  Vast literature has compared the recordings of biological neurons in the
brain to deep neural networks. The ultimate goal is to interpret deep networks
or to better understand and encode biological neural systems. Recently, there
has been a debate on whether system identification is possible and how much it
can tell us about the brain computation. System identification recognizes
whether one model is more valid to represent the brain computation over
another. Nonetheless, previous work did not consider the time aspect and how
video and dynamics (e.g., motion) modelling in deep networks relate to these
biological neural systems within a large-scale comparison. Towards this end, we
propose a system identification study focused on comparing single image vs.
video understanding models with respect to the visual cortex recordings. Our
study encompasses two sets of experiments; a real environment setup and a
simulated environment setup. The study also encompasses more than 30 models
and, unlike prior works, we focus on convolutional vs. transformer-based,
single vs. two-stream, and fully vs. self-supervised video understanding
models. The goal is to capture a greater variety of architectures that model
dynamics. As such, this signifies the first large-scale study of video
understanding models from a neuroscience perspective. Our results in the
simulated experiments, show that system identification can be attained to a
certain level in differentiating image vs. video understanding models.
Moreover, we provide key insights on how video understanding models predict
visual cortex responses; showing video understanding better than image
understanding models, convolutional models are better in the early-mid regions
than transformer based except for multiscale transformers that are still good
in predicting these regions, and that two-stream models are better than single
stream.


  Machine \emph{unlearning}, which involves erasing knowledge about a
\emph{forget set} from a trained model, can prove to be costly and infeasible
by existing techniques. We propose a nearly compute-free zero-shot unlearning
technique based on a discrete representational bottleneck. We show that the
proposed technique efficiently unlearns the forget set and incurs negligible
damage to the model's performance on the rest of the data set. We evaluate the
proposed technique on the problem of \textit{class unlearning} using three
datasets: CIFAR-10, CIFAR-100, and LACUNA-100. We compare the proposed
technique to SCRUB, a state-of-the-art approach which uses knowledge
distillation for unlearning. Across all three datasets, the proposed technique
performs as well as, if not better than SCRUB while incurring almost no
computational cost.


  We present the results of detailed analysis of X-ray data in 3-20 keV range
from a ~70 day outburst of the neutron star transient Aquila X-1 during
September-November 2000. Optical monitoring with the YALO 1m telescope was used
to trigger X-ray observations with Rossi X-ray Timing Explorer (RXTE) in order
to follow the outburst from a very early stage. In this paper we discuss the
correlated evolution in time of features in the spectral and temporal domains,
for the entire outburst. The state transition from low/hard state to high/soft
state during the rise of the outburst occurs at higher luminosity than the
transition back to low/hard state during the decay, as has also been observed
in other outbursts. Fourier power spectra at low frequencies show a broken
power law continuum during the rising phase, with the break frequency
increasing with time. During the decline from maximum the source evolves to a
position in the hardness-intensity plane as well as in the color-color diagram
which is similar to, but distinct from, the canonical high/soft state. High
frequency quasi-periodic oscillations from 636-870 Hz were seen only during
this transitional state.


  Spin $\frac{1}{2}$ honeycomb materials have gained substantial interest due
to their exotic magnetism and possible application in quantum computing.
However, in all current materials out-of-plane interactions are interfering
with the in-plane order, hence a true 2D magnetic honeycomb system is still of
demand. Here, we report the exfoliation of the magnetic semiconductor
$\alpha$-RuCl$_3$ into the first halide monolayers and the magnetic
characterization of the spin $\frac{1}{2}$ honeycomb arrangement of
turbostratically stacked RuCl$_3$ monolayers. The exfoliation is based on a
reductive lithiation/hydration approach, which gives rise to a loss of
cooperative magnetism due to the disruption of the spin $\frac{1}{2}$ state by
electron injection into the layers. After an oxidative treatment, cooperative
magnetism similar to the bulk is restored. The oxidized pellets of restacked
single layers feature a magnetic transition at T$_N$ = 7 K in the in-plane
direction, while the magnetic properties in the out-of-plane direction vastly
differ from bulk $\alpha$-RuCl$_3$. The macroscopic pellets of RuCl$_3$
therefore behave like a stack of monolayers without any symmetry relation in
the stacking direction. The deliberate introduction of turbostratic disorder to
manipulate the spin structure of RuCl$_3$ is of interest for research in
frustrated magnetism and complex magnetic order as predicted by the
Kitaev-Heisenberg model.


  We discuss the orbital effect of a tilted magnetic field on the quantum Hall
effect in parabolic quantum wells. Many-body states realized at the fractional
1/3 and 1/2 filling of the second electronic subband are studied using
finite-size exact diagonalization. In both cases, we obtain the phase diagram
consisting of a fractional quantum Hall fluid phase that persists for moderate
tilts, and eventually undergoes a direct transition to the stripe phase. It is
shown that tilting of the field probes the geometrical degree of freedom of
fractional quantum Hall fluids, and can be partly related to the effect of
band-mass anisotropy.


  Let n be any positive integer and p any prime. Also, let X be any spectrum
and let K(n) denote the nth Morava K-theory spectrum. Then we construct a
descent spectral sequence with abutment pi_*(L_{K(n)}(X)) and E_2-term equal to
the continuous cohomology of G_n, the extended Morava stabilizer group, with
coefficients in a certain discrete G_n-module that is built from various
homotopy fixed point spectra of the Morava module of X. This spectral sequence
can be contrasted with the K(n)-local E_n-Adams spectral sequence for
pi_*(L_{K(n)}(X)), whose E_2-term is not known to always be equal to a
continuous cohomology group.


  Using the integral field spectroscopic data from Mapping Nearby Galaxies at
Apache Point Observatory survey, we study the kinematics and stellar population
properties of the two counter-rotating stellar disks in a nearby galaxy SDSS
J074834.64+444117.8. We disentangle the two stellar disks by three methods,
including CaII $\lambda$8542 double Gaussian fit, pPXF spectral decomposition,
and orbit-based dynamical model. These three different methods give consistent
stellar kinematics. The pPXF spectral decomposition provides the spectra of two
stellar disks, with one being more luminous across the whole galaxy named
primary disk, and the other named secondary disk. The primary disk is
counter-rotating with ionized gas, while the secondary disk is co-rotating with
ionized gas. The secondary disk has younger stellar population and poorer
stellar metallicity than the primary disk. We estimate the stellar mass ratio
between the primary and secondary disks to be $\sim$5.2. The DESI $g$, $r$, $z$
color image doesn't show any merger remnant feature in this galaxy. These
findings support a scenario that the counter-rotating stellar disks in SDSS
J074834.64+444117.8 formed through gas accretion from the cosmic web or a
gas-rich companion.


  Magnetic proximity effect between topological insulator (TI) and
ferromagnetic insulator (FMI) is considered to have great potential in
spintronics. However, a complete determination of interfacial magnetic
structure has been highly challenging. We theoretically investigate the
interlayer exchange coupling of two FMIs separated by a TI thin film, and show
that the particular electronic states of the TI contributing to the proximity
effect can be directly identified through the coupling behavior between two
FMIs, together with a tunability of coupling constant. Such FMI/TI/FMI
structure not only serves as a platform to clarify the magnetic structure of
FMI/TI interface, but also provides insights into designing the magnetic
storage devices with ultrafast response.


  Cylindrical algebraic decomposition (CAD) is an important tool for the study
of real algebraic geometry with many applications both within mathematics and
elsewhere. It is known to have doubly exponential complexity in the number of
variables in the worst case, but the actual computation time can vary greatly.
It is possible to offer different formulations for a given problem leading to
great differences in tractability. In this paper we suggest a new measure for
CAD complexity which takes into account the real geometry of the problem. This
leads to new heuristics for choosing: the variable ordering for a CAD problem,
a designated equational constraint, and formulations for truth-table invariant
CADs (TTICADs). We then consider the possibility of using Groebner bases to
precondition TTICAD and when such formulations constitute the creation of a new
problem.


  We characterize preduals and K\"othe duals to a class of Sobolev multiplier
type spaces. Our results fit in well with the modern theory of function spaces
of harmonic analysis and are also applicable to nonlinear partial differential
equations. We make use of several tools from nonlinear potential theory,
weighted norm inequalities, and the theory of Banach function spaces to obtain
our results.


  The speed at which two remote parties can exchange secret keys over a
fixed-length fiber-optic cable in continuous-variable quantum key distribution
(CV-QKD) is currently limited by the computational complexity of
post-processing algorithms for key reconciliation. Multi-edge low-density
parity-check (LDPC) codes with low code rates and long block lengths were
proposed for CV-QKD, in order to extend the maximum reconciliation distance
between the two remote parties. Key reconciliation over multiple dimensions has
been shown to further improve the error-correction performance of multi-edge
LDPC codes in CV-QKD, thereby increasing both the secret key rate and distance.
However, the computational complexity of LDPC decoding for long block lengths
on the order of 10^6 bits remains a challenge. This work introduces a
quasi-cyclic (QC) code construction for multi-edge LDPC codes that is highly
suitable for hardware-accelerated decoding on a modern graphics processing unit
(GPU). When combined with an 8-dimensional reconciliation scheme, the LDPC
decoder achieves a raw decoding throughput of 1.72Mbit/s and an information
throughput of 7.16Kbit/s using an NVIDIA GeForce GTX 1080 GPU at a maximum
distance of 160km with a secret key rate of 4.10x10^{-7} bits/pulse for a rate
0.02 multi-edge code with block length of 10^6 bits when finite-size effects
are considered. This work extends the previous maximum CV-QKD distance of 100km
to 160km, while delivering between 1.07x and 8.03x higher decoded information
throughput over the upper bound on the secret key rate for a lossy channel. The
GPU-based QC-LDPC decoder achieves a 1.29x improvement in throughput over the
best existing GPU decoder implementation for a rate 1/10 multi-edge LDPC code
with block length of 2^{20} bits. These results show that LDPC decoding is no
longer the computational bottleneck in long-distance CV-QKD.


  We analyze the \textit{Large Deviation Probability (LDP)} of linear factor
models generated from non-identically distributed components with
\textit{regularly-varying} tails, a large subclass of heavy tailed
distributions. An efficient sampling method for LDP estimation of this class is
introduced and theoretically shown to exponentially outperform the crude
Monte-Carlo estimator, in terms of the coverage probability and the confidence
interval's length. The theoretical results are empirically validated through
stochastic simulations on independent non-identically Pareto distributed
factors. The proposed estimator is available as part of a more comprehensive
\texttt{Betta} package.


  We have presented the structural, elastic, optoelectronic and transport
properties of Sr3SnO under pressure by using first principles method. The
application of hydrostatic pressure causes charge transfer from Sr(5s) orbital
to Sn(5p) and O(2p) orbitals. The increasing trend of Pughs ratio under
pressure implies that the material tends to be ductile at high pressure. The
semiconductor-metal transition occurs at 14 GPa and the density of states at
the Fermi level is significantly increased at this pressure. The refractive
index, optical conductivity, and absorption of Sr3SnO have been found to be
high and comparable to that for typical materials used in photovoltaic. The
material becomes n-type from 12 GPa and Hall coefficient also confirm it. Large
Seebeck coefficient obtained at 12 GPa. Thus, Sr3SnO is a potential
thermoelectric material possessing both p- and n-type nature. The detail
physics of these changes under pressure has been explained within the available
theory.


  We develop further the effective fluid theory of stationary branes. This
formalism applies to stationary blackfolds as well as to other equilibrium
brane systems at finite temperature. The effective theory is described by a
Lagrangian containing the information about the elastic dynamics of the brane
embedding as well as the hydrodynamics of the effective fluid living on the
brane. The Lagrangian is corrected order-by-order in a derivative expansion,
where we take into account the dipole moment of the brane which encompasses
finite-thickness corrections, including transverse spin. We describe how to
extract the thermodynamics from the Lagrangian and we obtain constraints on the
higher-derivative terms with one and two derivatives. These constraints follow
by comparing the brane thermodynamics with the conserved currents associated
with background Killing vector fields. In particular, we fix uniquely the one-
and two-derivative terms describing the coupling of the transverse spin to the
background space-time. Finally, we apply our formalism to two blackfold
examples, the black tori and charged black rings and compare the latter to a
numerically generated solution.


  It is crucial to ask how agents can achieve goals by generating action plans
using only partial models of the world acquired through habituated
sensory-motor experiences. Although many existing robotics studies use a
forward model framework, there are generalization issues with high degrees of
freedom. The current study shows that the predictive coding (PC) and active
inference (AIF) frameworks, which employ a generative model, can develop better
generalization by learning a prior distribution in a low dimensional latent
state space representing probabilistic structures extracted from well
habituated sensory-motor trajectories. In our proposed model, learning is
carried out by inferring optimal latent variables as well as synaptic weights
for maximizing the evidence lower bound, while goal-directed planning is
accomplished by inferring latent variables for maximizing the estimated lower
bound. Our proposed model was evaluated with both simple and complex robotic
tasks in simulation, which demonstrated sufficient generalization in learning
with limited training data by setting an intermediate value for a
regularization coefficient. Furthermore, comparative simulation results show
that the proposed model outperforms a conventional forward model in
goal-directed planning, due to the learned prior confining the search of motor
plans within the range of habituated trajectories.


  We construct a family of three-body spin-1/2 Hamiltonians with a
super-extensive set of infinitely long-lived multi-magnon states. A magnon in
each such state carries either quasi-momentum zero or fixed $p_0\neq$ 0, and
energy $\Omega$ . These multi-magnon states provide an archetypal example of
quantum many-body scars: they are eigenstates at finite energy density that
violate the eigenstate thermalization hypothesis, and lead to persistent
oscillations in local observables in certain quench experiments. On the
technical side, we demonstrate the systematic derivation of scarred
Hamiltonians that satisfy a restricted spectrum generating algebra using an
operator basis built out of irreducible tensor operators. This operator basis
can be constructed for any spin, spatial dimension or continuous non-Abelian
symmetry that generates the scarred subspace.


  Large Language Model (LLM) -in-the-loop applications have been shown to
effectively interpret the human user's commands, make plans, and operate
external tools/systems accordingly. Still, the operation scope of the LLM agent
is limited to passively following the user, requiring the user to frame his/her
needs with regard to the underlying tools/systems. We note that the potential
of an LLM-Agent User Interface (LAUI) is much greater. A user mostly ignorant
to the underlying tools/systems should be able to work with a LAUI to discover
an emergent workflow. Contrary to the conventional way of designing an
explorable GUI to teach the user a predefined set of ways to use the system, in
the ideal LAUI, the LLM agent is initialized to be proficient with the system,
proactively studies the user and his/her needs, and proposes new interaction
schemes to the user. To illustrate LAUI, we present Flute X GPT, a concrete
example using an LLM agent, a prompt manager, and a flute-tutoring multi-modal
software-hardware system to facilitate the complex, real-time user experience
of learning to play the flute.


  Dropout has been commonly used to quantify prediction uncertainty, i.e, the
variations of model predictions on a given input example. However, using
dropout in practice can be expensive as it requires running dropout inferences
many times.
  In this paper, we study how to estimate dropout prediction uncertainty in a
resource-efficient manner. We demonstrate that we can use neuron activation
strengths to estimate dropout prediction uncertainty under different dropout
settings and on a variety of tasks using three large datasets, MovieLens,
Criteo, and EMNIST. Our approach provides an inference-once method to estimate
dropout prediction uncertainty as a cheap auxiliary task. We also demonstrate
that using activation features from a subset of the neural network layers can
be sufficient to achieve uncertainty estimation performance almost comparable
to that of using activation features from all layers, thus reducing resources
even further for uncertainty estimation.


  This research paper presents a novel approach to enhance optimization
performance through the hybridization of Gaussian Crunching Search (GCS) and
Powell's Method for derivative-free optimization. While GCS has shown promise
in overcoming challenges faced by traditional derivative-free optimization
methods [1], it may not always excel in finding the local minimum. On the other
hand, some traditional methods may have better performance in this regard.
However, GCS demonstrates its strength in escaping the trap of local minima and
approaching the global minima. Through experimentation, we discovered that by
combining GCS with certain traditional derivative-free optimization methods, we
can significantly boost performance while retaining the respective advantages
of each method. This hybrid approach opens up new possibilities for optimizing
complex systems and finding optimal solutions in a range of applications.


  We consider an extended inflation model in the frame of Higgs portal model,
assuming a nonminimal coupling of the scalar field to the gravity. Using the
new data from Planck $2013$ and other relevant astrophysical data, we obtain
the relation between the nonminimal coupling $\xi$ and the self-coupling
$\lambda$ needed to drive the inflation, and find that this inflationary model
is favored by the astrophysical data. Furthermore, we discuss the constraints
on the model parameters from the experiments of particle physics, especially
the recent Higgs data at the LHC.


  We prove explicit congruences modulo powers of arbitrary primes for three
smallest parts functions: one for partitions, one for overpartitions, and one
for partitions without repeated odd parts. The proofs depend on $\ell$-adic
properties of certain modular forms and mock modular forms of weight $3/2$ with
respect to the Hecke operators $T(\ell^{2m})$.


  Combining multiple machine learning models into an ensemble is known to
provide superior performance levels compared to the individual components
forming the ensemble. This is because models can complement each other in
taking better decisions. Instead of just combining the models, we propose a
self-paced ensemble learning scheme in which models learn from each other over
several iterations. During the self-paced learning process based on
pseudo-labeling, in addition to improving the individual models, our ensemble
also gains knowledge about the target domain. To demonstrate the generality of
our self-paced ensemble learning (SPEL) scheme, we conduct experiments on three
audio tasks. Our empirical results indicate that SPEL significantly outperforms
the baseline ensemble models. We also show that applying self-paced learning on
individual models is less effective, illustrating the idea that models in the
ensemble actually learn from each other.


  Waveguides can be employed to test non-linear effects in electrodynamics. We
solve Born-Infeld equations for TE waves in a rectangular waveguide. We show
that the energy velocity acquires a dependence on the amplitude, and harmonic
components appear as a consequence of the non-linear behavior.


  We study a class of DFSZ-like models for the QCD axion that can address
observed anomalies in stellar cooling. Stringent constraints from SN1987A and
neutron stars are avoided by suppressed couplings to nucleons, while axion
couplings to electrons and photons are sizable. All axion couplings depend on
few parameters that also control the extended Higgs sector, in particular
lepton flavor-violating couplings of the Standard Model-like Higgs boson $h$.
This allows us to correlate axion and Higgs phenomenology, and we find that
that ${\rm BR}(h \to \tau e)$ can be as large as the current experimental bound
of 0.22%, while ${\rm BR} (h \to \mu \mu)$ can be larger than in the Standard
Model by up to 70%. Large parts of the parameter space will be tested by the
next generation of axion helioscopes such as the IAXO experiment.


  Retrieval-Augmented Generation (RAG) has recently emerged as a promising
solution to alleviate Large Language Model (LLM)'s deficiency in lack of
knowledge. Existing RAG datasets, however, do not adequately represent the
diverse and dynamic nature of real-world Question Answering (QA) tasks. To
bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual
question answering benchmark of 4,409 question-answer pairs and mock APIs to
simulate web and Knowledge Graph (KG) search. CRAG is designed to encapsulate a
diverse array of questions across five domains and eight question categories,
reflecting varied entity popularity from popular to long-tail, and temporal
dynamisms ranging from years to seconds. Our evaluation on this benchmark
highlights the gap to fully trustworthy QA. Whereas most advanced LLMs achieve
<=34% accuracy on CRAG, adding RAG in a straightforward manner improves the
accuracy only to 44%. State-of-the-art industry RAG solutions only answer 63%
questions without any hallucination. CRAG also reveals much lower accuracy in
answering questions regarding facts with higher dynamism, lower popularity, or
higher complexity, suggesting future research directions. The CRAG benchmark
laid the groundwork for a KDD Cup 2024 challenge, attracting thousands of
participants and submissions within the first 50 days of the competition. We
commit to maintaining CRAG to serve research communities in advancing RAG
solutions and general QA solutions.


  A multi-component lattice Boltzmann model recently introduced (R. Benzi et
al. Phys. Rev. Lett 102, 026002 (2009)) to describe some dynamical behaviors of
soft-flowing materials is theoretically analyzed. Equilibrium and transport
properties are derived within the framework of a continuum free-energy
formulation, and checked against numerical simulations. Due to the competition
between short-range inter-species repulsion and mid-range intra-species
attraction, the model is shown to give rise to a very rich configurational
dynamics of the density field, exhibiting numerous features of soft-flowing
materials, such as long-time relaxation due to caging effects, enhanced
viscosity and structural arrest, ageing under moderate shear and shear-thinning
flow above a critical shear threshold.


  We prove that a non-elementary relatively hyperbolic group is statistically
hyperbolic with respect to every finite generating set. We also establish
statistical hyperbolicity for certain direct products of two groups, one of
which is relatively hyperbolic.


  Cherenkov Telescopes (as Magic, Hess and Veritas), while pointing
horizontally should reveal also the fluorescence flare tails of nearby
down-going air-showers. Such air-showers, born at higher (tens km) altitudes,
are growing and extending up to lowest atmospheres (EeVs) or up to higher (few
km) quotas (PeVs). Viceversa, as it has been foreseen and only recently
observed, the opposite takes place. Fluorescence Telescopes made for UHECR
detection may be blazed by inclined Cherenkov lights. The geomagnetic splitting
may tag the energy as well as the inclined shower footprint as seen in a recent
peculiar event in AUGER. Additional stereoscopic detection may define the event
origination distance and its consequent primary composition, extending our
understanding on UHECR composition, while unveling a novel tau Neutrino
Astronomy.


  Recent experimental findings on anomalous diffusion have demanded novel
models that combine annealed (temporal) and quenched (spatial or static)
disorder mechanisms. The comb-model is a simplified description of diffusion on
percolation clusters, where the comb-like structure mimics quenched disorder
mechanisms and yields a subdiffusive regime. Here we extend the comb-model to
simultaneously account for quenched and annealed disorder mechanisms. To do so,
we replace usual derivatives in the comb diffusion equation by different
fractional time-derivative operators and the conventional comb-like structure
by a generalized fractal structure. Our hybrid comb-models thus represent a
diffusion where different comb-like structures describe different quenched
disorder mechanisms, and the fractional operators account for various annealed
disorders mechanisms. We find exact solutions for the diffusion propagator and
mean square displacement in terms of different memory kernels used for defining
the fractional operators. Among other findings, we show that these models
describe crossovers from subdiffusion to Brownian or confined diffusions,
situations emerging in empirical results. These results reveal the critical
role of interactions between geometrical restrictions and memory effects on
modeling anomalous diffusion.


  Development of NTD Ge sensors has been initiated for low temperature (mK)
thermometry in The India-based Tin detector (TIN.TIN). NTD Ge sensors are
prepared by thermal neutron irradiation of device grade Ge samples at Dhruva
reactor, BARC, Mumbai. Detailed measurements have been carried out in
irradiated samples for estimating the carrier concentration and fast neutron
induced defects. The Positron Annihilation Lifetime Spectroscopy (PALS)
measurements indicated monovacancy type defects for all irradiated samples,
while Channeling studies employing RBS with 2 MeV alpha particles, revealed no
significant defects in the samples exposed to fast neutron fluence of $\sim
4\times10^{16}/cm^2$. Both PALS and Channeling studies have shown that vacuum
annealing at 600 $^\circ$C for $\sim2$ hours is sufficient to recover the
damage in the irradiated samples, thereby making them suitable for the sensor
development.


  Let's Plays of video games represent a relatively unexplored area for
experimental AI in games. In this short paper, we discuss an approach to
generate automated commentary for Let's Play videos, drawing on convolutional
deep neural networks. We focus on Let's Plays of the popular game Minecraft. We
compare our approach and a prior approach and demonstrate the generation of
automated, artificial commentary.


  In this paper we present a new camera calibration method aimed at finding a
straight-line locus, in a special colour feature space, that is traversed by
daylights and as well also approximately followed by specular points. The aim
of the calibration is to enable recovering the colour of the illuminant in a
scene, using the calibrated camera. First we prove theoretically that any
candidate specular points, for an image that is generated by a specific camera
and taken under a daylight, must lie on a straight line in log-chromaticity
space, for a chromaticity that is generated using a geometric-mean denominator.
Use is made of the assumptions that daylight illuminants can be approximated
using Planckians and that camera sensors are narrowband or can be made so by
spectral sharpening. Then we show how a particular camera can be calibrated so
as to discover this locus. As applications we use this curve for illuminant
detection, and also for re-lighting of images to show they would appear under
lighting having a different colour temperature.


  Ultralight axions and dark photons are well-motivated dark matter candidates.
Inside the plasma, once the mass of ultralight dark matter candidates equals
the plasma frequency, they can resonantly convert into electromagnetic waves,
due to the coupling between the ultralight dark matter particles and the
standard model photons. The converted electromagnetic waves are monochromatic.
In this article, we review the development of using radio detectors to search
for ultralight dark matter conversions in the solar corona and solar wind
plasma.


  Network Intrusion Detection Systems (NIDS) are a fundamental tool in
cybersecurity. Their ability to generalize across diverse networks is a
critical factor in their effectiveness and a prerequisite for real-world
applications. In this study, we conduct a comprehensive analysis on the
generalization of machine-learning-based NIDS through an extensive
experimentation in a cross-dataset framework. We employ four machine learning
classifiers and utilize four datasets acquired from different networks:
CIC-IDS-2017, CSE-CIC-IDS2018, LycoS-IDS2017, and LycoS-Unicas-IDS2018.
Notably, the last dataset is a novel contribution, where we apply corrections
based on LycoS-IDS2017 to the well-known CSE-CIC-IDS2018 dataset. The results
show nearly perfect classification performance when the models are trained and
tested on the same dataset. However, when training and testing the models in a
cross-dataset fashion, the classification accuracy is largely commensurate with
random chance except for a few combinations of attacks and datasets. We employ
data visualization techniques in order to provide valuable insights on the
patterns in the data. Our analysis unveils the presence of anomalies in the
data that directly hinder the classifiers capability to generalize the learned
knowledge to new scenarios. This study enhances our comprehension of the
generalization capabilities of machine-learning-based NIDS, highlighting the
significance of acknowledging data heterogeneity.


  The transmission phase across a quantum dot (QD) is expected to show
mesoscopic behavior, where the appearance of a phase lapse between Coulomb
peaks (CPs) as a function of the gate voltage depends on the orbital parity
relation between the corresponding CPs. On the other hand, such mesoscopic
behavior has been observed only in a limited QD configuration (a few-electron
and single-level transport regime) and universal phase lapses by $\pi$ between
consecutive CPs have been reported for all the other configurations. Here, we
report on the measurement of a transmission phase across a QD around the
crossover between single-level and multilevel transport regimes employing an
original two-path quantum interferometer. We find mesoscopic behavior for the
studied QD. Our results show that the universal phase lapse, a longstanding
puzzle of the phase shift, is absent for a standard QD, where several tens of
successive well-separated CPs are observed.


  Machine Reading Comprehension (MRC) aims to extract answers to questions
given a passage. It has been widely studied recently, especially in open
domains. However, few efforts have been made on closed-domain MRC, mainly due
to the lack of large-scale training data. In this paper, we introduce a
multi-target MRC task for the medical domain, whose goal is to predict answers
to medical questions and the corresponding support sentences from medical
information sources simultaneously, in order to ensure the high reliability of
medical knowledge serving. A high-quality dataset is manually constructed for
the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with
detailed analysis conducted. We further propose the Chinese medical BERT model
for the task (CMedBERT), which fuses medical knowledge into pre-trained
language models by the dynamic fusion mechanism of heterogeneous features and
the multi-task learning strategy. Experiments show that CMedBERT consistently
outperforms strong baselines by fusing context-aware and knowledge-aware token
representations.


  The diagnosis of cyber-physical systems aims to detect faulty behaviour, its
root cause and a mitigation or even prevention policy. Therefore, diagnosis
relies on a representation of the system's functional and faulty behaviour
combined with observations of the system taken at runtime. The main challenges
are the time-intensive building of a model, possible state-explosion while
searching for the root cause and interpretability of the results. In this paper
we propose a scalable algorithm tackling these challenges. We use a Bayesian
network to learn a structured model automatically and optimise the model by a
genetic algorithm. Our approach differs from existing work in two aspects:
instead of selecting features prior to the analysis we learn a global
representation using all available information which is then transformed to a
smaller, label-specific one and we focus on interpretability to facilitate
repairs. The evaluation shows that our approach is able to learn a model with
equal performance to state-of-the-art algorithms while giving better
interpretability and having a reduced size.


  A generation of neutrino experiments have established that neutrinos mix and
probably have mass. The mixing phenomenon points to processes beyond those of
the Standard Model, possibly at the Grand Unification energy scale. A extensive
sequence of of experiments will be required to measure precisely all the
parameters of the neutrino mixing matrix, culminating with the discovery and
study of leptonic CP violation. As a first step, extensions of conventional
pion/kaon decay beams, such as off-axis beams or low-energy super-beams, have
been considered. These could yield first observations of $\nu_\mu \to \nu_e$
transitions at the atmospheric frequency, which have not yet been observed, and
a first measurement of $\theta_{13}$. Experiments with much better flux control
can be envisaged if the neutrinos are obtained from the decays of stored
particles. One such possibility is the concept of beta beams provided by the
decays of radioactive nuclei, that has been developed within the context of
these studies. These would provide a pure (anti-)electron-neutrino beam of a
few hundred MeV, and beautiful complementarity with a high-intensity,
low-energy conventional beam, enabling experimental probes of T violation as
well as CP violation. Ultimately, a definitive and complete set of measurements
would offered by a Neutrino Factory based on a muon storage ring. This powerful
machine offers the largest reach for CP violation, even for very small values
of $\theta_{13}$.


  This paper studies some $C^0-$aspects of the action of the identity component
(w.r.t the $C^\infty-$compact open topology) $Diff^\Omega_0(M) $ in the group
of volume-preserving diffeomorphisms on the space $\mathcal Z^1(M)$ of all
closed $1-$forms of a compact connected oriented manifold $(M, \Omega)$, and
use this to improve the volume-capacity-inequality. We recover that when $M$ is
closed, the group $Diff^\Omega_0(M) $ is $C^0-$closed in the identity component
of the group of all diffeomorphisms of $M$. In the context of $C^0-$symplectic
geometry, when $M$ is Lefschetz, we prove the $C^0-$flux conjecture, and show
that the commutator subgroup $[SSympeo(M, \omega), SSympeo(M, \omega)]$
coincides with $[Hameo(M,\omega), Hameo(M,\omega)]$.


  We present here an extension of our model for the pion, which we used
previously to calculate its diagonal structure function, to the off-forward
case. The imaginary part of the off-forward gamma^* pi -> gamma^* pi scattering
amplitude is evaluated in the chiral limit (m_pi=0) and related to the
twist-two and twist-three generalised parton distributions H, H3, H3tilde.
Non-perturbative effects, linked to the size of the pion and still preserving
gauge invariance, are included. Remarkable new relations between H, H3 and
H3tilde are obtained and discussed.


  The purpose of this paper is to give the flavor of the subject of
self-similar tilings in a relatively elementary setting, and to provide a novel
method for the construction of such polygonal tilings.


  In this contribution to Peter Suranyi Festschrift, we study the
Halliday-Suranyi perturbation method for calculating the energy eigenvalues of
the quartic anharmonic oscillator.


  We study electron transport in a normal-metal ring modeled by the tight
binding lattice Hamiltonian, coupled to two electron reservoirs. First,
Buttiker's model of incorporating inelastic scattering, hence decoherence and
dissipation, has been extended by connecting each site of the open ring to
one-dimensional leads for uniform dephasing in the ring threaded by magnetic
flux. We show with this extension conductance remains symmetric under flux
reversal, and Aharonov-Bohm oscillations with changing magnetic flux reduce to
zero as a function of the decoherence parameter, thus indicating dephasing in
the ring. This extension enables us to find local chemical potential profiles
of the ring sites with changing magnetic flux and the decoherence parameter
analogously to the four probe measurement. The local electrochemical potential
oscillates in the ring sites because of quantum-interference effects. It
predicts that measured four-point resistance also fluctuates and even can be
negative. Then we point out the role of the closed ring's electronic
eigenstates in the persistent current around Fano antiresonances of an
asymmetric open ring for both ideal leads and tunnel barriers. Determining the
real eigenvalues of the non-Hermitian effective Hamiltonian of the ring, we
show that there exist discrete bound states in the continuum of scattering
states for the asymmetric ring even in the absence of magnetic flux. Our
approach involves quantum Langevin equations and non-equilibrium Green's
functions.


  Delimited control operator shift0 exhibits versatile capabilities: it can
express layered monadic effects, or equivalently, algebraic effects. Little did
we know it can express lambda calculus too! We present $ \Lambda_\$ $, a
call-by-value lambda calculus extended with shift0 and control delimiter $ \$ $
with carefully crafted reduction theory, such that the lambda calculus with
beta and eta reductions can be isomorphically embedded into $ \Lambda_\$ $ via
a right inverse of a continuation-passing style translation. While call-by-name
reductions of lambda calculus can trivially simulate its call-by-value version,
we show that addition of shift0 and $ \$ $ is the golden mean of expressive
power that suffices to simulate beta and eta reductions while still admitting a
simulation back. As a corollary, calculi $ \Lambda\mu_v $, $ \lambda_\$ $, $
\Lambda_\$ $ and $ \lambda $ all correspond equationally.


  Most meta-learning methods assume that the (very small) context set used to
establish a new task at test time is passively provided. In some settings,
however, it is feasible to actively select which points to label; the potential
gain from a careful choice is substantial, but the setting requires major
differences from typical active learning setups. We clarify the ways in which
active meta-learning can be used to label a context set, depending on which
parts of the meta-learning process use active learning. Within this framework,
we propose a natural algorithm based on fitting Gaussian mixtures for selecting
which points to label; though simple, the algorithm also has theoretical
motivation. The proposed algorithm outperforms state-of-the-art active learning
methods when used with various meta-learning algorithms across several
benchmark datasets.


  Condensed matter systems provide a rich setting to realize Dirac and Majorana
fermionic excitations and the possibility to manipulate them in materials for
potential applications. Recently, it has been proposed that Weyl fermions,
which are chiral, massless particles, can emerge in certain bulk materials or
in topological insulator multilayers and can produce unusual transport
properties, such as charge pumping driven by a chiral anomaly. A pair of Weyl
fermions protected by crystalline symmetry, effectively forming a massless
Dirac fermion, has been predicted to appear as low energy excitations in a
number of candidate materials termed three-dimensional (3D) Dirac semimetals.
Here we report scanning tunneling microscopy (STM) measurements at sub-Kelvin
temperatures and high magnetic fields on one promising host material, the II-V
semiconductor Cd3As2. Our study provides the first atomic scale probe of
Cd3As2, showing that defects mostly influence the valence band, consistent with
the observation of ultra-high mobility carriers in the conduction band. By
combining Landau level spectroscopy and quasiparticle interference (QPI), we
distinguish a large spin-splitting of the conduction band in a magnetic field
and its extended Dirac-like dispersion above the expected regime. A model band
structure consistent with our experimental findings suggests that for a
specific orientation of the applied magnetic field, Weyl fermions are the
low-energy excitations in Cd3As2.


  We show that the one-particle density matrix $\rho$ can be used to
characterize the interaction-driven many-body localization transition in closed
fermionic systems. The natural orbitals (the eigenstates of $\rho$) are
localized in the many-body localized phase and spread out when one enters the
delocalized phase, while the occupation spectrum (the set of eigenvalues of
$\rho$) reveals the distinctive Fock-space structure of the many-body
eigenstates, exhibiting a step-like discontinuity in the localized phase. The
associated one-particle occupation entropy is small in the localized phase and
large in the delocalized phase, with diverging fluctuations at the transition.
We analyze the inverse participation ratio of the natural orbitals and find
that it is independent of system size in the localized phase.


  In-band full-duplex systems are able to transmit and receive information
simultaneously on the same frequency band. Due to the strong self-interference
caused by the transmitter to its own receiver, the use of non-linear digital
self-interference cancellation is essential. In this work, we present a
hardware architecture for a neural network based non-linear self-interference
canceller and we compare it with our own hardware implementation of a
conventional polynomial based canceller. We show that, for the same
cancellation performance, the neural network canceller has a significantly
higher throughput and requires fewer hardware resources.


  Hall viscosity is a dissipationless transport coefficient whose value is
quantized in units of the density in some topological phases and may be used as
a measure of topological order. I give an overview of the Hall viscosity, its
relation to Hall conductivity in Galilean invariant theories and its
realization in effective theories.


  We propose a single time-scale actor-critic algorithm to solve the linear
quadratic regulator (LQR) problem. A least squares temporal difference (LSTD)
method is applied to the critic and a natural policy gradient method is used
for the actor. We give a proof of convergence with sample complexity
$\mathcal{O}(\varepsilon^{-1} \log(\varepsilon^{-1})^2)$. The method in the
proof is applicable to general single time-scale bilevel optimization problem.
We also numerically validate our theoretical results on the convergence.


  Sr$_{2}$IrO$_{4}$ with strong spin-orbit coupling (SOC) and Hubbard repulsion
(U) hosts Mott insulating states. The similar crystal structure, magnetic and
electronic properties, particularly the $d$-wave gap observed in
Sr$_{2}$IrO$_{4}$ enhanced the analogies to cuprate high-$T_{c}$
superconductor, La$_{2}$CuO$_{4}$. The incomplete analogy was due to the lack
of broken inversion symmetry phases observed in Sr$_{2}$IrO$_{4}$. Here, under
high pressure and high temperature conditions, we report a non-centrosymmetric
Sr$_{2}$IrO$_{4}$. The crystal structure and its noncentrosymmetric character
were determined by single crystal X-ray diffraction and high-resolution
scanning transmission electron microscopy (HR-STEM). The magnetic
characterization confirms the Ir$^{4+}$ with $S$ = 1/2 at low temperature in
Sr$_{2}$IrO$_{4}$ with magnetic ordering occurred at around 86 K, where a
larger moment is observed than the ambient pressure Sr$_{2}$IrO$_{4}$.
Moreover, the resistivity measurement shows three-dimensional Mott
variable-range hopping existed in the system. This non-centrosymmetric
Sr$_{2}$IrO$_{4}$ phase appears to be a unique material to offer further
understanding of high-$T_{c}$ superconductivity.


  We study the semimartingale properties for the generalized fractional
Brownian motion (GFBM) introduced by Pang and Taqqu (2019) and discuss the
applications of the GFBM and its mixtures to financial asset pricing. The GFBM
is self-similar and has non-stationary increments, whose Hurst index $H \in
(0,1)$ is determined by two parameters. We identify the regions of these two
parameter values where the GFBM is a semimartingale. Specifically, in one
region resulting in $H\in (1/2,1)$, it is in fact a process of finite variation
and differentiable, and in another region also resulting in $H\in (1/2,1)$ it
is not a semimartingale. For regions resulting in $H \in (0,1/2]$ except a line
segment resulting in the standard Brownian motion and time-changed Brownian
motion cases, the GFBM is also not a semimartingale.
  We next show that the mixed process made up of an independent BM and a GFBM
is a semimartingale when the parameters lie in the two regions aforementioned
resulting in $H \in (1/2,1)$ for the GFBM, as well as when the parameters lie
in the line segment resulting in a standard BM or time-changed BM with $H \in
(0,1/2]$ for the GFBM. We derive the associated equivalent Brownian measure in
the cases when $H\in (1/2,1)$. } This result is in great contrast with the
mixed FBM with $H \in \{1/2\}\cup(3/4,1]$ proved by Cheridito (2001) and shows
the significance of the additional parameter introduced in the GFBM.
  We then study the semimartingale asset pricing theory with the mixed GFBM, in
presence of long range dependence, and discuss the implications of using GFBM
on arbitrage theory.


  This report presents the results of applying different compression algorithms
to the network protocol of an online game. The algorithm implementations
compared are zlib, liblzma and my own implementation based on LZ77 and a
variation of adaptive Huffman coding. The comparison data was collected from
the game TomeNET. The results show that adaptive coding is especially useful
for compressing large amounts of very small packets.


  In this paper we classify all four dimensional real Lie bialgebras of
symplectic type. The classical r- matrices for these Lie bialgebras and Poisson
structures on all of the related four dimensional Poisson-Lie groups are also
obtained. Some new integrable models for which the Poisson-Lie group plays the
role as a phase space and its dual Lie group plays the role of a symmetry group
of the system, are obtained.


  Galaxy clusters play a crucial role in constraining cosmological parameters.
Cluster detection and characterization have therefore become an important field
of modern cosmology. In this work, we aim at constructing reasonably
representative composite luminosity function of cluster members in the WISE
survey that can be used in the future as an input in matching algorithms for
cluster detection. We use a sample of massive clusters from the redMaPPer
catalog to match the positions of the WISE sources. We build the composite
luminosity function of the WISE members in different redshift bins. We find
that galaxy cluster members have a characteristic composite luminosity
function, with a clear change in the slope at a given magnitude apparent
$M^{*}$, which becomes fainter with increasing redshift. The best-fit
bright-end slope $\beta$ is compatible with a constant value of $3.54$ with no
clear trend with redshift, while the faint-end slope $\alpha$ is remarkably
different, ranging from $2$ to $3$. We present the first characterization of
the composite luminosity function of the WISE counterparts of redMaPPer cluster
members and in this way we provide an element for building cluster detection
techniques based on matching algorithms.


  Many large-scale optimization problems can be expressed as composite
optimization models. Accelerated first-order methods such as the fast iterative
shrinkage-thresholding algorithm (FISTA) have proven effective for numerous
large composite models. In this paper, we present a new variation of FISTA, to
be called C-FISTA, which obtains global linear convergence for a broader class
of composite models than many of the latest FISTA variants. We demonstrate the
versatility and effectiveness of C-FISTA by showing C-FISTA outperforms current
first-order solvers on both group Lasso and group logistic regression models.
Furthermore, we utilize Fenchel duality to prove C-FISTA provides global linear
convergence for a large class of convex models without the loss of global
linear convergence.


  We construct the generic component of the moduli space of the germs of
Legendrian curves with generic plane projection topologicaly equivalent to a
curve y^n = x^m, (n,m)=1.


  Computational screening in heterogeneous catalysis relies increasingly on
machine learning models for predicting key input parameters due to the high
cost of computing these directly using first-principles methods. This becomes
especially relevant when considering complex materials spaces, e.g. alloys, or
complex reaction mechanisms with adsorbates that may exhibit bi- or
higher-dentate adsorption motifs. Here we present a data-efficient approach to
the prediction of binding motifs and associated adsorption enthalpies of
complex adsorbates at transition metals (TMs) and their alloys based on a
customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian Process
Regression. The model shows good predictive performance, not only for the
elemental TMs on which it was trained, but also for an alloy based on these
TMs. Furthermore, incorporation of minimal new training data allows for
predicting an out-of-domain TM. We believe the model may be useful in active
learning approaches, for which we present an ensemble uncertainty estimation
approach.


  Our study of the energy distribution has shown that the strength of the first
order transition in the four-dimensional compact U(1) lattice gauge theory
decreases when the coupling $\lambda$ of the monopole term increases. The
disappearance of the energy gap for sufficiently large values of $\lambda$
indicates that the transition ultimately becomes of second order. In our
present investigation, based on a finite-size analysis, we show that already at
$\lambda= 0.9$ the critical exponent is characteristic of a second-order
transition. Interestingly, this exponent turns out to be definitely different
from that of the Gaussian case. We observe that the monopole density becomes
constant in the second order region. In addition we find the rather surprising
result that the phase transition persists up to very large values of $\lambda$,
where the transition moves to (large) negative $\beta$.


  We report the first measurements of the dynamics of liquid germanium (l-Ge)
by quasi-elastic neutron scattering on time-of-flight and triple-axis
spectrometers. These results are compared with simulation data of the structure
and dynamics of l-Ge which have been obtained with ab initio density functional
theory methods. The simulations accurately reproduce previous results from
elastic and inelastic scattering experiments, as well as the q-dependence of
the width of the quasi-elastic signal of the new experimental data. In order to
understand some special features of the structure of the liquid we have also
simulated amorphous Ge. Overall we find that the atomistic model represents
accurately the average structure of real l-Ge as well as the time dependent
structural fluctuations. The new quasi-elastic neutron scattering data allows
us to investigate to what extent simple theoretical models can be used to
describe diffusion in l-Ge.


  Controlling electrically-stimulated quantum light sources (QLS) is key for
developing integrated and low-scale quantum devices. The mechanisms leading to
quantum emission are complex, as a large number of electronic states of the
system impacts the emission dynamics. Here, we use a scanning tunneling
microscope (STM) to excite a model QLS, namely a single molecule. The
luminescence spectra reveal two lines, associated to the emission of the
neutral and positively charged molecule, both exhibiting single-photon source
behavior. In addition, we find a correlation between the charged and neutral
molecule's emission, the signature of a photon cascade. By adjusting the
charging/discharging rate, we can control these emission statistics. This
generic strategy is further established by a rate equation model revealing the
complex internal dynamics of the molecular junction.


  Laws that govern land acquisition can lock in old paradigms. We study one
such case, the Coal Bearing Areas Act of 1957 (CBAA) which provides minimal
social and environmental safegaurds, and deviates in important ways from the
Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation
and Resettlement Act 2013 (LARR). The lack of due diligence protocol in the
CBAA confers an undue comparative advantage to coal development, which is
inconsistent with India's stance to phase down coal use, reduce air pollution,
and advance modern sources of energy. We argue that the premise under which the
CBAA was historically justified is no longer valid due to a significant change
in the local context. Namely, the environmental and social costs of coal energy
are far more salient and the market has cleaner energy alternatives that are
cost competitive. We recommend updating land acquisition laws to bring coal
under the general purview of LARR or, at minimum, amending the CBAA to ensure
adequate environmental and social safeguards are in place, both in letter and
practice.


  Most approaches to deep neural network compression via pruning either
evaluate a filter's importance using its weights or optimize an alternative
objective function with sparsity constraints. While these methods offer a
useful way to approximate contributions from similar filters, they often either
ignore the dependency between layers or solve a more difficult optimization
objective than standard cross-entropy. Our method, Mutual Information-based
Neuron Trimming (MINT), approaches deep compression via pruning by enforcing
sparsity based on the strength of the relationship between filters of adjacent
layers, across every pair of layers. The relationship is calculated using
conditional geometric mutual information which evaluates the amount of similar
information exchanged between the filters using a graph-based criterion. When
pruning a network, we ensure that retained filters contribute the majority of
the information towards succeeding layers which ensures high performance. Our
novel approach outperforms existing state-of-the-art compression-via-pruning
methods on the standard benchmarks for this task: MNIST, CIFAR-10, and
ILSVRC2012, across a variety of network architectures. In addition, we discuss
our observations of a common denominator between our pruning methodology's
response to adversarial attacks and calibration statistics when compared to the
original network.


  The global popularity of microblogs has led to an increasing accumulation of
large volumes of text data on microblogging platforms such as Twitter. These
corpora are untapped resources to understand social expressions on diverse
subjects. Microblog analysis aims to unlock the value of such expressions by
discovering insights and events of significance hidden among swathes of text.
Besides velocity; diversity of content, brevity, absence of structure and
time-sensitivity are key challenges in microblog analysis. In this paper, we
propose an unsupervised incremental machine learning and event detection
technique to address these challenges. The proposed technique separates a
microblog discussion into topics to address the key problem of diversity. It
maintains a record of the evolution of each topic over time. Brevity,
time-sensitivity and unstructured nature are addressed by these individual
topic pathways which contribute to generate a temporal, topic-driven structure
of a microblog discussion. The proposed event detection method continuously
monitors these topic pathways using multiple domain-independent event
indicators for events of significance. The autonomous nature of topic
separation, topic pathway generation, new topic identification and event
detection, appropriates the proposed technique for extensive applications in
microblog analysis. We demonstrate these capabilities on tweets containing
#microsoft and tweets containing #obama.


  The latest results in electroweak physics from proton anti-proton collisions
at the Fermilab Tevatron recorded by the CDF detector are presented. The
results provide constraints on parton distribution functions, the mass of the
Higgs boson and beyond the Standard Model physics.


  We introduce the notion of the $k$-closure of a group of automorphisms of a
locally finite tree, and give several examples of the construction. We show
that the $k$-closure satisfies a new property of automorphism groups of trees
that generalises Tits' Property $P$. We prove that, apart from some degenerate
cases, any non-discrete group acting on a tree with this property contains an
abstractly simple subgroup.


  We implement a double stochastic process as the mathematical model for the
spatial point patterns of urban facilities. We find that the model with power
covariance function can produce the best fit not only to $K$ function (whose
derivative gives the radial distribution $\rho(t) = K'(t)/2\pi t$) but also to
additional facts of spatial point patterns. These facts include the
mean-variance relationship of number of events in a series of expanding bins,
and other statistics beyond the first two orders, such as inter-event
distribution function $H(t)$ and nearest neighbor distribution functions $G(t)$
and $F(t)$.


  Motivated by the work of David Singmaster, we study the number of times an
integer can appear among the Stirling numbers of both kinds. We provide an
upper bound for the occurrences of all the positive integers, and present
certain questions for further study. Some numerical results and conjectures
concerning the related diohantine equations are collected.


  We investigate the dynamical interplay between topological state of matter
and a non-Markovian dissipation, which gives rise to a new and crucial time
scale into the system dynamics due to its quantum memory. We specifically study
a one-dimensional polaronic topological superconductor with phonon-dressed
$p$-wave pairing, when a fast temperature increase in surrounding phonons
induces an open-system dynamics. We show that when the memory depth increases,
the Majorana edge dynamics transits from relaxing monotonically to a plateau of
substantial value into a collapse-and-buildup behavior, even when the polaron
Hamiltonian is close to the topological phase boundary. Above a critical memory
depth, the system can approach a new dressed state of topological
superconductor in dynamical equilibrium with phonons, with nearly full buildup
of Majorana correlation.


  We report an experimental study of the scaling of zero-bias conductance peaks
compatible with Majorana zero modes as a function of magnetic field, tunnel
coupling, and temperature in one-dimensional structures fabricated from an
epitaxial semiconductor-superconductor heterostructure. Results are consistent
with theory, including a peak conductance that is proportional to tunnel
coupling, saturates at $2e^2/h$, decreases as expected with field-dependent
gap, and collapses onto a simple scaling function in the dimensionless ratio of
temperature and tunnel coupling.


  Touchdown is shown to be the only possible finite time singularity that may
take place in a free boundary problem modeling a three-dimensional
microelectromechanical system. The proof relies on the energy structure of the
problem and uses smoothing effects of the semigroup generated in $L^1$ by the
bi-Laplacian with clamped boundary conditions.


  Here we derive a nonsmooth maximum principle for optimal control problems
with both state and mixed constraints. Crucial to our development is a
convexity assumption on the "velocity set". The approach consists of applying
known penalization techniques for state constraints together with recent
results for mixed constrained problems.


  Data Warehouses (DWs) are core components of Business Intelligence (BI).
Missing data in DWs have a great impact on data analyses. Therefore, missing
data need to be completed. Unlike other existing data imputation methods mainly
adapted for facts, we propose a new imputation method for dimensions. This
method contains two steps: 1) a hierarchical imputation and 2) a k-nearest
neighbors (KNN) based imputation. Our solution has the advantage of taking into
account the DW structure and dependency constraints. Experimental assessments
validate our method in terms of effectiveness and efficiency.


  This article is a pedagogical review of Monte Carlo methods for the
self-avoiding walk, with emphasis on the extraordinarily efficient algorithms
developed over the past decade.


  We study the process $e^+e^-\to\pi^+\pi^-\pi^+\pi^-\gamma$, with a hard
photon radiated from the initial state. About 60,000 fully reconstructed events
have been selected from 89 $fb^{-1}$ of BaBar data. The invariant mass of the
hadronic final state defines the effective \epem center-of-mass energy, so that
these data can be compared with the corresponding direct $e^+e^-$ measurements.
  From the $4\pi$-mass spectrum, the cross section for the process
$e^+e^-\to\pi^+\pi^-\pi^+\pi^-$ is measured for center-of-mass energies from
0.6 to 4.5 $GeV/c^2$. The uncertainty in the cross section measurement is
typically 5%. We also measure the cross sections for the final states $K^+ K^-
\pi^+\pi^-$ and $K^+ K^- K^+ K^-$. We observe the $J/\psi$ in all three final
states and measure the corresponding branching fractions. We search for X(3872)
in $J/\psi (\to\mu^+\mu^-) \pi^+\pi^-$ and obtain an upper limit on the product
of the $e^+e^-$ width of the X(3872) and the branching fraction for $X(3872)
\to J/\psi\pi^+\pi^-$.


  We study the effect of confinement on the three-dimensional linear
instability of fastly rotating two-dimensional turbulent flows. Using the large
scale friction to model the effect of top and bottom boundaries, we study the
onset of three-dimensional perturbations on a rapidly rotating flow. The
friction term is taken to affect both the evolution of the two-dimensional
turbulent flow and the perturbations that evolve on top of it. Using direct
numerical simulations, the threshold for the onset of three-dimensional
perturbations is traced out as a function of the control parameters. As
reported in the earlier work (K. Seshasayanan and B. Gallet. 2020. Onset of
three-dimensionality in rapidly rotating turbulent flows), two different
mechanisms, namely the centrifugal and parametric type instabilities, are
responsible for the destabilisation across the wide range of parameters
explored in this study. In the turbulent regime, we find that the large scale
friction term does not affect the threshold in the case of centrifugal
instability while in the case of the parametric instability the instability
threshold is shifted to larger Rossby numbers. For the parametric instability,
the length scale of the unstable mode is found to scale as the inverse square
root of the rotation rate and the growth rate of the unstable mode is found to
be correlated with the minimum of the determinant of the strain rate tensor of
the underlying two-dimensional turbulent flow, showing resemblance with
elliptical type instabilities. Results from the turbulent flow are then
compared with the oscillatory Kolmogorov flow, which undergoes a parametric
instability resulting into inertial waves. The dependence of the threshold on
the aspect ratio of the system is discussed for both the turbulent and the
oscillating Kolmogorov flows.


  Data clustering is a common unsupervised learning method frequently used in
exploratory data analysis. However, identifying relevant structures in
unlabeled, high-dimensional data is nontrivial, requiring iterative
experimentation with clustering parameters as well as data features and
instances. The number of possible clusterings for a typical dataset is vast,
and navigating in this vast space is also challenging. The absence of
ground-truth labels makes it impossible to define an optimal solution, thus
requiring user judgment to establish what can be considered a satisfiable
clustering result. Data scientists need adequate interactive tools to
effectively explore and navigate the large clustering space so as to improve
the effectiveness of exploratory clustering analysis. We introduce
\textit{Clustrophile~2}, a new interactive tool for guided clustering analysis.
\textit{Clustrophile~2} guides users in clustering-based exploratory analysis,
adapts user feedback to improve user guidance, facilitates the interpretation
of clusters, and helps quickly reason about differences between clusterings. To
this end, \textit{Clustrophile~2} contributes a novel feature, the Clustering
Tour, to help users choose clustering parameters and assess the quality of
different clustering results in relation to current analysis goals and user
expectations. We evaluate \textit{Clustrophile~2} through a user study with 12
data scientists, who used our tool to explore and interpret sub-cohorts in a
dataset of Parkinson's disease patients. Results suggest that
\textit{Clustrophile~2} improves the speed and effectiveness of exploratory
clustering analysis for both experts and non-experts.


  We study a class of time functions called uniform temporal functions in the
general context of globally hyperbolic closed cone fields. We prove some
existence results for uniform temporal functions, and prove the density of
uniform temporal functions in Cauchy causal functions.


  The alternative replica technique which involve summation over all integer
momenta of the partition function and which does not require analytic
continuation to non-integer values of the replica parameter $n$ is discussed.
In terms of this technique (which does not involve any replica symmetry
breaking "magic operations") rigorous solution for the average free energy of
the Random Energy Model is recovered in a very simple way.


  The final state interactions in $K^\pm\to\pi^\pm\pi^0\pi^0$ decays are
considered using the methods of non-relativistic quantum mechanics. We show how
to take into account the largest electromagnetic effects in the analysis of
experimental data, using the amplitudes calculated earlier. We propose the
relevant expressions for amplitude corrections valid both above and below the
two charged pion production threshold $M_{\pi^0\pi^0}=2m_{\pi^\pm}$, including
the average effect for the threshold bin. These formulae can be used in the
procedure of pion scattering lengths measurement from $M_{\pi^0\pi^0}$
spectrum.


  The reactions pp -> pf (X0) ps, where X0 is observed decaying to
pi0pi0pi0pi0, pi+pi-pi0pi0 and pi+pi-pi+pi-, have been studied at 450 GeV/c.
There is evidence for an a2(1320)pi decay mode of the eta2(1645) and eta2(1870)
in the pi+pi-pi0pi0 and pi+pi-pi+pi- final states. The f2(1950) is consistent
with being a single resonance with a dominant f2(1270)pipi decay mode. The
f0(1370) is found to decay dominantly to rho-rho while the f0(1500) is found to
decay to rho-rho and sigma-sigma.


  We propose neural control variates (NCV) for unbiased variance reduction in
parametric Monte Carlo integration. So far, the core challenge of applying the
method of control variates has been finding a good approximation of the
integrand that is cheap to integrate. We show that a set of neural networks can
face that challenge: a normalizing flow that approximates the shape of the
integrand and another neural network that infers the solution of the integral
equation. We also propose to leverage a neural importance sampler to estimate
the difference between the original integrand and the learned control variate.
To optimize the resulting parametric estimator, we derive a theoretically
optimal, variance-minimizing loss function, and propose an alternative,
composite loss for stable online training in practice. When applied to light
transport simulation, neural control variates are capable of matching the
state-of-the-art performance of other unbiased approaches, while providing
means to develop more performant, practical solutions. Specifically, we show
that the learned light-field approximation is of sufficient quality for
high-order bounces, allowing us to omit the error correction and thereby
dramatically reduce the noise at the cost of negligible visible bias.


  We introduce a systematic formalism for two-resonator circuit QED, where two
on-chip microwave resonators are simultaneously coupled to one superconducting
qubit. Within this framework, we demonstrate that the qubit can function as a
quantum switch between the two resonators, which are assumed to be originally
independent. In this three-circuit network, the qubit mediates a geometric
second-order circuit interaction between the otherwise decoupled resonators. In
the dispersive regime, it also gives rise to a dynamic second-order
perturbative interaction. The geometric and dynamic coupling strengths can be
tuned to be equal, thus permitting to switch on and off the interaction between
the two resonators via a qubit population inversion or a shifting of the qubit
operation point. We also show that our quantum switch represents a flexible
architecture for the manipulation and generation of nonclassical microwave
field states as well as the creation of controlled multipartite entanglement in
circuit QED. In addition, we clarify the role played by the geometric
interaction, which constitutes a fundamental property characteristic of
superconducting quantum circuits without counterpart in quantum-optical
systems. We develop a detailed theory of the geometric second-order coupling by
means of circuit transformations for superconducting charge and flux qubits.
Furthermore, we show the robustness of the quantum switch operation with
respect to decoherence mechanisms. Finally, we propose a realistic design for a
two-resonator circuit QED setup based on a flux qubit and estimate all the
related parameters. In this manner, we show that this setup can be used to
implement a superconducting quantum switch with available technology.


  We consider the zero-average Gaussian free field on a certain class of finite
$d$-regular graphs for fixed $d\geq 3$. This class includes $d$-regular
expanders of large girth and typical realisations of random $d$-regular graphs.
We show that the level set of the zero-average Gaussian free field above level
$h$ exhibits a phase transition at level $h_\star$, which agrees with the
critical value for level-set percolation of the Gaussian free field on the
infinite $d$-regular tree. More precisely, we show that, with probability
tending to one as the size of the finite graphs tends to infinity, the level
set above level $h$ does not contain any connected component of larger than
logarithmic size whenever $h>h_\star$, and on the contrary, whenever
$h<h_\star$, a linear fraction of the vertices is contained in connected
components of the level set above level $h$ having a size of at least a small
fractional power of the total size of the graph. It remains open whether in the
supercritical phase $h<h_\star$, as the size of the graphs tends to infinity,
one observes the emergence of a (potentially unique) giant connected component
of the level set above level $h$. The proofs in this article make use of
results from the accompanying paper [AC1].


  Davemaoite, i.e., $CaSiO_3$ perovskite (CaPv), is the third most abundant
phase in the lower mantle and exhibits a tetragonal-cubic phase transition at
high pressures and temperatures. The phase boundary in CaPv has recently been
proposed to be close to the cold slab adiabatic and cause mid-mantle seismic
wave speed anomalies (Thomson et al., Nature 572, 643, 2019). In this study, we
utilized accurate deep-learning-based simulations and thermodynamic integration
techniques to compute free energies at temperatures ranging from 300 to 3,000 K
and pressures up to 130 GPa. Our results indicate that CaPv exhibits a single
cubic phase throughout lower-mantle conditions. This suggests that the phase
diagram proposed by Thomson et al. requires revision, and mid-mantle seismic
anomalies are likely attributable to other mechanisms.


  The ANTARES project aims at the construction of a neutrino telescope 2500 m
below the surface of the Mediterranean sea, close to the southern French coast.
The apparatus will consist of a 3D array of photomultiplier tubes, which
detects the Cherenkov light emitted by upward going neutrino-induced muons.
High-energy neutrinos may be produced in powerful cosmic accelerators, such as,
gamma-ray bursters, active galactic nuclei, supernova remnants, and
microquasars. We have estimated the event rate in ANTARES of neutrinos coming
from these sources, and particularly for a microquasar model, and found that
for some of these sources the detection rate can be up to several events per
year.


  This submission has been withdrawn by the authors because it is a duplicate
of arXiv:hep-ph/0611336. It was due to a technical submission error by the
authors.


  Twisted assemblies of filaments in ropes, cables and bundles are essential
structural elements in wide use in macroscopic materials as well as within the
cells and tissues of living organisms. We develop the unique, non-linear
elastic properties of twisted filament bundles that derive from generic
properties of two-dimensional line-ordered materials. Continuum elasticity
reveals a formal equivalence between the elastic stresses induced by bundle
twist and those induced by the positive curvature in thin, elastic sheets.
These geometrically-induced stresses can be screened by 5-fold disclination
defects in lattice packing, and we predict a discrete spectrum elastic energy
groundstates associated with integer numbers of disclinations in cylindrical
bundles. Finally, we show that elastic-energy groundstates are extremely
sensitive to defect position in the cross-section, with off-center
disclinations driving the entire bundle to buckle, adopting globally writhing
configurations.


  We study coherent electron transport in a one-dimensional wire with disorder
modeled as a chain of randomly positioned scatterers. We derive analytical
expressions for all statistical moments of the wire resistance $\rho$. By means
of these expressions we show analytically that the distribution $P(f)$ of the
variable $f=\ln(1+\rho)$ is not exactly Gaussian even in the limit of weak
disorder. In a strict mathematical sense, this conclusion is found to hold not
only for the distribution tails but also for the bulk of the distribution
$P(f)$.


  For stripes in doped antiferromagnets, we find that the ratio of spin and
charge correlation lenghts, $\xi_{s}/\xi_{c}$, provide a sharp criterion for
determining the dominant form of disorder in the system. If stripes disorder is
controlled by topological defects then $\xi_{s}/\xi_{c}\lesssim 1$. In contast,
if stripes correlations are disordered primarily by non-topological elastic
deformations (i.e., a Bragg-Glass type of disorder) then $1<\xi
_{s}/\xi_{c}\lesssim 4$ is expected. Therefore, the observation of $\xi
_{s}/\xi_{c}\approx 4$ in $(LaNd)_{2-x}Sr_{x}CuO_{4}$ and $\xi_{s}/\xi
_{c}\approx 3$ in $La_{2/3}Sr_{1/3}NiO_{4}$ invariably implies that the stripes
are in a Bragg glass type state, and topological defects are much less relevant
than commonly assumed. Expected spectral properties are discussed. Thus, we
establish the basis for any theoretical analysis of the experimentally
obsereved glassy state in these material.


  This paper outlines, and through stylized examples evaluates a novel and
highly effective computational technique in quantitative finance. Empirical
Risk Minimization (ERM) and neural networks are key to this approach. Powerful
open source optimization libraries allow for efficient implementations of this
algorithm making it viable in high-dimensional structures. The free-boundary
problems related to American and Bermudan options showcase both the power and
the potential difficulties that specific applications may face. The impact of
the size of the training data is studied in a simplified Merton type problem.
The classical option hedging problem exemplifies the need of market generators
or large number of simulations.


  Next generation deep neural networks for classification hosted on embedded
platforms will rely on fast, efficient, and accurate learning algorithms.
Initialization of weights in learning networks has a great impact on the
classification accuracy. In this paper we focus on deriving good initial
weights by modeling the error function of a deep neural network as a
high-dimensional landscape. We observe that due to the inherent complexity in
its algebraic structure, such an error function may conform to general results
of the statistics of large systems. To this end we apply some results from
Random Matrix Theory to analyse these functions. We model the error function in
terms of a Hamiltonian in N-dimensions and derive some theoretical results
about its general behavior. These results are further used to make better
initial guesses of weights for the learning algorithm.


  Contribution to the discussion of "When should meta-analysis avoid making
hidden normality assumptions?" by Dan Jackson and Ian R. White (2018;
https://doi.org/10.1002/bimj.201800071).


  A gamma-ray burst (GRB) is a strong and fast gamma-ray emission from the
explosion of stellar systems (massive stars or coalescing binary compact
stellar remnants), happening at any possible redshift, and detected by space
missions. Although GRBs are the most energetic events after the Big Bang,
systematic search (started after the first localization in 1997) led to only
374 spectroscopic redshift measurements. For less than half, the host galaxy is
detected and studied in some detail. Despite the small number of known hosts,
their impact on our understanding of galaxy formation and evolution is immense.
These galaxies offer the opportunity to explore regions which are
observationally hostile, due to the presence of gas and dust, or the large
distances reached. The typical long-duration GRB host galaxy at low redshift is
small, star-forming and metal poor, whereas, at intermediate redshift, many
hosts are massive, dusty and chemically evolved. Going even farther in the past
of the Universe, at z > 5, long-GRB hosts have never been identified, even with
the deepest NIR space observations, meaning that these galaxies are very small
(stellar mass < 10^7 M_sun). We considered the possibility that some high-z
GRBs occurred in primordial globular clusters, systems that evolved drastically
since the beginning, but would have back then the characteristics necessary to
host a GRB. At that time, the fraction of stellar mass contained in proto
globular clusters might have been orders of magnitude higher than today. Plus,
these objects contained in the past many massive fast rotating binary systems,
which are also regarded as a favorable situation for GRBs. The common factor
for all long GRBs at any redshift is the stellar progenitor: it is a very
massive rare/short-lived star, present in young regions, whose redshift
evolution is closely related to the star-formation history of the Universe.


  (Abridged). Motivated by a growing concern that masses of circumstellar disks
may have been systematically underestimated by conventional observational
methods, we present a numerical hydrodynamics study of time-averaged disk
masses (<M_d>) around low-mass Class 0, Class I, and Class II objects. Mean
disk masses (\overline{M}_d}) are then calculated by weighting the
time-averaged disk masses according to the corresponding stellar masses using a
power-law weight function with a slope typical for the Kroupa initial mass
function of stars. Two distinct types of disks are considered: self-gravitating
disks, in which mass and angular momentum are redistributed exclusively by
gravitational torques, and viscous disks, in which both the gravitational and
viscous torques are at work. We find that self-gravitating disks have mean
masses that are slowly increasing along the sequence of stellar evolution
phases. More specifically, Class 0/I/II self-gravitating disks have mean masses
\overline{M}_d=0.09, 0.10, and 0.12 M_sun, respectively. Viscous disks have
similar mean masses (\overline{M}_d=0.10-0.11 M_sun) in the Class 0/I phases
but almost a factor of 2 lower mean mass in the Class II phase
(\overline{M}_d,CII=0.06 M_sun). In each evolution phase, time-averaged disk
masses show a large scatter around the mean value. Our obtained mean disk
masses are larger than those recently derived by Andrews & Williams and Brown
et al., regardless of the physical mechanisms of mass transport in the disk.


  I describe a special class of meson-like \Lambda_Q excited states and present
evidence supporting the similarity of their spin-independent spectra to those
of mesons. I then examine spin-dependent forces in these baryons, showing that
predicted effects of spin-orbit forces are small for them for the same reason
they are small for the analogous mesons: a fortuitous cancellation between
large spin-orbit forces due to one-gluon-exchange and equally large inverted
spin-orbit forces due to Thomas precession in the confining potential. In
addition to eliminating the baryon spin-orbit puzzle in these states, this
solution provides a new perspective on spin-orbit forces in all baryons.


  We study impacts of dimension-five lepton-number violating operators
associated with two same-sign weak bosons, $\ell^\pm \ell^{\prime \pm} W^\mp
W^\mp$, on current and future experiments for neutrino oscillation,
lepton-number violating rare processes and high-energy collider experiments.
These operators can contain important information on the origin of tiny
neutrino masses, which is independent of that from the so-called Weinberg
operator. We examine constraints on the coefficients of the operators by the
neutrino oscillation data. Upper bounds on the coefficients are also
investigated by using the data for processes of lepton number violation such as
neutrinoless double beta decays and $\mu^-$-$e^+$ conversion. These operators
can also be directly tested by searching for lepton-number violating dilepton
production via the same-sign W boson fusion process at high-energy hadron
colliders like the Large Hadron Collider. We find that these operators can be
considerably probed by these current and future experiments.


  We present the results of a multi-wavelength (FUV to I-band) survey of the
stellar populations of the globular cluster NGC 6752, using STIS, ACS and WFC3
on board the Hubble Space Telescope. We have confirmed that two previously
identified CV candidates are, in fact, dwarf novae which underwent outbursts
during our observations. We have also identified previously unknown optical
counterparts to two X-ray sources. We estimate the position of the centre of
the cluster, and show that the stellar density profile is not well described by
a single King model, indicating that this cluster is in a core-collapsed or
post-core collapse phase. The colour-magnitude diagram shows a well-populated
horizontal branch, numerous blue stragglers and white dwarfs (WDs), as well as
87 sources in the gap region where we expect to find WD - main sequence
binaries, including cataclysmic variables (CVs). The X-ray sources and WD
binary systems are the most centrally concentrated populations, with
dynamically estimated characteristic masses >1.1Msun and >0.8Msun,
respectively.


  We study the action of the group of contact diffeomorphisms on CR
deformations of compact three-dimensional CR manifolds. Using anisotropic
function spaces and an anisotropic structure on the space of contact
diffeomorphisms, we establish the existence of local transverse slices to the
action of the contact diffeomorphism group in the neighbourhood of a fixed
embeddable strongly pseudoconvex CR structure.


  Periodic outbursts are observed in many AGNs and usually explained with a
supermassive black hole binary (SMBHB) scenario. However, multiple periods are
observed in some AGNs and cannot be explained with it. Here we analyze the
periodicity of the radio light curves of AO 0235+164 at multi-frequencies and
report the discovery of six QPOs in integer ratio 1:2:3:4:5:6 of QPO
frequencies, of which the second with period $P_2 = (5.46 \pm 0.47) {\rm yr}$
is the strongest. We fit the radio light curves and show that the initial
phases of six QPOs have zero or $\pi$ differences relative to each other. We
suggest a harmonic relationship of QPOs. The centroid frequency, relative
strength, harmonic relationship and relative initial phases of QPOs are
independent of radio frequency. The harmonic QPOs are likely due to the
quasi-periodic injection of plasma from an oscillating accretion disk into the
jet. We estimate the supermassive black hole mass $M_{\rm BH} \simeq (4.72\pm
2.04) \times 10^8 M_\odot$ and the accretion rate $\dot{m}\simeq 0.007$. With
the knowledge of accretion disk, it implies that the inner region of accretion
disk of AO 0235+164 is a radiatively inefficient accretion flow. The
oscillation accretion is due to the p-mode oscillation of the thick disk
probably excited by a SMBHB. The theoretical predications of fundamental
oscillation frequency and the harmonics are well consistent with the
observations. Harmonic QPOs would be absent when the thick disk becomes
geometrically thin due to the increase of accretion rate. We discuss the
observations of AO 0235+164 basing on the SMBHB-thick disk oscillation
scenario.


  We report the discovery of an esdL3 subdwarf, ULAS J020858.62+020657.0, and a
usdL4.5 subdwarf, ULAS J230711.01+014447.1. They were identified as L subdwarfs
by optical spectra obtained with the Gran Telescopio Canarias, and followed up
by optical-to-near-infrared spectroscopy with the Very Large Telescope. We also
obtained an optical-to-near-infrared spectrum of a previously known L subdwarf,
ULAS J135058.85+081506.8, and reclassified it as a usdL3 subdwarf. These three
objects all have typical halo kinematics. They have $T_{\rm eff}$ around
2050$-$2250 K, $-$1.8 $\leq$ [Fe/H] $\leq -$1.5, and mass around
0.0822$-$0.0833 M$_{\odot}$, according to model spectral fitting and
evolutionary models. These sources are likely halo transitional brown dwarfs
with unsteady hydrogen fusion, as their masses are just below the
hydrogen-burning minimum mass, which is $\sim$ 0.0845 M$_{\odot}$ at [Fe/H] =
$-$1.6 and $\sim$ 0.0855 M$_{\odot}$ at [Fe/H] = $-$1.8. Including these, there
are now nine objects in the `halo brown dwarf transition zone', which is a
`substellar subdwarf gap' that spans a wide temperature range within a narrow
mass range of the substellar population.


  In this paper we extend our analyses of the decay constant and distribution
amplitude with an improved holographic wavefunction to the heavy-light
pseudoscalar mesons. In the evaluations, the helicity-dependence of the
holographic wavefunction is considered; and an independent mass scale parameter
is employed to moderate the strong suppression induced by the heavy quark.
Under the constraints from decay constants and masses of pseudoscalar mesons,
the $\chi^2$-analyses for the holographic parameters exhibit a rough
consistence with the results obtained by fitting the Regge trajectory. With the
fitted parameters, the results for the decay constants and distribution
amplitudes are presented. We then show their application in evaluating the
$B_{d,s}\to D_{d,s}P$ decays, in which the power-suppressed spectator
scattering and weak annihilation corrections are first estimated. Numerically,
the spectator scattering and weak annihilation corrections present a negative
shift of about $ 0.7\%$ on the branching fractions; while, the predictions are
still larger than the experimental data. Such small negative shift confirms the
estimation based on the power counting rules.


  Experiments at the Relativistic Heavy Ion Collider (RHIC) have measured the
net polarization of $\Lambda$ and $\bar{\Lambda}$ hyperons and attributed it to
a coupling between their spin and the vorticity of the fluid created in heavy
ion collisions. Equipartition of energy is generally assumed, but the dynamical
mechanism which polarizes them has yet to be determined. We consider two such
mechanisms: vorticity fluctuations and helicity flip in scatterings between
strange quarks and light quarks and gluons. With reasonable parameters both
mechanisms lead to equilibration times orders of magnitude too large to be
relevant to heavy ion collisions. Our conclusion is that strange quark spin or
helicity is unchanged from the time they are created to the time they
hadronize. A corollary is that vorticity fluctuations do not affect the hyperon
spin either.


  For any positive integer $m$ and an odd prime $p$; let
$\mathbb{F}_{q}+u\mathbb{F}_{q}$, where $q=p^{m}$, be a ring extension of the
ring $\mathbb{F}_{p}+u\mathbb{F}_{p}.$
  In this paper, we construct linear codes over
$\mathbb{F}_{p}+u\mathbb{F}_{p}$ by using trace function defined on
$\mathbb{F}_{q}+u\mathbb{F}_{q}$ and determine their Hamming weight
distributions by employing symplectic-weight distributions of their Gray
images.


  Optomechanical and electromechanical cavities have been widely used in
quantum memories and quantum transducers. We theoretically investigate the
robustness of opto(electro)-mechanical quan- tum memories against the noise of
the control laser. By solving the Langevin equations and using the covariance
matrix formalism in the presence of laser noise, the storing fidelity of
Gaussian states is obtained. It is shown that, the destructive effect of phase
noise is more significant in higher values of coupling laser amplitude and
optomechanical coupling strength G. However, by further increasing coupling
coefficient, the interaction time between photons and phonons decreases below
the coherence time of laser frequency noise and the destructive effect of laser
phase noise on the storing fidelity drops as well.


  This is the second paper of a two-paper series on smart e-mobility. The
concept defined in the first paper is further elaborated and mathematically
formulated in here. A case study is conducted to evaluate the differences
between the proposed electric-vehicle-based and the existing
charging-station-based emobility systems. Each of the four issues identified in
the first paper are individually examined and omission of corresponding
constraints are analyzed and quantified.


  As an efficient image contrast enhancement (CE) tool, adaptive gamma
correction (AGC) was previously proposed by relating gamma parameter with
cumulative distribution function (CDF) of the pixel gray levels within an
image. ACG deals well with most dimmed images, but fails for globally bright
images and the dimmed images with local bright regions. Such two categories of
brightness-distorted images are universal in real scenarios, such as improper
exposure and white object regions. In order to attenuate such deficiencies,
here we propose an improved AGC algorithm. The novel strategy of negative
images is used to realize CE of the bright images, and the gamma correction
modulated by truncated CDF is employed to enhance the dimmed ones. As such,
local over-enhancement and structure distortion can be alleviated. Both
qualitative and quantitative experimental results show that our proposed method
yields consistently good CE results.


  We consider uniformly random strictly upper-triangular matrices in
$\operatorname{Mat}_n(\mathbb{F}_q)$. For such a matrix $A_n$, we show that
$n-\operatorname{rank}(A_n) \approx \log_q n$ as $n \to \infty$, and find that
the fluctuations around this limit are finite-order and given by explicit
$\mathbb{Z}$-valued random variables. More generally, we consider the random
partition whose parts are the sizes of the nilpotent Jordan blocks of $A_n$:
its $k$ largest parts (rows) were previously shown by Borodin to have jointly
Gaussian fluctuations as $N \to \infty$, and its columns correspond to
differences $\operatorname{rank}(A_n^{i-1}) - \operatorname{rank}(A_n^i)$. We
show the fluctuations of the columns converge jointly to a discrete random
point configuration $\mathcal{L}_{t,\chi}$ introduced in arXiv:2310.12275. The
proofs use an explicit integral formula for the probabilities at finite $N$,
obtained by de-Poissonizing a corresponding one in arXiv:2310.12275, which is
amenable to asymptotic analysis.


  We derive new rotating, non-asymptotically flat black ring solutions in
five-dimensional Einstein-Maxwell-dilaton gravity with dilaton coupling
constant $\alpha=\sqrt{8/3}$ which arises from a six-dimensional Kaluza-Klein
theory. As a limiting case we also find new rotating, non-asymptotically flat
five-dimensional black holes. The solutions are analyzed and the mass, angular
momentum and charge are computed. A Smarr-like relation is found. It is shown
that the first law of black hole thermodynamics is satisfied.


  Relationship between the speed limit on the brane and in the bulk is
discussed. We assume that the speed of light, similar to the 4-dimensional
gravitational constant, is not a primary fundamental constant but depends on
the gravitational potential of the brane. This opens the way to explain the
hierarchy between the Plank and Higgs scales even within the simplest
5-dimensional model.


  We establish the Lagrangian nature of the discrete isospectral and
isomonodromic dynamical systems corresponding to the re-factorization
transformations of the rational matrix functions on the Riemann sphere.
Specifically, in the isospectral case we generalize the Moser-Veselov approach
to integrability of discrete systems via the re-factorization of matrix
polynomials to a more general class of matrix rational functions that have a
simple divisor and, in the quadratic case, explicitly write the Lagrangian
function for such systems. Next we show that if we let certain parameters in
this Lagrangian to be time-dependent, the resulting Euler-Lagrange equations
describe the isomonodromic transformations of systems of linear difference
equations. It is known that in some special cases such equations reduce to the
difference Painlev\'e equation. As an example, we show how to obtain the
difference Painlev\`e V equation in this way, and hence we establish that this
equation can be written in the Lagrangian form.


  Subjective perceptual image quality can be assessed in lab studies by human
observers. Objective image quality assessment (IQA) refers to algorithms for
estimation of the mean subjective quality ratings. Many such methods have been
proposed, both for blind IQA in which no original reference image is available
as well as for the full-reference case. We compared 8 state-of-the-art
algorithms for blind IQA and showed that an oracle, able to predict the best
performing method for any given input image, yields a hybrid method that could
outperform even the best single existing method by a large margin. In this
contribution we address the research question whether established methods to
learn such an oracle can improve blind IQA. We applied AutoFolio, a
state-of-the-art system that trains an algorithm selector to choose a
well-performing algorithm for a given instance. We also trained deep neural
networks to predict the best method. Our results did not give a positive
answer, algorithm selection did not yield a significant improvement over the
single best method. Looking into the results in depth, we observed that the
noise in images may have played a role in why our trained classifiers could not
predict the oracle. This motivates the consideration of noisiness in IQA
methods, a property that has so far not been observed and that opens up several
interesting new research questions and applications.