  Computer Aided Control System Design (CACSD) allows to analyze complex
interconnected systems and design controllers achieving challenging control
requirements. We extend CACSD to systems with time delays and illustrate the
functionality of Control System Toolbox in MATLAB for such systems. We easily
define systems in time and frequency domain system representations and build
the overall complex system by interconnecting subsystems. We analyze the
overall system in time and frequency domains and design PID controllers
satisfying design requirements. Various visualization tools are used for
analysis and design verification. Our goal is to introduce these
functionalities to researchers and engineers and to discuss the open directions
in computer algorithms for control system design.


  The propagation of extremely energetic nu_tau's through the Earth is studied
by means of a detailed Monte Carlo simulation. All major mechanisms of nu_tau
interactions and tau energy loss as well as all its relevant decay modes are
properly taken into account. The detection possibility of nu_tau's emerging
from Earth in forthcoming neutrino telescopes is addressed


  During software maintenance and evolution, developers need to deal with a
large number of change requests by modifying existing code or adding code into
the system. An efficient tackling of change request calls for an accurate
localising of software changes, i.e. identifying which code are problematic and
where new files should be added for any type of change request at hand, such as
a bug report or a feature request. Existing automatic techniques for this
change localisation problem are limited in two aspects: on the one hand, they
are only limited to tackle a specific type of change request; on the other
hand, they are focused on finding files that should be modified for a change
request, yet barely capable of recommending what files or packages might be
newly created. To address the limitations, we are inspired to propose a
generalised change localisation approach to identify the to-be-modified files
(mostly for bugs), and at the same time point out where new files or packages
should be created (mostly for new feature requests) for an arbitrary type of
change request. In order to tackle the key challenge of predicting
to-be-created program elements, our proposed SeekChanges approach leverages the
hierarchical package structure for Java projects, and model the change
localisation problem as a structured information retrieval (IR) task. A
systematic investigation of three structured IR strategies is carried out for
scoring and ranking both the files that should be modified and the software
packages in which the new files should be created to address change requests.
Extensive experiments on four open source Java projects from the Apache
Software Foundation demonstrate that structured IR strategies have a good
performance on recommending newly created files, while the overall performance
of localising change requests is equally satisfactory.


  The formulae for correlators K2 and K3 of a given particle observable (e.g.
energy or transverse momentum) accounting for the track reconstruction
efficiency are presented. Similar to the case of an ideal detector, the
correlators can be expressed through the event-by-event fluctuation of the
observable single event mean and the observable pseudo-central moments.
However, on the contrary to the ideal case, this splitting does not allow for a
substantial reduction of the computation time.


  (Abridged) We use N-body simulations to study the evolution of dwarf
spheroidal galaxies (dSphs) driven by galactic tides. We adopt a
cosmologically-motivated model where dSphs are approximated by a King model
embedded within an NFW halo. We find that these NFW-embedded King models are
extraordinarily resilient to tides; the stellar density profile still resembles
a King model even after losing more than 99% of the stars. As tides strip the
galaxy, the stellar luminosity, velocity dispersion, central surface
brightness, and core radius decrease monotonically. Remarkably, we find that
the evolution of these parameters is solely controlled by the total amount of
mass lost from within the luminous radius. Of all parameters, the core radius
is the least affected: after losing 99% of the stars, R_c decreases by just a
factor of ~2. Interestingly, tides tend to make dSphs more dark-matter
dominated because the tightly bound central dark matter ``cusp'' is more
resilient to disruption than the ``cored'' King profile. We examine whether the
extremely large M/L ratios of the newly-discovered ultra-faint dSphs might have
been caused by tidal stripping of once brighter systems. Although dSph tidal
evolutionary tracks parallel the observed scaling relations in the
luminosity-radius plane, they predict too steep a change in velocity dispersion
compared with the observational estimates hitherto reported in the literature.
The ultra-faint dwarfs are thus unlikely to be the tidal remnants of systems
like Fornax, Draco, or Sagittarius. Despite spanning four decades in
luminosity, dSphs appear to inhabit halos of comparable peak circular velocity,
lending support to scenarios that envision dwarf spheroidals as able to form
only in halos above a certain mass threshold.


  We present an extended analytic formula for the calculation of the
temperature profile along a bondwire embedded in a package. The resulting
closed formula is built by coupling the heat transfer equations of the bondwire
and the surrounding moulding compound by means of auxiliary variables that stem
from an \emph{ad-hoc} linearisation and mediate the wire-mould thermal
interaction. The model, which corrects typical simplifications in previously
introduced analytic models, is also optimised against carefully taken
experimental samples representing fusing events of bondwires within real
packages.


  Massive compact systems at 0.2<z<0.6 are the missing link between the
predominantly compact population of massive quiescent galaxies at high redshift
and their analogs and relics in the local volume. The evolution in number
density of these extreme objects over cosmic time is the crucial constraining
factor for the models of massive galaxy assembly. We select a large sample of
~200 intermediate-redshift massive compacts from the BOSS spectroscopic dataset
by identifying point-like SDSS photometric sources with spectroscopic
signatures of evolved redshifted galaxies. A subset of our targets have
publicly available high-resolution ground-based images that we use to augment
the dynamical and stellar population properties of these systems by their
structural parameters. We confirm that all BOSS compact candidates are as
compact as their high-redshift massive counterparts and less than half the size
of similarly massive systems at z~0. We use the completeness-corrected numbers
of BOSS compacts to compute lower limits on their number densities in narrow
redshift bins spanning the range of our sample. The abundance of extremely
dense quiescent galaxies at 0.2<z<0.6 is in excellent agreement with the number
densities of these systems at high redshift. Our lower limits support the
models of massive galaxy assembly through a series of minor mergers over the
redshift range 0<z<2.


  We use the results of CMS and ATLAS searches for resonances that decay to
$\tau\nu$ or $tb$ and $\tau^+\tau^-$ or $t\bar t$ final states to constrain the
parameters of non-universal $W^\prime$ and $Z^\prime$ gauge bosons that couple
preferentially to the third generation. For the former we consider production
from $c\bar{b}$ annihilation and find very weak constraints on the strength of
the interaction and only for the mass range between 800 and 1100 GeV from the
$pp \to \tau_h p_T^{\rm miss}$ channel. The constraints on the latter are much
stronger and arise from both $t\bar t$ and $\tau^+\tau^-$ production. Treated
separately, we find that the weak constraints on the $W^\prime$ still permit an
explanation of the $R(D^{(\star)})$ anomalies with a light sterile neutrino
whereas the stronger constraints on the $Z^\prime$ exclude significant light
sterile neutrino contributions to the $K \to \pi \nu \bar\nu$ rates. Within
specific models the masses of $W^\prime$ and $Z^\prime$ are of course related
and we briefly discuss the consequences.


  We summarize three recent efforts to constrain the first few moments of
cosmic creation before and during the epoch of inflation. We consider two means
to explain a slight dip in the power spectrum of the cosmic microwave
background for multipoles in the range of $\ell= 10-30$ from both the {\it
Planck} and {\it WMAP} data. We show that such a dip could be the result of
resonant creation of a massive particle that couples to the inflaton field. For
best-fit models, the epoch of resonant particle creation reenters the horizon
at wave numbers of $k_* \sim 0.00011 \pm 0.0004 $ ($h$ Mpc$^{-1}$). The
amplitude and location of these features correspond to the creation of a number
of degenerate fermion species of mass $\sim 15/\lambda^{3/2} $ $m_{pl}$ during
inflation where $\lambda$ is the coupling constant between the inflaton field
and the created fermion species. Alternatively, one can explain the existence
of such a dip as due to a jump in the inflation generating potential. We show
that such a jump can also resolve the excessively large dark flow predicted
from the M-theory landscape. Finally, we summarize our efforts to quantify
constraints on the cosmic dark flow from a new analysis of the Type Ia
supernova distance-redshift relation.


  Let $S$ be a semigroup and $X$ a Banach space. The functional equation $\phi
(xyz)+ \phi (x) + \phi (y) + \phi (z) = \phi (xy) + \phi (yz) + \phi (xz)$ is
said to be stable for the pair $(X, S)$ if and only if $f: S\to X$ satisfying
$\| f(xyz)+f(x) + f(y) + f(z) - f(xy)- f(yz)-f(xz)\| \leq \delta $ for some
positive real number $\delta$ and all $x, y, z \in S$, there is a solution
$\phi : S \to X$ such that $f-\phi$ is bounded. In this paper, among others, we
prove the following results: 1) this functional equation, in general, is not
stable on an arbitrary semigroup; 2) this equation is stable on periodic
semigroups; 3) this equation is stable on abelian semigroups; 4) any semigroup
with left (or right) law of reduction can be embedded into a semigroup with
left (or right) law of reduction where this equation is stable.


  We present some variants of the Kaplansky condition for a K-Hermite ring $R$
to be an elementary divisor ring; for example, a commutative K-Hermite ring $R$
is an EDR iff for any elements $x,y,z\in R$ such that $(x,y)=(1)$, there exists
an element $\lambda\in R$ such that $x+\lambda y=uv$, where
$(u,z)=(v,1-z)=(1)$.
  We present an example of a a B\'ezout domain that is an elementary divisor
ring, but it does not have almost stable range 1, thus answering a question of
Warren Wm. McGovern.


  Large language models (LLMs) have shown great capabilities in various tasks
but also exhibited memorization of training data, raising tremendous privacy
and copyright concerns. While prior works have studied memorization during
pre-training, the exploration of memorization during fine-tuning is rather
limited. Compared to pre-training, fine-tuning typically involves more
sensitive data and diverse objectives, thus may bring distinct privacy risks
and unique memorization behaviors. In this work, we conduct the first
comprehensive analysis to explore language models' (LMs) memorization during
fine-tuning across tasks. Our studies with open-sourced and our own fine-tuned
LMs across various tasks indicate that memorization presents a strong disparity
among different fine-tuning tasks. We provide an intuitive explanation of this
task disparity via sparse coding theory and unveil a strong correlation between
memorization and attention score distribution.


  Graph clustering is a central topic in unsupervised learning with a multitude
of practical applications. In recent years, multi-view graph clustering has
gained a lot of attention for its applicability to real-world instances where
one has access to multiple data sources. In this paper we formalize a new
family of models, called \textit{multi-view stochastic block models} that
captures this setting.
  For this model, we first study efficient algorithms that naively work on the
union of multiple graphs. Then, we introduce a new efficient algorithm that
provably outperforms previous approaches by analyzing the structure of each
graph separately. Furthermore, we complement our results with an
information-theoretic lower bound studying the limits of what can be done in
this model. Finally, we corroborate our results with experimental evaluations.


  Model interpretability methods are often used to explain NLP model decisions
on tasks such as text classification, where the output space is relatively
small. However, when applied to language generation, where the output space
often consists of tens of thousands of tokens, these methods are unable to
provide informative explanations. Language models must consider various
features to predict a token, such as its part of speech, number, tense, or
semantics. Existing explanation methods conflate evidence for all these
features into a single explanation, which is less interpretable for human
understanding.
  To disentangle the different decisions in language modeling, we focus on
explaining language models contrastively: we look for salient input tokens that
explain why the model predicted one token instead of another. We demonstrate
that contrastive explanations are quantifiably better than non-contrastive
explanations in verifying major grammatical phenomena, and that they
significantly improve contrastive model simulatability for human observers. We
also identify groups of contrastive decisions where the model uses similar
evidence, and we are able to characterize what input tokens models use during
various language generation decisions.


  We review the basic theory of the parton pseudodistributions approach and its
applications to lattice extractions of parton distribution functions. The
crucial idea of the approach is the realization that the correlator $M(z,p)$ of
the parton fields is a function ${\cal M} (\nu, -z^2)$ of Lorentz invariants
$\nu =-(zp)$, the Ioffe time, and the invariant interval $z^2$. This
observation allows to extract the Ioffe-time distribution ${\cal M} (\nu,
-z^2)$ from Euclidean separations $z$ accessible on the lattice. Another basic
feature is the use of the ratio ${\mathfrak M} (\nu,-z^2) \equiv {\cal M} (\nu,
-z^2)/{\cal M} (0, -z^2)$, that allows to eliminate artificial ultraviolet
divergence generated by the gauge link for space-like intervals. The remaining
$z^2$-dependence of the reduced Ioffe-time distribution ${\mathfrak M}
(\nu,-z^2) $ corresponds to perturbative evolution, and can be converted into
the scale-dependence of parton distributions $f(x,\mu^2)$ using matching
relations. The $\nu$-dependence of ${\mathfrak M} (\nu,-z^2) $ governs the
$x$-dependence of parton densities $f(x,\mu^2)$. The perturbative evolution was
successfully observed in exploratory quenched lattice calculation. The analysis
of its precise data provides a framework for extraction of parton densities
using the pseudodistributions approach. It was used in the recently performed
calculations of the nucleon and pion valence quark distributions. We also
discuss matching conditions for the pion distribution amplitude and generalized
parton distributions, the lattice studies of which are now in progress.


  In a remarkably insightful pair of papers recently, Sica demonstrated that:
dichotomic data taken in any experiment that violates Bell's inequalities
``cannot represent any data streams that could possibly exist or be imagined''
if it is to be consistent with the derivation of the inequalities.\cite{sica}
The present writer maintains, however, that corrections in the formulation of
Bell's analysis loosen restrictions imposed by Bell inequalities. Moreover, it
is argued that the resolution proposed by Sica for the conflict arising from
the fact that real data does violate Bell inequalities, namely that the
functional form of the correlations considered by Bell must be amended, is
untenable on physical grounds. Finally, an alternate resolution is proposed.


  Alternative scenarios to the Big Bang singularity have been subject of
intense research for several decades by now. Most popular in this sense have
been frameworks were such singularity is replaced by a bounce around some
minimal cosmological volume or by some early quantum phase. This latter
scenario was devised a long time ago and referred as an "emergent universe" (in
the sense that our universe emerged from a constant volume quantum phase). We
show here that within an improved framework of canonical quantum gravity (the
so called Quantum Reduced Loop Gravity) the Friedmann equations for cosmology
are modified in such a way to replace the big bang singularity with a short
bounce {preceded by a metastable quantum phase in which the volume of the
universe oscillates between a series of local maxima and minima}. We call this
hybrid scenario an "emergent-bouncing universe" since after a pure oscillating
quantum phase the classical Friedmann spacetime emerges. Perspective
developments and possible tests of this scenario are discussed in the end.


  Miniaturized heat engines constitutes a fascinating field of current
research. They are being studied theoretically as well as experimentally, with
experiments involving colloidal particles and harmonic traps and even bacterial
baths acting like thermal baths. They are interesting to study because usual
equilibrium thermodynamic notions can not be applied directly to these systems.
These systems are micron sized or even smaller and they are subjected to laud
thermal fluctuations. Thus one needs to study the behavior of such systems in
terms of these fluctuations. Average thermodynamic quantities like work done,
heat exchanged, efficiency loose meaning unless otherwise supported by their
full probability distributions. Earlier studies on micro-engines are concerned
with applying Carnot or Stirling engine protocols to miniaturized systems,
where system undergoes typical two isothermal and two adiabatic changes. Unlike
these models we for the first time, study a prototype system of two classical
Ising spins driven by time dependent, phase different, external magnetic
fields. These spins are {\it simultaneously} in contact with two heat
reservoirs at different temperatures for the full duration of the driving
protocol. Performance of the model as an engine or a refrigerator depends only
on a single parameter namely the phase between two external drivings. We study
this system in terms of fluctuations in efficiency and coefficient of
performance (COP). We also find full distributions of these quantities
numerically and also study the tails of these distributions. We also study
reliability of this engine. We find the fluctuations dominate mean values of
efficiency and COP and their probability distributions are broad with power law
tails.


  We present nested sequent systems for propositional G\"odel-Dummett logic and
its first-order extensions with non-constant and constant domains, built atop
nested calculi for intuitionistic logics. To obtain nested systems for these
G\"odel-Dummett logics, we introduce a new structural rule, called the
"linearity rule," which (bottom-up) operates by linearizing branching structure
in a given nested sequent. In addition, an interesting feature of our calculi
is the inclusion of reachability rules, which are special logical rules that
operate by propagating data and/or checking if data exists along certain paths
within a nested sequent. Such rules require us to generalize our nested
sequents to include signatures (i.e. finite collections of variables) in the
first-order cases, thus giving rise to a generalization of the usual nested
sequent formalism. Our calculi exhibit favorable properties, admitting the
height-preserving invertibility of every logical rule and the
(height-preserving) admissibility of a large collection of structural and
reachability rules. We prove all of our systems sound and cut-free complete,
and show that syntactic cut-elimination obtains for the intuitionistic systems.
We conclude the paper by discussing possible extensions and modifications,
putting forth an array of structural rules that could be used to provide a
sizable class of intermediate logics with cut-free nested sequent systems.


  We study, from the point of view of CR geometry, the orbits M of a real form
G of a complex semisimple Lie group G in a complex flag manifold G/Q. In
particular we characterize those that are of finite type and satisfy some Levi
nondegeneracy conditions. These properties are also graphically described by
attaching to them some cross-marked diagrams that generalize those for minimal
orbits that we introduced in a previous paper. By constructing canonical
fibrations over real flag manifolds, with simply connected complex fibers, we
are also able to compute their fundamental group.


  Given the Riemann, or the Weyl, or a generalized curvature tensor K, a
symmetric tensor $b_{ij}$ is named `compatible' with the curvature tensor if
$b_i{}^m K_{jklm} + b_j{}^m K_{kilm} + b_k{}^m K_{ijlm} = 0$. Amongst showing
known and new properties, we prove that they form a special Jordan algebra,
i.e. the symmetrized product of K-compatible tensors is K-compatible.


  In this paper we present an exhaustive description of the basic types of
CNTFETs. In particular we review two models, already proposed by us, which
allow an easy implementation in circuit simulators, both in analog and in
digital applications.


  Aleatoric uncertainty is an intrinsic property of ill-posed inverse and
imaging problems. Its quantification is vital for assessing the reliability of
relevant point estimates. In this paper, we propose an efficient framework for
quantifying aleatoric uncertainty for deep residual learning and showcase its
significant potential on image restoration. In the framework, we divide the
conditional probability modeling for the residual variable into a deterministic
homo-dimensional level, a stochastic low-dimensional level and a merging level.
The low-dimensionality is especially suitable for sparse correlation between
image pixels, enables efficient sampling for high dimensional problems and acts
as a regularizer for the distribution. Preliminary numerical experiments show
that the proposed method can give not only state-of-the-art point estimates of
image restoration but also useful associated uncertainty information.


  We define in the space of n by m matrices of rank n, n less or equal than m,
the condition Riemannian structure as follows: For a given matrix A the tangent
space of A is equipped with the Hermitian inner product obtained by multiplying
the usual Frobenius inner product by the inverse of the square of the smallest
singular value of A denoted sigma_n(A). When this smallest singular value has
multiplicity 1, the function A -> log (sigma_n(A)^(-2)) is a convex function
with respect to the condition Riemannian structure that is t -> log
(sigma_n(A(t))^(-2)) is convex, in the usual sense for any geodesic A(t). In a
more abstract setting, a function alpha defined on a Riemannian manifold
(M,<,>) is said to be self-convex when log alpha (gamma(t)) is convex for any
geodesic in (M,<,>). Necessary and sufficient conditions for self-convexity are
given when alpha is C^2. When alpha(x) = d(x,N)^(-2) where d(x,N) is the
distance from x to a C^2 submanifold N of R^j we prove that alpha is
self-convex when restricted to the largest open set of points x where there is
a unique closest point in N to x. We also show, using this more general notion,
that the square of the condition number ||A|||_F / sigma_n(A) is self-convex in
projective space and the solution variety.


  The paper presents a new algorithm for the image inpainting problem. The
algorithm is using a recently designed versatile library of quasi-analytic
complex-valued wavelet packets (qWPs) which originate from polynomial splines
of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs
oriented in multiple directions. For example, a set of the fourth-level qWPs
comprises 62 different directions. The properties of the presented qWPs such as
refined frequency resolution, directionality of waveforms with unlimited number
of orientations, (anti-)symmetry of waveforms and windowed oscillating
structure of waveforms with a variety of frequencies, make them efficient in
image processing applications, in particular, in dealing with the inpainting
problem addressed in the paper. The obtained results for this problem are quite
competitive with the best state-of-the-art algorithms. The inpainting is
implemented by an iterative scheme, which, in essence, is the Split Bregman
Iteration (SBI) procedure supplied with an adaptive variable soft thresholding
based on the Bivariate Shrinkage algorithm. In the inpainting experiments,
performance comparison between the qWP-based methods and the state-of-the-art
algorithms is presented.


  We have obtained ground-based I, J and K band images of the spiral galaxy,
Messier 74 (NGC 628). This galaxy has been shown to possess a circumnuclear
ring of star formation from both near-infrared spectroscopy of CO absorption
and sub-millimetre imaging of CO emission. Circumnuclear rings of star
formation are believed to exist only as a result of a bar potential. In this
paper we show evidence for a weak oval distortion in the centre of M74. We use
the results of Combes & Gerin (1985) to suggest that this weak oval potential
is responsible for the circumnuclear ring of star formation observed in M74.


  We present an extension of Staruszkiewicz's modification of the
Schr\"{o}dinger equation which preserves its main and unique feature: in the
natural system of units the modification terms do not contain any dimensional
constants. The extension, similarly as the original, is formulated in a
three-dimensional space and derives from a Galilean invariant Lagrangian. It is
pointed out that this model of nonlinearity violates the separability of
compound systems in the fundamentalist approach to this issue. In its general
form, this modification does not admit stationary states for all potentials for
which such states exist in linear quantum mechanics. This is, however, possible
for a suitable choice of its free parameters. It is only in the original
Staruszkiewicz modification that the energy of these states remains unchanged,
which marks the uniqueness of this variant of the modification.


  We consider the problem of estimating the probability of a large loss from a
financial portfolio, where the future loss is expressed as a conditional
expectation. Since the conditional expectation is intractable in most cases,
one may resort to nested simulation. To reduce the complexity of nested
simulation, we present a method that combines multilevel Monte Carlo (MLMC) and
quasi-Monte Carlo (QMC). In the outer simulation, we use Monte Carlo to
generate financial scenarios. In the inner simulation, we use QMC to estimate
the portfolio loss in each scenario. We prove that using QMC can accelerate the
convergence rates in both the crude nested simulation and the multilevel nested
simulation. Under certain conditions, the complexity of MLMC can be reduced to
$O(\epsilon^{-2}(\log \epsilon)^2)$ by incorporating QMC. On the other hand, we
find that MLMC encounters catastrophic coupling problem due to the existence of
indicator functions. To remedy this, we propose a smoothed MLMC method which
uses logistic sigmoid functions to approximate indicator functions. Numerical
results show that the optimal complexity $O(\epsilon^{-2})$ is almost attained
when using QMC methods in both MLMC and smoothed MLMC, even in moderate high
dimensions.


  Interpretability in machine learning is critical for the safe deployment of
learned policies across legally-regulated and safety-critical domains. While
gradient-based approaches in reinforcement learning have achieved tremendous
success in learning policies for continuous control problems such as robotics
and autonomous driving, the lack of interpretability is a fundamental barrier
to adoption. We propose Interpretable Continuous Control Trees (ICCTs), a
tree-based model that can be optimized via modern, gradient-based,
reinforcement learning approaches to produce high-performing, interpretable
policies. The key to our approach is a procedure for allowing direct
optimization in a sparse decision-tree-like representation. We validate ICCTs
against baselines across six domains, showing that ICCTs are capable of
learning policies that parity or outperform baselines by up to 33% in
autonomous driving scenarios while achieving a 300x-600x reduction in the
number of parameters against deep learning baselines. We prove that ICCTs can
serve as universal function approximators and display analytically that ICCTs
can be verified in linear time. Furthermore, we deploy ICCTs in two realistic
driving domains, based on interstate Highway-94 and 280 in the US. Finally, we
verify ICCT's utility with end-users and find that ICCTs are rated easier to
simulate, quicker to validate, and more interpretable than neural networks.


  In this note we consider weighted $(PLB)$-spaces of ultradifferentiable
functions defined via a weight function and a weight system, as introduced in
our previous work [4]. We provide a complete characterization of when these
spaces are ultrabornological and barrelled in terms of the defining weight
system, thereby improving the main Theorem 5.1 of [4]. In particular, we obtain
that the multiplier space of the Gelfand-Shilov space
$\Sigma^{r}_{s}(\mathbb{R}^{d})$ of Beurling type is ultrabornological, whereas
the one of the Gelfand-Shilov space $\mathcal{S}^{r}_{s}(\mathbb{R}^{d})$ of
Roumieu type is not barrelled.


  Existence of planets is binaries with relatively small separations (around 20
AU), such as \alpha Centauri or \gamma Cephei poses severe challenges to
standard planet formation theories. The problem lies in the vigorous secular
excitation of planetesimal eccentricities at separations of several AU, where
some of the planets are found, by the massive, eccentric stellar companions.
High relative velocities of planetesimals preclude their growth in mutual
collisions for a wide range of sizes, from below 1 km up to several hundred km,
resulting in fragmentation barrier to planet formation. Here we show that rapid
apsidal precession of planetesimal orbits, caused by the gravity of the
circumstellar protoplanetary disk, acts to strongly reduce eccentricity
excitation, lowering planetesimal velocities by an order of magnitude or even
more at 1 AU. By examining the details of planetesimal dynamics we demonstrate
that this effect eliminates fragmentation barrier for in-situ growth of
planetesimals as small as < 10 km even at separations as wide as 2.6 AU
(semi-major axis of the giant planet in HD 196885), provided that the
circumstellar protoplanetary disk is relatively massive, ~0.1 M_Sun.


  Dual quaternions can represent rigid body motion in 3D spaces, and have found
wide applications in robotics, 3D motion modelling and control, and computer
graphics. In this paper, we introduce three different right linear independency
for a set of dual quaternion vectors, and study some related basic properties
for the set of dual quaternion vectors and dual quaternion matrices. We present
a minimax principle for right eigenvalues of dual quaternion Hermitian
matrices. Based upon a newly established Cauchy-Schwarz inequality for dual
quaternion vectors and singular value decomposition of dual quaternion
matrices, we propose an important inequality for singular values of dual
quaternion matrices. We finally introduce the concept of generalized inverse of
dual quaternion matrices, and present the necessary and sufficient conditions
for a dual quaternion matrix to be one of four types of generalized inverses of
another dual quaternion matrix.


  It is well-known that exploiting label correlations is crucially important to
multi-label learning. Most of the existing approaches take label correlations
as prior knowledge, which may not correctly characterize the real relationships
among labels. Besides, label correlations are normally used to regularize the
hypothesis space, while the final predictions are not explicitly correlated. In
this paper, we suggest that for each individual label, the final prediction
involves the collaboration between its own prediction and the predictions of
other labels. Based on this assumption, we first propose a novel method to
learn the label correlations via sparse reconstruction in the label space.
Then, by seamlessly integrating the learned label correlations into model
training, we propose a novel multi-label learning approach that aims to
explicitly account for the correlated predictions of labels while training the
desired model simultaneously. Extensive experimental results show that our
approach outperforms the state-of-the-art counterparts.


  We perform spatio-temporal analysis of public sentiment using geotagged photo
collections. We develop a deep learning-based classifier that predicts the
emotion conveyed by an image. This allows us to associate sentiment with place.
We perform spatial hotspot detection and show that different emotions have
distinct spatial distributions that match expectations. We also perform
temporal analysis using the capture time of the photos. Our spatio-temporal
hotspot detection correctly identifies emerging concentrations of specific
emotions and year-by-year analyses of select locations show there are strong
temporal correlations between the predicted emotions and known events.


  The equivariant Gromov--Hausdorff convergence of metric spaces is studied.
Where all isometry groups under consideration are compact Lie, it is shown that
an upper bound on the dimension of the group guarantees that the convergence is
by Lie homomorphisms. Additional lower bounds on curvature and volume
strengthen this result to convergence by monomorphisms, so that symmetries can
only increase on passing to the limit.


  Studies of the phase diagram of the coupled sine circle map lattice have
identified the presence of two distinct universality classes of spatiotemporal
intermittency viz. spatiotemporal intermittency of the directed percolation
class with a complete set of directed percolation exponents, and spatial
intermittency which does not belong to this class. We show that these two types
of behavior are special cases of a spreading regime where each site can infect
its neighbors permitting an initial disturbance to spread, and a non-spreading
regime where no infection is possible, with the two regimes being separated by
a line, the infection line. The coupled map lattice can be mapped on to an
equivalent cellular automaton which shows a transition from a probabilistic
cellular automaton to a deterministic cellular automaton at the infection line.
The origins of the spreading-non-spreading transition in the coupled map
lattice, as well as the probabilistic to deterministic transition in the
cellular automaton lie in a dynamical phenomenon, an attractor-widening crisis
at the infection line. Indications of unstable dimension variability are seen
in the neighborhood of the infection line. This may provide useful pointers to
the spreading behavior seen in other extended systems.


  We calculate renormalised vacuum expectation values of electromagnetic
stress-energy tensor in the static spherically-symmetrical wormhole topology.
We find that for metric tensor sufficiently slow varied with distance violation
of the averaged weak energy condition takes place irrespectively of the
detailed form of metric. This is a necessary condition for the electromagnetic
vacuum to be able to support the wormhole geometry.


  The temperature dependence of the solid-liquid interfacial free energy,
{\gamma}, is investigated for Al and Ni at the undercooled temperature regime
based on a recently developed persistent-embryo method. The atomistic
description of the nucleus shape is obtained from molecular dynamics
simulations. The computed {\gamma} shows a linear dependence on the
temperature. The values of {\gamma} extrapolated to the melting temperature
agree well with previous data obtained by the capillary fluctuation method.
Using the temperature dependence of {\gamma}, we estimate the nucleation free
energy barrier in a wide temperature range from the classical nucleation
theory. The obtained data agree very well with the results from the brute-force
molecular dynamics simulations.


  In Spring 2005, the blazar 3C454.3 underwent a dramatic outburst at all
wavelengths from mm to X-rays. This prompted INTEGRAL observations,
accomplished in 15-18 May 2005. The source was detected by the INTEGRAL
instruments from 3 to 200 keV in a bright state (~5 x 10E-10 cgs), at least a
factor of 2-3 higher than previously observed. This is one of the brightest
blazar detections achieved by INTEGRAL. During the 2.5 days of INTEGRAL
monitoring, we detected a ~20% decrease in the hard X-rays (20-40 keV),
indicating that we have sampled the decaying part of the flare. The decrease is
less apparent in the soft X-rays (5-15 keV). The simultaneous optical
variations are weakly correlated with those at soft X-rays, and not clearly
correlated with those at hard X-rays. The spectral energy distribution exhibits
two components, as typically seen in blazars, which can be modeled with
synchrotron radiation and inverse Compton scattering occurring in a region
external to the broad line region.


  Health behaviors are plagued by self-control problems, and commitment devices
are frequently proposed as a solution. We show that a simple alternative works
even better: appointments. We randomly offer HIV testing appointments and
financial commitment devices to high-risk men in Malawi. Appointments are much
more effective than financial commitment devices, more than doubling testing
rates. In contrast, most men who take up financial commitment devices lose
their investments. Appointments address procrastination without the potential
drawback of commitment failure, and also address limited memory problems.
Appointments have the potential to increase demand for healthcare in the
developing world.


  Deformation modeling of cardiac muscle is an important issue in the field of
cardiac analysis. For this reason, many approaches have been developed to best
estimate the cardiac muscle deformation, and to obtain a practical model to use
in diagnostic procedures. But there are some conditions, like in case of
myocardial infarction, in which the regular modeling approaches are not useful.
In this section, using a point-wise approach in deformation estimation, we try
to estimate the deformation under some abnormal conditions of cardiac muscle.
First, the endocardial and epicardial contour points are ordered with respect
to the center of gravity of endocardial contour and boundary point displacement
vectors are extracted. Then to solve the governing equation of deformation,
which is an elliptic equation, we apply boundary conditions in accordance with
the computed displacement vectors and then the Finite Element method (FEM) will
be used to solve the governing equation. Using obtained displacement field
through the cardiac muscle, strain map is extracted to show the mechanical
behavior of cardiac muscle. To validate the proposed algorithm in case of
infracted muscle, a non-homogeneous ring is modeled using ANSYS under a uniform
time varying internal pressure, which is the case in real cardiac muscle
deformation and then the proposed algorithm implemented in MATLAB and the
results for such problem are extracted.


  Consider a semi-regular hexagon on the triangular lattice (that is, the
lattice consisting of unit equilateral triangles, drawn so that one family of
lines is vertical). Rhombus (or lozenge) tilings of this region may be
represented in at least two very different ways: as families of
non-intersecting lattice paths; or alternatively as perfect matchings of a
certain sub-graph of the hexagonal lattice. In this article we show how the
lattice path representation of tilings may be utilised in order to calculate
the entries of the inverse Kasteleyn matrix that arises from interpreting
tilings as perfect matchings. Our main result gives precisely the inverse
Kasteleyn matrix (up to a possible change in sign) for a semi-regular hexagon
of side lengths $a,b,c,a,b,c$ (going clockwise from the south-west side). Not
only does this theorem generalise a number of known results regarding tilings
of hexagons that contain punctures, but it also provides a new formulation
through which we may attack problems in statistical physics such as Ciucu's
electrostatic conjecture.


  One implication of Bell's theorem is that there cannot in general be hidden
variable models for quantum mechanics that both are noncontextual and retain
the structure of a classical probability space. Thus, some hidden variable
programs aim to retain noncontextuality at the cost of using a generalization
of the Kolmogorov probability axioms. We generalize a theorem of Feintzeig
(2015) to show that such programs are committed to the existence of a finite
null cover for some quantum mechanical experiments, i.e., a finite collection
of probability zero events whose disjunction exhausts the space of experimental
possibilities.


  We report on spectroscopic and photometric observations of the AM CVn system
ASASSN-21br, which was discovered in outburst by the All-Sky Automated Survey
for Supernovae in February 2021. The outburst lasted for around three weeks,
and exhibited a pronounced brightness dip for $\approx$ 4 days, during which
the spectra showed a sudden transition from emission- to absorption-line
dominated. Only $\approx$ 60 AM CVn systems with derived orbital periods are
found in the Galaxy, therefore increasing the sample of AM CVn systems with
known orbital periods is of tremendous importance to (1) constrain the physical
mechanisms of their outbursts and (2) establish a better understanding of the
low-frequency background noise of future gravitational wave surveys.
Time-resolved photometry taken during the outburst of ASASSN-21br showed
modulation with a period of around 36.65 minutes, which is likely the superhump
or orbital period of the system. Time-resolved spectroscopy taken with the
Southern African Large Telescope did not show any sign of periodicity in the He
I absorption lines. This is possibly due to the origin of these lines in the
outbursting accretion disc, which makes it challenging to retrieve periodicity
from the spectral lines. Future follow up spectral observations during
quiescence might allow us better constrain the orbital period of ASASSN-21br.


  We develop an unsupervised deep learning method to solve the barrier options
under the Bergomi model. The neural networks serve as the approximate option
surfaces and are trained to satisfy the PDE as well as the boundary conditions.
Two singular terms are added to the neural networks to deal with the non-smooth
and discontinuous payoff at the strike and barrier levels so that the neural
networks can replicate the asymptotic behaviors of barrier options at short
maturities. After that, vanilla options and barrier options are priced in a
single framework. Also, neural networks are employed to deal with the high
dimensionality of the function input in the Bergomi model. Once trained, the
neural network solution yields fast and accurate option values.


  Continuing our analytic computation of the first-order self-force
contribution to Detweiler's redshift variable we provide the exact expressions
of the ninth and ninth-and-a-half post-Newtonian terms.


  We study analytically the distribution of fluctuations of the quantities
whose average yield the usual two-point correlation and linear response
functions in three unfrustrated models: the random walk, the $d$ dimensional
scalar field and the 2d XY model. In particular we consider the time dependence
of ratios between composite operators formed with these fluctuating quantities
which generalize the largely studied fluctuation-dissipation ratio, allowing us
to discuss the relevance of the effective temperature notion beyond linear
order. The behavior of fluctuations in the aforementioned solvable cases is
compared to numerical simulations of the 2d clock model with $p=6,12$ states.


  We study the possibility of standard model Higgs boson acting as an inflaton
field in the framework of Horava-Lifshitz Gravity. Under this framework, we
showed that it is possible for the Higgs field to produce right amount of
inflation and generate scale invariant power spectrum with the correct
experimental value. Thanks to the foliation preserving diffeomorphism and
anisotropic space-time scaling, it essentially helps us to construct this model
without the pre-existing inconsistency coming from cosmological and particle
physics constraints. We do not need to introduce any non-minimal or higher
derivative coupling term in an arbitrary basis either.


  In cost sharing games, the existence and efficiency of pure Nash equilibria
fundamentally depends on the method that is used to share the resources' costs.
We consider a general class of resource allocation problems in which a set of
resources is used by a heterogeneous set of selfish users. The cost of a
resource is a (non-decreasing) function of the set of its users. Under the
assumption that the costs of the resources are shared by uniform cost sharing
protocols, i.e., protocols that use only local information of the resource's
cost structure and its users to determine the cost shares, we exactly quantify
the inefficiency of the resulting pure Nash equilibria. Specifically, we show
tight bounds on prices of stability and anarchy for games with only submodular
and only supermodular cost functions, respectively, and an asymptotically tight
bound for games with arbitrary set-functions. While all our upper bounds are
attained for the well-known Shapley cost sharing protocol, our lower bounds
hold for arbitrary uniform cost sharing protocols and are even valid for games
with anonymous costs, i.e., games in which the cost of each resource only
depends on the cardinality of the set of its users.


  Configuring the antenna arrays is the main source of overhead in millimeter
wave (mmWave) communication systems. In high mobility scenarios, the problem is
exacerbated, as achieving the highest rates requires frequent link
reconfiguration. One solution is to exploit spatial congruence between signals
at different frequency bands and extract mmWave channel parameters from side
information obtained in another band. In this paper we propose the concept of
out-of-band information aided mmWave communication. We analyze different
strategies to leverage information derived from sensors or from other
communication systems operating at sub-6 GHz bands to help configure the mmWave
communication link. The overhead reductions that can be obtained when
exploiting out-of-band information are characterized in a preliminary study.
Finally, the challenges associated with using out-of-band signals as a source
of side information at mmWave are analyzed in detail.


  Define a permutation to be any sequence of distinct positive integers. Given
two permutations p and s on disjoint underlying sets, we denote by p sh s the
set of shuffles of p and s (the set of all permutations obtained by
interleaving the two permutations). A permutation statistic is a function St
whose domain is the set of permutations such that St(p) only depends on the
relative order of the elements of p. A permutation statistic is shuffle
compatible if the distribution of St on p sh s depends only on St(p) and St(s)
and their lengths rather than on the individual permutations themselves. This
notion is implicit in the work of Stanley in his theory of P-partitions. The
definition was explicitly given by Gessel and Zhuang who proved that various
permutation statistics were shuffle compatible using mainly algebraic means.
This work was continued by Grinberg. The purpose of the present article is to
use bijective techniques to give demonstrations of shuffle compatibility. In
particular, we show how a large number of permutation statistics can be shown
to be shuffle compatible using a few simple bijections. Our approach also leads
to a method for constructing such bijective proofs rather than having to treat
each one in an ad hoc manner. Finally, we are able to prove a conjecture of
Gessel and Zhuang about the shuffle compatibility of a certain statistic.


  We study the reinforcement learning problem for discounted Markov Decision
Processes (MDPs) under the tabular setting. We propose a model-based algorithm
named UCBVI-$\gamma$, which is based on the \emph{optimism in the face of
uncertainty principle} and the Bernstein-type bonus. We show that
UCBVI-$\gamma$ achieves an $\tilde{O}\big({\sqrt{SAT}}/{(1-\gamma)^{1.5}}\big)$
regret, where $S$ is the number of states, $A$ is the number of actions,
$\gamma$ is the discount factor and $T$ is the number of steps. In addition, we
construct a class of hard MDPs and show that for any algorithm, the expected
regret is at least $\tilde{\Omega}\big({\sqrt{SAT}}/{(1-\gamma)^{1.5}}\big)$.
Our upper bound matches the minimax lower bound up to logarithmic factors,
which suggests that UCBVI-$\gamma$ is nearly minimax optimal for discounted
MDPs.


  We study bisymmetric modes of angular wavenumber 2 for flat stellar disks in
potentials with smooth cores. Stars either all circulate in the same direction
or a small fraction may counter-rotate. The bisymmetric modes are unstable
unless there is a sufficiently large external halo or bulge. We find two modes
for each disk: a more central fundamental mode and a more extensive and more
spiral (trailing) secondary mode. The fundamental mode is particularly
sensitive to the orbital population in the central part of the disk. Depending
on that population, it varies from a small compact bar to a trailing spiral
that is almost as wound as the secondary mode. All modes rotate too rapidly for
there to be an inner Lindblad resonance. They transfer angular momentum from
the central to the outer regions of the disk. Most of them release
gravitational energy and convert it to kinetic energy, which also flows
outwards through the disk.


  V1432 Aquilae (=RX J1940.2-1025) is the X-ray bright, eclipsing magnetic
cataclysmic variable ~37' away from the Seyfert galaxy, NGC 6814. Due to a 0.3%
difference between the orbital (12116.3 s) and the spin (12150 s) periods, the
accretion geometry changes over the ~50 day beat period. Here we report the
results of an RXTE campaign to observe the eclipse 25 times, as well as of
archival observations with ASCA and BeppoSAX. Having confirmed that the eclipse
is indeed caused by the secondary, we use the eclipse timings and profiles to
map the accretion geometry as a function of the beat phase. We find that the
accretion region is compact, and that it moves relative to the center of white
dwarf on the beat period. The amplitude of this movement suggest a low-mass
white dwarf, in contrast to the high mass previously estimated from its X-ray
spectrum. The size of the X-ray emission region appears to be larger than in
other eclipsing magnetic CVs. We also report on the RXTE data as well as the
long-term behavior of NGC 6814, indicating flux variability by a factor of at
least 10 on time scales of years.


  We prove the higher dimensional case of the o-minimal variant of Zilber's
Restricted Trichotomy Conjecture. More precisely, let $\mathcal R$ be an
o-minimal expansion of a real closed field, let $M$ be an interpretable set in
$\mathcal R$, and let $\mathcal M=(M,...)$ be a reduct of the induced structure
on $M$. If $\mathcal M$ is strongly minimal and not locally modular, then
$\dim_{\mathcal R}(M)=2$. As an application, we prove the Zilber trichotomy for
all strongly minimal structures interpreted in the theory of compact complex
manifolds.


  Contributions of three-nucleon forces (3NF) to proton-deuteron scattering
observables at energies below the deuteron breakup threshold are studied by
solving the Faddeev equation that includes the Coulomb interaction. At E_p=3.0
MeV, we find that the central part of a two-pion exchange 3NF removes the
discrepancy between measured cross sections and the calculated ones by
two-nucleon forces, and improves the agreement with T_{22} experimental data.
However, the tensor part of the 3NF fails in reproducing data of the analyzing
power T_{21} by giving worse agreement between the measured and the calculated.
Detailed examinations of scattering amplitudes suggest that a P-wave
contribution in spin quartet tensor amplitudes has unsuitable sign for
reproducing the T_{21} data.


  Topological entanglements are abundant, and often detrimental, in polymeric
systems in biology and materials science. Here we theoretically investigate the
topological simplification of knots by diffusing slip-links (SLs), which may
represent biological or synthetic molecules, such as proteins on the genome or
cyclodextrines in slide-ring gels. We find that SLs entropically compete with
knots and can localise them, greatly facilitating their downstream
simplification by transient strand-crossing. We further show that the
efficiency of knot localisation strongly depends on the topology of the SL
network and, informed by our findings, discuss potential strategies to control
the topology of biological and synthetic materials.


  What can we learn from a connectome? We constructed a simplified model of the
first two stages of the fly visual system, the lamina and medulla. The
resulting hexagonal lattice convolutional network was trained using
backpropagation through time to perform object tracking in natural scene
videos. Networks initialized with weights from connectome reconstructions
automatically discovered well-known orientation and direction selectivity
properties in T4 neurons and their inputs, while networks initialized at random
did not. Our work is the first demonstration, that knowledge of the connectome
can enable in silico predictions of the functional properties of individual
neurons in a circuit, leading to an understanding of circuit function from
structure alone.


  This letter describes a method for estimating regions of attraction and
bounds on permissible perturbation amplitudes in nonlinear fluids systems. The
proposed approach exploits quadratic constraints between the inputs and outputs
of the nonlinearity on elliptical sets. This approach reduces conservatism and
improves estimates for regions of attraction and bounds on permissible
perturbation amplitudes over related methods that employ quadratic constraints
on spherical sets. We present and investigate two algorithms for performing the
analysis: an iterative method that refines the analysis by solving a sequence
of semi-definite programs, and another based on solving a generalized
eigenvalue problem with lower computational complexity, but at the cost of some
precision in the final solution. The proposed algorithms are demonstrated on
low-order mechanistic models of transitional flows. We further compare accuracy
and computational complexity with analysis based on sum-of-squares optimization
and direct-adjoint looping methods.


  In a recent letter Stafford, Baeriswyl and Burki have provided a simple model
for the elastic force in metallic constrictions of nanometer size. By employing
a semiclassical Weyl expansion for the density of states of transverse modes,
we show that for any smooth cross section, there is a universal contribution (4
epsilon_F)/(9 lambda_F) to the average force which is absent in the presence of
a hole.


  Despite their massive size, successful deep artificial neural networks can
exhibit a remarkably small difference between training and test performance.
Conventional wisdom attributes small generalization error either to properties
of the model family, or to the regularization techniques used during training.
  Through extensive systematic experiments, we show how these traditional
approaches fail to explain why large neural networks generalize well in
practice. Specifically, our experiments establish that state-of-the-art
convolutional networks for image classification trained with stochastic
gradient methods easily fit a random labeling of the training data. This
phenomenon is qualitatively unaffected by explicit regularization, and occurs
even if we replace the true images by completely unstructured random noise. We
corroborate these experimental findings with a theoretical construction showing
that simple depth two neural networks already have perfect finite sample
expressivity as soon as the number of parameters exceeds the number of data
points as it usually does in practice.
  We interpret our experimental findings by comparison with traditional models.


  We propose a realistic scheme to create motional entangled states of a few
bosonic atoms. It can experimentally be realized with a gas of ultra cold
bosonic atoms trapped in a deep optical lattice potential. By simultaneously
deforming and rotating the trapping potential on each lattice site it is
feasible to adiabatically create a variety of entangled states on each lattice
well. We fully address the case of N=2 and N=4 atoms per well and identify a
sequence of fractional quantum Hall states: the Pfaffian state, the
1/2-Laughlin quasiparticle and the 1/2-Laughlin state. Exact knowledge of the
spectrum has allowed us to design adiabatic paths to these states, with all
times and parameters well within the reach of current experimental setups.
  We further discuss the detection of these states by measuring different
properties as their density profile, angular momentum or correlation functions.


  We study ultra-cold neutral fermion superfluids in the presence of fictitious
magnetic fields, as well as charged fermion superfluids in the presence of real
magnetic fields. Charged fermion superfluids undergo a phase transition from
type-I to type-II superfluidity, where the magnetic properties of the
superfluid change from being a perfect diamagnet without vortices to a partial
diamagnet with the emergence of the Abrikosov vortex lattice. The transition
from type-I to type-II superfluidity is tunned by changing the scattering
parameter (interaction) for fixed density. We also find that neutral fermion
superfluids such as $^6$Li and $^{40}$K are extreme type-II superfluids, and
that they are more robust to the penetration of a fictitious magnetic field in
the BCS-BEC crossover region near unitarity, where the critical fictitious
magnetic field reaches a maximum as a function of the scattering parameter
(interaction).


  Noises, artifacts, and loss of information caused by the magnetic resonance
(MR) reconstruction may compromise the final performance of the downstream
applications. In this paper, we develop a re-weighted multi-task deep learning
method to learn prior knowledge from the existing big dataset and then utilize
them to assist simultaneous MR reconstruction and segmentation from the
under-sampled k-space data. The multi-task deep learning framework is equipped
with two network sub-modules, which are integrated and trained by our designed
iterative teacher forcing scheme (ITFS) under the dynamic re-weighted loss
constraint (DRLC). The ITFS is designed to avoid error accumulation by
injecting the fully-sampled data into the training process. The DRLC is
proposed to dynamically balance the contributions from the reconstruction and
segmentation sub-modules so as to co-prompt the multi-task accuracy. The
proposed method has been evaluated on two open datasets and one in vivo
in-house dataset and compared to six state-of-the-art methods. Results show
that the proposed method possesses encouraging capabilities for simultaneous
and accurate MR reconstruction and segmentation.


  For any closed surface $S$ of genus $g \geq 2$, we show that the deformation
space of marked hyperbolic 3-manifolds homotopy equivalent to $S$, $AH(S \times
I)$, is not locally connected. This proves a conjecture of Bromberg who
recently proved that the space of Kleinian punctured torus groups is not
locally connected. Playing an essential role in our proof is a new version of
the filling theorem that is based on the theory of cone-manifold deformations
developed by Hodgson, Kerckhoff, and Bromberg.


  The usage of channelling radiation arising from a high energy electron beam
traversing a crystal for the production of an intense low emittance muon beam
is investigated. The optimal energy and divergence of the electron beam are
computed for few crystal types based on analytical expressions of the radiation
spectrum and of the muon pair production cross section. It is shown that the
required beam properties seem within reach and that the source performance may
be comparable to other muon sources currently considered.


  In this paper, we show several vanishing type theorems for $p$-harmonic
$\ell$-forms on Riemannian manifolds ($p\geq2$). First of all, we consider
complete non-compact immersed submanifolds $M^n$ of ${N}^{n+m}$ with flat
normal bundle, we prove that any $p$-harmonic $\ell$-forms on $M$ is trivial if
$N$ has pure curvature tensor and $M$ satisfies some geometric condition. Then,
we obtain a vanishing theorem on Riemannian manifolds with weighted
Poincar\'{e} inequality. Final, we investigate complete simply connected,
locally conformally flat Riemannian manifolds $M$ and point out that there is
no nontrivial $p$-harmonic $\ell$-form on $M$ provided that
$\operatorname{Ric}$ has suitable bound.


  We investigate the efficiency at maximum power of an irreversible Carnot
engine performing finite-time cycles between two reservoirs at temperatures
$T_h$ and $T_c$ $(T_c<T_h)$, taking into account of internally dissipative
friction in two "adiabatic" processes. In the frictionless case, the
efficiencies at maximum power output are retrieved to be situated between
$\eta_{_C}/$ and $\eta_{_C}/(2-\eta_{_C})$, with $\eta_{_C}=1-T_c/{T_h}$ being
the Carnot efficiency. The strong limits of the dissipations in the hot and
cold isothermal processes lead to the result that the efficiency at maximum
power output approaches the values of $\eta_{_C}/$ and
$\eta_{_C}/(2-\eta_{_C})$, respectively. When dissipations of two isothermal
and two adiabatic processes are symmetric, respectively, the efficiency at
maximum power output is founded to be bounded between 0 and the Curzon-Ahlborn
(CA) efficiency $1-\sqrt{1-\eta{_C}}$, and the the CA efficiency is achieved in
the absence of internally dissipative friction.


  The Simple Spectral Access (SSA) Protocol (SSAP) defines a uniform interface
to remotely discover and access one dimensional spectra. SSA is a member of an
integrated family of data access interfaces altogether comprising the Data
Access Layer (DAL) of the IVOA. SSA is based on a more general data model
capable of describing most tabular spectrophotometric data, including time
series and spectral energy distributions (SEDs) as well as 1-D spectra; however
the scope of the SSA interface as specified in this document is limited to
simple 1-D spectra, including simple aggregations of 1-D spectra. The form of
the SSA interface is simple: clients first query the global resource registry
to find services of interest and then issue a data discovery query to selected
services to determine what relevant data is available from each service; the
candidate datasets available are described uniformly in a VOTable format
document which is returned in response to the query. Finally, the client may
retrieve selected datasets for analysis. Spectrum datasets returned by an SSA
spectrum service may be either precomputed, archival datasets, or they may be
virtual data which is computed on the fly to respond to a client request.
Spectrum datasets may conform to a standard data model defined by SSA, or may
be native spectra with custom project-defined content. Spectra may be returned
in any of a number of standard data formats. Spectral data is generally stored
externally to the VO in a format specific to each spectral data collection;
currently there is no standard way to represent astronomical spectra, and
virtually every project does it differently. Hence spectra may be actively
mediated to the standard SSA-defined data model at access time by the service,
so that client analysis programs do not have to be familiar with the
idiosyncratic details of each data collection to be accessed.


  Polymer field-effect transistors with 2D graphene electrodes are devices that
merge the best of two worlds: on the one hand, the low-cost and processability
of organic materials and, on the other hand, the chemical robustness, extreme
thinness and flexibility of graphene. Here, we demonstrate the tuning of the
ambipolar nature of the semiconductor polymer N2200 from Polyera ActiveInk by
incorporating graphene electrodes in a transistor geometry. Our devices show a
balanced ambipolar behavior with high current ON-OFF ratio and charge carrier
mobilities. These effects are caused by both the effective energy barrier
modulation and by the weak electric field screening effect at the
graphene-polymer interface. Our results provide a strategy to integrate 2D
graphene electrodes in ambipolar transistors in order to improve and modulate
their characteristics, paving the way for the design of novel organic
electronic devices.


  This contribution deals with a two-level discrete decision problem, a
so-called Stackelberg strategic game: A Subset Sum setting is addressed with a
set $N$ of items with given integer weights. One distinguished player, the
leader, may alter the weights of the items in a given subset $L\subset N$, and
a second player, the follower, selects a solution $A\subseteq N$ in order to
utilize a bounded resource in the best possible way. Finally, the leader
receives a payoff from those items of its subset $L$ that were included in the
overall solution $A$, chosen by the follower. We assume that the follower
applies a publicly known, simple, heuristic algorithm to determine its solution
set, which avoids having to solve NP-hard problems.
  Two variants of the problem are considered, depending on whether the leader
is able to control (i.e., change) the weights of its items (i) in the objective
function or (ii) in the bounded resource constraint. The leader's objective is
the maximization of the overall weight reduction, for the first variant, or the
maximization of the weight increase for the latter one. In both variants there
is a trade-off for each item between the contribution value to the leader's
objective and the chance of being included in the follower's solution set.
  We analyze the leader's pricing problem for a natural greedy strategy of the
follower and discuss the complexity of the corresponding problems. We show that
setting the optimal weight values for the leader is, in general, NP-hard. It is
even NP-hard to provide a solution within a constant factor of the best
possible solution. Exact algorithms, based on dynamic programming and running
in pseudopolynomial time, are provided. The additional cases, in which the
follower faces a continuous (linear relaxation) version of the above problems,
are shown to be straightforward to solve.


  The notion of almost elementariness for a locally compact Hausdorff \'{e}tale
groupoid $\mathcal{G}$ with a compact unit space was introduced by the authors
as a sufficient condition ensuring the reduced groupoid $C^*$-algebra
$C^*_r(\mathcal{G})$ is (tracially) $\mathcal{Z}$-stable and thus classifiable
under additional natural assumption. In this paper, we explore the converse
direction and show that many groupoids in the literature serving as models for
classifiable $C^*$-algebras are almost elementary. In particular, for a large
class $\mathcal{C}$ of Elliott invariants and a $C^*$-algebra $A$ with
$\operatorname{Ell}(A)\in \mathcal{C}$, we show that $A$ is classifiable if and
only if $A$ possesses a minimal, effective, amenable, second countable, almost
elementary groupoid model, which leads to a groupoid-theoretic characterization
of classifiability of $C^*$-algebras with certain Elliott invariants. Moreover,
we build a connection between almost elementariness and pure infiniteness for
groupoids and study obstructions to obtaining a transformation groupoid model
for the Jiang-Su algebra $\mathcal{Z}$.


  A variety of CP violating asymmetries in $B^0$ decays is predicted in the
Standard Model in terms of CKM (Cabibbo-Kobayashi-Maskawa) complex phases.
These phases can also be determined from rate measurements of certain $B^+$ and
$B^-$ decays. We focus on the following processes: $B^0\to\psi
K_S,~\pi^+\pi^-,~\pi^0\pi^0, ~\rho^{\pm}\pi^{\mp},~B_s\to D^+_s K^-,~\pi^0\eta,
~B^+\to D^0(\Dbar)K^+,~\pi^+\pi^0,~\pi^0 K^+,~ \pi^+ K^0,~\eta K^+$ and their
charge-conjugates. Complications due to gluonic-penguin and electroweak-penguin
amplitudes are dealt with and are resolved. The importance of final state
interaction phases in charged $B$ decay asymmetries is demonstrated in
$B^+\to\chi_{c0}\pi^+,~~\chi_{c0}\to\pi^+\pi^-$ through the effect of the
$\chi_{c0}$ width.


  Federated learning (FL) is a promising approach for training decentralized
data located on local client devices while improving efficiency and privacy.
However, the distribution and quantity of the training data on the clients'
side may lead to significant challenges such as class imbalance and non-IID
(non-independent and identically distributed) data, which could greatly impact
the performance of the common model. While much effort has been devoted to
helping FL models converge when encountering non-IID data, the imbalance issue
has not been sufficiently addressed. In particular, as FL training is executed
by exchanging gradients in an encrypted form, the training data is not
completely observable to either clients or servers, and previous methods for
class imbalance do not perform well for FL. Therefore, it is crucial to design
new methods for detecting class imbalance in FL and mitigating its impact. In
this work, we propose a monitoring scheme that can infer the composition of
training data for each FL round, and design a new loss function --
\textbf{Ratio Loss} to mitigate the impact of the imbalance. Our experiments
demonstrate the importance of acknowledging class imbalance and taking measures
as early as possible in FL training, and the effectiveness of our method in
mitigating the impact. Our method is shown to significantly outperform previous
methods, while maintaining client privacy.


  The B-physics potential of the ATLAS experiment at LHC is described.
Simulation results are shown for the measurement of sin(2beta), with an
emphasis on new tagging techniques. Other CP-violation measurements are
described briefly. New limits are shown for the reach of the x_s-measurement,
resulting from increased statistics and improved fitting methods. Some rare
decay modes of B-mesons can be easily seen in ATLAS. Analyses of channels B->
mu+mu-(X) are presented here.


  We present a detailed study of a scotogenic model accommodating dark matter,
neutrino masses and the anomalous magnetic moment of the muon while being
consistent with the existing constraints on flavour violating decays of the
leptons. Moreover, this model offers the possibility to explain the baryon
asymmetry of the Universe via leptogenesis. We determine the viable regions of
the model's parameter space in view of dark matter and flavour constraints
using a Markov Chain Monte Carlo setup combined with a particular procedure to
accommodate neutrino masses and the anomalous magnetic moment of the muon at
the same time. We also discuss briefly the resulting collider phenomenology.


  We show for $n,k\geq1$, and an $n$-dimensional complex vector space $V$ that
if an element $A\in\text{End}(V)[[z]]$ has constant term similar to a Jordan
block, then there exists a polynomial gauge transformation $g$ such that the
first $k$ coefficients of $gAg^{-1}$ have a controlled normal form.
Furthermore, we show that this normal form is unique by demonstrating explicit
relationships between the first $nk$ coefficients of the Puiseux series
expansion of the eigenvalues of $A$ and the entries of the first $k$
coefficients of $gAg^{-1}$.


  How dust scatters and absorbs starlight in the interstellar medium (ISM)
contains important clues about the size and composition of interstellar dust.
While the ultraviolet (UV) and visible interstellar extinction is well studied
and can be closely fitted in terms of various dust mixtures (e.g., the
silicategraphite mixture), the infrared (IR) extinction is not well understood,
particularly, the mid-IR extinction in the 3-8 micron wavelength range is
rather flat (or "gray") and is inconsistent with the standard Mathis, Rumpl, &
Nordsieck (MRN) silicate-graphite dust model. We attempt to reproduce the flat
IR extinction by exploring various dust sizes and species, including amorphous
silicate, graphite, amorphous carbon and iron. We find that the flat IR
extinction is best explained in terms of micrometer-sized amorphous carbon dust
which consumes ~ 60 carbon atoms per million hydrogen atoms (i.e., C/H ~ 60
ppm). To account for the observed UV/visible and near-IR extinction, the
silicate-graphite model requires Si/H ~ 34 ppm and C/H ~ 292 ppm. We conclude
that the extinction from the UV to the mid-IR could be closely reproduced by a
mixture of submicrometer-sized amorphous silicate dust, submicrometer-sized
graphitic dust, and micrometer-sized amorphous carbon dust, at the expense of
excess C available in the ISM (i.e., this model requires a solid-phase C
abundance of C/H ~ 352 ppm, considerably exceeding what could be available in
the ISM).


  There are a number of theoretical predictions for astrophysical and
cosmological objects, which emit high frequency ($10^6-10^9$~Hz) Gravitation
Waves (GW) or contribute somehow to the stochastic high frequency GW
background. Here we propose a new sensitive detector in this frequency band,
which is based on existing cryogenic ultra-high quality factor quartz Bulk
Acoustic Wave cavity technology, coupled to near-quantum-limited SQUID
amplifiers at $20$~mK. We show that spectral strain sensitivities reaching
$10^{-22}$ per $\sqrt{\text{Hz}}$ per mode is possible, which in principle can
cover the frequency range with multiple ($>100$) modes with quality factors
varying between $10^6-10^{10}$ allowing wide bandwidth detection. Due to its
compactness and well established manufacturing process, the system is easily
scalable into arrays and distributed networks that can also impact the overall
sensitivity and introduce coincidence analysis to ensure no false detections.


  Surgical robots have had clinical use since the mid 1990s. Robot-assisted
surgeries offer many benefits over the conventional approach including lower
risk of infection and blood loss, shorter recovery, and an overall safer
procedure for patients. The past few decades have shown many emerging surgical
robotic platforms that can work in complex and confined channels of the
internal human organs and improve the cognitive and physical skills of the
surgeons during the operation. Advanced technologies for sensing, actuation,
and intelligent control have enabled multiple surgical devices to
simultaneously operate within the human body at low cost and with more
efficiency. Despite advances, current surgical intervention systems are not
able to execute autonomous tasks and make cognitive decisions that are
analogous to that of humans. This paper will overview a historical development
of surgery from conventional open to robotic-assisted approaches with
discussion on the capabilities of advanced intelligent systems and devices that
are currently implemented in existing surgical robotic systems. It will also
revisit available autonomous surgical platforms with comments on the essential
technologies, existing challenges, and suggestions for the future development
of intelligent robotic-assisted surgical systems towards the achievement of
fully autonomous operation.


  We present the fifth simulation in the CGOLS project -- a set of isolated
starburst galaxy simulations modeled over large scales ($10\kpc$) at uniformly
high resolution ($\Delta x \approx 5\pc$). Supernova feedback in this
simulation is implemented as a disk-wide distribution of clusters, and we
assess the impact of this geometry on several features of the resulting
outflow, including radial profiles of various phases; mass, momentum, and
energy outflow rates; covering fraction of cool gas; mock absorption-line
spectra; and X-ray surface brightness. In general, we find that the outflow
generated by this model is cooler, slower, and contains more mass in the cool
phase than a more centrally concentrated outflow driven by a similar number of
supernovae. In addition, the energy loading factors in the hot phase are an
order-of-magnitude lower, indicating much larger losses due to radiative
cooling in the outflow. However, coupling between the hot and cool phases is
more efficient than in the nuclear burst case, with almost 50\% of the total
outflowing energy flux carried by the cool phase at a radial distance of 5 kpc.
These physical differences have corresponding signatures in observable
quantities: the covering fraction of cool gas is much larger, and there is
greater evidence of absorption in low and intermediate ionization-energy lines.
Taken together, our simulations indicate that centrally-concentrated starbursts
are more effective at driving hot, low-density outflows that will expand far
into the halo, while galaxy-wide bursts may be more effective at removing cool
gas from the disk.


  The IA-automorphism group is the group of automorphisms of the free group
$F_n$ that act trivially on the abelianization $F_n^{\mathrm{ab}}$. This group
is in many ways analoguous to Torelli groups of surfaces and their higher
dimensional analogues. In recent work, the stable rational cohomology of such
groups was studied by Kupers and Randal-Williams, using the machinery of
so-called Brauer categories. In this paper, we adapt their methods to study the
stable rational cohomology of the IA-automorphism group. We obtain a
conjectural description of the algebraic part of the stable rational cohomology
and prove that it holds up to degree $Q+1$, given the assumption that the
stable cohomology groups are stably finite dimensional in degrees up to $Q$. In
particular, this allows us to compute the algebraic part of the stable
cohomology in degree 2, which we show agrees with the part generated by the
first cohomology group via the cup product map and which has previously been
computed by Pettet.
  In the appendix, written by Mai Katada, it is shown how the results of the
paper can be applied to compute the stable Albanese (co)homology of the
IA-automorphism group.


  We explore the amplification of magnetic seed fields during the formation of
the first stars and galaxies. During gravitational collapse, turbulence is
created from accretion shocks, which may act to amplify weak magnetic fields in
the protostellar cloud. Numerical simulations showed that such turbulence is
sub-sonic in the first star-forming minihalos, and highly supersonic in the
first galaxies with virial temperatures larger than 10^4 K. We investigate the
magnetic field amplification during the collapse both for Kolmogorov and
Burgers-type turbulence with a semi-analytic model that incorporates the
effects of gravitational compression and small-scale dynamo amplification. We
find that the magnetic field may be substantially amplified before the
formation of a disk. On scales of 1/10 of the Jeans length, saturation occurs
after ~10^8 yr. Although the saturation behaviour of the small-scale dynamo is
still somewhat uncertain, we expect a saturation field strength of the order
~10^{-7} n^{0.5} G in the first star-forming halos, with n the number density
in cgs units. In the first galaxies with higher turbulent velocities, the
magnetic field strength may be increased by an order of magnitude, and
saturation may occur after 10^6 to 10^7 yr. In the Kolmogorov case, the
magnetic field strength on the integral scale (i.e. the scale with most
magnetic power) is higher due to the characteristic power-law indices, but the
difference is less than a factor of 2 in the saturated phase. Our results thus
indicate that the precise scaling of the turbulent velocity with length scale
is of minor importance. They further imply that magnetic fields will be
significantly enhanced before the formation of a protostellar disk, where they
may change the fragmentation properties of the gas and the accretion rate.


  Electromagnetic properties provide information about the structure of
strongly interacting systems and allow for independent tests of hadronic
models. The radiative decay of the Delta(1700) is studied, which appears
dynamically generated in a coupled channel approach from the rescattering of
the (3/2^+) decuplet of baryons with the (0^-) octet of pseudoscalar mesons.
The radiative decay is predicted from the well-known couplings of the photon to
the mesons and hadrons which constitute this resonance in the dynamical
picture.


  We consider the problem of exploring an anonymous unoriented ring of size $n$
by $k$ identical, oblivious, asynchronous mobile robots, that are unable to
communicate, yet have the ability to sense their environment and take decisions
based on their local view. Previous works in this weak scenario prove that $k$
must not divide $n$ for a deterministic solution to exist. Also, it is known
that the minimum number of robots (either deterministic or probabilistic) to
explore a ring of size $n$ is 4. An upper bound of 17 robots holds in the
deterministic case while 4 probabilistic robots are sufficient. In this paper,
we close the complexity gap in the deterministic setting, by proving that no
deterministic exploration is feasible with less than five robots whenever the
size of the ring is even, and that five robots are sufficient for any $n$ that
is coprime with five. Our protocol completes exploration in O(n) robot moves,
which is also optimal.


  Mixed action lattice QCD with Borici-Creutz valence quarks on staggered sea
is investigated. The counter terms in Borici-Creutz action are fixed
nonperturbatively to restore the broken parity and time symmetries. On symmetry
restoration, the usual signatures of partial quenching / unitarity violation
like negative scalar correlator are observed. The size of unitarity violation
due to different discretization of valence and sea quark is determined by
measuring $\Delta_{\rm mix}$ and is found to be comparable with other mixed
action studies.


  One of the main advantages of silicon spin qubits over other solid-state
qubits is their inherent scalability and compatibility with the 300 mm CMOS
fabrication technology that is already widely used in the semiconductor
industry, whilst maintaining high readout and gate fidelities. We demonstrate
detection of a single electron spin using energy-selective readout in a
CMOS-fabricated nanowire device with an integrated charge detector. We measure
a valley splitting of 0.3 meV and 0.16 meV in two similar devices. The
anisotropy of the spin-valley mixing is measured and shown to follow the
dependence expected from the symmetry of the local confinement, indicating low
disorder in the region of the quantum dot. Finally the charge noise in the
spin-valley coupling regime is investigated and found to induce fluctuations in
the qubit energy in the range of $0.6GHz/\sqrt{Hz}$.


  The Burning Index (BI) produced daily by the United States government's
National Fire Danger Rating System is commonly used in forecasting the hazard
of wildfire activity in the United States. However, recent evaluations have
shown the BI to be less effective at predicting wildfires in Los Angeles
County, compared to simple point process models incorporating similar
meteorological information. Here, we explore the forecasting power of a suite
of more complex point process models that use seasonal wildfire trends, daily
and lagged weather variables, and historical spatial burn patterns as
covariates, and that interpolate the records from different weather stations.
Results are compared with models using only the BI. The performance of each
model is compared by Akaike Information Criterion (AIC), as well as by the
power in predicting wildfires in the historical data set and residual analysis.
We find that multiplicative models that directly use weather variables offer
substantial improvement in fit compared to models using only the BI, and, in
particular, models where a distinct spatial bandwidth parameter is estimated
for each weather station appear to offer substantially improved fit.


  In this letter, we report the gigantic thermal Hall effect $\kappa_{xy}$ in
the low-field correlated-paramagnetic state of the frustrated pyrochlore
Yb$_2$Ti$_2$O$_7$. We observed a record magnitude for the thermal Hall angle in
an insulator, $\left|\kappa_{xy}\right|/\kappa_{xx}\sim 2\,\%$. The signal
onsets at $T\sim 3\,$K and is severely weakened around the transition to canted
ferromagnetic order at $T_\text{CFM}=0.275\,$K. Besides the large
$\kappa_{xy}>0$ of the fluctuating regime, a sign change towards negative
$\kappa_{xy}$ occurs at the lowest temperatures and in moderate field, where
sharp magnon excitations appear in the inelastic neutron scattering spectra. We
analyze the magnon-Hall signal and its suppression with field
semi-quantitatively. A contribution of phonon skew scattering to $\kappa_{xy}$
is ruled out by a comparison of $\kappa_{xx}$ for Tb-, Yb-, and Y-based rare
earth pyrochlore titanates. These results represent the first report of
non-vanishing $\kappa_{xy}$ measured in a dilution refrigerator ($T <
0.29\,$K).


  There has been a massive increase in research interest towards applying data
driven methods to problems in mechanics. While traditional machine learning
(ML) methods have enabled many breakthroughs, they rely on the assumption that
the training (observed) data and testing (unseen) data are independent and
identically distributed (i.i.d). Thus, traditional ML approaches often break
down when applied to real world mechanics problems with unknown test
environments and data distribution shifts. In contrast, out-of-distribution
(OOD) generalization assumes that the test data may shift (i.e., violate the
i.i.d. assumption). To date, multiple methods have been proposed to improve the
OOD generalization of ML methods. However, because of the lack of benchmark
datasets for OOD regression problems, the efficiency of these OOD methods on
regression problems, which dominate the mechanics field, remains unknown. To
address this, we investigate the performance of OOD generalization methods for
regression problems in mechanics. Specifically, we identify three OOD problems:
covariate shift, mechanism shift, and sampling bias. For each problem, we
create two benchmark examples that extend the Mechanical MNIST dataset
collection, and we investigate the performance of popular OOD generalization
methods on these mechanics-specific regression problems. Our numerical
experiments show that in most cases, while the OOD generalization algorithms
perform better compared to traditional ML methods on these OOD problems, there
is a compelling need to develop more robust OOD generalization methods that are
effective across multiple OOD scenarios. Overall, we expect that this study, as
well as the associated open access benchmark datasets, will enable further
development of OOD generalization methods for mechanics specific regression
problems.


  Adapting large pre-trained models (PTMs) through fine-tuning imposes
prohibitive computational and storage burdens. Recent studies of
parameter-efficient tuning (PET) find that only optimizing a small portion of
parameters conditioned on PTMs could yield on-par performance compared to
conventional fine-tuning. Generally, PET methods exquisitely design
parameter-efficient modules (PET modules) which could be applied to arbitrary
fine-grained positions inside PTMs. However, the effectiveness of these
fine-grained positions largely relies on sophisticated manual designation,
thereby usually producing sub-optimal results. In contrast to the manual
designation, we explore constructing PET modules in an automatic manner. We
automatically \textbf{S}earch for the \textbf{S}parse \textbf{S}tructure of
\textbf{P}arameter-\textbf{E}fficient \textbf{T}uning (S$^3$PET). Based on a
unified framework of various PET methods, S$^3$PET conducts the differentiable
PET structure search through bi-level optimization and proposes shifted global
sigmoid method to explicitly control the number of trainable parameters.
Extensive experiments show that S$^3$PET surpasses manual and random structures
with less trainable parameters. The searched structures preserve more than 99\%
fine-tuning performance with 0.01\% trainable parameters. Moreover, the
advantage of S$^3$PET is amplified with extremely low trainable parameters
budgets (0.0009\%$\sim$0.01\%). The searched structures are transferable and
explainable, providing suggestions and guidance for the future design of PET
methods.


  Robots operating in human environments must be able to rearrange objects into
semantically-meaningful configurations, even if these objects are previously
unseen. In this work, we focus on the problem of building physically-valid
structures without step-by-step instructions. We propose StructDiffusion, which
combines a diffusion model and an object-centric transformer to construct
structures given partial-view point clouds and high-level language goals, such
as "set the table". Our method can perform multiple challenging
language-conditioned multi-step 3D planning tasks using one model.
StructDiffusion even improves the success rate of assembling physically-valid
structures out of unseen objects by on average 16% over an existing multi-modal
transformer model trained on specific structures. We show experiments on
held-out objects in both simulation and on real-world rearrangement tasks.
Importantly, we show how integrating both a diffusion model and a
collision-discriminator model allows for improved generalization over other
methods when rearranging previously-unseen objects. For videos and additional
results, see our website: https://structdiffusion.github.io/.


  Recently, a simple combination of passage retrieval using off-the-shelf IR
techniques and a BERT reader was found to be very effective for question
answering directly on Wikipedia, yielding a large improvement over the previous
state of the art on a standard benchmark dataset. In this paper, we present a
data augmentation technique using distant supervision that exploits positive as
well as negative examples. We apply a stage-wise approach to fine tuning BERT
on multiple datasets, starting with data that is "furthest" from the test data
and ending with the "closest". Experimental results show large gains in
effectiveness over previous approaches on English QA datasets, and we establish
new baselines on two recent Chinese QA datasets.


  Designing efficient and labor-saving prosthetic hands requires powerful hand
gesture recognition algorithms that can achieve high accuracy with limited
complexity and latency. In this context, the paper proposes a compact deep
learning framework referred to as the CT-HGR, which employs a vision
transformer network to conduct hand gesture recognition using highdensity sEMG
(HD-sEMG) signals. The attention mechanism in the proposed model identifies
similarities among different data segments with a greater capacity for parallel
computations and addresses the memory limitation problems while dealing with
inputs of large sequence lengths. CT-HGR can be trained from scratch without
any need for transfer learning and can simultaneously extract both temporal and
spatial features of HD-sEMG data. Additionally, the CT-HGR framework can
perform instantaneous recognition using sEMG image spatially composed from
HD-sEMG signals. A variant of the CT-HGR is also designed to incorporate
microscopic neural drive information in the form of Motor Unit Spike Trains
(MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS).
This variant is combined with its baseline version via a hybrid architecture to
evaluate potentials of fusing macroscopic and microscopic neural drive
information. The utilized HD-sEMG dataset involves 128 electrodes that collect
the signals related to 65 isometric hand gestures of 20 subjects. The proposed
CT-HGR framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the
above-mentioned dataset utilizing 32, 64, 128 electrode channels. The average
accuracy over all the participants using 32 electrodes and a window size of
31.25 ms is 86.23%, which gradually increases till reaching 91.98% for 128
electrodes and a window size of 250 ms. The CT-HGR achieves accuracy of 89.13%
for instantaneous recognition based on a single frame of HD-sEMG image.


  Quantum entanglement in mechanical systems is not only a key signature of
macroscopic quantum effects, but has wide applications in quantum technologies.
Here we proposed an effective approach for creating strong steady-state
entanglement between two directly coupled mechanical oscillators (or a
mechanical oscillator and a microwave resonator) in a modulated optomechanical
system. The entanglement is achieved by combining the processes of a cavity
cooling and the two-mode parametric interaction, which can surpass the bound on
the maximal stationary entanglement from the two-mode parametric interaction.
In principle, our proposal allows one to cool the system from an initial
thermal state to an entangled state with high purity by a monochromatic driving
laser. Also, the obtained entangled state can be used to implement the
continuous-variable teleportation with high fidelity. Moreover, our proposal is
robust against the thermal fluctuations of the mechanical modes under the
condition of strong optical pumping.


  Matrix-multiplication units (MXUs) are now prevalent in every computing
platform. The key attribute that makes MXUs so successful is the semiring
structure, which allows tiling for both parallelism and data reuse.
Nonetheless, matrix-multiplication is not the only algorithm with such
attributes. We find that many algorithms share the same structure and differ in
only the core operation; for example, using add-minimum instead of
multiply-add. Algorithms with a semiring-like structure therefore have
potential to be accelerated by a general-purpose matrix operation architecture,
instead of common MXUs.
  In this paper, we propose SIMD$^2$, a new programming paradigm to support
generalized matrix operations with a semiring-like structure. SIMD$^2$
instructions accelerate eight more types of matrix operations, in addition to
matrix multiplications. Since SIMD$^2$ instructions resemble a
matrix-multiplication instruction, we are able to build SIMD$^2$ architecture
on top of any MXU architecture with minimal modifications. We developed a
framework that emulates and validates SIMD$^2$ using NVIDIA GPUs with Tensor
Cores. Across 8 applications, SIMD2 provides up to 38.59$\times$ speedup and
more than 10.63$\times$ on average over optimized CUDA programs, with only 5%
of full-chip area overhead.


  The upcoming video coding standard, Versatile Video Coding (VVC), has shown
great improvement compared to its predecessor, High Efficiency Video Coding
(HEVC), in terms of bitrate saving. Despite its substantial performance,
compressed videos might still suffer from quality degradation at low bitrates
due to coding artifacts such as blockiness, blurriness and ringing. In this
work, we exploit Convolutional Neural Networks (CNN) to enhance quality of VVC
coded frames after decoding in order to reduce low bitrate artifacts. The main
contribution of this work is the use of coding information from the compressed
bitstream. More precisely, the prediction information of intra frames is used
for training the network in addition to the reconstruction information. The
proposed method is applied on both luminance and chrominance components of
intra coded frames of VVC. Experiments on VVC Test Model (VTM) show that, both
in low and high bitrates, the use of coding information can improve the BD-rate
performance by about 1% and 6% for luma and chroma components, respectively.


  I derive a set of canonical elements that are useful for collision orbits
(perihelion distance approaching zero at fixed semimajor axis). The coordinates
are the mean anomaly and the two spherical polar angles at aphelion.


  The temperature and yttrium-iron-garnet (YIG) thickness dependences of the
spin Peltier effect (SPE) have been investigated using a Pt/YIG junction system
at temperatures ranging from room temperature to the Curie temperature of YIG
by the lock-in thermography method. By analyzing the YIG thickness dependence
using an exponential decay model, the characteristic length of SPE in YIG is
estimated to be 0.9 $\mu$m near room temperature and almost constant even near
the Curie temperature. The high-temperature behavior of SPE is clearly
different from that of the spin Seebeck effect, providing a clue for
microscopically understanding the reciprocal relation between them.


  A detailed study of stellar populations in Milky Way (MW) satellite galaxies
remains an observational challenge due to their faintness and fewer
spectroscopically confirmed member stars. We use unsupervised machine learning
methods to identify new members for nine nearby MW satellite galaxies using
Gaia data release-3 (Gaia DR3) astrometry and the Dark Energy Survey (DES) and
the DECam Local Volume Exploration Survey (DELVE) photometry. Two density-based
clustering algorithms, DBSCAN and HDBSCAN, have been used in the
four-dimensional astrometric parameter space to identify member stars belonging
to MW satellite galaxies. Our results indicate that we can recover more than
80% of the known spectroscopically confirmed members in most of the satellite
galaxies and also reject 95-100% of spectroscopic non-members. We have also
added many new members using this method. We compare our results with previous
studies that also use photometric and astrometric data and discuss the
suitability of density-based clustering methods for MW satellite galaxies