  Results of transmission experiments of vacuum ultraviolet light through a
11.6 cm long cell filled with pure and xenon-doped liquid argon are described.
Pure liquid argon shows no attenuation down to the experimental
short-wavelength cut-off at 118nm. Based on a conservative approach, a lower
limit of 1.10 m for the attenuation length of its own scintillation light could
be derived. Adding xenon to liquid argon at concentrations on the order of
parts per million leads to strong xenon-related absorption features which are
used for a tentative assignment of the recently found near-infrared emission
observed in electron-beam excited liquid argon-xenon mixtures. Two of the three
absorption features can be explained by perturbed xenon transitions and the
third one by a trapped exciton (Wannier-Mott) impurity state. A calibration
curve connecting the equivalent width of the absorption line at 140 nm with
xenon concentration is provided.


  Both astronomical observations of the interaction of Type II supernova
remnants (SNR) with dense interstellar clouds as well as cosmochemical studies
of the abundances of daughter products of short-lived radioisotopes (SLRIs)
formed by supernova nucleosynthesis support the hypothesis that the Solar
Systems SLRIs may have been derived from a supernova. This paper continues a
series devoted to examining whether such a shock wave could have triggered the
dynamical collapse of a dense, presolar cloud core and simultaneously injected
sufficient abundances of SLRIs to explain the cosmochemical evidence. Here we
examine the effects of shock waves striking clouds whose spin axes are oriented
perpendicular, rather than parallel, to the direction of propagation of the
shock front. The models start with 2.2 solar mass cloud cores and shock speeds
of 20 or 40 km/sec. Central protostars and protoplanetary disks form in all
models, though with disk spin axes aligned somewhat randomly. The disks derive
most of their angular momentum not from the initial cloud rotation, but from
the Rayleigh-Taylor fingers that also inject shock wave SLRIs. Injection
efficiencies, fi, the fraction of the incident shock wave material injected
into the collapsing cloud core, are 0.04 - 0.1 in these models, similar to when
the rotation axis is parallel to the shock propagation direction. Evidently
altering the rotation axis orientation has only a minor effect on the outcome,
strengthening the case for this scenario as an explanation for the Solar
Systems SLRIs.


  We consider correlators of N=4 super Yang Mills of the form A ~ < O_1 O_2
O*_1 O*_2 >, where the operators O_1 and O_2 are scalar primaries. In
particular, we analyze this correlator in the planar limit and in a Lorentzian
regime corresponding to high energy interactions in AdS. The planar amplitude
is dominated by a Regge pole whose nature varies as a function of the 't Hooft
coupling g^2. At large g, the pole corresponds to graviton exchange in AdS,
whereas at weak g, the pole is that of the hard perturbative BFKL pomeron. We
concentrate on the weak coupling regime and analyze pomeron exchange directly
in position space. The analysis relies heavily on the conformal symmetry of the
transverse space E^2 and of its holographic dual hyperbolic space H_3,
describing with an unified language, both the weak and strong 't Hooft coupling
regimes. In particular, the form of the impact factors is highly constrained in
position space by conformal invariance. Finally, the analysis suggests a
possible AdS eikonal resummation of multi-pomeron exchanges implementing AdS
unitarity, which differs from the usual 4-dimensional eikonal exponentiation.
Relations to violations of 4-dimensional unitarity at high energy and to the
onset of nonlinear effects and gluon saturation become immediate questions for
future research.


  The finite-bandwidth conductance of a Luttinger liquid (LL) with a cluster of
impurities is studied and its variation with respect to temperature is shown.
The calculations are done using the correlation functions obtained using the
powerful non-chiral bosonization technique (NCBT) . The results are compared
with those obtained by Matveev, Yue and Glazman [K. Matveev et al., Phys. Rev.
Lett. 71, 3351 (1993)] who deal with a weakly interacting LL. By contrast, NCBT
correctly provides the conductance for all values of the interaction strength
(as well as the sign). In addition to finding perfect agreement with the
results of Matveev et al. for both weakly repulsive and weakly attractive
mutual interactions, we are also able to probe novel physics seen when the
repulsion is strong - in the form of a weakly temperature dependent conductance
when there is a definite relationship between the transmission amplitude of the
non-interacting system and the holon velocity. Secondly, an unusual high
conductance for strongly repulsive mutual interactions is observed for a weak
barrier at low temperatures. Lastly, inclusion of backward scattering leads to
the non-monotonic temperature dependence of conductance when dealing with
fermions with spin. This work is also important as a validation of the NCBT
itself.


  Parallel recordings of neural spike counts have revealed the existence of
context-dependent noise correlations in neural populations. Theories of
population coding have also shown that such correlations can impact the
information encoded by neural populations about external stimuli. Although
studies have shown that these correlations often have a low-dimensional
structure, it has proven difficult to capture this structure in a model that is
compatible with theories of rate coding in correlated populations. To address
this difficulty we develop a novel model based on conditional finite mixtures
of independent Poisson distributions. The model can be conditioned on context
variables (e.g. stimuli or task variables), and the number of mixture
components in the model can be cross-validated to estimate the dimensionality
of the target correlations. We derive an expectation-maximization algorithm to
efficiently fit the model to realistic amounts of data from large neural
populations. We then demonstrate that the model successfully captures
stimulus-dependent correlations in the responses of macaque V1 neurons to
oriented gratings. Our model incorporates arbitrary nonlinear
context-dependence, and can thus be applied to improve predictions of neural
activity based on deep neural networks.


  A novel approach to computing time-harmonic solutions of Maxwell's equations
by time-domain simulations is presented. The method, EM-WaveHoltz, results in a
positive definite system of equations which makes it amenable to iterative
solution with the conjugate gradient method or with GMRES. Theoretical results
guaranteeing the convergence of the method away from resonances is presented.
Numerical examples illustrating the properties of EM-WaveHoltz are given.


  This paper proposes an improved Goodness of Pronunciation (GoP) that utilizes
Uncertainty Quantification (UQ) for automatic speech intelligibility assessment
for dysarthric speech. Current GoP methods rely heavily on neural
network-driven overconfident predictions, which is unsuitable for assessing
dysarthric speech due to its significant acoustic differences from healthy
speech. To alleviate the problem, UQ techniques were used on GoP by 1)
normalizing the phoneme prediction (entropy, margin, maxlogit, logit-margin)
and 2) modifying the scoring function (scaling, prior normalization). As a
result, prior-normalized maxlogit GoP achieves the best performance, with a
relative increase of 5.66%, 3.91%, and 23.65% compared to the baseline GoP for
English, Korean, and Tamil, respectively. Furthermore, phoneme analysis is
conducted to identify which phoneme scores significantly correlate with
intelligibility scores in each language.


  The energetics of transformation of a planar fragment of a graphite monolayer
into a spherical cluster is studied. The path considered is that a flat cluster
rolls up into a segment of a spherical shell. The energy landscape of the
process is presented. A simple model, formerly invented for calculating the
carbon nanocluster formation energy, is used to evaluate the energies of
intermediate states. Although the spherical-shell closed cluster has the lowest
energy, curving of a plane fragment into a segment has an energy barrier. The
barrier height goes to zero for clusters with the number of atoms greater than
some $N_{\rm th}$, for which the cluster size is found analytically.


  Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a
foundation of many multimedia retrieval systems. Because it offers low
responses times, Product Quantization (PQ) is a popular solution. PQ compresses
high-dimensional vectors into short codes using several sub-quantizers, which
enables in-RAM storage of large databases. This allows fast answers to NN
queries, without accessing the SSD or HDD. The key feature of PQ is that it can
compute distances between short codes and high-dimensional vectors using
cache-resident lookup tables. The efficiency of this technique, named
Asymmetric Distance Computation (ADC), remains limited because it performs many
cache accesses.
  In this paper, we introduce Quick ADC, a novel technique that achieves a 3 to
6 times speedup over ADC by exploiting Single Instruction Multiple Data (SIMD)
units available in current CPUs. Efficiently exploiting SIMD requires
algorithmic changes to the ADC procedure. Namely, Quick ADC relies on two key
modifications of ADC: (i) the use 4-bit sub-quantizers instead of the standard
8-bit sub-quantizers and (ii) the quantization of floating-point distances.
This allows Quick ADC to exceed the performance of state-of-the-art systems,
e.g., it achieves a Recall@100 of 0.94 in 3.4 ms on 1 billion SIFT descriptors
(128-bit codes).


  Results of a recent calculation of the effective $\dS$ Hamiltonian at the
next-to-leading order will be presented\footnote{Work done in collaboration
with A. J. Buras and M. E. Lautenbacher.}. These, together with an improved
treatment of hadronic matrix elements, are used to evaluate the measure for
direct CP-violation, $\epe$, at the next-to-leading order. Taking
$\mt=130\,GeV$, $\Lms=300\,MeV$ and calculating $\langle Q_6 \rangle$ and
$\langle Q_8 \rangle$ in the $1/N$ approach, we find in the NDR scheme $\epe =
(6.7 \pm 2.6)\times 10^{-4}$ in agreement with the experimental findings of
E731. We point out however that the increase of $\langle Q_6 \rangle$ by only a
factor of two gives $\epe = (20.0 \pm 6.5)\times 10^{-4}$ in agreement with the
result of NA31. The dependencies of $\epe$ on $\Lms$, $\mt$, and some
$B$-parameters, parameterizing hadronic matrix elements, are briefly discussed.


  The neutron-star Low-Mass X-ray Binary Aquila X-1 was observed seven times in
total with the Suzaku X-ray observatory from September 28 to October 30 in
2007, in the decaying phase of an outburst. In order to constrain the
flux-dependent accretion geometry of this source over wider energy bands than
employed in most of previous works, the present study utilized two out of the
seven data sets. The 0.8-31 keV spectrum on September 28, taken with the XIS
and HXD-PIN for an exposure of 13.8 ks, shows an absorbed 0.8-31 keV flux of
$3.6\times 10^{-9}$ erg s$^{-1}$ cm$^{-2}$, together with typical
characteristics of the soft state of this type of objects. The spectrum was
successfully explained by an optically-thick disk emission plus a Comptonized
blackbody component. Although these results are in general agreement with
previous studies, the significance of a hard tail recently reported using the
same data was inconclusive in our analysis. The spectrum acquired on October 9
for an exposure of 19.7 ks was detected over a 0.8-100 keV band with the XIS,
HXD-PIN, and HXD-GSO, at an absorbed flux of $8.5\times 10^{-10}$ erg s$^{-1}$
cm$^{-2}$ (in 0.8-100 keV). It shows characteristics of the hard state, and was
successfully explained by the same two continuum components but with rather
different parameters including much stronger thermal Comptonization, of which
the seed photon source was identified with blackbody emission from the
neutron-star surface. As a result, the accretion flow in the hard state is
inferred to take a form of an optically-thick and geometrically-thin disk down
to a radius of $21\pm 4$ km from the neutron star, and then turn into an
optically-thin nearly-spherical hot flow.


  Standard methods of data assimilation assume prior knowledge of a model that
describes the system dynamics and an observation function that maps the model
state to a predicted output. An accurate mapping from model state to
observation space is crucial in filtering schemes when adjusting the estimate
of the system state during the filter's analysis step. However, in many
applications the true observation function may be unknown and the available
observation model may have significant errors, resulting in a suboptimal state
estimate. We propose a method for observation model error correction within the
filtering framework. The procedure involves an alternating minimization
algorithm used to iteratively update a given observation function to increase
consistency with the model and prior observations, using ideas from attractor
reconstruction. The method is demonstrated on the Lorenz 1963 and Lorenz 1996
models, and on a single-column radiative transfer model with multicloud
parameterization.


  An analytical mean field approach for describing the time evolution of film
growth by seeding has been developed. The modeling deals with the generic case
of anisotropic growth with different growth rates, respectively on -- and
normal to -- the substrate plane. The finite size of the seeds is considered by
including spatial correlation effects among seeds through hard-core
interactions. The approach, based on probability theory, provides solution in
closed form for mean film thickness as a function of substrate coverage, seed
density and initial size of the seeds. For negligible values of the initial
coverage of the substrate by seeds, manageable analytical expressions are
attained. The model has been validated by comparison with experimental data
available in the literature. This study is significant in connection to the
possibility of determining optimal growth conditions for ultrathin
nanocrystalline diamond (NCD) film. In fact, the knowledge of the
seeding/nucleation density that allows a given minimum average thickness of
continuous film is of utmost importance for the development of technologically
advanced applications.


  Selfie soft biometrics has great potential for various applications ranging
from marketing, security and online banking. However, it faces many challenges
since there is limited control in data acquisition conditions. This chapter
presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach
that increases the resolution of low quality periocular iris images cropped
from selfie images of subject's faces. This work shows that increasing image
resolution (2x and 3x) can improve the sex-classification rate when using a
Random Forest classifier. The best sex-classification rate was 90.15% for the
right and 87.15% for the left eye. This was achieved when images were upscaled
from 150x150 to 450x450 pixels. These results compare well with the state of
the art and show that when improving image resolution with the SRCNN the
sex-classification rate increases. Additionally, a novel selfie database
captured from 150 subjects with an iPhone X was created (available upon
request).


  We study in this short comment the analogies and the differences that exist
between several local hidden variable models.


  Grain boundary diffusion in severely deformed Al-based AA5024 alloy is
investigated. Different states are prepared by combination of equal channel
angular processing and heat treatments, with the radioisotope $^{57}$Co being
employed as a sensitive probe of a given grain boundary state. Its diffusion
rates near room temperature (320~K) are utilized to quantify the effects of
severe plastic deformation and a presumed formation of a previously reported
deformation-modified state of grain boundaries, solute segregation at the
interfaces, increased dislocation content after deformation and of the
precipitation behavior on the transport phenomena along grain boundaries. The
dominant effect of nano-sized Al$_3$Sc-based precipitates is evaluated using
density functional theory and the Eshelby model for the determination of
elastic stresses around the precipitates.


  The Near-Field Microwave Microscope (NSMM) can be used to measure ohmic
losses of metallic thin films. We report on the presence of a new length scale
in the probe-to- sample interaction for the NSMM. We observe that this length
scale plays an important role when the tip to sample separation is less than
about 10nm. Its origin can be modeled as a tiny protrusion at the end of the
tip. The protrusion causes deviation from a logarithmic increase of capacitance
versus decreasing height of the probe above the sample. We model this
protrusion as a cone at the end of a sphere above an infinite plane. By fitting
the frequency shift of the resonator versus height data (which is directly
related to capacitance versus height) for our experimental setup, we find the
protrusion size to be 3nm to 5nm. For one particular tip, the frequency shift
of the NSMM relative to 2 micrometers away saturates at a value of about -1150
kHz at a height of 1nm above the sample, where the nominal range of sheet
resistance values of the sample are 15 ohms to 150 ohms. Without the
protrusion, the frequency shift would have followed the logarithmic dependence
and reached a value of about -1500 kHz.


  Small disturbances can trigger functional breakdowns in complex systems. A
challenging task is to infer the structural cause of a disturbance in a
networked system, soon enough to prevent a catastrophe. We present a graph
neural network approach, borrowed from the deep learning paradigm, to infer
structural perturbations from functional time series. We show our data-driven
approach outperforms typical reconstruction methods while meeting the accuracy
of Bayesian inference. We validate the versatility and performance of our
approach with epidemic spreading, population dynamics, and neural dynamics, on
various network structures: random networks, scale-free networks, 25 real
food-web systems, and the C. Elegans connectome. Moreover, we report that our
approach is robust to data corruption. This work uncovers a practical avenue to
study the resilience of real-world complex systems.


  Using the replica method, we develop an analytical approach to compute the
characteristic function for the probability $\mathcal{P}_N(K,\lambda)$ that a
large $N \times N$ adjacency matrix of sparse random graphs has $K$ eigenvalues
below a threshold $\lambda$. The method allows to determine, in principle, all
moments of $\mathcal{P}_N(K,\lambda)$, from which the typical sample to sample
fluctuations can be fully characterized. For random graph models with localized
eigenvectors, we show that the index variance scales linearly with $N \gg 1$
for $|\lambda| > 0$, with a model-dependent prefactor that can be exactly
calculated. Explicit results are discussed for Erd\"os-R\'enyi and regular
random graphs, both exhibiting a prefactor with a non-monotonic behavior as a
function of $\lambda$. These results contrast with rotationally invariant
random matrices, where the index variance scales only as $\ln N$, with an
universal prefactor that is independent of $\lambda$. Numerical diagonalization
results confirm the exactness of our approach and, in addition, strongly
support the Gaussian nature of the index fluctuations.


  We propose a machine-learning approach to model long-term out-of-sample
dynamics of brain activity from task-dependent fMRI data. Our approach is a
three stage one. First, we exploit Diffusion maps (DMs) to discover a set of
variables that parametrize the low-dimensional manifold on which the emergent
high-dimensional fMRI time series evolve. Then, we construct
reduced-order-models (ROMs) on the embedded manifold via two techniques:
Feedforward Neural Networks (FNNs) and the Koopman operator. Finally, for
predicting the out-of-sample long-term dynamics of brain activity in the
ambient fMRI space, we solve the pre-image problem coupling DMs with Geometric
Harmonics (GH) when using FNNs and the Koopman modes per se. For our
illustrations, we have assessed the performance of the two proposed schemes
using a benchmark fMRI dataset with recordings during a visuo-motor task. The
results suggest that just a few (for the particular task, five) non-linear
coordinates of the high-dimensional fMRI time series provide a good basis for
modelling and out-of-sample prediction of the brain activity. Furthermore, we
show that the proposed approaches outperform the one-step ahead predictions of
the naive random walk model, which, in contrast to our scheme, relies on the
knowledge of the signals in the previous time step. Importantly, we show that
the proposed Koopman operator approach provides, for any practical purposes,
equivalent results to the FNN-GH approach, thus bypassing the need to train a
non-linear map and to use GH to extrapolate predictions in the ambient fMRI
space; one can use instead the low-frequency truncation of the DMs function
space of L^2-integrable functions, to predict the entire list of coordinate
functions in the fMRI space and to solve the pre-image problem.


  We consider collision of two particles in the vicinity of the extremal
acceleration horizon (charged or rotating) that includes the Bertotti-Robinson
space-time and the geometry of the Kerr throat. It is shown that the energy in
the centre of mass frame E_{c.m.} can become indefinitely large if parameters
of one of the particles are fine-tuned, so the Ba\~nados-Silk-West (BSW) effect
manifests itself. There exists coordinate transformation which brings the
metric into the form free of the horizon. This leads to some paradox since (i)
the BSW effect exists due to the horizon, (ii) E_{c.m.} is a scalar and cannot
depend on the frame. Careful comparison of near-horizon trajectories in both
frames enables us to resolve this paradox. Although globally the space-time
structure of the metrics with acceleration horizons and black holes are
completely different, locally the vicinity of the extremal black hole horizon
can be approximated by the metric of the acceleration one. The energy of one
particle from the viewpoint of the Kruskal observer (or the one obtained from
it by finite local boost) diverges although in the stationary frame energies of
both colliding particles are finite. This suggests a new explanation of the BSW
effect for black holes given from the viewpoint of an observer who crosses the
horizon. It is complementary to the previously found explanation from the point
of view of a static or stationary observer.


  The Lottery Ticket Hypothesis continues to have a profound practical impact
on the quest for small scale deep neural networks that solve modern deep
learning tasks at competitive performance. These lottery tickets are identified
by pruning large randomly initialized neural networks with architectures that
are as diverse as their applications. Yet, theoretical insights that attest
their existence have been mostly focused on deep fully-connected feed forward
networks with ReLU activation functions. We prove that also modern
architectures consisting of convolutional and residual layers that can be
equipped with almost arbitrary activation functions can contain lottery tickets
with high probability.


  The heavy-tailed behavior of the generalized extreme-value distribution makes
it a popular choice for modeling extreme events such as floods, droughts,
heatwaves, wildfires, etc. However, estimating the distribution's parameters
using conventional maximum likelihood methods can be computationally intensive,
even for moderate-sized datasets. To overcome this limitation, we propose a
computationally efficient, likelihood-free estimation method utilizing a neural
network. Through an extensive simulation study, we demonstrate that the
proposed neural network-based method provides Generalized Extreme Value (GEV)
distribution parameter estimates with comparable accuracy to the conventional
maximum likelihood method but with a significant computational speedup. To
account for estimation uncertainty, we utilize parametric bootstrapping, which
is inherent in the trained network. Finally, we apply this method to 1000-year
annual maximum temperature data from the Community Climate System Model version
3 (CCSM3) across North America for three atmospheric concentrations: 289 ppm
$\mathrm{CO}_2$ (pre-industrial), 700 ppm $\mathrm{CO}_2$ (future conditions),
and 1400 ppm $\mathrm{CO}_2$, and compare the results with those obtained using
the maximum likelihood approach.


  In the present work, the atomic and the electronic structures of Au3N, AuN
and AuN2 are investigated using first-principles density-functional theory
(DFT). We studied cohesive energy vs. volume data for a wide range of possible
structures of these nitrides. Obtained data was fitted to a Birch-Murnaghan
third-order equation of state (EOS) so as to identify the most likely
candidates for the true crystal structure in this subset of the infinite
parameter space, and to determine their equilibrium structural parameters. The
analysis of the electronic properties was achieved by the calculations of the
band structure and the total and partial density of states (DOS). Some possible
pressure-induced structural phase transitions have been pointed out. Further,
we carried out GW0 calculations within the random-phase approximation (RPA) to
the dielectric tensor to investigate the optical spectra of the experimentally
suggested modification: Au3N(D0_9). Obtained results are compared with
experiment and with some available previous calculations.


  In this study, approximate generalized quantal distribution functions and
their applications, which appeared in the literature so far, have been
summarized. Making use of the generalized Planck radiation law, which have been
obtained by the authors of the present manuscript [Physica A240 (1997) 657],
some alternative bounds for the nonextensivity parameter $q$ has been
estimated. It has been shown that these results are similar to those obtained
by Tsallis et al. [Phys. Rev. E52 (1995) 1447] and by Plastino et al. [Phys.
Lett. A207 (1995) 42].


  We develop a new tool, namely polynomial and linear algebraic methods, for
studying systems of word equations. We illustrate its usefulness by giving
essentially simpler proofs of several hard problems. At the same time we prove
extensions of these results. Finally, we obtain the first nontrivial upper
bounds for the fundamental problem of the maximal size of independent systems.
These bounds depend quadratically on the size of the shortest equation. No
methods of having such bounds have been known before.


  Recent developments in matrix-product-state (MPS) investigations of many-body
localization (MBL) are reviewed, with a discussion of benefits and limitations
of the method. This approach allows one to explore the physics around the MBL
transition in systems much larger than those accessible to exact
diagonalization. System sizes and length scales that can be controllably
accessed by the MPS approach are comparable to those studied in
state-of-the-art experiments. Results for 1D, quasi-1D, and 2D random systems,
as well as 1D quasi-periodic systems are presented. On time scales explored (up
to $t \approx 300$ in units set by the hopping amplitude), a slow, subdiffusive
transport in a rather broad disorder range on the ergodic side of the MBL
transition is found. For 1D random spin chains, which serve as a "standard
model" of the MBL transition, the MPS study demonstrates a substantial drift of
the critical point $W_c(L)$ with the system size $L$: while for $L \approx 20$
we find $W_c \approx 4$, as also given by exact diagonalization, the MPS
results for $L = 50$--100 provide evidence that the critical disorder
saturates, in the large-$L$ limit, at $W_c \approx 5.5$. For quasi-periodic
systems, these finite-size effects are much weaker, which suggests that they
can be largely attributed to rare events. For quasi-1D ($d\times L$, with $d
\ll L$) and 2D ($L\times L$) random systems, the MPS data demonstrate an
unbounded growth of $W_c$ in the limit of large $d$ and $L$, in agreement with
analytical predictions based on the rare-event avalanche theory.


  We study bound states embedded into the continuum of edge states in
two-dimensional topological insulators. These states emerge in the presence of
a short-range potential of a structural defect coupled to the boundary. In this
case the edge states flow around the defect and have two resonances in the
local density of states. The bound state in continuum (BIC) arises due to an
interference of the resonances when they are close to the degeneracy. We find
the condition under which the BIC appears, study the spacial distribution of
the electron density, and show that the BIC has a helical structure with an
electron current circulating around the defect.


  Ryabinkin, Kohut, and Staroverov (RKS) [Phys. Rev. Lett. 115, 083001 (2015)]
devised an iterative method for reducing many-electron wave functions to
Kohn-Sham exchange-correlation potentials, $v_\text{XC}(\mathbf{r})$. For a
given type of wave function, the RKS method is exact (Kohn-Sham-compliant) in
the basis-set limit; in a finite basis set, it produces an approximation to the
corresponding basis-set-limit $v_\text{XC}(\mathbf{r})$. The original RKS
procedure works very well for large basis sets but sometimes fails for commonly
used (small and medium) sets. We derive a modification of the method's working
equation that makes the RKS procedure robust for all Gaussian basis sets and
increases the accuracy of the resulting exchange-correlation potentials with
respect to the basis-set limit.


  Cauchy's condensation test allows to determine the convergence of a monotone
series by looking at a weighted subseries that only involves terms of the
original series indexed by the powers of two. It is natural to ask whether the
converse is also true: Is it possible to determine the convergence of an
arbitrary subseries of a monotone series by looking at a suitably weighted
version of the original series? In this note we show that the answer is
affirmative and introduce a new convergence test particularly designed for this
purpose.


  Temporal data, notably time series and spatio-temporal data, are prevalent in
real-world applications. They capture dynamic system measurements and are
produced in vast quantities by both physical and virtual sensors. Analyzing
these data types is vital to harnessing the rich information they encompass and
thus benefits a wide range of downstream tasks. Recent advances in large
language and other foundational models have spurred increased use of these
models in time series and spatio-temporal data mining. Such methodologies not
only enable enhanced pattern recognition and reasoning across diverse domains
but also lay the groundwork for artificial general intelligence capable of
comprehending and processing common temporal data. In this survey, we offer a
comprehensive and up-to-date review of large models tailored (or adapted) for
time series and spatio-temporal data, spanning four key facets: data types,
model categories, model scopes, and application areas/tasks. Our objective is
to equip practitioners with the knowledge to develop applications and further
research in this underexplored domain. We primarily categorize the existing
literature into two major clusters: large models for time series analysis
(LM4TS) and spatio-temporal data mining (LM4STD). On this basis, we further
classify research based on model scopes (i.e., general vs. domain-specific) and
application areas/tasks. We also provide a comprehensive collection of
pertinent resources, including datasets, model assets, and useful tools,
categorized by mainstream applications. This survey coalesces the latest
strides in large model-centric research on time series and spatio-temporal
data, underscoring the solid foundations, current advances, practical
applications, abundant resources, and future research opportunities.


  Superdiffusion is surprisingly easily observed even in systems without the
integrability underpinning this phenomenon. Indeed, the classical Heisenberg
chain -- one of the simplest many-body systems, and firmly believed to be
non-integrable -- evinces a long-lived regime of anomalous, superdiffusive spin
dynamics at finite temperature. Similarly, superdiffusion persists for long
timescales, even at high temperature, for small perturbations around a related
integrable model. Eventually, however, ordinary diffusion is believed to be
asymptotically restored. We examine the timescales governing the lifetime of
the superdiffusive regime, and argue that it diverges algebraically fast --
both in deviation from the integrable limit, and at low temperature, where we
find $t^* \sim T^{-\zeta}$ with an exponent possibly as large as $\zeta = 8$.
This can render the crossover to ordinary diffusion practically inaccessible.


  We show that the open problem presented in "Geometric Folding Algorithms:
Linkages, Origami, Polyhedra" [DO07] is solved by a theorem of Burago and
Zalgaller [BZ96] from more than a decade earlier.


  This paper is concerned with the verification of finite Markov chains against
parametrized LTL (pLTL) formulas. In pLTL, the until-modality is equipped with
a bound that contains variables; e.g., $\Diamond_{\le x}\ \varphi$ asserts that
$\varphi$ holds within $x$ time steps, where $x$ is a variable on natural
numbers. The central problem studied in this paper is to determine the set of
parameter valuations $V_{\prec p} (\varphi)$ for which the probability to
satisfy pLTL-formula $\varphi$ in a Markov chain meets a given threshold $\prec
p$, where $\prec$ is a comparison on reals and $p$ a probability. As for pLTL
determining the emptiness of $V_{> 0}(\varphi)$ is undecidable, we consider
several logic fragments. We consider parametric reachability properties, a
sub-logic of pLTL restricted to next and $\Diamond_{\le x}$, parametric B\"uchi
properties and finally, a maximal subclass of pLTL for which emptiness of $V_{>
0}(\varphi)$ is decidable.


  We study the polarizations induced by the Galileon as a stochastic
gravitational wave background in the cross correlated power in a pulsar timing
array. Working within Galileon gravity, we first show that the scalar
gravitational wave signature of the Galileon is encoded solely in its effective
mass, which is controlled by the bare mass, conformal coupling, and a tadpole.
Then, we study the phenomenology of the Galileon induced scalar polarizations
and place observational constraints on these using the present NANOGrav data
set. Our results feature longitudinal spatial correlation, indicative of a
$10^{-22}$ eV {subluminal} Galileon, and show the Galileon polarizations as a
statistically compelling source of the observed spatial correlation across
millisecond pulsars, if there is any.


  We construct a $C^2$ symplectic twist map f of the annulus that has an
essential invariant curve C such that: - C is not differentiable; - the
dynamics of f restricted to C is conjugated to the one of a Denjoy
counter-example; - C is at the boundary of an instability zone for f.


  We study the problem of exponentially small splitting of separatrices of one
degree of freedom classical Hamiltonian systems with a non-autonomous
perturbation fast and periodic in time. We provide a result valid for general
systems which are algebraic or trigonometric polynomials in the state
variables. It consists on obtaining a rigorous proof of the asymptotic formula
for the measure of the splitting. We obtain that the splitting has the
asymptotic behavior $K \varepsilon^{\beta} \text{e}^{-a/\varepsilon}$,
identifying the constants $K,\beta,a$ in terms of the system features.
  We consider several cases. In some cases, assuming the perturbation is small
enough, the values of $K,\beta$ coincide with the classical Melnikov approach.
We identify the limit size of the perturbation for which this theory holds
true. However for the limit cases, which appear naturally both in averaging and
bifurcation theories, we encounter that, generically, $K$ and $\beta$ are not
well predicted by Melnikov theory.


  We study photodetection in graphene near a local electrostatic gate, which
enables active control of the potential landscape and carrier polarity. We find
that a strong photoresponse only appears when and where a p-n junction is
formed, allowing on-off control of photodetection. Photocurrents generated near
p-n junctions do not require biasing and can be realized using submicron gates.
Locally modulated photoresponse enables a new range of applications for
graphene-based photodetectors including, for example, pixilated infrared
imaging with control of response on subwavelength dimensions.


  In this paper, we show that the transient waveforms arising from several
localised pulses in a micro-structured material can be reproduced by a
corresponding generalised continuum of the relaxed micromorphic type.
Specifically, we compare the dynamic response of a bounded micro-structured
material to that of bounded continua with special kinematic properties: (i) the
relaxed micromorphic continuum and (ii) an equivalent Cauchy linear elastic
continuum. We show that, while the Cauchy theory is able to describe the
overall behaviour of the metastructure only at low frequencies, the relaxed
micromorphic model goes far beyond by giving a correct description of the pulse
propagation in the frequency band-gap and at frequencies intersecting the
optical branches. In addition, we observe a computational time reduction
associated with the use of the relaxed micromorphic continuum, compared to the
sensible computational time needed to perform a transient computation in a
micro-structured domain.


  Consider $A(x,D):C^{\infty}(\Omega,E) \rightarrow C^\infty(\Omega,F)$ an
elliptic and canceling linear differential operator of order $\nu$ with smooth
complex coefficients in $\Omega \subset \mathbb{R}^{N}$ from a finite dimension
complex vector space $E$ to a finite dimension complex vector space $F$ and
$A^{*}(x,D)$ {its} adjoint. In this work we characterize the (local) continuous
solvability of the partial differential equation $A^{*}(x,D)v=f$ (in the
distribution sense) for a given distribution $f$; more precisely we show that
any $x_0\in\Omega$ is contained in a neighborhood $U\subset \Omega$ in which
its continuous solvability is characterized by the following condition on $f$:
for every $\epsilon>0$ and any compact set $K \subset \subset U$, there exists
$\theta=\theta(K,\epsilon)>0$ such that the following holds for all smooth
function $\varphi$ supported in $K$: \begin{equation}\nonumber \left|
f(\varphi) \right| \leq \theta\|\varphi\|_{W^{\nu-1,1}} + \epsilon\|A(x,D)
\varphi\|_{L^{1}}, \end{equation} where $W^{\nu-1,1}$ stands for the homogenous
Sobolev space of all $L^1$ functions whose derivatives of order $\nu-1$ belongs
to $L^{1}(U)$. This characterization implies and extends results obtained
before for operators associated to elliptic complex of vector fields (see
\cite{MP}); we also provide local analogues, for a large range of differential
operators, to global results obtained for the classical divergence operator in
[4] and [9].


  The transmission and reflection spectra from a right-handed chiral sculptured
zirconia thin film are calculated using the piecewise homogeneity approximation
method and the Bruggeman homogenization formalism by considering that the
propagation of both dispersive and non-dispersive dielectric function occurs
for axial and non-axial states. The comparison of spectral results shows that
the dispersion of the dielectric function has a considerable effect on the
results. In axial excitation of cross-polarized reflectances and co-polarized
transmittances the dispersion effect becomes more pronounced at wavelengths
further away from the homogenization wavelength. This is also true in case of
non-axial excitation of circular transmittances, while there are considerable
differences for cross-polarized reflectances where (wavelength) the first Bragg
peak occurs. At wavelengths in the vicinity of the homogenization wavelength
the dispersion effect of the dielectric function in becomes more significant.


  Minimising opposing forces is a matter of interest to most cyclists. These
forces arise from passage through air ("drag") and interaction with the road
surface ("resistance"). Recent work recognises that resistance forces arise not
only from the deformation of the tyre ("rolling resistance") but also from
irregularities in the road surface ("roughness resistance"), which lead to
power dissipation in the body of the rider through vibration. The latter effect
may also have an adverse impact on human health. In this work we offer a
quantitative theory of roughness resistance and vibration that links these
effects to a surface characterisation in terms of the International Roughness
Index (IRI). We show that the roughness resistance and the Vibration Dose Value
(or VDV, the usual vibration dosage metric) can be expressed in terms of
elementary formulae. The roughness resistance depends only on the vertical
stiffness of the bicycle and the roughness index. Surprisingly, other
apparently relevant parameters, such as physiological characteristics of the
bicycle rider and other features of the bicycle, do not enter. For roads of
moderate roughness, roughness resistance is larger than rolling resistance. For
very rough roads, roughness resistance is larger than aerodynamic drag. So only
on roads of high quality (in most jurisdictions, accounting for less than 10~\%
of the total) can roughness resistance be ignored. Roughness resistance can be
mitigated by reducing the vertical stiffness of the bicycle. In common with
other recent reports, we find that almost any cycling activity will breach
public health guidelines relating to Vibration Dose Value.


  We give conditions that ensure that an operator satisfying a Piestch
domination in a given setting also satisfies a Piestch domination in a
different setting. From this we derive that a bounded mutlilinear operator $T$
is Lipschitz $p$-summing if and only if the mapping $ f_T(x_1\otimes\cdots
\otimes x_n):=T(x_1,\ldots, x_n)$ is Lipschitz $p$-summing. The results are
based on the projective tensor norm. An example with the Hilbert tensor norm is
provided to show that the statement may not hold when a reasonable cross-norm
other than the projective tensor norm is considered.


  We demonstrate that efficient optical pumping of nuclear spins in
semiconductor quantum dots (QDs) can be achieved by resonant pumping of
optically "forbidden" transitions. This process corresponds to one-to-one
conversion of a photon absorbed by the dot into a polarized nuclear spin, which
also has potential for initialization of hole spin in QDs. Pumping via the
"forbidden" transition is a manifestation of the "optical solid effect", an
optical analogue of the effect previously observed in electron spin resonance
experiments in the solid state. We find that by employing this effect, nuclear
polarization of 65% can be achieved, the highest reported so far in optical
orientation studies in QDs. The efficiency of the spin pumping exceeds that
employing the allowed transition, which saturates due to the low probability of
electron-nuclear spin flip-flop.


  Asymptotically safe quantum fluctuations of gravity can uniquely determine
the value of the gauge coupling for a large class of grand unified models. In
turn, this makes the electromagnetic fine-structure constant calculable. The
balance of gravity and matter fluctuations results in a fixed point for the
running of the gauge coupling. It is approached as the momentum scale is
lowered in the transplanckian regime, leading to a uniquely predicted value of
the gauge coupling at the Planck scale. The precise value of the predicted
fine-structure constant depends on the matter content of the grand unified
model. It is proportional to the gravitational fluctuation effects for which
computational uncertainties remain to be settled.


  If a state is passive for uniformly accelerated observers in n-dimensional
anti-de Sitter space-time (i.e. cannot be used by them to operate a perpetuum
mobile), they will (a) register a universal value of the Unruh temperature, (b)
discover a PCT symmetry, and (c) find that observables in complementary
wedge-shaped regions necessarily commute with each other in this state. The
stability properties of such a passive state induce a "geodesic causal
structure" on AdS and concommitant locality relations. It is shown that
observables in these complementary wedge-shaped regions fulfill strong
additional independence conditions. In two-dimensional AdS these even suffice
to enable the derivation of a nontrivial, local, covariant net indexed by
bounded spacetime regions. All these results are model-independent and hold in
any theory which is compatible with a weak notion of space-time localization.
Examples are provided of models satisfying the hypotheses of these theorems.


  This paper is devoted to a geometric-measure-theoretic study of the brand new
affine BV-capacity which is essentially different from the classic BV-capacity
in dimension greater than one.


  We establish by exact, nonperturbative methods a universality for the
correlation functions in Kraichnan's ``rapid-change'' model of a passively
advected scalar field. We show that the solutions for separated points in the
convective range of scales are unique and independent of the particular
mechanism of the scalar dissipation. Any non-universal dependences therefore
must arise from the large length-scale features. The main step in the proof is
to show that solutions of the model equations are unique even in the idealized
case of zero diffusivity, under a very modest regularity requirement
(square-integrability). Within this regularity class the only zero-modes of the
global many-body operators are shown to be trivial ones (i.e. constants). In a
bounded domain of size $L$, with physical boundary conditions, the
``ground-state energy'' is strictly positive and scales as $L^{-\gamma}$ with
an exponent $\gamma >0$.


  We present a theorem for verification of optimality of controlled diffusions
under the average cost criterion with near-monotone running cost, without
invoking any blanket stability assumptions. The implications of this result to
the policy iteration algorithm are also discussed.


  Domain generalizable person re-identification aims to apply a trained model
to unseen domains. Prior works either combine the data in all the training
domains to capture domain-invariant features, or adopt a mixture of experts to
investigate domain-specific information. In this work, we argue that both
domain-specific and domain-invariant features are crucial for improving the
generalization ability of re-id models. To this end, we design a novel
framework, which we name two-stream adaptive learning (TAL), to simultaneously
model these two kinds of information. Specifically, a domain-specific stream is
proposed to capture training domain statistics with batch normalization (BN)
parameters, while an adaptive matching layer is designed to dynamically
aggregate domain-level information. In the meantime, we design an adaptive BN
layer in the domain-invariant stream, to approximate the statistics of various
unseen domains. These two streams work adaptively and collaboratively to learn
generalizable re-id features. Our framework can be applied to both
single-source and multi-source domain generalization tasks, where experimental
results show that our framework notably outperforms the state-of-the-art
methods.


  We analyze the diffusion processes associated to equations of Wright-Fisher
type in one spatial dimension. These are defined by a degenerate second order
operator on the interval [0, 1], where the coefficient of the second order term
vanishes simply at the endpoints, and the first order term is an
inward-pointing vector field. We consider various aspects of this problem,
motivated by applications in population genetics, including a sharp regularity
theory for the zero flux boundary conditions, as well as a derivation of the
precise asymptotics for solutions of this equation, both as t goes to 0 and
infinity, and as x goes to 0, 1.


  Graph data widely exists in real life, with large amounts of data and complex
structures. It is necessary to map graph data to low-dimensional embedding.
Graph classification, a critical graph task, mainly relies on identifying the
important substructures within the graph. At present, some graph classification
methods do not combine the multi-granularity characteristics of graph data.
This lack of granularity distinction in modeling leads to a conflation of key
information and false correlations within the model. So, achieving the desired
goal of a credible and interpretable model becomes challenging. This paper
proposes a causal disentangled multi-granularity graph representation learning
method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the
important substructures and bias parts within the graph from a
multi-granularity perspective. The disentanglement of the CDM-GNN model reveals
important and bias parts, forming the foundation for its classification task,
specifically, model interpretations. The CDM-GNN model exhibits strong
classification performance and generates explanatory outcomes aligning with
human cognitive patterns. In order to verify the effectiveness of the model,
this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six
state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are
employed for comparison purposes. Additionally, a qualitative analysis of the
interpretation results is conducted.


  Annotation of medical images has been a major bottleneck for the development
of accurate and robust machine learning models. Annotation is costly and
time-consuming and typically requires expert knowledge, especially in the
medical domain. Here, we propose to use minimal user interaction in the form of
extreme point clicks in order to train a segmentation model that can, in turn,
be used to speed up the annotation of medical images. We use extreme points in
each dimension of a 3D medical image to constrain an initial segmentation based
on the random walker algorithm. This segmentation is then used as a weak
supervisory signal to train a fully convolutional network that can segment the
organ of interest based on the provided user clicks. We show that the network's
predictions can be refined through several iterations of training and
prediction using the same weakly annotated data. Ultimately, our method has the
potential to speed up the generation process of new training datasets for the
development of new machine learning and deep learning-based models for, but not
exclusively, medical image analysis.


  We present archival ALMA observations of the HD 141569 circumstellar disk at
345, 230, and 100 GHz. These data detect extended millimeter emission that is
exterior to the inner disk. We find through simultaneous visibility modeling of
all three data sets that the system's morphology is described well by a
two-component disk model. The inner disk ranges from approximately 16 to 45 au
with a spectral index of 1.81 (q = 2.95) and the outer disk ranges from 95 to
300 au with a spectral index of 2.28 (q = 3.21). Azimuthally averaged radial
emission profiles derived from the continuum images at each frequency show
potential emission that is consistent with the visibility modeling. The
analysis presented here shows that at ~5 Myr HD 141569's grain size
distribution is steeper, and therefore more evolved, in the outer disk than in
the inner disk.


  We study transitions between phases of matter with topological order. By
studying these transitions in exactly solvable lattice models we show how
universality classes may be identified and critical properties described. As a
familiar example to elucidate our results concretely, we describe in detail a
transition between a fully gapped achiral 2D $p$-wave superconductor ($p+ip$
for pseudospin up/$p-ip$ for pseudospin down) to an $s$-wave superconductor
which we show to be in the 2D transverse field Ising universality class.


  In this letter, we have considered the universe is filled with the mixture of
tachyonic field and scalar or phantom field. If the tachyonic field interacts
with scalar or phantom field, the interaction term decays with time and the
energy for scalar field is transferred to tachyonic field or the energy for
phantom field is transferred to tachyonic field. The tachyonic field and scalar
field potentials always decrease, but phantom field potential always increases.


  The present status and recent developments in the theory of light hydrogenic
atoms, electronic and muonic, are extensively reviewed. The discussion is based
on the quantum field theoretical approach to loosely bound composite systems.
The basics of the quantum field theoretical approach, which provide the
framework needed for a systematic derivation of all higher order corrections to
the energy levels, are briefly discussed. The main physical ideas behind the
derivation of all binding, recoil, radiative, radiative-recoil, and
nonelectromagnetic spin-dependent and spin-independent corrections to energy
levels of hydrogenic atoms are discussed and, wherever possible, the
fundamental elements of the derivations of these corrections are provided. The
emphasis is on new theoretical results which were not available in earlier
reviews. An up-to-date set of all theoretical contributions to the energy
levels is contained in the paper. The status of modern theory is tested by
comparing the theoretical results for the energy levels with the most precise
experimental results for the Lamb shifts and gross structure intervals in
hydrogen, deuterium, and helium ion $He^+$, and with the experimental data on
the hyperfine splitting in muonium, hydrogen and deuterium.


  Positive-unlabeled learning (PUL) aims at learning a binary classifier from
only positive and unlabeled training data. Even though real-world applications
often involve imbalanced datasets where the majority of examples belong to one
class, most contemporary approaches to PUL do not investigate performance in
this setting, thus severely limiting their applicability in practice. In this
work, we thus propose to tackle the issues of imbalanced datasets and model
calibration in a PUL setting through an uncertainty-aware pseudo-labeling
procedure (PUUPL): by boosting the signal from the minority class,
pseudo-labeling expands the labeled dataset with new samples from the unlabeled
set, while explicit uncertainty quantification prevents the emergence of
harmful confirmation bias leading to increased predictive performance. Within a
series of experiments, PUUPL yields substantial performance gains in highly
imbalanced settings while also showing strong performance in balanced PU
scenarios across recent baselines. We furthermore provide ablations and
sensitivity analyses to shed light on PUUPL's several ingredients. Finally, a
real-world application with an imbalanced dataset confirms the advantage of our
approach.


  We study A-discriminants from a non-Archimedean point of view, refining
earlier work on the tropical discriminant. In particular, we study the case
where $A$ is a collection of n+m+1 points in Z^n in general position, and give
an algorithm to compute the image of the A-discriminant variety under the
non-Archimedean evaluation map. When m=2, our approach yields tight lower and
upper bounds, of order quadratic in n. We also detail a Sage package for
plotting certain p-adic discriminant amoebae, and present explicit examples of
point sets yielding discriminant amoebae with extremal behavior.


  Advances in micro-electro-mechanical (MEMS) techniques enable inertial
measurements units (IMUs) to be small, cheap, energy efficient, and widely used
in smartphones, robots, and drones. Exploiting inertial data for accurate and
reliable navigation and localization has attracted significant research and
industrial interest, as IMU measurements are completely ego-centric and
generally environment agnostic. Recent studies have shown that the notorious
issue of drift can be significantly alleviated by using deep neural networks
(DNNs), e.g. IONet. However, the lack of sufficient labelled data for training
and testing various architectures limits the proliferation of adopting DNNs in
IMU-based tasks. In this paper, we propose and release the Oxford Inertial
Odometry Dataset (OxIOD), a first-of-its-kind data collection for
inertial-odometry research, with all sequences having ground-truth labels. Our
dataset contains 158 sequences totalling more than 42 km in total distance,
much larger than previous inertial datasets. Another notable feature of this
dataset lies in its diversity, which can reflect the complex motions of
phone-based IMUs in various everyday usage. The measurements were collected
with four different attachments (handheld, in the pocket, in the handbag and on
the trolley), four motion modes (halting, walking slowly, walking normally, and
running), five different users, four types of off-the-shelf consumer phones,
and large-scale localization from office buildings. Deep inertial tracking
experiments were conducted to show the effectiveness of our dataset in training
deep neural network models and evaluate learning-based and model-based
algorithms. The OxIOD Dataset is available at: http://deepio.cs.ox.ac.uk


  We study two delay-coupled FitzHugh-Nagumo systems, introducing a mismatch
between the delay times, as the simplest representation of interacting neurons.
We demonstrate that the presence of delays can cause periodic oscillations
which coexist with a stable fixed point. Periodic solutions observed are of two
types, which we refer to as a "long" and a "short" cycle, respectively.


  Train-time data poisoning attacks threaten machine learning models by
introducing adversarial examples during training, leading to misclassification.
Current defense methods often reduce generalization performance, are
attack-specific, and impose significant training overhead. To address this, we
introduce a set of universal data purification methods using a stochastic
transform, $\Psi(x)$, realized via iterative Langevin dynamics of Energy-Based
Models (EBMs), Denoising Diffusion Probabilistic Models (DDPMs), or both. These
approaches purify poisoned data with minimal impact on classifier
generalization. Our specially trained EBMs and DDPMs provide state-of-the-art
defense against various attacks (including Narcissus, Bullseye Polytope,
Gradient Matching) on CIFAR-10, Tiny-ImageNet, and CINIC-10, without needing
attack or classifier-specific information. We discuss performance trade-offs
and show that our methods remain highly effective even with poisoned or
distributionally shifted generative model training data.


  The present paper gives an account for the general mathematical reader of the
life and work of Martin Davis. Since two rather comprehensive autobiographical
accounts and two long biographical interviews already exist, the present work
focusses on Davis's scientific achievements, including work on computably
enumerable sets, universal Turing machines, the hyperarithmetical hierarchy,
neural networks, Hilbert's Tenth Problem, and automated reasoning.


  Topology in quantum many-body systems has profoundly changed our
understanding of quantum phases of matter. The paradigmatic model that has
played an instrumental role in elucidating these effects is the
antiferromagnetic spin-1 Haldane chain. Its ground state is a disordered state,
with symmetry-protected fourfold-degenerate edge states due to fractional spin
excitations. In the bulk, it is characterised by vanishing two-point spin
correlations, gapped excitations, and a characteristic non-local order
parameter. More recently it was understood that the Haldane chain forms a
specific example of a more general classification scheme of symmetry protected
topological (SPT) phases of matter that is based on ideas connecting to quantum
information and entanglement. Here, we realise such a topological Haldane phase
with Fermi-Hubbard ladders in an ultracold-atom quantum simulator. We directly
reveal both edge and bulk properties of the system through the use of
single-site and particle-resolved measurements as well as non-local correlation
functions. Continuously changing the Hubbard interaction strength of the system
allows us to investigate the robustness of the phase to charge (density)
fluctuations far from the regime of the Heisenberg model employing a novel
correlator.


  The top-quark mass M_top is measured using top quark-antiquark pairs produced
in proton-antiproton collisions at a center-of-mass energy of 1.96 TeV and
decaying into a fully hadronic final state. The full data set collected with
the CDFII detector at the Fermilab Tevatron Collider, corresponding to an
integrated luminosity of 9.3 fb-1, is used. Events are selected that have six
to eight jets, at least one of which is identified as having originated from a
b quark. In addition, a multivariate algorithm, containing multiple kinematic
variables as inputs, is used to discriminate signal events from background
events due to QCD multijet production. Templates for the reconstructed
top-quark mass are combined in a likelihood fit to measure M_top with a
simultaneous calibration of the jet-energy scale. A value of M_top = 175.07+-
1.19(stat)+1.55-1.58(syst) GeV/c^2 is obtained for the top-quark mass.


  We consider an one-phase free boundary problem for a degenerate fully
non-linear elliptic operators with non-zero right hand side. We use the
approach present in \cite{DeSilva} to prove that flat free boundaries and
Lipschitz free boundaries are $C^{1, \gamma}$.


  In this paper we prove that assuming Schanuel's conjecture, an exponential
polynomial in one variable over the algebraic numbers has only finitely many
algebraic solutions. This implies a positive answer to Shapiro's conjecture for
exponential polynomials over the algebraic numbers for pseudoexponential fields
as well as for any algebraically closed exponential field satisfying Schanuel's
conjecture.


  The NEMO (NEutrino Mediterranean Observatory) Collaboration installed, 25 km
E offshore the port of Catania (Sicily) at 2000 m depth, an underwater
laboratory to perform long-term tests of prototypes and new technologies for an
underwater high energy neutrino km$^3$-scale detector in the Mediterranean Sea.
In this framework the collaboration deployed and successfully operated for
about two years, starting form January 2005, an experimental apparatus for
on-line monitoring of deep-sea noise. The station was equipped with 4
hydrophones and it is operational in the range 30 Hz - 43 kHz. This interval of
frequencies matches the range suitable for the proposed acoustic detection
technique of high energy neutrinos. Hydrophone signals were digitized
underwater at 96 kHz sampling frequency and 24 bits resolution. A custom
software was developed to record data on high resolution 4-channels digital
audio file. This paper deals with the data analysis procedure and first results
on the determination of sea noise sound pressure density curves. The stored
data library, consisting of more than 2000 hours of recordings, is a unique
tool to model underwater acoustic noise at large depth, to characterise its
variations as a function of environmental parameters, biological sources and
human activities (ship traffic, ...), and to determine the presence of
cetaceans in the area.


  Let $G$ be a minimal split Kac-Moody group over a valued field {\mathcal{K}.
Motivated by the representation theory of $G$, we define two topologies of
topological group on $G$, which take into account the topology on {\mathcal{K}.


  Meta-learning has been the most common framework for few-shot learning in
recent years. It learns the model from collections of few-shot classification
tasks, which is believed to have a key advantage of making the training
objective consistent with the testing objective. However, some recent works
report that by training for whole-classification, i.e. classification on the
whole label-set, it can get comparable or even better embedding than many
meta-learning algorithms. The edge between these two lines of works has yet
been underexplored, and the effectiveness of meta-learning in few-shot learning
remains unclear. In this paper, we explore a simple process: meta-learning over
a whole-classification pre-trained model on its evaluation metric. We observe
this simple method achieves competitive performance to state-of-the-art methods
on standard benchmarks. Our further analysis shed some light on understanding
the trade-offs between the meta-learning objective and the whole-classification
objective in few-shot learning.


  We study ${\Lambda}K^+$ pair production in the interaction of protons of 2.83
GeV kinetic energy with C, Cu, Ag, and Au target nuclei in the framework of the
nuclear spectral function approach for incoherent primary proton--nucleon and
secondary pion--nucleon production processes, and processes associated with the
creation of intermediate ${\Sigma^0}K^+$ pairs. The approach accounts for the
initial proton and final $\Lambda$ hyperon absorption, final $K^+$ meson
distortion in nuclei, target nucleon binding, and Fermi motion, as well as
nuclear mean-field potential effects on these processes. We calculate the
$\Lambda$ momentum dependence of the absolute ${\Lambda}K^+$ yield from the
target nuclei considered, in the kinematical conditions of the ANKE experiment,
performed at COSY, within the different scenarios for the $\Lambda$-nucleus
effective scalar potential. We show that the above observable is appreciably
sensitive to this potential in the low-momentum region. Therefore, direct
comparison of the results of our calculations with the data from the
ANKE-at-COSY experiment can help to determine the above potential at finite
momenta. We also demonstrate that the two-step pion--nucleon production
channels dominate in the low-momentum ${\Lambda}K^+$ production in the chosen
kinematics and, therefore, they have to be taken into account in the analysis
of these data.


  Images recorded with the Gemini Multi-Object Spectrograph (GMOS) on Gemini
South are combined with archival images from other facilities to search for
variable stars in the southern portion of the nearby disk galaxy NGC 247.
Fifteen new periodic and non-periodic variables are identified. These include
three Cepheids with periods < 25 days, four semi-regular variables, one of
which shows light variations similar to those of R CrB stars, five variables
with intrinsic visible/red brightnesses and colors that are similar to those of
luminous blue variables (LBVs), and three fainter blue variables, one of which
may be a non-eclipsing close binary system. The S Doradus instability strip
defines the upper envelope of a distinct sequence of objects on the (i', g'-i')
color-magnitude diagram (CMD) of NGC 247. The frequency of variability with an
amplitude > 0.1 magnitude in the part of the CMD that contains LBVs over the
seven month period when the GMOS images were recorded is ~ 0.2. The light curve
of the B[e] supergiant J004702.18--204739.9, which is among the brightest stars
in NGC 247, is also examined. Low amplitude variations on day-to-day timescales
are found, coupled with a systematic trend in mean brightness over a six month
time interval.


  We explore the possibility that extrasolar planets might be found in the 1:1
mean-motion resonance. There are a variety of stable co-orbtial configurations,
and we specifically examine three different versions of the 1:1 resonance.
These include tadpole and horseshoe type orbits, as well as a more exotic
configuration which occurs when one planet has a highly eccentric orbit while
the other planet moves on a nearly circular orbit. We show that pairs of
planets in 1:1 resonance yield characteristic radial velocity signatures which
are not prone to the sin(i) degeneracy. Indeed, Keplerian fits to the radial
velocities cannot reveal the presence of two planets in the 1:1 resonance. We
discuss a dynamical fitting method for such systems, and illustrate its use
with a simulated data set. Finally, we argue that hydrodynamical simulations
and torqued three-body calculations indicate that 1:1 resonant pairs might
readily form and migrate within protostellar disks.


  Automotive radar has increasingly attracted attention due to growing interest
in autonomous driving technologies. Acquiring situational awareness using
multimodal data collected at high sampling rates by various sensing devices
including cameras, LiDAR, and radar requires considerable power, memory and
compute resources which are often limited at an edge device. In this paper, we
present a novel adaptive radar sub-sampling algorithm designed to identify
regions that require more detailed/accurate reconstruction based on prior
environmental conditions' knowledge, enabling near-optimal performance at
considerably lower effective sampling rates. Designed to robustly perform under
variable weather conditions, the algorithm was shown on the Oxford raw radar
and RADIATE dataset to achieve accurate reconstruction utilizing only 10% of
the original samples in good weather and 20% in extreme (snow, fog) weather
conditions. A further modification of the algorithm incorporates object motion
to enable reliable identification of important regions. This includes
monitoring possible future occlusions caused by objects detected in the present
frame. Finally, we train a YOLO network on the RADIATE dataset to perform
object detection directly on RADAR data and obtain a 6.6% AP50 improvement over
the baseline Faster R-CNN network.


  Understanding the interplay between charge, nematic, and structural ordering
tendencies in cuprate superconductors is critical to unraveling their complex
phase diagram. Using pump-probe time-resolved resonant x-ray scattering on the
(0 0 1) Bragg peak at the Cu $L_3$ and O $K$ resonances, we investigate
non-equilibrium dynamics of $Q_a = Q_b = 0$ nematic order and its association
with both charge density wave (CDW) order and lattice dynamics in
La$_{1.65}$Eu$_{0.2}$Sr$_{0.15}$CuO$_4$. The orbital selectivity of the
resonant x-ray scattering cross-section allows nematicity dynamics associated
with the planar O 2$p$ and Cu 3$d$ states to be distinguished from the response
of anisotropic lattice distortions. A direct time-domain comparison of CDW
translational-symmetry breaking and nematic rotational-symmetry breaking
reveals that these broken symmetries remain closely linked in the photoexcited
state, consistent with the stability of CDW topological defects in the
investigated pump fluence regime.


  We present a simple and general factorization law for quantum systems shared
by two parties, which describes the time evolution of entanglement upon passage
of either component through an arbitrary noisy channel. The robustness of
entanglement-based quantum information processing protocols is thus easily and
fully characterized by a single quantity.


  A neural solver and differentiable simulation of the quantum transmitting
boundary model is presented for the inverse quantum transport problem. The
neural solver is used to engineer continuous transmission properties and the
differentiable simulation is used to engineer current-voltage characteristics.


  Motivated by recent experimental breakthroughs, we propose a strategy to
design two-dimensional spin lattices with competing interactions that lead to
non-trivial emergent quantum states. We consider $S=1/2$ nanographenes with
$C_3$ symmetry as building blocks, and we leverage the potential to control
both the sign and the strength of exchange with first neighbours to build a
family of spin models. Specifically, we consider the case of a Heisenberg model
in a triangle-decorated honeycomb lattice with competing ferromagnetic and
antiferromagnetic interactions whose ratio can be varied in a wide range. Based
on exact diagonalization of both fermionic and spin models we predict a quantum
phase transition between a valence bond crystal of spin singlets with triplon
excitations living in a Kagom\'e lattice and a N\'eel phase of effective
$S=3/2$ in the limit of dominant ferromagnetic interactions.


  We re-express the Rashba and Dresselhaus interactions as non-Abelian
spin-orbit gauges and provide a new perspective in understanding the persistent
spin helix [Phys. Rev. Lett. 97, 236601 (2006)]. A spin-orbit interacting
system can be transformed into a free electron gas in the equal-strength
Rashba-Dresselhaus [001] linear model, the Dresselhaus [110] linear model, and
a one-dimensional system. A general tight-binding Hamiltonian for non-uniform
spin-orbit interactions and hoppings along arbitrary directions, within the
framework of finite difference method, is obtained. As an application based on
this Hamiltonian, a quantum square ring in contact with two ideal leads is
found to exhibit four states, insulating, spin-filtering, spin-flipping, and
spin-keeping states.


  We compute radiative correction to the lightest neutral Higgs mass ($m_h$)
induced by the Kaluza-Klein (KK) towers of fermions and sfermions in a minimal
supersymmetric scenario embeded in a 5-dimensional warped space. The Higgs is
confined to the TeV brane. The KK spectra of matter supermultiplets is tied to
the explanation of the fermion mass hierarchy problem. We demonstrate that for
a reasonable choice of extra-dimensional parameters, the KK-induced radiative
correction can enhance the upper limit on $m_h$ by as much as 100 GeV beyond
the 4d limit of 135 GeV.


  API search involves finding components in an API that are relevant to a
programming task. For example, a programmer may need a function in a C library
that opens a new network connection, then another function that sends data
across that connection. Unfortunately, programmers often have trouble finding
the API components that they need. A strong scientific consensus is emerging
towards developing interactive tool support that responds to conversational
feedback, emulating the experience of asking a fellow human programmer for
help. A major barrier to creating these interactive tools is implementing
dialogue management for API search. Dialogue management involves determining
how a system should respond to user input, such as whether to ask a
clarification question or to display potential results. In this paper, we
present a dialogue manager for interactive API search that considers search
results and dialogue history to select efficient actions. We implement two
dialogue policies: a hand-crafted policy and a policy optimized via
reinforcement learning. We perform a synthetics evaluation and a human
evaluation comparing the policies to a generic single-turn, top-N policy used
by source code search engines.


  We analyze in the four-generation model the first measurement of the
branching ratio of rare kaon decay $K^+\to\pi^+\nu\bar{nu}$, along with the
other processes of $K_L-K_S$ mass difference $\Delta m_K$, CP-violating
parameter $\epsilon_K, B_d-\bar{B_d}$ mixing, $B_s-\bar{B_s}$ mixing,
$B(K_L\to\mu\bar{\mu})$, and the upper bound values of $D^0-\bar{D^0}$ mixing
and $B(K_L\to\pi^0\nu\bar{\nu})$, and try to search for mixing of the fourth
generation in the hierarchical mixing scheme of the Wolfenstein
parametrization. Using the results for the mixing of the fourth generation, we
discuss predictions of the $D^0-\bar{D^0}$ mixing ($\Delta m_D$) and the
branching ratio of directly CP-violating decay process
$K_L\to\pi^0\nu\bar{\nu}$, and the effects on the CP asymmetry in neutral B
meson decays and the unitarity triangle.


  Aims. The purpose of this work is to investigate a new frequency separation
of stellar p-modes and its characteristics. Methods. Frequency separations are
deduced from the asymptotic formula of stellar p-modes. Then, using the
theoretical adiabatic frequencies of stellar model, we compute the frequency
separations. Results. A new separation $\sigma_{l-1 l+1}(n)$, which is similar
to the scaled small separation $d_{l l+2}(n)/(2l+3)$, is obtained from the
asymptotic formula of stellar p-modes. The separations $\sigma_{l-1 l+1}(n)$
and $d_{l l+2}(n)/(2l+3)$ have the same order. And like the small separation,
$\sigma_{l-1 l+1}(n)$ is mainly sensitive to the conditions in the stellar
core. However, with the decrease of the central hydrogen abundance of stars,
the $\sigma_{02}$ and $\sigma_{13}$ more and more deviate from the scaled small
separation. This characteristic could be used to extract the information on the
central hydrogen abundance of stars.


  We provide a general scheme, in the combined frameworks of Mathematical
Scattering Theory and Factorization Method, for inverse scattering for the
couple of self-adjoint operators $(\widetilde\Delta,\Delta)$, where $\Delta$ is
the free Laplacian in $L^{2}({\mathbb R}^{3})$ and $\widetilde\Delta$ is one of
its singular perturbations, i.e., such that the set $\{u\in H^{2}({\mathbb
R}^{3})\cap \text{dom}(\widetilde\Delta)\, :\, \Delta u=\widetilde\Delta u\}$
is dense. Typically $\widetilde\Delta$ corresponds to a self-adjoint
realization of the Laplace operator with some kind of boundary conditions
imposed on a null subset; in particular our results apply to standard, either
separating or semi-transparent, boundary conditions at $\Gamma=\partial\Omega$,
where $\Omega\subset{\mathbb R}^{3}$ is a bounded Lipschitz domain. Similar
results hold in the case the boundary conditions are assigned only on
$\Sigma\subset\Gamma$, a relatively open subset with a Lipschitz boundary. We
show that either $\Gamma$ or $\Sigma$ are determined by the knowledge of the
Scattering Matrix, equivalently of the Far Field Operator, at a single
frequency.


  In this paper we propose spatial filters for a linear regression model which
are based on the minimum-variance pseudo-unbiased reduced-rank estimation
(MV-PURE) framework. As a sample application, we consider the problem of
reconstruction of brain activity from electroencephalographic (EEG) or
magnetoencephalographic (MEG) measurements. The proposed filters come in two
versions depending on whether or not the EEG/MEG forward model explicitly
considers interfering activity in the way of brain activity originating in
regions different to those of main interest, but measured as correlated with
signals of interest by the EEG/MEG sensor array. In both cases, the proposed
filters are equipped with a rank-selection criterion minimizing the mean-square
error (MSE) of the filter output. Therefore, we consider them as novel
nontrivial generalizations of well-known linearly constrained minimum variance
(LCMV) and nulling filters. In order to facilitate reproducibility of our
research, we provide (jointly with this paper) comprehensive simulation
framework that allows for estimation of error of signal reconstruction for a
number of spatial filters applied to MEG or EEG signals. Based on this
framework, chief properties of proposed filters are verified in a set of
detailed simulations.


  Virtual interventions enable the physics-based simulation of device
deployment within coronary arteries. This framework allows for counterfactual
reasoning by deploying the same device in different arterial anatomies.
However, current methods to create such counterfactual arteries face a
trade-off between controllability and realism. In this study, we investigate
how Latent Diffusion Models (LDMs) can custom synthesize coronary anatomy for
virtual intervention studies based on mid-level anatomic constraints such as
topological validity, local morphological shape, and global skeletal structure.
We also extend diffusion model guidance strategies to the context of
morpho-skeletal conditioning and propose a novel guidance method for continuous
attributes that adaptively updates the negative guiding condition throughout
sampling. Our framework enables the generation and editing of coronary anatomy
in a controllable manner, allowing device designers to derive mechanistic
insights regarding anatomic variation and simulated device deployment.


  $\renewcommand{\Re}{\mathbb{R}}$ We develop a general randomized technique
for solving "implic it" linear programming problems, where the collection of
constraints are defined implicitly by an underlying ground set of elements. In
many cases, the structure of the implicitly defined constraints can be
exploited in order to obtain efficient linear program solvers.
  We apply this technique to obtain near-optimal algorithms for a variety of
fundamental problems in geometry. For a given point set $P$ of size $n$ in
$\Re^d$, we develop algorithms for computing geometric centers of a point set,
including the centerpoint and the Tukey median, and several other more involved
measures of centrality. For $d=2$, the new algorithms run in $O(n\log n)$
expected time, which is optimal, and for higher constant $d>2$, the expected
time bound is within one logarithmic factor of $O(n^{d-1})$, which is also
likely near optimal for some of the problems.


  In this work the concept of a novel slow neutron collimator and the way to
operate it are presented. The idea is based on the possibility to decouple the
device field-of-view from its collimation power. A multi-channel geometry is
proposed consisting of a chess-board structure where highly neutron-absorbing
channels are alternated to air channels. A borated polymer was purposely
developed to produce the attenuating components in the form of square-sectioned
long rods. A scalable structure consisting of multiple collimation sectors can
be arranged. The geometrical parameter LD, corresponding to the ratio between
the length of a channel and its width, defines the collimation power. Several
sectors can be arranged one after the other to reach relevant collimation
powers. Each sector, 100 mm long, is composed by several channels with D = 2.5
mm corresponding to an L/D coefficient of 40. The target field of view is 50x50
mm2. This novel collimator, developed inside the INFN-ANET collaboration, due
to its intrinsic compactness, will be of great importance to enhance the
neutron imaging capability of small to medium-size neutron sources.


  Here we report the size reduction and effects on nitrogen-vacancy centres in
nanodiamonds by air oxidation using a combined atomic force and confocal
microscope. The average height reduction of individual crystals as measured by
atomic force microscopy was 10.6 nm/h at 600 {\deg}C air oxidation at
atmospheric pressure. The oxidation process modified the surface including
removal of non-diamond carbon and organic material which also led to a decrease
in background fluorescence. During the course of the nanodiamond size
reduction, we observed the annihilation of nitrogen-vacancy centres which
provided important insight into the formation of colour centres in small
crystals. In these unirradiated samples, the smallest nanodiamond still hosting
a stable nitrogen-vacancy centre observed was 8 nm.


  The Large Language Model (LLM) is widely employed for tasks such as
intelligent assistants, text summarization, translation, and multi-modality on
mobile phones. However, the current methods for on-device LLM deployment
maintain slow inference speed, which causes poor user experience. To facilitate
high-efficiency LLM deployment on device GPUs, we propose four optimization
techniques: (a) a symbolic expression-based approach to support dynamic shape
model inference; (b) operator optimizations and execution priority setting to
enhance inference speed and reduce phone lagging; (c) an FP4 quantization
method termed M0E4 to reduce dequantization overhead; (d) a sub-tensor-based
technique to eliminate the need for copying KV cache after LLM inference.
Furthermore, we implement these methods in our mobile inference engine,
Transformer-Lite, which is compatible with both Qualcomm and MTK processors. We
evaluated Transformer-Lite's performance using LLMs with varied architectures
and parameters ranging from 2B to 14B. Specifically, we achieved prefill and
decoding speeds of 121 token/s and 14 token/s for ChatGLM2 6B, and 330 token/s
and 30 token/s for smaller Gemma 2B, respectively. Compared with CPU-based
FastLLM and GPU-based MLC-LLM, our engine attains over 10x speedup for the
prefill speed and 2~3x speedup for the decoding speed.


  We consider a spatially extended mesoscopic FitzHugh-Nagumo model with strong
local interactions and prove that its asymptotic limit converges towards the
classical nonlocal reaction-diffusion FitzHugh-Nagumo system. As the local
interactions strongly dominate, the weak solution to the mesoscopic equation
under consideration converges to the local equilibrium, which has the form of
Dirac distribution concentrated to an averaged membrane potential. Our approach
is based on techniques widely developed in kinetic theory (Wasserstein
distance, relative entropy method), where macroscopic quantities of the
mesoscopic model are compared with the solution to the nonlocal
reaction-diffusion system. This approach allows to make the rigorous link
between microscopic and reaction-diffusion models.


  Determinantal consensus clustering is a promising and attractive alternative
to partitioning about medoids and k-means for ensemble clustering. Based on a
determinantal point process or DPP sampling, it ensures that subsets of similar
points are less likely to be selected as centroids. It favors more diverse
subsets of points. The sampling algorithm of the determinantal point process
requires the eigendecomposition of a Gram matrix. This becomes computationally
intensive when the data size is very large. This is particularly an issue in
consensus clustering, where a given clustering algorithm is run several times
in order to produce a final consolidated clustering. We propose two efficient
alternatives to carry out determinantal consensus clustering on large datasets.
They consist in DPP sampling based on sparse and small kernel matrices whose
eigenvalue distributions are close to that of the original Gram matrix.


  Microservice architectures have gained popularity as one of the preferred
architectural approaches to develop large-scale systems, replacing the monolith
architecture approach. Similarly, strategic Domain-Driven Design (DDD) gained
traction as the preferred architectural design approach for the development of
microservices. However, DDD and its strategic patterns are open-ended by
design, leading to a gap between the concepts of DDD and the design of
microservices. This gap is especially evident in migration tools that identify
microservices from monoliths, where candidate decompositions into microservices
provide little in terms of DDD refactoring and visualization. This paper
proposes a solution to this problem by extending the operational pipeline of a
multi-strategy microservice identification tool, called Mono2Micro, with a DDD
modeling tool that provides a language, called Context Mapper DSL (CML), for
formalizing the most relevant DDD concepts. The extension maps the content of
the candidate decompositions, which include clusters, entities, and
functionalities, to CML constructs that represent DDD concepts such as Bounded
Context, Aggregate, Entity, and Service, among others. The results are
validated with a case study by comparing the candidate decompositions resulting
from a real-world monolith application with and without CML translation.


  In this paper, we proposed a single-source surface integral formulation to
accurately solve the scattering problems by 2D penetrable objects. In this
method, the objects are replaced by their surrounding medium through enforcing
a surface equivalent electric current to ensure fields exactly the same as
those in the original scattering problem. The equivalent electric current is
obtained through emitting the equivalent magnetic current by enforcing that the
electric fields in the original and equivalent problems equal to each other.
Through solving the Helmholtz equation inside objects by the scalar second
Green theorem, we could accurately model arbitrarily shaped objects. Then, we
solve the exterior scattering problems through the combined integral equation
(CFIE) with the equivalent electric current. The proposed formulation only
requires a single electric current source to model penetrable objects. At last,
two numerical experiments are carried out to validate its accuracy, stability
and capability of handling non-smoothing objects.


  Recent studies have demonstrated that defocusing cubic nonlinearity with
local strength growing from the center to the periphery faster than $r^{D}$, in
space of dimension $D$ with radial coordinate $r$, supports a vast variety of
robust bright solitons. In the framework of the same model, but with a weaker
spatial-growth rate, $\sim r^{\alpha }$ with $\alpha \leq D$, we here test the
possibility to create stable\textit{\ localized continuous waves} (LCWs) in
one- and two-dimensional (1D and 2D) geometries, \textit{% localized dark
solitons} (LDSs) in 1D, and \textit{localized dark vortices} (LDVs) in 2D,
which are all realized as loosely confined states with a divergent norm.
Asymptotic tails of the solutions, which determine the divergence of the norm,
are constructed in a universal analytical form by means of the Thomas-Fermi
approximation (TFA). Global approximations for the LCWs, LDSs, and LDVs are
constructed on the basis of interpolations between analytical approximations
available far from (TFA) and close to the center. In particular, the
interpolations for the 1D LDS, as well as for the 2D LDVs, are based on a
\textquotedblleft deformed-tanh" expression, which is suggested by the usual 1D
dark-soliton solution. In addition to the 1D fundamental LDSs with the single
notch, and 2D vortices with $S=1$, higher-order LDSs with multiple notches are
found too, as well as double LDVs, with $S=2$. Stability regions for the modes
under the consideration are identified by means of systematic simulations, the
LCWs being completely stable in 1D and 2D, as they are ground states in the
corresponding settings. Basic evolution scenarios are identified for those
vortices which are unstable. The settings considered in this work may be
implemented in nonlinear optics and in Bose-Einstein condensates.


  This paper investigates the Diversity Multiplexing Tradeoff (DMT) of the
generalized quantize-and-forward (GQF) relaying scheme over the slow fading
half-duplex multiple-access relay channel (HD-MARC). The compress-and-forward
(CF) scheme has been shown to achieve the optimal DMT when the channel state
information (CSI) of the relay-destination link is available at the relay.
However, having the CSI of relay-destination link at relay is not always
possible due to the practical considerations of the wireless system. In
contrast, in this work, the DMT of the GQF scheme is derived without
relay-destination link CSI at the relay. It is shown that even without
knowledge of relay-destination CSI, the GQF scheme achieves the same DMT,
achievable by CF scheme with full knowledge of CSI.


  Performance of neural network models relies on the availability of large
datasets with minimal levels of uncertainty. Transfer Learning (TL) models have
been proposed to resolve the issue of small dataset size by letting the model
train on a bigger, task-related reference dataset and then fine-tune on a
smaller, task-specific dataset. In this work, we apply a transfer learning
approach to improve predictive power in noisy data systems with large variable
confidence datasets. We propose a deep neural network method called Filtered
Transfer Learning (FTL) that defines multiple tiers of data confidence as
separate tasks in a transfer learning setting. The deep neural network is
fine-tuned in a hierarchical process by iteratively removing (filtering) data
points with lower label confidence, and retraining. In this report we use FTL
for predicting the interaction of drugs and proteins. We demonstrate that using
FTL to learn stepwise, across the label confidence distribution, results in
higher performance compared to deep neural network models trained on a single
confidence range. We anticipate that this approach will enable the machine
learning community to benefit from large datasets with uncertain labels in
fields such as biology and medicine.


  We suggest electronic circuits with memristors (resistors with memory) that
operate as memcapacitors (capacitors with memory) and meminductors (inductors
with memory). Using a memristor emulator, the suggested circuits have been
built and their operation has been demonstrated, showing a useful and
interesting connection between the three memory elements.


  A new black hole X-ray binary (BHXRB) MAXI J1535-571 was discovered by MAXI
during its outburst in 2017. Using observations taken by the first Chinese
X-ray satellite, the Hard X-ray Modulation Telescope (dubbed as Insight-HXMT),
we perform a joint spectral analysis (2-150 keV) in both energy and time
domains. The energy spectra provide the essential input for probing the
intrinsic Quasi-Periodic Oscillation (QPO) fractional rms spectra (FRS). Our
results show that during the intermediate state, the energy spectra are in
general consistent with those reported by Swift/XRT and NuSTAR. However, the
QPO FRS become harder and the FRS residuals may suggest the presence of either
an additional power-law component in the energy spectrum or a turn-over in the
intrinsic QPO FRS at high energies.


  The apparent lack of cold molecular gas in blue compact dwarf (BCD) galaxies
is at variance with their intense star-formation episode. The CO molecule,
often used a tracer of H2 through a conversion function, is selectively
photodissociated in dust-poor environments and, as a result, a potentially
large fraction of H2 is expected to reside in the so-called CO-dark gas, where
it could be traced instead by infrared cooling lines [CI], [CII], and [OI].
Although the fraction of CO-dark gas to total molecular gas is expected to be
relatively large in metal-poor galaxies, many uncertainties remain due to the
difficulty in identifying the main heating mechanism associated to the cooling
lines observed in such galaxies. Investigations of the Herschel Dwarf Galaxy
Survey show that the heating mechanism in the neutral gas of BCDs cannot be
dominated by the photoelectric effect on dust grains below some threshold
metallicity, implying that other heating mechanisms need to be invoked, along
with a new interpretation of the corresponding infrared line diagnostics. In
the study presented here and in Lebouteiller et al. 2017, we use optical and
infrared lines to constrain the physical conditions in the HII region + HI
region of the BCD IZw18 (18Mpc; 2% solar metallicity) within a consistent
photoionization and photodissociation model. We show that the HI region is
entirely heated by a single ultraluminous X-ray source with important
consequences on the applicability of [CII] to trace the star-formation rate and
to trace the CO-dark gas. We derive stringent upper limits on the size of H2
clumps that may be detected in the future with JWST and IRAM/NOEMA. We also
show that the nature of the X-ray source can be inferred from the corresponding
signatures in the ISM. Finally we speculate that star formation may be quenched
in extremely metal-poor dwarf galaxies due to X-ray photoionization.


  We explore nonlinear perturbations in different static fluid systems. We find
that the equations, corresponding to the perturbation of the integrals of
motion, i.e; Bernoulli's constant and the mass flow rate, satisfy massless
scalar field equation in a time dependent acoustic metric. When one is
interested up to the second order behaviour of the perturbations, the emergent
time dependent acoustic metric of the system, derived from the massless scalar
field equations of the perturbations of the integrals of motion, has some
astounding similarities with the metric describing gravitational wave in
Minkowski spacetime.


  We show that the considered in previous paper \cite{BurGrBag} lepton bag
model, generating the external gravitational and electromagnetic fields of the
Kerr-Newman (KN) solution is supersymmetric and represents a BPS-saturated
soliton, interpolating between internal vacuum state and external KN solution.
We obtain Bogomolnyi equations for this phase transition, and show that
Bogomolnyi bound determines all important features of this bag model, including
its stable shape.
  In particular, for stationary KN solution the BPS-bound provides stability of
the ellipsoidal form of the bag and formation of the ring-string structure at
its border, while for the periodic electromagnetic excitations of the KN
solution, the BPS-bound controls deformation of the surface of the bag,
reproducing the known flexibility of bag models.


  This work studies Nash equilibrium seeking for a class of stochastic
aggregative games, where each player has an expectation-valued objective
function depending on its local strategy and the aggregate of all players'
strategies. We propose a distributed algorithm with operator extrapolation, in
which each player maintains an estimate of this aggregate by exchanging this
information with its neighbors over a time-varying network, and updates its
decision through the mirror descent method. An operator extrapolation at the
search direction is applied such that the two step historical gradient samples
are utilized to accelerate the convergence. Under the strongly monotone
assumption on the pseudo-gradient mapping, we prove that the proposed algorithm
can achieve the optimal convergence rate of $\mathcal{O}(1/k)$ for Nash
equilibrium seeking of stochastic games. Finally, the algorithm performance is
demonstrated via numerical simulations.


  In recent years, a better understanding of the Monte Carlo method has
provided us with many new techniques in different areas of statistical physics.
Of particular interest are so called cluster methods, which exploit the
considerable algorithmic freedom given by the detailed balance condition.
Cluster algorithms appear, among other systems, in classical spin models, such
as the Ising model, in lattice quantum models (bosons, quantum spins and
related systems) and in hard spheres and other `entropic' systems for which the
configurational energy is either zero or infinite. In this chapter, we discuss
the basic idea of cluster algorithms with special emphasis on the pivot cluster
method for hard spheres and related systems, for which several recent
applications are presented.We provide less technical detail but more context
than in the original papers.