title: Challenging Common Assumptions in Convex Reinforcement Learning

abstract:
  The classic Reinforcement Learning (RL) formulation concerns the maximization
of a scalar reward function. More recently, convex RL has been introduced to
extend the RL formulation to all the objectives that are convex functions of
the state distribution induced by a policy. Notably, convex RL covers several
relevant applications that do not fall into the scalar formulation, including
imitation learning, risk-averse RL, and pure exploration. In classic RL, it is
common to optimize an infinite trials objective, which accounts for the state
distribution instead of the empirical state visitation frequencies, even though
the actual number of trajectories is always finite in practice. This is
theoretically sound since the infinite trials and finite trials objectives can
be proved to coincide and thus lead to the same optimal policy. In this paper,
we show that this hidden assumption does not hold in the convex RL setting. In
particular, we show that erroneously optimizing the infinite trials objective
in place of the actual finite trials one, as it is usually done, can lead to a
significant approximation error. Since the finite trials setting is the default
in both simulated and real-world RL, we believe shedding light on this issue
will lead to better approaches and methodologies for convex RL, impacting
relevant research areas such as imitation learning, risk-averse RL, and pure
exploration among others.


title:
Data Heterogeneity-Robust Federated Learning via Group Client Selection
  in Industrial IoT

abstract:
  Nowadays, the industrial Internet of Things (IIoT) has played an integral
role in Industry 4.0 and produced massive amounts of data for industrial
intelligence. These data locate on decentralized devices in modern factories.
To protect the confidentiality of industrial data, federated learning (FL) was
introduced to collaboratively train shared machine learning models. However,
the local data collected by different devices skew in class distribution and
degrade industrial FL performance. This challenge has been widely studied at
the mobile edge, but they ignored the rapidly changing streaming data and
clustering nature of factory devices, and more seriously, they may threaten
data security. In this paper, we propose FedGS, which is a hierarchical
cloud-edge-end FL framework for 5G empowered industries, to improve industrial
FL performance on non-i.i.d. data. Taking advantage of naturally clustered
factory devices, FedGS uses a gradient-based binary permutation algorithm
(GBP-CS) to select a subset of devices within each factory and build
homogeneous super nodes participating in FL training. Then, we propose a
compound-step synchronization protocol to coordinate the training process
within and among these super nodes, which shows great robustness against data
heterogeneity. The proposed methods are time-efficient and can adapt to dynamic
environments, without exposing confidential industrial data in risky
manipulation. We prove that FedGS has better convergence performance than
FedAvg and give a relaxed condition under which FedGS is more
communication-efficient. Extensive experiments show that FedGS improves
accuracy by 3.5% and reduces training rounds by 59% on average, confirming its
superior effectiveness and efficiency on non-i.i.d. data.


title: Resonant resurgent asymptotics from quantum field theory

abstract:
  We perform an all-order resurgence analysis of a quantum field theory
renormalon that contributes to an anomalous dimension in six-dimensional scalar
$\phi^3$ theory and is governed by a third-order nonlinear differential
equation. We augment the factorially divergent perturbative expansion
associated to the renormalon by asymptotic expansions to all instanton orders,
in a conjectured and well-tested formula. A distinctive feature of this
renormalon singularity is the appearance of logarithmic terms, starting at
second-instanton order in the trans-series. To highlight this and to illustrate
our methods, we also analyze the trans-series for a closely related
second-order nonlinear differential equation that exhibits a similarly resonant
structure but lacks logarithmic contributions.


title:
Modeling the kinematics of the decelerating jets from the black hole
  X-ray binary MAXI J1348$-$630

abstract:
  Black hole low mass X-ray binaries (BH LMXBs) can launch powerful outflows in
the form of discrete ejecta. Observing the entire trajectory of these ejecta
allows us to model their motion with great accuracy, and this is essential for
measuring their physical properties. In particular, observing the final
deceleration phase, often poorly sampled, is fundamental to obtain a reliable
estimate of the jet's energy. During its 2019/2020 outburst, the BH LMXB MAXI
J1348$-$630 launched a single-sided radio-emitting jet that was detected at
large scales after a strong deceleration due to the interaction with the
interstellar medium (ISM). We successfully modelled the jet motion with a
dynamical external shock model, which allowed us to constrain the jet initial
Lorentz factor $\Gamma_0 = 1.85^{+0.15}_{-0.12}$, inclination angle $\theta =
29.3_{-3.2}^{+2.7}$ deg and ejection date $t_{\rm ej} = 21.5_{-3.0}^{+1.8}$
(MJD $-$ $58500$). Under simple assumptions on the jet opening angle and on the
external ISM density, we find that the jet has a large initial kinetic energy
$E_0 = 4.6^{+20.0}_{-3.4} \times 10^{46}$ erg, far greater than what commonly
measured for LMXBs from the jet's synchrotron emission. This implies that
discrete ejecta radiate away only a small fraction of their total energy, which
is instead transferred to the environment. The jet power estimate is larger
than the simultaneous available accretion power, and we present several options
to mitigate this discrepancy. We infer that MAXI J1348$-$630 is likely embedded
in an ISM cavity with internal density $n = 0.0010^{+0.0005}_{-0.0003}$
cm$^{-3}$ and radius $R_{\rm c} = 0.61^{+0.11}_{-0.09}$ pc, which could have
been produced by the system's previous activity, as proposed for other BH
LMXBs.


title:
Channel State Acquisition in FDD Massive MIMO: Rate-Distortion Bound and
  Effectiveness of "Analog" Feedback

abstract:
  We consider the problem of estimating channel fading coefficients (modeled as
a correlated Gaussian vector) via Downlink (DL) training and Uplink (UL)
feedback in wideband FDD massive MIMO systems. Using rate-distortion theory, we
derive optimal bounds on the achievable channel state estimation error in terms
of the number of training pilots in DL ($\beta_{tr}$) and feedback dimension in
UL ($\beta_{fb}$), with random, spatially isotropic pilots. It is shown that
when the number of training pilots exceeds the channel covariance rank ($r$),
the optimal rate-distortion feedback strategy achieves an estimation error
decay of $\Theta (SNR^{-\alpha})$ in estimating the channel state, where
$\alpha = min (\beta_{fb}/r , 1)$ is the so-called quality scaling exponent. We
also discuss an "analog" feedback strategy, showing that it can achieve the
optimal quality scaling exponent for a wide range of training and feedback
dimensions with no channel covariance knowledge and simple signal processing at
the user side. Our findings are supported by numerical simulations comparing
various strategies in terms of channel state mean squared error and achievable
ergodic sum-rate in DL with zero-forcing precoding.


title: Network resilience in the aging brain

abstract:
  Degeneration and adaptation are two competing sides of the same coin called
resilience in the progressive processes of brain aging or diseases.
Degeneration accumulates during brain aging and other cerebral activities,
causing structural atrophy and dysfunction. At the same time, adaptation allows
brain network reorganize to compensate for structural loss to maintain
cognition function. Although hidden resilience mechanism is critical and
fundamental to uncover the brain aging law, due to the lack of datasets and
appropriate methodology, it remains essentially unknown how these two processes
interact dynamically across brain networks. To quantitatively investigate this
complex process, we analyze aging brains based on 6-year follow-up multimodal
neuroimaging database from 63 persons. We reveal the critical mechanism of
network resilience that various perturbation may cause fast brain structural
atrophy, and then brain can reorganize its functional layout to lower its
operational efficiency, which helps to slow down the structural atrophy and
finally recover its functional efficiency equilibrium. This empirical finding
could be explained by our theoretical model, suggesting one universal
resilience dynamical function. This resilience is achieved in the brain
functional network with evolving percolation and rich-club features. Our
findings can help to understand the brain aging process and design possible
mitigation methods to adjust interaction between degeneration and adaptation
from resilience viewpoint.


title: The nature of the on-street parking search

abstract:
  Parking occupancy in the area is defined by three major parameters - the rate
of cars arrivals, the dwell time of already parked cars, and the willingness of
drivers who are searching but yet did not find a vacant parking spot, to
continue their search. We investigate a series of theoretical and numeric
models, deterministic and stochastic, that describe parking dynamics in the
area as dependent on these parameters, over the entire spectrum of the demand
to supply ratio, focusing on the case when the demand is close to or above the
supply. We demonstrate that a simple deterministic model provides a good
analytical approximation for the major characteristics of the parking system -
the average fraction of cars among the arriving that will find parking in the
area, the average number of cars that cruise for parking, and average cruising
time. Stochastic models make it possible to estimate the distributions of these
characteristics as well as the parameters that are related to the variance of
these distributions, like the fraction of the arriving cars that find parking
in less than t minutes.


title:
Ammonia mapping observations toward the Galactic massive star-forming
  region Sh 2-255 and Sh 2-257

abstract:
  We performed NH$_3\ (J,K)=(1,1),(2,2),$ and $(3,3)$ mapping observations
toward the Galactic massive star-forming region Sh 2-255 and Sh 2-257 using the
Nobeyama 45-m telescope as a part of the KAGONMA (KAgoshima Galactic Object
survey with the Nobeyama 45-metre telescope by Mapping in Ammonia lines)
project. NH$_3$ (1,1) has an intensity peak at the cluster S255 N, is
distributed over 3 pc $\times$ 2 pc and is located between two HII regions. The
kinetic temperature derived from the NH$_3 (2,2)/(1,1)$ ratio was $\sim 35$ K
near the massive cluster S255 IR. These clusters also show emission with a
large line width of $\sim$ 3-4 km s$^{-1}$. Based on the reported data we
suggest that NH$_3$ gas in these regions is affected by stellar feedback from
embedded YSO clusters in S255 IR and S255 N. We also detected NH$_3$ (1,1)
emission in a region west of the main gas clump at the location of a
concentration of Class II YSOs adjacent to the HII regions Sh 2-254. The
presence of Class II YSOs implies $\sim$ 2 Myr of star formation, younger than
Sh 2-254 ($\sim 5$ Myr), thus we suggest that star formation in the western
region could be influenced by the older HII region Sh 2-254.


title: Oriented Random Walk on the Heisenberg Group and Percolation

abstract:
  It is shown that oriented random walk on the Heisenberg group admits
exponential intersection tail. As a corollary we get that on any transitive
graph of polynomial volume growth, which is not a finite extension of
$\mathbb{Z}, \mathbb{Z}^2$, the infinite cluster of percolation with retention
parameter $p$, close enough to $1$, is transient.


title:
Controlling spin pumping into superconducting Nb by proximity-induced
  spin-triplet Cooper pairs

abstract:
  Proximity-induced long-range spin-triplet supercurrents, important for the
field of superconducting spintronics, are generated in
superconducting/ferromagnetic heterostructures when interfacial magnetic
inhomogeneities responsible for spin mixing and spin flip scattering are
present. The multilayer stack Nb/Cr/Fe/Cr/Nb has been shown to support such
exotic currents when fabricated into Josephson junction devices. However,
creating pure spin currents controllably in superconductors outside of the
Josephson junction architecture is a bottleneck to progress. Recently,
ferromagnetic resonance was proposed as a possible direction, the signature of
pure supercurrent creation being an enhancement of the Gilbert damping below
the superconducting critical temperature, but the necessary conditions are
still poorly established. Consistent with theoretical prediction, we
demonstrate conclusively that pumping pure spin currents into a superconductor
is only possible when conditions supporting proximity-induced spin-triplet
effects are satisfied. Our study is an important step forward for
superconducting pure spin current creation and manipulation, considerably
advancing the field of superconducting spintronics.


title:
Evaluating the extent to which homeostatic plasticity learns to compute
  prediction errors in unstructured neuronal networks

abstract:
  The brain is believed to operate in part by making predictions about sensory
stimuli and encoding deviations from these predictions in the activity of
"prediction error neurons." This principle defines the widely influential
theory of predictive coding. The precise circuitry and plasticity mechanisms
through which animals learn to compute and update their predictions are
unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism
for training neuronal networks to perform predictive coding. Homeostatic
plasticity causes neurons to maintain a steady, baseline firing rate in
response to inputs that closely match the inputs on which a network was
trained, but firing rates can deviate away from this baseline in response to
stimuli that are mismatched from training. We combine computer simulations and
mathematical analysis systematically to test the extent to which randomly
connected, unstructured networks compute prediction errors after training with
homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity
alone is sufficient for computing prediction errors for trivial time-constant
stimuli, but not for more realistic time-varying stimuli. We use a mean-field
theory of plastic networks to explain our findings and characterize the
assumptions under which they apply.


title:
Circular photogalvanic effects in topological insulator/ferromagnet
  hybrid structures

abstract:
  We study laser driven spin-current effects at
ferromagnet/topological-insulator interfaces by two complementary experimental
approaches. The DC photocurrent is studied in ferromagnet/topological-insulator
bilayers with high spatial resolution. Dynamic interface currents are explored
via the emission of terahertz radiation emitted by these currents with high
temporal resolution. From our experiments, we reveal a lateral and dynamic
interaction of the ferromagnet and the topological insulator interface.


title: Bus Factor In Practice

abstract:
  Bus factor is a metric that identifies how resilient is the project to the
sudden engineer turnover. It states the minimal number of engineers that have
to be hit by a bus for a project to be stalled. Even though the metric is often
discussed in the community, few studies consider its general relevance.
Moreover, the existing tools for bus factor estimation focus solely on the data
from version control systems, even though there exists other channels for
knowledge generation and distribution. With a survey of 269 engineers, we find
that the bus factor is perceived as an important problem in collective
development, and determine the highest impact channels of knowledge generation
and distribution in software development teams. We also propose a multimodal
bus factor estimation algorithm that uses data on code reviews and meetings
together with the VCS data. We test the algorithm on 13 projects developed at
JetBrains and compared its results to the results of the state-of-the-art tool
by Avelino et al. against the ground truth collected in a survey of the
engineers working on these projects. Our algorithm is slightly better in terms
of both predicting the bus factor as well as key developers compared to the
results of Avelino et al. Finally, we use the interviews and the surveys to
derive a set of best practices to address the bus factor issue and proposals
for the possible bus factor assessment tool.


title:
Steady critical accretion onto black holes: selfgravity and sonic point
  characteristics

abstract:
  The spherically symmetric steady accretion of polytropic perfect fluids onto
a black hole is the simplest flow model that can demonstrate effects of
backreaction (selfgravity). It has been discovered 16 years ago that
backreaction does not influence some ("intensive") characteristics of sonic
points, under suitable conditions. Herein we consider a wider class of
equations of state, with polytropic indices in the range (1,2], and establish
detailed boundary conditions that allow one to prove this fact. We find also
numerical examples showing limits of our analytic criteria - if suitable
analytic conditions are not satisfied, then selfgravity influences all
characteristics of sonic points. That fact constrains the applicability of the
recent proposal of Baumgarte and Shapiro to estimate the lifetime of black
holes within compact stellar objects.


title: Reliable Community Search in Dynamic Networks

abstract:
  Local community search is an important research topic to support complex
network data analysis in various scenarios like social networks, collaboration
networks, and cellular networks. The evolution of networks over time has
motivated several recent studies to identify local communities from dynamic
networks. However, they only utilized the aggregation of disjoint structural
information to measure the quality of communities, which ignores the
reliability of communities in a continuous time interval. To fill this research
gap, we propose a novel $(\theta,k)$-$core$ reliable community (CRC) model in
the weighted dynamic networks, and define the problem of the most reliable
community search that couples the desirable properties of connection strength,
cohesive structure continuity, and the maximal member engagement. To solve this
problem, we first develop an online CRC search algorithm by proposing a
definition of eligible edge set and deriving the eligible edge set based
pruning rules. % called the Eligible Edge Filtering-based CRC algorithm. After
that, we devise a Weighted Core Forest-Index and index-based dynamic
programming CRC search algorithm, which can prune a large number of
insignificant intermediate results according to the maintained weight and
structure information in the index, as well as the proposed upper bound
properties. % our proposed pruning properties and upper bound properties.
Finally, we conduct extensive experiments to verify the efficiency of our
proposed algorithms and the effectiveness of our proposed community model on
eight real datasets under different parameter settings.


title:
Stochastic Homogenisation of nonconvex functionals in the space of
  $\mathbb{A}$-weakly differentiable maps

abstract:
  We prove the $\Gamma$-convergence of sequences of differentially constrained,
random integral functionals of the form \begin{equation*} \int_{U}
f\Big(\omega, x/\varepsilon, \mathbb{A} u\Big) \mathrm{d} x \end{equation*} for
the class of vectorial differential operators $\mathbb{A}$ with
finite-dimensional nullspaces. This work is intended to generalise results for
the full gradient and to cover the cases of symmetric gradients and the
deviatoric operator. The homogenisation procedure is carried out by employing a
variant of the blow-up method in the setting of $\mathbb{A}$-weakly
differentiable maps along with the Akcloglu-Krengel subadditive ergodic
theorem.


title:
Observation of Weak Anti-localization in thin films of the Topological
  Semimetal Candidate PdSb$_{2}$

abstract:
  We report results of a magneto-transport study on thin films of the
topological semi-metal candidate PdSb$_{2}$ (PS). We observe a positive
correction to magneto-conductivity at low temperatures, which is a signature of
weak anti-localization (WAL). We analyze the WAL data within the
Hikami-Larkin-Nagaoka (HLN) theory and extract the dephasing length
(L$_{\phi}$) whose temperature dependence reveals the various phase relaxation
mechanisms. From the WAL effect, we also extract $\alpha$ (the number of
transport channels). The evolution of $\alpha$ with temperature and film
thickness reflects how the coupling between different conducting channels
(Topological surface channels and bulk channels) changes. Additionally, the
electron-electron interaction (EEI) effect was observed in the
temperature-dependent conductivity at low temperatures. From the EEI effect, we
get an alternate estimate of the number of transport channels and obtain a
value similar to that obtained from the analysis of the WAL effect. This
suggests that in disordered films, the EEI effect can be used to get
information about the coupling of the topological surface states with each
other and with bulk states.


title:
First-Order to Second-Order Phase Transition Changeover and Latent Heats
  of q-State Potts Models in d=2,3 from a Simple Migdal-Kadanoff Adaptation

abstract:
  The changeover from first-order to second-order phase transitions in q-state
Potts models is obtained at q_c=2 in spatial dimension d=3 and essentially at
q_c=4 in d=2, using a physically intuited simple adaptation of the
Migdal-Kadanoff renormalization-group transformation. This simple procedure
yields the latent heats at the first-order phase transitions. In both d=2 and
3, the calculated phase transition temperatures, respectively compared with the
exact self-duality and Monte Carlo results, are dramatically improved.


title: Comparative assessment of federated and centralized machine learning

abstract:
  Federated Learning (FL) is a privacy preserving machine learning scheme,
where training happens with data federated across devices and not leaving them
to sustain user privacy. This is ensured by making the untrained or partially
trained models to reach directly the individual devices and getting locally
trained "on-device" using the device owned data, and the server aggregating all
the partially trained model learnings to update a global model. Although almost
all the model learning schemes in the federated learning setup use gradient
descent, there are certain characteristic differences brought about by the
non-IID nature of the data availability, that affects the training in
comparison to the centralized schemes. In this paper, we discuss the various
factors that affect the federated learning training, because of the non-IID
distributed nature of the data, as well as the inherent differences in the
federating learning approach as against the typical centralized gradient
descent techniques. We empirically demonstrate the effect of number of samples
per device and the distribution of output labels on federated learning. In
addition to the privacy advantage we seek through federated learning, we also
study if there is a cost advantage while using federated learning frameworks.
We show that federated learning does have an advantage in cost when the model
sizes to be trained are not reasonably large. All in all, we present the need
for careful design of model for both performance and cost.


title:
Serpentinization in the thermal evolution of icy Kuiper belt objects in
  the early Solar system

abstract:
  Here we present an improved algorithm to model the serpentinization process
in planetesimals in the early Solar system. Although it is hypothesized that
serpentinization-like reactions played an important role in the thermal
evolution of planetesimals, few and restricted models are available in this
topic. These processes may be important as the materials involved were abundant
in these objects. Our model is based on the model by (Gobi & Kereszturi 2017),
and contains improvements in the consideration of heat capacities and
lithospheric pressure, and in the calculation of the amount of interfacial
water. Comparison of our results with previous calculations shows that there
are significant differences in the e.g. the serpentinization time -- the time
necessary to consume most of the reactants at specific initial conditions -- or
the amount of heat produced by this process. In a simple application we show
that in icy bodies, under some realistic conditions, below the melting point of
water ice, serpentinization reaction using interfacial water may be able to
proceed and eventually push the local temperature above the melting point to
start a 'runaway' serpentinization. According to our calculations in objects
with radii R $\gtrsim$ 200 km serpentinization might have quickly reformed
nearly the whole interior of these bodies in the early Solar system.


title:
Applications of the Lieb--Thirring and other bounds for orthonormal
  systems in mathematical hydrodynamics

abstract:
  We discuss the estimates for the $L^p$-norms of systems of functions that are
orthonormal in $L^2$ and $H^1$, respectively, and their essential role in
deriving good or even optimal bounds for the dimension of global attractors for
the classical Navier--Stokes equations and for a class of $\alpha$-models
approximating them. New applications to interpolation inequalities on the 2D
torus are also given.


title: Deep learning study of an electromagnetic calorimeter

abstract:
  The accurate and precise extraction of information from a modern particle
physics detector, such as an electromagnetic calorimeter, may be complicated
and challenging. In order to overcome the difficulties we propose processing
the detector output using the deep-learning methodology. Our algorithmic
approach makes use of a known network architecture, which is being modified to
fit the problems at hand. The results are of high quality (biases of order 2%)
and, moreover, indicate that most of the information may be derived from only a
fraction of the detector. We conclude that such an analysis helps us
understanding the essential mechanism of the detector and should be performed
as a part of its designing procedure.


title: A covariant non-local phase field model of Bohm's potential

abstract:
  Assuming that the energy of a gas depends non-locally on the logarithm of its
mass density, the body force in the resulting equation of motion consists of
the sum of density gradient terms. Truncating this series after the second
term, Bohm's quantum potential and the Madelung equations are obtained, showing
explicitly that some of the hypotheses that led to the formulationb of quantum
mechanics do admit a classical interpretation based on non-locality. Here, we
generalize this approach imposing a finite speed of propagation of any
perturbation, thus determining a covariant formulation of the Madelung
equation.


title:
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep
  RL in Large Networked Systems

abstract:
  Learning effective policies for real-world problems is still an open
challenge for the field of reinforcement learning (RL). The main limitation
being the amount of data needed and the pace at which that data can be
obtained. In this paper, we study how to build lightweight simulators of
complicated systems that can run sufficiently fast for deep RL to be
applicable. We focus on domains where agents interact with a reduced portion of
a larger environment while still being affected by the global dynamics. Our
method combines the use of local simulators with learned models that mimic the
influence of the global system. The experiments reveal that incorporating this
idea into the deep RL workflow can considerably accelerate the training process
and presents several opportunities for the future.


title: MAQRO -- BPS 2023 Research Campaign Whitepaper

abstract:
  The objective of the proposed MAQRO mission is to harness space for achieving
long free-fall times, extreme vacuum, nano-gravity, and cryogenic temperatures
to test the foundations of physics in macroscopic quantum experiments. This
will result in the development of novel quantum sensors and a means to probe
the foundations of quantum physics at the interface with gravity. Earlier
studies showed that the proposal is feasible but that several critical
challenges remain, and key technologies need to be developed. These new
technologies will open up the potential for achieving additional science
objectives. The proposed research campaign aims to advance the state of the art
and to perform the first macroscopic quantum experiments in space. Experiments
on the ground, in micro-gravity, and in space will drive the proposed research
campaign during the current decade to enable the implementation of MAQRO within
the subsequent decade.


title:
Heteroepitaxy of Large-Area, Monocrystalline Lead Halide Perovskite
  Films on Gallium Arsenide

abstract:
  Lead halide perovskite materials have been emerging as promising candidates
for high-performance optoelectronic devices. Significant efforts have sought to
realize monocrystalline perovskite films at a large scale. Here, we epitaxially
grow monocrystalline methylammonium lead tribromide (MAPbBr3) films on
lattice-matched gallium arsenide (GaAs) substrates at a centimeter scale. In
particular, a solution-processed lead(II) sulfide (PbS) layer provides a
lattice-matched and chemical protective interface for the solid-gas reaction to
form MAPbBr3 films on GaAs. Structure characterizations identify the crystal
orientations in the trilayer MAPbBr3/PbS/GaAs epi-structure and confirm the
monocrystalline nature of MAPbBr3 on PbS/GaAs. The dynamic evolution of surface
morphologies during the growth indicates a two-step epitaxial process. These
fundamental understandings and practical growth techniques offer a viable
guideline to approach high-quality perovskite films for previously inaccessible
applications.


title:
Bending Graphs: Hierarchical Shape Matching using Gated Optimal
  Transport

abstract:
  Shape matching has been a long-studied problem for the computer graphics and
vision community. The objective is to predict a dense correspondence between
meshes that have a certain degree of deformation. Existing methods either
consider the local description of sampled points or discover correspondences
based on global shape information. In this work, we investigate a hierarchical
learning design, to which we incorporate local patch-level information and
global shape-level structures. This flexible representation enables
correspondence prediction and provides rich features for the matching stage.
Finally, we propose a novel optimal transport solver by recurrently updating
features on non-confident nodes to learn globally consistent correspondences
between the shapes. Our results on publicly available datasets suggest robust
performance in presence of severe deformations without the need for extensive
training or refinement.


title: Bose-Einstein condensation on hyperbolic spaces

abstract:
  A well-known conjecture in mathematical physics asserts that the interacting
Bose gas exhibits Bose-Einstein condensation (BEC) in the thermodynamic limit.
We consider the Bose gas on certain hyperbolic spaces. In this setting, one
obtains a short proof of BEC in the infinite-volume limit from the existence of
a volume-independent spectral gap of the Laplacian.


title:
Electronic energies from coupled fermionic 'Zombie' states imaginary
  time evolution

abstract:
  Zombie States are a recently introduced formalism to describe coupled
coherent Fermionic states which address the Fermionic sign problem in a
computationally tractable manner. Previously it has been shown that Zombie
States with fractional occupations of spin-orbitals obeyed the correct
Fermionic creation and annihilation algebra and presented results for real-time
evolution [Dmitrii V. Shalashilin, J. Chem. Phys. 148, 194109 (2018)]. In this
work we extend and build on this formalism by developing efficient algorithms
for evaluating the Hamiltonian and other operators between Zombie States and
address their normalization. We also show how imaginary time propagation can be
used to find the ground state of a system. We also present a biasing method,
for setting up a basis set of random Zombie States, that allow much smaller
basis sizes to be used while still accurately describing the electronic
structure Hamiltonian and its ground state and describe a technique of wave
function "cleaning" which removes the contributions of configurations with the
wrong number of electrons, improving the accuracy further. We also show how
low-lying excited states can be calculated efficiently using a Gram-Schmidt
orthogonalization procedure.The proposed algorithm of imaginary time
propagation on a biased random grids of Zombie States may present an
alternative to existing Quantum Monte Carlo methods.


title:
Statistics of Entanglement Transformation with Hierarchies among
  Catalysts

abstract:
  The distribution of typical bipartite pure states is studied within the
framework of state transformation via local operation and classical
communication (LOCC). We report the statistics of comparable and incomparable
states in different dimensions for single- and multi-copy regimes and establish
a connection between state transformation and the difference between the
entanglement contents of the initial and the target states. From the analysis
of catalyst resources, required to further otherwise impossible LOCC
transformations between pairs, we demonstrate a universal pattern in the
average and minimum entanglement of the randomly generated catalysts.
Furthermore, we introduce a concept of hierarchy between different kinds of
catalysts and show how they can not only aid in the conversion of incomparable
states but can also act as a less costly resource towards this goal. We confirm
the existence of catalysts, referred to as strong catalysts, which can activate
LOCC transformation between pairs at the single-copy level, when it is
initially impossible even with multiple copies.


title: Runge-Kutta-Nystr\"om symplectic splitting methods of order 8

abstract:
  Different families of Runge-Kutta-Nystr\"om (RKN) symplectic splitting
methods of order 8 are presented for second-order systems of ordinary
differential equations and are tested on numerical examples. They show a better
efficiency than state-of-the-art symmetric compositions of 2nd-order symmetric
schemes and RKN splitting methods of orders 4 and 6 for medium to high
accuracy. For some particular examples, they are even more efficient than
extrapolation methods for high accuracies and integrations over relatively
short time intervals.


title:
A Method for Counting, Tracking and Monitoring of Visitors with RFID
  sensors

abstract:
  This publication presents a method responsible for counting tracking and
monitoring visitors inside a building. The site examined is Manos Hatzidakis'
House, situated in Xanthi. Specifically, we have conducted a study, which
provides recommendations, regarding the installation of sensors in the
building. We also present the communication protocols of the computer network
used in order to ensure the efficient communication between the space examined
and the sensor network. Finally, we describe the process of creating a website,
which is designed to store and view the data.


title:
Design and Development of Automated Threat Hunting in Industrial Control
  Systems

abstract:
  Traditional industrial systems, e.g., power plants, water treatment plants,
etc., were built to operate highly isolated and controlled capacity. Recently,
Industrial Control Systems (ICSs) have been exposed to the Internet for ease of
access and adaptation to advanced technologies. However, it creates security
vulnerabilities. Attackers often exploit these vulnerabilities to launch an
attack on ICSs. Towards this, threat hunting is performed to proactively
monitor the security of ICS networks and protect them against threats that
could make the systems malfunction. A threat hunter manually identifies threats
and provides a hypothesis based on the available threat intelligence. In this
paper, we motivate the gap in lacking research in the automation of threat
hunting in ICS networks. We propose an automated extraction of threat
intelligence and the generation and validation of a hypothesis. We present an
automated threat hunting framework based on threat intelligence provided by the
ICS MITRE ATT&CK framework to automate the tasks. Unlike the existing hunting
solutions which are cloud-based, costly and prone to human errors, our solution
is a central and open-source implemented using different open-source
technologies, e.g., Elasticsearch, Conpot, Metasploit, Web Single Page
Application (SPA), and a machine learning analyser. Our results demonstrate
that the proposed threat hunting solution can identify the network's attacks
and alert a threat hunter with a hypothesis generated based on the techniques,
tactics, and procedures (TTPs) from ICS MITRE ATT&CK. Then, a machine learning
classifier automatically predicts the future actions of the attack.


title:
Linear transformations of vertex operator presentations of
  Hall-Littlewood polynomials

abstract:
  We study the effect of linear transformations on quantum fields with
applications to vertex operator presentations of symmetric functions.
Properties of linearly transformed quantum fields and corresponding
transformations of Hall-Littlewood polynomials are described, including
preservation of commutation relations, stability, explicit combinatorial
formulas and generating functions. We prove that specializations of linearly
transformed Hall-Littlewood polynomials describe all polynomial tau functions
of the KP and the BKP hierarchy. Examples of linear transformations are related
to multiparameter symmetric functions, Grothendieck polynomials, deformations
by cyclotomic polynomials, and some other variations of Schur symmetric
functions that exist in the literature.


title: Byzantine-Robust Decentralized Learning via Self-Centered Clipping

abstract:
  In this paper, we study the challenging task of Byzantine-robust
decentralized training on arbitrary communication graphs. Unlike federated
learning where workers communicate through a server, workers in the
decentralized environment can only talk to their neighbors, making it harder to
reach consensus. We identify a novel dissensus attack in which few malicious
nodes can take advantage of information bottlenecks in the topology to poison
the collaboration. To address these issues, we propose a Self-Centered Clipping
(SCClip) algorithm for Byzantine-robust consensus and optimization, which is
the first to provably converge to a $O(\delta_{\max}\zeta^2/\gamma^2)$
neighborhood of the stationary point for non-convex objectives under standard
assumptions. Finally, we demonstrate the encouraging empirical performance of
SCClip under a large number of attacks.


title:
QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty
  Data

abstract:
  In this work, we explore the problem of correctly and efficiently answering
complex SPJ queries issued directly on top of dirty data. We introduce QueryER,
a framework that seamlessly integrates Entity Resolution into Query Processing.
QueryER executes analysis-aware deduplication by weaving ER operators into the
query plan. The experimental evaluation of our approach exhibits that it adapts
to the workload and scales on both real and synthetic datasets.


title: Spectral multipliers in a general Gaussian setting

abstract:
  We investigate a class of spectral multipliers for an Ornstein-Uhlenbeck
operator $\mathcal L$ in $\mathbb R^n$, with drift given by a real matrix $B$
whose eigenvalues have negative real parts. We prove that if $m$ is a function
of Laplace transform type defined in the right half-plane, then $m(\mathcal L)$
is of weak type $(1, 1)$ with respect to the invariant measure in $\mathbb
R^n$. The proof involves many estimates of the relevant integral kernels and
also a bound for the number of zeros of the time derivative of the Mehler
kernel, as well as an enhanced version of the Ornstein-Uhlenbeck maximal
operator theorem.


title:
Multiplicity dependence of charged-particle jet production in pp
  collisions at $\sqrt{s} = 13$ TeV

abstract:
  The multiplicity dependence of jet production in pp collisions at the
centre-of-mass energy of $\sqrt{s} = 13\ \mathrm{TeV}$ is studied for the first
time. Jets are reconstructed from charged particles using the
anti-$k_\mathrm{T}$ algorithm with resolution parameters $R$ varying from $0.2$
to $0.7$. The jets are measured in the pseudorapidity range $|\eta_{\rm jet}|<
0.9-R$ and in the transverse momentum range $5<p_\mathrm{T,jet}^{\rm ch}<140\
\mathrm{GeV}/c$. The multiplicity intervals are categorised by the ALICE
forward detector V0. The $p_{\mathrm{T}}$ differential cross section of
charged-particle jets are compared to leading order (LO) and next-to-leading
order (NLO) perturbative quantum chromodynamics (pQCD) calculations. It is
found that the data are better described by the NLO calculation, although the
NLO prediction overestimates the jet cross section below $20\ \mathrm{GeV}/c$.
The cross section ratios for different $R$ are also measured and compared to
model calculations. These measurements provide insights into the angular
dependence of jet fragmentation. The jet yield increases with increasing
self-normalised charged-particle multiplicity. This increase shows only a weak
dependence on jet transverse momentum and resolution parameter at the highest
multiplicity. While such behaviour is qualitatively described by the present
version of PYTHIA, quantitative description may require implementing new
mechanisms for multi-particle production in hadronic collisions.


title:
First measurement of the absorption of $^{3}\overline{\rm He}$ nuclei in
  matter and impact on their propagation in the galaxy

abstract:
  Antimatter particles such as positrons and antiprotons abound in the cosmos.
Much less common are light antinuclei, composed of antiprotons and
antineutrons, which can be produced in our galaxy via high-energy cosmic-ray
collisions with the interstellar medium or could also originate from the
annihilation of the still undiscovered dark-matter particles. On Earth, the
only way to produce and study antinuclei with high precision is to create them
at high-energy particle accelerators like the Large Hadron Collider (LHC).
Though the properties of elementary antiparticles have been studied in detail,
knowledge of the interaction of light antinuclei with matter is rather limited.
This work focuses on the determination of the disappearance probability of
\ahe\ when it encounters matter particles and annihilates or disintegrates. The
material of the ALICE detector at the LHC serves as a target to extract the
inelastic cross section for \ahe\ in the momentum range of $1.17 \leq p < 10$
GeV/$c$. This inelastic cross section is measured for the first time and is
used as an essential input to calculations of the transparency of our galaxy to
the propagation of $^{3}\overline{\rm He}$ stemming from dark-matter decays and
cosmic-ray interactions within the interstellar medium. A transparency of about
50% is estimated using the GALPROP program for a specific dark-matter profile
and a standard set of propagation parameters. For cosmic-ray sources, the
obtained transparency with the same propagation scheme varies with increasing
$^{3}\overline{\rm He}$ momentum from 25% to 90%. The absolute uncertainties
associated to the $^{3}\overline{\rm He}$ inelastic cross section measurements
are of the order of 10%$-$15%. The reported results indicate that
$^{3}\overline{\rm He}$ nuclei can travel long distances in the galaxy, and can
be used to study cosmic-ray interactions and dark-matter decays.


title: Multi-Objective Optimization, different approach to query a database

abstract:
  The datasets available nowadays are very rich and complex, but how do we
reach the information we are looking for? In this survey, two different
approaches to query a dataset are analyzed and algorithms for each type are
explained. Specifically, the TA and NRA have been analyzed for the Top-K query
and the Basic Block Nested Loops has been examined for the skyline query.
Moreover, it's explained the core idea behind the Prioritized and Flexible
skyline. In the end, the pros and cons of each type of analyzed query have been
evaluated based on different criteria.


title:
Minimal Length of Nontrivial Solutions of the Isometry Equation and
  MacWilliams Extension Property with Respect to Weighted Poset Metric

abstract:
  For $R\triangleq Mat_{m}(\mathbb{F})$, the ring of all the $m\times m$
matrices over the finite field $\mathbb{F}$ with $|\mathbb{F}|=q$, and the left
$R$-module $A\triangleq Mat_{m,k}(\mathbb{F})$ with $m+1\leqslant k$, by
deriving the minimal length of solutions of the related isometry equation,
Dyshko has proved in \cite{3,4} that the minimal code length $n$ for $A^{n}$
not to satisfy the MacWilliams extension property with respect to Hamming
weight is equal to $\prod_{i=1}^{m}(q^{i}+1)$. In this paper, using the
M\"{o}bius functions, we derive the minimal length of nontrivial solutions of
the isometry equation with respect to a finite lattice. For the finite vector
space $\mathbf{H}\triangleq\prod_{i\in\Omega}\mathbb{F}^{k_{i}}$, a poset
$\mathbf{P}=(\Omega,\preccurlyeq_{\mathbf{P}})$ and a map
$\omega:\Omega\longrightarrow\mathbb{R}^{+}$ give rise to the
$(\mathbf{P},\omega)$-weight on $\mathbf{H}$, which has been proposed by Hyun,
Kim and Park in \cite{18}. For such a weight, we study the relations between
the MacWilliams extension property and other properties including admitting
MacWilliams identity, Fourier-reflexivity of involved partitions and Unique
Decomposition Property defined for $(\mathbf{P},\omega)$. We give necessary and
sufficient conditions for $\mathbf{H}$ to satisfy the MacWilliams extension
property with the additional assumption that either $\mathbf{P}$ is
hierarchical or $\omega$ is identically $1$, i.e., $(\mathbf{P},\omega)$-weight
coincides with $\mathbf{P}$-weight, which further allow us to partly answer a
conjecture proposed by Machado and Firer in \cite{22}.


title:
Inverse Low Gain Avalanche Detector (iLGAD) Periphery Design for X-Ray
  Applications

abstract:
  LGAD technology is established within the field of particle physics, as the
baseline technology for the timing detectors of both the ATLAS and CMS upgrades
at the HL-LHC. Pixelated LGADs have been proposed for the High Granularity
Timing Detector (HGTD) and for the Endcap Timing Layer (ETL) of the ATLAS and
CMS experiments, respectively. The drawback of segmenting an LGAD is the
non-gain area between pixels and the consequent reduction in the fill factor.
In this sense, inverse LGAD (iLGAD) technology has been proposed by IMB-CNM to
enhance the fill factor and to reach excellent tracking capabilities. In this
work, we explore the use of iLGAD sensors for X-Ray applications by developing
a new generation of iLGADs. The periphery of the first iLGAD generation is
optimized by means of TCAD tools, making them suitable for X-Ray irradiations
thanks to the double side optimization. The fabricated iLGAD sensors exhibit
good electrical performances before and after an X-Ray irradiation. The second
iLGAD generation is able to withstand the same voltage, as contrary to the
first iLGAD generation after irradiation.


title:
Covariate Selection Based on a Model-free Approach to Linear Regression
  with Exact Probabilities

abstract:
  In this paper we give a completely new approach to the problem of covariate
selection in linear regression. A covariate or a set of covariates is included
only if it is better in the sense of least squares than the same number of
Gaussian covariates consisting of i.i.d. $N(0,1)$ random variables. The
Gaussian P-value is defined as the probability that the Gaussian covariates are
better. It is given in terms of the Beta distribution, it is exact and it holds
for all data making it model-free free. The covariate selection procedures
require only a cut-off value $\alpha$ for the Gaussian P-value: the default
value in this paper is $\alpha=0.01$. The resulting procedures are very simple,
very fast, do not overfit and require only least squares. In particular there
is no regularization parameter, no data splitting, no use of simulations, no
shrinkage and no post selection inference is required. The paper includes the
results of simulations, applications to real data sets and theorems on the
asymptotic behaviour under the standard linear model. Here the step-wise
procedure performs overwhelmingly better than any other procedure we are aware
of. An R-package {\it gausscov} is available.


title: Newton Type Methods for solving a Hasegawa-Mima Plasma Model

abstract:
  In [1], the non-linear space-time Hasegawa-Mima plasma equation is formulated
as a coupled system of two linear PDE's, a solution of which is a pair (u, w).
The first equation is of hyperbolic type and the second of elliptic type.
Variational frames for obtaining weak solutions to the initial value
Hasegawa-Mima problem with periodic boundary conditions were also derived. In a
more recent work [2], a numerical approach consisting of a finite element
space-domain combined with an Euler-implicit time scheme was used to discretize
the coupled variational Hasegawa-Mima model. A semi-linear version of this
implicit nonlinear scheme was tested for several types of initial conditions.
This semi-linear scheme proved to lack efficiency for long time, which
necessitates imposing a cap on the magnitude of the solution. To circumvent
this difficulty, in this paper, we use Newton-type methods (Newton, Chord and
an introduced Modified Newton method) to solve numerically the fully-implicit
non-linear scheme. Testing these methods in FreeFEM++ indicates significant
improvements as no cap needs to be imposed for long time. In the sequel, we
demonstrate the validity of these methods by proving several results, in
particular the convergence of the implemented methods.


title:
Absolute convergence factors of Lipshitz class functions for general
  Fourier series

abstract:
  The main aim of this paper is to investigate the sequences of positive
numbers, for which multiplication with Fourier coefficients of functions $f\in$
Lip1 class provides absolute convergence of Fourier series. In particular we
found special conditions for the functions of orthonormal system, for which the
above sequences are absolute convergence factors of Fourier series of functions
of Lip1 class. It is established that the resulting conditions are best
possible in certain sense.


title: Periods of singular double octic Calabi-Yau threefolds and modular forms

abstract:
  By the modularity theorem every rigid Calabi-Yau threefold $X$ has associated
modular form $f$ such that the equality of $L$-functions $L(X,s)=L(f,s)$ holds.
In this case period integrals of $X$ are expected to be expressible in terms of
the special values $L(f,1)$ and $L(f,2)$. We propose a similar interpretation
of period integrals of a nodal model of $X$. It is given in terms of certain
variants of a Mellin transform of $f$. We provide numerical evidence towards
this interpretation based on a case of double octics.


title:
Effects of Temperature and Magnetization on the Mott-Anderson Physics in
  one-dimensional Disordered Systems

abstract:
  We investigate the Mott-Anderson physics in interacting disordered
one-dimensional chains through the average single-site entanglement quantified
by the linear entropy, which is obtained via density-functional theory
calculations. We show that the minimum disorder strength required to the
so-called full Anderson localization $-$ characterized by the real-space
localization of pairs $-$ is strongly dependent on the interaction regime. The
degree of localization is found to be intrinsically related to the interplay
between the correlations and the disorder potential. In magnetized systems, the
minimum entanglement characteristic of the full Anderson localization is split
into two, one for each of the spin species. We show that although all types of
localization eventually disappear with increasing temperature, the full
Anderson localization persists for higher temperatures than the Mott-like
localization.

categories: cond-mat.str-el cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.quant-gas quant-ph


title: The Diffusion Metrics of African Swine Fever in Wild Boar

abstract:
  To control African Swine Fever efficiently, easily interpretable metrics of
the outbreak dynamics are needed to plan and adapt the required measures. We
found that the spread pattern of African swine fever cases in wild boar follows
the mechanics of a diffusion process, at least in the early phase, for the
cases that occurred in Germany. Following incursion into a previously
unaffected area, infection disseminates locally within a naive and abundant
wild boar population. Using real case data for Germany, we derive statistics
about the time differences and distances between consecutive case reports. With
the use of these statistics, we generate an ensemble of random walkers
(continuous time random walks, CTRW) that resemble the properties of the
observed outbreak pattern as one possible realization of all possible disease
dissemination patterns. The trained random walker ensemble yields the diffusion
constant, the affected area, and the outbreak velocity of early ASF spread in
wild boar. These quantities are easy to interpret, robust, and may be
generalized or adapted to different regions. Therefore, diffusion metrics can
be useful descriptors of early disease dynamics and help facilitate efficient
control of African Swine Fever.


title:
Robotic Aerial 6G Small Cells with Grasping End Effectors for mmWave
  Relay Backhauling

abstract:
  Deployment of small cells in dense urban areas dedicated to the heterogeneous
network (HetNet) and associated relay nodes for improving backhauling is
expected to be an important structural element in the design of beyond 5G (B5G)
and 6G wireless access networks. A key operational aspect in HetNets is how to
optimally implement the wireless backhaul links to efficiently support the
traffic demand. In this work, we utilize the recently proposed Robotic Aerial
Small Cells (RASCs) that are able to grasp at different tall urban landforms as
wireless relay nodes for backhauling. This can be considered as an alternative
to fixed small cells (FSCs) which lack flexibility since once installed their
position cannot be altered. More specifically, on-demand deployment of RASCs is
considered for constructing a millimeter-wave (mmWave) backhaul network to
optimize available network capacity using a network flow-based mixed integer
linear programming (MILP) formulation. Numerical investigations reveal that for
the same required achievable throughput, the number of RASCs required are 25\%
to 65\% less than the number of required FSCs. This result can have significant
implications in reducing required wireless network equipment (capex) to provide
a given network capacity and allows for an efficient and flexible network
densification.


title:
Extending turbulence model uncertainty quantification using machine
  learning

abstract:
  In order to achieve a more virtual design and certification process of jet
engines in aviation industry, the uncertainty bounds for computational fluid
dynamics have to be known. This work shows the application of a machine
learning methodology to quantify the epistemic uncertainties of turbulence
models. The underlying method in order to estimate the uncertainty bounds is
based on an eigenspace perturbation of the Reynolds stress tensor in
combination with random forests.


title: Some problems of convergence of general Fourier series

abstract:
  S. Banach \cite{Banach} proved that good differential properties of function
do not guarantee the a.e. convergence of the Fourier series of this function
with respect to general orthonormal systems (ONS). On the other hand it is very
well known that a sufficient condition for the a.e. convergence of an
orthonormal series is given by the Menshov-Rademacher Theorem.
  The paper deals with sequence of positive numbers $(d_n)$ such that
multiplying the Fourier coefficients $(C_n(f))$ of functions with bounded
variation by these numbers one obtains a.e. convergent series of the form
$\sum_{n=1}^{\infty}d_n C_n(f) \varphi_n (x).$ It is established that the
resulting conditions are best possible.


title:
Doubly Robust Off-Policy Evaluation for Ranking Policies under the
  Cascade Behavior Model

abstract:
  In real-world recommender systems and search engines, optimizing ranking
decisions to present a ranked list of relevant items is critical. Off-policy
evaluation (OPE) for ranking policies is thus gaining a growing interest
because it enables performance estimation of new ranking policies using only
logged data. Although OPE in contextual bandits has been studied extensively,
its naive application to the ranking setting faces a critical variance issue
due to the huge item space. To tackle this problem, previous studies introduce
some assumptions on user behavior to make the combinatorial item space
tractable. However, an unrealistic assumption may, in turn, cause serious bias.
Therefore, appropriately controlling the bias-variance tradeoff by imposing a
reasonable assumption is the key for success in OPE of ranking policies. To
achieve a well-balanced bias-variance tradeoff, we propose the Cascade Doubly
Robust estimator building on the cascade assumption, which assumes that a user
interacts with items sequentially from the top position in a ranking. We show
that the proposed estimator is unbiased in more cases compared to existing
estimators that make stronger assumptions. Furthermore, compared to a previous
estimator based on the same cascade assumption, the proposed estimator reduces
the variance by leveraging a control variate. Comprehensive experiments on both
synthetic and real-world data demonstrate that our estimator leads to more
accurate OPE than existing estimators in a variety of settings.


title: On the Number of Graphs with a Given Histogram

abstract:
  Let $G$ be a large (simple, unlabeled) dense graph on $n$ vertices. Suppose
that we only know, or can estimate, the empirical distribution of the number of
subgraphs $F$ that each vertex in $G$ participates in, for some fixed small
graph $F$. How many other graphs would look essentially the same to us, i.e.,
would have a similar local structure? In this paper, we derive upper and lower
bounds on the number graphs whose empirical distribution lies close (in the
Kolmogorov-Smirnov distance) to that of $G$. Our bounds are given as solutions
to a maximum entropy problem on random graphs of a fixed size $k$ that does not
depend on $n$, under $d$ global density constraints. The bounds are
asymptotically close, with a gap that vanishes with $d$ at a rate that depends
on the concentration function of the center of the Kolmogorov-Smirnov ball.


title: Weakly Supervised Nuclei Segmentation via Instance Learning

abstract:
  Weakly supervised nuclei segmentation is a critical problem for pathological
image analysis and greatly benefits the community due to the significant
reduction of labeling cost. Adopting point annotations, previous methods mostly
rely on less expressive representations for nuclei instances and thus have
difficulty in handling crowded nuclei. In this paper, we propose to decouple
weakly supervised semantic and instance segmentation in order to enable more
effective subtask learning and to promote instance-aware representation
learning. To achieve this, we design a modular deep network with two branches:
a semantic proposal network and an instance encoding network, which are trained
in a two-stage manner with an instance-sensitive loss. Empirical results show
that our approach achieves the state-of-the-art performance on two public
benchmarks of pathological images from different types of organs.


title: Broadband quantum spectroscopy at the fingerprint mid-infrared region

abstract:
  Numerous molecules exhibit unique absorption bands in the fingerprint
infrared (IR) region, allowing chemical identification and detection. This
makes IR spectroscopy an essential tool in biomedical diagnostics, sensing, and
material characterization. However, measurements in the IR range are
challenging due to the relatively poor performance, need for cryogenic cooling,
and high cost of IR light sources and photodetectors. Here, we demonstrate that
the mid-IR fingerprints of the sample can be revealed from measurements in the
near-IR range using conventional silicon photodetectors. Our approach relies on
the quantum interference of correlated photon pairs produced in strongly
frequency non-degenerate parametric down-conversion. Our technique
simultaneously measures the absorption coefficient and refractive index in the
broad fingerprint region with high accuracy. As a proof-of-concept, we perform
spectroscopy of nitrous oxide gas in the 7.4-8.4 {\mu}m wavelength range, with
the detection in the 865-877 nm range. This work extends the applicability of
quantum interferometry to a range of broad practical appeals, such as detection
of atmosphere pollutants, breath diagnostics, chemical safety, and others.


title:
Unified theory of atom-centered representations and message-passing
  machine-learning schemes

abstract:
  Data-driven schemes that associate molecular and crystal structures with
their microscopic properties share the need for a concise, effective
description of the arrangement of their atomic constituents. Many types of
models rely on descriptions of atom-centered environments, that are associated
with an atomic property or with an atomic contribution to an extensive
macroscopic quantity. Frameworks in this class can be understood in terms of
atom-centered density correlations (ACDC), that are used as a basis for a
body-ordered, symmetry-adapted expansion of the targets. Several other schemes,
that gather information on the relationship between neighboring atoms using
"message-passing" ideas, cannot be directly mapped to correlations centered
around a single atom. We generalize the ACDC framework to include
multi-centered information, generating representations that provide a complete
linear basis to regress symmetric functions of atomic coordinates, and provides
a coherent foundation to systematize our understanding of both atom-centered
and message-passing, invariant and equivariant machine-learning schemes.


title: Perpetual maintenance of machines with different urgency requirements

abstract:
  A garden $G$ is populated by $n\ge 1$ bamboos $b_1, b_2, ..., b_n$ with the
respective daily growth rates $h_1 \ge h_2 \ge \dots \ge h_n$. It is assumed
that the initial heights of bamboos are zero. The robotic gardener maintaining
the garden regularly attends bamboos and trims them to height zero according to
some schedule. The Bamboo Garden Trimming Problem (BGT) is to design a
perpetual schedule of cuts to maintain the elevation of the bamboo garden as
low as possible. The bamboo garden is a metaphor for a collection of machines
which have to be serviced, with different frequencies, by a robot which can
service only one machine at a time. The objective is to design a perpetual
schedule of servicing which minimizes the maximum (weighted) waiting time for
servicing.
  We consider two variants of BGT. In discrete BGT the robot trims only one
bamboo at the end of each day. In continuous BGT the bamboos can be cut at any
time, however, the robot needs time to move from one bamboo to the next.
  For discrete BGT, we show a simple $4$-approximation algorithm and, by
exploiting relationship between BGT and the classical Pinwheel scheduling
problem, we derive a $2$-approximation algorithm for the general case and a
tighter approximation when the growth rates are balanced. A by-product of this
last approximation algorithm is that it settles one of the conjectures about
the Pinwheel problem. For continuous BGT, we propose approximation algorithms
which achieve approximation ratios $O(\log (h_1/h_n))$ and $O(\log n)$.


title: Tuning terahertz transitions in cyclo[n]carbon rings

abstract:
  We develop an analytic model for an ideal polyyne ring which describes the
induced THz gap in the molecular spectrum due to the Stark effect. This simple
model can also be used to describe an odd-dimered cyclocarbon which has
undergone a spontaneous symmetry-breaking event (due to the Jahn-Teller effect)
as an effective dipole across an ideal ring. We show that both the size of the
gap, and the strength of optical transitions across it, can be modulated by
varying the external electric field strength. A THz emission scheme based on
optical excitation is proposed, thus paving the way for a new class of THz
emitters based on cyclo[n]carbon.


title:
Scattering in Terms of Bohmian Conditional Wave Functions for Scenarios
  with Non-Commuting Energy and Momentum Operators

abstract:
  Without access to the full quantum state, modeling quantum transport in
mesoscopic systems requires dealing with a limited number of degrees of
freedom. In this work, we analyze the possibility of modeling the perturbation
induced by the non-simulated degrees of freedom on the simulated ones as a
transition between single-particle pure states. First, we show that Bohmian
conditional wave functions (BCWF) allow a rigorous discussion of the dynamics
of electrons inside open quantum systems in terms of such single-particle pure
states, either under Markovian or non-Markovian conditions. Second, we discuss
the practical application of the method for modeling light-matter interaction
phenomena in a resonant tunneling device (RTD), where a single photon is
interacting with a single electron. Third, we emphasize the importance of
interpreting such scattering mechanism as a transition between initial and
final single-particle BCWF with well-defined energies (rather than with
well-defined momenta).


title:
Magnetostatic levitation and two related linear PDEs in unbounded
  domains

abstract:
  We consider a problem occurring in a magnetostatic levitation. The problem
leads to a linear PDE in a strip. In engineering literature a particular
solution is obtained. Such a solution enables one to compute lift and drag
forces of the levitating object. It is in agreement with the experiment. We
show that such a solution is unique in a class of bounded regular functions.
Moreover, as a byproduct, we obtain nonstandard uniqueness results in two
linear PDEs in unbounded domains. One of them is an Eigenvalue problem for the
Laplacian in the strip in the nonstandard class of functions.


title: Toric Geometry of Entropic Regularization

abstract:
  Entropic regularization is a method for large-scale linear programming.
Geometrically, one traces intersections of the feasible polytope with scaled
toric varieties, starting at the Birch point. We compare this to log-barrier
methods, with reciprocal linear spaces, starting at the analytic center. We
revisit entropic regularization for unbalanced optimal transport, and we
develop the use of optimal conic couplings. We compute the degree of the
associated toric variety, and we explore algorithms like iterative scaling.


title: Band structure of n- and p-doped core-shell nanowires

abstract:
  We investigate the electronic band structure of modulation-doped GaAs/AlGaAs
core-shell nanowires for both n- and p-doping. We developed an 8-band
Burt-Foreman k.p Hamiltonian approach to describe coupled conduction and
valence bands in heterostructured nanowires of arbitrary composition, growth
directions, and doping. Coulomb interactions with the electron/hole gas are
taken into account within a mean-field self-consistent approach. We map the
ensuing multi-band envelope function and Poisson equations to optimized,
non-uniform real-space grids by the finite element method. Self-consistent
charge density, single-particle subbands, density of states and absorption
spectra are obtained at different doping regimes. For n-doped samples, the
large restructuring of the electron gas for increasing doping results in the
formation of quasi-1D electron channels at the core/shell interface. Strong
heavy-hole/light-hole coupling of hole state leads to non parabolic dispersions
with mass inversion, similarly to planar structures, which persist at large
dopings, giving rise to direct LH and indirect HH gaps. In p-doped samples the
hole gas forms an almost isotropic, ring-like cloud for a large range of
doping. Here, as a result of the increasing localization, HH and LH states
uncouple, and mass inversion takes place at a threshold density. Similar
evolution is obtained at fixed doping as a function of temperature. We show
that signatures of the evolution of band structure can be singled out in the
anisotropy of linearly polarized optical absorption.


title: Proto-exact categories of modules over semirings and hyperrings

abstract:
  \emph{Proto-exact categories}, introduced by Dyckerhoff and Kapranov, are a
generalization of Quillen exact categories which provide a framework for
defining algebraic K-theory and Hall algebras in a \emph{non-additive} setting.
This formalism is well-suited to the study of categories whose objects have
strong combinatorial flavor.
  In this paper, we show that the categories of modules over semirings and
hyperrings - algebraic structures which have gained prominence in tropical
geometry - carry proto-exact structures.
  In the first part, we prove that the category of modules over a semiring is
equipped with a proto-exact structure; modules over an idempotent semiring have
a strong connection to matroids. We also prove that the category of algebraic
lattices $\mathcal{L}$ has a proto-exact structure, and furthermore that the
subcategory of $\mathcal{L}$ consisting of finite lattices is equivalent to the
category of finite $\mathbb{B}$-modules as proto-exact categories, where
$\mathbb{B}$ is the \emph{Boolean semifield}. We also discuss some relations
between $\mathcal{L}$ and geometric lattices (simple matroids) from this
perspective.
  In the second part, we prove that the category of modules over a hyperring
has a proto-exact structure. In the case of finite modules over the
\emph{Krasner hyperfield} $\mathbb{K}$, a well-known relation between finite
$\mathbb{K}$-modules and finite incidence geometries yields a combinatorial
interpretation of exact sequences.


title:
Value distribution of exponential polynomials and their role in the
  theories of complex differential equations and oscillation theory

abstract:
  An exponential polynomial is a finite linear sum of terms $P(z)e^{Q(z)}$,
where $P(z)$ and $Q(z)$ are polynomials. The early results on the value
distribution of exponential polynomials can be traced back to Georg P\'olya's
paper published in 1920, while the latest results have come out in 2021.
Despite of over a century of research work, many intriguing problems on value
distribution of exponential polynomials still remain unsolved. The role of
exponential polynomials and their quotients in the theories of
linear/non-linear differential equations, oscillation theory and
differential-difference equations will also be discussed. Thirteen open
problems are given to motivate the readers for further research in these
topics.


title:
CoST: Contrastive Learning of Disentangled Seasonal-Trend
  Representations for Time Series Forecasting

abstract:
  Deep learning has been actively studied for time series forecasting, and the
mainstream paradigm is based on the end-to-end training of neural network
architectures, ranging from classical LSTM/RNNs to more recent TCNs and
Transformers. Motivated by the recent success of representation learning in
computer vision and natural language processing, we argue that a more promising
paradigm for time series forecasting, is to first learn disentangled feature
representations, followed by a simple regression fine-tuning step -- we justify
such a paradigm from a causal perspective. Following this principle, we propose
a new time series representation learning framework for time series forecasting
named CoST, which applies contrastive learning methods to learn disentangled
seasonal-trend representations. CoST comprises both time domain and frequency
domain contrastive losses to learn discriminative trend and seasonal
representations, respectively. Extensive experiments on real-world datasets
show that CoST consistently outperforms the state-of-the-art methods by a
considerable margin, achieving a 21.3% improvement in MSE on multivariate
benchmarks. It is also robust to various choices of backbone encoders, as well
as downstream regressors. Code is available at
https://github.com/salesforce/CoST.


title: Formulating bulk viscosity for neutron star simulations

abstract:
  In order to extract the precise physical information encoded in the
gravitational and electromagnetic signals from powerful neutron-star merger
events, we need to include as much of the relevant physics as possible in our
numerical simulations. This presents a severe challenge, given that many of the
involved parameters are poorly constrained. In this paper we focus on the role
of nuclear reactions. Combining a theoretical discussion with an analysis
connecting to state-of-the-art simulations, we outline multiple arguments that
lead to a reactive system being described in terms of a bulk viscosity. The
results demonstrate that in order to properly account for nuclear reactions,
future simulations must be able to handle different regimes where rather
different assumptions/approximations are appropriate. We also touch upon the
link to models based on the large-eddy-strategy required to capture turbulence.


title: An all Mach number finite volume method for isentropic two-phase flow

abstract:
  We present an implicit-explicit finite volume scheme for isentropic two phase
flow in all Mach number regimes. The underlying model belongs to the class of
symmetric hyperbolic thermodynamically compatible models. The key element of
the scheme consists of a linearisation of pressure and enthalpy terms at a
reference state. The resulting stiff linear parts are integrated implicitly,
whereas the non-linear higher order and transport terms are treated explicitly.
Due to the flux splitting, the scheme is stable under a CFL condition which
determined by the resolution of the slow material waves and allows large time
steps even in the presence of fast acoustic waves. Further the singular Mach
number limits of the model are studied and the asymptotic preserving property
of the scheme is proven. In numerical simulations the consistency with single
phase flow, accuracy and the approximation of material waves in different Mach
number regimes are assessed.


title: Projected Sensivity to Dimension-6 Triple Gauge Couplings at the FCC-hh

abstract:
  In this study, we investigate the process pp \rightarrow W^{\pm} \gamma+X for
the physics potential of the FCC-hh with \sqrt{s}=100 TeV to examine the
anomalous WW\gamma couplings defined by three CP-conserving and two
CP-violating effective operators of dimension-6. The analysis containing the
realistic detector effects is carried out in the mode where W^{\pm} bosons in
the final state decay into the leptonic channel. The best sensitivities
obtained from the process pp \rightarrow W^{\pm} \gamma+X TeV^{-2}, [-0.88;
0.88] TeV^{-2} and [-1.12; 1.23] TeV^{-2}, while C_{\tilde{W}WW}/ \Lambda^{2}
and C_{\tilde{W}}/ \Lambda^{2} couplings defined by CP-violating effective
Lagrangian are obtained as [-0.03; 0.03] TeV^{-2} and [-0.47; 0.47] TeV^{-2} at
the FCC-hh with \sqrt{s}=100 TeV, L_{int}=30 ab^{-1}. However, if the
systematic uncertainty is included, we obtain reduced sensitivities on the
anomalous WW\gamma couplings . The results are compared for assumed systematics
of 5\% and 10\%.


title: Excited doubly heavy baryon production via $W^+$ boson decays

abstract:
  In this paper, decay widths of the doubly heavy baryons ($\Xi_{cc}
~\text{and} ~\Xi_{bc}$) production are theoritically calculated in the whole
phase space through $W^+ \to \Xi_{cc}+ \bar{c}+\bar{s}$ and $W^+ \to \Xi_{bc}+
\bar{b}+\bar{s}$, within the framework of nonrelativistic QCD (NRQCD).
Differential widths $d\Gamma/ds_{12}$, $d\Gamma/ds_{23}$,
$d\Gamma/dcos\theta_{12}$, and $d\Gamma/dcos\theta_{13}$ are also given. In
addition to the ordinary S wave contributions for the baryons, we specifically
calculate P wave contributions as a comparison, namely the high excited states
of the intermediate diquark, including $[^1P_1]$ and $[^3P_J]$ (with $J=0,1,2$)
in both color anti-triplet state $\overline{\mathbf{3}}$ and color sextuplet
state $\mathbf{6}$. It shows that the contribution from P wave is about one
order lower than S wave. According to the results, we can expect plentiful
events produced at the LHC, i.e., $3.69\times10^5$ $\Xi_{cc}$ events and
$4.91\times10^4$ $\Xi_{bc}$ events per year.


title:
Fixed Points and 2-Cycles of Synchronous Dynamic Coloring Processes on
  Trees

abstract:
  This paper considers synchronous discrete-time dynamical systems on graphs
based on the threshold model. It is well known that after a finite number of
rounds these systems either reach a fixed point or enter a 2-cycle. The problem
of finding the fixed points for this type of dynamical system is in general
both NP-hard and #P-complete. In this paper we give a surprisingly simple
graph-theoretic characterization of fixed points and 2-cycles for the class of
finite trees. Thus, the class of trees is the first nontrivial graph class for
which a complete characterization of fixed points exists. This characterization
enables us to provide bounds for the total number of fixed points and pure
2-cycles. It also leads to an output-sensitive algorithm to efficiently
generate these states.


title: Are Bundles Good Deals for FOML?

abstract:
  Bundled products are often offered as good deals to customers. When we bundle
quantifiers and modalities together (as in $\exists x \Box$, $\Diamond \forall
x$ etc.) in first-order modal logic (FOML), we get new logical operators whose
combinations produce interesting fragments of FOML without any restriction on
the arity of predicates, the number of variables, or the modal scope. It is
well-known that finding decidable fragments of FOML is hard, so we may ask: do
bundled fragments that exploit the distinct expressivity of FOML constitute
good deals in balancing the expressivity and complexity? There are a few
positive earlier results on some particular fragments. In this paper, we try to
fully map the terrain of bundled fragments of FOML in (un)decidability, and in
the cases without a definite answer yet, we show that they lack the finite
model property. Moreover, whether the logics are interpreted over constant
domains (across states/worlds) or increasing domains presents another layer of
complexity. We also present the \textit{loosely bundled fragment}, which
generalizes the bundles and yet retain decidability (over increasing domain
models).


title: A Psychoacoustic Quality Criterion for Path-Traced Sound Propagation

abstract:
  In developing virtual acoustic environments, it is important to understand
the relationship between the computation cost and the perceptual significance
of the resultant numerical error. In this paper, we propose a quality criterion
that evaluates the error significance of path-tracing-based sound propagation
simulators. We present an analytical formula that estimates the error signal
power spectrum. With this spectrum estimation, we can use a modified Zwicker's
loudness model to calculate the relative loudness of the error signal masked by
the ideal output. Our experimental results show that the proposed criterion can
explain the human perception of simulation error in a variety of cases.


title:
THz non-linear optical response in cuprates: predominance of the BCS
  response over the Higgs mode

abstract:
  Recent experiments with strong THz fields in unconventional cuprates
superconductors have clearly evidenced an increase of the non-linear optical
response below the superconducting critical temperature Tc. As in the case of
conventional superconductors, a theoretical estimate of the various effects
contributing to the non-linear response is needed in order to interpret the
experimental findings. Here we report a detailed quantitative analysis of the
non-linear THz optical kernel in cuprates within a realistic model, accounting
for the band structure and disorder level appropriate for these systems. We
show that the BCS quasiparticle response is the dominant contribution for
cuprates, and its polarization dependence accounts very well for the
third-harmonic generation measurements. On the other hand, the polarization
dependence of the THz Kerr effect is only partly captured by our calculations,
suggesting the presence of additional effects when the system is probed using
light pulses with different central frequencies.


title: The extremal process of a cascading family of branching Brownian motion

abstract:
  We study the asymptotic behaviour of the extremal process of a cascading
family of branching Brownian motions. This is a particle system on the real
line such that each particle has a type in addition to his position. Particles
of type $1$ move on the real line according to Brownian motions and branch at
rate $1$ into two children of type $1$. Furthermore, at rate $\alpha$, they
give birth to children too of type $2$. Particles of type $2$ move according to
standard Brownian motion and branch at rate $1$, but cannot give birth to
descendants of type $1$. We obtain the asymptotic behaviour of the extremal
process of particles of type $2$.


title:
Performance evaluation of DMUs using hybrid fuzzy multi-objective data
  envelopment analysis

abstract:
  The objective of this paper is to evaluate the performance of decision-making
units (DMUs) using a hybrid fuzzy multi-objective (FMO) data envelopment
analysis (DEA) approach. This study develops fuzzy multi-objective optimistic
(FMOO) and pessimistic (FMOP) DEA models for performance evaluation of DMUs. To
rank the DMUs, a ranking approach is used that can simultaneously integrate
optimistic and pessimistic efficiencies. A comparison of the proposed approach
with the existing approach is made with the help of an example. Finally, a
real-life application of developed fuzzy optimistic and pessimistic DEA models
in the education sector is presented.


title: Backscatter-Aided NOMA V2X Communication under Channel Estimation Errors

abstract:
  Backscatter communications (BC) has emerged as a promising technology for
providing low-powered transmissions in nextG (i.e., beyond 5G) wireless
networks. The fundamental idea of BC is the possibility of communications among
wireless devices by using the existing ambient radio frequency signals.
Non-orthogonal multiple access (NOMA) has recently attracted significant
attention due to its high spectral efficiency and massive connectivity. This
paper proposes a new optimization framework to minimize total transmit power of
BC-NOMA cooperative vehicle-to-everything networks (V2XneT) while ensuring the
quality of services. More specifically, the base station (BS) transmits a
superimposed signal to its associated roadside units (RSUs) in the first time
slot. Then the RSUs transmit the superimposed signal to their serving vehicles
in the second time slot exploiting decode and forward protocol. A backscatter
device (BD) in the coverage area of RSU also receives the superimposed signal
and reflect it towards vehicles by modulating own information. Thus, the
objective is to simultaneously optimize the transmit power of BS and RSUs along
with reflection coefficient of BDs under perfect and imperfect channel state
information. The problem of energy efficiency is formulated as non-convex and
coupled on multiple optimization variables which makes it very complex and hard
to solve. Therefore, we first transform and decouple the original problem into
two sub-problems and then employ iterative sub-gradient method to obtain an
efficient solution. Simulation results demonstrate that the proposed BC-NOMA
V2XneT provides high energy efficiency than the conventional NOMA V2XneT
without BC.


title: MiDaS: Representative Sampling from Real-world Hypergraphs

abstract:
  Graphs are widely used for representing pairwise interactions in complex
systems. Since such real-world graphs are large and often evergrowing, sampling
a small representative subgraph is indispensable for various purposes:
simulation, visualization, stream processing, representation learning,
crawling, to name a few. However, many complex systems consist of group
interactions (e.g., collaborations of researchers and discussions on online Q&A
platforms), and thus they can be represented more naturally and accurately by
hypergraphs (i.e., sets of sets) than by ordinary graphs. Motivated by the
prevalence of large-scale hypergraphs, we study the problem of representative
sampling from real-world hypergraphs, aiming to answer (Q1) what a
representative sub-hypergraph is and (Q2) how we can find a representative one
rapidly without an extensive search. Regarding Q1, we propose to measure the
goodness of a sub-hypergraph by comparing it with the entire hypergraph in
terms of ten graph-level, hyperedge-level, and node-level statistics. Regarding
Q2, we first analyze the characteristics of six intuitive approaches in 11
real-world hypergraphs. Then, based on the analysis, we propose MiDaS, which
draws hyperedges with a bias towards those with high-degree nodes. Through
extensive experiments, we demonstrate that MiDaS is (a) Representative: finding
overall the most representative samples among 13 considered approaches, (b)
Fast: several orders of magnitude faster than the strongest competitors, which
performs an extensive search, and (c) Automatic: rapidly searching a proper
degree of bias.


title: Pentagon relation in quantum cluster scattering diagrams

abstract:
  We formulate the pentagon relation for quantum dilogarithm elements in the
structure group of a quantum cluster scattering diagram (QCSD). As an
application, we show the nonpositivity of a certain class of nonskew-symmetric
QCSDs. Also, we explicitly present various consistency relations for QCSDs of
rank 2 completely or up to some degree, many of which are new in the
literature.


title:
A two-stage numerical approach for the sparse initial source
  identification of a diffusion-advection equation

abstract:
  We consider the problem of identifying a sparse initial source condition to
achieve a given state distribution of a diffusion-advection partial
differential equation after a given final time. The initial condition is
assumed to be a finite combination of Dirac measures. The locations and
intensities of this initial condition are required to be identified. This
problem is known to be exponentially ill-posed because of the strong diffusive
and smoothing effects. We propose a two-stage numerical approach to treat this
problem. At the first stage, to obtain a sparse initial condition with the
desire of achieving the given state subject to a certain tolerance, we propose
an optimal control problem involving sparsity-promoting and
ill-posedness-avoiding terms in the cost functional, and introduce a
generalized primal-dual algorithm for this optimal control problem. At the
second stage, the initial condition obtained from the optimal control problem
is further enhanced by identifying its locations and intensities in its
representation of the combination of Dirac measures. This two-stage numerical
approach is shown to be easily implementable and its efficiency in short time
horizons is promisingly validated by the results of numerical experiments. Some
discussions on long time horizons are also included.


title:
Lipschitz continuity and Bochner-Eells-Sampson inequality for harmonic
  maps from $\mathrm{RCD}(K,N)$ spaces to $\mathrm{CAT}(0)$ spaces

abstract:
  We establish Lipschitz regularity of harmonic maps from $\mathrm{RCD}(K,N)$
metric measure spaces with lower Ricci curvature bounds and dimension upper
bounds in synthetic sense with values into $\mathrm{CAT}(0)$ metric spaces with
non-positive sectional curvature. Under the same assumptions, we obtain a
Bochner-Eells-Sampson inequality with a Hessian type-term. This gives a fairly
complete generalization of the classical theory for smooth source and target
spaces to their natural synthetic counterparts and an affirmative answer to a
question raised several times in the recent literature.
  The proofs build on a new interpretation of the interplay between Optimal
Transport and the Heat Flow on the source space and on an original perturbation
argument in the spirit of the viscosity theory of PDEs.


title:
Form-factors and complete basis of observables via separation of
  variables for higher rank spin chains

abstract:
  Integrable sl(N) spin chains, which we consider in this paper, are not only
the prototypical example of quantum integrable systems but also systems with a
wide range of applications. For these models we use the Functional Separation
of Variables (FSoV) technique with a new tool called Character Projection to
compute all matrix elements of a complete set of operators, which we call
principal operators, in the basis diagonalising the tower of conserved charges
as determinants in Q-functions. Building up on these results we then derive
similar determinant forms for the form-factors of combinations of multiple
principal operators between arbitrary factorizable states, which include, in
particular, off-shell Bethe vectors and Bethe vectors with arbitrary twists. We
prove that the set of principal operators generates the complete spin chain
Yangian. Furthermore, we derive the representation of these operators in the
SoV bases allowing one to compute correlation functions with an arbitrary
number of principal operators. Finally, we show that the available combinations
of multiple insertions includes Sklyanin`s SoV B operator. As a result, we are
able to derive the B operator for sl(N) spin chains using a minimal set of
ingredients, namely the FSoV method and the structure of the SoV basis.


title:
Energy Efficiency Optimization for Backscatter Enhanced NOMA Cooperative
  V2X Communications under Imperfect CSI

abstract:
  Automotive-Industry 5.0 will use beyond fifth-generation (B5G) technologies
to provide robust, computationally intelligent, and energy-efficient data
sharing among various onboard sensors, vehicles, and other devices. Recently,
ambient backscatter communications (AmBC) have gained significant interest in
the research community for providing battery-free communications. AmBC can
modulate useful data and reflect it towards near devices using the energy and
frequency of existing RF signals. However, obtaining channel state information
(CSI) for AmBC systems would be very challenging due to no pilot sequences and
limited power. As one of the latest members of multiple access technology,
non-orthogonal multiple access (NOMA) has emerged as a promising solution for
connecting large-scale devices over the same spectral resources in B5G wireless
networks. Under imperfect CSI, this paper provides a new optimization framework
for energy-efficient transmission in AmBC enhanced NOMA cooperative
vehicle-to-everything (V2X) networks. We simultaneously minimize the total
transmit power of the V2X network by optimizing the power allocation at BS and
reflection coefficient at backscatter sensors while guaranteeing the individual
quality of services. The problem of total power minimization is formulated as
non-convex optimization and coupled on multiple variables, making it complex
and challenging. Therefore, we first decouple the original problem into two
sub-problems and convert the nonlinear rate constraints into linear
constraints. Then, we adopt the iterative sub-gradient method to obtain an
efficient solution. For comparison, we also present a conventional NOMA
cooperative V2X network without AmBC. Simulation results show the benefits of
our proposed AmBC enhanced NOMA cooperative V2X network in terms of total
achievable energy efficiency.


title:
Massless minimal quantum scalar field with an asymmetric self
  interaction in de Sitter spacetime

abstract:
  Massless minimally coupled quantum scalar field with an asymmetric self
interaction, $V(\phi)=\lambda \phi^4/4!+ \beta \phi^3/3! $ (with $\lambda >0$)
is considered in the $(3+1)$-dimensional inflationary de Sitter spacetime. This
potential is bounded from below irrespective of the sign of $\beta$. Earlier
computations mostly considered the symmetric self interaction. First using the
in-in formalism, the renormalised quantum correction to the cubic potential
appearing in the energy-momentum tensor at two loop is computed, which is the
leading order correction in this case. The quantum correction to the kinetic
term at two loop are subleading compared to the above result at late
cosmological times. Next, using some of these results we compute the
renormalised vacuum expectation value of $ \phi$, by computing the tadpoles at
${\cal O}(\beta)$ and ${\cal O}(\lambda \beta)$. Due to the appearance of the
de Sitter breaking logarithm in the massless minimal scalar's propagators, the
tadpoles cannot be completely renormalised away in this case. All these
results, as usual, show secularly growing logarithms at late cosmological
times. We next use a recently proposed renormalisation group inspired formalism
to resum perturbative secular effects, to compute a non-perturbative $\langle
\phi \rangle$ at late cosmological times. The result turns out to be
approximately one order of magnitude less compared to the position of the
minima of $V(\phi)$, $\phi=-3\beta/\lambda$. Estimation on the possible shift
of the inflationary cosmological constant due to this $\langle \phi \rangle$ is
also made.


title:
Approximate NFA Universality and Related Problems Motivated by
  Information Theory

abstract:
  In coding and information theory, it is desirable to construct maximal codes
that can be either variable length codes or error control codes of fixed
length. However deciding code maximality boils down to deciding whether a given
NFA is universal, and this is a hard problem (including the case of whether the
NFA accepts all words of a fixed length). On the other hand, it is acceptable
to know whether a code is `approximately' maximal, which then boils down to
whether a given NFA is `approximately' universal. Here we introduce the notion
of a $(1-\epsilon)$-universal automaton and present polynomial randomized
approximation algorithms to test NFA universality and related hard automata
problems, for certain natural probability distributions on the set of words. We
also conclude that the randomization aspect is necessary, as approximate
universality remains hard for any fixed polynomially computable $\epsilon$.


title:
Clutter Edges Detection Algorithms for Structured Clutter Covariance
  Matrices

abstract:
  This letter deals with the problem of clutter edge detection and localization
in training data. To this end, the problem is formulated as a binary hypothesis
test assuming that the ranks of the clutter covariance matrix are known, and
adaptive architectures are designed based on the generalized likelihood ratio
test to decide whether the training data within a sliding window contains a
homogeneous set or two heterogeneous subsets. In the design stage, we utilize
four different covariance matrix structures (i.e., Hermitian, persymmetric,
symmetric, and centrosymmetric) to exploit the a priori information. Then, for
the case of unknown ranks, the architectures are extended by devising a
preliminary estimation stage resorting to the model order selection rules.
Numerical examples based on both synthetic and real data highlight that the
proposed solutions possess superior detection and localization performance with
respect to the competitors that do not use any a priori information.


title:
The Littlewood conjecture for a restricted class of pairs of real
  numbers

abstract:
  We prove that the Littlewood conjecture is satisfied for a restricted class
of pairs $(\alpha,\beta)$ of badly approximable numbers. We use the
localization of the roots of a cubic equation with coefficients depending on
the diophantine properties for the considered pair $(\alpha,\beta)$. The
estimates of the roots rely on the properties of the denominators of the
convergents of the continued fraction expansion of $\alpha$ and $\beta$.


title:
Temporal light control in complex media through the singular value
  decomposition of the time-gated transmission matrix

abstract:
  The complex temporal behavior of an ultrashort pulse of light propagating
through a multiply scattering medium can be characterized experimentally
through a time-gated transmission matrix. Using a spatial light modulator, we
demonstrate here, that injecting singular vectors of this matrix allows us to
optimally control energy deposition at any controllable delay time. Our
approach provides insights into fundamental aspects of multi-spectral light
scattering and could find applications in imaging or coherent control.


title:
Probing the SO$_2$ adsorption mechanism in Hofmann clathrates via
  inelastic neutron scattering and density functional theory calculations

abstract:
  The adsorption mechanism of SO2 in the Hofmann-like coordination polymer
Fe(pz)[Pt(CN)4] is studied using inelastic neutron scattering and density
functional theory calculations. We find that the most important spectral change
upon gas adsorption is the blueshift of the low energy peak found at 100 cm-1,
a feature that is fully confirmed by the computed neutron-weighted phonon
density of states. Our calculations suggest that the origin of this change is
twofold: i) an increase in the force constant of the cyanide out-of-plane
movement due to the binding of the gas onto the Pt(CN)4 plane, and ii) the
hampered rotation of the pyrazine due to steric hindrance. The high energy
region of the neutron scattering data whose spectral weight is dominated by the
internal vibrations of the pyrazine is negligibly affected by the presence of
the gas as expected from a physisorption type of binding.