  We report the discovery of a very cool, isolated brown dwarf, UGPS 0722-05,
with the UKIDSS Galactic Plane Survey. The near-infrared spectrum displays
deeper H2O and CH4 troughs than the coolest known T dwarfs and an unidentified
absorption feature at 1.275 um. We provisionally classify the object as a T10
dwarf but note that it may in future come to be regarded as the first example
of a new spectral type. The distance is measured by trigonometric parallax as
d=4.1{-0.5}{+0.6} pc, making it the closest known isolated brown dwarf. With
the aid of Spitzer/IRAC we measure H-[4.5] = 4.71. It is the coolest brown
dwarf presently known -- the only known T dwarf that is redder in H-[4.5] is
the peculiar T7.5 dwarf SDSS J1416+13B, which is thought to be warmer and more
luminous than UGPS 0722-05. Our measurement of the luminosity, aided by
Gemini/T-ReCS N band photometry, is L = 9.2 +/- 3.1x10^{-7} Lsun. Using a
comparison with well studied T8.5 and T9 dwarfs we deduce Teff=520 +/- 40 K.
This is supported by predictions of the Saumon & Marley models. With apparent
magnitude J=16.52, UGPS 0722-05 is the brightest T dwarf discovered by UKIDSS
so far. It offers opportunities for future study via high resolution
near-infrared spectroscopy and spectroscopy in the thermal infrared.


  We study eigenvalues of the Dirac operator with canonical form
  \begin{equation}
  L_{p,q} \begin{pmatrix}
  u \\ v
  \end{pmatrix}=
  \begin{pmatrix}
  0 & -1 \\ 1 & 0
  \end{pmatrix}\frac{d}{dt}
  \begin{pmatrix}
  u \\ v
  \end{pmatrix}+\begin{pmatrix}
  -p & q \\ q & p
  \end{pmatrix}\begin{pmatrix}
  u \\ v
  \end{pmatrix},\nonumber
  \end{equation}
  where $ p$ and $q$ are real functions. Under the assumption that
  \begin{equation}
  \limsup_{x\to \infty}x\sqrt{p^2(x)+q^2(x)}<\infty,\nonumber
  \end{equation}
  the essential spectrum of $L_{p,q}$ is $(-\infty,\infty)$.
  We prove that $L_{p,q}$ has no eigenvalues if
  $$\limsup_{x\to \infty}x\sqrt{p^2(x)+q^2(x)}<\frac{1}{2}.$$ Given any $A\geq
\frac{1}{2}$ and any $\lambda\in\R$, we construct functions $p$ and $q$ such
that $\limsup_{x\to \infty}x\sqrt{p^2(x)+q^2(x)}=A$ and $\lambda$ is an
eigenvalue of the corresponding Dirac operator $L_{p,q}$. We also construct
functions $p$ and $q$ so that the corresponding Dirac operator $L_{p,q}$ has
any prescribed set {(finitely or countably many)} of eigenvalues.


  Let $k$ be an algebraically closed field of prime characteristic $p$ and $P$
a finite $p$-group. We compute the Scott $kG$-module with vertex $P$ when
$\mathcal{F}$ is a constrained fusion system on $P$ and $G$ is Park's group for
$\mathcal{F}$. In the case $\mathcal{F}$ is a fusion system of the quadratic
group $Qd(p)=(\mathbb{Z}/p \times \mathbb{Z}/p)\rtimes {\mathrm{SL}}(2,p)$ on a
Sylow $p$-subgroup $P$ of $Qd(p)$ and $G$ is Park's group for $\mathcal{F}$, we
prove that the Scott $kG$-module with vertex $P$ is Brauer indecomposable.


  Multispectral pedestrian detection has attracted increasing attention from
the research community due to its crucial competence for many around-the-clock
applications (e.g., video surveillance and autonomous driving), especially
under insufficient illumination conditions. We create a human baseline over the
KAIST dataset and reveal that there is still a large gap between current top
detectors and human performance. To narrow this gap, we propose a network
fusion architecture, which consists of a multispectral proposal network to
generate pedestrian proposals, and a subsequent multispectral classification
network to distinguish pedestrian instances from hard negatives. The unified
network is learned by jointly optimizing pedestrian detection and semantic
segmentation tasks. The final detections are obtained by integrating the
outputs from different modalities as well as the two stages. The approach
significantly outperforms state-of-the-art methods on the KAIST dataset while
remain fast. Additionally, we contribute a sanitized version of training
annotations for the KAIST dataset, and examine the effects caused by different
kinds of annotation errors. Future research of this problem will benefit from
the sanitized version which eliminates the interference of annotation errors.


  Topological order offers possibilities for processing quantum information
which can be immune to imperfections. However, the question of its stability
out of equilibrium is relevant for experiments, where coupling to an
environment is unavoidable. In this work we demonstrate the robustness of
certain aspects of $Z_2 \times Z_2$ symmetry-protected topological (SPT) order
against a wide class of dissipation channels in the Lindblad and quantum
trajectory formalisms of an open quantum system. This is illustrated using the
one-dimensional $ZXZ$ cluster Hamiltonian along with Pauli-string jump
operators. We show that certain choices of dissipation retaining strong
symmetries support a steady-state manifold consisting of two non-local logical
qubits, and for Hamiltonian perturbations preserving the global symmetry, the
manifold remains long-lived. In contrast, this metastability is destroyed upon
breaking the above-mentioned symmetry. While the localized edge qubits of the
cluster Hamiltonian are not conserved by the Lindbladian evolution, they do
correspond to weak symmetries and thus retain a memory of their initial state
at all times in the quantum trajectories. We utilize this feature to construct
protocols to retrieve the quantum information either by monitoring jumps or
error mitigation. Our work thus proposes a novel framework to study the
dynamics of dissipative SPT phases and opens the possibility of engineering
entangled states relevant to quantum information processing.


  Clouds also form in atmospheres of planets that orbit other stars than our
Sun, in so-called extrasolar planets or exoplanets. Exoplanet atmospheres can
be chemically extremely rich. Exoplanet clouds are therefor made of a mix of
materials that changes throughout the atmosphere. They affect the atmospheres
through element depletion and through absorption and scattering, hence, they
have a profound impact on the atmosphere's energy budget. While astronomical
observations point us to the presence of extrasolar clouds and make first
suggestions on particle sizes and material compositions, we require fundamental
and complex modelling work to merge the individual observations into a coherent
picture. Part of this is to develop an understanding for cloud formation in
non-terrestrial environments.


  In this article, new results are presented for the zero-temperature
ground-state properties of the spin-half transverse Ising model on various
lattices using three different approximate techniques. These are, respectively,
the coupled cluster method, the correlated basis function method, and the
variational quantum Monte Carlo method. The methods, at different levels of
approximation, are used to study the ground-state properties of these systems,
and the results are found to be in excellent agreement both with each other and
with results of exact calculations for the linear chain and results of exact
cumulant series expansions for lattices of higher spatial dimension. The
different techniques used are compared and contrasted in the light of these
results, and the constructions of the approximate ground-state wave functions
are especially discussed.


  In this paper we introduce the notion of an unknotting index for virtual
knots. We give some examples of computation by using writhe invariants, and
discuss a relationship between the unknotting index and the virtual knot
module. In particular, we show that for any non-negative integer $n$ there
exists a virtual knot whose unknotting index is $(1,n)$.


  Using a simple model based on the characteristics of sunspots and faculae in
solar active regions, the effects of surface brightness inhomogeneities on the
position of the photocentre of the disk of a solar-like, magnetically active
star, are studied. A general law is introduced, giving the maximum amplitude of
the photocentre excursion produced by a distribution of active regions with a
given surface filling factor. The consequences for the detection of extrasolar
planets by means of the astrometric method are investigated with some
applications to forthcoming space missions, such as GAIA and SIM, as well as to
ground-based interferometric measurements. Spurious detections of extrasolar
planets can indeed be caused by activity-induced photocentre oscillations,
requiring a simultaneous monitoring of the optical flux and a determination of
the rotation period and of the level of activity of the target stars for an
appropriate discrimination.


  The usage of Lithium-ion (Li-ion) batteries has gained widespread popularity
across various industries, from powering portable electronic devices to
propelling electric vehicles and supporting energy storage systems. A central
challenge in Li-ion battery reliability lies in accurately predicting their
Remaining Useful Life (RUL), which is a critical measure for proactive
maintenance and predictive analytics. This study presents a novel approach that
harnesses the power of multiple denoising modules, each trained to address
specific types of noise commonly encountered in battery data. Specifically, a
denoising auto-encoder and a wavelet denoiser are used to generate
encoded/decomposed representations, which are subsequently processed through
dedicated self-attention transformer encoders. After extensive experimentation
on NASA and CALCE data, a broad spectrum of health indicator values are
estimated under a set of diverse noise patterns. The reported error metrics on
these data are on par with or better than the state-of-the-art reported in
recent literature.


  Phishing is the combination of social engineering and technical exploits
designed to convince a victim to provide personal information, usually for the
monetary gain of the attacker. Phishing has become the most popular practice
among the criminals of the Web. Phishing attacks are becoming more frequent and
sophisticated. The impact of phishing is drastic and significant since it can
involve the risk of identity theft and financial losses. Phishing scams have
become a problem for online banking and e-commerce users. In this paper we
propose a novel approach to detect phishing attacks. We implemented a prototype
web browser which can be used as an agent and processes each arriving email for
phishing attacks. Using email data collected over a period time we demonstrate
data that our approach is able to detect more phishing attacks than existing
schemes.


  On 2015 November 13, the small artificial object designated WT1190F entered
the Earth atmosphere above the Indian Ocean offshore Sri Lanka after being
discovered as a possible new asteroid only a few weeks earlier. At ESA's
SSA-NEO Coordination Centre we took advantage of this opportunity to organize a
ground-based observational campaign, using WT1190F as a test case for a
possible similar future event involving a natural asteroidal body.


  In this note we study several topics related to the schema of local
reflection $\mathsf{Rfn}(T)$ and its partial and relativized variants. Firstly,
we introduce the principle of uniform reflection with $\Sigma_n$-definable
parameters, establish its relationship with the relativized local reflection
principles and corresponding versions of induction with definable parameters.
Using this schema we give a new model-theoretic proof of the
$\Sigma_{n+2}$-conservativity of uniform $\Sigma_{n+1}$-reflection over
relativized local $\Sigma_{n+1}$-reflection. We also study the proof-theoretic
strength of Feferman's theorem, i.e., the assertion of $1$-provability in $S$
of the local reflection schema $\mathsf{Rfn}(S)$, and its generalized versions.
We relate this assertion to the uniform $\Sigma_2$-reflection schema and, in
particular, obtain an alternative axiomatization of $\mathsf{I}\Sigma_1$.


  We develop a statistical theory of networks. A network is a set of vertices
and links given by its adjacency matrix $\c$, and the relevant statistical
ensembles are defined in terms of a partition function $Z=\sum_{\c} \exp
{[}-\beta \H(\c) {]}$. The simplest cases are uncorrelated random networks such
as the well-known Erd\"os-R\'eny graphs. Here we study more general
interactions $\H(\c)$ which lead to {\em correlations}, for example, between
the connectivities of adjacent vertices. In particular, such correlations occur
in {\em optimized} networks described by partition functions in the limit
$\beta \to \infty$. They are argued to be a crucial signature of evolutionary
design in biological networks.


  One of the very small number of serious alternatives to the usual concept of
an astrophysical black hole is the "gravastar" model developed by Mazur and
Mottola; and a related phase-transition model due to Laughlin et al. We
consider a generalized class of similar models that exhibit continuous pressure
-- without the presence of infinitesimally thin shells. By considering the
usual TOV equation for static solutions with negative central pressure, we find
that gravastars cannot be perfect fluids -- anisotropic pressures in the
"crust" of a gravastar-like object are unavoidable. The anisotropic TOV
equation can then be used to bound the pressure anisotropy. The transverse
stresses that support a gravastar permit a higher compactness than is given by
the Buchdahl--Bondi bound for perfect fluid stars. Finally we comment on the
qualitative features of the equation of state that gravastar material must have
if it is to do the desired job of preventing horizon formation.


  This paper studies a Value-at-Risk (VaR)-regulated optimal portfolio problem
of the equity holders of a participating life insurance contract. In a setting
with unhedgeable mortality risk and complete financial market, the optimal
solution is given explicitly for contracts with mortality risk using a
martingale approach for constrained non-concave optimization problems. We show
that regulatory VaR constraints for participating insurance contracts lead to
more prudent investment than in the case of no regulation. This result is
contrary to the situation where the insurer maximizes the utility of the total
wealth of the company (without distinguishing between contributions of equity
holders and policyholders), in which case a VaR constraint may induce the
insurer to take excessive risks leading to higher losses than in the case of no
regulation. Compared to the unregulated problem, the VaR-constrained strategy
leads to a higher expected utility for the policyholders, highlighting the
potential usefulness of a VaR-regulation in the context of insurance. The
prudent investment behavior is more significant if a VaR-type regulation is
replaced by a portfolio insurance (PI)-type regulation. Furthermore, a stricter
regulation (a smaller allowed default probability in the VaR problem or a
higher minimum guarantee level in the PI problem) enhances the benefit of the
policyholder but deteriorates that of the insurer. For both types of
regulation, the gains in terms of expected utility are greater for higher
participation rates, while being smaller for higher bonus rates. We also extend
our analysis to frameworks where dividend and premature death benefit payments
are made at an intermediate time date.


  Small perturbations in spherical and thin disk stellar clusters surrounding
massive a black hole are studied. Due to the black hole, stars with
sufficiently low angular momentum escape from the system through the loss cone.
We show that stability properties of spherical clusters crucially depend on
whether the distribution of stars is monotonic or non-monotonic in angular
momentum. It turns out that only non-monotonic distributions can be unstable.
At the same time the instability in disk clusters is possible for both types of
distributions.


  Exploring the fundamental constituents of the matter around us and in the
Universe, as well as their interactions, is among the premier goals of physics.
Investigating ultrarelativistic collisions in particle accelerators has
delivered answers to these questions many times in the past decades. In this
paper we focus on the research aimed at recreating the matter that filled the
Universe in the first microsecond after the Big Bang -- but this time in
collisions of heavy ions. In particular we discuss the technique called
femtoscopy, which provides us a tool to understand the space-time structure of
particle creation in heavy-ion collisions. We utilize Levy-stable distributions
to investigate this structure and explore its dependence on particle momentum
and collision energy.


  For a many-body system of arbitrary dimension, we consider fermionic ground
states of non-interacting Hamiltonians invariant under a $C_2$ cyclic group.
The absolute difference $\Delta$ between the number of occupied symmetric and
anti-symmetric single-particle states is an adiabatic invariant. We prove lower
bounds on the configurational and the number entropy based on this invariant.
In band insulators, the topological invariant $\Delta$ and the entropy bounds
can be directly determined from high symmetry points in the Brillouin zone.


  In higher education, accreditation is a quality assurance process, where an
institution demonstrates a commitment to delivering high quality programs and
services to their students. For business schools nationally and internationally
the Association to Advance Collegiate Schools of Business (AACSB) accreditation
is the gold standard. For a business school to receive and subsequently
maintain accreditation, the school must undertake a rigorous, time consuming
reporting and peer review process, to demonstrate alignment with the AACSB
Standards. For this project we create a hybrid context retrieval augmented
generation pipeline that can assist in the documentation alignment and
reporting process necessary for accreditation. We implement both a vector
database and knowledge graph, as knowledge stores containing both institutional
data and AACSB Standard data. The output of the pipeline can be used by
institution stakeholders to build their accreditation report, dually grounded
by the context from the knowledge stores. To develop our knowledge graphs we
utilized both a manual construction process as well as an LLM Augmented
Knowledge Graph approach. We evaluated the pipeline using the RAGAs framework
and observed optimal performance on answer relevancy and answer correctness
metrics.


  Adiabatic process has found many important applications in modern physics,
the distinct merit of which is that it does not need accurate control over the
timing of the process. However, it is a slow process, which limits the
application in quantum computation, due to the limited coherent times of
typical quantum systems. Here, we propose a scheme to implement quantum state
conversion in opto-electro-mechanical systems via shortcut to adiabaticity,
where the process can be greatly speeded up while the precise timing control is
still not necessary. In our scheme, only by modifying the coupling strength, we
can achieve fast quantum state conversion with high fidelity, where the
adiabatic condition does not need to be met. In addition, the population of the
unwanted intermediate state can be further suppressed. Therefore, our protocol
presents an important step towards practical state conversion between optical
and microwave photons, and thus may find many important applications in hybrid
quantum information processing.


  I provide for the first time in English a line-by-line translation of the
entire text of the monumental 1840 memoir of Olinde Rodrigues, "On the
geometrical laws governing the motions of a solid system..." published in
French in the Journal de Math\'{e}matiques Pures et Appliqu\'{e}es. I accompany
the translation with copious notes in italics, in which I explicate some
passages whose meaning is obscure in the direct translation, supply detailed
proofs, where lacking, of assertions in the original, and clarify the overall
organization of the memoir and the relation of its sections to one another.


  Nascent neutron stars with millisecond periods and magnetic fields in excess
of $10^{16}$ Gauss can drive highly energetic and asymmetric explosions known
as magnetar-powered supernovae. These exotic explosions are one theoretical
interpretation for supernovae Ic-BL which are sometimes associated with long
gamma-ray bursts. Twisted magnetic field lines extract the rotational energy of
the neutron star and release it as a disk wind or a jet with energies greater
than 10$^{52}$ erg over $\sim 20$ sec. What fractions of the energy of the
central engine go into the wind and the jet remain unclear. We have performed
two-dimensional hydrodynamical simulations of magnetar-powered supernovae (SNe)
driven by disk winds and jets with the CASTRO code to investigate the effect of
the central engine on nucleosynthetic yields, mixing, and light curves. We find
that these explosions synthesize less than 0.05 Msun of Ni and that this mass
is not very sensitive to central engine type. The morphology of the explosion
can provide a powerful diagnostic of the properties of the central engine. In
the absence of a circumstellar medium these events are not very luminous, with
peak bolometric magnitudes $M_b \sim -16.5 $ due to low Ni production.


  Self-adjusting networks (SANs) have the ability to adapt to communication
demand by dynamically adjusting the workload (or demand) embedding, i.e., the
mapping of communication requests into the network topology. SANs can thus
reduce routing costs for frequently communicating node pairs by paying a cost
for adjusting the embedding. This is particularly beneficial when the demand
has structure, which the network can adapt to. Demand can be represented in the
form of a demand graph, which is defined by the set of network nodes (vertices)
and the set of pairwise communication requests (edges). Thus, adapting to the
demand can be interpreted by embedding the demand graph to the network
topology. This can be challenging both when the demand graph is known in
advance (offline) and when it revealed edge-by-edge (online). The difficulty
also depends on whether we aim at constructing a static topology or a dynamic
(self-adjusting) one that improves the embedding as more parts of the demand
graph are revealed. Yet very little is known about these self-adjusting
embeddings.
  In this paper, the network topology is restricted to a line and the demand
graph to a ladder graph, i.e., a $2^n$ grid, including all possible subgraphs
of the ladder. We present an online self-adjusting network that matches the
known lower bound asymptotically and is $12$-competitive in terms of request
cost. As a warm up result, we present an asymptotically optimal algorithm for
the cycle demand graph. We also present an oracle-based algorithm for an
arbitrary demand graph that has a constant overhead.


  We characterize those compact sets for which the Dirichlet problem has a
solution within the class of continuous $m$-subharmonic functions defined on a
compact set, and then within the class of $m$-harmonic functions.


  Annotating a large-scale in-the-wild person re-identification dataset
especially of marathon runners is a challenging task. The variations in the
scenarios such as camera viewpoints, resolution, occlusion, and illumination
make the problem non-trivial. Manually annotating bounding boxes in such
large-scale datasets is cost-inefficient. Additionally, due to crowdedness and
occlusion in the videos, aligning the identity of runners across multiple
disjoint cameras is a challenge. We collected a novel large-scale in-the-wild
video dataset of marathon runners. The dataset consists of hours of recording
of thousands of runners captured using 42 hand-held smartphone cameras and
covering real-world scenarios. Due to the presence of crowdedness and occlusion
in the videos, the annotation of runners becomes a challenging task. We propose
a new scheme for tackling the challenges in the annotation of such large
dataset. Our technique reduces the overall cost of annotation in terms of time
as well as budget. We demonstrate performing fps analysis to reduce the effort
and time of annotation. We investigate several annotation methods for
efficiently generating tight bounding boxes. Our results prove that
interpolating bounding boxes between keyframes is the most efficient method of
bounding box generation amongst several other methods and is 3x times faster
than the naive baseline method. We introduce a novel way of aligning the
identity of runners in disjoint cameras. Our inter-camera alignment tool
integrated with the state-of-the-art person re-id system proves to be
sufficient and effective in the alignment of the runners across multiple
cameras with non-overlapping views. Our proposed framework of annotation
reduces the annotation cost of the dataset by a factor of 16x, also effectively
aligning 93.64% of the runners in the cross-camera setting.


  Generative Adversarial Networks (GANs) are one of the most recent deep
learning models that generate synthetic data from limited genuine datasets.
GANs are on the frontier as further extension of deep learning into many
domains (e.g., medicine, robotics, content synthesis) requires massive sets of
labeled data that is generally either unavailable or prohibitively costly to
collect. Although GANs are gaining prominence in various fields, there are no
accelerators for these new models. In fact, GANs leverage a new operator,
called transposed convolution, that exposes unique challenges for hardware
acceleration. This operator first inserts zeros within the multidimensional
input, then convolves a kernel over this expanded array to add information to
the embedded zeros. Even though there is a convolution stage in this operator,
the inserted zeros lead to underutilization of the compute resources when a
conventional convolution accelerator is employed. We propose the GANAX
architecture to alleviate the sources of inefficiency associated with the
acceleration of GANs using conventional convolution accelerators, making the
first GAN accelerator design possible. We propose a reorganization of the
output computations to allocate compute rows with similar patterns of zeros to
adjacent processing engines, which also avoids inconsequential multiply-adds on
the zeros. This compulsory adjacency reclaims data reuse across these
neighboring processing engines, which had otherwise diminished due to the
inserted zeros. The reordering breaks the full SIMD execution model, which is
prominent in convolution accelerators. Therefore, we propose a unified
MIMD-SIMD design for GANAX that leverages repeated patterns in the computation
to create distinct microprograms that execute concurrently in SIMD mode.


  Cantor's first set theory paper (1874) establishes the uncountability of
$\mathbb{R}$. We study this most basic mathematical fact formulated in the
language of higher-order arithmetic. In particular, we investigate the logical
and computational properties of NIN (resp. NBI), i.e. the third-order
statement: there is no injection (resp. bijection) from $[0,1]$ to
$\mathbb{N}$. Working in Kohlenbach's higher-order Reverse Mathematics, we show
that NIN and NBI are hard to prove in terms of (conventional) comprehension
axioms, while many basic theorems, like Arzela's convergence theorem for the
Riemann integral (1885), are shown to imply NIN and/or NBI. Working in Kleene's
higher-order computability theory based on S1-S9, we show that the following
fourth-order process based on NIN is similarly hard to compute: for a given
$[0,1]\rightarrow \mathbb{N}$-function, find reals in the unit interval that
map to the same natural number.


  We find a Simons type formula for submanifolds with parallel mean curvature
vector (pmc submanifolds) in product spaces $M^n(c)\times\mathbb{R}$, where
$M^n(c)$ is a space form with constant sectional curvature $c$, and then we use
it to prove a gap theorem for the mean curvature of certain complete
proper-biharmonic pmc submanifolds, and classify proper-biharmonic pmc surfaces
in $\mathbb{S}^n(c)\times\mathbb{R}$.


  Sparse linear (or generalized linear) models combine a standard likelihood
function with a sparse prior on the unknown coefficients. These priors can
conveniently be expressed as a maximization over zero-mean Gaussians with
different variance hyperparameters. Standard MAP estimation (Type I) involves
maximizing over both the hyperparameters and coefficients, while an empirical
Bayesian alternative (Type II) first marginalizes the coefficients and then
maximizes over the hyperparameters, leading to a tractable posterior
approximation. The underlying cost functions can be related via a dual-space
framework from Wipf et al. (2011), which allows both the Type I or Type II
objectives to be expressed in either coefficient or hyperparmeter space. This
perspective is useful because some analyses or extensions are more conducive to
development in one space or the other. Herein we consider the estimation of a
trade-off parameter balancing sparsity and data fit. As this parameter is
effectively a variance, natural estimators exist by assessing the problem in
hyperparameter (variance) space, transitioning natural ideas from Type II to
solve what is much less intuitive for Type I. In contrast, for analyses of
update rules and sparsity properties of local and global solutions, as well as
extensions to more general likelihood models, we can leverage coefficient-space
techniques developed for Type I and apply them to Type II. For example, this
allows us to prove that Type II-inspired techniques can be successful
recovering sparse coefficients when unfavorable restricted isometry properties
(RIP) lead to failure of popular L1 reconstructions. It also facilitates the
analysis of Type II when non-Gaussian likelihood models lead to intractable
integrations.


  Building on recent work by Chandar et al. (2020), we construct X-ray
luminosity functions (XLFs) for different classes of X-ray binary (XRB) donors
in the nearby star-forming galaxy M83 through a novel methodology: rather than
classifying low- vs. high-mass XRBs based on the scaling of the number of X-ray
sources with stellar mass and star formation rate, respectively, we utilize
multi-band Hubble Space Telescope imaging data to classify each
Chandra-detected compact X-ray source as a low-mass (i.e. donor mass <~ 3 solar
masses), high-mass (donor mass >~ 8 solar masses) or intermediate-mass XRB
based on either the location of its candidate counterpart on optical
color-magnitude diagrams or the age of its host star cluster. In addition to
the the standard (single and/or truncated) power-law functional shape, we
approximate the resulting XLFs with a Schechter function. We identify a
marginally significant (at the 1-to-2 sigma level) exponential downturn for the
high-mass XRB XLF, at logLx ~ 38.48^{+0.52}_{-0.33} (in log CGS units). In
contrast, the low- and intermediate-mass XRB XLFs, as well as the total XLF of
M83, are formally consistent with sampling statistics from a single power-law.
Our method suggests a non-negligible contribution from low- and possibly
intermediate-mass XRBs to the total XRB XLF of M83, i.e. between 20 and 50%, in
broad agreement with X-ray based XLFs. More generally, we caution against
considerable contamination from X-ray emitting supernova remnants to the
published, X-ray based XLFs of M83, and possibly all actively star-forming
galaxies.


  The evaluation stack for minimal logarithmic stable maps is constructed,
parameterizing families of standard log points in the target log scheme. This
construction provides the ingredients necessary to define appropriate
evaluation maps for minimal log stable maps and establish the logarithmic
Gromov-Witten theory of a log-smooth Deligne- Faltings log scheme.


  We study the phase diagram of quark matter at finite temperature (T) and
chemical potential (mu) in the strong coupling region of lattice QCD for color
SU(3). Baryon has effects to extend the hadron phase to a larger mu direction
relative to Tc at low temperatures in the strong coupling limit. With the 1/g^2
corrections, Tc is found to decrease rapidly as g decreases, and the shape of
the phase diagram becomes closer to that expected in the real world.


  The interplay between light and matter attracts tremendous interest for
exploring novel topological quantum states and their phase transitions. Here we
show by first-principles calculations and the Floquet theorem that a carbon
allotrope bct-C$_{16}$, a typical nodal-line semimetal, exhibits exotic
photoinduced Floquet mixed-Weyl semimetallic features. Under the irradiation of
a linearly polarized light, bct-C$_{16}$ undergos a topological phase
transition from a Driac nodal-line semimetal to a Weyl semimetal with two pairs
of tunable Weyl points. With increasing the light intensity, left-handed Weyl
points evolve from type-I into type-II while right-handed ones are always
preserved to be type-I, giving rise to photo-dressed unconventional Weyl pairs
composed of distinct types of Weyl points. Importantly, a special Weyl pair
formed by type-I and type-III Weyl points is present at the boundary between
type-I and type-II states. The Floquet Fermi arcs connecting the projections of
two different types of Weyl points are clearly visible, further revealing their
unique topological features. Our work not only realizes promising
unconventional Weyl pairs but also paves a reliable avenue for investigating
light-induced topological phase transitions.


  In this article, we analyse the pole-skipping phenomena of finite temperature
Yang-Mills theory with quark flavors which is dual to D3-D7 brane system in the
bulk. We also consider the external electric field in the boundary field theory
which is dual to the world volume electric field on the D7 brane. We will work
in the probe limit where the D7 branes do not back-react to the D3 brane
background. In this scenario, we decode the characteristic parameters of the
chaos namely, Lyapunov exponent $\lambda_{L}$ and butterfly velocity $v_b$ from
the pole-skipping points by performing the near-horizon analysis of the
linearised Einstein equations. Unlike pure Yang-Mills, once charged quarks with
a background electric field are added into the system, the characteristic
parameters of the chaos show non-trivial dependence on the quark mass and
external electric field. We have observed that $\lambda_{L}$ and $v_b$
decreases with increasing electric field. This suggests the existence of a
critical electric field at which chaos parameters vanish. In the presence of a
strong electric field, therefore, the dual system of Yang-Mills gauge fields
along with dissociated quarks shows non-chaotic behaviour. We further perform
the pole-skipping analysis for the gauge invariant sound, shear, and tensor
modes of the perturbation in the bulk and discuss their physical importance in
the holographic context.


  Hybrid magnonics has recently attracted intensive attentions as a promising
platform for coherent information processing. In spite of its rapid
development, on-demand control over the interaction of magnons with other
information carriers, in particular microwave photons in electromagnonic
systems, has been long missing, significantly limiting the broad applications
of hybrid magnonics. Here, we show that by introducing Floquet engineering into
cavity electromagnonics, coherent control on the magnon-microwave photon
coupling can be realized. Leveraging the periodic temporal modulation from a
Floquet drive, our first-of-its-kind Floquet cavity electromagnonic system can
manipulate the interaction between hybridized cavity electromagnonic modes on
demand. Moreover, we demonstrate a new coupling regime in such systems: the
Floquet ultrastrong coupling, where the Floquet splitting is comparable with or
even larger than the level spacing of the two interacting modes, resulting in
the breakdown of the rotating wave approximation. Our findings open up new
directions for magnon-based coherent signal processing.


  Mendelian randomization is the use of genetic variants to make causal
inferences from observational data. The field is currently undergoing a
revolution fuelled by increasing numbers of genetic variants demonstrated to be
associated with exposures in genome-wide association studies, and the public
availability of summarized data on genetic associations with exposures and
outcomes from large consortia. A Mendelian randomization analysis with many
genetic variants can be performed relatively simply using summarized data.
However, a causal interpretation is only assured if each genetic variant
satisfies the assumptions of an instrumental variable. To provide some
protection against failure of these assumptions, robust methods for
instrumental variable analysis have been proposed. Here, we develop three
extensions to instrumental variable methods using: i) robust regression, ii)
the penalization of weights from candidate instruments with heterogeneous
causal estimates, and iii) L1 penalization. Results from a wide variety of
robust methods, including the recently-proposed MR-Egger and median-based
methods, are compared in an extensive simulation study. We demonstrate that two
methods, robust regression in an inverse-variance weighted method and a simple
median of the causal estimates from the individual variants, have considerably
improved Type 1 error rates compared with conventional methods in a wide
variety of scenarios when up to 30% of the genetic variants are invalid
instruments. While the MR-Egger method gives unbiased estimates when its
assumptions are satisfied, these estimates are less efficient than those from
other methods and are highly sensitive to violations of the assumptions.
Methods that make different assumptions should be used routinely to assess the
robustness of findings from applied Mendelian randomization investigations with
multiple genetic variants.


  Traffic prediction is a spatiotemporal predictive task that plays an
essential role in intelligent transportation systems. Today, graph
convolutional neural networks (GCNNs) have become the prevailing models in the
traffic prediction literature since they excel at extracting spatial
correlations. In this work, we classify the components of successful GCNN
prediction models and analyze the effects of matrix factorization, attention
mechanism, and weight sharing on their performance. Furthermore, we compare
these variations against random forests, a traditional regression method that
predates GCNNs by over 15 years. We evaluated these methods using simulated
data of two regions in Toronto as well as real-world sensor data from selected
California highways. We found that incorporating matrix factorization,
attention, and location-specific model weights either individually or
collectively into GCNNs can result in a better overall performance. Moreover,
although random forest regression is a less compact model, it matches or
exceeds the performance of all variations of GCNNs in our experiments. This
suggests that the current graph convolutional methods may not be the best
approach to traffic prediction and there is still room for improvement.
Finally, our findings also suggest that for future research on GCNN for traffic
prediction to be credible, researchers must include performance comparison to
random forests.


  A dedicated in situ heating setup in a scanning electron microscope (SEM)
followed by an ex situ atomic force microscopy (AFM) and electron backscatter
diffraction (EBSD) is used to characterize the nucleation and early growth
stages of Fe-Al intermetallics (IMs) at 596 {\deg}C. A location tracking is
used to interpret further characterization. Ex situ AFM observations reveal a
slight shrinkage and out of plane protrusion of the IM at the onset of IM
nucleation followed by directional growth. The formed interfacial IM compounds
were identified by ex situ EBSD. It is now clearly demonstrated that the
{\theta}-phase nucleates first prior to the diffusion-controlled growth of the
{\eta}-phase. The {\theta}-phase prevails the intermetallic layer.


  Spin fluctuation in LiV2O4 is revisited by examining the earlier result of
muon spin rotation/ relaxation measurements. Instead of a relationship for the
localized electron limit, one for itinerant electron systems between muon
depolarization rate and spin fluctuation rate ({\nu}_D) is employed to
re-analyze data, which reveals that {\nu}D varies linearly with temperature
({\nu}_D {\propto} T) over a range 10^8-10^{12} /s for 0.02 {\le} T < 102 K.
Such a linear-T behavior as well as the magnitude of {\nu}_D is fully
consistent with that of the magnetic relaxation rate previously observed by
inelastic neutron scattering (INS), demonstrating that {\mu}SR and INS have a
common time window over the fluctuation spectrum. The linear-T dependence of
{\nu}_D is understood as a specific feature predicted by a Hubbard model for
intersecting one-dimensional (1D) chains. This quasi-1D character, which is
co-existent with enhanced uniform susceptibility at low temperatures, supports
the scenario of 1D-to-3D crossover for the microscopic origin of heavy-fermion
behavior in LiV2O4.


  In contrast to most microscopic theories of superconductivity based on the
reciprocal space, the Bogoliubov-de Gennes (BdG) formalism provides a
real-space alternative for addressing inhomogeneous systems. In this article,
we study the superconducting states in correlated single-walled carbon
nanotubes (SWNTs) with curvature and spin-orbit corrections, as well as the
inter-tube interaction through a connecting molecule using an attractive
Hubbard model. The results reveal a close relationship between the on-site
superconducting gap and the single-electron local density of states. For the
limiting case of independent large-diameter nanotubes, the BdG equations can be
reduced to the standard Bardeen-Cooper-Schrieffer one with analytical
solutions. Moreover, an optimal separation between nanotubes is found, which
leads to a maximal superconducting critical temperature. This finding has a
remarkable accordance with the experimental data obtained from Buckypapers
built of boron doped SWNTs under external pressure.


  Hundreds of substellar companions to solar-type stars will be discovered with
the Kepler satellite. Kepler's extreme photometric precision gives access to
low-amplitude stellar variability contributed by a variety of physical
processes. We discuss in detail the periodic flux modulations arising from the
tidal force on the star due to a substellar companion. An analytic expression
for the variability is derived in the equilibrium-tide approximation. We
demonstrate analytically and through numerical solutions of the linear,
nonadiabatic stellar oscillation equations that the equilibrium-tide formula
works extremely well for stars of mass <1.4 Msun with thick surface convection
zones. More massive stars with largely radiative envelopes do not conform to
the equilibrium-tide approximation and can exhibit flux variations $\ga$10
times larger than naive estimates. Over the full range of stellar masses
considered, we treat the oscillatory response of the convection zone by
adapting a prescription that A. J. Brickhill developed for pulsating white
dwarfs. Compared to other sources of periodic variability, the ellipsoidal
lightcurve has a distinct dependence on time and system parameters. We suggest
that ellipsoidal oscillations induced by giant planets may be detectable from
as many as ~100 of the 10^5 Kepler target stars. (Abridged)


  We assume that we observe $N$ independent copies of a diffusion process on a
time interval $[0,2T]$. For a given time $t$, we estimate the transition
density $p_t(x,y)$, namely the conditional density of $X_{t + s}$ given $X_s =
x$, under conditions on the diffusion coefficients ensuring that this quantity
exists. We use a least squares projection method on a product of finite
dimensional spaces, prove risk bounds for the estimator and propose an
anisotropic model selection method, relying on several reference norms. A
simulation study illustrates the theoretical part for Ornstein-Uhlenbeck or
square-root (Cox-Ingersoll-Ross) processes.


  Looking back at seven decades of highly extensive application in the
semiconductor industry, silicon and its native oxide SiO$_2$ are still at the
heart of several technological developments. Recently, the fabrication of
ultra-thin oxide layers has become essential for keeping up with trends in
down-scaling of nanoelectronic devices and for the realization of novel device
technologies. With this comes a need for better understanding of the atomic
configuration at the Si/SiO$_2$ interface. Classical force fields offer
flexible application and relatively low computational costs, however, suffer
from limited accuracy. Ab-initio methods give much better results but are
extremely costly. Machine learning force fields (MLFF) offer the possibility to
combine the benefits of both worlds. We train a MLFF for the simulation of the
dry thermal oxidation process of a Si substrate. The training data is generated
by density functional theory calculations. The obtained structures are in line
with ab-initio simulations as well as with experimental observations. Compared
to a classical force field, the most recent reactive force field (reaxFF), the
resulting configurations are vastly improved.


  Hierarchical data representations in the context of classi cation and data
clustering were put forward during the fties. Recently, hierarchical image
representations have gained renewed interest for segmentation purposes. In this
paper, we briefly survey fundamental results on hierarchical clustering and
then detail recent paradigms developed for the hierarchical representation of
images in the framework of mathematical morphology: constrained connectivity
and ultrametric watersheds. Constrained connectivity can be viewed as a way to
constrain an initial hierarchy in such a way that a set of desired constraints
are satis ed. The framework of ultrametric watersheds provides a generic scheme
for computing any hierarchical connected clustering, in particular when such a
hierarchy is constrained. The suitability of this framework for solving
practical problems is illustrated with applications in remote sensing.


  Until recently, the most accurate methods for time series classification were
limited by high computational complexity. ROCKET achieves state-of-the-art
accuracy with a fraction of the computational expense of most existing methods
by transforming input time series using random convolutional kernels, and using
the transformed features to train a linear classifier. We reformulate ROCKET
into a new method, MINIROCKET, making it up to 75 times faster on larger
datasets, and making it almost deterministic (and optionally, with additional
computational expense, fully deterministic), while maintaining essentially the
same accuracy. Using this method, it is possible to train and test a classifier
on all of 109 datasets from the UCR archive to state-of-the-art accuracy in
less than 10 minutes. MINIROCKET is significantly faster than any other method
of comparable accuracy (including ROCKET), and significantly more accurate than
any other method of even roughly-similar computational expense. As such, we
suggest that MINIROCKET should now be considered and used as the default
variant of ROCKET.


  A simple model for the solidification of globular grains in metallic alloys
is presented. Based on the Voronoi diagram of the nuclei centers, it accounts
for the curvature of the grains near triple junctions. The predictions of this
model are close to those of more refined approaches such as the phase field
method, but with a computation cost decreased by several orders of magnitude.
Therefore, this model is ideally suited for granular simulations linking the
behavior of individual grains to macroscopic properties of the material.


  In a complex system, perturbations propagate by following paths on the
network of interactions among the system's units. In contrast to what happens
with the spreading of epidemics, observations of general perturbations are
often very sparse in time (there is a single observation of the perturbed
system) and in "space" (only a few perturbed and unperturbed units are
observed). A major challenge in many areas, from biology to the social
sciences, is to infer the propagation paths from observations of the effects of
perturbation under these sparsity conditions. We address this problem and show
that it is possible to go beyond the usual approach of using the shortest paths
connecting the known perturbed nodes. Specifically, we show that a simple and
general probabilistic model, which we solved using belief propagation, provides
fast and accurate estimates of the probabilities of nodes being perturbed.


  We have measured the surface acoustic wave velocity shift in a GaAs/AlGaAs
heterostructure containing a two-dimensional electron system (2DES) in a
low-density regime ($<$ $10^{10}$ cm$^{-2}$) at zero magnetic field. The
interaction of the surface acoustic wave with the 2DES is not well described by
a simple model using low-frequency conductivity measurements. We speculate that
this conflict is a result of inhomogeneities in the 2DES which become very
important at low density. This has implications for the putative
metal-insulator transition in two dimensions.


  Extensive first principles calculations are carried out to investigate
gold-promoted TiO2(110) surfaces in terms of structure optimizations,
electronic structure analyses, ab initio thermodynamics calculations of surface
phase diagrams, and ab initio molecular dynamics simulations. All computations
rely on density functional theory in the generalized gradient approximation
(PBE) and account for on-site Coulomb interactions via inclusion of a Hubbard
correction, PBE+U, where U is computed from linear response theory. This
approach is validated by investigating the interaction between TiO2(110)
surfaces and typical probe species (H, H2O, CO). Relaxed structures and binding
energies are compared to both data from the literature and plain PBE results.
The main focus of the study is on the properties of gold-promoted titania
surfaces and their interactions with CO. Both PBE+U and PBE optimized
structures of Au adatoms adsorbed on stoichiometric and reduced TiO2 surfaces
are computed, along with their electronic structure. The charge rearrangement
induced by the adsorbates at the metal/oxide contact are also analyzed and
discussed. By performing PBE+U ab initio molecular dynamics simulations, it is
demonstrated that the diffusion of Au adatoms on the stoichiometric surface is
highly anisotropic. The metal atoms migrate either along the top of the
bridging oxygen rows, or around the area between these rows, from one bridging
position to the next along the [001] direction. Approximate ab initio
thermodynamics predicts that under O-rich conditions, structures obtained by
substituting a Ti5c atom with an Au atom are thermodynamically stable over a
wide range of temperatures and pressures.


  New structure function results from H1 are presented. The measurements cover
a huge kinematical range for Q^2, the four momentum transfer squared, from
0.35, GeV^2 to 30,000, GeV^2, and for Bjorken x between \sim 5\cdot 10^{-6} and
0.65. At Q^2>100, GeV^2, full HERA, I data have been analyzed. The data are
compared with a new QCD analysis. The impact of the HERA, I data on the parton
density functions is discussed.


  We develop a fast and reliable method for solving large-scale optimal
transport (OT) problems at an unprecedented combination of speed and accuracy.
Built on the celebrated Douglas-Rachford splitting technique, our method
tackles the original OT problem directly instead of solving an approximate
regularized problem, as many state-of-the-art techniques do. This allows us to
provide sparse transport plans and avoid numerical issues of methods that use
entropic regularization. The algorithm has the same cost per iteration as the
popular Sinkhorn method, and each iteration can be executed efficiently, in
parallel. The proposed method enjoys an iteration complexity $O(1/\epsilon)$
compared to the best-known $O(1/\epsilon^2)$ of the Sinkhorn method. In
addition, we establish a linear convergence rate for our formulation of the OT
problem. We detail an efficient GPU implementation of the proposed method that
maintains a primal-dual stopping criterion at no extra cost. Substantial
experiments demonstrate the effectiveness of our method, both in terms of
computation times and robustness.


  We study spin-2 deformed-AKLT models on the square lattice, specifically a
two-parameter family of $O(2)$-symmetric ground-state wavefunctions as defined
by Niggemann, Kl\"umper, and Zittartz, who found previously that the phase
diagram consists of a N\'eel-ordered phase and a disordered phase which
contains the AKLT point. Using tensor-network methods, we not only confirm the
N\'eel phase but also find an XY phase with quasi-long-range order and a region
adjacent to it, within the AKLT phase, with very large correlation length, and
investigate the consequences of a perfectly factorizable point at the corner of
that phase.


  In this work, we present a subdomain discontinuous least-squares (SDLS)
scheme for neutronics problems. Least-squares (LS) methods are known to be
inaccurate for problems with sharp total-cross section interfaces. In addition,
the least-squares scheme is known not to be globally conservative in
heterogeneous problems. In problems where global conservation is important,
e.g. k-eigenvalue problems, conservative treatment must be applied. We, in this
study, proposed a SDLS method that retains global conservation. Such a method
resembles the LS formulation in each subdomain without material interface and
differs from LS in that an additional least-squares interface term appears for
each interface. Scalar flux is continuous in each subdomain with continuous
finite element method (CFEM) while discontinuous on interfaces for every pair
of contiguous subdomains. SDLS numerical results are compared with those
obtained from other numerical methods with test problems having material
interfaces. High accuracy of scalar flux in fixed-source problems and
$k_\mathrm{eff}$ in eigenvalue problems are demonstrated.


  We have constructed a nonlinear multi-graviton theory. An application of this
theory to cosmology is discussed. We found that scale factors in a solution for
this theory repeat acceleration and deceleration.


  In this article we aim to develop from first principles a theory of sum sets
and partial sum sets, which are defined analogously to difference sets and
partial difference sets. We obtain non-existence results and characterisations.
In particular, we show that any sum set must exhibit higher-order regularity
and that an abelian sum set is necessarily a reversible difference set. We next
develop several general construction techniques under the hypothesis that the
over-riding group contains a normal subgroup of order 2. Finally, by exploiting
properties of dihedral groups and Frobenius groups, several infinite classes of
sum sets and partial sum sets are introduced.


  A lattice QCD calculation of the kaon $B$ parameter $B_K$ is carried out with
the Wilson quark action in the quenched approximation at $\beta=6/g^2=5.9-6.5$.
The mixing problem of the $\Delta s=2$ four-quark operators is solved
non-perturbatively with full use of chiral Ward identities employing four
external quarks with an equal off-shell momentum in the Landau gauge. This
method, without invoking any effective theory, enables us to construct the weak
four-quark operators exhibiting good chiral behavior. Our results for $B_K$
with the non-perturbative mixing coefficients show small scaling violation
beyond the lattice cut-off $a^{-1}\sim 2.5 $GeV. Our estimate concludes
$B_K(NDR, 2 GeV)=0.69(7)$ at $a^{-1}=2.7-4.3$GeV, which agrees with the value
obtained with the Kogut-Susskind quark action. For comparison we also calculate
$B_K$ with one-loop perturbative mixing coefficients. While this yields
incorrect values at finite lattice spacing, a linear extrapolation to the
continuum limit as a function of $a$ leads to a result consistent with those
obtained with the Ward identity method.


  We refine and generalize several interpolation inequalities bounding the
$L^p$ norm of a probability density with respect to the reference measure $\mu$
by its Sobolev norm and the Kantorovich distance to $\mu$ on a smooth weighted
Riemannian manifold satisfying $CD(0, \infty)$ condition.


  Let ${\rm gp}(G)$ be the general position number of a graph $G$. It is proved
that ${\rm gp}(G-x)\leq 2{\rm gp}(G)$ holds for any vertex $x$ of a connected
graph $G$ and that if $x$ lies in some ${\rm gp}$-set of $G$, then ${\rm gp}(G)
- 1 \le {\rm gp}(G-x)$. Constructions are given which show that ${\rm gp}(G-x)$
can be much larger than ${\rm gp}(G)$ also when $G-x$ is connected. For
diameter $2$ graphs it is proved that ${\rm gp}(G-x) \le {\rm gp}(G)$, and that
${\rm gp}(G-x) \ge {\rm gp}(G) - 1$ when the diameter of $G-x$ remains $2$. It
is demonstrated that ${\rm gp}(G)/2\le {\rm gp}(G-e)\leq 2{\rm gp}(G)$ holds
for any edge $e$ of a graph $G$. For diameter $2$ graphs these results sharpens
to ${\rm gp}(G)-1\le {\rm gp}(G-e)\leq\ {\rm gp}(G) + 1$. All these bounds are
proved to be sharp.


  The irreducible characters of the symmetric group are a symmetric polynomial
in the eigenvalues of a permutation matrix. They can therefore be realized as a
symmetric function that can be evaluated at a set of variables and form a basis
of the symmetric functions. This basis of the symmetric functions is of
non-homogeneous degree and the (outer) product structure coefficients are the
stable Kronecker coefficients.
  We introduce the irreducible character basis by defining it in terms of the
induced trivial characters of the symmetric group which also form a basis of
the symmetric functions. The irreducible character basis is closely related to
character polynomials and we obtain some of the change of basis coefficients by
making this connection explicit. Other change of basis coefficients come from a
representation theoretic connection with the partition algebra, and still
others are derived by developing combinatorial expressions.
  This document is an extended abstract which can be used as a review reference
so that this basis can be implemented in Sage. A more complete version of the
results in this abstract can be found in {\tt arXiv:1605.06672}.


  Given a triple cover p: X --> Y of varieties, we produce a new variety Z and
a birational morphism f: Z --> X which is an isomorphism away from the
fat-point ramification locus of p. The variety Z has a natural interpretation
in terms of the data describing the triple cover, and the morphism f has an
elegant geometric description.


  This paper presents a study of accuracy issues in thermal modeling of high
power LED modules on system level. Both physical as well as numerical accuracy
issues are addressed. Incorrect physical assumptions may result in seemingly
correct, but erroneous results. It is therefore important to motivate the
underlying key physical assumptions of a thermal model. In this paper thermal
measurements are used to calibrate a computational fluid dynamics (CFD) model
of a high power LED module model at a reference application condition, and to
validate it at other application conditions.


  This paper investigates the impact of domain-specific model fine-tuning and
of reasoning mechanisms on the performance of question-answering (Q&A) systems
powered by large language models (LLMs) and Retrieval-Augmented Generation
(RAG). Using the FinanceBench SEC financial filings dataset, we observe that,
for RAG, combining a fine-tuned embedding model with a fine-tuned LLM achieves
better accuracy than generic models, with relatively greater gains attributable
to fine-tuned embedding models. Additionally, employing reasoning iterations on
top of RAG delivers an even bigger jump in performance, enabling the Q&A
systems to get closer to human-expert quality. We discuss the implications of
such findings, propose a structured technical design space capturing major
technical components of Q&A AI, and provide recommendations for making
high-impact technical choices for such components. We plan to follow up on this
work with actionable guides for AI teams and further investigations into the
impact of domain-specific augmentation in RAG and into agentic AI capabilities
such as advanced planning and reasoning.


  We demonstrate, both analytically and numerically, that learning dynamics of
neural networks is generically attracted towards a self-organized critical
state. The effect can be modeled with quartic interactions between
non-trainable variables (e.g. states of neurons) and trainable variables (e.g.
weight matrix). Non-trainable variables are rapidly driven towards stochastic
equilibrium and trainable variables are slowly driven towards learning
equilibrium described by a scale-invariant distribution on a wide range of
scales. Our results suggest that the scale invariance observed in many physical
and biological systems might be due to some kind of learning dynamics and
support the claim that the universe might be a neural network.


  6-DoF object-agnostic grasping in unstructured environments is a critical yet
challenging task in robotics. Most current works use non-optimized approaches
to sample grasp locations and learn spatial features without concerning the
grasping task. This paper proposes GraNet, a graph-based grasp pose generation
framework that translates a point cloud scene into multi-level graphs and
propagates features through graph neural networks. By building graphs at the
scene level, object level, and grasp point level, GraNet enhances feature
embedding at multiple scales while progressively converging to the ideal
grasping locations by learning. Our pipeline can thus characterize the spatial
distribution of grasps in cluttered scenes, leading to a higher rate of
effective grasping. Furthermore, we enhance the representation ability of
scalable graph networks by a structure-aware attention mechanism to exploit
local relations in graphs. Our method achieves state-of-the-art performance on
the large-scale GraspNet-1Billion benchmark, especially in grasping unseen
objects (+11.62 AP). The real robot experiment shows a high success rate in
grasping scattered objects, verifying the effectiveness of the proposed
approach in unstructured environments.


  We first re-prove with a more complete method that the the minimum standard
model, with the inclusion of the CKM-matrix, requires the T-odd/P-odd total
cross section of two spin-1/2 particles to vanish in all orders\cite{gxu}. Then
we study the contribution to T-odd/P-odd total scattering cross sections from
various channels within the Higgs sector, and optimize conditions for possible
experimental measurements of these effects. These studies show that such
contributions can appear at tree level, and that the spin dependent cross
section asymmetry is measurable if the lightest Higgs paarticle is not too
massive, {\it e.g.} $m_H \sim 200 GeV$, and if suitable reaction channels and
beam energies and luminosities are chosen.


  The Garvey-Kelson relations are used in an iterative process to predict
nuclear masses in the neighborhood of nuclei with measured masses. Average
errors in the predicted masses for the first three iteration shells are smaller
than those obtained with the best nuclear mass models. Their quality is
comparable with the Audi-Wapstra extrapolations, offering a simple and
reproducible procedure for short range mass predictions. A systematic study of
the way the error grows as a function of the iteration and the distance to the
known masses region, shows that a correlation exists between the error and the
residual neutron-proton interaction, produced mainly by the implicit assumption
that $V_{np}$ varies smoothly along the nuclear landscape.


  Despite their drawbacks and unintended consequences, social media networks
have recently emerged as a crucial resource for individuals in distress,
particularly during times of crisis. These platforms serve as a means to seek
assistance and support, share reliable information, and appeal for action and
solidarity. In this paper, we examine the online networks of support during the
Russia-Ukraine conflict by analyzing four major social media networks: Twitter,
Facebook, Instagram, and YouTube. Using a large dataset of 68 million posts, we
explore the temporal patterns and interconnectedness between these platforms
and online support websites. Our analysis highlights the prevalence of
crowdsourcing and crowdfunding websites as the two main support platforms to
mobilize resources and solicit donations, revealing their purpose and contents,
and investigating different support-seeking and -receiving practices. Overall,
our study underscores the potential of social media in facilitating online
support in distressed environments through grassroots mobilization,
contributing to the growing body of research on the positive impact of online
platforms in promoting social good and protecting vulnerable populations during
times of crisis and conflict.


  Using HST, we identify circumnuclear ($100$-$500$ pc scale) structures in
nine new H$_2$O megamaser host galaxies to understand the flow of matter from
kpc-scale galactic structures down to the supermassive black holes (SMBHs) at
galactic centers. We double the sample analyzed in a similar way by Greene et
al. (2013) and consider the properties of the combined sample of 18 sources. We
find that disk-like structure is virtually ubiquitous when we can resolve
$<200$ pc scales, in support of the notion that non-axisymmetries on these
scales are a necessary condition for SMBH fueling. We perform an analysis of
the orientation of our identified nuclear regions and compare it with the
orientation of megamaser disks and the kpc-scale disks of the hosts. We find
marginal evidence that the disk-like nuclear structures show increasing
misalignment from the kpc-scale host galaxy disk as the scale of the structure
decreases. In turn, we find that the orientation of both the $\sim100$ pc scale
nuclear structures and their host galaxy large-scale disks is consistent with
random with respect to the orientation of their respective megamaser disks.


  In this paper, we associate the idea of derivation languages with flat
splicing systems and compare the families of derivation languages (Szilard and
control languages) of these systems with the family of languages in Chomsky
hierarchy. We show that the family of Szilard languages of labeled flat finite
splicing systems of type $( m, n)$ (i.e., $SZLS_{n, FIN}^{m}$ ) and $REG$, $CF$
and $CS$ are incomparable. Also, it is decidable whether or not $SZ_{n,
FIN}^{m}(\mathscr{LS}) \subseteq R$ and $R \subseteq SZ_{n,
FIN}^{m}(\mathscr{LS})$ for any regular language $R$ and labeled flat finite
splicing system $\mathscr{LS}$. Also, any non-empty regular, non-empty
context-free and recursively enumerable language can be obtained as homomorphic
image of Szilard language of the labeled flat finite splicing systems of type
$(1, 2), (2, 2)$ and $(4, 2)$ respectively. We also introduce the idea of
control languages for labeled flat finite splicing systems and show that any
non-empty regular and context-free language can be obtained as a control
language of labeled flat finite splicing systems of type $(1,2)$ and $(2, 2)$
respectively. At the end, we show that any recursively enumerable language can
be obtained as a control language of labeled flat finite splicing systems of
type $(4,2)$ when $\lambda$-labeled rules are allowed.


  The geometric models for the module category and derived category of any
gentle algebra were introduced to realize the objects in module category and
derived category by permissible curves and admissible curves respectively. The
present paper firstly unifies these two realizations of objects in module
category and derived category via same surface for any gentle algebra, by the
rotation of permissible curves corresponding to the objects in the module
category. Secondly, the geometric characterization of the cohomology of
complexes over gentle algebras is established by the truncation of projective
permissible curves. It is worth mentioning that the rotation of permissible
curves and the truncation of projective permissible curves are mutually inverse
processes to some extent. As applications, an alternative proof of ``no gaps"
theorem as to cohomological length for the bounded derived categories of gentle
algebras is provided in terms of the geometric characterization of the
cohomology of complexes. Moreover, we obtain a geometric proof for the strong
Nakayama conjecture for gentle algebras. Finally, we contain two examples to
illustrate our results.


  We have searched the Forth Catalogue of Nearby Stars for halo stars and
identified 15 subdwarfs and a high velocity white dwarf in the solar
neighbourhood. This search was motivated mainly by the recent determinations of
MACHO masses of about 0.5 M_sun, which are typical for halo dwarfs. The local
mass density of the stars is 1.5 10^{-4} M_sun pc^{-3}, which is only 3% of the
current estimate of the local mass density of the MACHO population. We compare
the local density of subdwarfs with constraints set by HST observations of
distant red dwarfs. Using models of the stellar halo with density laws that
fall off like r^{-\alpha}, \alpha = 3.5 to 4, we find that the HST constraints
can only be matched, if we assume that the stellar halo is flattened with an
axial ratio of about 0.6. The non-detection of the analogues of MACHOs in the
solar neighbourhood allows to set an upper limit to the luminosity of MACHOs of
M_B > 21 magnitudes.


  Thermodynamic quantities of Coulomb plasmas consisting of point-like ions
immersed in a compressible, polarizable electron background are calculated for
ion charges Z=1 to 26 and for a wide domain of plasma parameters ranging from
the Debye-Hueckel limit to the crystallization point and from the region of
nondegenerate to fully degenerate nonrelativistic or relativistic electrons.
The calculations are based on the linear-response theory for the electron-ion
interaction, including the local-field corrections in the electronic dielectric
function. The thermodynamic quantities are calculated in the framework of the
N-body hypernetted-chain equations and fitted by analytic expressions. We
present also accurate analytic approximations for the free energy of the ideal
electron gas at arbitrary degeneracy and relativity and for the excess free
energy of the one-component plasma of ions (OCP) derived from Monte Carlo
simulations.
  The extension to multi-ionic mixtures is discussed within the framework of
the linear mixing rule. These formulae provide a completely analytic, accurate
description of the thermodynamic quantities of fully ionized electron-ion
Coulomb plasmas, a useful tool for various applications from liquid state
theory to dense stellar matter.


  The spontaneous emission rate (SER) is an important figure of merit for any
quantum bit (qubit), as it can play a significant role in the control and
decoherence of the qubit. As a result, accurately characterizing the SER for
practical devices is an important step in the design of quantum information
processing devices. Here, we specifically focus on the experimentally popular
platform of a transmon qubit, which is a kind of superconducting circuit qubit.
Despite the importance of understanding the SER of these qubits, it is often
determined using approximate circuit models or is inferred from measurements on
a fabricated device. To improve the accuracy of predictions in the design
process, it is better to use full-wave numerical methods that can make a
minimal number of approximations in the description of practical systems. In
this work, we show how this can be done with a recently developed field-based
description of transmon qubits coupled to an electromagnetic environment. We
validate our model by computing the SER for devices similar to those found in
the literature that have been well-characterized experimentally. We further
cross-validate our results by comparing them to simplified lumped element
circuit and transmission line models as appropriate.


  In this paper we present a gauge-invariant formalism for perturbations of the
brane-world model developed by the author, A. Ishibashi and O. Seto recently,
and analyze the behavior of cosmological perturbations in a spatially flat
expanding universe realized as a boundary 3-brane in AdS$^5$ in terms of this
formalism. For simplicity we restrict arguments to scalar perturbations. We
show that the behavior of cosmological perturbations on superhorizon scales in
the brane-world model is the same as that in the standard no-extradimension
model, irrespective of the initial condition for bulk perturbations, in the
late stage when the cosmic expansion rate $H$ is smaller than the inverse of
the bulk curvature scale $\ell$. Further, we give rough estimates which
indicate that in the early universe when $H$ is much larger than $1/\ell$,
perturbations in these two models behave quite differently, and the
conservation of the Bardeen parameter does not hold for superhorizon
perturbations in the brane-world model.


  The exact analytical formulas for the transverse momentum distributions of
the Bose-Einstein, Fermi-Dirac and Maxwell-Boltzmann statistics of particles
with nonzero mass in the framework of the Tsallis normalized and Tsallis
unnormalized (also known as Tsallis-1 and Tsallis-2) statistics have been
consistently derived. The final exact results were expressed in terms of the
series expansions in the integral representation. The zeroth term approximation
to both quantum and classical statistics of particles has been introduced. We
have revealed that the phenomenological classical Tsallis distribution (widely
used in high energy physics) is equal to the distribution of the Tsallis
unnormalized statistics in the zeroth term approximation, but the
phenomenological quantum Tsallis distributions (introduced by definition on the
basis of the generalized entropy of the ideal gas) do not correspond to the
distributions of the Tsallis statistics. We have found that in the ranges of
the entropic parameter relevant to the processes of high-energy physics ($q<1$
for Tsallis-1 and $q>1$ for Tsallis-2) the Tsallis statistics is divergent.
Therefore, to obtain physical results, we have regularized the Tsallis
statistics by introducing an upper cut-off in the series expansion. The exact
numerical results for the Bose-Einstein, Fermi-Dirac and Maxwell-Boltzmann
statistics of particles in the Tsallis normalized and unnormalized statistics
have been obtained. We observed that the exact results of the Tsallis
statistics strongly enhanced the production of high-$p_{T}$ hadrons in
comparison with the usual phenomenological Tsallis distribution function at the
same values of $q$. The $q$-duality of the Tsallis normalized and unnormalized
statistics for the massive particles was studied.


  Sensor noise sources cause differences in the signal recorded across pixels
in a single image and across multiple images. This paper presents a Bayesian
approach to decomposing and characterizing the sensor noise sources involved in
imaging with digital cameras. A Bayesian probabilistic model based on the
(theoretical) model for noise sources in image sensing is fitted to a set of a
time-series of images with different reflectance and wavelengths under
controlled lighting conditions. The image sensing model is a complex model,
with several interacting components dependent on reflectance and wavelength.
The properties of the Bayesian approach of defining conditional dependencies
among parameters in a fully probabilistic model, propagating all sources of
uncertainty in inference, makes the Bayesian modeling framework more attractive
and powerful than classical methods for approaching the image sensing model. A
feasible correspondence of noise parameters to their expected theoretical
behaviors and well calibrated posterior predictive distributions with a small
root mean square error for model predictions have been achieved in this study,
thus showing that the proposed model accurately approximates the image sensing
model. The Bayesian approach could be extended to formulate further components
aimed at identifying even more specific parameters of the imaging process.


  Novel classes of dynamical systems are introduced, including many-body
problems characterized by nonlinear equations of motion of Newtonian type
("acceleration equals forces") which determine the motion of points in the
complex plane. These models are solvable, namely their configuration at any
time can be obtained from the initial data by algebraic operations, amounting
to the determination of the zeros of a known time-dependent polynomial in the
independent variable z. Some of these models are multiply periodic, isochronous
or asymptotically isochronous; others display scattering phenomena.


  Recently a duality between a family of \mathcal{N}=2 supersymmetric higher
spin theories on AdS3, and the 't Hooft like limit of a class of Kazama-Suzuki
models (that are parametrised by N and k) was proposed. The higher spin
theories can be described by a Chern-Simons theory based on the
infinite-dimensional Lie algebra shs[\mu], and under the duality, \mu is to be
identified with \lambda=N/(N+k+1). Here we elucidate the structure of the
(quantum) asymptotic symmetry algebra sW_{\infty}[\mu] for arbitrary \mu and
central charge c. In particular, we show that for each value of the central
charge, there are generically four different values of \mu that describe the
same sW_{\infty} algebra. Among other things this proves that the quantum
symmetries on both sides of the duality agree; this equivalence does not just
hold in the 't Hooft limit, but even at finite N and k.


  By using photon pairs created in parametric down conversion, we report on an
experiment, which demonstrates that measurement can recover the quantum
entanglement of two qubit system in a pure dephasing environment. The
concurrence of the final state with and without measurement are compared and
analyzed. Furthermore, we verify that recovered states can still violate Bell's
inequality, that is, to say, such recovered states exhibit nonlocality. In the
context of quantum entanglement, sudden death and rebirth provide clear
evidence, which verifies that entanglement dynamics of the system is sensitive
not only to its environment, but also on its initial state.


  We consider a nearly collisionless plasma consisting of a species of `test
particles' in 1D-1V, stirred by an externally imposed stochastic electric
field. The mean effect on the particle distribution function is stochastic
heating. Accompanying this heating is the generation of fine-scale structure in
the distribution function, which we characterize with the collisionless
(Casimir) invariant $C_2 \propto \iint dx dv \, \langle f^2 \rangle$. We find
that $C_2$ is transferred from large scales to small scales in both position
and velocity space via a phase-space cascade enabled by both particle streaming
and nonlinear interactions between particles and the stochastic electric field.
We compute the steady-state fluxes and spectrum of $C_2$ in Fourier space, with
$k$ and $s$ denoting spatial and velocity wavenumbers, respectively. Whereas
even the linear phase mixing alone would lead to a constant flux of $C_2$ to
high $s$ (towards the collisional dissipation range) at every $k$, the
nonlinearity accelerates this cascade by intertwining velocity and position
space so that the flux of $C_2$ is to both high $k$ and high $s$
simultaneously. Integrating over velocity (spatial) wavenumbers, the $k$-space
($s$-space) flux of $C_2$ is constant down to a dissipation length (velocity)
scale that tends to zero as the collision frequency does, even though the rate
of collisional dissipation remains finite. The resulting spectrum in the
inertial range is a self-similar function in the $(k,s)$ plane, with power-law
asymptotics at large $k$ and $s$. We argue that stochastic heating is made
irreversible by this entropy cascade and that, while collisional dissipation
accessed via phase mixing occurs only at small spatial scales rather than at
every scale as it would in a linear system, the cascade makes phase mixing even
more effective overall in the nonlinear regime than in the linear one.


  Analysis of the radio-metric data from Pioneer 10 and 11 spacecrafts has
indicated the presence of an unmodeled acceleration starting at 20 AU, which
has become known as the Pioneer anomaly. The nature of this acceleration is
uncertain. In this paper we give a description of the effect and review some
relevant mechanisms proposed to explain the observed anomaly. We also discuss
on some future projects to investigate this phenomenon.


  Carlsson's construction is a simplicial group whose geometric realization is
the loop space of the 1-stunted reduced Borel construction. Our main results
are: i) Given a pointed simplicial set acted upon by the discrete cyclic group
C_2 of order 2, if the orbit projection has a section, then this loop space has
a mod 2 homology decomposition; ii) If the reduced diagonal map of the
C_2-invariant set is homologous to zero, then the pinched sets in the above
homology decomposition themselves have homology decompositions in terms of the
C_2-invariant set and the orbit space. Result i) generalizes a previous
homology decomposition of the second author for trivial actions. To illustrate
these two results, we completely compute the mod 2 Betti numbers for an
example.


  X-ray reflection off dense molecular clouds in the Galactic Centre region has
established itself as a powerful probe for the past activity record of the
supermassive black hole Sgr A* on a timescale of a few hundred years. Detailed
studies of the reflection signal from individual clouds allow us to estimate
parameters of the brightest flare(s) and explore properties of the dense gas
distribution inside and around them. On the other hand, wide area surveys
covering the full Central Molecular Zone (CMZ) are crucial to spot brightening
of the new clouds and long-term decay of the flux from the once bright ones.
Here we present and discuss the data obtained by the SRG/eROSITA telescope in
the course of its commissioning observations in late 2019 in regard to the
X-ray reflection off CMZ clouds located to the East of Sgr A*. We measure the
hard X-ray (4-8 keV) flux from the currently brightest (in reflected emission)
molecular complex Sgr A and derive upper limits for other molecular complexes.
We confirm that the Sgr A complex keeps being bright at the level of
$4\times10^{-13}\, {\rm erg\,s^{-1}\,cm^{-2}\,arcmin^{-2}}$, making it an
excellent candidate for the deep forthcoming high resolution imaging and
polarimetric observations. We also discuss distinct features of the reflected
emission from the clouds for which the primary illumination front has already
passed away and the signal is dominated by multiply scattered radiation.


  We analyze constraints on the anomalous $Wtb$ couplings from $B$-physics
experiments, performing a correlated analysis and allowing all anomalous
couplings to differ simultaneously from their Standard-Model (SM) values. The
$B$-physics observables allow one to probe three linear combinations out of the
four anomalous couplings, which parameterize the $Wtb$ vertex under the
assumption that the SM symmetries remain the symmetries of the effective
theory. The constraints in this work are obtained by taking into account the
following $B$-physics observables: the $\bar B^0-B^0$ oscillations, the
leptonic $B\to \mu^+\mu^-$ decays, the inclusive radiative $B\to X_s\gamma$
decays, and the differential branching fractions in the semileptonic inclusive
$B\to X_s \mu^+ \mu^-$ and exclusive $B\to (K,K^*)\mu^+\mu^-$ decays at small
$q^2$, $q$ the momentum of the $\mu^+\mu^-$ pair. We find that the SM values of
the anomalous couplings belong to the 95\% CL allowed region obtained this way
but lie beyond the 68\% allowed region. We also report that the distributions
of the anomalous couplings obtained within our scenario differ from the results
of the 1D scenario, when only one of the couplings is allowed to deviate from
its SM value.


  Here we demonstrate that a term in the nuclear spin Hamiltonian, the
antisymmetric \textit{J}-coupling, is fundamentally connected to molecular
chirality. We propose and simulate a nuclear magnetic resonance (NMR)
experiment to observe this interaction and differentiate between enantiomers
without adding any additional chiral agent to the sample. The antisymmetric
\textit{J}-coupling may be observed in the presence of molecular orientation by
an external electric field. The opposite parity of the antisymmetric coupling
tensor and the molecular electric dipole moment yields a sign change of the
observed coupling between enantiomers. We show how this sign change influences
the phase of the NMR spectrum and may be used to discriminate between
enantiomers.


  High-bandwidth, fiber-based optical cavities are a promising building block
for future quantum networks. They are used to resonantly couple stationary
qubits such as single or multiple atoms with photons routing quantum
information into a fiber network at high rates. In high-bandwidth cavities,
standard fluorescence imaging on the atom-cavity resonance line for controlling
atom positions is impaired since the Purcell effect strongly suppresses
all-directional fluorescence. Here, we restore imaging of $^{87}$Rb atoms
strongly coupled to such a fiber Fabry-P\'erot cavity by detecting the repumper
fluorescence which is generated by continuous and three-dimensional Raman
sideband cooling. We have carried out a detailed spectroscopic investigation of
the repumper-induced differential light shifts affecting the Raman resonance,
dependent on intensity and detuning. Our analysis identifies a compromise
regime between imaging signal-to-noise ratio and survival rate, where physical
insight into the role of dipole-force fluctuations in the heating dynamics of
trapped atoms is gained.


  We have analysed high-precision photometry of 77 double-mode RR Lyrae (RRd)
stars observed by NASA's Kepler-K2 mission during Campaigns 1-18. Among those
stars, we have identified several low-period ratio variables, belonging either
to a subclass of "anomalous" RRd stars (Soszy\'nski et al., 2016) or to a
separate shorter-period subclass recently identified by Prudil et al. (2017). A
non-radial mode with the period ratio of $P_{\rm x}/P_{\rm 1} \sim 0.615$ has
been detected in most RRd stars of our sample. In majority of these variables,
at least one subharmonic of the non-radial frequency is also present. Our
findings indicate that excitation of the non-radial mode is a common property
of the RRd stars. The same non-radial pulsation is also commonly detected in
the RRc stars. We show that properties of this puzzling mode are essentially
the same in both groups of RR Lyrae-type pulsators.


  The well known Boole-Shannon expansion of Boolean functions in several
variables (with co-efficients in a Boolean algebra $B$) is also known in more
general form in terms of expansion in a set $\Phi$ of orthonormal functions.
However, unlike the one variable step of this expansion an analogous
elimination theorem and consistency is not well known. This article proves such
an elimination theorem for a special class of Boolean functions denoted
$B(\Phi)$. When the orthonormal set $\Phi$ is of polynomial size in number $n$
of variables, the consistency of a Boolean equation $f=0$ can be determined in
polynomial number of $B$-operations. A characterization of $B(\Phi)$ is also
shown and an elimination based procedure for computing consistency of Boolean
equations is proposed.


  We report on existence and properties of discrete gap solitons in zigzag
arrays of alternating waveguides with positive and negative refractive indices.
Zigzag quasi-one-dimensional configuration of waveguide array introduces strong
next-to-nearest neighbor interaction in addition to nearest-neighbor coupling.
Effective diffraction can be controlled both in size and in sign by the value
of the next-to-nearest neighbor coupling coefficient and even can be cancelled.
In the regime where instabilities occur, we found different families of
discrete solitons bifurcating from gap edges of the linear spectrum. We show
that both staggered and unstaggered discrete solitons can become highly
localized states near the zero diffraction points even for low powers.
Stability analysis has shown that found soliton solutions are stable over a
wide range of parameters and can exist in focusing, defocusing and even in
alternating focusing-defocusing array.


  We present a detailed analysis of the specific star formation rate -- stellar
mass ($\mathrm{sSFR}-M_*$) of $z\le 0.13$ disk central galaxies using a
morphologically selected mass-complete sample ($M_* \ge 10^{9.5} M_{\odot}$).
Considering samples of grouped and ungrouped galaxies, we find the
$\mathrm{sSFR}-M_*$ relations of disk-dominated central galaxies to have no
detectable dependence on host dark-matter halo (DMH) mass, even where
weak-lensing measurements indicate a difference in halo mass of a factor
$\gtrsim5$. We further detect a gradual evolution of the $\mathrm{sSFR}-M_*$
relation of non-grouped (field) central disk galaxies with redshift, even over
a $\Delta z \approx 0.04$ ($\approx5\cdot10^{8}\mathrm{yr}$) interval, while
the scatter remains constant. This evolution is consistent with extrapolation
of the "main-sequence-of-star-forming-galaxies" from previous literature that
uses larger redshift baselines and coarser sampling.
  Taken together, our results present new constraints on the paradigm under
which the SFR of galaxies is determined by a self-regulated balance between gas
inflows and outflows, and consumption of gas by star-formation in disks, with
the inflow being determined by the product of the cosmological accretion rate
and a fuelling-efficiency -- $\dot{M}_{\mathrm{b,halo}}\zeta$. In particular,
maintaining the paradigm requires $\dot{M}_{\mathrm{b,halo}}\zeta$ to be
independent of the mass $M_{\mathrm{halo}}$ of the host DMH. Furthermore, it
requires the fuelling-efficiency $\zeta$ to have a strong redshift dependence
($\propto (1+z)^{2.7}$ for $M_*=10^{10.3} M_{\odot}$ over $z=0 - 0.13$), even
though no morphological transformation to spheroids can be invoked to explain
this in our disk-dominated sample. The physical mechanisms capable of giving
rise to such dependencies of $\zeta$ on $M_{\mathrm{halo}}$ and $z$ for disks
are unclear.


  Abstractive text summarization is one of the areas influenced by the
emergence of pre-trained language models. Current pre-training works in
abstractive summarization give more points to the summaries with more words in
common with the main text and pay less attention to the semantic similarity
between generated sentences and the original document. We propose ARMAN, a
Transformer-based encoder-decoder model pre-trained with three novel objectives
to address this issue. In ARMAN, salient sentences from a document are selected
according to a modified semantic score to be masked and form a pseudo summary.
To summarize more accurately and similar to human writing patterns, we applied
modified sentence reordering. We evaluated our proposed models on six
downstream Persian summarization tasks. Experimental results show that our
proposed model achieves state-of-the-art performance on all six summarization
tasks measured by ROUGE and BERTScore. Our models also outperform prior works
in textual entailment, question paraphrasing, and multiple choice question
answering. Finally, we established a human evaluation and show that using the
semantic score significantly improves summarization results.


  We studied the frequency and quality factor of mechanical plasmonic
nanoresonators as a function of temperature, ranging from ambient to 4 K. Our
investigation focused on individual gold nanorods and nanodisks of various
sizes. We observed that oscillation frequencies increase linearly as
temperature decreases until saturation is reached at cryogenic temperatures.
This behavior is explained by the temperature dependence of the elastic
modulus, with a Debye temperature compatible with reported bulk values for
gold. To describe the behavior of the quality factor, we developed a model
considering the nanostructures as anelastic solids, identifying a dissipation
peak around 150 K due to a thermally activated process, likely of the
Niblett-Wilks mechanism type. Importantly, our findings suggest that external
dissipation factors are more critical to improving quality factors than
internal friction, which can be increased by modifying the nanoresonator's
environment. Our results enable the design of structures with high vibration
frequencies and quality factors by effectively controlling external losses.


  Efficient retrieval of information is of key importance when using Big Data
systems. In large scale-out data architectures, data are distributed and
replicated across several machines. Queries/tasks to such data architectures,
are sent to a router which determines the machines containing the requested
data. Ideally, to reduce the overall cost of analytics, the smallest set of
machines required to satisfy the query should be returned by the router.
Mathematically, this can be modeled as the set cover problem, which is NP-hard,
thus making the routing process a balance between optimality and performance.
Even though an efficient greedy approximation algorithm for routing a single
query exists, there is currently no better method for processing multiple
queries than running the greedy set cover algorithm repeatedly for each query.
This method is impractical for Big Data systems and the state-of-the-art
techniques route a query to all machines and choose as a cover the machines
that respond fastest. In this paper, we propose an efficient technique to
speedup the routing of a large number of real-time queries while minimizing the
number of machines that each query touches (query span). We demonstrate that by
analyzing the correlation between known queries and performing query
clustering, we can reduce the set cover computation time, thereby significantly
speeding up routing of unknown queries. Experiments show that our incremental
set cover-based routing is 2.5 times faster and can return on average 50% fewer
machines per query when compared to repeated greedy set cover and baseline
routing techniques.


  As part of the Hubble Deep Field South program, a set of shorter 2-orbit
observations were obtained of the area adjacent to the deep fields. The WFPC2
flanking fields cover a contiguous solid angle of 48 square arcminutes.
Parallel observations with the STIS and NICMOS instruments produce a patchwork
of additional fields with optical and near-infrared (1.6 micron) response.
Deeper parallel exposures with WFPC2 and NICMOS were obtained when STIS
observed the NICMOS deep field. These deeper fields are offset from the rest,
and an extended low surface brightness object is visible in the deeper WFPC2
flanking field. In this data paper, which serves as an archival record of the
project, we discuss the observations and data reduction, and present SExtractor
source catalogs and number counts derived from the data. Number counts are
broadly consistent with previous surveys from both ground and space. Among
other things, these flanking field observations are useful for defining slit
masks for spectroscopic follow-up over a wider area around the deep fields, for
studying large-scale structure that extends beyond the deep fields, for future
supernova searches, and for number counts and morphological studies, but their
ultimate utility will be defined by the astronomical community.


  Recognition of objects with subtle differences has been used in many
practical applications, such as car model recognition and maritime vessel
identification. For discrimination of the objects in fine-grained detail, we
focus on deep embedding learning by using a multi-task learning framework, in
which the hierarchical labels (coarse and fine labels) of the samples are
utilized both for classification and a quadruplet-based loss function. In order
to improve the recognition strength of the learned features, we present a novel
feature selection method specifically designed for four training samples of a
quadruplet. By experiments, it is observed that the selection of very hard
negative samples with relatively easy positive ones from the same coarse and
fine classes significantly increases some performance metrics in a fine-grained
dataset when compared to selecting the quadruplet samples randomly. The feature
embedding learned by the proposed method achieves favorable performance against
its state-of-the-art counterparts.


  Teaching to improve student models (e.g., knowledge distillation) is an
extensively studied methodology in LLMs. However, for humans, teaching not only
improves students but also improves teachers. We ask: Can LLMs also learn by
teaching (LbT)? If yes, we can potentially unlock the possibility of
continuously advancing the models without solely relying on human-produced data
or stronger models. In this paper, we provide a preliminary exploration of this
ambitious agenda. We show that LbT ideas can be incorporated into existing LLM
training/prompting pipelines and provide noticeable improvements. Specifically,
we design three methods, each mimicking one of the three levels of LbT in
humans: observing students' feedback, learning from the feedback, and learning
iteratively, with the goals of improving answer accuracy without training and
improving models' inherent capability with fine-tuning. The findings are
encouraging. For example, similar to LbT in human, we see that: (1) LbT can
induce weak-to-strong generalization: strong models can improve themselves by
teaching other weak models; (2) Diversity in students might help: teaching
multiple students could be better than teaching one student or the teacher
itself. We hope that this early promise can inspire future research on LbT and
more broadly adopting the advanced techniques in education to improve LLMs. The
code is available at https://github.com/imagination-research/lbt.