  We consider an extension of the Standard Model with a complex singlet scalar,
where a global $U(1)$ symmetry is explicitly broken to $\mathbb{Z}_3$ symmetry.
We study the two-step electroweak phase transition in the model and find that
it can be of first-order if the heavy scalar mass falls in the range of
$1-2$~TeV and the mixing angle $\left | \theta \right |\gtrsim 0.2$
($11.5^{\circ}$). The Higgs signal strength measurements at the LHC, on the
other hand, restrict the mixing angle $\left | \theta \right |\lesssim 0.4$
($23^{\circ}$). Future colliders including high-luminosity LHC can probe the
remaining parameter space of first-order phase transition in this scenario.
After the $U(1)$ symmetry breaking, the pseudo-Goldstone boson becomes a dark
matter candidate due to a hidden $\mathbb{Z}_2$ symmetry of the model. We find
that the pseudo-Goldstone boson can make up a small fraction of the observed
dark matter and escape from the constraints of current direct detection. We
also show that the stochastic gravitational wave signals from the phase
transition are potentially discoverable with future space-based
interferometers.


  Robust zero modes supported by defects is one of the key features of
topological matter. Its presence renders a system topologically inhomegeneuous,
thus having no well-defined global topological invariant. The quantities
labeling different areas of the sample according to their topological state
were dubbed local topological markers. Here we study their dynamics and the
possibility to control their distribution over the sample. We suggest a new
perspective on the evolution of local markers. It gives a clear physical
description of the markers evolution in terms of response functions and the
ease of measurement. Furthermore, new markers' equations of motion are truly
local, being ensured that the current of the marker exists and obeys the
lattice continuity equation. The formalism presented does not rely on the
single-particle quantities therefore might be extended to interacting systems.


  In this paper, we study shearing spherically symmetric homogeneous density
fluids in comoving coordinates. It is found that the expansion of the
four-velocity of a perfect fluid is homogeneous, whereas its shear is generated
by an arbitrary function of time M(t), related to the mass function of the
distribution. This function is found to bear a functional relationship with
density. The field equations are reduced to two coupled first order ordinary
differential equations for the metric coefficients, g 11 and g 22. We have
explored a class of solutions assuming that M is a linear function of the
density. This class embodies, as a subcase, the complete class of shear-free
solutions. We have discussed the off quoted work of Kustaanheimo (1947) and
have noted that it deals with shear-free fluids having anisotropic pressure. It
is shown that the anisotropy of the fluid is characterized by an arbitrary
function of time. We have discussed some issues of historical priorities and
credentials related to shear-free solutions. Recent controversial claims by
Mitra (2011, 2012) have also been addressed. We found that the singularity and
the shearing motion of the fluid are closely related. Hence, there is a need
for fresh look to the solutions obtained earlier in comoving coordinates.
Keywords (separated by '-') Shearing solution - Perfect fluids - Homogeneous
density


  Based upon a previous work of Manjunath and Sturmfels for a finite, complete,
undirected graph, and a refined algorithm by Er\"ocal, Motsak, Schreyer and
Steenpa{\ss} for computing syzygies, we display a free resolution of the
lattice ideal associated to a finite, strongly connected, weighted, directed
graph. Moreover, the resolution is minimal precisely when the digraph is
strongly complete.


  Linguistic representations derived from text alone have been criticized for
their lack of grounding, i.e., connecting words to their meanings in the
physical world. Vision-and-Language (VL) models, trained jointly on text and
image or video data, have been offered as a response to such criticisms.
However, while VL pretraining has shown success on multimodal tasks such as
visual question answering, it is not yet known how the internal linguistic
representations themselves compare to their text-only counterparts. This paper
compares the semantic representations learned via VL vs. text-only pretraining
for two recent VL models using a suite of analyses (clustering, probing, and
performance on a commonsense question answering task) in a language-only
setting. We find that the multimodal models fail to significantly outperform
the text-only variants, suggesting that future work is required if multimodal
pretraining is to be pursued as a means of improving NLP in general.


  Modern particle physics relies on high energy particle accelerators to
provide collisions of various types of elementary particles in order to deduce
fundamental laws of physics or properties of individual particles. The only way
to generate particle collisions at extremely high energies is to collide
particles of counter-rotating beams...called "particle-colliders". This
write-up gives a short briefing on the physics motivation of various particle
colliders ($e^+e^-$ colliders, $pp$ colliders, ...), a summary of the
historical evolution and a mathematical treatment to describe collider
performance.


  The representation degeneration problem in Contextual Word Representations
(CWRs) hurts the expressiveness of the embedding space by forming an
anisotropic cone where even unrelated words have excessively positive
correlations. Existing techniques for tackling this issue require a learning
process to re-train models with additional objectives and mostly employ a
global assessment to study isotropy. Our quantitative analysis over isotropy
shows that a local assessment could be more accurate due to the clustered
structure of CWRs. Based on this observation, we propose a local cluster-based
method to address the degeneration issue in contextual embedding spaces. We
show that in clusters including punctuations and stop words, local dominant
directions encode structural information, removing which can improve CWRs
performance on semantic tasks. Moreover, we find that tense information in verb
representations dominates sense semantics. We show that removing dominant
directions of verb representations can transform the space to better suit
semantic applications. Our experiments demonstrate that the proposed
cluster-based method can mitigate the degeneration problem on multiple tasks.


  Let $D$ be a non-empty open subset of $\R^m,\,m\ge 2$, with boundary
$\partial D$, with finite Lebesgue measure $|D|$, and which satisfies a
parabolic Harnack principle. Let $K$ be a compact, non-polar subset of $D$. We
obtain the leading asymptotic behaviour as $\varepsilon\downarrow 0$ of the
$L^{\infty}$ norm of the torsion function with a Neumann boundary condition on
$\partial D$, and a Dirichlet boundary condition on $\partial (\varepsilon K)$,
in terms of the first eigenvalue of the Laplacian with corresponding boundary
conditions. These estimates quantify those of Burdzy, Chen and Marshall who
showed that $D\setminus K$ is a non-trap domain.


  Water scarcity is an urgent issue to be resolved and improving irrigation
water-use efficiency through closed-loop control is essential. The complex
agro-hydrological system dynamics, however, often pose challenges in
closed-loop control applications. In this work, we propose a two-layer neural
network (NN) framework to approximate the dynamics of the agro-hydrological
system. To minimize the prediction error, a linear bias correction is added to
the proposed model. The model is employed by a model predictive controller with
zone tracking (ZMPC), which aims to keep the root zone soil moisture in the
target zone while minimizing the total amount of irrigation. The performance of
the proposed approximation model framework is shown to be better compared to a
benchmark long-short-term-memory (LSTM) model for both open-loop and
closed-loop applications. Significant computational cost reduction of the ZMPC
is achieved with the proposed framework. To handle the tracking offset caused
by the plant-model-mismatch of the proposed NN framework, a shrinking target
zone is proposed for the ZMPC. Different hyper-parameters of the shrinking zone
in the presence of noise and weather disturbances are investigated, of which
the control performance is compared to a ZMPC with a time-invariant target
zone.


  A new variational principle for optimizing thermal density matrices is
introduced. As a first application, the variational many body density matrix is
written as a determinant of one body density matrices, which are approximated
by Gaussians with the mean, width and amplitude as variational parameters. The
method is illustrated for the particle in an external field problem, the
hydrogen molecule and dense hydrogen where the molecular, the dissociated and
the plasma regime are described. Structural and thermodynamic properties
(energy, equation of state and shock Hugoniot) are presented.


  We study a model of opinion exchange in social networks where a state of the
world is realized and every agent receives a zero-mean noisy signal of the
realized state. It is known from [Golub and Jackson 2010] that under DeGroot
dynamics [DeGroot 1974] agents reach a consensus that is close to the state of
the world when the network is large. The DeGroot dynamics, however, is highly
non-robust and the presence of a single ``stubborn agent'' that does not adhere
to the updating rule can sway the public consensus to any other value. We
introduce a variant of DeGroot dynamics that we call \emph{
$\frac{1}{m}$-DeGroot}. $\frac{1}{m}$-DeGroot dynamics approximates standard
DeGroot dynamics to the nearest rational number with $m$ as its denominator and
like the DeGroot dynamics it is Markovian and stationary. We show that in
contrast to standard DeGroot dynamics, $\frac{1}{m}$-DeGroot dynamics is highly
robust both to the presence of stubborn agents and to certain types of
misspecifications.


  Utilizing observations from the ELFIN satellites, we present a statistical
study of $\sim$2000 events in 2019-2020 characterizing the occurrence in
magnetic local time (MLT) and latitude of $\geq$50 keV electron isotropy
boundaries (IBs) at Earth, and the dependence of associated precipitation on
geomagnetic activity. The isotropy boundary for an electron of a given energy
is the magnetic latitude poleward of which persistent isotropized pitch-angle
distributions ($J_{prec}/J_{perp}\sim 1$) are first observed to occur,
interpreted as resulting from magnetic field-line curvature scattering (FLCS)
in the equatorial magnetosphere. We find that energetic electron IBs can be
well-recognized on the nightside from dusk until dawn, under all geomagnetic
activity conditions, with a peak occurrence rate of almost 90% near $\sim$22
hours in MLT, remaining above 80% from 21 to 01 MLT. The IBs span a wide range
of IGRF magnetic latitudes from $60^\circ$-$74^\circ$, with a maximum
occurrence between $66^\circ$-$71^\circ$ (L of 6-8), shifting to lower
latitudes and pre-midnight local times with activity. The precipitating energy
flux of $\geq$50 keV electrons averaged over the IB-associated latitudes varies
over four orders of magnitude, up to $\sim$1 erg/cm$^2$-s, and often includes
electron energies exceeding 1 MeV. The local time distribution of IB-associated
energies and precipitating fluxes also exhibit peak values near midnight for
low activity, shifting toward pre-midnight for elevated activity. The
percentage of the total energy deposited over the high-latitude regions
($55^\circ$ to $80^\circ$; or IGRF $L\gtrsim 3$) attributed to IBs is 10-20%,
on average, or about 10 MW of total atmospheric power input, but at times can
be up to $\sim$100% of the total $\geq$50 keV electron energy deposition over
the entire sub-auroral and auroral zone region, exceeding 1 GW in atmospheric
power input.


  We study stationary and dynamical properties of the many-body Landau-Zener
dynamics of a Bose quantum fluid confined in two coupled one-dimensional
chains, using a many-body generalization recently reported [Y.-A. Chen et al.],
within the decoupling approximation and the one-level band scheme. The energy
spectrum evidences the structure of the avoided level crossings as a function
of the on-site inter particle interaction strength. On the dynamical side, a
phase diagram of the transfer efficiency across ground-state and inverse sweeps
is presented. A totally different scenario with respect to the original
single-particle Landau-Zener scheme is found for ground-state sweeps, in which
a breakdown of the adiabatic region emerges as the sweep rate decreases. On the
contrary, the transfer efficiency across inverse sweeps reveals consistent
results with the single-particle Landau-Zener predictions. In the strong
coupling regime, we find that there is a critical value of the on-site
interaction for which the transfer of particles starts to vanish independently
of the sweep rate. Our results are in qualitative agreement with those of the
experimental counterpart.


  Considered is the problem of local equivalence of generic four-dimensional
metrics possessing two commuting and orthogonally transitive Killing vector
fields. A sufficient set of eight differential invariants is explicitly
constructed, among them four of first order and four of second order in terms
of metric coefficients. In vacuum case the four first-order invariants suffice
to distinguish generic metrics.


  Given a graph whose edges are labeled by ideals of a commutative ring R with
identity, a generalized spline is a vertex labeling by the elements of R such
that the difference of the labels on adjacent vertices lies in the ideal
associated to the edge. The set of generalized splines has a ring and an
R-module structure. We study the module structure of generalized splines where
the base ring is a greatest common divisor domain. We give basis criteria for
generalized splines on cycles, diamond graphs and trees by using determinantal
techniques. In the last section of the paper, we define a graded module
structure for generalized splines and give some applications of the basis
criteria for cycles, diamond graphs and trees.


  Large Language Models (LLMs) have shown remarkable capabilities in zero-shot
learning applications, generating responses to queries using only pre-training
information without the need for additional fine-tuning. This represents a
significant departure from traditional machine learning approaches. Previous
research has indicated that LLMs may exhibit serial position effects, such as
primacy and recency biases, which are well-documented cognitive biases in human
psychology. Our extensive testing across various tasks and models confirms the
widespread occurrence of these effects, although their intensity varies. We
also discovered that while carefully designed prompts can somewhat mitigate
these biases, their effectiveness is inconsistent. These findings underscore
the significance of serial position effects during the inference process,
particularly in scenarios where there are no ground truth labels, highlighting
the need for greater focus on addressing these effects in LLM applications.


  The net value of the fund is affected by performance and market, and the
researchers try to quantify these effects to predict the future net value by
establishing different models. The current prediction models usually can only
reflect the linear variation law, poorly handled or selectively ignore their
nonlinear characteristics, so the prediction results are usually less accurate.
This paper uses a fund prediction method based on the ARIMA-LSTM hybrid model.
After preprocessing the historical data, the first filter out the linear data
characteristics with the ARIMA model, then pass the data to the LSTM model to
extract the nonlinear characteristic by residual, and finally superposition the
respective prediction values of the two models to obtain the prediction results
of the hybrid model. Empirically shows that the methods in the paper are more
accurate and applicable than traditional fund prediction methods.


  This paper introduces a new unsupervised method for dimensionality reduction
via regression (DRR). The algorithm belongs to the family of invertible
transforms that generalize Principal Component Analysis (PCA) by using
curvilinear instead of linear features. DRR identifies the nonlinear features
through multivariate regression to ensure the reduction in redundancy between
he PCA coefficients, the reduction of the variance of the scores, and the
reduction in the reconstruction error. More importantly, unlike other nonlinear
dimensionality reduction methods, the invertibility, volume-preservation, and
straightforward out-of-sample extension, makes DRR interpretable and easy to
apply. The properties of DRR enable learning a more broader class of data
manifolds than the recently proposed Non-linear Principal Components Analysis
(NLPCA) and Principal Polynomial Analysis (PPA). We illustrate the performance
of the representation in reducing the dimensionality of remote sensing data. In
particular, we tackle two common problems: processing very high dimensional
spectral information such as in hyperspectral image sounding data, and dealing
with spatial-spectral image patches of multispectral images. Both settings pose
collinearity and ill-determination problems. Evaluation of the expressive power
of the features is assessed in terms of truncation error, estimating
atmospheric variables, and surface land cover classification error. Results
show that DRR outperforms linear PCA and recently proposed invertible
extensions based on neural networks (NLPCA) and univariate regressions (PPA).


  Minimal log discrepancies (mld's) are related not only to termination of log
flips, and thus to the existence of log flips but also to the ascending chain
condition (acc) of some global invariants and invariants of singularities in
the Log Minimal Model Program (LMMP). In this paper, we draw clear links
between several central conjectures in the LMMP. More precisely, our main
result states that the LMMP, the acc conjecture for mld's and the boundedness
of canonical Mori-Fano varieties in dimension $\le d$ imply the following: the
acc conjecture for $a$-lc thresholds, in particular, for canonical and log
canonical (lc) thresholds in dimension $\le d$; the acc conjecture for lc
thresholds in dimension $\le d+1$; termination of log flips in dimension $\le
d+1$ for effective pairs; and existence of pl flips in dimension $\le d+2$.
This also gives new proofs of some well-known and new results in the field in
low dimensions: the acc conjecture holds for $a$-lc thresholds of surfaces; the
acc conjecture holds for lc thresholds of 3-folds; termination of 3-fold log
flips holds for effective pairs; and the existence of 4-fold pl flips holds.


  We show direct and conceptually simple reductions between the classical
learning with errors (LWE) problem and its continuous analog, CLWE (Bruna,
Regev, Song and Tang, STOC 2021). This allows us to bring to bear the powerful
machinery of LWE-based cryptography to the applications of CLWE. For example,
we obtain the hardness of CLWE under the classical worst-case hardness of the
gap shortest vector problem. Previously, this was known only under quantum
worst-case hardness of lattice problems. More broadly, with our reductions
between the two problems, any future developments to LWE will also apply to
CLWE and its downstream applications.
  As a concrete application, we show an improved hardness result for density
estimation for mixtures of Gaussians. In this computational problem, given
sample access to a mixture of Gaussians, the goal is to output a function that
estimates the density function of the mixture. Under the (plausible and widely
believed) exponential hardness of the classical LWE problem, we show that
Gaussian mixture density estimation in $\mathbb{R}^n$ with roughly $\log n$
Gaussian components given $\mathsf{poly}(n)$ samples requires time
quasi-polynomial in $n$. Under the (conservative) polynomial hardness of LWE,
we show hardness of density estimation for $n^{\epsilon}$ Gaussians for any
constant $\epsilon > 0$, which improves on Bruna, Regev, Song and Tang (STOC
2021), who show hardness for at least $\sqrt{n}$ Gaussians under polynomial
(quantum) hardness assumptions.
  Our key technical tool is a reduction from classical LWE to LWE with
$k$-sparse secrets where the multiplicative increase in the noise is only
$O(\sqrt{k})$, independent of the ambient dimension $n$.


  We develop a model characterizing all possible knots and links arising from
recombination starting with a twist knot substrate, extending previous work of
Buck and Flapan. We show that all knot or link products fall into three
well-understood families of knots and links, and prove that given a positive
integer $n$, the number of product knots and links with minimal crossing number
equal to $n$ grows proportionally to $n^5$. In the (common) case of twist knot
substrates whose products have minimal crossing number one more than the
substrate, we prove that the types of products are tightly prescribed. Finally,
we give two simple examples to illustrate how this model can help determine
previously uncharacterized experimental data.


  We prove that the Teichmueller disc stabilized by the Arnoux-Yoccoz
pseudo-Anosov diffeomorphism contains at least two closed Teichmueller
geodesics. This proves that the corresponding flat surface does not have a
cyclic Veech group.
  In addition, we prove that this Teichmueller disc is dense inside the
hyperelliptic locus of the connected component H^odd(2,2). The proof uses
Ratner's theorems.
  Rephrasing our results in terms of quadratic differentials, we show that
there exists a holomorphic quadratic differential, on a genus 2 surface, with
the two following properties. (1) The Teichmueller disc is dense inside the
moduli space of holomorphic quadratic differentials (which are not the global
square of any Abelian differentials). (2) The stabilizer of the PSL(2,R)-action
contains two non-commuting pseudo-Anosov diffeomorphisms.


  We define weighted mean summability method of double sequences in
intuitionistic fuzzy normed spaces($IFNS$), and obtain necessary and sufficient
Tauberian conditions under which convergence of double sequences in $IFNS$
follows from their weighted mean summability. This study reveals also Tauberian
results for some known summation methods in the special cases.


  The far-ultraviolet (FUV) number counts of galaxies constrain the evolution
of the star-formation rate density of the universe. We report the FUV number
counts computed from FUV imaging of several fields including the Hubble Ultra
Deep Field, the Hubble Deep Field North, and small areas within the GOODS-North
and -South fields. These data were obtained with the Hubble Space Telescope
Solar Blind Channel of the Advance Camera for Surveys. The number counts sample
a FUV AB magnitude range from 21-29 and cover a total area of 15.9 arcmin^2, ~4
times larger than the most recent HST FUV study. Our FUV counts intersect
bright FUV GALEX counts at 22.5 mag and they show good agreement with recent
semi-analytic models based on dark matter "merger trees" by Somerville et al.
(2011). We show that the number counts are ~35% lower than in previous HST
studies that use smaller areas. The differences between these studies are
likely the result of cosmic variance; our new data cover more lines of sight
and more area than previous HST FUV studies. The integrated light from field
galaxies is found to contribute between 65.9 +/-8 - 82.6 +/-12
photons/s/cm^2/sr/angstrom to the FUV extragalactic background. These
measurements set a lower limit for the total FUV background light.


  In a recent work [R. Shojaei et al, Physical Review E 100, 022303 (2019)] the
Authors calculate numerically the critical temperature $T_c$ of the
balanced-imbalanced phase transition in a fully connected graph. According to
their findings, $T_c$ decreases with the number of nodes $N$. Here we calculate
the same critical temperature using the heat-bath algorithm. We show that $T_c$
increases with $N$ as $N^{\gamma}$, with $\gamma$ close to 0.5 or 1.0. This
value depends on the initial fraction of positive bonds.


  We compute the spectrum of the "all ones" hypermatrix using the Poisson
product formula. This computation includes a complete description of the
eigenvalues' multiplicities, a seemingly elusive aspect of the spectral theory
of tensors. We also give a general distributional picture of the spectrum as a
point-set in the complex plane, and use our techniques to analyze the spectrum
of "sunflower hypergraphs", a class that has played a prominent role in
extremal hypergraph theory.


  We study the interband Landau-Zener tunneling of a quantum particle in the
Hall configuration, i.e., in the presence of normal to the lattice plane gauge
field (for example, magnetic field for a charged particle) and in-plane
potential field (electric field for a charged particle). The interband
tunneling is induced by the potential field and for vanishing gauge field is
described by the common Landau-Zener theory. We generalize this theory for
non-zero gauge field. The depletion rates of low-energy bands are calculated by
using semi-analytical method of the truncated Floquet matrix.


  Researchers and practitioners increasingly consider a human-centered
perspective in the design of machine learning-based applications, especially in
the context of Explainable Artificial Intelligence (XAI). However, clear
methodological guidance in this context is still missing because each new
situation seems to require a new setup, which also creates different
methodological challenges. Existing case study collections in XAI inspired us;
therefore, we propose a similar collection of case studies for human-centered
XAI that can provide methodological guidance or inspiration for others. We want
to showcase our idea in this workshop by describing three case studies from our
research. These case studies are selected to highlight how apparently small
differences require a different set of methods and considerations. With this
workshop contribution, we would like to engage in a discussion on how such a
collection of case studies can provide a methodological guidance and critical
reflection.


  Recently a five-dimensional Kaluza-Klein black hole solution with squashed
horizon has been found in hep-th/0510094. The black hole spacetime is
asymptotically locally flat and has a spatial infinity $S^1 \hookrightarrow
S^{2}$. By using "boundary counterterm" method and generalized Abbott-Deser
method, we calculate the mass of this black hole. When an appropriate
background is chosen, the generalized Abbott-Deser method gives the same mass
as the "boundary counterterm" method. The mass is found to satisfy the first
law of black hole thermodynamics. The thermodynamic properties of the
Kaluza-Klein black hole are discussed and are compared to those of its
undeformed counterpart, a five-dimensional Reissner-Nordstr\"om black hole.


  We consider a class of optimization problems involving the optimal operation
of a single lossy energy storage system that incurs energy loss when charging
or discharging. Such inefficiencies in the energy storage dynamics are known to
result in a nonconvex set of feasible charging and discharging power profiles.
In this letter, we provide an equivalent reformulation for this class of
optimization problems, along with sufficient conditions for the convexity of
the proposed reformulation. The conditions provided generalize existing
conditions for convexity in the literature.


  Despite the fact that image captioning models have been able to generate
impressive descriptions for a given image, challenges remain: (1) the
controllability and diversity of existing models are still far from
satisfactory; (2) models sometimes may produce extremely poor-quality captions.
In this paper, two novel methods are introduced to solve the problems
respectively. Specifically, for the former problem, we introduce a control
signal which can control the macroscopic sentence attributes, such as sentence
quality, sentence length, sentence tense and number of nouns etc. With such a
control signal, the controllability and diversity of existing captioning models
are enhanced. For the latter problem, we innovatively propose a strategy that
an image-text matching model is trained to measure the quality of sentences
generated in both forward and backward directions and finally choose the better
one. As a result, this strategy can effectively reduce the proportion of
poorquality sentences. Our proposed methods can be easily applie on most image
captioning models to improve their overall performance. Based on the Up-Down
model, the experimental results show that our methods achieve BLEU-
4/CIDEr/SPICE scores of 37.5/120.3/21.5 on MSCOCO Karpathy test split with
cross-entropy training, which surpass the results of other state-of-the-art
methods trained by cross-entropy loss.


  Let \(G\) be a finite group, and let \(\Delta(G)\) denote the \emph{prime
graph} built on the set of degrees of the irreducible complex characters of
\(G\). It is well known that, whenever \(\Delta(G)\) is connected, the diameter
of \(\Delta(G)\) is at most \(3\). In the present paper, we provide a
description of the finite solvable groups for which the diameter of this graph
attains the upper bound. This also enables us to confirm a couple of
conjectures proposed by M.L. Lewis.


  We revisit static, spherically symmetric solutions to AdS-Einstein gravity
with a conformally coupled scalar field (and no self-interaction potential) in
four dimensions. We first observe that a convenient choice of coordinates leads
to a significant simplification of the field equations, which enables one to
identify various roots of the indicial equations and thus distinct branches of
solutions. Next, we construct an explicit 2-parameter hairy black hole solution
in terms of an infinite power series around the event horizon. The black hole
is non-extremal with a regular scalar field on and outside the event horizon,
and it reduces to the Schwarzschild-AdS metric in the limit of vanishing hair.
Its properties are illustrated for various values of the parameters and
compared with previous numerical results by other authors. In addition, the
analysis reveals the presence of a photon sphere and how the scalar field
affects its size and the angular radius of the corresponding shadow. The
thermodynamics of the solution is also briefly discussed.


  This note is about the Hilbert square $X=S^{[2]}$, where $S$ is a general
$K3$ surface of degree $10$, and the anti-symplectic birational involution
$\iota$ of $X$ constructed by O'Grady. The main result is that the action of
$\iota$ on certain pieces of the Chow groups of $X$ is as expected by Bloch's
conjecture. Since $X$ is birational to a double EPW sextic $X^\prime$, this has
consequences for the Chow ring of the EPW sextic $Y\subset{\mathbb{P}}^5$
associated to $X^\prime$.


  We report on our new analysis of the spatial and kinematical distribution of
warm and cold molecular gas in NGC 6240, which was undertaken to explore the
origin of its unusually luminous H$_2$ emission. By comparing three-dimensional
emission-line data (in space and velocity) of CO (J=2-1) in the radio and H$_2$
in the near infrared, we are able to study the H$_2$ emitting efficiency,
defined in terms of the intensity ratio of H$_2$ to CO [$I$(H$_2$)/$I$(CO)], as
a function of velocity. The integrated H$_2$ emitting efficiency is calculated
by integrating the velocity profile of H$_2$ emitting efficiency in blue, red,
and total (blue + red) velocity regions of the profile. We find that (1) both
the total H$_2$ emitting efficiency and the blue-to-red ratio of the efficiency
are larger in regions surrounding the CO and H$_2$ intensity peaks, and (2) the
H$_2$ emitting efficiency and the kinematical conditions in the warm molecular
gas are closely related to each other. A collision between the molecular gas
concentration and the external superwind outflow from the southern nucleus
seems plausible to explain these characteristics, since it can reproduce the
enhanced emitting efficiency of blueshifted H$_2$ around the molecular gas
concentration, if we assume that the superwind blows from the southern nucleus
toward us, hitting the entire gas concentration from behind. In this model,
internal cloud-cloud collisions within the molecular gas concentration are
enhanced by the interaction with the superwind outflow, and efficient and
intense shock-excited H$_2$ emission is expected as a result of the
cloud-crushing mechanism.


  Given a discrete group and a unitary representation on a Hilbert space
$\mathcal{H}$, we prove that the notions of operator Bracket map and Gramian
coincide on a dense set of $\mathcal{H}$. As a consequence, combining this
result with known frame theory, we can recover in a simple way all previous
Bracket characterizations of Riesz and frame sequences generated by a single
element under a unitary representation.


  Spiral galaxies offer a unique opportunity to study the role of star
formation in galaxy evolution and to test various theoretical star formation
schemes. I review some recent relevant work on the evolution of spiral
galaxies. Detailed models are used for the chemical and spectrophotometric
evolution, with metallicity dependent stellar yields, tracks and spectra. The
models are ``calibrated'' on the Milky Way disk and generalised to other
spirals with some simple scaling relations, obtained in the framework of Cold
Dark Matter models for galaxy formation. The results compare favourably to the
main observables of present day spirals, provided a crucial assumption is made:
massive disks form their stars earlier than low mass ones. It is not clear
whether this picture is compatible with currently popular hierarchical models
of galaxy evolution. The resulting abundance gradients are found to be
anticorrelated to the disk scalelength, support radially dependent star
formation efficiencies and point to a kind of ``homologuous evolution'' for
spirals.


  I would like to tell a story. A story about a beautiful mathematical
relationship that elucidates the computational view on the classic subject of
trigonometry. All stories need a language, and for this particular story an
algorithmic language ought to do well. What makes a language algorithmic? From
our perspective as the functional programming community, an algorithmic
language provides means to express computation in terms of functions, with no
implementation-imposed limitations. We develop a new algorithm for the
computation of trigonometric functions on dyadic rationals, together with the
language used to express it, in Scheme. We provide a mechanically-derived
algorithm for the computation of the inverses of our target functions. We
address efficiency and accuracy concerns that pertain to the implementation of
the proposed algorithm either in hardware or software.


  Warning: This paper contains examples of offensive language targetting
marginalized population. The digitization of historical texts invites
researchers to explore the large-scale corpus of historical texts with
computational methods. In this study, we present computational text analysis on
a relatively understudied topic of how Asian workers are represented in
historical newspapers in the United States. We found that the word "coolie" was
semantically different in some States (e.g., Massachusetts, Rhode Island,
Wyoming, Oklahoma, and Arkansas) with the different discourses around coolie.
We also found that then-Confederate newspapers and then-Union newspapers formed
distinctive discourses by measuring over-represented words. Newspapers from
then-Confederate States associated coolie with slavery-related words. In
addition, we found Asians were perceived to be inferior to European immigrants
and subjected to the target of racism. This study contributes to supplementing
the qualitative analysis of racism in the United States with quantitative
discourse analysis.


  We quantitatively cross-associate the 18811 ROSAT Bright Source Catalog
(RASS/BSC) X-ray sources with optical sources in the USNO-A2 catalog,
calculating the the probability of unique association (Pid) between each
candidate within 75 arcsec of the X-ray source position, on the basis of
optical magnitude and proximity. We present catalogs of RASS/BSC sources for
which the probability of association is >98%, >90%, and >50%, which contain
2705, 5492, and 11301 unique USNO-A2 optical counterparts respectively down to
the stated level of significance. We include in this catalog a list of objects
in the SIMBAD database within 10 arcsec of the USNO position, as an aid to
identification and source classification. The catalog is more useful than
previous catalogs which either rely on plausibility arguments for association,
or do not aid in selecting a counterpart between multiple off-band sources in
the field. We find that a fraction ~65.8% of RASS/BSC sources have an
identifiable optical counterpart, down to the magnitude limit of the USNO
catalog which could be identified by their spatial proximity and high optical
brightness.


  We give an introduction to the calculation of path integrals on a lattice,
with the quantum harmonic oscillator as an example. In addition to providing an
explicit computational setup and corresponding pseudocode, we pay particular
attention to the existence of autocorrelations and the calculation of reliable
errors. The over-relaxation technique is presented as a way to counter strong
autocorrelations. The simulation methods can be extended to compute observables
for path integrals in other settings.


  Static stripe order is detrimental to superconductivity. Yet, it has been
proposed that transverse stripe fluctuations may enhance the inter-stripe
Josephson coupling and thus promote superconductivity. Direct experimental
studies of stripe dynamics, however, remain difficult. From a strong-coupling
perspective, transverse stripe fluctuations are realized in the form of dynamic
"kinks" -- sideways shifting stripe sections. Here, we show how modest uniaxial
pressure tuning reorganizes directional kink alignment. Our starting point is
La$_{1.88}$Sr$_{0.12}$CuO$_4$, where transverse kink ordering results in a
rotation of stripe order away from the crystal axis. Application of mild
uniaxial pressure changes the ordering pattern and pins the stripe order to the
crystal axis. This reordering occurs at a much weaker pressure than that to
detwin the stripe domains and suggests a rather weak transverse stripe
stiffness. Weak spatial stiffness and transverse quantum fluctuations are
likely key prerequisites for stripes to coexist with superconductivity.


  Partial label learning (PLL) is a class of weakly supervised learning where
each training instance consists of a data and a set of candidate labels
containing a unique ground truth label. To tackle this problem, a majority of
current state-of-the-art methods employs either label disambiguation or
averaging strategies. So far, PLL methods without such techniques have been
considered impractical. In this paper, we challenge this view by revealing the
hidden power of the oldest and naivest PLL method when it is instantiated with
deep neural networks. Specifically, we show that, with deep neural networks,
the naive model can achieve competitive performances against the other
state-of-the-art methods, suggesting it as a strong baseline for PLL. We also
address the question of how and why such a naive model works well with deep
neural networks. Our empirical results indicate that deep neural networks
trained on partially labeled examples generalize very well even in the
over-parametrized regime and without label disambiguations or regularizations.
We point out that existing learning theories on PLL are vacuous in the
over-parametrized regime. Hence they cannot explain why the deep naive method
works. We propose an alternative theory on how deep learning generalize in PLL
problems.


  ``The right to be forgotten'' ensured by laws for user data privacy becomes
increasingly important. Machine unlearning aims to efficiently remove the
effect of certain data points on the trained model parameters so that it can be
approximately the same as if one retrains the model from scratch. This work
proposes stochastic gradient Langevin unlearning, the first unlearning
framework based on noisy stochastic gradient descent (SGD) with privacy
guarantees for approximate unlearning problems under convexity assumption. Our
results show that mini-batch gradient updates provide a superior
privacy-complexity trade-off compared to the full-batch counterpart. There are
numerous algorithmic benefits of our unlearning approach, including complexity
saving compared to retraining, and supporting sequential and batch unlearning.
To examine the privacy-utility-complexity trade-off of our method, we conduct
experiments on benchmark datasets compared against prior works. Our approach
achieves a similar utility under the same privacy constraint while using $2\%$
and $10\%$ of the gradient computations compared with the state-of-the-art
gradient-based approximate unlearning methods for mini-batch and full-batch
settings, respectively.


  Colliding black holes are systems of profound interest in both gravitational
wave astronomy and in gravitation theory, and a variety of methods have been
developed for modeling their dynamics in detail. The features of these dynamics
are determined by the masses of the holes and by the magnitudes and axes of
their spins. While masses and spin magnitudes can be defined in reasonably
unambiguous ways, the spin axis is a concept which despite great physical
importance is seriously undermined by the coordinate freedom of general
relativity. Despite a great wealth of detailed numerical simulations of generic
spinning black hole collisions, very little attention has gone into defining or
justifying the definitions of the spin axis used in the numerical relativity
literature. In this paper, we summarize and contrast the various spin direction
measures available in the SpEC code, including a comparison with a method
common in other codes, we explain why these measures have shown qualitatively
different nutation features than one would expect from post-Newtonian theory,
and we derive and implement new measures that give much better agreement.


  Some of the important non-classical aspects of quantum mechanics can be
described in more intuitive terms if they are reformulated in a geometrical
picture based on an extension of the classical phase space. This contribution
presents various phase-space properties of moments describing a quantum state
and its dynamics. An example of a geometrical reformulation of a non-classical
quantum effect is given by an equivalence between conditions imposed by
uncertainty relations and centrifugal barriers, respectively.


  Contrastive learning (CL) aims to learn useful representation without relying
on expert annotations in the context of medical image segmentation. Existing
approaches mainly contrast a single positive vector (i.e., an augmentation of
the same image) against a set of negatives within the entire remainder of the
batch by simply mapping all input features into the same constant vector.
Despite the impressive empirical performance, those methods have the following
shortcomings: (1) it remains a formidable challenge to prevent the collapsing
problems to trivial solutions; and (2) we argue that not all voxels within the
same image are equally positive since there exist the dissimilar anatomical
structures with the same image. In this work, we present a novel Contrastive
Voxel-wise Representation Learning (CVRL) method to effectively learn low-level
and high-level features by capturing 3D spatial context and rich anatomical
information along both the feature and the batch dimensions. Specifically, we
first introduce a novel CL strategy to ensure feature diversity promotion among
the 3D representation dimensions. We train the framework through bi-level
contrastive optimization (i.e., low-level and high-level) on 3D images.
Experiments on two benchmark datasets and different labeled settings
demonstrate the superiority of our proposed framework. More importantly, we
also prove that our method inherits the benefit of hardness-aware property from
the standard CL approaches.


  We prove an analog of the Tits alternative for rational functions. In
particular, we show that if $S$ is a finitely generated semigroup of rational
functions over the complex numbers, then either $S$ has polynomially bounded
growth or $S$ contains a nonabelian free semigroup. We also show that if f and
g are polarizable maps over any field that do not have the same set of
preperiodic points, then the semigroup generated by f and g contains a
nonabelian free semigroup.


  Landau-Zener transition (LZT) has been explored in a variety of physical
systems for coherent population transfer between different quantum states. In
recent years, there have been various proposals for applying LZT to quantum
information processing because when compared to the methods using ac pulse for
coherent population transfer, protocols based on LZT are less sensitive to
timing errors. However, the effect of finite range of qubit energy available to
LZT based state control operations has not been thoroughly examined. In this
work, we show that using the well-known Landau-Zener formula in the vicinity of
an avoided energy-level crossing will cause considerable errors due to coherent
oscillation of the transition probability in a single-passage LZT experiment.
The data agree well with the numerical simulations which take the transient
dynamics of LZT into account. These results not only provide a closer view on
the issue of finite-time LZT but also shed light on its effects on the quantum
state manipulation.


  A proof is given, which relies on the commutator algebra of the unitary Lie
groups, that quantum gates operating on just two bits at a time are sufficient
to construct a general quantum circuit. The best previous result had shown the
universality of three-bit gates, by analogy to the universality of the Toffoli
three-bit gate of classical reversible computing. Two-bit quantum gates may be
implemented by magnetic resonance operations applied to a pair of electronic or
nuclear spins. A ``gearbox quantum computer'' proposed here, based on the
principles of atomic force microscopy, would permit the operation of such
two-bit gates in a physical system with very long phase breaking (i.e., quantum
phase coherence) times. Simpler versions of the gearbox computer could be used
to do experiments on Einstein-Podolsky-Rosen states and related entangled
quantum states.


  We theoretically study the non-monotonic (re-entrant) activated dynamics
associated with a repulsive glass to fluid to attractive glass transition in
high density particle suspensions interacting via strong short range attractive
forces. The classic theoretical projection approximation that replaces all
microscopic forces by a single effective force determined solely by equilibrium
pair correlations is revisited based on the projectionless dynamic theory (PDT)
that avoids force projection. A hybrid-PDT is formulated that explicitly
quantifies how attractive forces induce dynamical constraints, while singular
hard core interactions are treated based on the projection approach. Both the
effects of interference between repulsive and attractive forces, and structural
changes due to attraction-induced bond formation that competes with caging, are
included. Combined with the microscopic Elastically Collective Nonlinear
Langevin Equation (ECNLE) theory of activated relaxation, the resultant
approach appears to properly capture both the re-entrant dynamic crossover
behavior and the strong non-monotonic variation of the activated structural
relaxation time with attraction strength and range at very high volume
fractions. Qualitative differences with ECNLE theory-based results that adopt
the full projection approximation are identified, and testable predictions
made. The new formulation appears qualitatively consistent with multiple
experimental and simulation studies, and provides a new perspective for the
overall problem that is rooted in activated motion and interference between
repulsive and attractive forces. This is conceptually distinct from empirical
shifting or other ad hoc modifications of ideal mode coupling theory which do
not take into account activated dynamics. Implications for thermal glass
forming liquids are briefly discussed.


  This paper studies fingerprinting (traitor tracing) games in which the number
of colluders and the collusion channel are unknown. The fingerprints are
embedded into host sequences representing signals to be protected and provide
the receiver with the capability to trace back pirated copies to the colluders.
The colluders and the fingerprint embedder are subject to signal fidelity
constraints. Our problem setup unifies the signal-distortion and Boneh-Shaw
formulations of fingerprinting. The fundamental tradeoffs between fingerprint
codelength, number of users, number of colluders, fidelity constraints, and
decoding reliability are then determined. Several bounds on fingerprinting
capacity have been presented in recent literature. This paper derives exact
capacity formulas and presents a new randomized fingerprinting scheme with the
following properties: (1) the encoder and receiver assume a nominal coalition
size but do not need to know the actual coalition size and the collusion
channel; (2) a tunable parameter $\Delta$ trades off false-positive and
false-negative error exponents; (3) the receiver provides a reliability metric
for its decision; and (4) the scheme is capacity-achieving when the
false-positive exponent $\Delta$ tends to zero and the nominal coalition size
coincides with the actual coalition size.
  A fundamental component of the new scheme is the use of a "time-sharing"
randomized sequence. The decoder is a maximum penalized mutual information
decoder, where the significance of each candidate coalition is assessed
relative to a threshold, and the penalty is proportional to the coalition size.
A much simpler {\em threshold decoder} that satisfies properties (1)---(3)
above but not (4) is also given.


  Scintillating LFS-3 and LFS-8 crystals were exposed to a 155 MeV/c proton
fluence $\Phi_{p}=(4.4\pm0.4)\cdot10^{12}cm^{-2}$. There was negligible
reduction in transmission spectrum of LFS-3 crystal measured in 30 days after
irradiation.


  We propose an elasticity theory for one and two dimensional arrays of
globular proteins for which the free energy is affected by relative position
and relative rotation between neighboring molecules. The kinematics of such
assemblies is described, the conditions of compatibility are found, a form of
the free energy is given, and formulas for applied forces and moments are
developed. It is shown that fully relaxed states of sheets consist of helically
deformed sheets which themselves are composed of helical chains of molecules in
rational directions.
  We apply the theory to the fascinating contractile deformation that occurs in
the tail sheath of the virus bacteriophage T4, which aids its invasion of its
bacterial host. Using electron density maps of extended and contracted sheath,
we approximate the domains of each molecule by ellipsoids and then evaluate our
formulas for the position and orientation of each molecule. We show that, with
the resulting kinematic description, the configurations of extended and
contracted tail sheath are generated by a simple formula. We proposed a
constrained version of the theory based on measurements on extended and
contracted sheath. Following a suggestion of Pauling [{\em Discussions of the
Faraday Society} {\bf 13}, 170-6 (1953)], we develop a simple model of the
molecular interaction. The resulting free energy is found to have a double-well
structure. Certain simple deformations are studied (tension, torsion
inflation); the theory predicts a first-order Poynting effect and some
unexpected relations among moduli. Finally, the force of penetration is given,
and a possibly interesting program of epitaxial growth and patterning of such
sheets is suggested.


  The state-of-the-art in time series classification has come a long way, from
the 1NN-DTW algorithm to the ROCKET family of classifiers. However, in the
current fast-paced development of new classifiers, taking a step back and
performing simple baseline checks is essential. These checks are often
overlooked, as researchers are focused on establishing new state-of-the-art
results, developing scalable algorithms, and making models explainable.
Nevertheless, there are many datasets that look like time series at first
glance, but classic algorithms such as tabular methods with no time ordering
may perform better on such problems. For example, for spectroscopy datasets,
tabular methods tend to significantly outperform recent time series methods. In
this study, we compare the performance of tabular models using classic machine
learning approaches (e.g., Ridge, LDA, RandomForest) with the ROCKET family of
classifiers (e.g., Rocket, MiniRocket, MultiRocket). Tabular models are simple
and very efficient, while the ROCKET family of classifiers are more complex and
have state-of-the-art accuracy and efficiency among recent time series
classifiers. We find that tabular models outperform the ROCKET family of
classifiers on approximately 19% of univariate and 28% of multivariate datasets
in the UCR/UEA benchmark and achieve accuracy within 10 percentage points on
about 50% of datasets. Our results suggest that it is important to consider
simple tabular models as baselines when developing time series classifiers.
These models are very fast, can be as effective as more complex methods and may
be easier to understand and deploy.


  We show that Geroch decomposition leads us to Maxwell-like representation of
gravity in $(3+1)$ metrics decomposition that may be perceived as Lorentz
invariant version of GEM. For such decomposition we derive four-potential
$V^\mu$ and gravitational field tensor $F^{\mu\nu}$ that is associated with
gravitational interaction. Next we show that gravitational four-current $J^\mu$
derived for introduced four-potential produce energy-stress tensor and
reproduce main General Relativity formula. Next we introduce valid Lagrangian
and equations of motion that explains obtained results. At the end we introduce
new approach to quantization of gravity that results in proper quantum values
and is open to further generalization.


  The discrete height abelian sandpile model was introduced by Bak, Tang &
Wiesenfeld and Dhar as an example for the concept of self-organized
criticality. When the model is modified to allow grains to disappear on each
toppling, it is called bulk-dissipative. We provide a detailed study of a
continuous height version of the abelian sandpile model, called the abelian
avalanche model, which allows an arbitrarily small amount of dissipation to
take place on every toppling. We prove that for non-zero dissipation, the
infinite volume limit of the stationary measure of the abelian avalanche model
exists and can be obtained via a weighted spanning tree measure. We show that
in the whole non-zero dissipation regime, the model is not critical, i.e.,
spatial covariances of local observables decay exponentially. We then study the
zero dissipation limit and prove that the self-organized critical model is
recovered, both for the stationary measure and for the dynamics. We obtain
rigorous bounds on toppling probabilities and introduce an exponent describing
their scaling at criticality. We rigorously establish the mean-field value of
this exponent for $d > 4$.


  In this paper, we calculate the branching ratios for $B^+\to D_s^+\eta,
B^+\to D_s^+\eta^{\prime}$, $B^+\to D_s^{*+}\eta$ and $ B^+\to
D_s^{*+}\eta^{\prime}$ decays by employing the perturbative QCD (pQCD)
factorization approach. Under the two kinds of $\eta-\eta^{\prime}$ mixing
schemes, the quark-flavor mixing scheme and the singlet-octet mixing scheme, we
find that the calculated branching ratios are consistent with the currently
available experimental upper limits. We also considered the so called
"$f_{D_s}$ puzzle", by using two groups of parameters about the $D^{(*)}_s$
meson decay constants, that is $f_{D_s}=241$ MeV, $f_{D^*_s}=272$ MeV and
$f_{D_s}=274$ MeV, $f_{D^*_s}=312$ MeV, to calculate the branching ratios for
the considered decays. We find that the results change $30\%$ by using these
two different groups of paramters.


  Diabetic retinopathy (DR) is a prevalent complication of diabetes associated
with a significant risk of vision loss. Timely identification is critical to
curb vision impairment. Algorithms for DR staging from digital fundus images
(DFIs) have been recently proposed. However, models often fail to generalize
due to distribution shifts between the source domain on which the model was
trained and the target domain where it is deployed. A common and particularly
challenging shift is often encountered when the source- and target-domain
supports do not fully overlap. In this research, we introduce DRStageNet, a
deep learning model designed to mitigate this challenge. We used seven publicly
available datasets, comprising a total of 93,534 DFIs that cover a variety of
patient demographics, ethnicities, geographic origins and comorbidities. We
fine-tune DINOv2, a pretrained model of self-supervised vision transformer, and
implement a multi-source domain fine-tuning strategy to enhance generalization
performance. We benchmark and demonstrate the superiority of our method to two
state-of-the-art benchmarks, including a recently published foundation model.
We adapted the grad-rollout method to our regression task in order to provide
high-resolution explainability heatmaps. The error analysis showed that 59\% of
the main errors had incorrect reference labels. DRStageNet is accessible at URL
[upon acceptance of the manuscript].


  In this study, we examine introductory physics students' ability to perform
analogical reasoning between two isomorphic problems which employ the same
underlying physics principles but have different surface features. Three
hundred and sixty two students from a calculus-based and an algebra-based
introductory physics course were given a quiz in the recitation in which they
had to first learn from a solved problem provided and take advantage of what
they learned from it to solve another problem (which we call the quiz problem)
which was isomorphic. Previous research suggests that the multiple-concept quiz
problem is challenging for introductory students. Students in different
recitation classes received different interventions in order to help them
discern and exploit the underlying similarities of the isomorphic solved and
quiz problems. We also conducted think-aloud interviews with four introductory
students in order to understand in-depth the difficulties they had and explore
strategies to provide better scaffolding. We found that most students were able
to learn from the solved problem to some extent with the scaffolding provided
and invoke the relevant principles in the quiz problem. However, they were not
necessarily able to apply the principles correctly. Research suggests that more
scaffolding is needed to help students in applying these principles
appropriately. We outline a few possible strategies for future investigation.


  Microwave sensors integrated with microfluidic platforms can provide the size
and permittivity of single cells and microparticles. Amongst the microwave
sensor topologies, the planar arrangement of electrodes is a popular choice
owing to the ease of fabrication. Unfortunately, planar electrodes generate a
non-uniform electric field which causes the responsivity of the sensor to
depend on the vertical position of a microparticle in the microfluidic channel.
To overcome this problem, we fabricated three-dimensional (3D) electrodes at
the coplanar sensing region of an underlying microwave resonator. The 3D
electrodes are based on SU8 polymer which is then metallized by sputter
coating. With this system, we readily characterized a mixture composed of 12 um
and 20 um polystyrene particles and demonstrated separation without any
position-related calibration. The ratio of the electronic response of the two
particle types is approximately equal to the ratio of the particle volumes,
which indicates the generation of a uniform electric field at the sensing
region. The current work obviates the need for using multiple coplanar
electrodes and extensive processing of the data for the calibration of particle
height in a microfluidic channel: as such, it enables the fabrication of more
sophisticated microwave resonators for environmental and biological
applications.


  A critical problem in the field of post hoc explainability is the lack of a
common foundational goal among methods. For example, some methods are motivated
by function approximation, some by game theoretic notions, and some by
obtaining clean visualizations. This fragmentation of goals causes not only an
inconsistent conceptual understanding of explanations but also the practical
challenge of not knowing which method to use when.
  In this work, we begin to address these challenges by unifying eight popular
post hoc explanation methods (LIME, C-LIME, KernelSHAP, Occlusion, Vanilla
Gradients, Gradients x Input, SmoothGrad, and Integrated Gradients). We show
that these methods all perform local function approximation of the black-box
model, differing only in the neighbourhood and loss function used to perform
the approximation. This unification enables us to (1) state a no free lunch
theorem for explanation methods, demonstrating that no method can perform
optimally across all neighbourhoods, and (2) provide a guiding principle to
choose among methods based on faithfulness to the black-box model. We
empirically validate these theoretical results using various real-world
datasets, model classes, and prediction tasks.
  By bringing diverse explanation methods into a common framework, this work
(1) advances the conceptual understanding of these methods, revealing their
shared local function approximation objective, properties, and relation to one
another, and (2) guides the use of these methods in practice, providing a
principled approach to choose among methods and paving the way for the creation
of new ones.


  We construct integrals of motion (IM) for the sine-Gordon model with boundary
at the free Fermion point which correctly determine the boundary S matrix. The
algebra of these IM (``boundary quantum group'' at q=1) is a one-parameter
family of infinite-dimensional subalgebras of twisted affine sl(2). We also
propose the structure of the fractional-spin IM away from the free Fermion
point.


  This paper presents a new large multiview dataset called HUMBI for human body
expressions with natural clothing. The goal of HUMBI is to facilitate modeling
view-specific appearance and geometry of gaze, face, hand, body, and garment
from assorted people. 107 synchronized HD cameras are used to capture 772
distinctive subjects across gender, ethnicity, age, and physical condition.
With the multiview image streams, we reconstruct high fidelity body expressions
using 3D mesh models, which allows representing view-specific appearance using
their canonical atlas. We demonstrate that HUMBI is highly effective in
learning and reconstructing a complete human model and is complementary to the
existing datasets of human body expressions with limited views and subjects
such as MPII-Gaze, Multi-PIE, Human3.6M, and Panoptic Studio datasets.


  We show why and how the $I\bar I$ valley trajectory commonly used in the
literature so far is in fact unsatisfactory. A better $I\bar I$ valley is
suggested. We also give analytic expressions for the multiple
instanton-antiinstanton configurations in the pure Yang-Mills theory. These
formulas make it possible to go beyond the dilute gas approximation and
calculate the multi-body interactions among instantons and antiinstantons.


  We analyze the covertime of a biased random walk on the random graph
$G_{n,p}$. The walk is biased towards visiting vertices of low degree and this
makes the covertime less than in the unbiased case


  Time-bin encoding is a robust form of optical quantum information, especially
for transmission in optical fibers. To read out the information, the separation
of the time bins must be larger than the detector time resolution, typically on
the order of nanoseconds for photon counters. In the present work, we
demonstrate a technique using a nonlinear interaction between chirped entangled
time-bin photons and shaped laser pulses to perform projective measurements on
arbitrary time-bin states with picosecond-scale separations. We demonstrate a
tomographically-complete set of time-bin qubit projective measurements and show
the fidelity of operations is sufficiently high to violate the CHSH-Bell
inequality by more than 6 standard deviations.


  The upper bound and the lower bound of average numbers of divisors of Euler
Phi function and Carmichael Lambda function are obtained by Luca and Pomerance
(see \cite{LP}). We improve the lower bound and provide a heuristic argument
which suggests that the upper bound given by \cite{LP} is indeed close to the
truth.


  We classify gamma-ray bursts (GRBs) according to their observed durations and
physical properties of their spectra. We find that long/hard bursts (of
duration T_90 > 2.5 s, and typical photon energy E_p > 0.8 MeV corresponding to
BATSE's energy fluence hardness H^e_{32} > 3) show the strongest deviation from
the three-dimensional Euclidean brightness distribution. The majority of GRBs,
i.e., short bursts (T_90 < 2.5 s) and long/soft bursts (with T_90 > 2.5 s, and
H^e_{32} < 3) show little, if any, deviations from the Euclidean distribution.
These results contradict the prediction of simple extragalactic GRB models that
the most distant bursts should be the most affected by cosmological energy
redshift and time-dilation (long/soft GRBs). The strongly non-Euclidean GRB
subclass has very hard spectra of typical photon energy above 1 MeV, i.e.,
outside the ideal energy range for optimal detection by BATSE. We discuss
possible explanations of this puzzling feature of GRBs.


  Cutting mozzarella with a dull blade results in poorly shaped slices: the
process occurs in a configuration so deformed as to yield unexpectedly curved
surfaces. We study the rich morphogenetics arising from such process through
the example of coring: when a thin cylindrical hollow punch is pushed into a
soft elastomer, the extracted core is "clarinet-shaped", reaching diameters far
smaller than those of the tool. With contributions from fracture mechanics and
large strain theory, we build a simple yet quantitative understanding of the
observed shapes, revealing the crucial role of friction.


  We have contacted single wall carbon nanotubes grown by chemical vapor
deposition to superconducting Ti/Al/Ti electrodes. The device, we here report
on is in the Kondo regime exhibiting a four-fold shell structure, where a clear
signature of the superconducting electrodes is observed below the critical
temperature. Multiple Andreev reflections are revealed by sub-gap structure and
a narrow peak in the differential conductance around zero bias is seen
depending on the shell filling. We interpret the peak as a proximity induced
supercurrent and examine its interplay with Kondo resonances.


  Generalizing Eisenbud's matrix factorizations, we define factorization
categories. Following work of Positselski, we define their associated derived
categories. We construct specific resolutions of factorizations built from a
choice of resolutions of their components. We use these resolutions to lift
fully-faithfulness statements from derived categories of Abelian categories to
derived categories of factorizations and to construct a spectral sequence
computing the morphism spaces in the derived categories of factorizations from
Ext-groups of their components in the underlying Abelian category.


  Here we show that micro-swimmers can form a concealed swarm through
synergistic cooperation in suppressing one another's disturbing flows. We then
demonstrate how such a concealed swarm can actively gather around a favorite
spot, point toward a target, or track a desired trajectory in space, while
minimally disturbing the ambient fluid. Our findings provide a clear road map
to control and lead flocks of swimming micro-robots in stealth versus fast
modes, tuned through their active collaboration in minimally disturbing the
host medium.


  We report the observation of surface solitons in chirped semi-infinite
waveguide arrays whose waveguides exhibit exponentially decreasing refractive
indices. We show that the power threshold for surface wave formation decreases
with an increase of the array chirp and that for sufficiently large chirp
values linear surface modes are supported.


  Let $X$ be the space of isometry classes of ordered sextuples of points in
the hyperbolic plane such that the product of the six corresponding rotations
of angle $\pi$ is the identity. This space $X$ is closely related to the
PSL$_2(\mathbb{R})$-character variety of the genus 2 surface $\Sigma$. In this
article we study the topology and the natural symplectic structure on $X$, and
we describe the action of the mapping class group of $\Sigma$ on $X$. This
completes the classification of the ergodic components of the character variety
in genus 2 initiated in our previous work.


  The Steiner tree problem can be stated in terms of finding a connected set of
minimal length containing a given set of finitely many points. We show how to
formulate it as a mass-minimization problem for $1$-dimensional currents with
coefficients in a suitable normed group. The representation used for these
currents allows to state a calibration principle for this problem. We also
exhibit calibrations in some examples.


  In our monograph with B. Roynette and M. Yor, we construct a sigma-finite
measure related to penalisations of different stochastic processes, including
the Brownian motion in dimension 1 or 2, and a large class of linear
diffusions. In the last chapter of the monograph, we define similar measures
from recurrent Markov chains satisfying some technical conditions. In the
present paper, we give a classification of these measures, in function of the
minimal Martin boundary of the Markov chain considered at the beginning. We
apply this classification to the examples considered at the end of our
monograph.


  Mukai proved that most prime Fano fourfolds of degree 10 and index 2 are
contained in a Grassmannian G(2,5). They are all unirational and some are
rational, as remarked by Roth in 1949. We show that their middle cohomology is
of K3 type and that their period map is dominant, with smooth 4-dimensional
fibers, onto a 20-dimensional bounded symmetric period domain of type IV.
Following Hassett, we say that such a fourfold is special if it contains a
surface whose cohomology class does not come from the Grassmannian G(2,5).
Special fourfolds correspond to a countable union of hypersurfaces in the
period domain, labelled by a positive integer d, the discriminant. We describe
special fourfolds for some low values of d. We also characterize those integers
d for which special fourfolds do exist.


  K-means is undoubtedly the most widely used partitional clustering algorithm.
Unfortunately, due to its gradient descent nature, this algorithm is highly
sensitive to the initial placement of the cluster centers. Numerous
initialization methods have been proposed to address this problem. In this
paper, we first present an overview of these methods with an emphasis on their
computational efficiency. We then compare eight commonly used linear time
complexity initialization methods on a large and diverse collection of data
sets using various performance criteria. Finally, we analyze the experimental
results using non-parametric statistical tests and provide recommendations for
practitioners. We demonstrate that popular initialization methods often perform
poorly and that there are in fact strong alternatives to these methods.


  The spherical limit of strongly commensurate dirty-bosons is studied
perturbatively at weak disorder and numerically at strong disorder in two
dimensions (2D). We argue that disorder is not perfectly screened by
interactions, and consequently that the ground state in the effective Anderson
localisation problem always remains localised. As a result there is only a
gapped Mott insulator phase in the theory. Comparisons with other studies and
the parallel with disordered fermions in 2D are discussed. We conjecture that
while for the physical cases N=2 (XY) and N=1 (Ising) the theory should have
the ordered phase, it may not for N=3 (Heisenberg).


  In this paper we build Non-singular Morse-Smale flows on S^3 with unknotted
and unlinked saddle orbits by identifying fat round handles along their
boundaries. This way of building the flows enables to get their phase
portraits. We also show that the presence of heteroclinic trajectories imposes
an order in the round handle decomposition of these flows; this order is total
for NMS flows composed of one repulsive, one attractive and n unknotted saddle
orbits, for n >1.


  The gamma-ray blazar PKS 1510-089 has a highly superluminal milli-arcsecond
jet at a position angle (PA) of -28 degrees and an arcsecond jet with an
initial PA of 155 degrees. With a PA difference of 177 degrees between the
arcsecond and milli-arcsecond jets, PKS 1510-089 is perhaps the most highly
misaligned radio jet ever observed and serves as a graphic example of
projection effects in a highly beamed relativistic jet. Here we present the
results of observations designed to bridge the gap between the milli-arcsecond
and arcsecond scales. We find that a previously detected ``counter-feature'' to
the arcsecond jet is directly fed by the milli-arcsecond jet. This feature is
located 0.3" from the core, corresponding to a de-projected distance of 30
kiloparsecs. The feature appears to be dominated by shocked emission and has an
almost perfectly ordered magnetic field along its outside edge. We conclude
that it is most likely a shocked bend, viewed end-on, where the jet crosses our
line of sight to form the southern arcsecond jet. While the bend appears to be
nearly 180 degrees when viewed in projection, we estimate the intrinsic bending
angle to be between 12 and 24 degrees. The cause of the bend is uncertain;
however, we favor a scenario where the jet is bent after it departs the galaxy,
either by ram pressure due to winds in the intracluster medium or simply by the
density gradient in the transition to the intergalactic medium.


  We report near-infrared long-baseline interferometric observations of the
Hyades multiple system HD 284163, made with the CHARA array, as well as almost
43 yr of high-resolution spectroscopic monitoring at the CfA. Both types of
observations resolve the 2.39 d inner binary, and also an outer companion in a
43.1 yr orbit. Our observations, combined with others from the literature,
allow us to solve for the 3D inner and outer orbits, which are found to be at
nearly right angles to each other. We determine the dynamical masses of the
three stars (good to better than 1.4% for the inner pair), as well as the
orbital parallax. The secondary component (0.5245 +/- 0.0047 MSun) is now the
lowest mass star with a dynamical mass measurement in the cluster. A comparison
of these measurements with current stellar evolution models for the age and
metallicity of the Hyades shows good agreement. All three stars display
significant levels of chromospheric activity, consistent with the
classification of HD 284163 as an RS CVn object. We present evidence that a
more distant fourth star is physically associated, making this a hierarchical
quadruple system.


  The current microscopic picture of plasticity in amorphous materials assumes
local failure events to produce displacement fields complying with linear
elasticity. Indeed, the flow properties of nonaffine systems such as foams,
emulsions and granular materials close to jamming, that produce a fluctuating
displacement fields when failing, are still controversial. Here we show, via a
thorough numerical investigation of jammed materials, that nonaffinity induces
a critical scaling of the flow properties dictated by the distance to the
jamming point. We rationalize this critical behavior introducing a new
universal jamming exponent and hyperscaling relations, and use these results to
describe the volume fraction dependence of the friction coefficient.


  We investigate the interaction mechanism of pure quantum gravity in Regge
discretization. We compute volume-volume and link-link correlation functions.
In a preliminary analysis the forces turn out to be of Yukawa type, at least on
our finite lattice being away from the continuum limit.


  Centrality dependence of the directed flow of protons in Au+Au collisions at
the beam energy of 1.23A GeV collected by the HADES experiment at GSI is
presented. Measurements are performed with respect to the spectators plane
estimated using the Forward Wall hodoscope. Biases due to non-flow correlations
and correlated detector effects are evaluated. The corresponding systematic
uncertainties are quantified using estimates of the spectators plane from
various forward rapidity regions constructed from groups of Forward Wall
channels and protons reconstructed with the HADES tracking system.


  This paper demonstrates the potential of vibration-based Foundation Models
(FMs), pre-trained with unlabeled sensing data, to improve the robustness of
run-time inference in (a class of) IoT applications. A case study is presented
featuring a vehicle classification application using acoustic and seismic
sensing. The work is motivated by the success of foundation models in the areas
of natural language processing and computer vision, leading to generalizations
of the FM concept to other domains as well, where significant amounts of
unlabeled data exist that can be used for self-supervised pre-training. One
such domain is IoT applications. Foundation models for selected sensing
modalities in the IoT domain can be pre-trained in an environment-agnostic
fashion using available unlabeled sensor data and then fine-tuned to the
deployment at hand using a small amount of labeled data. The paper shows that
the pre-training/fine-tuning approach improves the robustness of downstream
inference and facilitates adaptation to different environmental conditions.
More specifically, we present a case study in a real-world setting to evaluate
a simple (vibration-based) FM-like model, called FOCAL, demonstrating its
superior robustness and adaptation, compared to conventional supervised deep
neural networks (DNNs). We also demonstrate its superior convergence over
supervised solutions. Our findings highlight the advantages of vibration-based
FMs (and FM-inspired selfsupervised models in general) in terms of inference
robustness, runtime efficiency, and model adaptation (via fine-tuning) in
resource-limited IoT settings.


  Optimal sex allocation theory is one of the most intricately developed areas
of evolutionary ecology. Under a range of conditions, particularly under
population sub-division, selection favours sex being allocated to offspring
non-randomly, generating non-binomial variances of offspring group sex ratios.
Detecting non-binomial sex allocation is complicated by stochastic
developmental mortality, as offspring sex can often only be identified on
maturity with the sex of non-maturing offspring remaining unknown. We show that
current approaches for detecting non-binomiality have limited ability to detect
non-binomial sex allocation when developmental mortality has occurred. We
present a new procedure using an explicit model of sex allocation and mortality
and develop a Bayesian model selection approach (available as an R package). We
use the double and multiplicative binomial distributions to model over- and
under-dispersed sex allocation and show how to calculate Bayes factors for
comparing these alternative models to the null hypothesis of binomial sex
allocation. The ability to detect non-binomial sex allocation is greatly
increased, particularly in cases where mortality is common. The use of Bayesian
methods allows for the quantification of the evidence in favour of each
hypothesis, and our modelling approach provides an improved descriptive
capability over existing approaches. We use a simulation study to demonstrate
substantial improvements in power for detecting non-binomial sex allocation in
situations where current methods fail, and we illustrate the approach in real
scenarios using empirically obtained datasets on the sexual composition of
groups of gregarious parasitoid wasps.


  We report of a series of laboratory experiments and numerical simulations of
freely-decaying rotating turbulent flows confined in domains with variable
height. We show that the vertical confinement has important effects on the
formation of large-scale columnar vortices, the hallmark of rotating
turbulence, and in particular delays the development of the cyclone-anticyclone
asymmetry. We compare the experimental and numerical results face-to-face,
showing the robustness of the results.


  This tutorial introduces quantum computing with a focus on the applicability
of formal methods in this relatively new domain. We describe quantum circuits
and convey an understanding of their inherent combinatorial nature and the
exponential blow-up that makes them hard to analyze. Then, we show how weighted
model counting (\#SAT) can be used to solve hard analysis tasks for quantum
circuits.
  This tutorial is aimed at everyone in the formal methods community with an
interest in quantum computing. Familiarity with quantum computing is not
required, but basic linear algebra knowledge (particularly matrix
multiplication and basis vectors) is a prerequisite. The goal of the tutorial
is to inspire the community to advance the development of quantum computing
with formal methods.


  While existing predictive frameworks are able to handle Euclidean structured
data (i.e, brain images), they might fail to generalize to geometric
non-Euclidean data such as brain networks. Besides, these are rooted the sample
selection step in using Euclidean or learned similarity measure between
vectorized training and testing brain networks. Such sample connectomic
representation might include irrelevant and redundant features that could
mislead the training sample selection step. Undoubtedly, this fails to exploit
and preserve the topology of the brain connectome. To overcome this major
drawback, we propose Residual Embedding Similarity-Based Network selection
(RESNets) for predicting brain network evolution trajectory from a single
timepoint. RESNets first learns a compact geometric embedding of each training
and testing sample using adversarial connectome embedding network. This nicely
reduces the high-dimensionality of brain networks while preserving their
topological properties via graph convolutional networks. Next, to compute the
similarity between subjects, we introduce the concept of a connectional brain
template (CBT), a fixed network reference, where we further represent each
training and testing network as a deviation from the reference CBT in the
embedding space. As such, we select the most similar training subjects to the
testing subject at baseline by comparing their learned residual embeddings with
respect to the pre-defined CBT. Once the best training samples are selected at
baseline, we simply average their corresponding brain networks at follow-up
timepoints to predict the evolution trajectory of the testing network. Our
experiments on both healthy and disordered brain networks demonstrate the
success of our proposed method in comparison to RESNets ablated versions and
traditional approaches.


  We consider finite element approximations of ill-posed elliptic problems with
conditional stability. The notion of {\emph{optimal error estimates}} is
defined including both convergence with respect to mesh parameter and
perturbations in data. The rate of convergence is determined by the conditional
stability of the underlying continuous problem and the polynomial order of the
finite element approximation space. A proof is given that no finite element
approximation can converge at a better rate than that given by the definition,
justifying the concept. A recently introduced class of finite element methods
with weakly consistent regularisation is recalled and the associated error
estimates are shown to be quasi optimal in the sense of our definition.


  We report the results of deep H-alpha imaging of the ionized gas surrounding
the low-redshift (z=0.0638) quasar MR 2251-178 using the TAURUS Tunable Filter
(TTF) on the Anglo-Australian Telescope. Our observations reach a 2-sigma
detection level of ~5 x 10^-18 erg s^-1 cm^-2 arcsec^-2, more than an order of
magnitude deeper than conventional narrowband images previously published on
this object. Our data reveal a spiral complex that extends more or less
symmetrically over ~200 kpc, making it the largest known quasar nebula. The
total mass of ionized gas is 6 x 10^10 solar masses (upper limit), a large
fraction of which is in a very faint, diffuse component. The large and
symmetric extent of the gaseous envelope favors a model in which the
filamentary and diffuse emission arises from a large cloud complex,
photoionized by the bright quasar. A crude kinematic analysis reveals
relatively smooth rotation, suggesting that the envelope did not originate with
a cooling flow, a past merger event, or an interaction with the nearby galaxy
G1.


  Dynamical quantum field theories (QFTs), such as those in which spacetimes
are equipped with a metric and/or a field in the form of a smooth map to a
target manifold, can be formulated axiomatically using the language of
$\infty$-categories. According to a geometric version of the cobordism
hypothesis, such QFTs collectively assemble themselves into objects in an
$\infty$-topos of smooth spaces. We show how this allows one to define and
study generalized global symmetries of such QFTs. The symmetries are themselves
smooth, so the `higher-form' symmetry groups can be endowed with, e.g., a Lie
group structure.
  Among the more surprising general implications for physics are, firstly, that
QFTs in spacetime dimension $d$, considered collectively, can have $d$-form
symmetries, going beyond the known $(d-1)$-form symmetries of individual QFTs
and, secondly, that a global symmetry of a QFT can be anomalous even before we
try to gauge it, due to a failure to respect either smoothness (in that a
symmetry of an individual QFT does not smoothly extend to QFTs collectively) or
locality (in that a symmetry of an unextended QFT does not extend to an
extended one).
  Smoothness anomalies are shown to occur even in 2-state systems in quantum
mechanics (here formulated axiomatically by equipping $d=1$ spacetimes with a
metric, an orientation, and perhaps some unitarity structure). Locality
anomalies are shown to occur even for invertible QFTs defined on $d=1$
spacetimes equipped with an orientation and a smooth map to a target manifold.
These correspond in physics to topological actions for a particle moving on the
target and the relation to an earlier classification of such actions using
invariant differential cohomology is elucidated.


  It is shown that Euclidean field theory with polynomial interaction, can be
regularized using the wavelet representation of the fields. The connections
between wavelet based regularization and stochastic quantization are
considered.


  This paper represents a groundbreaking advancement in Parkinson disease (PD)
research by employing a novel machine learning framework to categorize PD into
distinct subtypes and predict its progression. Utilizing a comprehensive
dataset encompassing both clinical and neurological parameters, the research
applies advanced supervised and unsupervised learning techniques. This
innovative approach enables the identification of subtle, yet critical,
patterns in PD manifestation, which traditional methodologies often miss.
Significantly, this research offers a path toward personalized treatment
strategies, marking a major stride in the precision medicine domain and
showcasing the transformative potential of integrating machine learning into
medical research.


  Predicting missing links in incomplete complex networks efficiently and
accurately is still a challenging problem. The recently proposed CAR
(Cannistrai-Alanis-Ravai) index shows the power of local link/triangle
information in improving link-prediction accuracy. With the information of
level-2 links, which are links between common-neighbors, most classical
similarity indices can be improved. Nevertheless, calculating the number of
level-2 links makes CAR index not efficient enough. Inspired by the idea of
employing local link/triangle information, we propose a new similarity index
with more local structure information. In our method, local link/triangle
structure information can be conveyed by clustering coefficient of common
neighbors directly. The reason why clustering coefficient has good
effectiveness in estimating the contribution of a common-neighbor is because
that it employs links existing between neighbors of the common-neighbor and
these links have the same structural position with the candidate link to this
common-neighbor. Ten real-world networks drawn from five various fields are
used to test the performance of our method against to classical similarity
indices and recently proposed CAR index. Two estimators: precision and AUP, are
used to evaluate the accuracy of link prediction algorithms. Generally
speaking, our new index only performs competitively with CAR, but it is a good
complement to CAR for networks with not very high LCP-corr, which is a measure
to estimate the correlation between number of common-neighbors and number of
links between common-neighbors. Besides, the proposed index is also more
efficient than CAR index.


  Qualitative reasoning involves expressing and deriving knowledge based on
qualitative terms such as natural language expressions, rather than strict
mathematical quantities. Well over 40 qualitative calculi have been proposed so
far, mostly in the spatial and temporal domains, with several practical
applications such as naval traffic monitoring, warehouse process optimisation
and robot manipulation. Even if a number of specialised qualitative reasoning
tools have been developed so far, an important barrier to the wider adoption of
these tools is that only qualitative reasoning is supported natively, when
real-world problems most often require a combination of qualitative and other
forms of reasoning. In this work, we propose to overcome this barrier by using
ASP as a unifying formalism to tackle problems that require qualitative
reasoning in addition to non-qualitative reasoning. A family of ASP encodings
is proposed which can handle any qualitative calculus with binary relations.
These encodings are experimentally evaluated using a real-world dataset based
on a case study of determining optimal coverage of telecommunication antennas,
and compared with the performance of two well-known dedicated reasoners.
Experimental results show that the proposed encodings outperform one of the two
reasoners, but fall behind the other, an acceptable trade-off given the added
benefits of handling any type of reasoning as well as the interpretability of
logic programs. This paper is under consideration for acceptance in TPLP.


  We present remote Operating System detection as an inference problem: given a
set of observations (the target host responses to a set of tests), we want to
infer the OS type which most probably generated these observations. Classical
techniques used to perform this analysis present several limitations. To
improve the analysis, we have developed tools using neural networks and
Statistics tools. We present two working modules: one which uses DCE-RPC
endpoints to distinguish Windows versions, and another which uses Nmap
signatures to distinguish different version of Windows, Linux, Solaris,
OpenBSD, FreeBSD and NetBSD systems. We explain the details of the topology and
inner workings of the neural networks used, and the fine tuning of their
parameters. Finally we show positive experimental results.


  The accuracy of object detectors and trackers is most commonly evaluated by
the Intersection over Union (IoU) criterion. To date, most approaches are
restricted to axis-aligned or oriented boxes and, as a consequence, many
datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented
boxes cannot accurately capture an object's shape. To address this, a number of
densely segmented datasets has started to emerge in both the object detection
and the object tracking communities. However, evaluating the accuracy of object
detectors and trackers that are restricted to boxes on densely segmented data
is not straightforward. To close this gap, we introduce the relative
Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU
with the optimal box for the segmentation to generate an accuracy measure that
ranges between 0 and 1 and allows a more precise measurement of accuracies.
Furthermore, it enables an efficient and easy way to understand scenes and the
strengths and weaknesses of an object detection or tracking approach. We
display how the new measure can be efficiently calculated and present an
easy-to-use evaluation framework. The framework is tested on the DAVIS and the
VOT2016 segmentations and has been made available to the community.


  The objective of this paper is to describe an approach to detect the slip and
contact force in real-time feedback. In this novel approach, the DAVIS camera
is used as a vision tactile sensor due to its fast process speed and high
resolution. Two hundred experiments were performed on four objects with
different shapes, sizes, weights, and materials to compare the accuracy and
response of the Baxter robot grippers to avoid slipping. The advanced approach
is validated by using a force-sensitive resistor (FSR402). The events captured
with the DAVIS camera are processed with specific algorithms to provide
feedback to the Baxter robot aiding it to detect the slip.


  Segmentation of cardiac images, particularly late gadolinium-enhanced
magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased
cardiac structures, is a crucial first step for clinical diagnosis and
treatment. However, direct segmentation of LGE-MRIs is challenging due to its
attenuated contrast. Since most clinical studies have relied on manual and
labor-intensive approaches, automatic methods are of high interest,
particularly optimized machine learning approaches. To address this, we
organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs,
currently the world's largest cardiac LGE-MRI dataset, and associated labels of
the left atrium segmented by three medical experts, ultimately attracting the
participation of 27 international teams. In this paper, extensive analysis of
the submitted algorithms using technical and biological metrics was performed
by undergoing subgroup analysis and conducting hyper-parameter analysis,
offering an overall picture of the major design choices of convolutional neural
networks (CNNs) and practical considerations for achieving state-of-the-art
left atrium segmentation. Results show the top method achieved a dice score of
93.2% and a mean surface to a surface distance of 0.7 mm, significantly
outperforming prior state-of-the-art. Particularly, our analysis demonstrated
that double, sequentially used CNNs, in which a first CNN is used for automatic
region-of-interest localization and a subsequent CNN is used for refined
regional segmentation, achieved far superior results than traditional methods
and pipelines containing single CNNs. This large-scale benchmarking study makes
a significant step towards much-improved segmentation methods for cardiac
LGE-MRIs, and will serve as an important benchmark for evaluating and comparing
the future works in the field.


  Spectral components of one-dimensional Schr\"odinger operator with complex
potential are investigated. An effective upper bound for the total number of
eigenvalues and spectral singularities is established. For dissipative
Schr\"odinger operator sufficient condition is found which guarantees the
absence of singular component in the continuous spectrum and spectral
decomposition is exposed.


  We consider the solution $u$ to the one-dimensional parabolic Anderson model
with homogeneous initial condition $u(0, \cdot) \equiv 1$, arbitrary drift and
a time-independent potential bounded from above. Under ergodicity and
independence conditions we derive representations for both the quenched
Lyapunov exponent and, more importantly, the $p$-th annealed Lyapunov exponents
for {\it all} $p \in (0, \infty).$ These results enable us to prove the
heuristically plausible fact that the $p$-th annealed Lyapunov exponent
converges to the quenched Lyapunov exponent as $p \downarrow 0.$ Furthermore,
we show that $u$ is $p$-intermittent for $p$ large enough. As a byproduct, we
compute the optimal quenched speed of the random walk appearing in the
Feynman-Kac representation of $u$ under the corresponding Gibbs measure. In
this context, depending on the negativity of the potential, a phase transition
from zero speed to positive speed appears.


  We investigate some spectral properties of the weighted quaternionic Cauchy
transform when acting on the right quaternionic Hilbert space of Gaussian
integrable functions. We study its boundedness, compactness, and memberships to
the $k$-Schatten class, and we identify its range. This is done by means of its
restriction to the n-th S-polyregular Bargmann space of the second kind, for
which we provide an explicit closed expression for its action on the
quaternionic It\^o--Hermite polynomials constituting an orthogonal basis. We
also exhibit an orthogonal basis of eigenfunctions of its n-Bergman projection
leading to the explicit determination of its singular values. The obtained
results generalize those given for weighted Cauchy transform on the complex
plane to the quaternionic setting.