  Domain adaptive semantic segmentation aims to transfer knowledge from a
labeled source domain to an unlabeled target domain. However, existing methods
primarily focus on directly learning qualified target features, making it
challenging to guarantee their discrimination in the absence of target labels.
This work provides a new perspective. We observe that the features learned with
source data manage to keep categorically discriminative during training,
thereby enabling us to implicitly learn adequate target representations by
simply \textbf{pulling target features close to source features for each
category}. To this end, we propose T2S-DA, which we interpret as a form of
pulling Target to Source for Domain Adaptation, encouraging the model in
learning similar cross-domain features. Also, considering the pixel categories
are heavily imbalanced for segmentation datasets, we come up with a dynamic
re-weighting strategy to help the model concentrate on those underperforming
classes. Extensive experiments confirm that T2S-DA learns a more discriminative
and generalizable representation, significantly surpassing the
state-of-the-art. We further show that our method is quite qualified for the
domain generalization task, verifying its domain-invariant property.


  Hawking radiation is often intuitively visualized as particles that have
tunneled across the horizon. Yet, at first sight, it is not apparent where the
barrier is. Here I show that the barrier depends on the tunneling particle
itself. The key is to implement energy conservation, so that the black hole
contracts during the process of radiation. A direct consequence is that the
radiation spectrum cannot be strictly thermal. The correction to the thermal
spectrum is of precisely the form that one would expect from an underlying
unitary quantum theory. This may have profound implications for the black hole
information puzzle.


  Competitive Self-Play (CSP) based Multi-Agent Reinforcement Learning (MARL)
has shown phenomenal breakthroughs recently. Strong AIs are achieved for
several benchmarks, including Dota 2, Glory of Kings, Quake III, StarCraft II,
to name a few. Despite the success, the MARL training is extremely data
thirsty, requiring typically billions of (if not trillions of) frames be seen
from the environment during training in order for learning a high performance
agent. This poses non-trivial difficulties for researchers or engineers and
prevents the application of MARL to a broader range of real-world problems. To
address this issue, in this manuscript we describe a framework, referred to as
TLeague, that aims at large-scale training and implements several main-stream
CSP-MARL algorithms. The training can be deployed in either a single machine or
a cluster of hybrid machines (CPUs and GPUs), where the standard Kubernetes is
supported in a cloud native manner. TLeague achieves a high throughput and a
reasonable scale-up when performing distributed training. Thanks to the modular
design, it is also easy to extend for solving other multi-agent problems or
implementing and verifying MARL algorithms. We present experiments over
StarCraft II, ViZDoom and Pommerman to show the efficiency and effectiveness of
TLeague. The code is open-sourced and available at
https://github.com/tencent-ailab/tleague_projpage


  We present a methodology for the estimation of optical network physical layer
parameters from signal to noise ratio via history matching. An expensive
network link simulator is emulated by a Gaussian process surrogate model, which
is used to estimate a set of physical layer parameters from simulated ground
truth data. The a priori knowledge assumed consists of broad parameter bounds
obtained from the literature and specification sheets of typical network
components, and the physics-based model of the simulator. Accurate estimation
of the physical layer parameters is demonstrated with a signal to noise ratio
penalty of 1~dB or greater, using only 3 simulated measurements. The proposed
approach is highly flexible, allowing for the calibration of any unknown
simulator input from broad a priori bounds. The role of this method in the
improvement of optical network modeling is discussed.


  The independent control of two magnetic electrodes and spin-coherent
transport in magnetic tunnel junctions are strictly required for tunneling
magnetoresistance, while junctions with only one ferromagnetic electrode
exhibit tunneling anisotropic magnetoresistance dependent on the anisotropic
density of states with no room temperature performance so far. Here we report
an alternative approach to obtaining tunneling anisotropic magnetoresistance in
alfa-FeRh-based junctions driven by the magnetic phase transition of alfa-FeRh
and resultantly large variation of the density of states in the vicinity of MgO
tunneling barrier, referred to as phase transition tunneling anisotropic
magnetoresistance. The junctions with only one alfa-FeRh magnetic electrode
show a magnetoresistance ratio up to 20% at room temperature. Both the polarity
and magnitude of the phase transition tunneling anisotropic magnetoresistance
can be modulated by interfacial engineering at the alfa-FeRh/MgO interface.
Besides the fundamental significance, our finding might add a different
dimension to magnetic random access memory and antiferromagnet spintronics.


  Deposition of clean and defect-free atomically thin two-dimensional
crystalline flakes on surfaces by mechanical exfoliation of layered bulk
materials has proven to be a powerful technique, but it requires a fast,
reliable and non-destructive way to identify the atomically thin flakes among a
crowd of thick flakes. In this work, we provide general guidelines to identify
ultrathin flakes of TaSe2 by means of optical microscopy and Raman
spectroscopy. Additionally, we determine the optimal substrates to facilitate
the optical identification of atomically thin TaSe2 crystals. Experimental
realization and isolation of ultrathin layers of TaSe2 enables future studies
on the role of the dimensionality in interesting phenomena such as
superconductivity and charge density waves.


  Lack of directional bonding between two-dimensional crystals like graphene or
monolayer transition metal dichalcogenides provides unusual freedom in
selection of components for vertical van der Waals heterostructures. However,
even for identical layers, their stacking, in particular the relative angle
between their crystallographic directions, modifies properties of the
structure. We demonstrate that the interatomic coupling between two
two-dimensional crystals can be determined from angle-resolved photoemission
spectra of a trilayer structure with one aligned and one twisted interface.
Each of the interfaces provides complementary information and together they
enable self-consistent determination of the coupling. We parametrize
interatomic coupling for carbon atoms by studying twisted trilayer graphene and
show that the result can be applied to structures with different twists and
number of layers. Our approach demonstrates how to extract fundamental
information about interlayer coupling in a stack of two-dimensional crystals
and can be applied to many other van der Waals interfaces.


  We investigate a class of hierarchical mixtures-of-experts (HME) models where
exponential family regression models with generalized linear mean functions of
the form psi(ga+fx^Tfgb) are mixed. Here psi(...) is the inverse link function.
Suppose the true response y follows an exponential family regression model with
mean function belonging to a class of smooth functions of the form psi(h(fx))
where h(...)in W_2^infty (a Sobolev class over [0,1]^{s}). It is shown that the
HME probability density functions can approximate the true density, at a rate
of O(m^{-2/s}) in L_p norm, and at a rate of O(m^{-4/s}) in Kullback-Leibler
divergence. These rates can be achieved within the family of HME structures
with no more than s-layers, where s is the dimension of the predictor fx. It is
also shown that likelihood-based inference based on HME is consistent in
recovering the truth, in the sense that as the sample size n and the number of
experts m both increase, the mean square error of the predicted mean response
goes to zero. Conditions for such results to hold are stated and discussed.


  We consider boundary value problems for quasilinear first-order
one-dimensional hyperbolic systems in a strip. The boundary conditions are
supposed to be of a smoothing type, in the sense that the $L^2$-generalized
solutions to the initial-boundary value problems become eventually $C^2$-smooth
for any initial $L^2$-data. We investigate small global classical solutions and
obtain the existence and uniqueness result under the condition that the
evolution family generated by the linearized problem has exponential dichotomy
on R. We prove that the dichotomy survives under small perturbations in the
leading coefficients of the hyperbolic system. Assuming that the coefficients
of the hyperbolic system are almost periodic, we prove that the bounded
solution is almost periodic also.


  We present a simple interpolation formula using dimensional limits $D=1$ and
$D=\infty$ to obtain the $D=3$ ground-state energies of atoms and molecules.
For atoms, these limits are linked by first-order perturbation terms of
electron-electron interactions. This unorthodox approach is illustrated by
ground-states for two, three, and four electron atoms, with modest effort to
obtain fairly accurate results. Also, we treat the ground-state of H$_2$ over a
wide range of the internuclear distance R, and compares well with the standard
exact results from the Full Configuration Interaction method. Similar
dimensional interpolations may be useful for complex many-body systems.


  Peculiar velocities are a precious tool to study the large-scale distribution
of matter in the local universe and test cosmological models. However, present
measurements of peculiar velocities are based on empirical distance indicators,
which introduce large error bars. Here we present a new method to measure the
peculiar velocities, by directly estimating luminosity distances through
waveform signals from inspiralling compact binaries and measuring redshifts
from electromagnetic (EM) counterparts. In the future, with the distance
uncertainty of GW events reducing to $0.1$ per cent by future GW detectors, the
uncertainty of the peculiar velocity can be reduced to $10$ km/s at 100 mega
parsecs. We find that dozens of GW events with EM counterparts can provide a
Hubble constant $H_0$ uncertainty of $0.5\%$ and the growth rate of structure
with a $0.6\%$ precision in the third-generation ground-base GW detectors,
which can reconcile the $H_0$ tension and determine the origins for cosmic
accelerated expansion.


  Quantum dynamics simulations (QDSs) are one of the most highly anticipated
applications of quantum computing. Quantum circuit depth for implementing
Hamiltonian simulation algorithms is commonly time dependent so that long time
dynamics simulations become impratical on near-term quantum processors. The
Hamiltonian simulation algorithm based on Cartan decomposition (CD) provides an
appealing scheme for QDSs with fixed-depth circuits while limited to
time-independent case. In this work, we generalize this CD-based Hamiltonian
simulation algorithm for studying time-dependent systems by combining it with
variational Hamiltonian simulation. The time-dependent and time-independent
parts of the Hamiltonian are treated with the variational approach and the
CD-based Hamiltonian simulation algorithms, respectively. As such, only
fixed-depth quantum circuits are required in this hybrid Hamiltonian simulation
algorithm while still maintaining high accuracy. We apply this new algorithm to
study the response of spin and molecular systems to $\delta$-kick electric
fields and obtain accurate spectra for these excitation processes.


  We consider connectivity properties of the Branching Interlacements model in
$\mathbb{Z}^d,~d\ge5$, recently introduced by Angel, R\'ath and Zhu in 2016.
Using stochastic dimension techniques we show that every two vertices visited
by the branching interlacements are connected via at most $\lceil d/4\rceil$
conditioned critical branching random walks from the underlying Poisson
process, and that this upper bound is sharp. In particular every such two
branching random walks intersect if and only if $5\le d\le 8$. The stochastic
dimension of branching random walk result is of independent interest. We
additionally obtain heat kernel bounds for branching random walks conditioned
on survival.


  We propose a manifestly Lorentz covariant, non-commutative Dirac equation for
charged particles interacting with an electromagnetic field. The equation is
formulated on the operator level, but operators are not composed through the
normal operator product, but a modified product that restores the Lorentz
covariance. This equation is solved for the free particle and a particle moving
in a constant magnetic field. An abstract action, constructed on the operator
level, that yields this equation as equation of motion is also derived. To
relate this formalism to current formulations of non-commutative quantum field
theories, this action is written in a coherent state basis, leading to an
action in 4-dimensional Minkowski space-time. The resulting action differs from
existing non-commutative actions, but still exhibits non-commutative effects
through non-locality.


  Cross-modality generation is an emerging topic that aims to synthesize data
in one modality based on information in a different modality. In this paper, we
consider a task of such: given an arbitrary audio speech and one lip image of
arbitrary target identity, generate synthesized lip movements of the target
identity saying the speech. To perform well in this task, it inevitably
requires a model to not only consider the retention of target identity,
photo-realistic of synthesized images, consistency and smoothness of lip images
in a sequence, but more importantly, learn the correlations between audio
speech and lip movements. To solve the collective problems, we explore the best
modeling of the audio-visual correlations in building and training a
lip-movement generator network. Specifically, we devise a method to fuse audio
and image embeddings to generate multiple lip images at once and propose a
novel correlation loss to synchronize lip changes and speech changes. Our final
model utilizes a combination of four losses for a comprehensive consideration
in generating lip movements; it is trained in an end-to-end fashion and is
robust to lip shapes, view angles and different facial characteristics.
Thoughtful experiments on three datasets ranging from lab-recorded to lips
in-the-wild show that our model significantly outperforms other
state-of-the-art methods extended to this task.


  The structure of a spherically symmetric stable dark 'star' is discussed, at
zero temperature, containing 1) a core of quarks in the deconfined phase and
antileptons 2) a shell of hadrons in particular $n$, $p$, $\Lambda$ and
$\Sigma^-$ and leptons or antileptons and 3) a shell of hydrogen in the
superfluid phase. If the superfluid hydrogen phase goes over into the
electromagnetic plasma phase at densities well below one atom / $(10 fm)^{3}$,
as is usually assumed, the hydrogen shell is insignificant for the mass and the
radius of the 'star'. These quantities are then determined approximatively :
mass = 1.8 solar masses and radius = 9.2 km. On the contrary if densities of
the order of one atom / $(10 fm)^{3}$ do form a stable hydrogen superfluid
phase, we find a large range of possible masses from 1.8 to 375 solar masses.
The radii vary accordingly from 9 to 1200 km.


  The question of what physical mechanisms shape planetary nebulae into their
observed morphologies remains open. However, intensified efforts since the last
meeting in this series, Asymmetrical Planetary Nebulae IV, in July 2007 have
yielded some excellent results. In this review we concentrate on those
developments that have taken place in the last three years, with emphasis on
results obtained since the review by De Marco (2009).


  Additive interventions are a recently-proposed mechanism for controlling
target-side attributes in neural machine translation. In contrast to tag-based
approaches which manipulate the raw source sequence, interventions work by
directly modulating the encoder representation of all tokens in the sequence.
We examine the role of additive interventions in a large-scale multi-domain
machine translation setting and compare its performance in various inference
scenarios. We find that while the performance difference is small between
intervention-based systems and tag-based systems when the domain label matches
the test domain, intervention-based systems are robust to label error, making
them an attractive choice under label uncertainty. Further, we find that the
superiority of single-domain fine-tuning comes under question when training
data size is scaled, contradicting previous findings.


  GJ 1214b is the most studied sub-Neptune exoplanet to date. Recent
measurements have shown its near-infrared transmission spectrum to be flat,
pointing to a high-altitude opacity source in the exoplanet's atmosphere,
either equilibrium condensate clouds or photochemical hazes. Many photometric
observations have been reported in the optical by different groups, though
simultaneous measurements spanning the entire optical regime are lacking. We
present an optical transmission spectrum ($4,500-9,260$\AA) of GJ 1214b in 14
bins measured with Magellan/IMACS repeatedly over three transits. We measure a
mean planet-to-star radius ratio of ${R_{p}/R_{s} = 0.1146\pm{2\times10^{-4}}}$
and mean uncertainty of $\sigma(R_{p}/R_{s})=8.7\times10^{-4}$ in the spectral
bins. The optical transit depths are shallower on average than observed in the
near-infrared. We present a model for jointly incorporating the effects of a
composite photosphere and atmospheric transmission (CPAT) through the
exoplanet's limb, and use it to examine the cases of absorber and temperature
heterogeneities in the stellar photosphere. We find the optical and
near-infrared measurements are best explained by the combination of (1)
photochemical haze in the exoplanetary atmosphere with a mode particle size
$r=0.1~\mu$m and haze-forming efficiency $f_{haze}=10 \%$ and (2) faculae in
the unocculted stellar disk with a temperature contrast $\Delta
T=354^{+46}_{-46}$ K, assuming 3.2% surface coverage. The CPAT model can be
used to assess potential contributions of heterogeneous stellar photospheres to
observations of exoplanet transmission spectra, which will be important for
searches for spectral features in the optical.


  Graph data structures are fundamental for studying connected entities. With
an increase in the number of applications where data is represented as graphs,
the problem of graph generation has recently become a hot topic. However,
despite its significance, conditional graph generation that creates graphs with
desired features is relatively less explored in previous studies. This paper
addresses the problem of class-conditional graph generation that uses class
labels as generation constraints by introducing the Class Conditioned Graph
Generator (CCGG). We built CCGG by injecting the class information as an
additional input into a graph generator model and including a classification
loss in its total loss along with a gradient passing trick. Our experiments
show that CCGG outperforms existing conditional graph generation methods on
various datasets. It also manages to maintain the quality of the generated
graphs in terms of distribution-based evaluation metrics.


  Recently in boson star models in framework of Brans-Dicke theory, three
possible definitions of mass have been identified, all identical in general
relativity, but different in scalar-tensor theories of gravity.It has been
conjectured that it's the tensor mass which peaks, as a function of the central
density, at the same location where the particle number takes its maximum.This
is a very important property which is crucial for stability analysis via
catastrophe theory. This conjecture has received some numerical support. Here
we give an analytical proof of the conjecture in framework of the generalized
scalar-tensor theory of gravity, confirming in this way the numerical
calculations.


  In this article we develop an image based rendering technique based on light
field reconstruction from a limited set of perspective views acquired by
cameras. Our approach utilizes sparse representation of epipolar-plane images
in a directionally sensitive transform domain, obtained by an adapted discrete
shearlet transform. The used iterative thresholding algorithm provides
high-quality reconstruction results for relatively big disparities between
neighboring views. The generated densely sampled light field of a given 3D
scene is thus suitable for all applications which requires light field
reconstruction. The proposed algorithm is compared favorably against state of
the art depth image based rendering techniques.


  In these lectures we provide an introduction to the theory of QCD at very
high baryon density. We begin with a review of some aspects of quantum
many-body system that are relevant in the QCD context. We also provide a brief
review of QCD and its symmetries. The main part of these lectures is devoted to
the phenomenon of color superconductivity. We discuss the use of weak coupling
methods and study the phase structure as a function of the number of flavors
and their masses. We also introduce effective theories that describe low energy
excitations at high baryon density. Finally, we use effective field theory
methods in order to study the effects of a non-zero strange quark mass.


  We focus on large blackouts in electric distribution systems caused by
extreme winds. Such events have a large cost and impact on customers. To
quantify resilience to these events, we formulate large event risk and show how
to calculate it from the historical outage data routinely collected by
utilities' outage management systems. Risk is defined using an event cost
exceedance curve. The tail of this curve and the large event risk is described
by the probability of a large cost event and the slope magnitude of the tail on
a log-log plot. Resilience can be improved by planned investments to upgrade
system components or speed up restoration. The benefits that these investments
would have had if they had been made in the past can be quantified by
"rerunning history" with the effects of the investment included, and then
recalculating the large event risk to find the improvement in resilience. An
example using utility data shows a 12% and 22% reduction in the probability of
a large cost event due to 10% wind hardening and 10% faster restoration
respectively. This new data-driven approach to quantify resilience and
resilience investments is realistic and much easier to apply than complicated
approaches based on modeling all the phases of resilience. Moreover, an appeal
to improvements to past lived experience may well be persuasive to customers
and regulators in making the case for resilience investments.


  We report the discovery of a trend of increasing barium abundance with
decreasing age for a large sample of Galactic open clusters. The observed
pattern of [Ba/Fe] vs. age can be reproduced with a Galactic chemical evolution
model only assuming a higher Ba yield from the $s$-process in low-mass stars
than the average one suggested by parametrized models of neutron-capture
nucleosynthesis. We show that this is possible in a scenario where the
efficiency of the extra-mixing processes producing the neutron source $^{13}$C
is anti-correlated with the initial mass, with a larger efficiency for lower
masses. This is similar to the known trend of extended mixing episodes acting
in H-rich layers and might suggest a common physical mechanism.


  Recent advances in time-domain astronomy have led to fresh observational
insights into intermediate polars, a subtype of magnetic cataclysmic variables
generally accreting via a partial accretion disc. These new discoveries include
detections of superhumps, low states, and outbursts. However, these studies
have largely relied on relative photometry. Here we tabulate the absolute G
magnitudes of confirmed intermediate polars, plot them against their orbital
periods, and compare the results to similar studies of dwarf novae during
quiescence and in outburst. This exercise suggests the presence of two distinct
luminosity classes of intermediate polars, with practical and physical
implications for the studies of low states and outbursts. In particular, we
point out that two of the optically luminous systems showing short outbursts
are also seen to exhibit superhumps, suggesting that they may be caused by the
same underlying mechanism.


  In this paper, we use multithreaded fast Fourier transforms provided in three
highly optimized packages, FFTW-2.1.5, FFTW-3.3.7, and Intel MKL FFT, to
present a novel model-based parallel computing technique as a very effective
and portable method for optimization of scientific multithreaded routines for
performance, especially in the current multicore era where the processors have
abundant number of cores. We propose two optimization methods, PFFT-FPM and
PFFT-FPM-PAD, based on this technique. They compute 2D-DFT of a complex signal
matrix of size NxN using p abstract processors. Both algorithms take as inputs,
discrete 3D functions of performance against problem size of the processors and
output the transformed signal matrix. Based on our experiments on a modern
Intel Haswell multicore server consisting of 36 physical cores, the average and
maximum speedups observed for PFFT-FPM using FFTW-3.3.7 are 1.9x and 6.8x
respectively and the average and maximum speedups observed using Intel MKL FFT
are 1.3x and 2x respectively. The average and maximum speedups observed for
PFFT-FPM-PAD using FFTW-3.3.7 are 2x and 9.4x respectively and the average and
maximum speedups observed using Intel MKL FFT are 1.4x and 5.9x respectively.


  The analyzing power for proton-carbon elastic scattering in the
coulomb-nuclear interference region of momentum transfer,
$9.0\times10^{-3}<-t<4.1\times10^{-2}$ (GeV/$c)^{2}$, was measured with a 21.7
GeV/$c$ polarized proton beam at the Alternating Gradient Synchrotron of
Brookhaven National Laboratory. The ratio of hadronic spin-flip to non-flip
amplitude, $r_5$, was obtained from the analyzing power to be $\text{Re}
r_5=0.088\pm 0.058$ and $\text{Im} r_5=-0.161\pm 0.226$.


  The observation that every two-person adversarial game is an affine
transformation of a zero-sum game is traceable to Luce & Raiffa (1957) and made
explicit in Aumann (1987). Recent work of (ADP) Adler et al. (2009), and of
Raimondo (2023) in increasing generality, proves what has so far remained a
conjecture. We present two proofs of an even more general formulation: the
first draws on multilinear utility theory developed by Fishburn & Roberts
(1978); the second is a consequence of the ADP proof itself for a special case
of a two-player game with a set of three actions.


  We prove that two general Enriques surfaces defined over an algebraically
closed field of characteristic different from $2$ are isomorphic if their
Kuznetsov components are equivalent. We apply the same techniques to give a new
simple proof of a conjecture by Ingalls and Kuznetsov relating the derived
categories of the blow-up of general Artin\textendash Mumford quartic double
solids and of the associated Enriques surfaces. This paper originated from one
of the problem sections at the workshop \emph{Semiorthogonal decompositions,
stability conditions and sheaves of categories}, Universit\'e de Toulouse, May
2--5, 2018.


  We analyse biased ensembles of trajectories for a two-dimensional system of
particles, evolving by Langevin dynamics in a channel geometry. This bias
controls the degree of particle clustering. On biasing to large clustering, we
observe a dynamical phase transition where the particles break symmetry and
accumulate at one of the walls. We analyse the mechanical properties of this
symmetry-broken state using the Irving-Kirkwood stress tensor. The biased
ensemble is characterised by body forces which originate in random thermal
noises, but have finite averages in the presence of the bias. We discuss the
connection of these forces to Doob's transform and optimal control theory.


  We firstly describe a maximal inequality for dual Sobolev spaces W^{-1,p}.
This one corresponds to a "Sobolev version" of usual properties of the
Hardy-Littlewood maximal operator in Lebesgue spaces. Even in the euclidean
space, this one seems to be new and we develop arguments in the general
framework of Riemannian manifold. Then we present an application to obtain
interpolation results for Sobolev spaces.


  We investigate the quality factor and RMS amplitude of the lower kHz QPOs
from XTE J1701-462, a unique X-ray source which was observed in both the
so-called Z and atoll states. Correcting for the frequency drift of the QPO, we
show that, as in all sources for which such a correction can be applied, the
quality factor and RMS amplitude drops sharply above above a critical
frequency. For XTE J1701-462 this frequency is estimated to be ~800 Hz, where
the quality factor reaches a maximum of ~200 (e.g. a value consistent with the
one observed from more classical systems, such as 4U~1636-536). Such a drop has
been interpreted as the signature of the innermost stable circular orbit, and
that interpretation is consistent with the observations we report here. The kHz
QPOs in the Z state are much less coherent and lower amplitude than they are in
the atoll state. We argue that the change of the QPO properties between the two
source states is related to the change of the scale height of the accretion
disk; a prediction of the toy model proposed by barret et al. (2007). As a
by-product of our analysis, we also increased the significance of the upper kHz
QPO detected in the atoll phase up to 4.8 sigma (single trial significance),
and show that the frequency separation (266.5+/-13.1 Hz) is comparable with the
one measured from simultaneous twin QPOs the Z phase.


  We propose a simple mechanical model for relativistic explosions with both
forward and reverse shocks, which allows one to do fast calculations of GRB
afterglow. The blast wave in the model is governed by pressures P_F and P_R at
the forward and reverse shocks. We show that the simplest assumption P_F=P_R is
in general inconsistent with energy conservation law. The model is applied to
GRBs with non-uniform ejecta. Such "stratified fireballs" are likely to emerge
with a monotonic velocity profile after an internal-shock stage. We calculate
the early afterglow emission expected from stratified fireballs.


  The purpose of this note is to provide an expository introduction to some
more curious integral formulas and transformations involving generating
functions. We seek to generalize these results and integral representations
which effectively provide a mechanism for converting between a sequence's
ordinary and exponential generating function (OGF and EGF, respectively) and
vice versa. The Laplace transform provides an integral formula for the
EGF-to-OGF transformation, where the reverse OGF-to-EGF operation requires more
careful integration techniques. We prove two variants of the OGF-to-EGF
transformation integrals from the Hankel loop contour for the reciprocal gamma
function and from Fourier series expansions of integral representations for the
Hadamard product of two generating functions, respectively. We also suggest
several generalizations of these integral formulas and provide new examples
along the way.


  The SMC complex, MukBEF, is important for chromosome organization and
segregation in Escherichia coli. Fluorescently tagged MukBEF forms distinct
spots (or 'foci') in the cell, where it is thought to carry out most of its
chromosome associated activities. This chapter outlines the technique of
Fluorescence Recovery After Photobleaching (FRAP) as a method to study the
properties of YFP-tagged MukB in fluorescent foci. This method can provide
important insight into the dynamics of MukB on DNA and be used to study its
biochemical properties in vivo.


  GFDM is a new modulation format whose advantages compared to OFDM reportedly
make it a preferable modulation format for 5G. However, the non-orthogonal
nature of GFDM with matched filtering (MF) receiver for pulses with good
time-frequency localization is one of its disadvantages, leading to the
proposal of WCP-COQAM, employing offset quadrature amplitude modulation (OQAM).
In this paper, we prove that a pulse satisfying orthogonality conditions for
OQAM-OFDM will also satisfy orthogonality with WCP-COQAM, thus the pulse design
methods developed for OQAM-OFDM can also be used with WCP-COQAM. This statement
is also verified by the simulation based results.


  In 1994, M. M. Popov [On integrability in F-spaces, Studia Math. no 3,
205-220] showed that the fundamental theorem of calculus fails, in general, for
functions mapping from a compact interval of the real line into the lp-spaces
for 0<p<1, and the question arose whether such a significant result might hold
in some non-Banach spaces. In this article we completely settle the problem by
proving that the fundamental theorem of calculus breaks down in the context of
any non-locally convex quasi-Banach space. Our approach introduces the tool of
Riemann-integral averages of continuous functions, and uses it to bring out to
light the differences in behavior of their approximates in the lack of local
convexity. As a by-product of our work we solve a problem raised in [F. Albiac
and J.L. Ansorena, Lipschitz maps and primitives for continuous functions in
quasi-Banach space, Nonlinear Anal. 75 (2012), no. 16, 6108-6119] on the
different types of spaces of differentiable functions with values on a
quasi-Banach space.


  The lower energy thresholds of large wide-field gamma-ray observatories are
often determined by their capability to deal with the very low-energy cosmic
ray background. In fact, in observatories with areas of tens or hundreds of
thousands of square meters, the number of background events generated by the
superposition of random, very low energy cosmic rays is huge and may exceed by
far the possible signal events. In this article, we argue that a trigger
strategy based on pattern recognition of the shower front can significantly
reject the background, keeping a good efficiency and a good angular accuracy
(few square degrees) for gamma rays with energies as low as tens of GeV. In
this way, alerts can be followed or emitted within time lapses of the order of
the second, enabling wide-field gamma-ray observatories to better contribute to
global multi-messenger networks of astrophysical observatories.


  We generalize the bifurcation technique of Bando-Mabuchi in the context of
extremal metrics.


  We reexamine the recently introduced basis-set correction theory based on
density-functional theory consisting in correcting the basis-set incompleteness
error of wave-function methods using a density functional. We use a
one-dimensional model Hamiltonian with delta-potential interactions which has
the advantage of making easier to perform a more systematic analysis than for
three-dimensional Coulombic systems while keeping the essence of the slow basis
convergence problem of wave-function methods. We provide some mathematical
details about the theory and propose a new variant of basis-set correction
which has the advantage of being suited to the development of an adapted
local-density approximation. We show indeed how to develop a local-density
approximation for the basis-set correction functional which is automatically
adapted to the basis set employed, without resorting to range-separated
density-functional theory as in previous works, but using instead a finite
uniform electron gas whose electron-electron interaction is projected on the
basis set. The work puts the basis-set correction theory on firmer grounds and
provides an interesting strategy for the improvement of this approach.


  We present a new view of Feynman diagrams for the field theory of transport
on percolation clusters. The diagrams for random resistor networks are
interpreted as being resistor networks themselves. This simplifies the field
theory considerably as we demonstrate by calculating the fractal dimension
$D_B$ of the percolation backbone to three loop order. Using renormalization
group methods we obtain $D_B = 2 + \epsilon /21 - 172\epsilon^2 /9261 + 2
\epsilon^3 (- 74639 + 22680 \zeta (3))/4084101$, where $\epsilon = 6-d$ with
$d$ being the spatial dimension and $\zeta (3) = 1.202057..$.


  It is well known that the observed Higgs mass is more naturally explained in
the NMSSM than in the MSSM. Without any violation of this success, there are
variants on the NMSSM which can lead to new phenomenologies. In this study we
propose a new variant of NMSSM by imposing an unbroken $R$ symmetry. We firstly
identify the minimal structure of such scenario from the perspective of both
simplicity and viability, then compare model predictions to current
experimental limits, and finally highlight main features that differ from the
well-known scenarios.


  As mobile health (mHealth) studies become increasingly productive due to the
advancements in wearable and mobile sensor technology, our ability to monitor
and model human behavior will be constrained by participant receptivity. The
reliance on subjective responses for health constructs poses challenges,
especially in populations with lower receptivity rates. Researchers have
proposed machine-learning approaches to optimize survey timing and delivery to
address this. However, there are concerns regarding potential biases or
unintended influences on participant responses. Our study delves into factors
impacting receptivity to ecological momentary assessments (EMA) in a 10-day
mHealth study, exploring physiological relationships indicative of receptivity
and affect. Utilizing data from 45 participants with wearable devices measuring
various biometrics, we employ unsupervised (k-means clustering) and supervised
(Random Forest and Neural Networks) machine learning methods to infer affect
during non-responses. Findings reveal that triggering EMAs based on a
receptivity model reduces reported negative affect by over 3 points (0.29
standard deviations). The predicted affect during non-responses exhibits a
bimodal distribution, suggesting more frequent initiation during states of
higher positive emotions. The study underscores a clear relationship between
affect and receptivity, impacting mHealth study efficacy, especially those
using machine learning for EMA triggering. Therefore, we propose a smart
trigger that promotes EMA receptivity without influencing affect during sampled
time points as future work.


  This paper examines the economic and security implications of Proof-of-Stake
(POS) designs, providing a survey of POS design choices and their underlying
economic principles in prominent POS-blockchains. The paper argues that
POS-blockchains are essentially platforms that connect three groups of agents:
users, validators, and investors. To meet the needs of these groups,
blockchains must balance trade-offs between security, user adoption, and
investment into the protocol. We focus on the security aspect and identify two
different strategies: increasing the quality of validators (static security)
vs. increasing the quantity of stakes (dynamic security). We argue that quality
comes at the cost of quantity, identifying a trade-off between the two
strategies when designing POS systems. We test our qualitative findings using
panel analysis on collected data. The analysis indicates that enhancing the
quality of the validator set through security measures like slashing and
minimum staking amounts may decrease dynamic security. Further, the analysis
reveals a strategic divergence among blockchains, highlighting the absence of a
single, universally optimal staking design solution. The optimal design hinges
upon a platform's specific objectives and its developmental stage. This
research compels blockchain developers to meticulously assess the trade-offs
outlined in this paper when developing their staking designs.


  Open-Domain Question Answering (ODQA) aims to answer questions without
explicitly providing specific background documents. This task becomes notably
challenging in a zero-shot setting where no data is available to train tailored
retrieval-reader models. While recent Large Language Models (LLMs) like GPT-3
have demonstrated their effectiveness in zero-shot ODQA using direct prompting
methods, these methods still fall short of fully harnessing the potential of
LLMs when implicitly invoked. In this paper, we propose a Self-Prompting
framework to explicitly utilize the massive knowledge encoded in the parameters
of LLMs and their strong instruction understanding abilities. Concretely, we
prompt LLMs step by step to generate multiple pseudo QA pairs with background
passages and explanations entirely from scratch. These generated elements are
then utilized for in-context learning. Experimental results show that our
method significantly surpasses previous state-of-the-art zero-shot methods on
three widely-used ODQA datasets and even achieves comparable performance with
various customized fine-tuned models on full training data. Our code is
available at https://github.com/lockon-n/self-prompting.


  We explore populations of ultra-diffuse galaxies (UDGs) in isolated,
satellite, and cluster environments using the Romulus25 and RomulusC
simulations, including how the populations vary with UDG definition and viewing
orientation. Using a fiducial definition of UDGs, we find that isolated UDGs
have notably larger semi-major (b/a) and smaller semi-minor (c/a) axis ratios
than their non-UDG counterparts, i.e., they are more oblate, or diskier. This
is in line with previous results that adopted the same UDG definition and
showed that isolated UDGs form via early, high-spin mergers. However, the
choice of UDG definition can drastically affect what subsets of a dwarf
population are classified as UDGs, changing the number of UDGs by up to
approximately 45% of the dwarf population. We also find that a galaxy's
classification as a UDG is dependent on its viewing orientation, and this
dependence decreases as environmental density increases. Overall, we conclude
that some definitions for UDGs used in the literature manage to isolate a
specific formation mechanism for isolated dwarfs, while less restrictive
definitions erase a link to the formation mechanism. Thus, how we define UDG
populations must be considered if we want to understand the formation and
evolution of UDGs.


  We investigate multipartite entanglement in a non-interacting fermion gas, as
a function of fermion separation, starting from the many particle fermion
density matrix. We prove that all multiparticle entanglement can be built only
out of two-fermion entanglement. Although from the Pauli exclusion principle we
would always expect entanglement to decrease with fermion distance, we
surprisingly find the opposite effect for certain fermion configurations. The
von Neumann entropy is found to be proportional to the volume for a large
number of particles even when they are arbitrarily close to each other. We will
illustrate our results using different configurations of two, three, and four
fermions at zero temperature although all our results can be applied to any
temperature and any number of particles.


  Objective: A clinical decision support tool that automatically interprets
EEGs can reduce time to diagnosis and enhance real-time applications such as
ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a
specificity below 5% was the minimum requirement for clinical acceptance. We
propose a highperformance classification system based on principles of big data
and machine learning. Methods: A hybrid machine learning system that uses
hidden Markov models (HMM) for sequential decoding and deep learning networks
for postprocessing is proposed. These algorithms were trained and evaluated
using the TUH EEG Corpus, which is the world's largest publicly available
database of clinical EEG data. Results: Our approach delivers a sensitivity
above 90% while maintaining a specificity below 5%. This system detects three
events of clinical interest: (1) spike and/or sharp waves, (2) periodic
lateralized epileptiform discharges, (3) generalized periodic epileptiform
discharges. It also detects three events used to model background noise: (1)
artifacts, (2) eye movement (3) background. Conclusions: A hybrid HMM/deep
learning system can deliver a low false alarm rate on EEG event detection,
making automated analysis a viable option for clinicians. Significance: The TUH
EEG Corpus enables application of highly data consumptive machine learning
algorithms to EEG analysis. Performance is approaching clinical acceptance for
real-time applications.


  In response to an increasing availability of statistically rich observational
data sets, the performance and applicability of traditional Atmospheric
Cherenkov Telescope analyses in the regime of systematically dominated
measurement uncertainties is examined. In particular, the effect of systematic
uncertainties affecting the relative normalisation of fiducial ON and
OFF-source sampling regions - often denoted as {\alpha} - is investigated using
combined source analysis as a representative example case. The traditional
summation of accumulated ON and OFF-source event counts is found to perform
sub-optimally in the studied contexts and requires careful calibration to
correct for unexpected and potentially misleading statistical behaviour. More
specifically, failure to recognise and correct for erroneous estimates of
{\alpha} is found to produce substantial overestimates of the combined
population significance which worsen with increasing target multiplicity. An
alternative joint likelihood technique is introduced, which is designed to
treat systematic uncertainties in a uniform and statistically robust manner.
This alternate method is shown to yield dramatically enhanced performance and
reliability with respect to the more traditional approach.


  Scene coordinates regression (SCR), i.e., predicting 3D coordinates for every
pixel of a given image, has recently shown promising potential. However,
existing methods remain limited to small scenes memorized during training, and
thus hardly scale to realistic datasets and scenarios. In this paper, we
propose a generalized SCR model trained once to be deployed in new test scenes,
regardless of their scale, without any finetuning. Instead of encoding the
scene coordinates into the network weights, our model takes as input a database
image with some sparse 2D pixel to 3D coordinate annotations, extracted from
e.g. off-the-shelf Structure-from-Motion or RGB-D data, and a query image for
which are predicted a dense 3D coordinate map and its confidence, based on
cross-attention. At test time, we rely on existing off-the-shelf image
retrieval systems and fuse the predictions from a shortlist of relevant
database images w.r.t. the query. Afterwards camera pose is obtained using
standard Perspective-n-Point (PnP). Starting from selfsupervised CroCo
pretrained weights, we train our model on diverse datasets to ensure
generalizabilty across various scenarios, and significantly outperform other
scene regression approaches, including scene-specific models, on multiple
visual localization benchmarks. Finally, we show that the database
representation of images and their 2D-3D annotations can be highly compressed
with negligible loss of localization performance.


  A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was
proposed for Facial Expression Recognition (FER) to explicitly reduce high
inter-subject variations caused by identity-related facial attributes, e.g.,
age, race, and gender. As part of an end-to-end system, a cGAN was designed to
transform a given input facial expression image to an "average" identity face
with the same expression as the input. Then, identity-free FER is possible
since the generated images have the same synthetic "average" identity and
differ only in their displayed expressions. Experiments on four facial
expression datasets, one with spontaneous expressions, show that IF-GAN
outperforms the baseline CNN and achieves state-of-the-art performance for FER.


  The intrinsic temporality of learning demands the adoption of methodologies
capable of exploiting time-series information. In this study we leverage the
sequence data framework and show how data-driven analysis of temporal sequences
of task completion in online courses can be used to characterise personal and
group learners' behaviors, and to identify critical tasks and course sessions
in a given course design. We also introduce a recently developed probabilistic
Bayesian model to learn sequence trajectories of students and predict student
performance. The application of our data-driven sequence-based analyses to data
from learners undertaking an on-line Business Management course reveals
distinct behaviors within the cohort of learners, identifying learners or
groups of learners that deviate from the nominal order expected in the course.
Using course grades a posteriori, we explore differences in behavior between
high and low performing learners. We find that high performing learners follow
the progression between weekly sessions more regularly than low performing
learners, yet within each weekly session high performing learners are less tied
to the nominal task order. We then model the sequences of high and low
performance students using the probablistic Bayesian model and show that we can
learn engagement behaviors associated with performance. We also show that the
data sequence framework can be used for task centric analysis; we identify
critical junctures and differences among types of tasks within the course
design. We find that non-rote learning tasks, such as interactive tasks or
discussion posts, are correlated with higher performance. We discuss the
application of such analytical techniques as an aid to course design,
intervention, and student supervision.


  We investigate the time-dependent behavior of Crab-like pulsar wind nebulae
(PWNe) generating a set of models using 4 different initial spin-down
luminosities ($L_0 =\{1,0.1,0.01,0.001\} \times L_{0, {\rm Crab}}$), 8 values
of magnetic fraction ($\eta =$ 0.001, 0.01, 0.03, 0.1, 0.5, 0.9, 0.99, and
0.999, i.e., from fully particle dominated to fully magnetically dominated
nebulae), and 3 distinctive ages: 940, 3000, and 9000 years. We find that the
self-synchrotron Compton (SSC) contribution is irrelevant for $L_{SD}$=0.1, 1,
and 10% of the Crab power, disregarding the age and the magnetic fraction. SSC
only becomes relevant for highly energetic ($\sim 70%$ of the Crab), particle
dominated nebulae at low ages (of less than a few kyr), located in a FIR
background with relatively low energy density. Since no pulsar other than Crab
is known to have these features, these results clarify why the Crab Nebula, and
only it, is SSC dominated. No young PWN would be detectable at TeV energies if
the pulsar's spin-down power is 0.1% Crab or lower. For 1% of the Crab
spin-down, only particle dominated nebulae can be detected by H.E.S.S.-like
telescopes when young enough (with details depending on the precise injection
and environmental parameters). Above 10% of the Crab's power, all PWNe are
detectable by H.E.S.S.-like telescopes if they are particle dominated, no
matter the age. The impact of the magnetic fraction on the final SED is varied
and important, generating order of magnitude variations in the luminosity
output for systems that are otherwise the same (equal $P$, $\dot P$, injection,
and environment).


  The extraction of two- and three-body hadronic scattering amplitudes and the
properties of the low-lying hadronic resonances from the finite-volume energy
levels in lattice QCD represents a rapidly developing field of research. The
use of various modifications of the L\"uscher finite-volume method has opened a
path to calculate infinite-volume scattering amplitudes on the lattice. Many
new results have been obtained recently for different two- and three-body
scattering processes, including the extraction of resonance poles and their
properties from lattice data. Such studies, however, require robust
parametrizations of the infinite-volume scattering amplitudes, which rely on
basic properties of $S$-matrix theory and -- preferably -- encompass systems
with quark masses at and away from the physical point. Parametrizations of this
kind, provided by unitarized Chiral Perturbation Theory, are discussed in this
review. Special attention is paid to three-body systems on the lattice, owing
to the rapidly growing interest in the field. Here, we briefly survey the
formalism, chiral extrapolation, as well as finite-volume analyses of lattice
data.


  Large language models (LLMs) trained on massive corpora demonstrate
impressive capabilities in a wide range of tasks. While there are ongoing
efforts to adapt these models to languages beyond English, the attention given
to their evaluation methodologies remains limited. Current multilingual
benchmarks often rely on back translations or re-implementations of English
tests, limiting their capacity to capture unique cultural and linguistic
nuances. To bridge this gap for the Korean language, we introduce the HAE-RAE
Bench, a dataset curated to challenge models lacking Korean cultural and
contextual depth. The dataset encompasses six downstream tasks across four
domains: vocabulary, history, general knowledge, and reading comprehension.
Unlike traditional evaluation suites focused on token and sequence
classification or mathematical and logical reasoning, the HAE-RAE Bench
emphasizes a model's aptitude for recalling Korean-specific knowledge and
cultural contexts. Comparative analysis with prior Korean benchmarks indicates
that the HAE-RAE Bench presents a greater challenge to non-Korean models by
disturbing abilities and knowledge learned from English being transferred.


  Restricted by the limited interaction area of native AR glasses (e.g., touch
bars), it is challenging to create sketches in AR glasses. Recent works have
attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures
to expand the interactive spaces and can work as the 2D/3D sketching input
interfaces for AR glasses. Between them, mobile devices allow for accurate
sketching but are often heavy to carry, while sketching with bare hands is
zero-burden but can be inaccurate due to arm instability. In addition, mid-air
bare-hand sketching can easily lead to social misunderstandings and its
prolonged use can cause arm fatigue. As a new attempt, in this work, we present
WristSketcher, a new AR system based on a flexible sensing wristband for
creating 2D dynamic sketches, featuring an almost zero-burden authoring model
for accurate and comfortable sketch creation in real-world scenarios.
Specifically, we have streamlined the interaction space from the mid-air to the
surface of a lightweight sensing wristband, and implemented AR sketching and
associated interaction commands by developing a gesture recognition method
based on the sensing pressure points on the wristband. The set of interactive
gestures used by our WristSketcher is determined by a heuristic study on user
preferences. Moreover, we endow our WristSketcher with the ability of animation
creation, allowing it to create dynamic and expressive sketches. Experimental
results demonstrate that our WristSketcher i) faithfully recognizes users'
gesture interactions with a high accuracy of 96.0%; ii) achieves higher
sketching accuracy than Freehand sketching; iii) achieves high user
satisfaction in ease of use, usability and functionality; and iv) shows
innovation potentials in art creation, memory aids, and entertainment
applications.


  This paper presents a system consisting of the FPGA IP core, the simple
network protocol and the Linux device driver, capable of efficient and reliable
data transmission from a low resources FPGA chip to the Linux-based embedded
computer system, via a private Ethernet network (consisting of a single segment
or a few segments connected via an Ethernet switch). The embedded system may
optionally process the acquired data, and distribute them further, using
standard network protocols. Proposed design targets cost-efficient multichannel
data acquisition systems, in which multiple FPGA based front end boards (FEB)
should transmit the stream of acquired data to the computer network,
responsible for their final processing and archiving. The presented solution
allows to minimize the cost of data concentration due to use of inexpensive
Ethernet network infrastructure. The work is mainly focused on minimization of
resources consumption in the FPGA, and minimization of acknowledge latency in
the Linux based system - which allows to achieve high throughput in spite of
use of inexpensive FPGA chips with small internal memory.


  We discuss the Lagrangian property and the conservation of the kinetic energy
for solutions of the 2D incompressible Euler equations. Existence of Lagrangian
solutions is known when the initial vorticity is in $L^p$ with $1\leq p\leq
\infty$. Moreover, if $p\geq 3/2$ all weak solutions are conservative. In this
work we prove that solutions obtained via the vortex method are Lagrangian, and
that they are conservative if $p>1$.


  Using HF+BCS method we study light nuclei with nuclear charge in the range $2
\leq Z \leq 8$ and lying near the neutron drip line. The HF method uses
effective Skyrme forces and allows for axial deformations. We find that the
neutron drip line forms stability peninsulas at $^{18}$He and $^{40}$C. These
isotopes are found to be stable against one neutron emission and possess the
highest known neutron to proton ratio in stable nuclei.


  In 2020, amidst the COVID pandemic and a polarized political climate, the
Sleeping Giants online activist movement gained traction in Brazil. Its
rationale was simple: to curb the spread of misinformation by harming the
advertising revenue of sources that produce this type of content. Like its
international counterparts, Sleeping Giants Brasil (SGB) campaigned against
media outlets using Twitter to ask companies to remove ads from the targeted
outlets. This work presents a thorough quantitative characterization of this
activism model, analyzing the three campaigns carried out by SGB between May
and September 2020. To do so, we use digital traces from both Twitter and
Google Trends, toxicity and sentiment classifiers trained for the Portuguese
language, and an annotated corpus of SGB's tweets. Our key findings were
threefold. First, we found that SGB's requests to companies were largely
successful (with 83.85\% of all 192 targeted companies responding positively)
and that user pressure was correlated to the speed of companies' responses.
Second, there were no significant changes in the online attention and the user
engagement going towards the targeted media outlets in the six months that
followed SGB's campaign (as measured by Google Trends and Twitter engagement).
Third, we observed that user interactions with companies changed only
transiently, even if the companies did not respond to SGB's request. Overall,
our results paint a nuanced portrait of internet activism. On the one hand,
they suggest that SGB was successful in getting companies to boycott specific
media outlets, which may have harmed their advertisement revenue stream. On the
other hand, they also suggest that the activist movement did not impact the
online attention these media outlets received nor the online image of companies
that did not respond positively to their requests.


  For a closed, connected direct product Riemannian manifold $(M,
g)=(M_1\times\cdots\times M_l, g_1\oplus\cdots\oplus g_l)$, we define its
multiconformal class $ [\![ g ]\!]$ as the totality $\{f_1^2g_1\oplus
\cdots\oplus f_l^2g_l\}$ of all Riemannian metrics obtained from multiplying
the metric $g_i$ of each factor $M_i$ by a function $f_i^2>0$ on the total
space $M$. A multiconformal class $ [\![ g ]\!]$ contains not only all warped
product type deformations of $g$ but also the whole conformal class
$[\tilde{g}]$ of every $\tilde{g}\in [\![ g ]\!]$. In this article, we prove
that $ [\![ g ]\!]$ carries a metric of positive scalar curvature if and only
if the conformal class of some factor $(M_i, g_i)$ does, under the technical
assumption $\dim M_i\ge 2$. We also show that, even in the case where every
factor $(M_i, g_i)$ has positive scalar curvature, $ [\![ g ]\!]$ carries a
metric of scalar curvature constantly equal to $-1$ and with arbitrarily large
volume, provided $l\ge 2$ and $\dim M\ge 3$. In this case, such negative scalar
curvature metrics within $ [\![ g ]\!]$ for $l=2$ cannot be of any warped
product type.


  Two-player quantitative zero-sum games provide a natural framework to
synthesize controllers with performance guarantees for reactive systems within
an uncontrollable environment. Classical settings include mean-payoff games,
where the objective is to optimize the long-run average gain per action, and
energy games, where the system has to avoid running out of energy.
  We study average-energy games, where the goal is to optimize the long-run
average of the accumulated energy. We show that this objective arises naturally
in several applications, and that it yields interesting connections with
previous concepts in the literature. We prove that deciding the winner in such
games is in NP inter coNP and at least as hard as solving mean-payoff games,
and we establish that memoryless strategies suffice to win. We also consider
the case where the system has to minimize the average-energy while maintaining
the accumulated energy within predefined bounds at all times: this corresponds
to operating with a finite-capacity storage for energy. We give results for
one-player and two-player games, and establish complexity bounds and memory
requirements.


  Analyzing global starburst properties in various kinds of starburst and
post-starburst galaxies and relating them to the properties of the star cluster
populations they form, I explore the conditions for the formation of massive,
compact, long-lived star clusters. The aim is to find out whether the relative
amount of star formation that goes into star cluster formation as opposed to
field star formation, and into the formation of massive long-lived clusters in
particular, is universal or scales with star formation rate, burst strength,
star formation efficiency, galaxy or gas mass, and whether or not there are
special conditions or some threshold for the formation of star clusters that
merit to be called globular clusters a few gigayears later.


  We present the study of a proof-of-concept integrated device that can be used
as a nonlinear broadband isolator. The device is based on the asymmetric
loading of a highly-confining silicon-slot photonic coupler with graphene
layers, whose ultrafast and low-threshold saturable absorption can be exploited
for nonreciprocal transmission between the cross-ports of the coupler. The
structure is essentially a non-Hermitian system, whose exceptional points are
briefly discussed. The nonlinear device is modeled with a coupled Schrodinger
equation system whose validity is checked by full-vector finite element-based
beam-propagation method simulations in CW. The numerically computed performance
reveals a nonreciprocal intensity range (NRIR) in the vicinity of 100 mW peak
power with a bandwidth spanning tens of nanometers, from CW down to ps-long
pulses. Finally, the combination of saturable absorption and self-phase
modulation (Kerr effect) in graphene is studied, indicating the existence of
two NRIR with opposite directionality.


  Star luminosity function, already recognized as an age indicator for old
galactic globular clusters, can be used to contrains the age of younger stellar
systems like the nearby dwarfs spheroidal Leo I. We compare the observed
luminosity function of Leo I, presented by Lee et al. 1993, with theoretical
expectations for three selected ages, 1, 1.5 and 2 billion years, deriving an
age of about 1.5 Gyr. This result does not appear critically affected by
assumptions about the cluster distance modulus or the Initial Mass Function.


  Recently, researchers have turned their attention to recommender systems that
use only minimal necessary data. This trend is informed by the idea that
recommender systems should use no more user interactions than are needed in
order to provide users with useful recommendations. In this position paper, we
make the case for applying the idea of minimal necessary data to recommender
systems that use user reviews. We argue that the content of individual user
reviews should be subject to minimization. Specifically, reviews used as
training data to generate recommendations or reviews used to help users decide
on purchases or consumption should be automatically edited to contain only the
information that is needed.


  Large-scale simulations of plastic deformation and phase transformations in
alloys require reliable classical interatomic potentials. We construct an
embedded-atom method potential for niobium as the first step in alloy potential
development. Optimization of the potential parameters to a well-converged set
of density-functional theory (DFT) forces, energies, and stresses produces a
reliable and transferable potential for molecular dynamics simulations. The
potential accurately describes properties related to the fitting data, and also
produces excellent results for quantities outside the fitting range. Structural
and elastic properties, defect energetics, and thermal behavior compare well
with DFT results and experimental data, e.g., DFT surface energies are
reproduced with less than 4% error, generalized stacking-fault energies differ
from DFT values by less than 15%, and the melting temperature is within 2% of
the experimental value.


  Ray tracing plays a vital role in black hole imaging, modeling the emission
mechanisms of pulsars, and deriving signatures from physics beyond the Standard
Model. In this work we focus on one specific application of ray tracing,
namely, predicting radio signals generated from the resonant conversion of
axion dark matter in the strongly magnetized plasma surrounding neutron stars.
The production and propagation of low-energy photons in these environments are
sensitive to both the anisotropic response of the background plasma and curved
spacetime; here, we employ a fully covariant framework capable of treating both
effects. We implement this both via forward and backward ray tracing. In
forward ray tracing, photons are sampled at the point of emission and
propagated to infinity, whilst in the backward-tracing approach, photons are
traced backwards from an image plane to the point of production. We explore
various approximations adopted in prior work, quantifying the importance of
gravity, plasma anisotropy, the neutron star mass and radius, and imposing the
proper kinematic matching of the resonance. Finally, using a more realistic
model for the charge distribution of magnetar magnetospheres, we revisit the
sensitivity of current and future radio and sub-mm telescopes to spectral lines
emanating from the Galactic Center Magnetar, showing such observations may
extend sensitivity to axion masses $m_a \sim \mathcal{O}({\rm few}) \times
10^{-3}$ eV, potentially even probing parameter space of the QCD axion.


  An innumerable number of individual choices go into discovering a new book.
There are unmistakably two groups of booklovers: those who like to search
online, follow other people's latest readings, or simply react to a system's
recommendations; and those who love to wander between library stacks, lose
themselves behind bookstore shelves, or simply hide behind piles of
(un)organized books. Depending on which group a person may fall into, there are
two distinct and corresponding mediums that inform his or her choices: digital,
that provides efficient retrieval of information online, and physical, a more
tactile pursuit that leads to unexpected discoveries and promotes serendipity.
How could we possibly bridge the gap between these seemingly disparate mediums
into an integrated system that can amplify the benefits they both offer? In
this paper, we present the BookVIS application, which uses book-related data
and generates personalized visualizations to follow users in their quest for a
new book. In this new redesigned version, the app brings associative visual
connections to support intuitive exploration of easily retrieved digital
information and its relationship with the physical book in hand. BookVIS keeps
track of the user's reading preferences and generates a dataSelfie as an
individual snapshot of a personal taste that grows over time. Usability testing
has also been conducted and has demonstrated the app's ability to identify
distinguishable patterns in readers' tastes that could be further used to
communicate personal preferences in new "shelf-browsing" iterations. By
efficiently supplementing the user's cognitive information needs while still
supporting the spontaneity and enjoyment of the book browsing experience,
BookVIS bridges the gap between real and online realms, and maximizes the
engagement of personalized mobile visual clues.


  In this paper we prove gradient estimates of both elliptic and parabolic
types, specifically, of Souplet-Zhang, Hamilton and Li-Yau types for positive
smooth solutions to a class of nonlinear parabolic equations involving the
Witten or drifting Laplacian on smooth metric measure spaces. These estimates
are established under various curvature conditions and lower bounds on the
generalised Bakry-\'Emery Ricci tensor and find utility in proving elliptic and
parabolic Harnack-type inequalities as well as general elliptic and parabolic
Liouville-type and other global constancy results. Several applications and
consequences are presented and discussed.


  The Callovo-Oxfordian (COx) claystone is considered as a candidate host rock
for a deep geological radioactive waste repository in France. Due to the
exothermic waste packages, the rock is expected to be submitted to temperatures
up to 90 {\deg}C. The temperature rise induces deformations of the host rock,
together with an increase in pore pressures, involving complex
thermo-hydro-mechanical (THM) couplings. This study aims to better characterize
the THM response of the COx claystone to temperature changes in the laboratory.
To this end, claystone specimens were tested in a temperature controlled, high
pressure isotropic compression cell, under stress conditions close to the
in-situ ones. Thermal loads were applied on the specimens along different
heating and cooling paths. A temperature corrected strain gage system provided
precise measurements of the anisotropic strain response of the specimens.
Drained and undrained thermal expansion coefficients in both transversely
isotropic directions were determined. The measurement of pore pressure changes
in undrained condition yielded the thermal pressurization coefficient. All
parameters were analysed for their compatibility within the thermo-poro-elastic
framework, and their stress and temperature dependency was identified.


  We find a general formula for the operator mixing on the $\mathbb{S}^4$ of
chiral primary operators (CPO) for the ${\cal N}=4$ theory at large $N$ in
terms of Chebyshev polynomials. As an application, we compute the correlator of
a CPO and a Wilson loop, reproducing an earlier result by Giombi and Pestun
obtained from a two-matrix model proposal. Finally, we discuss a simple method
to obtain correlators in general ${\cal N}=2$ superconformal field theories in
perturbation theory in terms of correlators of the ${\cal N}=4$ theory.


  We propose a novel photonic structure composed of metal nanolayer, Bragg
mirror and metal nanolayer. The structure supports resonances that are
transitional between Fabry-Perot and Tamm modes. When the dielectric contrast
of the DBR is removed these modes are a pair of conventional Fabry-Perot
resonances. They spectrally merge into a Tamm mode at high contrast. Such
behavior differs from the results for structures supporting Tamm modes reported
earlier. The optical properties of the structure in the frequency range of the
DBR stop band, including highly beneficial 50% transmittivity through thick
structures, are determined by the introduced in the paper hybrid resonances.
The results can find a wide range of photonic applications.


  In this paper we employ homogenization techniques to provide a rigorous
derivation of the Darcy scale model for precipitation and dissolution in porous
media proposed in [19]. The starting point is the pore scale model in [12],
which is a coupled system of evolution equations, involving a parabolic
equation and an ordinary differential equation. The former models ion transport
and is defined in a periodically perforated medium. It is further coupled
through the boundary conditions to the latter, defined on the boundaries of the
perforations and modelling the dissolution and precipitation of the
precipitate.
  The main challenge is in dealing with the dissolution and precipitation
rates, which involve a monotone but multi-valued mapping. Due to this, the
micro-scale solution lacks regularity. With $\ep$ being the scale parameter
(the ratio between the micro scale and the macro scale length), we adopt the
2-scale framework to achieve the convergence of the homogenization procedure as
$\ep$ approaches zero.


  Phase diagrams form the basis for the study of material science, and the
profiles of phase-transition lines separating different thermodynamic phases
include comprehensive information about thermodynamic quantities, such as
latent heat. However, in some materials exhibiting field-induced first-order
transitions (FOTs), the equilibrium phase-transition line is hidden by the
hysteresis region associated with the FOT; thus, it cannot be directly
determined from measurements of resistivity, magnetization, etc. Here, we
demonstrate a thermodynamics-based method for determining the hidden
equilibrium FOT line. This method is verified for the FOT between
antiferromagnetic and ferrimagnetic states in magneto-electric compounds
(Fe$_{0.95}$Zn$_{0.05}$)$_{2}$Mo$_{3}$O$_{8}$. The equilibrium FOT line
determined based on the Clausius-Clapeyron equation exhibits a reasonable
profile in terms of the third law of thermodynamics, and it shows marked
differences from the midpoints of the hysteresis region. Our findings highlight
that care should be taken for referring to the hysteresis midpoint line when
discussing field-induced latent heat or magnetocaloric effects.


  We present Chandra gas temperature maps for two hot, intermediate-redshift
clusters A665 and A2163. Both show strong temperature variations in their
central r=1 Mpc regions, naturally interpreted as product of the subcluster
mergers. The A665 map reveals a shock in front of the cool core, while the
temperature structure of A2163 is more complicated. On a larger linear scale,
our data on A2163 indicate a radial temperature decline in agreement with
earlier ASCA results, although the uncertainties are large. Both these clusters
exhibit previously known synchrotron radio halos. Comparison of the radio
images and the gas temperature maps indicates that radio emission predominantly
comes from the hot gas regions, providing a strong argument in favor of the
hypothesis that relativistic electrons are accelerated in merger shocks.


  Geometrically frustrated magnetic molecules have attracted a lot of interest
in the field of molecular magnetism as well as frustrated Heisenberg
antiferromagnets. In this article we demonstrate how an approximate
diagonalization scheme can be used in order to obtain thermodynamic and
spectroscopic information about frustrated magnetic molecules. To this end we
theoretically investigate an antiferromagnetically coupled spin system with
cuboctahedral structure modeled by an isotropic Heisenberg Hamiltonian.


  The prevalence and mobility of smartphones make these a widely used tool for
environmental health research. However, their potential for determining
aggregated air quality index (AQI) based on PM2.5 concentration in specific
locations remains largely unexplored in the existing literature. In this paper,
we thoroughly examine the challenges associated with predicting
location-specific PM2.5 concentration using images taken with smartphone
cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to
its significant air pollution levels and the large population exposed to it.
Our research involves the development of a Deep Convolutional Neural Network
(DCNN), which we train using over a thousand outdoor images taken and
annotated. These photos are captured at various locations in Dhaka, and their
labels are based on PM2.5 concentration data obtained from the local US
consulate, calculated using the NowCast algorithm. Through supervised learning,
our model establishes a correlation index during training, enhancing its
ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC).
This enables the algorithm to calculate an equivalent daily averaged AQI index
from a smartphone image. Unlike, popular overly parameterized models, our model
shows resource efficiency since it uses fewer parameters. Furthermore, test
results indicate that our model outperforms popular models like ViT and INN, as
well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in
predicting location-specific PM2.5 concentration. Our dataset is the first
publicly available collection that includes atmospheric images and
corresponding PM2.5 measurements from Dhaka. Our codes and dataset are
available at https://github.com/lepotatoguy/aqi.


  The Euclidean distance degree of an algebraic variety is a well-studied topic
in applied algebra and geometry. It has direct applications in geometric
modeling, computer vision, and statistics. We use non-proper Morse theory to
give a topological interpretation of the Euclidean distance degree of an affine
variety in terms of Euler characteristics. As a concrete application, we solve
the open problem in computer vision of determining the Euclidean distance
degree of the affine multiview variety.


  Accurate results are obtained for the low temperature magnetization versus
magnetic field of Heisenberg spin rings consisting of an even number N of
intrinsic spins s = 1/2, 1, 3/2, 2, 5/2, 3, 7/2 with nearest-neighbor
antiferromagnetic (AF) exchange by employing a numerically exact quantum Monte
Carlo method. A straightforward analysis of this data, in particular the values
of the level-crossing fields, provides accurate results for the lowest energy
eigenvalue E(N,S,s) for each value of the total spin quantum number S. In
particular, the results are substantially more accurate than those provided by
the rotational band approximation. For s <= 5/2, data are presented for all
even N <= 20, which are particularly relevant for experiments on finite
magnetic rings. Furthermore, we find that for s > 1 the dependence of E(N,S,s)
on s can be described by a scaling relation, and this relation is shown to hold
well for ring sizes up to N = 80 for all intrinsic spins in the range 3/2 <= s
<= 7/2. Considering ring sizes in the interval 8 <= N <= 50, we find that the
energy gap between the ground state and the first excited state approaches zero
proportional to 1/N^a, where a = 0.76 for s = 3/2 and a = 0.84 for s = 5/2.
Finally, we demonstrate the usefulness of our present results for E(N,S,s) by
examining the Fe12 ring-type magnetic molecule, leading to a new, more accurate
estimate of the exchange constant for this system than has been obtained
heretofore.


  Modes emerging out of a collapsing black hole are red-shifted to such an
extent that Hawking radiation at future null infinity consists of modes that
have energies beyond the Planck scale at past null infinity. This indicates
that physics at the Planck scale may modify the spectrum of Hawking radiation
and the associated stress-energy tensor of the quantum field. Recently, it has
been shown that, the T-duality symmetry of string fluctuations along compact
extra dimensions leads to a modification of the standard propagator of point
particles in quantum field theory. At low energies (when compared to the string
scale), the modified propagator is found to behave as though the spacetime
possesses a minimal length, say, $\lp$, which we shall assume to be of the
order of the Planck length. We utilize the duality approach to evaluate the
modified propagator around the rotating Banados-Teitelboim-Zanelli black hole
and show that the propagator is finite in the coincident limit. We compute the
stress-energy tensor associated with the modified Green's function and
illustrate graphically that the quantum gravitational corrections turn out to
be negligibly small. We conclude by briefly commenting on the results we have
obtained.


  The source coding problem with action-dependent side information at the
decoder has recently been introduced to model data acquisition in
resource-constrained systems. In this paper, an efficient algorithm for
numerical computation of the rate-distortion-cost function for this problem is
proposed, and a convergence proof is provided. Moreover, a two-stage code
design based on multiplexing is put forth, whereby the first stage encodes the
actions and the second stage is composed of an array of classical Wyner-Ziv
codes, one for each action. Specific coding/decoding strategies are designed
based on LDGM codes and message passing. Through numerical examples, the
proposed code design is shown to achieve performance close to the lower bound
dictated by the rate-distortion-cost function.


  Data curation tasks that prepare data for analytics are critical for turning
data into actionable insights. However, due to the diverse requirements of
applications in different domains, generic off-the-shelf tools are typically
insufficient. As a result, data scientists often have to develop
domain-specific solutions tailored to both the dataset and the task, e.g.
writing domain-specific code or training machine learning models on a
sufficient number of annotated examples. This process is notoriously difficult
and time-consuming. We present SEED, an LLM-as-compiler approach that
automatically generates domain-specific data curation solutions via Large
Language Models (LLMs). Once the user describes a task, input data, and
expected output, the SEED compiler produces a hybrid pipeline that combines LLM
querying with more cost-effective alternatives, such as vector-based caching,
LLM-generated code, and small models trained on LLM-annotated data. SEED
features an optimizer that automatically selects from the four LLM-assisted
modules and forms a hybrid execution pipeline that best fits the task at hand.
To validate this new, revolutionary approach, we conducted experiments on $9$
datasets spanning over $5$ data curation tasks. In comparison to solutions that
use the LLM on every data record, SEED achieves state-of-the-art or comparable
few-shot performance, while significantly reducing the number of LLM calls.


  Twisted Van der Waals systems offer the unprecedented possibility to tune
different states of correlated quantum matter with an external non-invasive
electrostatic doping. The nature of the superconducting order presents a
recurring open question in this context. In this work, we quantitatively assess
the case of spin-fluctuation-mediated pairing for $\Gamma$-valley twisted
transition metal dichalcogenide homobilayers. We self-consistently and
dynamically calculate the doping dependent superconducting transition
temperature $T_{\mathrm{c}}$ revealing a superconducting dome with a maximal
$T_{\mathrm{c}}\approx 0.1-1$ K depending on twist angle. We compare our
results with conventional phonon-mediated superconductivity and identify clear
fingerprints in the doping dependence of $T_{\mathrm{c}}$, which allow
experiments to distinguish between different pairing mechanisms.


  We give necessary conditions for the surjectivity of the higher Gaussian maps
on a polarized K3 surface. As an application, we show that the higher $k$-th
Gauss map for a general curve of genus $g$ (that depends quadratically with
$k$) is surjective.


  In a recent paper the notion of {\em quantum perceptron} has been introduced
in connection with projection operators. Here we extend this idea, using these
kind of operators to produce a {\em clustering machine}, i.e. a framework which
generates different clusters from a set of input data. Also, we consider what
happens when the orthonormal bases first used in the definition of the
projectors are replaced by frames, and how these can be useful when trying to
connect some noised signal to a given cluster.


  This paper presents a time-efficient scheme for Mars exploration by the
cooperation of multiple drones and a rover. To maximize effective coverage of
the Mars surface in the long run, a comprehensive framework has been developed
with joint consideration for limited energy, sensor model, communication range
and safety radius, which we call TIME-SC2 (TIme-efficient Mars Exploration of
Simultaneous Coverage and Charging). First, we propose a multi-drone coverage
control algorithm by leveraging emerging deep reinforcement learning and design
a novel information map to represent dynamic system states. Second, we propose
a near-optimal charging scheduling algorithm to navigate each drone to an
individual charging slot, and we have proven that there always exists feasible
solutions. The attractiveness of this framework not only resides on its ability
to maximize exploration efficiency, but also on its high autonomy that has
greatly reduced the non-exploring time. Extensive simulations have been
conducted to demonstrate the remarkable performance of TIME-SC2 in terms of
time-efficiency, adaptivity and flexibility.


  Recently, zero-shot text-to-speech (TTS) systems, capable of synthesizing any
speaker's voice from a short audio prompt, have made rapid advancements.
However, the quality of the generated speech significantly deteriorates when
the audio prompt contains noise, and limited research has been conducted to
address this issue. In this paper, we explored various strategies to enhance
the quality of audio generated from noisy audio prompts within the context of
flow-matching-based zero-shot TTS. Our investigation includes comprehensive
training strategies: unsupervised pre-training with masked speech denoising,
multi-speaker detection and DNSMOS-based data filtering on the pre-training
data, and fine-tuning with random noise mixing. The results of our experiments
demonstrate significant improvements in intelligibility, speaker similarity,
and overall audio quality compared to the approach of applying speech
enhancement to the audio prompt.


  Quantum information and communication processing within quantum networks
usually employs identical particles. Despite this, the physical role of quantum
statistical nature of particles in large-scale networks remains elusive. Here,
we show that just the indistinguishability of fermions makes it possible a new
mechanism of entanglement transfer in many-node quantum networks. This process
activates remote entanglement among distant sites, which do not share a common
past, by only locally counting identical particles and classical communication.
These results constitute the key achievement of the present technique and open
the way to a more stable multistage transfer of nonlocal quantum correlations
based on fermions.


  We studied the quantum correlation between the photon pairs generated by
biexciton cascade decays of self-assembled quantum dots, and determined the
temperature behavior associated with so-called sudden change of the quantum
correlation. The relationship between the fine structure splitting and the
sudden change temperature is also provided. Our study indicates that this
correlation behavior sudden change temperature is independent on the back
ground noise in the system and far lower than entanglement sudden death
temperature, therefore it should be easier to observe the phenomenon of
correlation sudden change in experiments than to observe entanglement sudden
death.


  An edge coloring of a graph $G$ with colors $1,2,\ldots ,t$ is called an
interval $t$-coloring if for each $i\in \{1,2,\ldots,t\}$ there is at least one
edge of $G$ colored by $i$, and the colors of edges incident to any vertex of
$G$ are distinct and form an interval of integers. A graph $G$ is interval
colorable, if there is an integer $t\geq 1$ for which $G$ has an interval
$t$-coloring. Let $\mathfrak{N}$ be the set of all interval colorable graphs.
In 2004 Kubale and Giaro showed that if $G,H\in \mathfrak{N}$, then the
  Cartesian product of these graphs belongs to $\mathfrak{N}$. Also, they
formulated a similar problem for the lexicographic product as an open problem.
In this paper we first show that if $G\in \mathfrak{N}$, then $G[nK_{1}]\in
\mathfrak{N}$ for any $n\in \mathbf{N}$. Furthermore, we show that if $G,H\in
\mathfrak{N}$ and $H$ is a regular graph, then strong and lexicographic
products of graphs $G,H$ belong to $\mathfrak{N}$. We also prove that tensor
and strong tensor products of graphs $G,H$ belong to $\mathfrak{N}$ if $G\in
\mathfrak{N}$ and $H$ is a regular graph.


  A deformed dielectric microcavity is used as an experimental platform for the
analysis of the statistics of chaotic resonances, in the perspective of testing
fractal Weyl laws at optical frequencies. In order to surmount the difficulties
that arise from reading strongly overlapping spectra, we exploit the mixed
nature of the phase space at hand, and only count the high-Q whispering-gallery
modes (WGMs) directly. That enables us to draw statistical information on the
more lossy chaotic resonances, coupled to the high-Q regular modes via
dynamical tunneling. Three different models [classical, Random-Matrix-Theory
(RMT) based, semiclassical] to interpret the experimental data are discussed.
On the basis of least-squares analysis, theoretical estimates of Ehrenfest
time, and independent measurements, we find that a semiclassically modified
RMT-based expression best describes the experiment in all its realizations,
particularly when the resonator is coupled to visible light, while RMT alone
still works quite well in the infrared. In this work we reexamine and
substantially extend the results of a short paper published earlier [L. Wang,
D. Lippolis, Z.-Y. Li, X.-F. Jiang, Q. Gong, and Y.-F. Xiao, Phys. Rev. E 93,
040201(R) (2016)].


  Wearable cameras allow to acquire images and videos from the user's
perspective. These data can be processed to understand humans behavior. Despite
human behavior analysis has been thoroughly investigated in third person
vision, it is still understudied in egocentric settings and in particular in
industrial scenarios. To encourage research in this field, we present MECCANO,
a multimodal dataset of egocentric videos to study humans behavior
understanding in industrial-like settings. The multimodality is characterized
by the presence of gaze signals, depth maps and RGB videos acquired
simultaneously with a custom headset. The dataset has been explicitly labeled
for fundamental tasks in the context of human behavior understanding from a
first person view, such as recognizing and anticipating human-object
interactions. With the MECCANO dataset, we explored five different tasks
including 1) Action Recognition, 2) Active Objects Detection and Recognition,
3) Egocentric Human-Objects Interaction Detection, 4) Action Anticipation and
5) Next-Active Objects Detection. We propose a benchmark aimed to study human
behavior in the considered industrial-like scenario which demonstrates that the
investigated tasks and the considered scenario are challenging for
state-of-the-art algorithms. To support research in this field, we publicy
release the dataset at https://iplab.dmi.unict.it/MECCANO/.


  With the rapid proliferation of smart devices in wireless networks, more
powerful technologies are expected to fulfill the network requirements of high
throughput, massive connectivity, and diversify quality of service. To this
end, rate splitting multiple access (RSMA) is proposed as a promising solution
to improve spectral efficiency and provide better fairness for the
next-generation mobile networks. In this paper, the outage performance of
uplink RSMA transmission with randomly deployed users is investigated, taking
both user scheduling schemes and power allocation strategies into
consideration. Specifically, the greedy user scheduling (GUS) and cumulative
distribution function (CDF) based user scheduling (CUS) schemes are considered,
which could maximize the rate performance and guarantee scheduling fairness,
respectively. Meanwhile, we re-investigate cognitive power allocation (CPA)
strategy, and propose a new rate fairness-oriented power allocation (FPA)
strategy to enhance the scheduled users' rate fairness. By employing order
statistics and stochastic geometry, an analytical expression of the outage
probability for each scheduling scheme combining power allocation is derived to
characterize the performance. To get more insights, the achieved diversity
order of each scheme is also derived. Theoretical results demonstrate that both
GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity
orders, and the application of CPA strategy in RSMA can effectively eliminate
the secondary user's diversity order constraint from the primary user.
Simulation results corroborate the accuracy of the analytical expressions, and
show that the proposed FPA strategy can achieve excellent rate fairness
performance in high signal-to-noise ratio region.


  An iterative approach is introduced, which allows the efficient solution of
the hierarchical equations of motion (HEOM) for the steady state of open
quantum systems. The approach combines the method of matrix equations with an
efficient preconditioning technique to reduce the numerical effort of solving
the HEOM. Illustrative applications to simulate nonequilibrium charge transport
in single-molecule junctions demonstrate the performance of the method.


  A major challenge of our time is reducing disparities in access to and
effective use of digital technologies, with recent discussions highlighting the
role of AI in exacerbating the digital divide. We examine user characteristics
that predict usage of the AI-powered conversational agent ChatGPT. We combine
behavioral and survey data in a web tracked sample of N=1376 German citizens to
investigate differences in ChatGPT activity (usage, visits, and adoption)
during the first 11 months from the launch of the service (November 30, 2022).
Guided by a model of technology acceptance (UTAUT- 2), we examine the role of
socio-demographics commonly associated with the digital divide in ChatGPT
activity and explore further socio-political attributes identified via
stability selection in Lasso regressions. We confirm that lower age and higher
education affect ChatGPT usage, but do not find that gender or income do. We
find full-time employment and more children to be barriers to ChatGPT activity.
Using a variety of social media was positively associated with ChatGPT
activity. In terms of political variables, political knowledge and political
self-efficacy as well as some political behaviors such as voting, debating
political issues online and offline and political action online were all
associated with ChatGPT activity, with online political debating and political
self-efficacy negatively so. Finally, need for cognition and communication
skills such as writing, attending meetings, or giving presentations, were also
associated with ChatGPT engagement, though chairing/organizing meetings was
negatively associated. Our research informs efforts to address digital
disparities and promote digital literacy among underserved populations by
presenting implications, recommendations, and discussions on ethical and social
issues of our findings.


  Finding unequivocal evidence of dark matter interactions in a particle
detector is a major objective of physics research. Liquid argon time projection
chambers offer a path to probe Weakly Interacting Massive Particles scattering
cross sections on nucleus down to the so-called neutrino floor, in a mass range
from few GeV's to hundredths of TeV's. Based on the successful operation of the
DarkSide-50 detector at LNGS, a new and more sensitive experiment,
DarkSide-20k, has been designed and is now under construction. A thorough
understanding of the DarkSide-50 detector response and, therefore, of all kind
of observed events, is essential for an optimal design of the new experiment.
In this paper, we report on a particular set of events, which were not used for
dark matter searches. Namely, standard two-pulse scintillation-ionization
signals accompanied by a small amplitude third pulse, originating from single
or few electrons, in a time window of less than a maximum drift time. We
compare our findings to those of a recent paper of the LUX Collaboration
(D.S.Akerib et al. Phys.Rev.D 102, 092004). Indeed, both experiments observe
events related to photoionization of the cathode. From the measured rate of
these events, we estimate for the first time the quantum efficiency of the
tetraphenyl butadiene deposited on the DarkSide-50 cathode at wavelengths
around 128 nm, in liquid argon. Also, both experiments observe events likely
related to photoionization of impurities in the liquid. The probability of
photoelectron emission per unit length turns out to be one order of magnitude
smaller in DarkSide-50 than in LUX. This result, together with the much larger
measured electron lifetime, coherently hints toward a lower concentration of
contaminants in DarkSide-50 than in LUX.