  We consider a quantum dimer model (QDM) on the kagome lattice which was
introduced recently [Phys. Rev. Lett. 89, 137202 (2002)]. It realizes a Z_2
liquid phase and its spectrum was obtained exactly. It displays a topological
degeneracy when the lattice has a non-trivial geometry (cylinder, torus, etc).
We discuss and solve two extensions of the model where perturbations along
lines are introduced: first the introduction of a potential energy term
repelling (or attracting) the dimers along a line is added, second a
perturbation allowing to create, move or destroy monomers. For each of these
perturbations we show that there exists a critical value above which, in the
thermodynamic limit, the degeneracy of the ground-state is lifted from 2 (on a
cylinder) to 1. In both cases the exact value of the gap between the first two
levels is obtained by a mapping to an Ising chain in transverse field. This
model provides an example of solvable Hamiltonian for a topological quantum bit
where the two perturbations act as a diagonal and a transverse operator in the
two-dimensional subspace. We discuss how crossing the transitions may be used
in the manipulation of the quantum bit to optimize simultaneously the frequency
of operation and the losses due to decoherence.


  New neutrino-nucleus interaction cross-section measurements are required to
improve nuclear models sufficiently for future long-baseline neutrino
experiments to meet their sensitivity goals. A time projection chamber (TPC)
filled with a high-pressure gas is a promising detector to characterise the
neutrino sources planned for such experiments. A gas-filled TPC is ideal for
measuring low-energy particles as they travel much further in gas than solid or
liquid neutrino detectors. Using a high-pressure gas increases the target
density, resulting in more neutrino interactions. This paper will examine the
suitability of multiwire proportional chambers (MWPCs) taken from the ALICE TPC
to be used as the readout chambers of a high-pressure gas TPC. These chambers
were previously operated at atmospheric pressure. We tested one such MWPC at up
to almost 5 bar absolute (barA) with the UK high-pressure test stand at Royal
Holloway, University of London. This paper reports the successful operation of
an ALICE TPC outer readout chamber (OROC) at pressures up to 4.8 bar absolute
with Ar-CH$_{4}$ mixtures with a CH$_{4}$ content between 2.8% and 5.0%, and so
far up to 4 bar absolute with Ar-CO$_{2}$ (90-10). We measured the charge gain
of this OROC using signals induced by an $^{55}$Fe source. The largest gain
achieved at 4.8 bar was $64\pm2)\cdot10^{3}$ at stable conditions with an anode
wire voltage of 2990 V in Ar-CH$_{4}$ (95.9-4.1). In Ar-CO$_{2}$ a gain of
$(4.2\pm0.1)\cdot10^{3}$ was observed at an anode voltage of 2975 V at 4 barA
gas pressure. Based on all our gain measurements, we extrapolate that, at the
10 barA pressure necessary to fit 1 tonne of gas into the ALICE TPC volume, a
gain of 5000 in Ar-CO$_{2}$ (90-10) (10000 in Ar-CH$_{4}$ with $\sim\!$ 4%
CH$_{4}$ content) may be achieved with an OROC anode voltage of 4.2 V ($\sim\!$
3.1 kV).


  A QCD-based treatment of projectile size fluctuations is used to compute
inelastic diffractive cross sections $\sigma_{diff}$ for coherent
hadron-nuclear processes. We find that fluctuations near the average size give
the major contribution to the cross section with $ \le few \%$ contribution
from small size configurations.
  The computed values of $\sigma_{diff}$ are consistent with the limited
available data. The importance of coherent diffraction studies for a wide range
of projectiles for high energy Fermilab fixed target experiments is emphasized.
The implications of these significant color fluctuations for relativistic heavy
ion collisions are discussed.


  Stress is known as one of the major factors threatening human health. A large
number of studies have been performed in order to either assess or relieve
stress by analyzing the brain and heart-related signals. In this study, signals
produced by functional Near-Infrared Spectroscopy (fNIRS) of the brain recorded
from 10 healthy volunteers are employed to assess the stress induced by the
Montreal Imaging Stress Task by means of a deep learning system. The proposed
deep learning system consists of two main parts: First, the one-dimensional
convolutional neural network is employed to build informative feature maps.
Then, a stack of deep fully connected layers is used to predict the stress
existence probability. Experiment results showed that the trained fNIRS model
performs stress classification by achieving 88.52 -+ 0.77% accuracy. Employment
of the proposed deep learning system trained on the fNIRS measurements leads to
higher stress classification accuracy than the existing methods proposed in
fNIRS studies in which the same experimental procedure has been employed. The
proposed method suggests better stability with lower variation in prediction.
Furthermore, its low computational cost opens up the possibility to be applied
in real-time stress assessment.


  We investigate the two classes of finite subgroups of SU(3) that are called
type C and D in the book of Miller, Blichfeldt and Dickson. We present two
theorems which fully determine the form of the generators in a suitable basis.
After exploring further properties of these groups, we are able to construct a
complete list of infinite series in which these groups are arranged. For type C
there are infinitely many series whereas for type D there are only two.
Explicit examples of these series are presented which illustrate the general
results.


  We present an experimental study on optical properties and dynamics of direct
and spatially and momentum indirect excitons in AlGaAs/AlAs quantum wells near
the crossover between $\varGamma$- and $X$-valley confined electron states. The
time-integrated photoluminescence experiment at $T=$4.8 K revealed three
simultaneously observed optical transitions resulting from (a) a direct exciton
recombination, involving an electron and a hole states both located in the
$\varGamma$-valley in the quantum well layer, and (b) two spatially and
momentum indirect excitons, comprising of the confined electron states in the
$X$-valley in the AlAs barrier with different effective masses and quantum well
holes in the $\varGamma$-valley. The measured spatial extent, density
dependence and temperature dependence of the structure photoluminescence
revealed characteristics necessary to pinpoint the states' nature and provided
their characterization crucial in future device design. Temporal dynamics
observed in the time-resolved photoluminescence measurement showed the
complexity of the capture and recombination dynamics, largely affected by
nonradiative processes, what is additionally critical in the system's use. This
solid state platform hosting both direct and indirect excitons in a highly
tunable monolithic system can benefit and underline the operation principles of
novel electronic and photonic devices.


  We present dyonic multi-membrane solutions of the N=2 D=8 supergravity theory
that serves as the effective field theory of the $T^2$-compactified type II
superstring theory. The `electric' charge is fractional for generic asymptotic
values of an axion field, as for D=4 dyons. These membrane solutions are
supersymmetric, saturate a Bogomolnyi bound, fill out orbits of an $Sl(2;\Z)$
subgroup of the type II D=8 T-duality group, and are non-singular when
considered as solutions of $T^3$-compactified D=11 supergravity. On $K_3$
compactification to D=4, the conjectured type II/heterotic equivalence allows
the $Sl(2;\Z)$ group to be reinterpreted as the S-duality group of the
toroidally compactified heterotic string and the dyonic membranes wrapped
around homology two-cycles of $K_3$ as S-duals of perturbative heterotic string
states.


  We study three dimensional oceanic Lagrangian Coherent Structures (LCSs) in
the Benguela region, as obtained from an output of the ROMS model. To do that
we first compute Finite-Size Lyapunov exponent (FSLE) fields in the region
volume, characterizing mesoscale stirring and mixing. Average FSLE values show
a general decreasing trend with depth, but there is a local maximum at about
100 m depth. LCSs are extracted as ridges of the calculated FSLE fields. They
present a "curtain-like" geometry in which the strongest attracting and
repelling structures appear as quasivertical surfaces. LCSs around a particular
cyclonic eddy, pinched off from the upwelling front are also calculated. The
LCSs are confirmed to provide pathways and barriers to transport in and out of
the eddy.


  Despite the significant achievements of Vision Transformers (ViTs) in various
vision tasks, they are constrained by the quadratic complexity. Recently, State
Space Models (SSMs) have garnered widespread attention due to their global
receptive field and linear complexity with respect to the input length,
demonstrating substantial potential across fields including natural language
processing and computer vision. To improve the performance of SSMs in vision
tasks, a multi-scan strategy is widely adopted, which leads to significant
redundancy of SSMs. For a better trade-off between efficiency and performance,
we analyze the underlying reasons behind the success of the multi-scan
strategy, where long-range dependency plays an important role. Based on the
analysis, we introduce Multi-Scale Vision Mamba (MSVMamba) to preserve the
superiority of SSMs in vision tasks with limited parameters. It employs a
multi-scale 2D scanning technique on both original and downsampled feature
maps, which not only benefits long-range dependency learning but also reduces
computational costs. Additionally, we integrate a Convolutional Feed-Forward
Network (ConvFFN) to address the lack of channel mixing. Our experiments
demonstrate that MSVMamba is highly competitive, with the MSVMamba-Tiny model
achieving 82.8% top-1 accuracy on ImageNet, 46.9% box mAP, and 42.2% instance
mAP with the Mask R-CNN framework, 1x training schedule on COCO, and 47.6% mIoU
with single-scale testing on ADE20K.Code is available at
\url{https://github.com/YuHengsss/MSVMamba}.


  Spin subsystem of the copper oxides within the polar Jahn-Teller centers
model corresponds to a singlet-triplet magnet where the local boson movement
accompanied by the induced longitudinal spin fluctuations. These fluctuations
determine the features of the magnetic inelastic scattering in the high-Tc
cuprates.


  This is an expository article describing the conformalized mean curvature
flow, originally introduced by Kazhdan, Solomon, and Ben-Chen. We are
interested in applying mean curvature flow to surface parametrizations. We
discuss our own implementation of their algorithm and some limitations.


  Memristors have been at the forefront of nanoelectronics research for the
last decade, offering a valuable component to reconfigurable computing. Their
attributes have been studied extensively along with applications that leverage
their state-dependent programmability in a static fashion. However, practical
applications of memristor-based AC circuits have been rather sparse, with only
a few examples found in the literature where their use is emulated at higher
frequencies. In this work, we study the behavior of metal-oxide memristors
under an AC perturbation in a range of frequencies, from 10^3 to 10^7 Hz.
Metal-oxide memristors are found to behave as RC low-pass filters and they
present a variable cut-off frequency when their state is switched, thus
providing a window of reconfigurability when used as filters. We further study
this behaviour across distinct material systems and we show that the usable
reconfigurability window of the devices can be tailored to encompass specific
frequency ranges by amending the devices' capacitance. This study extends
current knowledge on metal-oxide memristors by characterising their frequency
dependent characteristics, providing useful insights for their use in
reconfigurable AC circuits.


  It is well-known that the sum over topologies in quantum gravity is
ill-defined, due to a super-exponential growth of the number of geometries as a
function of the space-time volume, leading to a badly divergent gravitational
path integral. Not even in dimension 2, where a non-perturbative quantum
gravity theory can be constructed explicitly from a (regularized) path
integral, has this problem found a satisfactory solution. -- In the present
work, we extend a previous 2d Lorentzian path integral, regulated in terms of
Lorentzian random triangulations, to include space-times with an arbitrary
number of handles. We show that after the imposition of physically motivated
causality constraints, the combined sum over geometries and topologies is
well-defined and possesses a continuum limit which yields a concrete model of
space-time foam in two dimensions.


  We establish quantitative stability results for the entropy power inequality
(EPI). Specifically, we show that if uniformly log-concave densities nearly
saturate the EPI, then they must be close to Gaussian densities in the
quadratic Wasserstein distance. Further, if one of the densities is log-concave
and the other is Gaussian, then the deficit in the EPI can be controlled in
terms of the $L^1$-Wasserstein distance. As a counterpoint, an example shows
that the EPI can be unstable with respect to the quadratic Wasserstein distance
when densities are uniformly log-concave on sets of measure arbitrarily close
to one. Our stability results can be extended to non-log-concave densities,
provided certain regularity conditions are met. The proofs are based on optimal
transportation.


  This is a reply to the comment from Khemani, Moessner and Sondhi (KMS)
[arXiv:2109.00551] on our manuscript [Phys. Rev. Lett. 118, 030401 (2017)]. The
main new claim in KMS is that the short-ranged model does not support an MBL
DTC phase. We show that, even for the parameter values they consider and the
system sizes they study, the claim is an artifact of an unusual choice of range
for the crucial plots. Conducting a standard finite-size scaling analysis on
the same data strongly suggests that the system is in fact a many-body
localized (MBL) discrete time crystal (DTC). Furthermore, we have carried out
additional simulations at larger scales, and provide an analytic argument,
which fully support the conclusions of our original paper. We also show that
the effect of boundary conditions, described as essential by KMS, is exactly
what one would expect, with boundary effects decreasing with increasing system
size. The other points in KMS are either a rehashing of points already in the
literature (for the long-ranged model) or are refuted by a proper finite-size
scaling analysis.


  We present a survey on the rotational and physical properties of the
dynamically low inclination Cold Classical trans-Neptunian objects. The Cold
Classicals (CCs) are primordial planetesimals and contain relevant information
about the early phase of our Solar System and planet formation over the first
100 million years after the formation of the Sun. Our project makes use of the
Magellan and the Lowell's Discovery Channel Telescopes for photometric
purposes. We obtained partial/complete lightcurves for 42 CCs. We use
statistical tests to derive general properties about the shape and rotational
frequency distributions of the CC population, and infer that the CCs have
slower rotations and are more elongated/deformed than the other trans-Neptunian
objects. Based on the available full lightcurves, the mean rotational period of
the CC population is 9.48$\pm$1.53h whereas the mean period of the rest of the
trans-Neptunian objects is 8.45$\pm$0.58h. About 65% of the trans-Neptunian
objects (excluding the CCs) have a lightcurve amplitude below 0.2mag compared
to the 36% of CCs with small amplitude. We present the full lightcurve of one
new likely contact binary: 2004 VC131 with a potential density of 1gcc for a
mass ratio of 0.4. We also have hints that 2004 MU8 and 2004 VU75 are maybe
potential contact binaries based on their sparse lightcurves but more data are
needed to confirm such a find. Assuming equal-sized binaries, we find that only
~10-25% of the Cold Classicals could be contact binaries, suggesting that there
is a deficit of contact binaries in this population compared to previous
estimates and compared to the abundant (~40-50%) possible contact binaries in
the 3:2 resonant population. This estimate is a lower limit and will increase
if non equal-sized contact binaries are also considered. Finally, we put in
context the early results of the New Horizons flyby of (486958) 2014 MU69.


  We present results from SPH-cosmological simulations, including
self-consistent modelling of SN feedback and chemical evolution, of galaxies
belonging to two clusters and twelve groups. We reproduce the mass-metallicity
(ZM) relation of galaxies classified in two samples according to their
star-forming activity, as parametrized by their sSFR, across a redshift range
up to z=2.
  Its slope shows irrelevant evolution in the passive sample, being steeper in
groups than in clusters. However, the sub-sample of high-mass passive galaxies
only is characterized by a steep increase of the slope with redshift, from
which it can be inferred that the bulk of the slope evolution of the ZM
relation is driven by the more massive passive objects. (...ABRIDGED...)
  The ZM relation for the star-forming sample reveals an increasing scatter
with redshift, indicating that it is still being built at early epochs. The
star-forming galaxies make up a tight sequence in the SFR-M_* plane at high
redshift, whose scatter increases with time alongside with the consolidation of
the passive sequence. We also confirm the anti-correlation between sSFR and
stellar mass, pointing at a key role of the former in determining the galaxy
downsizing, as the most significant means of diagnostics of the star formation
efficiency. Likewise, an anti-correlation between sSFR and metallicity can be
established for the star-forming galaxies, while on the contrary more active
galaxies in terms of simple SFR are also metal-richer.
  We discuss these results in terms of the mechanisms driving the evolution
within the high- and low-mass regimes at different epochs: mergers,
feedback-driven outflows and the intrinsic variation of the star formation
efficiency.


  Cooperative problems under continuous control have always been the focus of
multi-agent reinforcement learning. Existing algorithms suffer from the problem
of uneven learning degree with the increase of the number of agents. In this
paper, a new structure for a multi-agent actor critic is proposed, and the
self-attention mechanism is applied in the critic network and the value
decomposition method used to solve the uneven problem. The proposed algorithm
makes full use of the samples in the replay memory buffer to learn the behavior
of a class of agents. First, a new update method is proposed for policy
networks that promotes learning efficiency. Second, the utilization of samples
is improved, at the same time reflecting the ability of perspective-taking
among groups. Finally, the "deceptive signal" in training is eliminated and the
learning degree among agents is more uniform than in the existing methods.
Multiple experiments were conducted in two typical scenarios of a multi-agent
particle environment. Experimental results show that the proposed algorithm can
perform better than the state-of-the-art ones, and that it exhibits higher
learning efficiency with an increasing number of agents.


  Coronary heart disease (CHD) caused by hardening of artery walls due to
cholesterol known as atherosclerosis is responsible for large number of deaths
world-wide. The disease progression is slow, asymptomatic and may lead to
sudden cardiac arrest, stroke or myocardial infraction. Presently, imaging
techniques are being employed to understand the molecular and metabolic
activity of atherosclerotic plaques to estimate the risk. Though imaging
methods are able to provide some information on plaque metabolism they lack the
required resolution and sensitivity for detection. In this paper we consider
the clinical observations and habits of individuals for predicting the risk
factors of CHD. The identification of risk factors helps in stratifying
patients for further intensive tests such as nuclear imaging or coronary
angiography. We present a novel approach for predicting the risk factors of
atherosclerosis with an in-built imputation algorithm and particle swarm
optimization (PSO). We compare the performance of our methodology with other
machine learning techniques on STULONG dataset which is based on longitudinal
study of middle aged individuals lasting for twenty years. Our methodology
powered by PSO search has identified physical inactivity as one of the risk
factor for the onset of atherosclerosis in addition to other already known
factors. The decision rules extracted by our methodology are able to predict
the risk factors with an accuracy of $99.73%$ which is higher than the
accuracies obtained by application of the state-of-the-art machine learning
techniques presently being employed in the identification of atherosclerosis
risk studies.


  The ultrafast and ultracold electron source, based on laser cooling and
trapping of an atomic gas and its subsequent near-threshold photoionization, is
capable of generating electron bunches with a high transverse brightness at
energies of roughly 10 keV. This paper investigates the possibility of
increasing the range of applications of this source by accelerating the bunch
using radio-frequency electromagnetic fields. Bunch energies of $\sim35$ keV
are measured by analyzing the diffraction patterns generated from a
mono-crystalline gold sample. Further analysis points to a largely preserved
normalized transverse emittance during acceleration.


  Photoluminescence (PL) from excitons serves as a powerful tool to
characterize the optoelectronic property and band structure of semiconductors,
especially for atomically thin 2D transition metal chalcogenide (TMD)
materials. However, PL quenches quickly when the thickness of TMD material
increases from monolayer to few-layers, due to the change from direct to
indirect band transition. Here we show that PL can be recovered by engineering
multilayer heterostructures, with the band transition reserved to be direct
type. We report emission from layer engineered interlayer excitons from these
multilayer heterostructures. Moreover, as desired for valleytronic devices, the
lifetime, valley polarization, and the valley lifetime of the generated
interlayer excitons can all be significantly improved as compared with that in
the monolayer-monolayer heterostructure. Our results pave the way for
controlling the properties of interlayer excitons by layer engineering.


  A definition of the Laplacian on Cartesian products with mixed boundary
conditions using quadratic forms is proposed. Its consistency with the standard
definition for homogeneous and certain mixed boundary conditions is proved and,
as a consequence, tensor representations of the corresponding Sobolev spaces of
first order are derived. Moreover, a criterion for the domain to belong to the
Sobolev space of second order is proved.


  In the present paper we describe the action of (not necessarily line) bundles
of finite order on the $K$-functor in terms of classifying spaces. This
description might provide with an approach for more general twistings in
$K$-theory than ones related to the action of the Picard group.


  Neural architecture search (NAS) automatically finds the best task-specific
neural network topology, outperforming many manual architecture designs.
However, it can be prohibitively expensive as the search requires training
thousands of different networks, while each can last for hours. In this work,
we propose the Graph HyperNetwork (GHN) to amortize the search cost: given an
architecture, it directly generates the weights by running inference on a graph
neural network. GHNs model the topology of an architecture and therefore can
predict network performance more accurately than regular hypernetworks and
premature early stopping. To perform NAS, we randomly sample architectures and
use the validation accuracy of networks with GHN generated weights as the
surrogate search signal. GHNs are fast -- they can search nearly 10 times
faster than other random search methods on CIFAR-10 and ImageNet. GHNs can be
further extended to the anytime prediction setting, where they have found
networks with better speed-accuracy tradeoff than the state-of-the-art manual
designs.


  The identification of a non-trivial band topology usually relies on directly
probing the protected surface/edge states. But, it is difficult to achieve
electronically in narrow-gap topological materials due to the small (meV)
energy scales. Here, we demonstrate that band inversion, a crucial ingredient
of the non-trivial band topology, can serve as an alternative, experimentally
accessible indicator. We show that an inverted band can lead to a four-fold
splitting of the non-zero Landau levels, contrasting the two-fold splitting
(spin splitting only) in the normal band. We confirm our predictions in
magneto-transport experiments on a narrow-gap strong topological insulator,
zirconium pentatelluride (ZrTe$_5$), with the observation of additional
splittings in the quantum oscillations and also an anomalous peak in the
extreme quantum limit. Our work establishes an effective strategy for
identifying the band inversion as well as the associated topological phases for
future topological materials research.


  Retail central bank digital currency (rCBDC) is seen as a key upgrade of the
monetary system in the 21st century. However, privacy concerns are the main
impediment to rCBDC's development and roll-out. On the one hand, the rights of
people to keep their transactions private should be protected, including
against central bank surveillance. On the other hand, the central bank needs to
ensure that no over-issuance of money or other frauds occur, demanding a
certain form of knowledge of rCBDC transactions to safeguard against malicious
users. This work focuses on rCBDC architectures based on the unspent
transaction output (UTXO) data model and tackles the research problem of
preserving a sufficient degree of privacy for UTXO transaction records while
allowing the central bank to verify their correctness. User privacy is not
adequately addressed in the UTXO-based rCBDC architectures. Using evolving
public keys as pseudonyms to hide the real identities of users only solves the
privacy issue partially. Some information could still be leaked out. This work
investigates techniques to address the shortcomings of the pseudonym approach.
First, a Pedersen commitment scheme is applied to hide the transaction values
of a UTXO transaction while allowing the central bank to verify that no
over-issuance of rCBDC has occurred in the transaction.This work uses a Schnorr
signature to prove no over-issuance of money, which reduces overheads and
enables a non-interactive proof. Then, Coinjoin is applied to aggregate UTXO
transactions from different users into one larger UTXO transaction to obfuscate
the payer-payee relationship while preserving the correctness of the amount of
money flow. This work applies k-anonymity to analyse the privacy guarantee of
Coinjoin. By modelling the transaction traffic by a Poisson process, the
trade-off between anonymity and transaction confirmation time of Coinjoin is
analysed.


  The Cable-Driven Parallel Robots (CDPRs) have gained significant attention
due to their high payload capacity and large workspace. When deploying CDPRs in
practice, one of the challenges is kinematic modeling. Unlike serial
mechanisms, CDPRs have a simple inverse kinematics problem but a complex
forward kinematics (FK) issue. Therefore, the development of accurate and
efficient FK solvers has been a prominent research focus in CDPR applications.
By observing the topology within CDPRs, in this paper, we propose a graph-based
representation to model CDPRs and introduce CafkNet, a fast and general FK
solver, leveraging Graph Neural Network (GNN). CafkNet is extensively tested on
3D and 2D CDPRs in different configurations, both in simulators and real
scenarios. The results demonstrate its ability to learn CDPRs' internal
topology and accurately solve the FK problem. Then, the zero-shot
generalization from one configuration to another is validated. Also, the
sim2real gap can be bridged by CafkNet using both simulation and real-world
data. To the best of our knowledge, it is the first study that employs the GNN
to solve FK problem for CDPRs.


  Supermassive stars born from pristine gas in atomically-cooled haloes are
thought to be the progenitors of supermassive black holes at high redshifts.
However, the way they accrete their mass is still an unsolved problem. In
particular, for accretion to proceed, a large amount of angular momentum has to
be extracted from the collapsing gas. Here, we investigate the constraints
stellar evolution imposes on this angular momentum problem. We present an
evolution model of a supermassive Population III star including simultaneously
accretion and rotation. We find that, for supermassive stars to form by
accretion, the accreted angular momentum has to be about 1% of the Keplerian
angular momentum. This tight constraint comes from the $\Omega\Gamma$-limit, at
which the combination of radiation pressure and centrifugal force cancels
gravity. It implies that supermassive stars are slow rotators, with a surface
velocity less than 10-20% of their first critical velocity, at which the
centrifugal force alone cancels gravity. At such low velocities, the
deformation of the star due to rotation is negligible.


  Recent work in compressed sensing theory shows that $n\times N$ independent
and identically distributed (IID) sensing matrices whose entries are drawn
independently from certain probability distributions guarantee exact recovery
of a sparse signal with high probability even if $n\ll N$. Motivated by signal
processing applications, random filtering with Toeplitz sensing matrices whose
elements are drawn from the same distributions were considered and shown to
also be sufficient to recover a sparse signal from reduced samples exactly with
high probability. This paper considers Toeplitz block matrices as sensing
matrices. They naturally arise in multichannel and multidimensional filtering
applications and include Toeplitz matrices as special cases. It is shown that
the probability of exact reconstruction is also high. Their performance is
validated using simulations.


  We present a numerical study of the structural correlations associated to gas
adsorption/desorption in silica aerogels in order to provide a theoretical
interpretation of scattering experiments. Following our earlier work, we use a
coarse-grained lattice-gas description and determine the nonequilibrium
behavior of the adsorbed gas within a local mean-field analysis.
  We focus on the differences between the adsorption and desorption mechanisms
and their signature in the fluid-fluid and gel-fluid structure factors as a
function of temperature. At low temperature, but still in the regime where the
isotherms are continuous, we find that the adsorbed fluid density, during both
filling and draining, is correlated over distances that may be much larger than
the gel correlation length. In particular, extended fractal correlations may
occur during desorption, indicating the existence of a ramified cluster of
vapor filled cavities. This also induces an important increase of the
scattering intensity at small wave vectors. The similarity and differences with
the scattering of fluids in other porous solids such as Vycor are discussed.


  Chiral symmetry and heavy quark symmetry constrain the interactions of mesons
containing a single heavy quark with low-momentum pions. Chiral corrections to
the Isgur-Wise function for $\bar B_s \rightarrow D_s^{(*)}$ versus $\bar B
\rightarrow D^{(*)}$ semileptonic decay, the ratio of heavy meson decay
constants $f_{D_s}/ f_D$ or $f_{B_s}/ f_B$, the amplitudes for $B_s - \bar B_s$
versus $B^0 - \bar B{\,}^0$ mixing, and the heavy meson mass splittings are
calculated in chiral perturbation theory. (Invited talk Rencontres de Moriond
1993)


  We report electronic control and measurement of an imbalance between spin-up
and spin-down electrons in micron-scale open quantum dots. Spin injection and
detection was achieved with quantum point contacts tuned to have spin-selective
transport, with four contacts per dot for realizing a non-local spin-valve
circuit. This provides an interesting system for studies of spintronic effects
since the contacts to reservoirs can be controlled and characterized with high
accuracy. We show how this can be used to extract in a single measurement the
relaxation time for electron spins inside the dot ~ 300 ps and the degree of
spin polarization of the contacts P ~ 0.8.


  We study the dependence of the transverse mass distribution of the charged
lepton and the missing energies on the parton distributions (PDFs) adapted to
the $W$ boson mass measurements at the CDF and ATLAS experiments. We compare
the shape variations of the distribution induced by different PDFs and find
that spread of predictions from different PDF sets can be much larger than the
PDF uncertainty predicted by a specific PDF set. We suggest analyzing the
experimental data using up-to-date PDFs for a better understanding of the PDF
uncertainties in the $W$ boson mass measurements. We further carry out a series
of Lagrange multiplier scans to identify the constraints on the transverse mass
distribution imposed by individual data sets in the CT18 global analysis. In
the case of CDF measurement, the distribution is mostly sensitive to the
$d$-quark PDFs at the intermediate $x$ region that is largely constrained by
the DIS and Drell-Yan data on the deuteron target, as well as the Tevatron
lepton charge asymmetry data.


  Let T be an aperiodic and repetitive tiling of R^d with finite local
complexity. Let O be its tiling space with canonical transversal X. The tiling
equivalence relation R_X is the set of pairs of tilings in X which are
translates of each others, with a certain (etale) topology. In this paper R_X
is reconstructed as a generalized "tail equivalence" on a Bratteli diagram,
with its standard AF-relation as a subequivalence relation.
  Using a generalization of the Anderson-Putnam complex, O is identified with
the inverse limit of a sequence of finite CW-complexes. A Bratteli diagram B is
built from this sequence, and its set of infinite paths dB is homeomorphic to
X. The diagram B is endowed with a horizontal structure: additional edges that
encode the adjacencies of patches in T. This allows to define an etale
equivalence relation R_B on dB which is homeomorphic to R_X, and contains the
AF-relation of "tail equivalence".


  Estimation of reliability and hazard rate is one of the most important
problems raised in many applications especially in engineering studies as well
as human lifetime. In this regard, different methods of estimation have been
used. Each method exploits various tools and suffers from problems such as
complexity of computations, low precision, and so forth. This study is employed
the E-Bayesian method, for estimating the parameter and survival functions of
the Weibull Generalized Exponential distribution. The estimators are obtained
under squared error and LINEX loss functions under progressive type-II censored
samples. E-Bayesian estimations are derived based on three priors of
hyperparameters to investigate the influence of different priors on
estimations. The asymptotic behaviours of E-Bayesian estimations have been
investigated as well as relationships among them. Finally, a comparison among
the maximum likelihood, Bayes, and E-Bayesian estimations are made, using real
data and Monte Carlo simulation. Results show that the new method is more
efficient than previous methods.


  Training certifiable neural networks enables one to obtain models with
robustness guarantees against adversarial attacks. In this work, we introduce a
framework to bound the adversary-free region in the neighborhood of the input
data by a polyhedral envelope, which yields finer-grained certified robustness.
We further introduce polyhedral envelope regularization (PER) to encourage
larger polyhedral envelopes and thus improve the provable robustness of the
models. We demonstrate the flexibility and effectiveness of our framework on
standard benchmarks; it applies to networks of different architectures and
general activation functions. Compared with the state-of-the-art methods, PER
has very little computational overhead and better robustness guarantees without
over-regularizing the model.


  Analyzing the records of Advanced LIGO and Virgo gravitational observatories,
we found a permanent time-domain asymmetry inherent only to the signals of
their gravitational detectors. Experiments with different periodic signals,
Gaussian and non-Gaussian noises, made it possible to conclude that the noise
of gravitational detectors is an unusual mixture of signals. We also developed
a specialized Pearson correlation analyzer to detect gravitational-wave (GW)
events. It turned out that the LIGO and Virgo detectors' output signals contain
a significant (-6dB) component demonstrating the properties of records of
confirmed resolved GW events. It allows us to argue that the gravitational
background is largely due to the processes of stellar masses merging. Since the
specific signal is registered by the detectors continuously, we can consider
the sub-kilohertz band gravitational background field as discovered. Our
analysis method also allows us to estimate the contribution of the
gravitational background component to the total signal energy. With its help,
it will be possible not only to provide the radio-frequency estimation of the
magnitude of gravitational disturbances but also to obtain the GW background
sky map.


  We study the switch distribution, introduced by Van Erven et al. (2012),
applied to model selection and subsequent estimation. While switching was known
to be strongly consistent, here we show that it achieves minimax optimal
parametric risk rates up to a $\log\log n$ factor when comparing two nested
exponential families, partially confirming a conjecture by Lauritzen (2012) and
Cavanaugh (2012) that switching behaves asymptotically like the Hannan-Quinn
criterion. Moreover, like Bayes factor model selection but unlike standard
significance testing, when one of the models represents a simple hypothesis,
the switch criterion defines a robust null hypothesis test, meaning that its
Type-I error probability can be bounded irrespective of the stopping rule.
Hence, switching is consistent, insensitive to optional stopping and almost
minimax risk optimal, showing that, Yang's (2005) impossibility result
notwithstanding, it is possible to `almost' combine the strengths of AIC and
Bayes factor model selection.


  We address a cognitive radio scenario, where a number of secondary users
performs identification of which primary user, if any, is transmitting, in a
distributed way and using limited location information. We propose two fully
distributed algorithms: the first is a direct identification scheme, and in the
other a distributed sub-optimal detection based on a simplified Neyman-Pearson
energy detector precedes the identification scheme. Both algorithms are studied
analytically in a realistic transmission scenario, and the advantage obtained
by detection pre-processing is also verified via simulation. Finally, we give
details of their fully distributed implementation via consensus averaging
algorithms.


  This is about the paper in the title published by K. Hila and J. Dine in Acta
Math. Sinica, English Series, 29, no. 3, (2013), 609--624.


  We study the statistical properties of recurrence times in the self-excited
Hawkes conditional Poisson process, the simplest extension of the Poisson
process that takes into account how the past events influence the occurrence of
future events. Specifically, we analyze the impact of the power law
distribution of fertilities with exponent \alpha, where the fertility of an
event is the number of aftershocks of first generation that it triggers, on the
probability distribution function (pdf) f(\tau) of the recurrence times \tau
between successive events. The other input of the model is an exponential Omori
law quantifying the pdf of waiting times between an event and its first
generation aftershocks, whose characteristic time scale is taken as our time
unit. At short time scales, we discover two intermediate power law asymptotics,
f(\tau) ~ \tau^{-(2-\alpha)} for \tau << \tau_c and f(\tau) ~ \tau^{-\alpha}
for \tau_c << \tau << 1, where \tau_c is associated with the self-excited
cascades of aftershocks. For 1 << \tau << 1/\nu, we find a constant plateau
f(\tau) ~ const, while at long times, 1/\nu < \tau, f(\tau) ~ e^{-\nu \tau} has
an exponential tail controlled by the arrival rate \nu of exogenous events.
These results demonstrate a novel mechanism for the generation of power laws in
the distribution of recurrence times, which results from a power law
distribution of fertilities in the presence of self-excitation and cascades of
triggering.


  In this paper we calculate the number of equivariant diffeomorphism classes
of small covers over a prism.


  In a probabilistic framework of the interpretation of the initial mass
function (IMF), the IMF cannot be arbitrarily normalized to the total mass, M,
or number of stars, N, of the system. Hence, the inference of M and N when
partial information about the studied system is available must be revised.
(i.e., the contribution to the total quantity cannot be obtained by simple
algebraic manipulations of the IMF). We study how to include constraints in the
IMF to make inferences about different quantities characterizing stellar
systems. It is expected that including any particular piece of information
about a system would constrain the range of possible solutions. However,
different pieces of information might be irrelevant depending on the quantity
to be inferred. In this work we want to characterize the relevance of the
priors in the possible inferences. Assuming that the IMF is a probability
distribution function, we derive the sampling distributions of M and N of the
system constrained to different types of information available. We show that
the value of M that would be inferred must be described as a probability
distribution Phi[M; m_a, N_a, Phi(N)] that depends on the completeness limit of
the data, m_a, the number of stars observed down to this limit, N_a, and the
prior hypothesis made on the distribution of the total number of stars in
clusters, Phi(N).


  We show that an Anosov map has a geodesic axis on the curve graph of a torus.
The direct corollary of our result is the stable translation length of an
Anosov map on the curve graph is always a positive integer. As the proof is
constructive, we also provide an algorithm to calculate the exact translation
length for any given Anosov map. The application of our result is threefold:
(a) to determine which word realizes the minimal translation length on the
curve graph within a specific class of words, (b) to establish the effective
bound on the ratio of translation lengths of an Anosov map on the curve graph
to that on Teichm\"uller space, and (c) to estimate the overall growth of the
number of Anosov maps which have a sufficient number of Anosov maps with the
same translation length.


  We consider the question of computing invariant measures from an abstract
point of view. We work in a general framework (computable metric spaces,
computable measures and functions) where this problem can be posed precisely.
We consider invariant measures as fixed points of the transfer operator and
give general conditions under which the transfer operator is (sufficiently)
computable. In this case, a general result ensures the computability of
isolated fixed points and hence invariant measures (in given classes of
"regular" measures). This implies the computability of many SRB measures.
  On the other hand, not all computable dynamical systems have a computable
invariant measure. We exhibit two interesting examples of computable dynamics,
one having an SRB measure which is not computable and another having no
computable invariant measure at all, showing some subtlety in this kind of
problems.


  Recently, Class-Agnostic Counting (CAC) problem has garnered increasing
attention owing to its intriguing generality and superior efficiency compared
to Category-Specific Counting (CSC). This paper proposes a novel ExpressCount
to enhance zero-shot object counting by delving deeply into language-guided
exemplar learning. Specifically, the ExpressCount is comprised of an innovative
Language-oriented Exemplar Perceptron and a downstream visual Zero-shot
Counting pipeline. Thereinto, the perceptron hammers at exploiting accurate
exemplar cues from collaborative language-vision signals by inheriting rich
semantic priors from the prevailing pre-trained Large Language Models (LLMs),
whereas the counting pipeline excels in mining fine-grained features through
dual-branch and cross-attention schemes, contributing to the high-quality
similarity learning. Apart from building a bridge between the LLM in vogue and
the visual counting tasks, expression-guided exemplar estimation significantly
advances zero-shot learning capabilities for counting instances with arbitrary
classes. Moreover, devising a FSC-147-Express with annotations of meticulous
linguistic expressions pioneers a new venue for developing and validating
language-based counting models. Extensive experiments demonstrate the
state-of-the-art performance of our ExpressCount, even showcasing the accuracy
on par with partial CSC models.


  We use temperature-dependent resistivity in small-angle twisted double
bilayer graphene to measure bandwidths and gaps of the bands. This
electron-hole asymmetric system has one set of non-dispersing bands that splits
into two flat bands with the electric field - distinct from the twisted bilayer
system. With electric field, the gap between two emergent flat bands increases
monotonically and bandwidth is tuned from 1 meV to 15 meV. These two flat bands
with gap result in a series of thermally induced insulator to metal transitions
- we use a model, at charge neutrality, to measure the bandwidth using only
transport measurements. Having two flat bands with tunable gap and bandwidth
offers an opportunity to probe the emergence of correlations.


  Within the Minimal Supersymmetric Standard Model (MSSM) the production and
decay of superpartners can give rise to backgrounds for Higgs boson searches.
Here MSSM background processes to the vector boson fusion channel with the
Higgs boson decaying into two tau leptons or two W-bosons are investigated,
giving rise to dilepton plus missing transverse momentum signals of the Higgs
boson. Starting from a scenario with relatively small masses of the
supersymmetric (SUSY) particles, with concomitant large cross section of the
background processes, one obtains a first conservative estimate of the
background. Light chargino pair production plus two jets, lightest and
next-to-lightest neutralino production plus two jets as well as slepton pair
production plus two jets are identified as important contributions to the
irreducible SUSY background. Light chargino and next-to-lightest neutralino
production plus two jets and next-to-lightest neutralino pair production plus
two jets give rise to reducible backgrounds, which can be larger than the
irreducible ones in some scenarios. The relevant distributions are shown and
additional cuts for MSSM background reduction are discussed. Extrapolation to
larger squark masses is performed and shows that MSSM backgrounds are quite
small for squark masses at the current exclusion limits.


  We have analyzed the archival data of FUV observations for the region of GSH
006-15+7, a large shell-like structure discovered by Moss et al. (2012) from
the H I velocity maps. FUV emission is seen to be enhanced in the lower
supershell region. The FUV emission is considered to come mainly from the
scattering of interstellar photons by dust grains. A corresponding Monte Carlo
simulation indicates that the distance to the supershell is 1300 +- 800 pc,
which is similar to the previous estimation of 1500 +- 500 pc based on
kinematic considerations. The spectrum at lower Galactic latitudes of the
supershell exhibits molecular hydrogen fluorescence lines; a simulation model
for this candidate photodissociation region (PDR) yields an H_2 column density
of N(H_2) = 10^{18.0-20.0} cm^{-2} with a rather high total hydrogen density of
n_H ~ 30 cm^{-3}.


  The main goal of this paper is to show that a (not necessarily densely
defined or closed) symmetric operator $A$ acting on a real or complex Hilbert
space is selfadjoint exactly when $I+A^2$ is a full range operator.


  In this digital era, our privacy is under constant threat as our personal
data and traceable online/offline activities are frequently collected,
processed and transferred by many software applications. Privacy attacks are
often formed by exploiting vulnerabilities found in those software
applications. The Common Weakness Enumeration (CWE) and Common Vulnerabilities
and Exposures (CVE) systems are currently the main sources that software
engineers rely on for understanding and preventing publicly disclosed software
vulnerabilities. However, our study on all 922 weaknesses in the CWE and
156,537 vulnerabilities registered in the CVE to date has found a very small
coverage of privacy-related vulnerabilities in both systems, only 4.45\% in CWE
and 0.1\% in CVE. These also cover only a small number of areas of privacy
threats that have been raised in existing privacy software engineering
research, privacy regulations and frameworks, and relevant reputable
organisations. The actionable insights generated from our study led to the
introduction of 11 new common privacy weaknesses to supplement the CWE system,
making it become a source for both security and privacy vulnerabilities.


  Let $0 \to A \to L \to B \to 0$ be a short exact sequence of Lie algebras
over a field $F$, where $A$ is abelian. We show that the obstruction for a pair
of automorphisms in $\Aut(A) \times \Aut(B)$ to be induced by an automorphism
in $\Aut(L)$ lies in the Lie algebra cohomology $\Ha^2(B;A)$. As a consequence,
we obtain a four term exact sequence relating automorphisms, derivations and
cohomology of Lie algebras. We also obtain a more explicit necessary and
sufficient condition for a pair of automorphisms in
$\Aut\big(L_{n,2}^{(1)}\big) \times \Aut\big(L_{n,2}^{ab}\big)$ to be induced
by an automorphism in $\Aut\big(L_{n,2}\big)$, where $L_{n,2}$ is a free
nilpotent Lie algebra of rank $n$ and step $2$.


  Multiwavelength data indicate that the X-ray emitting plasma in the cores of
galaxy clusters is not cooling catastrophically. To large extent, cooling is
offset by heating due to active galactic nuclei (AGN) via jets. The cool-core
clusters, with cooler/denser plasmas, show multiphase gas and signs of some
cooling in their cores. These observations suggest that the cool core is
locally thermally unstable while maintaining global thermal equilibrium. Using
high-resolution, three-dimensional simulations we study the formation of
multiphase gas in cluster cores heated by highly-collimated bipolar AGN jets.
Our key conclusion is that spatially extended multiphase filaments form only
when the instantaneous ratio of the thermal instability and free-fall
timescales (t_TI/t_ff) falls below a critical threshold of \approx 10. When
this happens, dense cold gas decouples from the hot ICM phase and generates
inhomogeneous and spatially extended Halpha filaments. These cold gas clumps
and filaments `rain' down onto the central regions of the core, forming a cold
rotating torus and in part feeding the supermassive black hole. Consequently,
the self-regulated feedback enhances AGN heating and the core returns to a
higher entropy level with t_TI/t_ff > 10. Eventually the core reaches
quasi-stable global thermal equilibrium, and cold filaments condense out of the
hot ICM whenever t_TI/t_ff \lesssim 10. This occurs despite the fact that the
energy from AGN jets is supplied to the core in a highly anisotropic fashion.
The effective spatial redistribution of heat is enabled in part by the
turbulent motions in the wake of freely-falling cold filaments. Increased AGN
activity can locally reverse the cold gas flow, launching cold filamentary gas
away from the cluster center. Our criterion for the condensation of spatially
extended cold gas is in agreement with observations and previous idealized
simulations.


  In deep inelastic electron-photon scattering in leading order QED,
egamma->egamma*gamma->effbar, there are four non-zero structure functions. We
calculate them for real photons retaining the full dependence on the fermion
mass, and show numerical results of its effect.


  We study model predictions for three high-redshift galaxy populations: Lyman
break galaxies at z~3 (LBGs), optically selected star-forming galaxies at z~2
(BXs), and distant red galaxies at z~2 (DRGs).Our galaxy formation model
simultaneously reproduces the abundances, redshift distributions and clustering
of all three observed populations. The star formation rates (SFRs) of model
LBGs and BXs are lower than those quoted for real samples, reflecting different
initial mass functions and scatter in model dust properties. About 85% of model
galaxies selected as DRGs are star-forming, with SFRs ranging up to 100
M_sun/yr. Model LBGs, BXs and DRGs together account for less than half of all
star formation over the range 1.5<z<3.2. Model BXs have metallicities which
agree roughly with observation, but model LBGs are only slightly more
metal-poor, in disagreement with recent observational results. Model galaxies
are predominantly disk-dominated. About 30% of model galaxies with
M>10^{11}M_sun are classified as LBGs or BXs at the relevant redshifts, while
65% are classified as DRGs. Almost all model LBGs and BXs are central galaxies,
but about a quarter of DRGs are satellites. Half of all LBG descendants at z=2
would be identified as BX's, but very few as DRGs. Clustering increases with
decreasing redshift for descendants of all three populations, becoming stronger
than that of L^* galaxies by z=0, when many have become satellite galaxies.
Their stellar mass growth is dominated by star formation until z~1 and
thereafter by mergers. Most LBGs and DRGs end up as red ellipticals, while BXs
have a more varied fate. 99% of local galaxies with M>10^{11.5}M_sun are
predicted to have at least one LBG/BX/DRG progenitor, and over 70% above
10^{11}M_sun. (abbreviated)


  Multi-Level Intermediate Representation (MLIR) is an effort to enable faster
compiler development by providing an extensible framework for downstream
developers to define custom IRs with MLIR dialects. MLIR dialects define new
IRs that are tailored for specific domains. The diversity and rapid evolution
of these IRs make it impractical to pre-define custom generator logic for every
available dialect. We design a new approach called SynthFuzz that automatically
infers and applies custom mutations from existing tests. Inferred custom
mutations are parameterized and context-dependent such that they can be
concretized depending on the target context. By doing this, we obviate the need
to manually write custom mutations for newly introduced MLIR dialects. Further,
SynthFuzz increases the chance of finding effective edit locations and reduces
the chance of inserting invalid edit content by performing k-ancestor-prefix
and l-sibling-postfix matching. We compare SynthFuzz to three baselines:
Grammarinator -- a grammar-based fuzzer without custom mutators, MLIRSmith -- a
custom test generator for MLIR, and NeuRI -- a custom test generator with
support for parameterized generation. We conduct this comparison on 4 different
MLIR projects where each project defines a new set of MLIR dialects that would
take months of effort to manually write custom input generation and mutation
logic. We show that SynthFuzz on average improves input diversity by
1.51$\times$, which increases branch coverage by 1.16$\times$. Further, we show
that our context dependent custom mutation increases the proportion of valid
tests by up to 1.11$\times$, indicating that SynthFuzz correctly concretizes
its parameterized mutations with respect to the target context. Mutation
parameterization reduces the fraction of tests violating general MLIR
constraints by 0.57$\times$, increasing the time spent fuzzing dialect-specific
code.


  The methods of the probability theory have been used in order to build up a
new model of hysteresis. It turns out that the reversal points of the control
parameter (e. g., the magnetic field) are Markov points which determine the
stochastic evolution of the process. It has been shown that the branches of the
hysteresis loop are converging to fixed limit curves when the number of cyclic
back-and-forth variations of the control parameter between two consecutive
reversal points is large enough. This convergence to limit curves gives a clear
explanation of the accommodation process. The accommodated minor loops show the
return-point memory property but this property is obviously absent in the case
of non-accommodated minor loops which are not congruent and generally not
closed. In contrast to the traditional Preisach model the reversal point
susceptibilities are non-zero finite values. The stochastic model can provide a
good approximation of the Raylaigh quadratic law when the external parameter
varies between two sufficiently small values.


  In application of the Balancing Domain Decomposition by Constraints (BDDC) to
a case with many substructures, solving the coarse problem exactly becomes the
bottleneck which spoils scalability of the solver. However, it is
straightforward for BDDC to substitute the exact solution of the coarse problem
by another step of BDDC method with subdomains playing the role of elements. In
this way, the algorithm of three-level BDDC method is obtained. If this
approach is applied recursively, multilevel BDDC method is derived. We present
a detailed description of a recently developed parallel implementation of this
algorithm. The implementation is applied to an engineering problem of linear
elasticity and a benchmark problem of Stokes flow in a cavity. Results by the
multilevel approach are compared to those by the standard (two-level) BDDC
method.


  Stream Learning (SL) requires models to rapidly adapt to continuous data
streams, setting it apart from traditional Continual Learning (CL). Recent SL
methods emphasize efficiency by selecting data subsets for training, but they
often struggle due to their reliance on static, rule-based selection algorithms
that cannot effectively adapt to the changing importance of data. In this work,
we introduce StreamPrompt, a method that enhances data selection through
dynamic, learnable prompts. These dynamic prompts serve two purposes beyond
guiding model inference: 1) optimizing data selection, and 2) guiding updates
to the rehearsal buffer. This approach addresses the challenges of adaptability
and computational efficiency in processing continuous data streams. Moreover,
StreamPrompt introduces Prompt Attunement,a mechanism that enhances the
efficiency of prompt learning. By leveraging attention layers from vision
transformers and softly combining their outputs with a gate unit, Prompt
Attunementrefines prompts with minimal computational resources. Comprehensive
evaluations demonstrate StreamPrompts superior performance over
state-of-the-art, with significant improvements in accuracy and reductions in
training time. These results underscore the efficacy and efficiency of
StreamPrompt, establishing its potential as a scalable and effective solution
for the evolving demands of SL. Our code is available at
https://github.com/intellistream/Efficient-Stream-Learning.


  The quiet solar corona consists of myriads of loop-like features, with
magnetic fields originating from network and internetwork regions on the solar
surface. The continuous interaction between these different magnetic patches
leads to transient brightenings or bursts that might contribute to the heating
of the solar atmosphere. However, it remains unclear whether such transients,
which are mostly observed in the EUV, play a significant role in atmospheric
heating. We revisit the open question of these bursts as a prelude to the new
high-resolution EUV imagery expected from the recently launched Solar Orbiter.
We use EUV images recorded by the SDO/AIA to investigate statistical properties
of the bursts. We detect the bursts in the 171 {\AA} filter images of AIA in an
automated way through a pixel-wise analysis by imposing different intensity
thresholds. By exploiting the high cadence (12 s) of the AIA observations, we
find that the distribution of lifetimes of these events peaks at about 120 s.
The sizes of the detected bursts are limited by the spatial resolution, which
indicates that a larger number of events might be hidden in the AIA data. We
estimate that about 100 new bursts appear per second on the whole Sun. The
detected bursts have nanoflare-like energies of $10^{24}$\,erg per event. Based
on this, we estimate that at least 100 times more events of a similar nature
would be required to account for the energy that is required to heat the
corona. When AIA observations are considered alone, the EUV bursts discussed
here therefore play no significant role in the coronal heating of the quiet
Sun. If the coronal heating of the quiet Sun is mainly bursty, then the
high-resolution EUV observations from Solar Orbiter may be able to reduce the
deficit in the number of EUV bursts seen with SDO/AIA at least partly by
detecting more such events.


  The results of space-like separated measurements are independent of distant
measurement settings, a property one might call two-way no-signalling. In
contrast, time-like separated measurements are only one-way no-signalling since
the past is independent of the future but not vice-versa. For this reason
temporal correlations that are formally identical to non-classical spatial
correlations can still be modelled classically. We define non-classical
temporal correlations as the ones which cannot be simulated by propagating in
time a classical information content of a quantum system. We first show that
temporal correlations between results of any projective quantum measurements on
a qubit can be simulated classically. Then we present a sequence of POVM
measurements on a single $m$-level quantum system that cannot be explained by
propagating in time $m$-level classical system and using classical computers
with unlimited memory.


  Considering the single scalar field models of dark energy, i.e. the
quintessence and phantom models, it is shown that the quantum effects can cause
the system crosses the w = -1 line. This phenomenon does not occur in classical
level. The quantum effects are described via the account of conformal anomaly.


  Minimizing the Action integral of a Lagrangian provides the Euler-Lagrange
equation of motion in the elegant machinery of Lagrangian Mechanics. However
two relations define the divergence of current and energy-momentum, and provide
an alternative motivation for the Euler-Lagrange equation without invoking the
considerable machinery required for the principle of least Action. The
derivation of these two relations proceeds with only local differential
operations on the Lagrangian, and without a global integration defining an
Action. The two relations connect local continuity equations (as a vanishing
divergence) for current and energy-momentum to Lorentz force, symmetry, and the
Euler-Lagrange equation. The Euler-Lagrange equation is common to both
relations so providing sufficient motivation for acceptance as equations of
motion. The essential relationships between the central concepts of
energy-momentum, force, current, symmetry and equation of motion provide
pedagogically interesting clarity. The student will see that how these concepts
relate to each other as well as the definition of each concept in isolation.


  Let $K$ be a local field of characteristic 0 with residue characteristic $p$.
Let $G$ be an extraspecial $p$-group and let $L/K$ be a totally ramified
$G$-extension. In this paper we find sufficient conditions for $L/K$ to admit a
Galois scaffold. This leads to sufficient conditions for the ring of integers
$\mathfrak{O}_L$ to be free of rank 1 over its associated order
$\mathfrak{A}_{L/K}$, and to stricter conditions which imply that
$\mathfrak{A}_{L/K}$ is a Hopf order in the group ring $K[G]$.


  We survey existence and regularity results for semi-linear wave equations. In
particular, we review the recent regularity results for the $u^5$-Klein Gordon
equation by Grillakis and this author and give a self-contained, slightly
simplified proof.


  We analyze the oxygen abundances of a stellar sample representative of the
two major Galactic populations: the thin and thick disks. The aim is to
investigate the differences between members of the Galactic disks and to
contribute to the understanding on the origin of oxygen chemical enrichment in
the Galaxy. The analysis is based on the [O\,{\sc i}]=6300.30\,\AA~ oxygen line
in HR spectra ($R\sim$52,500) obtained from the GES Survey. By comparing the
observed spectra with a theoretical dataset, computed in LTE with the SPECTRUM
synthesis and ATLAS12 codes, we derive the oxygen abundances of 516 FGK dwarfs
for which we have previously measured carbon abundances. Based on kinematic,
chemical and dynamical considerations we identify 20 thin and 365 thick disk
members. We study potential trends of both subsamples in terms of their
chemistry ([O/H], [O/Fe], [O/Mg], and [C/O] versus [Fe/H] and [Mg/H]), age, and
position in the Galaxy. Main results are: (a) [O/H] and [O/Fe] ratios versus
[Fe/H] show systematic differences between thin and thick disk stars with
enhanced O abundance of thick disk stars with respect to thin disk members and
a monotonic decrement of [O/Fe] with increasing metallicity, even at metal-rich
regime; (b) a smooth correlation of [O/Mg] with age in both populations,
suggesting that this abundance ratio can be a good proxy of stellar ages within
the Milky Way; (c) thin disk members with [Fe/H]$\simeq0$ display a [C/O] ratio
smaller than the solar value, suggesting a possibly outward migration of the
Sun from lower Galactocentric radii.


  In this paper, we find trajectory planning and power allocation for a
vehicular network in which an unmanned-aerial-vehicle (UAV) is considered as a
relay to extend coverage for two disconnected far vehicles. We show that in a
two-user network with an amplify-and-forward (AF) relay,
non-orthogonal-multiple-access (NOMA) always has better or equal sum-rate in
comparison to orthogonal-multiple-access (OMA) at high signal-to-noise-ratio
(SNR) regime. However, for the cases where i) base station (BS)-to-relay link
is weak, or ii) two users have similar links, or iii) BS-to-relay link is
similar to relay-to-weak user link, applying NOMA has negligible sum-rate gain.
Hence, due to the complexity of successive-interference-cancellation (SIC)
decoding in NOMA, we propose a dynamic NOMA/OMA scheme in which OMA mode is
selected for transmission when applying NOMA has only negligible gain. Also, we
show that OMA always has better min-rate than NOMA at high SNR regime. Further,
we formulate two optimization problems which maximize the sum-rate and min-rate
of the two vehicles. These problems are non-convex, and hence we propose an
iterative algorithm based on alternating-optimization (AO) method which solves
trajectory and power allocation sub-problems by successive-convex-approximation
(SCA) and difference-of-convex (DC) methods, respectively. Finally, the
above-mentioned performance is confirmed by simulations.


  We consider the most general AdS$_3$ solutions of type IIB supergravity
admitting a dynamical SU$(3)$ structure and preserving $\mathcal{N}=(0,2)$
supersymmetry. The analysis is broken into three distinct classes depending on
whether the dynamical SU$(3)$ structure degenerates to a strict SU$(3)$
structure. The first class we consider allows for a holomorphically varying
axio-dilaton consistent with the presence of $(p,q)$ 7-branes in addition to
D3-branes and $(p,q)$ 5-branes. In the remaining two classes the axio-dilaton
may vary but does not do so holomorphically. The second class of solution
allows for 5-branes and 1-branes but no D3-branes, whilst in the final class
all branes can be present. We illustrate our results with examples of such
solutions including a new infinite family with all fluxes but the axion turned
on.


  The characterisation of the multiplicity of high-mass stars is of fundamental
importance to understand their evolution, the diversity of observed
core-collapse supernovae and the formation of gravitational wave progenitor
systems. Despite that, until recently, one of the final phases of massive star
evolution -- the cool supergiant phase -- has received comparatively little
attention. In this study we aim to explore the multiplicity among the cool
supergiants (CSGs) in the Large and Small Magellanic Clouds (LMC and SMC,
respectively). To do this we compile extensive archival radial velocity (RV)
measurements for over 1000 CSGs from the LMC and SMC, spanning a baseline of
over 40 years. By statistically correcting the RV measurements of each stellar
catalogue to the Gaia DR2 reference frame we are able to effectively compare
these diverse observations. We identify 45 CSGs where RV variations cannot be
explained through intrinsic variability, and are hence considered binary
systems. We obtain a minimum binary fraction of $15\pm4\%$ for the SMC and of
$14\pm5\%$ for the LMC. Combining these results, we determine a minimum binary
fraction of $15\pm3\%$ for CSGs. These results are in good agreement with
previous results which apply a correction to account for observational biases.
These results add strength to the hypothesis that the binary fraction of CSGs
is significantly lower than their main-sequence counterparts. Going forward, we
stress the need for long-baseline multi-epoch spectroscopic surveys to cover
the full parameter space of CSG binary systems.


  We have studied the relationship between the nuclear (high-resolution) radio
emission, at 8.4 GHz (3.6 cm) and 1.4 GHz (20 cm), the [O IV] 25.89um, [Ne III]
15.56um and [Ne II] 12.81um emission lines and the black hole mass accretion
rate for a sample of Seyfert galaxies. In order to characterize the radio
contribution for the Seyfert nuclei we used the 8.4GHz/[O IV] ratio, assuming
that [O IV] scales with the luminosity of the AGN. From this we find that
Seyfert 1's (i.e., Seyfert 1.0's, 1.2's, and 1.5's) and Seyfert 2's (i.e.,
Seyfert 1.8's, 1.9's, and 2.0's) have similar radio contributions, relative to
the AGN. On the other hand, sources in which the [Ne II] emission is dominated
either by the AGN or star formation have statistically different radio
contributions, with star formation dominated sources more "radio loud", by a
factor of ~2.8 on average, than AGN dominated sources. We show that star
formation dominated sources with relatively larger radio contribution have
smaller mass accretion rates. Overall, we suggest that 8.4GHz/[O IV], or
alternatively, 1.4GHz/[O IV] ratios, can be used to characterize the radio
contribution, relative to the AGN, without the limitation of previous methods
that rely on optical observables.


  In the context of the teleparallel equivalent of general relativity, we show
that the energy-momentum density for the gravitational field can be described
by a true spacetime tensor. It is also invariant under local (gauge)
translations of the tangent space coordinates, but transforms covariantly only
under global Lorentz transformations. When the gauge gravitational field
equation is written in a purely spacetime form, it becomes the teleparallel
equivalent of Einstein's equation, and we recover M{\o}ller's expression for
the canonical gravitational energy-momentum pseudotensor.


  Near-infrared emission lines provide unique diagnostics of the geometry,
structure, kinematics, and excitation of eta Carinae's circumstellar ejecta,
and give clues to the nature of its wind. The infrared spectrum is a strong
function of position in eta Car's nebula, with a mix of intrinsic and reflected
emission. Molecular hydrogen traces cool gas and dust in the polar lobes, while
[Fe II] blankets their inner surfaces. These lines reveal the back wall of the
SE polar lobe for the first time, and give the clearest picture yet of the 3-D
geometry. Additionally, collisionally-excited [Fe II] reveals the kinematic
structure of a recently discovered `Little Homunculus' expanding inside the
larger one. Equatorial gas in the `Fan', on the other hand, shows a spectrum
indicating recombination and fluorescent pumping. Some equatorial ejecta glow
in the He I 10830 line, showing evidence for material ejected in the 1890
outburst of eta Car. Closer to the star, the compact `Weigelt blobs' are
marginally resolved, allowing their infrared spectrum to be separated from the
star for the first time. In general, infrared spectra reveal a coherent,
directional dependence of excitation in the Homunculus: polar ejecta are
collisionally excited, whereas equatorial ejecta are dominated by fluorescence
and normal photoexcitation. These are important clues to the geometry of the
central star's UV radiation field. Reflected near-infrared emission lines also
reveal interesting latitudinal dependence in the stellar wind.


  The Reynolds Averaged Navier Stokes (RANS) models are the most common form of
model in turbulence simulations. They are used to calculate Reynolds stress
tensor and give robust results for engineering flows. But RANS model
predictions have large error and uncertainty. In past, there has been some work
towards using data-driven methods to increase their accuracy. In this work we
outline a machine learning approach to aid the use of the Eigenspace
Perturbation Method to predict the uncertainty in the turbulence model
prediction. We use a trained neural network to predict the discrepancy in the
shape of the RANS predicted Reynolds stress ellipsoid. We apply the model to a
number of turbulent flows and demonstrate how the approach correctly identifies
the regions in which modeling errors occur when compared to direct numerical
simulation (DNS), large eddy simulation (LES) or experimental results from
previous works.


  Unabated coal power in India must be phased out by mid-century to achieve
global climate targets under the Paris Agreement. Here we estimate the costs of
hybrid power plants - lithium-ion battery storage with wind and solar PV - to
replace coal generation. We design least cost mixes of these technologies to
supply stylized baseload and load-following generation profiles in three Indian
states - Karnataka, Gujarat, and Tamil Nadu. Our analysis shows that
availability of low cost capital, solar PV capital costs of at least $250/kW,
and battery storage capacity costs at least 50% cheaper than current levels
will be required to phase out existing coal power plants. Phaseout by 2040
requires a 6% annual decline in the cost of hybrid systems over the next two
decades. We find that replacing coal generation with hybrid systems 99% of the
hours over multiple decades is roughly 40% cheaper than 100% replacement,
indicating a key role for other low cost grid flexibility mechanisms to help
hasten coal phaseout. Solar PV is more suited to pairing with short duration
storage than wind power. Overall, our results describe the challenging
technological and policy advances needed to achieve the temperature goals of
the Paris Agreement.


  Due to its safety-critical property, the image-based diagnosis is desired to
achieve robustness on out-of-distribution (OOD) samples. A natural way towards
this goal is capturing only clinically disease-related features, which is
composed of macroscopic attributes (e.g., margins, shapes) and microscopic
image-based features (e.g., textures) of lesion-related areas. However, such
disease-related features are often interweaved with data-dependent (but disease
irrelevant) biases during learning, disabling the OOD generalization. To
resolve this problem, we propose a novel framework, namely Domain Invariant
Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant
disease-related features from multiple domains. Specifically, we first propose
a Bayesian network, which explicitly decomposes the latent variables into
disease-related and other disease-irrelevant parts that are provable to be
disentangled from each other. Guided by this, we reformulate the objective
function based on Variational Auto-Encoder, in which the encoder in each domain
has two branches: the domain-independent and -dependent ones, which
respectively encode disease-related and -irrelevant features. To better capture
the macroscopic features, we leverage the observed clinical attributes as a
goal for reconstruction, via Graph Convolutional Network (GCN). Finally, we
only implement the disease-related features for prediction. The effectiveness
and utility of our method are demonstrated by the superior OOD generalization
performance over others on mammogram benign/malignant diagnosis.


  Intent Classification (IC) and Slot Labeling (SL) models, which form the
basis of dialogue systems, often encounter noisy data in real-word
environments. In this work, we investigate how robust IC/SL models are to noisy
data. We collect and publicly release a test-suite for seven common noise types
found in production human-to-bot conversations (abbreviations, casing,
misspellings, morphological variants, paraphrases, punctuation and synonyms).
On this test-suite, we show that common noise types substantially degrade the
IC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models.
By leveraging cross-noise robustness transfer -- training on one noise type to
improve robustness on another noise type -- we design aggregate
data-augmentation approaches that increase the model performance across all
seven noise types by +10.8% for IC accuracy and +15 points for SL F1 on
average. To the best of our knowledge, this is the first work to present a
single IC/SL model that is robust to a wide range of noise phenomena.


  Skeleton-based gait recognition models usually suffer from the robustness
problem, as the Rank-1 accuracy varies from 90\% in normal walking cases to
70\% in walking with coats cases. In this work, we propose a state-of-the-art
robust skeleton-based gait recognition model called Gait-TR, which is based on
the combination of spatial transformer frameworks and temporal convolutional
networks. Gait-TR achieves substantial improvements over other skeleton-based
gait models with higher accuracy and better robustness on the well-known gait
dataset CASIA-B. Particularly in walking with coats cases, Gait-TR get a 90\%
Rank-1 gait recognition accuracy rate, which is higher than the best result of
silhouette-based models, which usually have higher accuracy than the
silhouette-based gait recognition models. Moreover, our experiment on CASIA-B
shows that the spatial transformer can extract gait features from the human
skeleton better than the widely used graph convolutional network.


  We provide a generalization of Bianchi's triply conjugate systems containing
a family of deformations of 2-dimensional quadrics together with its
B\"{a}cklund transformation to higher dimensions.


  Background: Cardiovascular magnetic resonance imaging (CMR) is a
wellestablished imaging tool for diagnosing and managing cardiac conditions.
The integration of exercise stress with CMR (ExCMR) can enhance its diagnostic
capacity. Despite recent advances in CMR technology, quantitative ExCMR during
exercise remains technically challenging due to motion artifacts and limited
spatial and temporal resolution. Methods: This study investigated the
feasibility of biventricular functional and hemodynamic assessment using
real-time (RT) ExCMR during a staged exercise protocol in 24 healthy
volunteers. We applied a coil reweighting technique and employed high
acceleration rates to minimize motion blurring and artifacts. We further
applied a beat-selection technique that identified beats from the endexpiratory
phase to minimize the impact of respiration-induced through-plane motion.
Additionally, results from six patients were presented to demonstrate clinical
feasibility. Results: Our findings indicated a consistent decrease in
end-systolic volume and stable end-diastolic volume across exercise
intensities, leading to increased stroke volume and ejection fraction. The
selection of end-expiratory beats enhanced the repeatability of cardiac
function parameters, as shown by scan-rescan tests in nine volunteers. High
scores from a blinded image quality assessment indicated that coil reweighting
effectively minimized motion artifacts. Conclusions: This study demonstrated
the feasibility of RT ExCMR with inmagnet exercise in healthy subjects and
patients. Our results indicate that high acceleration rates, coil reweighting,
and selection of respiratory phase-specific heartbeats enhance image quality
and repeatability of quantitative RT ExCMR.


  COVID-19 is an infectious disease that mostly affects the respiratory system.
At the time of this research being performed, there were more than 1.4 million
cases of COVID-19, and one of the biggest anxieties is not just our health, but
our livelihoods, too. In this research, authors investigate the impact of
COVID-19 on the global economy, more specifically, the impact of COVID-19 on
financial movement of Crude Oil price and three U.S. stock indexes: DJI, S&P
500 and NASDAQ Composite. The proposed system for predicting commodity and
stock prices integrates the Stationary Wavelet Transform (SWT) and
Bidirectional Long Short-Term Memory (BDLSTM) networks. Firstly, SWT is used to
decompose the data into approximation and detail coefficients. After
decomposition, data of Crude Oil price and stock market indexes along with
COVID-19 confirmed cases were used as input variables for future price movement
forecasting. As a result, the proposed system BDLSTM+WT-ADA achieved
satisfactory results in terms of five-day Crude Oil price forecast.


  Interaction between supernova (SN) ejecta and dense circumstellar medium
(CSM) with a flat density structure ($\rho \propto r^{-s}, s < 1.5$) was
recently proposed as a possible mechanism behind interacting SNe that exhibit
exceptionally long rise times exceeding 100 days. In such a configuration, the
interaction luminosity keeps rising until the reverse shock propagates into the
inner layers of the SN ejecta. We investigate the light curves of SNe
interacting with a flatly distributed CSM in detail, incorporating the effects
of photon diffusion inside the CSM into the model. We show that three physical
processes - the shock breakout, the propagation of the reverse shock into the
inner ejecta, and the departure of the shock from the dense CSM - predominantly
determine the qualitative behaviour of the light curves. Based on the presence
and precedence of these processes, the light curves of SNe interacting with
flatly distributed CSM can be classified into five distinct morphological
classes. We also show that our model can qualitatively reproduce doubly peaked
SNe whose peaks are a few tens of days apart, such as SN 2005bf and SN 2022xxf.
Our results show that the density distribution of the CSM is an important
property of CSM that contributes to the diversity in light curves of
interacting SNe.


  Some features of the Pioneer anomaly are discussed in context of author's
explanation of this effect as a deceleration of the probes in the graviton
background. It is noted that if the model is true then the best parameter of
the anomalous acceleration should be not the distance to the Sun but a cosine
of the angle between a velocity of the probe and its radius-vector.


  Following notation introduced in the recent paper \cite{Rdef}, this paper is
aimed to present in detail an example of a "small" geometric transition which
is not a "simple" one i.e. a deformation of a conifold transition. This is
realized by means of a detailed analysis of the Kuranishi space of a Namikawa
cuspidal fiber product, which in particular improves the conclusion of
Y.~Namikawa in Remark 2.8 and Example 1.11 of \cite{N}. The physical interest
of this example is presenting a geometric transition which can't be immediately
explained as a massive black hole condensation to a massless one, as described
by A.~Strominger \cite{Strominger95}.


  We suggest an all-optical scheme for the storage, retrieval and processing of
a single-photon wave packet through its off-resonant Raman interaction with a
series of coherent control beams. These control beams, each with distinct
carrier frequency, are distributed along the way of single-photon propagation,
thus effectively forming a gradient absorption structure which can be
controlled in various ways to achieve different single-photon processing
functionalities. Such a controllable frequency comb is a hybrid of Raman,
gradient echo memory (GEM) and atomic frequency comb (AFC) methods, therefore
demonstrates many of their advantages all together in one.


  To analyze phase transitions in a nonequilibrium system we study its grand
canonical partition function as a function of complex fugacity. Real and
positive roots of the partition function mark phase transitions. This behavior,
first found by Yang and Lee under general conditions for equilibrium systems,
can also be applied to nonequilibrium phase transitions. We consider a
one-dimensional diffusion model with periodic boundary conditions. Depending on
the diffusion rates, we find real and positive roots and can distinguish two
regions of analyticity, which can identified with two different phases. In a
region of the parameter space both of these phases coexist. The condensation
point can be computed with high accuracy.


  We study the Abramovich--Vistoli moduli space of genus zero orbifold stable
maps to [Sym^2 P^2], the stack symmetric square of P^2. This compactifies the
moduli space of stable maps from hyperelliptic curves to P^2, and we show that
all genus zero Gromov--Witten invariants are determined from trivial
enumerative geometry of hyperelliptic curves. We also show how the genus zero
Gromov--Witten invariants can be used to determine the number of hyperelliptic
curves of degree d and genus g interpolating 3d + 1 generic points in P^2.
Comparing our method to that of Graber for calculating the same numbers, we
verify an example of the crepant resolution conjecture.


  We have performed a Monte-Calro simulation for Galactic population of pulsars
and for the $\gamma$-ray observations. We apply outer gap model for the
$\gamma$-ray emission process, and study the radiation characteristics as a
function of the magnetic inclination angle $(\alpha)$ and the Earth viewing
angle ($\zeta$). In our model, the $\gamma$-ray flux and the spectral cut-off
energy tend to decrease as the inclination and viewing angles deviate from
$90^{\circ}$. The emerging spectrum above 100 MeV becomes soft with a photon
index $p\sim 1.8-2$ for $\zeta \rightarrow 90^{\circ}$ and $p\sim 1.2-1.3$ for
$\zeta \ll 90^{\circ}$. Our simulation predicts that the pulsars with larger
inclination angles ($\alpha=70-90^{\circ}$) and larger viewing angles
($\zeta=70-90^{\circ}$) have been preferentially detected by the $Fermi$
$\gamma$-ray telescope, and hence the observed pulse profiles of the
$\gamma$-ray pulsars have the double peak structure rather than single peak. In
the simulation, most $\gamma$-ray millisecond pulsars are categorized as the
radio-quiet $\gamma$-ray pulsars, because its radio fluxes are under the
sensitivities of the major radio surveys. Even we drastically increase the
radio sensitivity by a factor of ten, the number of radio-selected millisecond
pulsars detected by the $Fermi$ ten years observations is still much less than
the expected $\gamma$-ray-selected millisecond pulsars, indicating the
radio-quiet millisecond pulsars must contribute to the $Fermi$ unidentified
sources and/or the $\gamma$-ray background radiations. The Unique radiation
properties of the low-efficient $\gamma$-ray pulsar, PSR J0659+1414, can be
explained by the present our gap model with a viewing geometry of $\alpha\sim
\zeta=40^{\circ}-50^{\circ}$.


  Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of
unseen classes by resorting to a small set of support images that contain
pixel-level object annotations. Existing methods have demonstrated that the
domain agent-based attention mechanism is effective in FSVOS by learning the
correlation between support images and query frames. However, the agent frame
contains redundant pixel information and background noise, resulting in
inferior segmentation performance. Moreover, existing methods tend to ignore
inter-frame correlations in query videos. To alleviate the above dilemma, we
propose a holistic prototype attention network (HPAN) for advancing FSVOS.
Specifically, HPAN introduces a prototype graph attention module (PGAM) and a
bidirectional prototype attention module (BPAM), transferring informative
knowledge from seen to unseen classes. PGAM generates local prototypes from all
foreground features and then utilizes their internal correlations to enhance
the representation of the holistic prototypes. BPAM exploits the holistic
information from support images and video frames by fusing co-attention and
self-attention to achieve support-query semantic consistency and inner-frame
temporal consistency. Extensive experiments on YouTube-FSVOS have been provided
to demonstrate the effectiveness and superiority of our proposed HPAN method.


  A key challenge in Imitation Learning (IL) is that optimal state actions
demonstrations are difficult for the teacher to provide. For example in
robotics, providing kinesthetic demonstrations on a robotic manipulator
requires the teacher to control multiple degrees of freedom at once. The
difficulty of requiring optimal state action demonstrations limits the space of
problems where the teacher can provide quality feedback. As an alternative to
state action demonstrations, the teacher can provide corrective feedback such
as their preferences or rewards. Prior work has created algorithms designed to
learn from specific types of noisy feedback, but across teachers and tasks
different forms of feedback may be required. Instead we propose that in order
to learn from a diversity of scenarios we need to learn from a variety of
feedback. To learn from a variety of feedback we make the following insight:
the teacher's cost function is latent and we can model a stream of feedback as
a stream of loss functions. We then use any online learning algorithm to
minimize the sum of these losses. With this insight we can learn from a
diversity of feedback that is weakly correlated with the teacher's true cost
function. We unify prior work into a general corrective feedback meta-algorithm
and show that regardless of feedback we can obtain the same regret bounds. We
demonstrate our approach by learning to perform a household navigation task on
a robotic racecar platform. Our results show that our approach can learn
quickly from a variety of noisy feedback.


  In this work we propose composition products in the class of complex harmonic
functions so that the composition of two such functions is again a complex
harmonic function. From here we begin the study of the iterations of the
functions of this class showing briefly their potential to be a topic of future
research. In parallel, we define and study composition operators whose symbols
belong to a Hardy space of complex harmonic functions also introduced in the
work. All this constitutes a previous work for the research of semigroups and
evolutionary families composed of complex harmonic functions.


  We provide here a set of three OpenMP parallelized FORTRAN 90/95 programs to
compute the ground states and the dynamics of trapped spin-2 Bose-Einstein
condensates (BECs) with anisotropic spin-orbit (SO) coupling by solving a set
of five coupled Gross-Pitaevskii equations using a time-splitting Fourier
spectral method. Depending on the nature of the problem, without any loss of
generality, we have employed the Cartesian grid spanning either three-, two-,
or one-dimensional space for numerical discretization. To illustrate the
veracity of the package, wherever feasible, we have compared the numerical
ground state solutions of the full mean-field model with those from the
simplified scalar models. The two set of results show excellent agreement, in
particular, through the equilibrium density profiles, energies and chemical
potentials of the ground-states. We have also presented test results for OpenMP
performance parameters like speedup and the efficiency of the three codes.


  Bosons have a natural instinct to condense at zero temperature. It is a
long-standing challenge to create a high-dimensional quantum liquid that does
not exhibit long-range order at the ground state, as either extreme
experimental parameters or sophisticated designs of microscopic Hamiltonian are
required for suppressing the condensation. Here, we show that ultra cold atoms
with synthetic spin-orbit coupling provide physicists a simple and practical
scheme to produce a two-dimensional algebraic quantum liquid at the ground
state. This quantum liquid arises at a critical Lifshitz point, where the
single-particle ground state shrinks to a point from a circle in the momentum
space, and many fundamental properties of two-dimensional bosons are changed in
its proximity. Such an ideal simulator of the quantum Lifshitz model allows
experimentalists to directly visualize and explore the deconfinement transition
of topological excitations, an intriguing phenomenon that is difficult to
access in other systems.


  The task of cross-view image geo-localization aims to determine the
geo-location (GPS coordinates) of a query ground-view image by matching it with
the GPS-tagged aerial (satellite) images in a reference dataset. Due to the
dramatic changes of viewpoint, matching the cross-view images is challenging.
In this paper, we propose the GeoCapsNet based on the capsule network for
ground-to-aerial image geo-localization. The network first extracts features
from both ground-view and aerial images via standard convolution layers and the
capsule layers further encode the features to model the spatial feature
hierarchies and enhance the representation power. Moreover, we introduce a
simple and effective weighted soft-margin triplet loss with online batch hard
sample mining, which can greatly improve image retrieval accuracy. Experimental
results show that our GeoCapsNet significantly outperforms the state-of-the-art
approaches on two benchmark datasets.


  Phosphorus (P) is one of the essential elements for life due to its central
role in biochemical processes. Recent searches have shown that P-bearing
molecules (in particular PN and PO) are present in star-forming regions,
although their formation routes remain poorly understood. In this Letter, we
report observations of PN and PO towards seven molecular clouds located in the
Galactic Center, which are characterized by different types of chemistry. PN is
detected in five out of seven sources, whose chemistry is thought to be
shock-dominated. The two sources with PN non-detections correspond to clouds
exposed to intense UV/X-rays/cosmic-ray radiation. PO is detected only towards
the cloud G+0.693$-$0.03, with a PO/PN abundance ratio of $\sim$1.5. We
conclude that P-bearing molecules likely form in shocked gas as a result of
dust grain sputtering, while are destroyed by intense UV/X-ray/cosmic ray
radiation.


  The field of image synthesis is currently flourishing due to the advancements
in diffusion models. While diffusion models have been successful, their
computational intensity has prompted the pursuit of more efficient
alternatives. As a representative work, non-autoregressive Transformers (NATs)
have been recognized for their rapid generation. However, a major drawback of
these models is their inferior performance compared to diffusion models. In
this paper, we aim to re-evaluate the full potential of NATs by revisiting the
design of their training and inference strategies. Specifically, we identify
the complexities in properly configuring these strategies and indicate the
possible sub-optimality in existing heuristic-driven designs. Recognizing this,
we propose to go beyond existing methods by directly solving the optimal
strategies in an automatic framework. The resulting method, named AutoNAT,
advances the performance boundaries of NATs notably, and is able to perform
comparably with the latest diffusion models at a significantly reduced
inference cost. The effectiveness of AutoNAT is validated on four benchmark
datasets, i.e., ImageNet-256 & 512, MS-COCO, and CC3M. Our code is available at
https://github.com/LeapLabTHU/ImprovedNAT.


  We present an alternative scheme for obtaining effective power dissipation in
planar composites, extending the recently proposed concept of metagrating
(MGs), sparse arrangements of polarizable particles (meta-atoms), to realize
multifunctional absorbers. In contrast to typical metasurface solutions, where
periodicities are limited to half of a wavelength at most to avoid high-order
Floquet-Bloch modes, we purposely consider large-period MGs, relying on their
proven ability to effectively mitigate spurious scattering. The absorption
process is thus implemented via precise engineering of the mutual coupling
between numerous individual scatterers fitting in the enlarged period, with
these additional degrees of freedom further utilized to enforce the perfect
absorption conditions for multiple excitation angles simultaneously. The
resultant devices, utilizing a standard printed circuit board configuration
obtained semianalytically while featuring relaxed fabrication demands, exhibit
high absorption across a wide angular range, useful for radar cross section
reduction and energy harvesting applications.