  We present a novel transformer-based architecture for global multi-object
tracking. Our network takes a short sequence of frames as input and produces
global trajectories for all objects. The core component is a global tracking
transformer that operates on objects from all frames in the sequence. The
transformer encodes object features from all frames, and uses trajectory
queries to group them into trajectories. The trajectory queries are object
features from a single frame and naturally produce unique trajectories. Our
global tracking transformer does not require intermediate pairwise grouping or
combinatorial association, and can be jointly trained with an object detector.
It achieves competitive performance on the popular MOT17 benchmark, with 75.3
MOTA and 59.1 HOTA. More importantly, our framework seamlessly integrates into
state-of-the-art large-vocabulary detectors to track any objects. Experiments
on the challenging TAO dataset show that our framework consistently improves
upon baselines that are based on pairwise association, outperforming published
works by a significant 7.7 tracking mAP. Code is available at
https://github.com/xingyizhou/GTR.


  We study the question of dualizability in higher Morita categories of locally
presentable tensor categories and braided tensor categories. Our main results
are that the 3-category of rigid tensor categories with enough compact
projectives is 2-dualizable, that the 4-category of rigid braided tensor
categories with enough compact projectives is 3-dualizable, and that (in
characteristic zero) the 4-category of braided fusion categories is
4-dualizable. Via the cobordism hypothesis, this produces respectively 2, 3 and
4-dimensional framed local topological field theories. In particular, we
produce a framed 3-dimensional local TFT attached to the category of
representations of a quantum group at any value of $q$.


  The simulation of electromagnetic devices with complex geometries and
large-scale discrete systems benefits from advanced computational methods like
IsoGeometric Analysis and Domain Decomposition. In this paper, we employ both
concepts in an Isogeometric Tearing and Interconnecting method to enable the
use of parallel computations for magnetostatic problems. We address the
underlying non-uniqueness by using a graph-theoretic approach, the tree-cotree
decomposition. The classical tree-cotree gauging is adapted to be feasible for
parallelization, which requires that all local subsystems are uniquely
solvable. Our contribution consists of an explicit algorithm for constructing
compatible trees and combining it with a dual-primal approach to enable
parallelization. The correctness of the proposed approach is proved and
verified by numerical experiments, showing its accuracy, scalability and
optimal convergence.


  Totally asymmetric simple exclusion processes, consisting of two coupled
parallel lattice chains with particles interacting with hard-core exclusion and
moving along the channels and between them, are considered. In the limit of
strong coupling between the channels, the particle currents, density profiles
and a phase diagram are calculated exactly by mapping the system into an
effective one-channel totally asymmetric exclusion model. For intermediate
couplings, a simple approximate theory, that describes the particle dynamics in
vertical clusters of two corresponding parallel sites exactly and neglects the
correlations between different vertical clusters, is developed. It is found
that, similarly to the case of one-channel totally asymmetric simple exclusion
processes, there are three stationary state phases, although the phase
boundaries and stationary properties strongly depend on inter-channel coupling.
An extensive computer Monte Carlo simulations fully support the theoretical
predictions.


  This paper proposes a novel Subdivision-Fusion Model (SFM) to recognize human
actions. In most action recognition tasks, overlapping feature distribution is
a common problem leading to overfitting. In the subdivision stage of the
proposed SFM, samples in each category are clustered. Then, such samples are
grouped into multiple more concentrated subcategories. Boundaries for the
subcategories are easier to find and as consequence overfitting is avoided. In
the subsequent fusion stage, the multi-subcategories classification results are
converted back to the original category recognition problem. Two methods to
determine the number of clusters are provided. The proposed model has been
thoroughly tested with four popular datasets. In the Hollywood2 dataset, an
accuracy of 79.4% is achieved, outperforming the state-of-the-art accuracy of
64.3%. The performance on the YouTube Action dataset has been improved from
75.8% to 82.5%, while considerably improvements are also observed on the KTH
and UCF50 datasets.


  We consider propagation of heavy quarks in QCD matter. Because of large quark
mass, the radiative quark energy loss appears to be qualitatively different
from that of light quarks at all energies of practical importance. Finite quark
mass effects lead to an in-medium enhancement of the heavy-to-light D/\pi ratio
at moderately large (5--10 GeV) transverse momenta. For hot QCD matter a large
enhancement is expected, whose magnitude and shape are exponentially sensitive
to the density of colour charges in the medium.


  We present a calculation of the rate of information release from a
Schwarzschild BH. We have recently extended Hawking's theory of black hole (BH)
evaporation to account for quantum fluctuations of the background geometry, as
well as for back-reaction and time-dependence effects. Our main result has been
a two-point function matrix for the radiation that consists of Hawking's
thermal matrix plus off-diagonal corrections that are initially small and
become more important as the evaporation proceeds. Here, we show that, if the
phases and amplitudes of the radiation matrix are recorded over the lifetime of
the BH, then the radiation purifies in a continuous way. We conjecture that our
results establish the maximal rate at which information can be released from a
semiclassical BH, to be contrasted with the minimal rate that was predicted by
Page on the basis of generic unitarity arguments. When the phases of the
radiation matrix are not tracked, we show that it purifies only parametrically
close to the end of the BH evaporation and does so extremely fast. Our main
technical tool in the quantitative treatment of this purification is the purity
of the radiation matrix and, its inverse, the participation ratio. These can be
related to the Renyi entropy of the density matrix of the emitted radiation.


  We show that the Fremlin tensor product $C(X)\bar{\otimes}C(Y)$ is not square
mean complete when X and Y are uncountable metrizable compact spaces. This
motivates the definition of complexification of Archimedean vector lattices,
the Fremlin tensor product of Archimedean complex vector lattices, and a theory
of powers of Archimedean complex vector lattices.


  Transcription of broadcast news is an interesting and challenging application
for large-vocabulary continuous speech recognition (LVCSR). We present in
detail the structure of a manually segmented and annotated corpus including
over 160 hours of German broadcast news, and propose it as an evaluation
framework of LVCSR systems. We show our own experimental results on the corpus,
achieved with a state-of-the-art LVCSR decoder, measuring the effect of
different feature sets and decoding parameters, and thereby demonstrate that
real-time decoding of our test set is feasible on a desktop PC at 9.2% word
error rate.


  We introduce an adic (Bratteli-Vershik) dynamical system based on a diagram
whose path counts from the root are the Delannoy numbers. We identify the
ergodic invariant measures, prove total ergodicity for each of them, and
initiate the study of the dimension group and other dynamical properties.


  Magnons are the elementary excitations of the magnetic order that carry spin,
momentum, and energy. Here we compare the magnon with the ferron, i.e. the
elementary excitation of the electric dipolar order that transports
polarization and heat in ferroelectrics.


  Atrial Fibrillation (AF) is a heart's arrhythmia which, despite being often
asymptomatic, represents an important risk factor for stroke, therefore being
able to predict AF at the electrocardiogram exam, would be of great impact on
actively targeting patients at high risk. In the present work we use
Convolution Neural Networks to analyze ECG and predict Atrial Fibrillation
starting from realistic datasets, i.e. considering fewer ECG than other studies
and extending the maximal distance between ECG and AF diagnosis. We achieved
75.5% (0.75) AUC firstly increasing our dataset size by a shifting technique
and secondarily using the dilation parameter of the convolution neural network.
In addition we find that, contrarily to what is commonly used by clinicians
reporting AF at the exam, the most informative leads for the task of predicting
AF are D1 and avR. Similarly, we find that the most important frequencies to
check are in the range of 5-20 Hz. Finally, we develop a net able to manage at
the same time the electrocardiographic signal together with the electronic
health record, showing that integration between different sources of data is a
profitable path. In fact, the 2.8% gain of such net brings us to a 78.6% (std
0.77) AUC. In future works we will deepen both the integration of sources and
the reason why we claim avR is the most informative lead.


  In this work, we investigate the radiation-induced segregation (RIS)
resulting from the coupling between the atomic and point defect (PD) fluxes
towards the structural defects of the microstructure. This flux coupling
depends on the migration mechanisms of PDs and atoms, including thermal
diffusion mechanisms and forced atomic relocations (FAR) occurring in
displacement cascades. We derive an analytic model of the PD and solute RIS
profiles accounting for PD production and mutual recombination, the FAR
mechanism, and the overall sink strength of the microstructure controlling the
elimination of PDs at structural defects. From this model, we present a
parametric investigation of diffusion and RIS properties in dilute Fe-$B$ ($B$
= P, Mn, Cr, Si, Ni, and Cu) binary alloys, in the form of quantitative
temperature/radiation flux/sink strength maps. As in previous works, we
distinguish three kinetic domains for the diffusion and RIS properties: the
recombination domain, the sink domain, and the thermal domain. Both our
analytical approach and numerical applications demonstrate that the diffusion
and RIS behaviors of PDs and solute atoms largely differ from one kinetic
domain to another. Moreover, at high radiation flux, low temperature, and large
sink strength, FARs tend to destroy the solute RIS profiles and therefore
reduce the overall amount of RIS by forcing the mixing of solute and host
atoms, especially close to PD sinks. Finally, we provide quantitative criteria
to emulate in-reactor RIS behaviors by ion irradiation.


  Fluid turbulence is commonly associated with stronger drag, greater heat
transfer, and more efficient mixing than in laminar flows. In many natural and
industrial settings, turbulent liquid flows contain suspensions of dispersed
bubbles and light particles. Recently, much attention has been devoted to
understanding the behavior and underlying physics of such flows by use of both
experiments and high-resolution direct numerical simulations. This review
summarizes our present understanding of various phenomenological aspects of
bubbly and buoyant particle-laden turbulent flows. We begin by discussing
different dynamical regimes, including those of crossing trajectories and
wake-induced oscillations of rising particles, and regimes in which bubbles and
particles preferentially accumulate near walls or within vortical structures.
We then address how certain paradigmatic turbulent flows, such as homogeneous
isotropic turbulence, channel flow, Taylor-Couette turbulence, and thermally
driven turbulence, are modified by the presence of these dispersed bubbles and
buoyant particles. We end with a list of summary points and future research
questions.


  Remote sensing image fusion technology (pan-sharpening) is an important means
to improve the information capacity of remote sensing images. Inspired by the
efficient arameter space posteriori sampling of Bayesian neural networks, in
this paper we propose a Bayesian Generative Adversarial Network based on
Preconditioned Stochastic Gradient Langevin Dynamics (PGSLD-BGAN) to improve
pan-sharpening tasks. Unlike many traditional generative models that consider
only one optimal solution (might be locally optimal), the proposed PGSLD-BGAN
performs Bayesian inference on the network parameters, and explore the
generator posteriori distribution, which assists selecting the appropriate
generator parameters. First, we build a two-stream generator network with PAN
and MS images as input, which consists of three parts: feature extraction,
feature fusion and image reconstruction. Then, we leverage Markov discriminator
to enhance the ability of generator to reconstruct the fusion image, so that
the result image can retain more details. Finally, introducing Preconditioned
Stochastic Gradient Langevin Dynamics policy, we perform Bayesian inference on
the generator network. Experiments on QuickBird and WorldView datasets show
that the model proposed in this paper can effectively fuse PAN and MS images,
and be competitive with even superior to state of the arts in terms of
subjective and objective metrics.


  Trit-plane coding enables deep progressive image compression, but it cannot
use autoregressive context models. In this paper, we propose the context-based
trit-plane coding (CTC) algorithm to achieve progressive compression more
compactly. First, we develop the context-based rate reduction module to
estimate trit probabilities of latent elements accurately and thus encode the
trit-planes compactly. Second, we develop the context-based distortion
reduction module to refine partial latent tensors from the trit-planes and
improve the reconstructed image quality. Third, we propose a retraining scheme
for the decoder to attain better rate-distortion tradeoffs. Extensive
experiments show that CTC outperforms the baseline trit-plane codec
significantly in BD-rate on the Kodak lossless dataset, while increasing the
time complexity only marginally. Our codes are available at
https://github.com/seungminjeon-github/CTC.


  A characteristic polynomial is an important invariant in the field of
hyperplane arrangement. For the Linial arrangement of any irreducible root
system, Postnikov and Stanley conjectured that all roots of the characteristic
polynomial have the same real part. In relation to this conjecture, Yoshinaga
obtained an explicit relationship between the characteristic quasi-polynomial
and the Ehrhart quasi-polynomial for the fundamental alcove. In this paper, we
calculate Yoshinaga's explicit formula through the decomposition of the Ehrhart
quasi-polynomial into several quasi-polynomials and a modified shift operator,
and obtain new formulas for the characteristic quasi-polynomial of the Linial
arrangement. In particular, when the parameter of the Linial arrangement is
relatively prime to the period of the Ehrhart quasi-polynomial, we prove the
Postnikov-Stanley Linial arrangement conjecture. This generalizes some of the
results for the root systems of classical types that have been proved by
Postnikov-Stanley and Athanasiadis. For other cases, we verify this conjecture
for exceptional root systems using a computational approach.


  Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to
overcome the transparency limitations of black-box models. Due to the growing
attention in graph learning, we focus on the concepts of CE for GNNs. We
analysed the SoA to provide a taxonomy, a uniform notation, and the
benchmarking datasets and evaluation metrics. We discuss fourteen methods,
their evaluation protocols, twenty-two datasets, and nineteen metrics. We
integrated the majority of methods into the GRETEL library to conduct an
empirical evaluation to understand their strengths and pitfalls. We highlight
open challenges and future work.


  Real-time semantic segmentation of remote sensing imagery is a challenging
task that requires a trade-off between effectiveness and efficiency. It has
many applications including tracking forest fires, detecting changes in land
use and land cover, crop health monitoring, and so on. With the success of
efficient deep learning methods (i.e., efficient deep neural networks) for
real-time semantic segmentation in computer vision, researchers have adopted
these efficient deep neural networks in remote sensing image analysis. This
paper begins with a summary of the fundamental compression methods for
designing efficient deep neural networks and provides a brief but comprehensive
survey, outlining the recent developments in real-time semantic segmentation of
remote sensing imagery. We examine several seminal efficient deep learning
methods, placing them in a taxonomy based on the network architecture design
approach. Furthermore, we evaluate the quality and efficiency of some existing
efficient deep neural networks on a publicly available remote sensing semantic
segmentation benchmark dataset, the OpenEarthMap. The experimental results of
an extensive comparative study demonstrate that most of the existing efficient
deep neural networks have good segmentation quality, but they suffer low
inference speed (i.e., high latency rate), which may limit their capability of
deployment in real-time applications of remote sensing image segmentation. We
provide some insights into the current trend and future research directions for
real-time semantic segmentation of remote sensing imagery.


  Let $H$ be an infinite dimensional separable Hilbert space and $B(H)$ the
C*-algebra of bounded operators on $H.$ Suppose that $T_1,T_2,..., T_n$ are
self-adjoint operators in $B(H).$ We show that, if commutators $[T_i, T_j]$ are
sufficiently small in norm, then ``Approximately Macroscopically Unique" states
always exist for any values in a synthetic spectrum of the $n$-tuple of
self-adjoint operators. This is achieved under the circumstance for which the
$n$-tuple may not be approximated by commuting ones. This answers a question
proposed by David Mumford for measurements in quantum theory. If commutators
are not small in norm but small modulo compact operators, then ``Approximate
Macroscopic Uniqueness" states also exist.


  Polycrystalline silicon core fibers (SCFs) fabricated via the molten core
drawing (MCD) method are emerging as a flexible optoelectronic platform. Here,
the optical transmission properties of MCD SCFs that have been tapered down to
a few micrometer-sized core dimensions are characterized from the Telecom band
to the mid-infrared spectal regime. The SCFs exhibit low linear losses on the
order of a few dB/cm over the entire wavelength range. Characterization of the
two-photon absorption coefficient \b{eta}_TPA and nonlinear refractive index
n_2 of the SCFs reveals values consistent with previous measurements of single
crystal silicon materials, indicating the high optical quality of the
polysilicon core material. The high nonlinear figure of merit obtained for
wavelengths above 2{\mu}m highlight the potential for these fibers to find
application in infrared nonlinear photonics.


  Survival competing risks models are very useful for studying the incidence of
diseases whose occurrence competes with other possible diseases or health
conditions. These models perform properly when working with terminal events,
such as death, that imply the conclusion of the corresponding study. But they
do not allow the treatment of scenarios with non-terminal competing events that
may occur sequentially. Multi-state models are complex survival models. They
focus on pathways defined by the temporal and sequential occurrence of numerous
events of interest and thus they are suitable for connecting competing
non-terminal events as well as to manage other survival scenarios with higher
complexity. We discuss competing risks within the framework of multi-state
models and clarify the usefulness of both models for analysing epidemiological
data. We highlight the power of multi-state models through a real-world study
of recurrent hip fracture from Bayesian inferential methodology.


  Visible-Infrared cross-modality person re-identification (VI-ReID), whose aim
is to match person images between visible and infrared modality, is a
challenging cross-modality image retrieval task. Batch Hard Triplet loss is
widely used in person re-identification tasks, but it does not perform well in
the Visible-Infrared person re-identification task. Because it only optimizes
the hardest triplet for each anchor image within the mini-batch, samples in the
hardest triplet may all belong to the same modality, which will lead to the
imbalance problem of modality optimization. To address this problem, we adopt
the batch all triplet selection strategy, which selects all the possible
triplets among samples to optimize instead of the hardest triplet. Furthermore,
we introduce Unified Batch All Triplet loss and Cosine Softmax loss to
collaboratively optimize the cosine distance between image vectors. Similarly,
we rewrite the Hetero Center Triplet loss, which is proposed for VI-ReID task,
into a batch all form to improve model performance. Extensive experiments
indicate the effectiveness of the proposed methods, which outperform
state-of-the-art methods by a wide margin.


  The structural, electronic, and elastic properties of three mixed transition
metal carbonitrides TiNxC1-x, ZrNxC1-x, and HfNxC1-x (0<x<1) with the rock-salt
structure were calculated at ambient and elevated up to 50 GPa hydrostatic
pressures in the framework of the density functional theory methods. The
lattice constants, densities, and bulk moduli of the considered compounds were
shown to behave as linear functions of the nitrogen concentration x. The
obtained linear dependencies of all these parameters allow for getting their
estimates at any value of x in the range from 0 to 1. Gradual enhancement of
the ionicity of the chemical bonds with gradual replacement of carbon by
nitrogen was demonstrated by calculating the bond orders and electron density
difference distributions.


  We discuss the stability of the neutrino oscillation parameters at low energy
scale including self-complementarity (SC) relations among mixing angles under
radiative corrections with the variation of SUSY breaking scale ($m_s$) in both
normal and inverted hierarchical cases. We observe that the neutrino
oscillation parameters including the SC relation maintains stability at the
electroweak scale within $1\sigma$ range of the latest global fit data. NH case
maintains more stability than IH case. All the numerical values related to the
absolute neutrino masses viz., $\Sigma |m_i|$, $m_{\beta}$ and $m_{ \beta
\beta}$ are found to lie below the observational upper bound.


  The MAGIC telescope took data of very high energy gamma-ray emission from the
blazar Markarian 421 (Mkn 421) between November 2004 and April 2005. We present
a combined analysis of data samples recorded under different observational
conditions, down to gamma-ray energies of 100 GeV. The flux was found to vary
between 0.5 -- 2 Crab units (integrated above 200 GeV), considered a low state
when compared to known data. Although the flux varied on a day-by-day basis, no
short-term variability was observed, although there is some indication that not
all nights are in an equally quiescent state. The results at higher energies
were found to be consistent with previous observations. A clear correlation is
observed between gamma-rays and X-rays fluxes, whereas no significant
correlation between gamma-rays and optical data is seen. The spectral energy
distribution between 100 GeV and 3 TeV shows a clear deviation from a power
law, more clearly and at lower flux than previous observations at higher
energies. The deviation persists after correcting for the effect of attenuation
by the extragalactic background light, and most likely is source-inherent.
There is a rather clear indication of an inverse Compton peak around 100 GeV.
The spectral energy distribution of Mkn 421 can be fitted by a one-zone
synchrotron self-compton model suggesting once again a leptonic origin of the
very high energy gamma-ray emission from this blazar.


  We describe experiments towards building a conversational digital assistant
that considers the preferred conversational style of the user. In particular,
these experiments are designed to measure whether users prefer and trust an
assistant whose conversational style matches their own. To this end we
conducted a user study where subjects interacted with a digital assistant that
responded in a way that either matched their conversational style, or did not.
Using self-reported personality attributes and subjects' feedback on the
interactions, we built models that can reliably predict a user's preferred
conversational style.


  In an Anderson localized system, a quantum particle with a nonzero initial
velocity returns, on average, to its origin. This recently discovered behavior
is known as the quantum boomerang effect. Time reversal invariance was
initially thought to be a necessary condition for the existence of this
phenomenon. We theoretically analyze the impact of the symmetry breaking on the
phenomenon using a one-dimensional system with a spin-orbit coupling and show
that the time reversal invariance is not necessary for the boomerang effect to
occur.


  Federated learning enables a cluster of decentralized mobile devices at the
edge to collaboratively train a shared machine learning model, while keeping
all the raw training samples on device. This decentralized training approach is
demonstrated as a practical solution to mitigate the risk of privacy leakage.
However, enabling efficient FL deployment at the edge is challenging because of
non-IID training data distribution, wide system heterogeneity and
stochastic-varying runtime effects in the field. This paper jointly optimizes
time-to-convergence and energy efficiency of state-of-the-art FL use cases by
taking into account the stochastic nature of edge execution. We propose AutoFL
by tailor-designing a reinforcement learning algorithm that learns and
determines which K participant devices and per-device execution targets for
each FL model aggregation round in the presence of stochastic runtime variance,
system and data heterogeneity. By considering the unique characteristics of FL
edge deployment judiciously, AutoFL achieves 3.6 times faster model convergence
time and 4.7 and 5.2 times higher energy efficiency for local clients and
globally over the cluster of K participants, respectively.


  The search for the rare \BMuMu and \BsMuMu decays in pp collisions at $\sqrt
s = 7$~GeV and $\sqrt s = 8$~GeV, collected at the LHC in 2011 and 2012, is
briefly reviewed. The data analyzed by CMS correspond to a total integrated
luminosity of 5 and 20 $\rm fb^{-1}$, respectively. The time-integrated average
branching fraction $\left<{\mathcal B }( { B^{0}_{s}\rightarrow \mu^+\mu^-}
)\right>$ has been measured to be $(3.0 ^{+1.0}_{-0.9})\times 10^{-9}$ in
accordance with the Standard Model predictions, while an upper limit $\left<{
\mathcal B }( B^{0}\rightarrow \mu^+\mu^-)\right> < 1.1\times 10^{-9}) $ has
been placed on the other investigated decay at 95% CL. A preliminary
combination with the results from LHCb is also presented for both channels, and
prospects for the future are briefly discussed.


  By the Magnus-Floquet approach we calculate the effective Hamiltonian for a
charged particle on the lattice subject to a homogeneous high frequency
oscillating electric field. The obtained result indicate the absence of dynamic
localization of the particle for any value of the lattice constant and electric
field applied, which completes the limit results obtained by Dunlap and Kenkre.


  As an effective form of narrative visualization, visual data stories are
widely used in data-driven storytelling to communicate complex insights and
support data understanding. Although important, they are difficult to create,
as a variety of interdisciplinary skills, such as data analysis and design, are
required. In this work, we introduce Erato, a human-machine cooperative data
story editing system, which allows users to generate insightful and fluent data
stories together with the computer. Specifically, Erato only requires a number
of keyframes provided by the user to briefly describe the topic and structure
of a data story. Meanwhile, our system leverages a novel interpolation
algorithm to help users insert intermediate frames between the keyframes to
smooth the transition. We evaluated the effectiveness and usefulness of the
Erato system via a series of evaluations including a Turing test, a controlled
user study, a performance validation, and interviews with three expert users.
The evaluation results showed that the proposed interpolation technique was
able to generate coherent story content and help users create data stories more
efficiently.


  We recommend a deeper extension to the High-Latitute Wide Area Survey planned
to be conducted by the Nancy Grace Roman Space Telescope (\emph{Roman}). While
this deeper-tier survey extension can support a range of astrophysical
investigations, it is particularly well suited to characterize the dark matter
substructure in galactic halos and reveal the microphysics of dark matter
through gravitational lensing. We quantify the expected yield of \emph{Roman}
for finding galaxy-galaxy-type gravitational lenses and motivate observational
choices to optimize the \emph{Roman} core community surveys for studying dark
matter substructure. In the proposed survey, we expect to find, on average, one
strong lens with a characterizable substructure per \emph{Roman} tile (0.28
squared degrees), yielding approximately 500 such high-quality lenses. With
such a deeper legacy survey, \emph{Roman} will outperform any current and
planned telescope within the next decade in its potential to characterize the
concentration and abundance of dark matter subhalos in the mass range
10$^7$-10$^{11}$\,M$_{\odot}$.


  Neural Collapse (NC) gives a precise description of the representations of
classes in the final hidden layer of classification neural networks. This
description provides insights into how these networks learn features and
generalize well when trained past zero training error. However, to date, (NC)
has only been studied in the final layer of these networks. In the present
paper, we provide the first comprehensive empirical analysis of the emergence
of (NC) in the intermediate hidden layers of these classifiers. We examine a
variety of network architectures, activations, and datasets, and demonstrate
that some degree of (NC) emerges in most of the intermediate hidden layers of
the network, where the degree of collapse in any given layer is typically
positively correlated with the depth of that layer in the neural network.
Moreover, we remark that: (1) almost all of the reduction in intra-class
variance in the samples occurs in the shallower layers of the networks, (2) the
angular separation between class means increases consistently with hidden layer
depth, and (3) simple datasets require only the shallower layers of the
networks to fully learn them, whereas more difficult ones require the entire
network. Ultimately, these results provide granular insights into the
structural propagation of features through classification neural networks.


  Probabilistic load forecasts provide comprehensive information about future
load uncertainties. In recent years, many methodologies and techniques have
been proposed for probabilistic load forecasting. Forecast combination, a
widely recognized best practice in point forecasting literature, has never been
formally adopted to combine probabilistic load forecasts. This paper proposes a
constrained quantile regression averaging (CQRA) method to create an improved
ensemble from several individual probabilistic forecasts. We formulate the CQRA
parameter estimation problem as a linear program with the objective of
minimizing the pinball loss, with the constraints that the parameters are
nonnegative and summing up to one. We demonstrate the effectiveness of the
proposed method using two publicly available datasets, the ISO New England data
and Irish smart meter data. Comparing with the best individual probabilistic
forecast, the ensemble can reduce the pinball score by 4.39% on average. The
proposed ensemble also demonstrates superior performance over nine other
benchmark ensembles.


  Background: Caenorhabditis elegans is a major model system in biology, yet
very little is known about its biology outside the laboratory. Especially, its
unusual mode of reproduction with self-fertile hermaphrodites and facultative
males raises the question of its frequency of outcrossing in natural
populations. Results: We describe the first analysis of C. elegans individuals
sampled directly from natural populations. C. elegans is found predominantly in
the dauer stage and with a very low frequency of males compared with
hermaphrodites. While C. elegans was previously shown to display a low
worldwide genetic diversity, we find by comparison a surprisingly high local
genetic diversity of C. elegans populations; this local diversity is
contributed in great part by immigration of new alleles rather than by
mutation. Our results on heterozygote frequency, male frequency and linkage
disequilibrium furthermore show that selfing is the predominant mode of
reproduction in C. elegans natural populations, yet that infrequent outcrossing
events occur, at a rate of approximately 1%. Conclusions: Our results give a
first insight in the biology of C. elegans in the natural populations. They
demonstrate that local populations of C. elegans are genetically diverse and
that a low frequency of outcrossing allows for the recombination of these
locally diverse genotypes.


  Let X be a smooth projective variety of dimension n in P^r. We study the
fibers of a general linear projection pi: X --> P^{n+c}, with c > 0. When n is
small it is classical that the degree of any fiber is bounded by n/c+1, but
this fails for n >> 0. We describe a new invariant of the fiber that agrees
with the degree in many cases and is always bounded by n/c+1. This implies, for
example, that if we write a fiber as the disjoint union of schemes Y' and Y''
such that Y' is the union of the locally complete intersection components of Y,
then deg Y'+deg Y''_red <= n/c+1 and this formula can be strengthened a little
further. Our method also gives a sharp bound on the subvariety of P^r swept out
by the l-secant lines of X for any positive integer l, and we discuss a
corresponding bound for highly secant linear spaces of higher dimension. These
results extend Ziv Ran's "Dimension+2 Secant Lemma".


  A weak-coupling scaling diagram for the Lyapunov exponent and the integrated
density of states near a band edge of a random Jacobi matrix is obtained. The
analysis is based on the use of a Fokker-Planck operator describing the
drift-diffusion of the Pr\"ufer phases.


  We systematically analyze the nonlinear partial differential equation that
determines the behaviour of a bounded radiating spherical mass in general
relativity. Four categories of solution are possible. These are identified in
terms of restrictions on the gravitational potentials. One category of solution
can be related to the horizon function transformation which was recently
introduced. A Lie symmetry analysis of the resulting Riccati equation shows
that several new classes of exact solutions are possible. The relationship
between the horizon function, Euclidean star models and other earlier
investigations is clarified.


  In this paper we introduce a knowledge engine, which learns and shares
knowledge representations, for robots to carry out a variety of tasks. Building
such an engine brings with it the challenge of dealing with multiple data
modalities including symbols, natural language, haptic senses, robot
trajectories, visual features and many others. The \textit{knowledge} stored in
the engine comes from multiple sources including physical interactions that
robots have while performing tasks (perception, planning and control),
knowledge bases from the Internet and learned representations from several
robotics research groups.
  We discuss various technical aspects and associated challenges such as
modeling the correctness of knowledge, inferring latent information and
formulating different robotic tasks as queries to the knowledge engine. We
describe the system architecture and how it supports different mechanisms for
users and robots to interact with the engine. Finally, we demonstrate its use
in three important research areas: grounding natural language, perception, and
planning, which are the key building blocks for many robotic tasks. This
knowledge engine is a collaborative effort and we call it RoboBrain.


  We present algorithms to perform modular polynomial multiplication or modular
dot product efficiently in a single machine word. We pack polynomials into
integers and perform several modular operations with machine integer or
floating point arithmetic. The modular polynomials are converted into integers
using Kronecker substitution (evaluation at a sufficiently large integer). With
some control on the sizes and degrees, arithmetic operations on the polynomials
can be performed directly with machine integers or floating point numbers and
the number of conversions can be reduced. We also present efficient ways to
recover the modular values of the coefficients. This leads to practical gains
of quite large constant factors for polynomial multiplication, prime field
linear algebra and small extension field arithmetic.


  We formulate a theory of invariants for the spin symmetric group in some
suitable modules which involve the polynomial and exterior algebras. We solve
the corresponding graded multiplicity problem in terms of specializations of
the Schur Q-functions and a shifted q-hook formula. In addition, we provide a
bijective proof for a formula of the principal specialization of the Schur
Q-functions.


  Affordance learning considers the interaction opportunities for an actor in
the scene and thus has wide application in scene understanding and intelligent
robotics. In this paper, we focus on contextual affordance learning, i.e.,
using affordance as context to generate a reasonable human pose in a scene.
Existing scene-aware human pose generation methods could be divided into two
categories depending on whether using pose templates. Our proposed method
belongs to the template-based category, which benefits from the representative
pose templates. Moreover, inspired by recent transformer-based methods, we
associate each query embedding with a pose template, and use the interaction
between query embeddings and scene feature map to effectively predict the scale
and offsets for each pose template. In addition, we employ knowledge
distillation to facilitate the offset learning given the predicted scale.
Comprehensive experiments on Sitcom dataset demonstrate the effectiveness of
our method.


  The two important issues concerning the theory of inclusive decays are
studied: (1) evaluation of expectation values of four-quark operators in order
to systematically include the spectator effects and (2) estimation of duality
breaking corrections in heavy quark expansion. Our study leads to the
theoretical prediction of the ratio of lifetimes of $\Lambda_b$ and $B$ close
to the experimental one and the duality breaking corrections of about 10% of
the leading decay rate.


  Here we advocate an observational strategy to help prioritize exoplanet
observations. It starts with more easily obtained observational data, and ranks
exoplanets for more difficult follow-up observations based on the likelihood of
avoiding planets for which oxygen is a false positives or even an inconclusive
signature of life. We find that for oxygen to be a robust biosignature, both
land and surface water must be present. Landless exoplanets have much slower
biogeochemical cycles, so while oxygenic photosynthesizing life could exist on
such planets, it could not produce oxygen at a rate competitive with abiotic
rates such as photolysis. These habitable planets, whose life would not be
detectable, should be avoided.


  In this work we study the main properties and the one-loop renormalization of
a Yang-Mills theory in which the kinetic term contains also a fourth-order
differential operator; in particular, we add to the Yang-Mills Lagrangian the
most general contribution of mass dimension six, weighted with a dimensionful
parameter. This model is renormalizable; in the literature two values for the
beta function for the gauge coupling have been reported, one obtained using the
heat kernel approach and one with Feynman diagrams. In this work we repeat the
computation using heat kernel techniques confirming the latter result. We also
considered coupling with matter.
  We then study the supersymmetric extension of the model; this is a nontrivial
task because of the complicate structure of the higher-derivative term. Some
partial results were known, but a computation of the beta functions for the
full supersymmetric non-Abelian higher-derivative gauge theory was missing. We
make use of the (unextended) supersymmetric higher-derivative Lagrangian
density for the Yang-Mills field in six spacetime dimensions derived in
arXiv:hep-th/0505082; by dimensional reduction we obtain the N=1 and N=2
supersymmetric higher-derivative super-Yang-Mills Lagrangian in four spacetime
dimensions, whose beta function we evaluate using heat kernels. We also deduce
the beta function for N=4 supersymmetry.


  Gaussian Processes are used in many applications to model spatial phenomena.
Within this context, a key issue is to decide the set of locations where to
take measurements so as to obtain a better approximation of the underlying
function. Current state of the art techniques select such set to minimize the
posterior variance of the Gaussian process. We explore the feasibility of
solving this problem by proposing a novel Quadratic Unconstrained Binary
Optimization (QUBO) model. In recent years this QUBO formulation has gained
increasing attention since it represents the input for the specialized quantum
annealer D-Wave machines. Hence, our contribution takes an important first step
towards the sampling optimization of Gaussian processes in the context of
quantum computation. Results of our empirical evaluation shows that the optimum
of the QUBO objective function we derived represents a good solution for the
above mentioned problem. In fact we are able to obtain comparable and in some
cases better results than the widely used submodular technique.


  The effects of strains on the low-energy electronic properties of double-Weyl
phases are studied in solids and cold-atom optical lattices. The principal
finding is that deformations do not couple, in general, to the low-energy
effective Hamiltonian as a pseudoelectromagnetic gauge potential. The response
of an optical lattice to strains is simpler, but still only one of several
strain-induced terms in the corresponding low-energy Hamiltonian can be
interpreted as a gauge potential. Most interestingly, the strains can induce a
nematic order parameter that splits a double-Weyl node into a pair of Weyl
nodes with the unit topological charges. The effects of deformations on the
motion of wavepackets in the double-Weyl optical lattice model are studied. It
is found that, even in the undeformed lattices, the wavepackets with opposite
topological charges can be spatially split. Strains, however, modify their
velocities in a very different way and lead to a spin polarization of the
wavepackets.


  Today, there are millions of third-party Android applications. Some of these
applications are buggy or even malicious. To identify such applications, novel
frameworks for automated black-box testing and dynamic analysis are being
developed by the Android community, including Google. Code coverage is one of
the most common metrics for evaluating effectiveness of these frameworks.
Furthermore, code coverage is used as a fitness function for guiding
evolutionary and fuzzy testing techniques. However, there are no reliable tools
for measuring fine-grained code coverage in black-box Android app testing.
  We present the Android Code coVerage Tool, ACVTool for short, that
instruments Android apps and measures the code coverage in the black-box
setting at the class, method and instruction granularities. ACVTool has
successfully instrumented 96.9% of apps in our experiments. It introduces a
negligible instrumentation time overhead, and its runtime overhead is
acceptable for automated testing tools. We show in a large-scale experiment
with Sapienz, a state-of-art testing tool, that the fine-grained
instruction-level code coverage provided by ACVTool helps to uncover a larger
amount of faults than coarser-grained code coverage metrics.


  Recent progress in neural machine translation (NMT) has made it possible to
translate successfully between monolingual language pairs where large parallel
data exist, with pre-trained models improving performance even further.
Although there exists work on translating in code-mixed settings (where one of
the pairs includes text from two or more languages), it is still unclear what
recent success in NMT and language modeling exactly means for translating
code-mixed text. We investigate one such context, namely MT from code-mixed
Modern Standard Arabic and Egyptian Arabic (MSAEA) into English. We develop
models under different conditions, employing both (i) standard end-to-end
sequence-to-sequence (S2S) Transformers trained from scratch and (ii)
pre-trained S2S language models (LMs). We are able to acquire reasonable
performance using only MSA-EN parallel data with S2S models trained from
scratch. We also find LMs fine-tuned on data from various Arabic dialects to
help the MSAEA-EN task. Our work is in the context of the Shared Task on
Machine Translation in Code-Switching. Our best model achieves $\bf25.72$ BLEU,
placing us first on the official shared task evaluation for MSAEA-EN.


  In order to investigate the capability of the preseismic electric field
"strange attractor like" precursor as a time predictor of a large EQ within a
short time window (short-term prediction), the specific methodology was applied
on the Earth's electric field recorded during a rather long seismically active
period (December 1st, 2007 - April 30th, 2008) of Greece. During this period of
time a number (8) of large (Ms > 5.5R) earthquakes took place. The particular
analysis is presented in detail for the following EQs: the Monemvasia EQ
(January 6th 2008, Ms = 6.6R), the Methoni EQs (February 14th 2008 Ms = 6.7R,
February 19th 2008 Ms = 5.6R, February 20th 2008 Ms = 6.5R, February 26th 2008
Ms = 5.7R), the Skyros EQ (March 19th 2008 Ms = 5.5R) and the Mid Southern
Creta EQ (March 28th 2008 Ms = 5.6R). The obtained results from the analysis of
the afore mentioned EQs, in conjunction to the ones obtained from an earlier
presentation of the particular methodology (Thanassoulas et al. 2008a),
suggest: an average time of initiation of the preseismic precursor of the order
of (9) days before the EQ occurrence, a precursor average duration of the order
of (7) days, while the elapsed time between the end of the precursor till the
EQ occurrence time is, at average, only (2) days. These results suggest an
objective and easy to apply method for the short-term EQ prediction, based on
the registration of the Earth's electric field on ground surface.


  A family of solutions of the Jacobi PDEs is investigated. This family is
$n$-dimensional, of arbitrary nonlinearity and can be globally analyzed (thus
improving the usual local scope of Darboux theorem). As an outcome of this
analysis it is demonstrated that such Poisson structures lead to integrable
systems. The solution family embraces as particular cases different systems of
applied interest that are also regarded as examples.


  We prove that an arc-homogeneous Euclidean neighborhood retract is a homology
manifold.


  Two commonly used ideas in the development of citation-based research
performance indicators are the idea of normalizing citation counts based on a
field classification scheme and the idea of recursive citation weighing (like
in PageRank-inspired indicators). We combine these two ideas in a single
indicator, referred to as the recursive mean normalized citation score
indicator, and we study the validity of this indicator. Our empirical analysis
shows that the proposed indicator is highly sensitive to the field
classification scheme that is used. The indicator also has a strong tendency to
reinforce biases caused by the classification scheme. Based on these
observations, we advise against the use of indicators in which the idea of
normalization based on a field classification scheme and the idea of recursive
citation weighing are combined.


  The article proves an assertion analogous to the Littlewood-Paley theorem for
the orthoprojectors onto wavelet subspaces corresponding to the
multidimensional multiresolution analysis generated as tensor product of smooth
finite scaling functions of one variable.


  This study investigated the impacts of the COVID-19 pandemic on the
electricity consumption of a university dormitory building in the southern U.S.
The historical electricity consumption data of this university dormitory
building and weather data of an on-campus weather station, which were collected
from January 1st, 2017 to July 31st, 2020, were used for analysis. Four inverse
data-driven prediction models, i.e., Artificial Neural Network, Long Short-Term
Memory Recurrent Neural Network, eXtreme Gradient Boosting, and Light Gradient
Boosting Machine, were exploited to account for the influence of the weather
conditions. The results suggested that the total electricity consumption of the
objective building decreased by nearly 41% (about 276,000 kWh (942 MMBtu))
compared with the prediction value during the campus shutdown due to the
COVID-19. Besides, the daily load ratio (DLR) varied significantly as well. In
general, the DLR decreased gradually from 80% to nearly 40% in the second half
of March 2020, maintained on a relatively stable level between 30% to 60% in
April, May, and June 2020, and then slowly recovered to 80% of the normal
capacity in July 2020.


  We calculate the "observed at infinity" image and spectrum of the accretion
structure in Sgr A*, by modelling it as an optically thin, constant angular
momentum ion torus in hydrodynamic equilibrium. The physics we consider
includes a two-temperature plasma, a toroidal magnetic field, as well as
radiative cooling by bremsstrahlung, synchrotron and inverse Compton processes.
Our relativistic model has the virtue of being fully analytic and very simple,
depending only on eight tunable parameters: the black hole spin and the
inclination of the spin axis to our line of sight, the torus angular momentum,
the polytropic index, the magnetic to total pressure ratio, the central values
of density and electron temperature and the ratio of electron to ion
temperatures. The observed image and spectrum are calculated numerically using
the ray-tracing code GYOTO. Our results demonstrate that the ion torus model is
able to account for the main features of the accretion structure surrounding
Sgr A*.


  The BPS Skyrme model has been demonstrated already to provide a physically
intriguing and quantitatively reliable description of nuclear matter. Indeed,
the model has both the symmetries and the energy-momentum tensor of a perfect
fluid, and thus represents a field theoretic realization of the "liquid
droplet" model of nuclear matter. In addition, the classical soliton solutions
together with some obvious corrections (spin-isospin quantization, Coulomb
energy, proton-neutron mass difference) provide an accurate modeling of nuclear
binding energies for heavier nuclei. These results lead to the rather natural
proposal to try to describe also neutron stars by the BPS Skyrme model coupled
to gravity. We find that the resulting self-gravitating BPS Skyrmions provide
excellent results as well as some new perspectives for the description of bulk
properties of neutron stars when the parameter values of the model are
extracted from nuclear physics. Specifically, the maximum possible mass of a
neutron star before black-hole formation sets in is a few solar masses, the
precise value depending on the precise values of the model parameters, and the
resulting neutron star radius is of the order of 10 km.


  Vision transformers (ViTs) are top performing models on many computer vision
benchmarks and can accurately predict human behavior on object recognition
tasks. However, researchers question the value of using ViTs as models of
biological learning because ViTs are thought to be more data hungry than
brains, with ViTs requiring more training data to reach similar levels of
performance. To test this assumption, we directly compared the learning
abilities of ViTs and animals, by performing parallel controlled rearing
experiments on ViTs and newborn chicks. We first raised chicks in impoverished
visual environments containing a single object, then simulated the training
data available in those environments by building virtual animal chambers in a
video game engine. We recorded the first-person images acquired by agents
moving through the virtual chambers and used those images to train self
supervised ViTs that leverage time as a teaching signal, akin to biological
visual systems. When ViTs were trained through the eyes of newborn chicks, the
ViTs solved the same view invariant object recognition tasks as the chicks.
Thus, ViTs were not more data hungry than newborn visual systems: both learned
view invariant object representations in impoverished visual environments. The
flexible and generic attention based learning mechanism in ViTs combined with
the embodied data streams available to newborn animals appears sufficient to
drive the development of animal-like object recognition.


  Our toolchain for accelerating application called Courier-FPGA, is designed
for utilize the processing power of CPU-FPGA platforms for software programmers
and non-expert users. It automatically gathers runtime information of library
functions from a running target binary, and constructs the function call graph
including input-output data. Then, it uses corresponding predefined hardware
modules if these are ready for FPGA and prepares software functions on CPU by
using Pipeline Generator. The Pipeline Generator builds a pipeline control
program by using Intel Threading Building Block to run both hardware modules
and software functions in parallel. Finally, Courier-FPGA dynamically replaces
the original functions in the binary and accelerates it by using the built
pipeline. Courier-FPGA performs these acceleration processes without user
intervention, source code tweaks or re-compilations of the binary. We describe
the technical details of this mixed software hardware pipeline on CPU-FPGA
platforms in this paper. In our case study, Courier-FPGA was used to accelerate
a corner detection using the Harris-Stephens method application binary on the
Zynq platform. A series of functions were off-loaded, and speed up 15.36 times
was achieved by using the built pipeline.


  In this paper, we investigate how stochastic reaction processes are affected
by external perturbations. We describe an extension of the deterministic
metabolic control analysis (MCA) to the stochastic regime. We introduce
stochastic sensitivities for mean and covariance values of reactant
concentrations and reaction fluxes and show that there exist MCA-like summation
theorems among these sensitivities. The summation theorems for flux variances
are shown to depend on the size of the measurement time window ($\epsilon$),
within which reaction events are counted for measuring a single flux. The
degree of the $\epsilon$-dependency can become significant for processes
involving multi-time-scale dynamics and is estimated by introducing a new
measure of time scale separation. This $\epsilon$-dependency is shown to be
closely related to the power-law scaling observed in flux fluctuations in
various complex networks. We propose a systematic way to control fluctuations
of reactant concentrations while minimizing changes in mean concentration
levels. Such orthogonal control is obtained by introducing a control vector
indicating the strength and direction of parameter perturbations leading to a
sensitive control. We also propose a possible implication in the control of
flux fluctuation: The control distribution for flux fluctuations changes with
the measurement time window size, $\epsilon$. When a control engineer applies a
specific control operation on a reaction system, the system can respond
contrary to what is expected, depending on the time window size $\epsilon$.


  We present the results on elliptic flow with multi-strange baryons produced
in Pb-Pb collisions at \sqrt{s_{NN}} = 2.76 TeV. The analysis is performed with
the ALICE detector at LHC. Multi-strange baryons are reconstructed via their
decay topologies and the v_2 values are analyzed with the two-particle scalar
product method. The p_T differential v_2 values are compared to the VISH2+1
model calculation and to the STAR measurements at 200 GeV in Au+Au collisions.
We found that the model describes \Xi and \Omega v_2 measurements within
experimental uncertainties. The differential flow of \Xi and \Omega is similar
to the STAR measurements at 200 GeV in Au+Au collisions.


  The physical mechanism of phenomenon is explained as a result of inequality
of forward and reversed processes in optics. The importance of experimental
study of its invariance relation is discussed.


  The concept of proximate order is widely used in the theories of entire,
meromorphic, subharmonic and plurisubharmonic functions. We give a general
interpretation of this concept as a proximate growth function relative to a
model growth function. If a function is the proximate growth function with
respect to the identity function on the positive semi-axis, then the logarithm
of this function is the classical proximate order. Our definition uses only one
condition. This form of definition is also new for the classical proximate
order.


  We present a first-principles computational study of the thermodynamics of
carbon defects in hexagonal boron nitride (hBN). The defects considered are
carbon monomers, dimers, trimers, and larger carbon clusters, as well as
complexes of carbon with vacancies, antisites, and substitutional oxygen. Our
calculations show that monomers ($\text{C}_{\text{B}}$, $\text{C}_{\text{B}}$),
dimers, trimers, and $\text{C}_{\text{N}}\text{O}_{\text{N}}$ pairs are the
most prevalent species under most growth conditions. Compared to these defects,
larger carbon clusters, as well as complexes of carbon with vacancies and
antisites, occur at much smaller concentrations. Our results are discussed in
view of the relevance of carbon defects in single-photon emission in hBN.


  An electric monopole solution to the equations of Maxwell and Einstein's
general relativity is displayed. It differs from the usual one in that all
components of the metric vanish at large spatial distances from the charge
rather than approaching the Minkowski metric. This enables us to find an
approximate solution to that for many charges.


  Even if the tuning between the first and second register of a clarinet has
been optimized by instrument makers, the lowest twelfths remain slightly too
large (inharmonicity). In this article, we study the problem from two different
points of view. First, we systematically review various physical reasons why
this inharmonicity may take place, and the effect of different bore
perturbations inserted in cylindrical instruments. Applications to a real
clarinet resonator and comparisons with impedance measurements are then
presented. A commonly accepted idea is that the register hole is the dominant
cause for this inharmonicity: it is natural to expect that opening this hole
will raise the resonance frequencies of the instrument, except for the note for
which the hole is at the pressure node. We show that the real situation is
actually more complicated because other effects, such as open holes or bore
taper and bell, introduce resonance shifts that are comparable but with
opposite sign, so that a relatively good overall compensation takes place. The
origin of the observed inharmonicity in playing frequencies is therefore
different. In a second part, we use an elementary model of the clarinet in
order to isolate the effect of the register hole: a perfect cylindrical tube
without closed holes. Optimization techniques are then used to calculate an
optimum location for the register hole; the result turns out to be close to the
location chosen by clarinet makers. Finally, attempts are made numerically to
improve the situation by introducing small perturbations in the higher part of
the cylindrical resonator, but no satisfactory improvement is obtained.


  The advanced state of cosmological observations constantly tests the
alternative theories of gravity that originate from Einstein's theory. However,
this is not restricted to modifications to general relativity. In this sense,
we work in the context of Weyl's theory, more specifically, on a particular
black hole solution for a charged massive source, which is confronted with the
classical test of the geodetic precession, to obtain information about the
parameters associated with this theory. To fully assess this spacetime, the
complete geodesic structure for massive test particles is presented.


  With tremendous growing interests in Big Data systems, analyzing and
facilitating their performance improvement become increasingly important.
Although there have much research efforts for improving Big Data systems
performance, efficiently analysing and diagnosing performance bottlenecks over
these massively distributed systems remain a major challenge. In this paper, we
propose a spatio-temporal correlation analysis approach based on stage
characteristic and distribution characteristic of Big Data applications, which
can associate the multi-level performance data fine-grained. On the basis of
correlation data, we define some priori rules, select features and vectorize
the corresponding datasets for different performance bottlenecks, such as,
workload imbalance, data skew, abnormal node and outlier metrics. And then, we
utilize the data and model driven algorithms for bottlenecks detection and
diagnosis. In addition, we design and develop a lightweight, extensible tool
HybridTune, and validate the diagnosis effectiveness of our tool with
BigDataBench on several benchmark experiments in which the outperform
state-of-the-art methods. Our experiments show that the accuracy of
abnormal/outlier detection we obtained reaches about 80%. At last, we report
several Spark and Hadoop use cases, which are demonstrated how HybridTune
supports users to carry out the performance analysis and diagnosis efficiently
on the Spark and Hadoop applications, and our experiences demonstrate
HybridTune can help users find the performance bottlenecks and provide
optimization recommendations.


  We study the low temperature properties of a helium-4 fluid confined in
nanopores, using large-scale Quantum Monte Carlo simulations with realistic
He-He and He-pore interactions. In the narrow-pore limit, the system can be
described by the quantum hydrodynamic theory known as Luttinger liquid theory
with a large Luttinger parameter, corresponding to the dominance of solid
tendencies and strong susceptibility to pinning by a periodic or random
potential from the pore walls. On the other hand, for wider pores, the central
region appears to behave like a Luttinger liquid with a smaller Luttinger
parameter, and may be protected from pinning by the wall potential, offering
the possibility of experimental detection of a Luttinger liquid.


  We consider two problems that are attracting increasing attention in clinical
dose finding studies. First, we assess the similarity of two non-linear
regression models for two non-overlapping subgroups of patients over a
restricted covariate space. To this end, we derive a confidence interval for
the maximum difference between the two given models. If this confidence
interval excludes the equivalence margins, similarity of dose response can be
claimed. Second, we address the problem of demonstrating the similarity of two
target doses for two non-overlapping subgroups, using again a confidence
interval based approach. We illustrate the proposed methods with a real case
study and investigate their operating characteristics (coverage probabilities,
Type I error rates, power) via simulation.


  The evolution of industry has enabled the integration of physical and digital
systems, facilitating the collection of extensive data on manufacturing
processes. This integration provides a reliable solution for improving process
quality and managing equipment health. However, data collected from real
manufacturing processes often exhibit challenging properties, such as severe
class imbalance, high rates of missing values, and noisy features, which hinder
effective machine learning implementation. In this study, a rare class
prediction approach is developed for in situ data collected from a smart
semiconductor manufacturing process. The primary objective is to build a model
that addresses issues of noise and class imbalance, enhancing class separation.
The developed approach demonstrated promising results compared to existing
literature, which would allow the prediction of new observations that could
give insights into future maintenance plans and production quality. The model
was evaluated using various performance metrics, with ROC curves showing an AUC
of 0.95, a precision of 0.66, and a recall of 0.96


  Edge magnetoplasmon is an emergent chiral bosonic mode promising for studying
electronic quantum optics. While the plasmon transport has been investigated
with various techniques for decades,its coupling to a mesoscopic device
remained unexplored. Here, we demonstrate the coupling between a single plasmon
mode in a quantum Hall plasmon resonator and a double quantum dot (DQD).
Resonant plasmon-assisted tunneling is observed in the DQD through absorbing or
emitting plasmons stored in the resonator. By using the DQD as a spectrometer,
the plasmon energy and the coupling strength are evaluated, which can be
controlled by changing the electrostatic environment of the quantum Hall edge.
The observed plasmon-electron coupling encourages us for studying strong
coupling regimes of plasmonic cavity quantum electrodynamics.


  To date, most existing self-supervised learning methods are designed and
optimized for image classification. These pre-trained models can be sub-optimal
for dense prediction tasks due to the discrepancy between image-level
prediction and pixel-level prediction. To fill this gap, we aim to design an
effective, dense self-supervised learning method that directly works at the
level of pixels (or local features) by taking into account the correspondence
between local features. We present dense contrastive learning, which implements
self-supervised learning by optimizing a pairwise contrastive (dis)similarity
loss at the pixel level between two views of input images. Compared to the
baseline method MoCo-v2, our method introduces negligible computation overhead
(only <1% slower), but demonstrates consistently superior performance when
transferring to downstream dense prediction tasks including object detection,
semantic segmentation and instance segmentation; and outperforms the
state-of-the-art methods by a large margin. Specifically, over the strong
MoCo-v2 baseline, our method achieves significant improvements of 2.0% AP on
PASCAL VOC object detection, 1.1% AP on COCO object detection, 0.9% AP on COCO
instance segmentation, 3.0% mIoU on PASCAL VOC semantic segmentation and 1.8%
mIoU on Cityscapes semantic segmentation. Code is available at:
https://git.io/AdelaiDet


  Due to its wide reaching implications for everything from identifying
hotspots of income inequality to political redistricting, there is a rich body
of literature across the sciences quantifying spatial patterns in socioeconomic
data. In particular, the variability of indicators relevant to social and
economic well-being between localized populations is of great interest, as it
pertains to the spatial manifestations of inequality and segregation. However,
heterogeneity in population density, sensitivity of statistical analyses to
spatial aggregation, and the importance of pre-drawn political boundaries for
policy intervention may decrease the efficacy and relevance of existing methods
for analyzing spatial socioeconomic data. Additionally, these measures commonly
lack either a framework for comparing results for qualitative and quantitative
data on the same scale, or a mechanism for generalization to multi-region
correlations. To mitigate these issues associated with traditional spatial
measures, here we view local deviations in socioeconomic variables from a
topological lens rather than a spatial one, and use a novel information
theoretic network approach based on the Generalized Jensen Shannon Divergence
to distinguish distributional quantities across adjacent regions. We apply our
methodology in a series of experiments to study the network of neighboring
census tracts in the continental US, quantifying the decay in two-point
distributional correlations across the network, examining the county-level
socioeconomic disparities induced from the aggregation of tracts, and
constructing an algorithm for the division of a city into homogeneous clusters.
These results provide a new framework for analyzing the variation of attributes
across regional populations, and shed light on new, universal patterns in
socioeconomic attributes.


  We discuss and briefly overview recent progress with studying fluctuations in
scattering on a resonance state coupled to the background of many chaotic
states. Such a problem arises naturally, e.g., when dealing with wave
propagation in the presence of a complex environment. Using a statistical model
based on random matrix theory, we obtain a number of nonperturbative results
for various statistics of scattering characteristics. This includes the joint
and marginal distributions of the reflection and transmission intensities and
phases, which are derived exactly at arbitrary coupling to the background with
finite absorption. The intensities and phases are found to exhibit highly
non-trivial statistical correlations, which remain essential even in the limit
of strong absorption. In the latter case, we also consider the relevant
approximations and their accuracy. As an application, we briefly discuss the
statistics of the phase rigidity (or mode complexness) in such a scattering
situation.


  In this paper, we show theoretically that there exists quasi-liquid on the
edges and vertices of snow crystals between -4C and -22C, while the basal and
prism faces have no quasi-liquid layers. Investigating the macroscopic theory
of quasi-liquid and applying to the edges and vertices of the crystal, we find
that the quasi-liquid becomes unstable above the critical supersaturation
point, which is above the water saturation point. The thickness of this
unstable quasi-liquid layer continues growing indefinitely. We interpret this
behavior as corresponding to continuous production and overflow onto
neighboring faces in a real system. We hypothesize that the unstable growth of
snow crystals originates from the edges and vertices, and it is due to the
overflow of quasi-liquid from the edges and vertices onto the neighboring
faces, which are rough and lack quasi-liquid. Our hypothesis accounts for the
qualitative behavior of the relations between the morphological instability and
the water saturation in the snow phase diagram.


  A perfect code in a graph is an independent set of the graph such that every
vertex outside the set is adjacent to exactly one vertex in the set. A
circulant graph is a Cayley graph of a cyclic group. In this paper we study
perfect codes in circulant graphs of degree $p^l - 1$, where $p$ is a prime and
$l \ge 1$. We obtain a necessary and sufficient condition for such a circulant
graph to admit perfect codes, give a construction of all such circulant graphs
which admit perfect codes, and prove a lower bound on the number of distinct
perfect codes in such a circulant graph. This extends known results for the
case $l=1$ and provides insight on the general problem on the existence and
structure of perfect codes in circulant graphs.


  The inelastic x-ray scattering spectrum for phonons of $\Delta_{1}$-symmetry
including the CuO bond-stretching phonon dispersion is analyzed by a Lorentz
fit in HgBa$_{2}$CuO$_{4}$ and Bi$_{2}$Sr$_{2}$CuO$_{6}$, respectively, using
recently calculated phonon frequencies as input parameters. The resulting mode
frequencies of the fit are almost all in good agreement with the calculated
data. An exception is the second highest $\Delta_{1}$-branch compromising the
bond-stretching modes which disagrees in both compounds with the calculations.
This branch unlike the calculations shows an anomalous softening with a minimum
around the wavevector $\vc{q}=\frac{2\pi}{a}(0.25, 0, 0)$. Such a disparity
with the calculated results, that are based on the assumption of an undisturbed
translation- and point group invariant electronic structure of the CuO plane,
indicates some {\it static} charge inhomogenities in the measured probes. Most
likely these will be charge stripes along the CuO bonds which have the
strongest coupling to certain longitudinal bond-stretching modes that in turn
selfconsistently induce corresponding {\it dynamic} charge inhomogenities. The
symmetry breaking by the mix of dynamic and static charge inhomogenities can
lead to a reconstruction of the Fermi surface into small pockets.


  I give a conjectural generating function for the numbers of $\delta$-nodal
curves in a linear system of dimension $\delta$ on an algebraic surface. It
reproduces the results of Vainsencher for the case $\delta\le 6$ and
Kleiman-Piene for the case $\delta\le 8$. The numbers of curves are expressed
in terms of five universal power series, three of which I give explicitly as
quasimodular forms. This gives in particular the numbers of curves of arbitrary
genus on a K3 surface and an abelian surface in terms of quasimodular forms,
generalizing the formula of Yau-Zaslow for rational curves on K3 surfaces. The
coefficients of the other two power series can be determined by comparing with
the recursive formulas of Caporaso-Harris for the Severi degrees in $\P_2$. We
verify the conjecture for genus 2 curves on an abelian surface. We also discuss
a link of this problem with Hilbert schemes of points.


  Evaluation of keyword spotting (KWS) systems that detect keywords in speech
is a challenging task under realistic privacy constraints. The KWS is designed
to only collect data when the keyword is present, limiting the availability of
hard samples that may contain false negatives, and preventing direct estimation
of model recall from production data. Alternatively, complementary data
collected from other sources may not be fully representative of the real
application. In this work, we propose an evaluation technique which we call
AB/BA analysis. Our framework evaluates a candidate KWS model B against a
baseline model A, using cross-dataset offline decoding for relative recall
estimation, without requiring negative examples. Moreover, we propose a
formulation with assumptions that allow estimation of relative false positive
rate between models with low variance even when the number of false positives
is small. Finally, we propose to leverage machine-generated soft labels, in a
technique we call Semi-Supervised AB/BA analysis, that improves the analysis
time, privacy, and cost. Experiments with both simulation and real data show
that AB/BA analysis is successful at measuring recall improvement in
conjunction with the trade-off in relative false positive rate.


  The Mackey-type identity mentioned in the title relates the operations of
parabolic induction and restriction for invariant functions on the Lie algebras
of the finite unitary groups $U(N, q^2)$. This result is applied to
constructing positive harmonic functions on a new branching graph with a
negative Hall-Littlewood parameter, as introduced in the authors' paper [Adv.
Math. vol. 395 (2022), 108087; arXiv:2102.01947].This in turn implies the
existence of an infinite-parameter family of invariant measures for the
coadjoint action of an infinite-dimensional analogue of the groups $U(N, q^2)$.


  The effects of a minimal length are investigated within an algebraically
extended theory of General Relativity (GR). Former attempts, to include a
minimal length in GR are first resumed, with a conformal factor of the metric
as a consequence. Effective potentials for various black hole masses (as ratios
to the minimal length) are deduced. It is found that the existence of a minimal
length has, for a small mass black hole, important effects on the effective
potential near the event horizon, creating barriers which inhibit that
particles can pass the event horizon. Further, a new limit for the minimal mass
of a black hole is derive


  We make explicit the exponential bound on the degrees of the polynomials
appearing in the Effective Quillen-Suslin Theorem, and apply it jointly with
the Hilbert-Burch Theorem to show that the syzygy module of a sequence of m
polynomials in n variables defining a complete intersection ideal of grade two
is free, and that a basis of it can be computed with bounded degrees. In the
known cases, these bounds improve previous results.


  Artificial multiferroics consist of two types of ferroic materials, typically
a ferroelectric and ferromagnet, often coupled interfacially by
magnetostriction induced by the lattice elongations in the ferroelectric. In
BaTiO3 the magnitude of strain induced by these elongations is heavily
temperature dependent, varying greatly between each of the polar crystal phases
and exerting a huge influence over the properties of a coupled magnetic film.
Here we demonstrate that temperature, and thus strain, is an effective means of
controlling the magnetic anisotropy in BaTiO3(111)/CoFeB heterostructures. We
investigate the three polar phases of BaTiO3: tetragonal (T) at room
temperature, orthorhombic (O) below 280 K and rhombohedral (R) below 190 K,
across a total range of 77 K to 420 K. We find two distinct responses; a
step-like change in the anisotropy across the low-temperature phase
transitions, and a sharp high-temperature reduction around the ferroelectric
Curie temperature, measured from hard axis hysteresis loops. Using our
measurements of this anisotropy strength we are then able to show by
micromagnetic simulation the behaviour of all possible magnetic domain wall
states and determine their scaling as a function of temperature. The most
significant changes occur in the head-to-head domain wall states, with a
maximum change of 210 nm predicted across the entire range effectively doubling
the size of the domain wall as compared to room temperature. Notably, similar
changes are seen for both high and low temperatures which suggest different
routes for potential control of magnetic anisotropy and elastically pinned
magnetic domain walls.


  G\"ahler ([3],[4]) introduced the concept of 2-metric as a possible
generalization of usual notion of a metric space. In many cases the results
obtained in the usual metric spaces and 2-metric spaces are found to be
unrelated (see [5]). Mustafa and Sims [8] took a different approach and
introduced the notion of $G$-metric. The author [6] generalized the notion of
G-metric to more than three variables and introduced the concept of $K$-metric
as a function $K\colon X^n \to \mathbb{R}^+$, $(n\ge 3)$. In this paper, We
improve the definition of $K$-metric by making symmetry condition more general.
This improved metric denoted by $G_n$ is called the Generalized $n$-metric. We
develop the theory for generalized $n$-metric spaces and obtain some fixed
point theorems.


  For a harmonic oscillator, Nos\'e's single-thermostat approach to simulating
Gibbs' canonical ensemble with dynamics samples only a small fraction of the
phase space. Nos\'e's approach has been improved in a series of three steps: [
1 ] several two-thermostat sets of motion equations have been found which cover
the complete phase space in an ergodic fashion, [ 2 ] sets of single-thermostat
motion equations, exerting "weak control" over both forces and momenta, have
been shown to be ergodic, and [ 3 ] sets of single-thermostat motion equations
exerting weak control over two velocity moments provide ergodic phase-space
sampling for the oscillator and for the rigid pendulum, but not for the quartic
oscillator or for the Mexican Hat potential. The missing fourth step, motion
equations providing ergodic sampling for anharmonic potentials requires a
further advance. The 2016 Ian Snook Prize will be awarded to the author(s) of
the most interesting original submission addressing the problem of finding
ergodic algorithms for Gibbs' canonical ensemble using a single thermostat.


  The classical Skolem--Noether Theorem [Giraud, 71] shows us (1) how we can
assign to an Azumaya algebra $A$ on a scheme $X$ a cohomological Brauer class
in $H^2(X,\mathbf G_m)$ and (2) how Azumaya algebras correspond to twisted
vector bundles. The Derived Skolem--Noether Theorem [Lieblich, 09] generalizes
this result to weak algebras in the derived 1-category locally quasi-isomorphic
to derived endomorphism algebras of perfect complexes. We show that in general
for a co-family $\mathscr C^\otimes$ of presentable monoidal quasi-categories
with descent over a quasi-category with a Grothendieck topology, there is a
fibre sequence giving in particular the above correspondences. For a totally
supported perfect complex $E$ over a quasi-compact and quasi-separated scheme
$X$, the long exact sequence on homotopy group sheaves splits giving equalities
$\pi_i(\mathop{\mathrm{Aut}}_{\mathop{\mathrm{Perf}}}
E,\mathrm{id}_E)=\pi_i(\mathop{\mathrm{Aut}}_{\mathop{\mathrm{Alg}}\mathop{\mathrm{Perf}}}\mathop{\mathbf
R\mathrm{End} E},\mathrm{id}_{\mathop{\mathbf R\mathrm{End} E}})$ for $i\ge1$.
Further applications include complexes in Derived Algebraic Geometry, module
spectra in Spectral Algebraic Geometry and ind-coherent sheaves and crystals in
Derived Algeraic Geometry in characteristic 0.


  We have accurately measured the bispectrum for four scale-free models of
structure formation with the spectral index $n=1$, 0, -1, and -2. The
measurement is based on a new method that can effectively eliminate the alias
and numerical artifacts, and reliably extend the analysis into the strongly
non-linear regime. The work makes use of a set of state-of-the art N-body
simulations that have significantly increased the resolution range compared
with the previous studies on the subject. With these measured results, we
demonstrated that the measured bispectrum depends on the shape and size of
$k$-triangle even in the strongly nonlinear regime. It increases with
wavenumber and decreases with the spectral index. These results are in contrast
with the hypothesis that the reduced bispectrum is a constant in the strongly
non-linear regime. We also show that the fitting formula of Scoccimarro &
Frieman (1999) does not describe our simulation results well (with a typical
error about 40 percent). In the end, we present a new fitting formula for the
reduced bispectrum that is valid for $-2 \leq n \leq 0$ with a typical error of
10 percent only.


  Deep neural networks are able to solve tasks across a variety of domains and
modalities of data. Despite many empirical successes, we lack the ability to
clearly understand and interpret the learned internal mechanisms that
contribute to such effective behaviors or, more critically, failure modes. In
this work, we present a general method for visualizing an arbitrary neural
network's inner mechanisms and their power and limitations. Our dataset-centric
method produces visualizations of how a trained network attends to components
of its inputs. The computed "attention masks" support improved interpretability
by highlighting which input attributes are critical in determining output. We
demonstrate the effectiveness of our framework on a variety of deep neural
network architectures in domains from computer vision, natural language
processing, and reinforcement learning. The primary contribution of our
approach is an interpretable visualization of attention that provides unique
insights into the network's underlying decision-making process irrespective of
the data modality.


  Poly-Cauchy numbers with level $2$ are defined by inverse sine hyperbolic
functions with the inverse relation from sine hyperbolic functions. In this
paper, we show several convolution identities of poly-Cauchy numbers with level
$2$. In particular, that of three poly-Cauchy numbers with level $2$ can be
expressed as a simple form. In the sequel, we introduce the Stirling numbers of
the first kind with level $2$


  Barren Plateaus are a formidable challenge for hybrid quantum-classical
algorithms that lead to flat plateaus in the loss function landscape making it
difficult to take advantage of the expressive power of parameterized quantum
circuits with gradient-based methods. Like in classical neural network models,
parameterized quantum circuits suffer the same vanishing gradient issue due to
large parameter spaces with non-convex landscapes. In this review, we present
an overview of the different genesis for barren plateaus, mathematical
formalisms of common themes around barren plateaus, and dives into gradients.
The central objective is to provide a conceptual perspective between classical
and quantum interpretations of vanishing gradients as well as dive into
techniques involving cost functions, entanglement, and initialization
strategies to mitigate barren plateaus. Addressing barren plateaus paves the
way towards feasibility of many classically intractable applications for
quantum simulation, optimization, chemistry, and quantum machine learning.


  In previous work, we have found new static, spherically symmetric boson star
solutions which generalize the standard boson stars by allowing a particular
superposition of scalar fields in which each of the fields is characterized by
a fixed value of its non-vanishing angular momentum number $\ell$. We call such
solutions "$\ell$-boson stars". Here, we perform a series of fully non-linear
dynamical simulations of perturbed $\ell$-boson stars in order to study their
stability, and the final fate of unstable configurations. We show that for each
value of $\ell$, the configuration of maximum mass separates the parameter
space into stable and unstable regions. Stable configurations, when perturbed,
oscillate around the unperturbed solution and very slowly return to a
stationary configuration. Unstable configurations, in contrast, can have three
different final states: collapse to a black hole, migration to the stable
branch, or explosion (dissipation) to infinity. Just as it happens with
$\ell=0$ boson stars, migration to the stable branch or dissipation to infinity
depends on the sign of the total binding energy of the star: bound unstable
stars collapse to black holes or migrate to the stable branch, whereas unbound
unstable stars either collapse to a black hole or explode to infinity. Thus,
the parameter $\ell$ allows us to construct a new set of stable configurations.
All our simulations are performed in spherical symmetry, leaving a more
detailed stability analysis including non-spherical perturbations for future
work.


  In todays world, Cloud computing has attracted research communities as it
provides services in reduced cost due to virtualizing all the necessary
resources. Even modern business architecture depends upon Cloud computing .As
it is a internet based utility, which provides various services over a network,
it is prone to network based attacks. Hence security in clouds is the most
important in case of cloud computing. Cloud Security concerns the customer to
fully rely on storing data on clouds. That is why Cloud security has attracted
attention of the research community. This paper will discuss securing the data
in clouds by implementing key agreement, encryption and signature
verification/generation with hyperelliptic curve cryptography.


  We theoretically investigate multiple-$Q$ spin textures, which are composed
of superpositions of spin density waves with different wave numbers, for an
effective spin model of centrosymmetric itinerant magnets. Our focus is on the
interplay between biquadratic interactions arising from the spin-charge
coupling and magnetic anisotropy caused by the spin-orbit coupling. Taking into
account two types of the magnetic anisotropy, single-ion anisotropy and
bond-dependent anisotropy, we elucidate magnetic phase diagrams for an
archetypal triangular-lattice system in the absence and presence of an external
magnetic field. In the case of the single-ion anisotropy, we find a plethora of
multiple-$Q$ instabilities depending on the strength and the sign of the
anisotropy (easy plane or easy axis), including a skyrmion crystal with
topological number of two. In an external magnetic field, we find that a
skyrmion crystal with topological number of one is stabilized by the in-plane
(out-of-plane) magnetic field under the easy-plane (easy-axis) anisotropy. We
also examine the stability of the field-induced skyrmion crystal by rotating
the field direction. As a biproduct, we show that a triple-$Q$ state with
nonzero chirality appears in the presence of the biquadratic interaction and
the easy-axis anisotropy. Meanwhile, we find that the bond-dependent anisotropy
also stabilizes both types of skyrmion crystals. We show that, however, for the
skyrmion crystal with topological number of one, Bloch- and N\'eel-type
skyrmion crystals are selectively realized depending on the sign of the
bond-dependent anisotropy. Moreover, we find yet another multiple-$Q$ states,
including two types of meron crystals with the skyrmion numbers of one and two.
The systematic investigation will provide a reference to complex magnetic
textures in centrosymmetric magnetic metals.


  A search is presented for massive long-lived particles decaying into a muon
and two quarks. The dataset consists of proton-proton interactions at
centre-of-mass energies of 7 and 8 TeV, corresponding to integrated
luminosities of 1 and 2 1/fb, respectively. The analysis is performed assuming
a set of production mechanisms with simple topologies, including the production
of a Higgs-like particle decaying into two long-lived particles. The mass range
from 20 to 80 GeV and lifetimes from 5 to 100 ps are explored. Results are also
interpreted in terms of neutralino production in different supersymmetric
models, with masses in the 23-198 GeV range. No excess above the background
expectation is observed and upper limits are set on the production
cross-section for various points in the parameter space of theoretical models.


  While AAPT and many other physics organizations have been introducing a
series of effective practices around diversity and inclusion at the national
level in recent years, it was wondered if these were being adopted at the local
level. It is hoped that section members and Section Representatives will decide
to further expand the actions of national leadership to make their own section
meeting more inclusive. In order to assess if this was in fact the case, a
survey on diversity practices that have been used the the national level and
can be implemented at the section level was sent to AAPT's Section Reps mailing
list in the spring of 2018, with a follow-up survey in winter 2020. Feedback in
both cycles suggested that a guide for section leadership would be useful. The
Northern California/Nevada section has made progress in implementing some of
the effective practices from the national meetings into our local section
meetings, we share these efforts in the hope that they assist our fellow
sections.


  Although transmission of a data packet containing sensory information in a
networked control system improves the quality of regulation, it has indeed a
price from the communication perspective. It is, therefore, rational that such
a data packet be transmitted only if it is valuable in the sense of a
cost-benefit analysis. Yet, the fact is that little is known so far about this
valuation of information and its connection with traditional event-triggered
communication. In the present article, we study this intrinsic property of
networked control systems by formulating a rate-regulation tradeoff between the
packet rate and the regulation cost with an event trigger and a controller as
two distributed decision makers, and show that the valuation of information is
conceivable and quantifiable grounded on this tradeoff. In particular, we
characterize an equilibrium in the rate-regulation tradeoff, and quantify the
value of information $\text{VoI}_k$ there as the variation in a so-called value
function with respect to a piece of sensory information that can be
communicated to the controller at each time $k$. We prove that, for a
multi-dimensional Gauss-Markov process, $\text{VoI}_k$ is a symmetric function
of the discrepancy between the state estimates at the event trigger and the
controller, and that a data packet containing sensory information at time $k$
should be transmitted to the controller only if $\text{VoI}_k$ is nonnegative.
Moreover, we discuss that $\text{VoI}_k$ can be computed with arbitrary
accuracy, and that it can be approximated by a closed-form quadratic function
with a performance guarantee.


  The concept of depth represents methods to measure how deep an arbitrary
point is positioned in a dataset and can be seen as the opposite of
outlyingness. It has proved very useful and a wide range of methods have been
developed based on the concept.
  To address the well-known computational challenges associated with the depth
concept, we suggest to estimate Tukey depth contours using recently developed
incremental quantile estimators. The suggested algorithm can estimate depth
contours when the dataset in known in advance, but also recursively update and
even track Tukey depth contours for dynamically varying data stream
distributions. Tracking was demonstrated in a real-life data example where
changes in human activity was detected in real-time from accelerometer
observations.


  Quasi-degenerate neutrino mass models (QDN) which can explain the current
data on neutrino masses and mixings,are studied. In the first part, we study
the effect of CP-phases on QDN mass matrix obeying $\mu-\tau$ symmetry in
normal hierarchical (QD-NH) and inverted hierarchical (QD-IH) patterns.The
numerical predictions are consistent with observed data on solar mixing angle,
absolute neutrino mass parameter consistent with neutrinoless double beta decay
mass parameter and sum of three absolute neutrino masses from cosmological
bound.The neutrino mass matrix is parameterized using only two unknown
parameters. The second part deals with the estimation of observed baryon
asymmetry of the universe. The prediction is nearly consistent with observation
with flavoured thermal leptogenesis scenario. QD-NH model appears to be more
favourable than those of QD-IH models.The present analysis shows that the three
absolute neutrino masses may exhibit quasi-degenerate pattern in nature. They
are far from discrimination at the moment.


  Role of different extensions of Nambu\textendash Jona-Lasinio (NJL) model
like addition of vector interaction, Polyakov loop extended version (PNJL) and
the entangled PNJL (EPNJL) models on transport coefficients like shear
viscosity, bulk viscosity, electrical conductivity and thermal conductivity are
critically analyzed. We have considered the standard expressions of transport
coefficients, obtained in relaxation time approximation of kinetic theory.
Influence of temperature dependent order parameters on temperature profile of
transport coefficients are analyzed. Causal aspect of massless case to these
different extended components of mean field models are also picturized, where
an approximated lower and upper bound are drawn for shear relaxation time.


  Achievable degrees-of-freedom (DoF) of the large-scale interfering two-way
relay network is investigated. The network consists of $K$ pairs of
communication nodes (CNs) and $N$ relay nodes (RNs). It is assumed that $K\ll
N$ and each pair of CNs communicates with each other through one of the $N$
relay nodes without a direct link between them. Interference among RNs is also
considered. Assuming local channel state information (CSI) at each RN, a
distributed and opportunistic RN selection technique is proposed for the
following three promising relaying protocols: amplify--forward,
decode--forward, and compute--forward. As a main result, the asymptotically
achievable DoF is characterized as $N$ increases for the three relaying
protocols. In particular, a sufficient condition on $N$ required to achieve the
certain DoF of the network is analyzed. Through extensive simulations, it is
shown that the proposed RN selection techniques outperform conventional schemes
in terms of achievable rate even in practical communication scenarios. Note
that the proposed technique operates with a distributed manner and requires
only local CSI, leading to easy implementation for practical wireless systems.