  The C-19 stream is the most metal poor stellar system ever discovered, with a
mean metallicity $[Fe/H] = -3.38\pm0.06$. Its low metallicity dispersion
($\sigma_{\rm [Fe/H]}$ $<$ 0.18 at the 95\% confidence level) as well as
variations in sodium abundances strongly suggest a globular cluster origin. In
this work, we use VLT/UVES spectra of seven C-19 stars to derive more precise
velocity measurements for member stars, and to identify two new members with
radial velocities and metallicities consistent with the stream's properties.
One of these new member stars is located 30 $\deg$ away from the previously
identified body of C-19, implying that the stream is significantly more
extended than previously known and that more members likely await discovery. In
the main part of C-19, we measure a radial velocity dispersion $\sigma_v$ =
6.2$^{+2.0}_{-1.4}$ km s$^{-1}$ from nine members, and a stream width of
0.56$\deg\pm0.08\deg$, equivalent to $\sim$158 pc at a heliocentric distance of
18 kpc. These confirm that C-19 is comparatively hotter, dynamically, than
other known globular cluster streams and shares the properties of faint dwarf
galaxy streams. On the other hand, the variations in the Na abundances of the
three newly observed bright member stars, the variations in Mg and Al for two
of them, and the normal Ba abundance of the one star where it can be measured
provide further evidence for a globular cluster origin. The tension between the
dynamical and chemical properties of C-19 suggests that its progenitor
experienced a complex birth environment or disruption history.


  In the context of the U(1) slave boson theory we derive a critical field
theory near the quantum critical point of the Kondo lattice model in two
spatial dimensions. First we argue that strong gauge fluctuations in the U(1)
slave boson theory give rise to confinement between spinons and holons, thus
causing "neutralized" spinons in association with the slave boson U(1) gauge
field. Second we show that critical fluctuations of Kondo singlets near the
quantum critical point result in a new U(1) gauge field. This emergent gauge
field has nothing to do with the slave boson U(1) gauge field. Third we find
that the slave boson U(1) gauge field can be exactly integrated out in the low
energy limit. As a result we find a critical field theory in terms of
renormalized conduction electrons and neutralized spinons interacting via the
new emergent U(1) gauge field. Based on this critical field theory we obtain
the temperature dependence of specific heat and the imaginary part of the
self-energy of the renormalized electrons. These quantities display non-Fermi
liquid behavior near the quantum critical point.


  We consider an isotropic homogeneous cosmological model with five interacting
elements: first, the dynamic aether presented by a unit timelike vector field,
second, the pseudoscalar field describing an axionic component of the dark
matter, third, the cosmic dark energy, described by a rheologic fluid, fourth,
the non-axionic dark matter coupled to the dark energy, fifth, the gravity
field. We show that the early evolution of the Universe described by this model
can include two specific epochs: the first one can be characterized as a
super-inflation, the second epoch is associated with an oscillatory regime. The
dynamic aether carries out a regulatory mission; the rheologic dark fluid
provides the specific features of the spacetime evolution. The oscillations of
the scale factor and of the Hubble function are shown to switch on the
parametric (Floquet - type) mechanism of the axion number growth.


  Networked epidemic models have been widely adopted to describe propagation
phenomena. The endemic equilibrium of these models is of great significance in
the field of viral marketing, innovation dissemination, and information
diffusion. However, its stability conditions have not been fully explored. In
this paper we study the stability of the endemic equilibrium of a networked
Susceptible-Infected-Susceptible (SIS) epidemic model with heterogeneous
transition rates in a discrete-time manner. We show that the endemic
equilibrium, if it exists, is asymptotically stable for any nontrivial initial
condition. Under mild assumptions on initial conditions, we further prove that
during the spreading process there exists no overshoot with respect to the
endemic equilibrium. Finally, we conduct numerical experiments on real-world
networks to demonstrate our results.


  With the rapid advancement of graphical processing units, Physics-Informed
Neural Networks (PINNs) are emerging as a promising tool for solving partial
differential equations (PDEs). However, PINNs are not well suited for solving
PDEs with multiscale features, particularly suffering from slow convergence and
poor accuracy. To address this limitation of PINNs, this article proposes
physics-informed cell representations for resolving multiscale Poisson problems
using a model architecture consisting of multilevel multiresolution grids
coupled with a multilayer perceptron (MLP). The grid parameters (i.e., the
level-dependent feature vectors) and the MLP parameters (i.e., the weights and
biases) are determined using gradient-descent based optimization. The
variational (weak) form based loss function accelerates computation by allowing
the linear interpolation of feature vectors within grid cells. This cell-based
MLP model also facilitates the use of a decoupled training scheme for Dirichlet
boundary conditions and a parameter-sharing scheme for periodic boundary
conditions, delivering superior accuracy compared to conventional PINNs.
Furthermore, the numerical examples highlight improved speed and accuracy in
solving PDEs with nonlinear or high-frequency boundary conditions and provide
insights into hyperparameter selection. In essence, by cell-based MLP model
along with the parallel tiny-cuda-nn library, our implementation improves
convergence speed and numerical accuracy.


  A correspondence between the sextic anharmonic oscillator and a pair of
third-order ordinary differential equations is used to investigate the
phenomenon of quasi-exact solvability for eigenvalue problems involving
differential operators with order greater than two. In particular, links with
Bender-Dunne polynomials and resonances between independent solutions are
observed for certain second-order cases, and extended to the higher-order
problems.


  The Schr\"odinger equation and Bloch theorem are applied to examine a system
of protons confined within a periodic potential, accounting for deviations from
ideal harmonic behavior due to real-world conditions like truncated and
non-quadratic potentials, in both one-dimensional and three-dimensional
scenarios. Numerical computation of the energy spectrum of bound eigenfunctions
in both cases reveals intriguing structures, including bound states with
degeneracy matching the site number $N_w$, reminiscent of a finite harmonic
oscillator spectrum. In contrast to electronic energy bands, the proton system
displays a greater number of possible bound states due to the significant mass
of protons. Extending previous research, this study rigorously determines the
constraints on energy gap and oscillation amplitude of the previously
identified coherent states. The deviations in energy level spacing identified
in the computed spectrum, leading to minor splitting of electromagnetic modes,
are analyzed and found not to hinder the onset of coherence. Finally, a more
precise value of the energy gap is determined for the proton coherent states,
ensuring their stability against thermal decoherence up to the melting
temperature of the hosting metal.


  The relation between Latttice Boltzmann Method, which has recently become
popular, and the Kinetic Schemes, which are routinely used in Computational
Fluid Dynamics, is explored. A new discrete velocity model for the numerical
solution of the Navier-Stokes equations for incompressible fluid flow is
presented by combining both the approaches. The new scheme can be interpreted
as a pseudo-compressibility method and, for a particular choice of parameters,
this interpretation carries over to the Lattice Boltzmann Method.


  In the era of Industry 4.0, Additive Manufacturing (AM), particularly metal
AM, has emerged as a significant contributor due to its innovative and
cost-effective approach to fabricate highly intricate geometries. Despite its
potential, this industry still lacks real-time capable process monitoring
algorithms. Recent advancements in this field suggest that Melt Pool (MP)
signatures during the fabrication process contain crucial information about
process dynamics and quality. To obtain this information, various sensory
approaches, such as high-speed cameras-based vision modules are employed for
online fabrication monitoring. However, many conventional in-depth analyses
still cannot process all the recorded data simultaneously. Although
conventional Image Processing (ImP) solutions provide a targeted tunable
approach, they pose a trade-off between convergence certainty and convergence
speed. As a result, conventional methods are not suitable for a dynamically
changing application like MP monitoring. Therefore, this article proposes the
implementation of a Tunable Deep Image Processing (TDIP) method to address the
data-rich monitoring needs in real-time. The proposed model is first trained to
replicate an ImP algorithm with tunable features and methodology. The TDIP
model is then further improved to account for MP geometries and fabrication
quality based on the vision input and process parameters. The TDIP model
achieved over 94% estimation accuracy with more than 96% R2 score for quality,
geometry, and MP signature estimation and isolation. The TDIP model can process
500 images per second, while conventional methods taking a few minutes per
image. This significant processing time reduction enables the integration of
vision-based monitoring in real-time for processes and quality estimation.


  Motivated by the decidability question for the theory of real exponentiation
and by the Transfer Conjecture for o-minimal exponential fields, we show that,
under the assumption of Schanuel's Conjecture, the prime model of real
exponentiation is embeddable into any o-minimal exponential field, where the
embedding is not necessarily elementary. This is a consequence of an
unconditional model theoretic embeddability result that we obtain by applying
K\H{o}nig's Lemma.


  Location data obtained from smartphones is increasingly finding use cases in
disaster risk management. Where traditionally, CDR has provided the predominant
digital footprint for human mobility, GPS data now has immense potential in
terms of improved spatiotemporal accuracy, volume, availability, and
accessibility. GPS data has already proven invaluable in a range of pre- and
post-disaster use cases, such as quantifying displacement, measuring rates of
return and recovery, evaluating accessibility to critical resources, planning
for resilience. Despite its popularity and potential, however, the use of GPS
location data in DRM is still nascent, with several use cases yet to be
explored. In this paper, we consider the 2017 Puebla Earthquake in Mexico to
(i) validate and expand upon post-disaster analysis applications using GPS
data, and (ii) illustrate the use of a new toolkit, Mobilkit, to facilitate
scalable, replicable extensions of this work for a wide range of disasters,
including earthquakes, typhoons, flooding, and beyond.


  We report a Cu $K$-edge resonant inelastic x-ray scattering (RIXS) study of
charge-transfer excitations in the 2-8 eV range in the structurally simple
compound HgBa$_2$CuO$_{4+\delta}$ at optimal doping ($T_{\rm c} = 96.5 $ K).
The spectra exhibit a significant dependence on the incident photon energy
which we carefully utilize to resolve a multiplet of weakly-dispersive ($ <
0.5$ eV) electron-hole excitations, including a mode at 2 eV. The observation
of this 2 eV excitation suggests the existence of a charge-transfer pseudogap
deep in the superconducting phase. Quite generally, our data demonstrate the
importance of exploring the incident photon energy dependence of the RIXS cross
section.


  In this paper, using the Greiner's approach to heat kernel asymptotics, we
give new proofs of the equivariant Gauss-Bonnet-Chern formula and the variation
formulas for the equivariant Ray-Singer metric, which are originally due to J.
M. Bismut and W. Zhang.


  Aerial platforms are expected to deliver enhanced and seamless connectivity
in the fifth generation (5G) wireless networks and beyond (B5G). This is
generally achievable by supporting advanced onboard communication features
embedded in heavy and energy-intensive equipment. Alternatively, reconfigurable
smart surfaces (RSS), which smartly exploit/recycle signal reflections in the
environment, are increasingly being recognized as a new wireless communication
paradigm to improve communication links. In fact, their reduced cost, low power
use, light weight, and flexible deployment make them an attractive candidate
for integration with 5G/B5G technologies. In this article, we discuss
comprehensive approaches to the integration of RSS in aerial platforms. First,
we present a review of RSS technology, its operations and types of
communication. Next, we describe how RSS can be used in aerial platforms, and
we propose a control architecture workflow. Then, several potential use cases
are presented and discussed. Finally, associated research challenges are
identified.


  Semantic communication is considered the future of mobile communication,
which aims to transmit data beyond Shannon's theorem of communications by
transmitting the semantic meaning of the data rather than the bit-by-bit
reconstruction of the data at the receiver's end. The semantic communication
paradigm aims to bridge the gap of limited bandwidth problems in modern
high-volume multimedia application content transmission. Integrating AI
technologies with the 6G communications networks paved the way to develop
semantic communication-based end-to-end communication systems. In this study,
we have implemented a semantic communication-based end-to-end image
transmission system, and we discuss potential design considerations in
developing semantic communication systems in conjunction with physical channel
characteristics. A Pre-trained GAN network is used at the receiver as the
transmission task to reconstruct the realistic image based on the Semantic
segmented image at the receiver input. The semantic segmentation task at the
transmitter (encoder) and the GAN network at the receiver (decoder) is trained
on a common knowledge base, the COCO-Stuff dataset. The research shows that the
resource gain in the form of bandwidth saving is immense when transmitting the
semantic segmentation map through the physical channel instead of the ground
truth image in contrast to conventional communication systems. Furthermore, the
research studies the effect of physical channel distortions and quantization
noise on semantic communication-based multimedia content transmission.


  We study self-consistent cosmological solutions for an Einstein Universe in a
graph-based induced gravity model. The graph-based field theory has been
proposed by the present authors to generalize dimensional deconstruction. In
this paper, we consider self-consistent Einstein equations for a ``graph theory
space''. Especially, we demonstrate specific results for cycle graphs.


  We study the statistical properties of the sampled networks by a random
walker. We compare topological properties of the sampled networks such as
degree distribution, degree-degree correlation, and clustering coefficient with
those of the original networks. From the numerical results, we find that most
of topological properties of the sampled networks are almost the same as those
of the original networks for $\gamma \lesssim 3$. In contrast, we find that the
degree distribution exponent of the sampled networks for $\gamma>3$ somewhat
deviates from that of the original networks when the ratio of the sampled
network size to the original network size becomes smaller. We also apply the
sampling method to various real networks such as collaboration of movie actor,
world wide web, and peer-to-peer networks. All topological properties of the
sampled networks show the essentially same as the original real networks.


  We observed v=1, J=1-0 43-GHz SiO maser emission toward the Mira variable IK
Tauri (IK Tau) using the Very Long Baseline Array (VLBA). The images resulting
from these observations show that SiO masers form a highly elliptical ring of
emission approximately 58 x 32 mas with an axial ratio of 1.8:1. The major axis
of this elliptical distribution is oriented at position angle of ~59 deg. The
line-of-sight velocity structure of the SiO masers has an apparent axis of
symmetry consistent with the elongation axis of the maser distribution.
Relative to the assumed stellar velocity of 35 km/s, the blue- and red-shifted
masers were found to lie to the northwest and southeast of this symmetry axis
respectively. This velocity structure suggests a NW-SE rotation of the SiO
maser shell with an equatorial velocity, which we determine to be ~3.6 km/s.
Such a NW-SE rotation is in agreement with a circumstellar envelope geometry
invoked to explain previous water and OH maser observations. In this geometry,
water and OH masers are preferentially created in a region of enhanced density
along the NE-SW equator orthogonal to the rotation/polar axis suggested by the
SiO maser velocities.


  van Dokkum et al. (arXiv:2309.07969) reported the discovery of JWST-ER1, a
strong lensing object at redshift $z\approx2$, using data from the James Webb
Space Telescope. The lens mass within the Einstein ring is $5.9$ times higher
than the expected stellar mass from a Chabrier initial mass function,
indicating a high dark matter density. In this work, we show that a cold dark
matter halo, influenced by gas-driven adiabatic contraction, can account for
the observed lens mass. We interpret the measurement of JWST-ER1 in the
self-interacting dark matter scenario and show that the cross section per
particle mass $\sigma/m\approx0.1~{\rm cm^2/g}$ is generally favored.
Intriguingly, $\sigma/m\approx0.1~{\rm cm^2/g}$ can also be consistent with the
strong lensing observations of early-type galaxies at redshift $z\approx0.2$,
where adiabatic contraction is not observed overall.


  In a previous paper, we tried to test the Kerr nature of the stellar-mass
black hole in GRS 1915+105 by analyzing NuSTAR data of 2012 with our reflection
model RELXILL_NK. We found that the choice of the intensity profile of the
reflection component is crucial and eventually we were not able to get any
constraint on the spacetime metric around the black hole in GRS 1915+105. In
the present paper, we study the same source with Suzaku data of 2007. We
confirm that the intensity profile plays an important role, but now we find
quite stringent constraints consistent with the Kerr hypothesis. The key
differences with respect to our previous study are likely the lower disk
temperature in the Suzaku observation and the higher energy resolution near the
iron line of the Suzaku data. We also apply different RELXILL flavors
(different descriptions of the coronal spectrum and variable disk electron
density) obtaining essentially the same results. We thus conclude that this
choice is not very important for our tests of the Kerr hypothesis while the
intensity profile does play an important role, and that with high quality data
it is possible to measure both the spacetime metric and the intensity profile.


  We study the 2-D, time-dependent hydrodynamics of radiation-driven winds from
accretion disks in which the radiation force is mediated by spectral lines. If
the dominant contribution to the total radiation field comes from the disk,
then we find the outflow is intrinsically unsteady and characterised by large
amplitude velocity and density changes. Both infall and outflow can occur in
different regions of the wind at the same time. On the other hand, if the total
luminosity of the system is dominated by the central star, then the outflow is
steady. In either case, we find the 2-D structure of the wind consists of a
dense, slow outflow, typically confined to angles within about 45$^o$ of the
equatorial plane, that is bounded on the polar side by a high -velocity, lower
density stream. The flow geometry is controlled largely by the geometry of the
radiation field. Global properties e.g., the total mass loss rate and terminal
velocity depend more on the system luminosity and are insensitive to geometry.
Matter is fed into the fast wind from within a few stellar radii of the central
star. Our solutions agree qualitatively with the kinematics of outflows in CV
systems inferred from spectroscopic observations. We predict that low
luminosity systems may display unsteady behavior in wind-formed spectral lines.
Our study also has application to winds from active galactic nuclei and from
high mass YSOs.


  In the present paper we report the discovery of a new hot Jupiter,
EPIC211089792 b, first detected by the Super-WASP observatory and then by the
K2 space mission during its campaign 4. The planet has a period of 3.25d, a
mass of 0.73 +/- 0.04 Mjup, and a radius of 1.19 +/- 0.02 Rjup. The host star
is a relatively bright (V=12.5) G7 dwarf with a nearby K5V companion. Based on
stellar rotation and the abundance of Lithium, we find that the system might be
as young as about 450 Myr. The observation of the Rossiter-McLaughlin effect
shows the planet is aligned with respect to the stellar spin. Given the deep
transit (20mmag), the magnitude of the star and the presence of a nearby
stellar companion, the planet is a good target for both space- and ground-based
transmission spectroscopy, in particular in the near-infrared where the both
stars are relatively bright.


  We provide several examples of bounded Laurent monomials of minors of a
totally positive matrix, which can not be factored into a product of so called
primitive ratios, thus showing that the conjecture about factorization of
bounded ratios stated in [3] by Fallat, Gekhtman, and Johnson does not hold.
However, all found examples satisfy subtraction-free conjecture stated also in
[3]. In addition, we show that the set of all bounded ratios form a polyhedral
cone of dimension $\binom{2n}{n}-2n$.


  The physics at Frascati in the years 60's - 70's is reviewed together with
the role played by Bruno Touschek


  The concept of local pressure is pivotal to describe many important physical
phenomena, such as buoyancy or atmospheric phenomena, which always require the
consideration of space-varying pressure fields. These fields have precise
definitions within the phenomenology of hydro-thermodynamics, but a simple and
pedagogical microscopic description based on Statistical Mechanical is still
lacking in present literature. In this paper, we propose a new microscopic
definition of the local pressure field inside a classical fluid, relying on a
local barometer potential that is built into the many-particle Hamiltonian.
Such a setup allows the pressure to be locally defined, at an arbitrary point
inside the fluid, simply by doing a standard ensemble average of the radial
force exerted by the barometer potential on the gas' particles. This setup is
further used to give a microscopic derivation of the generalized Archimedes's
buoyancy principle, in the presence of an arbitrary external field. As
instructive examples, buoyancy force fields are calculated for ideal fluids in
the presence of: i) a uniform force field, ii) a spherically symmetric harmonic
confinement field, and iii) a centrifugal rotating frame.


  Histological evaluation of tissue samples is a typical approach to identify
colorectal cancer metastases in the peritoneum. For immediate assessment,
reliable and real-time in-vivo imaging would be required. For example,
intraoperative confocal laser microscopy has been shown to be suitable for
distinguishing organs and also malignant and benign tissue. So far, the
analysis is done by human experts. We investigate the feasibility of automatic
colon cancer classification from confocal laser microscopy images using deep
learning models. We overcome very small dataset sizes through transfer learning
with state-of-the-art architectures. We achieve an accuracy of 89.1% for cancer
detection in the peritoneum which indicates viability as an intraoperative
decision support system.


  Despite recent progress in generative image modeling, successfully generating
high-resolution, diverse samples from complex datasets such as ImageNet remains
an elusive goal. To this end, we train Generative Adversarial Networks at the
largest scale yet attempted, and study the instabilities specific to such
scale. We find that applying orthogonal regularization to the generator renders
it amenable to a simple "truncation trick," allowing fine control over the
trade-off between sample fidelity and variety by reducing the variance of the
Generator's input. Our modifications lead to models which set the new state of
the art in class-conditional image synthesis. When trained on ImageNet at
128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of
166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous
best IS of 52.52 and FID of 18.6.


  Plasmodium of Physarum polycephalum is a large cell, visible by unaided eye,
which exhibits sophisticated patterns of foraging behaviour. The plasmodium's
behaviour is well interpreted in terms of computation, where data are spatially
extended configurations of nutrients and obstacles, and results of computation
are networks of protoplasmic tubes formed by the plasmodium. In laboratory
experiments and numerical simulation we show that if plasmodium of Physarum is
inoculated in a maze's peripheral channel and an oat flake (source of
attractants) in a the maze's central chamber then the plasmodium grows toward
target oat flake and connects the flake with the site of original inoculation
with a pronounced protoplasmic tube. The protoplasmic tube represents a path in
the maze. The plasmodium solves maze in one pass because it is assisted by a
gradient of chemo-attractants propagating from the target oat flake.


  We study two small quantum systems coupled to the same reservoir which is in
thermal equilibrium. By studying the particle density and the energy density in
the two systems before and after they contact each other, we find that the two
systems are not in thermal equilibrium with each other. Our result shows that
the zeroth law of thermodynamics is broken in small quantum systems at low
temperatures. Therefore, the traditional way of defining temperature fails due
to the breakdown of the transitive relation of thermal equilibrium. Then we
show a different way of defining temperature by attaching an auxiliary site,
which plays the role of a thermometer, to the small quantum system.


  Given the recent developments in quantum techniques, modeling the physical
Hamiltonian of a target quantum many-body system is becoming an increasingly
practical and vital research direction. Here, we propose an efficient strategy
combining maximum likelihood estimation, gradient descent, and quantum
many-body algorithms. Given the measurement outcomes, we optimize the target
model Hamiltonian and density operator via a series of descents along the
quantum likelihood gradient, which we prove is negative semi-definite with
respect to the negative-log-likelihood function. In addition to such
optimization efficiency, our maximum-likelihood-estimate Hamiltonian learning
respects the locality of a given quantum system, therefore, extends readily to
larger systems with available quantum many-body algorithms. Compared with
previous approaches, it also exhibits better accuracy and overall stability
toward noises, fluctuations, and temperature ranges, which we demonstrate with
various examples.


  Spot instances offer a cost-effective solution for applications running in
the cloud computing environment. However, it is challenging to run long-running
jobs on spot instances because they are subject to unpredictable evictions.
Here, we present Spot-on, a generic software framework that supports
fault-tolerant long-running workloads on spot instances through checkpoint and
restart. Spot-on leverages existing checkpointing packages and is compatible
with the major cloud vendors. Using a genomics application as a test case, we
demonstrated that Spot-on supports both application-specific and transparent
checkpointing methods. Compared to running applications using on-demand
instances, it allows the completion of these workloads for a significant
reduction in computing costs. Compared to running applications using
application-specific checkpoint mechanisms, transparent checkpoint-protected
applications reduce runtime by up to 40%, leading to further cost savings of up
to 86%.


  We revisit inflationary cosmology of axion models in the light of recent
developments on the inflaton decay in supergravity. We find that all the
cosmological difficulties, including gravitino, axino overproduction and
axionic isocurvature fluctuation, can be avoided if the saxion field has large
initial amplitude during inflation and decays before big-bang nucleosynthesis.


  We propose the Chiarella-Heston model, a new agent-based model for improving
the effectiveness of deep hedging strategies. This model includes momentum
traders, fundamental traders, and volatility traders. The volatility traders
participate in the market by innovatively following a Heston-style volatility
signal. The proposed model generalises both the extended Chiarella model and
the Heston stochastic volatility model, and is calibrated to reproduce as many
empirical stylized facts as possible. According to the stylised facts distance
metric, the proposed model is able to reproduce more realistic financial time
series than three baseline models: the extended Chiarella model, the Heston
model, and the Geometric Brownian Motion. The proposed model is further
validated by the Generalized Subtracted L-divergence metric. With the proposed
Chiarella-Heston model, we generate a training dataset to train a deep hedging
agent for optimal hedging strategies under various transaction cost levels. The
deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and
is trained to maximize profits and minimize risks. Our testing results reveal
that the deep hedging agent, trained with data generated by our proposed model,
outperforms the baseline in most transaction cost levels. Furthermore, the
testing process, which is conducted using empirical data, demonstrates the
effective performance of the trained deep hedging agent in a realistic trading
environment.


  Observations of the eclipsing dwarf nova IP Peg during outburst reveal metal
lines in emission, such as Mg II 4481 A. Analysis using Doppler tomography
locates emission of helium and metal lines on the inner Roche lobe of the
secondary star. Such multi-line Roche-lobe imaging presents a new tool in
mapping the red star's ionization structure.


  We establish a Grothendieck--Lefschetz theorem for smooth ample subvarieties
of smooth projective varieties over an algebraically closed field of
characteristic zero and, more generally, for smooth subvarieties whose
complement has small cohomological dimension. A weaker statement is also proved
in a more general context and in all characteristics. Several applications are
included.


  Twin vertices of a graph have the same open neighbourhood. If they are not
adjacent, then they are called duplicates and contribute the eigenvalue zero to
the adjacency matrix. Otherwise they are termed co-duplicates, when they
contribute $-1$ as an eigenvalue of the adjacency matrix. On removing a twin
vertex from a graph, the spectrum of the adjacency matrix does not only lose
the eigenvalue $0$ or $-1$. The perturbation sends a rippling effect to the
spectrum. The simple eigenvalues are displaced. We obtain a closed formula for
the characteristic polynomial of a graph with twin vertices in terms of two
polynomials associated with the perturbed graph. These are used to obtain
estimates of the displacements in the spectrum caused by the perturbation.


  The work presented here is a novel biological approach for the compliant
control of a robotic arm in real time (RT). We integrate a spiking cerebellar
network at the core of a feedback control loop performing torque-driven
control. The spiking cerebellar controller provides torque commands allowing
for accurate and coordinated arm movements. To compute these output motor
commands, the spiking cerebellar controller receives the robot's sensorial
signals, the robot's goal behavior, and an instructive signal. These input
signals are translated into a set of evolving spiking patterns representing
univocally a specific system state at every point of time.
Spike-timing-dependent plasticity (STDP) is then supported, allowing for
building adaptive control. The spiking cerebellar controller continuously
adapts the torque commands provided to the robot from experience as STDP is
deployed. Adaptive torque commands, in turn, help the spiking cerebellar
controller to cope with built-in elastic elements within the robot's actuators
mimicking human muscles (inherently elastic). We propose a natural integration
of a bio inspired control scheme, based on the cerebellum, with a compliant
robot. We prove that our compliant approach outperforms the accuracy of the
default factory-installed position control in a set of tasks used for
addressing cerebellar motor behavior: controlling six degrees of freedom (DoF)
in smooth movements, fast ballistic movements, and unstructured scenario
compliant movements.


  Planning in public transportation is traditionally done in a sequential
process: After the network design process, the lines and their frequencies are
planned. When these are fixed, a timetable is determined and based on the
timetable, the vehicle and crew schedules are optimized. After each step,
passenger routes are adapted to model the behavior of the passengers as
realistically as possible. It has been mentioned in many publications that such
a sequential process is sub-optimal, and integrated approaches, mainly
heuristics, are under consideration. Sequential planning is not only common in
public transportation planning but also in many other applied problems, among
others in supply chain management, or in organizing hospitals efficiently.
  The contribution of this paper hence is two-fold: on the one hand, we develop
an integrated integer programming formulation for the three planning stages
line planning, (periodic) timetabling, and vehicle scheduling which also
includes the integrated optimization of the passenger routes. This gives us an
exact formulation rewriting the sequential approach as an integrated problem.
We discuss properties of the integrated formulation and apply it experimentally
to data sets from the LinTim library.
  On the other hand, we propose a mathematical formulation for general
sequential processes which can be used to build integrated formulations. For
comparing sequential processes with their integrated counterparts we analyze
the price of sequentiality, i.e., the ratio between the solution obtained by
the sequential process and an integrated solution. We also experiment with
different possibilities for partial integration of a subset of the sequential
problems and again illustrate our results using the case of public
transportation. The obtained results may be useful for other sequential
processes.


  We study static, spherically symmetric solutions with an electric field in an
extension of general relativity (GR) containing a Ricci-squared term and
formulated in the Palatini formalism. We find that all the solutions present a
central core whose area is proportional to the Planck area times the number of
charges. Far from the core, curvature invariants quickly tend to those of the
usual Reissner-Nordstr\"om solution, though the structure of horizons may be
different. In fact, besides the structures found in the Reissner-Nordstr\"om
solution of GR, we find black hole solutions with just one nondegenerate
horizon (Schwarzschild-like), and nonsingular black holes and naked cores. The
charge-to-mass ratio of the nonsingular solutions implies that the core matter
density is independent of the specific amounts of charge and mass and of order
the Planck density. We discuss the physical implications of these results for
astrophysical and microscopic black holes, construct the Penrose diagrams of
some illustrative cases, and show that the maximal analytical extension of the
nonsingular solutions implies a bounce of the radial coordinate.


  Good quality network connectivity is ever more important. For hybrid fiber
coaxial (HFC) networks, searching for upstream high noise in the past was
cumbersome and time-consuming. Even with machine learning due to the
heterogeneity of the network and its topological structure, the task remains
challenging. We present the automation of a simple business rule (largest
change of a specific value) and compare its performance with state-of-the-art
machine-learning methods and conclude that the precision@1 can be improved by
2.3 times. As it is best when a fault does not occur in the first place, we
secondly evaluate multiple approaches to forecast network faults, which would
allow performing predictive maintenance on the network.


  The interplanar bond strength in graphite has been identified to be very low
owing to the contribution of the van der Waals interaction. However, in this
study, we use microscopic picosecond ultrasound to demonstrate that the elastic
constant, $C_{33}$, along the $c$ axis of defect-free monocrystalline graphite
exceeds 45 GPa, which is higher than reported values by 20\%. Existing theories
fail to reproduce this strongly correlated interplanar system, and our results,
thus, indicate the necessity for improvement. Since the LDA+U+RPA method,
including both random phase approximation correlation and short-range
correlation in $p$ Wannier orbitals, shows better agreement with the
observation than LDA or even than ACFDT-RPA, the experimental results indicate
non-negligible electron correlation effects with respect to both the
short-range and long-range interactions.


  Experimental data from Maxwell's demon experiments on granular gas are
revisited. It is shown that the transition nature changes from critical to
sub-critical via a tri-critical point when frequency of vibration is increased
continuously. So (i) the transition is not hyper-critical as asserted
previously, but (ii) the fluctuations amplitude is spontaneously reinforced at
the tri-critical point compared to the one at other frequencies. So, (iii) this
analysis still contradicts a recent study which asserts that the bifurcation is
always critical, and that fluctuations shall depend on the number of grains
only. Beside, (iv) it is proposed a way to build an experiment, based on some
modification of the "Maxwell's demon in granular gas" set up, which undergoes a
hyper-critical bifurcation, with the use of some controlled feed-back of the
flows. This last idea can be generalised and used in other domains where
bifurcation theory applies, i.e. physics, chemistry, population dynamics,
economy, to improve regulation, but generating hyper- fluctuations. Finally it
is shown that the dynamical result is sensitive to the very-details of the
regulation.


  This paper examines the transformative role of Large Language Models (LLMs)
in education and their potential as learning tools, despite their inherent
risks and limitations. The authors propose seven approaches for utilizing AI in
classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator,
and AI-student, each with distinct pedagogical benefits and risks. The aim is
to help students learn with and about AI, with practical strategies designed to
mitigate risks such as complacency about the AI's output, errors, and biases.
These strategies promote active oversight, critical assessment of AI outputs,
and complementarity of AI's capabilities with the students' unique insights. By
challenging students to remain the "human in the loop," the authors aim to
enhance learning outcomes while ensuring that AI serves as a supportive tool
rather than a replacement. The proposed framework offers a guide for educators
navigating the integration of AI-assisted learning in classrooms


  The delensing procedure is an effective tool for removing lensing-induced
$B$-mode polarization in the Cosmic Microwave Background to allow for deep
searches of primordial $B$-modes. However, the delensing algorithm existing in
the literature breaks down if the target $B$-mode signals overlap significantly
with the lensing $B$-mode ($\sim 300<l<2000$) in multipole-$l$ space. In this
paper, we identify the cause of the breakdown to be correlations between the
input $B$-map and the deflection field estimator ($EB$). The amplitude of this
bias is quantified by numerical simulations and compared to the analytically
derived functional form. We also propose a revised delensing procedure that
circumvents the bias. While the newly identified bias does not affect the
search of degree scale $B$-mode generated by tensor perturbations, the modified
delensing algorithm makes it possible to perform deep searches of high-$l$
$B$-modes such as those generated by patchy reionization, cosmic strings, or
rotation of polarization angles. Finally, we estimate how well future
polarization experiments can do in detecting tensor- and cosmic string-
generated $B$-mode after delensing and comment on different survey strategies.


  Photons may evade a synchrotron radiation constraint on quantum gravity by
violating the equivalence principle.


  Acquiring the mathematical, conceptual, and problem-solving skills required
in university-level physics courses is hard work, and the average student often
lacks the knowledge and study skills they need to succeed in the introductory
courses. Here we propose a new pedagogical model and a straight-forwardly
reproducible set of internet-based testing tools. Our work to address some of
the most important student deficiencies is based on three fundamental
principles: balancing skill level and challenge, providing clear goals and
feedback at every stage, and allowing repetition without penalty. Our tools
include an Automated Mathematics Evaluation System (AMES), a Computerized
Homework Assignment Grading System (CHAGS), and a set of after-homework quizzes
and mini-practice exams (QUizzes Intended to Consolidate Knowledge, or QUICK).
We describe how these tools are incorporated into the course, and present some
preliminary results on their effectiveness.


  In a recent letter we have outlined some issues on GW 150914, we hereby give
additional details. We analyze the event GW 150914 announced by the Advanced
Laser Interferometer Gravitational-Wave Observatory (LIGO) as the
gravitational-wave emission of a black-hole binary merger. We show that the
parameters of the coalescing system and of the newly formed Kerr black-hole can
be extracted from basic results of the gravitational-wave emission during the
inspiraling and merger phases without sophisticate numerical simulations. Our
strikingly accurate estimates are based on textbook formulas describing two
different regimes: 1) the binary inspiraling analysis treated in Landau and
Lifshitz textbook, 2) the plunge of a particle into a black-hole, treated in
the Rees-Ruffini-Wheeler textbook as well as 3) the transition between these
two regimes following Detweiler's treatment of a particle infalling with
non-zero angular momentum onto a black-hole. It is stressed that in order to
infer any astrophysical information on the masses of the system both regimes
have to be independently and observationally constrained by LIGO, which does
not appear to be the case.


  We propose an energy-efficient controller to minimize the energy consumption
of a mobile robot by dynamically manipulating the mechanical and computational
actuators of the robot. The mobile robot performs real-time vision-based
applications based on an event-based camera. The actuators of the controller
are CPU voltage/frequency for the computation part and motor voltage for the
mechanical part. We show that independently considering speed control of the
robot and voltage/frequency control of the CPU does not necessarily result in
an energy-efficient solution. In fact, to obtain the highest efficiency, the
computation and mechanical parts should be controlled together in synergy. We
propose a fast hill-climbing optimization algorithm to allow the controller to
find the best CPU/motor configuration at run-time and whenever the mobile robot
is facing a new environment during its travel. Experimental results on a robot
with Brushless DC Motors, Jetson TX2 board as the computing unit, and a
DAVIS-346 event-based camera show that the proposed control algorithm can save
battery energy by an average of 50.5%, 41%, and 30%, in low-complexity,
medium-complexity, and high-complexity environments, over baselines.


  Context. Wavelength, photon spin angular momentum (PSAM), and photon orbital
angular momentum (POAM), completely describe the state of a photon or an
electric field (an ensemble of photons). Wavelength relates directly to energy
and linear momentum, the corresponding kinetic quantities. PSAMand POAM,
themselves kinetic quantities, are colloquially known as polarization and
optical vortices, respectively. Astrophysical sources emit photons that carry
this information. Aims. PSAM characteristics of an electric field (intensity)
are compactly described by the Jones (Stokes/Mueller) calculus. Similarly, I
created calculi to represent POAM characteristics of electric fields and
intensities in an astrophysical context. Adding wavelength dependence to all of
these calculi is trivial. The next logical steps are to 1) form photon total
angular momentum (PTAM = POAM + PSAM) calculi; 2) prove their validity using
operators and expectation values; and 3) show that instrumental PSAM can affect
measured POAM values for certain types electric fields. Methods. I derive the
PTAM calculi of electric fields and intensities by combining the POAM and PSAM
calculi. I show how these quantities propagate from celestial sphere to image
plane. I also form the PTAMoperator (the sum of the POAMand PSAMoperators),
with and without instrumental PSAM, and calculate the corresponding expectation
values. Results. Apart from the vector, matrix, dot product, and direct product
symbols, the PTAM and POAM calculi appear superficially identical. I provide
tables with all possible forms of PTAM calculi. I prove that PTAM expectation
values are correct for instruments with and without instrumental PSAM. I also
show that POAM measurements of "unfactored" PTAM electric fields passing
through non-zero instrumental circular PSAM can be biased. Conclusions. The
combined PTAM calculi provide insight into how to mathematically model PTAM
sources and calibrate POAMand PSAM- induced POAM measurement errors.


  The folding dynamics of proteins at the single molecule level has been
studied with single-molecule force spectroscopy (SMFS) experiments for twenty
years, but a common standardized method for the analysis of the collected data
and for the sharing among the scientific community members is still not
available. We have developed a new open source tool, Fodis, for the analysis of
the Force-distance curves obtained in SMFS experiments, providing an almost
automatic processing, analysis and classification of the obtained data. Our
method provides also a classification of the possible unfolding pathways and
structural heterogeneity, present during the unfolding of proteins.


  We study the online busy time scheduling model on heterogeneous machines. In
our setting, unit-length jobs arrive online with a deadline that is known to
the algorithm at the job's arrival time. An algorithm has access to machines,
each with different associated capacities and costs. The goal is to schedule
jobs on machines before their deadline, so that the total cost incurred by the
scheduling algorithm is minimized. Relatively little is known about online busy
time scheduling when machines are heterogeneous (i.e., have different costs and
capacities), despite this being the most practical model for clients using
cloud computing services. We make significant progress in understanding this
model by designing an 8-competitive algorithm for the problem on unit-length
jobs, and providing a lower bound on the competitive ratio of 2. We further
prove that our lower bound is tight in the natural setting when jobs have
agreeable deadlines.


  We prove a version of faithfully flat descent in rigid analytic geometry, for
almost perfect complexes and without finiteness assumptions on the rings
involved. This extends results of Drinfeld for vector bundles.


  The caustic technique measures the mass of galaxy clusters in both their
virial and infall regions and, as a byproduct, yields the list of cluster
galaxy members. Here we use 100 galaxy clusters with mass M200>=1E14 Msun/h
extracted from a cosmological N-body simulation of a LambdaCDM universe to test
the ability of the caustic technique to identify the cluster galaxy members. We
identify the true three-dimensional members as the gravitationally bound
galaxies. The caustic technique uses the caustic location in the redshift
diagram to separate the cluster members from the interlopers. We apply the
technique to mock catalogues containing 1000 galaxies in the field of view of
12 Mpc/h on a side at the cluster location. On average, this sample size
roughly corresponds to 180 real galaxy members within 3r200, similar to recent
redshift surveys of cluster regions. The caustic technique yields a
completeness, the fraction of identified true members, fc=0.95 (+- 0.03) within
3r200. The contamination increases from fi=0.020 (+0.046;-0.015) at r200 to
fi=0.08 (+0.11;-0.05) at 3r200. No other technique for the identification of
the members of a galaxy cluster provides such large completeness and small
contamination at these large radii. The caustic technique assumes spherical
symmetry and the asphericity of the cluster is responsible for most of the
spread of the completeness and the contamination. By applying the technique to
an approximately spherical system obtained by stacking the individual clusters,
the spreads decrease by at least a factor of two. We finally estimate the
cluster mass within 3r200 after removing the interlopers: for individual
clusters, the mass estimated with the virial theorem is unbiased and within 30
per cent of the actual mass; this spread decreases to less than 10 per cent for
the spherically symmetric stacked cluster.


  We study a P\'olya-type urn model defined as follows. Start at time 0 with a
single ball of some colour. Then, at each time n>0, choose a ball from the urn
uniformly at random. With probability 1/2<p<1, return the ball to the urn along
with another ball of the same colour. With probability 1-p, recolour the ball
to a new colour and then return it to the urn. This is equivalent to the
supercritical case of a random graph model studied by Backhausz and M\'ori, and
Th\"ornblad. We prove that, with probability 1, there is a dominating colour,
in the sense that, after some random but finite time, there is a colour that
always has the most number of balls. A crucial part of the proof is the
analysis of an urn model with two colours, in which the observed ball is
returned to the urn along with another ball of the same colour with probability
p, and removed with probability 1-p. Our results here generalise a classical
result about the P\'olya urn model (which corresponds to p=1).


  We prove coding theorems for two scenarios of cooperating encoders for the
multiple access channel with two classical inputs and one quantum output. In
the first scenario (ccq-MAC with common messages), the two senders each have
their private messages, but would also like to transmit common messages. In the
second scenario (ccq-MAC with conferencing encoders), each sender has its own
set of messages, but they are allowed to use a limited amount of noiseless
classical communication amongst each other prior to encoding their messages.
This conferencing protocol may depend on each individual message they intend to
send. The two scenarios are related to each other not only in spirit - the
existence of near-optimal codes for the ccq-MAC with common messages is used
for proving the existence of near-optimal codes for the ccq-MAC with
conferencing encoders.


  We consider the possibility that future wide-field time-domain optical
imaging surveys may be able to discover gravitationally lensed quasar pairs
through serendipitous measurements of their time delays. We discuss the merits
such a discovery technique would have relative to conventional lens searches.
Using simulated quasar lightcurves, we demonstrate that in a survey which
observes objects several times each lunar cycle over the course of five years,
it is possible to improve the efficiency of a gravitational lens search by 2-3
orders of magnitude through the use of time delay selection. In the most
advantageous scenario considered, we are able to improve efficiency by a factor
of 1000 with no loss of completeness. In the least advantageous scenario, we
are able to improve efficiency by a factor of 110 while reducing completeness
by a factor of 9. We show that window function effects associated with the
length of the observing season are more important than the total number of
datapoints in determining the effectiveness of this method. We also
qualitatively discuss several complications which might be relevant to a real
time delay search.


  We identify and quantify the set of constraints that neutron star-normal star
binaries must satisfy in order to become observable LMXBs. These constraints
are related to (i) the thermal and hydrostatic equilibrium of the donors, (ii)
the degree to which the mass transfer process is conservative, and (iii) the
age of the systems. They divide the parameter space of potential LMXBs in
several distinct parts, of which those that actually become LMXBs at the onset
of mass transfer occupy only a small part. Of the remainder, many become
unstable to dynamical time scale mass transfer either at the onset or later in
the course of mass transfer, and enter common envelope evolution. Others
experience super-Eddington mass transfer but may eventually survive to become
LMXBs. These survivors arguably include binary millisecond pulsars with orbital
periods in excess of $100$\,d, ultrashort-period LMXBs with hydrogen-deficient
donors, and long-period LMXBs with giant donors.


  Surveys have indicated that the remanufacturing industry is concerned about
the necessity of agile and prioritized core sorting due to its potential
benefits to optimal core inventory and condition assessment, both at equipment
and component levels. As such, core sorting holds a pivotal role in generalized
remanufacturing operations, however, extreme value core arrivals, its
stochastic nature and resulting sorting issues warrant targeted modelling and
analysis. This paper is devoted to triage as an agile and quality-based sorting
strategy in extreme value scenarios, which can be utilized as a complementary
core sorting strategy. A statistical model of extreme core arrivals is
developed based on Extreme Value (EV) theory and related Generalized Extreme
Value (GEV) and Fr\'echet (Fisher-Tippett type-II) distributions. The model is
applied to extreme arrivals of valves in an industrial valve repair shop in
order to formulate extreme arrival sorting strategies. Using a large sample
size, distribution parameters are estimated and the stochastic behaviour of the
extreme valve arrivals is evaluated and verified. A generic analogy between
medical triage and remanufacturing triage is discussed, because triage can be
used to address extreme core arrivals, associated statistical distributions,
and their effect on core condition assessment in order to enhance management of
unpredictable consequences of extreme core arrivals.


  In recent years, end-to-end (E2E) based automatic speech recognition (ASR)
systems have achieved great success due to their simplicity and promising
performance. Neural Transducer based models are increasingly popular in
streaming E2E based ASR systems and have been reported to outperform the
traditional hybrid system in some scenarios. However, the joint optimization of
acoustic model, lexicon and language model in neural Transducer also brings
about challenges to utilize pure text for language model adaptation. This
drawback might prevent their potential applications in practice. In order to
address this issue, in this paper, we propose a novel model, factorized neural
Transducer, by factorizing the blank and vocabulary prediction, and adopting a
standalone language model for the vocabulary prediction. It is expected that
this factorization can transfer the improvement of the standalone language
model to the Transducer for speech recognition, which allows various language
model adaptation techniques to be applied. We demonstrate that the proposed
factorized neural Transducer yields 15% to 20% WER improvements when
out-of-domain text data is used for language model adaptation, at the cost of a
minor degradation in WER on a general test set.


  In existing theoretical approaches to core-level excitations of
transition-metal ions in solids relaxation and polarization effects due to the
inner core hole are often ignored or described phenomenologically. Here we set
up an ab initio computational scheme that explicitly accounts for such physics
in the calculation of x-ray absorption and resonant inelastic x-ray scattering
spectra. Good agreement is found with experimental transition-metal $L$-edge
data for the strongly correlated $d^9$ cuprate Li$_2$CuO$_2$, for which we
determine the absolute scattering intensities. The newly developed methodology
opens the way for the investigation of even more complex $d^n$ electronic
structures of group VI B to VIII B correlated oxide compounds.


  An overview is given of recent advances in the nonequilibrium statistical
mechanics of quantum systems and, especially, of time-reversal symmetry
relations that have been discovered in this context. The systems considered are
driven out of equilibrium by time-dependent forces or by coupling to large
reservoirs of particles and energy. The symmetry relations are established for
the exchange of energy and particles between the subsystem and its environment.
These results have important consequences. In particular, generalizations of
the Kubo formula and the Casimir-Onsager reciprocity relations can be deduced
beyond linear response properties. Applications to electron quantum transport
in mesoscopic semiconducting circuits are discussed.


  This paper reports state-of-the-art electronic structure calculations on the
deposition of strontium on the technologically relevant, (001) orientated
silicon surface. We identified the surface reconstructions from zero to four
thirds monolayers and relate them to experimentally reported data. A phase
diagram is proposed. We predict phases at 1/6, 1/4, 1/2, 2/3 and 1 monolayers.
Our results are expected to provide valuable information in order to understand
heteroepitaxial growth of a prominent class of high-K oxides around SrTiO3. The
insight obtained for strontium is expected to be transferable to other alkaline
earth metals.


  It is well accepted that a magnetic flux rope (MFR) is a critical component
of many coronal mass ejections (CMEs), yet how it evolves toward eruption
remains unclear. Here we investigate the continuous evolution of a pre-existing
MFR, which is rooted in strong photospheric magnetic fields and electric
currents. The evolution of the MFR is observed by the Solar Terrestrial
Relations Observatory (STEREO) and the Solar Dynamics Observatory (SDO) from
multiple viewpoints. From STEREO's perspective, the MFR starts to rise slowly
above the limb five hours before it erupts as a halo CME on 2012 June 14. In
SDO observations, conjugate dimmings develop on the disk, simultaneously with
the gradual expansion of the MFR, suggesting that the dimmings map the MFR's
feet. The evolution comprises a two-stage gradual expansion followed by another
stage of rapid acceleration/eruption. Quantitative measurements indicate that
magnetic twist of the MFR increases from 1.0 +/- 0.5 to 2.0 +/- 0.5 turns
during the five-hour expansion, and further increases to about 4.0 turns per AU
when detected as a magnetic cloud at 1 AU two day later. In addition, each
stage is preceded by flare(s), implying reconnection is actively involved in
the evolution and eruption of the MFR. The implications of these measurements
on the CME initiation mechanisms are discussed.


  We study the necessity of predictive information in a class of queueing
admission control problems, where a system manager is allowed to divert
incoming jobs up to a fixed rate, in order to minimize the queueing delay
experienced by the admitted jobs.
  Spencer et al. (2014) show that the system's delay performance can be
significantly improved by having access to future information in the form of a
lookahead window, during which the times of future arrivals and services are
revealed. They prove that, while delay under an optimal online policy diverges
to infinity in the heavy-traffic regime, it can stay bounded by making use of
future information. However, the diversion polices of Spencer et al. (2014)
require the length of the lookahead window to grow to infinity at a non-trivial
rate in the heavy-traffic regime, and it remained open whether substantial
performance improvement could still be achieved with less future information.
  We resolve this question to a large extent by establishing an asymptotically
tight lower bound on how much future information is necessary to achieve
superior performance, which matches the upper bound of Spencer et al. (2014) up
to a constant multiplicative factor. Our result hence demonstrates that the
system's heavy-traffic delay performance is highly sensitive to the amount of
future information available. Our proof is based on analyzing certain excursion
probabilities of the input sample paths, and exploiting a connection between a
policy's diversion decisions and subsequent server idling, which may be of
independent interest for related dynamic resource allocation problems.


  We propose a novel experiment to identify the symmetry of superconductivity
on the basis of theoretical results for differential conductance of a normal
metal connected to a superconductor. The proximity effect from the
superconductor modifies the conductance of the remote current depending
remarkably on the pairing symmetry: spin-singlet or spin-triplet. The clear-cut
difference in the conductance is explained by symmetry of Cooper pairs in a
normal metal with respect to frequency. In the spin-triplet case, the anomalous
transport is realized due to an odd-frequency symmetry of Cooper pairs.


  The celebrated Heinz inequality asserts that $ 2|||A^{1/2}XB^{1/2}|||\leq
|||A^{\nu}XB^{1-\nu}+A^{1-\nu}XB^{\nu}|||\leq |||AX+XB|||$ for $X \in
\mathbb{B}(\mathscr{H})$, $A,B\in \+$, every unitarily invariant norm
$|||\cdot|||$ and $\nu \in [0,1]$. In this paper, we present several
improvement of the Heinz inequality by using the convexity of the function
$F(\nu)=|||A^{\nu}XB^{1-\nu}+A^{1-\nu}XB^{\nu}|||$, some integration techniques
and various refinements of the Hermite--Hadamard inequality. In the setting of
matrices we prove that \begin{eqnarray*}
&&\hspace{-0.5cm}\left|\left|\left|A^{\frac{\alpha+\beta}{2}}XB^{1-\frac{\alpha+\beta}{2}}+A^{1-\frac{\alpha+\beta}{2}}XB^{\frac{\alpha+\beta}{2}}\right|\right|\right|\leq\frac{1}{|\beta-\alpha|}
\left|\left|\left|\int_{\alpha}^{\beta}\left(A^{\nu}XB^{1-\nu}+A^{1-\nu}XB^{\nu}\right)d\nu\right|\right|\right|\nonumber\\
&&\qquad\qquad\leq
\frac{1}{2}\left|\left|\left|A^{\alpha}XB^{1-\alpha}+A^{1-\alpha}XB^{\alpha}+A^{\beta}XB^{1-\beta}+A^{1-\beta}XB^{\beta}\right|\right|\right|\,,
\end{eqnarray*} for real numbers $\alpha, \beta$.


  We show that the elasticity Hilbert complex with mixed boundary conditions on
bounded strong Lipschitz domains is closed and compact. The crucial results are
compact embeddings which follow by abstract arguments using functional analysis
together with particular regular decompositions. Higher Sobolev order results
are proved as well. This paper extends recent results on the de Rham Hilbert
complex with mixed boundary conditions from [11] and recent results on the
elasticity Hilbert complex with empty or full boundary conditions from [15].


  In this paper, Bayesian optimisation is used to simultaneously search the
optimal values of the shape parameter and the radius in radial basis function
partition of unity interpolation problem. It is a probabilistic iterative
approach that models the error function with a step-by-step self-updated
Gaussian process, whereas partition of unity leverages a mesh-free method that
allows us to reduce cost-intensive computations when the number of scattered
data is very large, as the entire domain is decomposed into several smaller
subdomains of variable radius. Numerical experiments on the scattered data
interpolation problem show that the combination of these two tools sharply
reduces the search time with respect to other techniques such as the leave one
out cross validation.


  We consider a reconciliation problem, where two hosts wish to synchronize
their respective sets. Efficient solutions for minimizing the communication
cost between the two hosts have been previously proposed in the literature.
However, they rely on prior knowledge about the size of the set differences
between the two sets to be reconciled. In this paper, we propose a method which
can achieve comparable efficiency without assuming this prior knowledge. Our
method uses compressive sensing techniques which can leverage the expected
sparsity in set differences. We study the performance of the method via
theoretical analysis and numerical simulations.


  With the explosive growth of internet technology, easy transfer of digital
multimedia is feasible. However, this kind of convenience with which authorized
users can access information, turns out to be a mixed blessing due to
information piracy. The emerging field of Digital Rights Management (DRM)
systems addresses issues related to the intellectual property rights of digital
content. In this paper, an object-oriented (OO) DRM system, called "Imaging
System with Watermarking and Attack Resilience" (ISWAR), is presented that
generates and authenticates color images with embedded mechanisms for
protection against infringement of ownership rights as well as security
attacks. In addition to the methods, in the object-oriented sense, for
performing traditional encryption and decryption, the system implements methods
for visible and invisible watermarking. This paper presents one visible and one
invisible watermarking algorithm that have been integrated in the system. The
qualitative and quantitative results obtained for these two watermarking
algorithms with several benchmark images indicate that high-quality watermarked
images are produced by the algorithms. With the help of experimental results it
is demonstrated that the presented invisible watermarking techniques are
resilient to the well known benchmark attacks and hence a fail-safe method for
providing constant protection to ownership rights.


  The leading order corrections to Reissner--Nordstrom solutions of the
Einstein's equations on noncommutative space time have been worked out basing
on a noncommutative gauge theory of gravity. From the corrcted metric the
horizons have been derived and the curvature scalar is also computed. The
introduction of noncommutativity leads to the removal of the coordinate
singularities.


  Superconductivity in low-dimensional compounds has long attracted much
interest. Here we report superconductivity in a low-dimensional ternary
telluride Ta4Pd3Te16 in which the repeating layers contain edge-sharing
octahedrally-coordinated PdTe2 chains along the crystallographic b axis.
Measurements of electrical resistivity, magnetic susceptibility and specific
heat on the Ta4Pd3Te16 crystals, grown via a self-flux method, consistently
demonstrate bulk superconductivity at 4.6 K. Further analyses of the data
indicate significant electron-electron interaction, which allows electronic
Cooper pairing in the present system.


  A key goal of freeform manufacturing is to achieve speed, precision and
design freedom at the same time. Volumetric additive manufacturing via
tomographic reconstruction offers high speed but cannot achieve optical
resolution at high design freedom. Here we combine polyspectral tomographic
reconstruction with pseudo-negative illumination to resolve the trilemma. We
used the same units on a digital micromirror device to modulate two colors of
illumination simultaneously to control the chemical stability of binary
photoinhibition. The methodology enables a typical 4K projector to 3D-print up
to 8.9 billion voxels in a 30x30x50 mm vial at two-digit-m-resolution in
minutes, with prolonged process window and geometrically fidelous greyscale
printing. These results are linked to, and thus can be improved directly upon
upgrading, the precision of the optomechanical modality.


  We consider numerical solution of Coupled Nonlinear Schr\"{o}dinger Equation.
We prove stability and convergence in the $L_2$ space for an explicit scheme
which estimations is used for implicit scheme and compare both method. As a
test we compare numerical solutions of Manakov system with known analytical
solitonic solutions and as example of general system - evolution of two
impulses with different group velocity (model of pulses interaction in optic
fibers). Last example, a rectangular pulse evolution, shows asymptotic behavior
typical for Nonlinear Schr\"{o}dinger Equation asymptotics with the same
initial condition.


  Higher-order tensor datasets arise commonly in recommendation systems,
neuroimaging, and social networks. Here we develop probable methods for
estimating a possibly high rank signal tensor from noisy observations. We
consider a generative latent variable tensor model that incorporates both high
rank and low rank models, including but not limited to, simple hypergraphon
models, single index models, low-rank CP models, and low-rank Tucker models.
Comprehensive results are developed on both the statistical and computational
limits for the signal tensor estimation. We find that high-dimensional latent
variable tensors are of log-rank; the fact explains the pervasiveness of
low-rank tensors in applications. Furthermore, we propose a polynomial-time
spectral algorithm that achieves the computationally optimal rate. We show that
the statistical-computational gap emerges only for latent variable tensors of
order 3 or higher. Numerical experiments and two real data applications are
presented to demonstrate the practical merits of our methods.


  Recently E. E. Donets, D. V. Galtsov, and the author reported the results of
numerical and analytical investigation of the SU(2) Einstein-Yang-Mills black
hole interior solutions (gr-qc/9612067). It was shown that a generic interior
solution develops a new type of an infinitely oscillating behavior with
exponentially growing amplitude. Numerical data for three sequential
oscillations were presented. The numerical integration technique was not
discussed. Later P. Breitenlohner, G. Lavrelashvili, and D. Maison confirmed
our main results (gr-qc/9703047). But they have made some misleading
statements. In particular, they claimed, discussing the oscillations, that ``as
one performs the numerical integration one quickly runs into serious
problems...'' so that ``it is practically impossible to follow more than one or
two of them numerically'' because ``the numerical integration procedure breaks
down''. It is shown here that trivial logarithmic substitutions and integration
along the integral curve solve these ``serious problems'' easily.


  The temperature at the end of reheating and the length of this cosmological
phase can be bound to the inflationary observables if one considers the
cosmological evolution from the time of Hubble crossing until today. There are
many examples in the literature where it is made for single-field inflationary
models and a constant equation of state during reheating. We adopt two simple
varying equation of state parameters during reheating, combine the allowed
range of the reheating parameters with the observational limits of the scalar
perturbations spectral index and compare the constraints of some inflationary
models with the case of a constant equation of state parameter during
reheating.


  In this paper, we consider the following Choquard type equation
\begin{equation} \left\{\begin{aligned} &-\Delta u+\lambda
u=\gamma(\Phi_N(|x|)\ast|u|^p)u \ \ \mbox{in $\mathbb{R}^N$}, \\
&\lim\limits_{|x|\to\infty}u(x)=0,\\ \end{aligned}\right. \end{equation}
  where $N\geq2,\lambda>0,\gamma>0, p\in[1,2]$ and $\Phi_N(|x|)$ denotes the
fundamental solution of the Laplacian $-\Delta$ on $\mathbb{R}^N$. This
equation does not have a variational frame when $p\neq 2.$ Instead of
variational methods, we prove the existence and uniqueness of positive radial
solutions of the above equation via the shooting method by establishing some
new differential inequalities. The proofs are based on an analysis of the
corresponding system of second-order differential equations, and our results
extend the existing ones in the literature from $p=2$ to $p\in[1,2]$.


  The periodic s-d(f) exchange model is characterized by a wide variety of
interesting applications, a simple mathematical structure and a limited number
of reliable approximations which take care of the quantum nature of the
participating spins. We suggest the use of a projection-operator method for
getting information perturbationally, which are not accessible via diagrammatic
approaches. In this paper we present in particular results beyond perturbation
theory, which are obtained such that almost all exactly known limiting cases
are incorporated correctly. We discuss a variety of possible methods and
evaluate their consequences for one-particle properties. These considerations
serve as a guideline for a more effective approach to the model.


  Multimodal Large Language Models (MLLMs) have demonstrated remarkable
capabilities in processing and generating content across multiple data
modalities, including text, images, audio, and video. However, a significant
drawback of MLLMs is their reliance on static training data, leading to
outdated information and limited contextual awareness. This static nature
hampers their ability to provide accurate, up-to-date responses, particularly
in dynamic or rapidly evolving contexts. Integrating Multimodal
Retrieval-augmented Generation (Multimodal RAG) offers a promising solution,
but the system would inevitably encounter the multi-granularity noisy
correspondence (MNC) problem, which involves two types of noise: coarse-grained
(query-caption) and fine-grained (query-image). This noise hinders accurate
retrieval and generation. In this work, we propose \textbf{RagLLaVA}, a novel
framework with knowledge-enhanced reranking and noise-injected training, to
address these limitations. We instruction-tune the MLLM with a simple yet
effective instruction template to induce its ranking ability and serve it as a
reranker to precisely filter the top-k retrieved images. For generation, we
inject visual noise during training at the data and token levels to enhance the
generator's robustness. Extensive experiments are conducted on the subsets of
two datasets that require retrieving and reasoning over images to answer a
given query. Our results demonstrate the superiority of RagLLaVA in retrieving
accurately and generating robustly. Code and models are available at
https://github.com/IDEA-FinAI/RagLLaVA.


  SAX J1819.3-2525 is a nearby X-ray transient which exhibited a fast and large
X-ray outburst on Sep. 15, 1999 (Smith et al. 1999). The Wide Field Cameras and
the Narrow Field Instruments (NFI) on board BeppoSAX observed SAX J1819.3-2525
at various stages of its activity before that, in the spring and fall of 1999.
The fluxes range between 0.012 and 0.3 Crab units (2-10 keV). The NFI
observation is unique because it is the longest semi-continuous observation of
SAX J1819.3-2525 so far, and it offers a study of the spectrum at a relatively
high resolution of 8% full width at half maximum at 6 keV. We discuss the
observations with emphasis on the X-ray spectrum. A strong Fe-K emission line
was detected in SAX J1819.3-2525 with an equivalent width between 0.3 and 1
keV. The line energy is up to 6.85 +/- 0.02 keV and suggests the presence of
highly ionized iron. We identify this as fluorescent emission from a
photo-ionized plasma. The continuum spectrum is typical for a low-mass X-ray
binary in which emission from an accretion disk corona plays an important role.
There is no sign of an eclipse or periodic signal due to the binary orbit in
this exposure, despite the fact that the twin jets seen at radio wavelengths
suggest a high inclination angle.


  A popular metric to evaluate the performance of fuzzers is branch coverage.
However, we argue that focusing solely on covering many different branches
(i.e., the richness) is not sufficient since the majority of the covered
branches may have been exercised only once, which does not inspire a high
confidence in the reliability of the covered code. Instead, the distribution of
the executed branches (i.e., the evenness) should also be considered. That is,
behavioral diversity is only given if the generated inputs not only trigger
many different branches, but also trigger them evenly often with diverse
inputs. We introduce BeDivFuzz, a feedback-driven fuzzing technique for
generator-based fuzzers. BeDivFuzz distinguishes between structure-preserving
and structure-changing mutations in the space of syntactically valid inputs,
and biases its mutation strategy towards validity and behavioral diversity
based on the received program feedback. We have evaluated BeDivFuzz on Ant,
Maven, Rhino, Closure, Nashorn, and Tomcat. The results show that BeDivFuzz
achieves better behavioral diversity than the state of the art, measured by
established biodiversity metrics, namely the Hill numbers, from the field of
ecology.


  Iron oxides are fundamental components of planet-forming materials.
Understanding the Fe-O system's behavior and properties under high pressure can
help us identify many new phases and states possible in exoplanetary interiors,
especially terrestrial ones. Using the adaptive genetic algorithm (AGA), we
investigate the structure of iron oxides for a wide range of stoichiometries
($0.25\leq x_O \leq 0.8$) at 1, 2, and 3 TPa. Five unreported ground-state
structures with Fe$_2$O, FeO, Fe$_3$O$_5$, FeO$_2$, and FeO$_4$ compositions
are identified. The calculated density of states (DOS) suggests that, except
for FeO$_4$, all phases are metallic, but their carrier densities decrease with
increasing pressure and oxygen content. The cluster alignment analysis of
stable and metastable phases shows that several motifs may co-exist in a
structure of iron oxides with low O content. In contrast, most iron oxides with
high O content adopt a simple BCC motif at TPa pressures. Our results provide a
crystal structure database of iron oxides for modeling and understanding the
interiors of exoplanets.


  We study representations of hemistrict Lie 2-algebras and give a functorial
construction of their cohomology. We prove that both the cohomology of an
injective hemistrict Lie 2-algebra $L$ and the cohomology of the semistrict Lie
2-algebra obtained from skew-symmetrization of $L$ are isomorphic to the
Chevalley-Eilenberg cohomology of the induced Lie algebra
$L_{\operatorname{Lie}}$.


  A recent trend in the signal/image processing literature is the optimization
of Fourier sampling schemes for specific datasets of signals. In this paper, we
explain why choosing optimal non Cartesian Fourier sampling patterns is a
difficult nonconvex problem by bringing to light two optimization issues. The
first one is the existence of a combinatorial number of spurious minimizers for
a generic class of signals. The second one is a vanishing gradient effect for
the high frequencies. We conclude the paper by showing how using large datasets
can mitigate first effect and illustrate experimentally the benefits of using
stochastic gradient algorithms with a variable metric.


  Multiple robots could perceive a scene (e.g., detect objects) collaboratively
better than individuals, although easily suffer from adversarial attacks when
using deep learning. This could be addressed by the adversarial defense, but
its training requires the often-unknown attacking mechanism. Differently, we
propose ROBOSAC, a novel sampling-based defense strategy generalizable to
unseen attackers. Our key idea is that collaborative perception should lead to
consensus rather than dissensus in results compared to individual perception.
This leads to our hypothesize-and-verify framework: perception results with and
without collaboration from a random subset of teammates are compared until
reaching a consensus. In such a framework, more teammates in the sampled subset
often entail better perception performance but require longer sampling time to
reject potential attackers. Thus, we derive how many sampling trials are needed
to ensure the desired size of an attacker-free subset, or equivalently, the
maximum size of such a subset that we can successfully sample within a given
number of trials. We validate our method on the task of collaborative 3D object
detection in autonomous driving scenarios.


  Hyperspectral image fusion (HIF) is critical to a wide range of applications
in remote sensing and many computer vision applications. Most traditional HIF
methods assume that the observation model is predefined or known. However, in
real applications, the observation model involved are often complicated and
unknown, which leads to the serious performance drop of many advanced HIF
methods. Also, deep learning methods can achieve outstanding performance, but
they generally require a large number of image pairs for model training, which
are difficult to obtain in realistic scenarios. Towards these issues, we
proposed a self-regression learning method that alternatively reconstructs
hyperspectral image (HSI) and estimate the observation model. In particular, we
adopt an invertible neural network (INN) for restoring the HSI, and two
fully-connected network (FCN) for estimating the observation model. Moreover,
\emph{SoftMax} nonlinearity is applied to the FCN for satisfying the
non-negative, sparsity and equality constraints. Besides, we proposed a local
consistency loss function to constrain the observation model by exploring
domain specific knowledge. Finally, we proposed an angular loss function to
improve spectral reconstruction accuracy. Extensive experiments on both
synthetic and real-world dataset show that our model can outperform the
state-of-the-art methods


  In this paper, a new approach is proposed for automated software maintenance.
The tool is able to perform 26 different refactorings. It also contains a large
selection of metrics to measure the impact of the refactorings on the software
and six different search based optimization algorithms to improve the software.
This tool contains both mono-objective and multi-objective search techniques
for software improvement and is fully automated. The paper describes the
various capabilities of the tool, the unique aspects of it, and also presents
some research results from experimentation. The individual metrics are tested
across five different codebases to deduce the most effective metrics for
general quality improvement. It is found that the metrics that relate to more
specific elements of the code are more useful for driving change in the search.
The mono-objective genetic algorithm is also tested against the multi-objective
algorithm to see how comparable the results gained are with three separate
objectives. When comparing the best solutions of each individual objective the
multi-objective approach generates suitable improvements in quality in less
time, allowing for rapid maintenance cycles.


  Let $R$ be a commutative ring with unit. We develop a Hochschild cohomology
theory in the category $\mathcal{F}$ of linear functors defined from an
essentially small symmetric monoidal category enriched in $R$-Mod, to $R$-Mod.
The category $\mathcal{F}$ is known to be symmetric monoidal too, so one can
consider monoids in $\mathcal{F}$ and modules over these monoids, which allows
for the possibility of a Hochschild cohomology theory. The emphasis of the
article is in considering natural hom constructions appearing in this context.
These homs, together with the abelian structure of $\mathcal{F}$ lead to nice
definitions and provide effective tools to prove the main properties and
results of the classical Hochschild cohomology theory.


  Clear presentation of uncertainty is an exception rather than rule in media
articles, data-driven reports, and consumer applications, despite proposed
techniques for communicating sources of uncertainty in data. This work
considers, Why do so many visualization authors choose not to visualize
uncertainty? I contribute a detailed characterization of practices,
associations, and attitudes related to uncertainty communication among
visualization authors, derived from the results of surveying 90 authors who
regularly create visualizations for others as part of their work, and
interviewing thirteen influential visualization designers. My results highlight
challenges that authors face and expose assumptions and inconsistencies in
beliefs about the role of uncertainty in visualization. In particular, a clear
contradiction arises between authors' acknowledgment of the value of depicting
uncertainty and the norm of omitting direct depiction of uncertainty. To help
explain this contradiction, I present a rhetorical model of uncertainty
omission in visualization-based communication. I also adapt a formal
statistical model of how viewers judge the strength of a signal in a
visualization to visualization-based communication, to argue that uncertainty
communication necessarily reduces degrees of freedom in viewers' statistical
inferences. I conclude with recommendations for how visualization research on
uncertainty communication could better serve practitioners' current needs and
values while deepening understanding of assumptions that reinforce uncertainty
omission.


  We compute the trace of an endomorphism in equivariant bivariant K-theory for
a compact group G in several ways: geometrically using geometric
correspondences, algebraically using localisation, and as a Hattori-Stallings
trace. This results in an equivariant version of the classical Lefschetz
fixed-point theorem, which applies to arbitrary equivariant correspondences,
not just maps.


  We present a new approach for identifying the Tip of the Red Giant Branch
(TRGB) which, as we show, works robustly even on sparsely populated targets.
Moreover, the approach is highly adaptable to the available data for the
stellar population under study, with prior information readily incorporable
into the algorithm. The uncertainty in the derived distances is also made
tangible and easily calculable from posterior probability distributions. We
provide an outline of the development of the algorithm and present the results
of tests designed to characterize its capabilities and limitations. We then
apply the new algorithm to three M31 satellites: Andromeda I, Andromeda II and
the fainter Andromeda XXIII, using data from the Pan-Andromeda Archaeological
Survey (PAndAS), and derive their distances as $731^{(+ 5) + 18}_{(- 4) - 17}$
kpc, $634^{(+ 2) + 15}_{(- 2) - 14}$ kpc and $733^{(+ 13)+ 23}_{(- 11) - 22}$
kpc respectively, where the errors appearing in parentheses are the components
intrinsic to the method, while the larger values give the errors after
accounting for additional sources of error. These results agree well with the
best distance determinations in the literature and provide the smallest
uncertainties to date. This paper is an introduction to the workings and
capabilities of our new approach in its basic form, while a follow-up paper
shall make full use of the method's ability to incorporate priors and use the
resulting algorithm to systematically obtain distances to all of M31's
satellites identifiable in the PAndAS survey area.


  In this thesis, Maggiore's method (MM), which evaluates the transition
frequency that appears in the adiabatic invariant from the highly damped
quasinormal mode frequencies (QNMFs), is used to investigate the entropy and
area spectra of the Garfinkle-Horowitz-Strominger black hole (GHSBH), the
four-dimensional Lifshitz black hole with zero dynamical exponent (ZZLBH), and
the rotating linear dilaton black hole (RLDBH), respectively. For studying the
GHSBH's quantization, instead of ordinary quasinormal modes (QNMs), the boxed
QNMs (BQNMs) that are the characteristic resonance spectra of the confined
scalar fields in the GHSBH geometry are computed. To this end, it is assumed
that the GHSBH has a confining mirror placed in the vicinity of its event
horizon. It is then shown how the complex resonance frequencies of this caged
black hole (BH) are computed considering the scalar perturbations around the
event horizon. Additionally, the resonance modes (RMs) and the RM frequencies
(RMFs) of the GHSBH are computed by using the confined scalar waves with high
azimuthal quantum number. The entropy and area quantizations are shown to be
independent of the GHSBH's parameters, however, both spectra are equally
spaced.


  The Heisenberg limit provides a quadratic improvement over the standard
quantum limit, and is the maximum quantum advantage that linear quantum sensors
could provide over classical methods. It remains elusive, however, because of
the inevitable presence of noise decohering quantum sensors. Namely, if
infinite rounds of quantum error correction corrects any part of a quantum
sensor's signal, a no-go result purports that the standard quantum limit
scaling can not be exceeded. We side-step this no-go result by using an optimal
finite number of rounds of quantum error correction, such that even if part of
the signal is corrected away, our quantum field sensing protocol can approach
the Heisenberg limit in the face of a linear rate of deletion errors. Here, we
focus on quantum error correction codes within the symmetric subspace.
Symmetric states of collective angular momentum are good candidates for
multi-qubit probe states in quantum sensors because they are easy to prepare
and can be controlled without requiring individual addressability. We show how
the representation theory of the symmetric group can inform practical decoding
algorithms for permutation-invariant codes. These decoding algorithms involve
measurements of total angular momentum, quantum Schur transforms or logical
state teleportations, and geometric pulse gates. Our completion of the theory
of permutation-invariant codes allows near-term implementation of quantum error
correction using simple quantum control techniques.


  As the main means of communication for deaf people, sign language has a
special grammatical order, so it is meaningful and valuable to develop a
real-time translation system for sign language. In the research process, we
added a TSM module to the lightweight neural network model for the large
Chinese continuous sign language dataset . It effectively improves the network
performance with high accuracy and fast recognition speed. At the same time, we
improve the Bert-Base-Chinese model to divide Chinese sentences into words and
mapping the natural word order to the statute sign language order, and finally
use the corresponding word videos in the isolated sign language dataset to
generate the sentence video, so as to achieve the function of text-to-sign
language translation. In the last of our research we built a system with sign
language recognition and translation functions, and conducted performance tests
on the complete dataset. The sign language video recognition accuracy reached
about 99.3% with a time of about 0.05 seconds, and the sign language generation
video time was about 1.3 seconds. The sign language system has good performance
performance and is feasible.


  We introduce and study three new statistics on the hyperoctahedral group
$B_{n}$, and show that they give two generalizations of Carlitz's identity for
the descent number and major index over $S_{n}$. This answers a question posed
by Foata.


  We first construct generalized Riemann-normal coordinates by using
autoparallels, instead of geodesics, in an arbitrary Riemann-Cartan spacetime.
With the aid of generalized Riemann-normal coordinates and their associated
orthonormal frames, we obtain a momentum-space representation of the Feynman
propagator for scalar fields, which is a direct generalization of Bunch and
Parker's works to curved spacetime with torsion. We further derive the
proper-time representation in $n$ dimensional Riemann-Cartan spacetime from the
momentum-space representation. It leads us to obtain the renormalization of
one-loop effective Lagrangians of free scalar fields by using dimensional
regularization. When torsion tensor vanishes, our resulting momentum-space
representation returns to the standard Riemannian results.


  Uncertainties in flavor and mass eigenstates of neutrinos are considered
within the majorization approach. Nontrivial bounds reflect the fact that
neutrinos cannot be simultaneously in flavor and mass eigenstates. As
quantitative measures of uncertainties, both the R\'{e}nyi and Tsallis
entropies are utilized. Within the current amount of experience concerning the
mixing matrix, majorization uncertainty relations need to put values of only
two parameters, viz. $\theta_{12}$ and $\theta_{13}$. That is, the majorization
approach is applicable within the same framework as the Maassen-Uffink relation
recently utilized in this context. We also consider the case of detection
inefficiencies, since it can naturally be incorporated into the entropic
framework. Short comments on applications of entropic uncertainty relations
with quantum memory are given.


  In Reinforcement Learning (RL), it is common to use optimistic initialization
of value functions to encourage exploration. However, such an approach
generally depends on the domain, viz., the scale of the rewards must be known,
and the feature representation must have a constant norm. We present a simple
approach that performs optimistic initialization with less dependence on the
domain.


  We revisit lower bounds on the regret in the case of multi-armed bandit
problems. We obtain non-asymptotic, distribution-dependent bounds and provide
straightforward proofs based only on well-known properties of Kullback-Leibler
divergences. These bounds show in particular that in an initial phase the
regret grows almost linearly, and that the well-known logarithmic growth of the
regret only holds in a final phase. The proof techniques come to the essence of
the information-theoretic arguments used and they are deprived of all
unnecessary complications.


  Sarcasm is a form of figurative language where the intended meaning of a
sentence differs from its literal meaning. This poses a serious challenge to
several Natural Language Processing (NLP) applications such as Sentiment
Analysis, Opinion Mining, and Author Profiling. In this paper, we present our
participating system to the intended sarcasm detection task in English and
Arabic languages. Our system\footnote{The source code of our system is
available at \url{https://github.com/AbdelkaderMH/iSarcasmEval}} consists of
three deep learning-based models leveraging two existing pre-trained language
models for Arabic and English. We have participated in all sub-tasks. Our
official submissions achieve the best performance on sub-task A for Arabic
language and rank second in sub-task B. For sub-task C, our system is ranked
7th and 11th on Arabic and English datasets, respectively.


  A recent line of works apply machine learning techniques to assist or rebuild
cost-based query optimizers in DBMS. While exhibiting superiority in some
benchmarks, their deficiencies, e.g., unstable performance, high training cost,
and slow model updating, stem from the inherent hardness of predicting the cost
or latency of execution plans using machine learning models. In this paper, we
introduce a learning-to-rank query optimizer, called Lero, which builds on top
of a native query optimizer and continuously learns to improve the optimization
performance. The key observation is that the relative order or rank of plans,
rather than the exact cost or latency, is sufficient for query optimization.
Lero employs a pairwise approach to train a classifier to compare any two plans
and tell which one is better. Such a binary classification task is much easier
than the regression task to predict the cost or latency, in terms of model
efficiency and accuracy. Rather than building a learned optimizer from scratch,
Lero is designed to leverage decades of wisdom of databases and improve the
native query optimizer. With its non-intrusive design, Lero can be implemented
on top of any existing DBMS with minimal integration efforts. We implement Lero
and demonstrate its outstanding performance using PostgreSQL. In our
experiments, Lero achieves near optimal performance on several benchmarks. It
reduces the plan execution time of the native optimizer in PostgreSQL by up to
70% and other learned query optimizers by up to 37%. Meanwhile, Lero
continuously learns and automatically adapts to query workloads and changes in
data.


  We have developed a scanning near-field microwave microscope that operates at
cryogenic temperatures. Our system uses an open-ended coaxial probe with a 200
mm inner conductor diameter and operates from 77 to 300 K in the 0.01-20 GHz
frequency range. In this paper, we present microwave images of the electric
field distribution above a Tl2Ba2CaCu2O8 microstrip resonator at 77 K, measured
at several heights. In addition, we describe the use of a frequency-following
circuit to study the influence of the probe on the resonant frequency of the
device.