title:
PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic
  Information for Language Identification

abstract:
  We propose a novel model to hierarchically incorporate phoneme and
phonotactic information for language identification (LID) without requiring
phoneme annotations for training. In this model, named PHO-LID, a
self-supervised phoneme segmentation task and a LID task share a convolutional
neural network (CNN) module, which encodes both language identity and
sequential phonemic information in the input speech to generate an intermediate
sequence of phonotactic embeddings. These embeddings are then fed into
transformer encoder layers for utterance-level LID. We call this architecture
CNN-Trans. We evaluate it on AP17-OLR data and the MLS14 set of NIST LRE 2017,
and show that the PHO-LID model with multi-task optimization exhibits the
highest LID performance among all models, achieving over 40% relative
improvement in terms of average cost on AP17-OLR data compared to a CNN-Trans
model optimized only for LID. The visualized confusion matrices imply that our
proposed method achieves higher performance on languages of the same cluster in
NIST LRE 2017 data than the CNN-Trans model. A comparison between predicted
phoneme boundaries and corresponding audio spectrograms illustrates the
leveraging of phoneme information for LID.


title:
Transformer-based Multimodal Information Fusion for Facial Expression
  Analysis

abstract:
  Human affective behavior analysis has received much attention in
human-computer interaction (HCI). In this paper, we introduce our submission to
the CVPR 2022 Competition on Affective Behavior Analysis in-the-wild (ABAW). To
fully exploit affective knowledge from multiple views, we utilize the
multimodal features of spoken words, speech prosody, and facial expression,
which are extracted from the video clips in the Aff-Wild2 dataset. Based on
these features, we propose a unified transformer-based multimodal framework for
Action Unit detection and also expression recognition. Specifically, the static
vision feature is first encoded from the current frame image. At the same time,
we clip its adjacent frames by a sliding window and extract three kinds of
multimodal features from the sequence of images, audio, and text. Then, we
introduce a transformer-based fusion module that integrates the static vision
features and the dynamic multimodal features. The cross-attention module in the
fusion module makes the output integrated features focus on the crucial parts
that facilitate the downstream detection tasks. We also leverage some data
balancing techniques, data augmentation techniques, and postprocessing methods
to further improve the model performance. In the official test of ABAW3
Competition, our model ranks first in the EXPR and AU tracks. The extensive
quantitative evaluations, as well as ablation studies on the Aff-Wild2 dataset,
prove the effectiveness of our proposed method.


title: A Framework for Fast Polarity Labelling of Massive Data Streams

abstract:
  Many of the existing sentiment analysis techniques are based on supervised
learning, and they demand the availability of valuable training datasets to
train their models. When dataset freshness is critical, the annotating of high
speed unlabelled data streams becomes critical but remains an open problem. In
this paper, we propose PLStream, a novel Apache Flink-based framework for fast
polarity labelling of massive data streams, like Twitter tweets or online
product reviews. We address the associated implementation challenges and
propose a list of techniques including both algorithmic improvements and system
optimizations. A thorough empirical validation with two real-world workloads
demonstrates that PLStream is able to generate high quality labels (almost 80%
accuracy) in the presence of high-speed continuous unlabelled data streams
(almost 16,000 tuples/sec) without any manual efforts.


title: MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data

abstract:
  Training of speech enhancement systems often does not incorporate knowledge
of human perception and thus can lead to unnatural sounding results.
Incorporating psychoacoustically motivated speech perception metrics as part of
model training via a predictor network has recently gained interest. However,
the performance of such predictors is limited by the distribution of metric
scores that appear in the training data. In this work, we propose MetricGAN+/-
(an extension of MetricGAN+, one such metric-motivated system) which introduces
an additional network - a "de-generator" which attempts to improve the
robustness of the prediction network (and by extension of the generator) by
ensuring observation of a wider range of metric scores in training.
Experimental results on the VoiceBank-DEMAND dataset show relative improvement
in PESQ score of 3.8% (3.05 vs 3.22 PESQ score), as well as better
generalisation to unseen noise and speech.


title: Fields of invariants for unipotent radicals of parabolic subgroups

abstract:
  The paper is devoted to the problem of finding free generators in the fields
of invariants for actions of unipotent groups on affine varieties. We consider
the case when the unipotent group is the unipotent radical in an arbitrary
parabolic subgroup in the reductive group of classical type GL(n), SL(n), O(n)
or Sp(2n). In the explicit form, we present a system of free generators in the
field of invariants for the action of the unipotent radical on the reductive
group by conjugation.


title:
High Phonon Scattering Rates Suppress Thermal Conductivity in
  Hyperstoichiometric Uranium Dioxide

abstract:
  Uranium dioxide (UO$_2$), one of the most important nuclear fuels, can
accumulate excess oxygen atoms as interstitial defects, which significantly
impacts thermal properties. In this study, thermal conductivities and inelastic
neutron scattering measurements on UO$_2$ and UO$_{2+x}$ (x=0.3, 0.4, 0.8,
0.11) were performed at low temperatures (2-300 K). The thermal conductivity of
UO$_{2+x}$ is significantly suppressed compared to UO$_2$ except near the
N\'eel temperature TN= 30.8 K, where it is independent of x. Phonon
measurements demonstrate that the heat capacities and phonon group velocities
of UO$_2$ and UO$_{2+x}$ are similar and that the suppressed thermal
conductivity in UO$_{2+x}$ results from high phonon scattering rates. These new
insights advance our fundamental understanding of thermal transport properties
in advanced nuclear fuels.


title:
Effective calculation of the Green's function in the time domain on
  near-term quantum processors

abstract:
  We propose an improved quantum algorithm to calculate the Green's function
through real-time propagation, and use it to compute the retarded Green's
function for the 2-, 3- and 4-site Hubbard models. This novel protocol
significantly reduces the number of controlled operations when compared to
those previously suggested in literature. Such reduction is quite remarkable
when considering the 2-site Hubbard model, for which we show that it is
possible to obtain the exact time propagation of the $|N\pm 1\rangle$ states by
exponentiating one single Pauli component of the Hamiltonian, allowing us to
perform the calculations on an actual superconducting quantum processor.


title:
A boundary-penalized isogeometric analysis for second-order hyperbolic
  equations

abstract:
  Explicit time-marching schemes are popular for solving time-dependent partial
differential equations; one of the biggest challenges these methods suffer is
increasing the critical time-marching step size that guarantees numerical
stability. In general, there are two ways to increase the critical step size.
One is to reduce the stiffness of the spatially discretized system, while the
other is to design time-marching schemes with larger stability regions. In this
paper, we focus on the recently proposed explicit generalized-$\alpha$ method
for second-order hyperbolic equations and increase the critical step size by
reducing the stiffness of the isogeometric-discretized system. In particular,
we apply boundary penalization to lessen the system's stiffness. For $p$-th
order $C^{p-1}$ isogeometric elements, we show numerically that the critical
step size increases by a factor of $\sqrt{\frac{p^2-3p+6}{4}}$, which indicates
the advantages of using the proposed method, especially for high-order
elements. Various examples in one, two, and three dimensions validate the
performance of the proposed technique.


title: Topological Hybrids of Magnons and Magnon Bound Pairs

abstract:
  We consider quantum condensed matter systems without particle-number
conservation. Since the particle number is not a good quantum number, states
belonging to different particle-number sectors can hybridize, which causes
topological anticrossings in the spectrum. The resulting spectral gaps support
chiral edge excitations whose wavefunction is a superposition of states in the
two hybridized sectors. This situation is realized in fully saturated
spin-anisotropic quantum magnets without spin conservation, in which single
magnons hybridize with magnon bound pairs, i.e., two-magnon bound states. The
resulting chiral edge excitations are exotic composites that carry mixed
spin-multipolar character, inheriting spin-dipolar and spin-quadrupolar
character from their single-particleness and two-particleness, respectively. In
contrast to established topological magnons, the topological effects discussed
here are of genuine quantum mechanical origin and vanish in the classical
limit. We discuss implications for both intrinsic anomalous Hall-type transport
and beyond-spintronics computation paradigms. We conclude that fully polarized
quantum magnets are a promising platform for topology caused by hybridizations
between particle-number sectors, complementing the field of ultracold atoms
working with a conserved number of particles.


title: Five theorems on Gorenstein global dimensions

abstract:
  We expand on two existing characterizations of rings of Gorenstein (weak)
global dimension zero and give two new characterizations of rings of finite
Gorenstein (weak) global dimension. We also include the answer to a question of
Y.~Xiang on Gorenstein weak global dimension of group rings.


title: A Fast Diagnostic to Inform Screening of Discarded or Retired Batteries

abstract:
  With the increased pervasiveness of Lithium-ion batteries, there is growing
concern for the amount of retired batteries that will be entering the waste
stream. Although these batteries no longer meet the demands of their first
application, many still have a significant portion of their initial capacity
remaining for use in secondary applications. Yet, direct repurposing is
generally not possible and each cell in a battery must be evaluated, increasing
the cost of the repurposed packs due to the time intensive screening process.
In this paper, a rapid assessment of the internal resistance of a cell is
proposed. First, this method of measuring the resistance is completed on cells
from twelve retired battery packs and one fresh pack using a hybrid pulse power
characterization (HPPC) test as a benchmark for the analysis. Results from
these tests show relatively constant resistance measurements across mid to high
terminal voltages, allowing this metric to be independent of state of charge
(SOC). Then, the relation between internal resistance and capacity across the
various packs is discussed. Initial experimental results from this study show a
correlation between internal resistance and capacity which can be approximated
with a linear fit, suggesting internal resistance measurements taken above a
threshold cell terminal voltage may be a suitable initial screening metric for
the capacity of retired cells without knowledge of the SOC.


title: Dynamically-Scaled Deep Canonical Correlation Analysis

abstract:
  Canonical Correlation Analysis (CCA) is a method for feature extraction of
two views by finding maximally correlated linear projections of them. Several
variants of CCA have been introduced in the literature, in particular, variants
based on deep neural networks for learning highly correlated nonlinear
transformations of two views. As these models are parameterized conventionally,
their learnable parameters remain independent of the inputs after the training
process, which may limit their capacity for learning highly correlated
representations. We introduce a novel dynamic scaling method for training an
input-dependent canonical correlation model. In our deep-CCA models, the
parameters of the last layer are scaled by a second neural network that is
conditioned on the model's input, resulting in a parameterization that is
dependent on the input samples. We evaluate our model on multiple datasets and
demonstrate that the learned representations are more correlated in comparison
to the conventionally-parameterized CCA-based models and also obtain preferable
retrieval results. Our code is available at
https://github.com/tomerfr/DynamicallyScaledDeepCCA.


title:
Long hauling eco-driving: heavy-duty trucks operational modes control
  with integrated road slope preview

abstract:
  In this paper, a complete eco-driving strategy for heavy-duty trucks (HDT)
based on a finite number of driving modes with corresponding gear shifting is
developed to cope with different route events and with road slope data. The
problem is formulated as an optimal control problem with respect to fuel
consumption and trip duration, and solved using a Pontryagin minimum principle
(PMP) algorithm for a path search problem, such that computations can be
carried out online, in real-time. The developed eco-driving assistance system
(EDAS) provides a velocity profile and a sequence of driving modes (and gears)
recommendation to the driver, without actively controlling the HDT (human in
the loop) and, in practice, allows contextual feedback incorporation from the
driver for safety. Simulation results show that the developed methodology is
able to provide a velocity profile for a complete route based on known road
events and slope information while satisfying all truck operational
constraints.


title:
A Levenberg-Marquardt algorithm for sparse identification of dynamical
  systems

abstract:
  Low complexity of a system model is essential for its use in real-time
applications. However, sparse identification methods commonly have stringent
requirements that exclude them from being applied in an industrial setting. In
this paper, we introduce a flexible method for the sparse identification of
dynamical systems described by ordinary differential equations. Our method
relieves many of the requirements imposed by other methods that relate to the
structure of the model and the data set, such as fixed sampling rates, full
state measurements, and linearity of the model. The Levenberg-Marquardt
algorithm is used to solve the identification problem. We show that the
Levenberg-Marquardt algorithm can be written in a form that enables parallel
computing, which greatly diminishes the time required to solve the
identification problem. An efficient backward elimination strategy is presented
to construct a lean system model.


title:
Co-loading of doxorubicin and iron oxide nanocubes in polycaprolactone
  fibers for combining Magneto-Thermal and chemotherapeutic effects on cancer
  cells

abstract:
  Among the strategies to fight cancer, multi-therapeutic approaches are
considered as a wise choice to put in place multiple weapons to suppress
tumors. In this work, to combine chemotherapeutic effects to magnetic
hyperthermia when using biocompatible scaffolds, we have established an
electrospinning method to produce nanofibers of polycaprolactone loaded with
magnetic nanoparticles as heat mediators to be selectively activated under
alternating magnetic field and doxorubicin as a chemotherapeutic drug.
Production of the fibers was investigated with iron oxide nanoparticles of
peculiar cubic shape (at 15 and 23 nm in cube edges) as they provide benchmark
heat performance under clinical magnetic hyperthermia conditions. With 23 nm
nanocubes when included into the fibers, an arrangement in chains was obtained.
This linear configuration of magnetic nanoparticles resemble that of the
magnetosomes, produced by magnetotactic bacteria, and our magnetic fibers
exhibited remarkable heating effects as the magnetosomes. Magnetic fiber
scaffolds showed excellent biocompatibility on fibroblast cells when missing
the chemotherapeutic agent and when not exposed to magnetic hyperthermia as
shown by viability assays. On the contrary, the fibers containing both magnetic
nanocubes and doxorubicin showed significant cytotoxic effects on cervical
cancer cells following the exposure to magnetic hyperthermia. Notably, these
tests were conducted at magnetic hyperthermia field conditions of clinical use.
As here shown, on the doxorubicin sensitive cervical cancer cells, the
combination of heat damage by magnetic hyperthermia with enhanced diffusion of
doxorubicin at therapeutic temperature are responsible for a more effective
oncotherapy.


title:
Subwavelength field localization based on dielectric mesoscale particle
  with single and blind nanohole array

abstract:
  Some new unusual physical phenomena and effects associated with dielectric
mesoscale particles with Mie size parameter near 10 were studied and have been
discovered during the last decade. In this paper, we propose nanoholes
structured wavelength-scaled dielectric cubic particle with refractive index
near two, where the array of nanoholes can act as a plurality of near-field
probes to simultaneously illuminate the sample surface and it has the potential
of surpassing the performance of most existing nearfield imaging approaches. We
also offer the concept of the single nano-structuring of a dielectric cylinder
or sphere made from conventional optical materials. The choice of the diameter
of the nanohole in the particle "transfers" it into the resonance mode, when
the characteristics of the field localized in the shadow part of the particle
are determined not by the wavelength, but by the size of the nanohole. Thus,
the diameter of the focused spot at the exit from the particle can be much
smaller than the solid immersion diffraction limit.


title: An aperiodic monotile for the tiler

abstract:
  Can the entire plane be paved with a single tile that forces aperiodicity?
This is known as the ein Stein problem (in German, ein Stein means one tile).
This paper presents an aperiodic monotile for the tiler. It is based on the
monotile developed by Taylor and Socolar (whose aperiodicity is forced by means
of a non-connected tile that is mainly hexagonal) and motif-based hexagonal
tilings that followed this major discovery. The proposed monotile consists of
two layers. No motif is needed to make the monotile aperiodic. Additional
motifs can be added to the monotile to provide some insights. The proof of
aperiodicity is presented with the use of such motifs.


title:
The restriction from below of the subharmonic function by the logarithm
  of the module of entire function

abstract:
  Let $u\not\equiv -\infty$ be a subharmonic function on the complex plane
$\mathbb C$. Then for any function $r\colon\mathbb C\to (0,1]$ satisfying the
condition $$\inf_{z\in\mathbb C}\frac{\ln r(z)}{\ln(2+|z|)}>-\infty,$$ there is
an entire function $f\not\equiv 0$ such that $$ \ln |f(z)|\leq
\frac{1}{2\pi}\int_0^{2\pi}u(z+r(z)e^{i\theta})\,{\mathrm
d}\theta\quad\text{for all $z\in\mathbb C$.}$$ A similar result is established
for subharmonic functions of finite order with inequalities of the form
$\ln|f(z)|\leq u(z)$ at all points $z\in\mathbb C\setminus E$, where the
exceptional set $E$ is small in terms of $d$-dimensional Hausdorff content of
$E$ with variable radius $r$.


title: Rank-Metric Codes and Their Applications

abstract:
  The rank metric measures the distance between two matrices by the rank of
their difference. Codes designed for the rank metric have attracted
considerable attention in recent years, reinforced by network coding and
further motivated by a variety of applications. In code-based cryptography, the
hardness of the corresponding generic decoding problem can lead to systems with
reduced public-key size. In distributed data storage, codes in the rank metric
have been used repeatedly to construct codes with locality, and in coded
caching, they have been employed for the placement of coded symbols. This
survey gives a general introduction to rank-metric codes, explains their most
important applications, and highlights their relevance to these areas of
research.


title: A Blueprint for Four-states Quantum Formal System

abstract:
  A blueprint for a formal system for describing living systems, based on the
entanglement and parallel computation of systems consisting of two qubits shall
be proposed. In particular, isomorphism with the S = {T, A, G, C}, | S | = 22 =
4 system of DNA sequences consisting of nucleotide symbols are aimed for. In
this simple system, two qubits belong to system A and the other two qubits
belong to system B, which are entangled and inseparable. System A and System B
are derived from a parent-mother diploid living system.


title: Modules in Robinson Spaces

abstract:
  A Robinson space is a dissimilarity space $(X,d)$ (i.e., a set $X$ of size
$n$ and a dissimilarity $d$ on $X$) for which there exists a total order $<$ on
$X$ such that $x<y<z$ implies that $d(x,z)\ge \max\{ d(x,y), d(y,z)\}$.
Recognizing if a dissimilarity space is Robinson has numerous applications in
seriation and classification. An mmodule of $(X,d)$ (generalizing the notion of
a module in graph theory) is a subset $M$ of $X$ which is not distinguishable
from the outside of $M$, i.e., the distance from any point of $X\setminus M$ to
all points of $M$ is the same. If $p$ is any point of $X$, then $\{ p\}$ and
the maximal by inclusion mmodules of $(X,d)$ not containing $p$ define a
partition of $X$, called the copoint partition. In this paper, we investigate
the structure of mmodules in Robinson spaces and use it and the copoint
partition to design a simple and practical divide-and-conquer algorithm for
recognition of Robinson spaces in optimal $O(n^2)$ time.


title:
On the (Limited) Generalization of MasterFace Attacks and Its Relation
  to the Capacity of Face Representations

abstract:
  A MasterFace is a face image that can successfully match against a large
portion of the population. Since their generation does not require access to
the information of the enrolled subjects, MasterFace attacks represent a
potential security risk for widely-used face recognition systems. Previous
works proposed methods for generating such images and demonstrated that these
attacks can strongly compromise face recognition. However, previous works
followed evaluation settings consisting of older recognition models, limited
cross-dataset and cross-model evaluations, and the use of low-scale testing
data. This makes it hard to state the generalizability of these attacks. In
this work, we comprehensively analyse the generalizability of MasterFace
attacks in empirical and theoretical investigations. The empirical
investigations include the use of six state-of-the-art FR models, cross-dataset
and cross-model evaluation protocols, and utilizing testing datasets of
significantly higher size and variance. The results indicate a low
generalizability when MasterFaces are training on a different face recognition
model than the one used for testing. In these cases, the attack performance is
similar to zero-effort imposter attacks. In the theoretical investigations, we
define and estimate the face capacity and the maximum MasterFace coverage under
the assumption that identities in the face space are well separated. The
current trend of increasing the fairness and generalizability in face
recognition indicates that the vulnerability of future systems might further
decrease. Future works might analyse the utility of MasterFaces for
understanding and enhancing the robustness of face recognition models.


title: Laser ablation and fragmentation of Boron in liquids

abstract:
  Nanoparticles of elemental Boron are generated for the first time by laser
ablation of a sintered Boron target in liquid isopropanol and subsequent laser
fragmentation of the suspension. For this purpose an ytterbium doped fiber
laser was used at wavelength of 1060-1070 nm, pulse repetition rate of 20 kHz,
and pulse duration of 200 ns. The size of Boron nanoparticles after ablation
and fragmentation of the suspension is around 30 nm. Nanoparticles are made
mostly of Boron and carbon, some particles have carbon shell. Allotropic
composition of nanoparticles differs from that of the initial Boron target.
Possible applications are discussed.


title: Climate impacts of particle physics

abstract:
  The pursuit of particle physics requires a stable and prosperous society.
Today, our society is increasingly threatened by global climate change.
Human-influenced climate change has already impacted weather patterns, and
global warming will only increase unless deep reductions in emissions of CO$_2$
and other greenhouse gases are achieved. Current and future activities in
particle physics need to be considered in this context, either on the moral
ground that we have a responsibility to leave a habitable planet to future
generations, or on the more practical ground that, because of their scale,
particle physics projects and activities will be under scrutiny for their
impact on the climate. In this white paper for the U.S. Particle Physics
Community Planning Exercise ("Snowmass"), we examine several contexts in which
the practice of particle physics has impacts on the climate. These include the
construction of facilities, the design and operation of particle detectors, the
use of large-scale computing, and the research activities of scientists. We
offer recommendations on establishing climate-aware practices in particle
physics, with the goal of reducing our impact on the climate. We invite members
of the community to show their support for a sustainable particle physics field
(https://indico.fnal.gov/event/53795/).


title: Ly{\alpha} irradiation of solid-state formamide

abstract:
  Formamide (NH$_2$CHO), a potential prebiotic precursor, has been proposed to
play an important role in the context of origin of life on our planet. It has
been observed in different environments in space including the protostellar
regions and comets. The abundance and stability of NH$_2$CHO in the early
stages of star formation can be better understood by incorporating the
formation and destruction data in the astrochemical models. We carried out an
experimental investigation to study the destruction of pure NH$_2$CHO ice at 12
K by the interaction of Ly$\alpha$ (121.6 nm) photons. The UV photo destruction
of NH$_2$CHO was studied using Fourier-transform infrared spectroscopy. After
UV processing, the intensity of NH$_2$CHO IR bands decreases and new bands
corresponding to HCN, CO, NH$_4^+$ OCN$^-$, HNCO, and CO$_2$ appeared in the
spectrum. Destruction and cumulative product formation cross-sections were
derived. The comparison of destruction rate derived from the cross-section in
cold and dense molecular cloud for different energetic processing agents,
reveals that UV photons induces an order of magnitude higher NH$_2$CHO
destruction than cosmic rays, but three orders of magnitude lower than for H
atoms.


title: Dynamics of droplets driven by electrowetting

abstract:
  Even though electrowetting-on-dielectric (EWOD) is a useful strategy in a
wide array of biological and engineering processes with numerous
droplet-manipulation applications, there is still a lack of complete
theoretical interpretation on the dynamics of electrowetting. In this paper, we
present an effective theoretical model and use Onsager variational principle to
successfully derive the governing equations of non-equilibrium electrowetting
dynamics for a droplet in both overdamped and underdamped regimes. It is found
that the spreading and retraction dynamics of a droplet on EWOD substrates can
be fairly well captured in the overdamped regime. By varying liquid viscosity,
droplet size, and applied voltage, we confirm that the transient dynamics of EW
can be characterized by a timescale independent of liquid viscosity, droplet
size and applied voltage. Our model provides a complete fundamental explanation
of EW-driven spreading dynamics, which is important for a wide range of
applications, from self-cleaning to novel optical and digital microfluidic
devices.


title: On the dimension of angles and their units

abstract:
  We examine implications of angles having their own dimension, in the same
sense as do lengths, masses, {\it etc.} The conventional practice in scientific
applications involving trigonometric or exponential functions of angles is to
assume that the argument is the numerical part of the angle when expressed in
units of radians. It is also assumed that the functions are the corresponding
radian-based versions. These (usually unstated) assumptions generally allow one
to treat angles as if they had no dimension and no units, an approach that
sometimes leads to serious difficulties. Here we consider arbitrary units for
angles and the corresponding generalizations of the trigonometric and
exponential functions. Such generalizations make the functions complete, that
is, independent of any particular choice of unit for angles. They also provide
a consistent framework for including angle units in computer algebra programs.


title: Proper time operator and its uncertainty relation

abstract:
  We study the quantum properties of an oscillator in proper time. This proper
time oscillator is a particle model with mass that is on shell. Its internal
time can be treated as a self-adjoint operator. The displaced time and
displaced time rate of the oscillator obey an uncertainty relation resembling
the one between position and momentum, which is different from the usual
energy-time uncertainty relation. In addition, we demonstrate that a matter
field with proper time oscillators satisfies the Klein-Gordon equation. It has
properties of a zero-spin quantum field. The formulations adopted permits a
more symmetrical treatment between time and space in a matter field.


title:
The impossibility of one-way superluminal signaling in the framework of
  modern Physics

abstract:
  This is a response to the note criticizing my paper A New Paradox in
Superluminal Signaling. The criticism is based, among other things, on
arbitrarily postulated statement about invarience of direction of superluminal
information transfer. The analysis presented in my response shows the absence
of any grounds for such statement and confirms my original proof of the
impossibility of one-way superluminal signaling. The analysis also unveils many
other inconsistencies in the discussed note and shows that all its criticism is
unsubstantiated.
  Key words: superluminal signaling, tachyons, the information flow


title: Favorit: farmers volatility risk treatment

abstract:
  This paper seeks to develop a strategy based on analytics, for an individual
Indian farmer to tackle market price fluctuations. The idea is to select a
month (or a week or a day) on which to take the produce to market for a good
return on the sale. The choice is based on the history of price data and
associated variability. Market-wise price data for the last decade or so are
used. These ideas are applied to three vegetable crops, tomato, onion, and
coriander for some markets in the state of Maharashtra. It is proposed that
similar work should be done crop-wise and market-wise for different parts of
India by local academic groups from any of the subjects such as statistics,
analytics, data science, agriculture, business management, commerce, and
economics. The objective is to mitigate the adverse impact of price fluctuation
on farmers.


title: Dynamics of higher-order lumps in the Davey-Stewartson II equation

abstract:
  A family of higher-order rational lumps on non-zero constant background of
Davey-Stewartson (DS) II equation are investigated. These solutions have
multiple peaks whose heights and trajectories are approximately given by
asymptotical analysis. It is found that the heights are time-dependent and for
large time they approach the same constant height value of the first-order
fundamental lump. The resulting trajectories are considered and it is found
that the scattering angle can assume arbitrary values in the interval of
$(\frac{\pi}{2}, \pi)$ which is markedly distinct from the necessary orthogonal
scattering for the higher-order lumps on zero background. Additionally, it is
illustrated that the higher-order lumps containing multi-peaked $n$-lumps can
be regarded as a nonlinear superposition of $n$ first-order ones as
$|t|\rightarrow\infty$.


title: On independent domination in direct products

abstract:
  In \cite{nr-1996} Nowakowski and Rall listed a series of conjectures
involving several different graph products. In particular, they conjectured
that $i(G\times H) \ge i(G)i(H)$ where $i(G)$ is the independent domination
number of $G$ and $G\times H$ is the direct product of graphs $G$ and $H$. We
show this conjecture is false, and, in fact, construct pairs of graphs for
which $\min\{i(G), i(H)\} - i(G\times H)$ is arbitrarily large. We also give
the exact value of $i(G\times K_n)$ when $G$ is either a path or a cycle.


title: The moduli of annuli in random conformal geometry

abstract:
  We obtain exact formulae for three basic quantities in random conformal
geometry that depend on the modulus of an annulus. The first is for the law of
the modulus of the Brownian annulus describing the scaling limit of uniformly
sampled planar maps with annular topology, which is as predicted from the ghost
partition function in bosonic string theory. The second is for the law of the
modulus of the annulus bounded by a loop of a simple conformal loop ensemble
(CLE) on a disk and the disk boundary. The formula is as conjectured from the
partition function of the O$(n)$ loop model on the annulus derived by Cardy
(2006). The third is for the annulus partition function of the SLE$_{8/3}$ loop
introduced by Werner (2008). It again confirms a prediction of Cardy (2006).
The physics principle underlying our proofs is that 2D quantum gravity coupled
with conformal matters can be decomposed into three conformal field theories
(CFT): the matter CFT, the Liouville CFT, and the ghost CFT. At the technical
level, we rely on two types of integrability in Liouville quantum gravity, one
from the scaling limit of random planar maps, the other from the Liouville CFT.
We expect our method to be applicable to a variety of questions related to the
random moduli of non-simply-connected random surfaces.


title: PRTT: Precomputed Radiance Transfer Textures

abstract:
  Precomputed Radiance Transfer (PRT) can achieve high quality renders of
glossy materials at real-time framerates. PRT involves precomputing a
k-dimensional transfer vector of Spherical Harmonic (SH) coefficients at
specific points for a scene. Most prior art precomputes transfer at vertices of
the mesh and interpolates color for interior points. They require finer mesh
tessellations for high quality renderings. In this paper, we explore and
present the use of textures for storing transfer. Using transfer textures
decouples mesh resolution from transfer storage and sampling which is useful
especially for glossy renders. We further demonstrate glossy inter-reflections
by precomputing additional textures. We thoroughly discuss practical aspects of
transfer textures and analyze their performance in real-time rendering
applications. We show equivalent or higher render quality and FPS and
demonstrate results on several challenging scenes.


title: Tight Bounds for Repeated Balls-into-Bins

abstract:
  We study the repeated balls-into-bins process introduced by Becchetti,
Clementi, Natale, Pasquale and Posta (2019). This process starts with $m$ balls
arbitrarily distributed across $n$ bins. At each step $t=1,2,\ldots$, we select
one ball from each non-empty bin, and then place it into a bin chosen
independently and uniformly at random.
  We prove the following results:
  $\quad \bullet$ For any $n \leq m \leq \mathrm{poly}(n)$, we prove a lower
bound of $\Omega(m/n \cdot \log n)$ on the maximum load. For the special case
$m=n$, this matches the upper bound of $O(\log n)$, as shown in [BCNPP19]. It
also provides a positive answer to the conjecture in [BCNPP19] that for $m=n$
the maximum load is $\omega(\log n/ \log \log n)$ in a polynomially large
window. For $m \in [\omega(n), n \log n]$, our new lower bound disproves the
conjecture in [BCNPP19] that the maximum load remains $O(\log n)$.
  $\quad \bullet$ For any $n \leq m \leq \mathrm{poly}(n)$, we prove an upper
bound of $O(m/n \cdot \log n)$ on the maximum load for a polynomially large
window. This matches our lower bound up to multiplicative constants.
  $\quad \bullet$ For any $m \geq n$, our analysis also implies an $O( m^2 /
n)$ waiting time to a configuration with $O(m/n \cdot \log m)$ maximum load,
even for worst-case initial distributions.
  $\quad \bullet$ For $m \geq n$, we show that every ball visits every bin in
$O(m \log m)$ steps. For $m = n$, this improves the previous upper bound of
$O(n \log^2 n)$ in [BCNPP19]. We also prove that the upper bound is tight up to
multiplicative constants for any $n \leq m \leq \mathrm{poly}(n)$.


title:
Revisiting timelike geodesics in the Schwarzschild spacetime: general
  expressions in terms of Weierstrass elliptic functions

abstract:
  The theory of Schwarzschild timelike geodesics is revisited. Basing on a
result by Weierstrass and Biermann, we derive a formula describing all non
radial, time-like trajectories in terms of Weierstrass elliptic functions.
Quite remarkably, a single formula works for an entire geodesic trajectory,
even if it passes through turning points. Using this formula, we derive
expressions for the proper and coordinate time along the geodesic.


title: Performance evaluation of volatility estimation methods for Exabel

abstract:
  Quantifying both historic and future volatility is key in portfolio risk
management. This note presents and compares estimation strategies for
volatility estimation in an estimation universe consisting on 28 629 unique
companies from February 2010 to April 2021, with 858 different portfolios. The
estimation methods are compared in terms of how they rank the volatility of the
different subsets of portfolios. The overall best performing approach estimates
volatility from direct entity returns using a GARCH model for variance
estimation.


title:
Repulsive Clustering Based Pilot Assignment for Cell-Free Massive MIMO
  Systems

abstract:
  Thanks to its capability to provide a uniform service rate for the User
Equipments (UEs), Cell-free (CF) massive Multiple-Input, Multiple-Output
(mMIMO), has recently attracted considerable attention, both in academia and in
industry, and so is considered as one of the potential technologies for
beyond-5G and 6G. However, the reuse of the same pilot signals by multiple
users can create the so-called pilot contamination problem, which can hinder
the CF mMIMO from unlocking its full performance. In this paper, we address the
challenge by formulating the pilot assignment as a maximally diverse clustering
problem and propose an efficient yet straightforward repulsive clustering-based
pilot assignment scheme to mitigate the effects of pilot contamination on CF
mMIMO. The numerical results show the superiority of the proposed technique
compared to some other methods with respect to the achieved uplink per-user
rate.


title: Gauss maps of harmonic and minimal great circle fibrations

abstract:
  We investigate Gauss maps associated to great circle fibrations of $S^3$. We
show that the associated Gauss map to such a fibration is harmonic
(respectively minimal) if and only if the unit vector field generating the
great circle foliation is harmonic (respectively minimal). These results can be
viewed as analogues of the classical theorem of Ruh and Vilms about the
harmonicity of the Gauss map of a minimal submanifold in the euclidean space.
Moreover, we prove that a harmonic or minimal unit vector field in $S^3$ with
great circle integral curves is a Hopf vector field.


title: A theory of the dark matter

abstract:
  In an earlier paper I proposed a highly symmetric semi-classical initial
condition to describe the universe in the period leading up to the electroweak
transition and completely determine all cosmology after that. Nothing beyond
the Standard Model is assumed. Inflation is not needed. The initial symmetry
allows no adjustable parameters. It is a complete theory of the Standard Model
cosmological epoch, predictive and falsifiable. Here, the time evolution of the
initial condition is calculated in the classical approximation. The fields with
nontrivial classical values are the SU(2)-weak gauge field (the cosmological
gauge field or CGF) and the Higgs field. The CGF produces the electroweak
transition then evolves as a non-relativistic perfect fluid
($w_{\mathrm{CGF}}\approx 0$). At the present time, i.e. when $H=H_{0}$, the
CGF energy density satisfies $\Omega_{\Lambda}+\Omega_{\mathrm{CGF}}=1$. The
CGF is the dark matter. The dark matter is a classical phenomenon of the
Standard Model. The classsical universe contains only the dark matter, no
ordinary matter. At next to leading order the fluctuations of the Standard
Model fields will provide a calculable, relatively small amount of ordinary
matter such that
$\Omega_{\Lambda}+\Omega_{\mathrm{CGF}}+\Omega_{\mathrm{ordinary}}=1$.


title: Spectral decomposition of some non-self-adjoint operators

abstract:
  We consider non-self-adjoint operators in Hilbert spaces of the form
$H=H_0+CWC$, where $H_0$ is self-adjoint, $W$ is bounded and $C$ is a metric
operator, $C$ bounded and relatively compact with respect to $H_0$. We suppose
that $C(H_0-z)^{-1}C$ is uniformly bounded in
$z\in\mathbb{C}\setminus\mathbb{R}$. We define the spectral singularities of
$H$ as the points of the essential spectrum
$\lambda\in\sigma_{\mathrm{ess}}(H)$ such that $C(H\pm i\varepsilon)^{-1}CW$
does not have a limit as $\varepsilon\to0^+$. We prove that the spectral
singularities of $H$ are in one-to-one correspondence with the eigenvalues,
associated to resonant states, of an extension of $H$ to a larger Hilbert
space. Next, we show that the asymptotically disappearing states for $H$, i.e.
the set of vectors $\varphi$ such that $e^{\pm itH}\varphi\to0$ as
$t\to\infty$, coincide with the generalized eigenstates of $H$ corresponding to
eigenvalues $\lambda\in\mathbb{C}$, $\mp\mathrm{Im}(\lambda)>0$. Finally, we
define the absolutely continuous spectral subspace of $H$ and show that it
satisfies $\mathcal{H}_{\mathrm{ac}}(H)=\mathcal{H}_{\mathrm{p}}(H^*)^\perp$,
where $\mathcal{H}_{\mathrm{p}}(H^*)$ stands for the point spectrum of $H^*$.
We thus obtain a direct sum decomposition of the Hilbert spaces in terms of
spectral subspaces of $H$. One of the main ingredients of our proofs is a
spectral resolution formula for a bounded operator $r(H)$ regularizing the
identity at spectral singularities. Our results apply to Schr\"odinger
operators with complex potentials.


title: Verification of safety critical control policies using kernel methods

abstract:
  Hamilton-Jacobi reachability methods for safety-critical control have been
well studied, but the safety guarantees derived rely on the accuracy of the
numerical computation. Thus, it is crucial to understand and account for any
inaccuracies that occur due to uncertainty in the underlying dynamics and
environment as well as the induced numerical errors. To this end, we propose a
framework for modeling the error of the value function inherent in
Hamilton-Jacobi reachability using a Gaussian process. The derived safety
controller can be used in conjuncture with arbitrary controllers to provide a
safe hybrid control law. The marginal likelihood of the Gaussian process then
provides a confidence metric used to determine switches between a least
restrictive controller and a safety controller. We test both the prediction as
well as the correction capabilities of the presented method in a classical
pursuit-evasion example.


title: Exabel's Factor Model

abstract:
  Factor models have become a common and valued tool for understanding the
risks associated with an investing strategy. In this report we describe
Exabel's factor model, we quantify the fraction of the variability of the
returns explained by the different factors, and we show some examples of annual
returns of portfolios with different factor exposure.


title: Continuous Software Engineering in the Wild

abstract:
  Software is becoming a critical component of most products and organizational
functions. The ability to continuously improve software determines how well the
organization can respond to market opportunities. Continuous software
engineering promises numerous advantages over sprint-based or plan-driven
development. However, implementing a continuous software engineering pipeline
in an existing organization is challenging.
  In this invited position paper, we discuss the adoption challenges and argue
for a more systematic methodology to drive the adoption of continuous
engineering. Our discussion is based on ongoing work with several industrial
partners as well as experience reported in both state-of-practice and
state-of-the-art.
  We conclude that the adoption of continuous software engineering primarily
requires analysis of the organization, its goals, and constraints. One size
does not fit all purposes, meaning that many of the principles behind
continuous engineering are relevant for most organizations, but the level of
realization and the benefits may still vary. The main hindrances to continuous
flow of software arise from sub-optimal organizational structures and the lack
of alignment. Once those are removed, the organization can implement automation
to further improve the software delivery.


title: Towards Reproducible Network Traffic Analysis

abstract:
  Analysis techniques are critical for gaining insight into network traffic
given both the higher proportion of encrypted traffic and increasing data
rates. Unfortunately, the domain of network traffic analysis suffers from a
lack of standardization, leading to incomparable results and barriers to
reproducibility. Unlike other disciplines, no standard dataset format exists,
forcing researchers and practitioners to create bespoke analysis pipelines for
each individual task. Without standardization researchers cannot compare
"apples-to-apples", preventing us from knowing with certainty if a new
technique represents a methodological advancement or if it simply benefits from
a different interpretation of a given dataset.
  In this work, we examine irreproducibility that arises from the lack of
standardization in network traffic analysis. First, we study the literature,
highlighting evidence of irreproducible research based on different
interpretations of popular public datasets. Next, we investigate the underlying
issues that have lead to the status quo and prevent reproducible research.
Third, we outline the standardization requirements that any solution aiming to
fix reproducibility issues must address. We then introduce pcapML, an open
source system which increases reproducibility of network traffic analysis
research by enabling metadata information to be directly encoded into raw
traffic captures in a generic manner. Finally, we use the standardization
pcapML provides to create the pcapML benchmarks, an open source leaderboard
website and repository built to track the progress of network traffic analysis
methods.


title:
The power of qutrits for non-adaptive measurement-based quantum
  computing

abstract:
  Non-locality is not only one of the most prominent quantum features but can
also serve as a resource for various information-theoretical tasks. Analysing
it from an information-theoretical perspective has linked it to applications
such as non-adaptive measurement-based quantum computing (NMQC). In this type
of quantum computing the goal is to output a multivariate function. The success
of such a computation can be related to the violation of a generalised Bell
inequality. So far, the investigation of binary NMQC with qubits has shown that
quantum correlations can compute all Boolean functions using at most $2^n-1$
qubits, whereas local hidden variables (LHVs) are restricted to linear
functions. Here, we extend these results to NMQC with qutrits and prove that
quantum correlations enable the computation of all ternary functions using the
generalised qutrit Greenberger-Horne-Zeilinger (GHZ) state as a resource and at
most $3^n-1$ qutrits. This yields a corresponding generalised GHZ type paradox
for any ternary function that LHVs cannot compute. We give an example for an
$n$-variate function that can be computed with only $n+1$ qutrits, which leads
to convenient generalised qutrit Bell inequalities whose quantum bound is
maximal. Finally, we prove that not all functions can be computed efficiently
with qutrit NMQC by presenting a counterexample.


title:
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture
  Search

abstract:
  Optimizing resource utilization in target platforms is key to achieving high
performance during DNN inference. While optimizations have been proposed for
inference latency, memory footprint, and energy consumption, prior
hardware-aware neural architecture search (NAS) methods have omitted resource
utilization, preventing DNNs to take full advantage of the target inference
platforms. Modeling resource utilization efficiently and accurately is
challenging, especially for widely-used array-based inference accelerators such
as Google TPU. In this work, we propose a novel hardware-aware NAS framework
that does not only optimize for task accuracy and inference latency, but also
for resource utilization. We also propose and validate a new computational
model for resource utilization in inference accelerators. By using the proposed
NAS framework and the proposed resource utilization model, we achieve 2.8 - 4x
speedup for DNN inference compared to prior hardware-aware NAS methods while
attaining similar or improved accuracy in image classification on CIFAR-10 and
Imagenet-100 datasets.


title: Factor Graphs for Quantum Information Processing

abstract:
  [...] In this thesis, we are interested in generalizing factor graphs and the
relevant methods toward describing quantum systems. Two generalizations of
classical graphical models are investigated, namely double-edge factor graphs
(DeFGs) and quantum factor graphs (QFGs). Conventionally, a factor in a factor
graph represents a nonnegative real-valued local functions. Two different
approaches to generalize factors in classical factor graphs yield DeFGs and
QFGs, respectively. We proposed/re-proposed and analyzed generalized versions
of belief-propagation algorithms for DeFGs/QFGs. As a particular application of
the DeFGs, we investigate the information rate and their upper/lower bounds of
classical communications over quantum channels with memory. In this study, we
also propose a data-driven method for optimizing the upper/lower bounds on
information rate.


title:
Federated Learning Approach for Lifetime Prediction of Semiconductor
  Lasers

abstract:
  A new privacy-preserving federated learning framework allowing laser
manufacturers to collaboratively build a robust ML-based laser lifetime
prediction model, is proposed. It achieves a mean absolute error of 0.1 years
and a significant performance improvement


title: A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction

abstract:
  A hybrid prognostic model based on convolutional neural networks (CNN) and
long short-term memory (LSTM) is proposed to predict the laser remaining useful
life (RUL). The experimental results show that it outperforms the conventional
methods.


title:
A Framework for Controlling Multi-Robot Systems Using Bayesian
  Optimization and Linear Combination of Vectors

abstract:
  We propose a general framework for creating parameterized control schemes for
decentralized multi-robot systems. A variety of tasks can be seen in the
decentralized multi-robot literature, each with many possible control schemes.
For several of them, the agents choose control velocities using algorithms that
extract information from the environment and combine that information in
meaningful ways. From this basic formation, a framework is proposed that
classifies each robots' measurement information as sets of relevant scalars and
vectors and creates a linear combination of the measured vector sets. Along
with an optimizable parameter set, the scalar measurements are used to generate
the coefficients for the linear combination. With this framework and Bayesian
optimization, we can create effective control systems for several multi-robot
tasks, including cohesion and segregation, pattern formation, and
searching/foraging.


title:
Continuous entanglement distribution over a transnational 248 km fibre
  link

abstract:
  Entanglement is the basis of many quantum applications. The technically most
mature of them, quantum key distribution, harnesses quantum correlations of
entangled photons to produce cryptographic keys of provably unbreakable
security. A key challenge in this context is the establishment of continuously
working, reliable long-distance distributions of entanglement. However,
connections via satellites don't allow for interruption-free operation, and
deployed fibre implementations have so far been limited to less than 100 km by
losses, a few hours of duty time, or use trusted nodes. Here, we present a
continuously working international link between Austria and Slovakia, directly
distributing polarization-entangled photon pairs via 248 km of deployed
telecommunication fibre. Despite 79 dB loss, we measure stable pair rates of 9
s$^{-1}$ over an exemplary operation time of 110 hours. We mitigate multi-pair
detections with strict temporal filtering, enabled by nonlocal compensation of
chromatic dispersion. Fully automatized active polarization stabilization keeps
the entangled state's visibility at 86 % for altogether 82 hours, producing 403
kbit of quantum-secure key at a rate of 1.4 bits/s. Our work paves the way for
low-maintenance, ultra-stable quantum communication over long distances,
independent of cloud coverage and time of day, thus constituting an important
step towards the quantum internet.


title:
On the conformal spin dependence of the perturbative QCD vacuum
  singularity

abstract:
  We study the four-gluon scattering amplitude in the high energy limit of QCD
written in terms of its conformal expansion. We highlight the need to include
both even and odd conformal spin contributions in order to map it to an
iterative representation in rapidity and transverse momentum space which we
have evaluated numerically. By Fourier expanding in a set of three azimuthal
angles, we find a new form for the amplitude in terms of $_4F_3$ hypergeometric
functions. An alternative formulation is possible when connecting this Fourier
expansion with Bessel kernels studied in analytic number theory.


title:
On the equivalence of classical Helmoltz equation and fractional
  Helmoltz equation with arbitrary order

abstract:
  We show the equivalence of the classical Helmoltz equation and the fractional
Helmoltz equation with arbitrary order. This improves a recent result of Guan,
Murugan and Wei \cite{gmw2022}


title:
A Novel Simulation-Based Quality Metric for Evaluating Grasps on 3D
  Deformable Objects

abstract:
  Evaluation of grasps on deformable 3D objects is a little-studied problem,
even if the applicability of rigid object grasp quality measures for deformable
ones is an open question. A central issue with most quality measures is their
dependence on contact points which for deformable objects depend on the
deformations. This paper proposes a grasp quality measure for deformable
objects that uses information about object deformation to calculate the grasp
quality. Grasps are evaluated by simulating the deformations during grasping
and predicting the contacts between the gripper and the grasped object. The
contact information is then used as input for a new grasp quality metric to
quantify the grasp quality. The approach is benchmarked against two classical
rigid-body quality metrics on over 600 grasps in the Isaac gym simulation and
over 50 real-world grasps. Experimental results show an average improvement of
18\% in the grasp success rate for deformable objects compared to the classical
rigid-body quality metrics.


title:
Thermodynamic bounds via bulk-boundary correspondence: speed limit,
  thermodynamic uncertainty relation, and Heisenberg principle

abstract:
  Strongly correlated and coupled systems are difficult to analyze, because of
inability to apply perturtative approaches. The bulk-boundary correspondence
gives a guiding principle for tackling such systems, which states that all
information of the bulk of a system is encoded in its boundary. In this paper,
we apply the concept of the bulk-boundary correspondence to thermodynamic
bounds described by classical and quantum Markov processes. Using the
continuous matrix product state, we can convert a Markov process to a quantum
field, which correspond to the boundary and the bulk, respectively, and jump
events in the Markov process are represented by particle creation in the bulk
quantum field. Introducing the time evolution of the bulk quantum field, we
apply the notion of the geometric bound to the time evolution. We find that the
geometric bound reduces to the speed limit relation when we represent the bound
in terms of boundary quantities, whereas it reduces to the thermodynamic
uncertainty relation when the bound is expressed by quantities of the bulk
quantum field. Our results show that the speed limit and the thermodynamic
uncertainty relations can be derived from the same ancestral inequality,
showing that they are two faces of the same geometric bound. Moreover, we show
that the Heisenberg uncertainty relation in the bulk quantum field reduces to
the thermodynamic uncertainty relation in the Markov process.


title:
Exact and numerical solutions of the Riemann problem for a conservative
  model of compressible two-phase flows

abstract:
  In this work we study the solution of the Riemann problem for the barotropic
version of the conservative symmetric hyperbolic and thermodynamically
compatible (SHTC) two-phase flow model introduced in
\cite{Romenski2007,Romenski2009}. All characteristic fields are carefully
studied and explicit expressions are derived for the Riemann invariants and the
Rankine-Hugoniot conditions. Due to the presence of multiple characteristics in
the system under consideration, non-standard wave phenomena can occur.
Therefore we briefly review admissibility conditions for discontinuities and
then discuss possible wave interactions. In particular we will show that
overlapping rarefaction waves are possible and moreover we may have shocks that
lie inside a rarefaction wave. In contrast to nonconservative two phase flow
models, such as the Baer-Nunziato system, we can use the advantage of the
conservative form of the model under consideration. Furthermore, we show the
relation between the considered conservative SHTC system and the corresponding
barotropic version of the nonconservative Baer-Nunziato model. Additionally, we
derive the reduced four equation Kapila system for the case of instantaneous
relaxation, which is the common limit system of both, the conservative SHTC
model and the non-conservative Baer-Nunziato model. Finally, we compare exact
solutions of the Riemann problem with numerical results obtained for the
conservative two-phase flow model under consideration, for the non-conservative
Baer-Nunziato system and for the Kapila limit. The examples underline the
previous analysis of the different wave phenomena, as well as differences and
similarities of the three systems.


title: Polarization-resolving graphene-based mid-infrared detector

abstract:
  The ability to resolve the polarization of light with on-chip devices
represents an urgent problem in optoelectronics. The detectors with
polarization resolution demonstrated so far mostly require multiple oriented
detectors or movable external polarizers. Here, we experimentally demonstrate
the feasibility to resolve the polarization of mid-infrared light with a single
chemical-vapor-deposited graphene-channel device with dissimilar metal
contacts. This possibility stems from an unusual dependence of photoresponse at
graphene-metal junctions on gate voltage and polarization angle. Namely, there
exist certain gate voltages providing the polarization-insensitive signal;
operation at these voltages can be used for power calibration of the detector.
At other gate voltages, the detector features very strong polarization
sensitivity, with the ratio of signals for two orthogonal polarizations
reaching ~10. Operation at these voltages can provide information about
polarization angles, after the power calibration. We show that such unusual
gate- and polarization-dependence of photosignal can appear upon competition of
isotropic and anisotropic photovoltage generation pathways and discuss the
possible physical candidates.


title: A Survey about application based on RF signals

abstract:
  With the popularity of commercial wireless devices, the research based on RF
signal has developed rapidly in the past decade. RF signals have great
advantages in environmental perception. Wireless signals are transmitted
through the transmitter. With the propagation of these signals in the medium,
they are reflected from different objects and people in space to the receiver.
In this process, rich environmental perception information is carried. These
ubiquitous perceptual information has rich application significance and bright
prospects in the fields of indoor positioning, motion perception, behavior
recognition, human-computer interaction, fall detection, health monitoring,
smart home, search and rescue and so on. This paper will discuss and analyze
the current research results by combing some existing literature and the
research status of relevant researchers in relevant fields at this stage, so as
to understand the future development trend of RF signal based applications.


title: A Survey On Semantic Steganography Systems

abstract:
  Steganography is the practice of concealing a message within some other
carrier or cover message. It is used to allow the sending of hidden information
through communication channels where third parties would only be aware of the
explicit information in the carrier message. With the growth of internet
surveillance and the increased need for secret communication, steganography
systems continue to find new applications. In semantic steganography, the
redundancies in the semantics of a language are used to send a text
steganographic message. In this article we go over the concepts behind semantic
steganography and propose a hierarchy for classifying systems within the
context of text steganography and steganography in general. After laying this
groundwork we list systems for semantic steganography that have been published
in the past and review their properties. Finally, we comment on and briefly
compare the described systems.


title:
Impact of demand growth on decarbonizing India's electricity sector and
  the role for energy storage

abstract:
  Global energy sector decarbonization efforts are contingent on technology
choices for energy production and end-use in emerging markets such as India,
where air conditioning is expected to be a major driver for electricity demand
growth. Here, we use an integrated demand-supply framework to quantify the
impacts of demand growth and temporal patterns on long-term electricity system
evolution. Under projected renewables and Li-ion storage cost declines, our
supply-demand modeling points to renewables contributing substantially (46-67%)
to meet annual electricity demand in India by 2030. However, without
appropriate policy measures to phase out existing coal generation, even such
rapid adoption of renewable energy coupled with one or more technological
levers such as low-cost energy storage and demand-side measures such as setting
aggressive AC efficiency standards and deploying distribution level storage,
are insufficient to reduce annual CO2 emissions in 2050 vs. 2020 because of the
relatively higher growth rate of projected electricity demand over this period.


title: Investigation of four rotating radio transients properties at 111 MHz

abstract:
  The analysis of individual pulses of four rotating radio transients (RRATs),
previously discovered in a monitoring survey running for 5.5 years at the
frequency of 111 MHz, is presented. At a time interval equivalent to five days
of continuous observations for each RRAT, 90, 389, 206, and 157 pulses were
detected in J0640+07, J1005+30, J1132+25, and J1336+33, respectively. The
investigated RRATs have a different distribution of the pulses amplitude. For
J0640+07 and J1132+25, the distribution is described by a single exponent over
the entire range of flux densities. For J1005+30 and J1336+33, it is a
lognormal function with a power law tail. For J0640+07 and J1005+30, we have
detected pulses with a signal-to-noise (S/N) ratio of few hundreds. For
J1132+25 and J1336+33, the S/N of the strongest pulses reaches several tens.
These RRATs show strong changing of character of emission. When strengths of
pulse amplitudes significantly changed, we see long intervals of absence of
emission or its strong attenuation. The analysis carried out in this work shows
that it is possible that all the studied RRATs are, apparently, pulsars with
giant pulses.


title:
An Attention-based Method for Action Unit Detection at the 3rd ABAW
  Competition

abstract:
  Facial Action Coding System is an approach for modeling the complexity of
human emotional expression. Automatic action unit (AU) detection is a crucial
research area in human-computer interaction. This paper describes our
submission to the third Affective Behavior Analysis in-the-wild (ABAW)
competition 2022. We proposed a method for detecting facial action units in the
video. At the first stage, a lightweight CNN-based feature extractor is
employed to extract the feature map from each video frame. Then, an attention
module is applied to refine the attention map. The attention encoded vector is
derived using a weighted sum of the feature map and the attention scores later.
Finally, the sigmoid function is used at the output layer to make the
prediction suitable for multi-label AUs detection. We achieved a macro F1 score
of 0.48 on the ABAW challenge validation set compared to 0.39 from the baseline
model.


title: Lie algebra actions on module categories for truncated shifted Yangians

abstract:
  We develop a theory of parabolic induction and restriction functors relating
modules over Coulomb branch algebras, in the sense of
Braverman-Finkelberg-Nakajima. Our functors generalize Bezrukavnikov-Etingof's
induction and restriction functors for Cherednik algebras, but their definition
uses different tools.
  After this general definition, we focus on quiver gauge theories attached to
a quiver $\Gamma$. The induction and restriction functors allow us to define a
categorical action of the corresponding symmetric Kac-Moody algebra
$\mathfrak{g}_{\Gamma}$ on category $ \mathcal O $ for these Coulomb branch
algebras. When $ \Gamma $ is of Dynkin type, the Coulomb branch algebras are
truncated shifted Yangians and quantize generalized affine Grassmannian slices.
Thus, we regard our action as a categorification of the geometric Satake
correspondence.
  To establish this categorical action, we define a new class of "flavoured"
KLRW algebras, which are similar to the diagrammatic algebras originally
constructed by the second author for the purpose of tensor product
categorification. We prove an equivalence between the category of
Gelfand-Tsetlin modules over a Coulomb branch algebra and the modules over a
flavoured KLRW algebra. This equivalence relates the categorical action by
induction and restriction functors to the usual categorical action on modules
over a KLRW algebra.


title:
Solving the Federated Edge Learning Participation Dilemma: A Truthful
  and Correlated Perspective

abstract:
  An emerging computational paradigm, named federated edge learning (FEL),
enables intelligent computing at the network edge with the feature of
preserving data privacy for edge devices. Given their constrained resources, it
becomes a great challenge to achieve high execution performance for FEL. Most
of the state-of-the-arts concentrate on enhancing FEL from the perspective of
system operation procedures, taking few precautions during the composition step
of the FEL system. Though a few recent studies recognize the importance of FEL
formation and propose server-centric device selection schemes, the impact of
data sizes is largely overlooked. In this paper, we take advantage of game
theory to depict the decision dilemma among edge devices regarding whether to
participate in FEL or not given their heterogeneous sizes of local datasets.
For realizing both the individual and global optimization, the server is
employed to solve the participation dilemma, which requires accurate
information collection for devices' local datasets. Hence, we utilize mechanism
design to enable truthful information solicitation. With the help of correlated
equilibrium, we derive a decision making strategy for devices from the global
perspective, which can achieve the long-term stability and efficacy of FEL. For
scalability consideration, we optimize the computational complexity of the
basic solution to the polynomial level. Lastly, extensive experiments based on
both real and synthetic data are conducted to evaluate our proposed mechanisms,
with experimental results demonstrating the performance advantages.


title: Bounds for Bias-Adjusted Treatment Effect in Linear Econometric Models

abstract:
  In linear econometric models with proportional selection on unobservables,
omitted variable bias in estimated treatment effects are real roots of a cubic
equation involving estimated parameters from a short and intermediate
regression. The roots of the cubic are functions of $\delta$, the degree of
selection on unobservables, and $R_{max}$, the R-squared in a hypothetical long
regression that includes the unobservable confounder and all observable
controls. In this paper I propose and implement a novel algorithm to compute
roots of the cubic equation over relevant regions of the $\delta$-$R_{max}$
plane and use the roots to construct bounding sets for the true treatment
effect. The algorithm is based on two well-known mathematical results: (a) the
discriminant of the cubic equation can be used to demarcate regions of unique
real roots from regions of three real roots, and (b) a small change in the
coefficients of a polynomial equation will lead to small change in its roots
because the latter are continuous functions of the former. I illustrate my
method by applying it to the analysis of maternal behavior on child outcomes.


title:
Nanoconfinement Facilitates Reactions of Carbon Dioxide in Supercritical
  Water

abstract:
  The reactions of CO$_2$ in water under extreme pressure-temperature
conditions are of great importance to the carbon storage and transport below
Earth's surface, which substantially affect the carbon budget in the
atmosphere. We applied ab initio molecular dynamics simulations to study
aqueous carbon solutions nanoconfined by graphene and stishovite (SiO$_2$) at
10 GPa and 1000$\sim$1400 K. We found that CO$_2$(aq) reacts more in
nanoconfinement than in bulk. The stishovite-water interface makes the
solutions more acidic, which shifts the chemical equilibria, and the interface
chemistry also affects the reaction mechanisms. Our findings suggest that
CO$_2$(aq) in deep Earth may be more active than previously thought, and
confining CO$_2$ and water in nanopores may enhance the efficiency of mineral
carbonation.


title:
Entropy-Based Theory of Thermomagnetic Phenomena: Poynting Vector,
  Vorticity, and Advanced Sensing

abstract:
  We show that in the linear response approximation only entropy provides
coupling between thermal and electric phenomena. The dissipationless quantum
currents -- magnetization, superconducting, persistent and topological edge
currents -- do not produce and transfer entropy and may be excluded from final
formulas for thermomagnetic coefficients. The magnetization energy flux, eM X
E, in crossed electric and magnetic fields strongly modifies the Poynting
vector in magnetic materials and metamaterials, but do not contribute to the
heat current. Calculating entropy fluxes of fluctuating Cooper pairs, we find
the fluctuation Nernst coefficient in pure superconductors. To account electron
scattering, we generalize the gauge-invariant Kubo formalism developed for the
Hall effect to thermomagnetic entropy transfer. We also introduce the
thermomagnetic entropy per unit charge and derive the Nernst coefficient
proportional to the difference of the thermoelectric and thermomagnetic
entropies. This explains the Sondheimer cancellation and high sensitivity of
thermomagnetic phenomena to correlations. In 2D superconductors, the transport
entropy transferred by a vortex moving through the background formed by
vortex-antivortex pairs is the configuration entropy of kB ln 2, which strongly
exceeds the intrinsic entropy of vortex core. Beyond the linear response, the
non-entropic forces can lead to phenomena unexpected from thermodynamics, such
as vortex attraction to the moving hot spot. Quantum currents do not transfer
entropy and may be used as ideal connectors to quantum nanodetectors.


title:
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention
  in Mobile Crowdsensing

abstract:
  Mobile Crowdsensing (MCS) is a sensing paradigm that has transformed the way
that various service providers collect, process, and analyze data. MCS offers
novel processes where data is sensed and shared through mobile devices of the
users to support various applications and services for cutting-edge
technologies. However, various threats, such as data poisoning, clogging task
attacks and fake sensing tasks adversely affect the performance of MCS systems,
especially their sensing, and computational capacities. Since fake sensing task
submissions aim at the successful completion of the legitimate tasks and mobile
device resources, they also drain MCS platform resources. In this work, Self
Organizing Feature Map (SOFM), an artificial neural network that is trained in
an unsupervised manner, is utilized to pre-cluster the legitimate data in the
dataset, thus fake tasks can be detected more effectively through less
imbalanced data where legitimate/fake tasks ratio is lower in the new dataset.
After pre-clustered legitimate tasks are separated from the original dataset,
the remaining dataset is used to train a Deep Neural Network (DeepNN) to reach
the ultimate performance goal. Pre-clustered legitimate tasks are appended to
the positive prediction outputs of DeepNN to boost the performance of the
proposed technique, which we refer to as pre-clustered DeepNN (PrecDeepNN). The
results prove that the initial average accuracy to discriminate the legitimate
and fake tasks obtained from DeepNN with the selected set of features can be
improved up to an average accuracy of 0.9812 obtained from the proposed machine
learning technique.


title: Stateful to Stateless: Modelling Stateless Ethereum

abstract:
  The concept of 'Stateless Ethereum' was conceived with the primary aim of
mitigating Ethereum's unbounded state growth. The key facilitator of Stateless
Ethereum is through the introduction of 'witnesses' into the ecosystem. The
changes and potential consequences that these additional data packets pose on
the network need to be identified and analysed to ensure that the Ethereum
ecosystem can continue operating securely and efficiently. In this paper we
propose a Bayesian Network model, a probabilistic graphical modelling approach,
to capture the key factors and their interactions in Ethereum mainnet, the
public Ethereum blockchain, focussing on the changes being introduced by
Stateless Ethereum to estimate the health of the resulting Ethereum ecosystem.
We use a mixture of empirical data and expert knowledge, where data are
unavailable, to quantify the model. Based on the data and expert knowledge
available to use at the time of modelling, the Ethereum ecosystem is expected
to remain healthy following the introduction of Stateless Ethereum.


title:
Terphenylthiazole-based self-assembled monolayers on cobalt with high
  conductance photo-switching ratio for spintronics

abstract:
  Two new photo-switchable terphenylthiazoles molecules are synthesized and
self-assembled as monolayers on Au and on ferromagnetic Co electrodes. The
electron transport properties probed by conductive atomic force microscopy in
ultra-high vacuum reveal a conductance of the light-induced closed (c) form
larger than for the open (o) form. We report an unprecedented conductance ratio
up to 380 between the closed and open forms on Co for the molecule with the
anchoring group (thiol) on the side of the two N atoms of the thiazole unit.
This result is rationalized by Density Functional Theory (DFT) calculations
coupled to the Non-Equilibrium Green's function (NEGF) formalism. These
calculations show that the high conductance in the closed form is due to a
strong electronic coupling between the terphenylthiazole molecules and the Co
electrode that manifests by a resonant transmission peak at the Fermi energy of
the Co electrode with a large broadening. This behavior is not observed for the
same molecules self-assembled on gold electrodes. These high conductance ratios
make these Co-based molecular junctions attractive candidates to develop and
study switchable molecular spintronic devices.


title:
Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking
  Neural Networks

abstract:
  Spiking Neural Networks (SNNs) compute in an event-based matter to achieve a
more efficient computation than standard Neural Networks. In SNNs, neuronal
outputs (i.e. activations) are not encoded with real-valued activations but
with sequences of binary spikes. The motivation of using SNNs over conventional
neural networks is rooted in the special computational aspects of SNNs,
especially the very high degree of sparsity of neural output activations. Well
established architectures for conventional Convolutional Neural Networks (CNNs)
feature large spatial arrays of Processing Elements (PEs) that remain highly
underutilized in the face of activation sparsity. We propose a novel
architecture that is optimized for the processing of Convolutional SNNs (CSNNs)
that feature a high degree of activation sparsity. In our architecture, the
main strategy is to use less but highly utilized PEs. The PE array used to
perform the convolution is only as large as the kernel size, allowing all PEs
to be active as long as there are spikes to process. This constant flow of
spikes is ensured by compressing the feature maps (i.e. the activations) into
queues that can then be processed spike by spike. This compression is performed
in run-time using dedicated circuitry, leading to a self-timed scheduling. This
allows the processing time to scale directly with the number of spikes. A novel
memory organization scheme called memory interlacing is used to efficiently
store and retrieve the membrane potentials of the individual neurons using
multiple small parallel on-chip RAMs. Each RAM is hardwired to its PE, reducing
switching circuitry and allowing RAMs to be located in close proximity to the
respective PE. We implemented the proposed architecture on an FPGA and achieved
a significant speedup compared to other implementations while needing less
hardware resources and maintaining a lower energy consumption.


title: Kibble-Zurek scaling from linear response theory

abstract:
  While quantum phase transitions share many characteristics with thermodynamic
phase transitions, they are also markedly different as they occur at zero
temperature. Hence, it is not immediately clear whether tools and frameworks
that capture the properties of thermodynamic phase transitions also apply in
the quantum case. Concerning the crossing of thermodynamic critical points and
describing its non-equilibrium dynamics, the Kibble-Zurek mechanism and linear
response theory have been demonstrated to be among the very successful
approaches. In the present work, we show that these two approaches are
consistent also in the description of quantum phase transitions, and that
linear response theory can even inform arguments of the Kibble-Zurek mechanism.
In particular, we show that the relaxation time provided by linear response
theory gives a rigorous argument for why to identify the "gap" as a relaxation
rate, and we verify that the excess work computed from linear response theory
exhibits Kibble-Zurek scaling.


title:
Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck I:
  Construction of CMB Lensing Maps and Modeling Choices

abstract:
  Joint analyses of cross-correlations between measurements of galaxy
positions, galaxy lensing, and lensing of the cosmic microwave background (CMB)
offer powerful constraints on the large-scale structure of the Universe. In a
forthcoming analysis, we will present cosmological constraints from the
analysis of such cross-correlations measured using Year 3 data from the Dark
Energy Survey (DES), and CMB data from the South Pole Telescope (SPT) and
Planck. Here we present two key ingredients of this analysis: (1) an improved
CMB lensing map in the SPT-SZ survey footprint, and (2) the analysis
methodology that will be used to extract cosmological information from the
cross-correlation measurements. Relative to previous lensing maps made from the
same CMB observations, we have implemented techniques to remove contamination
from the thermal Sunyaev Zel'dovich effect, enabling the extraction of
cosmological information from smaller angular scales of the cross-correlation
measurements than in previous analyses with DES Year 1 data. We describe our
model for the cross-correlations between these maps and DES data, and validate
our modeling choices to demonstrate the robustness of our analysis. We then
forecast the expected cosmological constraints from the galaxy survey-CMB
lensing auto and cross-correlations. We find that the galaxy-CMB lensing and
galaxy shear-CMB lensing correlations will on their own provide a constraint on
$S_8=\sigma_8 \sqrt{\Omega_{\rm m}/0.3}$ at the few percent level, providing a
powerful consistency check for the DES-only constraints. We explore scenarios
where external priors on shear calibration are removed, finding that the joint
analysis of CMB lensing cross-correlations can provide constraints on the shear
calibration amplitude at the 5 to 10% level.


title:
Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck
  II: Cross-correlation measurements and cosmological constraints

abstract:
  Cross-correlations of galaxy positions and galaxy shears with maps of
gravitational lensing of the cosmic microwave background (CMB) are sensitive to
the distribution of large-scale structure in the Universe. Such
cross-correlations are also expected to be immune to some of the systematic
effects that complicate correlation measurements internal to galaxy surveys. We
present measurements and modeling of the cross-correlations between galaxy
positions and galaxy lensing measured in the first three years of data from the
Dark Energy Survey with CMB lensing maps derived from a combination of data
from the 2500 deg$^2$ SPT-SZ survey conducted with the South Pole Telescope and
full-sky data from the Planck satellite. The CMB lensing maps used in this
analysis have been constructed in a way that minimizes biases from the thermal
Sunyaev Zel'dovich effect, making them well suited for cross-correlation
studies. The total signal-to-noise of the cross-correlation measurements is
23.9 (25.7) when using a choice of angular scales optimized for a linear
(nonlinear) galaxy bias model. We use the cross-correlation measurements to
obtain constraints on cosmological parameters. For our fiducial galaxy sample,
which consist of four bins of magnitude-selected galaxies, we find constraints
of $\Omega_{m} = 0.272^{+0.032}_{-0.052}$ and $S_{8} \equiv \sigma_8
\sqrt{\Omega_{m}/0.3}= 0.736^{+0.032}_{-0.028}$ ($\Omega_{m} =
0.245^{+0.026}_{-0.044}$ and $S_{8} = 0.734^{+0.035}_{-0.028}$) when assuming
linear (nonlinear) galaxy bias in our modeling. Considering only the
cross-correlation of galaxy shear with CMB lensing, we find $\Omega_{m} =
0.270^{+0.043}_{-0.061}$ and $S_{8} = 0.740^{+0.034}_{-0.029}$. Our constraints
on $S_8$ are consistent with recent cosmic shear measurements, but lower than
the values preferred by primary CMB measurements from Planck.


title: M-SENA: An Integrated Platform for Multimodal Sentiment Analysis

abstract:
  M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims
to facilitate advanced research by providing flexible toolkits, reliable
benchmarks, and intuitive demonstrations. The platform features a fully modular
video sentiment analysis framework consisting of data management, feature
extraction, model training, and result analysis modules. In this paper, we
first illustrate the overall architecture of the M-SENA platform and then
introduce features of the core modules. Reliable baseline results of different
modality features and MSA benchmarks are also reported. Moreover, we use model
evaluation and analysis tools provided by M-SENA to present intermediate
representation visualization, on-the-fly instance test, and generalization
ability test results. The source code of the platform is publicly available at
https://github.com/thuiar/M-SENA.


title:
Plasma Processing for In-Situ Field Emission Mitigation of
  Superconducting Radiofrequency (SRF) Cryomodules

abstract:
  Field emission (FE) is one of the main limiting factors of superconducting
radio-frequency (SRF) cavities operating in accelerators and it occurs whenever
contaminants, like dust, metal flakes or even absorbates, are present on the
surface of the cavity high electric field region. Field emission reduces the
maximum achievable accelerating field and generates free electrons that may
interact with the beam, damage or activate the beamline. One practical method
that can be used to mitigate this problem is in-situ plasma cleaning, or plasma
processing. The development of a processing that can be applied in-situ is
extremely advantageous, since it enables the recovery of the cryomodule
performance without the need of disassembling the whole cryomodule, which is an
extremely expensive and time-consuming process. On the other hand, plasma
processing only requires the cryomodule warm-up to room-temperature and the
subsequent processing of the contaminated cavities. The entire process is
reasonably quick and involves a limited number of personnel. For these reasons
we would like to advocate for continuing to invest in the R\&D of plasma
processing to optimize its applicability in cryomodules and for extending the
technique to other frequency ranges and cavities geometries.


title:
Machine learning aided atomic structure identification of interfacial
  ionic hydrates from atomic force microscopy images

abstract:
  Relevant to broad applied fields and natural processes, interfacial ionic
hydrates has been widely studied by ultrahigh-resolution atomic force
microscopy (AFM). However, the complex relationship between AFM signal and the
investigated system makes it difficult to determine the atomic structure of
such complex system from AFM images alone. Using machine learning, we achieved
precise identification of the atomic structures of interfacial water/ionic
hydrates based on AFM images, including the position of each atom and the
orientation of water molecules. Furthermore, it was found that structure
prediction of ionic hydrates can be achieved cost-effectively by transfer
learning using neural network (NN) trained with easily available interfacial
water data. Thus, this work provides an efficient and economical methodology
which not only opens up avenues to determine atomic structures of more complex
systems from AFM images, but may also help to interpret other science-wide
studies involving sophisticated experimental results.


title:
Volume Approximation of Strongly ${\mathbb C}$-Convex Domains by Random
  Polyhedra

abstract:
  Polyhedral-type approximations of convex-like domains in $\mathbb{C}^d$ have
been considered recently by the second author. In particular, the decay rate of
the error in optimal volume approximation as a function of the number of facets
has been obtained. In this article, we take these studies further by
investigating polyhedra constructed using random points (Poisson or binomial
process) on the boundary of a strongly $\mathbb{C}$-convex domain. We determine
the rate of error in volume approximation of the domain by random polyhedra,
and conjecture the precise value of the minimal limiting constant. Analogous to
the real case, the exponent appearing in the error rate of random volume
approximation coincides with that of optimal volume approximation, and can be
interpreted in terms of the Hausdorff dimension of a naturally-occurring metric
space. Moreover, the limiting constant is conjectured to depend on the
M\"obius-Fefferman measure, which is a complex analogue of the Blaschke surface
area measure. Finally, we also prove $L^1$-convergence, variance bounds, and
normal approximation.


title:
ShareTrace: Contact Tracing with Asynchronous, Parallel Message Passing
  on a Temporal Graph

abstract:
  Proximity-based contact tracing relies on user device interaction to estimate
the spread of disease. ShareTrace is one such approach that has been shown to
provide improved efficacy in tracking the spread of disease by also considering
(in)direct forms of contact. In this work, we aim to provide an efficient and
scalable formulation of ShareTrace by utilizing asynchronous, parallel,
non-iterative message passing on a temporal graph. We also introduce a unique
form of reachability, message reachability, that accounts for the dynamic
nature of message passing and the temporal graph. Our evaluation on both
synthetic and real-world temporal graphs indicates that correct parameter
values optimize for accuracy and efficiency. In addition, we demonstrate that
message reachability can accurately estimate the impact of the risk of a user
on their contacts.


title: SMEMO: Social Memory for Trajectory Forecasting

abstract:
  Effective modeling of human interactions is of utmost importance when
forecasting behaviors such as future trajectories. Each individual, with its
motion, influences surrounding agents since everyone obeys to social
non-written rules such as collision avoidance or group following. In this paper
we model such interactions, which constantly evolve through time, by looking at
the problem from an algorithmic point of view, i.e. as a data manipulation
task. We present a neural network based on an end-to-end trainable working
memory, which acts as an external storage where information about each agent
can be continuously written, updated and recalled. We show that our method is
capable of learning explainable cause-effect relationships between motions of
different agents, obtaining state-of-the-art results on multiple trajectory
forecasting datasets.


title:
Benchmarking GNOF against FCI in challenging systems in one, two and
  three dimensions

abstract:
  This work assess the reliability of the recently proposed [Phys. Rev. Lett.
127, 233001, 2021] global natural orbital functional (GNOF) in the treatment of
the strong electron correlation regime. We use a benchmark set of four hydrogen
model systems of different dimensionalities and distinctive electronic
structures: a 1D chain, a 2D ring, a 2D sheet, and a 3D close-packed pyramid.
We show that GNOF without hybridization to other electronic structure methods
and free of tuned parameters succeeds in treating weak and strong correlation
in a more balanced way than the functionals that have preceded it.


title: Effect of Surfactant on Cloud Formation

abstract:
  Atmospheric aerosols usually consist of inorganic salts and organic
substances, including surfactants at a significant concentration. Most
importantly, the latter can reduce the surface tension at the liquid-vapor
surfaces, where they preferentially adsorb due to their amphiphilic structure.
As a result, processes such as droplet coalescence, development of
precipitation and ultimately cloud lifetime, crucially depend on the presence
of surfactants in the aerosols. Here, we present a numerical model for cloud
droplet formation, which is based on the Lagrangian particle-based
microphysics-scheme super-droplet method and takes into account the presence of
surfactant in the droplets. Our results show that surfactant facilitates cloud
formation by increasing the number and size of activated droplets, which
concentrate at the bottom of the cloud, while the largest droplets are
concentrated at the top of the cloud. This indicates a circulation of droplets
that involves activation and growth processes from the bottom of the cloud
towards the top. Moreover, our conclusions are independent of the particular
approach used for modeling the diffusion of Eulerian variables due to the
subgrid-scale turbulence. We anticipate that our results will enrich our
understanding of the role of surfactants in the behavior of atmospheric
aerosols and will pave the way for further developments in the numerical
modeling of systems with surfactants at macroscopic scales.


title:
High precision measurement of the half-life of the 391.6 keV metastable
  level in 239Pu

abstract:
  Materials selection for rare event physics requires high performance
detectors and customized analyses. In this context a novel $\beta$-$\gamma$
detection system, comprised of a liquid scintillator in coincidence with a
HPGe, was developed with the main purpose of studying ultra trace contamination
of uranium, thorium and potassium in liquid samples. In the search for
$^{238}\textrm{U}$ contaminations through neutron activation analysis, since
the activation product $^{239}\textrm{Np}$ decays to a relatively long-lived
isomeric state of $^{239}\textrm{Pu}$, it is possible to perform a time
selection of these events obtaining a strong background suppression.
Investigating the time distribution of the coincidences between the $\beta^-$
decay of $^{239}\textrm{Np}$ and the delayed events following the de-excitation
of the $^{239}\textrm{Pu}$ isomeric level at 391.6 keV, a precision measurement
of the half-life of this level was conducted. The half-life of the 391.6 keV
$^{239}\textrm{Pu}$ level resulted $190.2 \pm 0.2$ ns, thus increasing the
precision by about a factor of 20 over previous measurements.


title:
Motile topological defects hinder dynamical arrest in dense liquids of
  active ellipsoids

abstract:
  Recent numerical studies have identified the persistence time of active
motion as a critical parameter governing glassy dynamics in dense active
matter. Here we studied dynamics in liquids of granular active ellipsoids with
tunable persistence and velocity. We show that increasing the persistence time
at moderate supercooling is equivalent to increasing the strength of attraction
in equilibrium liquids and results in reentrant dynamics not just in the
translational degrees of freedom, as anticipated, but also in the orientational
ones. However, at high densities, motile topological defects, unique to active
liquids of elongated particles, hindered dynamical arrest. Most remarkably, for
the highest activity, we observed intermittent dynamics due to the
jamming-unjamming of these defects for the first time.


title: Deep Multi-View Learning for Tire Recommendation

abstract:
  We are constantly using recommender systems, often without even noticing.
They build a profile of our person in order to recommend the content we will
most likely be interested in. The data representing the users, their
interactions with the system or the products may come from different sources
and be of a various nature. Our goal is to use a multi-view learning approach
to improve our recommender system and improve its capacity to manage multi-view
data. We propose a comparative study between several state-of-the-art
multi-view models applied to our industrial data. Our study demonstrates the
relevance of using multi-view learning within recommender systems.


title: State and parameter estimation for retinal laser treatment

abstract:
  Adequate therapeutic retinal laser irradiation needs to be adapted to the
local absorption. This leads to time-consuming treatments as the laser power
needs to be successively adjusted to avoid under- and overtreatment caused by
too low or too high temperatures. Closed-loop control can overcome this burden
by means of temperature measurements. To allow for model predictive control
schemes, the current state and the spot-dependent absorption need to be
estimated. In this paper, we thoroughly compare moving horizon estimator (MHE)
and extended Kalman filter (EKF) designs for joint state and parameter
estimation. We consider two different scenarios, the estimation of one or two
unknown absorption coefficients. For one unknown parameter, both estimators
perform very similar. For two unknown parameters, we found that the MHE
benefits from active parameter constraints at the beginning of the estimation,
whereas after a settling time both estimators perform again very similar as
long as the parameters are inside the considered parameter bounds.