  Recently, thermal Hall effect has been observed in the paramagnetic state of
Volborthite, which consists of distorted Kagome layers with $S=1/2$ local
moments. Despite the appearance of a magnetic order below $1 \, \mathrm{K}$,
the response to external magnetic field and unusual properties of the
paramagnetic state above $1 \, \mathrm{K}$ suggest possible realization of
exotic quantum phases. Motivated by these discoveries, we investigate possible
spin liquid phases with fermionic spinon excitations in a non-symmorphic
version of the Kagome lattice, which belongs to the two-dimensional
crystallographic group $p2gg$. This non-symmorphic structure is consistent with
the spin model obtained in the density functional theory (DFT) calculation.
Using projective symmetry group (PSG) analysis and fermionic parton mean field
theory, we identify twelve distinct $\mathbb{Z}_2$ spin liquid states, four of
which are found to have correspondence in the eight Schwinger boson spin liquid
states we classified earlier. We focus on the four fermionic states with
bosonic counterpart and find that the spectrum of their corresponding root
$U(1)$ states feature spinon Fermi surface. The existence of spinon Fermi
surface in candidate spin liquid states may offer a possible explanation of the
finite thermal Hall conductivity observed in Volborthite.


  We reinvestigate the twisted N=4 supersymmetry present in Schwarz type
topological field models. We show that Chern-Simons theory in three dimensions
can be untwisted to a kind of sigma-model with reversed statistics only in the
free case. By dimensional reduction we define then the two-dimensional
BF-model. We establish an analog result concerning the untwisting. As a
consequence of the definition through dimensional reduction we find new
fermionic scalar symmetries that have been overlooked so far in the literature.


  In this paper, two sequences of minimal isoparametric hypersurfaces are
constructed via representations of Clifford algebras. Based on these, we give
estimates on eigenvalues of the Laplacian of the focal submanifolds of
isoparametric hypersurfaces in unit spheres. This improves results of [TY13]
and [TXY14].
  Eells and Lemaire [EL83] posed a problem to characterize the compact
Riemannian manifold M for which there is an eigenmap from M to S^n. As another
application of our constructions, the focal maps give rise to many examples of
eigenmaps from minimal isoparametric hypersurfaces to unit spheres.
  Most importantly, by investigating the second fundamental forms of focal
submanifolds of isoparametric hypersurfaces in unit spheres, we provide
infinitely many counterexamples to two conjectures of Leung [Le91] (posed in
1991) on minimal submanifolds in unit spheres. Notice that these conjectures of
Leung have been proved in the case that the normal connection is flat [HV01].


  Adaptive dimensionality reduction in high-dimensional problems is a key topic
in statistics. The multiplicative gamma process takes a relevant step in this
direction, but improved studies on its properties are required to ease
implementation. This note addresses such aim.


  We investigate the dynamics and the phase-space evolution for the scalar
nonmetricity cosmology with a Chameleon mechanism. In particular, we consider a
spatially flat Friedmann--Lema\^{\i}tre--Robertson--Walker geometry and within
the framework of scalar nonmetricity theory, we consider a generalization of
the Brans-Dicke theory in nonmetricity gravity. Introducing a pressureless gas
as the matter source, we also incorporate a coupling function responsible for
the interaction. Our findings reveal that the choice of connection in
nonmetricity gravity significantly impacts the interaction between the scalar
field and the matter source. For one particular connection, we discover the
absence of asymptotic solutions with a nontrivial interacting component. More
precisely, in this scenario, the matter source does not directly interact with
the scalar field; however, there is an interaction with the dynamical degrees
of freedom provided by the connection.


  Recent success in deep reinforcement learning is having an agent learn how to
play Go and beat the world champion without any prior knowledge of the game. In
that task, the agent has to make a decision on what action to take based on the
positions of the pieces. Person Search is recently explored using natural
language based text description of images for video surveillance applications
(S.Li et.al). We see (Fu.et al) provides an end to end approach for
object-based retrieval using deep reinforcement learning without constraints
placed on which objects are being detected. However, we believe for real-world
applications such as person search defining specific constraints which identify
a person as opposed to starting with a general object detection will have
benefits in terms of performance and computational resources required. In our
task, Deep reinforcement learning would localize the person in an image by
reshaping the sizes of the bounding boxes. Deep Reinforcement learning with
appropriate constraints would look only for the relevant person in the image as
opposed to an unconstrained approach where each individual objects in the image
are ranked. For person search, the agent is trying to form a tight bounding box
around the person in the image who matches the description. The bounding box is
initialized to the full image and at each time step, the agent makes a decision
on how to change the current bounding box so that it has a tighter bound around
the person based on the description of the person and the pixel values of the
current bounding box. After the agent takes an action, it will be given a
reward based on the Intersection over Union (IoU) of the current bounding box
and the ground truth box. Once the agent believes that the bounding box is
covering the person, it will indicate that the person is found.


  To push upper boundaries of effective thermal conductivity in polymer
composites, a fundamental understanding of thermal transport mechanisms is
crucial. Although there is intensive simulation research, systematic
experimental investigation on thermal transport in polymer composites is
limited. To better understand thermal transport processes, we design polymer
composites with perfect fillers (graphite) and defective fillers (graphite
oxide); we choose polar polyvinyl alcohol (PVA) as a matrix model; and we
identify how thermal transport occurs across heterogeneous interfaces. Measured
thermal conductivities of in PVA/defective filler composites is higher than
those of PVA/perfect filler composites, while measured thermal conductivities
in defective fillers are lower than those of perfect fillers. An effective
quantum mechanical model is developed, showing that the vibrational state of
the defective level plays a critical role in enhancing the thermal conductivity
with increased defect concentration. Our experimental and model results have
suggested that defects in polymer composites may enhance thermal transport in
polymer composites by promoting vibrational resonant couplings.


  We present a path planning problem for a pursuer to intercept a target
traveling on a circle. The pursuer considered here has limited yaw rate, and
therefore its path should satisfy the kinematic constraints. We assume that the
distance between initial position of the pursuer and any point on the target
circle is greater than four times the minimum turn radius of the pursuer. We
prove the continuity of the Dubins paths of type Circle-Straight line-Circle
with respect to the position on the target circle. This is used to prove that
the optimal interception path is a Dubins path, and we present an iterative
algorithm to find the optimal interception point on the target circle.


  We examine the validity of the 1-loop approximation to the effective
potential at finite temperatures and present a simple test for its reliability.
As an application we study the standard electroweak potential, showing that for
a Higgs mass above 70 GeV, and afirly independent of the top mass (with $m_{t}
> 90 GeV$, the 1-loop approximation is no longer valid for temperatures in the
neighborhood of the critical temperature.


  Learning the true density in high-dimensional feature spaces is a well-known
problem in machine learning. In this work, we consider generative autoencoders
based on maximum-mean discrepancy (MMD) and provide theoretical insights. In
particular, (i) we prove that MMD coupled with Coulomb kernels has optimal
convergence properties, which are similar to convex functionals, thus improving
the training of autoencoders, and (ii) we provide a probabilistic bound on the
generalization performance, highlighting some fundamental conditions to achieve
better generalization. We validate the theory on synthetic examples and on the
popular dataset of celebrities' faces, showing that our model, called Coulomb
autoencoders, outperform the state-of-the-art.


  In this paper we will present the results of Artin--Markov on braid groups by
using the Groebner--Shirshov basis. As a consequence we can reobtain the normal
form of Artin--Markov--Ivanovsky as an easy corollary.


  We calculate the pi-/pi+ ratio for Pb+Pb at CERN/SPS energies and for Au+Au
at BNL/AGS energies using a (3+1) dimensional hydrodynamical model. Without
consideration of Coulomb effect an enhancement of this ratio at low mt is found
compatible with that observed in these experiments. Our calculations are based
on previous (3+1) dimensional hydrodynamical simulations (HYLANDER), which
described many other aspects of experimental data. In this model the observed
enhancement is a consequence of baryon and strangeness conservation and of
chemical equilibration of the system and is caused by the decay of produced
hyperons, which leads to a difference in the total number of positive and
negative pions as well. Based on the same approach, we also present results for
the pi-/pi+ ratio for S+S (CERN/SPS) collisions, where we find a similar
effect. The absence of the enhancement of the pi-/pi+ ratio in the S+S data
presented by the NA44 Collaboration, if confirmed, could indicate that chemical
equilibration has not yet been estabilished in this reaction.


  We present a new approach-the ALVar estimator-to estimation of asymptotic
variance in sequential Monte Carlo methods, or, particle filters. The method,
which adjusts adaptively the lag of the estimator proposed in [Olsson, J. and
Douc, R. (2019). Numerically stable online estimation of variance in particle
filters. Bernoulli, 25(2), pp. 1504-1535] applies to very general distribution
flows and particle filters, including auxiliary particle filters with adaptive
resampling. The algorithm operates entirely online, in the sense that it is
able to monitor the variance of the particle filter in real time and with, on
the average, constant computational complexity and memory requirements per
iteration. Crucially, it does not require the calibration of any algorithmic
parameter. Estimating the variance only on the basis of the genealogy of the
propagated particle cloud, without additional simulations, the routine requires
only minor code additions to the underlying particle algorithm. Finally, we
prove that the ALVar estimator is consistent for the true asymptotic variance
as the number of particles tends to infinity and illustrate numerically its
superiority to existing approaches.


  Monolayer transition metal dichalcogenides (TMDs) show an optimal
surface-to-volume ratio and are thus promising candidates for novel molecule
sensor devices. It was recently predicted that a certain class of molecules
exhibiting a large dipole moment can be detected through the activation of
optically inaccessible (dark) excitonic states in absorption spectra of
tungsten-based TMDs. In this work, we investigate the molecule signatures in
photoluminescence spectra in dependence of a number of different experimentally
accessible quantities, such as excitation density, temperature as well as
molecular characteristics including the dipole moment and its orientation,
molecule-TMD distance, molecular coverage and distribution. We show that under
certain optimal conditions, even room temperature detection of molecules can be
achieved.


  Clauser-Horne-Shimony-Holt inequality for bipartite systems of 4-dimension is
studied in detail by employing the unbiased eight-port beam splitters
measurements. The uniform formulae for the maximum and minimum values of this
inequality for such measurements are obtained. Based on these formulae, we show
that an optimal non-maximally entangled state is about 6% more resistant to
noise than the maximally entangled one. We also give the optimal state and the
optimal angles which are important for experimental realization.


  The detailed structure of few-cycle superradiant pulses generated from a
free-electron laser (FEL) oscillator was experimentally revealed for the first
time. As predicted in numerical simulations, the obtained pulse structures have
ringing, i.e., a train of subpulses following the main pulse. Owing to the
phase retrieval with a combination of linear and nonlinear autocorrelation
measurements, the temporal phase was also obtained with the pulse shape, and
$\pi$-phase jumps between each pulse were confirmed. This particular pulse
shape with $\pi$-phase jumps is a common feature of Burnham-Chiao ringing or
superradiance ringing. Using unaveraged FEL simulations, the ringing was found
to originate from repeated formation and deformation of microbunches across
successive bunch slices. The simulation results imply that there should be
superluminal propagation of the FEL pulse due to strong gain modulation along
the undulator of the FEL oscillator.


  We use a nanowire quantum dot to probe high-frequency current fluctuations in
a nearby quantum point contact. The fluctuations drive charge transitions in
the quantum dot, which are measured in real-time with single-electron detection
techniques. The quantum point contact (GaAs) and the quantum dot (InAs) are
fabricated in different material systems, which indicates that the interactions
are mediated by photons rather than phonons. The large energy scales of the
nanowire quantum dot allow radiation detection in the long-wavelength infrared
regime.


  We present here a brief summary of new results on heavy quarks and heavy
quarkonia from the PHENIX experiment as presented at the "Quark Gluon Plasma
Thermalization" Workshop in Vienna, Austria in August 2005, directly following
the International Quark Matter Conference in Hungary.


  We searched for the emission of microwave radiation in the Ku band generated
by a 95 keV electron beam in air. We unequivocally detected the radiation, and
measured its yield and angular dependence. Both the emitted power and its
angular pattern are well described by a model, where microwave photons are
generated via bremsstrahlung in the free-electron atomic-nucleus collisions,
during the slowdown of the electrons. As a consequence, the radiation is not
isotropic but peaked in the forward direction. The emission yield scales
proportionally with the number of electrons. This contrasts a previous claim
that the yield scales with the number squared, due to coherence. With a Monte
Carlo simulation we extrapolate our results to the Ultra High Energy Cosmic Ray
energy range.


  Working in a semi-abelian context, we use Janelidze's theory of generalised
satellites to study universal properties of the Everaert long exact homology
sequence. This results in a new definition of homology which does not depend on
the existence of projective objects. We explore the relations with other
notions of homology, and thus prove a version of the higher Hopf formulae. We
also work out some examples.


  The Erdos-Szekeres theorem states that for any natural k there is a natural
number g(k) such that any set of at least g(k) points on a plane in general
position contains a set of k points that are the extreme points of a convex
polytope. We generalize and refine this theorem, having the general-position
condition removed and a convex polygon defined as an ordered sequence of points
such that the union of the edges of the polygon coincides with the boundary of
its convex hull.


  We introduce a protocol for quantum secret sharing based on reusable
entangled states. The entangled state between the sender and the receiver acts
only as a carrier to which data bits are entangled by the sender and
disentangled from it by the receivers, all by local actions of simple gates.
  We also show that the interception by Eve or the cheating of one of the
receivers introduces a quantum bit error rate (QBER) larger than
  25 percent which can be detected by comparing a subsequence of the bits.


  We show that any two geometric triangulations of a closed hyperbolic,
spherical or Euclidean manifold are related by a sequence of Pachner moves and
barycentric subdivisions of bounded length. This bound is in terms of the
dimension of the manifold, the number of top dimensional simplexes and bound on
the lengths of edges of the triangulation. This leads to an algorithm to check
from the combinatorics of the triangulation and bounds on lengths of edges, if
two geometrically triangulated closed hyperbolic or low dimensional spherical
manifolds are isometric or not.


  The chirality induced spin selectivity (CISS) effect is a fascinating
phenomena correlating molecular structure with electron spin-polarisation in
excited state measurements. Experimental procedures to quantify the
spin-filtering magnitude relies generally on averaging data sets, especially
those from magnetic field dependent conductive-AFM. We investigate the
underlying observed disorder in the IV spectra and the origin of spikes
superimposed. We demonstrate and explain that a dynamic, voltage sweep rate
dependent, phenomena can give rise to complex IV curves for chiral crystals of
coronene bisimide. The redox group, able to capture localized charge states,
acts as an impurity state interfering with a continuum, giving rise to Fano
resonances. We introduce a novel mechanism for the dynamic transport which
might also provide insight into the role of spin-polarization. Crucially,
interference between charge localisation and delocalisation during transport
may be important properties into understanding the CISS phenomena.


  We argue that classical bulk gravitational radiation effects in AdS/CFT,
previously ignored because of their subleading nature in the 1/Nc-expansion,
are magnified by powers of large Lorentz factors gamma for ultrarelativistic
jets, thereby dominating other forms of jet energy loss in holography at finite
temperature. We make use of the induced gravitational self-force in thermal
AdS5 to estimate its effects. In a thermal medium, relativistic jets may loose
most of their energy through longitudinal drag caused by the energy accumulated
in their nearby field as they zip through the strongly coupled plasma.


  We consider the following problem: a team of robots is deployed in an unknown
environment and it has to collaboratively build a map of the area without a
reliable infrastructure for communication. The backbone for modern mapping
techniques is pose graph optimization, which estimates the trajectory of the
robots, from which the map can be easily built. The first contribution of this
paper is a set of distributed algorithms for pose graph optimization: rather
than sending all sensor data to a remote sensor fusion server, the robots
exchange very partial and noisy information to reach an agreement on the pose
graph configuration. Our approach can be considered as a distributed
implementation of the two-stage approach of Carlone et al., where we use the
Successive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as
workhorses to split the computation among the robots. As a second contribution,
we extend %and demonstrate the applicability of the proposed distributed
algorithms to work with object-based map models. The use of object-based models
avoids the exchange of raw sensor measurements (e.g., point clouds) further
reducing the communication burden. Our third contribution is an extensive
experimental evaluation of the proposed techniques, including tests in
realistic Gazebo simulations and field experiments in a military test facility.
Abundant experimental evidence suggests that one of the proposed algorithms
(the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS
requires minimal information exchange, has an anytime flavor, scales well to
large teams, is robust to noise, and is easy to implement. Our field tests show
that the combined use of our distributed algorithms and object-based models
reduces the communication requirements by several orders of magnitude and
enables distributed mapping with large teams of robots in real-world problems.


  We present a general method for studying coupled qubits driven by
adiabatically changing external parameters. Extended calculations are provided
for a two-bit Hamiltonian whose eigenstates can be used as logical states for a
quantum CNOT gate. From a numerical analysis of the stationary Schroedinger
equation we find a set of parameters suitable for representing CNOT, while from
a time-dependent study the conditions for adiabatic evolution are determined.
  Specializing to a concrete physical system involving SQUIDs, we determine
reasonable parameters for experimental purposes. The dissipation for SQUIDs is
discussed by fitting experimental data. The low dissipation obtained supports
the idea that adiabatic operations could be performed on a time scale shorter
than the decoherence time.


  Object detection applied to LiDAR point clouds is a relevant task in
robotics, and particularly in autonomous driving. Single frame methods,
predominant in the field, exploit information from individual sensor scans.
Recent approaches achieve good performance, at relatively low inference time.
Nevertheless, given the inherent high sparsity of LiDAR data, these methods
struggle in long-range detection (e.g. 200m) which we deem to be critical in
achieving safe automation. Aggregating multiple scans not only leads to a
denser point cloud representation, but it also brings time-awareness to the
system, and provides information about how the environment is changing.
Solutions of this kind, however, are often highly problem-specific, demand
careful data processing, and tend not to fulfil runtime requirements. In this
context we propose TimePillars, a temporally-recurrent object detection
pipeline which leverages the pillar representation of LiDAR data across time,
respecting hardware integration efficiency constraints, and exploiting the
diversity and long-range information of the novel Zenseact Open Dataset (ZOD).
Through experimentation, we prove the benefits of having recurrency, and show
how basic building blocks are enough to achieve robust and efficient results.


  One of the most common methods for statistical inference is the maximum
likelihood estimator (MLE). The MLE needs to compute the normalization constant
in statistical models, and it is often intractable. Using unnormalized
statistical models and replacing the likelihood with the other scoring rule are
a good way to circumvent such high computation cost, where the scoring rule
measures the goodness of fit of the model to observed samples. The scoring rule
is closely related to the Bregman divergence, which is a discrepancy measure
between two probability distributions. In this paper, the purpose is to provide
a general framework of statistical inference using unnormalized statistical
models on discrete sample spaces. A localized version of scoring rules is
important to obtain computationally efficient estimators. We show that the
local scoring rules are related to the localized version of Bregman
divergences. Through the localized Bregman divergence, we investigate the
statistical consistency of local scoring rules. We show that the consistency is
determined by the structure of neighborhood system defined on discrete sample
spaces. In addition, we show a way of applying local scoring rules to
classification problems. In numerical experiments, we investigated the relation
between the neighborhood system and the estimation accuracy.


  Action recognition models have shown a promising capability to classify human
actions in short video clips. In a real scenario, multiple correlated human
actions commonly occur in particular orders, forming semantically meaningful
human activities. Conventional action recognition approaches focus on analyzing
single actions. However, they fail to fully reason about the contextual
relations between adjacent actions, which provide potential temporal logic for
understanding long videos. In this paper, we propose a prompt-based framework,
Bridge-Prompt (Br-Prompt), to model the semantics across adjacent actions, so
that it simultaneously exploits both out-of-context and contextual information
from a series of ordinal actions in instructional videos. More specifically, we
reformulate the individual action labels as integrated text prompts for
supervision, which bridge the gap between individual action semantics. The
generated text prompts are paired with corresponding video clips, and together
co-train the text encoder and the video encoder via a contrastive approach. The
learned vision encoder has a stronger capability for ordinal-action-related
downstream tasks, e.g. action segmentation and human activity recognition. We
evaluate the performances of our approach on several video datasets: Georgia
Tech Egocentric Activities (GTEA), 50Salads, and the Breakfast dataset.
Br-Prompt achieves state-of-the-art on multiple benchmarks. Code is available
at https://github.com/ttlmh/Bridge-Prompt


  We show that if $E \subset \mathbb{F}_q^d$, the $d$-dimensional vector space
over the finite field with $q$ elements, and $|E| \geq \rho q^d$, where $
q^{-\frac{1}{2}}\ll \rho \leq 1$, then $E$ contains an isometric copy of at
least $c \rho^{d-1} q^{d+1 \choose 2}$ distinct $(d+1)$-point configurations.


  A neutron radiography testing station has been developed exploiting the
neutron beam of CERN's n\_TOF Experimental Area 2, located at the shortest
distance to the neutron producing-target. The characteristics of the n\_TOF
neutron beam for the imaging setup are presented in this paper, together with
the obtained experimental results. The possible developments of neutron imaging
capabilities of the n\_TOF facility in terms of detection-systems and beam-line
upgrades are as well outlined.


  This article continues 1205.0410 [hep-th]. Considered is the N=1 SQCD-like
theory with SU(N_c) colors and 3N_c/2 <N_F <2N_c flavors of light quarks
Q_i,\bar Q_j, and with the additional N^2_F colorless flavored fields Phi_{ij}
with the large mass parameter \mu_Phi>>Lambda. The mass spectra of this Phi
theory (and its dual variant, the dPhi theory) are calculated at different
values of \mu_Phi/Lambda >>1 within the dynamical scenario which implies that
quarks can be in two different phases only: either this is the HQ (heavy quark)
phase where they are confined, or they are higgsed at appropriate values of the
lagrangian parameters. It is shown that at the left end of the conformal
window, i.e. at 0<(2N_F-3N_c)/N_F << 1, the mass spectra of the direct and dual
theories are parametrically different.


  The Hopfield model is a paradigmatic model of neural networks that has been
analyzed for many decades in the statistical physics, neuroscience, and machine
learning communities. Inspired by the manifold hypothesis in machine learning,
we propose and investigate a generalization of the standard setting that we
name Random-Features Hopfield Model. Here $P$ binary patterns of length $N$ are
generated by applying to Gaussian vectors sampled in a latent space of
dimension $D$ a random projection followed by a non-linearity. Using the
replica method from statistical physics, we derive the phase diagram of the
model in the limit $P,N,D\to\infty$ with fixed ratios $\alpha=P/N$ and
$\alpha_D=D/N$. Besides the usual retrieval phase, where the patterns can be
dynamically recovered from some initial corruption, we uncover a new phase
where the features characterizing the projection can be recovered instead. We
call this phenomena the learning phase transition, as the features are not
explicitly given to the model but rather are inferred from the patterns in an
unsupervised fashion.


  In recent years, there has been discussion in the literature regarding
methods of estimating the magnitude of the minimum principal stress from
subsurface fracture injection tests, commonly called Diagnostic Fracture
Injection Tests (DFITs). McClure et al. (2016) used modeling and mathematical
analysis to propose that changes should be made to common interpretation
techniques. Subsequently, Craig et al. (2017) attempted to refute those
findings using field and laboratory observations. This discussion paper reviews
the interpretations from Craig et al. (2017) and concludes that their
conclusions are unsupported by the data presented.


  The rapid development of digital economy has led to the emergence of various
black and shadow internet industries, which pose potential risks that can be
identified and managed through digital risk management (DRM) that uses
different techniques such as machine learning and deep learning. The evolution
of DRM architecture has been driven by changes in data forms. However, the
development of AI-generated content (AIGC) technology, such as ChatGPT and
Stable Diffusion, has given black and shadow industries powerful tools to
personalize data and generate realistic images and conversations for fraudulent
activities. This poses a challenge for DRM systems to control risks from the
source of data generation and to respond quickly to the fast-changing risk
environment. This paper aims to provide a technical analysis of the challenges
and opportunities of AIGC from upstream, midstream, and downstream paths of
black/shadow industries and suggest future directions for improving existing
risk control systems. The paper will explore the new black and shadow
techniques triggered by generative AI technology and provide insights for
building the next-generation DRM system.


  We consider a simple one-dimensional time-dependent model for bone
regeneration in the presence of a bio-resorbable polymer scaffold. Within the
framework of the model, we optimize the effective mechanical stiffness of the
polymer scaffold together with the regenerated bone matrix. The result of the
optimization procedure is a scaffold porosity distribution which maximizes the
stiffness of the scaffold-bone system over the regeneration time, such that the
propensity for mechanical failure is reduced.


  We investigate the complex geometry of total spaces of reductive principal
bundles over compact base spaces and establish a close relation between the
K\"ahler property of the base, momentum maps for the action of a maximal
compact subgroup on the total space, and the K\"ahler property of special
equivariant compactifications. We provide many examples illustrating that the
main result is optimal.


  Epigenome modulation in response to the environment potentially provides a
mechanism for organisms to adapt, both within and between generations. However,
neither the extent to which this occurs, nor the molecular mechanisms involved
are known. Here we investigate DNA methylation variation in Swedish Arabidopsis
thaliana accessions grown at two different temperatures. Environmental effects
on DNA methylation were limited to transposons, where CHH methylation was found
to increase with temperature. Genome-wide association mapping revealed that the
extensive CHH methylation variation was strongly associated with genetic
variants in both cis and trans, including a major trans-association close to
the DNA methyltransferase CMT2. Unlike CHH methylation, CpG gene body
methylation (GBM) on the coding region of genes was not affected by growth
temperature, but was instead strongly correlated with the latitude of origin.
Accessions from colder regions had higher levels of GBM for a significant
fraction of the genome, and this was correlated with elevated transcription
levels for the genes affected. Genome-wide association mapping revealed that
this effect was largely due to trans-acting loci, a significant fraction of
which showed evidence of local adaptation. These findings constitute the first
direct link between DNA methylation and adaptation to the environment, and
provide a basis for further dissecting how environmentally driven and
genetically determined epigenetic variation interact and influence organismal
fitness.


  Communication and computation services supporting Connected and Automated
Vehicles (CAVs) are characterized by stringent requirements, in terms of
response time and reliability. Fulfilling these requirements is crucial for
ensuring road safety and traffic optimization. The conceptually simple solution
of hosting these services in the vehicles increases their cost (mainly due to
the installation and maintenance of computation infrastructure) and may drain
their battery excessively. Such disadvantages can be tackled via Multi-Access
Edge Computing (MEC), consisting in deploying computation capability in network
nodes deployed close to the devices (vehicles in this case), such as to satisfy
the stringent CAV requirements. However, it is not yet clear under which
conditions MEC can support CAV requirements and for which services. To shed
light on this question, we conduct a simulation campaign using well-known
open-source simulation tools, namely OMNeT++, Simu5G, Veins, INET, and SUMO. We
are thus able to provide a reality check on MEC for CAV, pinpointing what are
the computation capacities that must be installed in the MEC, to support the
different services, and the amount of vehicles that a single MEC node can
support. We find that such parameters must vary a lot, depending on the service
considered. This study can serve as a preliminary basis for network operators
to plan future deployment of MEC to support CAV.


  We present the results of medium-resolution spectroscopy of five nearby
Herbig Ae/Be stars at 4.7 micron: UX Ori, HD 34282, HD 50138, V380 Ori, HK Ori.
The goal was to search for CO fundamental ro-vibrational emission. None of the
targets show CO features, either in absorption nor in emission. We derive a
5sigma upper limit of < 10^-12 cm^-2 to the column density of hot CO (T ~ 1500
K) in the sources. These upper limits are considerably lower than the values of
Herbig Ae/Be stars for which warm and hot CO emission has been reported.The
non-detection of CO v=1-0 emission in these five targets suggest that Herbig
Ae/Be stars are not a homogeneous group with respect to the structure of the
gaseous disk and/or the amount of CO in the inner 50 AU of their disks.


  Following the recent research enthusiasm on the effect of geometry on
near-field heat transfer (NFHT) enhancement, we present an analysis based on
simplified yet highly efficient graphene and nanotube models. Two geometries
are considered: that of two parallel infinite "graphene" surfaces and that of a
one-dimensional infinite "nanotube" line in parallel with an infinite surface.
Due to its symmetry, the former is in principal simpler to analyze and even so,
earlier works suggested that the application of a full model in this problem
still demands heavy computations. Among other findings, our simplified
computation - having successfully replicated the results of relevant earlier
works - suggests a sharper NFHT enhancement dependence on distance for the
line-surface system, namely $J\sim d^{-5.1}$ as compared to $J\sim d^{-2.2}$
for the parallel surface. Such comparisons together with applications of our
efficient approach would be the important first steps in the attempt to find a
general rule describing geometric dependence of NFHT.


  Let $\F_q$ be the finite field with $q$ elements, $p=\Char \F_q$. The group
$\GL_2(\F_q)$ acts naturally in the set of irreducible polynomials over $\F_q$
of degree at least $2$. In this paper we are interested in the characterization
and number of the irreducible polynomials that are fixed by the elements of a
subgroup $H$ of $\GL_2(\F_q)$. We make a complete characterization of the fixed
polynomials in the case when $H$ has only elements of the form
$\left(\begin{matrix} 1&b\\ 0&1 \end{matrix}\right)$, corresponding to
translations $x\mapsto x+b$ and, as a consequence, the case when $H$ is a
$p-$subgroup of $\GL_2(\F_q)$. This paper also contains alternative solutions
for the cases when $H$ is generated by an element of the form
$\left(\begin{matrix} a&0\\ 0&1 \end{matrix}\right)$, obtained by Garefalakis
(2010) and $H=\rm{PGL}$$_2(\F_q)$, obtained by Stichtenoth and Topuzoglu
(2011).


  Edge-magic total labeling was introduced by \cite{GS}. The number of
edge-magic solutions for cycles have been explored in \cite{BS}. This sequence
is mentioned in On Line Encyclopedia of Integer Sequences (OEIS) \cite{oeis}.
In this short note, we enumerate the number of unique edge-magic total
labelings on Path $P_n$


  Generating realistic motions for digital humans is a core but challenging
part of computer animations and games, as human motions are both diverse in
content and rich in styles. While the latest deep learning approaches have made
significant advancements in this domain, they mostly consider motion synthesis
and style manipulation as two separate problems. This is mainly due to the
challenge of learning both motion contents that account for the inter-class
behaviour and styles that account for the intra-class behaviour effectively in
a common representation. To tackle this challenge, we propose a denoising
diffusion probabilistic model solution for styled motion synthesis. As
diffusion models have a high capacity brought by the injection of
stochasticity, we can represent both inter-class motion content and intra-class
style behaviour in the same latent. This results in an integrated, end-to-end
trained pipeline that facilitates the generation of optimal motion and
exploration of content-style coupled latent space. To achieve high-quality
results, we design a multi-task architecture of diffusion model that
strategically generates aspects of human motions for local guidance. We also
design adversarial and physical regulations for global guidance. We demonstrate
superior performance with quantitative and qualitative results and validate the
effectiveness of our multi-task architecture.


  Reservoir Computing is a class of Recurrent Neural Networks with internal
weights fixed at random. Stability relates to the sensitivity of the network
state to perturbations. It is an important property in Reservoir Computing as
it directly impacts performance. In practice, it is desirable to stay in a
stable regime, where the effect of perturbations does not explode
exponentially, but also close to the chaotic frontier where reservoir dynamics
are rich. Open questions remain today regarding input regularization and
discontinuous activation functions. In this work, we use the recurrent kernel
limit to draw new insights on stability in reservoir computing. This limit
corresponds to large reservoir sizes, and it already becomes relevant for
reservoirs with a few hundred neurons. We obtain a quantitative
characterization of the frontier between stability and chaos, which can greatly
benefit hyperparameter tuning. In a broader sense, our results contribute to
understanding the complex dynamics of Recurrent Neural Networks.


  We describe an application of variational Monte Carlo to two-dimensional
fermionic systems within the recently developed tensor-network string-bond
state (SBS) ansatz. We use a combination of variational Monte Carlo and
stochastic optimization to optimize the matrix-product state matrices
representing the ground state. We present results for a two-dimensional
spinless fermion model including nearest-neighbor Coulomb interactions and
determine using finite-size scaling the phase boundary between charge-ordered
insulating and metallic phases. This approach can treat frustrated systems and
be easily extended to for fermions with spin.


  We investigate the role of quantum fluctuations in topological quantum phase
transitions of quantum spin Hall insulators and quantum anomalous Hall
insulators. Employing the variational cluster approximation to obtain the
single-particle Green's function of the interacting many-body system, we
characterize different phases by direct calculation of the recently proposed
topological order parameter for interacting systems. We pinpoint the influence
of quantum fluctuations on the quantum spin Hall to Mott insulator transition
in several models. Furthermore, we propose a general mechanism by which a
topological quantum phase transition can be driven by the divergence of the
self energy induced by interactions.


  Following up on earlier work, we investigate possible effects of a dressed
quark-gluon vertex in heavy-light mesons. In particular, we study corrections
to the popular rainbow-ladder truncation of the Dyson-Schwinger-Bethe-Salpeter
equation system. We adopt a simple interaction kernel which reduces the
resulting set of coupled integral equations to a set of coupled algebraic
equations, which are solved numerically. In this way, we extend previous
studies to quark-antiquark systems with unequal current-quark masses, at first
for the pseudoscalar case, and investigate the resulting set of problems and
solutions. We attempt to find patterns in - as well as to quantify corrections
to - the rainbow-ladder truncation. In addition, we open this approach to
phenomenological predictions of the heavy quark symmetry.


  Pruning is a widely used technique for reducing the size of deep neural
networks while maintaining their performance. However, such a technique,
despite being able to massively compress deep models, is hardly able to remove
entire layers from a model (even when structured): is this an addressable task?
In this study, we introduce EGP, an innovative Entropy Guided Pruning algorithm
aimed at reducing the size of deep neural networks while preserving their
performance. The key focus of EGP is to prioritize pruning connections in
layers with low entropy, ultimately leading to their complete removal. Through
extensive experiments conducted on popular models like ResNet-18 and Swin-T,
our findings demonstrate that EGP effectively compresses deep neural networks
while maintaining competitive performance levels. Our results not only shed
light on the underlying mechanism behind the advantages of unstructured
pruning, but also pave the way for further investigations into the intricate
relationship between entropy, pruning techniques, and deep learning
performance. The EGP algorithm and its insights hold great promise for
advancing the field of network compression and optimization. The source code
for EGP is released open-source.


  We investigate simplified models involving an inert scalar triplet and
vector-like leptons that can account for the muon $g-2$ anomaly. These
simplified scenarios are embedded in a model that features W' and Z' bosons,
which are subject to stringent collider bounds. The constraints coming from the
muon $g-2$ anomaly are put into perspective with collider bounds, as well as
bounds coming from lepton flavor violation searches. The region of parameter
space that explains the $g-2$ anomaly is shown to be within reach of lepton
flavor violation probes and future colliders such as HL-LHC and HE-LHC.


  The emergence of various notions of ``consistency'' in diffusion models has
garnered considerable attention and helped achieve improved sample quality,
likelihood estimation, and accelerated sampling. Although similar concepts have
been proposed in the literature, the precise relationships among them remain
unclear. In this study, we establish theoretical connections between three
recent ``consistency'' notions designed to enhance diffusion models for
distinct objectives. Our insights offer the potential for a more comprehensive
and encompassing framework for consistency-type models.


  A differential cluster variation method (DCVM) is proposed for analysis of
spinoidal decomposition in alloys. In this method, lattice symmetry operations
in the presence of an infinitesimal composition gradient are utilized to deduce
the connection equations for the correlation functions and to reduce the number
of independent variables in the cluster variation analysis.
  Application of the method is made to calculate the gradient energy
coefficient in the Cahn-Hilliard free energy function and the fastest growing
wavelength for spinodal decomposition in Al-Li alloys. It is shown that the
gradient coefficient of congruently ordered Al-Li alloys is much larger than
that of the disordered system. In such an alloy system, the calculated fastest
growing wavelength is approximately 10 nm, which is an order of magnitude
larger than the experimentally observed domain size. This may provide a
theoretical explanation why spinodal decomposition after a congruent ordering
is dominated by the antiphase boundaries.


  In this paper we continue investigations of the Parker-shearing instability
performing numerical simulations of the magnetic flux tube dynamics in the thin
flux-tube approximation. We show that evolution of flux tubes resulting from
numerical simulations is very similar to that of linear solutions if the
vertical displacements are smaller than the vertical scale height $H$ of the
galactic disc. If the vertical displacements are comparable to $H$, the
vertical growth of perturbations is faster in the nonlinear range than in the
linear one and we observe a rapid inflation of the flux tube at its top, which
leads to a singularity in numerical simulations, if only the cosmic rays are
taken into account. Then we perform simulations for the case of nonuniform
external medium, which show that the dominating wavelength of the Parker
instability is the same as the wavelength of modulations of external medium. As
a consequence of this fact, in the case of dominating cosmic ray pressure, the
dynamo $\alpha$ effect related to these short wavelength modulations is much
more efficient than that related to the linearly most unstable long wavelengths
modes of the Parker instability. Under the influence of differential forces
resulting from differential rotation and the density waves, the $\alpha$-effect
is essentially magnified in the spiral arms and diminished in the interarm
regions, what confirms our previous results obtained in the linear
approximation.


  The Baryonic Tully-Fisher Relation (BTFR) has applications in galaxy
evolution as a testbed for the galaxy-halo connection and in observational
cosmology as a redshift-independent secondary distance indicator. We use the
31,000+ galaxy ALFALFA sample -- which provides redshifts, velocity widths, and
HI content for a large number of gas-bearing galaxies in the local universe --
to fit and test an extensive local universe BTFR. This BTFR is designed to be
as inclusive of ALFALFA and comparable samples as possible. Velocity widths
measured via an automated method and $M_{b}$ proxies extracted from survey data
can be uniformly and efficiently measured for other samples, giving this
analysis broad applicability. We also investigate the role of sample
demographics in determining the best-fit relation. We find that the best-fit
relations are changed significantly by changes to the sample mass range and to
second order, mass sampling, gas fraction, different stellar mass and velocity
width measurements. We use a subset of ALFALFA with demographics that reflect
the full sample to measure a robust BTFR slope of $3.30\pm0.06$. We apply this
relation and estimate source distances, finding general agreement with
flow-model distances as well as average distance uncertainties of $\sim0.17$
dex for the full ALFALFA sample. We demonstrate the utility of these distance
estimates by applying them to a sample of sources in the Virgo vicinity,
recovering signatures of infall consistent with previous work.


  We prove new upper bounds on the tolerable level of noise in a quantum
circuit. We consider circuits consisting of unitary k-qubit gates each of whose
input wires is subject to depolarizing noise of strength p, as well as
arbitrary one-qubit gates that are essentially noise-free. We assume that the
output of the circuit is the result of measuring some designated qubit in the
final state. Our main result is that for p>1-\Theta(1/\sqrt{k}), the output of
any such circuit of large enough depth is essentially independent of its input,
thereby making the circuit useless. For the important special case of k=2, our
bound is p>35.7%. Moreover, if the only allowed gate on more than one qubit is
the two-qubit CNOT gate, then our bound becomes 29.3%. These bounds on p are
notably better than previous bounds, yet are incomparable because of the
somewhat different circuit model that we are using. Our main technique is the
use of a Pauli basis decomposition, which we believe should lead to further
progress in deriving such bounds.


  We have studied the rich dynamics of a damped particle inside an external
double-well potential under the influence of state-dependent time-delayed
feedback. In certain regions of the parameter space, we observe multistability
with the existence of two different attractors (limit cycle or strange
attractor) with well separated mean Lyapunov energies forming a two-level
system. Bifurcation analysis reveals that, as the effects of the time-delay
feedback are enhanced, chaotic transitions emerge between the two wells of the
double-well potential for the attractor corresponding to the fundamental energy
level. By computing the residence time distributions and the scaling laws near
the onset of chaotic transitions, we rationalize this apparent tunneling-like
effect in terms of the crisis-induced intermittency phenomenon. Further, we
investigate the first passage times in this regime and observe the appearance
of a Cantor-like fractal set in the initial history space, a characteristic
feature of hyperbolic chaotic scattering. The non-integer value of the
uncertainty dimension indicates that the residence time inside each well is
unpredictable. Finally, we demonstrate the robustness of this tunneling
intermittency as a function of the memory parameter by calculating the largest
Lyapunov exponent.


  Despite their success with unstructured data, deep neural networks are not
yet a panacea for structured tabular data. In the tabular domain, their
efficiency crucially relies on various forms of regularization to prevent
overfitting and provide strong generalization performance. Existing
regularization techniques include broad modelling decisions such as choice of
architecture, loss functions, and optimization methods. In this work, we
introduce Tabular Neural Gradient Orthogonalization and Specialization
(TANGOS), a novel framework for regularization in the tabular setting built on
latent unit attributions. The gradient attribution of an activation with
respect to a given input feature suggests how the neuron attends to that
feature, and is often employed to interpret the predictions of deep networks.
In TANGOS, we take a different approach and incorporate neuron attributions
directly into training to encourage orthogonalization and specialization of
latent attributions in a fully-connected network. Our regularizer encourages
neurons to focus on sparse, non-overlapping input features and results in a set
of diverse and specialized latent units. In the tabular domain, we demonstrate
that our approach can lead to improved out-of-sample generalization
performance, outperforming other popular regularization methods. We provide
insight into why our regularizer is effective and demonstrate that TANGOS can
be applied jointly with existing methods to achieve even greater generalization
performance.


  Spin-orbit coupling (SOC) plays a crucial role in determining the spin
structure of an odd parity psedospin-triplet Cooper pairing state. Here, we
present a thorough study of how SOC lifts the degeneracy among different p-wave
pseudospin-triplet pairing states in a widely used microscopic model for
$\mathrm{Sr_2 Ru O_4}$, combining a Ginzburg-Landau (GL) free energy expansion,
a symmetry analysis of the model, and numerical weak-coupling renormalization
group (RG) and random phase approximation (RPA) calculations. These analyses
are then used to critically re-examine previous numerical results on the
stability of chiral p-wave pairing. The symmetry analysis can serve as a guide
for future studies, especially numerical calculations, on the pairing
instability in $\mathrm{Sr_2 Ru O_4}$ and can be useful for studying other
multi-band spin-triplet superconductors where SOC plays an important role.


  Differential Dynamic Programming (DDP) is an efficient computational tool for
solving nonlinear optimal control problems. It was originally designed as a
single shooting method and thus is sensitive to the initial guess supplied.
This work considers the extension of DDP to multiple shooting (MS), improving
its robustness to initial guesses. A novel derivation is proposed that accounts
for the defect between shooting segments during the DDP backward pass, while
still maintaining quadratic convergence locally. The derivation enables
unifying multiple previous MS algorithms, and opens the door to many smaller
algorithmic improvements. A penalty method is introduced to strategically
control the step size, further improving the convergence performance. An
adaptive merit function and a more reliable acceptance condition are employed
for globalization. The effects of these improvements are benchmarked for
trajectory optimization with a quadrotor, an acrobot, and a manipulator. MS-DDP
is also demonstrated for use in Model Predictive Control (MPC) for dynamic
jumping with a quadruped robot, showing its benefits over a single shooting
approach.


  In this paper we give a purely categorical construction of d-fold matrix
factorizations of a natural transformation, for any even integer d. This
recovers the classical definition of those for regular elements in commutative
rings due to Eisenbud. We explore some natural functors between associated
triangulated categories, and show that when d=2 these are full and faithful,
and in some cases equivalences.


  We study the $v$-adic distance from the torsion of a Drinfeld module to an
affine variety.


  The reassembling of a simple connected graph G = (V,E) is an abstraction of a
problem arising in earlier studies of network analysis. The reassembling
process has a simple formulation (there are several equivalent formulations)
relative to a binary tree B (reassembling tree), with root node at the top and
$n$ leaf nodes at the bottom, where every cross-section corresponds to a
partition of V such that:
  - the bottom (or first) cross-section (all the leaves) is the finest
partition of V with n one-vertex blocks,
  - the top (or last) cross-section (the root) is the coarsest partition with a
single block, the entire set V,
  - a node (or block) in an intermediate cross-section (or partition) is the
result of merging its two children nodes (or blocks) in the cross-section (or
partition) below it.
  The maximum edge-boundary degree encountered during the reassembling process
is what we call the alpha-measure of the reassembling, and the sum of all
edge-boundary degrees is its beta-measure. The alpha-optimization (resp.
beta-optimization) of the reassembling of G is to determine a reassembling tree
B that minimizes its alpha-measure (resp. beta-measure).
  There are different forms of reassembling. In an earlier report, we studied
linear reassembling, which is the case when the height of B is (n-1). In this
report, we study balanced reassembling, when B has height [log n].
  The two main results in this report are the NP-hardness of alpha-optimization
and beta-optimization of balanced reassembling. The first result is obtained by
a sequence of polynomial-time reductions from minimum bisection of graphs
(known to be NP-hard), and the second by a sequence of polynomial-time
reductions from clique cover of graphs (known to be NP-hard).


  In this paper we analyse the five-factor capital market model of Munk et
al.(2004). The model features a Vasicek interest rate model, an equity index
with mean-reverting excess return and an index for realized inflation with
mean-reverting expectation. The primary aim of the analysis is to facilitate
so-called exact simulation from the model on a set of discrete time points. It
turns out that this can be achieved by sampling from a (degenerate)
seven-dimensional normal distribution. We derive the distributional results
necessary and describe how to overcome the rank deficiency of the
variance-covariance matrix in practice.
  The tradeable assets in the original model consist of cash, nominal bonds and
stocks. We extend the investment universe to also include inflation bonds by
deriving the arbitrage free break-even inflation (BEI) curve for a
three-parameter specification of the two market prices of inflation risk.
Finally, we provide a number of auxiliary results regarding the dynamics of
constant-maturity nominal and inflation bond indices, the distribution of the
stock index in nominal and real terms, and the distribution of the Sharpe ratio
for individual assets and portfolios with an application to factor investing.


  We use the replica method in order to obtain an expression for the
variational free energy of an Ising ferromagnet on a Viana-Bray lattice in the
presence of random external fields. Introducing a global order parameter, in
the replica-symmetric context, the problem is reduced to the analysis of the
solutions of a nonlinear integral equation. At zero temperature, and under some
restrictions on the form of the random fields, we are able to perform a
detailed analysis of stability of the replica-symmetric solutions. In contrast
to the behaviour of the Sherrington-Kirkpatrick model for a spin glass in a
uniform field, the paramagnetic solution is fully stable in a sufficiently
large random field.


  We study the geometric structure of Lorentzian spin manifolds, which admit
imaginary Killing spinors. The discussion is based on the cone construction and
a normal form classification of skew-adjoint operators in signature $(2,n-2)$.
Derived geometries include Brinkmann spaces, Lorentzian Einstein-Sasaki spaces
and certain warped product structures. Exceptional cases with decomposable
holonomy of the cone are possible.


  We study the interaction of xenon with an 850 eV photon energy FEL pulse. We
do so by employing a Monte-Carlo technique. We compute the single-photon
ionisation cross sections and Auger rates, used in the Monte-Carlo technique,
by adopting to atoms a formalism we previously developed for diatomic
molecules. We determine the yields of the ion states of driven xenon and
compare with previously obtained experimental results. To better understand the
yields obtained, we identify the prevalent pathways leading to the formation of
each final ion state of xenon. We gain further insight into the high yields of
highly-charged ion states by comparing the yields and dominant pathways of
these ion states of xenon when driven by different FEL pulses that have the
same energy. We show that higher-charged ion states have higher yields when
xenon is driven by longer-duration pulses due to Auger cascades taking place
between subsequent single-photon ionisations.


  Finding upper and lower bounds to integrals with respect to copulas is a
quite prominent problem in applied probability. In their 2014 paper, Hofer and
Iaco showed how particular two dimensional copulas are related to optimal
solutions of the two dimensional assignment problem. Using this, they managed
to approximate integrals with respect to two dimensional copulas. In this
paper, we will further illuminate this connection, extend it to d-dimensional
copulas and therefore generalize the method from Hofer and Iaco to arbitrary
dimensions. We also provide convergence statements. As an example, we consider
three dimensional dependence measures.


  The spectral decomposition of a real skew-symmetric matrix $A$ can be
mathematically transformed into a specific structured singular value
decomposition (SVD) of $A$. Based on such equivalence, a skew-symmetric Lanczos
bidiagonalization (SSLBD) method is proposed for the specific SVD problem that
computes extreme singular values and the corresponding singular vectors of $A$,
from which the eigenpairs of $A$ corresponding to the extreme conjugate
eigenvalues in magnitude are recovered pairwise in real arithmetic. A number of
convergence results on the method are established, and accuracy estimates for
approximate singular triplets are given. In finite precision arithmetic, it is
proven that the semi-orthogonality of each set of basis vectors and the
semi-biorthogonality of two sets of basis vectors suffice to compute the
singular values accurately. A commonly used efficient partial
reorthogonalization strategy is adapted to maintaining the needed
semi-orthogonality and semi-biorthogonality. For a practical purpose, an
implicitly restarted SSLBD algorithm is developed with partial
reorthogonalization. Numerical experiments illustrate the effectiveness and
overall efficiency of the algorithm.


  This paper introduces new optimality-preserving operators on Q-functions. We
first describe an operator for tabular representations, the consistent Bellman
operator, which incorporates a notion of local policy consistency. We show that
this local consistency leads to an increase in the action gap at each state;
increasing this gap, we argue, mitigates the undesirable effects of
approximation and estimation errors on the induced greedy policies. This
operator can also be applied to discretized continuous space and time problems,
and we provide empirical results evidencing superior performance in this
context. Extending the idea of a locally consistent operator, we then derive
sufficient conditions for an operator to preserve optimality, leading to a
family of operators which includes our consistent Bellman operator. As
corollaries we provide a proof of optimality for Baird's advantage learning
algorithm and derive other gap-increasing operators with interesting
properties. We conclude with an empirical study on 60 Atari 2600 games
illustrating the strong potential of these new operators.


  Pervasive pre-Riesz spaces are defined by means of vector lattice covers. To
avoid the computation of a vector lattice cover, we give two distinct intrinsic
characterizations of pervasive pre-Riesz spaces. We introduce weakly pervasive
pre-Riesz spaces and observe that this property can be easily checked in
examples. We relate weakly pervasive pre-Riesz spaces to pre-Riesz spaces with
the Riesz decomposition property.


  In the light-cone closed string and toroidal membrane theories, we associate
the global constraints with gauge symmetries. In the closed string case, we
show that the physical states defined by the BRS charge satisfy the
level-matching condition. In the toroidal membrane case, we show that the
Faddeev-Popov ghost and anti-ghost corresponding to the global constraints are
essentially free even if we adopt any gauge fixing condition for the local
constraint. We discuss the quantum double-dimensional reduction of the wrapped
supermembrane with the global constraints.


  Open neutrino physics issues require precision studies, both theoretical and
experimental ones, and towards this aim coherent neutral current
neutrino-nucleus scattering events are expected to be observed soon. In this
work, we explore $\nu$-nucleus processes from a nuclear theory point of view
and obtain results with high confidence level based on accurate nuclear
structure cross sections calculations. Besides cross sections, the present
study includes simulated signals expected to be recorded by nuclear detectors,
differential event rates as well as total number of events predicted to be
measured. Our original cross sections calculations are focused on measurable
rates for the Standard Model process, but we also perform calculations for
various channels of the non-standard neutrino-nucleus reactions and come out
with promising results within the current upper limits of the corresponding
exotic parameters. We concentrate on the possibility of detecting (i) supernova
neutrinos by using massive detectors like those of the GERDA and SuperCDMS dark
matter experiments and (ii) laboratory neutrinos produced near the spallation
neutron source facilities (at Oak Ridge National Lab) by the COHERENT
experiment. Our nuclear calculations take advantage of the relevant
experimental sensitivity and employ the severe bounds extracted for the exotic
parameters entering the Lagrangians of various particle physics models and
specifically those resulting from the charged lepton flavour violating $\mu^{-}
\rightarrow e^{-}$ experiments (Mu2e and COMET experiments).


  We discuss the molecular diffusion transport in infinitely dilute liquid
solutions under non-isothermal conditions. This discussion is motivated by an
occurring misinterpretation of thermodynamic transport equations written in
terms of chemical potential in the presence of temperature gradient. The
transport equations contain the contributions owned by a gauge transformation
related to the fact that chemical potential is determined up to the summand of
form (AT+B) with arbitrary constants A and B, where constant A is owned by the
entropy invariance with respect to shifts by a constant value and B is owned by
the potential energy invariance with respect to shifts by a constant value. The
coefficients of the cross-effect terms in thermodynamic fluxes are contributed
by this gauge transformation and, generally, are not the actual cross-effect
physical transport coefficients. Our treatment is based on consideration of the
entropy balance and suggests a promising hint for attempts of evaluation of the
thermal diffusion constant from the first principles. We also discuss the
impossibility of the "barodiffusion" for dilute solutions, understood in a
sense of diffusion flux driven by the pressure gradient itself. When one speaks
of "barodiffusion" terms in literature, these terms typically represent the
drift in external potential force field (e.g., electric or gravitational
fields), where in the final equations the specific force on molecules is
substituted with an expression with the hydrostatic pressure gradient this
external force field produces. Obviously, the interpretation of the latter as
barodiffusion is fragile and may hinder the accounting for the diffusion fluxes
produced by the pressure gradient itself.


  Topological materials with unconventional electronic properties have been
investigated intensively for both fundamental and practical interests.
Thousands of topological materials have been identified by symmetry-based
analysis and ab initio calculations. However, the predicted magnetic
topological insulators with genuine full band gaps are rare. Here we employ
this database and supervisedly train neural networks to develop a heuristic
chemical rule for electronic topology diagnosis. The learned rule is
interpretable and diagnoses with a high accuracy whether a material is
topological using only its chemical formula and Hubbard $U$ parameter. We next
evaluate the model performance in several different regimes of materials.
Finally, we integrate machine-learned rule with ab initio calculations to
high-throughput screen for magnetic topological insulators in 2D material
database. We discover 6 new classes (15 materials) of Chern insulators, among
which 4 classes (7 materials) have full band gaps and may motivate for
experimental observation. We anticipate the machine-learned rule here can be
used as a guiding principle for inverse design and discovery of new topological
materials.


  Machine learning often needs to model density from a multidimensional data
sample, including correlations between coordinates. Additionally, we often have
missing data case: that data points can miss values for some of coordinates.
This article adapts rapid parametric density estimation approach for this
purpose: modelling density as a linear combination of orthonormal functions,
for which $L^2$ optimization says that (independently) estimated coefficient
for a given function is just average over the sample of value of this function.
Hierarchical correlation reconstruction first models probability density for
each separate coordinate using all its appearances in data sample, then adds
corrections from independently modelled pairwise correlations using all samples
having both coordinates, and so on independently adding correlations for
growing numbers of variables using often decreasing evidence in data sample. A
basic application of such modelled multidimensional density can be imputation
of missing coordinates: by inserting known coordinates to the density, and
taking expected values for the missing coordinates, or even their entire joint
probability distribution. Presented method can be compared with cascade
correlations approach, offering several advantages in flexibility and accuracy.
It can be also used as artificial neuron: maximizing prediction capabilities
for only local behavior - modelling and predicting local connections.


  Recently, many healthcare organizations are adopting CRM as a strategy, which
involves using technology to organize, automate, and coordinate business
processes, in managing interactions with their patients. CRM with the Web
technology provides healthcare providers the ability to broaden their services
beyond usual practices, and thus offers suitable environment using latest
technology to achieve superb patient care. This paper discusses and
demonstrates how a new approach in CRM based on Web 2.0 will help the
healthcare providers improving their customer support, avoiding conflict, and
promoting better health to patient. With this new approach patients will
benefit from the customized personal service with full information access to
perform self managed their own health. It also helps healthcare providers
retaining the right customer. A conceptual framework of the new approach will
be discussed.


  We study the two-matrix model for double-scaled SYK model, called ETH matrix
model introduced by Jafferis et al [arXiv:2209.02131]. If we set the parameters
$q_A,q_B$ of this model to zero, the potential of this two-matrix model is
given by the Gaussian terms and the $q$-commutator squared interaction. We find
that this model is solvable in the large $N$ limit and we explicitly construct
the planar one- and two-point function of resolvents in terms of elliptic
functions.


  The pair-potential and current density around a single vortex of the
two-dimensional chiral p-wave superconductor with ${\mib d}={\mib z}(p_x \pm
\iu p_y)$ are determined self-consistently within the quasiclassical theory of
superconductivity. Shrinking of the vortex core at low temperatures are
considered numerically and analytically. Temperature-dependences of the spatial
variation of pair-potential and circular current around the core and density of
states at zero energy are the same as those in the isotropic s-wave case. When
the senses of vorticity and chirality are opposite, however, we find two novel
results; 1) the scattering rate due to non-magnetic impurities is considerably
suppressed, compared to that in the s-wave vortex. From this observation, we
expect that the chiral p-wave superconductors provide the best chance to
observe the shrinking of the vortex ("Kramer-Pesch effect") experimentally. 2)
The pair-potential of chiral p-wave superconductors inside vortex core recovers
a combined time-reversal-Gauge symmetry, although this symmetry is broken in
the region far from the vortex core. This local recovery of symmetry leads to
the suppression of the impurity effect inside vortex core.


  We obtain a very simple formula for the generating function of bipartite
(resp. quasi-bipartite) planar maps with boundaries (holes) of prescribed
lengths, which generalizes certain expressions obtained by Eynard in a book to
appear. The formula is derived from a bijection due to Bouttier, Di Francesco
and Guitter combined with a process (reminiscent of a construction of Pitman)
of aggregating connected components of a forest into a single tree. The formula
naturally extends to $p$-constellations and quasi-$p$-constellations with
boundaries (the case $p=2$ corresponding to bipartite maps).


  In this paper we investigate the impact of the higher - twist effects,
resummed by the non - linear approaches for the QCD dynamics, on the inclusive
observables that will be measured in future electron - ion colliders. We assume
a phenomenological model for the dipole - nucleus scattering amplitude which
takes into account the non - linear corrections and estimate the contribution
of the different twists for $F_2^A(x,Q^2)$, $F_L^A(x,Q^2)$ and $\partial F_2^A
/\partial ln Q^2$ considering different values of the Bjorken - $x$ variable,
photon virtuality $Q^2$ and atomic number $A$. A comparison with the full
predictions is performed and the impact of the distinct twists is estimated.
Our results indicate that a future analysis of the logarithmic $Q^2$ slope and
longitudinal structure function will allow us to probe the presence of the non
- linear effects on the QCD dynamics.


  Let $(R,\frak m)$ be a commutative noetherian local ring. In this paper, we
prove that if $\frak m$ is decomposable, then for any finitely generated
$R$-module $M$ of infinite projective dimension $\frak m$ is a direct summand
of (a direct sum of) syzygies of $M$. Applying this result to the case where
$\frak m$ is quasi-decomposable, we obtain several classfications of
subcategories, including a complete classification of the thick subcategories
of the singularity category of $R$.


  We consider the hard leptoproduction of a photon off nuclei up to spin-1. As
a new result we present here the general azimuthal angular dependence of the
differential cross section for a spin-1 target. Its twist-two Fourier
coefficients of the interference and squared deeply virtual Compton scattering
amplitude are evaluated in leading order approximation of perturbation theory
in terms of generalized parton distributions, while the pure Bethe--Heitler
cross section is exactly calculated in terms of electromagnetic form factors.
Relying on a simple model for the nucleon generalized parton distribution $H$,
which describes the existing DVCS data for a proton target, we estimate the
size of unpolarized cross sections, beam and longitudinal target spin as well
as unpolarized charge asymmetries for present fixed target experiments with
nuclei. These estimates are confronted with preliminary HERMES data for
deuterium and neon.


  The Borsuk-Ulam theorem states that a continuous function $f:S^n \to \R^n$
has a point $x\in S^n$ with $f(x)=f(-x)$. We give an analogue of this theorem
for digital images, which are modeled as discrete spaces of adjacent pixels
equipped with $\Z^n$-valued functions.
  In particular, for a concrete two-dimensional rectangular digital image whose
pixels all have an assigned "brightness" function, we prove that there must
exist a pair of opposite boundary points whose brightnesses are approximately
equal. This theorem applies generally to any integer-valued function on an
abstract simple graph.
  We also discuss generalizations to digital images of dimension 3 and higher.
We give some partial results for higher dimensional images, and show a counter
example which demonstrates that the full results obtained in lower dimensions
cannot hold generally.


  Gravity-induced non-Gaussianity can provide important clues to Modified
Gravity (MG) Theories. Several recent studies have suggested using the {\it
Integrated Bispectrum} (IB) as a probe for squeezed configuration of
bispectrum. Extending previous studies on the IB, we include redshift-space
distortions to study a class of (parametrised) MG theories that include the
string-inspired Dvali, Gabadadze \& Porrati (DGP) model. Various contributions
from redshift-space distortions are derived in a transparent manner, and
squeezed contributions from these terms are derived separately. Results are
obtained using the Zel'dovich Approximation (ZA). Results are also presented
for projected surveys (2D). We use the Press-Schechter (PS) and Sheth-Torman
(ST) mass functions to compute the IB for collapsed objects that can readily be
extended to peak-theory based approaches. The {\em cumulant correlators} (CCs)
generalise the ordinary {\em cumulants} and are known to probe collapsed
configurations of higher order correlation functions. We generalise the concept
of CCs to halos of different masses. We also introduce a generating function
based approach to analyse more general non-local biasing models. The Fourier
representations of the CCs, the skew-spectrum, or the kurt-spctra are discussed
in this context. The results are relevant for the study of the Minkowski
Functionals (MF) of collapsed tracers in redshift-space.} \keywords {Cosmology,
Large Scale Structure, Modified Theories of Gravity


  Theories of gravity that incorporate new scalar degrees of freedom typically
require screening mechanisms to ensure consistency with Solar System tests. One
widely-studied mechanism -- the chameleon -- can lead to violations of the
equivalence principle (EP), as screened and unscreened objects fall
differently. If the stars are screened but the surrounding dark matter is not,
this leads to asymmetry between leading and trailing streams. We provide
analytic estimates of the magnitude of this effect for realistic Galactic mass
distributions. Using a restricted N-body code, we simulate 4 satellites with a
range of masses and orbits, together with a variety of strengths of the fifth
force and screening levels of the Milky Way and satellite. The ratio of the
cumulative number function of stars in the leading and trailing stream as a
function of longitude from the satellite is computable from simulations,
measurable from the stellar data and can provide a direct test. We forecast
constraints for streams at large Galactocentric distances, using the specific
example case of Hu-Sawicki gravity. Streams with apocentres between 100 and 200
kpc provide attainable constraints at the level of $|f_{R0}| = 10^{-7}$. Still
more stringent constraints at the level of $10^{-7.5}$ or even $10^{-8}$ are
plausible provided the environmental screening of the satellite is accounted
for. These would be among the tightest astrophysical constraints to date. We
note further signatures of chameleon gravity: (i) the trailing stellar stream
may become detached from the dark matter progenitor if all the stars are lost,
(ii) in the extreme fifth force regime, striations in the stellar trailing tail
may develop, (iii) if the satellite is fully screened, its orbital frequency is
lower than that of the associated dark matter, which is preferentially
liberated into the leading tidal tail.


  Memristor, memory resistor, is an emerging technology for computational
memory. Number of different memristor models are available based on the
physical experiments. To use memristor as a computational memory element, one
should know how the internal state modulates in time when driven by current or
voltage. In this paper, we examine three widely used models and make a
comparison of how internal state in these models changes with respect to input
current or voltage. In Strukov model, internal state changes linearly with the
input current. However, the linearity of internal state modulation in Yang
model can be controlled. On the other hand, Pickett model shows non linear
variation in internal state with the input current.


  We propose several improvements for Linear Programming (LP) decoding
algorithms for High Density Parity Check (HDPC) codes. First, we use the
automorphism groups of a code to create parity check matrix diversity and to
generate valid cuts from redundant parity checks. Second, we propose an
efficient mixed integer decoder utilizing the branch and bound method. We
further enhance the proposed decoders by removing inactive constraints and by
adapting the parity check matrix prior to decoding according to the channel
observations. Based on simulation results the proposed decoders achieve near-ML
performance with reasonable complexity.


  We extend the well-studied midrapidity TRENTo initial-conditions model to
three dimensions, thus facilitating (3+1)D modeling and analysis of
ultrarelativistic heavy-ion collisions at RHIC and LHC energies. TRENTo-3D is a
fast, parametric model of the 3D initial-state geometry, capable of providing
initial conditions for (3+1)D models of quark--gluon plasma formation and
evolution. It builds on TRENTo's success at modeling the initial nuclear
participant thicknesses, longitudinally extending the initial deposition to
form a central fireball near midrapidity and two fragmentation regions at
forward and backward rapidities. We validate the new model through a
large-scale Bayesian calibration, utilizing as observables the rapidity
distributions of charged hadrons. For computational efficiency the present
effort employs a (1+1)D linearized approximation of ideal hydrodynamics as a
stand-in for quark--gluon plasma dynamics. This calibration serves as model
validation and paves the way for utilizing TRENTo-3D as an initial-conditions
model for state-of-the-art simulation incorporating (3+1)D relativistic viscous
hydrodynamics.


  A physical mechanism that drives FU Orionis-type outbursts is reconsidered.
We study the effect of inner part of a circumstellar disk covering a region
from near the central star to the radius of approximately $5$ AU (hereafter,
the inner disk). Using the fluctuated mass accretion rate onto the inner disk
$\dot{M}_{\rm out}$, we consider the viscous evolution of the inner disk and
the time variability of the mass accretion rate onto the central star
$\dot{M}_{\rm in}$ by means of numerical calculation of an unsteady viscous
accretion disk in a one-dimensional axisymmetric model. First, we calculate the
evolution of the inner disk assuming an oscillating $\dot{M}_{\rm out}$. It is
shown that the time variability of $\dot{M}_{\rm in}$ does not coincide with
$\dot{M}_{\rm out}$ due to viscous diffusion. Second, we investigate the
properties of spontaneous outbursts with temporally constant $\dot{M}_{\rm
out}$. Outburst occur only in a limited range of mass accretion rates onto the
inner disk $10^{-10}<\dot{M}_{\rm out}< 3\times 10^{-6}~{\rm M}_{\odot} {\rm
yr}^{-1}$ due to gravo-magneto limit cycle (GML). Finally, we discuss the case
with a combination of episodic $\dot{M}_{\rm out}$ and accretion outbursts
cause by the GML in the inner disk. The GML can drive accretion outbursts onto
the star even for the case of fluctuating $\dot{M}_{\rm out}$, although
fluctuations of $\dot{M}$ decay during transmitting the inner disk inwards. We
newly identified two modes of outburst which are spontaneous one and stimulated
one. In a stimulated mode of outburst, $\dot{M}_{\rm out}$ does appear directly
in $\dot{M}_{\rm in}$ (the latter defining the stellar accretion luminosity).
In a spontaneous mode of outburst, $\dot{M}_{\rm out}$ appears as the interval
between outbursts.


  It is important to study the fine structures of solar filaments with
high-resolution observations since it can help us understand the magnetic and
thermal structures of the filaments and their dynamics. In this paper, we study
a newly-formed filament located inside the active region NOAA 11762, which was
observed by the 1.6 m New Solar Telescope (NST) at Big Bear Solar Observatory
(BBSO) from 16:40:19 UT to 17:07:58 UT on 2013 June 5. As revealed by the
H$\alpha$ filtergrams, cool material is seen to be injected into the filament
spine with a speed of 5--10 km s$^{-1}$. At the source of the injection,
brightenings are identified in the chromosphere, which is accompanied by
magnetic cancellation in the photosphere, implying the importance of magnetic
reconnection in replenishing the filament with plasmas from the lower
atmosphere. Counter-streamings are detected near one endpoint of the filament,
with the plane-of-the-sky speed being 7--9 km s$^{-1}$ in the H$\alpha$
red-wing filtergrams and 9--25 km s$^{-1}$ in the blue-wing filtergrams. The
observations are indicative of that this active region filament is supported by
a sheared arcade without magnetic dips, and the counter-streamings are due to
unidirectional flows with alternative directions, rather than due to the
longitudinal oscillations of filament threads as in many other filaments.


  Neural Machine Translation systems built on top of Transformer-based
architectures are routinely improving the state-of-the-art in translation
quality according to word-overlap metrics. However, a growing number of studies
also highlight the inherent gender bias that these models incorporate during
training, which reflects poorly in their translations. In this work, we
investigate whether these models can be instructed to fix their bias during
inference using targeted, guided instructions as contexts. By translating
relevant contextual sentences during inference along with the input, we observe
large improvements in reducing the gender bias in translations, across three
popular test suites (WinoMT, BUG, SimpleGen). We further propose a novel metric
to assess several large pre-trained models (OPUS-MT, M2M-100) on their
sensitivity towards using contexts during translation to correct their biases.
Our approach requires no fine-tuning and thus can be used easily in production
systems to de-bias translations from stereotypical gender-occupation bias 1. We
hope our method, along with our metric, can be used to build better, bias-free
translation systems.


  We consider the Riemann Mapping Theorem in the case of a bounded simply
connected and semianalytic domain. We show that the germ at 0 of the Riemann
map (i.e. biholomorphic map) from the upper half plane to such a domain can be
realized in a certain quasianalytic class if the angle of the boundary at the
point to which 0 is mapped, is greater than 0. This quasianalytic class was
introduced and used by Ilyashenko in his work on Hilbert's 16th problem. With
this result we can prove that the Riemann map from a bounded simply connected
semianalytic domain onto the unit ball is definable in an o-minimal structure,
provided that at singular boundary points the angles of the boundary are
irrational multiples of $\pi$.


  Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations, as induced by a viewpoint change or an object
deformation, by learning a deep neural network that detects landmarks
consistently with such visual effects. Furthermore, we show that the learned
landmarks establish meaningful correspondences between different object
instances in a category without having to impose this requirement explicitly.
We assess the method qualitatively on a variety of object types, natural and
man-made. We also show that our unsupervised landmarks are highly predictive of
manually-annotated landmarks in face benchmark datasets, and can be used to
regress these with a high degree of accuracy.


  We establish the self-averaging properties of the Wigner transform of a
mixture of states in the regime when the correlation length of the random
medium is much longer than the wave length but much shorter than the
propagation distance. The main ingredients in the proof are the error estimates
for the semiclassical approximation of the Wigner transform by the solution of
the Liouville equations, and the limit theorem for two-particle motion along
the characteristics of the Liouville equations. The results are applied to a
mathematical model of the time-reversal experiments for the acoustic waves, and
self-averaging properties of the re-transmitted wave are proved.


  The realization of ${\cal N}=2$ four dimensional super Yang-Mills theories in
terms of a single Dirichlet fivebrane in type IIB string theory is considered.
A classical brane computation reproduces the full quantum low energy effective
action. This result has a simple explanation in terms of mirror symmetry.


  In this paper we study the structure of the set $\mbox{Hom}(X,\mathbb{R})$ of
all lattice homomorphisms from a Banach lattice $X$ into $\mathbb{R}$. Using
the relation among lattice homomorphisms and disjoint families, we prove that
the topological dual of the free Banach lattice $FBL(A)$ generated by a set $A$
contains a disjoint family of cardinality $2^{|A|}$, answering a question of B.
de Pagter and A.W. Wickstead. We also deal with norm-attaining lattice
homomorphisms. For classical Banach lattices, as $c_0$, $L_p$-, and
$C(K)$-spaces, every lattice homomorphism on it attains its norm, which shows,
in particular, that there is no James theorem for this class of functions. We
prove that, indeed, every lattice homomorphism on $X$ and $C(K,X)$ attains its
norm whenever $X$ has order continuous norm. On the other hand, we provide what
seems to be the first example in the literature of a lattice homomorphism which
does not attain its norm. In general, we study the existence and
characterization of lattice homomorphisms not attaining their norm in free
Banach lattices. As a consequence, it is shown that no Bishop-Phelps type
theorem holds true in the Banach lattice setting, i.e. not every lattice
homomorphism can be approximated by norm-attaining lattice homomorphisms.


  We study over-parameterized classifiers where Empirical Risk Minimization
(ERM) for learning leads to zero training error. In these over-parameterized
settings there are many global minima with zero training error, some of which
generalize better than others. We show that under certain conditions the
fraction of "bad" global minima with a true error larger than {\epsilon} decays
to zero exponentially fast with the number of training data n. The bound
depends on the distribution of the true error over the set of classifier
functions used for the given classification problem, and does not necessarily
depend on the size or complexity (e.g. the number of parameters) of the
classifier function set. This insight may provide a novel perspective on the
unexpectedly good generalization even of highly over-parameterized neural
networks. We substantiate our theoretical findings through experiments on
synthetic data and a subset of MNIST. Additionally, we assess our hypothesis
using VGG19 and ResNet18 on a subset of Caltech101.


  We investigate sums of mixed powers involving two squares, two cubes, and
various higher powers, concentrating on situations inaccessible to the
Hardy-Littlewood method.


  The deep learning based time-domain models, e.g. Conv-TasNet, have shown
great potential in both single-channel and multi-channel speech enhancement.
However, many experiments on the time-domain speech enhancement model are done
in simulated conditions, and it is not well studied whether the good
performance can generalize to real-world scenarios. In this paper, we aim to
provide an insightful investigation of applying multi-channel Conv-TasNet based
speech enhancement to both simulation and real data. Our preliminary
experiments show a large performance gap between the two conditions in terms of
the ASR performance. Several approaches are applied to close this gap,
including the integration of multi-channel Conv-TasNet into the beamforming
model with various strategies, and the joint training of speech enhancement and
speech recognition models. Our experiments on the CHiME-4 corpus show that our
proposed approaches can greatly reduce the speech recognition performance
discrepancy between simulation and real data, while preserving the strong
speech enhancement capability in the frontend.


  During recent years perturbative fixed order and resummation calculations
have decreased uncertainties on predictions for gluon fusion Higgs production
cross sections tremendously. Most exciting results have been published just
this year. In these proceedings I present an overview of recent and most recent
developments of these calculations that allow theory predictions to compete
with the experimental precision reached by future collider upgrades.


  Full Heusler compounds with Cobalt as a primary element show anomalous
transport properties owing to the Weyl fermions and broken time-reversal
symmetry. We present here the study of anomalous Hall effect (AHE) in Co2FeGe
Heusler compound. The experiment reveals anomalous Hall conductivity (AHC) 100
S/cm at room temperature with an intrinsic contribution of 78 S/cm . The
analysis of anomalous Hall resistivity suggests the scattering independent
intrinsic mechanism dominates the overall behaviour of anomalous Hall
resistivity. The first principles calculation reveals that the Berry curvature
originated by gapped nodal line near EF is the main source of AHE in Co2FeGe
Heusler compound. The theoretically calculated AHC is in agreement with the
experiment.


  The integrability of the geodesic flow on the three-folds $\mathcal M^3$
admitting $SL(2,\mathbb R)$-geometry in Thurston's sense is investigated. The
main examples are the quotients $\mathcal M^3_\Gamma=\Gamma\backslash
PSL(2,\mathbb R)$, where $\Gamma \subset PSL(2,\mathbb R)$ is a cofinite
Fuchsian group. We show that the corresponding phase space $T^*M_\Gamma^3$
contains two open regions with integrable and chaotic behaviour with zero and
positive topological entropy respectively.
  As a concrete example we consider the case of modular 3-fold with the modular
group $\Gamma=PSL({2,\mathbb Z})$, when $\mathcal M^3_\Gamma$ is known to be
homeomorphic to the complement of a trefoil knot $\mathcal K$ in 3-sphere. Ghys
proved a remarkable fact that the lifts of the periodic geodesics to the
modular surface to $\mathcal M^3_\Gamma$ produce the same isotopy class of
knots, which appeared in the chaotic version of the celebrated Lorenz system
and were extensively studied by Birman and Williams. We show that in the
integrable limit of the geodesic system on $\mathcal M^3_\Gamma$ they are
replaced by the simple class of cable knots of trefoil.


  We discuss the effects of finite perturbations in fully developed turbulence
by introducing a measure of the chaoticity degree associated to a given scale
of the velocity field. This allows one to determine the predictability time for
non-infinitesimal perturbations, generalizing the usual concept of maximum
Lyapunov exponent. We also determine the scaling law for our indicator in the
framework of the multifractal approach. We find that the scaling exponent is
not sensitive to intermittency corrections, but is an invariant of the
multifractal models. A numerical test of the results is performed in the shell
model for the turbulent energy cascade.


  We present the results of a multi-line and continuum study towards the source
IRAS 23385+6053,performed with the IRAM-30m telescope, the Plateau de Bure
Interferometer, the Very Large Array Interferometer and the James Clerk Maxwell
Telescope. The new results confirm our earlier findings, namely that IRAS
23385+6053 is a good candidate high-mass protostellar object, precursor of an
ultracompact H$_{II}$ region. The source is roughly composed of two regions: a
molecular core $\sim0.03\div0.04$ pc in size, with a temperature of $\sim40$ K
and an H$_{2}$ volume density of the order of 10$^{7}$ cm$^{-3}$, and an
extended halo of diameter $\leq$0.4 pc, with an average kinetic temperature of
$\sim 15$ K and H$_{2}$ volume density of the order of 10$^{5}$ cm$^{-3}$. The
core temperature is much smaller than what is typically found in molecular
cores of the same diameter surrounding massive ZAMS stars. We deduce that the
core luminosity is between 150 and $1.6\times10^{4}L_{\odot}$, and we believe
that the upper limit is near the ``true'' source luminosity. Moreover, by
comparing the H$_{2}$ volume density obtained at different radii from the IRAS
source, we find that the halo has a density profile of the type $n_{\rm
H_{2}}\propto r^{-2.3}$. This suggests that the source is gravitationally
unstable. Finally, we demonstrate that the temperature at the core surface is
consistent with a core luminosity of $10^3 L_{\odot}$ and conclude that we
might be observing a protostar still accreting material from its parental
cloud, whose mass at present is $\sim 6 M_{\odot}$.


  Predicting traffic incident duration is a major challenge for many traffic
centres around the world. Most research studies focus on predicting the
incident duration on motorways rather than arterial roads, due to a high
network complexity and lack of data. In this paper we propose a bi-level
framework for predicting the accident duration on arterial road networks in
Sydney, based on operational requirements of incident clearance target which is
less than 45 minutes. Using incident baseline information, we first deploy a
classification method using various ensemble tree models in order to predict
whether a new incident will be cleared in less than 45min or not. If the
incident was classified as short-term, then various regression models are
developed for predicting the actual incident duration in minutes by
incorporating various traffic flow features. After outlier removal and
intensive model hyper-parameter tuning through randomized search and
cross-validation, we show that the extreme gradient boost approach outperformed
all models, including the gradient-boosted decision-trees by almost 53%.
Finally, we perform a feature importance evaluation for incident duration
prediction and show that the best prediction results are obtained when
leveraging the real-time traffic flow in vicinity road sections to the reported
accident location.