title:
Hyperk\"{a}hler, Bi-hypercomplex, Generalized Hyperk\"{a}hler Structures
  and T-duality

abstract:
  We investigate comprehensive relations among T-duality, complex and
bi-hermitian structures $(J_+, J_-)$ in two-dimensional $\mathcal{N} =(2,2)$
sigma models with/without twisted chiral multiplets. The bi-hermitian
structures $(J_+,J_-)$ embedded in generalized K\"{a}hler structures
$(\mathcal{J}_+,\mathcal{J}_-)$ are organized into the algebra of the
tri-complex numbers. We newly write down an analogue of the Buscher rule by
which the T-duality transformation of the bi-hermitian and K\"{a}hler
structures are apparent. We also study the bi-hypercomplex and hyperk\"{a}hler
cases in $\mathcal{N} = (4,4)$ theories. They are expressed, as a T-duality
covariant fashion, in the generalized hyperk\"{a}hler structures and form the
split-bi-quaternion algebras. As a concrete example, we show the explicit
T-duality relation between the hyperk\"{a}hler structures of the KK-monopole
(Taub-NUT space) and the bi-hypercomplex structures of the H-monopole (smeared
NS5-brane). Utilizing this result, we comment on a T-duality relation for the
worldsheet instantons in these geometries.


title: On a class of nonlocal problems with fractional gradient constraint

abstract:
  We consider a Hilbertian and a charges approach to fractional gradient
constraint problems of the type $|D^\sigma u|\leq g$, involving the
distributional fractional Riesz gradient $D^\sigma$, $0<\sigma <1$, extending
previous results on the existence of solutions and Lagrange multipliers of
these nonlocal problems.
  We also prove their convergence as $\sigma\nearrow1$ towards their local
counterparts with the gradient constraint $|D u|\leq g$.


title: Broadcast Approach Meets Network Coding for Data Streaming

abstract:
  For data streaming applications, existing solutions are not yet able to close
the gap between high data rates and low delay. This work considers the problem
of data streaming under mixed delay constraints over a single communication
channel with delayed feedback. We propose a novel layered adaptive causal
random linear network coding (LAC-RLNC) approach with forward error correction.
LAC-RLNC is a variable-to-variable coding scheme, i.e., variable recovered
information data at the receiver over variable short block length and rate is
proposed. Specifically, for data streaming with base and enhancement layers of
content, we characterize a high dimensional throughput-delay trade-off managed
by the adaptive causal layering coding scheme. The base layer is designed to
satisfy the strict delay constraints, as it contains the data needed to allow
the streaming service. Then, the sender can manage the throughput-delay
trade-off of the second layer by adjusting the retransmission rate a priori and
posterior as the enhancement layer, that contains the remaining data to augment
the streaming service's quality, is with the relax delay constraints. We
numerically show that the layered network coding approach can dramatically
increase performance. We demonstrate that LAC-RLNC compared with the
non-layered approach gains a factor of three in mean and maximum delay for the
base layer, close to the lower bound, and factor two for the enhancement layer.


title:
A Riemann Manifold Model Framework for Longitudinal Changes in Physical
  Activity Patterns

abstract:
  Physical activity (PA) is significantly associated with many health outcomes.
The wide usage of wearable accelerometer-based activity trackers in recent
years has provided a unique opportunity for in-depth research on PA and its
relations with health outcomes and interventions. Past analysis of activity
tracker data relies heavily on aggregating minute-level PA records into
day-level summary statistics, in which important information of PA
temporal/diurnal patterns is lost. In this paper we propose a novel functional
data analysis approach based on Riemann manifolds for modeling PA and its
longitudinal changes. We model smoothed minute-level PA of a day as
one-dimensional Riemann manifolds and longitudinal changes in PA in different
visits as deformations between manifolds. The variability in changes of PA
among a cohort of subjects is characterized via variability in the deformation.
Functional principal component analysis is further adopted to model the
deformations and PC scores are used as a proxy in modeling the relation between
changes in PA and health outcomes and/or interventions. We conduct
comprehensive analyses on data from two clinical trials: Reach for Health (RfH)
and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect
of interventions on longitudinal changes in PA patterns and how different modes
of changes in PA influence weight loss, respectively. The proposed approach
reveals unique modes of changes including overall enhanced PA, boosted morning
PA, and shifts of active hours specific to each study cohort. The results bring
new insights into the study of longitudinal changes in PA and health and have
the potential to facilitate designing of effective health interventions and
guidelines.


title: Ill-posedness of quintic fourth order Schr\"odinger equation

abstract:
  We prove that the solution map, associated to the quintic fourth order
nonlinear Schr\"odinger equation, exhibits the norm inflation phenomenon at
every point in the Sobolev spaces of super-critical regularity. Indeed, we
prove this result separately in the cases of negative and of positive
regularity. In the negative regularity case, we prove the result for both the
defocusing and focusing equations: in the one dimensional case, the associated
solution map exhibits norm inflation in Sobolev spaces of super-critical
regularity (including the critical index); in the higher dimensional case, the
solution map exhibits the same phenomenon in spaces of negative regularity.
Meanwhile, in the case of positive regularity, we prove the result for the
defocusing equation in dimensions $d=3,4,5$. Our proofs are based on the
"high-to-low" and "low-to-high" frequency cascades respectively.


title:
Free-breathing motion compensated 4D (3D+respiration) T2-weighted turbo
  spin-echo MRI for body imaging

abstract:
  Purpose: To develop and evaluate a free-breathing respiratory motion
compensated 4D (3D+respiration) $T_2$-weighted turbo spin echo sequence with
application to radiology and MR-guided radiotherapy.
  Methods: k-space data are continuously acquired using a rewound Cartesian
acquisition with spiral profile ordering (rCASPR) to provide matching contrast
to the conventional linear phase encode ordering and to sort data into multiple
respiratory phases. Low-resolution respiratory-correlated 4D images were
reconstructed with compressed sensing and used to estimate non-rigid
deformation vector fields, which were subsequently used for a motion
compensated image reconstruction. rCASPR sampling was compared to linear and
CASPR sampling in terms of point-spread-function (PSF) and image contrast with
in silico, phantom and in vivo experiments. Reconstruction parameters for
low-resolution 4D-MRI (spatial resolution and temporal regularization) were
determined using a grid search. The proposed motion compensated rCASPR was
evaluated in eight healthy volunteers and compared to free-breathing scans with
linear sampling. Image quality was compared based on visual inspection and
quantitatively by means of the gradient entropy.
  Results: rCASPR provided a superior PSF (similar in ky and narrower in kz)
and showed no considerable differences in images contrast compared to linear
sampling. The optimal 4D-MRI reconstruction parameters were spatial
resolution=$4.5 mm^3$ and $\lambda_t=10^{-4}$. The groupwise average gradient
entropy was 22.31 for linear, 22.20 for rCASPR, 22.14 for soft-gated rCASPR and
22.02 for motion compensated rCASPR.
  Conclusion: The proposed motion compensated rCASPR enables high quality
free-breathing T2-TSE with minimal changes in image contrast and scan time. The
proposed method therefore enables direct transfer of clinically used 3D TSE
sequences to free-breathing.


title: Mass Production of 2021 KMTNet Microlensing Planets I

abstract:
  We inaugurate a program of ``mass production'' of microlensing planets
discovered in 2021 KMTNet data, with the aim of laying the basis for future
statistical studies. While we ultimately plan to quickly publish all 2021
planets meeting some minimal criteria, the current sample of four was chosen
simply on the basis of having low initial estimates of the planet-host mass
ratio, $q$. It is therefore notable that 2 members of this sample suffer from a
degeneracy in the normalized source radius $\rho$ that arises from different
morphologies of closely spaced caustics. All four planets
[KMT-2021-BLG-(1391,1253,1372,0748)] have well-characterized mass ratios, $q$,
and therefore are suitable for mass-ratio frequency studies. Both of the $\rho$
degeneracies can be resolved by future adaptive optics (AO) observations on 30m
class telescopes. We provide general guidance for such AO observations for all
events in anticipation of the prospect that they will revolutionize the field
of microlensing planets.


title: CECILIA: Comprehensive Secure Machine Learning Framework

abstract:
  Since machine learning algorithms have proven their success in data mining
tasks, the data with sensitive information enforce privacy preserving machine
learning algorithms to emerge. Moreover, the increase in the number of data
sources and the high computational power required by those algorithms force
individuals to outsource the training and/or the inference of a machine
learning model to the clouds providing such services. To address this dilemma,
we propose a secure 3-party computation framework, CECILIA, offering privacy
preserving building blocks to enable more complex operations privately. Among
those building blocks, we have two novel methods, which are the exact
exponential of a public base raised to the power of a secret value and the
inverse square root of a secret Gram matrix. We employ CECILIA to realize the
private inference on pre-trained recurrent kernel networks, which require more
complex operations than other deep neural networks such as convolutional neural
networks, on the structural classification of proteins as the first study ever
accomplishing the privacy preserving inference on recurrent kernel networks.
The results demonstrate that we perform the exact and fully private exponential
computation, which is done by approximation in the literature so far. Moreover,
we can also perform the exact inverse square root of a secret Gram matrix
computation up to a certain privacy level, which has not been addressed in the
literature at all. We also analyze the scalability of CECILIA to various
settings on a synthetic dataset. The framework shows a great promise to make
other machine learning algorithms as well as further computations privately
computable by the building blocks of the framework.


title: The Input and Output Entropies of the $k$-Deletion/Insertion Channel

abstract:
  The channel output entropy of a transmitted word is the entropy of the
possible channel outputs and similarly the input entropy of a received word is
the entropy of all possible transmitted words. The goal of this work is to
study these entropy values for the $k$-deletion, $k$-insertion channel, where
exactly $k$ symbols are deleted, inserted in the transmitted word,
respectively. If all possible words are transmitted with the same probability
then studying the input and output entropies is equivalent. For both the
1-insertion and 1-deletion channels, it is proved that among all words with a
fixed number of runs, the input entropy is minimized for words with a skewed
distribution of their run lengths and it is maximized for words with a balanced
distribution of their run lengths. Among our results, we establish a conjecture
by Atashpendar et al. which claims that for the binary 1-deletion channel, the
input entropy is maximized for the alternating words.


title:
Harnessing $\mathcal{PT}$-symmetry in non-Hermitian stiffness-modulated
  waveguides

abstract:
  The recent progress in the context of elastic metamaterials and modulated
waveguides with digitally controllable properties has opened new pathways to
overcome the limitations dictated by Hermitian Hamiltonians in mechanics. Among
the possible implementations, non-Hermitian, $\mathcal{PT}$-symmetric systems
with balanced gain and loss have emerged as an elegant mechanism to access
novel functionalities by lifting the non-Hermitian degeneracies (exceptional
points). Motivated by this, the paper deals with a non-Hermitian and
$\mathcal{PT}$-symmetric elastic waveguide with complex stiffness-modulation.
The strength of the stiffness-modulations, tailored in the form of a balanced
gain/loss, delineates a transition from unbroken to broken
$\mathcal{PT}$-symmetric phases, where distinct Bloch-wave modes coalesce into
exceptional points. It is shown that, in the unbroken $\mathcal{PT}$-symmetric
regime, and due to the interplay between real and imaginary components of the
elasticity, the waveguide operates as a phononic filter. When the strength of
the gain/loss interactions increases, the frequency gap closes and the bulk
bands degenerate into an exceptional point, where the system operates as a
waveguide with asymmetric scattering capabilities. The paper provides a
connection between the distinct wave modes that populate the non-Hermitian
degeneracies and the directional reflection/transmission capabilities. The
asymmetric behavior is herein explained by combining the dispersion properties
of a $\mathcal{PT}$-symmetric rod, obtained through the plane wave expansion
method (PWEM), and the scattering matrix method (SMM) for a modulated slab
series-connected to semi-infinite media.


title: Context Autoencoder for Self-Supervised Representation Learning

abstract:
  We present a novel masked image modeling (MIM) approach, context autoencoder
(CAE), for self-supervised learning. We randomly partition the image into two
sets: visible patches and masked patches. The CAE architecture consists of: (i)
an encoder that takes visible patches as input and outputs their latent
representations, (ii) a latent context regressor that predicts the masked patch
representations from the visible patch representations that are not updated in
this regressor, (iii) a decoder that takes the estimated masked patch
representations as input and makes predictions for the masked patches, and (iv)
an alignment module that aligns the masked patch representation estimation with
the masked patch representations computed from the encoder.
  In comparison to previous MIM methods that couple the encoding and decoding
roles, e.g., using a single module in BEiT, our approach attempts
to~\emph{separate the encoding role (content understanding) from the decoding
role (making predictions for masked patches)} using different modules,
improving the content understanding capability. In addition, our approach makes
predictions from the visible patches to the masked patches in \emph{the latent
representation space} that is expected to take on semantics. In addition, we
present the explanations about why contrastive pretraining and supervised
pretraining perform similarly and why MIM potentially performs better. We
demonstrate the effectiveness of our CAE through superior transfer performance
in downstream tasks: semantic segmentation, and object detection and instance
segmentation.


title: Alexander polynomials and signatures of some high-dimensional knots

abstract:
  We give necessary and sufficient conditions for an integer to be the
signature of a (4q-1)-knot in the (4q+1)-sphere with a given square-free
Alexander polynomial.


title: QUARK: A Framework for Quantum Computing Application Benchmarking

abstract:
  Quantum computing (QC) is anticipated to provide a speedup over classical HPC
approaches for specific problems in optimization, simulation, and machine
learning. With the advances in quantum computing toward practical applications,
the need to analyze and compare different quantum solutions increases. While
different low-level benchmarks for QC exist, these benchmarks do not provide
sufficient insights into real-world application-level performance. We propose
an application-centric benchmark method and the QUantum computing Application
benchmaRK (QUARK) framework to foster the investigation and creation of
application benchmarks for QC. This paper establishes three significant
contributions: (1) it makes a case for application-level benchmarks and
provides an in-depth "pen and paper" benchmark formulation of two reference
problems: robot path and vehicle option optimization from the industrial
domain; (2) it proposes the open-source QUARK framework for designing,
implementing, executing, and analyzing benchmarks; (3) it provides multiple
reference implementations for these two reference problems based on different
known, and where needed, extended, classical and quantum algorithmic approaches
and analyzes their performance on different types of infrastructures.


title:
Structural Deformation and Metal-Semiconductor Transition in Coupled
  Carbon Chains

abstract:
  The transition between gapped (semiconducting) and gapless (metallic) phases
and tunability of bandgap in materials is a very lucrative yet considerably
challenging goal for new-age device preparation. For bulk materials and for
two-dimensional layered systems, this is a rapidly expanding field. We
theoretically propose a one-dimensional pure carbon material with a tunable
bandgap. We find that two parallel coupled polyyne chains show metallic
behaviour with bands crossing on the Fermi level, unlike the single
semiconducting chain. The number of nodal points (two) is robust under
transverse and longitudinal strain, indicating the symmetry-protected nature of
the metallic phase. Sliding one chain with respect to the other breaks
reflection symmetry and a clear bandgap opens up at the nodes, leading to a
gapped phase. By varying the slide parameter, the bandgap can be tuned
efficiently. This work initiates and indicates possible topological phases of
real one-dimensional materials without the involvement of edge modes.


title:
Inexistence of Non-Product Hessian Rank 1 Affinely Homogeneous
  Hypersurfaces $H^n$ in $\mathbb{R}^{n+1}$ in Dimension $n \geqslant 5$

abstract:
  Equivalences under the affine group ${\rm Aff} (\mathbb{R}^3)$ of constant
Hessian rank $1$ surfaces $S^2 \subset \mathbb{R}^3$, sometimes called
parabolic, were, among other objects, studied by Doubrov, Komrakov, Rabinovich,
Eastwood, Ezhov, Olver, Chen, Merker, Arnaldsson, Valiquette. Especially,
homogeneous models and algebras of differential invariants in various branches
have been fully understood.
  Then what about higher dimensions? We consider hypersurfaces $H^n \subset
\mathbb{R}^{n+1}$ graphed as $\{ u = F(x_1, \dots, x_n) \}$ whose Hessian
matrix $(F_{x_i x_j})$, a relative affine invariant, is, similarly, of constant
rank $1$. Are there homogeneous models?
  Complete explorations were done by the author on a computer in dimensions $n
= 2, 3, 4, 5, 6, 7$. The first, expected outcome, was to obtain a complete
classification of homogeneous models in dimensions $n = 2, 3, 4$ (forthcoming
article, case $n = 2$ already known). The second, unexpected outcome, was that
in dimensions $n = 5, 6, 7$, there are no affinely homogenous models!
  (Except those that are affinely equivalent to a product of $\mathbb{R}^m$
with a homogeneous model in dimensions $2, 3, 4$.)
  The present article establishes such a non-existence result in every
dimension $n \geqslant 5$, based on the production of a normal form for $\{ u =
F(x_1, \dots, x_n) \}$ under ${\rm Aff} (\mathbb{R}^{n+1})$, up to order
$\leqslant n+5$, valid in any dimension $n \geqslant 2$.


title: A comprehensive benchmark analysis for sand dust image reconstruction

abstract:
  Numerous sand dust image enhancement algorithms have been proposed in recent
years. To our best acknowledge, however, most methods evaluated their
performance with no-reference way using few selected real-world images from
internet. It is unclear how to quantitatively analysis the performance of the
algorithms in a supervised way and how we could gauge the progress in the
field. Moreover, due to the absence of large-scale benchmark datasets, there
are no well-known reports of data-driven based method for sand dust image
enhancement up till now. To advance the development of deep learning-based
algorithms for sand dust image reconstruction, while enabling supervised
objective evaluation of algorithm performance. In this paper, we presented a
comprehensive perceptual study and analysis of real-world sand dust images,
then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for training
Convolutional Neural Networks (CNNs) and evaluating algorithms performance. In
addition, we adopted the existing image transformation neural network trained
on SIRB as baseline to illustrate the generalization of SIRB for training CNNs.
Finally, we conducted the qualitative and quantitative evaluation to
demonstrate the performance and limitations of the state-of-the-arts (SOTA),
which shed light on future research in sand dust image reconstruction.


title: Coded Caching Does Not Generally Benefit From Selfish Caching

abstract:
  In typical coded caching scenarios, the content of a central library is
assumed to be of interest to all receiving users. However, in a realistic
scenario the users may have diverging interests which may intersect to various
degrees. What happens for example if each file is of potential interest to,
say, $40\,\%$ of the users and each user has potential interest in $40\,\%$ of
the library? What if then each user caches selfishly only from content of
potential interest? In this work, we formulate the symmetric selfish coded
caching problem, where each user naturally makes requests from a subset of the
library, which defines its own file demand set (FDS), and caches selfishly only
contents from its own FDS. For the scenario where the different FDSs
symmetrically overlap to some extent, we propose a novel information-theoretic
converse that reveals, for such general setting of symmetric FDS structures,
that selfish coded caching yields a load performance which is strictly worse
than that in standard coded caching.


title:
Mental Stress Detection using Data from Wearable and Non-wearable
  Sensors: A Review

abstract:
  This paper presents a comprehensive review of methods covering significant
subjective and objective human stress detection techniques available in the
literature. The methods for measuring human stress responses could include
subjective questionnaires (developed by psychologists) and objective markers
observed using data from wearable and non-wearable sensors. In particular,
wearable sensor-based methods commonly use data from electroencephalography,
electrocardiogram, galvanic skin response, electromyography, electrodermal
activity, heart rate, heart rate variability, and photoplethysmography both
individually and in multimodal fusion strategies. Whereas, methods based on
non-wearable sensors include strategies such as analyzing pupil dilation and
speech, smartphone data, eye movement, body posture, and thermal imaging.
Whenever a stressful situation is encountered by an individual, physiological,
physical, or behavioral changes are induced which help in coping with the
challenge at hand. A wide range of studies has attempted to establish a
relationship between these stressful situations and the response of human
beings by using different kinds of psychological, physiological, physical, and
behavioral measures. Inspired by the lack of availability of a definitive
verdict about the relationship of human stress with these different kinds of
markers, a detailed survey about human stress detection methods is conducted in
this paper. In particular, we explore how stress detection methods can benefit
from artificial intelligence utilizing relevant data from various sources. This
review will prove to be a reference document that would provide guidelines for
future research enabling effective detection of human stress conditions.


title: Temperature dependence of energy loss in gas-filled bouncing balls

abstract:
  The "coefficient of restitution" $\epsilon$ is a measure of the energy which
is retained when a ball bounces. This can be easily and accurately measured in
an "at home" experiment. Here, for a gas-filled ball such as a tennis ball, we
construct a simple two-parameter model to describe how $\epsilon$ changes as a
function of temperature. A comparison with data shows good agreement.


title: Josephson oscillation in the dissipative Bose-Hubbard dimer

abstract:
  We analyze Josephson's oscillation of Bose particles in the open
(dissipative) Bose-Hubbard dimer. First, we excite the dimer from the vacuum
state into a state suitable for observing the oscillation by using a special
protocol for external driving. Next, we switch off the driving and observe the
oscillation. It is shown that the main mechanism for the decay of Josephson's
oscillation is the dephasing due to fluctuating number of particles in open
systems. An analytical estimate for the decay time is obtained.


title: Structure-Aware Transformer for Graph Representation Learning

abstract:
  The Transformer architecture has gained growing attention in graph
representation learning recently, as it naturally overcomes several limitations
of graph neural networks (GNNs) by avoiding their strict structural inductive
biases and instead only encoding the graph structure via positional encoding.
Here, we show that the node representations generated by the Transformer with
positional encoding do not necessarily capture structural similarity between
them. To address this issue, we propose the Structure-Aware Transformer, a
class of simple and flexible graph transformers built upon a new self-attention
mechanism. This new self-attention incorporates structural information into the
original self-attention by extracting a subgraph representation rooted at each
node before computing the attention. We propose several methods for
automatically generating the subgraph representation and show theoretically
that the resulting representations are at least as expressive as the subgraph
representations. Empirically, our method achieves state-of-the-art performance
on five graph prediction benchmarks. Our structure-aware framework can leverage
any existing GNN to extract the subgraph representation, and we show that it
systematically improves performance relative to the base GNN model,
successfully combining the advantages of GNNs and transformers.


title: Momentum recoil in the relativistic two-body problem: higher-order tails

abstract:
  In the description of the relativistic two-body interaction, together with
the effects of energy and angular momentum losses due to the emission of
gravitational radiation, one has to take into account also the loss of linear
momentum, which is responsible for the recoil of the center-of-mass of the
system. We compute higher-order tail (i.e., tail-of-tail and tail-squared)
contributions to the linear momentum flux for a nonspinning binary system
either along hyperboliclike or ellipticlike orbits. The corresponding orbital
averages are evaluated at their leading post-Newtonian approximation, using
harmonic coordinates and working in the Fourier domain. The final expressions
are given in a large-eccentricity (or large-angular momentum) expansion along
hyperboliclike orbits and in a small-eccentricity expansion along ellipticlike
orbits. We thus complete a previous analysis focusing on both energy and
angular momentum losses [Phys. Rev. D \textbf{104}, no.10, 104020 (2021)],
providing brick-type results which will be useful, e.g., in the high-accurate
determination of the radiated impulses of the two bodies undergoing a
scattering process.


title:
Deep Networks on Toroids: Removing Symmetries Reveals the Structure of
  Flat Regions in the Landscape Geometry

abstract:
  We systematize the approach to the investigation of deep neural network
landscapes by basing it on the geometry of the space of implemented functions
rather than the space of parameters. Grouping classifiers into equivalence
classes, we develop a standardized parameterization in which all symmetries are
removed, resulting in a toroidal topology. On this space, we explore the error
landscape rather than the loss. This lets us derive a meaningful notion of the
flatness of minimizers and of the geodesic paths connecting them. Using
different optimization algorithms that sample minimizers with different
flatness we study the mode connectivity and other characteristics. Testing a
variety of state-of-the-art architectures and benchmark datasets, we confirm
the correlation between flatness and generalization performance; we further
show that in function space flatter minima are closer to each other and that
the barriers along the geodesics connecting them are small. We also find that
minimizers found by variants of gradient descent can be connected by zero-error
paths with a single bend. We observe similar qualitative results in neural
networks with binary weights and activations, providing one of the first
results concerning the connectivity in this setting. Our results hinge on
symmetry removal, and are in remarkable agreement with the rich phenomenology
described by some recent analytical studies performed on simple shallow models.


title:
The Exact Load-Memory Tradeoff of Multi-Access Coded Caching With
  Combinatorial Topology

abstract:
  Recently, Muralidhar et al. proposed a novel multi-access system model where
each user is connected to multiple caches in a manner that follows the
well-known combinatorial topology of combination networks. For such
multi-access topology, the same authors proposed an achievable scheme, which
stands out for the unprecedented coding gains even with very modest cache
resources. In this paper, we identify the fundamental limits of such
multi-access setting with exceptional potential, providing an
information-theoretic converse which establishes, together with the inner bound
by Muralidhar et al., the exact optimal performance under uncoded prefetching.


title: Towards Learning Through Open-Domain Dialog

abstract:
  The development of artificial agents able to learn through dialog without
domain restrictions has the potential to allow machines to learn how to perform
tasks in a similar manner to humans and change how we relate to them. However,
research in this area is practically nonexistent. In this paper, we identify
the modifications required for a dialog system to be able to learn from the
dialog and propose generic approaches that can be used to implement those
modifications. More specifically, we discuss how knowledge can be extracted
from the dialog, used to update the agent's semantic network, and grounded in
action and observation. This way, we hope to raise awareness for this subject,
so that it can become a focus of research in the future.


title: Smooth multisoliton solutions of the Geng-Xue equation

abstract:
  We present a reciprocal transformation which links the Geng-Xue equation to a
particular reduction of the first negative flow of the Boussinesq hierarchy. We
discuss two reductions of the reciprocal transformation for the
Degasperis-Procesi and Novikov equations, respectively. With the aid of the
Darboux transformation and the reciprocal transformation, we obtain a compact
parametric representation for the smooth soliton solutions such as multi-kink
solutions of the Geng-Xue equation.


title: Quantum States of a Two-Level Atom trapped in a Helical Optical Tube

abstract:
  We investigate the quantized states of a two-level Rb atom which is trapped
by the optical dipole potential when the atom interacts with a helical optical
tube (HOT) light field. The analysis shows that the stationary states can be
defined by a triplet of quantum numbers and the corresponding wavefunctions
exhibit a twisted, spiral-like 3D spatial shape.


title: Dynamics of rotation in chiral nuclei

abstract:
  The dynamics of chiral nuclei is investigated for the first time with the
time-dependent and tilted axis cranking covariant density functional theories
on a three-dimensional space lattice in a microscopic and self-consistent way.
The experimental energies of the two pairs of the chiral doublet bands in
$^{135}$Nd are well reproduced without any adjustable parameters beyond the
well-defined density functional. A novel mechanism, i.e., chiral precession, is
revealed from the microscopic dynamics of the total angular momentum in the
body-fixed frame, whose harmonicity is associated with a transition from the
planar into aplanar rotations with the increasing spin. This provides a fully
microscopic and dynamical view to understand the chiral excitations in nuclei.


title: Hybrid quantum annealing for larger-than-QPU lattice-structured problems

abstract:
  Quantum processing units (QPUs) executing annealing algorithms have shown
promise in optimization and simulation applications. Hybrid algorithms are a
natural bridge to additional applications of larger scale. We present a
straightforward and effective method for solving larger-than-QPU
lattice-structured Ising optimization problems. Performance is compared against
simulated annealing with promising results, and improvement is shown as a
function of the generation of D-Wave QPU used.


title: Metric-valued regression

abstract:
  We propose an efficient algorithm for learning mappings between two metric
spaces, $\X$ and $\Y$. Our procedure is strongly Bayes-consistent whenever $\X$
and $\Y$ are topologically separable and $\Y$ is "bounded in expectation" (our
term; the separability assumption can be somewhat weakened). At this level of
generality, ours is the first such learnability result for unbounded loss in
the agnostic setting. Our technique is based on metric medoids (a variant of
Fr\'echet means) and presents a significant departure from existing methods,
which, as we demonstrate, fail to achieve Bayes-consistency on general
instance- and label-space metrics. Our proofs introduce the technique of {\em
semi-stable compression}, which may be of independent interest.


title: A new face swap method for image and video domains: a technical report

abstract:
  Deep fake technology became a hot field of research in the last few years.
Researchers investigate sophisticated Generative Adversarial Networks (GAN),
autoencoders, and other approaches to establish precise and robust algorithms
for face swapping. Achieved results show that the deep fake unsupervised
synthesis task has problems in terms of the visual quality of generated data.
These problems usually lead to high fake detection accuracy when an expert
analyzes them. The first problem is that existing image-to-image approaches do
not consider video domain specificity and frame-by-frame processing leads to
face jittering and other clearly visible distortions. Another problem is the
generated data resolution, which is low for many existing methods due to high
computational complexity. The third problem appears when the source face has
larger proportions (like bigger cheeks), and after replacement it becomes
visible on the face border. Our main goal was to develop such an approach that
could solve these problems and outperform existing solutions on a number of
clue metrics. We introduce a new face swap pipeline that is based on
FaceShifter architecture and fixes the problems stated above. With a new eye
loss function, super-resolution block, and Gaussian-based face mask generation
leads to improvements in quality which is confirmed during evaluation.


title:
Data set creation and empirical analysis for detecting signs of
  depression from social media postings

abstract:
  Depression is a common mental illness that has to be detected and treated at
an early stage to avoid serious consequences. There are many methods and
modalities for detecting depression that involves physical examination of the
individual. However, diagnosing mental health using their social media data is
more effective as it avoids such physical examinations. Also, people express
their emotions well in social media, it is desirable to diagnose their mental
health using social media data. Though there are many existing systems that
detects mental illness of a person by analysing their social media data,
detecting the level of depression is also important for further treatment.
Thus, in this research, we developed a gold standard data set that detects the
levels of depression as `not depressed', `moderately depressed' and `severely
depressed' from the social media postings. Traditional learning algorithms were
employed on this data set and an empirical analysis was presented in this
paper. Data augmentation technique was applied to overcome the data imbalance.
Among the several variations that are implemented, the model with Word2Vec
vectorizer and Random Forest classifier on augmented data outperforms the other
variations with a score of 0.877 for both accuracy and F1 measure.


title:
Holographic superconductors with higher order corrections in
  lower-dimensional Gauss-Bonnet gravity

abstract:
  We study the properties of holographic $s$-wave and $p$-wave superconductors
with higher order corrections in Gauss-Bonnet gravity in $3$ and
$4$-dimensional space-time. We employ shooting method to solve equations of
motion numerically and obtain the effect of different values of mass, nonlinear
and Gauss-Bonnet parameters on critical temperature and condensation. Based on
our results, increasing each of these three parameters leads to lower
temperatures and larger values of condensation. This phenomenon is rooted in
the fact that conductor/superconductor phase transition faces with difficulty
for higher effect of nonlinear and Gauss-Bonnet terms in the presence of a
massive field. In addition, we study the electrical conductivity in holographic
setup. In $D=4$, real and imaginary parts of conductivity in holographic $s$-
and $p$-wave models behave similarly and follow the same trend as higher
dimensions by showing the delta function and pole at low frequency regime that
Kramers-Kronig relation can connect these two parts of conductivity to each
other. We observe the appearance of a gap energy at $\omega_{g}\approx 8T_{c}$
at which shifts toward higher frequencies by diminishing temperature and
increasing the effect of nonlinear and Gauss-Bonnet terms. Conductivity in $D =
3$ is far different from other dimensions. Even the real and imaginary parts in
$s$- and $p$-wave modes pursue various trends. For example in $\omega
\rightarrow 0$ limit, imaginary part in holographic $s$-wave model tends to
infinity but in $p$-wave model approaches to zero. However, the real parts in
both models show a delta function behavior. In general, real and imaginary
parts of conductivity in all cases that we study tend to a constant value in
$\omega \rightarrow \infty$ regime.


title: Machine Learning of Combinatorial Rules in Mechanical Metamaterials

abstract:
  Combinatorial problems arising in puzzles, origami, and (meta)material design
have rare sets of solutions, which define complex and sharply delineated
boundaries in configuration space. These boundaries are difficult to capture
with conventional statistical and numerical methods. Here we show that
convolutional neural networks can learn to recognize these boundaries for
combinatorial mechanical metamaterials, down to finest detail, despite using
heavily undersampled training sets, and can successfully generalize. This
suggests that the network infers the underlying combinatorial rules from the
sparse training set, opening up new possibilities for complex design of
(meta)materials.


title:
A First Glimpse at the Shell Structure beyond $^{54}$Ca: Spectroscopy of
  $^{55}$K, $^{55}$Ca, and $^{57}$Ca

abstract:
  States in the $N=35$ and 37 isotopes $^{55,57}$Ca have been populated by
direct proton-induced nucleon removal reactions from $^{56,58}$Sc and $^{56}$Ca
beams at the RIBF. In addition, the $(p,2p)$ quasi-free single-proton removal
reaction from $^{56}$Ca was studied. Excited states in $^{55}$K, $^{55}$Ca, and
$^{57}$Ca were established for the first time via in-beam $\gamma$-ray
spectroscopy. Results for the proton and neutron removal reactions from
$^{56}$Ca to states in $^{55}$K and $^{55}$Ca for the level energies, excited
state lifetimes, and exclusive cross sections agree well with state-of-the-art
theoretical calculations using different approaches. The observation of a
short-lived state in $^{57}$Ca suggests a transition in the calcium isotopic
chain from single-particle dominated states at $N=35$ to collective excitations
at $N=37$.


title: Using Partial Monotonicity in Submodular Maximization

abstract:
  Over the last two decades, submodular function maximization has been the
workhorse of many discrete optimization problems in machine learning
applications. Traditionally, the study of submodular functions was based on
binary function properties. However, such properties have an inherit weakness,
namely, if an algorithm assumes functions that have a particular property, then
it provides no guarantee for functions that violate this property, even when
the violation is very slight. Therefore, recent works began to consider
continuous versions of function properties. Probably the most significant among
these (so far) are the submodularity ratio and the curvature, which were
studied extensively together and separately.
  The monotonicity property of set functions plays a central role in submodular
maximization. Nevertheless, and despite all the above works, no continuous
version of this property has been suggested to date (as far as we know). This
is unfortunate since submoduar functions that are almost monotone often arise
in machine learning applications. In this work we fill this gap by defining the
monotonicity ratio, which is a continues version of the monotonicity property.
We then show that for many standard submodular maximization algorithms one can
prove new approximation guarantees that depend on the monotonicity ratio;
leading to improved approximation ratios for the common machine learning
applications of movie recommendation, quadratic programming and image
summarization.


title:
Unifying Architectures, Tasks, and Modalities Through a Simple
  Sequence-to-Sequence Learning Framework

abstract:
  In this work, we pursue a unified paradigm for multimodal pretraining to
break the scaffolds of complex task/modality-specific customization. We propose
OFA, a unified multimodal pretrained model that unifies modalities (i.e.,
cross-modality, vision, language) and tasks (e.g., image generation, visual
grounding, image captioning, image classification, text generation, etc.) to a
simple sequence-to-sequence learning framework based on the encoder-decoder
architecture. OFA performs pretraining and finetuning with task instructions
and introduces no extra task-specific layers for finetuning. Experimental
results show that OFA achieves new state-of-the-arts on a series of multimodal
tasks, including image captioning (COCO test CIDEr: 149.6), text-to-image
generation (COCO test FID: 10.5), VQA (test-std acc.: 80.02), SNLI-VE (test
acc.: 90.20), and referring expression comprehension (RefCOCO / RefCOCO+ /
RefCOCOg test acc.: 92.93 / 90.10 / 85.20). Through extensive analyses, we
demonstrate that OFA reaches comparable performance with uni-modal pretrained
models (e.g., BERT, MAE, MoCo v3, SimCLR v2, etc.) in uni-modal tasks,
including NLU, NLG, and image classification, and it effectively transfers to
unseen tasks and domains. Code shall be released soon at
http://github.com/OFA-Sys/OFA


title: Planetesimal/Debris discs

abstract:
  This review chapter for young researchers presents our current understanding
of debris discs. It introduces some of their basic properties and observables,
and describes how we think they form and collisionally evolve. Special emphasis
is dedicated to ALMA observations of the dust and gas, which constrains the
distribution of planetesimals, their volatile composition, and potential
volatile delivery to planetary atmospheres.


title: Heavy quarks at finite temperature

abstract:
  New incarnations of heavy-ion collision experiments are turning our attention
to hard processes and a more fine-grained resolution of the QGP. In this
endeavor quarkonia or open heavy flavors turn out to be versatile probes, which
are usually described through models based on perturbative QCD, AdS, and
effective field theories. The lattice provides nonperturbative input and
constraints to such models.\newline In-medium bottomonia, the complex static
quark-antiquark potential, and the heavy-quark momentum diffusion coefficient
are key quantities where lattice gauge theory has recently achieved significant
progress with impact for heavy-ion phenomenology. We review these lattice
results, relate them to phenomenological applications, and close with an
outlook.


title:
Enabling Automatic Repair of Source Code Vulnerabilities Using
  Data-Driven Methods

abstract:
  Users around the world rely on software-intensive systems in their day-to-day
activities. These systems regularly contain bugs and security vulnerabilities.
To facilitate bug fixing, data-driven models of automatic program repair use
pairs of buggy and fixed code to learn transformations that fix errors in code.
However, automatic repair of security vulnerabilities remains under-explored.
In this work, we propose ways to improve code representations for vulnerability
repair from three perspectives: input data type, data-driven models, and
downstream tasks. The expected results of this work are improved code
representations for automatic program repair and, specifically, fixing security
vulnerabilities.


title: Control of cascading failures in dynamical models of power grids

abstract:
  In this paper, we introduce a distributed control strategy to prevent
dynamically-induced cascading failures in power grids. We model power grids
using complex networks and nonlinear dynamics to provide a coarse-grained
description of the electro-mechanical phenomena taking place on them (in
particular, we use coupled swing equations) and restrict our analysis to
cascades of line failures, i.e., failures due to power flows exceeding the
maximum capacity of a line. We formulate a distributed control protocol relying
on the same topology of the physical layer and apply it to several power grid
models, including a small-size illustrative example with five nodes, the
Italian high-voltage (380kV) power grid, and the IEEE 118-bus system. Our
results indicate that the approach is capable of preventing cascading failures,
either controlling each node of the network or a suitable subset of them.


title: Multi-Objective Quality Diversity Optimization

abstract:
  In this work, we consider the problem of Quality-Diversity (QD) optimization
with multiple objectives. QD algorithms have been proposed to search for a
large collection of both diverse and high-performing solutions instead of a
single set of local optima. Thriving for diversity was shown to be useful in
many industrial and robotics applications. On the other hand, most real-life
problems exhibit several potentially antagonist objectives to be optimized.
Hence being able to optimize for multiple objectives with an appropriate
technique while thriving for diversity is important to many fields. Here, we
propose an extension of the MAP-Elites algorithm in the multi-objective
setting: Multi-Objective MAP-Elites (MOME). Namely, it combines the diversity
inherited from the MAP-Elites grid algorithm with the strength of
multi-objective optimizations by filling each cell with a Pareto Front. As
such, it allows to extract diverse solutions in the descriptor space while
exploring different compromises between objectives. We evaluate our method on
several tasks, from standard optimization problems to robotics simulations. Our
experimental evaluation shows the ability of MOME to provide diverse solutions
while providing global performances similar to standard multi-objective
algorithms.


title: Non-traditional intervals and their use. Which ones really make sense?

abstract:
  The paper discusses the question of why intervals, which are the main object
of Interval Analysis, have exactly the form that we know well and habitually
use, and not some other. In particular, we investigate why traditional
intervals are closed, i.\,e. contain their endpoints, and also what is wrong
with an empty interval. The second question considered in the work is how
expedient it is to expand the set of traditional intervals by some other
objects. We show that improper ("reversed") intervals and the arithmetic of
such intervals (Kaucher complete interval arithmetic) are very useful from many
different points of view.


title: Evaluation of Runtime Monitoring for UAV Emergency Landing

abstract:
  To certify UAV operations in populated areas, risk mitigation strategies --
such as Emergency Landing (EL) -- must be in place to account for potential
failures. EL aims at reducing ground risk by finding safe landing areas using
on-board sensors. The first contribution of this paper is to present a new EL
approach, in line with safety requirements introduced in recent research. In
particular, the proposed EL pipeline includes mechanisms to monitor learning
based components during execution. This way, another contribution is to study
the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within
the context of a real-world critical system. A new evaluation methodology is
introduced, and applied to assess the practical safety benefits of three MLRM
mechanisms. The proposed approach is compared to a default mitigation strategy
(open a parachute when a failure is detected), and appears to be much safer.


title: The Importance of Non-Markovianity in Maximum State Entropy Exploration

abstract:
  In the maximum state entropy exploration framework, an agent interacts with a
reward-free environment to learn a policy that maximizes the entropy of the
expected state visitations it is inducing. Hazan et al. (2019) noted that the
class of Markovian stochastic policies is sufficient for the maximum state
entropy objective, and exploiting non-Markovianity is generally considered
pointless in this setting. In this paper, we argue that non-Markovianity is
instead paramount for maximum state entropy exploration in a finite-sample
regime. Especially, we recast the objective to target the expected entropy of
the induced state visitations in a single trial. Then, we show that the class
of non-Markovian deterministic policies is sufficient for the introduced
objective, while Markovian policies suffer non-zero regret in general. However,
we prove that the problem of finding an optimal non-Markovian policy is at
least NP-complete. Despite this negative result, we discuss avenues to address
the problem in a tractable way and how non-Markovian exploration could benefit
the sample efficiency of online reinforcement learning in future works.


title: Longest Cycle above Erd\H{o}s-Gallai Bound

abstract:
  In 1959, Erd\H{o}s and Gallai proved that every graph G with average vertex
degree ad(G)\geq 2 contains a cycle of length at least ad(G). We provide an
algorithm that for k\geq 0 in time 2^{O(k)} n^{O(1)} decides whether a
2-connected n-vertex graph G contains a cycle of length at least ad(G)+k. This
resolves an open problem explicitly mentioned in several papers. The main
ingredients of our algorithm are new graph-theoretical results interesting on
their own.


title: Perfect state transfer on semi-Cayley graphs over abelian groups

abstract:
  In this paper, we consider the problem on the existence of perfect state
transfer(PST for short) on semi-Cayley graphs over abelian groups (which are
not necessarily regular), i.e on the graphs having semiregular and abelian
subgroups of automorphisms with two orbits of equal size. We stablish a
characterization of semi-Cayley graphs over abelian groups having PST. As a
result, we give a characterization of Cayley graphs over groups with an abelian
subgroup of index 2 having PST, which improves the earlier results on Cayley
graphs over abelian groups, dihedral groups and dicyclic group and determines
Cayley graphs over generalized dihedral groups and generalized dicyclic groups
having PST.


title: Proper condensates and off-diagonal long range order

abstract:
  Within the framework of the algebra of canonical commutation relations in
Euclidean space, a long range order between particles in bounded regions is
established in states with a sufficiently large particle number. It occurs
whenever homogeneous proper (infinite) condensates form locally in the states
in the limit of infinite densities. The condensates are described by
eigenstates of the momentum operator, covering also those cases, where they are
streaming with a constant velocity. The arguments given are model independent
and lead to a new criterion for the occurrence of condensates. It makes use of
a novel approach to the identification of condensates, based on a
characterization of regular and singular wave functions.


title: Flexible scenario for heavy element research

abstract:
  Detection system and software package coded in Builder C ++ are developed for
the new analog spectrometer of the DGFRS-2 setup installed on the new DC-280
cyclotron. This software allows providing data acquisition using a 48x128 DSSSD
(Double Side Silicon Strip Detector) detector and a multi-wire gaseous
detector. The main feature of this software package is the use of flexible
real-time algorithms to provide deep suppression of the background in reactions
for the synthesis of superheavy elements, using the detection of correlations
ER-{\alpha} (Recoil-alpha particle). The block-diagram of analog spectrometer
is presented.


title: Sequence of phase transitions in a model of interacting rods

abstract:
  In a system of interacting thin rigid rods of equal length $2 \ell$ on a
two-dimensional grid of lattice spacing $a$, we show that there are multiple
phase transitions as the coupling strength $\kappa=\ell/a$ and the temperature
are varied. There are essentially two classes of transitions. One corresponds
to the Ising-type spontaneous symmetry breaking transition and the second
belongs to less-studied phase transitions of geometrical origin. The latter
class of transitions appear at fixed values of $\kappa$ irrespective of the
temperature, whereas the critical coupling for the spontaneous symmetry
breaking transition depends on it. By varying the temperature, the phase
boundaries may cross each other, leading to a rich phase behaviour with
infinitely many phases. Our results are based on Monte Carlo simulations on the
square lattice, and a fixed-point analysis of a functional flow equation on a
Bethe lattice.


title:
Splitting Kronecker squares, 2-decomposition numbers, Catalan
  Combinatorics, and the Saxl conjecture

abstract:
  While there has been some progress on the decomposition of Kronecker products
of characters of the symmetric groups in recent times, results on the symmetric
and alternating part of Kronecker squares are still scarce. Here, new results
(and conjectures) are presented on this splitting of the squares that
contribute to a refined understanding of the Kronecker squares. Furthermore,
connections to 2-modular decomposition numbers, Catalan combinatorics, and to
the Saxl conjecture are discussed which further motivate the study of these
splittings.


title: Quantum Jet Bundles

abstract:
  We formulate a notion of jet bundles over a possibly noncommutative algebra
$A$ equipped with a torsion free connection. Among the conditions needed for
3rd-order jets and above is that the connection also be flat and its
`generalised braiding tensor' $\sigma:\Omega^1\otimes_A\Omega^1\to
\Omega^1\otimes_A
  \Omega^1$ obey the Yang-Baxter equation or braid relations. We also cover the
case of jet bundles of a given `vector bundle' over $A$ in the form of a
bimodule $E$ with flat bimodule connection with its braiding $\sigma_E$ obeying
the coloured braid relations. Examples include the permutation group $S_3$ with
its 2-cycles calculus, $M_2(\Bbb C)$ and the bicrossproduct model quantum
spacetime in two dimensions.


title:
Artificial Intelligence based tool wear and defect prediction for
  special purpose milling machinery using low-cost acceleration sensor
  retrofits

abstract:
  Milling machines form an integral part of many industrial processing chains.
As a consequence, several machine learning based approaches for tool wear
detection have been proposed in recent years, yet these methods mostly deal
with standard milling machines, while machinery designed for more specialized
tasks has gained only limited attention so far. This paper demonstrates the
application of an acceleration sensor to allow for convenient condition
monitoring of such a special purpose machine, i.e. round seam milling machine.
We examine a variety of conditions including blade wear and blade breakage as
well as improper machine mounting or insufficient transmission belt tension. In
addition, we presents different approaches to supervised failure recognition
with limited amounts of training data. Hence, aside theoretical insights, our
analysis is of high, practical importance, since retrofitting older machines
with acceleration sensors and an on-edge classification setup comes at low cost
and effort, yet provides valuable insights into the state of the machine and
tools in particular and the production process in general.


title:
Weak dissipation drives and enhances Wada basins in three-dimensional
  chaotic scattering

abstract:
  Chaotic scattering in three dimensions has not received as much attention as
in two dimensions so far. In this paper, we deal with a three-dimensional open
Hamiltonian system whose Wada basin boundaries become non Wada when the
critical energy value is surpassed in the absence of dissipation. In
particular, we study here the dissipation effects on this topological change,
which has no analogy in two dimensions. Hence, we find that non-Wada basins,
expected in the absence of dissipation, transform themselves into partially
Wada basins when a weak dissipation reduces the system energy below the
critical energy. We provide numerical evidence of the emergence of the Wada
points on the basin boundaries under weak dissipation. According to the paper
findings, Wada basins are typically driven, enhanced and, consequently,
structurally stable under weak dissipation in three-dimensional open
Hamiltonian systems.


title:
Addressing modern and practical challenges in machine learning: A survey
  of online federated and transfer learning

abstract:
  Online federated learning (OFL) and online transfer learning (OTL) are two
collaborative paradigms for overcoming modern machine learning challenges such
as data silos, streaming data, and data security. This survey explored OFL and
OTL throughout their major evolutionary routes to enhance understanding of
online federated and transfer learning. Besides, practical aspects of popular
datasets and cutting-edge applications for online federated and transfer
learning are highlighted in this work. Furthermore, this survey provides
insight into potential future research areas and aims to serve as a resource
for professionals developing online federated and transfer learning frameworks.


title: Distributionally Robust Fair Principal Components via Geodesic Descents

abstract:
  Principal component analysis is a simple yet useful dimensionality reduction
technique in modern machine learning pipelines. In consequential domains such
as college admission, healthcare and credit approval, it is imperative to take
into account emerging criteria such as the fairness and the robustness of the
learned projection. In this paper, we propose a distributionally robust
optimization problem for principal component analysis which internalizes a
fairness criterion in the objective function. The learned projection thus
balances the trade-off between the total reconstruction error and the
reconstruction error gap between subgroups, taken in the min-max sense over all
distributions in a moment-based ambiguity set. The resulting optimization
problem over the Stiefel manifold can be efficiently solved by a Riemannian
subgradient descent algorithm with a sub-linear convergence rate. Our
experimental results on real-world datasets show the merits of our proposed
method over state-of-the-art baselines.


title: Mathematical models of confirmation bias

abstract:
  Confirmation bias is a cognitive bias that adversely affects management
decisions, and mathematical modelling is an aid to its detailed understanding.
Bias in opinion update about the value of a parameter is modelled here assuming
that observations are discounted depending on their distance from prior
opinion. The models allow belief persistence, attitude polarization, and the
irrational primacy effect to be explored. A general framework for exploring
large-sample properties of these models is given, and an attempt made to
classify the models. An interesting result is that in some models the influence
of an observation always increases with distance from the prior opinion,
whereas in others observations greatly at odds with prior opinion are given
very little weight. The models could be useful to those exploring these
phenomena in detail.


title:
Tailoring accidental double bound states in the continuum in
  all-dielectric metasurfaces

abstract:
  Bound states in the continuum (BICs) have been thoroughly investigated due to
their formally divergent Q-factor, especially those emerging in all-dielectric,
nanostructured metasurfaces from symmetry protection at the $\Gamma$ point
(in-plane wavevector $k_{||}=0$). Less attention has been paid to accidental
BICs that may appear at any other $k_{||}\not =0$ in the band structure of
supported modes, being in turn difficult to predict. Here we make use of a
coupled electric/magnetic dipole model to determine analytical conditions for
the emergence of accidental BICs, valid for any planar array of meta-atoms that
can be described by dipolar resonances, which is the case of many
nanostructures in the optical domain. This is explored for all-dielectric
nanospheres through explicit analytical conditions that allow us in turn to
predict accidental BIC positions in the parameter space $(\omega,\bf{k_{||}}$).
Finally, such conditions are exploited to determine not only single, but also
double (for both linear polarizations) accidental BICs occurring at the same
position in the dispersion relation $\omega-\bf{k_{||}}$ for realistic
semiconductor nanodisk meta-atoms. This might pave the way to a variety of
BIC-enhanced light-matter interaction phenomena at the nanoscale such as lasing
or non-linear conversion, that benefit from emerging at wavevectors away from
the $\Gamma$ point (off-normal incidence) overlapping for both linear
polarizations.


title:
Imposing Temporal Consistency on Deep Monocular Body Shape and Pose
  Estimation

abstract:
  Accurate and temporally consistent modeling of human bodies is essential for
a wide range of applications, including character animation, understanding
human social behavior and AR/VR interfaces. Capturing human motion accurately
from a monocular image sequence is still challenging and the modeling quality
is strongly influenced by the temporal consistency of the captured body motion.
Our work presents an elegant solution for the integration of temporal
constraints in the fitting process. This does not only increase temporal
consistency but also robustness during the optimization. In detail, we derive
parameters of a sequence of body models, representing shape and motion of a
person, including jaw poses, facial expressions, and finger poses. We optimize
these parameters over the complete image sequence, fitting one consistent body
shape while imposing temporal consistency on the body motion, assuming linear
body joint trajectories over a short time. Our approach enables the derivation
of realistic 3D body models from image sequences, including facial expression
and articulated hands. In extensive experiments, we show that our approach
results in accurately estimated body shape and motion, also for challenging
movements and poses. Further, we apply it to the special application of sign
language analysis, where accurate and temporal consistent motion modelling is
essential, and show that the approach is well-suited for this kind of
application.


title: A short note on the orbit growth of sofic shifts

abstract:
  A sofic shift is a shift space consisting of bi-infinite labels of paths from
a labelled graph. Being a dynamical system, the distribution of its closed
orbits may indicate the complexity of the space. For this purpose, prime orbit
and Mertens' orbit counting functions are introduced as a way to describe the
growth of the closed orbits. The asymptotic behaviours of these counting
functions can be implied from the analiticity of the Artin-Mazur zeta function
of the space. Despite having a closed-form expression, the zeta function is
expressed implicitly in terms of several signed subset matrices, and this makes
the study on its analyticity to be seemingly difficult. In this paper, we will
prove the asymptotic behaviours of the counting functions for a sofic shift via
its zeta function. This involves investigating the properties of the said
matrices. Suprisingly, the proof is rather short and only uses well-known facts
about a sofic shift, especially on its minimal right-resolving presentation.


title: AMS-02 antiprotons and dark matter: Trimmed hints and robust bounds

abstract:
  Based on 4 yr AMS-02 antiproton data, we present bounds on the dark matter
(DM) annihilation cross section vs. mass for some representative final state
channels. We use recent cosmic-ray propagation models, a realistic treatment of
experimental and theoretical errors, and an updated calculation of input
antiproton spectra based on a recent release of the PYTHIA code. We find that
reported hints of a DM signal are statistically insignificant; an adequate
treatment of errors is crucial for credible conclusions. Antiproton bounds on
DM annihilation are among the most stringent ones, probing thermal DM up to the
TeV scale. The dependence of the bounds upon propagation models and the DM halo
profile is also quantified. A preliminary estimate reaches similar conclusions
when applied to the 7 years AMS-02 dataset, but also suggests extra caution as
for possible future claims of DM excesses.


title: Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests

abstract:
  Non-parametric two-sample tests (TSTs) that judge whether two sets of samples
are drawn from the same distribution, have been widely used in the analysis of
critical data. People tend to employ TSTs as trusted basic tools and rarely
have any doubt about their reliability. This paper systematically uncovers the
failure mode of non-parametric TSTs through adversarial attacks and then
proposes corresponding defense strategies. First, we theoretically show that an
adversary can upper-bound the distributional shift which guarantees the
attack's invisibility. Furthermore, we theoretically find that the adversary
can also degrade the lower bound of a TST's test power, which enables us to
iteratively minimize the test criterion in order to search for adversarial
pairs. To enable TST-agnostic attacks, we propose an ensemble attack (EA)
framework that jointly minimizes the different types of test criteria. Second,
to robustify TSTs, we propose a max-min optimization that iteratively generates
adversarial pairs to train the deep kernels. Extensive experiments on both
simulated and real-world datasets validate the adversarial vulnerabilities of
non-parametric TSTs and the effectiveness of our proposed defense.


title: Fair Interpretable Representation Learning with Correction Vectors

abstract:
  Neural network architectures have been extensively employed in the fair
representation learning setting, where the objective is to learn a new
representation for a given vector which is independent of sensitive
information. Various representation debiasing techniques have been proposed in
the literature. However, as neural networks are inherently opaque, these
methods are hard to comprehend, which limits their usefulness. We propose a new
framework for fair representation learning that is centered around the learning
of "correction vectors", which have the same dimensionality as the given data
vectors. Correction vectors may be computed either explicitly via architectural
constraints or implicitly by training an invertible model based on Normalizing
Flows. We show experimentally that several fair representation learning models
constrained in such a way do not exhibit losses in ranking or classification
performance. Furthermore, we demonstrate that state-of-the-art results can be
achieved by the invertible model. Finally, we discuss the law standing of our
methodology in light of recent legislation in the European Union.


title: Call-by-Value Solvability and Multi Types

abstract:
  This paper provides a characterization of call-by-value solvability using
call-by-value multi types. Our work is based on Accattoli and Paolini's
characterization of call-by-value solvable terms as those terminating with
respect to the solving strategy of the value substitution calculus, a
refinement of Plotkin's call-by-value $\lambda$-calculus. Here we show that the
solving strategy terminates on a term $t$ if and only if $t$ is typable in a
certain way in the multi type system induced by Ehrhard's call-by-value
relational semantics. Moreover, we show how to extract from the type system
exact bounds on the length of the solving evaluation and on the size of its
normal form, adapting de Carvalho's technique for call-by-name.


title:
Advection-enhanced kinetics in microtiter plates for improved surface
  assay quantitation and multiplexing capabilities

abstract:
  Surface assays such as ELISA are pervasive in clinics and research and
predominantly standardized in microtiter plates (MTP). MTPs provide many
advantages but are often detrimental to surface assay efficiency due to
inherent mass transport limitations. Microscale flows can overcome these and
largely improve assay kinetics. However, the disruptive nature of microfluidics
with existing labware and protocols has narrowed its transformative potential.
We present WellProbe, a novel microfluidic concept compatible with MTPs. With
it, we show that immunoassays become more sensitive at low concentrations (up
to 9-fold signal improvement in 12-fold less time), richer in information with
3-4 different kinetic conditions, and can be used to estimate kinetic
parameters, minimize washing steps and non-specific binding, and identify
compromised results. We further multiplex single-well assays combining
WellProbes kinetic regions with tailored microarrays. Finally, we demonstrate
our system in a context of immunoglobulin subclass evaluation, increasingly
regarded as clinically relevant.


title: Is Metaverse LAND a good investment? It depends on your unit of account!

abstract:
  The Sandbox metaverse LAND non-fungible token (NFT) prices increased by than
300 times (in USD) between December 2019 and January 2022, but when measured in
its native utility token (SAND), the increase is only 3 times. Depending on how
prices are denominated, investment returns and effective transaction prices
vary. We analyze more than 71,000 transactions and find that users are willing
to pay 3-4% more when transactions are settled in SAND, and 30% less when
settled in wETH (a smart contract version of ETH) when compared to ETH, so unit
of account matters. Our results contribute to the discussions of
blockchain-based, virtual economy management and the digitalization of money
(Brunnermeier et al., 2019).


title:
Stripped-envelope supernova light-curve bumps caused by variable thermal
  energy injection from magnetar spin down

abstract:
  Luminosity evolution of some stripped-envelope supernovae such as
superluminous supernovae is difficult to be explained by the canonical 56Ni
nuclear decay heating. A popular alternative heating source is rapid spin down
of strongly-magnetized rapidly-rotating neutron stars (magnetars) and it has
been shown that magnetar spin down can explain luminosity evolution of
superluminous supernovae. Recent observations have indicated that superluminous
supernovae often have bumpy light curves with multiple luminosity peaks. The
cause of bumpy light curves is still unknown. In this study, we investigate the
possibility that the light-curve bumps are caused by variabilities in the
thermal energy injection from magnetar spin down. We find that a temporal
increase in the thermal energy injection can lead to multiple luminosity peaks.
The multiple luminosity peaks caused by the variable thermal energy injection
is found to be accompanied by significant photospheric temperature increase,
and photospheric radii are not significantly changed. We show that the bumpy
light curves of SN 2015bn and SN 2019stc can be reproduced by temporarily
increasing magnetar spin-down energy input by a factor of 2-3 for 5-20 days.
However, not all the light-curve bumps are accompanied by the clear
photospheric temperature increase as predicted by our synthetic models. In
particular, the secondary light-curve bump of SN 2019stc is accompanied by a
temporal increase in photospheric radii rather than temperature, which is not
seen in our synthetic models. We, therefore, conclude that not all the
light-curve bumps observed in luminous supernovae are caused by the variable
thermal energy injection from magnetar spin down and some bumps are likely
caused by a different mechanism. Our study demonstrates the importance of
photosphere information to identify the cause of the bumpy light curves.


title:
Gender stereotypes in the mediated personalization of politics:
  Empirical evidence from a lexical, syntactic and sentiment analysis

abstract:
  The media attention to the personal sphere of famous and important
individuals has become a key element of the gender narrative. Here we combine
lexical, syntactic and sentiment analysis to investigate the role of gender in
the personalization of a wide range of political office holders in Italy during
the period 2017-2020. On the basis of a score for words that is introduced to
account for gender unbalance in both representative and news coverage, we show
that the political personalization in Italy is more detrimental for women than
men, with the persistence of entrenched stereotypes including a masculine
connotation of leadership, the resulting women's unsuitability to hold
political functions, and a greater deal of focus on their attractiveness and
body parts. In addition, women politicians are covered with a more negative
tone than their men counterpart when personal details are reported. Further,
the major contribution to the observed gender differences comes from online
news rather than print news, suggesting that the expression of certain
stereotypes may be better conveyed when click baiting and personal targeting
have a major impact.


title: Temporal Point Cloud Completion with Pose Disturbance

abstract:
  Point clouds collected by real-world sensors are always unaligned and sparse,
which makes it hard to reconstruct the complete shape of object from a single
frame of data. In this work, we manage to provide complete point clouds from
sparse input with pose disturbance by limited translation and rotation. We also
use temporal information to enhance the completion model, refining the output
with a sequence of inputs. With the help of gated recovery units(GRU) and
attention mechanisms as temporal units, we propose a point cloud completion
framework that accepts a sequence of unaligned and sparse inputs, and outputs
consistent and aligned point clouds. Our network performs in an online manner
and presents a refined point cloud for each frame, which enables it to be
integrated into any SLAM or reconstruction pipeline. As far as we know, our
framework is the first to utilize temporal information and ensure temporal
consistency with limited transformation. Through experiments in ShapeNet and
KITTI, we prove that our framework is effective in both synthetic and
real-world datasets.


title: Streaming readout for next generation electron scattering experiment

abstract:
  Current and future experiments at the high intensity frontier are expected to
produce an enormous amount of data that needs to be collected and stored for
offline analysis. Thanks to the continuous progress in computing and networking
technology, it is now possible to replace the standard `triggered' data
acquisition systems with a new, simplified and outperforming scheme. `Streaming
readout' (SRO) DAQ aims to replace the hardware-based trigger with a much more
powerful and flexible software-based one, that considers the whole detector
information for efficient real-time data tagging and selection. Considering the
crucial role of DAQ in an experiment, validation with on-field tests is
required to demonstrate SRO performance. In this paper we report results of the
on-beam validation of the Jefferson Lab SRO framework. We exposed different
detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator
hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B
production electron beam, with increasingly complex experimental conditions. By
comparing the data collected with the SRO system against the traditional DAQ,
we demonstrate that the SRO performs as expected. Furthermore, we provide
evidence of its superiority in implementing sophisticated AI-supported
algorithms for real-time data analysis and reconstruction.


title:
Machine Translation from Signed to Spoken Languages: State of the Art
  and Challenges

abstract:
  Automatic translation from signed to spoken languages is an interdisciplinary
research domain, lying on the intersection of computer vision, machine
translation and linguistics. Nevertheless, research in this domain is performed
mostly by computer scientists in isolation. As the domain is becoming
increasingly popular - the majority of scientific papers on the topic of sign
language translation have been published in the past three years - we provide
an overview of the state of the art as well as some required background in the
different related disciplines. We give a high-level introduction to sign
language linguistics and machine translation to illustrate the requirements of
automatic sign language translation. We present a systematic literature review
to illustrate the state of the art in the domain and then, harking back to the
requirements, lay out several challenges for future research. We find that
significant advances have been made on the shoulders of spoken language machine
translation research. However, current approaches are often not linguistically
motivated or are not adapted to the different input modality of sign languages.
We explore challenges related to the representation of sign language data, the
collection of datasets, the need for interdisciplinary research and
requirements for moving beyond research, towards applications. Based on our
findings, we advocate for interdisciplinary research and to base future
research on linguistic analysis of sign languages. Furthermore, the inclusion
of deaf and hearing end users of sign language translation applications in use
case identification, data collection and evaluation is of the utmost importance
in the creation of useful sign language translation models. We recommend
iterative, human-in-the-loop, design and development of sign language
translation models.


title: Unsupervised Long-Term Person Re-Identification with Clothes Change

abstract:
  We investigate unsupervised person re-identification (Re-ID) with clothes
change, a new challenging problem with more practical usability and scalability
to real-world deployment. Most existing re-id methods artificially assume the
clothes of every single person to be stationary across space and time. This
condition is mostly valid for short-term re-id scenarios since an average
person would often change the clothes even within a single day. To alleviate
this assumption, several recent works have introduced the clothes change facet
to re-id, with a focus on supervised learning person identity discriminative
representation with invariance to clothes changes. Taking a step further
towards this long-term re-id direction, we further eliminate the requirement of
person identity labels, as they are significantly more expensive and more
tedious to annotate in comparison to short-term person re-id datasets. Compared
to conventional unsupervised short-term re-id, this new problem is drastically
more challenging as different people may have similar clothes whilst the same
person can wear multiple suites of clothes over different locations and times
with very distinct appearance. To overcome such obstacles, we introduce a novel
Curriculum Person Clustering (CPC) method that can adaptively regulate the
unsupervised clustering criterion according to the clustering confidence.
Experiments on three long-term person re-id datasets show that our CPC
outperforms SOTA unsupervised re-id methods and even closely matches the
supervised re-id models.


title: Generalized Minkowski weights and Chow rings of T-varieties

abstract:
  We give a combinatorial characterization of Fulton's operational Chow
cohomology groups of a complete rational T-variety of complexity one in terms
of so called generalized Minkowsky weights. In the contraction-free case, we
also describe the intersection product with Cartier invariant divisors in terms
of the combinatorial data. In particular this provides a new way of computing
top intersection numbers of invariant Cartier divisors combinatorially.


title: Energy relaxation and electron-phonon coupling in laser-excited metals

abstract:
  The rate of energy transfer between electrons and phonons is investigated by
a first principles framework for electron temperatures up to $T_e=50000$ K
while considering the lattice at ground state. Two typical but differently
complex metals are investigated, namely Aluminium and Copper. In order to
reasonably take the electronic excitation effect into account, we adopt finite
temperature density functional theory and linear response to determine the
electron-temperature-dependent Eliashberg function and electron density of
states. Of the three branch-dependent electron-phonon coupling strengths, the
longitudinal acoustic mode plays a dominant role in the electron-phonon
coupling for Aluminium for all temperatures considered here, but for Copper it
only dominates above an electron temperature of $T_e=40000$ K. The second
moment of the Eliashberg function and the electron phonon coupling constant at
room temperature $T_e=315$ K show good agreement with other results. For
increasing electron temperatures, we show the limits of the $T=0$ approximation
for the Eliashberg function. Our present work provides a rich perspective on
the phonon dynamics and this will help to improve insight into the underlying
mechanism of energy flow in ultra-fast laser-metal interaction.


title: Nanomaterials for Quantum Information Science and Engineering

abstract:
  Quantum information science and engineering (QISE) which entails use of
quantum mechanical states for information processing, communications, and
sensing and the area of nanoscience and nanotechnology have dominated condensed
matter physics and materials science research in the 21st century. Solid state
devices for QISE have, to this point, predominantly been designed with bulk
materials as their constituents. In this review, we consider how nanomaterials
(i.e. materials with intrinsic quantum confinement) may offer inherent
advantages over conventional materials for QISE. We identify the materials
challenges for specific types of qubits, and we identify how emerging
nanomaterials may overcome these challenges. Challenges for and progress
towards nanomaterials based quantum devices are identified. We aim to help
close the gap between the nanotechnology and quantum information communities
and inspire research that will lead to next generation quantum devices for
scalable and practical quantum applications.

categories: quant-ph cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.optics


title: Auto-Lambda: Disentangling Dynamic Task Relationships

abstract:
  Understanding the structure of multiple related tasks allows for multi-task
learning to improve the generalisation ability of one or all of them. However,
it usually requires training each pairwise combination of tasks together in
order to capture task relationships, at an extremely high computational cost.
In this work, we learn task relationships via an automated weighting framework,
named Auto-Lambda. Unlike previous methods where task relationships are assumed
to be fixed, Auto-Lambda is a gradient-based meta learning framework which
explores continuous, dynamic task relationships via task-specific weightings,
and can optimise any choice of combination of tasks through the formulation of
a meta-loss; where the validation loss automatically influences task weightings
throughout training. We apply the proposed framework to both multi-task and
auxiliary learning problems in computer vision and robotics, and show that
Auto-Lambda achieves state-of-the-art performance, even when compared to
optimisation strategies designed specifically for each problem and data domain.
Finally, we observe that Auto-Lambda can discover interesting learning
behaviors, leading to new insights in multi-task learning. Code is available at
https://github.com/lorenmt/auto-lambda.


title: Document-Level Event Extraction via Human-Like Reading Process

abstract:
  Document-level Event Extraction (DEE) is particularly tricky due to the two
challenges it poses: scattering-arguments and multi-events. The first challenge
means that arguments of one event record could reside in different sentences in
the document, while the second one reflects one document may simultaneously
contain multiple such event records. Motivated by humans' reading cognitive to
extract information of interests, in this paper, we propose a method called HRE
(Human Reading inspired Extractor for Document Events), where DEE is decomposed
into these two iterative stages, rough reading and elaborate reading.
Specifically, the first stage browses the document to detect the occurrence of
events, and the second stage serves to extract specific event arguments. For
each concrete event role, elaborate reading hierarchically works from sentences
to characters to locate arguments across sentences, thus the
scattering-arguments problem is tackled. Meanwhile, rough reading is explored
in a multi-round manner to discover undetected events, thus the multi-events
problem is handled. Experiment results show the superiority of HRE over prior
competitive methods.


title:
Effective Variable Depth Local Search for the Budgeted Maximum Coverage
  Problem

abstract:
  We address the Budgeted Maximum Coverage Problem (BMCP), which is a natural
and more practical extension of the standard 0-1 knapsack problem and the set
cover problem. Given m elements with nonnegative weights, n subsets of elements
with nonnegative costs, and a total budget, BMCP aims to select some subsets
such that the total cost of selected subsets does not exceed the budget, and
the total weight of associated elements is maximized. In this paper, we propose
a variable depth local search algorithm (VDLS) for the BMCP. VDLS first
generates an initial solution by a greedy algorithm, then iteratively improves
the solution through a partial depth-first search method, that can improve the
solution by simultaneously changing the states (selected or not) of multiple
subsets. Such method allows VDLS to explore the solution space widely and
deeply, and to yield high-quality solutions. We further propose a neighbour
structure to boost the algorithm performance, that is, both subsets have a
neighbour relation if they have at least one common associated element. By
applying the neighbour structure, VDLS can adjust the selected subsets while
losing as few covered elements as possible. Since the existing BMCP benchmarks
only have simple structures and small scales, we design 60 new instances with
relatively large scales and complex structures to enrich the diversity of the
BMCP instances. Experimental results on 30 public instances and 60 new
instances we designed demonstrate that VDLS significantly outperforms the
existing heuristic and the general CPLEX exact solver, for the BMCP.


title:
A revisit of generalized scaling of forced turbulence through flux
  analysis

abstract:
  In this investigation, we theoretically studied the transports of kinetic
energy and scalar variance in a turbulence (with stratification-like scalar
background) driven by a scalar-based volume force, which has a general form as
$M{\nabla}^{\beta} s'$ associated with scalar fluctuations $s'$, in wavenumber
space relies on flux conservation equation. The equation has one real solution
and two complex solutions. The real solution leads to symmetric cascade
processes, including inertial subrange (constant fluxes of kinetic energy and
scalar variance), CEF subrange (quasi-constant flux of kinetic energy), CSF
subrange (quasi-constant flux of scalar variance), and dissipation subrange
(nonconstant fluxes of kinetic energy and scalar variance). When ${\beta}<2/3$,
the CEF subrange locates on the lower wavenumber side of the CSF subrange.
While ${\beta}>2/3$, the CEF subrange locates on the higher wavenumber side of
the CSF subrange. The scaling exponents in the power spectra of kinetic energy
and scalar variance strictly depend on ${\beta}$. In the CEF subrange,
${\xi}_u=-5/3$, while ${\xi}_s=-(6{\beta}+1)/3$. In the CSF subrange,
${\xi}_u=(4{\beta}-11)/5$ and ${\xi}_s=-(2{\beta}+7)/5$. The investigations on
the 2nd solution show the symmetry of the cascade process is broken. When
${\beta}<2/3$, we only observe a new subrange with non-constant ${\Pi}_u$ and
${\Pi}_s$, where ${\xi}_u={\xi}_s=-3$. However, when $2/3<{\beta}<4$, only the
CEF subrange is present. For the 3rd solution, there is only the CEF subrange
in the range $0<={\beta}<=2.657$. Beyond this range, the 3rd solution becomes
physically inappropriate. Thus, a complete transport picture of both kinetic
energy and scalar variance has been established for the type of forced
turbulence.


title:
An explanation of the muon puzzle of ultrahigh-energy cosmic rays and
  the role of the Forward Physics Facility for model improvement

abstract:
  We investigate the observed muon deficit in air shower simulations when
compared to ultrahigh-energy cosmic ray (UHECR) data. Based upon the observed
enhancement of strangeness production in high-energy hadronic collisions
reported by the ALICE Collaboration, the concomitant $\pi \leftrightarrow K$
swap is considered as the keystone to resolve the muon anomaly through its
corresponding impact on the shower development. We construct a toy model in
terms of the $\pi \leftrightarrow K$ swapping probability $F_s$. We present a
parametrization of $F_s$ in terms of the pseudorapidity that can accommodate
the UHECR data. Looking to the future, we explore potential strategies for
model improvement using the massive amounts of data to be collected by LHC
neutrino detectors, such as FASER$\nu$ and experiments at the Forward Physics
Facility. We calculate the corresponding sensitivity to $F_s$ and show that
these experiments will be able to probe the model phase space.


title:
A mathematical model for inter-specific seagrass interactions:
  reproducing field observations for C. nodosa and C. prolifera

abstract:
  Seagrasses are vital organisms in coastal waters, and the drastic demise of
their population in the last decades has worrying implications for marine
ecosystems. Spatial models for seagrass meadows provide a mathematical
framework to study their dynamical processes and emergent collective behavior.
These models are crucial to predict the response of seagrasses to different
global warming scenarios, analyze the resilience of existing seagrass patterns,
and optimize restoration strategies. In this article, we propose a model based
on the plant clonal growth rules that includes interactions among different
species of seagrasses. We present a theoretical analysis of the model
considering the specific case of the seagrass-seaweed interaction between
Cymodocea nodosa and Caulerpa prolifera. Our simulations successfully reproduce
field observations of shoot densities in mixed meadows in the Ebro River Delta
in the Mediterranean Sea. Besides, the proposed model allows us to investigate
the possible underlying mechanisms that mediate the interaction among
macrophytes.


title:
Learn over Past, Evolve for Future: Search-based Time-aware
  Recommendation with Sequential Behavior Data

abstract:
  The personalized recommendation is an essential part of modern e-commerce,
where user's demands are not only conditioned by their profile but also by
their recent browsing behaviors as well as periodical purchases made some time
ago. In this paper, we propose a novel framework named Search-based Time-Aware
Recommendation (STARec), which captures the evolving demands of users over time
through a unified search-based time-aware model. More concretely, we first
design a search-based module to retrieve a user's relevant historical
behaviors, which are then mixed up with her recent records to be fed into a
time-aware sequential network for capturing her time-sensitive demands. Besides
retrieving relevant information from her personal history, we also propose to
search and retrieve similar user's records as an additional reference. All
these sequential records are further fused to make the final recommendation.
Beyond this framework, we also develop a novel label trick that uses the
previous labels (i.e., user's feedbacks) as the input to better capture the
user's browsing pattern. We conduct extensive experiments on three real-world
commercial datasets on click-through-rate prediction tasks against
state-of-the-art methods. Experimental results demonstrate the superiority and
efficiency of our proposed framework and techniques. Furthermore, results of
online experiments on a daily item recommendation platform of Company X show
that STARec gains average performance improvement of around 6% and 1.5% in its
two main item recommendation scenarios on CTR metric respectively.


title: Mock theta functions and characters of N=3 superconformal modules

abstract:
  In this paper we study the characters of N=3 superconformal modules by using
the Zwegers' theory on modification of mock theta functions.


title: FL_PyTorch: optimization research simulator for federated learning

abstract:
  Federated Learning (FL) has emerged as a promising technique for edge devices
to collaboratively learn a shared machine learning model while keeping training
data locally on the device, thereby removing the need to store and access the
full data in the cloud. However, FL is difficult to implement, test and deploy
in practice considering heterogeneity in common edge device settings, making it
fundamentally hard for researchers to efficiently prototype and test their
optimization algorithms. In this work, our aim is to alleviate this problem by
introducing FL_PyTorch : a suite of open-source software written in python that
builds on top of one the most popular research Deep Learning (DL) framework
PyTorch. We built FL_PyTorch as a research simulator for FL to enable fast
development, prototyping and experimenting with new and existing FL
optimization algorithms. Our system supports abstractions that provide
researchers with a sufficient level of flexibility to experiment with existing
and novel approaches to advance the state-of-the-art. Furthermore, FL_PyTorch
is a simple to use console system, allows to run several clients simultaneously
using local CPUs or GPU(s), and even remote compute devices without the need
for any distributed implementation provided by the user. FL_PyTorch also offers
a Graphical User Interface. For new methods, researchers only provide the
centralized implementation of their algorithm. To showcase the possibilities
and usefulness of our system, we experiment with several well-known
state-of-the-art FL algorithms and a few of the most common FL datasets.


title: Sequential Channel Synthesis

abstract:
  The channel synthesis problem has been widely investigated over the last
decade. In this paper, we consider the sequential version in which the encoder
and the decoder work in a sequential way. Under a mild assumption on the target
joint distribution we provide a complete (single-letter) characterization of
the solution for the point-to-point case, which shows that the canonical
symbol-by-symbol mapping is not optimal in general, but is indeed optimal if we
make some additional assumptions on the encoder and decoder. We also extend
this result to the broadcast scenario and the interactive communication
scenario. We provide bounds in the broadcast setting and a complete
characterization of the solution under a mild condition on the target joint
distribution in the interactive communication case. Our proofs are based on a
R\'enyi entropy method.


title:
NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural
  Networks

abstract:
  This paper proposes a fast and scalable method for uncertainty quantification
of machine learning models' predictions. First, we show the principled way to
measure the uncertainty of predictions for a classifier based on
Nadaraya-Watson's nonparametric estimate of the conditional label distribution.
Importantly, the approach allows to disentangle explicitly aleatoric and
epistemic uncertainties. The resulting method works directly in the feature
space. However, one can apply it to any neural network by considering an
embedding of the data induced by the network. We demonstrate the strong
performance of the method in uncertainty estimation tasks on a variety of
real-world image datasets, such as MNIST, SVHN, CIFAR-100 and several versions
of ImageNet.