  The ability to endow maps of indoor scenes with semantic information is an
integral part of robotic agents which perform different tasks such as target
driven navigation, object search or object rearrangement. The state-of-the-art
methods use Deep Convolutional Neural Networks (DCNNs) for predicting semantic
segmentation of an image as useful representation for these tasks. The accuracy
of semantic segmentation depends on the availability and the amount of labeled
data from the target environment or the ability to bridge the domain gap
between test and training environment. We propose RegConsist, a method for
self-supervised pre-training of a semantic segmentation model, exploiting the
ability of the agent to move and register multiple views in the novel
environment. Given the spatial and temporal consistency cues used for pixel
level data association, we use a variant of contrastive learning to train a
DCNN model for predicting semantic segmentation from RGB views in the target
environment. The proposed method outperforms models pre-trained on ImageNet and
achieves competitive performance when using models that are trained for exactly
the same task but on a different dataset. We also perform various ablation
studies to analyze and demonstrate the efficacy of our proposed method.


  Stacking is an important process within logistics. Some notable examples of
items to be stacked are steel bars or steel plates in a steel yard or
containers in a container terminal or on a ship. We say that two items are
conflicting if their storage time intervals overlap in which case one of the
items needs to be rehandled if the items are stored at the same LIFO storage
location. We consider the problem of stacking items using $k$ LIFO locations
with a minimum number of conflicts between items sharing a location. We present
an extremely simple online stacking algorithm that is oblivious to the storage
time intervals and storage locations of all other items when it picks a storage
location for an item. The risk of assigning the same storage location to two
conflicting items is proved to be of the order $1/k^2$ under mild assumptions
on the distribution of the storage time intervals for the items. Intuitively,
it seems natural to pick a storage location uniformly at random in the
oblivious setting implying a risk of $1/k$ so the risk for our algorithm is
surprisingly low. Our results can also be expressed within the context of the
MAX $k$-CUT problem for circle graphs. The results indicate that circle graphs
on average have relatively big $k$-cuts compared to the total number of edges.


  The Taylor Interpolation through FFT (TI-FFT) algorithm for the computation
of the electromagnetic wave propagation in the quasi-planar geometry within the
half-space is proposed in this article. There are two types of TI-FFT
algorithm, i.e., the spatial TI-FFT and the spectral TI-FFT. The former works
in the spatial domain and the latter works in the spectral domain. It has been
shown that the optimized computational complexity is the same for both types of
TI-FFT algorithm, which is N_r^{opt} N_o^{opt} O (N log_2 N) for an N = N_x
\times N_y computational grid, where N_r^{opt} is the optimized number of
slicing reference planes and N_o^{opt} is the optimized order of Taylor series.
Detailed analysis shows that N_o^{opt} is closely related to the algorithm's
computational accuracy \gamma_{TI}, which is given as N_o^{opt} ~ -
ln(\gamma_{TI}) and the optimized spatial slicing spacing between two adjacent
spatial reference planes \delta_z^{opt} only depends on the characteristic
wavelength \lambda_c of the electromagnetic wave, which is given as
\delta_z^{opt} ~ 1/17 \lambda_c. The planar TI-FFT algorithm allows a large
sampling spacing required by the sampling theorem. What's more, the algorithm
is free of singularities and it works particularly well for the narrow-band
beam and the quasi-planar geometry.


  The largest X9.3 solar flare in solar cycle 24 and the preceding X2.2 flare
occurred on September 6, 2017, in the solar active region NOAA 12673. This
study aims to understand the onset mechanism of these flares via analysis of
multiple observational datasets from the Hinode and Solar Dynamics Observatory
and results from a non-linear force-free field extrapolation. The most
noticeable feature is the intrusion of a major negative-polarity region,
appearing similar to a peninsula, oriented northwest into a neighboring
opposite-polarity region. We also observe proxies of magnetic reconnection
caused by related to the intrusion of the negative peninsula: rapid changes of
the magnetic field around the intruding negative peninsula; precursor
brightening at the tip of the negative peninsula, including a cusp-shaped
brightening that shows a transient but significant downflow (~100 km/s) at a
leg of the cusp; a dark tube-like structure that appears to be a magnetic flux
rope that erupted with the X9.3 flare; and coronal brightening along the dark
tube-like structure that appears to represent the electric current generated
under the flux rope. Based on these observational features, we propose that (1)
the intrusion of the negative peninsula was critical in promoting the push-mode
magnetic reconnection that forms and grows a twisted magnetic flux rope that
erupted with the X2.2 flare, (2) the continuing intrusion progressing even
beyond the X2.2 flare is further promoted to disrupt the equilibrium that leads
the reinforcement of the magnetic flux rope that erupted with the X9.3 flare.


  The convex dimension of a $k$-uniform hypergraph is the smallest dimension
$d$ for which there is an injective mapping of its vertices into $\mathbb{R}^d$
such that the set of $k$-barycenters of all hyperedges is in convex position.
We completely determine the convex dimension of complete $k$-uniform
hypergraphs, which settles an open question by Halman, Onn and Rothblum, who
solved the problem for complete graphs. We also provide lower and upper bounds
for the extremal problem of estimating the maximal number of hyperedges of
$k$-uniform hypergraphs on $n$ vertices with convex dimension $d$. To prove
these results, we restate them in terms of affine projections that preserve the
vertices of the hypersimplex. More generally, we provide a full
characterization of the projections that preserve its $i$-dimensional skeleton.
In particular, we obtain a hypersimplicial generalization of the linear van
Kampen-Flores theorem: for each $n$, $k$ and $i$ we determine onto which
dimensions can the $(n,k)$-hypersimplex be linearly projected while preserving
its $i$-skeleton. Our results have direct interpretations in terms of $k$-sets
and $(i,j)$-partitions, and are closely related to the problem of finding large
convexly independent subsets in Minkowski sums of $k$ point sets.


  Recently, great progress has been made for online handwritten Chinese
character recognition due to the emergence of deep learning techniques.
However, previous research mostly treated each Chinese character as one class
without explicitly considering its inherent structure, namely the radical
components with complicated geometry. In this study, we propose a novel
trajectory-based radical analysis network (TRAN) to firstly identify radicals
and analyze two-dimensional structures among radicals simultaneously, then
recognize Chinese characters by generating captions of them based on the
analysis of their internal radicals. The proposed TRAN employs recurrent neural
networks (RNNs) as both an encoder and a decoder. The RNN encoder makes full
use of online information by directly transforming handwriting trajectory into
high-level features. The RNN decoder aims at generating the caption by
detecting radicals and spatial structures through an attention model. The
manner of treating a Chinese character as a two-dimensional composition of
radicals can reduce the size of vocabulary and enable TRAN to possess the
capability of recognizing unseen Chinese character classes, only if the
corresponding radicals have been seen. Evaluated on CASIA-OLHWDB database, the
proposed approach significantly outperforms the state-of-the-art
whole-character modeling approach with a relative character error rate (CER)
reduction of 10%. Meanwhile, for the case of recognition of 500 unseen Chinese
characters, TRAN can achieve a character accuracy of about 60% while the
traditional whole-character method has no capability to handle them.


  We prove that a Shimura curve in the Siegel modular variety is not
generically contained in the open Torelli locus as long as the rank of unitary
part in its canonical Higgs bundle satisfies a numerical upper bound. As an
application we show that the Coleman-Oort conjecture holds for Shimura curves
associated to partial corestriction upon a suitable choice of parameters, which
generalizes a construction due to Mumford.


  Automatic differentiation (AD), a technique for constructing new programs
which compute the derivative of an original program, has become ubiquitous
throughout scientific computing and deep learning due to the improved
performance afforded by gradient-based optimization. However, AD systems have
been restricted to the subset of programs that have a continuous dependence on
parameters. Programs that have discrete stochastic behaviors governed by
distribution parameters, such as flipping a coin with probability $p$ of being
heads, pose a challenge to these systems because the connection between the
result (heads vs tails) and the parameters ($p$) is fundamentally discrete. In
this paper we develop a new reparameterization-based methodology that allows
for generating programs whose expectation is the derivative of the expectation
of the original program. We showcase how this method gives an unbiased and
low-variance estimator which is as automated as traditional AD mechanisms. We
demonstrate unbiased forward-mode AD of discrete-time Markov chains,
agent-based models such as Conway's Game of Life, and unbiased reverse-mode AD
of a particle filter. Our code package is available at
https://github.com/gaurav-arya/StochasticAD.jl.


  This paper gives detailed information on the structure of the infrared
singularities for the process e+ e- --> 3 jets at next-to-next-to-leading order
in perturbation theory. Particular emphasis is put on singularities associated
to soft gluons. The knowledge of the singularity structure allows the
construction of appropriate subtraction terms, which in turn can be implemented
into a numerical Monte Carlo program.


  Young open clusters are likely sites of cosmic ray acceleration as indicated
by recent detections of the TeV gamma-ray sources in the directions of two open
clusters (Cyg OB2 and Westerlund 2) and their directional proximity to some
unidentified EGRET sources. In fact, up to now a few different scenarios for
acceleration of particles inside open clusters have been considered, i.e.
shocks in massive star winds, pulsars and their nebulae, supernova shocks,
massive compact binaries. Here we consider in detail the radiation processes
due to both electrons and hadrons accelerated inside the open cluster. As a
specific scenario, we apply the acceleration process at the shocks arising in
the winds of WR type stars. Particles diffuse through the medium of the open
cluster during the activity time of the acceleration scenario defined by the
age of the WR star. They interact with the matter and radiation, at first
inside the open cluster and, later in the dense surrounding clouds. We
calculate the broad band spectrum in different processes for three example open
clusters (Berk 87, Cyg OB2, Westerlund 2) for which the best observational
constraints on the spectra are at present available. It is assumed that the
high energy phenomena, observed from the X-ray up to the GeV-TeV gamma-ray
energies, are related to each other. We conclude that the most likely
description of the radiation processes in these objects is achieved in the
hybrid (leptonic-hadronic) model in which leptons are responsible for the
observed X-ray and GeV gamma-ray emission and hadrons are responsible for the
TeV gamma-ray emission, which is produced directly inside and in dense clouds
surrounding the open cluster.


  Recently, skeleton-based human action has become a hot research topic because
the compact representation of human skeletons brings new blood to this research
domain. As a result, researchers began to notice the importance of using RGB or
other sensors to analyze human action by extracting skeleton information.
Leveraging the rapid development of deep learning (DL), a significant number of
skeleton-based human action approaches have been presented with fine-designed
DL structures recently. However, a well-trained DL model always demands
high-quality and sufficient data, which is hard to obtain without costing high
expenses and human labor. In this paper, we introduce a novel data augmentation
method for skeleton-based action recognition tasks, which can effectively
generate high-quality and diverse sequential actions. In order to obtain
natural and realistic action sequences, we propose denoising diffusion
probabilistic models (DDPMs) that can generate a series of synthetic action
sequences, and their generation process is precisely guided by a
spatial-temporal transformer (ST-Trans). Experimental results show that our
method outperforms the state-of-the-art (SOTA) motion generation approaches on
different naturality and diversity metrics. It proves that its high-quality
synthetic data can also be effectively deployed to existing action recognition
models with significant performance improvement.


  It is well known that the linear-noise approximation (LNA) exactly agrees
with the chemical master equation, up to second-order moments, for chemical
systems composed of zero and first-order reactions. Here we show that this is
also a property of the LNA for a subset of chemical systems with second-order
reactions. This agreement is independent of the number of interacting
molecules.


  To maximize the received power at a user equipment, the problem of optimizing
a reconfigurable intelligent surface (RIS) with a limited phase range R <
2{\pi} and nonuniform discrete phase shifts with adjustable gains is addressed.
Necessary and sufficient conditions to achieve this maximization are given.
These conditions are employed in two algorithms to achieve the global optimum
in linear time for R {\ge} {\pi} and R < {\pi}, where R is the limited RIS
phase range. With a total number of N(2K + 1) complex vector additions, it is
shown for R {\ge} {\pi} and R < {\pi} that the global optimality is achieved in
NK or fewer and N(K + 1) or fewer steps, respectively, where N is the number of
RIS elements and K is the number of discrete phase shifts which may be placed
nonuniformly over the limited phase range R. In addition, we define two
quantization algorithms that we call nonuniform polar quantization (NPQ)
algorithm and extended nonuniform polar quantization (ENPQ) algorithm, where
the latter is a novel quantization algorithm for RISs with a significant phase
range restriction, i.e., R < {\pi}. With NPQ, we provide a closed-form solution
for the approximation ratio with which an arbitrary set of nonuniform discrete
phase shifts can approximate the continuous solution. We also show that with a
phase range limitation, equal separation among the nonuniform discrete phase
shifts maximizes the normalized performance. Furthermore, we show that the gain
of using K {\ge} 3 with R < {\pi}/2 and K {\ge} 4 with R < {\pi} is only
marginal. Finally, we prove that when R < 2{\pi}/3, ON/OFF selection for the
RIS elements brings significant performance compared to the case when the RIS
elements are strictly ON.


  The double helicity asymmetry in neutral pion production for p_T = 1 to 12
GeV/c has been measured with the PHENIX experiment in order to access the gluon
spin contribution, Delta-G, to the proton spin. Measured asymmetries are
consistent with zero, and at a theory scale of \mu^2 = 4 GeV^2 give
Delta-G^[0.02,0.3] = 0.1 to 0.2, with a constraint of -0.7 < Delta-G^[0.02,0.3]
< 0.5 at Delta-chi^2 = 9 (~3 sigma) for our sampled gluon momentum fraction (x)
range, 0.02 to 0.3. The results are obtained using predictions for our measured
asymmetries generated from four representative fits to polarized deep inelastic
scattering data. We also consider the dependence of the Delta-G constraint on
the choice of theoretical scale, a dominant uncertainty in these predictions.


  Large Language Models (LLMs) excel in various natural language processing
tasks but struggle with hallucination issues. Existing solutions have
considered utilizing LLMs' inherent reasoning abilities to alleviate
hallucination, such as self-correction and diverse sampling methods. However,
these methods often overtrust LLMs' initial answers due to inherent biases. The
key to alleviating this issue lies in overriding LLMs' inherent biases for
answer inspection. To this end, we propose a CounterFactual Multi-Agent Debate
(CFMAD) framework. CFMAD presets the stances of LLMs to override their inherent
biases by compelling LLMs to generate justifications for a predetermined
answer's correctness. The LLMs with different predetermined stances are engaged
with a skeptical critic for counterfactual debate on the rationality of
generated justifications. Finally, the debate process is evaluated by a
third-party judge to determine the final answer. Extensive experiments on four
datasets of three tasks demonstrate the superiority of CFMAD over existing
methods.


  Unbounded operators corresponding to nonlocal elliptic problems on a bounded
region $G\subset\mathbb R^2$ are considered. The domain of these operators
consists of functions from the Sobolev space $W_2^m(G)$ being generalized
solutions of the corresponding $2m$-order elliptic equation with right-hand
side from $L_2(G)$ and satisfying homogeneous nonlocal boundary conditions. It
is known that such unbounded operators have the Fredholm property. It is proved
in the paper that low-order terms in the differential equation do not affect
the index of the operator. Conditions under which nonlocal perturbations on the
boundary do not change the index are also formulated.


  In this paper, we focus on how current Machine Translation (MT) tools perform
on the translation of emotion-loaded texts by evaluating outputs from Google
Translate according to a framework proposed in this paper. We propose this
evaluation framework based on the Multidimensional Quality Metrics (MQM) and
perform a detailed error analysis of the MT outputs. From our analysis, we
observe that about 50% of the MT outputs fail to preserve the original emotion.
After further analysis of the errors, we find that emotion carrying words and
linguistic phenomena such as polysemous words, negation, abbreviation etc., are
common causes for these translation errors.


  This paper studies the question on whether machines can be rational. It
observes the existing reasons why humans are not rational which is due to
imperfect and limited information, limited and inconsistent processing power
through the brain and the inability to optimize decisions and achieve maximum
utility. It studies whether these limitations of humans are transferred to the
limitations of machines. The conclusion reached is that even though machines
are not rational advances in technological developments make these machines
more rational. It also concludes that machines can be more rational than
humans.


  We discuss Toeplitz operators on the Segal-Bargmann space as functional
realizations of anti-Wick operators on the Fock space. In the special case of
radial symbols we exploit the isometric mapping between the Segal-Bargmann
space and $l^2$ complex sequences in order to establish conditions such that an
equivalence between Toeplitz operators and diagonal operators on $l^2$ holds.
We also analyze the inverse problem of mapping diagonal operators on $l^2$ into
Toeplitz form. The composition problem of Toeplitz operators with radial
symbols is reviewed as an application. Our notation and basic examples make
contact with Quantum Mechanics literature.


  In numerically simulated vibrated beds of powder, we measure temperature
under convection by the generalized Einstein's relation. The spatial
temperature distribution turns out to be quite uniform except for the boundary
layers. In addition to this, temperature remains uniform even if segregation
occurs. This suggests the possibility that there exists some "thermal
equilibrium state" even in a vibrated bed of powder. This finding may lead to a
unified view of the dynamic steady state of granular matter.


  We construct new families of smooth Fano fourfolds with Picard rank 1, which
contain cylinders, i.e., Zariski open subsets of form $Z\times A^1$, where $Z$
is a quasiprojective variety. The affine cones over such a fourfold admit
effective $G_a$-actions. Similar constructions of cylindrical Fano threefolds
and fourfolds were done previously in [KPZ11, KPZ14, PZ15].


  We examine $(D-2)$-brane solutions in supergravities, showing that they fall
into four categories depending on the details of the dilaton coupling. In
general they describe domain walls, although in one of the four categories the
metric describes anti-de Sitter spacetime. We study this case, and its $S^1$
dimensional reduction to a more conventional domain wall in detail, focussing
in particular on the manner in which the unbroken supersymmetry of the anti-de
Sitter solution is partially broken by the dimensional reduction to the domain
wall.


  In the same spirit as heterotic weight lifting, B-L lifting is a way of
replacing the superfluous and ubiquitous U(1)_{B-L} with something else with
the same modular properties, but different conformal weights and ground state
dimensions. This method works in principle for all variants of (2,2)
constructions, such as orbifolds, Calabi-Yau manifolds, free bosons and
fermions and Gepner models, since it only modifies the universal SO(10) x E_8
part of the CFT. However, it can only yield chiral spectra if the ``internal"
sector of the theory provides a simple current of order 5. Here we apply this
new method to Gepner models. Including exceptional invariants, 86 of them have
the required order 5 simple current, and 69 of these yield chiral spectra.
Three family spectra occur abundantly.


  Metallic ferromagnetic transition metal dichalcogenides have emerged as
important building blocks for scalable magnonics and memory applications.
Downscaling such systems to the ultra-thin limit is critical to integrate them
into technology. Here, we achieved layer-by-layer control over the transition
metal dichalcogenide Cr$_{1.6}$Te$_{2}$ by using pulsed laser deposition, and
we uncovered the minimum critical thickness above which room temperature
magnetic order is maintained. The electronic and magnetic structure is explored
experimentally and theoretically and it is shown that the films exhibit strong
in-plane magnetic anisotropy as a consequence of large spin-orbit effects. Our
study elucidates both magnetic and electronic properties of Cr$_{1.6}$Te$_{2}$,
and corroborates the importance of intercalation to tune the magnetic
properties of nanoscale materials architectures.


  Visual information can serve as an effective cue for target speaker
extraction (TSE) and is vital to improving extraction performance. In this
paper, we propose AV-SepFormer, a SepFormer-based attention dual-scale model
that utilizes cross- and self-attention to fuse and model features from audio
and visual. AV-SepFormer splits the audio feature into a number of chunks,
equivalent to the length of the visual feature. Then self- and cross-attention
are employed to model and fuse the multi-modal features. Furthermore, we use a
novel 2D positional encoding, that introduces the positional information
between and within chunks and provides significant gains over the traditional
positional encoding. Our model has two key advantages: the time granularity of
audio chunked feature is synchronized to the visual feature, which alleviates
the harm caused by the inconsistency of audio and video sampling rate; by
combining self- and cross-attention, feature fusion and speech extraction
processes are unified within an attention paradigm. The experimental results
show that AV-SepFormer significantly outperforms other existing methods.


  We provide lower error bounds for randomized algorithms that approximate
integrals of functions depending on an unrestricted or even infinite number of
variables. More precisely, we consider the infinite-dimensional integration
problem on weighted Hilbert spaces with an underlying anchored decomposition
and arbitrary weights. We focus on randomized algorithms and the randomized
worst case error. We study two cost models for function evaluation which depend
on the number of active variables of the chosen sample points. Multilevel
algorithms behave very well with respect to the first cost model, while
changing dimension algorithms and also dimension-wise quadrature methods, which
are based on a similar idea, can take advantage of the more generous second
cost model. We prove the first non-trivial lower error bounds for randomized
algorithms in these cost models and demonstrate their quality in the case of
product weights. In particular, we show that the randomized changing dimension
algorithms provided in [L. Plaskota, G. W. Wasilkowski, J. Complexity 27
(2011), 505--518] achieve convergence rates arbitrarily close to the optimal
convergence rate.


  The random K-satisfiability (K-SAT) problem is an important problem for
studying typical-case complexity of NP-complete combinatorial satisfaction; it
is also a representative model of finite-connectivity spin-glasses. In this
paper we review our recent efforts on the solution space fine structures of the
random K-SAT problem. A heterogeneity transition is predicted to occur in the
solution space as the constraint density alpha reaches a critical value
alpha_cm. This transition marks the emergency of exponentially many solution
communities in the solution space. After the heterogeneity transition the
solution space is still ergodic until alpha reaches a larger threshold value
alpha_d, at which the solution communities disconnect from each other to become
different solution clusters (ergodicity-breaking). The existence of solution
communities in the solution space is confirmed by numerical simulations of
solution space random walking, and the effect of solution space heterogeneity
on a stochastic local search algorithm SEQSAT, which performs a random walk of
single-spin flips, is investigated. The relevance of this work to glassy
dynamics studies is briefly mentioned.


  We discuss the physics of the pot-in-pot cooler. By balancing temperature
decrease due to evaporation and temperature increase due to heat exchange, we
find the equilibrium temperature of the pot. In this simplified model, the
cooling jug acts as a psychrometer, and the theoretical prediction of our model
is in a good agreement with psychrometric tables. Next, we study dynamics of
the jug cooling. The cooling rate is limited by water vapour diffusion through
air, heat conduction through air, and heat conduction through the body of the
jug. The derived rate of temperature decrease is in general agreement with the
result of our experiment. In the end, we discuss some additional factors, such
as capillary effects in the raw clay, water viscosity in the capillaries, and
impact of complex shape of the jug.


  It is well-known that thermal equilibrium states in quantum statistical
mechanics and quantum field theory can be described in a mathematically
rigorous manner by means of the so-called Kubo-Martin-Schwinger (KMS)
condition, which is based on certain analyticity and periodicity properties of
correlation functions. On the other hand, the characterization of
non-equilibrium states which only locally have thermal properties still
constitutes a challenge in quantum field theory. We discuss a recent proposal
for characterization of such states by a generalized KMS condition. The
connection of this proposal to a proposal by D. Buchholz, I. Ojima and H.-J.
Roos for characterizing local thermal equilibrium states in quantum field
theory is discussed.


  Recently, simultaneously transmitting and reflecting reconfigurable
intelligent surfaces (STAR-RISs) have emerged as a novel technology that
provides 360 coverage and new degrees-of-freedom (DoFs). They are also capable
of manipulating signal propagation and simultaneous wireless information and
power transfer (SWIPT). This paper introduces a novel STAR-RIS-aided secure
SWIPT system for downlink multiple input single output rate-splitting multiple
access (RSMA) networks. The transmitter concurrently communicates with the
information receivers (IRs) and sends energy to untrusted energy receivers
(UERs). The UERs are also capable of wiretapping the IR streams. We assume that
the channel state information (CSI) of the IRs is known at the information
transmitter, but only imperfect CSI for the UERs is available at the energy
transmitter. By exploiting RSMA, the base station splits the messages of the
IRs into common and private parts. The former is encoded into a common stream
that can be decoded by all IRs, while the private messages are individually
decoded by their respective IRs. We find the precoders and STAR-RIS
configuration that maximizes the achievable worst-case sum secrecy rate of the
IRs under a total transmit power constraint, a sum energy constraint for the
UERs, and subject to constraints on the transmission and reflection
coefficients. The formulated problem is non-convex and has intricately coupled
variables. To tackle this challenge, a suboptimal two-step iterative algorithm
based on the sequential parametric convex approximation method is proposed.
Simulations demonstrate that the RSMA-based algorithm implemented with a
STAR-RIS enhances both the rate of confidential information transmission and
the total spectral efficiency. Furthermore, our method surpasses the
performance of both orthogonal multiple access (OMA) and non-OMA (NOMA).


  Various applications of quantum algebraic techniques in nuclear and molecular
physics are briefly reviewed. Emphasis is put in the study of the symmetries of
the anisotropic quantum harmonic oscillator with rational ratios of
frequencies, which underly the structure of superdeformed and hyperdeformed
nuclei, the Bloch--Brink $\alpha$-cluster model and possibly the shell
structure in deformed atomic clusters.


  We report absolute values for the radiative relaxation quantum yield of
synthetic eumelanin as a function of excitation energy. These values were
determined by correcting for pump beam attenuation and emission re-absorption
in both eumelanin samples and fluorescein standards over a large range of
concentrations. Our results confirm that eumelanins are capable of dissipating
>99.9% of absorbed UV and visible radiation through non-radiative means.
Furthermore, we have found that the radiative quantum yield of synthetic
eumelanin is excitation energy dependent. This observation is supported by
corrected emission spectra, which also show a clear dependence of both peak
position and peak width upon excitation energy. Our findings indicate that
photoluminescence emission in eumelanins is derived from ensembles of small
chemically distinct oligomeric units which can be selectively pumped. This
hypothesis lends support to the theory that the basic structural unit of
eumelanin is oligomeric rather than hetero-polymeric.


  Automatic reconfiguration of optical filters is the key to novel flexible RF
photonic receivers and Software Defined Radios (SDRs). Although silicon
photonics (SiP) is a promising technology platform to realize such receivers,
process variations and lack of in-situ tuning capability limits the adoption of
SiP filters in widely-tunable RF photonic receivers. To address this issue,
this work presents a first `in-situ' automatic reconfiguration algorithm and
demonstrates a software configurable integrated optical filter that can be
reconfigured on-the-fly based on user specifications. The presented
reconfiguration scheme avoids the use of expensive and bulky equipment such as
Optical Vector Network Analyzer (OVNA), does not use simulation data for
reconfiguration, reduces the total number of thermo-optic tuning elements
required and eliminates several time consuming configuration steps as in the
prior art. This makes this filter ideal in a real world scenario where user
specifies the filter center frequency, bandwidth, required rejection & filter
type (Butterworth, Chebyshev, etc.) and the filter is automatically configured
regardless of process, voltage & temperature (PVT) variations. We fabricated
our design in AIM Photonics' Active SiP process and have demonstrated our
reconfiguration algorithm for a second-order filter with 3dB bandwidth of 3
GHz, 2.2 dB insertion loss and >30 dB out-of-band rejection using only two
reference laser wavelength settings. Since the filter photonic integrated
circuit (PIC) is fabricated using a CMOS-compatible SiP foundry, the design is
manufacturable with repeatable and scalable performance suited for its
integration with electronics to realize complex chip-scale RF photonic systems.


  We present a simple density functional theory for the solid phases of systems
of particles interacting via soft-core potentials. In particular, we apply the
theory to particles interacting via repulsive point Yukawa and Gaussian pair
potentials. We find qualitative agreement with the established phase diagrams
for these systems. The theory is able to account for the bcc-fcc solid
transitions of both systems and the re-entrant melting that the Gaussian system
exhibits.


  Modern DDoS defense systems rely on probabilistic monitoring algorithms to
identify flows that exceed a volume threshold and should thus be penalized.
Commonly, classic sketch algorithms are considered sufficiently accurate for
usage in DDoS defense. However, as we show in this paper, these algorithms
achieve poor detection accuracy under burst-flood attacks, i.e., volumetric
DDoS attacks composed of a swarm of medium-rate sub-second traffic bursts.
Under this challenging attack pattern, traditional sketch algorithms can only
detect a high share of the attack bursts by incurring a large number of false
positives.
  In this paper, we present ALBUS, a probabilistic monitoring algorithm that
overcomes the inherent limitations of previous schemes: ALBUS is highly
effective at detecting large bursts while reporting no legitimate flows, and
therefore improves on prior work regarding both recall and precision. Besides
improving accuracy, ALBUS scales to high traffic rates, which we demonstrate
with an FPGA implementation, and is suitable for programmable switches, which
we showcase with a P4 implementation.


  In this work we present a multi-modal machine learning-based system, which we
call ACORN, to analyze videos of school classrooms for the Positive Climate
(PC) and Negative Climate (NC) dimensions of the CLASS observation protocol
that is widely used in educational research. ACORN uses convolutional neural
networks to analyze spectral audio features, the faces of teachers and
students, and the pixels of each image frame, and then integrates this
information over time using Temporal Convolutional Networks. The audiovisual
ACORN's PC and NC predictions have Pearson correlations of $0.55$ and $0.63$
with ground-truth scores provided by expert CLASS coders on the UVA Toddler
dataset (cross-validation on $n=300$ 15-min video segments), and a purely
auditory ACORN predicts PC and NC with correlations of $0.36$ and $0.41$ on the
MET dataset (test set of $n=2000$ videos segments). These numbers are similar
to inter-coder reliability of human coders. Finally, using Graph Convolutional
Networks we make early strides (AUC=$0.70$) toward predicting the specific
moments (45-90sec clips) when the PC is particularly weak/strong. Our findings
inform the design of automatic classroom observation and also more general
video activity recognition and summary recognition systems.


  We report on deep SCUBA-2 observations at 850$\mu$m and NOEMA spectroscopic
measurements at 2 mm of the environment surrounding the luminous, massive
($M_{*} \approx 2 \times 10^{11}$ M$_{\odot}$) Herschel-selected source
HerBS-70. This source was revealed by previous NOEMA observations to be a
binary system of dusty star-forming galaxies at $z= 2.3$, with the East
component (HerBS-70E) hosting an Active Galactic Nucleus (AGN). The SCUBA-2
observations detected, in addition to the binary system, twenty-one sources at
$> 3.5 \sigma$ over an area of $\sim 25$ square comoving Mpc with a sensitivity
of $\sigma_{850} = 0.75$ mJy. The surface density of continuum sources around
HerBS-70 is three times higher than for field galaxies. The NOEMA spectroscopic
measurements confirm the protocluster membership of three of the nine brightest
sources through their CO(4 - 3) line emission, yielding a volume density 36
times higher than for field galaxies. All five confirmed sub-mm galaxies in the
HerBS-70 system have relatively short gas depletion times ($80 - 500$ Myr),
indicating the onset of quenching for this protocluster core due to the
depletion of gas. The dark matter halo mass of the HerBS-70 system is estimated
around $5 \times{} 10^{13}$ M$_{\odot}$, with a projected current-day mass of
$10^{15}$ M$_{\odot}$, similar to the local Virgo and Coma clusters. These
observations support the claim that DSFGs, in particular the ones with observed
multiplicity, can trace cosmic overdensities.


  We propose a novel method for closed-form predictive distribution modeling
with neural nets. In quantifying prediction uncertainty, we build on Evidential
Deep Learning, which has been impactful as being both simple to implement and
giving closed-form access to predictive uncertainty. We employ it to model
aleatoric uncertainty and extend it to account also for epistemic uncertainty
by converting it to a Bayesian Neural Net. While extending its uncertainty
quantification capabilities, we maintain its analytically accessible predictive
distribution model by performing progressive moment matching for the first time
for approximate weight marginalization. The eventual model introduces a
prohibitively large number of hyperparameters for stable training. We overcome
this drawback by deriving a vacuous PAC bound that comprises the marginal
likelihood of the predictor and a complexity penalty. We observe on regression,
classification, and out-of-domain detection benchmarks that our method improves
model fit and uncertainty quantification.


  The Unruh's thermal state in the vicinity of the event horizon of the black
hole provides conditions where impinging particles can radiate other particles.
The subsequent decays may eventually lead to observable radiation of photons
and neutrinos induced even by massive particles with gravitational interaction
only. The hadronic particles will induce $\sim 30$ MeV gamma radiation from
$\pi^{0}$ decays.


  We study the effects of correlations on a one dimensional ring threaded by a
uniform magnetic flux. In order to describe the interaction between particles,
we work in the framework of the U $\infty$ Hubbard and $t$-$J$ models. We focus
on the dilute limit. Our results suggest the posibility that the persistent
current has an anomalous periodicity $\phi_{0}/p$, where $p$ is an integer in
the range $2\leq p\leq N_{e}$ ($N_{e}$ is the number of particles in the ring
and $\phi_{0}$ is the flux quantum). We found that this result depends neither
on disorder nor on the detailed form of the interaction, while remains the on
site infinite repulsion.


  We present a study of the effect of component separation on the recovered
cosmic microwave background (CMB) temperature distribution, considering
Gaussian and non-Gaussian input CMB maps. First, we extract the CMB component
from simulated Planck data (in small patches of the sky) using the maximum
entropy method (MEM), Wiener filter (WF) and a method based on the subtraction
of foreground templates plus a linear combination of frequency channels (LCFC).
We then apply a wavelet-based method to study the Gaussianity of the recovered
CMB and compare it with the same analysis for the input map. When the original
CMB map is Gaussian (and assuming that point sources have been removed), we
find that none of the methods introduce non-Gaussianity (NG) in the CMB
reconstruction. On the contrary, if the input CMB map is non-Gaussian, all the
studied methods produce a reconstructed CMB with lower detections of NG than
the original map. This effect is mainly due to the presence of instrumental
noise. In this case, MEM tends to produce slightly higher non-Gaussian
detections in the reconstructed map than WF whereas the detections are lower
for the LCFC. We have also studied the effect of point sources in the MEM
reconstruction. If no attempt to remove point sources is performed, they
clearly contaminate the CMB reconstruction, introducing spurious NG. When the
brightest point sources are removed from the data using the Mexican Hat
Wavelet, the Gaussian character of the CMB is preserved. However, when
analysing larger regions of the sky, the variance of our estimators will be
appreciably reduced and, in this case, we expect the point source residuals to
introduce spurious NG in the CMB. Thus, a careful subtraction (or masking) of
point source emission is crucial when studying the Gaussianity of the CMB.


  We revisit the well-known problem of sorting under partial information: sort
a finite set given the outcomes of comparisons between some pairs of elements.
The input is a partially ordered set P, and solving the problem amounts to
discovering an unknown linear extension of P, using pairwise comparisons. The
information-theoretic lower bound on the number of comparisons needed in the
worst case is log e(P), the binary logarithm of the number of linear extensions
of P. In a breakthrough paper, Jeff Kahn and Jeong Han Kim (J. Comput. System
Sci. 51 (3), 390-399, 1995) showed that there exists a polynomial-time
algorithm for the problem achieving this bound up to a constant factor. Their
algorithm invokes the ellipsoid algorithm at each iteration for determining the
next comparison, making it impractical.
  We develop efficient algorithms for sorting under partial information. Like
Kahn and Kim, our approach relies on graph entropy. However, our algorithms
differ in essential ways from theirs. Rather than resorting to convex
programming for computing the entropy, we approximate the entropy, or make sure
it is computed only once, in a restricted class of graphs, permitting the use
of a simpler algorithm. Specifically, we present:
  - an O(n^2) algorithm performing O(log n log e(P)) comparisons;
  - an O(n^2.5) algorithm performing at most (1+ epsilon) log e(P) + O_epsilon
(n) comparisons;
  - an O(n^2.5) algorithm performing O(log e(P)) comparisons.
  All our algorithms can be implemented in such a way that their computational
bottleneck is confined in a preprocessing phase, while the sorting phase is
completed in O(q) + O(n) time, where q denotes the number of comparisons
performed.


  In this work, we present a new iterative exact solution algorithm for the
weighted fair sequences problem, which is a recently introduced NP-hard
sequencing problem with applications in diverse areas such as TV advertisement
scheduling, periodic machine maintenance and real-time scheduling. In the
problem we are given an upper bound on the allowed solution sequence length and
a list of symbols. For each symbols, there is a positive weight and a number,
which gives the minimum times the symbol has to occur in a feasible solution
sequence. The goal is to find a feasible sequence, which minimizes the maximum
weight-distance product, which is calculated for each consecutive appearance of
each symbol in the sequence, including the last and first appearance in the
sequence, i.e., the sequence is considered to be circular for the calculation
of the objective function. Our proposed solution algorithm is based on a new
mixed-integer programming model for the problem for a fixed sequence length.
The model is enhanced with valid inequalities and variable fixings. We also
develop an extended model, which allows the definition of an additional set of
valid inequalities and present additional results which can allow us to skip
the solution of the mixed-integer program for some sequence lengths. We conduct
a computational study on the instances from literature to assess the efficiency
of our newly proposed solution approach. Our approach manages to solve 404 of
440 instances to optimality within the given timelimit, most of them within
five minutes. The previous best existing solution approach for the problem only
managed to solve 229 of these instances, and its exactness depends on an
unproven conjecture. Moreover, our approach is up to two magnitudes faster
compared to this best existing solution approach.


  We present a new X-ray spectroscopic study of $22$ luminous
($2\times10^{45}\lesssim L_{\rm bol}\rm /erg\,s^{-1} \lesssim 2\times10^{46}$)
active galactic nuclei (AGNs) at intermediate-redshift ($0.1 \lesssim z
\lesssim 0.4$), as part of the SUpermassive Black hole Winds in the x-rAYS
(SUBWAYS) sample, mostly composed of quasars (QSOs) and type\,1 AGN. Here, 17
targets were observed with \textit{XMM-Newton} between 2019--2020 and the
remaining 5 are from previous observations. The aim of this large campaign
($1.45\,\rm Ms$ duration) is to characterise the various manifestations of
winds in the X-rays driven from supermassive black holes in AGN. In this paper
we focus on the search and characterization of ultra-fast outflows (UFOs),
which are typically detected through blueshifted absorption troughs in the
Fe\,K band ($E>7\,\rm keV$). By following Monte Carlo procedures, we confirm
the detection of absorption lines corresponding to highly ionised iron (e.g.,
Fe\,\textsc{xxv}\,H$\alpha$, Fe\,\textsc{xxvi}\,Ly$\alpha$) in 7/22 sources at
the $\gtrsim95\%$ confidence level (for each individual line). The global
combined probability of such absorption features in the sample is $>99.9\%$.
The SUBWAYS campaign extends at higher luminosity and redshifts than previous
local studies on Seyferts, obtained using \xmm and \suzaku observations. We
find a UFO detection fraction of $\sim30\%$ on the total sample that is in
agreement with the previous findings. This work independently provides further
support for the existence of highly-ionised matter propagating at mildly
relativistic speed ($\gtrsim0.1c$) in a considerable fraction of AGN over a
broad range of luminosities, which is expected to play a key role in the
self-regulated AGN feeding-feedback cycle, as also supported by hydrodynamical
multiphase simulations.


  This letter expands the stability criterion for radially stratified,
vertically {unstratified} accretion disks incorporating thermal relaxation. We
find a linear amplification of epicyclic oscillations in these disks that
depends on the effective cooling time, i.e. an overstability. The growth rates
of the overstability vanish for both extreme cases, e.g. infinite cooling time
and instantaneous cooling, i.e. the adiabatic and fully isothermal cases.
However, for thermal relaxation times $\tau$ on the order of the orbital
frequency, $\tau\Omega \sim 1$, modes grow at a rate proportional to the square
of the Brunt-V\"ais\"al\"a frequency. The overstability is based on epicyclic
motions, with the thermal relaxation causing gas to heat while radially
displaced inwards, and cool while radially displaced outwards. This causes the
gas to have a lower density when moving outwards compared to when it moves
inwards, so it feels the outwards directed pressure force more strongly on that
leg of the journey. We suggest the term ``Convective Overstability" for the
phenomenon that has already been numerically studied in the non-linear regime
in the context of amplifying vortices in disks, under the name ``Subcritical
Baroclinic Instability". The point of the present paper is to make clear that
vortex formation in three-dimensional disks is neither subcritical, i.e. does
not need a finite perturbation, nor is it baroclinic in the sense of
geophysical fluid dynamics, which requires on vertical shear. We find that
Convective Overstability is a linear instability that will operate under a wide
range of physical conditions for circumstellar disks.


  Background: Several sources of noise obfuscate the identification of single
nucleotide variation (SNV) in next generation sequencing data. For instance,
errors may be introduced during library construction and sequencing steps. In
addition, the reference genome and the algorithms used for the alignment of the
reads are further critical factors determining the efficacy of variant calling
methods. It is crucial to account for these factors in individual sequencing
experiments.
  Results: We introduce a simple data-adaptive model for variant calling. This
model automatically adjusts to specific factors such as alignment errors. To
achieve this, several characteristics are sampled from sites with low mismatch
rates, and these are used to estimate empirical log-likelihoods. These
likelihoods are then combined to a score that typically gives rise to a mixture
distribution. From these we determine a decision threshold to separate
potentially variant sites from the noisy background.
  Conclusions: In simulations we show that our simple proposed model is
competitive with frequently used much more complex SNV calling algorithms in
terms of sensitivity and specificity. It performs specifically well in cases
with low allele frequencies. The application to next-generation sequencing data
reveals stark differences of the score distributions indicating a strong
influence of data specific sources of noise. The proposed model is specifically
designed to adjust to these differences.


  We have constructed a moderately large area (32 cm2), fine pixel (2.5 mm
pixel, 5 mm thick) CZT imaging detector which constitutes the first section of
a detector module (256 cm2) developed for a balloon-borne wide-field hard X-ray
telescope, ProtoEXIST1. ProtoEXIST1 is a prototype for the High Energy
Telescope (HET) in the Energetic X-ray imaging Survey Telescope (EXIST), a next
generation space-borne multi-wavelength telescope. We have constructed a large
(nearly gapless) detector plane through a modularization scheme by tiling of a
large number of 2 cm x 2 cm CZT crystals. Our innovative packaging method is
ideal for many applications such as coded-aperture imaging, where a large,
continuous detector plane is desirable for the optimal performance. Currently
we have been able to achieve an energy resolution of 3.2 keV (FWHM) at 59.6 keV
on average, which is exceptional considering the moderate pixel size and the
number of detectors in simultaneous operation. We expect to complete two
modules (512 cm2) within the next few months as more CZT becomes available. We
plan to test the performance of these detectors in a near space environment in
a series of high altitude balloon flights, the first of which is scheduled for
Fall 2009. These detector modules are the first in a series of progressively
more sophisticated detector units and packaging schemes planned for ProtoEXIST2
& 3, which will demonstrate the technology required for the advanced CZT
imaging detectors (0.6 mm pixel, 4.5 m2 area) required in EXIST/HET.


  Distributed representations of words and paragraphs as semantic embeddings in
high dimensional data are used across a number of Natural Language
Understanding tasks such as retrieval, translation, and classification. In this
work, we propose "Class Vectors" - a framework for learning a vector per class
in the same embedding space as the word and paragraph embeddings. Similarity
between these class vectors and word vectors are used as features to classify a
document to a class. In experiment on several sentiment analysis tasks such as
Yelp reviews and Amazon electronic product reviews, class vectors have shown
better or comparable results in classification while learning very meaningful
class embeddings.


  Deep Nonnegative Matrix Factorization (deep NMF) has recently emerged as a
valuable technique for extracting multiple layers of features across different
scales. However, all existing deep NMF models and algorithms have primarily
centered their evaluation on the least squares error, which may not be the most
appropriate metric for assessing the quality of approximations on diverse
datasets. For instance, when dealing with data types such as audio signals and
documents, it is widely acknowledged that $\beta$-divergences offer a more
suitable alternative. In this paper, we develop new models and algorithms for
deep NMF using some $\beta$-divergences, with a focus on the Kullback-Leibler
divergence. Subsequently, we apply these techniques to the extraction of facial
features, the identification of topics within document collections, and the
identification of materials within hyperspectral images.


  Extensive studies on thin films indicated a generic cubic current-voltage
$I-V$ dependence as a salient feature of charge transport by tunneling. A quick
glance at $I-V$ data for molecular junctions suggests a qualitatively similar
behavior. This would render model-based studies almost irrelevant, since,
whatever the model, its parameters can always be adjusted to fit symmetric
(asymmetric) $I-V$ curves characterized by two (three) expansion coefficients.
Here, we systematically examine popular models based on tunneling barrier or
tight-binding pictures and demonstrate that, for a quantitative description at
biases of interest ($V$ slightly higher than the transition voltage $V_t$),
cubic expansions do not suffice. A detailed collection of analytical formulae
as well as their conditions of applicability are presented to facilitate
experimentalists colleagues to process and interpret their experimental data by
obtained by measuring currents in molecular junctions. We discuss in detail the
limits of applicability of the various models and emphasize that uncritically
adjusting model parameters to experiment may be unjustified because the values
deduced in this way may fall in ranges rendering a specific model invalid or
incompatible to ab initio estimates. We exemplify with the benchmark case of
oligophenylene-based junctions, for which results of ab initio quantum chemical
calculations are also reported. As a specific issue, we address the impact of
the spatial potential profile and show that it is not notable up to biases V
somewhat larger than V_t, unlike at higher biases, where it may be responsible
for negative differential resistance effects.


  We consider infinite or periodic 2D triangular Ising lattices with arbitrary
positive or negative nearest-neighbor couplings $K_i(\vec{r})$, where $\vec{r}$
and $i$ indicate the bond position and orientation, respectively. Iterative
application of the star-triangle transformation to an initial lattice
$\mathcal{T}(0)$ with a set of couplings $\{K_i^{(0)}(\vec{r})\}$ generates a
sequence of lattices $\mathcal{T}(n)$, for $n=1,2,\ldots,$ with couplings
$\{K_i^{(n)}(\vec{r})\}$. When $\mathcal{T}(0)$ includes sufficiently strongly
frustrated plaquettes, complex couplings will appear. We show that,
nevertheless, the variables $1/\sinh 2K_i^{(n)}\!(\vec{r})$ remain confined to
the union ${\mathbb{R}} \cup \rm{i}{\mathbb{R}}$ of the real and the imaginary
axis. The same holds for a lattice with free boundaries, provided we
distinguish between "receding" and "advancing" boundaries, the latter having
degrees of freedom that must be fixed by an appropriately chosen protocol. This
study establishes a framework for future analytic and numerical work on such
frustrated Ising lattices.


  We discuss various symmetry properties of the N = 2 supersymmetric quantum
spin model in one (0 + 1)-dimension of spacetime and provide their relevance in
the realm of the mathematics of differential geometry. We show one-to-one
mapping between the continuous symmetry transformations (and corresponding
generators) and de Rham cohomological operators of differential geometry. One
of the novel observations is the existence of discrete symmetry transformations
which play a crucial role in providing the physical realization of the Hodge
duality ($\star$) operation. Thus, the present model provides a toy model for
the Hodge theory.


  Confidence bounds are an essential tool for rigorously quantifying the
uncertainty of predictions. In this capacity, they can inform the
exploration-exploitation trade-off and form a core component in many sequential
learning and decision-making algorithms. Tighter confidence bounds give rise to
algorithms with better empirical performance and better performance guarantees.
In this work, we use martingale tail bounds and finite-dimensional
reformulations of infinite-dimensional convex programs to establish new
confidence bounds for sequential kernel regression. We prove that our new
confidence bounds are always tighter than existing ones in this setting. We
apply our confidence bounds to the kernel bandit problem, where future actions
depend on the previous history. When our confidence bounds replace existing
ones, the KernelUCB (GP-UCB) algorithm has better empirical performance, a
matching worst-case performance guarantee and comparable computational cost.
Our new confidence bounds can be used as a generic tool to design improved
algorithms for other kernelised learning and decision-making problems.


  Mimicking the von Neumann version of Kustermans and Vaes' locally compact
quantum groups, Franck Lesieur had introduced a notion of measured quantum
groupoid, in the setting of von Neumann algebras. In this article, we suppose
that the basis of the measured quantum groupoid is central; in that case, we
prove that a specific sub-${\bf C}^*$ algebra is invariant under all the data
of the measured quantum groupoid; moreover, this sub-${\bf C}^*$-algebra is a
continuous field of ${\bf C}^*$-algebras; when the basis is central in both the
measured quantum groupoid and its dual, we get that the measured quantum
groupoid is a continuous field of locally compact quantum groups. On the other
hand, using this sub-${\bf C}^*$-algebra, we prove that any abelian measured
quantum groupoid comes from a locally compact groupoid.


  We propose and analyze a new type of stochastic first order method: gradient
descent with compressed iterates (GDCI). GDCI in each iteration first
compresses the current iterate using a lossy randomized compression technique,
and subsequently takes a gradient step. This method is a distillation of a key
ingredient in the current practice of federated learning, where a model needs
to be compressed by a mobile device before it is sent back to a server for
aggregation. Our analysis provides a step towards closing the gap between the
theory and practice of federated learning, and opens the possibility for many
extensions.


  The unified set of yields of particles produced in proton-proton collisions
at $\sqrt{s}$ = 17.3 GeV (laboratory beam momentum 158 GeV/c) is evaluated,
combining the experimental results of the NA49 and NA61/SHINE collaborations at
the CERN SPS. With the statistical hadronization code Thermal-Fist we confirm
the unacceptably high value of $\chi^2$, both in the canonical and grand
canonical - strangeness canonical approach, and the common volume for all the
hadrons. The use of the energy-dependent width of the Breit-Wigner
parametrization for the mass distributions of unstable particles improves the
quality of the description of particle yields only slightly. We confirm the
observation that exclusion of the $\phi$ meson yield makes the fit result
acceptable. The complete experimental data set of particle yields can be
reasonably fitted if the canonical volumes of hadrons without and with open
strangeness are allowed to vary independently. The canonical volume of
strangeness was found larger than that for non-strange hadrons, which is
compatible with the femtoscopy measurements of p+p system at $\sqrt{s} = $ 27.4
MeV and 900 MeV. The model with the best-fit parameters allows to predict the
yields of several not yet measured particles emitted from p+p at $\sqrt{s}$ =
17.3 GeV.


  Though the community has made great progress on Machine Reading Comprehension
(MRC) task, most of the previous works are solving English-based MRC problems,
and there are few efforts on other languages mainly due to the lack of
large-scale training data. In this paper, we propose Cross-Lingual Machine
Reading Comprehension (CLMRC) task for the languages other than English.
Firstly, we present several back-translation approaches for CLMRC task, which
is straightforward to adopt. However, to accurately align the answer into
another language is difficult and could introduce additional noise. In this
context, we propose a novel model called Dual BERT, which takes advantage of
the large-scale training data provided by rich-resource language (such as
English) and learn the semantic relations between the passage and question in a
bilingual context, and then utilize the learned knowledge to improve reading
comprehension performance of low-resource language. We conduct experiments on
two Chinese machine reading comprehension datasets CMRC 2018 and DRCD. The
results show consistent and significant improvements over various
state-of-the-art systems by a large margin, which demonstrate the potentials in
CLMRC task. Resources available: https://github.com/ymcui/Cross-Lingual-MRC


  Federated Learning (FL) enables model training across decentralized devices
by communicating solely local model updates to an aggregation server. Although
such limited data sharing makes FL more secure than centralized approached, FL
remains vulnerable to inference attacks during model update transmissions.
Existing secure aggregation approaches rely on differential privacy or
cryptographic schemes like Functional Encryption (FE) to safeguard individual
client data. However, such strategies can reduce performance or introduce
unacceptable computational and communication overheads on clients running on
edge devices with limited resources. In this work, we present EncCluster, a
novel method that integrates model compression through weight clustering with
recent decentralized FE and privacy-enhancing data encoding using probabilistic
filters to deliver strong privacy guarantees in FL without affecting model
performance or adding unnecessary burdens to clients. We performed a
comprehensive evaluation, spanning various datasets and architectures, to
demonstrate EncCluster's scalability across encryption levels. Our findings
reveal that EncCluster significantly reduces communication costs - below even
conventional FedAvg - and accelerates encryption by more than four times over
all baselines; at the same time, it maintains high model accuracy and enhanced
privacy assurances.


  We show that every locally compact group which acts faithfully on a connected
three-manifold is a Lie group. By known reductions, it suffices to show that
there is no faithful action of $\mathbb Z_p$ (the $p$-adic integers) on a
connected three-manifold. If $\mathbb Z_p$ acts faithfully on $M^3$, we find an
interesting $\mathbb Z_p$-invariant open set $U\subseteq M$ with
$H_2(U)=\mathbb Z$ and analyze the incompressible surfaces in $U$ representing
a generator of $H_2(U)$. It turns out that there must be one such
incompressible surface, say $F$, whose isotopy class is fixed by $\mathbb Z_p$.
An analysis of the resulting homomorphism $\mathbb Z_p\to\operatorname{MCG}(F)$
gives the desired contradiction. The approach is local on $M$.


  We investigate effects of nuclear medium on antiquark distribution in nuclei
applying the results of a recently developed relativistically-covariant
self-consistent model for the pion and the isobar. We take into account Fermi
motion including Pauli blocking and binding effects on the nucleons and medium
effects on the isobar and pion leading to modest enhancement of the pion
light-cone-momentum distribution in large nuclei. As a consequence the
Drell-Yan cross-section ratio with respect to the deuteron exceeds one only for
small values of the light-cone momentum.


  We have measured the branching fractions of the Cabibbo-suppressed decays
$\Lambda^+_c$ $\to$ $\Lambda^{0}$ $K^+$ and $\Lambda^+_c$ $\to$ $\Sigma^0$
$K^+$ %(measured with improved accuracy). relative to the Cabibbo-allowed decay
modes $\Lambda^+_c$ $\to$ $\Lambda^{0}$ $\pi^+$ and $\Lambda^+_c$ $\to$
$\Sigma^0$ $\pi^+$ to be $ 0.044 \pm 0.004 (\textnormal{stat.}) \pm 0.002
(\textnormal{syst.})$ and $ 0.040 \pm 0.005 (\textnormal{stat.}) \pm 0.004
(\textnormal{syst.})$, respectively. We also present the first observation of
$\Lambda^+_c$ $\to$ $\Lambda^{0}$ $K^+ \pi^+ \pi^-$ and have measured the
branching fraction relative to $\Lambda^+_c$ $\to$ $\Lambda^{0}$ $\pi^+$ to be
$0.266 \pm 0.027 (\textnormal{stat.}) \pm 0.032 (\textnormal{syst.})$. The
upper limit of the branching fraction into the decay $\Lambda^+_c$ $\to$
$\Sigma^0$ $K^+ \pi^+ \pi^-$ relative to $\Lambda^+_c$ $\to$ $\Sigma^0$ $\pi^+$
has been measured to be %7.6 \times 10^{-4}$ $ < 3.9 \times 10^{-2}$ at the 90%
confidence level. This analysis was performed using a data sample of
%approximately 125 fb$^{-1}$ (integrated luminosity) collected by the \babar$
$detector at the PEP-II asymmetric-energy $B$ Factory at the Stanford Linear
Accelerator Center. All results presented in this conference contribution are
preliminary.


  This report summarizes major findings from an independent evaluation of 2020
census operations. The American Statistical Association 2020 Census Quality
Indicators Task Force selected the authors to conduct the evaluation using
nonpublic operations data provided by the Census Bureau.
  The evaluation focused on the quality of state-level population counts
released by the Census Bureau for congressional apportionment. The authors
first partitioned the census enumeration process into five operation phases.
Within each phase, one or more activities considered to be critical to census
data quality were identified. Operational data from each activity were then
analyzed to assess the risk of error, particularly as they related to similar
activities in the 2010 census.
  Overall, the evaluation found that census operations relied on higher risk
activities at a higher rate in 2020 than in 2010, suggesting that the risk of
error may be higher in 2020 than in 2010. However, the available data were
insufficient to determine whether the apportionment counts are of lower quality
in 2020 than in 2010.


  The Goos-H\"anchen (G-H) effect for neutron reflection from condensed matter
is considered. An experiment to quantify the effect is proposed. The relation
of G-H shift to the neutron coherence length is considered.


  Overfitting frequently occurs in deep learning. In this paper, we propose a
novel regularization method called Drop-Activation to reduce overfitting and
improve generalization. The key idea is to drop nonlinear activation functions
by setting them to be identity functions randomly during training time. During
testing, we use a deterministic network with a new activation function to
encode the average effect of dropping activations randomly. Our theoretical
analyses support the regularization effect of Drop-Activation as implicit
parameter reduction and verify its capability to be used together with Batch
Normalization (Ioffe and Szegedy 2015). The experimental results on CIFAR-10,
CIFAR-100, SVHN, EMNIST, and ImageNet show that Drop-Activation generally
improves the performance of popular neural network architectures for the image
classification task. Furthermore, as a regularizer Drop-Activation can be used
in harmony with standard training and regularization techniques such as Batch
Normalization and Auto Augment (Cubuk et al. 2019). The code is available at
\url{https://github.com/LeungSamWai/Drop-Activation}.


  Testing and code reviews are known techniques to improve the quality and
robustness of software. Unfortunately, the complexity of modern software
systems makes it impossible to anticipate all possible problems that can occur
at runtime, which limits what issues can be found using testing and reviews.
Thus, it is of interest to consider autonomous self-healing software systems,
which can automatically detect, diagnose, and contain unanticipated problems at
runtime. Most research in this area has adopted a model-driven approach, where
actual behavior is checked against a model specifying the intended behavior,
and a controller takes action when the system behaves outside of the
specification. However, it is not easy to develop these specifications, nor to
keep them up-to-date as the system evolves. We pose that, with the recent
advances in machine learning, such models may be learned by observing the
system. Moreover, we argue that artificial immune systems (AISs) are
particularly well-suited for building self-healing systems, because of their
anomaly detection and diagnosis capabilities. We present the state-of-the-art
in self-healing systems and in AISs, surveying some of the research directions
that have been considered up to now. To help advance the state-of-the-art, we
develop a research agenda for building self-healing software systems using
AISs, identifying required foundations, and promising research directions.


  The quantum nature of nuclei plays an important role in the accurate
modelling of light atoms such as hydrogen, but it is often neglected in
simulations due to the high computational overhead involved. It has recently
been shown that zero-point energy effects can be included comparatively cheaply
in simulations of harmonic and quasi-harmonic systems by augmenting classical
molecular dynamics with a generalized Langevin equation (GLE). Here we describe
how a similar approach can be used to accelerate the convergence of path
integral (PI) molecular dynamics to the exact quantum mechanical result in more
strongly anharmonic systems exhibiting both zero point energy and tunnelling
effects. The resulting PI-GLE method is illustrated with applications to a
double-well tunnelling problem and to liquid water.


  We study in this work a continuum model derived from 1D
attachment-detachment-limited (ADL) type step flow on vicinal surface, $
u_t=-u^2(u^3)_{hhhh}$, where $u$, considered as a function of step height $h$,
is the step slope of the surface. We formulate a notion of weak solution to
this continuum model and prove the existence of a global weak solution, which
is positive almost everywhere. We also study the long time behavior of weak
solution and prove it converges to a constant solution as time goes to
infinity. The space-time H\"older continuity of the weak solution is also
discussed as a byproduct.


  The semiclassical, dynamical description of diffraction dissociation of
weakly bound nuclei is applied to analyze the decay-energy spectra of $^{15}$C
that have been measured at 68 MeV/nucleon on a Pb target. The optical
potentials that are used to describe the nuclear interaction of $^{15}$C with
the target nucleus are realistic because the fits to the two measured spectra,
one with a small and one with a very large acceptance angle, are consistent and
of similar quality. The cross section for the radiative neutron capture on
$^{14}$C to the 1/2$^+$ ground state of $^{15}$C is deduced from the analysis.
When combined with an estimated contribution from the capture to the 5/2$^+$
excited state of $^{15}$C, an excellent agreement with a recent direct capture
measurement is achieved.


  We present VLBI images of the RS CVn binary star HR 5110 (=BH CVn; HD
118216), obtained from observations made at 8.4 GHz on 1994 May 29/30 in
support of the NASA/Stanford Gravity Probe B project. Our images show an
emission region with a core-halo morphology. The core was 0.39 +/- 0.09 mas
(FWHM) in size, or 66% +/- 20% of the 0.6 +/- 0.1 mas diameter of the
chromospherically active K subgiant star in the binary system. The halo was
1.95 +/- 0.22 mas (FWHM) in size, or 1.8 +/- 0.2 times the 1.1 +/- 0.1 mas
separation of the centers of the K and F stars. The core increased
significantly in brightness over the course of the observations and seems to
have been the site of flare activity that generated an increase in the total
flux density of ~200% in 12 hours. The fractional circular polarization
simultaneously decreased from ~10% to 2.5%.


  The paper introduces particle swarm optimization as a viable strategy to find
numerical solution of Diophantine equation, for which there exists no general
method of finding solutions. The proposed methodology uses a population of
integer particles. The candidate solutions in the feasible space are optimized
to have better positions through particle best and global best positions. The
methodology, which follows fully connected neighborhood topology, can offer
many solutions of such equations.


  Adaptation to local environments often occurs through natural selection
acting on a large number of loci, each having a weak phenotypic effect. One way
to detect these loci is to identify genetic polymorphisms that exhibit high
correlation with environmental variables used as proxies for ecological
pressures. Here, we propose new algorithms based on population genetics,
ecological modeling, and statistical learning techniques to screen genomes for
signatures of local adaptation. Implemented in the computer program "latent
factor mixed model" (LFMM), these algorithms employ an approach in which
population structure is introduced using unobserved variables. These fast and
computationally efficient algorithms detect correlations between environmental
and genetic variation while simultaneously inferring background levels of
population structure. Comparing these new algorithms with related methods
provides evidence that LFMM can efficiently estimate random effects due to
population history and isolation-by-distance patterns when computing
gene-environment correlations, and decrease the number of false-positive
associations in genome scans. We then apply these models to plant and human
genetic data, identifying several genes with functions related to development
that exhibit strong correlations with climatic gradients.


  Solving a longstanding problem on equiangular lines, we determine, for each
given fixed angle and in all sufficiently large dimensions, the maximum number
of lines pairwise separated by the given angle.
  Fix $0 < \alpha < 1$. Let $N_\alpha(d)$ denote the maximum number of lines
through the origin in $\mathbb{R}^d$ with pairwise common angle $\arccos
\alpha$. Let $k$ denote the minimum number (if it exists) of vertices in a
graph whose adjacency matrix has spectral radius exactly
$(1-\alpha)/(2\alpha)$. If $k < \infty$, then $N_\alpha(d) = \lfloor
k(d-1)/(k-1) \rfloor$ for all sufficiently large $d$, and otherwise
$N_\alpha(d) = d + o(d)$. In particular, $N_{1/(2k-1)}(d) = \lfloor
k(d-1)/(k-1) \rfloor$ for every integer $k\ge 2$ and all sufficiently large
$d$.
  A key ingredient is a new result in spectral graph theory: the adjacency
matrix of a connected bounded degree graph has sublinear second eigenvalue
multiplicity.


  Some questions of application of trigonometric splines in problems of
spectral analysis are considered. The known effects of overlay in the frequency
and time domains are discussed; deployment effects in these areas are firstly
considered. The relation between discrete Fourier coefficients and Fourier
coefficients of trigonometric splines was obtained. The expediency of taking
into account the differential properties of the investigated signals that are
losing during the primary sampling is shown.


  Dimer models are a combinatorial tool to describe certain algebras that
appear as noncommutative crepant resolutions of toric Gorenstein singularities.
Unfortunately, not every dimer model gives rise to a noncommutative crepant
resolution. Several notions of consistency have been introduced to deal with
this problem. In this paper we study the major different notions in detail and
show that for dimer models on a torus they are all equivalent.


  Most ultra-luminous X-ray sources (ULXs) are now thought to be powered by
stellar-mass compact objects accreting at super-Eddington rates. While the
discovery of evolutionary cycles have marked a breakthrough in our
understanding of the accretion flow changes in the sub-Eddington regime in
Galactic Black Hole Binaries, their evidence in the super-Eddington regime
remained elusive. However, recent circumstantial evidence had hinted the
presence of a recurrent evolutionary cycle in two archetypal ULXs: Holmberg II
X-1 and NGC 5204 X-1. Here we build on our previous work and exploit the
long-term high-cadence monitoring of Swift-XRT in order to provide evidence of
the evolutionary cycle in these two sources and investigate the main physical
parameters inducing their spectral transitions. We study the long-term
evolution of both sources using hardness-intensity diagrams (HID) and by means
of Lomb-Scargle periodograms and Gaussian processes modelling to look for
periodic variability. We show that both sources follow a recurrent evolutionary
pattern in the HID that can be characterized by the hard ultraluminous (HUL)
and soft ultraluminous (SUL) spectral regimes, and a third state with
characteristics similar to the supersoft ultraluminous (SSUL) state. The
transitions between the soft states seem aperiodic, as revealed by timing
analysis of the light curve of Holmberg II X-1, albeit further investigation is
warranted. The light curve of NGC 5204 X-1 shows a periodicity of $\sim$ 200
days, possibly associated with the duration of the evolutionary cycle. We
support a scenario in which the spectral changes from HUL to SUL are due to a
periodic increase of the mass-transfer rate and subsequent narrowing of the
opening angle of the supercritical funnel. The narrower funnel, combined with
stochastic variability imprinted by the wind, might explain the SUL--SSUL
spectral changes.


  We study the world volume theory of D3-branes wrapping the Melvin universe
supported by background NSNS B-field. In the appropriate decoupling limit, the
open string dynamics is that of non-commutative guage field theory with
non-constant non-commutativity. We identify this model as a simple Melvin twist
of flat D3 branes. Along similar lines, one recognizes the model of Hashimoto
and Sethi as being the Melvin null twist, and the model of Dolan and Nappi as
being the null Melvin twist, of the flat D3-brane. This construction therefore
offers a unified perspective on most of the known explicit constructions of
non-commutative gauge theories as a decoupled theory of D-branes in a B-field
background. We also describe the world volume theory on the D3-brane in Melvin
universe which is decaying via the nucleation of monopole anti-monopole pair.


  We use an auxiliary field construction to discuss the hard thermal loop
effective action associated with massless thermal SU(N) QCD interacting with a
weak gravitational field. It is demonstrated that the previous attempt to
derive this effective action has only been partially successful and that it is
presently only known to first order in the graviton coupling constant. This is
still sufficient to enable a calculation of a symmetric traceless quark gluon
plasma energy momentum tensor. Finally, we comment on the conserved currents
and charges of the derived energy momentum tensor.


  Quadrature by Expansion (QBX) is a quadrature method for approximating the
value of the singular integrals encountered in the evaluation of layer
potentials. It exploits the smoothness of the layer potential by forming
locally-valid expansion which are then evaluated to compute the near or
on-surface value of the integral. Recent work towards coupling of a Fast
Multipole Method (FMM) to QBX yielded a first step towards the rapid evaluation
of such integrals (and the solution of related integral equations), albeit with
only empirically understood error behavior. In this paper, we improve upon this
approach with a modified algorithm for which we give a comprehensive analysis
of error and cost in the case of the Laplace equation in two dimensions. For
the same levels of (user-specified) accuracy, the new algorithm empirically has
cost-per-accuracy comparable to prior approaches. We provide experimental
results to demonstrate scalability and numerical accuracy.


  We consider the chance-constrained binary knapsack problem (CKP), where the
item weights are independent and normally distributed. We introduce a
continuous relaxation for the CKP, represented as a non-convex optimization
problem, which we call the non-convex relaxation. A comparative study shows
that the non-convex relaxation provides an upper bound for the CKP, at least as
tight as those obtained from other continuous relaxations for the CKP.
Furthermore, the quality of the obtained upper bound is guaranteed to be at
most twice the optimal objective value of the CKP. Despite its non-convex
nature, we show that the non-convex relaxation can be solved in polynomial
time. Subsequently, we proposed a polynomial-time 1/2-approximation algorithm
for the CKP based on this relaxation, providing a lower bound for the CKP.
Computational test results demonstrate that the non-convex relaxation and the
proposed approximation algorithm yields tight lower and upper bounds for the
CKP within a short computation time, ensuring the quality of the obtained
bounds.


  In honor of S.G.Dani's 65th birthday, we describe some of his many
contributions to the theory and applications of dynamical systems on
homogeneous spaces, with emphasis on unipotent flows.


  1. Introduction, 2. Gauge Theories, 3. The Standard Model of Electroweak
Interactions, 4. The Higgs Mechanism, 5. The CKM Matrix, 6. Renormalisation and
Higher Order Corrections, 7. Why we do Believe in the Standard Model: Precision
Tests, 7.1.Precision Electroweak Data and the Standard Model, 7.2.A More
General Analysis of Electroweak Data, 8. Why we do not Believe in the Standard
Model, 8.1.Conceptual Problems, 8.2.Hints from Experiment, -8.2.1 Unification
of Couplings, -8.2.2 Dark Matter, -8.2.3 Neutrino Masses, -8.2.4 Baryogenesis,
9. Status of the Search for the Higgs and for New Physics, 10.Conclusion


  We present an experimental feasible scheme to synthesize two-mode
continuous-variable entangled states of two superconducting resonators that are
interconnected by two gap-tunable superconducting qubits. We show that, with
each artificial atom suitably driven by a bichromatic microwave field to induce
sidebands in the qubit-resonator coupling, the stationary state of the photon
fields in the two resonators can be cooled and steered into a two-mode squeezed
vacuum state via a dissipative quantum dynamical process, while the
superconducting qubits remain in their ground states. In this scheme the qubit
decay plays a positive role and can help drive the system to the target state,
which thus converts a detrimental source of noise into a resource.


  Mathematical connection between the quantum teleportation, the most unique
feature of quantum information processing, and the black hole final state is
studied taking into account the non trivial spacetime geometry. We use the
twist operatation for the generalized entanglement measurement and the final
state boundary conditions to obtain transfer theorems for the black hole
evaporation. This would enable us to put together the universal quantum
teleportation and the black hole evaporation in the unified mathematical
footing. For a renormalized post selected final state of outgoing Hawking
radiation, we found that the measure of mixedness is preserved only in the
special case of final-state boundary condition in the micro-canonical form,
which resmebles perfect teleportation channel.


  A new measurement of a spatially extended gamma-ray signal from the center of
the Andromeda galaxy (M31) has been recently published by the Fermi-LAT
collaboration, reporting that the emission broadly resembles the so-called
Galactic center excess (GCE) of the Milky Way (MW). At the same time, evidence
is accumulating on a millisecond pulsar (MSPs) origin for the GCE. These
elements prompt us to compare the mentioned observations with what is, perhaps,
the simplest model for an MSP population, solely obtained by rescaling of the
MSP luminosity function determined in the local MW disk via the respective
stellar mass of the systems. It is remarkable that without free fitting
parameters, this model can account for both the energetics and the morphology
of the GCE within uncertainties. For M31, the estimated luminosity due to
primordial MSPs is expected to contribute only about a quarter of the detected
emission, although a dominant contribution cannot be excluded given the large
uncertainties. If correct, the model predicts that the M31 disk emission due to
MSP is not far below the present upper bound. We also discuss a few refinements
of this simple model. In particular, we use the correlation between globular
cluster gamma-ray luminosity and stellar encounter rate to gauge the dynamical
MSP formation in the bulge. This component is expected to contribute to the GCE
only at a level $\lesssim 5\%$, but it may be of some importance in explaining
the signal's morphology in the inner region of the Galaxy. We also comment on
some effects which may lead to violations of the simple scaling used, on
alternative models, and on future perspectives for improved diagnostics.


  In this work, we investigate two source coding models, a \emph{Helper}
problem and a \emph{Gray-Wyner} problem, under equivocation constraints.
Specifically, in the Helper problem, an encoder communicates with a legitimate
receiver through noise-free rate-limited public and private links; and an
external passive eavesdropper intercepts every information that is sent on the
public link. We study two classes of this model: i) when a pair of arbitrarily
correlated discrete memoryless sources is to be encoded such that one component
has to be recovered lossily at the legitimate receiver while the equivocation
about both components at the eavesdropper must be maintained no smaller than
some prescribed level; and ii) when the legitimate receiver reproduces both
components, one of which, that is recovered losslessly, has to be concealed
from the eavesdropper to some equivocation level. For both classes problems, we
establish single-letter characterizations of optimal
rate-distortion-equivocation tradeoffs in the discrete memoryless case. Next,
we extend our results to the case of two legitimate receivers, i.e., Gray-Wyner
network with equivocation constraints. Here, two legitimate receivers are
connected to the encoder each through a dedicated error-free private link as
well as a common error-free public link; and an external passive eavesdropper
overhears on the public link. We study two classes of this model that are
extensions of the aforementioned instances of Helper problems to the case of
two receivers. For each of the two classes, we establish a single-letter
characterization of the optimal rate-distortion-equivocation region. Throughout
the paper, the analysis sheds light on the role of the private links, and we
illustrate the results by computing them for some binary examples. Also, we
make some meaningful connections, e.g., with problems of secret-sharing and
encryption.


  The paper reports a new electrostatic-confinement based fusion approach,
where, a new non-equilibrium distribution function for an ion-beam, compressed
by an external electric force, has been derived. This distribution function
allows the system to possess appreciably low and insignificant thermal energy
irrespective of the energy per particle. The spread in the energy among the
particles is attributed to the collisions in presence of the external force,
whereas; for equilibrium, the spreading in energy is due to the absence of the
force. The reactivity for a deuterium-deuterium fusion, using the proposed
distribution function, has been computed. It is shown that the fusion time is
comparable to the energy confinement time, collision time and transit time of
the ion for beam energy greater than 160 keV. The estimated energy gain Q
(ratio of fusion power to the power consumed by the system) is around 10 for
beam energy 160 keV and ion density 1018 cm-3. The energy loss due to particle
scattering is estimated and is taken into consideration in the estimation of
energy gain. An outline of a possible device is proposed according to the model
and is not similar to conventionally used in IEC systems based on collisions of
the beam with a reflex beam or with background gas or plasma.


  Heavy vector-like quarks coupled to a scalar $S$ will induce a coupling of
this scalar to gluons and possibly (if electrically charged) photons. The decay
of the heavy quark into $Sq$, with $q$ being a Standard Model quark, provides,
if kinematically allowed, new channels for heavy quark searches. Inspired by
naturalness considerations, we consider the case of a vector-like partner of
the top quark. For illustration, we show that a singlet partner can be searched
for at the 13$\,$TeV LHC through its decay into a scalar resonance in the
$2\gamma+\ell + X$ final states, especially if the diphoton branching ratio of
the scalar $S$ is further enhanced by the contribution of non coloured
particles. We then show that conventional heavy quark searches are also
sensitive to this new decay mode, when $S$ decays hadronically, by slightly
tightening the current selection cuts. Finally, we comment about the
possibility of disentangling, by scrutinising appropriate kinematic
distributions, heavy quark decays to $St$ from other standard decay modes.


  In symmetric gravitating systems experiencing rapid mass loss, particle
orbits change almost instantaneously, which can lead to the development of a
sharply contoured density profile, including singular caustics for
collisionless systems. This framework can be used to model a variety of
dynamical systems, such as accretion disks following a massive black hole
merger and dwarf galaxies following violent early star formation feedback.
Particle interactions in the high-density peaks seem a promising source of
observable signatures of these mass loss events (i.e. a possible EM counterpart
for black hole mergers or strong gamma-ray emission from dark matter
annihilation around young galaxies), because the interaction rate depends on
the square of the density. We study post-mass-loss density profiles, both
analytic and numerical, in idealised cases and present arguments and methods to
extend to any general system. An analytic derivation is presented for particles
on Keplerian orbits responding to a drop in the central mass. We argue that
this case, with initially circular orbits, gives the most sharply contoured
profile possible. We find that despite the presence of a set of singular
caustics, the total particle interaction rate is reduced compared to the
unperturbed system; this is a result of the overall expansion of the system
dominating over the steep caustics. Finally we argue that this result holds
more generally, and the loss of central mass decreases the particle interaction
rate in any physical system.


  We describe a simple manganin foil resistance manometer for uniaxial stress
measurements. The manometer functions at low pressures and over a range of
temperatures. In this design no temperature seasoning is necessary, although
the manometer must be prestressed to the upper end of the desired pressure
range. The prestress pressure cannot be increased arbitrarily; irreversibility
arising from shear stress limits its range. Attempting larger pressures yields
irreproducible resistance measurements.


  To efficiently align the creation of defect center with photonics structure
in nanoscale precision is one of the outstanding challenges for realizing
high-performance photonic devices and the application in quantum technology
such as quantum sensing, scalable quantum systems, and quantum computing
network. Here, we propose a facile self-aligned patterning technique wholly
based on conventional engineering technology, with the doping precision can
reach ~15nm. Specifically, we demonstrate this technique by fabricating diamond
nanopillar sensor arrays, which show high consistency and near-optimal photon
counts, high yield approaching the theoretical limit, and high filtering
efficiency for different NV centers. Combined with appropriate crystal
orientation, a saturated fluorescence rate of 4.65 Mcps and the best reported
fluorescence-dependent detection sensitivity of 1900 cps^(-1/2) are achieved.
This technique applicable to all similar solid-state systems should facilitate
the development of parallel quantum sensing and scalable information
processing.


  The purpose of this paper is to study microlocal conditions for inclusion
relations between the ranges of square systems of pseudodifferential operators
which fail to be locally solvable. The work is an extension of earlier results
for the scalar case in this direction, where analogues of results by L.
H\"ormander about inclusion relations between the ranges of first order
differential operators with coefficients in $C^\infty$ which fail to be locally
solvable were obtained. We shall study the properties of the range of systems
of principal type with constant characteristics for which condition (\Psi) is
known to be equivalent to microlocal solvability.


  In this paper, we extend the results from Jiao et al. (2019) on distributed
linear quadratic control for leaderless multi-agent systems to the case of
distributed linear quadratic tracking control for leader-follower multi-agent
systems. Given one autonomous leader and a number of homogeneous followers, we
introduce an associated global quadratic cost functional. We assume that the
leader shares its state information with at least one of the followers and the
communication between the followers is represented by a connected simple
undirected graph. Our objective is to design distributed control laws such that
the controlled network reaches tracking consensus and, moreover, the associated
cost is smaller than a given tolerance for all initial states bounded in norm
by a given radius. We establish a centralized design method for computing such
suboptimal control laws, involving the solution of a single Riccati inequality
of dimension equal to the dimension of the local agent dynamics, and the
smallest and the largest eigenvalue of a given positive definite matrix
involving the underlying graph. The proposed design method is illustrated by a
simulation example.


  We consider tunneling of electromagnetic waves through a polariton band gap
of a 1-D chain of atoms. We analytically show that a defect embedded in the
structure gives rise to the resonance transmission at the frequency of a local
polariton state associated with the defect. Numerical Monte-Carlo simulations
are used to examine properties of the electromagnetic band arising inside the
polariton gap due to finite concentration of defects.


  A reference implementation of a new method in isogeometric analysis (IGA) is
presented. It delivers low-cost variable-scale approximations (surrogates) of
the matrices which IGA conventionally requires to be computed by element-scale
quadrature. To generate surrogate matrices, quadrature must only be performed
on a fraction of the elements in the computational domain. In this way,
quadrature determines only a subset of the entries in the final matrix. The
remaining matrix entries are computed by a simple B-spline interpolation
procedure. We present the modifications and extensions required for a reference
implementation in the open-source IGA software library GeoPDEs. The exposition
is fashioned to help facilitate similar modifications in other contemporary
software libraries.


  Multi-modal retrieval becomes increasingly popular in practice. However, the
existing retrievers are mostly text-oriented, which lack the capability to
process visual information. Despite the presence of vision-language models like
CLIP, the current methods are severely limited in representing the text-only
and image-only data. In this work, we present a new embedding model VISTA for
universal multi-modal retrieval. Our work brings forth threefold technical
contributions. Firstly, we introduce a flexible architecture which extends a
powerful text encoder with the image understanding capability by introducing
visual token embeddings. Secondly, we develop two data generation strategies,
which bring high-quality composed image-text to facilitate the training of the
embedding model. Thirdly, we introduce a multi-stage training algorithm, which
first aligns the visual token embedding with the text encoder using massive
weakly labeled data, and then develops multi-modal representation capability
using the generated composed image-text data. In our experiments, VISTA
achieves superior performances across a variety of multi-modal retrieval tasks
in both zero-shot and supervised settings. Our model, data, and source code are
available at https://github.com/FlagOpen/FlagEmbedding.


  Vehicle-pedestrian interaction (VPI) is one of the most challenging tasks for
automated driving systems. The design of driving strategies for such systems
usually starts with verifying VPI in simulation. This work proposed an improved
framework for the study of VPI in uncontrolled pedestrian crossing scenarios.
The framework admits the mutual effect between the pedestrian and the vehicle.
A multi-state social force based pedestrian motion model was designed to
describe the microscopic motion of the pedestrian crossing behavior. The
pedestrian model considers major interaction factors such as the accepted gap
of the pedestrian's decision on when to start crossing, the desired speed of
the pedestrian, and the effect of the vehicle on the pedestrian while the
pedestrian is crossing the road. Vehicle driving strategies focus on the
longitudinal motion control, for which the feedback obstacle avoidance control
and the model predictive control were tested and compared in the framework. The
simulation results verified that the proposed framework can generate a variety
of VPI scenarios, consisting of either the pedestrian yielding to the vehicle
or the vehicle yielding to the pedestrian. The framework can be easily extended
to apply different approaches to the VPI problems.