  Large-scale matrix data has been widely discovered and continuously studied
in various fields recently. Considering the multi-level factor structure and
utilizing the matrix structure, we propose a multilevel matrix factor model
with both global and local factors. The global factors can affect all matrix
times series, whereas the local factors are only allow to affect within each
specific matrix time series. The estimation procedures can consistently
estimate the factor loadings and determine the number of factors. We establish
the asymptotic properties of the estimators. The simulation is presented to
illustrate the performance of the proposed estimation method. We utilize the
model to analyze eight indicators across 200 stocks from ten distinct
industries, demonstrating the empirical utility of our proposed approach.


  The catastrophe theory is applied to a nuclear cluster model and an effective
model for QCD at low energy. The study of quantum phase transitions in the
cluster model was considered in an earlier publication, but restricted to
spherical clusters and on a semi-classical level. In the present contribution,
we include the case of deformed clusters and determine the spectrum numerically
as a function of an interaction parameter, where signatures of a quantum phase
transition can be seen. It is shown that in this more complicated case, with
deformation of the clusters, the catastrophe theory can be applied with some
interesting consequences. A further example of a many-body problem is
considered, namely an effective model of QCD, which is able to describe the low
energy hadron spectrum and, when temperature is introduced, even ratios of
particle-antiparticle productions. The catastrophe theory is able to provide
useful information on the phase transition from a perturbative to a
non-perturbative vacuum. This contributions shows the universal usefulness of
catastrophe theory, while more examples of applications to different fields are
mentioned in the Introduction.


  Quantum state tomography is a powerful, but resource-intensive, general
solution for numerous quantum information processing tasks. This motivates the
design of robust tomography procedures that use relevant resources as sparingly
as possible. Important cost factors include the number of state copies and
measurement settings, as well as classical postprocessing time and memory. In
this work, we present and analyze an online tomography algorithm designed to
optimize all the aforementioned resources at the cost of a worse dependence on
accuracy. The protocol is the first to give provably optimal performance in
terms of rank and dimension for state copies, measurement settings and memory.
Classical runtime is also reduced substantially and numerical experiments
demonstrate a favorable comparison with other state-of-the-art techniques.
Further improvements are possible by executing the algorithm on a quantum
computer, giving a quantum speedup for quantum state tomography.


  Understanding the influence of a product is crucially important for making
informed business decisions. This paper introduces a new type of skyline
queries, called uncertain reverse skyline, for measuring the influence of a
probabilistic product in uncertain data settings. More specifically, given a
dataset of probabilistic products P and a set of customers C, an uncertain
reverse skyline of a probabilistic product q retrieves all customers c in C
which include q as one of their preferred products. We present efficient
pruning ideas and techniques for processing the uncertain reverse skyline query
of a probabilistic product using R-Tree data index. We also present an
efficient parallel approach to compute the uncertain reverse skyline and
influence score of a probabilistic product. Our approach significantly
outperforms the baseline approach derived from the existing literature. The
efficiency of our approach is demonstrated by conducting extensive experiments
with both real and synthetic datasets.


  The flat matter dominated Universe that dominated cosmological model building
for much of the past 20 years does not correspond to the Universe in which we
live. This has profound implications both for our understanding of dark matter,
and also for our understanding of the future of the Universe. I review recent
developments here and present best fits for the current, sometimes crazy,
values of the major measured fundamental cosmological parameters. (Invited
review talk, 9th International Workshop on Neutrino Telescopes, Venice, March
2001. Note: this is an updated version of an article prepared for the
proceedings of IDM2000 in York, UK.)


  This paper proposes a do-it-all neural model of human hands, named LISA. The
model can capture accurate hand shape and appearance, generalize to arbitrary
hand subjects, provide dense surface correspondences, be reconstructed from
images in the wild and easily animated. We train LISA by minimizing the shape
and appearance losses on a large set of multi-view RGB image sequences
annotated with coarse 3D poses of the hand skeleton. For a 3D point in the hand
local coordinate, our model predicts the color and the signed distance with
respect to each hand bone independently, and then combines the per-bone
predictions using predicted skinning weights. The shape, color and pose
representations are disentangled by design, allowing to estimate or animate
only selected parameters. We experimentally demonstrate that LISA can
accurately reconstruct a dynamic hand from monocular or multi-view sequences,
achieving a noticeably higher quality of reconstructed hand shapes compared to
baseline approaches. Project page:
https://www.iri.upc.edu/people/ecorona/lisa/.


  For the case of finite life-time broadening the standard Kubo-formula for the
optical conductivity tensor is rederived in terms of Green's functions by using
contour integrations, whereby finite temperatures are accounted for by using
the Fermi-Dirac distribution function. For zero life-time broadening, the
present formalism is related to expressions well-known in the literature.
Numerical aspects of how to calculate the corresponding contour integrals are
also outlined.


  A large eddy simulation (LES) study of the flow around a 1/4 scale squareback
Ahmed body at $Re_H=33,333$ is presented. The study consists of both
wall-resolved (WRLES) and wall-modelled (WMLES) simulations, and investigates
the bimodal switching of the wake between different horizontal positions.
Within a non-dimensional time-window of 1050 convective flow units, both WRLES
and WMLES simulations, for which only the near-wall region of the turbulent
boundary layer is treated in a Reynolds-averaged sense, are able to capture
horizontal (spanwise) shifts in the wake's cross-stream orientation.
Equilibrium wall-models in the form of Spalding's law and the log-law of the
wall are successfully used. Once these wall-models are, however, applied to a
very coarse near-wall WMLES mesh, in which a portion of the turbulent boundary
layer's outer region dynamics is treated in a Reynolds-averaged manner as well,
large-scale horizontal shifts in the wake's orientation are no longer detected.
This suggests larger-scale flow structures found within the turbulent boundary
layer's outer domain are responsible for generating the critical amount of flow
intermittency needed to trigger a bimodal switching event. By looking at mean
flow structures, instantaneous flow features and their associated turbulent
kinetic energy (TKE) production, it becomes clear that the front separation
bubbles just aft of the Ahmed body nose generate high levels of TKE through the
shedding of large hairpin vortices. Only in the reference WRLES and
(relatively) fine near-wall mesh WMLES simulations are these features present,
exemplifying their importance in triggering a bimodal event. This motivates
studies on the suppression of wake bimodality by acting upon the front
separation bubbles.


  This paper reports our submission under the team name `SynthDetectives' to
the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the
task of AI-generated text detection. Our approach is novel in terms of its
choice of models in that we use accessible and lightweight models in the
ensemble. We show that ensembling the models results in an improved accuracy in
comparison with using them individually. Our approach achieves an accuracy
score of 0.9555 on the official test data provided by the shared task
organisers.


  Graphene quantum dots (GQDs) represent single layers up to dozens of graphene
layers smaller than 30 nm. GQDs are newish molecules that have aroused great
interest in research because of their exceptional and manageable optical,
electrical, chemical, and structural properties. In this work, we report
electrostatic potential energy maps, or molecular electrostatic potential
surfaces, illustrate the charge distributions of GQDs three-dimensionally.
Knowledge of the charge distributions can be used to determine how GQDs
interact with one another. To analyze the distribution of molecular charges
accurately, a large number of electrostatic potential energy values must be
calculated. The best way to transmit these data is to visualize them as in the
electrostatic potential map. A ZINDO semi-empirical quantum chemistry method
then imposes the calculated data onto an electron density model of the GQDs
derived from the Schr\"odinger equation. To make the electrostatic potential
energy data of GQDs easy to interpret, a color spectrum, with red as the lowest
electrostatic potential energy value and blue as the highest, is employed to
convey the varying intensities of the electrostatic potential energy values.
The results of the four GQD models suggest that the energy of the ionization
potential lies in a range of -7.20 eV to -5.31 eV and the electron affinity is
-2.65 to -0.24 eV.


  In this article we give some refinements of Simpson's Rule in cases when it
is not applicable in it's classical form i.e., when the target function is not
four times differentiable on a given interval. Some sharp two-sided
inequalities for an extended form of Simpson's Rule are also proven.


  We analyse the meson spectrum in quenched QCD using lattice gauge theory. By
studying hadron propagation with a variety of operators (both smeared and
local), we are able to extract the ground state and first excited state masses
with confidence. We pay attention to the correlations among the data used in
the fits to extract these masses and couplings. We compare the resulting hadron
spectrum with experiment and find evidence for a significant departure in the
pseudoscalar and vector meson masses.


  Sequencing a DNA strand, as part of the read process in DNA storage, produces
multiple noisy copies which can be combined to produce better estimates of the
original strand; this is called trace reconstruction. One can reduce the error
rate further by introducing redundancy in the write sequence and this is called
coded trace reconstruction. In this paper, we model the DNA storage channel as
an insertion-deletion-substitution (IDS) channel and design both encoding
schemes and low-complexity decoding algorithms for coded trace reconstruction.
  We introduce Trellis BMA, a new reconstruction algorithm whose complexity is
linear in the number of traces, and compare its performance to previous
algorithms. Our results show that it reduces the error rate on both simulated
and experimental data. The performance comparisons in this paper are based on a
new dataset of traces that will be publicly released with the paper. Our hope
is that this dataset will enable research progress by allowing objective
comparisons between candidate algorithms.


  We provide a novel proof that the set of directions that admit a saddle
connection on a meromorphic quadratic differential with at least one pole of
order at least two is closed, which generalizes a result of Bridgeland and
Smith, and Gaiotto, Moore, and Neitzke. Secondly, we show that this set has
finite Cantor-Bendixson rank and give a tight bound. Finally, we present a
family of surfaces realizing all possible Cantor-Bendixson ranks. The
techniques in the proof of this result exclusively concern Abelian
differentials on Riemann surfaces, also known as translation surfaces. The
concept of a "slit translation surface" is introduced as the primary tool for
studying meromorphic quadratic differentials with higher order poles.


  We study vertex algebras and their modules associated with possibly
degenerate even lattices, using an approach somewhat different from others.
Several known results are recovered and a number of new results are obtained.
We also study modules for Heisenberg algebras and we classify irreducible
modules satisfying certain conditions and obtain a complete reducibility
theorem.


  This paper addresses the following problem. \begin{equation}
  \left\{
  \begin{array}{lr}
  -{\Delta}u=\lambda I_\alpha*_\Omega u+|u|^{2^*-2}u\mbox{ in }\Omega
,\nonumber u\in H_0^1(\Omega).\nonumber
  \end{array}
  \right. \end{equation} Here, $\Omega$ is a bounded domain in $\mathbb{R}^N$
with $N\geq3$, $2^*=\frac{2N}{N-2}$, $\lambda\in\mathbb{R}$, $\lambda\in(0,N)$,
$I_\alpha$ is the Riesz potential and \begin{align} I_\alpha*_\Omega
u(x):=\int_\Omega
\frac{\Gamma(\frac{N-\alpha}{2})}{\Gamma(\frac{\alpha}{2})\pi^\frac{N}{2}2^\alpha|x-y|^{N-\alpha}}
u(y)dy. \nonumber \end{align} We study the non-existence, existence and
multiplicity results. Our argument combines Brezis-Nirenberg's method with the
regularity results involving potential terms. Especially, we study the
following nonlocal eigenvalue problem. \begin{equation}
  \left\{
  \begin{array}{lr}
  -{\Delta}u=\lambda I_\alpha*_\Omega u\mbox{ in }\Omega ,\nonumber
  \lambda\in\mathbb{R},\,u\in H_0^1(\Omega).\nonumber
  \end{array}
  \right. \end{equation}


  In this work we propose a novel self-attention mechanism model to address
electricity theft detection on an imbalanced realistic dataset that presents a
daily electricity consumption provided by State Grid Corporation of China. Our
key contribution is the introduction of a multi-head self-attention mechanism
concatenated with dilated convolutions and unified by a convolution of kernel
size $1$. Moreover, we introduce a binary input channel (Binary Mask) to
identify the position of the missing values, allowing the network to learn how
to deal with these values. Our model achieves an AUC of $0.926$ which is an
improvement in more than $17\%$ with respect to previous baseline work. The
code is available on GitHub at
https://github.com/neuralmind-ai/electricity-theft-detection-with-self-attention.


  Background and purpose: Deep Learning (DL) has been widely explored for
Organs at Risk (OARs) segmentation; however, most studies have focused on a
single modality, either CT or MRI, not both simultaneously. This study presents
a high-performing DL pipeline for segmentation of 30 OARs from MRI and CT scans
of Head and Neck (H&N) cancer patients.
  Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer
patients alongside annotation for 30 OARs from the H&N OAR CT & MR segmentation
challenge dataset were used to develop a segmentation pipeline. After cropping
irrelevant regions, rigid followed by non-rigid registration of CT and MRI
volumes was performed. Two versions of the CT volume, representing soft tissues
and bone anatomy, were stacked with the MRI volume and used as input to an
nnU-Net pipeline. Modality Dropout was used during the training to force the
model to learn from the different modalities. Segmentation masks were predicted
with the trained model for an independent set of 14 new patients. The mean Dice
Score (DS) and Hausdorff Distance (HD) were calculated for each OAR across
these patients to evaluate the pipeline.
  Results: This resulted in an overall mean DS and HD of 0.777 +- 0.118 and
3.455 +- 1.679, respectively, establishing the state-of-the-art (SOTA) for this
challenge at the time of submission.
  Conclusion: The proposed pipeline achieved the best DS and HD among all
participants of the H&N OAR CT and MR segmentation challenge and sets a new
SOTA for automated segmentation of H&N OARs.


  Spin-$S$ Heisenberg quantum antiferromagnets on the Kagome lattice offer,
when placed in a magnetic field, a fantastic playground to observe exotic
phases of matter with (magnetic analogs of) superfluid, charge, bond or nematic
orders, or a coexistence of several of the latter. In this context, we have
obtained the (zero temperature) phase diagrams up to $S=2$ directly in the
thermodynamic limit thanks to infinite Projected Entangled Pair States (iPEPS),
a tensor network numerical tool. We find incompressible phases characterized by
a magnetization plateau vs field and stabilized by spontaneous breaking of
point group or lattice translation symmetry(ies). The nature of such phases may
be semi-classical, as the plateaus at $\frac{1}{3}$th, $(1-\frac{2}{9S})$th and
$(1-\frac{1}{9S})$th of the saturated magnetization (the latter followed by a
macroscopic magnetization jump), or fully quantum as the spin-$\frac{1}{2}$
$\frac{1}{9}$-plateau exhibiting coexistence of charge and bond orders. Upon
restoration of the spin rotation $U(1)$ symmetry a finite compressibility
appears, although lattice symmetry breaking persists. For integer spin values
we also identify spin gapped phases at low enough field, such as the $S=2$
(topologically trivial) spin liquid with no symmetry breaking, neither spin nor
lattice.


  We suggest that the anomalous 4-jet events recently reported by ALEPH may be
the first indication of low scale technicolour. According to our interpretation
about half of the events are due to resonantly enhanced pair production of
charged technipions, of mass around 55 GeV, each decaying into a pair of jets,
the other half being due to standard processes. The resonant enhancement is due
to a nearby technirho resonance in the mass range 150-200 GeV, leading to
hugely enhanced signals at the forthcoming LEP run at $\sqrt{s}=160$ GeV since
the CM energy would be closer to the peak of the resonance.


  Photon blockade enhancement is an exciting and promising subject that has
been well studied for photons in cavities. However, whether photon blockade can
be enhanced in the output fields remains largely unexplored. We show that
photon blockade can be greatly enhanced in the mixing output field of a
nonlinear cavity and an auxiliary (linear) cavity, where no direct coupling
between the nonlinear and auxiliary cavities is needed. We uncover a
biquadratic scaling relation between the second-order correlation of the
photons in the output field and intracavity nonlinear interaction strength, in
contrast to a quadratic scaling relation for the photons in a nonlinear cavity.
We identify that this scaling enhancement of photon blockade in the output
field is induced by the destructive interference between two of the paths for
two photons passing through the two cavities. We then extend the theory to the
experimentally feasible Jaynes-Cummings model consisting of a two-level system
strongly coupled to one of the two uncoupled cavities, and also predict a
biquadratic scaling law in the mixing output field. Our proposed scheme is
universal and can be extended to enhance blockade in other bosonic systems.


  Vector vortex beams are a class of optical beams with singularities in their
space-variant polarization. Vector vortex beam lasers have applications in many
areas including imaging and communication, where vertical-cavity lasers
emitting Gaussian beams have been most widely used so far. Generation of vector
vortex beams from vertical-cavity lasers has required external control or
modulation. Here, by utilizing a polarization-selective subwavelength grating
as one of the reflectors in a vertical semiconductor microcavity, we design the
spin textures of the polariton mode and demonstrate polariton lasing in a
single-mode, radially polarized vector vortex beam. Polarization and phase
distributions of the emission are characterized by polarization-resolved
imaging and interferometry. This method of vector vortex laser beam generation
allows low threshold power, stable single-mode operation, scalability, and
on-chip integration, all of which are important for applications in imaging and
communication.


  The exclusion principle of Maldacena and Strominger is seen to follow from
deformed Heisenberg algebras associated with the chiral rings of S_N orbifold
CFTs. These deformed algebras are related to quantum groups at roots of unity,
and are interpreted as algebras of space-time field creation and annihilation
operators. We also propose, as space-time origin of the stringy exclusion
principle, that the $ADS_3 \times S^3$ space-time of the associated
six-dimensional supergravity theory acquires, when quantum effects are taken
into account, a non-commutative structure given by $SU_q(1,1) \times SU_q (2)$.
Both remarks imply that finite N effects are captured by quantum groups
$SL_q(2)$ with $q= e^{{i \pi \over {N + 1}}}$. This implies that a proper
framework for the theories in question is given by gravity on a non-commutative
spacetime with a q-deformation of field oscillators. An interesting consequence
of this framework is a holographic interpretation for a product structure in
the space of all unitary representations of the non-compact quantum group
$SU_q(1,1)$ at roots of unity.


  We consider a free boundary problem on cones depending on a parameter c and
study when the free boundary is allowed to pass through the vertex of the cone.
We show that when the cone is three-dimensional and c is large enough, the free
boundary avoids the vertex. We also show that when c is small enough but still
positive, the free boundary is allowed to pass through the vertex. This
establishes 3 as the critical dimension for which the free boundary may pass
through the vertex of a right circular cone. In view of the well-known
connection between area-minimizing surfaces and the free boundary problem under
consideration, our result is analogous to a result of Morgan that classifies
when an area-minimizing surface on a cone passes through the vertex.


  Microbially induced calcite precipitation (MICP) is a new and sustainable
technology which utilizes biochemical processes to create barriers by calcium
carbonate cementation; therefore, this technology has a potential to be used
for sealing leakage zones in geological formations. The complexity of current
MICP models and present computer power limit the size of numerical simulations.
We describe a mathematical model for MICP suitable for field-scale studies. The
main mechanisms in the conceptual model are as follow: suspended microbes
attach themselves to the pore walls to form biofilm, growth solution is added
to stimulate the biofilm development, the biofilm uses cementation solution for
production of calcite, and the calcite reduces the pore space which in turn
decreases the rock permeability. We apply the model to study the MICP
technology in two sets of reservoir properties including a well-established
field-scale benchmark system for CO$_2$ leakage. A two-phase flow model for
CO$_2$ and water is used to assess the leakage prior to and with MICP
treatment. Based on the numerical results, this study confirms the potential
for this technology to seal leakage paths in reservoir-caprock systems.


  Optimal transport provides a powerful mathematical framework with
applications spanning numerous fields. A cornerstone within this domain is the
$p$-Wasserstein distance, which serves to quantify the cost of transporting one
probability measure to another. While recent attempts have sought to extend
this measure to the realm of quantum states, existing definitions often present
certain limitations, such as not being faithful. In this work, we present a new
definition of quantum Wasserstein distances. This definition, leveraging the
coupling method and a metric applicable to pure states, draws inspiration from
a property characterising the classical Wasserstein distance - its
determination based on its value on point masses. Subject to certain continuity
properties, our definition exhibits numerous attributes expected of an optimal
quantum rendition of the Wasserstein distance. Notably, our approach seamlessly
integrates metrics familiar to quantum information theory, such as the trace
distance. Moreover, it provides an organic extension for metrics, like
Nielsen's complexity metric, allowing their application to mixed states with a
natural operational interpretation. Furthermore, we analyze this metric's
attributes in the context of random quantum states and unveil phase transitions
concerning the complexity of subsystems of random states.


  Endoscopic surgery relies on two-dimensional views, posing challenges for
surgeons in depth perception and instrument manipulation. While Simultaneous
Localization and Mapping (SLAM) has emerged as a promising solution to address
these limitations, its implementation in endoscopic procedures presents
significant challenges due to hardware limitations, such as the use of a
monocular camera and the absence of odometry sensors. This study presents a
robust deep learning-based SLAM approach that combines state-of-the-art and
newly developed models. It consists of three main parts: the Monocular Pose
Estimation Module that introduces a novel unsupervised method based on the
CycleGAN architecture, the Monocular Depth Estimation Module that leverages the
novel Zoe architecture, and the 3D Reconstruction Module which uses information
from the previous models to create a coherent surgical map. The performance of
the procedure was rigorously evaluated using three publicly available datasets
(Hamlyn, EndoSLAM, and SCARED) and benchmarked against two state-of-the-art
methods, EndoSFMLearner and EndoDepth. The integration of Zoe in the MDEM
demonstrated superior performance compared to state-of-the-art depth estimation
algorithms in endoscopy, whereas the novel approach in the MPEM exhibited
competitive performance and the lowest inference time. The results showcase the
robustness of our approach in laparoscopy, gastroscopy, and colonoscopy, three
different scenarios in endoscopic surgery. The proposed SLAM approach has the
potential to improve the accuracy and efficiency of endoscopic procedures by
providing surgeons with enhanced depth perception and 3D reconstruction
capabilities.


  We discuss the contribution of diffractive $Q \bar Q$ production to the
longitudinal double-spin asymmetry in polarized deep--inelastic $lp$
scattering. We show the strong dependence of the $A_{ll}$ asymmetry on the
pomeron spin structure.


  In this paper, we investigate a cell-free massive MIMO system with both
access points and user equipments equipped with multiple antennas over the
Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency
(SE) based on a two-layer decoding structure with maximum ratio (MR) or local
minimum mean-square error (MMSE) combining applied in the first layer and
optimal large-scale fading decoding method implemented in the second layer,
respectively. To maximize the weighted sum SE, an uplink precoding structure
based on an Iteratively Weighted sum-MMSE (I-WMMSE) algorithm using only
channel statistics is proposed. Furthermore, with MR combining applied in the
first layer, we derive novel achievable SE expressions and optimal precoding
structures in closed-form. Numerical results validate our proposed results and
show that the I-WMMSE precoding can achieve excellent sum SE performance.


  We consider a free boundary problem for the $p$-Laplace operator which is
related to the so-called Bernoulli free boundary problem. In this formulation,
the classical boundary gradient condition is replaced by a condition on the
distance between two different level surfaces of the solution. For suitable
scalings our model converges to the classical Bernoulli problem; one of the
advantages in this new formulation lies in the simplicity of the arguments,
since one does not need to consider the boundary gradient.
  We shall study this problem in convex and other regimes, and establish
existence and qualitative theory.


  In this paper, the set of all physical theories is represented by a countable
subset of the lattice of consequence operators defined on a language L. It is
established that there exists a unifying injection U defined on the set of
significant representations for natural-systems M. If W is a member of M, then
U selects an ultralogic C such that C(*W) restricted to L is a unification for
the set of all physical theories as each is applied to W. There also exists a
standard injection S that for each W in M selects a standard unifying
consequence operator D, where C(*W) is a subset of *D(*W). It is also shown how
to use these unifying operators and obtain a product operator that has the same
effect but, in a practical sense, this operator is applied to every
natural-system within our universe simultaneously.


  The Bialas-Bzdak model of elastic proton-proton scattering is generalized to
the case when the real part of the parton-parton level forward scattering
amplitude is non-vanishing. Such a generalization enables the model to describe
well the dip region of the differential cross-section of elastic scattering at
the ISR energies, and improves significantly the ability of the model to
describe also the recent TOTEM data at sqrt{s} = 7 TeV LHC energy. Within this
framework, both the increase of the total cross-section, as well as the
decrease of the location of the dip with increasing colliding energies, is
related to the increase of the quark-diquark distance and to the increase of
the "fragility" of the protons with increasing energies. In addition, we
present and test the validity of two new phenomenological relations: one of
them relates the total p+p cross-section to an effective, model-independent
proton radius, while the other relates the position of the dip in the
differential elastic cross-section to the measured value of the total
cross-section.


  The energy dependence of the cross section for exclusive electroproduction of
vector mesons is discussed as a way to learn about the interplay of soft and
hard interactions. The question of determining the scale of these processes is
addressed.


  To distinguish between the different models proposed to understand the excess
of faint field counts over the predictions from non-evolving models, a number
of redshift surveys have been undertaken. The answer has not arrived yet due to
the high rate of incompleteness achieved. Un-identified galaxies have been
shown to be bluer than identified ones. In this paper, it is argued that some
of these galaxies could be "quiescent" dwarfs, still quite blue, but with
featureless spectra.


  In this work, we studied the relaxation dynamics of coherences of different
order present in a system of two coupled nuclear spins. We used a previously
designed model for intrinsic noise present in such systems which considers the
Lindblad master equation for Markovian relaxation. We experimentally created
zero-, single- and double- quantum coherences in several two-spin systems and
performed a complete state tomography and computed state fidelity. We
experimentally measured the decay of zero- and double- quantum coherences in
these systems. The experimental data fitted well to a model that considers the
main noise channels to be a correlated phase damping channel acting
simultaneously on both spins in conjunction with a generalized amplitude
damping channel acting independently on both spins. The differential relaxation
of multiple-quantum coherences can be ascribed to the action of a correlated
phase damping channel acting simultaneously on both the spins.


  TensorX is a Python library for prototyping, design, and deployment of
complex neural network models in TensorFlow. A special emphasis is put on ease
of use, performance, and API consistency. It aims to make available high-level
components like neural network layers that are, in effect, stateful functions,
easy to compose and reuse. Its architecture allows for the expression of
patterns commonly found when building neural network models either on research
or industrial settings. Incorporating ideas from several other deep learning
libraries, it makes it easy to use components commonly found in
state-of-the-art models. The library design mixes functional dataflow
computation graphs with object-oriented neural network building blocks. TensorX
combines the dynamic nature of Python with the high-performance GPU-enabled
operations of TensorFlow.
  This library has minimal core dependencies (TensorFlow and NumPy) and is
distributed under Apache License 2.0 licence, encouraging its use in both an
academic and commercial settings. Full documentation, source code, and binaries
can be found in https://tensorx.org/.


  Quadrotors are agile platforms. With human experts, they can perform
extremely high-speed flights in cluttered environments. However, fully
autonomous flight at high speed remains a significant challenge. In this work,
we propose a motion planning algorithm based on the corridor-constrained
minimum control effort trajectory optimization (MINCO) framework. Specifically,
we use a series of overlapping spheres to represent the free space of the
environment and propose two novel designs that enable the algorithm to plan
high-speed quadrotor trajectories in real-time. One is a sampling-based
corridor generation method that generates spheres with large overlapped areas
(hence overall corridor size) between two neighboring spheres. The second is a
Receding Horizon Corridors (RHC) strategy, where part of the previously
generated corridor is reused in each replan. Together, these two designs
enlarge the corridor spaces in accordance with the quadrotor's current state
and hence allow the quadrotor to maneuver at high speeds. We benchmark our
algorithm against other state-of-the-art planning methods to show its
superiority in simulation. Comprehensive ablation studies are also conducted to
show the necessity of the two designs. The proposed method is finally evaluated
on an autonomous LiDAR-navigated quadrotor UAV in woods environments, achieving
flight speeds over 13.7 m/s without any prior map of the environment or
external localization facility.


  For a field $F$ and an integer $d\geq 1$, we consider the universal
associative $F$-algebra $A$ generated by two sets of $d+1$ mutually orthogonal
idempotents. We display four bases for the $F$-vector space $A$ that we find
attractive. We determine how these bases are related to each other. We describe
how the multiplication in $A$ looks with respect to our bases. Using our bases
we obtain an infinite nested sequence of 2-sided ideals for $A$. Using our
bases we obtain an infinite exact sequence involving a certain $F$-linear map
$\partial: A \to A$. We obtain several results concerning the kernel of
$\partial$; for instance this kernel is a subalgebra of $A$ that is free of
rank $d$.


  Multilayer networks allow for modeling complex relationships, where
individuals are embedded in multiple social networks at the same time. Given
the ubiquity of such relationships, these networks have been increasingly
gaining attention in the literature. This paper presents the first analysis of
the robustness of centrality measures against strategic manipulation in
multilayer networks. More specifically, we consider an "evader" who
strategically chooses which connections to form in a multilayer network in
order to obtain a low centrality-based ranking-thereby reducing the chance of
being highlighted as a key figure in the network-while ensuring that she
remains connected to a certain group of people. We prove that determining an
optimal way to "hide" is NP-complete and hard to approximate for most
centrality measures considered in our study. Moreover, we empirically evaluate
a number of heuristics that the evader can use. Our results suggest that the
centrality measures that are functions of the entire network topology are more
robust to such a strategic evader than their counterparts which consider each
layer separately.


  This paper adopts a two-stage sample robust optimization (SRO) model to
address the wind power penetrated unit commitment optimal energy flow (UC-OEF)
problem for IEGSs. The two-stage SRO model can be approximately transformed
into a computationally efficient form. Specifically, we employ linear decision
rules to simplify the proposed UC-OEF model. Moreover, we further enhance the
tractability of the simplified model by exploring its structural features and,
accordingly, develop a solution method.


  We present the results of a search for transversely polarised hidden photons
(HPs) with $\sim 3$ eV energies emitted from the Sun. These hypothetical
particles, known also as paraphotons or dark sector photons, are theoretically
well motivated for example by string theory inspired extensions of the Standard
Model. Solar HPs of sub-eV mass can convert into photons of the same energy
(photon$\leftrightarrow$HP oscillations are similar to neutrino flavour
oscillations). At SHIPS this would take place inside a long light-tight
high-vacuum tube, which tracks the Sun. The generated photons would then be
focused into a low-noise photomultiplier at the far end of the tube. Our
analysis of 330 h of data (and {330 h} of background characterisation) reveals
no signal of photons from solar hidden photon conversion. We estimate the rate
of newly generated photons due to this conversion to be smaller than 25
mHz/m$^2$ at the 95$%$ C.L. Using this and a recent model of solar HP emission,
we set stringent constraints on $\chi$, the coupling constant between HPs and
photons, as a function of the HP mass.


  Prompt treatment for melanoma is crucial. To assist physicians in identifying
lesion areas precisely in a quick manner, we propose a novel skin lesion
segmentation technique namely SLP-Net, an ultra-lightweight segmentation
network based on the spiking neural P(SNP) systems type mechanism. Most
existing convolutional neural networks achieve high segmentation accuracy while
neglecting the high hardware cost. SLP-Net, on the contrary, has a very small
number of parameters and a high computation speed. We design a lightweight
multi-scale feature extractor without the usual encoder-decoder structure.
Rather than a decoder, a feature adaptation module is designed to replace it
and implement multi-scale information decoding. Experiments at the ISIC2018
challenge demonstrate that the proposed model has the highest Acc and DSC among
the state-of-the-art methods, while experiments on the PH2 dataset also
demonstrate a favorable generalization ability. Finally, we compare the
computational complexity as well as the computational speed of the models in
experiments, where SLP-Net has the highest overall superiority


  Waveform physiological data is important in the treatment of critically ill
patients in the intensive care unit. Such recordings are susceptible to
artefacts, which must be removed before the data can be re-used for alerting or
reprocessed for other clinical or research purposes. Accurate removal of
artefacts reduces bias and uncertainty in clinical assessment, as well as the
false positive rate of intensive care unit alarms, and is therefore a key
component in providing optimal clinical care. In this work, we present
DeepClean; a prototype self-supervised artefact detection system using a
convolutional variational autoencoder deep neural network that avoids costly
and painstaking manual annotation, requiring only easily-obtained 'good' data
for training. For a test case with invasive arterial blood pressure, we
demonstrate that our algorithm can detect the presence of an artefact within a
10-second sample of data with sensitivity and specificity around 90%.
Furthermore, DeepClean was able to identify regions of artefact within such
samples with high accuracy and we show that it significantly outperforms a
baseline principle component analysis approach in both signal reconstruction
and artefact detection. DeepClean learns a generative model and therefore may
also be used for imputation of missing data.


  This paper addresses the strategic challenges of deposit banks, and payment
clearinghouses, posed by the growing role of mobile operators as collectors and
payment agents for flows of cash for themselves and third parties. Through
analysis and data analysis from selected operators, it is shown that mobile
operators achieve as money flow handlers levels of efficiency,
profitability,and risk control comparable with deposit banks. Furthermore, the
payment infrastructures deployed by both are found to be quite similar, and are
analyzed in relation to financial profitability, strategic challenges and
opportunities. This paves the way to either mobile operators taking a bigger
role,or for banks to tie up such operators to them even more tightly,or for
alliances/mergers to take place,all these options being subject to regulatory
evolution which is analyzed as well . The consequences are mapped out in
operational and regulatory terms


  A subgroup $H$ of a group $G$ is said to be {\it pronormal} in $G$ if $H$ and
$H^g$ are conjugate in $\langle H, H^g \rangle$ for each $g \in G$. Some
problems in Finite Group Theory, Combinatorics, and Permutation Group Theory
were solved in terms of pronormality, therefore, the question of pronormality
of a given subgroup in a given group is of interest. Subgroups of odd index in
finite groups satisfy a native necessary condition of pronormality. In this
paper we continue investigations on pronormality of subgroups of odd index and
consider the pronormality question for subgroups of odd index in some direct
products of finite groups.
  In particular, in this paper we prove that the subgroups of odd index are
pronormal in the direct product $G$ of finite simple symplectic groups over
fields of odd characteristics if and only if the subgroups of odd index are
pronormal in each direct factor of $G$. Moreover, deciding the pronormality of
a given subgroup of odd index in the direct product of simple symplectic groups
over fields of odd characteristics is reducible to deciding the pronormality of
some subgroup $H$ of odd index in a subgroup of $\prod_{i=1}^t \mathbb{Z}_3\wr
Sym_{n_i}$, where each $Sym_{n_i}$ acts naturally on $\{1,\dots, n_i\}$, such
that $H$ projects onto $\prod_{i=1}^t Sym_{n_i}$. Thus, in this paper we obtain
a criterion of pronormality of a subgroup $H$ of odd index in a subgroup of
$\prod_{i=1}^t \mathbb{Z}_{p_i}\wr Sym_{n_i}$, where each $p_i$ is a prime and
each $Sym_{n_i}$ acts naturally on $\{1,\dots, n_i\}$, such that $H$ projects
onto $\prod_{i=1}^t Sym_{n_i}$.


  Previous research has shown positive correlations between EEG alpha activity
and performing creative tasks. In this study, expert classical musicians (n=4)
were asked to play their instrument while being monitored with a wireless EEG
headset. Data was collected during two rehearsal types: (a) in their regular,
fixed ensemble;; (b) in an improvised, mixed ensemble with unfamiliar musicians
and less rehearsal time. A positive correlation was found between alpha power
and the improvised setup (p<0.01, d=0.4). A positive correlation was also found
between alpha power and more intense play (p<0.01, d=0.2). There was a negative
correlation between alpha power and arousal due to stress, e.g., frowning after
playing a false note (p<0.01, d=0.6). Finally, the real-time capabilities of
wireless EEG monitoring were explored with a data visualisation during live
performance on stage.


  The storage and retrieval of photonic quantum states, quantum memory, is a
key resource for a wide range of quantum applications. Here we investigate the
sensitivity of $\Lambda$-type quantum memory to experimental fluctuations and
drift. We use a variance-based approach, focusing on the effects of
fluctuations and drift on memory efficiency. We consider shot-to-shot
fluctuations of the memory parameters, and separately we consider longer
timescale drift of the control field parameters. We find the parameters that a
quantum memory is most sensitive to depend on the quantum memory protocol being
employed, where the observed sensitivity agrees with physical interpretation of
the protocols. We also present a general framework that is applicable to other
figures of merit beyond memory efficiency. These results have practical
ramifications for quantum memory experiments.


  We determine the number counts to second order in cosmological perturbation
theory in the Poisson gauge and allowing for anisotropic stress. The
calculation is performed using an innovative approach based on the recently
proposed "geodesic light-cone" gauge. This allows us to determine the number
counts in a purely geometric way, without using Einstein's equation. The result
is valid for general dark energy models and (most) modified gravity models. We
then evaluate numerically the relevant contributions to the number counts
bispectrum. In particular we consider the terms involving the density, redshift
space distortion and lensing.


  All quark mass matrices with texture zeroes obtained through weak basis
transformations are confronted with the experimental data. The reconstruction
of the quark mass matrices M_u and M_d at the electroweak scale is performed in
a weak basis where the matrices are Hermitian and have a maximum of three
vanishing elements. The same procedure is also accomplished for the Yukawa
coupling matrices at the grand unification scale in the context of the Standard
Model and its minimal supersymmetric extension as well as of the two Higgs
doublet model. The analysis of all viable power structures on the quark Yukawa
coupling matrices that could naturally appear from a Froggatt-Nielsen mechanism
is also presented.


  HighwayHash is a new pseudo-random function based on SIMD multiply and
permute instructions for thorough and fast hashing. It is 5.2 times as fast as
SipHash for 1 KiB inputs. An open-source implementation is available under a
permissive license. We discuss design choices and provide statistical analysis,
speed measurements and preliminary cryptanalysis. Assuming it withstands
further analysis, strengthened variants may also substantially accelerate file
checksums and stream ciphers.


  In this paper, we study heterodimensional cycles of two-parameter families of
3-dimensional diffeomorphisms some element of which contains nondegenerate
heterodimensional tangencies of the stable and unstable manifolds of two saddle
points with different indexes, and prove that such diffeomorphisms can be well
approximated by another element which has a quadratic homoclinic tangency
associated to one of these saddle points. Moreover, it is shown that the
tangency unfolds generically with respect to the family. This result together
with some theorem in Viana, we detect strange attractors appeared arbitrarily
close to the original element with the heterodimensional cycle.


  We classify Chern characters of semistable sheaves up to rank four in three
dimensional projective space. As a corollary we show that moduli spaces of
semistable sheaves between rank zero and four with maximal third Chern
character are smooth and irreducible.


  Influences of reaction zone thickening and local extinctions on the burning
rate of extremely turbulent hydrogen-enriched methane-air flames are
investigated using simultaneous planar laser-induced fluorescence of
formaldehyde molecule and hydroxyl radical as well as separate stereoscopic
particle image velocimetry techniques. Karlovitz numbers upto 76 are examined.
It is shown that, by increasing the turbulence intensity, the preheat and
reaction zone thicknesses can increase to values that are, respectively, 6.3
and 4.9 of the corresponding laminar flames. Broadening of these zones for
intensely turbulent hydrogen-enriched methane-air flames is shown
experimentally for the first time. Broadening of the reaction zone suggests
that the flamelet assumption used for development of the burning rate
formulations may not hold. Thus, a new formulation, which does not utilize the
flamelet assumption, is developed and used to calculate the burning rate of the
tested flames. It is shown that, at small turbulence intensities, the burning
rate values follow those of the local consumption speed, which is developed in
the literature based on the flamelet assumption. However, at large turbulence
intensities, the estimated burning rate features large values, and the ratio of
this parameter to the local consumption speed is consistent with the ratio of
the global and local consumption speeds reported in the literature. It is shown
that the ratio of the normalized burning rate to the normalized local
consumption speed is correlated with the broadening of reaction zone,
suggesting that the disparity between the values of the burning rate and local
consumption speed is linked to the reaction zone thickening. It is shown,
although the flame thickening increase the burning rate, local extinctions
decrease this parameter leading to the bending behavior reported in the
literature.


  We study the Cauchy problem for the 3D Gross-Pitaevskii equation. The global
well-posedness in the natural energy space was proved by G\'erard
\cite{Gerard}. In this paper we prove scattering for small data in the same
space with some additional angular regularity, and in particular in the radial
case we obtain small energy scattering.


  Two-sided marketplaces such as eBay, Etsy and Taobao have two distinct groups
of customers: buyers who use the platform to seek the most relevant and
interesting item to purchase and sellers who view the same platform as a tool
to reach out to their audience and grow their business. Additionally, platforms
have their own objectives ranging from growing both buyer and seller user bases
to revenue maximization. It is not difficult to see that it would be
challenging to obtain a globally favorable outcome for all parties. Taking the
search experience as an example, any interventions are likely to impact either
buyers or sellers unfairly to course correct for a greater perceived need. In
this paper, we address how a company-aligned search experience can be provided
with competing business metrics that E-commerce companies typically tackle. As
far as we know, this is a pioneering work to consider multiple different
aspects of business indicators in two-sided marketplaces to optimize a search
experience. We demonstrate that many problems are difficult or impossible to
decompose down to credit assigned scores on individual documents, rendering
traditional methods inadequate. Instead, we express market-level metrics as
constraints and discuss to what degree multiple potentially conflicting metrics
can be tuned to business needs. We further explore the use of policy learners
in the form of Evolutionary Strategies to jointly optimize both group-level and
market-level metrics simultaneously, side-stepping traditional cascading
methods and manual interventions. We empirically evaluate the effectiveness of
the proposed method on Etsy data and demonstrate its potential with insights.


  End-to-end (E2E) automatic speech recognition models like Recurrent Neural
Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR
applications like voice assistants. While E2E models are very effective at
learning representation of the training data they are trained on, their
accuracy on unseen domains remains a challenging problem. Additionally, these
models require paired audio and text training data, are computationally
expensive and are difficult to adapt towards the fast evolving nature of
conversational speech. In this work, we explore a contextual biasing approach
using likelihood-ratio that leverages text data sources to adapt RNN-T model to
new domains and entities. We show that this method is effective in improving
rare words recognition, and results in a relative improvement of 10% in 1-best
word error rate (WER) and 10% in n-best Oracle WER (n=8) on multiple
out-of-domain datasets without any degradation on a general dataset. We also
show that complementing the contextual biasing adaptation with adaptation of a
second-pass rescoring model gives additive WER improvements.


  New notions of the complexity function C(epsilon;t,s) and entropy function
S(epsilon;t,s) are introduced to describe systems with nonzero or zero Lyapunov
exponents or systems that exhibit strong intermittent behavior with
``flights'', trappings, weak mixing, etc. The important part of the new notions
is the first appearance of epsilon-separation of initially close trajectories.
The complexity function is similar to the propagator p(t0,x0;t,x) with a
replacement of x by the natural lengths s of trajectories, and its introduction
does not assume of the space-time independence in the process of evolution of
the system. A special stress is done on the choice of variables and the
replacement t by eta=ln(t), s by xi=ln(s) makes it possible to consider
time-algebraic and space-algebraic complexity and some mixed cases. It is shown
that for typical cases the entropy function S(epsilon;xi,eta) possesses
invariants (alpha,beta) that describe the fractal dimensions of the space-time
structures of trajectories. The invariants (alpha,beta) can be linked to the
transport properties of the system, from one side, and to the Riemann
invariants for simple waves, from the other side. This analog provides a new
meaning for the transport exponent mu that can be considered as the speed of a
Riemann wave in the log-phase space of the log-space-time variables. Some other
applications of new notions are considered and numerical examples are
presented.


  Least Squares regression was first introduced for the pricing of
American-style options, but it has since been expanded to include swing options
pricing. The swing options price may be viewed as a solution to a Backward
Dynamic Programming Principle, which involves a conditional expectation known
as the continuation value. The approximation of the continuation value using
least squares regression involves two levels of approximation. First, the
continuation value is replaced by an orthogonal projection over a subspace
spanned by a finite set of $m$ squared-integrable functions (regression
functions) yielding a first approximation $V^m$ of the swing value function. In
this paper, we prove that, with well-chosen regression functions, $V^m$
converges to the swing actual price $V$ as $m \to + \infty$. A similar result
is proved when the regression functions are replaced by neural networks. For
both methods (least squares or neural networks), we analyze the second level of
approximation involving practical computation of the swing price using Monte
Carlo simulations and yielding an approximation $V^{m, N}$ (where $N$ denotes
the Monte Carlo sample size). Especially, we prove that $V^{m, N} \to V^m$ as
$N \to + \infty$ for both methods and using Hilbert basis in the least squares
regression. Besides, a convergence rate of order
$\mathcal{O}\big(\frac{1}{\sqrt{N}} \big)$ is proved in the least squares case.
Several convergence results in this paper are based on the continuity of the
swing value function with respect to cumulative consumption, which is also
proved in the paper and has not been yet explored in the literature before for
the best of our knowledge.


  Despite significant progress in deep learning-based optical flow methods,
accurately estimating large displacements and repetitive patterns remains a
challenge. The limitations of local features and similarity search patterns
used in these algorithms contribute to this issue. Additionally, some existing
methods suffer from slow runtime and excessive graphic memory consumption. To
address these problems, this paper proposes a novel approach based on the RAFT
framework. The proposed Attention-based Feature Localization (AFL) approach
incorporates the attention mechanism to handle global feature extraction and
address repetitive patterns. It introduces an operator for matching pixels with
corresponding counterparts in the second frame and assigning accurate flow
values. Furthermore, an Amorphous Lookup Operator (ALO) is proposed to enhance
convergence speed and improve RAFTs ability to handle large displacements by
reducing data redundancy in its search operator and expanding the search space
for similarity extraction. The proposed method, Efficient RAFT
(Ef-RAFT),achieves significant improvements of 10% on the Sintel dataset and 5%
on the KITTI dataset over RAFT. Remarkably, these enhancements are attained
with a modest 33% reduction in speed and a mere 13% increase in memory usage.
The code is available at: https://github.com/n3slami/Ef-RAFT


  The natural language generation (NLG) component of a spoken dialogue system
(SDS) usually needs a substantial amount of handcrafting or a well-labeled
dataset to be trained on. These limitations add significantly to development
costs and make cross-domain, multi-lingual dialogue systems intractable.
Moreover, human languages are context-aware. The most natural response should
be directly learned from data rather than depending on predefined syntaxes or
rules. This paper presents a statistical language generator based on a joint
recurrent and convolutional neural network structure which can be trained on
dialogue act-utterance pairs without any semantic alignments or predefined
grammar trees. Objective metrics suggest that this new model outperforms
previous methods under the same experimental conditions. Results of an
evaluation by human judges indicate that it produces not only high quality but
linguistically varied utterances which are preferred compared to n-gram and
rule-based systems.


  We give a proof of the Universality Conjecture for orthogonal (beta=1) and
symplectic (beta=4) random matrix ensembles of Laguerre-type in the bulk of the
spectrum as well as at the hard and soft spectral edges. Our results are stated
precisely in the Introduction (Theorems 1.1, 1.4, 1.6 and Corollaries 1.2, 1.5,
1.7). They concern the appropriately rescaled kernels K_{n,beta}, correlation
and cluster functions, gap probabilities and the distributions of the largest
and smallest eigenvalues. Corresponding results for unitary (beta=2)
Laguerre-type ensembles have been proved by the fourth author in [23]. The
varying weight case at the hard spectral edge was analyzed in [13] for beta=2:
In this paper we do not consider varying weights.
  Our proof follows closely the work of the first two authors who showed in
[7], [8] analogous results for Hermite-type ensembles. As in [7], [8] we use
the version of the orthogonal polynomial method presented in [25], [22] to
analyze the local eigenvalue statistics. The necessary asymptotic information
on the Laguerre-type orthogonal polynomials is taken from [23].


  Mg/Si and Fe/Si ratios are important parameters that control the composition
of rocky planets. In this work we applied non-LTE correction to the Mg and Si
abundances of stars with and without planets to confirm/infirm our previous
findings that [Mg/Si] atmospheric abundance is systematically higher for
Super-Earth/Neptune-mass planet hosts than stars without planets. Our results
show that the small differences of stellar parameters observed in these two
groups of stars are not responsible for the already reported difference in the
[Mg/Si] ratio. Thus, the high [Mg/Si] ratio of Neptunian hosts is probably
related to the formation efficiency of these planets in such environments.


  In this work, we explore the applicability of cache occupancy attacks and the
implications of secured cache design rationales on such attacks. In particular,
we show that one of the well-known cache randomization schemes, MIRAGE, touted
to be resilient against eviction-based attacks, amplifies the chances of cache
occupancy attack, making it more vulnerable compared to contemporary designs.
We leverage MIRAGE's global eviction property to demonstrate covert channel
with byte-level granularity, with far less cache occupancy requirement (just
$10\%$ of LLC) than other schemes. For instance, ScatterCache (a randomisation
scheme with lesser security guarantees than MIRAGE) and generic set-associative
caches require $40\%$ and $30\%$ cache occupancy, respectively, to exhibit
covert communication. Furthermore, we extend our attack vectors to include
side-channel, template-based fingerprinting of workloads in a cross-core
setting. We demonstrate the potency of such fingerprinting on both inhouse LLC
simulator as well as on SPEC2017 workloads on gem5. Finally, we pinpoint
implementation inconsistencies in MIRAGE's publicly available gem5 artifact
which motivates a re-evaluation of the performance statistics of MIRAGE with
respect to ScatterCache and baseline set-associative cache. We find MIRAGE, in
reality, performs worse than what is previously reported in literature, a
concern that should be addressed in successor generations of secured caches.


  $\textbf{Theorem 1.2.}$ For a ring $A$, the following conditions are
equivalent. $\textbf{1)}$ $A$ is a right automorphism-invariant right
non-singular ring. $\textbf{2)}$ $A$ is a right automorphism-invariant regular
ring. $\textbf{3)}$ $A=S\times T$, where $S$ is a right injective regular ring
and $T$ is a strongly regular ring which contains all invertible elements of
its maximal right ring of quotients. $\textbf{Theorem 1.5.}$ For a ring $A$
with right Goldie radical $G(A_A)$, the following conditions are equivalent.
$\textbf{1)}$ $A/G(A_A)$ is a semiprime right Goldie ring. $\textbf{2)}$ Any
direct sum of automorphism-invariant non-singular right $A$-modules is an
automorphism-invariant module. $\textbf{3)}$ Any direct sum of
automorphism-invariant non-singular right $A$-modules is an injective module.


  A weak-lensing analysis of wide-field $V$- and $I$-band images centered on
the cluster Abell 1942 has uncovered a mass concentration $\sim 7$ arcminutes
South of the cluster center. A statistical analysis shows that the detections
are highly significant. No strong concentration of bright galaxies is seen at
the position of the mass concentration, though a slight galaxy number
overdensity and a weak extended X-ray source are present about 1' away from its
center.
  From the spatial dependence of the tangential alignment around the center of
the mass concentration, we inferred a lower bound on the mass inside a sphere
of radius $0.5 h^{-1}$\ts Mpc of $1\times 10^{14}h^{-1}M_\odot$, much higher
than crude mass estimates based on X-ray data. No firm conclusion can be
inferred about the nature of the clump. If it were a high-redshift cluster, the
weak X-ray flux would indicate that it had an untypically low X-ray luminosity
for its mass; if the X-ray emission were physically unrelated to the mass
concentration, this conclusion would be even stronger.
  The search for massive halos by weak lensing enables us for the first time to
select halos based on their mass properties only and to detect new types of
objects, e.g., dark halos. The mass concentration in the field of A1942 may be
the first example of such a halo.


  Quantifying the perceptual similarity of two images is a long-standing
problem in low-level computer vision. The natural image domain commonly relies
on supervised learning, e.g., a pre-trained VGG, to obtain a latent
representation. However, due to domain shift, pre-trained models from the
natural image domain might not apply to other image domains, such as medical
imaging. Notably, in medical imaging, evaluating the perceptual similarity is
exclusively performed by specialists trained extensively in diverse medical
fields. Thus, medical imaging remains devoid of task-specific, objective
perceptual measures. This work answers the question: Is it necessary to rely on
supervised learning to obtain an effective representation that could measure
perceptual similarity, or is self-supervision sufficient? To understand whether
recent contrastive self-supervised representation (CSR) may come to the rescue,
we start with natural images and systematically evaluate CSR as a metric across
numerous contemporary architectures and tasks and compare them with existing
methods. We find that in the natural image domain, CSR behaves on par with the
supervised one on several perceptual tests as a metric, and in the medical
domain, CSR better quantifies perceptual similarity concerning the experts'
ratings. We also demonstrate that CSR can significantly improve image quality
in two image synthesis tasks. Finally, our extensive results suggest that
perceptuality is an emergent property of CSR, which can be adapted to many
image domains without requiring annotations.


  Very recently, J 1128+5925 was found to show strong intraday variability at
radio wavelengths and may be a new source with annual modulation of the
timescale of its radio variability. Therefore, its radio variability can be
best explained via interstellar scintillation. Here we present the properties
of its optical variability for the first time after a monitoring program in
2007 May. Our observations indicate that in this period J 1128+5925 only showed
trivial optical variability on internight timescale, and did not show any clear
intranight variability. This behavior is quite different from its strong radio
intraday variability. Either this object was in a quiescent state in optical in
this period, or it is intrinsically not so active in optical as it is in radio
regimes.


  The ordered magnetic field observed via polarized synchrotron emission in
nearby disc galaxies can be explained by a mean-field dynamo operating in the
diffuse interstellar medium (ISM). Additionally, vertical-flux initial
conditions are potentially able to influence this dynamo via the occurrence of
the magneto-rotational instability (MRI). We aim to study the influence of
various initial field configurations on the saturated state of the mean-field
dynamo. This is motivated by the observation that different saturation behavior
was previously obtained for different supernova rates. We perform direct
numerical simulations (DNS) of three-dimensional local boxes of the vertically
stratified, turbulent interstellar medium, employing shearing-periodic boundary
conditions horizontally. Unlike in our previous work, we also impose a vertical
seed magnetic field. We run the simulations until the growth of the magnetic
energy becomes negligible. We furthermore perform simulations of equivalent 1D
dynamo models, with an algebraic quenching mechanism for the dynamo
coefficients. We compare the saturation of the magnetic field in the DNS with
the algebraic quenching of a mean-field dynamo. The final magnetic field
strength found in the direct simulation is in excellent agreement with a
quenched $\alpha\Omega$~dynamo. For supernova rates representative of the Milky
Way, field losses via a Galactic wind are likely responsible for saturation. We
conclude that the relative strength of the turbulent and regular magnetic
fields in spiral galaxies may depend on the galaxy's star formation rate. We
propose that a mean field approach with algebraic quenching may serve as a
simple sub-grid scale model for galaxy evolution simulations including a
prescribed feedback from magnetic fields.


  The analysis of the time ordered data of Dark Matter experiments is becoming
more and more challenging with the increase of sensitivity in the ongoing and
forthcoming projects. Combined with the well-known level of background events,
this leads to a rather high level of pile-up in the data. Ionization,
scintillation as well as bolometric signals present common features in their
acquisition timeline: low frequency baselines, random gaussian noise, parasitic
noise and signal characterized by well-defined peaks. In particular, in the
case of long-lasting signals such as bolometric ones, the pile-up of events may
lead to an inaccurate reconstruction of the physical signal (misidentification
as well as fake events). We present a general method to detect and extract
signals in noisy data with a high pile-up rate and qe show that events from few
keV to hundreds of keV can be reconstructed in time ordered data presenting a
high pile-up rate. This method is based on an iterative detection and fitting
procedure combined with prior wavelet-based denoising of the data and baseline
subtraction. {We have tested this method on simulated data of the MACHe3
prototype experiment and shown that the iterative fitting procedure allows us
to recover the lowest energy events, of the order of a few keV, in the presence
of background signals from a few to hundreds of keV. Finally we applied this
method to the recent MACHe3 data to successfully measure the spectrum of
conversion electrons from Co57 source and also the spectrum of the background
cosmic muons.


  The solar neutrino problem, atmospheric neutrino problem, and the existence
of hot dark matter can all be economically accounted for using only the three
known neutrinos if these neutrinos all have nearly degenerate masses of a few
eV. We show how to generate this pattern of neutrino masses in a natural way
within a `see-saw' framework.


  In this work, we consider the cosmological constraints on the interacting
dark energy models. We generalize the models considered previously by Guo {\it
et al.}, Costa and Alcaniz, and try to discuss two general types of models:
type I models are characterized by $\rho_{_X}/\rho_m=f(a)$ and $f(a)$ can be
any function of scale factor $a$, whereas type II models are characterized by
$\rho_m=\rho_{m0}\,a^{-3+\epsilon(a)}$ and $\epsilon(a)$ can be any function of
$a$. We obtain the cosmological constraints on the type I and II models with
power-law, CPL-like, logarithmic $f(a)$ and $\epsilon(a)$ by using the latest
observational data.


  A typical issue in Pattern Recognition is the non-uniformly sampled data,
which modifies the general performance and capability of machine learning
algorithms to make accurate predictions. Generally, the data is considered
non-uniformly sampled when in a specific area of data space, they are not
enough, leading us to misclassification problems. This issue cut down the goal
of the one-class classifiers decreasing their performance. In this paper, we
propose a one-class classifier based on the minimum spanning tree with a
dynamic decision boundary (OCdmst) to make good prediction also in the case we
have non-uniformly sampled data. To prove the effectiveness and robustness of
our approach we compare with the most recent one-class classifier reaching the
state-of-the-art in most of them.


  First, we present a combined analysis of ${\rm p}{\rm p} \to \mu^+ \nu_\mu
{\rm e}^- \bar \nu_{\rm e}$ and ${\rm p}{\rm p} \to \mu^+ \nu_\mu {\rm e}^-
\bar \nu_{\rm e} {\rm j}$ at next-to-leading order, including both QCD and
electroweak corrections. Second, we provide all-order predictions for ${\rm
p}{\rm p} \to \mu^+ \nu_\mu {\rm e}^- \bar \nu_{\rm p}+\mathrm{jets}$ using
merged parton-shower simulations that also include approximate EW effects. A
fully inclusive sample for WW production is compared to the fixed-order
computations for exclusive zero- and one-jet selections. The various
higher-order effects are studied in detail at the level of cross sections and
differential distributions for realistic experimental set-ups. Our study
confirms that merged predictions are significantly more stable than the
fixed-order ones in particular regarding ratios between the two processes.


  We show the existence of an infinite collection of hyperbolic knots where
each of which has in its exterior meridional essential planar surfaces of
arbitrarily large number of boundary components, or, equivalently, that each of
these knots has essential tangle decompositions of arbitrarily large number of
strings. Moreover, each of these knots has in its exterior meridional essential
surfaces of any positive genus and (even) number of boundary components. That
is, the compact surfaces that have a meridional essential embedding into a
hyperbolic knot exterior have meridional essential embeddings into each of
these hyperbolic knots exteriors.


  An adaptive funnel control method is considered for the regulation of the
output for a class of nonlinear infinite-dimensional systems on real Hilbert
spaces. After a decomposition of the state space and some change of variables
related to the Byrnes-Isidori form, it is shown that the funnel controller
presented in (Berger et al., 2020) achieves the control objective under some
assumptions on the nonlinear system dynamics, like well-posedness and
Bounded-Input-State Bounded-Output (BISBO) stability. The theory is applied to
the regulation of the temperature in a chemical plug-flow tubular reactor whose
reaction kinetics are modeled by the Arrhenius nonlinearity. Furthermore a
damped sine-Gordon model is shown to fit the required assumptions as well. The
theoretical results are illustrated by means of numerical simulations.


  Shear layer instability at the free surface of a water jet is studied. The
accompanying video shows experimental data recorded using measurement methods
such as Planar Laser Induced Fluorescence (PLIF) and Particle Image Velocity
(PIV). These results reveal the mechanisms leading to the formation of
capillary waves on the surface due to the roll-up of the shear layer. These
capillary waves eventually collide to each other, injecting vorticity in the
bulk of the flow. Shear layer and injected vorticity interact to form a counter
rotating vortex pair that moves down to the flow.


  We discuss two research areas dealing respectively with (1) a class of
multivariate medians and (2) a symmetrization algorithm for probability
measures.


  In a compressed Nuclear Matter (NM) an increasing pressure between the
nucleons starts to increase the ratio of a nucleon Fermi to average single
particle energy and in accordance with the Hugenholtz-van Hove theorem the
longitudinal Momentum Sum Rule (MSR) is broken in a Relativistic Mean Field
(RMF) approach. We propose to benefit from the concept of enthalpy in order to
show how to fulfill the MSR above a saturation density with pressure
corrections. As a result a nucleon mass can decrease with density, making the
Equation of State (EoS) softer. The course of the EoS in our modified RMF model
is close to a semi-empirical estimate and to results obtained from extensive
DBHF calculations with a Bonn A potential, which produce the EoS stiff enough
to describe neutron star properties (mass-radius constraint), especially the
most massive known neutron star. The presented model has proper saturation
properties, including good values of a compressibility and a spin-orbit term.


  In the last few years, image processing researchers spent a substantial
amount of time and effort developing and perfecting image quality assessment
algorithms. Bright-field microscopy, for example, produces images whose quality
is a bottleneck for consistent evaluation. For instance, when a stack of images
of a specimen is acquired in different focal plane configurations, there will
be a set of blurred or partially blurred elements in it, impairing proper
evaluation. This work aims to provide an image quality assessment metric,
without the presence of a reference image for comparison, to detect the blurred
and sharp images among the whole set of the stack, and elect the sharpest ones
for a further fusion process. The correlation of the results with subjective
labeling of the image sets showed that the proposed metric offers reliable
identification of the eligible images for fusion and suggests the application
in other real-world problems.


  We have performed an X-ray study of the nearby barred spiral galaxy NGC1672,
primarily to ascertain the effect of the bar on its nuclear activity. We use
both Chandra and XMM-Newton observations to investigate its X-ray properties,
together with supporting high-resolution optical imaging data from the Hubble
Space Telescope (HST), infrared imaging from the Spitzer Space Telescope, and
ATCA ground-based radio data. We detect 28 X-ray sources within the D25 area of
the galaxy, many of which correlate spatially with star-formation in the bar
and spiral arms, while two are identified as background galaxies in the HST
images. Nine of the X-ray sources are ULXs, with the three brightest (LX > 5E39
erg/s) located at the ends of the bar. With the spatial resolution of Chandra,
we are able to show for the first time that NGC1672 possesses a hard
(Gamma~1.5) nuclear X-ray source with a 2-10 keV luminosity of 4E38 erg/s. This
is surrounded by an X-ray bright circumnuclear star-forming ring, comprised of
point sources and hot gas, which dominates the 2-10 keV emission in the central
region of the galaxy. The spatially resolved multiwavelength photometry
indicates that the nuclear source is a low-luminosity AGN (LLAGN), but with
star formation activity close to the central black hole. A high-resolution
multiwavelength survey is required to fully assess the impact of both
large-scale bars and smaller-scale phenomena such as nuclear bars, rings and
nuclear spirals on the fueling of LLAGN.


  We give examples of situations -- stochastic production, military tactics,
corporate merger -- where it is beneficial to concentrate risk rather than to
diversify it, that is, to put all eggs in one basket. Our examples admit a dual
interpretation: as optimal strategies of a single player (the `principal') or,
alternatively, as dominant strategies in a non-cooperative game with multiple
players (the `agents').
  The key mathematical result can be formulated in terms of a convolution
structure on the set of increasing functions on a Boolean lattice (the lattice
of subsets of a finite set). This generalizes the well-known Harris inequality
from statistical physics and discrete mathematics; we give a simple
self-contained proof of this result, and prove a further generalization based
on the game-theoretic approach.


  It is unclear how changing the learning rule of a deep neural network alters
its learning dynamics and representations. To gain insight into the
relationship between learned features, function approximation, and the learning
rule, we analyze infinite-width deep networks trained with gradient descent
(GD) and biologically-plausible alternatives including feedback alignment (FA),
direct feedback alignment (DFA), and error modulated Hebbian learning (Hebb),
as well as gated linear networks (GLN). We show that, for each of these
learning rules, the evolution of the output function at infinite width is
governed by a time varying effective neural tangent kernel (eNTK). In the lazy
training limit, this eNTK is static and does not evolve, while in the rich
mean-field regime this kernel's evolution can be determined self-consistently
with dynamical mean field theory (DMFT). This DMFT enables comparisons of the
feature and prediction dynamics induced by each of these learning rules. In the
lazy limit, we find that DFA and Hebb can only learn using the last layer
features, while full FA can utilize earlier layers with a scale determined by
the initial correlation between feedforward and feedback weight matrices. In
the rich regime, DFA and FA utilize a temporally evolving and depth-dependent
NTK. Counterintuitively, we find that FA networks trained in the rich regime
exhibit more feature learning if initialized with smaller correlation between
the forward and backward pass weights. GLNs admit a very simple formula for
their lazy limit kernel and preserve conditional Gaussianity of their
preactivations under gating functions. Error modulated Hebb rules show very
small task-relevant alignment of their kernels and perform most task relevant
learning in the last layer.


  We study the mod-$\ell$ homotopy type of classifying spaces for
commutativity, $B(\mathbb{Z}, G)$, at a prime $\ell$. We show that the
mod-$\ell$ homology of $B(\mathbb{Z}, G)$ depends on the mod-$\ell$ homotopy
type of $BG$ when $G$ is a compact connected Lie group, in the sense that a
mod-$\ell$ homology isomorphism $BG \to BH$ for such groups induces a
mod-$\ell$ homology isomorphism $B(\mathbb{Z}, G) \to B(\mathbb{Z}, H)$. In
order to prove this result, we study a presentation of $B(\mathbb{Z}, G)$ as a
homotopy colimit over a topological poset of closed abelian subgroups,
expanding on an idea of Adem and G\'omez. We also study the relationship
between the mod-$\ell$ type of a Lie group $G(\mathbb{C})$ and the locally
finite group $G(\bar{\mathbb{F}}_p)$ where $G$ is a Chevalley group. We see
that the na\"ive analogue for $B(\mathbb{Z}, G)$ of the celebrated
Friedlander--Mislin result cannot hold, but we show that it does hold after
taking the homotopy quotient of a $G$ action on $B(\mathbb{Z}, G)$.


  Many contemporary software products have subsystems for automatic crash
reporting. However, it is well-known that the same bug can produce slightly
different reports. To manage this problem, reports are usually grouped, often
manually by developers. Manual triaging, however, becomes infeasible for
products that have large userbases, which is the reason for many different
approaches to automating this task. Moreover, it is important to improve
quality of triaging due to the big volume of reports that needs to be processed
properly. Therefore, even a relatively small improvement could play a
significant role in overall accuracy of report bucketing. The majority of
existing studies use some kind of a stack trace similarity metric, either based
on information retrieval techniques or string matching methods. However, it
should be stressed that the quality of triaging is still insufficient. In this
paper, we describe TraceSim -- a novel approach to address this problem which
combines TF-IDF, Levenshtein distance, and machine learning to construct a
similarity metric. Our metric has been implemented inside an industrial-grade
report triaging system. The evaluation on a manually labeled dataset shows
significantly better results compared to baseline approaches.


  Pre-trained language models like BERT achieve superior performances in
various NLP tasks without explicit consideration of syntactic information.
Meanwhile, syntactic information has been proved to be crucial for the success
of NLP applications. However, how to incorporate the syntax trees effectively
and efficiently into pre-trained Transformers is still unsettled. In this
paper, we address this problem by proposing a novel framework named
Syntax-BERT. This framework works in a plug-and-play mode and is applicable to
an arbitrary pre-trained checkpoint based on Transformer architecture.
Experiments on various datasets of natural language understanding verify the
effectiveness of syntax trees and achieve consistent improvement over multiple
pre-trained models, including BERT, RoBERTa, and T5.


  Self-consistent simulations of the ultrafast electron dynamics in thin metal
films are performed. A regime of nonlinear oscillations is observed, which
corresponds to ballistic electrons bouncing back and forth against the film
surfaces. When an oscillatory laser field is applied to the film, the field
energy is partially absorbed by the electron gas. Maximum absorption occurs
when the period of the external field matches the period of the nonlinear
oscillations, which, for sodium films, lies in the infrared range. Possible
experimental implementations are discussed.


  The COVID-19 pandemic has had far-reaching consequences, highlighting the
urgency for explanatory and predictive tools to track infection rates and
burden of care over time and space. However, the scarcity and inhomogeneity of
data is a challenge. In this research we develop a robust framework for
estimating and predicting the occupied beds of Intensive Care Units by
presenting an innovative Small Area Estimation methodology based on the
definition of mixed models with random regression coefficients. We applied it
to estimate and predict the daily occupancy of Intensive Care Unit beds by
COVID-19 in health areas of Castilla y Le\'on, from November 2020 to March
2022.


  We present simple new examples of pure-jump strict local martingales. The
examples are constructed as exponentials of self-exciting affine Markov
processes. We characterize the strict local martingale property of these
processes by an integral criterion and by non-uniqueness of an associated
ordinary differential equation. Finally we show an alternative construction for
our examples by an absolutely continuous measure change in the spirit of
(Delbaen and Schachermayer, PTRF 1995).


  Given an election, a preferred candidate p, and a budget, the SHIFT BRIBERY
problem asks whether p can win the election after shifting p higher in some
voters' preference orders. Of course, shifting comes at a price (depending on
the voter and on the extent of the shift) and one must not exceed the given
budget. We study the (parameterized) computational complexity of S HIFT BRIBERY
for multiwinner voting rules where winning the election means to be part of
some winning committee. We focus on the well-established SNTV, Bloc, k-Borda,
and Chamberlin-Courant rules, as well as on approximate variants of the
Chamberlin-Courant rule, since the original rule is NP-hard to compute. We show
that SHIFT BRIBERY tends to be harder in the multiwinner setting than in the
single-winner one by showing settings where SHIFT BRIBERY is easy in the
single-winner cases, but is hard (and hard to approximate) in the multiwinner
ones. Moreover, we show that the non-monotonicity of those rules which are
based on approximation algorithms for the Chamberlin-Courant rule sometimes
affects the complexity of SHIFT BRIBERY.


  For many applications with multivariate data, random field models capturing
departures from Gaussianity within realisations are appropriate. For this
reason, we formulate a new class of multivariate non-Gaussian models based on
systems of stochastic partial differential equations with additive type G noise
whose marginal covariance functions are of Mat\'ern type. We consider four
increasingly flexible constructions of the noise, where the first two are
similar to existing copula-based models. In contrast to these, the latter two
constructions can model non-Gaussian spatial data without replicates.
Computationally efficient methods for likelihood-based parameter estimation and
probabilistic prediction are proposed, and the flexibility of the suggested
models is illustrated by numerical examples and two statistical applications.


  Gravitational wave astronomy pipelines rely on template waveform models for
searches and parameter estimation purposes. For coalescing binary neutron stars
(BNS), such models need to accurately reproduce numerical relativity (NR) up to
merger, in order to provide robust estimate of the stars' equation of state -
dependent parameters. In this work we present an improved version of the
Effective One Body (EOB) model $\tt TEOBResumS$ for gravitational waves from
BNS systems. Building upon recent post-Newtonian calculations, we include
subleading order tidal terms in the waveform multipoles and EOB metric
potentials, as well as add up to 5.5PN terms in the gyro-gravitomagnetic
functions entering the spin-orbit sector of the model. In order to further
improve the EOB-NR agreement in the last few orbital cycles before merger, we
introduce next-to-quasicircular corrections in the waveform -- informed by a
large number of BNS NR simulations -- and introduce a new NR-informed parameter
entering the tidal sector of our conservative dynamics. The performance of our
model is then validated against 14 new eccentricity reduced simulations of
unequal mass, spinning binaries with varying equation of state. A time-domain
phasing analysis and mismatch computations demonstrate that the new model
overall improves over the previous version of $\tt TEOBResumS$. Finally, we
present a closed-form frequency domain representation of the (tidal) amplitude
and phase of the new model. This representation accounts for mass-ratio,
aligned spin and (resummed) spin-quadrupole effects in the tidal phase and --
within the calibration region -- it is faithful to the original model.


  The cause of quasi-periodic pulsations (QPP) in solar flares remains the
subject of debate. Recently, Nakariakov & Zimovets (2011) proposed a new model
suggesting that, in two-ribbon flares, such pulsations could be explained by
propagating slow waves. These waves may travel obliquely to the magnetic field,
reflect in the chromosphere and constructively interfere at a spatially
separate site in the corona, leading to quasi-periodic reconnection events
progressing along the flaring arcade. Such a slow wave regime would have
certain observational characteristics. We search for evidence of this
phenomenon during a selection of two-ribbon flares observed by RHESSI, SOHO and
TRACE; the flares of 2002 November 9, 2005 January 19 and 2005 August 22. We
were not able to observe a clear correlation between hard X-ray footpoint
separations and pulse timings during these events. Also, the motion of hard
X-ray footpoints is shown to be continuous within the observational error,
whereas a discontinuous motion might be anticipated in the slow wave model.
Finally, we find that for a preferential slow wave propagation angle of 25-28
degrees that is expected for the fastest waves, the velocities of the hard
X-ray footpoints lead to estimated pulse periods and ribbon lengths
significantly larger than the measured values. Hence, for the three events
studied, we conclude that the observational characteristics cannot be easily
explained via the Nakariakov & Zimovets (2011) propagating slow wave model when
only angles of 25-28 degrees are considered. We provide suggested flare
parameters to optimise future studies of this kind.


  We advocates here the use of (mathematical) logic for systems biology, as a
unified framework well suited for both modeling the dynamic behaviour of
biological systems, expressing properties of them, and verifying these
properties. The potential candidate logics should have a traditional proof
theoretic pedigree (including a sequent calculus presentation enjoying
cut-elimination and focusing), and should come with (certified) proof tools.
Beyond providing a reliable framework, this allows the adequate encodings of
our biological systems. We present two candidate logics (two modal extensions
of linear logic, called HyLL and SELL), along with biological examples. The
examples we have considered so far are very simple ones-coming with completely
formal (interactive) proofs in Coq. Future works includes using automatic
provers, which would extend existing automatic provers for linear logic. This
should enable us to specify and study more realistic examples in systems
biology, biomedicine (diagnosis and prognosis), and eventually neuroscience.


  We propose a novel model for lepton flavor and dark matter based on the
$SU(2)_D$ gauge symmetry and vector-like leptons in its fundamental
representations. We introduce a dark $SU(2)_D$ Higgs doublet and a Higgs
bi-doublet for the mass mixing between the vector-like lepton and the lepton.
As a result, the seesaw lepton masses are generated and there are sizable
one-loop contributions to the muon $g-2$ via the $SU(2)_D$ gauge bosons and the
relatively heavy vector-like lepton, as indicated in Fermilab E989. The
tree-level mass mixing between the $Z$ boson and the isospin neutral gauge
boson of $SU(2)_D$ in our model accounts for the shift in the $W$ boson mass,
being consistent with Tevatron CDFII. Finally, we show that the isospin charged
gauge boson of $SU(2)_D$ becomes a plausible candidate for dark matter with a
small mass splitting tied up to the modified $W$ boson mass, and there is a
viable parameter space where the favored corrections to the muon $g-2$ and the
$W$ boson mass and the dark matter constraints are simultaneously fulfilled.


  Many of the XYZ mesons discovered in the last decade can be identified as
bound states in Born-Oppenheimer (B-O) potentials for a heavy quark and
antiquark. They include quarkonium hybrids, which are bound states in excited
flavor-singlet B-O potentials, and quarkonium tetraquarks, which are bound
states in flavor-nonsinglet B-O potentials. We present simple parameterizations
of the deepest flavor-singlet B-O potentials. We infer the deepest
flavor-nonsinglet B-O potentials from lattice QCD calculations of static
adjoint mesons. Selection rules for hadronic transitions are used to identify
XYZ mesons that are candidates for ground-state energy levels in the B-O
potentials for charmonium hybrids and tetraquarks. The energies of the
lowest-energy charmonium hybrids are predicted by using the results of lattice
QCD calculations to calculate the energy splittings between the ground states
of different B-O potentials and using the Schroedinger equation to determine
the splittings between energy levels within a B-O potential.


  We present DIALIGHT, a toolkit for developing and evaluating multilingual
Task-Oriented Dialogue (ToD) systems which facilitates systematic evaluations
and comparisons between ToD systems using fine-tuning of Pretrained Language
Models (PLMs) and those utilising the zero-shot and in-context learning
capabilities of Large Language Models (LLMs). In addition to automatic
evaluation, this toolkit features (i) a secure, user-friendly web interface for
fine-grained human evaluation at both local utterance level and global dialogue
level, and (ii) a microservice-based backend, improving efficiency and
scalability. Our evaluations reveal that while PLM fine-tuning leads to higher
accuracy and coherence, LLM-based systems excel in producing diverse and
likeable responses. However, we also identify significant challenges of LLMs in
adherence to task-specific instructions and generating outputs in multiple
languages, highlighting areas for future research. We hope this open-sourced
toolkit will serve as a valuable resource for researchers aiming to develop and
properly evaluate multilingual ToD systems and will lower, currently still
high, entry barriers in the field.


  We perform Brownian dynamics simulations in two dimensions to study the
collective behavior of circle swimmers, which are driven by both, an
(effective) translational and rotational self-propulsion, and interact via
steric repulsion. We find that active rotation generally opposes
motility-induced clustering and phase separation, as demonstrated by a
narrowing of the coexistence region upon increase of the propulsion angular
velocity. Moreover, although the particles are intrinsically assigned to rotate
counterclockwise, a novel state of clockwise vortices emerges at an optimal
value of the effective propulsion torque. We propose a simple gear-like model
to capture the underlying mechanism of the clockwise vortices.


  The globular cluster system of a typical spheroidal galaxy makes up about
0.25% of the total galaxy mass (McLaughlin 1999). This is roughly the same mass
fraction as contained in the nuclear star clus- ter (or stellar nucleus)
present in most nearby low-mass galaxies. Motivated by this "coincidence", this
Letter discusses a scenario in which globular clusters of present-day galaxies
are the surviving nuclei of the dwarf galaxies that - according to the
hierarchical merging paradigm of galaxy forma- tion - constitute the "building
blocks" of present-day massive galaxies. This scenario, which was first
suggested by Freeman (1993), has become more attractive recently in the light
of studies that demonstrate a complex star formation history in a number of
massive globular clusters.


  I summarize highlights of the theory talks presented at the 37th Rencontres
de Moriond on QCD and High Energy Hadronic Interactions.


  The origin of biological motion can be traced back to the function of
molecular motor proteins. Cytoplasmic dynein and kinesin transport organelles
within our cells moving along a polymeric filament, the microtubule. The motion
of the myosin molecules along the actin filaments is responsible for the
contraction of our muscles. Recent experiments have been able to reveal some
important features of the motion of individual motor proteins, and a new
statistical physical description - often referred to as ``thermal ratchets'' -
has been developed for the description of motion of these molecules. In this
approach the motors are considered as Brownian particles moving along
one-dimensional periodic structures due to the effect of nonequilibrium
fluctuations. Assuming specific types of interaction between the particles the
models can be made more realistic. We have been able to give analytic solutions
for our model of kinesin with elastically coupled Brownian heads and for the
motion of the myosin filament where the motors are connected through a rigid
backbone. Our theoretical predictions are in a very good agreement with the
various experimental results. In addition, we have considered the effects
arising as a result of interaction among a large number of molecular motors,
leading to a number of novel cooperative transport phenomena.


  Recent studies on different topological materials in condensed matter physics
have provided the evidence of topological nature for bosonic particle like
phonons by performing various theoretical calculations and experimental
observations. Here, the topological behaviours of phonons of SnS, SnSe and SnTe
materials in rock-salt structure are investigated using $ab$-$initio$
methodology. For all these materials, the tilted linear band touching is
observed along with the presence of band inversion in direction X-W. The
topological point in phonon dispersion curve along X-W direction is found to be
$\sim$2.83, $\sim$2.46 and $\sim$2.51 THz for SnS, SnSe and SnTe, respectively.
The calculated numbers of Weyl points (WPs) is estimated to be 56, 24 and 54
for SnS, SnSe and SnTe, respectively. These WPs have shown conserved Chiral
charges for all corresponding materials. The surface local density of states is
computed for these compounds, where the surface arc is clearly seen. In case of
SnS, the surface arc is found at 2.0 - 2.2 THz energy region. Also, the
isofrequency surface states are investigated to observe the presence of Fermi
arc for SnX (X = S, Se, Te). The present first-principles calculation predicts
that SnS, SnSe and SnTe show type-II Weyl phononic behaviour along with
evidence of topological phononic surface states.