenerate heat equations with a nonlocal space term,
studying, in particular, their null controllability property. To this aim, we
first consider the associated nonhomogeneous degenerate heat equations: we
study their well posedness, the Carleman estimates for the associated adjoint
problems and, finally, the null controllability. Then, as a consequence, using
the Kakutani's fixed point Theorem, we deduce the null controllability property
for the initial nonlocal problems.


Abstract:
  Decades of deindustrialization have led to economic decline and population
loss throughout the U.S. Midwest, with the highest national poverty rates found
in Detroit, Cleveland, and Buffalo. This poverty is often confined to core
cities themselves, however, as many of their surrounding suburbs continue to
prosper. Poverty can therefore be highly concentrated at the MSA level, but
more evenly distributed within the borders of the city proper. One result of
this disparity is that if suburbanites consider poverty to be confined to the
central city, they might be less willing to devote resources to alleviate it.
But due to recent increases in suburban poverty, particularly since the 2008
recession, such urban-suburban gaps might be shrinking. Using Census
tract-level data, this study quantifies poverty concentrations for four "Rust
Belt" MSAs, comparing core-city and suburban concentrations in 2000, 2010, and
2015. There is evidence of a large gap between core cities and outlying areas,
which is closing in the three highest-poverty cities, but not in Milwaukee. A
set of four comparison cities show a smaller, more stable city-suburban divide
in the U.S. "Sunbelt," while Chicago resembles a "Rust Belt" metro.


Abstract:
  Recent observations by the {\it Juno} spacecraft have revealed that the tidal
Love number $k_2$ of Jupiter is $4\%$ lower than the hydrostatic value. We
present a simple calculation of the dynamical Love number of Jupiter that
explains the observed "anomaly". The Love number is usually dominated by the
response of the (rotation-modified) f-modes of the planet. Our method also
allows for efficient computation of high-order dynamical Love numbers. While
the inertial-mode contributions to the Love numbers are negligible, a
sufficiently strong stratification in a large region of the planet's interior
would induce significant g-mode responses and influence the measured Love
numbers.


Abstract:
  Conformal predictors are an important class of algorithms that allow
predictions to be made with a user-defined confidence level. They are able to
do this by outputting prediction sets, rather than simple point predictions.
The conformal predictor is valid in the sense that the accuracy of its
predictions is guaranteed to meet the confidence level, only assuming
exchangeability in the data. Since accuracy is guaranteed, the performance of a
conformal predictor is measured through the efficiency of the prediction sets.
Typically, a conformal predictor is built on an underlying machine learning
algorithm and hence its predictive power is inherited from this algorithm.
However, since the underlying machine learning algorithm is not trained with
the objective of minimizing predictive efficiency it means that the resulting
conformal predictor may be sub-optimal and not aligned sufficiently to this
objective. Hence, in this study we consider an approach to train the conformal
predictor directly with maximum predictive efficiency as the optimization
objective, and we focus specifically on the inductive conformal predictor for
classification. To do this, the conformal predictor is approximated by a
differentiable objective function and gradient descent used to optimize it. The
resulting parameter estimates are then passed to a proper inductive conformal
predictor to give valid prediction sets. We test the method on several real
world data sets and find that the method is promising and in most cases gives
improved predictive efficiency against a baseline conformal predictor.


Abstract:
  A recent experiment [K. H. Kim, et al., Science 370, 978 (2020)] showed that
it may be possible to detect a liquid-liquid phase transition (LLPT) in
supercooled water by subjecting high density amorphous ice (HDA) to ultrafast
heating, after which the sample reportedly undergoes spontaneous decompression
from a high density liquid (HDL) to a low density liquid (LDL) via a
first-order phase transition. Here we conduct computer simulations of the ST2
water model, in which a LLPT is known to occur. We subject various HDA samples
of this model to a heating and decompression protocol that follows a
thermodynamic pathway similar to that of the recent experiments. Our results
show that a signature of the underlying equilibrium LLPT can be observed in a
strongly out-of-equilibrium process that follows this pathway despite the very
high heating and decompression rates employed here. Our results are also
consistent with the phase diagram of glassy ST2 water reported in previous
studies.


Abstract:
  In recent years, the notion of Quantum Materials has emerged as a powerful
unifying concept across diverse fields of science and engineering, from
condensed-matter and cold atom physics to materials science and quantum
computing. Beyond traditional quantum materials such as unconventional
superconductors, heavy fermions, and multiferroics, the field has significantly
expanded to encompass topological quantum matter, two-dimensional materials and
their van der Waals heterostructures, Moire materials, Floquet time crystals,
as well as materials and devices for quantum computation with Majorana
fermions. In this Roadmap collection we aim to capture a snapshot of the most
recent developments in the field, and to identify outstanding challenges and
emerging opportunities. The format of the Roadmap, whereby experts in each
discipline share their viewpoint and articulate their vision for quantum
materials, reflects the dynamic and multifaceted nature of this research area,
and is meant to encourage exchanges and discussions across traditional
disciplinary boundaries. It is our hope that this collective vision will
contribute to sparking new fascinating questions and activities at the
intersection of materials science, condensed matter physics, device
engineering, and quantum information, and to shaping a clearer landscape of
quantum materials science as a new frontier of interdisciplinary scientific
inquiry.


Abstract:
  The paper contains several theoretical results related to the weighted
nonlinear least-squares problem for low-rank signal estimation, which can be
considered as a Hankel structured low-rank approximation problem. A
parameterization of the subspace of low-rank time series connected with
generalized linear recurrence relations (GLRRs) is described and its features
are investigated. It is shown how the obtained results help to describe the
tangent plane, prove optimization problem features and construct stable
algorithms for solving low-rank approximation problems. For the latter, a
stable algorithm for constructing the projection onto a subspace of time series
that satisfy a given GLRR is proposed and justified. This algorithm is used for
a new implementation of the known Gauss-Newton method using the variable
projection approach. The comparison by stability and computational cost is
performed theoretically and with the help of an example.


Abstract:
  We develop a new method for analyzing moduli problems related to the stack of
pure coherent sheaves on a polarized family of projective schemes. It is an
infinite-dimensional analogue of geometric invariant theory. We apply this to
two familiar moduli problems: the stack of $\Lambda$-modules and the stack of
pairs. In both examples, we construct a $\Theta$-stratification of the stack,
defined in terms of a polynomial numerical invariant, and we construct good
moduli spaces for the open substacks of semistable points. One of the essential
ingredients is the construction of higher dimensional analogues of the affine
Grassmannian for the moduli problems considered.


Abstract:
  Subset selection is a valuable tool for interpretable learning, scientific
discovery, and data compression. However, classical subset selection is often
eschewed due to selection instability, computational bottlenecks, and lack of
post-selection inference. We address these challenges from a Bayesian
perspective. Given any Bayesian predictive model $\mathcal{M}$, we elicit
predictively-competitive subsets using linear decision analysis. The approach
is customizable for (local) prediction or classification and provides
interpretable summaries of $\mathcal{M}$. A key quantity is the acceptable
family of subsets, which leverages the predictive distribution from
$\mathcal{M}$ to identify subsets that offer nearly-optimal prediction. The
acceptable family spawns new (co-) variable importance metrics based on whether
variables (co-) appear in all, some, or no acceptable subsets. Crucially, the
linear coefficients for any subset inherit regularization and predictive
uncertainty quantification via $\mathcal{M}$. The proposed approach exhibits
excellent prediction, interval estimation, and variable selection for simulated
data, including $p=400 > n$. These tools are applied to a large education
dataset with highly correlated covariates, where the acceptable family is
especially useful. Our analysis provides unique insights into the combination
of environmental, socioeconomic, and demographic factors that predict
educational outcomes, and features highly competitive prediction with
remarkable stability.


Abstract:
  As infamous invaders to the North American ecosystem, the Asian giant hornet
(Vespa mandarinia) is devastating not only to native bee colonies, but also to
local apiculture. One of the most effective way to combat the harmful species
is to locate and destroy their nests. By mobilizing the public to actively
report possible sightings of the Asian giant hornet, the governmentcould timely
send inspectors to confirm and possibly destroy the nests. However, such
confirmation requires lab expertise, where manually checking the reports one by
one is extremely consuming of human resources. Further given the limited
knowledge of the public about the Asian giant hornet and the randomness of
report submission, only few of the numerous reports proved positive, i.e.
existing nests. How to classify or prioritize the reports efficiently and
automatically, so as to determine the dispatch of personnel, is of great
significance to the control of the Asian giant hornet. In this paper, we
propose a method to predict the priority of sighting reports based on machine
learning. We model the problem of optimal prioritization of sighting reports as
a problem of classification and prediction. We extracted a variety of rich
features in the report: location, time, image(s), and textual description.
Based on these characteristics, we propose a classification model based on
logistic regression to predict the credibility of a certain report.
Furthermore, our model quantifies the impact between reports to get the
priority ranking of the reports. Extensive experiments on the public dataset
from the WSDA (the Washington State Department of Agriculture) have proved the
effectiveness of our method.


Abstract:
  Despite the recent success of reconciling spike-based coding with the error
backpropagation algorithm, spiking neural networks are still mostly applied to
tasks stemming from sensory processing, operating on traditional data
structures like visual or auditory data. A rich data representation that finds
wide application in industry and research is the so-called knowledge graph - a
graph-based structure where entities are depicted as nodes and relations
between them as edges. Complex systems like molecules, social networks and
industrial factory systems can be described using the common language of
knowledge graphs, allowing the usage of graph embedding algorithms to make
context-aware predictions in these information-packed environments. We propose
a spike-based algorithm where nodes in a graph are represented by single spike
times of neuron populations and relations as spike time differences between
populations. Learning such spike-based embeddings only requires knowledge about
spike times and spike time differences, compatible with recently proposed
frameworks for training spiking neural networks. The presented model is easily
mapped to current neuromorphic hardware systems and thereby moves inference on
knowledge graphs into a domain where these architectures thrive, unlocking a
promising industrial application area for this technology.


Abstract:
  Mutations sometimes increase contagiousness for evolving pathogens. During an
epidemic, scientists use viral genome data to infer a shared evolutionary
history and connect this history to geographic spread. We propose a model that
directly relates a pathogen's evolution to its spatial contagion dynamics --
effectively combining the two epidemiological paradigms of phylogenetic
inference and self-exciting process modeling -- and apply this
\emph{phylogenetic Hawkes process} to a Bayesian analysis of 23,422 viral cases
from the 2014-2016 Ebola outbreak in West Africa. The proposed model is able to
detect individual viruses with significantly elevated rates of spatiotemporal
propagation for a subset of 1,610 samples that provide genome data. Finally, to
facilitate model application in big data settings, we develop massively
parallel implementations for the gradient and Hessian of the log-likelihood and
apply our high performance computing framework within an adaptively
preconditioned Hamiltonian Monte Carlo routine.


Abstract:
  Understanding the interaction of massive black hole binaries with their
gaseous environment is crucial since at sub-parsec scales the binary is too
wide for gravitational wave emission to take over and to drive the two black
holes to merge. We here investigate the interaction between a massive black
hole binary and a self-gravitating circumbinary disc using 3D smoothed particle
hydrodynamics simulations. We find that, when the disc self-gravity regulates
the angular momentum transport, the binary semi-major axis decreases regardless
the choice of disc masses and temperatures, within the range we explored. In
particular, we find that the disc initial temperature (hence the disc aspect
ratio) has little effect on the evolution of the binary since discs with the
same mass self-regulate towards the same temperature. Initially warmer discs
cause the binary to shrink on a slightly shorter timescale until the disc has
reached the self-regulated equilibrium temperature. More massive discs drive
the binary semi-major axis to decrease at a faster pace compared to less
massive discs and result in faster binary eccentricity growth even after the
initial-condition-dependent transient evolution. Finally we investigate the
effect that the initial cavity size has on the binary-disc interaction and we
find that, in the self-gravitating regime, an initially smaller cavity leads to
a much faster binary shrinking, as expected. Our results are especially
important for very massive black hole binaries such as those in the PTA band,
for which gas self gravity cannot be neglected.


Abstract:
  When wave scattering systems are subject to certain symmetries, resonant
states may decouple from the far-field continuum; they remain localized to the
structure and cannot be excited by incident waves from the far field. In this
work, we use layer-potential techniques to prove the existence of such states,
known as bound states in the continuum, in systems of subwavelength resonators.
When the symmetry is slightly broken, this resonant state can be excited from
the far field. Remarkably, this may create asymmetric (Fano-type) scattering
behaviour where the transmission is fundamentally different for frequencies on
either side of the resonant frequency. Using asymptotic analysis, we compute
the scattering matrix of the system explicitly, thereby characterizing this
Fano-type transmission anomaly.


Abstract:
  We study the problem of regularization of inverse problems adopting a purely
data driven approach, by using the similarity to the method of regularization
by projection. We provide an application of a projection algorithm, utilized
and applied in frames theory, as a data driven reconstruction procedure in
inverse problems, generalizing the algorithm proposed by the authors in Inverse
Problems 36 (2020), n. 12, 125009, based on an orthonormalization procedure for
the training pairs. We show some numerical experiments, comparing the different
methods.


Abstract:
  We enumerate the number of staircase diagrams over classically finite
$E$-type Dynkin diagrams, extending the work of Richmond and Slofstra
(Staircase Diagrams and Enumeration of smooth Schubert varieties) and
completing the enumeration of staircase diagrams over finite type Dynkin
diagrams. The staircase diagrams are in bijection to smooth and rationally
smooth Schubert varieties over $E$-type thereby giving an enumeration of these
varieties.


Abstract:
  Machine learning, artificial intelligence, and deep learning have advanced
significantly over the past decade. Nonetheless, humans possess unique
abilities such as creativity, intuition, context and abstraction, analytic
problem solving, and detecting unusual events. To successfully tackle pressing
scientific and societal challenges, we need the complementary capabilities of
both humans and machines. The Federal Government could accelerate its
priorities on multiple fronts through judicious integration of citizen science
and crowdsourcing with artificial intelligence (AI), Internet of Things (IoT),
and cloud strategies.


Abstract:
  The affinoid envelope, $\widehat{U(\mathcal{L})}$ of a free, finitely
generated $\mathbb{Z}_p$-Lie algebra $\mathcal{L}$ has proven to be useful
within the representation theory of compact $p$-adic Lie groups. Our aim is to
further understand the algebraic structure of $\widehat{U(\mathcal{L})}$, and
to this end, we will define a Dixmier module over $\widehat{U(\mathcal{L})}$,
and prove that this object is generally irreducible in case where $\mathcal{L}$
is nilpotent. Ultimately, we will prove that all primitive ideals in the
affinoid envelope can be described in terms of the annihilators of Dixmier
modules, and using this, we aim towards proving that these algebras satisfy a
version of the classical Dixmier-Moeglin equivalence.


Abstract:
  We theoretically investigate electron-hole recollisions in high-harmonic
generation (HHG) in band-gap solids irradiated by linearly and elliptically
polarized drivers. We find that in many cases the emitted harmonics do not
originate in electron-hole pairs created at the minimum band gap, where the
tunneling probability is maximized, but rather in pairs created across an
extended region of the Brillouin zone (BZ). In these situations, the analogy to
gas-phase HHG in terms of the short- and long-trajectory categorizations is
inadequate. Our analysis methodology comprises three complementary levels of
theory: the numerical solutions to the semiconductor Bloch equations, an
extended semiclassical recollision model, and a quantum wave packet approach.
We apply this methodology to two general material types with representative
band structures: a bulk system and a hexagonal monolayer system. In the bulk,
the interband harmonics generated using elliptically-polarized drivers are
found to originate not from tunneling at the minimum band gap $\Gamma$, but
from regions away from it. In the monolayer system driven by linearly-polarized
pulses, tunneling regions near different symmetry points in the BZ lead to
distinct harmonic energies and emission profiles. We show that the imperfect
recollisions, where an electron-hole pair recollide while being spatially
separated, are important in both bulk and monolayer materials. The excellent
agreement between our three levels of theory highlights and characterizes the
complexity behind the HHG emission dynamics in solids, and expands on the
notion of interband HHG as always originating in trajectories tunnelled at the
minimum band gap. Our work furthers the fundamental understanding of HHG in
periodic systems and will benefit the future design of experiments.


Abstract:
  Within the transport model evaluation project (TMEP) of simulations for
heavy-ion collisions, the mean-field response is examined here. Specifically,
zero-sound propagation is considered for neutron-proton symmetric matter
enclosed in a periodic box, at zero temperature and around normal density. The
results of several transport codes belonging to two families (BUU-like and
QMD-like) are compared among each other and to exact calculations. For BUU-like
codes, employing the test particle method, the results depend on the
combination of the number of test particles and the spread of the profile
functions that weight integration over space. These parameters can be properly
adapted to give a good reproduction of the analytical zero-sound features.
QMD-like codes, using molecular dynamics methods, are characterized by large
damping effects, attributable to the fluctuations inherent in their phase-space
representation. Moreover, for a given nuclear effective interaction, they
generally lead to slower density oscillations, as compared to BUU-like codes.
The latter problem is mitigated in the more recent lattice formulation of some
of the QMD codes. The significance of these results for the description of real
heavy-ion collisions is discussed.


Abstract:
  In this paper, we present a physics-constrained deep neural network (PCDNN)
method for parameter estimation in the zero-dimensional (0D) model of the
vanadium redox flow battery (VRFB). In this approach, we use deep neural
networks (DNNs) to approximate the model parameters as functions of the
operating conditions. This method allows the integration of the VRFB
computational models as the physical constraints in the parameter learning
process, leading to enhanced accuracy of parameter estimation and cell voltage
prediction. Using an experimental dataset, we demonstrate that the PCDNN method
can estimate model parameters for a range of operating conditions and improve
the 0D model prediction of voltage compared to the 0D model prediction with
constant operation-condition-independent parameters estimated with traditional
inverse methods. We also demonstrate that the PCDNN approach has an improved
generalization ability for estimating parameter values for operating conditions
not used in the DNN training.


Abstract:
  Billiards in ellipses have a confocal ellipse or hyperbola as caustic. The
goal of this paper is to prove that for each billiard of one type there exists
an isometric counterpart of the other type. Isometry means here that the
lengths of corresponding sides are equal. The transition between these two
isometric billiard can be carried out continuosly via isometric focal billiards
in a fixed ellipsoid. The extended sides of these particular billiards in an
ellipsoid are focal axes, i.e., generators of confocal hyperboloids. This
transition enables to transfer properties of planar billiards to focal
billiards, in particular billiard motions and canonical parametrizations. A
periodic planar billiard and its associated Poncelet grid give rise to periodic
focal billiards and spatial Poncelet grids. If the sides of a focal billiard
are materialized as thin rods with spherical joints at the vertices and other
crossing points between different sides, then we obtain Henrici's hyperboloid,
which is flexible between the two planar limits.


Abstract:
  A search for laser light from Proxima Centauri was performed, including 107
high-resolution, optical spectra obtained between 2004 and 2019. Among them, 57
spectra contain multiple, confined spectral combs, each consisting of 10
closely-spaced frequencies of light. The spectral combs, as entities, are
themselves equally spaced with a frequency separation of 5800 GHz, rendering
them unambiguously technological in origin. However, the combs do not originate
at Proxima Centauri. Otherwise, the 107 spectra of Proxima Centauri show no
evidence of technological signals, including 29 observations between March and
July 2019 when the candidate technological radio signal, BLC1, was captured by
Breakthrough Listen. This search would have revealed lasers pointed toward
Earth having a power of 20 to 120 kilowatts and located within the 1.3au field
of view centered on Proxima Centauri, assuming a benchmark laser launcher
having a 10-meter aperture.


Abstract:
  Agent-based modelling is a powerful tool when simulating human systems, yet
when human behaviour cannot be described by simple rules or maximising one's
own profit, we quickly reach the limits of this methodology. Machine learning
has the potential to bridge this gap by providing a link between what people
observe and how they act in order to reach their goal. In this paper we use a
framework for agent-based modelling that utilizes human values like fairness,
conformity and altruism. Using this framework we simulate a public goods game
and compare to experimental results. We can report good agreement between
simulation and experiment and furthermore find that the presented framework
outperforms strict reinforcement learning. Both the framework and the utility
function are generic enough that they can be used for arbitrary systems, which
makes this method a promising candidate for a foundation of a universal
agent-based model.


Abstract:
  Drug combination therapy has become a increasingly promising method in the
treatment of cancer. However, the number of possible drug combinations is so
huge that it is hard to screen synergistic drug combinations through wet-lab
experiments. Therefore, computational screening has become an important way to
prioritize drug combinations. Graph neural network have recently shown
remarkable performance in the prediction of compound-protein interactions, but
it has not been applied to the screening of drug combinations. In this paper,
we proposed a deep learning model based on graph neural networks and attention
mechanism to identify drug combinations that can effectively inhibit the
viability of specific cancer cells. The feature embeddings of drug molecule
structure and gene expression profiles were taken as input to multi-layer
feedforward neural network to identify the synergistic drug combinations. We
compared DeepDDS with classical machine learning methods and other deep
learning-based methods on benchmark data set, and the leave-one-out
experimental results showed that DeepDDS achieved better performance than
competitive methods. Also, on an independent test set released by well-known
pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive
methods by more than 16\% predictive precision. Furthermore, we explored the
interpretability of the graph attention network, and found the correlation
matrix of atomic features revealed important chemical substructures of drugs.
We believed that DeepDDS is an effective tool that prioritized synergistic drug
combinations for further wet-lab experiment validation.


Abstract:
  Bell inequality can provide a useful witness for device-independent
applications with quantum (or post-quantum) eavesdroppers. This feature holds
only for single entangled systems. Our goal is to explore device-independent
model for quantum networks. We firstly propose a Bell inequality to verify the
genuinely multipartite nonlocality of connected quantum networks including
cyclic networks and universal quantum computational resources for
measurement-based computation model. This is further used to construct new
monogamy relation in a fully device-independent model with multisource quantum
resources. It is finally applied for multiparty quantum key distribution, blind
quantum computation, and quantum secret sharing. The present model can inspire
various large-scale applications on quantum networks in a device-independent
manner.


Abstract:
  In this paper the authors study quotients of the product of elliptic curves
by a rigid diagonal action of a finite group $G$. It is shown that only for $G
= \operatorname{He(3)}, \mathbb Z_3^2$, and only for dimension $\geq 4$ such an
action can be free. A complete classification of the singular quotients in
dimension 3 and the smooth quotients in dimension $4$ is given. For the other
finite groups a strong structure theorem for rigid quotients is proven.


Abstract:
  Log-based cyber threat hunting has emerged as an important solution to
counter sophisticated cyber attacks. However, existing approaches require
non-trivial efforts of manual query construction and have overlooked the rich
external knowledge about threat behaviors provided by open-source Cyber Threat
Intelligence (OSCTI). To bridge the gap, we build ThreatRaptor, a system that
facilitates cyber threat hunting in computer systems using OSCTI. Built upon
mature system auditing frameworks, ThreatRaptor provides (1) an unsupervised,
light-weight, and accurate NLP pipeline that extracts structured threat
behaviors from unstructured OSCTI text, (2) a concise and expressive
domain-specific query language, TBQL, to hunt for malicious system activities,
(3) a query synthesis mechanism that automatically synthesizes a TBQL query
from the extracted threat behaviors, and (4) an efficient query execution
engine to search the big system audit logging data.


Abstract:
  We develop a theory for the susceptible-infected-susceptible (SIS) epidemic
model on networks that incorporate both network structure and dynamic
correlations. This theory can account for the multistage onset of the epidemic
phase in scale-free networks. This phenomenon is characterized by multiple
peaks in the susceptibility as a function of the infection rate. It can be
explained by that, even under the global epidemic threshold, a hub can sustain
the epidemics for an extended period. Moreover, our approach improves
theoretical calculations of prevalence close to the threshold in heterogeneous
networks and also can predict the average risk of infection for neighbors of
nodes with different degree and state on uncorrelated static networks.


Abstract:
  This paper describes a search for galaxy centers with clear indications of
unusual stellar populations with an initial mass function flatter than Salpeter
at high stellar masses. Out of a sample of 668 face-on galaxies with stellar
masses in the range 10^10- 10^11 M_sol, I identify 15 galaxies with young to
intermediate age central stellar populations with unusual stellar population
gradients in the inner regions of the galaxy. In these galaxies, the 4000
Angstrom break is either flat or rising towards the center of the galaxy,
indicating that the central regions host evolved stars, but the H$\alpha$
equivalent width also rises steeply in the central regions. The ionization
parameter [OIII]/[OII] is typically low in these galactic centers, indicating
that ionizing sources are stellar rather than AGN. Wolf Rayet features
characteristic of hot young stars are often found in the spectra and these also
get progressively stronger at smaller galactocentric radii. These outliers are
compared to a control sample of galaxies of similar mass with young inner
stellar populations, but where the gradients in Halpha equivalent width and
4000 Angstrom break follow each other more closely. The outliers exhibit
central Wolf Rayet red bump excesses much more frequently, they have higher
central stellar and ionized gas metallicities, and they are also more
frequently detected at 20 cm radio wavelengths. I highlight one outlier where
the ionized gas is clearly being strongly perturbed and blown out either by
massive stars after they explode as supernovae, or by energy injection from
matter falling onto a black hole.


Abstract:
  We consider the problem of operator-valued kernel learning and investigate
the possibility of going beyond the well-known separable kernels. Borrowing
tools and concepts from the field of quantum computing, such as partial trace
and entanglement, we propose a new view on operator-valued kernels and define a
general family of kernels that encompasses previously known operator-valued
kernels, including separable and transformable kernels. Within this framework,
we introduce another novel class of operator-valued kernels called entangled
kernels that are not separable. We propose an efficient two-step algorithm for
this framework, where the entangled kernel is learned based on a novel
extension of kernel alignment to operator-valued kernels. We illustrate our
algorithm with an application to supervised dimensionality reduction, and
demonstrate its effectiveness with both artificial and real data for
multi-output regression.


Abstract:
  Turbulence has the potential for creating gas density enhancements that
initiate cloud and star formation (SF), and it can be generated locally by SF.
To study the connection between turbulence and SF, we looked for relationships
between SF traced by FUV images, and gas turbulence traced by kinetic energy
density (KED) and velocity dispersion ($v_{disp}$) in the LITTLE THINGS sample
of nearby dIrr galaxies. We performed 2D cross-correlations between FUV and KED
images, measured cross-correlations in annuli to produce correlation
coefficients as a function of radius, and determined the cumulative
distribution function of the cross correlation value. We also plotted on a
pixel-by-pixel basis the locally excess KED, $v_{disp}$, and HI mass surface
density, $\Sigma_{\rm HI}$, as determined from the respective values with the
radial profiles subtracted, versus the excess SF rate density $\Sigma_{\rm
SFR}$, for all regions with positive excess $\Sigma_{\rm SFR}$. We found that
$\Sigma_{\rm SFR}$ and KED are poorly correlated. The excess KED associated
with SF implies a $\sim0.5$% efficiency for supernova energy to pump local HI
turbulence on the scale of resolution here, which is a factor of $\sim2$ too
small for all of the turbulence on a galactic scale. The excess $v_{disp}$ in
SF regions is also small, only $\sim0.37$ km s$^{-1}$. The local excess in
$\Sigma_{\rm HI}$ corresponding to an excess in $\Sigma_{\rm SFR}$ is
consistent with an HI consumption time of $\sim1.6$ Gyr in the inner parts of
the galaxies. The similarity between this timescale and the consumption time
for CO implies that CO-dark molecular gas has comparable mass to HI in the
inner disks.


Abstract:
  Through CaH2 chemical reduction of a parent R3+Ni3+O3 perovskite form,
superconductivity was recently achieved in Sr-doped NdNiO2 on SrTiO3 substrate.
Using density functional theory (DFT) calculations, we find that stoichiometric
NdNiO2 is significantly unstable with respect to decomposition into 1/2[Nd2O3 +
NiO + Ni] with exothermic decomposition energy of +176 meV/atom, a considerably
higher instability than that for common ternary oxides. This poses the question
if the stoichiometric NdNiO2 nickelate compound used extensively to model the
electronic band structure of Ni-based oxide analog to cuprates and found to be
metallic is the right model for this purpose. To examine this, we study via DFT
the role of the common H impurity expected to be present in the process of
chemical reduction needed to obtain NdNiO2. We find that H can be incorporated
exothermically, i.e., spontaneously in NdNiO2, even from H2 gas. In the
concentrated limit, such impurities can result in the formation of a hydride
compound NdNiO2H, which has significantly reduced instability relative to
hydrogen-free NdNiO2. Interestingly, the hydrogenated form has a similar
lattice constant as the pure form (leading to comparable XRD patterns), but
unlike the metallic character of NdNiO2, the hydrogenated form is predicted to
be a wide gap insulator thus, requiring doping to create a metallic or
superconducting state, just like cuprates, but unlike unhydrogenated
nickelates. While it is possible that hydrogen would be eventually desorbed,
the calculation suggests that pristine NdNiO2 is hydrogen-stabilized. One must
exercise caution with theories predicting new physics in pristine
stoichiometric NdNiO2 as it might be an unrealizable compound. Experimental
examination of the composition of real NdNiO2 superconductors and the effect of
hydrogen on the superconductivity is called for.


Abstract:
  The dynamics of water molecules plays a vital role in understanding water. We
combined computer simulation and deep learning to study the dynamics of H-bonds
between water molecules. Based on ab initio molecular dynamics simulations and
a newly defined directed Hydrogen (H-) bond population operator, we studied a
typical dynamic process in bulk water: interchange, in which the H-bond donor
reverses roles with the acceptor. By designing a recurrent neural network-based
model, we have successfully classified the interchange and breakage processes
in water. We have found that the ratio between them is approximately 1:4, and
it hardly depends on temperatures from 280 to 360 K. This work implies that
deep learning has the great potential to help distinguish complex dynamic
processes containing H-bonds in other systems.


Abstract:
  Understanding and simulating how a quantum system interacts and exchanges
information or energy with its surroundings is a ubiquitous problem, one which
must be carefully addressed in order to establish a coherent framework to
describe the dynamics and thermodynamics of quantum systems. Significant effort
has been invested in developing various methods for tackling this issue and in
this Perspective we focus on one such technique, namely collision models, which
have emerged as a remarkably flexible approach. We discuss their application to
understanding non-Markovian dynamics and to studying the thermodynamics of
quantum systems, two areas in which collision models have proven to be
particularly insightful. Their simple structure endows them with extremely
broad applicability which has spurred their recent experimental demonstrations.
By focusing on these areas, our aim is to provide a succinct entry point to
this remarkable framework.


Abstract:
  Nucleons (protons and neutrons) are the building blocks of atomic nuclei, and
are responsible for more than 99\% of the visible matter in the universe.
Despite decades of efforts in studying its internal structure, there are still
a number of puzzles surrounding the proton such as its spin, and charge radius.
Accurate knowledge about the proton charge radius is not only essential for
understanding how quantum chromodynamics (QCD) works in the non-perturbative
region, but also important for bound state quantum electrodynamics (QED)
calculations of atomic energy levels. It also has an impact on the Rydberg
constant, one of the most precisely measured fundamental constants in nature.
This article reviews the latest situation concerning the proton charge radius
in light of the new experimental results from both atomic hydrogen spectroscopy
and electron scattering measurements, with particular focus on the latter. We
also present the related theoretical developments and backgrounds concerning
the determination of the proton charge radius using different experimental
techniques. We discuss upcoming experiments, and briefly mention the deuteron
charge radius puzzle at the end.


Abstract:
  Topological properties of the jacobian curve ${\mathcal
J}_{\mathcal{F},\mathcal{G}}$ of two foliations $\mathcal{F}$ and $\mathcal{G}$
are described in terms of invariants associated to the foliations. The main
result gives a decomposition of the jacobian curve ${\mathcal
J}_{\mathcal{F},\mathcal{G}}$ which depends on how similar are the foliations
$\mathcal{F}$ and $\mathcal{G}$. The similarity between foliations is codified
in terms of the Camacho-Sad indices of the foliations with the notion of
collinear point or divisor. Our approach allows to recover the results
concerning the factorization of the jacobian curve of two plane curves and of
the polar curve of a curve or a foliation.


Abstract:
  Relativistic jets and disc-winds are typically observed in BH-XRBs and AGNs.
However, many physical details of jet launching and the driving of disc winds
from the underlying accretion disc are still not fully understood. In this
study, we further investigate the role of the magnetic field strength and
structure in launching jets and disc winds. In particular, we explore the
connection between jet, wind, and the accretion disc around the central black
hole. We perform axisymmetric GRMHD simulations of the accretion-ejection
system using adaptive mesh refinement. Essentially, our simulations are
initiated with a thin accretion disc in equilibrium. An extensive parametric
study by choosing different combinations of magnetic field strength and initial
magnetic field inclination is also performed. Our study finds relativistic jets
driven by the Blandford \& Znajek (BZ) mechanism and the disc-wind driven by
the Blandford \& Payne (BP) mechanism. We also find that plasmoids are formed
due to the reconnection events, and these plasmoids advect with disc-winds. As
a result, the tension force due to the poloidal magnetic field is enhanced in
the inner part of the accretion disc, resulting in disc truncation and
oscillation. These oscillations result in flaring activities in the jet mass
flow rates. We find simulation runs with a lower value of the plasma-$\beta$,
and lower inclination angle parameters are more prone to the formation of
plasmoids and subsequent inner disc oscillations. Our models provide a possible
template to understand spectral state transition phenomena in BH-XRBs.


Abstract:
  Space-borne optical frequency references based on spectroscopy of atomic
vapors may serve as an integral part of compact optical atomic clocks, which
can advance global navigation systems, or can be utilized for earth observation
missions as part of laser systems for cold atom gradiometers. Nanosatellites
offer low launch-costs, multiple deployment opportunities and short payload
development cycles, enabling rapid maturation of optical frequency references
and underlying key technologies in space. Towards an in-orbit demonstration on
such a platform, we have developed a CubeSat-compatible prototype of an optical
frequency reference based on the D2-transition in rubidium. A frequency
instability of 1.7e-12 at 1 s averaging time is achieved. The optical module
occupies a volume of 35 cm^3, weighs 73 g and consumes 780 mW of power.


Abstract:
  At the heart of all automated driving systems is the ability to sense the
surroundings, e.g., through semantic segmentation of LiDAR sequences, which
experienced a remarkable progress due to the release of large datasets such as
SemanticKITTI and nuScenes-LidarSeg. While most previous works focus on sparse
segmentation of the LiDAR input, dense output masks provide self-driving cars
with almost complete environment information. In this paper, we introduce MASS
- a Multi-Attentional Semantic Segmentation model specifically built for dense
top-view understanding of the driving scenes. Our framework operates on pillar-
and occupancy features and comprises three attention-based building blocks: (1)
a keypoint-driven graph attention, (2) an LSTM-based attention computed from a
vector embedding of the spatial input, and (3) a pillar-based attention,
resulting in a dense 360-degree segmentation mask. With extensive experiments
on both, SemanticKITTI and nuScenes-LidarSeg, we quantitatively demonstrate the
effectiveness of our model, outperforming the state of the art by 19.0% on
SemanticKITTI and reaching 32.7% in mIoU on nuScenes-LidarSeg, where MASS is
the first work addressing the dense segmentation task. Furthermore, our
multi-attention model is shown to be very effective for 3D object detection
validated on the KITTI-3D dataset, showcasing its high generalizability to
other tasks related to 3D vision.


Abstract:
  Prediction tasks about students have practical significance for both student
and college. Making multiple predictions about students is an important part of
a smart campus. For instance, predicting whether a student will fail to
graduate can alert the student affairs office to take predictive measures to
help the student improve his/her academic performance. With the development of
information technology in colleges, we can collect digital footprints which
encode heterogeneous behaviors continuously. In this paper, we focus on
modeling heterogeneous behaviors and making multiple predictions together,
since some prediction tasks are related and learning the model for a specific
task may have the data sparsity problem. To this end, we propose a variant of
LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the
student profile-aware representation from heterogeneous behavior sequences. The
proposed soft-attention mechanism can dynamically learn different importance
degrees of different days for every student. In this way, heterogeneous
behaviors can be well modeled. In order to model interactions among multiple
prediction tasks, we propose a co-attention mechanism based unit. With the help
of the stacked units, we can explicitly control the knowledge transfer among
multiple tasks. We design three motivating behavior prediction tasks based on a
real-world dataset collected from a college. Qualitative and quantitative
experiments on the three prediction tasks have demonstrated the effectiveness
of our model.


Abstract:
  We tackle the problem of predicting saliency maps for videos of dynamic
scenes. We note that the accuracy of the maps reconstructed from the gaze data
of a fixed number of observers varies with the frame, as it depends on the
content of the scene. This issue is particularly pressing when a limited number
of observers are available. In such cases, directly minimizing the discrepancy
between the predicted and measured saliency maps, as traditional deep-learning
methods do, results in overfitting to the noisy data. We propose a noise-aware
training (NAT) paradigm that quantifies and accounts for the uncertainty
arising from frame-specific gaze data inaccuracy. We show that NAT is
especially advantageous when limited training data is available, with
experiments across different models, loss functions, and datasets. We also
introduce a video game-based saliency dataset, with rich temporal semantics,
and multiple gaze attractors per frame. The dataset and source code are
available at https://github.com/NVlabs/NAT-saliency.


Abstract:
  In this work, we present a comparative analysis of the trajectories estimated
from various Simultaneous Localization and Mapping (SLAM) systems in a
simulation environment for vineyards. Vineyard environment is challenging for
SLAM methods, due to visual appearance changes over time, uneven terrain, and
repeated visual patterns. For this reason, we created a simulation environment
specifically for vineyards to help studying SLAM systems in such a challenging
environment. We evaluated the following SLAM systems: LIO-SAM, StaticMapping,
ORB-SLAM2, and RTAB-MAP in four different scenarios. The mobile robot used in
this study equipped with 2D and 3D lidars, IMU, and RGB-D camera (Kinect v2).
The results show good and encouraging performance of RTAB-MAP in such an
environment.


Abstract:
  We present a study of the wrinkling modes, localized in the plane of single-
and few-layer graphene sheets embedded in or placed on a compliant
compressively strained matrix. We provide the analytical model based on
nonlinear elasticity of the graphene sheet, which shows that the compressive
surface stress results in spatial localization of the extended sinusoidal
wrinkling mode with soliton-like envelope with localization length, decreasing
with the overcritical external strain. The parameters of the extended
sinusoidal wrinkling modes are found from the conditions of anomalous softening
of flexural surface acoustic wave propagating along the graphene sheet in or on
the matrix. For relatively small overcritical external strain, the continuous
transition occurs from the sinusoidal wrinkling modes with soliton-like
envelope to the strongly localized modes with approximately one-period
sinusoidal profiles and amplitude- and external-strain-independent spatial
widths. Two types of graphene wrinkling modes with different symmetry are
described, when the in-plane antisymmetric or symmetric modes are presumably
realized in the graphene sheet embedded in or placed on a compliant strained
matrix. Strongly localized wrinkling modes can be realized without delamination
of the graphene sheet from the compliant matrix and are not equivalent to the
ripplocations in layered solids. Molecular-dynamics modeling confirms the
appearance of sinusoidal wrinkling modes in single- and few-layer graphene
sheets embedded in polyethylene matrix at T = 300K.


Abstract:
  A new method is proposed for human motion predition by learning temporal and
spatial dependencies in an end-to-end deep neural network. The joint
connectivity is explicitly modeled using a novel autoregressive structured
prediction representation based on flow-based generative models. We learn a
latent space of complex body poses in consecutive frames which is conditioned
on the high-dimensional structure input sequence. To construct each latent
variable, the general and local smoothness of the joint positions are
considered in a generative process using conditional normalizing flows. As a
result, all frame-level and joint-level continuities in the sequence are
preserved in the model. This enables us to parameterize the inter-frame and
intra-frame relationships and joint connectivity for robust long-term
predictions as well as short-term prediction. Our experiments on two
challenging benchmark datasets of Human3.6M and AMASS demonstrate that our
proposed method is able to effectively model the sequence information for
motion prediction and outperform other techniques in 42 of the 48 total
experiment scenarios to set a new state-of-the-art.


Abstract:
  We consider bootstrap percolation and diffusion in sparse random graphs with
fixed degrees, constructed by configuration model. Every node has two states:
it is either active or inactive. We assume that to each node is assigned a
nonnegative (integer) threshold. The diffusion process is initiated by a subset
of nodes with threshold zero which consists of initially activated nodes,
whereas every other node is inactive. Subsequently, in each round, if an
inactive node with threshold $\theta$ has at least $\theta$ of its neighbours
activated, then it also becomes active and remains so forever. This is repeated
until no more nodes become activated. The main result of this paper provides a
central limit theorem for the final size of activated nodes. Namely, under
suitable assumptions on the degree and threshold distributions, we show that
the final size of activated nodes has asymptotically Gaussian fluctuations.


Abstract:
  A novel class of methods for combining $p$-values to perform aggregate
hypothesis tests has emerged that exploit the properties of heavy-tailed Stable
distributions. These methods offer important practical advantages including
robustness to dependence and better-than-Bonferroni scaleability, and they
reveal theoretical connections between Bayesian and classical hypothesis tests.
The harmonic mean $p$-value (HMP) procedure is based on the convergence of
summed inverse $p$-values to the Landau distribution, while the Cauchy
combination test (CCT) is based on the self-similarity of summed
Cauchy-transformed $p$-values. The CCT has the advantage that it is analytic
and exact. The HMP has the advantage that it emulates a model-averaged Bayes
factor, is insensitive to $p$-values near 1, and offers multilevel testing via
a closed testing procedure. Here I investigate whether other Stable combination
tests can combine these benefits, and identify a new method, the L\'evy
combination test (LCT). The LCT exploits the self-similarity of sums of L\'evy
random variables transformed from $p$-values. Under arbitrary dependence, the
LCT possesses better robustness than the CCT and HMP, with two-fold worst-case
inflation at small significance thresholds. It controls the strong-sense
familywise error rate through a multilevel test uniformly more powerful than
Bonferroni. Simulations show that the LCT behaves like Simes' test in some
respects, with power intermediate between the HMP and Bonferroni. The LCT
represents an interesting and attractive addition to combined testing methods
based on heavy-tailed distributions.


Abstract:
  We shall present with examples how analysis of astronomy data can be used for
an educational purpose to train students in methods of data analysis,
statistics, programming skills and research problems. Special reference will be
made to our IAU-OAD project `Astronomy from Archival Data' where we are in the
process of building a repository of instructional videos and reading material
for undergraduate and postgraduate students. Virtual Observatory tools will
also be discussed and applied. As this is an ongoing project, by the time of
the conference we will have the projects and work done by students included in
our presentation. The material produced can be freely used by the community.


Abstract:
  Maxwell's boundary conditions (MBCs) were long known insufficient to
determine the optical responses of spatially dispersive medium. Supplementing
MBCs with additional boundary conditions (ABCs) has become a normal yet
controversial practice. Here the problem of ABCs is solved by analyzing some
subtle aspects of a physical surface. A generic theory is presented for
handling the interaction of light with the surfaces of an arbitrary medium and
applied to study the traditional problem of exciton polaritons. We show that
ABCs can always be adjusted to fit the theory but they can by no means be
construed as intrinsic surface characteristics, which are instead captured by a
\textit{surface response function} (SRF). Unlike any ABCs, a SRF describes
essentially non-local boundary effects. Methods for experimentally extracting
the spatial profile of this function are proposed.


Abstract:
  These Monte Carlo studies describe the impact of higher order effects in both
QCD and EW $t\bar{t}W$ production. Both next-to-leading inclusive and multileg
setups are studied for $t\bar{t}W$ QCD production.


Abstract:
  In this paper we show how rederive the Bogomolny's equations of generalized
Maxwell-Chern-Simons-Higgs model presented in Ref. \cite{Bazeia:2012ux} by
using BPS Lagrangian method. We also show that the other results
(identification, potential terms, Gauss's law constraint) in there can be
obtained rigorously with a particular form of the BPS Lagrangian density. In
this method, we find that the potential terms are the most general form that
could have the BPS vortex solutions. The Gauss's law constraint turns out to be
the Euler-Lagrange equations of the BPS Lagrangian density. We also find
another BPS vortex solutions by taking other identification between the neutral
scalar field and the electric scalar potential field, $N=\pm A_0$, which is
different by a relative sign to the identification in Ref.
\cite{Bazeia:2012ux}, $N=\mp A_0$,. We find the BPS vortex solutions have
negative electric charge which are related to the corresponding BPS vortes
solutions in Ref. \cite{Bazeia:2012ux} by tranforming the neutral scalar field
$N\to-N$. Other possible choice of BPS Lagrangian density might give different
Bogomolny's equations and the form of potential terms which will be discussed
in another work.


Abstract:
  We introduce the magnon circular photogalvanic effect enabled by stimulated
Raman scattering. This provides an all-optical pathway to the generation of
directed magnon currents with circularly polarized light in honeycomb
antiferromagnetic insulators. The effect is the leading order contribution to
magnon photocurrent generation via optical fields. Control of the magnon
current by the polarization and angle of incidence of the laser is
demonstrated. Experimental detection by sizeable inverse spin Hall voltages in
platinum contacts is proposed.


Abstract:
  Let $F: T^{n} \times I \to T^{n}$ be a homotopy on a n-dimensional torus. The
main purpose of this paper is to present a formula for the one-parameter
Nielsen number $N(F)$ of $F$ in terms of induced homomorphism. If $L(F)$ is the
one-parameter Lefschetz class of $F$ then $L(F)$ is given by $L(F) = \pm
N(F)\alpha,$ for some $\alpha \in H_{1}(\pi_{1}(T^{n}),\mathbb{Z}).$


Abstract:
  Neutrino non-standard interactions (NSI) can be constrained using coherent
elastic neutrino-nucleus scattering. We discuss here two aspects in this
respect, namely the effects of (i) charged current NSI in neutrino production
and (ii) CP-violating phases associated with neutral current NSI in neutrino
detection. Effects of CP-phases require the simultaneous presence of two
different flavor-changing neutral current NSI parameters. Applying these two
scenarios to the COHERENT measurement, we derive limits on charged current NSI
and find that more data is required to compete with the existing limits.
Regarding CP-phases, we show how the limits on the NSI parameters depend
dramatically on the values of the phases. Accidentally, the same parameters
influencing coherent scattering also show up in neutrino oscillation
experiments. We find that COHERENT provides complementary constraints on the
set of NSI parameters that can explain the discrepancy in the best-fit value of
the standard CP-phase obtained by T2K and NO$\nu$A, while the significance with
which the LMA-Dark solution is ruled out can be weakened by the presence of
additional NSI parameters introduced here.


Abstract:
  Scientific digital libraries play a critical role in the development and
dissemination of scientific literature. Despite dedicated search engines,
retrieving relevant publications from the ever-growing body of scientific
literature remains challenging and time-consuming. Indexing scientific articles
is indeed a difficult matter, and current models solely rely on a small portion
of the articles (title and abstract) and on author-assigned keyphrases when
available. This results in a frustratingly limited access to scientific
knowledge. The goal of the DELICES project is to address this pitfall by
exploiting semantic relations between scientific articles to both improve and
enrich indexing. To this end, we will rely on the latest advances in semantic
representations to both increase the relevance of keyphrases extracted from the
documents, and extend indexing to new terms borrowed from semantically similar
documents.


Abstract:
  We give a brief account of the history of neutrino, and how that most aloof
of all particles has shaped our search for a theory of fundamental interactions
ever since it was theoretically proposed. We introduce the necessary concepts
and phenomena in a non-technical language aimed at a physicist with some basic
knowledge of quantum mechanics. In showing that neutrino mass could be the door
to new physics beyond the Standard Model, we emphasize the need to frame the
issue in the context of a complete theory, with testable predictions accessible
to present and near future experiments. We argue in favor of the Minimal
Left-Right Symmetric theory as the strongest candidate for such theory,
connecting neutrino mass with parity breakdown in nature. This is the theory
that led originally to neutrino mass and the seesaw mechanism behind its
smallness, but even more important, the theory that sheds light on a
fundamental question that touches us all: the symmetry between left and right.


Abstract:
  Contextuality and entanglement are valuable resources for quantum computing
and quantum information. Bell inequalities are used to certify entanglement;
thus, it is important to understand why and how they are violated. Quantum
mechanics and behavioral sciences teach us that random variables measuring the
same content (the answer to the same Yes or No question) may vary, if measured
jointly with other random variables. Alice and Bob raw data confirm Einsteinian
non-signaling, but setting dependent experimental protocols are used to create
samples of coupled pairs of distant outcomes and to estimate correlations.
Marginal expectations, estimated using these final samples, depend on distant
settings. Therefore, a system of random variables measured in Bell tests is
inconsistently connected and it should be analyzed using a
Contextuality-by-Default approach, what is done for the first time in this
paper. The violation of Bell inequalities and inconsistent connectedness may be
explained using a contextual locally causal probabilistic model in which
setting dependent variables describing measuring instruments are correctly
incorporated. We prove that this model does not restrict experimenters freedom
of choice which is a prerequisite of science. Contextuality seems to be the
rule and not an exception; thus, it should be carefully tested.


Abstract:
  The agent-based Yard-Sale model of wealth inequality is generalized to
incorporate exponential economic growth and its distribution. The distribution
of economic growth is nonuniform and is determined by the wealth of each agent
and a parameter $\lambda$. Our numerical results indicate that the model has a
critical point at $\lambda=1$ between a phase for $\lambda < 1$ with economic
mobility and exponentially growing wealth of all agents and a non-stationary
phase for $\lambda \geq 1$ with wealth condensation and no mobility. We define
the energy of the system and show that the system can be considered to be in
thermodynamic equilibrium for $\lambda < 1$. Our estimates of various critical
exponents are consistent with a mean-field theory (see following paper). The
exponents do not obey the usual scaling laws unless a combination of parameters
that we refer to as the Ginzburg parameter is held fixed as the transition is
approached. The model illustrates that both poorer and richer agents benefit
from economic growth if its distribution does not favor the richer agents too
strongly. This work and the accompanying theory paper contribute to
understanding whether the methods of equilibrium statistical mechanics can be
applied to economic systems.