title:
Merger Effects on the Spin and Shape Alignments of Galaxy Stellar, Cold
  and Hot Gas, and Dark Matter Components

abstract:
  We present a numerical evidence supporting the scenario that the peculiar
alignments of the galaxy stellar spins with the major principal axes of the
local tidal tensors are produced during the quiescent evolution period when the
galaxies experience no recent merger events. Analyzing the merger tree from the
TNG300-1 simulation of the IllustrisTNG project, we find the latest merger
epochs, $a(z_{m})$, of the galaxies, and create four $a(z_{m})$-selected
samples that are controlled to share the identical mass and density
distributions. For each sample, we determine the spin and shape vectors of the
galaxy stellar, cold and hot gas, and dark matter components separately, and
compute the average strengths of their alignments with the principal directions
of the local tidal fields as well as their mutual alignment tendencies. It is
found that the stellar (cold gas) spin axes of the galaxies whose latest merger
events occur at earlier epochs are more strongly aligned (weakly anti-aligned)
with the major principal axes of the tidal fields. It is also shown that
although the mass-dependent transition of the galaxy DM spins have little
connection with the merger events, the morphologies, spin-shape and shape-shear
alignment strengths of the galaxy four components sensitively depend on
$a(z_{m})$. Noting that the stellar components of the galaxies which undergo
long quiescent evolution have distinctively oblate shapes and very strong
spin-shape alignments, we suggest that the local tidal field might be traced by
using the stellar shapes of galaxies without signatures of mergers as a proxy
of their stellar spins.


title: Glueball molecules

abstract:
  Experimental searches for pure glueball states have proven challenging and so
far yielded no results. This is believed to occur because glueballs mix with
the ordinary $q\bar q$ states with the same quantum numbers. We will discuss an
alternative mechanism, the formation of the glueball-meson molecular states. We
will argue that the wave functions of already observed excited meson states may
contain a significant part due to such molecular states. We discuss the
phenomenology of glueball molecules and comment on a possible charmless
component of the $XYZ$ states.


title:
Optimization-Based Ramping Reserve Allocation of BESS for AGC
  Enhancement

abstract:
  This paper presents a novel scheme termed Optimization-based Ramping Reserve
Allocation (ORRA) for addressing an ongoing challenge in Automatic Generation
Control (AGC) enhancement, i.e., the optimal coordination of multiple Battery
Energy Storage Systems (BESSs). While exploiting further the synergy between
BESSs and slow ramping resources, the proposed scheme offers an insight into
the energy-neutral operation, which is achieved by smoothly discontinuing the
BESS participation along with the minimization of Area Injection Error (AIE), a
variant of traditional Area Control Error (ACE). The first stage of ORRA is to
incorporate Neural Networks (NNs) with the AIE in order to ensure a zero-mean
of ramping reserves to be allocated among BESSs. These AIE signals are then
used to formulate the optimal coordination of BESS as an online optimization
problem, which is therefore feedback-driven. Finally, a distributed
optimization algorithm is developed to solve the formulated problem in
real-time, achieving a sublinear dynamic regret that quantifies the cost
difference to the trajectory computed by a centralized optimizer with perfect
global information. Consistent with the geographical distribution of BESSs, the
proposed ORRA is fully distributed such that the algorithm can be executed in
parallel at all nodes. Simulations on a modified IEEE 14-bus system are
performed to illustrate the effectiveness and important features of ORRA.


title:
A jumping problem for quasilinear elliptic equations with right hand
  side measure

abstract:
  We consider a quasilinear elliptic equation with right hand side measure, in
which the lower order term has a behavior of jumping type. By means of
techniques of degree theory, we prove the existence of one or two entropy
solutions.


title:
Neural annealing and visualization of autoregressive neural networks in
  the Newman-Moore model

abstract:
  Artificial neural networks have been widely adopted as ansatzes to study
classical and quantum systems. However, some notably hard systems such as those
exhibiting glassiness and frustration have mainly achieved unsatisfactory
results despite their representational power and entanglement content, thus,
suggesting a potential conservation of computational complexity in the learning
process. We explore this possibility by implementing the neural annealing
method with autoregressive neural networks on a model that exhibits glassy and
fractal dynamics: the two-dimensional Newman-Moore model on a triangular
lattice. We find that the annealing dynamics is globally unstable because of
highly chaotic loss landscapes. Furthermore, even when the correct ground state
energy is found, the neural network generally cannot find degenerate
ground-state configurations due to mode collapse. These findings indicate that
the glassy dynamics exhibited by the Newman-Moore model caused by the presence
of fracton excitations in the configurational space likely manifests itself
through trainability issues and mode collapse in the optimization landscape.


title:
On the resolution and Linear programming problems subjected by
  Aczel-Alsina Fuzzy relational equations

abstract:
  Aczel-Alsina t-norm belongs to the family of strict t-norms that are the most
applied fuzzy operators in various fuzzy modelling problems. In this paper, we
study a linear optimization problem where the feasible region is formed as a
system of fuzzy relational equations (FRE) defined by the Aczel-Alsina t-norm.
Since the feasible solutions set of FREs is non-convex and the finding of all
minimal solutions is an NP-hard problem, conventional methods may not be
directly employed. The resolution of the feasible region is completely
investigated. Based on some theoretical properties of the problem, an algorithm
is presented to find all the optimal solutions, and finally an example is
described to illustrate this algorithm.


title: Quantum dynamics corresponding to the classical BKL scenario

abstract:
  We quantize the solution to the Belinski-Khalatnikov-Lifshitz (BKL) scenario
using the affine coherent states quantization method. Quantization smears the
gravitational singularity avoiding its localization in the configuration space.
Classical chaotic behaviour of the BKL scenario becomes enhanced at the quantum
level. Our results strongly suggest that the generic singularity of general
relativity can be avoided at quantum level giving support to the expectation
that quantum gravity has good chance to be a regular theory.


title:
Enabling High-Performance and Energy-Efficient Hybrid
  Transactional/Analytical Databases with Hardware/Software Cooperation

abstract:
  A growth in data volume, combined with increasing demand for real-time
analysis (using the most recent data), has resulted in the emergence of
database systems that concurrently support transactions and data analytics.
These hybrid transactional and analytical processing (HTAP) database systems
can support real-time data analysis without the high costs of synchronizing
across separate single-purpose databases. Unfortunately, for many applications
that perform a high rate of data updates, state-of-the-art HTAP systems incur
significant losses in transactional (up to 74.6%) and/or analytical (up to
49.8%) throughput compared to performing only transactional or only analytical
queries in isolation, due to (1) data movement between the CPU and memory, (2)
data update propagation from transactional to analytical workloads, and (3) the
cost to maintain a consistent view of data across the system.
  We propose Polynesia, a hardware-software co-designed system for in-memory
HTAP databases that avoids the large throughput losses of traditional HTAP
systems. Polynesia (1) divides the HTAP system into transactional and
analytical processing islands, (2) implements new custom hardware that unlocks
software optimizations to reduce the costs of update propagation and
consistency, and (3) exploits processing-in-memory for the analytical islands
to alleviate data movement overheads. Our evaluation shows that Polynesia
outperforms three state-of-the-art HTAP systems, with average
transactional/analytical throughput improvements of 1.7x/3.7x, and reduces
energy consumption by 48% over the prior lowest-energy HTAP system.


title: Impact of Change Granularity in Refactoring Detection

abstract:
  Detecting refactorings in commit history is essential to improve the
comprehension of code changes in code reviews and to provide valuable
information for empirical studies on software evolution. Several techniques
have been proposed to detect refactorings accurately at the granularity level
of a single commit. However, refactorings may be performed over multiple
commits because of code complexity or other real development problems, which is
why attempting to detect refactorings at single-commit granularity is
insufficient. We observe that some refactorings can be detected only at coarser
granularity, that is, changes spread across multiple commits. Herein, this type
of refactoring is referred to as coarse-grained refactoring (CGR). We compared
the refactorings detected on different granularities of commits from 19
open-source repositories. The results show that CGRs are common, and their
frequency increases as the granularity becomes coarser. In addition, we found
that Move-related refactorings tended to be the most frequent CGRs. We also
analyzed the causes of CGR and suggested that CGRs will be valuable in
refactoring research.


title:
Complex valued semi-linear heat equations in super-critical spaces
  $E^s_\sigma$

abstract:
  We consider the Cauchy problem for the complex valued semi-linear heat
equation $$
  \partial_t u - \Delta u - u^m =0, \ \ u (0,x) = u_0(x), $$ where $m\geq 2$ is
an integer and the initial data belong to super-critical spaces $E^s_\sigma$
for which the norms are defined by $$ \|f\|_{E^s_\sigma} = \|\langle
\xi\rangle^\sigma 2^{s|\xi|}\widehat{f}(\xi)\|_{L^2}, \ \ \sigma \in
\mathbb{R}, \ s<0. $$ If $s<0$, then any Sobolev space $H^{r}$ is a subspace of
$E^s_\sigma$, i.e., $\cup_{r \in \mathbb{R}} H^r \subset E^s_\sigma$. We obtain
the global existence and uniqueness of the solutions if the initial data belong
to $E^s_\sigma$ ($s<0, \ \sigma \geq d/2-2/(m-1)$) and their Fourier transforms
are supported in the first octant, the smallness conditions on the initial data
in $E^s_\sigma$ are not required for the global solutions. Moreover, we show
that the error between the solution $u$ and the iteration solution $u^{(j)}$ is
$C^j/(j\,!)^2$. Similar results also hold if the nonlinearity $u^m$ is replaced
by an exponential function $e^u-1$.


title:
Unsupervised Learning Discriminative MIG Detectors in Nonhomogeneous
  Clutter

abstract:
  Principal component analysis (PCA) is a common used pattern analysis method
that maps high-dimensional data into a lower-dimensional space maximizing the
data variance, that results in the promotion of separability of data. Inspired
by the principle of PCA, a novel type of learning discriminative matrix
information geometry (MIG) detectors in the unsupervised scenario are
developed, and applied to signal detection in nonhomogeneous environments.
Hermitian positive-definite (HPD) matrices can be used to model the sample
data, while the clutter covariance matrix is estimated by the geometric mean of
a set of secondary HPD matrices. We define a projection that maps the HPD
matrices in a high-dimensional manifold to a low-dimensional and more
discriminative one to increase the degree of separation of HPD matrices by
maximizing the data variance. Learning a mapping can be formulated as a
two-step mini-max optimization problem in Riemannian manifolds, which can be
solved by the Riemannian gradient descent algorithm. Three discriminative MIG
detectors are illustrated with respect to different geometric measures, i.e.,
the Log-Euclidean metric, the Jensen--Bregman LogDet divergence and the
symmetrized Kullback--Leibler divergence. Simulation results show that
performance improvements of the novel MIG detectors can be achieved compared
with the conventional detectors and their state-of-the-art counterparts within
nonhomogeneous environments.


title:
Soft photon propagation in a hot and dense medium to next-to-leading
  order

abstract:
  We present the first complete calculation of the soft photon self-energy to
the next-to-leading order in a hot and/or dense ultrarelativistic plasma in
Quantum Electrodynamics (QED). The calculation is performed within the
real-time formalism utilizing dimensional regularization in $4-2\epsilon$
dimensions, while the result is reported including explicit $O(\epsilon)$ terms
in the zero-temperature limit. This information is required to extend the
perturbative calculation of the pressure of cold and dense QED matter to
partial next-to-next-to-next-to-leading order in a weak-coupling expansion,
reported in a companion paper. These results pave the way for a similar future
calculation in Quantum Chromodynamics.


title:
Towards the Semantic Weak Generalization Problem in Generative Zero-Shot
  Learning: Ante-hoc and Post-hoc

abstract:
  In this paper, we present a simple and effective strategy lowering the
previously unexplored factors that limit the performance ceiling of generative
Zero-Shot Learning (ZSL). We begin by formally defining semantic
generalization, then look into approaches for reducing the semantic weak
generalization problem and minimizing its negative influence on classifier
training. In the ante-hoc phase, we augment the generator's semantic input, as
well as relax the fitting target of the generator. In the post-hoc phase (after
generating simulated unseen samples), we derive from the gradient of the loss
function to minimize the gradient increment on seen classifier weights carried
by biased unseen distribution, which tends to cause misleading on intra-seen
class decision boundaries. Without complicated designs, our approach hit the
essential problem and significantly outperform the state-of-the-art on four
widely used ZSL datasets.


title: A missing link in the nitrogen-rich organic chain on Titan

abstract:
  Context. The chemical building blocks of life contain a large proportion of
nitrogen, an essential element. Titan, the largest moon of Saturn, with its
dense atmosphere of molecular nitrogen and methane, offers an exceptional
opportunity to explore how this element is incorporated into carbon chains
through atmospheric chemistry in our Solar System. A brownish dense haze is
consistently produced in the atmosphere and accumulates on the surface on the
moon. This solid material is nitrogen-rich and may contain prebiotic molecules
carrying nitrogen. Aims. To date, our knowledge of the processes leading to the
incorporation of nitrogen into organic chains has been rather limited. In the
present work, we investigate the formation of nitrogen-bearing ions in an
experiment simulating Titan s upper atmosphere, with strong implications for
the incorporation of nitrogen into organic matter on Titan. Methods. By
combining experiments and theoretical calculations, we show that the abundant
N2+ ion, produced at high altitude by extreme-ultraviolet solar radiation, is
able to form nitrogen-rich organic species. Results. An unexpected and
important formation of CH3N2+ and CH2N2+ diazo-ions is experimentally observed
when exposing a gas mixture composed of molecular nitrogen and methane to
extreme-ultraviolet radiation. Our theoretical calculations show that these
diazo-ions are mainly produced by the reaction of N2+ with CH3 radicals. These
small nitrogen-rich diazo-ions, with a N/C ratio of two, appear to be a missing
link that could explain the high nitrogen content in Titan s organic matter.
More generally, this work highlights the importance of reactions between ions
and radicals, which have rarely been studied thus far, opening up new
perspectives in astrochemistry.


title: Facility Location with Entrance Fees

abstract:
  In mechanism design, the facility location game is an extensively studied
problem. In the classical model, the cost of each agent is her distance to the
nearest facility. In this paper, we consider a new model, where there is a
location-dependent entrance fee to the facility. Thus, in our model, the cost
of each agent is the sum of the distance to the facility and the entrance fee
of the facility. This is a refined generalization of the classical model. We
study the model and design strategyproof mechanisms. For one and two
facilities, we provide upper and lower bounds for the approximation ratio given
by deterministic and randomized mechanisms, with respect to the utilitarian
objective and the egalitarian objective. Most of our bounds are tight and these
bounds are independent of the entrance fee functions. Our results are as
general as possible because the entrance fee function we consider is arbitrary.


title: Bounds on the closeness centrality of a graph

abstract:
  We present new bounds on the (normalised) closeness centrality
$\bar{\mathsf{C}}_C$ of connected graphs. The main result is the fundamental
and elegant new bound $\bar{\mathsf{C}}_C\bar{l}\geq 1$ relating the closeness
centrality to the mean distance $\bar{l}$ of a graph. Combining this bound with
known upper bounds on the mean distance, we find ten new lower bounds for the
closeness centrality of graphs, as well as new lower bounds for
self-complementary graphs, 2-connected graphs, and trees. We also provide
explicit expressions for the closeness centralities of twelve specific families
of graphs, as well as the closeness centralities for ten of these graphs.
Finally, we provide asymptotically tight lower and upper bounds for path graphs
$P_n$ and ladder graphs $L_n$. Elegantly and perhaps surprisingly, the
asymptotic limits of $n\bar{\mathsf{C}}_C\big(P_n\big)$ and of
$n\bar{\mathsf{C}}_C\big(L_n\big)$ both equal $\pi$.


title:
Self-embeddings of models of arithmetic; fixed points, small submodels,
  and extendability

abstract:
  In this paper we will show that for every cut $ I $ of any countable
nonstandard model $ \mathcal{M} $ of $ \mathrm{I}\Sigma_{1} $, each $ I $-small
$ \Sigma_{1} $-elementary submodel of $ \mathcal{M}$ is of the form of the set
of fixed points of some proper initial self-embedding of $ \mathcal{M} $ iff $
I $ is a strong cut of $ \mathcal{M} $. Especially, this feature will provide
us with some equivalent conditions with the strongness of the standard cut in a
given countable model $ \mathcal{M} $ of $ \mathrm{I}\Sigma_{1} $. In addition,
we will find some criteria for extendability of initial self-embeddings of
countable nonstandard models of $ \mathrm{I}\Sigma_{1} $ to larger models.


title: Computing the Collection of Good Models for Rule Lists

abstract:
  Since the seminal paper by Breiman in 2001, who pointed out a potential harm
of prediction multiplicities from the view of explainable AI, global analysis
of a collection of all good models, also known as a `Rashomon set,' has been
attracted much attention for the last years. Since finding such a set of good
models is a hard computational problem, there have been only a few algorithms
for the problem so far, most of which are either approximate or incomplete. To
overcome this difficulty, we study efficient enumeration of all good models for
a subclass of interpretable models, called rule lists. Based on a
state-of-the-art optimal rule list learner, CORELS, proposed by Angelino et al.
in 2017, we present an efficient enumeration algorithm CorelsEnum for exactly
computing a set of all good models using polynomial space in input size, given
a dataset and a error tolerance from an optimal model. By experiments with the
COMPAS dataset on recidivism prediction, our algorithm CorelsEnum successfully
enumerated all of several tens of thousands of good rule lists of length at
most $\ell = 3$ in around 1,000 seconds, while a state-of-the-art top-$K$ rule
list learner based on Lawler's method combined with CORELS, proposed by Hara
and Ishihata in 2018, found only 40 models until the timeout of 6,000 seconds.
For global analysis, we conducted experiments for characterizing the Rashomon
set, and observed large diversity of models in predictive multiplicity and
fairness of models.


title:
Improved far-field speech recognition using Joint Variational
  Autoencoder

abstract:
  Automatic Speech Recognition (ASR) systems suffer considerably when source
speech is corrupted with noise or room impulse responses (RIR). Typically,
speech enhancement is applied in both mismatched and matched scenario training
and testing. In matched setting, acoustic model (AM) is trained on
dereverberated far-field features while in mismatched setting, AM is fixed. In
recent past, mapping speech features from far-field to close-talk using
denoising autoencoder (DA) has been explored. In this paper, we focus on
matched scenario training and show that the proposed joint VAE based mapping
achieves a significant improvement over DA. Specifically, we observe an
absolute improvement of 2.5% in word error rate (WER) compared to DA based
enhancement and 3.96% compared to AM trained directly on far-field filterbank
features.


title: Real Kaehler submanifolds in codimension up to four

abstract:
  Let $f\colon M^{2n}\to\mathbb{R}^{2n+4}$ be an isometric immersion of a
Kaehler manifold of complex dimension $n\geq 5$ into Euclidean space with
complex rank at least $5$ everywhere. Our main result is that, along each
connected component of an open dense subset of $M^{2n}$, either $f$ is
holomorphic in $\mathbb{R}^{2n+4}\cong\mathbb{C}^{n+2}$ or it is in a unique
way a composition $f=F\circ h$ of isometric immersions. In the latter case, we
have that $h\colon M^{2n}\to N^{2n+2}$ is holomorphic and $F\colon
N^{2n+2}\to\mathbb{R}^{2n+4}$ belongs to the class, by now quite well
understood, of non-holomorphic Kaehler submanifold in codimension two.
Moreover, the submanifold $F$ is minimal if and only if $f$ is minimal.


title: Idempotents, free products and quandle coverings

abstract:
  In this paper we investigate idempotents in quandle rings and relate them
with quandle coverings. We prove that integral quandle rings of non-trivial
involutory coverings over nice base quandles have infinitely many non-trivial
idempotents and give their complete description. We show that the set of all
these idempotents forms a quandle in itself. As an application, we deduce that
the quandle ring of the knot quandle of a non-trivial long knot admit
non-trivial idempotents. We consider free products of quandles and prove that
integral quandle rings of free quandles have only trivial idempotents, giving
an infinite family of quandles with this property. We also give a description
of idempotents in quandle rings of unions and certain twisted unions of
quandles.


title: Complexity and Avoidance

abstract:
  In this dissertation we examine the relationships between the several
hierarchies, including the complexity, $\mathrm{LUA}$ (Linearly Universal
Avoidance), and shift complexity hierarchies, with an eye towards quantitative
bounds on growth rates therein. We show that for suitable $f$ and $p$, there
are $q$ and $g$ such that $\mathrm{LUA}(q) \leq_\mathrm{s} \mathrm{COMPLEX}(f)$
and $\mathrm{COMPLEX}(g) \leq_\mathrm{s} \mathrm{LUA}(p)$, as well as quantify
the growth rates of $q$ and $g$. In the opposite direction, we show that for
certain sub-identical $f$ satisfying $\lim_{n \to \infty}{f(n)/n}=1$ there is a
$q$ such that $\mathrm{COMPLEX}(f) \leq_\mathrm{w} \mathrm{LUA}(q)$, and for
certain fast-growing $p$ there is a $g$ such that $\mathrm{LUA}(p)
\leq_\mathrm{s} \mathrm{COMPLEX}(g)$, as well as quantify the growth rates of
$q$ and $g$.
  Concerning shift complexity, explicit bounds are given on how slow-growing
$q$ must be for any member of $\rm{LUA}(q)$ to compute $\delta$-shift complex
sequences. Motivated by the complexity hierarchy, we generalize the notion of
shift complexity to consider sequences $X$ satisfying $\operatorname{KP}(\tau)
\geq f(|\tau|) - O(1)$ for all substrings $\tau$ of $X$ where $f$ is any order
function. We show that for sufficiently slow-growing $f$, $f$-shift complex
sequences can be uniformly computed by $g$-complex sequences, where $g$ grows
slightly faster than $f$.
  The structure of the $\mathrm{LUA}$ hierarchy is examined using bushy tree
forcing, with the main result being that for any order function $p$, there is a
slow-growing order function $q$ such that $\mathrm{LUA}(p)$ and
$\mathrm{LUA}(q)$ are weakly incomparable. Using this, we prove new results
about the filter of the weak degrees of deep nonempty $\Pi^0_1$ classes and the
connection between the shift complexity and $\mathrm{LUA}$ hierarchies.


title:
Periodic Lp estimates by R-boundedness: Applications to the
  Navier-Stokes equations

abstract:
  General evolution equations in Banach spaces are investigated. Based on an
operator-valued version of de Leeuw's transference principle, time-periodic
$L^p$ estimates of maximal regularity type are established from
$\mathscr{R}$-bounds of the family of solution operators
($\mathscr{R}$-solvers) to the corresponding resolvent problems. With this
method, existence of time-periodic solutions to the Navier-Stokes equations is
shown for two configurations: in a periodically moving bounded domain and in an
exterior domain, subject to prescribed time-periodic forcing and boundary data.


title:
Large Scale Time-Series Representation Learning via Simultaneous Low and
  High Frequency Feature Bootstrapping

abstract:
  Learning representation from unlabeled time series data is a challenging
problem. Most existing self-supervised and unsupervised approaches in the
time-series domain do not capture low and high-frequency features at the same
time. Further, some of these methods employ large scale models like
transformers or rely on computationally expensive techniques such as
contrastive learning. To tackle these problems, we propose a non-contrastive
self-supervised learning approach efficiently captures low and high-frequency
time-varying features in a cost-effective manner. Our method takes raw time
series data as input and creates two different augmented views for two branches
of the model, by randomly sampling the augmentations from same family.
Following the terminology of BYOL, the two branches are called online and
target network which allows bootstrapping of the latent representation. In
contrast to BYOL, where a backbone encoder is followed by multilayer perceptron
(MLP) heads, the proposed model contains additional temporal convolutional
network (TCN) heads. As the augmented views are passed through large kernel
convolution blocks of the encoder, the subsequent combination of MLP and TCN
enables an effective representation of low as well as high-frequency
time-varying features due to the varying receptive fields. The two modules (MLP
and TCN) act in a complementary manner. We train an online network where each
module learns to predict the outcome of the respective module of target network
branch. To demonstrate the robustness of our model we performed extensive
experiments and ablation studies on five real-world time-series datasets. Our
method achieved state-of-art performance on all five real-world datasets.


title: Entropic Risk-Averse Generalized Momentum Methods

abstract:
  In the context of first-order algorithms subject to random gradient noise, we
study the trade-offs between the convergence rate (which quantifies how fast
the initial conditions are forgotten) and the "risk" of suboptimality, i.e.
deviations from the expected suboptimality. We focus on a general class of
momentum methods (GMM) which recover popular methods such as gradient descent
(GD), accelerated gradient descent (AGD), and heavy-ball (HB) method as special
cases depending on the choice of GMM parameters. We use well-known risk
measures "entropic risk" and "entropic value at risk" to quantify the risk of
suboptimality. For strongly convex smooth minimization, we first obtain new
convergence rate results for GMM with a unified theory that is also applicable
to both AGD and HB, improving some of the existing results for HB. We then
provide explicit bounds on the entropic risk and entropic value at risk of
suboptimality at a given iterate which also provides direct bounds on the
probability that the suboptimality exceeds a given threshold based on
Chernoff's inequality. Our results unveil fundamental trade-offs between the
convergence rate and the risk of suboptimality. We then plug the entropic risk
and convergence rate estimates we obtained in a computationally tractable
optimization framework and propose entropic risk-averse GMM (RA-GMM) and
entropic risk-averse AGD (RA-AGD) methods which can select the GMM parameters
to systematically trade-off the entropic value at risk with the convergence
rate. We show that RA-AGD and RA-GMM lead to improved performance on quadratic
optimization and logistic regression problems compared to the standard choice
of parameters. To our knowledge, our work is the first to resort to coherent
measures to design the parameters of momentum methods in a systematic manner.


title:
The Quadrupole Moment of Compact Binaries to the Fourth post-Newtonian
  Order: From Source to Canonical Moment

abstract:
  As a crucial step towards the completion of the fourth post-Newtonian (4PN)
gravitational-wave generation from compact binary systems, we obtain the
expressions of the so-called "canonical" multipole moments of the source in
terms of the "source" and "gauge" moments. The canonical moments describe the
propagation of gravitational waves outside the source's near zone, while the
source and gauge moments encode explicit information about the matter source.
Those two descriptions, in terms of two sets of canonical moments or in terms
of six sets of source and gauge moments, are isometric. We thus construct the
non-linear diffeomorphism between them up to the third post-Minkowskian order,
and we exhibit the concrete expression of the canonical mass-type quadrupole
moment at the 4PN order. This computation is one of the last missing pieces for
the determination of the gravitational-wave phasing of compact binary systems
at 4PN order.


title:
Colorectal cancer survival prediction using deep distribution based
  multiple-instance learning

abstract:
  Several deep learning algorithms have been developed to predict survival of
cancer patients using whole slide images (WSIs).However, identification of
image phenotypes within the WSIs that are relevant to patient survival and
disease progression is difficult for both clinicians, and deep learning
algorithms. Most deep learning based Multiple Instance Learning (MIL)
algorithms for survival prediction use either top instances (e.g., maxpooling)
or top/bottom instances (e.g., MesoNet) to identify image phenotypes. In this
study, we hypothesize that wholistic information of the distribution of the
patch scores within a WSI can predict the cancer survival better. We developed
a distribution based multiple-instance survival learning algorithm
(DeepDisMISL) to validate this hypothesis. We designed and executed experiments
using two large international colorectal cancer WSIs datasets - MCO CRC and
TCGA COAD-READ. Our results suggest that the more information about the
distribution of the patch scores for a WSI, the better is the prediction
performance. Including multiple neighborhood instances around each selected
distribution location (e.g., percentiles) could further improve the prediction.
DeepDisMISL demonstrated superior predictive ability compared to other recently
published, state-of-the-art algorithms. Furthermore, our algorithm is
interpretable and could assist in understanding the relationship between cancer
morphological phenotypes and patients cancer survival risk.


title: Quark Nuclear Physics with Heavy Quarks

abstract:
  Heavy quarks have been instrumental for progress in our exploration of strong
interactions. Quarkonium in particular, a heavy quark-antiquark nonrelativistic
bound state, has been at the root of several revolutions. Quarkonium is endowed
with a pattern of separated energy scales qualifying it as special probe of
complex environments. Its multiscale nature has made a description in Quantum
Field Theory particularly difficult up to the advent of Nonrelativistic
Effective Field Theories. We will focus on systems made by two or more heavy
quarks. After considering some historical approaches based on the potential
models and the Wilson loop approach, we will introduce the contemporary
nonrelativistic effective field theory descriptions, in particular potential
Nonrelativistic QCD which entails the Schoedinger equation as zero order
problem, define the potentials as matching coefficients and allows systematic
calculations of the physical properties. The effective field theory allows us
to explore quarkonium properties in the realm of QCD. In particular it allows
us to make calculations with unprecedented precision when high order
perturbative calculations are possible and to systematically factorize short
from long range contributions where observables are sensitive to the
nonperturbative dynamics of QCD. Such effective field theory treatment can be
extended at finite temperature and in presence of gluonic and light quark
excitations. We will show that in this novel theoretical framework, quarkonium
can play a crucial role for a number of problems at the frontier of our
research, from the investigation of the confinement dynamics in strong
interactions to the study of deconfinement and the phase diagram of nuclear
matter, to the precise determination of Standard Model parameters up to the
emergence of exotics X Y Z states of an unprecedented nature.


title:
Many-body localization of one-dimensional degenerate Fermi gases with
  cavity-assisted non-local quasiperiodic interactions

abstract:
  The localization properties of one-dimensional degenerate Fermi gases with
cavity-assisted non-local quasiperiodic interactions are numerically studied.
Although the cavity-induced interaction is typically nonlocal, it is proved
that the weak eigenstate thermalization hypothesis (ETH) is still applicable in
our system depending on the system parameters. We also find the segment of the
spectrum corresponding to infinite temperature varies for different system
parameters, which indicates the spectral range employed in the spectral
statistical analysis should be varied accordingly. The features of many-body
localization (MBL) are numerically identified by analyzing the spectral
statistics and the entanglement entropy using exact diagonalization. These
features are further confirmed by our time evolution results. In addition, the
number of cavity photons are found stable over long time dynamics in the MBL
phase. Such a feature can not only be utilized to nondestructively diagnose the
MBL phase by monitoring the number of leaking photons from the cavity, but
leveraged for constructing a device to produce a stable number of photons.


title: Planar Prop of Differential Operators

abstract:
  We propose a definition of differential operators of an associative algebra
$A$ in the spirit of Hochschild cohomology. Specifically we define $D(A)$ as
the zero cohomology of a certain bicomplex formed by Hom-spaces
$\mathrm{Hom}(A^{\otimes q}, A^{\otimes p})$. We show that it has a structure
of a planar prop, i.e. each differential operator has multiple inputs and
outputs and they can be composed along planar graphs. Furthermore, for a
formally smooth algebra we have the surjective symbol map from $D(A)$ to the
space of poly-derivations. We also consider another planar prop $E(A)$
generated by automorphisms of the trivial associative deformation of $A$ over
the completion of a free associative algebra. We construct a natural map from
$E(A)$ to $D(A)$ and identify its image.


title: Constructive Combinatorics of Dickson's Lemma

abstract:
  We study constructively the relations between the finite cases of Dickson's
lemma. Although there are many constructive proofs of them, the novel aspect of
our proofs is the extraction of a corresponding bound. We provide some new
one-step unprovability results i.e., results of the form "a finite case of
Dickson's lemma does not prove in one step a stronger case of it". Moreover, we
study the infinite cases of Dickson's lemma from the point of view of
constructive reverse mathematics. We work within Bishop's informal system of
constructive mathematics BISH.


title: Extensions of the Schur majorisation inequalities

abstract:
  We obtain some inequalities which are stronger than the Schur majorization
inequalities.


title: Is Universal Broadband Service Impossible?

abstract:
  Broadband Internet service is widely expected to be the fundamental universal
service for the 21st century. But more than a decade of national and
international struggles to close the digital divide between broadband haves and
have nots suggest that reaching global universality will be a very difficult
task. This paper argues that the strong guarantees made by the current
broadband paradigm - low latency and constant availability - are unnecessary
obstacles to its adoption as an affordable and universal digital service. We
show that there is nonetheless a plausible strategy for deploying a Basic
Broadband service that does not require such guarantees and is able to offer,
at reasonable cost, almost all the critical and valuable services and
applications currently delivered over low latency broadband, synchronous
telepresence excepted.


title: Satellite Image Time Series Analysis for Big Earth Observation Data

abstract:
  The development of analytical software for big Earth observation data faces
several challenges. Designers need to balance between conflicting factors.
Solutions that are efficient for specific hardware architectures can not be
used in other environments. Packages that work on generic hardware and open
standards will not have the same performance as dedicated solutions. Software
that assumes that its users are computer programmers are flexible but may be
difficult to learn for a wide audience. This paper describes sits, an
open-source R package for satellite image time series analysis using machine
learning. To allow experts to use satellite imagery to the fullest extent, sits
adopts a time-first, space-later approach. It supports the complete cycle of
data analysis for land classification. Its API provides a simple but powerful
set of functions. The software works in different cloud computing environments.
Satellite image time series are input to machine learning classifiers, and the
results are post-processed using spatial smoothing. Since machine learning
methods need accurate training data, sits includes methods for quality
assessment of training samples. The software also provides methods for
validation and accuracy measurement. The package thus comprises a production
environment for big EO data analysis. We show that this approach produces high
accuracy for land use and land cover maps through a case study in the Cerrado
biome, one of the world's fast moving agricultural frontiers for the year 2018.


title: A Generic Approach to Quantitative Verification

abstract:
  This thesis is concerned with quantitative verification, that is, the
verification of quantitative properties of quantitative systems. These systems
are found in numerous applications, and their quantitative verification is
important, but also rather challenging. In particular, given that most systems
found in applications are rather big, compositionality and incrementality of
verification methods are essential.
  In order to ensure robustness of verification, we replace the Boolean yes-no
answers of standard verification with distances. Depending on the application
context, many different types of distances are being employed in quantitative
verification. Consequently, there is a need for a general theory of system
distances which abstracts away from the concrete distances and develops
quantitative verification at a level independent of the distance. It is our
view that in a theory of quantitative verification, the quantitative aspects
should be treated just as much as input to a verification problem as the
qualitative aspects are. In this work we develop such a general theory of
quantitative verification. We assume as input a distance between traces, or
executions, and then employ the theory of games with quantitative objectives to
define distances between quantitative systems. Different versions of the
quantitative bisimulation game give rise to different types of distances,
viz.~bisimulation distance, simulation distance, trace equivalence distance,
etc., enabling us to construct a quantitative generalization of van Glabbeek's
linear-time--branching-time spectrum. We also extend our general theory of
quantitative verification to a theory of quantitative specifications. For this
we use modal transition systems, and we develop the quantitative properties of
the usual operators for behavioral specification theories.


title: A generalization of Alperin fusion theorem and its applications

abstract:
  Let $\mathcal F$ be a saturated fusion system on a finite $p$-group $S$, and
let $P$ be a strongly $\mathcal F$-closed subgroup of $S$. We define the
concept "$\mathcal F$-essential subgroups with respect to $P$", and show that
an $\mathcal F$-isomorphism between subgroups of $P$ can be factorised by some
automorphisms of $P$ and $\mathcal F$-essential subgroups with respect to $P$.
When $P$ is taken to be equal $S$, Alperin-Goldschmidt fusion theorem can be
obtained as a special case. We also show that $P\unlhd \mathcal F$ if and only
if there is no $\mathcal F$-essential subgroup with respect to $P$. The
following definition is made: a $p$-group $P$ is strongly resistant in
saturated fusion systems whenever there is an over $p$-group $S$ and a
saturated fusion system $\mathcal F$ on $S$ such that $P$ is strongly $\mathcal
F$-closed, $P\unlhd \mathcal F$. It is shown that several classes of $p$-groups
are strongly resistant, which appears as our second main theorem. We also give
a new necessary and sufficient criteria for a strongly $\mathcal F$-closed
subgroup to be normal in $\mathcal F$. These results are obtained as a
consequences of developing a theory of quasi and semi-saturated fusion systems,
which seems to be interesting for its own right.


title: Dictionary Attacks on Speaker Verification

abstract:
  In this paper, we propose dictionary attacks against speaker verification - a
novel attack vector that aims to match a large fraction of speaker population
by chance. We introduce a generic formulation of the attack that can be used
with various speech representations and threat models. The attacker uses
adversarial optimization to maximize raw similarity of speaker embeddings
between a seed speech sample and a proxy population. The resulting master voice
successfully matches a non-trivial fraction of people in an unknown population.
Adversarial waveforms obtained with our approach can match on average 69% of
females and 38% of males enrolled in the target system at a strict decision
threshold calibrated to yield false alarm rate of 1%. By using the attack with
a black-box voice cloning system, we obtain master voices that are effective in
the most challenging conditions and transferable between speaker encoders. We
also show that, combined with multiple attempts, this attack opens even more to
serious issues on the security of these systems.


title:
Autour de la d\'ecomposition des alg\`ebres d'exposant 2 sur les
  extensions multiquadratiques

abstract:
  For central simple algebras of exponent $2$ over fields of characteristic $2$
and $2$-cohomological dimension equal to $2$, we study the adapted
decomposition to some multiquadratic extensions of the base field. Several
remarkable properties are extended to multiquadratic extensions of separability
degree at most $4$. We also extend to the characteristic $2$ a result of
Elman-Lam-Tignol-Wadsworth by constructing an algebra of exponent $2$ and
degree $8$ containing a separable triquadratic extension but which admits no
adapted decomposition to this extension. As an application we give an
elementary proof of the non-excellence of separable biquadratic extensions.


title: Socle of Hamiltonian Group

abstract:
  The socle of a group $G$ is the subgroup generated by all minimal normal
subgroups of $G$. In this short note, we determine the socle of a Hamiltonian
group explicitly.


title:
A Comprehensive Test Pattern Generation Approach Exploiting SAT Attack
  for Logic Locking

abstract:
  The need for reducing manufacturing defect escape in today's safety-critical
applications requires increased fault coverage. However, generating a test set
using commercial automatic test pattern generation (ATPG) tools that lead to
zero-defect escape is still an open problem. It is challenging to detect all
stuck-at faults to reach 100% fault coverage. In parallel, the hardware
security community has been actively involved in developing solutions for logic
locking to prevent IP piracy. Locks (e.g., XOR gates) are inserted in different
locations of the netlist so that an adversary cannot determine the secret key.
Unfortunately, the Boolean satisfiability (SAT) based attack, introduced in
[1], can break different logic locking schemes in minutes. In this paper, we
propose a novel test pattern generation approach using the powerful SAT attack
on logic locking. A stuck-at fault is modeled as a locked gate with a secret
key. Our modeling of stuck-at faults preserves the property of fault activation
and propagation. We show that the input pattern that determines the key is a
test for the stuck-at fault. We propose two different approaches for test
pattern generation. First, a single stuck-at fault is targeted, and a
corresponding locked circuit with one key bit is created. This approach
generates one test pattern per fault. Second, we consider a group of faults and
convert the circuit to its locked version with multiple key bits. The inputs
obtained from the SAT tool are the test set for detecting this group of faults.
Our approach is able to find test patterns for hard-to-detect faults that were
previously failed in commercial ATPG tools. The proposed test pattern
generation approach can efficiently detect redundant faults present in a
circuit. We demonstrate the effectiveness of the approach on ITC'99 benchmarks.
The results show that we can achieve a perfect fault coverage reaching 100%.


title: Verbal quandles with one parameter

abstract:
  We find all words $W(x,y,z)$ in the free group $F(x,y,z)$, such that for
every group $G$ and an element $c\in G$ the algebraic system $(G,*_{W,c})$ with
the binary operation $*_{W,c}$ given by $a*_{W,c}b=W(a,b,c)$ for $a,b\in G$ is
a quandle. Such quandles are called verbal quandles with one parameter.


title:
A new particle approximation to the diffusive Dean-Kawasaki equation
  with colored noise

abstract:
  Following the idea of Q-Wiener process on Wasserstein space, we construct a
new particle approximation to the regularised martingale solution of the
diffusive Dean-Kawasaki equation on 1-D Torus with colored noise, whose spatial
correlated intensity is larger than 1.


title: Experimental optimal direction guessing with abstention

abstract:
  Sophisticated quantum measurements are fundamental to obtain a quantum
advantage in many informational problems. Here, we consider the task of
guessing a direction encoded in a two-qubit pure state. We experimentally
demonstrate that abstention can be used to recover optimal direction guessing
(measured in terms of the fidelity and maximum likelihood scores) even from non
ideal states. Our protocol uses nine-step photonic quantum walks to implement
the optimal five-output two-qubit collective measurements with fidelities above
0.9850. Thanks to abstention, we obtain more than a 10-fold improvement of the
direction guessing scores (in terms of deviation to the optimal guessing
scores). Our work demonstrates the versatility of photonic quantum walks for
implementing many-qubit sophisticated measurements.


title:
Polarization-independent resonant lattice Kerker effect enabled by
  phase-change material

abstract:
  Resonant lattice Kerker effect in periodic resonators is one of the most
interesting generalizations of the Kerker effect that relates to various
fascinating functionalities such as scattering management and Huygens
metasurfaces. However, so far this effect has been shown to be sensitive to the
incident polarization, restricting its applications. Here, we report, for the
first time, polarization-independent resonant lattice Kerker effect in
metasurfaces composed of periodic Ge$_2$Se$_2$Te$_5$ (GST) disks. For such a
metasurface of square lattice, the spectrally overlap of the electric dipole
and magnetic dipole surface lattice resonances can be realized by choosing an
appropriate GST crystalline fraction regardless of the incident polarization.
The operation wavelength and the required GST crystalline fraction can be
conveniently tuned over large ranges since these parameters scale linearly with
the disk size and the lattice period, greatly facilitating the design. Making
use of the obtained resonant lattice Kerker effect, we realize a reconfigurable
and polarization-independent lattice Huygens' metasurface with a dynamic phase
modulation of close to $2\pi$ and high transmittance. This work will advance
the engineering of the resonant lattice Kerker effect and promote its
applications in phase modulation and wavefront control.


title: EMOCA: Emotion Driven Monocular Face Capture and Animation

abstract:
  As 3D facial avatars become more widely used for communication, it is
critical that they faithfully convey emotion. Unfortunately, the best recent
methods that regress parametric 3D face models from monocular images are unable
to capture the full spectrum of facial expression, such as subtle or extreme
emotions. We find the standard reconstruction metrics used for training
(landmark reprojection error, photometric error, and face recognition loss) are
insufficient to capture high-fidelity expressions. The result is facial
geometries that do not match the emotional content of the input image. We
address this with EMOCA (EMOtion Capture and Animation), by introducing a novel
deep perceptual emotion consistency loss during training, which helps ensure
that the reconstructed 3D expression matches the expression depicted in the
input image. While EMOCA achieves 3D reconstruction errors that are on par with
the current best methods, it significantly outperforms them in terms of the
quality of the reconstructed expression and the perceived emotional content. We
also directly regress levels of valence and arousal and classify basic
expressions from the estimated 3D face parameters. On the task of in-the-wild
emotion recognition, our purely geometric approach is on par with the best
image-based methods, highlighting the value of 3D geometry in analyzing human
behavior. The model and code are publicly available at
https://emoca.is.tue.mpg.de.


title:
Dyonically Charged Black Holes Arising in Generalized Born--Infeld
  Theory of Electromagnetism

abstract:
  Black hole solutions to the Einstein equations coupled with the Born--Infeld
electromagnetism associated with generalized nonlinear electrodynamics,
carrying both electric and magnetic charges, often referred to as dyonically
charged black holes, of finite energies, are constructed. These solutions give
rise to relegated curvature singularities at the center of the matter sources
of the black holes and approach the classical Reissner--Nordstrom black hole
asymptotically.


title: Faster Learned Sparse Retrieval with Guided Traversal

abstract:
  Neural information retrieval architectures based on transformers such as BERT
are able to significantly improve system effectiveness over traditional sparse
models such as BM25. Though highly effective, these neural approaches are very
expensive to run, making them difficult to deploy under strict latency
constraints. To address this limitation, recent studies have proposed new
families of learned sparse models that try to match the effectiveness of
learned dense models, while leveraging the traditional inverted index data
structure for efficiency. Current learned sparse models learn the weights of
terms in documents and, sometimes, queries; however, they exploit different
vocabulary structures, document expansion techniques, and query expansion
strategies, which can make them slower than traditional sparse models such as
BM25. In this work, we propose a novel indexing and query processing technique
that exploits a traditional sparse model's "guidance" to efficiently traverse
the index, allowing the more effective learned model to execute fewer scoring
operations. Our experiments show that our guided processing heuristic is able
to boost the efficiency of the underlying learned sparse model by a factor of
four without any measurable loss of effectiveness.


title: Compression-Based Optimizations for Out-of-Core GPU Stencil Computation

abstract:
  An out-of-core stencil computation code handles large data whose size is
beyond the capacity of GPU memory. Whereas, such an code requires streaming
data to and from the GPU frequently. As a result, data movement between the CPU
and GPU usually limits the performance. In this work, compression-based
optimizations are proposed. First, an on-the-fly compression technique is
applied to an out-of-core stencil code, reducing the CPU-GPU memory copy.
Secondly, a single working buffer technique is used to reduce GPU memory
consumption. Experimental results show that the stencil code using the proposed
techniques achieved 1.1x speed and reduced GPU memory consumption by 33.0\% on
an NVIDIA Tesla V100 GPU.


title:
Variance expansion and Berry-Esseen bound for the number of vertices of
  a random polygon in a polygon

abstract:
  Fix a container polygon $P$ in the plane and consider the convex hull $P_n$
of $n\geq 3$ independent and uniformly distributed in $P$ random points. In the
focus of this paper is the vertex number of the random polygon $P_n$. The
precise variance expansion for the vertex number is determined up to the
constant-order term, a result which can be considered as a second-order
analogue of the classical expansion for the expectation of R\'enyi and Sulanke
(1963). Moreover, a sharp Berry-Esseen bound is derived for the vertex number
of the random polygon $P_n$, which is of the same order as the square-root of
the variance. The main idea behind the proof of both results is a decomposition
of the boundary of the random polygon $P_n$ into random convex chains and a
careful merging of the variance expansions and Berry-Esseen bounds for the
vertex numbers of the individual chains.


title: A Markovian Model for the Spread of the SARS-CoV-2 Virus

abstract:
  We propose a Markovian stochastic approach to model the spread of a
SARS-CoV-2-like infection within a closed group of humans. The model takes the
form of a Partially Observable Markov Decision Process (POMDP), whose states
are given by the number of subjects in different health conditions. The model
also exposes the different parameters that have an impact on the spread of the
disease and the various decision variables that can be used to control it (e.g,
social distancing, number of tests administered to single out infected
subjects). The model describes the stochastic phenomena that underlie the
spread of the epidemic and captures, in the form of deterministic parameters,
some fundamental limitations in the availability of resources (hospital beds
and test swabs). The model lends itself to different uses. For a given control
policy, it is possible to verify if it satisfies an analytical property on the
stochastic evolution of the state (e.g., to compute probability that the
hospital beds will reach a fill level, or that a specified percentage of the
population will die). If the control policy is not given, it is possible to
apply POMDP techniques to identify an optimal control policy that fulfils some
specified probabilistic goals. Whilst the paper primarily aims at the model
description, we show with numeric examples some of its potential applications.


title: Identification and Statistical Decision Theory

abstract:
  Econometricians have usefully separated study of estimation into
identification and statistical components. Identification analysis aims to
place an informative upper bound on what may be learned about population
parameters of interest with specified sample data. Statistical decision theory
has studied decision making with sample data without reference to
identification. This paper asks if and how identification analysis is useful to
statistical decision theory. I show that the answer is positive and simple when
the relevant parameter (true state of nature) is point identified. However, a
subtlety arises when the true state is partially identified, and a decision
must be made under ambiguity. Then the performance of some criteria,
particularly minimax regret, is enhanced by permitting randomized choice of an
action, which essentially requires availability of sample data. I show that an
informative upper bound on the performance of decision making holds when the
knowledge assumed in identification analysis is combined with sample data
enabling randomized choice. I emphasize that using sample data to randomize
choice is conceptually distinct from its traditional econometric use to infer
population parameters.


title: Non-commutative ambits and equivariant compactifications

abstract:
  We prove that an action $\rho:A\to M(C_0(\mathbb{G})\otimes A)$ of a locally
compact quantum group on a $C^*$-algebra has a universal equivariant
compactification, and prove a number of other category-theoretic results on
$\mathbb{G}$-equivariant compactifications: that the categories
compactifications of $\rho$ and $A$ respectively are locally presentable (hence
complete and cocomplete), that the forgetful functor between them is a
colimit-creating left adjoint, and that epimorphisms therein are surjective and
injections are regular monomorphisms.
  When $\mathbb{G}$ is regular coamenable we also show that the forgetful
functor from unital $\mathbb{G}$-$C^*$-algebras to unital $C^*$-algebras
creates finite limits and is comonadic, and that the monomorphisms in the
former category are injective.


title: Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation

abstract:
  Modern day conversational agents are trained to emulate the manner in which
humans communicate. To emotionally bond with the user, these virtual agents
need to be aware of the affective state of the user. Transformers are the
recent state of the art in sequence-to-sequence learning that involves training
an encoder-decoder model with word embeddings from utterance-response pairs. We
propose an emotion-aware transformer encoder for capturing the emotional
quotient in the user utterance in order to generate human-like empathetic
responses. The contributions of our paper are as follows: 1) An emotion
detector module trained on the input utterances determines the affective state
of the user in the initial phase 2) A novel transformer encoder is proposed
that adds and normalizes the word embedding with emotion embedding thereby
integrating the semantic and affective aspects of the input utterance 3) The
encoder and decoder stacks belong to the Transformer-XL architecture which is
the recent state of the art in language modeling. Experimentation on the
benchmark Facebook AI empathetic dialogue dataset confirms the efficacy of our
model from the higher BLEU-4 scores achieved for the generated responses as
compared to existing methods. Emotionally intelligent virtual agents are now a
reality and inclusion of affect as a modality in all human-machine interfaces
is foreseen in the immediate future.


title:
Multimedia Services Placement Algorithm for Cloud-Fog Hierarchical
  Environments

abstract:
  With the rapid development of mobile communication, multimedia services have
experienced explosive growth in the last few years. The high quantity of mobile
users, both consuming and producing these services to and from the Cloud
Computing (CC), can outpace the available bandwidth capacity. Fog Computing
(FG) presents itself as a solution to improve on this and other issues. With a
reduction in network latency, real-time applications benefit from improved
response time and greater overall user experience. Taking this into account,
the main goal of this work is threefold. Firstly, it is proposed a method to
build an environment based on Cloud-Fog Computing (CFC). Secondly, it is
designed two models based on Autoregressive Integrated Moving Average (ARIMA)
and Long Short-Term Memory (LSTM). The goal is to predict demand and reserve
the nodes' storage capacity to improve the positioning of multimedia services.
Later, an algorithm for the multimedia service placement problem which is aware
of data traffic prediction is proposed. The goal is to select the minimum
number of nodes, considering their hardware capacities for providing multimedia
services in such a way that the latency for servicing all the demands is
minimized. An evaluation with actual data showed that the proposed algorithm
selects the nodes closer to the user to meet their demands. This improves the
services delivered to end-users and enhances the deployed network to mitigate
provider costs. Moreover, reduce the demand to Cloud allowing turning off
servers in the data center not to waste energy


title:
Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for
  Nonconvex Smooth Optimization

abstract:
  An algorithm for solving nonconvex smooth optimization problems is proposed,
analyzed, and tested. The algorithm is an extension of the Trust Region
Algorithm with Contractions and Expansions (TRACE) [Math. Prog. 162(1):132,
2017]. In particular, the extension allows the algorithm to use inexact
solutions of the arising subproblems, which is an important feature for solving
large-scale problems. Inexactness is allowed in a manner such that the optimal
iteration complexity of ${\cal O}(\epsilon^{-3/2})$ for attaining an
$\epsilon$-approximate first-order stationary point is maintained while the
worst-case complexity in terms of Hessian-vector products may be significantly
improved as compared to the original TRACE. Numerical experiments show the
benefits of allowing inexact subproblem solutions and that the algorithm
compares favorably to a state-of-the-art technique.


title: A note on Pontryagin duality and continuous logic

abstract:
  We exhibit Pontryagin duality as a special case of Stone duality in a
continuous logic setting. That is, given an abelian topological group $A$, and
$\mathcal{F}$ the family (group) of continuous homomorphisms from $A$ to the
circle group $\mathbb{T}$, then viewing $(A,+)$ equipped with the collection
$\mathcal{F}$, as a continuous logic structure $M$, the local type space
$S_{\mathcal{F}}(M)$ is precisely the Pontryagin dual of the group
$\mathcal{F}$ where the latter is considered as a discrete group. We conclude,
using Pontryagin duality (between compact and discrete abelian groups) that
$S_{\mathcal{F}}(M)$ is the Bohr compactification of the topological group $A$.


title: A singularity problem for interacting massive vectors

abstract:
  Interacting massive spin-1 fields have been widely used in cosmology and
particle physics. We obtain a new condition on the validity of the classical
limit of these theories related to the non-trivial constraints that exist for
vector field components. A violation of this consistency condition causes a
singularity in the time derivative of the auxiliary component and could impact,
for example, the field's cosmic history and superradiance around black holes.
Such a condition is expected to exist generically in many other non-trivially
constrained systems.


title: Two-stage matching-adjusted indirect comparison

abstract:
  Anchored covariate-adjusted indirect comparisons inform reimbursement
decisions where there are no head-to-head trials between the treatments of
interest, there is a common comparator arm shared by the studies, and there are
patient-level data limitations. Matching-adjusted indirect comparison (MAIC) is
the most widely used covariate-adjusted indirect comparison method. MAIC has
poor precision and is inefficient when the effective sample size after
weighting is small. A modular extension to MAIC, termed two-stage
matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two
parametric models. One estimates the treatment assignment mechanism in the
study with individual patient data (IPD), the other estimates the trial
assignment mechanism. The resulting weights balance covariates between
treatment arms and across studies. A simulation study provides
proof-of-principle in an indirect comparison performed across two randomized
trials and explores the use of weight truncation in combination with MAIC for
the first time. Despite enforcing randomization and knowing the true treatment
assignment mechanism in the IPD trial, 2SMAIC yields improved precision and
efficiency with respect to MAIC in all scenarios, while maintaining similarly
low levels of bias. The two-stage approach is effective when sample sizes in
the IPD trial are low, as it controls for chance imbalances in prognostic
baseline covariates between study arms. It is not as effective when overlap
between the trials' target populations is poor and the extremity of the weights
is high. In these scenarios, truncation leads to substantial precision and
efficiency gains but induces considerable bias. The combination of a two-stage
approach with truncation produces the highest precision and efficiency
improvements.


title:
The Multiscale Structure of Neural Network Loss Functions: The Effect on
  Optimization and Origin

abstract:
  Local quadratic approximation has been extensively used to study the
optimization of neural network loss functions around the minimum. Though, it
usually holds in a very small neighborhood of the minimum, and cannot explain
many phenomena observed during the optimization process. In this work, we study
the structure of neural network loss functions and its implication on
optimization in a region beyond the reach of good quadratic approximation.
Numerically, we observe that neural network loss functions possesses a
multiscale structure, manifested in two ways: (1) in a neighborhood of minima,
the loss mixes a continuum of scales and grows subquadratically, and (2) in a
larger region, the loss shows several separate scales clearly. Using the
subquadratic growth, we are able to explain the Edge of Stability phenomenon[4]
observed for gradient descent (GD) method. Using the separate scales, we
explain the working mechanism of learning rate decay by simple examples.
Finally, we study the origin of the multiscale structure and propose that the
non-uniformity of training data is one of its cause. By constructing a
two-layer neural network problem we show that training data with different
magnitudes give rise to different scales of the loss function, producing
subquadratic growth or multiple separate scales.


title: Global bifurcation for corotating and counter-rotating vortex pairs

abstract:
  The existence of a local curve of corotating and counter-rotating vortex
pairs was proven by Hmidi and Mateu in via a desingularization of a pair of
point vortices. In this paper, we construct a global continuation of these
local curves. That is, we consider solutions which are more than a mere
perturbation of a trivial solution. Indeed, while the local analysis relies on
the study of the linear equation at the trivial solution, the global analysis
requires on a deeper understanding of topological properties of the nonlinear
problem. For our proof, we adapt the powerful analytic global bifurcation
theorem due to Buffoni and Toland, to allow for the singularity at the
bifurcation point. For both the corotating and the counter-rotating pairs,
along the global curve of solutions either the angular fluid velocity vanishes
or the two patches self-intersect.


title: Six Levels of Autonomous Process Execution Management (APEM)

abstract:
  Terms such as the Digital Twin of an Organization (DTO) and Hyperautomation
(HA) illustrate the desire to autonomously manage and orchestrate processes,
just like we aim for autonomously driving cars. Autonomous driving and
Autonomous Process Execution Management (APEM) have in common that the goals
are pretty straightforward and that each year progress is made, but fully
autonomous driving and fully autonomous process execution are more a dream than
a reality. For cars, the Society of Automotive Engineers (SAE) identified six
levels (0-5), ranging from no driving automation (SAE, Level 0) to full driving
automation (SAE, Level 5). This short article defines six levels of Autonomous
Process Execution Management (APEM). The goal is to show that the transition
from one level to the next will be gradual, just like for self-driving cars.


title:
Integrating User Experience into Agile -- An Experience Report on Lean
  UX and Scrum

abstract:
  The integration of Agile development and user experience (UX) has received
significant attention over the past decade. The literature contains several
process models and wide-ranging discussion about the benefits and challenges of
this integration. However, academia has given this integration short shrift. In
fact, there are very few publications covering educational courses dealing with
Agile development and UX. In this paper, we report on our experience of
designing and running a graduate software engineering course that covers the
integration of Lean UX into Scrum and employs gamification to improve student
engagement. We identified six lessons learned that new point to important
aspects to be considered when integrating Agile development and UX in academia.
For example, we discuss the complexity of managing Lean UX activities in short
sprints or how the design of a testable and tactical hypothesis can be one of
the most challenging aspects of the Lean UX process.


title:
Quantum chaos and H\'enon-Heiles model: Dirac's variational approach
  with Jackiw-Kerman function

abstract:
  A simple semiclassical H\'enon-Heiles model is constructed based on Dirac's
time-dependent variational principle. We obtain an effective semiclassical
Hamiltonian using a Hatree-type two-body trial wavefunction in the
Jackiw-Kerman form. Numerical results show that quantum effects can in fact
induce chaos in the non-chaotic regions of the classical H\'enon-Heiles model.


title: Around generalized external zomotopal algebras of graphs

abstract:
  Following [NSh] we introduce and study a family of deformations of external
zonotopal algebras associated to a given undirected graph with possibly
multiple edges, but no loops. This deformation is parameterised by a linear
space of univariate polynomials whose degrees are bounded by the total number
of edges in the considered graph. We discuss different properties of the
Hilbert series of these algebras and present the results of many calculations
carried out in Macaulay 2. Several conjectures are formulated.


title: N-polar GaN p-n junction diodes with low ideality factors

abstract:
  High-quality N-polar GaN p-n diodes are realized on single-crystal N-polar
GaN bulk substrate by plasma-assisted molecular beam epitaxy. The
room-temperature current-voltage characteristics reveal a high on/off current
ratio of 10^11 at 4 V and an ideality factor of 1.6. As the temperature
increases to 200 C, the apparent ideality factor gradually approaches 2. At
such high temperatures, Shockley-Read-Hall recombination times of 0.32-0.46 ns
are estimated. The measured electroluminescence spectrum is dominated by a
strong near-band edge emission, while deep level and acceptor-related
luminescence is greatly suppressed. A relatively high reverse breakdown field
of 2.4 MV/cm without field-plates is achieved. This work indicates that the
quality of N-polar GaN diodes is now approaching to that of their
state-of-the-art Ga-polar counterparts.


title:
On the size of good-for-games Rabin automata and its link with the
  memory in Muller games

abstract:
  In this paper, we look at good-for-games Rabin automata that recognise a
Muller language (a language that is entirely characterised by the set of
letters that appear infinitely often in each word). We establish that minimal
such automata are exactly of the same size as the minimal memory required for
winning Muller games that have this language as their winning condition. We
show how to effectively construct such minimal automata. Finally, we establish
that these automata can be exponentially more succinct than equivalent
deterministic ones, thus proving as a consequence that chromatic memory for
winning a Muller game can be exponentially larger than unconstrained memory.


title: Hardware Acceleration for Third-Generation FHE and PSI Based on It

abstract:
  With the expansion of cloud services, serious concerns about the privacy of
users' data arise due to the exposure of the unencrypted data to the server
during computation. Various security primitives are under investigation to
preserve privacy while evaluating private data, including Fully Homomorphic
Encryption (FHE), Private Set Intersection (PSI), and others. However, the
prohibitive processing time of these primitives hinders their practical
applications. This work proposes and implements an architecture for
accelerating third-generation FHE with Amazon Web Services (AWS) cloud FPGAs,
marking the first hardware acceleration solution for third-generation FHE. We
also introduce a novel unbalanced PSI protocol based on third-generation FHE,
optimized for the proposed hardware architecture. Several
algorithm-architecture co-optimization techniques are introduced to allow the
communication and computation costs to be independent of the Sender's set size.
The measurement results show that the proposed accelerator achieves $>21\times$
performance improvement compared to a software implementation for various
crucial subroutines of third-generation FHE and the proposed PSI.


title: Simulating Fluids in Real-World Still Images

abstract:
  In this work, we tackle the problem of real-world fluid animation from a
still image. The key of our system is a surface-based layered representation
deriving from video decomposition, where the scene is decoupled into a surface
fluid layer and an impervious background layer with corresponding
transparencies to characterize the composition of the two layers. The animated
video can be produced by warping only the surface fluid layer according to the
estimation of fluid motions and recombining it with the background. In
addition, we introduce surface-only fluid simulation, a $2.5D$ fluid
calculation version, as a replacement for motion estimation. Specifically, we
leverage the triangular mesh based on a monocular depth estimator to represent
the fluid surface layer and simulate the motion in the physics-based framework
with the inspiration of the classic theory of the hybrid Lagrangian-Eulerian
method, along with a learnable network so as to adapt to complex real-world
image textures. We demonstrate the effectiveness of the proposed system through
comparison with existing methods in both standard objective metrics and
subjective ranking scores. Extensive experiments not only indicate our method's
competitive performance for common fluid scenes but also better robustness and
reasonability under complex transparent fluid scenarios. Moreover, as the
proposed surface-based layer representation and surface-only fluid simulation
naturally disentangle the scene, interactive editing such as adding objects to
the river and texture replacing could be easily achieved with realistic
results.


title:
Casimir Self-Entropy of Nanoparticles with Classical Polarizabilities:
  Electromagnetic Field Fluctuations

abstract:
  Not only are Casimir interaction entropies not guaranteed to be positive, but
also, more strikingly, Casimir self-entropies of bodies can be negative. Here,
we attempt to interpret the physical origin and meaning of these negative
self-entropies by investigating the Casimir self-entropy of a neutral spherical
nanoparticle. After extracting the polarizabilities of such a particle by
examining the asymptotic behavior of the scattering Green's function, we
compute the corresponding free energy and entropy. Two models for the
nanoparticle, namely a spherical plasma $\delta$-function shell and a
homogeneous dielectric/diamagnetic ball, are considered at low temperature,
because that is all that can be revealed from a nanoparticle perspective. The
second model includes a contribution to the entropy from the bulk free energy,
referring to the situation where the medium inside or outside the ball fills
all space, which must be subtracted on physical grounds in order to maintain
consistency with van der Waals interactions, corresponding to the self-entropy
of each bulk. (The van der Waals calculation is described in Appendix A.) The
entropies so calculated agree with known results in the low-temperature limit,
appropriate for a small particle, and are negative. But we suggest that the
negative self-entropy is simply an interaction entropy, the difference between
the total entropy and the blackbody entropy of the two bulks, outside or inside
of the nanosphere. The vacuum entropy is always positive and overwhelms the
interaction entropy. Thus the interaction entropy can be negative, without
contradicting the principles of statistical thermodynamics. Because the entropy
of blackbody radiation by itself plays an important role, it is also discussed,
including dispersive effects, in detail.


title: An Item Response Theory Framework for Persuasion

abstract:
  In this paper, we apply Item Response Theory, popular in education and
political science research, to the analysis of argument persuasiveness in
language. We empirically evaluate the model's performance on three datasets,
including a novel dataset in the area of political advocacy. We show the
advantages of separating these components under several style and content
representations, including evaluating the ability of the speaker embeddings
generated by the model to parallel real-world observations about
persuadability.


title: Taming Hybrid-Cloud Fast and Scalable Graph Analytics at Twitter

abstract:
  We have witnessed a boosted demand for graph analytics at Twitter in recent
years, and graph analytics has become one of the key parts of Twitter's
large-scale data analytics and machine learning for driving engagement, serving
the most relevant content, and promoting healthier conversations. However,
infrastructure for graph analytics has historically not been an area of
investment at Twitter, resulting in a long timeline and huge engineering effort
for each project to deal with graphs at the Twitter scale. How do we build a
unified graph analytics user experience to fulfill modern data analytics on
various graph scales spanning from thousands to hundreds of billions of
vertices and edges?
  To bring fast and scalable graph analytics capability into production, we
investigate the challenges we are facing in large-scale graph analytics at
Twitter and propose a unified graph analytics platform for efficient, scalable,
and reliable graph analytics across on-premises and cloud, to fulfill the
requirements of diverse graph use cases and challenging scales. We also conduct
quantitative benchmarking on Twitter's production-level graph use cases between
popular graph analytics frameworks to certify our solution.


title:
Integrated Local Energy Decay for the Damped Wave Equation on Stationary
  Space-Times

abstract:
  We prove integrated local energy decay for the damped wave equation on
stationary, asymptotically flat space-times in $(1+3)$ dimensions. Local energy
decay constitutes a powerful tool in the study of dispersive partial
differential equations on such geometric backgrounds. By utilizing the
geometric control condition to handle trapped trajectories, we are able to
recover high frequency estimates without any loss. We may then apply known
estimates from Metcalfe, Sterbenz, and Tataru in the medium and low frequency
regimes in order to establish local energy decay. This generalizes the
integrated version of results in Bouclet and Royer from the setting of
asymptotically Euclidean manifolds to the full Lorentzian case.


title:
Farmer's Assistant: A Machine Learning Based Application for
  Agricultural Solutions

abstract:
  Farmers face several challenges when growing crops like uncertain irrigation,
poor soil quality, etc. Especially in India, a major fraction of farmers do not
have the knowledge to select appropriate crops and fertilizers. Moreover, crop
failure due to disease causes a significant loss to the farmers, as well as the
consumers. While there have been recent developments in the automated detection
of these diseases using Machine Learning techniques, the utilization of Deep
Learning has not been fully explored. Additionally, such models are not easy to
use because of the high-quality data used in their training, lack of
computational power, and poor generalizability of the models. To this end, we
create an open-source easy-to-use web application to address some of these
issues which may help improve crop production. In particular, we support crop
recommendation, fertilizer recommendation, plant disease prediction, and an
interactive news-feed. In addition, we also use interpretability techniques in
an attempt to explain the prediction made by our disease detection model.


title: Deep Learning for Medical Image Registration: A Comprehensive Review

abstract:
  Image registration is a critical component in the applications of various
medical image analyses. In recent years, there has been a tremendous surge in
the development of deep learning (DL)-based medical image registration models.
This paper provides a comprehensive review of medical image registration.
Firstly, a discussion is provided for supervised registration categories, for
example, fully supervised, dual supervised, and weakly supervised registration.
Next, similarity-based as well as generative adversarial network (GAN)-based
registration are presented as part of unsupervised registration. Deep iterative
registration is then described with emphasis on deep similarity-based and
reinforcement learning-based registration. Moreover, the application areas of
medical image registration are reviewed. This review focuses on monomodal and
multimodal registration and associated imaging, for instance, X-ray, CT scan,
ultrasound, and MRI. The existing challenges are highlighted in this review,
where it is shown that a major challenge is the absence of a training dataset
with known transformations. Finally, a discussion is provided on the promising
future research areas in the field of DL-based medical image registration.


title:
Decay/growth rates for inhomogeneous heat equations with memory. The
  case of small dimensions

abstract:
  We study the decay/growth rates in all $L^p$ norms of solutions to an
inhomogeneous nonlocal heat equation in $\mathbb{R}^N$ involving a Caputo
$\alpha$-time derivative and a power $\beta$ of the Laplacian when the spatial
dimension is small, $1\le N\le 4\beta$, thus completing the already available
results for large spatial dimensions. Rates depend not only on $p$, but also on
the space-time scale and on the time behavior of the spatial $L^1$ norm of the
forcing term.


title:
Applying Digital Twins in Metaverse: User Interface, Security and
  Privacy Challenges

abstract:
  Digital Twins (DTs) are a conventional and well-known concept, proposed in
70s, that are popular in a broad spectrum of sciences, industry innovations,
and consortium alliances. However, in the last few years, the growth of digital
assets and online communications has attracted attention to DTs as highly
accurate twins of physical objects. Metaverse, as a digital world, is a concept
proposed in 1992 and has also become a popular paradigm and hot topic in public
where DTs can play critical roles. This study first presents definitions,
applications, and general challenges of DT and Metaverse. It then offers a
three-layer architecture linking the physical world to the Metaverse through a
user interface. Further, it investigates the security and privacy challenges of
using DTs in Metaverse. Finally, a conclusion, including possible solutions for
mentioned challenges and future works, will be provided.


title: On a Frequency-Stabilized Single Current Inverse Source Formulation

abstract:
  Several strategies are available for solving the inverse source problem in
electromagnetics. Among them, many have been focusing in retrieving Love
currents by solving, after regularization, for Love's electric and magnetic
currents. In this work we present a dual-element discretization, analysis, and
stabilization of an inverse source formulation providing Love data by solving
for only one current. This results in substantial savings and allows for an
effective quasi-Helmholtz projector stabilization of the resulting operator.
Theoretical considerations are complemented by numerical tests showing
effectiveness and efficiency of the newly proposed method.


title: Extraordinary Disordered Hyperuniform Multifunctional Composites

abstract:
  A variety of performance demands are being placed on material systems,
including desirable mechanical, thermal, electrical, optical, acoustic and flow
properties. The purpose of the present article is to review the emerging field
of disordered hyperuniform composites and their novel multifunctional
characteristics. Disordered hyperuniform media are exotic amorphous states of
matter that are characterized by an anomalous suppression of large-scale
volume-fraction fluctuations compared to those in "garden-variety" disordered
materials. Such unusual composites can have advantages over their periodic
counterparts, such as unique or nearly optimal, direction-independent physical
properties and robustness against defects. It will be shown that disordered
hyperuniform composites and porous media can be endowed with a broad spectrum
of extraordinary physical properties, including photonic, phononic, transport,
chemical and mechanical characteristics that are only beginning to be discov


title:
Less is More: Reweighting Important Spectral Graph Features for
  Recommendation

abstract:
  As much as Graph Convolutional Networks (GCNs) have shown tremendous success
in recommender systems and collaborative filtering (CF), the mechanism of how
they, especially the core components (\textit{i.e.,} neighborhood aggregation)
contribute to recommendation has not been well studied. To unveil the
effectiveness of GCNs for recommendation, we first analyze them in a spectral
perspective and discover two important findings: (1) only a small portion of
spectral graph features that emphasize the neighborhood smoothness and
difference contribute to the recommendation accuracy, whereas most graph
information can be considered as noise that even reduces the performance, and
(2) repetition of the neighborhood aggregation emphasizes smoothed features and
filters out noise information in an ineffective way. Based on the two findings
above, we propose a new GCN learning scheme for recommendation by replacing
neihgborhood aggregation with a simple yet effective Graph Denoising Encoder
(GDE), which acts as a band pass filter to capture important graph features. We
show that our proposed method alleviates the over-smoothing and is comparable
to an indefinite-layer GCN that can take any-hop neighborhood into
consideration. Finally, we dynamically adjust the gradients over the negative
samples to expedite model training without introducing additional complexity.
Extensive experiments on five real-world datasets show that our proposed method
not only outperforms state-of-the-arts but also achieves 12x speedup over
LightGCN.


title:
A decay estimate for the Fourier transform of certain singular measures
  in $\mathbb{R}^{4}$ and applications

abstract:
  We consider, for a class of functions $\varphi : \mathbb{R}^{2} \setminus \{
{\bf 0} \} \to \mathbb{R}^{2}$ satisfying a nonisotropic homogeneity condition,
the Fourier transform $\hat{\mu}$ of the Borel measure on $\mathbb{R}^{4}$
defined by \[ \mu(E) = \int_{U} \chi_{E}(x, \varphi(x)) \, dx \] where $E$ is a
Borel set of $\mathbb{R}^{4}$ and $U = \{ (t^{\alpha_1}, t^{\alpha_2}s) : c < s
< d, \, 0 < t < 1 \}$. The aim of this article is to give a decay estimate for
$\hat{\mu}$, for the case where the set of nonelliptic points of $\varphi$ is a
curve in $\bar{U} \setminus \{ {\bf 0} \}$. From this estimate we obtain a
restriction theorem for the usual Fourier transform to the graph of
$\varphi_{U} : U \to \mathbb{R}^{2}$. We also give $L^{p}$-improving properties
for the convolution operator $T_{\mu} f = \mu \ast f$.


title: Skyrmionic Spin Structures in Layered Fe5GeTe2 Up To Room Temperature

abstract:
  The role of the crystal lattice, temperature and magnetic field for the spin
structure formation in the 2D van der Waals magnet Fe5GeTe2 is a key open
question. Using Lorentz transmission electron microscopy, we experimentally
observe topological spin structures up to room temperature in the metastable
pre-cooling and stable post-cooling phase of Fe5GeTe2. Over wide temperature
and field ranges, skyrmionic magnetic bubbles form without preferred chirality,
which is indicative of a centrosymmetric crystal structure. In the pre-cooling
phase, these bubbles are observable even without the application of an external
field, while in the post-cooling phase, a transformation from bubble domains to
stripe domains is seen. To understand the magnetic order in Fe5GeTe2 we compare
macroscopic magnetometry characterization results with microscopic density
functional theory calculation. Our results show that even up to room
temperature, topological spin structures can be stabilized in centrosymmetric
van der Waals magnets.


title:
Revisiting Multi-breathers in the discrete Klein-Gordon equation: A
  Spatial Dynamics Approach

abstract:
  We consider the existence and spectral stability of multi-breather structures
in the discrete Klein-Gordon equation, both for soft and hard symmetric
potentials. By taking a spatial dynamics approach and an appropriate
approximation of the system on a finite-dimensional Hilbert space, we use Lin's
method to construct multi-breathers from a sequence of well-separated copies of
a primary, single-site breather. We then locate the eigenmodes in the Floquet
spectrum associated with the interaction between the individual breathers of
such multi-breather states by reducing the spectral problem to a matrix
equation. Analytical expressions for these eigenmodes for the approximate
system are obtained in terms of the primary breather and its kernel
eigenfunctions, and these are found to be in very good agreement with the
numerical Floquet spectrum results. This is supplemented with results from
numerical timestepping experiments, which are interpreted using the spectral
computations.


title:
Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with
  Graph Neural Network Communication Layer for Open-ended Wildfire-Management
  Resource Distribution

abstract:
  Most real-world domains can be formulated as multi-agent (MA) systems.
Intentionality sharing agents can solve more complex tasks by collaborating,
possibly in less time. True cooperative actions are beneficial for egoistic and
collective reasons. However, teaching individual agents to sacrifice egoistic
benefits for a better collective performance seems challenging. We build on a
recently proposed Multi-Agent Reinforcement Learning (MARL) mechanism with a
Graph Neural Network (GNN) communication layer. Rarely chosen communication
actions were marginally beneficial. Here we propose a MARL system in which
agents can help collaborators perform better while risking low individual
performance. We conduct our study in the context of resource distribution for
wildfire management. Communicating environmental features and partially
observable fire occurrence help the agent collective to pre-emptively
distribute resources. Furthermore, we introduce a procedural training
environment accommodating auto-curricula and open-endedness towards better
generalizability. Our MA communication proposal outperforms a Greedy Heuristic
Baseline and a Single-Agent (SA) setup. We further demonstrate how
auto-curricula and openendedness improves generalizability of our MA proposal.


title:
An empirical study of the effect of background data size on the
  stability of SHapley Additive exPlanations (SHAP) for deep learning models

abstract:
  Nowadays, the interpretation of why a machine learning (ML) model makes
certain inferences is as crucial as the accuracy of such inferences. Some ML
models like the decision tree possess inherent interpretability that can be
directly comprehended by humans. Others like artificial neural networks (ANN),
however, rely on external methods to uncover the deduction mechanism. SHapley
Additive exPlanations (SHAP) is one of such external methods, which requires a
background dataset when interpreting ANNs. Generally, a background dataset
consists of instances randomly sampled from the training dataset. However, the
sampling size and its effect on SHAP remain to be unexplored. In our empirical
study on the MIMIC-III dataset, we show that the two core explanations - SHAP
values and variable rankings fluctuate when using different background datasets
acquired from random sampling, indicating that users cannot unquestioningly
trust the one-shot interpretation from SHAP. Luckily, such fluctuation
decreases with the increase of the background dataset size. Also, we notice an
U-shape in the stability assessment of SHAP variable rankings, demonstrating
that SHAP is more reliable in ranking the most and least important variables
compared to moderately important ones. Overall, our results suggest that users
should take into account how background data affects SHAP results, with
improved SHAP stability as the background sample size increases.


title: Learning to Attack Powergrids with DERs

abstract:
  In the past years, power grids have become a valuable target for
cyber-attacks. Especially the attacks on the Ukrainian power grid has sparked
numerous research into possible attack vectors, their extent, and possible
mitigations. However, many fail to consider realistic scenarios in which time
series are incorporated into simulations to reflect the transient behaviour of
independent generators and consumers. Moreover, very few consider the limited
sensory input of a potential attacker. In this paper, we describe a reactive
power attack based on a well-understood scenario. We show that independent
agents can learn to use the dynamics of the power grid against it and that the
attack works even in the face of other generator and consumer nodes acting
independently.


title: Efficient Certifiable Randomness from a Single Quantum Device

abstract:
  Brakerski et. al [BCM+18] introduced the model of cryptographic testing of a
single untrusted quantum device and gave a protocol for certifiable randomness
generation. We use the leakage resilience properties of the Learning With
Errors problem to address a key issue left open in previous work - the rate of
generation of randomness. Our new protocol can certify $\Omega(n)$ fresh bits
of randomness in constant rounds, where $n$ is a parameter of the protocol and
the total communication is $O(n)$, thus achieving a nearly optimal rate. The
proof that the output is statistically random is conceptually simple and
technically elementary.


title: Locally analytic vector bundles on the Fargues-Fontaine curve

abstract:
  In this article, we develop a version of Sen theory for equivariant vector
bundles on the Fargues-Fontaine curve. We show that every equivariant vector
bundle canonically descends to a locally analytic vector bundle. A comparison
with the theory of $(\varphi,\Gamma)$-modules in the cyclotomic case then
recovers the Cherbonnier-Colmez decompletion theorem. Next, we focus on the
subcategory of de Rham locally analytic vector bundles. Using the p-adic
monodromy theorem, we show that each locally analytic vector bundle
$\mathcal{E}$ has a canonical differential equation for which the space of
solutions has full rank. As a consequence, $\mathcal{E}$ and its sheaf of
solutions $\mathrm{Sol}(\mathcal{E})$ satisfy a Riemann-Hilbert type relation,
which gives a geometric interpretation of a result of Berger on
$(\varphi,\Gamma)$-modules. In particular, if V is a de Rham Galois
representation, its associated filtered $(\varphi,N,G_{K})$-module is realized
as the space of global solutions to the differential equation. A key to our
approach is a vanishing result for the higher locally analytic vectors of
representations satisfying the Tate-Sen formalism, which is also of independent
interest.


title: Thermal Evolution of Dirac Magnons in the Honeycomb Ferromagnet CrBr$_3$

abstract:
  CrBr$_3$ is an excellent realization of the two-dimensional honeycomb
ferromagnet, which offers a bosonic equivalent of graphene with Dirac magnons
and topological character. We perform inelastic neutron scattering (INS)
measurements using state-of-the-art instrumentation to update 50-year-old data,
thereby enabling a definitive comparison both with recent experimental claims
of a significant gap at the Dirac point and with theoretical predictions for
thermal magnon renormalization. We demonstrate that CrBr$_3$ has next-neighbor
$J_2$ and $J_3$ interactions approximately 5\% of $J_1$, an ideal Dirac magnon
dispersion at the K point, and the associated signature of isospin winding. The
magnon lifetime and the thermal band renormalization show the universal $T^2$
evolution expected from an interacting spin-wave treatment, but the measured
dispersion lacks the predicted van Hove features, highlighting the need for a
deeper theoretical analysis.