  Three-dimensional, crystalline Hopf insulators are generic members of unitary
Wigner-Dyson class, which can break all global discrete symmetries and point
group symmetries. In the absence of first Chern number for any two-dimensional
plane of Brillouin zone, the Hopf invariant $N_H \in \mathbb{Z}$. But in the
presence of Chern number $N_H \in \mathbb{Z}_{2q}$, where $q$ is the greatest
common divisor of Chern numbers for $xy$, $yz$, and $xz$ planes of Brillouin
zone. How does $N_H$ affect topological quantization of isotropic,
magneto-electric coefficient? We answer this question with calculations of
Witten effect for a test, magnetic monopole. Furthermore, we construct $N$-band
tight-binding models of Hopf insulators and demonstrate their topological
stability against spectral flattening.


  The specific heat Cp and other properties of glasses (ranging from amorphous
solids to disordered crystals) at low temperatures, are well known to be
markedly different from those in fully-ordered crystals. For decades, this
qualitative, and even quantitative, universal behavior of glasses has been
thoroughly studied. However, a clear understanding of its origin and
microscopic nature, needless to say a closed theory, is still lacking. To shed
light on this matter, I review the situation in this work, mainly by compiling
and discussing measured low-temperature Cp data of many glasses and disordered
crystals, as well as highlighting a few exceptions to that "universality rule".
Thus, one can see that, in contrast to other low-temperature properties of
glasses, the magnitude of the "glassy" Cp excess at low temperature is far from
being universal. Even worse, some molecular crystals without a clear sign of
disorder exhibit linear coefficients in Cp larger than those found in many
amorphous solids, whereas a few of the latter show negligible values.


  Domain Adaptation (DA) targets at adapting a model trained over the
well-labeled source domain to the unlabeled target domain lying in different
distributions. Existing DA normally assumes the well-labeled source domain is
class-wise balanced, which means the size per source class is relatively
similar. However, in real-world applications, labeled samples for some
categories in the source domain could be extremely few due to the difficulty of
data collection and annotation, which leads to decreasing performance over
target domain on those few-shot categories. To perform fair cross-domain
adaptation and boost the performance on these minority categories, we develop a
novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair
cross-domain classification. Specifically, generative feature augmentation is
explored to synthesize effective training data for few-shot source classes,
while effective cross-domain alignment aims to adapt knowledge from source to
facilitate the target learning. Experimental results on two large cross-domain
visual datasets demonstrate the effectiveness of our proposed method on
improving both few-shot and overall classification accuracy comparing with the
state-of-the-art DA approaches.


  Partial differential equation-based numerical solution frameworks for initial
and boundary value problems have attained a high degree of complexity. Applied
to a wide range of physics with the ultimate goal of enabling engineering
solutions, these approaches encompass a spectrum of spatiotemporal
discretization techniques that leverage solver technology and high performance
computing. While high-fidelity solutions can be achieved using these
approaches, they come at a high computational expense and complexity. Systems
with billions of solution unknowns are now routine. The expense and complexity
do not lend themselves to typical engineering design and decision-making, which
must instead rely on reduced-order models. Here we present an approach to
reduced-order modelling that builds off of recent graph theoretic work for
representation, exploration, and analysis on computed states of physical
systems (Banerjee et al., Comp. Meth. App. Mech. Eng., 351, 501-530, 2019). We
extend a non-local calculus on finite weighted graphs to build such models by
exploiting first order dynamics, polynomial expansions, and Taylor series. Some
aspects of the non-local calculus related to consistency of the models are
explored. Details on the numerical implementations and the software library
that has been developed for non-local calculus on graphs are described.
Finally, we present examples of applications to various quantities of interest
in mechano-chemical systems.


  We describe, in a very explicit way, a method for determining the spectra and
bases of all the corresponding eigenspaces of arbitrary lifts of graphs
(regular or not).


  Fork algebras are an extension of relation algebras obtained by extending the
set of logical symbols with a binary operator called fork. This class of
algebras was introduced by Haeberer and Veloso in the early 90's aiming at
enriching relation algebra, an already successful language for program
specification, with the capability of expressing some form of parallel
computation.
  The further study of this class of algebras led to many meaningful results
linked to interesting properties of relation algebras such as representability
and finite axiomatizability, among others. Also in the 90's, Veloso introduced
a subclass of relation algebras that are expansible to fork algebras, admitting
a large number of non-isomorphic expansions, referred to as explosive relation
algebras.
  In this work we discuss some general techniques for constructing algebras of
this type.


  In the first part of this talk there is given a very brief review of the
status of the Standard Model (SM). In the second part I discuss electroweak
interactions and physics beyond the SM.


  We propose the new nondissipative transport effect - the appearance of axial
current of thermal quasiparticles in the presence of background gravity with
torsion. For the non-interacting model of massless Dirac fermions the response
of the axial current to torsion is derived. The chiral vortical effect appears
to be the particular case of the chiral torsional effect. The proposed effect
may be observed in the condensed matter systems with emergent relativistic
invariance (Weyl/Dirac semimetals and the $^3$He-A superfluid).


  With the increasing attention to large vision-language models such as CLIP,
there has been a significant amount of effort dedicated to building efficient
prompts. Unlike conventional methods of only learning one single prompt, we
propose to learn multiple comprehensive prompts to describe diverse
characteristics of categories such as intrinsic attributes or extrinsic
contexts. However, directly matching each prompt to the same visual feature is
problematic, as it pushes the prompts to converge to one point. To solve this
problem, we propose to apply optimal transport to match the vision and text
modalities. Specifically, we first model images and the categories with visual
and textual feature sets. Then, we apply a two-stage optimization strategy to
learn the prompts. In the inner loop, we optimize the optimal transport
distance to align visual features and prompts by the Sinkhorn algorithm, while
in the outer loop, we learn the prompts by this distance from the supervised
data. Extensive experiments are conducted on the few-shot recognition task and
the improvement demonstrates the superiority of our method. The code is
available at https://github.com/CHENGY12/PLOT.


  We explore heat transfer in molecular junctions between two leads in the
absence of a finite net thermal bias. The application of an unbiased,
time-periodic temperature modulation of the leads entails a dynamical breaking
of reflection symmetry, such that a directed heat current may emerge (ratchet
effect). In particular, we consider two cases of adiabatically slow driving,
namely (i) periodic temperature modulation of only one lead and (ii)
temperature modulation of both leads with an ac driving that contains a second
harmonic, thus generating harmonic mixing. Both scenarios yield sizeable
directed heat currents which should be detectable with present techniques.
Adding a static thermal bias, allows one to compute the heat current-thermal
load characteristics which includes the ratchet effect of negative thermal bias
with positive-valued heat flow against the thermal bias, up to the thermal
stop-load. The ratchet heat flow in turn generates also an electric current. An
applied electric stop-voltage, yielding effective zero electric current flow,
then mimics a solely heat-ratchet-induced thermopower (``ratchet Seebeck
effect''), although no net thermal bias is acting. Moreover, we find that the
relative phase between the two harmonics in scenario (ii) enables steering the
net heat current into a direction of choice.


  Preparing the ground state of a system is an important task in physics. We
propose a quantum algorithm for preparing the ground state of a physical system
that can be simulated on a quantum computer. The system is coupled to an
ancillary qubit, by introducing a resonance mechanism between the ancilla qubit
and the system, and combined with measurements performed on the ancilla qubit,
the system can be evolved to monotonically converge to its ground state through
an iterative procedure. We have simulated the application of this algorithm for
the Afflect-Kennedy-Lieb-Tasaki model, whose ground state can be used as
resource state in one-way quantum computation.


  An attacker may use a variety of techniques to fool an automatic speaker
verification system into accepting them as a genuine user. Anti-spoofing
methods meanwhile aim to make the system robust against such attacks. The
ASVspoof 2017 Challenge focused specifically on replay attacks, with the
intention of measuring the limits of replay attack detection as well as
developing countermeasures against them. In this work, we propose our replay
attacks detection system - Attentive Filtering Network, which is composed of an
attention-based filtering mechanism that enhances feature representations in
both the frequency and time domains, and a ResNet-based classifier. We show
that the network enables us to visualize the automatically acquired feature
representations that are helpful for spoofing detection. Attentive Filtering
Network attains an evaluation EER of 8.99$\%$ on the ASVspoof 2017 Version 2.0
dataset. With system fusion, our best system further obtains a 30$\%$ relative
improvement over the ASVspoof 2017 enhanced baseline system.


  The invariant theory of Killing tensors (ITKT) is extended by introducing the
new concepts of covariants and joint invariants of (product) vector spaces of
Killing tensors defined in pseudo-Riemannian spaces of constant curvature. The
covariants are employed to solve the problem of classification of the
orthogonal coordinate webs generated by non-trivial Killing tensors of valence
two defined in the Euclidean and Minkowski planes. Illustrative examples are
provided.


  We study the two-body problem for two-dimensional electron systems in a
symmetrized Bernevig-Hughes-Zhang model which is widely used to describe
topological and conventional insulators. The main result is that two
interacting electrons can form bound states with the energy in the gap of the
band spectrum. The pairing mechanism can be interpreted as the formation of a
negative reduced effective mass of two electrons. The problem is complicated
because the relative motion of the electrons is coupled to the center-of-mass
motion. We consider the case of zero total momentum. Detail calculations are
carried out for the repulsive interaction potential of steplike form. The
states are classified according to their spin structure and two-particle basis
functions that form a given bound state. We analyze the spectra and electronic
structure of the bound states in the case of both topological and trivial
phases and especially focus on effects originating from the band inversion and
the coupling of the electron and hole bands. In the trivial phase and the
topological phase with the large coupling parameter $a$, the bound state
spectra are qualitatively similar. However, when $a$ is less a certain value,
the situation changes dramatically. In the topological phase, new states arise
with a higher binding energy at lower interaction potential, which evidences
that the band inversion can favor pairing the electrons.


  We introduce dessins d'enfants from the various existing points of view: As
topological covering spaces, as surfaces with triangulations, and as algebraic
curves with functions ramified over three points. We prove Belyi's theorem that
such curves are defined over number fields, and define the action of the
absolute Galois group Gal($\overline Q/Q$) on dessins d'enfants. We prove that
several kinds of dessins d'enfants are defined over their field of moduli:
regular dessins, dessins with no nontrivial automorphisms and dessins with one
face. In the last part, we give two examples of regular dessins d'enfants with
a field of moduli that is not an abelian extension of Q. Both of the examples
have genus 61 and field of moduli Q(2^(1/3)).


  We extend the formalism of Hamiltonian string bit models of quantum gravity
type in two spacetime dimensions to include couplings to particles. We find
that the single-particle closed and open universe models respectively behave
like empty open and closed universes, and that a system of two distinguishable
particles in a closed universe behaves like an empty closed universe. We then
construct a metamodel that contains all such models, and find that its
transition amplitude is exactly the same as the sl(2) gravity model.


  The minimum error entropy (MEE) criterion has been successfully used in
fields such as parameter estimation, system identification and the supervised
machine learning. There is in general no explicit expression for the optimal
MEE estimate unless some constraints on the conditional distribution are
imposed. A recent paper has proved that if the conditional density is
conditionally symmetric and unimodal (CSUM), then the optimal MEE estimate
(with Shannon entropy) equals the conditional median. In this study, we extend
this result to the generalized MEE estimation where the optimality criterion is
the Renyi entropy or equivalently, the \alpha-order information potential (IP).


  Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the
reliability of microservice systems. However, performing RCA on modern
microservice systems can be challenging due to their large scale, as they
usually comprise hundreds of components, leading significant human effort. This
paper proposes TraceDiag, an end-to-end RCA framework that addresses the
challenges for large-scale microservice systems. It leverages reinforcement
learning to learn a pruning policy for the service dependency graph to
automatically eliminates redundant components, thereby significantly improving
the RCA efficiency. The learned pruning policy is interpretable and fully
adaptive to new RCA instances. With the pruned graph, a causal-based method can
be executed with high accuracy and efficiency. The proposed TraceDiag framework
is evaluated on real data traces collected from the Microsoft Exchange system,
and demonstrates superior performance compared to state-of-the-art RCA
approaches. Notably, TraceDiag has been integrated as a critical component in
the Microsoft M365 Exchange, resulting in a significant improvement in the
system's reliability and a considerable reduction in the human effort required
for RCA.


  On the basis of the Hartree-Fock-Bogoliubov (HFB) plus quasiparticle random
phase approximation method (QRPA) based on the Green's function approach with
Skyrme force, we discuss the anomalous E2 properties of the first 2+ states in
neutron-rich nuclei 32Mg and 30Ne. The B(E2) values and the excitation energies
of the first 2+ states are well described within HFB plus QRPA calculations
with spherical symmetry. We conclude that pairing effects account largely for
the anomalously large B(E2) values and the low excitation energies in 32Mg and
30Ne.


  Recently, a scheme based on the method of weak measurements to register the
trajectories of photons passing through a nested Mach-Zehnder interferometer
was proposed [L. Vaidman, Phys. Rev. A \textbf{87}, 052104 (2013)] and then
realized [A. Danan, D. Farfurnik, S. Bar-Ad, et al., Phys. Rev. Lett.
\textbf{111}, 240402 (2013)]. Interpreting the results of the experiment, the
authors concluded that "the photons do not always follow continuous
trajectories". It is shown in this work that these results can be easily and
clearly explained in terms of traditional classical electrodynamics or quantum
mechanics implying the continuity of all possible paths of photons.
Consequently, a new concept of disconnected trajectories proposed by the
authors of work [Phys. Rev. Lett. \textbf{111}, 240402 (2013)] is unnecessary.


  While event handling is a key element in modern interactive programming, it
is unfortunate that its theoretical foundation is rather weak. To solve this
problem, we propose to adopt a game-logical approach of computability logic
\cite{Jap08} to event handling.


  In this paper, some special distance spectral properties of graphs are
considered. Concretely, we recursively construct an infinite family of trees
with distance eigenvalue $-1$, and determine all $\{C_3,C_4\}$-free connected
graphs with three distinct distance eigenvalues of which the smallest one is
equal to $-3$, which partially answers a problem posed by Koolen, Hayat and
Iqbal [Linear Algebra Appl. 505 (2016) 97--108]. Furthermore, we characterize
all trees with three distinct distance eigenvalues.


  In this paper, we mainly give characterizations of EP elements in terms of
equations. In addition, a related notion named a central EP element is defined
and investigated. Finally, we focus on characterizations of a generalized EP
element, i.e. *-DMP element.


  We show that non-topological solitons, known as Q-balls, are promising
candidates for self-interacting dark matter. They can satisfy the cross-section
requirements for a broad range of masses. Unlike previously considered
examples, Q-balls can stick together after collision, reducing the effective
self-interaction rate to a negligible value after a few collisions per
particle. This feature modifies predictions for halo formation. We also discuss
the possibility that Q-balls have large interaction cross-sections with
ordinary matter.


This article has been replaced by gr-qc/0412011


  We study the effective field theory of KKLT and LVS moduli stabilisation
scenarios coupled to an anti-D3-brane at the tip of a warped throat. We
describe the presence of the anti-brane in terms of a nilpotent goldstino
superfield in a supersymmetric effective field theory. The introduction of this
superfield produces a term that can lead to a de Sitter minimum. We fix the
Kaehler moduli dependence of the nilpotent field couplings by matching this
term with the anti-D3-brane uplifting contribution. The main result of this
paper is the computation, within this EFT, of the soft supersymmetry breaking
terms in both KKLT and LVS for matter living on D3-brane (leaving the D7-brane
analysis to an appendix). A handful of distinct phenomenological scenarios
emerge that could have low energy implications, most of them having a split
spectrum of soft masses. Some cosmological and phenomenological properties of
these models are discussed. We also check that the attraction between the
D3-brane and the anti-D3-brane does not affect the leading contribution to the
soft masses and does not destabilise the system.


  We investigate quantum phase transitions in the extended periodic Anderson
model, which includes electron correlations within and between itinerant and
localized bands. We calculate zero and finite temperature properties of the
system using the combination of dynamical mean-field theory and the numerical
renormalization group. At half filling, a phase transition between a Mott
insulating state and a Kondo insulating state occurs in the strong coupling
regime. We furthermore find that a metallic state is stabilized in the weak
coupling regime. This state should be adiabatically connected to the orbital
selective Mott state with one orbital localized and the other itinerant. The
effect of hole doping is also addressed.


  A model is studied for the theoretical description of nanoscale magnetic
films with high perpendicular anisotropy. In the model the magnetic film is
described in terms of single domain magnetic grains with Ising-like behavior,
interacting via exchange as well as via dipolar forces. Additionally, the model
contains an energy barrier and a coupling to an external magnetic field.
Disorder is taken into account in order to describe realistic domain and domain
wall structures. The influence of a finite temperature as well as the dynamics
can be modeled by a Monte Carlo simulation.
  Many of the experimental findings can be investigated and at least partly
understood by the model introduced above. For thin films the magnetisation
reversal is driven by domain wall motion. The results for the field and
temperature dependence of the domain wall velocity suggest that for thin films
hysteresis can be described as a depinning transition of the domain walls
rounded by thermal activation for finite temperatures.


  The vector-boson production cross-section for the Higgs boson decay in the $H
\rightarrow WW^{\ast} \rightarrow e\nu\mu\nu$ channel is measured as a function
of kinematic observables sensitive to the Higgs boson production and decay
properties as well as integrated in a fiducial phase space. The analysis is
performed using the proton-proton collision data collected by the ATLAS
detector in Run 2 of the LHC at $\sqrt{s}= 13$ $\text{TeV}$ center-of-mass
energy corresponding to an integrated luminosity of 139 fb$^{-1}$. The
different flavor final state is studied by selecting an electron and a muon
originating from a pair of $W$ bosons and compatible with the Higgs boson
decay. The data are corrected for the effects of detector inefficiency and
resolution, and the measurements are compared with different state-of-the-art
theoretical predictions. The differential cross-sections are used to constrain
anomalous interactions described by dimension-six operators in an Effective
Field Theory.


  The lack of X-ray pulsars with spin periods > 12 s raises the question of
where the population of evolved high magnetic field neutron stars has gone.
Unlike canonical radio pulsars, X-ray pulsars are not subject to physical
limits to the emission mechanism nor observational biases against the detection
of sources with longer periods. Here we show that a highly resistive layer in
the innermost part of the crust of neutron stars naturally limits the spin
period to a maximum value of about 10-20 s. This highly resistive layer is
expected if the inner crust is amorphous and heterogeneous in nuclear charge,
possibly due to the existence of a nuclear pasta phase. Our findings suggest
that the maximum period of isolated X-ray pulsars can be the first
observational evidence of an amorphous inner crust, which properties can be
further constrained by future X-ray timing missions combined with more detailed
models.


  We analyze a model of mutually-propelled filaments suspended in a
two-dimensional solvent. The system undergoes a mean-field isotropic-nematic
transition for large enough filament concentrations and the nematic order
parameter is allowed to vary in space and time. We show that the interplay
between non-uniform nematic order, activity and flow results in spatially
modulated relaxation oscillations, similar to those seen in excitable media. In
this regime the dynamics consists of nearly stationary periods separated by
"bursts" of activity in which the system is elastically distorted and solvent
is pumped throughout. At even higher activity the dynamics becomes chaotic.


  We use a Monte Carlo code to generate synthetic near-IR reflection nebulae
that resemble those (normally associated with a bipolar outflow cavity) seen
towards massive young stellar objects (YSOs). The 2D axi-symmetric calculations
use an analytic expression for a flattened infalling rotating envelope with a
bipolar cavity representing an outflow. We are interested in which aspects of
the circumstellar density distribution can be constrained by servations of
these reflection nebulae. We therefore keep the line of sight optical depth
constant in the model grid, as this is often constrained independently by
observations. It is found that envelopes with density distributions
corresponding to mass infall rates of ~ 1E-4 M_sun/yr (for an envelope radius
of 4700 AU) seen at an inclination angle of ~ 45 deg approximately reproduce
the morphology and extension of the sub-arcsecond nebulae observed in massive
YSOs. Based on the flux ratio between the approaching and receding lobe of the
nebula, we can constrain the system inclination angle. The cavity opening angle
is well constrained from the nebula opening angle. Our simulations indicate
that to constrain the outflow cavity shape and the degree of flattening in the
envelope, near-IR imaging with higher resolution and dynamic range than speckle
imaging in 4m-class telescopes is needed. The radiative transfer code is also
used to simulate the near-IR sub-arcsecond nebula seen in Mon R2 IRS3. We find
indications of a shallower opacity law in this massive YSO than in the
interstellar medium, or possibly a sharp drop in the envelope density
distribution at distances of ~ 1000 AU from the illuminating source.


  We consider the sample complexity of revenue maximization for multiple
bidders in unrestricted multi-dimensional settings. Specifically, we study the
standard model of $n$ additive bidders whose values for $m$ heterogeneous items
are drawn independently. For any such instance and any $\varepsilon>0$, we show
that it is possible to learn an $\varepsilon$-Bayesian Incentive Compatible
auction whose expected revenue is within $\varepsilon$ of the optimal
$\varepsilon$-BIC auction from only polynomially many samples.
  Our fully nonparametric approach is based on ideas that hold quite generally,
and completely sidestep the difficulty of characterizing optimal (or
near-optimal) auctions for these settings. Therefore, our results easily extend
to general multi-dimensional settings, including valuations that are not
necessarily even subadditive, and arbitrary allocation constraints. For the
cases of a single bidder and many goods, or a single parameter (good) and many
bidders, our analysis yields exact incentive compatibility (and for the latter
also computational efficiency). Although the single-parameter case is already
well-understood, our corollary for this case extends slightly the
state-of-the-art.


  Metal-based negative refractive index materials have been extensively studied
in the microwave region. However, negative-index metamaterials have not been
realized at near-IR or visible fre-quencies due to difficulties of fabrication
and to the generally poorer optical properties of metals at these wavelengths.
In this paper, we report the first fabrication and experimental verification of
a transversely structured metal-dielectric-metal multilayer exhibiting a
negative refractive in-dex around 2 mm. Both the amplitude and the phase of the
transmission and reflection were measured experimentally, and are in good
agreement with a rigorous coupled wave analysis.


  A period of a rational integral is the result of integrating, with respect to
one or several variables, a rational function over a closed path. This work
focuses particularly on periods depending on a parameter: in this case the
period under consideration satisfies a linear differential equation, the
Picard-Fuchs equation. I give a reduction algorithm that extends the
Griffiths-Dwork reduction and apply it to the computation of Picard-Fuchs
equations. The resulting algorithm is elementary and has been successfully
applied to problems that were previously out of reach.


  Fomin and Villanger (STACS 2010) proved that Maximum Independent Set,
Feedback Vertex Set, and more generally the problem of finding a maximum
induced subgraph of treewith at most a constant $t$, can be solved in
polynomial time on graph classes with polynomially many minimal separators. We
extend these results in two directions. Let $\Gpoly$ be the class of graphs
with at most $\poly(n)$ minimal separators, for some polynomial $\poly$.
  We show that the odd powers of a graph $G$ have at most as many minimal
separators as $G$. Consequently, \textsc{Distance-$d$ Independent Set}, which
consists in finding maximum set of vertices at pairwise distance at least $d$,
is polynomial on $\Gpoly$, for any even $d$. The problem is NP-hard on chordal
graphs for any odd $d \geq 3$.
  We also provide polynomial algorithms for Connected Vertex Cover and
Connected Feedback Vertex Set on subclasses of $\Gpoly$ including chordal and
circular-arc graphs, and we discuss variants of independent domination
problems.


  A general description of proton form factors is presented, in the whole
kinematical region. The existing data and selected phenomenological models are
briefly discussed.


  A lot of studies on the summary measures of predictive strength of
categorical response models consider the likelihood ratio index (LRI), also
known as the McFadden-$R^2$, a better option than many other measures. We
propose a simple modification of the LRI that adjusts for the effect of the
number of response categories on the measure and that also rescales its values,
mimicking an underlying latent measure. The modified measure is applicable to
both binary and ordinal response models fitted by maximum likelihood. Results
from simulation studies and a real data example on the olfactory perception of
boar taint show that the proposed measure outperforms most of the widely used
goodness-of-fit measures for binary and ordinal models. The proposed $R^2$
interestingly proves quite invariant to an increasing number of response
categories of an ordinal model.


  We discuss a robust method to simultaneously fit a complex model both to the
complex impedance and the noise data for transition-edge sensors (TES). It is
based on a differential evolution (DE) algorithm, providing accurate and
repeatable results with only a small increase in computational cost compared to
the standard least squares (LS) fitting method. Test fits are made using both
DE and LS methods, and the results compared with previously determined best
fits, with varying initial value deviations and limit ranges for the
parameters. The robustness of DE is demonstrated with successful fits even when
parameter limits up to a factor of 5 from the known values were used. It is
shown that the least squares fitting becomes unreliable beyond a 10% deviation
from the known values.


  The Imaging Atmospheric Cherenkov Telescope MAGIC I has recently been
extended to a stereoscopic system by adding a second 17 m telescope, MAGIC-II.
One of the major improvements of the second telescope is an improved camera.
The Camera Control Program is embedded in the telescope control software as an
independent subsystem.
  The Camera Control Program is an effective software to monitor and control
the camera values and their settings and is written in the visual programming
language LabVIEW. The two main parts, the Central Variables File, which stores
all information of the pixel and other camera parameters, and the Comm Control
Routine, which controls changes in possible settings, provide a reliable
operation. A safety routine protects the camera from misuse by accidental
commands, from bad weather conditions and from hardware errors by automatic
reactions.


  Lorentz invariance in the weak interaction has been tested rather poorly
compared to the electromagnetic interaction. In this work we show which tests
on the weak interaction should be considered. We focus on one particular test
that explores the spin degree of freedom in {\beta} decay. To connect various
phenomenological tests of Lorentz invariance in the weak interaction, we
exploit a new theoretical model that may provide a framework that relates the
different tests.


  In this paper, we derive explicit expressions for the moments and for the
mixed moments of the compression of a free unitary Brownian motion by a free
projection. While the moments of this non-normal operator are readily derived
using analytical or combinatorial methods, we only succeeded to derive its
mixed ones after solving a non-linear partial differential equation for their
two-variables generating function. Nonetheless, the combinatorics of non
crossing partitions lead to another expression of the lowest-order mixed
moment. We shall also give some interest in odd alternating moments. In
particular, we derive a linear partial differential equation for their
generating function and discuss the combinatorial approach to these moments
when the rank of the projection equals $1/2$.


  We consider the coordinate ring of a hyperelliptic curve and let
$\mathfrak{g}\otimes R$ be the corresponding current Lie algebra where
$\mathfrak g$ is a finite dimensional simple Lie algebra defined over $\mathbb
C$. We give a generator and relations description of the universal central
extension of $\mathfrak{g}\otimes R$ in terms of certain families of
polynomials $P_{k,i}$ and $Q_{k,i}$ and describe how the center $\Omega_R/dR$
decomposes into a direct sum of irreducible representations when the
automorphism group is $C_{2k}$ or $D_{2k}$.


  Bayesian inference is known to provide a general framework for incorporating
prior knowledge or specific properties into machine learning models via
carefully choosing a prior distribution. In this work, we propose a new type of
prior distributions for convolutional neural networks, deep weight prior (DWP),
that exploit generative models to encourage a specific structure of trained
convolutional filters e.g., spatial correlations of weights. We define DWP in
the form of an implicit distribution and propose a method for variational
inference with such type of implicit priors. In experiments, we show that DWP
improves the performance of Bayesian neural networks when training data are
limited, and initialization of weights with samples from DWP accelerates
training of conventional convolutional neural networks.


  We use QCD sum rules to test the nature of the recently observed mesons
Y(4260), Y(4350) and Y(4660), assumed to be exotic four-quark
$(c\bar{c}q\bar{q})$ or $(c\bar{c}s\bar{s})$ states with $J^{PC}=1^{--}$. We
work at leading order in $\alpha_s$, consider the contributions of higher
dimension condensates and keep terms which are linear in the strange quark mass
$m_s$. We find for the $(c\bar{c}s\bar{s})$ state a mass $m_Y=(4.65\pm 0.10)$
GeV which is compatible with the experimental candidate Y(4660), while for the
$(c\bar{c}q\bar{q})$ state we find a mass $m_Y=(4.49\pm 0.11)$ GeV, which is
higger than the mass of the experimental candidate Y(4350). With the tetraquark
structure we are working we can not explain the Y(4260) as a tetraquark state.
We also consider molecular $D_{s0}\bar{D}_s^*$ and $D_{0}\bar{D}^*$ states. For
the $D_{s0}\bar{D}_s^*$ molecular state we get $m_{D_{s0}\bar{D}_s^*}=(4.42\pm
0.10)$ GeV which is consistent, considering the errors, with the mass of the
meson Y(4350) and for the $D_{0}\bar{D}^*$ molecular state we get
$m_{D_{0}\bar{D}^*}=(4.27\pm 0.10)$ GeV in excelent agreement with the mass of
the meson Y(4260).


  A conjecture of Morel asserts that the sheaf of A^1-connected components of a
simplicial sheaf X is A^1-invariant. A conjecture of Asok-Morel asserts that
A^1-connected components of smooth k-schemes coincide with their
A^1-chain-connected components and are birational invariants of smooth proper
schemes. In this article, we exhibit examples of schemes for which Asok-Morel's
conjectures fail to hold and whose Sing_* is not A^1-local. We also give
equivalent conditions for Morel's conjecture to hold. A method suggested by
these results is then used to prove Morel's conjecture for non-uniruled
surfaces over a field k.


  We call a finitely complete category diexact if every Mal'cev relation admits
a pushout which is stable under pullback and itself a pullback. We prove three
results relating to diexact categories: firstly, that a category is a pretopos
if and only if it is diexact with a strict initial object; secondly, that a
category is diexact if and only if it is Barr-exact, and every pair of
monomorphisms admits a pushout which is stable and a pullback; and thirdly,
that a small category with finite limits and pushouts of Mal'cev spans is
diexact if and only if it admits a full structure-preserving embedding into a
Grothendieck topos.


  Operations Research approaches to surgical scheduling are becoming
increasingly popular in both theory and practice. Often these models neglect
stochasticity in order to reduce the computational complexity of the problem.
We wish to provide practitioners and hospital administrative staff with a
start-of-day probabilistic forecast for the occupancy of post-surgery recovery
spaces. The model minimises the maximum expected occupancy of the recovery
unit, thus levelling the workload of hospital staff, and reducing the
likelihood of bed shortages and bottlenecks. We show that a Poisson binomial
random variable models the number of patients in the recovery when
parameterised by the surgical case sequence. A mixed integer nonlinear
programming model for the surgical case sequencing problem reduces the maximum
expected occupancy in post-surgery recovery spaces. Simulated Annealing
produces good solutions in short amounts of computational time. We evaluate the
methodology with a full year of historical data. The solution techniques reduce
maximum expected recovery occupancy by 18% on average. This alleviates a large
amount of stress on staff in the postsurgery recovery spaces, reduces the
likelihood of bottlenecks, and improves the quality of care provided to
patients.


  We present ESPnet-ST, which is designed for the quick development of
speech-to-speech translation systems in a single framework. ESPnet-ST is a new
project inside end-to-end speech processing toolkit, ESPnet, which integrates
or newly implements automatic speech recognition, machine translation, and
text-to-speech functions for speech translation. We provide all-in-one recipes
including data pre-processing, feature extraction, training, and decoding
pipelines for a wide range of benchmark datasets. Our reproducible results can
match or even outperform the current state-of-the-art performances; these
pre-trained models are downloadable. The toolkit is publicly available at
https://github.com/espnet/espnet.


  Crop breeding is crucial in improving agricultural productivity while
potentially decreasing land usage, greenhouse gas emissions, and water
consumption. However, breeding programs are challenging due to long turnover
times, high-dimensional decision spaces, long-term objectives, and the need to
adapt to rapid climate change. This paper introduces the use of Reinforcement
Learning (RL) to optimize simulated crop breeding programs. RL agents are
trained to make optimal crop selection and cross-breeding decisions based on
genetic information. To benchmark RL-based breeding algorithms, we introduce a
suite of Gym environments. The study demonstrates the superiority of RL
techniques over standard practices in terms of genetic gain when simulated in
silico using real-world genomic maize data.


  Working with epitaxial films of Fe, we succeeded in independent control of
different scattering processes in the anomalous Hall effect. The result
appropriately accounted for the role of phonons, thereby clearly exposing the
fundamental flaws of the standard plot of the anomalous Hall resistivity versus
longitudinal resistivity. A new scaling has been thus established that allows
an unambiguous identification of the intrinsic Berry curvature mechanism as
well as the extrinsic skew scattering and side-jump mechanisms of the anomalous
Hall effect.


  In this paper, we introduced the concept of a $p$-ideal for a given ring. We
provide necessary and sufficient condition for $\dfrac{R[x]}{(f(x))}$ to be a
$p$-ring, where $R$ is a finite $p$-ring. It is also shown that the
amalgamation of rings, $A\bowtie^fJ$ is a $p$-ring if and only if so is $A$ and
$J$ is a $p$-ideal. Finally, we establish the transfer of this notion to
trivial ring extensions.


  Spinor gravity is a functional integral formulation of gravity based only on
fundamental spinor fields. The vielbein and metric arise as composite objects.
Due to the lack of local Lorentz-symmetry new invariants in the effective
gravitational action lead to a modification of Einstein's equations. We discuss
different geometrical viewpoints of spinor gravity with particular emphasis on
nonlinear fields. The effective gravitational field equations arise as
solutions to the lowest order Schwinger-Dyson equation for spinor gravity.


  COVID19 was first reported in England at the end of January 2020, and by
mid-June over 150,000 cases were reported. We assume that, similarly to
influenza-like illnesses, people who suffer from COVID19 may query for their
symptoms prior to accessing the medical system (or in lieu of it). Therefore,
we analyzed searches to Bing from users in England, identifying cases where
unexpected rises in relevant symptom searches occurred at specific areas of the
country. Our analysis shows that searches for "fever" and "cough" were the most
correlated with future case counts, with searches preceding case counts by
16-17 days. Unexpected rises in search patterns were predictive of future case
counts multiplying by 2.5 or more within a week, reaching an Area Under Curve
(AUC) of 0.64. Similar rises in mortality were predicted with an AUC of
approximately 0.61 at a lead time of 3 weeks. Thus, our metric provided Public
Health England with an indication which could be used to plan the response to
COVID19 and could possibly be utilized to detect regional anomalies of other
pathogens.


  We study the quark-gluon mixed condensate g<\bar{q} sigma G q>, which is
another chiral order parameter, in SU(3)c lattice QCD with the Kogut-Susskind
fermion at the quenched level. Using 100 gauge configurations on the 16^4
lattice with \beta = 6.0, we measure the mixed condensate at 16 points in each
gauge configuration for each current-quark mass of m_q=21, 36, 52 MeV. From the
1600 data for each m_q, we find the ratio between the mixed condensate and the
quark condensate, m_0^2 = g<\bar{q} sigma G q> / <\bar{q}q> \simeq 2.5 GeV^2 at
the lattice scale of 1/a \simeq 2 GeV in the chiral limit. The large value of
the mixed condensate suggests its importance in the operator product expansions
in QCD. We also show our preliminary results of the mixed condensate at finite
temperature.


  Over the past decade, significant advances have been made in the field of
image search for e-commerce applications. Traditional image-to-image retrieval
models, which focus solely on image details such as texture, tend to overlook
useful semantic information contained within the images. As a result, the
retrieved products might possess similar image details, but fail to fulfil the
user's search goals. Moreover, the use of image-to-image retrieval models for
products containing multiple images results in significant online product
feature storage overhead and complex mapping implementations. In this paper, we
report the design and deployment of the proposed Multi-modal Item Embedding
Model (MIEM) to address these limitations. It is capable of utilizing both
textual information and multiple images about a product to construct meaningful
product features. By leveraging semantic information from images, MIEM
effectively supplements the image search process, improving the overall
accuracy of retrieval results. MIEM has become an integral part of the Shopee
image search platform. Since its deployment in March 2023, it has achieved a
remarkable 9.90% increase in terms of clicks per user and a 4.23% boost in
terms of orders per user for the image search feature on the Shopee e-commerce
platform.


  We propose a novel end-to-end trainable framework for the graph decomposition
problem. The minimum cost multicut problem is first converted to an
unconstrained binary cubic formulation where cycle consistency constraints are
incorporated into the objective function. The new optimization problem can be
viewed as a Conditional Random Field (CRF) in which the random variables are
associated with the binary edge labels of the initial graph and the hard
constraints are introduced in the CRF as high-order potentials. The parameters
of a standard Neural Network and the fully differentiable CRF are optimized in
an end-to-end manner. Furthermore, our method utilizes the cycle constraints as
meta-supervisory signals during the learning of the deep feature
representations by taking the dependencies between the output random variables
into account. We present analyses of the end-to-end learned representations,
showing the impact of the joint training, on the task of clustering images of
MNIST. We also validate the effectiveness of our approach both for the feature
learning and the final clustering on the challenging task of real-world
multi-person pose estimation.


  Accessibility is one of the most important features in the design of robots
and their interfaces. This thesis proposes methods that improve the
accessibility of robots for three different target audiences: consumers,
researchers, and learners. In order for humans and robots to work together
effectively, they both must be able to communicate with each other. We tackle
the problem of generating route instructions that are readily understandable by
novice humans for the navigation of a priori unknown indoor environments. We
then move on to the related problem of enabling robots to understand natural
language utterances in the context of learning to operate articulated objects
(e.g., fridges, drawers) by leveraging kinematic models. Next, we turn our
focus to the development of accessible and reproducible robotic platforms for
scientific research. We propose a new concept for reproducible robotics
research that integrates development and benchmarking, so that reproducibility
is obtained "by design" from the beginning of the research and development
process. We then propose a framework called SHARC (SHared Autonomy for Remote
Collaboration), to improve accessibility for underwater robotic intervention
operations. SHARC allows multiple remote scientists to efficiently plan and
execute high-level sampling procedures using an underwater manipulator while
deferring low-level control to the robot. Lastly, we developed the first
hardware-based MOOC in AI and robotics. This course allows learners to study
autonomy hands-on by making real robots make their own decisions and accomplish
broadly defined tasks. We design a new robotic platform from the ground up to
support this new learning experience. A fully browser-based interface, based on
leading tools and technologies for code development, testing, validation, and
deployment serves to maximize the accessibility of these educational resources.


  In this work we study the electrized quark matter under finite temperature
and density conditions in the context of the SU(2) and SU(3)
Nambu--Jona-Lasinio models. To this end, we evaluate the effective quark masses
and the Schwinger quark-antiquark pair production rate. For the SU(3) NJL model
we incorporate in the Lagrangian the 't Hooft determinant and we present a set
of analytical expressions more convenient for numerical evaluations. We predict
a decrease of the pseudocritical electric field with the increase of the
temperature for both models and a more prominent production rate for the SU(3)
model when compared to the SU(2).


  In [F. Caselli, Involutory reflection groups and their models, J. Algebra 24
(2010), 370--393] there is constructed a uniform Gelfand model for all
non-exceptional irreducible complex reflection groups which are involutory.
Such model can be naturally decomposed into the direct sum of submodules
indexed by $S_n$-conjugacy classes, and we present here a general result that
relates the irreducible decomposition of these submodules with the projective
Robinson-Schensted correspondence. This description also reflects in a very
explicit way the existence of split representations for these groups.


  We present a method that assesses the theoretical detection limit of a
Bayesian Markov chain Monte Carlo search for a periodic gravitational wave
signal emitted by a neutron star. Inverse probability yields an upper limit
estimate for the strength when a signal could not be detected in an observed
data set. The proposed method is based on Bayesian model comparison that
automatically quantifies Occam s Razor. It limits the complexity of a model by
favoring the most parsimonious model that explains the data. By comparing the
model with a signal from a pulsar to the null model that assumes solely noise,
we derive the detection probability and an estimate for the upper limit that a
search, for example, for a narrow-band emission for SN1987a, might yield on
data at the sensitivity of LIGO data for an observation time of one year.


  We consider global properties of gravitomagnetism by investigating the
gravitomagnetic field of a rotating cosmic string. We show that although the
gravitomagnetic field produced by such a configuration of matter vanishes
locally, it can be detected globally. In this context we discuss the
gravitational analogue of the Aharonov-Bohm effect.


  There is a modular curve X'(6) of level 6 defined over Q whose Q-rational
points correspond to j-invariants of elliptic curves E over Q for which Q(E[2])
is a subfield of Q(E[3]). In this note we characterize the j-invariants of
elliptic curves with this property by exhibiting an explicit model of X'(6).
Our motivation is two-fold: on the one hand, X'(6) belongs to the list of
modular curves which parametrize non-Serre curves (and is not well-known), and
on the other hand, X'(6)(Q) gives an infinite family of examples of elliptic
curves with non-abelian "entanglement fields," which is relevant to the
systematic study of correction factors of various conjectural constants for
elliptic curves over Q.


  We develop a framework for efficient streaming reconstructions of turbulent
velocity fluctuations from limited sensor measurements with the goal of
enabling real-time applications. The reconstruction process is simplified by
computing linear estimators using flow statistics from an initial training
period and evaluating their performance during a subsequent testing period with
data obtained from direct numerical simulation. We address cases where (i) no,
(ii) limited, and (iii) full-field training data are available using estimators
based on (i) resolvent modes, (ii) resolvent-based estimation, and (iii)
spectral proper orthogonal decomposition modes. During training, we introduce
blockwise inversion to accurately and efficiently compute the resolvent
operator in an interpretable manner. During testing, we enable efficient
streaming reconstructions by using a temporal sliding discrete Fourier
transform to recursively update Fourier coefficients using incoming
measurements. We use this framework to reconstruct with minimal time delay the
turbulent velocity fluctuations in a minimal channel at ${\rm Re}_\tau \approx
186$ from sparse planar measurements. We evaluate reconstruction accuracy in
the context of the extent of data required and thereby identify potential use
cases for each estimator. The reconstructions capture large portions of the
dynamics from relatively few measurement planes when the linear estimators are
computed with sufficient fidelity. We also evaluate the efficiency of our
reconstructions and show that the present framework has the potential to help
enable real-time reconstructions of turbulent velocity fluctuations in an
analogous experimental setting.


  Singular degenerate differential operator equations are studied. The uniform
separability of boundary value problems for degenerate elliptic equation and
optimal regularity properties of Cauchy problem for degenerate parabolic
equation are obtained. These problems have a numeros applications which occur
in fluid mechanics, environmental engineering and atmospheric dispersion of
pollutants.


  In this paper we present a novel approach to graph (and structural) limits
based on model theory and analysis. The role of Stone and Gelfand dualities is
displayed prominently and leads to a general theory, which we believe is
naturally emerging. This approach covers all the particular examples of
structural convergence and it put the whole in new context. As an application,
it leads to new intermediate examples of structural convergence and to a "grand
conjecture" dealing with sparse graphs. We survey the recent developments.


  For an odd prime $p$, we say $f(X) \in {\mathbb F}_p[X]$ computes square
roots in $\mathbb F_p$ if, for all nonzero perfect squares $a \in \mathbb F_p$,
we have $f(a)^2 = a$.
  When $p \equiv 3 \mod 4$, it is well known that $f(X) = X^{(p+1)/4}$ computes
square roots. This degree is surprisingly low (and in fact lowest possible),
since we have specified $(p-1)/2$ evaluations (up to sign) of the polynomial
$f(X)$. On the other hand, for $p \equiv 1 \mod 4$ there was previously no
nontrivial bound known on the lowest degree of a polynomial computing square
roots in $\mathbb F_p$; it could have been anywhere between $\frac{p}{4}$ and
$\frac{p}{2}$.
  We show that for all $p \equiv 1 \mod 4$, the degree of a polynomial
computing square roots has degree at least $p/3$. Our main new ingredient is a
general lemma which may be of independent interest: powers of a low degree
polynomial cannot have too many consecutive zero coefficients. The proof method
also yields a robust version: any polynomial that computes square roots for
99\% of the squares also has degree almost $p/3$.
  In the other direction, a result of Agou, Deligl\'ese, and Nicolas (Designs,
Codes, and Cryptography, 2003) shows that for infinitely many $p \equiv 1 \mod
4$, the degree of a polynomial computing square roots can be as small as
$3p/8$.


  We present a derivation of the gap equation for the crystalline color
superconducting phase of QCD which begins from a one-loop Schwinger-Dyson
equation written using a Nambu-Gorkov propagator modified to describe the
spatially varying condensate. Some aspects of previous variational calculations
become more straightforward when rephrased beginning from a diagrammatic
starting point. This derivation also provides a natural base from which to
generalize the analysis to include quark masses, nontrivial crystal structures,
gluon propagation at asymptotic densities, and nonzero temperature. In this
paper, we analyze the effects of nonzero temperature on the crystalline color
superconducting phase.


  In the context of LLM geometries, we show that superpositions of classical
coherent states of trivial topology can give rise to new classical limits where
the topology of spacetime has changed. We argue that this phenomenon implies
that neither the topology nor the geometry of spacetime can be the result of an
operator measurement. We address how to reconcile these statements with the
usual semiclassical analysis of low energy effective field theory for gravity.


  We consider the Dirichlet Laplacian $A_q=-\Delta+q$ in a bounded domain
$\Omega \subset \mathbb{R}^d$, $d \ge 3$, with real-valued perturbation $q \in
L^{\max(2 , 3 d / 5)}(\Omega)$. We examine the stability issue in the inverse
problem of determining the electric potential $q$ from the asymptotic behavior
of the eigenvalues of $A_q$. Assuming that the boundary measurement of the
normal derivative of the eigenfunctions is a square summable sequence in
$L^2(\partial \Omega)$, we prove that $q$ can be H\"older stably retrieved
through knowledge of the asymptotics of the eigenvalues


  An interval edge t-coloring of a graph G is a proper edge coloring of G with
colors 1,2...,t such that at least one edge of G is colored by color
i,i=1,2...,t, and the edges incident with each vertex x are colored by d_{G}(x)
consecutive colors, where d_{G}(x) is the degree of the vertex x in G. For
Mobius ladders the existence of this coloring is proved and all possible
numbers of colors in such colorings are found.


  We have measured the mass of metals in the molecular gas in 13 submillimetre
galaxies at z~4 in which the gas, based on previous observations, lies in a
cold rotating disk. We measured the metal masses using either the submillimetre
line or continuum emission from three tracers of the overall metal content -
carbon atoms, carbon monoxide molecules and dust grains - using the first
simultaneous calibration of all three tracers (Dunne et al. 2022). We obtain
very similar mass estimates from the different tracers, which are similar to
the entire metal content of a present-day massive early-type galaxy. We used
the dynamical masses of these galaxies to set an upper limit on the mass of the
molecular gas in each galaxy, allowing us to set a lower limit on the metal
abundance in the gas, finding values for many of the galaxies well above the
solar value. We use chemical evolution models to show that such high metal
masses and abundances are what is expected shortly after the formation of a
galaxy for a top-heavy IMF. We suggest a scenario for galaxy evolution in which
massive galaxies reach a high metal abundance during their formation phase,
which is then gradually reduced by dry mergers with lower mass galaxies. We use
the chemical-evolution models to show that the metals in the outflows from
massive early-type galaxies in their formation phase can quantitatively explain
the long-standing puzzle that approximately 75% of the metals in clusters of
galaxies is in the intracluster gas rather than in the galaxies.


  We describe a method to control and detect in single-shot the electron spin
state of an individual donor in silicon with greatly enhanced sensitivity. A
silicon-based Single-Electron Transistor (SET) allows for spin-dependent
tunneling of the donor electron directly into the SET island during the
read-out phase. Simulations show that the charge transfer signals are typically
\Delta q > 0.2 e - over an order of magnitude larger than achievable with
metallic SETs on the SiO2 surface. A complete spin-based qubit structure is
obtained by adding a local Electron Spin Resonance line for coherent spin
control. This architecture is ideally suited to demonstrate and study the
coherent properties of donor electron spins, but can be expanded and integrated
with classical control electronics in the context of scale-up.


  Optical tweezer arrays of neutral atoms provide a versatile platform for
quantum simulation due to the range of interactions and Hamiltonians that can
be realized and explored. We propose to simulate a two-component Bose-Hubbard
model with power-law hopping using arrays of multilevel Rydberg atoms featuring
resonant dipolar interactions. The diversity of states that can be used to
encode the local Hilbert space of the Bose-Hubbard model enables control of the
relative hopping rate of each component and even the realization of spin-flip
hopping. We use numerical simulations to show how multilevel Rydberg atoms
provide an opportunity to explore the diverse non-equilibrium quench dynamics
of the model. For example, we demonstrate a separation of the relaxation
timescales of effective spin and charge degrees of freedom, and observe regimes
of slow relaxation when the effective hopping rates of the two components are
vastly different due to dynamical constraints arising from hardcore boson
interactions. We discuss prospects for studying these effects in
state-of-the-art Rydberg tweezer arrays.


  This paper is about line search for the generalized alternating projections
(GAP) method. This method is a generalization of the von Neumann alternating
projections method, where instead of performing alternating projections,
relaxed projections are alternated. The method can be interpreted as an
averaged iteration of a nonexpansive mapping. Therefore, a recently proposed
line search method for such algorithms is applicable to GAP. We evaluate this
line search and show situations when the line search can be performed with
little additional cost. We also present a variation of the basic line search
for GAP - the projected line search. We prove its convergence and show that the
line search condition is convex in the step length parameter. We show that
almost all convex optimization problems can be solved using this approach and
numerical results show superior performance with both the standard and the
projected line search, sometimes by several orders of magnitude, compared to
the nominal method.


  Multivariate time series forecasting is a challenging task because the data
involves a mixture of long- and short-term patterns, with dynamic
spatio-temporal dependencies among variables. Existing graph neural networks
(GNN) typically model multivariate relationships with a pre-defined spatial
graph or learned fixed adjacency graph. It limits the application of GNN and
fails to handle the above challenges. In this paper, we propose a novel
framework, namely static- and dynamic-graph learning-neural network (SDGL). The
model acquires static and dynamic graph matrices from data to model long- and
short-term patterns respectively. Static matric is developed to capture the
fixed long-term association pattern via node embeddings, and we leverage graph
regularity for controlling the quality of the learned static graph. To capture
dynamic dependencies among variables, we propose dynamic graphs learning method
to generate time-varying matrices based on changing node features and static
node embeddings. And in the method, we integrate the learned static graph
information as inductive bias to construct dynamic graphs and local
spatio-temporal patterns better. Extensive experiments are conducted on two
traffic datasets with extra structural information and four time series
datasets, which show that our approach achieves state-of-the-art performance on
almost all datasets. If the paper is accepted, I will open the source code on
github.


  Let H be a semisimple finite dimensional Hopf algebra over a field F of zero
characteristic. We prove three major theorems: 1. The Representability theorem
which states that every H-module (associative) F-algebra W satisfying an
ordinary PI, has the same H-identities as the Grassmann envelope of an
$H\otimes\left(F\mathbb{Z}/2\mathbb{Z}\right)^{*}$-module algebra which is
finite dimensional over a field extension of F. 2. The Specht problem for
H-module (ordinary) PI algebras. That is, every H-T-ideal $\Gamma$ which
contains an ordinary PI contains H-polynomials $f_{1},...,f_{s}$ which
generates $\Gamma$ as an H-T-ideal. 3. Amitsur's conjecture for H-module
algebras, saying that the exponent of the H-codimension sequence of an ordinary
PI H-module algebra is an integer.


  In this paper, a novel convexification approach for Small-Signal Stability
Constraint Optimal Power Flow (SSSC-OPF) has been presented that does not rely
on eigenvalue analysis. The proposed methodology is based on the sufficient
condition for the small-signal stability, developed as a Bilinear Matrix
Inequality (BMI), and uses network structure-preserving Differential Algebraic
Equation (DAE) modeling of the power system. The proposed formulation is based
on Semi-definite Programming (SDP) and objective penalization that has been
proposed for feasible solution recovery, making the method computationally
efficient for large-scale systems. A vector-norm based objective penalty
function has also been proposed for feasible solution recovery while working
over large and dense BMIs with matrix variables. An effectiveness study carried
out on WECC 9-bus, New England 39-bus, and IEEE 118-bus test systems show that
the proposed method is capable of achieving a stable equilibrium point without
inflicting a high stability-induced additional cost.


  Automatic Chinese classical poetry generation has attracted much research
interest, but achieving effective control over format and content
simultaneously remains challenging. Traditional systems usually accept keywords
as user inputs, resulting in limited control over content. Large language
models (LLMs) improve content control by allowing unrestricted user
instructions, but the token-by-token generation process frequently makes format
errors. Motivated by this, we propose CharPoet, a Chinese classical poetry
generation system based on token-free LLM, which provides effective control
over both format and content. Our token-free architecture generates in a
character-by-character manner, enabling precise control over the number of
characters. Pruned from existing token-based LLMs, CharPoet inherits their
pretrained capabilities and can generate poetry following instructions like
"Write me a poem for my mother's birthday." CharPoet achieves format accuracy
above 0.96, outperforming Jiuge-GPT-2 (0.91) and GPT-4 (0.38). In terms of
content quality, CharPoet surpasses traditional systems including Jiuge, and is
comparable to other LLMs. Our system is open source and available at
https://modelscope.cn/models/CharPoet/CharPoet. A video demonstration of
CharPoet is available at https://youtu.be/voZ25qEp3Dc.


  The technique of database refactoring is all about applying disciplined and
controlled techniques to change an existing database schema. The problem is to
successfully create a Database Refactoring Framework for databases. This paper
concentrates on the feasibility of adapting this concept to work as a generic
template. To retain the constraints regardless of the modifications to the
metadata, the paper proposes a MetaData Manipulation Tool to facilitate change.
The tool adopts a Template Design Pattern to make it database independent. The
paper presents a drawback of using java for constraint extraction and proposes
an alternative.


  We explain the anomalous features in the Hall data observed experimentally in
the normal state of the high-Tc superconductors. We show that a consistent
treatment of the local spin fluctuations in a model with x-ray edge
singularities in d=oo reproduces the temperature and the doping dependence of
the Hall constant as well as the Hall angle in the normal state. The model has
also been invoked to justify the marginal-Fermi-liquid behavior, and provides a
consistent explanation of the Hall anomalies for a non-Fermi liquid in d=oo.


  Face Recognition (FR) has been the interest to several researchers over the
past few decades due to its passive nature of biometric authentication. Despite
high accuracy achieved by face recognition algorithms under controlled
conditions, achieving the same performance for face images obtained in
surveillance scenarios, is a major hurdle. Some attempts have been made to
super-resolve the low-resolution face images and improve the contrast, without
considerable degree of success. The proposed technique in this paper tries to
cope with the very low resolution and low contrast face images obtained from
surveillance cameras, for FR under surveillance conditions. For Support Vector
Machine classification, the selection of appropriate kernel has been a widely
discussed issue in the research community. In this paper, we propose a novel
kernel selection technique termed as MFKL (Multi-Feature Kernel Learning) to
obtain the best feature-kernel pairing. Our proposed technique employs a
effective kernel selection by Multiple Kernel Learning (MKL) method, to choose
the optimal kernel to be used along with unsupervised domain adaptation method
in the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem.
Rigorous experimentation has been performed on three real-world surveillance
face datasets : FR\_SURV, SCface and ChokePoint. Results have been shown using
Rank-1 Recognition Accuracy, ROC and CMC measures. Our proposed method
outperforms all other recent state-of-the-art techniques by a considerable
margin.


  The correlation between static magnetoelectric coupling and magnetic
structures was investigated in $TbMn_{0.98}Fe_{0.02}O_{3}$ with magnetic field
up to 8 T and down to 2 K. Neutron diffraction experiments reveal a substantial
increase in the temperature dependence of the incommensurate modulation vector
of the antiferromagnetic phase, responsible for the significant increase in
magnetoelectric coupling, i.e., stronger changes of ferroelectric polarization
with applied magnetic field than in other multiferroic materials. This shows
that even a small 2% substitution of $Mn^{3+}$ by $Fe^{3+}$ significantly
enhances the destabilization of the incommensurably modulated magnetic
cycloidal structure of $TbMnO_{3}$ in a magnetic field above 5 T.


  The Pierre Auger Observatory, recently completed, has been operational since
2004. As a hybrid experiment, it allows for a wide range of measurements of
UHECR-induced extensive air showers (EAS), including measurements of the EAS
particle content on ground which is sensitive to high-energy hadronic
interactions. We present the results of several independent measurements of the
EAS muon content on ground in Auger data at a primary energy of 10 EeV. We
discuss implications on high-energy hadronic interaction models and cosmic ray
composition.


  This is an introductory course to the Lanczos Method and Density Matrix
Renormalization Group Algorithms(DMRG), two among the leading numerical
techniques applied in studies of low-dimensional quantum models. The idea of
studying the models on clusters of a finite size in order to extract their
physical properties is briefly discussed. The important role played by the
model symmetries is also examined. Special emphasis is given to the DMRG.


  Using the dynamical cluster approximation and quantum monte carlo we
calculate the single-particle spectra of the Hubbard model with next-nearest
neighbor hopping $t'$. In the underdoped region, we find that the pseudogap
along the zone diagonal in the electron doped systems is due to long range
antiferromagnetic correlations. The physics in the proximity of $(0,\pi)$ is
dramatically influenced by $t'$ and determined by the short range correlations.
The effect of $t'$ on the low energy ARPES spectra is weak except close to the
zone edge. The short range correlations are sufficient to yield a pseudogap
signal in the magnetic susceptibility, produce a concomitant gap in the
single-particle spectra near $(\pi,\pi/2)$ but not necessarily at a location in
the proximity of Fermi surface.


  The scope of the paper is threefold. First, we build on recent work by Hayden
to compute Hedden's tau-invariant $\tau_{\xi}(L)$ in the case when $\xi$ is a
Stein fillable contact structure, and $L$ is a transverse link arising as the
boundary of a $J$-holomorphic curve. This leads to a new proof of the relative
Thom conjecture for Stein domains. Secondly, we compare the invariant
$\tau_\xi$ to the Grigsby-Ruberman-Strle topological tau-invariant
$\tau_{\mathfrak s}$, associated to the $\text{Spin}^c$-structure $\mathfrak
s=\mathfrak s_\xi$ of the contact structure, to admit topological obstructions
for a link type to contain a holomorphically fillable transverse
representative. We combine the latter with a result of Mark and Tosun to
confirm a conjecture of Gompf: no standardly-oriented Brieskorn homology sphere
bounds a rational homology four-ball Stein filling, with any contact structure.
Finally, we use our main result together with methods from lattice cohomology
to compute the $\tau_{\mathfrak s}$-invariants of certain links in lens spaces,
and estimate their PL slice genus.


  Detrended fluctuation analysis is used to investigate correlations between
the monthly average of the maximum daily temperatures for different locations
in the continental US and the different climates these locations have. When we
plot the scaling exponents obtained from the detrended fluctuation analysis
versus the standard deviation of the temperature fluctuations we observe
crowding of data points belonging to the same climates. Thus, we conclude that
by observing the long-time trends in the fluctuations of temperature it would
be possible to distinguish between different climates.


  In the context of bacteria and models of their evolution under genome
rearrangement, we explore a novel application of group representation theory to
the inference of evolutionary history. Our contribution is to show, in a very
general maximum likelihood setting, how to use elementary matrix algebra to
sidestep intractable combinatorial computations and convert the problem into
one of eigenvalue estimation amenable to standard numerical approximation
techniques.


  Multiple parton interactions are typically implemented in Montecarlo codes by
assuming a Poissonian distribution of collisions with average number depending
on the impact parameter. A possible generalization, which links the process to
hadronic diffraction, is shortly discussed.


  When an explicit expression for a probability distribution function $F(x)$
can not be found, asymptotic properties of the tail probability function
$\bar{F}(x)=1-F(x)$ are very valuable, since they provide approximations or
bounds for system performance, and approaches for computing probability
distribution. In this paper, we study tail asymptotic properties for the number
of the customers in the $M/G/1$ retrial queueing system. For queueing systems,
studies on asymptotic tails have mainly concentrated on the first order
asymptotic behavior. To best our knowledge, there is no second order tail
asymptotic analysis for retrial queueing models with heavy-tailed service time.
Second order asymptotic expansions provide the refined asymptotic results from
the first order approximation, and are often more difficult to obtain as
expected. The main contribution of this paper is the second order asymptotic
analysis for the $M/G/1$ retrial queue.


  We develop a non-perturbative formalism for scalar metric fluctuations from a
5D extended version of general relativity in vacuum. In this work we
concentrate our efforts on calculations valid on large cosmological scales,
which are the dominant during the inflationary phase of the universe. The
resulting metric in this limit is obtained after implementing a planar
coordinate transformation on a 5D Ricci-flat metric solution. We calculate the
spectrum of these fluctuations with an effective 4D Schwarzschild-de Sitter
spacetime on cosmological scales, which is obtained after we make a static
foliation on the non-compact extra coordinate. Our results show how the squared
metric fluctuations of the primordial universe become scale invariant with the
inflationary expansion.


  Alternating quantifier depth is a natural measure of difficulty required to
express first order logical sentences. We define a sequence of first order
properties on rooted, locally finite trees in a recursive manner, and provide
rigorous arguments for finding the alternating quantifier depth of each
property in the sequence, using Ehrenfeucht-Fra\"{i}ss\'{e} games.


  We show how to formulate Yang-Mills Theory in \m{2+1} dimensions as a
hamitonian system within a simplicial regularization and construct its
quantization, with special attention to the mass gap. An approximate conformal
invariance of the hamiltonian of this theory is useful to construct a continuum
limit.


  Dual-comb microscopy (DCM), an interesting imaging modality based on the
optical-frequency-comb (OFC) mode and image pixel one-to-one correspondence,
benefits from scan-less full-field imaging and simultaneous confocal amplitude
and phase imaging. However, the two fully frequency-stabilized OFC sources
requirement hampers DCM practicality due to the complexity and costs. Here, a
bidirectional single-cavity dual-comb fiber laser (SCDCFL) is adopted as a DCM
low-complexity OFC source. Computational image correction reduces the image
blur caused by the SCDCFL residual timing jitter. Nanometer-order step surface
profilometry with a 14.0 nm uncertainty highlights the image-corrected DCM
effectiveness. The proposed method enhances the DCM versality and practicality.


  Motivated by the absence of cooperative Jahn-Teller effect and of magnetic
ordering in LiNiO$_2$, a layered oxide with triangular planes, we study a
general spin-orbital model on the triangular lattice. A mean-field approach
reveals the presence of several singlet phases between the SU(4) symmetric
point and a ferromagnetic phase, a conclusion supported by exact
diagonalizations of finite clusters. We argue that one of the phases,
characterized by a large number of low-lying singlets associated to dimer
coverings of the triangular lattice, could explain the properties of LiNiO$_2$,
while a ferro-orbital phase that lies nearby in parameter space leads to a new
prediction for the magnetic properties of NaNiO$_2$.


  We generalize the respective ``double recurrence'' results of Bourgain and of
the second author, which established for pairs of $L^{\infty}$ functions on a
finite measure space the a.e. convergence of the discrete bilinear ergodic
averages and of the discrete bilinear Hilbert averages defined by invertible
measure-preserving point transformations. Our generalizations are set in the
context of arbitrary sigma-finite measure spaces and take the form of a.e.
convergence of such discrete averages, as well as of their continuous variable
counterparts, when these averages are defined by Lebesgue space isometries and
act on $L^{p_{1}}\times L^{p_{2}}$ ($ 1<p_{1},p_{2}<\infty $,
$p_{1}^{-1}+p_{2}^{-1}<3/2$). In the setting of an arbitrary measure space,
this yields the a.e. convergence of these discrete bilinear averages when they
act on $L^{p_{1}}\times L^{p_{2}}$ and are defined by an invertible
measure-preserving point transformation.


  Strategic asset allocation requires an investor to select stocks from a given
basket of assets. The perspective of our investor is to maximize risk-adjusted
alpha returns relative to a benchmark index. Historical returns are used to
provide inputs into an optimization algorithm. Our approach uses Bayesian
regularization to not only provide stock selection but also optimal sequential
portfolio weights. By incorporating investor preferences with a number of
different regularization penalties we extend the approaches of Black (1992) and
Puelz (2015). We tailor standard sparse MCMC algorithms to calculate portfolio
weights and perform selection. We illustrate our methodology on stock selection
from the SP100 stock index and from the top fifty holdings of two hedge funds
Renaissance Technologies and Viking Global. Finally, we conclude with
directions for future research.


  Recent work has shown that machine learning (ML) models can be trained to
accurately forecast the dynamics of unknown chaotic dynamical systems.
Short-term predictions of the state evolution and long-term predictions of the
statistical patterns of the dynamics (``climate'') can be produced by employing
a feedback loop, whereby the model is trained to predict forward one time step,
then the model output is used as input for multiple time steps. In the absence
of mitigating techniques, however, this technique can result in artificially
rapid error growth. In this article, we systematically examine the technique of
adding noise to the ML model input during training to promote stability and
improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise
Training (LMNT), a regularization technique that deterministically approximates
the effect of many small, independent noise realizations added to the model
input during training. Our case study uses reservoir computing, a
machine-learning method using recurrent neural networks, to predict the
spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir
computers trained with noise or with LMNT produce climate predictions that
appear to be indefinitely stable and have a climate very similar to the true
system, while reservoir computers trained without regularization are unstable.
Compared with other regularization techniques that yield stability in some
cases, we find that both short-term and climate predictions from reservoir
computers trained with noise or with LMNT are substantially more accurate.
Finally, we show that the deterministic aspect of our LMNT regularization
facilitates fast hyperparameter tuning when compared to training with noise.


  Living organisms produce metabolites of many types via their metabolisms.
Especially, flavonoids, a kind of secondary metabolites, of plant species are
interesting examples. Since plant species are believed to have specific
flavonoids with respect to diverse environment, elucidation of design
principles of metabolite distributions across plant species is important to
understand metabolite diversity and plant evolution. In the previous work, we
found heterogeneous connectivity in metabolite distributions, and proposed a
simple model to explain a possible origin of heterogeneous connectivity. In
this paper, we show further structural properties in the metabolite
distribution among families inspired by analogy with plant-animal mutualistic
networks: nested structure and modular structure. An earlier model represents
that these structural properties in bipartite relationships are determined
based on traits of elements and external factors. However, we find that the
architecture of metabolite distributions is described by simple evolution
processes without trait-based mechanisms by comparison between our model and
the earlier model. Our model can better predict nested structure and modular
structure in addition to heterogeneous connectivity both qualitatively and
quantitatively. This finding implies an alternative possible origin of these
structural properties, and suggests simpler formation mechanisms of metabolite
distributions across plant species than expected.


  We consider a theory in which supersymmetry is partially spontaneously broken
and show that the dynamical fields in the same supersymmetric multiplet as the
Goldstino are Goldstone bosons whose corresponding generators are central
charges in the underlying supersymmetry algebra. We illustrate how this works
for four dimensional Born-Infeld theory and five brane of M theory. We
conjecture, with supporting arguments, that the dynamics of the branes of M
theory can be extended so as to possess an E_{11} symmetry.


  We propose a theoretically justified and practically applicable slice
sampling based Markov chain Monte Carlo (MCMC) method for approximate sampling
from probability measures on Riemannian manifolds. The latter naturally arise
as posterior distributions in Bayesian inference of matrix-valued parameters,
for example belonging to either the Stiefel or the Grassmann manifold. Our
method, called geodesic slice sampling, is reversible with respect to the
distribution of interest, and generalizes Hit-and-run slice sampling on
$\mathbb{R}^{d}$ to Riemannian manifolds by using geodesics instead of straight
lines. We demonstrate the robustness of our sampler's performance compared to
other MCMC methods dealing with manifold valued distributions through extensive
numerical experiments, on both synthetic and real data. In particular, we
illustrate its remarkable ability to cope with anisotropic target densities,
without using gradient information and preconditioning.


  Remote sensing images are useful for a wide variety of planet monitoring
applications, from tracking deforestation to tackling illegal fishing. The
Earth is extremely diverse -- the amount of potential tasks in remote sensing
images is massive, and the sizes of features range from several kilometers to
just tens of centimeters. However, creating generalizable computer vision
methods is a challenge in part due to the lack of a large-scale dataset that
captures these diverse features for many tasks. In this paper, we present
SatlasPretrain, a remote sensing dataset that is large in both breadth and
scale, combining Sentinel-2 and NAIP images with 302M labels under 137
categories and seven label types. We evaluate eight baselines and a proposed
method on SatlasPretrain, and find that there is substantial room for
improvement in addressing research challenges specific to remote sensing,
including processing image time series that consist of images from very
different types of sensors, and taking advantage of long-range spatial context.
Moreover, we find that pre-training on SatlasPretrain substantially improves
performance on downstream tasks, increasing average accuracy by 18% over
ImageNet and 6% over the next best baseline. The dataset, pre-trained model
weights, and code are available at https://satlas-pretrain.allen.ai/.


  A systematic analysis of giant quadrupole resonances is performed for several
nuclei, from $^{30}$Si to $^{208}$Pb, within the subtracted second
random--phase--approximation (SSRPA) model in the framework of the
energy--density--functional theory. Centroid energies and widths of the
isoscalar giant quadrupole resonances are compared with the corresponding
random--phase--approximation (RPA) values. We find lower SSRPA centroid
energies compared to the RPA values leading, in general, to a better agreement
with the experimental data. As far as the widths are concerned, we observe for
both SSRPA and RPA cases a global attenuation of the single--particle Landau
damping going from lighter to heavier nuclei and we obtain, systematically,
larger widths in the SSRPA model compared to the RPA case. For some selected
nuclei for which high--resolution ($p,p'$) experimental data are available,
namely $^{40}$Ca, $^{90}$Zr, $^{120}$Sn, and $^{208}$Pb, the theoretical
strength distributions are directly compared with the experimental spectra. We
observe a significant improvement, with respect to RPA results, in the
description of the spreading widths and of the fragmentation of the obtained
spectra, due to the coupling between 1 particle-1 hole and 2 particle-2 hole
configurations.


  Human visual perception is a complex, dynamic and fluctuating process. In
addition to the incoming visual stimulus, it is affected by many other factors
including temporal context, both external and internal to the observer. In this
study we investigate the dynamic properties of psychophysical responses to a
continuous stream of visual near-threshold detection tasks. We manipulate the
incoming signals to have temporal structures with various characteristic
timescales. Responses of human observers to these signals are analyzed using
tools that highlight their dynamical features as well.
  We find that two opposing biases shape perception, and operate over distinct
timescales. Positive recency appears over short times, e.g. consecutive trials.
Adaptation, entailing an increased probability of changed response, reflects
trends over longer times. Analysis of psychometric curves conditioned on
various temporal events reveals that the balance between the two biases can
shift depending on their interplay with the temporal properties of the input
signal. A simple mathematical model reproduces the experimental data in all
stimulus regimes. Taken together, our results support the view that visual
response fluctuations reflect complex internal dynamics, possibly related to
higher cognitive processes.


  Yellow Hypergiants (YHGs) and B[e] supergiants (B[e]SGs), though in different
phases in their evolution, display many features in common. This is partly due
to the fact that both types of objects undergo strong, often asymmetric mass
loss, and the ejected material accumulates in shells, rings, or disk-like
structures, giving rise to emission from warm molecules and dust. We performed
an optical spectroscopic survey of northern Galactic emission-line stars aimed
at identifying tracers for the structure and kinematics of circumstellar
environments. We identified two sets of lines, [O I] and [Ca II], which
originate from the discs of B[e]SGs. The same set of lines is observed in V1302
Aql and V509 Cas, which are both hot YHGs. While V1302 Aql is known to have a
disc-like structure, the kinematical broadening of the lines in V509 Cas
suggest a Keplerian disk or ring around this star alike their hotter B[e]SG
counterparts.


  A $(2+1)$-dimensional gapped quantum many-body system can have a
topologically protected energy current at its edge. The magnitude of this
current is determined entirely by the temperature and the chiral central
charge, a quantity associated with the effective field theory of the edge. We
derive a formula for the chiral central charge that, akin to the topological
entanglement entropy, is completely determined by the many-body ground state
wave function in the bulk. According to our formula, nonzero chiral central
charge gives rise to a topological obstruction that prevents the ground state
wave function from being real-valued in any local product basis.


  Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision
due to their compactness and continuous representation capabilities. However,
addressing dense correspondences and semantic relationships across DIF-encoded
shapes remains a critical challenge, limiting their applications in texture
transfer and shape analysis. Moreover, recent endeavors in 3D shape generation
using DIFs often neglect correspondence and topology preservation. This paper
presents HNDF (Hybrid Neural Diffeomorphic Flow), a method that implicitly
learns the underlying representation and decomposes intricate dense
correspondences into explicitly axis-aligned triplane features. To avoid
suboptimal representations trapped in local minima, we propose hybrid
supervision that captures both local and global correspondences. Unlike
conventional approaches that directly generate new 3D shapes, we further
explore the idea of shape generation with deformed template shape via
diffeomorphic flows, where the deformation is encoded by the generated triplane
features. Leveraging a pre-existing 2D diffusion model, we produce high-quality
and diverse 3D diffeomorphic flows through generated triplanes features,
ensuring topological consistency with the template shape. Extensive experiments
on medical image organ segmentation datasets evaluate the effectiveness of HNDF
in 3D shape representation and generation.


  We study random uniform permutations in an important class of
pattern-avoiding permutations: the separable permutations. We describe the
asymptotics of the number of occurrences of any fixed given pattern in such a
random permutation in terms of the Brownian excursion. In the recent
terminology of permutons, our work can be interpreted as the convergence of
uniform random separable permutations towards a "Brownian separable permuton".


  The first renormalisable quantum field theories on non-commutative space have
been found recently. We review this rapidly growing subject.


  In planar microcavities, the transverse-electric and transverse-magnetic
(TE-TM) mode splitting of cavity photons arises due to their different
penetration into the Bragg mirrors and can result in optical spin-orbit
coupling (SOC). In this work, we find that in a liquid crystal (LC) microcavity
filled with perovskite microplates, the pronounced TE-TM splitting gives rise
to a strong SOC that leads to the spatial instability of microcavity polariton
condensates under single-shot excitation. Spatially varying hole burning and
mode competition occurs between polarization components leading to different
condensate profiles from shot to shot. The single-shot polariton condensates
become stable when the SOC vanishes as the TE and TM modes are spectrally well
separated from each other, which can be achieved by application of an electric
field to our LC microcavity with electrically tunable anisotropy. Our findings
are well reproduced and traced back to their physical origin by our detailed
numerical simulations. With the electrical manipulation our work reveals how
the shot-to-shot spatial instability of spatial polariton profiles can be
engineered in anisotropic microcavities at room temperature, which will benefit
the development of stable polariton-based optoeletronic and light-emitting
devices.


  We propose AccoMontage-3, a symbolic music automation system capable of
generating multi-track, full-band accompaniment based on the input of a lead
melody with chords (i.e., a lead sheet). The system contains three modular
components, each modelling a vital aspect of full-band composition. The first
component is a piano arranger that generates piano accompaniment for the lead
sheet by transferring texture styles to the chords using latent chord-texture
disentanglement and heuristic retrieval of texture donors. The second component
orchestrates the piano accompaniment score into full-band arrangement according
to the orchestration style encoded by individual track functions. The third
component, which connects the previous two, is a prior model characterizing the
global structure of orchestration style over the whole piece of music. From end
to end, the system learns to generate full-band accompaniment in a
self-supervised fashion, applying style transfer at two levels of polyphonic
composition: texture and orchestration. Experiments show that our system
outperforms the baselines significantly, and the modular design offers
effective controls in a musically meaningful way.


  The vexing error of excess variance in the sizing of single particles
degrades accuracy in applications ranging from quality control of nanoparticle
products to hazard assessment of nanoplastic byproducts. The particular
importance of lipid nanoparticles for vaccine and medicine delivery motivates
this comment on a publication$^{\textrm{1}}$ in ACS Nano. In ref 1, the
benchmark measurements of a nanoparticle standard manifest large errors of the
size distribution that contradict the claim of validation. Such errors can bias
the correlation of fluorescence intensity as an optical proxy for the molecular
loading of lipid nanoparticles and give misleading insights from power-law
models of intensity$-$size data. Looking forward, measurement error models have
the potential to address this widespread issue.


  We study stochastic acceleration models for the Fermi bubbles. Turbulence is
excited just behind the shock front via Kelvin--Helmholtz, Rayleigh--Taylor, or
Richtmyer--Meshkov instabilities, and plasma particles are continuously
accelerated by the interaction with the turbulence. The turbulence gradually
decays as it goes away from the shock fronts. Adopting a phenomenological model
for the stochastic acceleration, we explicitly solve the temporal evolution of
the particle energy distribution in the turbulence. Our results show that the
spatial distribution of high-energy particles is different from those for a
steady solution. We also show that the contribution of electrons that escaped
from the acceleration regions significantly softens the photon spectrum. The
photon spectrum and surface brightness profile are reproduced by our models. If
the escape efficiency is very high, the radio flux from the escaped low-energy
electrons can be comparable to that of the WMAP haze. We also demonstrate
hadronic models with the stochastic acceleration, but they are unlikely in the
viewpoint of the energy budget.


  Savage et al. have recently put forward the claim that results from the
XENON10 experiment are incompatible with the totality of both DAMA/LIBRA and
CoGeNT experimental regions. In this brief note the source of this erroneous
conclusion is identified in a misinterpretation of the XENON10 efficiency in
the detection of S1 light from low-energy nuclear recoils.


  We present a scale-invariant theory, conformal gravity, which closely
resembles the geometrodynamical formulation of general relativity (GR). While
previous attempts to create scale-invariant theories of gravity have been based
on Weyl's idea of a compensating field, our direct approach dispenses with this
and is built by extension of the method of best matching w.r.t scaling
developed in the parallel particle dynamics paper by one of the authors. In
spatially-compact GR, there is an infinity of degrees of freedom that describe
the shape of 3-space which interact with a single volume degree of freedom. In
conformal gravity, the shape degrees of freedom remain, but the volume is no
longer a dynamical variable. Further theories and formulations related to GR
and conformal gravity are presented.
  Conformal gravity is successfully coupled to scalars and the gauge fields of
nature. It should describe the solar system observations as well as GR does,
but its cosmology and quantization will be completely different.


  Due to long-distance correlation and powerful pretrained models,
transformer-based methods have initiated a breakthrough in visual object
tracking performance. Previous works focus on designing effective architectures
suited for tracking, but ignore that data augmentation is equally crucial for
training a well-performing model. In this paper, we first explore the impact of
general data augmentations on transformer-based trackers via systematic
experiments, and reveal the limited effectiveness of these common strategies.
Motivated by experimental observations, we then propose two data augmentation
methods customized for tracking. First, we optimize existing random cropping
via a dynamic search radius mechanism and simulation for boundary samples.
Second, we propose a token-level feature mixing augmentation strategy, which
enables the model against challenges like background interference. Extensive
experiments on two transformer-based trackers and six benchmarks demonstrate
the effectiveness and data efficiency of our methods, especially under
challenging settings, like one-shot tracking and small image resolutions.