  The hadronic vacuum polarisation contributions to the anomalous magnetic
moment of the muon, $a_{\mu}^{\rm had, VP}$ have been re-evaluated from the
combination of $e^+e^-\rightarrow {\rm hadrons}$ cross section data. Focus has
been placed on the development of a new data combination method, which fully
incorporates all correlated statistical and systematic uncertainties in a bias
free approach. Using these combined data have resulted in estimates of the
hadronic vacuum polarisation contributions to $g-2$ of the muon of
$a_{\mu}^{\rm had, \, LO \, VP} = (693.27 \pm 2.46)\times 10^{-10}$ and
$a_{\mu}^{\rm had, \, NLO \, VP} = (-9.82 \pm 0.04)\times 10^{-10}$. The new
estimate for the Standard Model prediction is found to be $a_{\mu}^{\rm SM} =
(11\ 659 \ 182.05 \pm 3.56) \times 10^{-10}$, which is $3.7\sigma$ below the
current experimental measurement. In addition, the prediction for the hadronic
contribution to the QED coupling at the $Z$ boson mass has been calculated to
be $\Delta\alpha_{\rm had}^{(5)}(M_Z^2)= (276.11 \pm 1.11)\times 10^{-4}$,
resulting in $\alpha^{-1}(M_Z^2) = 128.946 \pm 0.015$.


  We introduce the notion of the power quandle of a group, an algebraic
structure that forgets the multiplication but keeps the conjugation and the
power maps. Compared with plain quandles, power quandles are much better
invariants of groups. We show that they determine the central quotient of any
group and the center of any finite group. Any group can be canonically
approximated by the associated group of its power quandle, which we show to be
a central extension, with a universal property, and a computable kernel. This
allows us to present any group as a quotient of a group with a
power-conjugation presentation by an abelian subgroup that is determined by the
power quandle and low-dimensional homological invariants.


  We numerically examine the transport of active run-and-tumble particles
driven with a drift force over random disordered landscapes comprised of fixed
obstacles. For increasing run lengths, the net particle transport initially
increases before reaching a maximum and decreasing at larger run lengths. The
transport reduction is associated with the formation of cluster or living
crystal states that become locally jammed or clogged by the obstacles. We also
find that the system dynamically jams at lower particle densities when the run
length is increased. Our results indicate that there is an optimal activity
level for active matter transport through quenched disorder, and could be
important for understanding biological transport in complex environments or for
applications of active matter particles in random media.


  We consider the information filtering problem, in which we face a stream of
items, and must decide which ones to forward to a user to maximize the number
of relevant items shown, minus a penalty for each irrelevant item shown.
Forwarding decisions are made separately in a personalized way for each user.
We focus on the cold-start setting for this problem, in which we have limited
historical data on the user's preferences, and must rely on feedback from
forwarded articles to learn which the fraction of items relevant to the user in
each of several item categories. Performing well in this setting requires
trading exploration vs. exploitation, forwarding items that are likely to be
irrelevant, to allow learning that will improve later performance. In a
Bayesian setting, and using Markov decision processes, we show how the
Bayes-optimal forwarding algorithm can be computed efficiently when the user
will examine each forwarded article, and how an upper bound on the
Bayes-optimal procedure and a heuristic index policy can be obtained for the
setting when the user will examine only a limited number of forwarded items. We
present results from simulation experiments using parameters estimated using
historical data from arXiv.org.


  Knowledge-based visual question answering (KB-VQA) is a challenging task,
which requires the model to leverage external knowledge for comprehending and
answering questions grounded in visual content. Recent studies retrieve the
knowledge passages from external knowledge bases and then use them to answer
questions. However, these retrieved knowledge passages often contain irrelevant
or noisy information, which limits the performance of the model. To address the
challenge, we propose two synergistic models: Knowledge Condensation model and
Knowledge Reasoning model. We condense the retrieved knowledge passages from
two perspectives. First, we leverage the multimodal perception and reasoning
ability of the visual-language models to distill concise knowledge concepts
from retrieved lengthy passages, ensuring relevance to both the visual content
and the question. Second, we leverage the text comprehension ability of the
large language models to summarize and condense the passages into the knowledge
essence which helps answer the question. These two types of condensed knowledge
are then seamlessly integrated into our Knowledge Reasoning model, which
judiciously navigates through the amalgamated information to arrive at the
conclusive answer. Extensive experiments validate the superiority of the
proposed method. Compared to previous methods, our method achieves
state-of-the-art performance on knowledge-based VQA datasets (65.1% on OK-VQA
and 60.1% on A-OKVQA) without resorting to the knowledge produced by GPT-3
(175B).


  The Narrow Line Seyfert 1 galaxy NGC4051 shows unusual low flux states,
lasting several months, when the 2-10 keV X-ray spectrum becomes unusually hard
(photon index<1) while the spectrum at lower X-ray energies is dominated by a
large soft excess. A Chandra TOO of the low state has shown that the soft
excess and hard components are variable and well-correlated. The variability of
the hard component rules out an origin in a distant reflector. Here we present
results from a recent XMM-Newton TOO of NGC4051 in the low state, which allows
a much more detailed examination of the nature of the hard and soft spectral
components in the low state. We demonstrate that the spectral shape in the low
state is consistent with the extrapolation of the spectral pivoting observed at
higher fluxes. The XMM-Newton data also reveals the warm absorbing gas in
emission, as the drop in the primary continuum flux unmasks prominent emission
lines from a range of ion species.


  We present a collection of $7582$ real-world elections divided into $25$
datasets from various sources ranging from sports competitions over music
charts to survey- and indicator-based rankings. We provide evidence that the
collected elections complement already publicly available data from the PrefLib
database, which is currently the biggest and most prominent source containing
$701$ real-world elections from $36$ datasets. Using the map of elections
framework, we divide the datasets into three categories and conduct an analysis
of the nature of our elections. To evaluate the practical applicability of
previous theoretical research on (parameterized) algorithms and to gain further
insights into the collected elections, we analyze different structural
properties of our elections including the level of agreement between voters and
election's distances from restricted domains such as single-peakedness. Lastly,
we use our diverse set of collected elections to shed some further light on
several traditional questions from social choice, for instance, on the number
of occurrences of the Condorcet paradox and on the consensus among different
voting rules.


  In this paper, we study the minimum time planar tilting maneuver of a
spacecraft, from the theoretical as well as from the numerical point of view,
with a particular focus on the chattering phenomenon. We prove that there exist
optimal chattering arcs when a singular junction occurs. Our study is based on
the Pontryagin Maximum Principle and on results by M.I. Zelikin and V.F.
Borisov. We give sufficient conditions on the initial values under which the
optimal solutions do not contain any singular arc, and are bang-bang with a
finite number of switchings. Moreover, we implement sub-optimal strategies by
replacing the chattering control with a fixed number of piecewise constant
controls. Numerical simulations illustrate our results.


  Video, as a key driver in the global explosion of digital information, can
create tremendous benefits for human society. Governments and enterprises are
deploying innumerable cameras for a variety of applications, e.g., law
enforcement, emergency management, traffic control, and security surveillance,
all facilitated by video analytics (VA). This trend is spurred by the rapid
advancement of deep learning (DL), which enables more precise models for object
classification, detection, and tracking. Meanwhile, with the proliferation of
Internet-connected devices, massive amounts of data are generated daily,
overwhelming the cloud. Edge computing, an emerging paradigm that moves
workloads and services from the network core to the network edge, has been
widely recognized as a promising solution. The resulting new intersection, edge
video analytics (EVA), begins to attract widespread attention. Nevertheless,
only a few loosely-related surveys exist on this topic. The basic concepts of
EVA (e.g., definition, architectures) were not fully elucidated due to the
rapid development of this domain. To fill these gaps, we provide a
comprehensive survey of the recent efforts on EVA. In this paper, we first
review the fundamentals of edge computing, followed by an overview of VA. EVA
systems and their enabling techniques are discussed next. In addition, we
introduce prevalent frameworks and datasets to aid future researchers in the
development of EVA systems. Finally, we discuss existing challenges and foresee
future research directions. We believe this survey will help readers comprehend
the relationship between VA and edge computing, and spark new ideas on EVA.


  To study the stability of heavy quarkonia in quark-gluon plasma, we use a
color-singlet Q-Qbar potential determined previously to be a well-defined
linear combination of the free energy F_1 and the internal energy U_1. Using
the lattice gauge results of Kaczmarek et al., the dissociation temperatures of
J/psi and chi_b in quenched QCD are found to be 1.62Tc and 1.18Tc respectively,
in good agreement with spectral function analyses. The dissociation temperature
of J/psi in full QCD with 2 flavors is 1.42Tc. Thus, the presence of dynamical
quarks in full QCD lowers the dissociation temperature of J/psi, but J/psi
remains bound up to 1.42Tc.


  We consider correlated L\'evy walks on a class of two- and three-dimensional
deterministic self-similar structures, with correlation between steps induced
by the geometrical distribution of regions, featuring different diffusion
properties. We introduce a geometric parameter $\alpha$, playing a role
analogous to the exponent characterizing the step-length distribution in random
systems. By a {\it single-long jump} approximation, we analytically determine
the long-time asymptotic behavior of the moments of the probability
distribution, as a function of $\alpha$ and of the dynamic exponent $z$
associated to the scaling length of the process. We show that our scaling
analysis also applies to experimentally relevant quantities such as escape-time
and transmission probabilities.
  Extensive numerical simulations corroborate our results which, in general,
are different from those pertaining to uncorrelated L\'evy-walks models.


  Self-supervised learning on graphs can be bifurcated into contrastive and
generative methods. Contrastive methods, also known as graph contrastive
learning (GCL), have dominated graph self-supervised learning in the past few
years, but the recent advent of graph masked autoencoder (GraphMAE) rekindles
the momentum behind generative methods. Despite the empirical success of
GraphMAE, there is still a dearth of theoretical understanding regarding its
efficacy. Moreover, while both generative and contrastive methods have been
shown to be effective, their connections and differences have yet to be
thoroughly investigated. Therefore, we theoretically build a bridge between
GraphMAE and GCL, and prove that the node-level reconstruction objective in
GraphMAE implicitly performs context-level GCL. Based on our theoretical
analysis, we further identify the limitations of the GraphMAE from the
perspectives of alignment and uniformity, which have been considered as two key
properties of high-quality representations in GCL. We point out that GraphMAE's
alignment performance is restricted by the masking strategy, and the uniformity
is not strictly guaranteed. To remedy the aforementioned limitations, we
propose an Alignment-Uniformity enhanced Graph Masked AutoEncoder, named
AUG-MAE. Specifically, we propose an easy-to-hard adversarial masking strategy
to provide hard-to-align samples, which improves the alignment performance.
Meanwhile, we introduce an explicit uniformity regularizer to ensure the
uniformity of the learned representations. Experimental results on benchmark
datasets demonstrate the superiority of our model over existing
state-of-the-art methods.


  Neural Architecture Search (NAS), aiming at automatically designing network
architectures by machines, is hoped and expected to bring about a new
revolution in machine learning. Despite these high expectation, the
effectiveness and efficiency of existing NAS solutions are unclear, with some
recent works going so far as to suggest that many existing NAS solutions are no
better than random architecture selection. The inefficiency of NAS solutions
may be attributed to inaccurate architecture evaluation. Specifically, to speed
up NAS, recent works have proposed under-training different candidate
architectures in a large search space concurrently by using shared network
parameters; however, this has resulted in incorrect architecture ratings and
furthered the ineffectiveness of NAS.
  In this work, we propose to modularize the large search space of NAS into
blocks to ensure that the potential candidate architectures are fully trained;
this reduces the representation shift caused by the shared parameters and leads
to the correct rating of the candidates. Thanks to the block-wise search, we
can also evaluate all of the candidate architectures within a block. Moreover,
we find that the knowledge of a network model lies not only in the network
parameters but also in the network architecture. Therefore, we propose to
distill the neural architecture (DNA) knowledge from a teacher model as the
supervision to guide our block-wise architecture search, which significantly
improves the effectiveness of NAS. Remarkably, the capacity of our searched
architecture has exceeded the teacher model, demonstrating the practicability
and scalability of our method. Finally, our method achieves a state-of-the-art
78.4\% top-1 accuracy on ImageNet in a mobile setting, which is about a 2.1\%
gain over EfficientNet-B0. All of our searched models along with the evaluation
code are available online.


  We consider the isoperimetric problem in $\mathbb R^n$ with density for the
planar case $n=2$. We show that, if the density is ${\rm C}^{0,\alpha}$, then
the boundary of any isoperimetric is of class ${\rm C}^{1,\frac
\alpha{3-2\alpha}}$. This improves the previously known regularity.


  Given any finite set of nonnegative integers, there exists a closed convex
set whose facial dimension signature coincides with this set of integers, that
is, the dimensions of its nonempty faces comprise exactly this set of integers.
In this work, we show that such sets can be realised as solution sets of
systems of finitely many convex quadratic inequalities, and hence are
representable via second-order cone programming problems, and are, in
particular, spectrahedral. It also follows that these sets are facially
exposed, in contrast to earlier constructions. We obtain a lower bound on the
minimum number of convex quadratic inequalities needed to represent a closed
convex set with prescribed facial dimension signature, and show that our bound
is tight for some special cases. Finally, we relate the question of finding
efficient representations with indecomposability of integer sequences and other
topics, and discuss a substantial number of open questions.


  Accelerating particles to relativistic energies over very short distances
using lasers has been a long standing goal in physics. Among the various
schemes proposed for electrons, vacuum laser acceleration has attracted
considerable interest and has been extensively studied theoretically because of
its appealing simplicity: electrons interact with an intense laser field in
vacuum and can be continuously accelerated, provided they remain at a given
phase of the field until they escape the laser beam. But demonstrating this
effect experimentally has proved extremely challenging, as it imposes stringent
requirements on the conditions of injection of electrons in the laser field.
Here, we solve this long-standing experimental problem for the first time by
using a plasma mirror to inject electrons in an ultraintense laser field, and
obtain clear evidence of vacuum laser acceleration. With the advent of PetaWatt
class lasers, this scheme could provide a competitive source of very high
charge (nC) and ultrashort relativistic electron beams.


  Double perovskites provide a unique opportunity to induce and control
multiferroic behaviors in oxide systems. The appealing possibility to design
materials with a strong coupling between the magnetization and the polarization
fields may be achieved in this family since these magnetic insulators can
present structural self-ordering in the appropriate growth conditions. We have
studied the functional properties of La2CoMnO6 and Bi2CoMnO6 epitaxial thin
films grown by pulsed laser deposition. Cation-ordered La2CoMnO6 films display
a magnetic Curie temperature of 250 K while cation-disordered Bi2CoMnO6 films
present ferromagnetism up to ~ 800 K. Such high transition temperature for
magnetic ordering can be further tuned by varying the strain in the films
indicating an important contribution from the structural characteristics of the
materials. Our approach might be generalized for other oxide systems. At this
end, our results are compared with other multiferroic systems. The roles of
various cations, their arrangements and structural effects are further
discussed.


  Worldwide, we are experiencing an unprecedented, accelerated loss of
biodiversity triggered by a bundle of anthropogenic threats such as habitat
destruction, environmental pollution and climate change. Despite all efforts of
the European biodiversity conservation policy, initiated 20 years ago by the
Habitats Directive that provided the legal basis for establishing the Natura
2000 network, the goal to halt the decline of biodiversity in Europe by 2010
has been missed. Hochkirch et al. (2013, Conserv. Lett. 6: 462-467) identified
four major shortcomings of the current implementation of the directive
concerning prioritization of the annexes, conservation plans, survey systems
and financial resources. However they did not account for the intended network
character of the Natura 2000 sites, an aspect of highest relevance. This
response letter deals with this shortcoming as it is the prerequisite, over any
other strategies, ensuring a Natura 2020 network being worth its name.


  In image-based camera localization systems, information about the environment
is usually stored in some representation, which can be referred to as a map.
Conventionally, most maps are built upon hand-crafted features. Recently,
neural networks have attracted attention as a data-driven map representation,
and have shown promising results in visual localization. However, these neural
network maps are generally hard to interpret by human. A readable map is not
only accessible to humans, but also provides a way to be verified when the
ground truth pose is unavailable. To tackle this problem, we propose Generative
Map, a new framework for learning human-readable neural network maps, by
combining a generative model with the Kalman filter, which also allows it to
incorporate additional sensor information such as stereo visual odometry. For
evaluation, we use real world images from the 7-Scenes and Oxford RobotCar
datasets. We demonstrate that our Generative Map can be queried with a pose of
interest from the test sequence to predict an image, which closely resembles
the true scene. For localization, we show that Generative Map achieves
comparable performance with current regression models. Moreover, our framework
is trained completely from scratch, unlike regression models which rely on
large ImageNet pretrained networks.


  We present a framework for the study of $q$-difference equations satisfied by
$q$-semi-classical orthogonal systems. As an example, we identify the
$q$-difference equation satisfied by a deformed version of the little
$q$-Jacobi polynomials as a gauge transformation of a special case of the
associated linear problem for $q$-$\mathrm{P}_{\mathrm{VI}}$. We obtain a
parameterization of the associated linear problem in terms of orthogonal
polynomial variables and find the relation between this parameterization and
that of Jimbo and Sakai.


  Recently the new method of the Cherenkov fields and loss factors of a
point-like electron bunch passing through longitudinally homogeneous structures
lined with arbitrary slowdown layers was proposed. It was shown that the
Cherenkov loss factor of the short bunch does not depend on the waveguide
system material and is a constant for any given transverse dimensions and
cross-section shapes of the waveguides. It was shown that with the proposed
approach one can use a relatively simple method for the calculation of the
total loss factor using an integral relation based on the cylindrical slowdown
waveguide model. With this paper, we demonstrate that the same integral
relation that we call relativistic Gauss theorem can be applied in case
impedance boundary conditions (IBC) also known as Leontovich boundary
conditions.


  It has been pointed out that the spurious Cosmic Microwave Background (CMB)
B-mode polarization signals caused by the absorption of the CMB monopole
component due to the Galactic interstellar matter, called the CMB shadow,
degrade the accuracy of detecting the CMB B-mode polarization signals imprinted
by primordial gravitational waves. We have made a realistic estimation using
simulated sky maps of how the CMB shadow affects forthcoming high-precision CMB
B-mode experiments for the first time. The Delta-map method, an internal
template method taking into account the first-order spatial variation of
foregrounds' spectral parameters, is applied as a foreground removal method. We
show that if the CMB shadow effects are not taken into account in the
foreground removal process, future observations would lead to the false
detection of the CMB B-mode polarization signals originating from primordial
gravitational waves. We also show that the effect of the CMB shadow can be
mitigated by our revised Delta-map method to target the CMB B-mode polarization
signals at the level of tensor-to-scalar ratio r=0.001.


  It is shown how the gluing theorem due to Corvino and Shoen can be naturally
extended to accommodate supertranslations at spatial infinity. Logarithmic
supertranslations play an intersting role in the construction.


  We calculate the effects of next-to-leading order perturbative QCD as well as
of the quark transverse motion and off-shellness on the Drell-Yan process cross
section. By studying the s->infinity behaviour of the cross section in these
approaches, we find that the effects of quark off-shellness and intrinsic-k_T
parametrize those of higher twists. In particular, the off-shellness of partons
generates part of the K-factor type corrections to the leading order cross
section. Higher twist contributions to the p_T-spectrum of the Drell-Yan pairs
are found to be large for presently accessible energies. The evolution of quark
off-shellness distribution with the hard scale is also studied.


  In this paper we introduce a variational model for the study of multilayer
films that allows for the treatment of both coherent and incoherent interfaces
between layers. The model is designed in the framework of the theory of Stress
Driven Rearrangement Instabilities, which are characterized by the competition
between elastic and surface energy effects. The surface of each film layer is
assumed to satisfy an ''exterior graph condition'', under which in particular
bulk cracks are allowed to be of non-graph type. By applying the direct method
of calculus of variations under a constraint on the number of connected
components of the cracks not connected to the surface of the film layers the
existence of energy minimizers is established in dimension 2. As a byproduct of
the analysis the state of art on the variational modeling of single-layered
films deposited on a fixed substrate is advanced by letting the substrate
surface free, by addressing the presence of multiple layers of various
materials, and by including the possibility of delamination between the various
film layers.


  Previous deep learning efforts have focused on improving the performance of
Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using
Convolutional Neural Networks (CNN). However, the features from CT scans alone
are not always sufficient for the diagnosis of PE. CT scans along with
electronic heath records (EHR) can provide a better insight into the patients
condition and can lead to more accurate PE diagnosis. In this paper, we propose
Pulmonary Embolism Detection using Contrastive Learning (PECon), a supervised
contrastive pretraining strategy that employs both the patients CT scans as
well as the EHR data, aiming to enhance the alignment of feature
representations between the two modalities and leverage information to improve
the PE diagnosis. In order to achieve this, we make use of the class labels and
pull the sample features of the same class together, while pushing away those
of the other class. Results show that the proposed work outperforms the
existing techniques and achieves state-of-the-art performance on the RadFusion
dataset with an F1-score of 0.913, accuracy of 0.90 and an AUROC of 0.943.
Furthermore, we also explore the explainability of our approach in comparison
to other methods. Our code is publicly available at
https://github.com/BioMedIA-MBZUAI/PECon.


  We continue the studies of localization of the U(1) gauge fields on domain
walls. Depending on dynamics of the bulk theory the gauge field localized on
the domain wall can be either in the Coulomb phase or squeezed into flux tubes
implying (Abelian) confinement of probe charges on the wall along the wall
surface. First, we consider a simple toy model with one flavor in the bulk at
weak coupling (a minimal model) realizing the latter scenario. We then suggest
a model presenting an extension of the Seiberg--Witten theory which is at
strong coupling, but all theoretical constructions are under full control if we
base our analysis on a dual effective action. Finally, we compare our findings
with the wall in a "nonminimal" theory with two distinct quark flavors that had
been studied previously. In this case the U(1) gauge field trapped on the wall
is exactly massless because it is the Goldstone boson of a U(1) symmetry in the
bulk spontaneously broken on the wall. The theory on the wall is in the Coulomb
phase. We explain why the mechanism of confinement discussed in the first part
of the paper does not work in this case, and strings are not formed on the
walls.


  The space of local operators in three-dimensional quantum electrodynamics
contains monopole operators that create $n$ units of gauge flux emanating from
the insertion point. This paper uses the state-operator correspondence to
calculate the anomalous dimensions of these monopole operators perturbatively
to next-to-leading order in the $1/N_f$ expansion, thus improving on the
existing leading order results in the literature. Here, $N_f$ is the number of
two-component complex fermion flavors. The scaling dimension of the $n=1$
monopole operator is $0.265 N_f - 0.0383 + O(1/N_f)$ at the infrared conformal
fixed point.


  We present a computer simulation study of a (6,12)-Lennard-Jones fluid
confined to a slit pore, formed by two uniform planes. These interact via
(3,9)-Lennard-Jones potential with the fluid particles. When the fluid
approaches the liquid-to-solid transition we first observe layering parallel to
the walls. In order to investigate the nature of the freezing transition we
performed a detailed analysis of the bond-orientational order parameter in the
layers. We found no signs of hexatic order which would indicate a melting
scenario of the Kosterlitz-Thouless type. An analysis of the mean-square
displacement shows that the particles can easily move between the layers,
making the crystallization a 3d-like process. This is consistent with the fact
that we observe a considerable hysteresis in the heating-freezing curves,
showing that the crystallization transition proceeds as an activated process.


  We prove that a usual differential operator is formally the limit of quantum
differential operators. For this purpose, we introduce the notion of twisted
differential operator of infinite level and prove that, formally, such an
object is independent of the choice of the twist. Our method provides explicit
formulas.


  The major system is a mnemonic system that can be used to memorize sequences
of numbers. In this work, we present a method to automatically generate
sentences that encode a given number. We propose several encoding models and
compare the most promising ones in a password memorability study. The results
of the study show that a model combining part-of-speech sentence templates with
an $n$-gram language model produces the most memorable password
representations.


The proof of Dey's new k-set bound is illustrated.


  This report presents an elementary theory of unification for positive
conjunctive queries. A positive conjunctive query is a formula constructed from
propositional constants, equations and atoms using the conjunction $\wedge$ and
the existential quantifier $\exists$. In particular, empty queries correspond
to existentially quantified systems of equations -- called $\cal E$-formulas.
We provide an algorithm which transforms any conjunctive query into a solved
form. We prove some lattice-theoretic properties of queries. In particular, the
quotient set of $\cal E$-formulas under an equivalence relation forms a
complete lattice. Then we present another lattice -- a lattice of finite
substitutions. We prove that the both lattices are isomorphic. Finally, we
introduce the notion of application of substitutions to formulas and clarify
its relationship to $\cal E$-formulas. This theory can be regarded as a basis
for alternative presentation of logic programming.


  This article reviews recent developments in tests of fundamental physics
using atoms and molecules, including the subjects of parity violation, searches
for permanent electric dipole moments, tests of the CPT theorem and Lorentz
symmetry, searches for spatiotemporal variation of fundamental constants, tests
of quantum electrodynamics, tests of general relativity and the equivalence
principle, searches for dark matter, dark energy and extra forces, and tests of
the spin-statistics theorem. Key results are presented in the context of
potential new physics and in the broader context of similar investigations in
other fields. Ongoing and future experiments of the next decade are discussed.


  Future searches for new physics beyond the Standard Model are without doubt
in need of a diverse approach and experiments with complementary sensitivities
to different types of classes of models. One of the directions that should be
explored is feebly interacting particles (FIPs) with masses below the
electroweak scale. The interest in FIPs has significantly increased in the last
ten years. Searches for FIPs at colliders have intrinsic limitations in the
region they may probe, significantly restricting exploration of the mass range
$m_{\text{FIP}} < 5-10$\,GeV/c$^2$. Beam dump-like experiments, characterized
by the possibility of extremely high luminosity at relatively high energies and
the effective coverage of the production and decay acceptance, are the perfect
option to generically explore the ``coupling frontier'' of the light FIPs.
Several proposals for beam-dump detectors are currently being considered by
CERN for implementation at the SPS ECN3 beam facility. In this we paper we
analyse in depth how the characteristic geometric parameters of a beam dump
experiment influence the signal yield. We apply an inclusive approach by
considering the phenomenology of different types of FIPs. From the various
production modes and kinematics, we demonstrate that the optimal layout that
maximises the production and decay acceptance consists of a detector located on
the beam-axis, at the shortest possible distance from the target defined by the
systems required to suppress the beam-induced backgrounds.


  Many systems can be described using graphs, or networks. Detecting
communities in these networks can provide information about the underlying
structure and functioning of the original systems. Yet this detection is a
complex task and a large amount of work was dedicated to it in the past decade.
One important feature is that communities can be found at several scales, or
levels of resolution, indicating several levels of organisations. Therefore
solutions to the community structure may not be unique. Also networks tend to
be large and hence require efficient processing. In this work, we present a new
algorithm for the fast detection of communities across scales using a local
criterion. We exploit the local aspect of the criterion to enable parallel
computation and improve the algorithm's efficiency further. The algorithm is
tested against large generated multi-scale networks and experiments demonstrate
its efficiency and accuracy.


  The many-particle ground state and excitations of self-assembled InAs quantum
dots are investigated using far-infrared (FIR) and capacitance spectroscopy.
The contributions of quantization and charging energies to the $n$--electron
energy spectrum are determined. For $n = 1$, 2, we find good agreement with a
parabolic model; for higher filling, the Coulomb interaction reveals itself not
only in the capacitance, but also in the FIR excitations


  In this paper, we propose to exploit the limited cache packets as side
information to cancel incoming interference at the receiver side. We consider a
stochastic network where the random locations of base stations and users are
modeled using Poisson point processes. Caching schemes to reap both the local
caching gain and the interference cancellation gain for the users are developed
based on two factors: the density of different user subsets and the packets
cached in the corresponding subsets. The packet loss rate (PLR) is analyzed,
which depends on both the cached packets and the channel state information
(CSI) available at the receiver. Theoretical results reveal the tradeoff
between caching resource and wireless resource. The performance for different
caching schemes are analyzed and the minimum achievable PLR for the distributed
caching is derived.


  The recently completed High Altitude Water Cherenkov (HAWC) gamma-ray
observatory has been taking data with a partial array for more than one year
and is now operating with >95% duty cycle in its full configuration. With an
instantaneous field of view of 2 sr, two-thirds of the sky is surveyed every
day at gamma-ray energies between approximately 100 GeV and 100 TeV. Any source
location in the field of view can be monitored each day, with an exposure of up
to $\sim$ 6 hours. These unprecedented observational capabilities allow us to
continuously scan the highly variable extra-galactic gamma-ray sky. By
monitoring the flaring behavior of Active Galactic Nuclei we aim to
significantly increase the observational data base for characterizing particle
acceleration mechanisms in these sources and for studying cosmological
properties like the extra-galactic background light. In this work we present
first studies of data taken between June 2013 and July 2014 with a partial
array configuration. Flux light curves, binned in week-long intervals, for the
TeV-emitting blazars Markarian 421 and 501 are discussed with respect to
indications of flaring states and we highlight coincident multi-wavelength
observations. Results for both sources show indications of gamma-ray flare
observations and demonstrate that a water Cherenkov detector can monitor
TeV-scale variability of extra-galactic sources on weekly time scales. The
analysis methods presented here can provide daily flux measurements with a
minimum time interval of one transit and will be applied to new data from the
completed HAWC array for monitoring of blazars and other transients.


  We present Karl G. Jansky Very Large Array (VLA) observations of the CO ($J =
2 \rightarrow 1$) line emission towards the $z = 6.419$ quasar SDSS
J$114816.64+525150.3$ (J$1148+5251$). The molecular gas is found to be
marginally resolved with a major axis of $0.9"$ (consistent with previous size
measurements of the CO ($J = 7 \rightarrow 6$) emission). We observe tentative
evidence for extended line emission towards the south west on a scale of
~$1.4"$, but this is only detected at $3.3\sigma$ significance and should be
confirmed. The position of the molecular emission region is in excellent
agreement with previous detections of low frequency radio continuum emission as
well as [C ii] line and thermal dust continuum emission. These CO ($J = 2
\rightarrow 1$) observations provide an anchor for the low excitation part of
the molecular line SED. We find no evidence for extended low excitation
component, neither in the spectral line energy distribution nor the image. We
fit a single kinetic gas temperature model of 50 K. We revisit the gas and
dynamical masses in light of this new detection of a low order transition of
CO, and confirm previous findings that there is no extended reservoir of cold
molecular gas in J$1148+5251$, and that the source departs substantially from
the low $z$ relationship between black hole mass and bulge mass. Hence, the
characteristics of J$1148+5251$ at $z = 6.419$ are very similar to $z$~$2$
quasars, in the lack of a diffuse cold gas reservoir and kpc-size compactness
of the star forming region.


  Suppose we have a signal y which we wish to represent using a linear
combination of a number of basis atoms a_i, y=sum_i x_i a_i = Ax. The problem
of finding the minimum L0 norm representation for y is a hard problem. The
Basis Pursuit (BP) approach proposes to find the minimum L1 norm representation
instead, which corresponds to a linear program (LP) that can be solved using
modern LP techniques, and several recent authors have given conditions for the
BP (minimum L1 norm) and sparse (minimum L0 solutions) representations to be
identical. In this paper, we explore this sparse representation problem} using
the geometry of convex polytopes, as recently introduced into the field by
Donoho. By considering the dual LP we find that the so-called polar polytope P
of the centrally-symmetric polytope P whose vertices are the atom pairs +-a_i
is particularly helpful in providing us with geometrical insight into
optimality conditions given by Fuchs and Tropp for non-unit-norm atom sets. In
exploring this geometry we are able to tighten some of these earlier results,
showing for example that the Fuchs condition is both necessary and sufficient
for L1-unique-optimality, and that there are situations where Orthogonal
Matching Pursuit (OMP) can eventually find all L1-unique-optimal solutions with
m nonzeros even if ERC fails for m, if allowed to run for more than m steps.


  We develop new perturbation techniques for conducting convergence analysis of
various first-order algorithms for a class of nonsmooth optimization problems.
We consider the iteration scheme of an algorithm to construct a perturbed
stationary point set-valued map, and define the perturbing parameter by the
difference of two consecutive iterates. Then, we show that the calmness
condition of the induced set-valued map, together with a local version of the
proper separation of stationary value condition, is a sufficient condition to
ensure the linear convergence of the algorithm. The equivalence of the calmness
condition to the one for the canonically perturbed stationary point set-valued
map is proved, and this equivalence allows us to derive some sufficient
conditions for calmness by using some recent developments in variational
analysis. These sufficient conditions are different from existing results
(especially, those error-bound-based ones) in that they can be easily verified
for many concrete application models. Our analysis is focused on the
fundamental proximal gradient (PG) method, and it enables us to show that any
accumulation of the sequence generated by the PG method must be a stationary
point in terms of the proximal subdifferential, instead of the limiting
subdifferential. This result finds the surprising fact that the solution
quality found by the PG method is in general superior. Our analysis also leads
to some improvement for the linear convergence results of the PG method in the
convex case. The new perturbation technique can be conveniently used to derive
linear rate convergence of a number of other first-order methods including the
well-known alternating direction method of multipliers and primal-dual hybrid
gradient method, under mild assumptions.


  The rare decays of $\Lambda_b$ baryon governed by the quark level transitions
$ b \to s$, are investigated in the fourth quark generation model popularly
known as SM4. Recently it has been shown that SM4, which is a very simple
extension of the standard model, can successfully explain several anomalies
observed in the CP violation parameters of $B$ and $B_s$ mesons. We find that
in this model due to the additional contributions coming from the heavy $t'$
quark in the loop, the branching ratios and other observables in rare
$\Lambda_b$ decays deviate significantly from their SM values. Some of these
modes are within the reach of LHCb experiment and search for such channels are
strongly argued.


  Availability attacks can prevent the unauthorized use of private data and
commercial datasets by generating imperceptible noise and making unlearnable
examples before release. Ideally, the obtained unlearnability prevents
algorithms from training usable models. When supervised learning (SL)
algorithms have failed, a malicious data collector possibly resorts to
contrastive learning (CL) algorithms to bypass the protection. Through
evaluation, we have found that most of the existing methods are unable to
achieve both supervised and contrastive unlearnability, which poses risks to
data protection. Different from recent methods based on contrastive error
minimization, we employ contrastive-like data augmentations in supervised error
minimization or maximization frameworks to obtain attacks effective for both SL
and CL. Our proposed AUE and AAP attacks achieve state-of-the-art worst-case
unlearnability across SL and CL algorithms with less computation consumption,
showcasing prospects in real-world applications.


  The objective of this research is to enable safety-critical systems to
simultaneously learn and execute optimal control policies in a safe manner to
achieve complex autonomy. Learning optimal policies via trial and error, i.e.,
traditional reinforcement learning, is difficult to implement in
safety-critical systems, particularly when task restarts are unavailable. Safe
model-based reinforcement learning techniques based on a barrier transformation
have recently been developed to address this problem. However, these methods
rely on full state feedback, limiting their usability in a real-world
environment. In this work, an output-feedback safe model-based reinforcement
learning technique based on a novel barrier-aware dynamic state estimator has
been designed to address this issue. The developed approach facilitates
simultaneous learning and execution of safe control policies for
safety-critical linear systems. Simulation results indicate that barrier
transformation is an effective approach to achieve online reinforcement
learning in safety-critical systems using output feedback.


  We present an analysis of the symmetries of the interference pattern of
critical currents through a two-dimensional
superconductor-semiconductor-superconductor junction, taking into account
Rashba and Dresselhaus spin-orbit interaction, an arbitrarily oriented magnetic
field, disorder, and structural asymmetries. We relate the symmetries of the
pattern to the absence or presence of symmetries in the Hamiltonian, which
provides a qualitative connection between easily measurable quantities and the
spin-orbit coupling and other symmetries of the junction. We support our
analysis with numerical calculations of the Josephson current based on a
perturbative expansion up to eighth order in tunnel coupling between the normal
region and the superconductors.


  Clustering can be used to extract insights from data or to verify some of the
assumptions held by the domain experts, namely data segmentation. In the
literature, few methods can be applied in clustering qualitative values using
the context associated with other variables present in the data, without losing
interpretability. Moreover, the metrics for calculating dissimilarity between
qualitative values often scale poorly for high dimensional mixed datasets.
  In this study, we propose a novel method for clustering qualitative values,
based on Hierarchical Clustering (HQC), and using Maximum Mean Discrepancy. HQC
maintains the original interpretability of the qualitative information present
in the dataset. We apply HQC to two datasets. Using a mixed dataset provided by
Spotify, we showcase how our method can be used for clustering music artists
based on the quantitative features of thousands of songs. In addition, using
financial features of companies, we cluster company industries, and discuss the
implications in investment portfolios diversification.


  Machine learning (ML) has emerged as a pervasive tool in science,
engineering, and beyond. Its success has also led to several synergies with
molecular dynamics (MD) simulations, which we use to identify and characterize
the major metastable states of molecular systems. Typically, we aim to
determine the relative stabilities of these states and how rapidly they
interchange. This information allows mechanistic descriptions of molecular
mechanisms, enables a quantitative comparison with experiments, and facilitates
their rational design. ML impacts all aspects of MD simulations -- from
analyzing the data and accelerating sampling to defining more efficient or more
accurate simulation models.


  Josephson junctions fabricated on the surface of three-dimensional
topological insulators (TI) show a few unusual properties distinct from
conventional Josephson junctions. In these devices, the Josephson coupling and
the supercurrent are mediated by helical metal, the two-dimensional surface of
the TI. A line junction of this kind is known to support Andreev bound states
at zero energy for phase bias \pi, and consequently the so-called fractional ac
Josephson effect. Motivated by recent experiments on TI-based Josephson
junctions, here we describe a convenient algorithm to compute the bound state
spectrum and the current-phase relation for junctions with finite length and
width. We present analytical results for the bound state spectrum, and discuss
the dependence of the current-phase relation on the length and width of the
junction, the chemical potential of the helical metal, and temperature. A
thorough understanding of the current-phase relation may help in designing
topological superconducting qubits and manipulating Majorana fermions.


  The dependence of the inelasticity in terms of the center of mass energy is
studied in the eikonal formalism, which provides connection between elastic and
inelastic channels. Due to the absence of inelasticity experimental datasets,
the present analysis is based on experimental information available on the full
phase space multiplicity distribution covering a large range of energy, namely
30 $<$ $\sqrt{s}$ $\leq$ 1800 GeV. Our results indicate that the decrease of
inelasticity is consequence of minijets production from semihard interactions
arising from the scattering of gluons carrying only a very small fractions of
the momenta from their parent protons. Alternative methods of estimating the
inelasticity are discussed and predictions to the LHC energies are presented.


  In creating his gravitational field equations Einstein unjustifiedly assumed
that inertial mass, and its energy equivalent, is a source of gravity. Denying
this assumption allows modifying the field equations to a form in which a
positive cosmological constant appears as a uniform density of gravitationally
repulsive matter. This repulsive matter is identified as the back sides of the
'drainholes' (called by some 'traversable wormholes') introduced by the author
in 1973, which attract on the high, front sides and repel more strongly on the
low, back sides. The field equations with a scalar field added produce
cosmological models that 'bounce' off a positive minimum of the scale factor
and accelerate throughout history. The 'dark drainholes' that radiate nothing
visible are hypothesized to constitute the 'dark matter' inferred from
observation, their excess of negative active mass over positive active mass
driving the accelerating expansion. For a universe with spatial curvature zero,
and the ratio of scale factor now to scale factor at bounce equal to the Hubble
radius over the Planck length, the model gives an elapsed time since the bounce
of two trillion years. The solutions for negative spatial curvature exhibit
early stage inflation of great magnitude in short times. Cosmic voids,
filaments, and walls are attributed to separation of the back sides of the
drainholes from the front, driven by their mutual attractive-repulsive
interactions.


  Deligne and Kato proved a formula computing the dimension of the nearby
cycles complex of an l-adic sheaf on a relative curve over an excellent
strictly henselian trait. In this article, we reprove this formula using
Abbes-Saito's ramification theory.


  Eight Accreting Millisecond X-ray Pulsars (AMXPs) are known to date. Optical
and NIR observations carried out during quiescence give a unique opportunity to
constrain the nature of the donor star and to investigate the origin of the
observed quiescent luminosity at long wavelengths. Using data obtained with the
ESO-Very Large Telescope, we performed a deep optical and NIR photometric study
of the fields of XTE J1814-338 and of the ultracompact systems XTE J0929-314
and XTE J1807-294 during quiescence in order to look for the presence of a
variable counterpart. If suitable candidates were found, we also carried out
optical spectroscopy. We present here the first multi-band (VR) detection of
the optical counterpart of XTE J1814-338 in quiescence together with its
optical spectrum. The optical light curve shows variability in both bands
consistent with a sinusoidal modulation at the known 4.3 hr orbital period and
presents a puzzling decrease of the V-band flux around superior conjunction
that may be interpreted as a partial eclipse. The marginal detection of the
very faint counterpart of XTE J0929-314 and deep upper limits for the
optical/NIR counterpart of XTE J1807-294 are also reported. We also briefly
discuss the results reported in the literature for the optical/NIR counterpart
of XTE J1751-305. Our findings are consistent with AMXPs being systems
containing an old, weakly magnetized neutron star, reactivated as a millisecond
radio pulsar during quiescence which irradiates the low-mass companion star.
The absence of type I X-ray bursts and of hydrogen and helium lines in outburst
spectra of ultracompact (P_orb < 1 hr) AMXPs suggests that the companion stars
are likely evolved dwarf stars.


  Since the early work by Ashkin in 1970, optical trapping has become one of
the most powerful tools for manipulating small particles, such as micron sized
beads or single atoms. The optical trapping mechanism is based on the
interaction energy of a dipole and the electric field of the laser light. In
atom trapping, the dominant contribution typically comes from the allowed
optical transition closest to the laser wavelength, whereas for mesoscopic
particles it is given by the bulk polarizability of the material. These two
different regimes of optical trapping have coexisted for decades without any
direct link, resulting in two very different contexts of applications: one
being the trapping of small objects mainly in biological settings, the other
one being dipole traps for individual neutral atoms in the field of quantum
optics. Here we show that for nanoscale diamond crystals containing artificial
atoms, so-called nitrogen vacancy (NV) color centers, both regimes of optical
trapping can be observed at the same time even in a noisy liquid environment.
For wavelengths in the vicinity of the zero-phonon line transition of the color
centers, we observe a significant modification ($10\%$) of the overall trapping
strength. Most remarkably, our experimental findings suggest that owing to the
large number of artificial atoms, collective effects greatly contribute to the
observed trapping strength modification. Our approach adds the powerful
atomic-physics toolbox to the field of nano-manipulation.


  The Onsager formula for the free energy of the two dimensional Ising model
with periodic boundary conditions is proved.


  Neutrino non-standard interactions (NSI), previously introduced for the sun,
are studied in the supernova context. For normal hierarchy the probability for
electron neutrinos and antineutrinos at low energy ($E\lesssim 0.8-0.9 MeV$) is
substantially increased with respect to the non-NSI case and joins its value
for inverse hierarchy which is constant with energy. Also for inverse hierarchy
the NSI and non-NSI probabilities are the same for each neutrino and
antineutrino species. These are the possible visible effects of NSI in the
supernova. The decay into antineutrinos, which has been previously shown to be
implied by dense matter, cannot be seen experimentally, owing to the smallness
of the antineutrino production probability.


  A systematic study of the ground state properties of the entire chains of
even even neutron magic nuclei represented by isotones of traditional neutron
magic numbers N = 8, 20, 40, 50, 82 and 126 has been carried out using
relativistic mean field (rmf) plus Bardeen Cooper Schrieffer (BCS) approach.
Our present investigation includes deformation, binding energy, two proton
separation energy, single particle energy, rms radii along with proton and
neutron density profiles, etc. Several of these results are compared with the
results calculated using non relativistic approach (Skyrme Hartree Fock method)
along with available experimental data and indeed they are found with excellent
agreement. In addition, the possible locations of the proton and neutron drip
lines, the (Z,N) values for the new shell closures, disappearance of
traditional shell closures as suggested by the detailed analyzes of results are
also discussed in detail.


  In this paper, we develop an approach to the problem of closing lemma based
on KAM normal form. The new approach differs from existing $C^1$ perturbation
approach and spectral approach, and can handle the high regularity, high
dimensional cases and even Riemannian metric perturbations. Moreover, the proof
is constructive and effective. We apply the method to the original nearly
integrable setting of Poincar\'e and confirm several old and new conjectures
with weak formulations. First, for Poincar\'e's original setting of nearly
integrable systems, we prove that for typical perturbations, periodic orbits
are asymptotically dense as the size of perturbation tends to zero. Second, we
prove that typical smooth perturbation of the geodesic flow on the flat torus
has asymptotically dense periodic orbits, which partially solves an open
problem since Pugh-Robinson's $C^1$-closing lemma. Third, we prove that for
typical Hamiltonian or contact perturbation of the geodesic flows of the
ellipsoid has asymptotically dense orbit on the energy level, which enhances
the recent researches on strong closing lemma, and also confirms partially a
conjecture of Fish-Hofer in this setting. We also discuss the relation of our
models to the recent researches on many-body localization in physics.


  It is pointed out that the recently discovered analogy between the QCD
description of the Pomeron exchange and the Heisenberg chain may allow to apply
lattice techniques to the high energy phenomena.


  For an $\aleph_1$-categorical atomic class, we clarify the space of types
over the unique model of size $\aleph_1$. Using these results, we prove that if
such a class has a model of size $\beth_1^+$ then it is $\omega$-stable.


  We consider temperate distributions on Euclidean spaces with uniformly
discrete support and locally finite spectrum. We find conditions on
coefficients of distributions under which they are finite sum of derivatives of
generalized lattice Dirac combs. These theorems are derived from properties of
families of discretely supported measures and almost periodic distributions.


  We classify nilpotent associative algebras of dimensions up to 4 over any
field. This is done by constructing the nilpotent associative algebras as
central extensions of algebras of smaller dimension, analogous to methods known
for nilpotent Lie algebras.


  We present the discovery of a rare system detected in the TESS data showing
three different eclipsing-like signals. TIC 452991707 and TIC 452991693 seem to
be the second such system on the sky, whose two components separated about 16"
are gravitationally bounded, or comprise a co-moving pair. The three periods
detected from the TESS data are: PA=1.46155 d, PB=1.77418 d, and PC=1.03989 d,
respectively. The A and B periods belong to TIC 452991707, while the C comes
from the component TIC 452991693. The pair A shows the deepest eclipses, and
its orbit is very slightly eccentric. The third period C has lowest amplitude
(eclipsing or ellipsoidal nature), but originates from TIC 452991693, which is
connected to A+B because both visual components share similar proper motion and
distance. Long-term collection of data from older photometry from various
surveys also shows that the two inner pairs A and B orbit around their
barycenter. Its period is probably of a few years, but for a final derivation
of its orbital parameters one needs more up-to-date data. Hence, we call for
new observations of this amazing system.


  In this paper we study the distribution of the temperature within a body
where the heat is transported only by radiation. Specifically, we consider the
situation where both emission-absorption and scattering processes take place.
We study the initial boundary value problem given by the coupling of the
radiative transfer equation with the energy balance equation on a convex domain
$ \Omega \subset \mathbb{R}^3 $ in the diffusion approximation regime, i.e.
when the mean free path of the photons tends to zero. Using the method of
matched asymptotic expansions we will derive the limit initial boundary value
problems for all different possible scaling limit regimes and we will classify
them as equilibrium or non-equilibrium diffusion approximation. Moreover, we
will observe the formation of boundary and initial layers for which suitable
equations are obtained. We will consider both stationary and time dependent
problems as well as different situations in which the light is assumed to
propagate either instantaneously or with finite speed.


  When considering the statistical properties of a bundle of cosmic microwave
background (CMB) photons propagating through space, the effect of `mixing
geodesics' appears with a distinct signature that depends on the geometry of
space. In a Universe with negative curvature this effect is expected to produce
elongated anisotropy spots on CMB maps. We used COBE-DMR data to look for such
effect. Based on the analysis of a measure of eccentricity of hot spots it
appears that there is a clear indication of an excess eccentricity of hot spots
with respect to that expected from noise alone. This result must be interpreted
with caution as this effect can be due in part to galactic emission. If the
detected eccentricity of anisotropy spots can be attributed to the effect of
mixing it implies the negative curvature of the Universe and a value of $\Omega
< 1$.


  Vibrations are a key source of image degradation in ground-based
instrumentation, especially for high-contrast imaging instruments. Vibrations
reduce the quality of the correction provided by the adaptive optics system,
blurring the science image and reducing the sensitivity of most science
modules. We studied vibrations using the Subaru Coronagraphic Extreme Adaptive
Optics (SCExAO) instrument at the Subaru Telescope as it is the most vibration
sensitive system installed on the telescope. We observed vibrations for all
targets, usually at low frequency, below 10 Hz. Using accelerometers on the
telescope, we confirmed that these vibrations were introduced by the telescope
itself, and not the instrument. It was determined that they were related to the
pitch of the encoders of the telescope drive system, both in altitude and
azimuth, with frequencies evolving proportionally to the rotational speed of
the telescope. Another strong vibration was found in the altitude axis of the
telescope, around the time of transit of the target, when the altitude rotation
speed is below 0.12 arcsec/s. These vibrations are amplified by the 10-Hz
control loop of the telescope, especially in a region between 4 and 6 Hz. In
this work, we demonstrate an accurate characterization of the frequencies of
the telescope vibrations using only the coordinates -right ascension and
declination- of the target, and provide a means by which we can predict them
for any telescope pointing. This will be a powerful tool that can be used by
more advanced wavefront control algorithms, especially predictive control, that
uses informations about the disturbance to calculate the best correction.


  In this paper, we study the special curves and ruled surfaces on helix
hypersurface whose tangent planes make a constant angle with a fixed direction
in Euclidean n-space Besides, we observe some special ruled surfaces in and
give requirement of being developable of the ruled surface. Also, we
investigate the helix surface generated by a plane curve in Euclidean 3-space .


  Urban planning applications (energy audits, investment, etc.) require an
understanding of built infrastructure and its environment, i.e., both
low-level, physical features (amount of vegetation, building area and geometry
etc.), as well as higher-level concepts such as land use classes (which encode
expert understanding of socio-economic end uses). This kind of data is
expensive and labor-intensive to obtain, which limits its availability
(particularly in developing countries). We analyze patterns in land use in
urban neighborhoods using large-scale satellite imagery data (which is
available worldwide from third-party providers) and state-of-the-art computer
vision techniques based on deep convolutional neural networks. For supervision,
given the limited availability of standard benchmarks for remote-sensing data,
we obtain ground truth land use class labels carefully sampled from open-source
surveys, in particular the Urban Atlas land classification dataset of $20$ land
use classes across $~300$ European cities. We use this data to train and
compare deep architectures which have recently shown good performance on
standard computer vision tasks (image classification and segmentation),
including on geospatial data. Furthermore, we show that the deep
representations extracted from satellite imagery of urban environments can be
used to compare neighborhoods across several cities. We make our dataset
available for other machine learning researchers to use for remote-sensing
applications.


  Hub-filament systems (HFSs) being the potential sites of formation of star
clusters and high mass stars, provide a test bed for the current theories that
attempt to explain star formation globally. It is thus important to study a
large number of HFSs using both intensity and velocity information to constrain
these objects better observationally. We present here a study of the
hub-filament system associated with G6.55-0.1 using newly obtained observations
of radio continuum and $J$=2--1 transition of CO, $^{13}$CO, and C$^{18}$O. The
radio continuum maps show multiple peaks that coincide with far-infrared dust
continuum peaks indicating the presence of more than one young massive stars in
the hub of the HFS. We used the velocity information from the C$^{18}$O(2--1)
map to (a) show that the source G6.55-0.1 is not physically associated with the
SNR W28 and (b) disentangle and identify the velocity components genuinely
associated with G6.55-0.1. Among the velocity-coherent structures identified,
the two filaments at 13.8 and 17.3 km s$^{-1}$ contribute a total mass
accretion rate of $\sim$3000 M$_{\odot}$ Myr$^{-1}$ to the hub. Both the
filaments also show V-shaped structure, characteristic of gravitational
collapse, in their velocity profile at the location of the hub. Estimated mass
per unit length of the segments of the filaments are smaller than the critical
line masses derived from virial equilibrium considerations. This suggests that
while the filaments are not gravitationally collapsing as a whole, the spectra
from the hub indicate that the inner parts are dynamically decoupled and
collapsing to form stars.


  Stacking, a heuristic technique for training deep residual networks by
progressively increasing the number of layers and initializing new layers by
copying parameters from older layers, has proven quite successful in improving
the efficiency of training deep neural networks. In this paper, we propose a
theoretical explanation for the efficacy of stacking: viz., stacking implements
a form of Nesterov's accelerated gradient descent. The theory also covers
simpler models such as the additive ensembles constructed in boosting methods,
and provides an explanation for a similar widely-used practical heuristic for
initializing the new classifier in each round of boosting. We also prove that
for certain deep linear residual networks, stacking does provide accelerated
training, via a new potential function analysis of the Nesterov's accelerated
gradient method which allows errors in updates. We conduct proof-of-concept
experiments to validate our theory as well.


  In this paper we study the problem of designing periodic orbits for a special
class of hybrid systems, namely mechanical systems with underactuated
continuous dynamics and impulse events. We approach the problem by means of
optimal control. Specifically, we design an optimal control based strategy that
combines trajectory optimization, dynamics embedding, optimal control
relaxation and root finding techniques. The proposed strategy allows us to
design, in a numerically stable manner, trajectories that optimize a desired
cost and satisfy boundary state constraints consistent with a periodic orbit.
To show the effectiveness of the proposed strategy, we perform numerical
computations on a compass biped model with torso.


  It is shown that for every $k\in\mathbb{N}$ and every spreading sequence
$\{e_n\}_{n\in\mathbb{N}}$ that generates a uniformly convex Banach space $E$,
there exists a uniformly convex Banach space $X_{k+1}$ admitting
$\{e_n\}_{n\in\mathbb{N}}$ as a $k+1$-iterated spreading model, but not as a
$k$-iterated one.


  Model-based clustering methods for continuous data are well established and
commonly used in a wide range of applications. However, model-based clustering
methods for categorical data are less standard. Latent class analysis is a
commonly used method for model-based clustering of binary data and/or
categorical data, but due to an assumed local independence structure there may
not be a correspondence between the estimated latent classes and groups in the
population of interest. The mixture of latent trait analyzers model extends
latent class analysis by assuming a model for the categorical response
variables that depends on both a categorical latent class and a continuous
latent trait variable; the discrete latent class accommodates group structure
and the continuous latent trait accommodates dependence within these groups.
Fitting the mixture of latent trait analyzers model is potentially difficult
because the likelihood function involves an integral that cannot be evaluated
analytically. We develop a variational approach for fitting the mixture of
latent trait models and this provides an efficient model fitting strategy. The
mixture of latent trait analyzers model is demonstrated on the analysis of data
from the National Long Term Care Survey (NLTCS) and voting in the U.S.
Congress. The model is shown to yield intuitive clustering results and it gives
a much better fit than either latent class analysis or latent trait analysis
alone.


  Aqueous foams coarsen with time due to gas diffusion through the liquid. The
mean bubble size grows, and small bubbles vanish. However, coarsening is little
understood for foams with an intermediate liquid content, particularly in the
presence of surfactant-induced attractive forces between the bubbles, measured
by the contact angle. Rigorous bubble growth laws have yet to be developed, and
the evolution of bulk foam properties is unclear. We present a quasi-static
numerical model for coarsening in two-dimensional wet foams, focusing on growth
laws and related bubble properties. The deformation of bubbles is modelled
using a finite-element approach, and the gas flow through both films and
Plateau borders is approximated. We give results for disordered two-dimensional
wet foams with 256 to 1024 bubbles, at liquid fractions from $2\%$ to beyond
the zero-contact-angle jamming transition, and with contact angles up to
$10^\circ$. Simple analytical models are developed to aid interpretation. We
find that nonzero contact angle causes a proxy of the initial coarsening rate
to plateau at large liquid fractions, and that the individual bubble growth
rates are closely related to their effective number of neighbours.


  A graph $G$ is said to be $p$-periodic, if the automorphism group $Aut(G)$
contains an element of order $p$ which preserves no edges. In this paper, we
investigate the behavior of graph polynomials (Negmai and Tutte) with respect
to graph periodicity. In particular, we prove that if $p$ is a prime, then the
coefficients of the Tutte polynomial of such a graph satisfy a certain
necessary condition. This result is illustrated by an example where the Tutte
polynomial is used to rule out the periodicity of the Frucht graph.


  A plasma haloscope has recently been proposed as a feasible approach to
extend the search for dark matter axions above 10 GHz ($\sim$ 40 $\mu$eV),
whereby the microwave cavity in a conventional axion haloscope is supplanted by
a wire array metamaterial. As the plasma frequency of a metamaterial is
determined by its unit cell, and is thus a bulk property, a metamaterial
resonator of any frequency can be made arbitrarily large, in contrast to a
microwave cavity which incurs a steep penalty in volume with increasing
frequency. We have investigated the basic properties of wire array
metamaterials through $S_{21}$ measurements in the 10 GHz range. Excellent
agreement with theoretical models is found, by which we project achievable
quality factors to be of order $10^{4}$ in an actual axion search. Furthermore,
schemes for tuning the array over a usable dynamic range ($30\%$ in frequency)
appear practical from an engineering perspective.


  Partially Observable Markov Decision Processes (POMDPs) are a powerful
framework for planning under uncertainty. They allow to model state uncertainty
as a belief probability distribution. Approximate solvers based on Monte Carlo
sampling show great success to relax the computational demand and perform
online planning. However, scaling to complex realistic domains with many
actions and long planning horizons is still a major challenge, and a key point
to achieve good performance is guiding the action-selection process with
domain-dependent policy heuristics which are tailored for the specific
application domain. We propose to learn high-quality heuristics from POMDP
traces of executions generated by any solver. We convert the belief-action
pairs to a logical semantics, and exploit data- and time-efficient Inductive
Logic Programming (ILP) to generate interpretable belief-based policy
specifications, which are then used as online heuristics. We evaluate
thoroughly our methodology on two notoriously challenging POMDP problems,
involving large action spaces and long planning horizons, namely, rocksample
and pocman. Considering different state-of-the-art online POMDP solvers,
including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed
in Answer Set Programming (ASP) yield performance superior to neural networks
and similar to optimal handcrafted task-specific heuristics within lower
computational time. Moreover, they well generalize to more challenging
scenarios not experienced in the training phase (e.g., increasing rocks and
grid size in rocksample, incrementing the size of the map and the aggressivity
of ghosts in pocman).


  The paper (in French) exemplifies graphically a solution of the heat equation
which is a 1-dimensional unfolding of an elliptic umbilic catastrophe. The
example is due to James Damon and adapts Thom-Mather's singularity theory to
multiscale models of scale-space analysis in image processing.


  An efficient numerical code FP3D has been developed to calculate particle
orbits and evaluate particle confinement in 3D magnetic fields including
stellarators and tokamaks with 3D fields. The magnetic field is either
calculated from coils directly or obtained from equilibrium codes. FP3D has
been verified with the 3D equilibrium code VMEC (S. P. Hirshman, Phys. Fluids
26, 3553 (1983)) for magnetic field calculation and with the drift-kinetic code
SFINCS (M. Landreman, Physics of Plasmas 21 (4) (2014)) for neoclassical
transport. The code has been applied successfully to the NCSX stellarator (B.
Nelson, Fusion Engineering and design 66 (2003)) for the calculation of
neoclassical transport coefficient with the 3D magnetic field obtained directly
from coils. FP3D is also used to calculate ripple losses in the tokamak EAST
(W. Yuanxi, Plasma Science Technology 8 (3) (2006)).


  The European Economic Area Electricity Network Benchmark (EEA-ENB) is a
multi-area power system representing the European network of transmission
systems for electricity to facilitate the application of distributed control
techniques. In the EEA-ENB we consider the Load Frequency Control (LFC) problem
in the presence of renewable energy sources (RESs), and energy storage systems
(ESSs). RESs are known to cause instability in power networks due to their
inertia-less and intermittent characteristics, while ESSs are introduced as a
resource to mitigate the problem. In the EEA-ENB, particular attention is
dedicated to Distributed Model Predictive Control (DMPC), whose application is
often limited to small and homogeneous test cases due to the lack of
standardized large-scale scenarios for testing, and due to the large
computation time required to obtain a centralized MPC action for performance
comparison with DMPC strategies under consideration. The second problem is
exacerbated when the scale of the system grows. To address these challenges and
to provide a real-world-based and control-independent benchmark, the EEA-ENB
has been developed. The benchmark includes a centralized MPC strategy providing
performance and computation time metrics to compare distributed control within
a repeatable and realistic simulation environment.


  A central goal in social science is to evaluate the causal effect of a
policy. One dominant approach is through panel data analysis in which the
behaviors of multiple units are observed over time. The information across time
and space motivates two general approaches: (i) horizontal regression (i.e.,
unconfoundedness), which exploits time series patterns, and (ii) vertical
regression (e.g., synthetic controls), which exploits cross-sectional patterns.
Conventional wisdom states that the two approaches are fundamentally different.
We establish this position to be partly false for estimation but generally true
for inference. In particular, we prove that both approaches yield identical
point estimates under several standard settings. For the same point estimate,
however, each approach quantifies uncertainty with respect to a distinct
estimand. In turn, the confidence interval developed for one estimand may have
incorrect coverage for another. This emphasizes that the source of randomness
that researchers assume has direct implications for the accuracy of inference.


  RCW49 and its ionizing cluster form an extensive, complex region, widely
studied at IR and optical wavelengths. Molonglo 843 MHz and ATCA data at 1.4
and 2.4 GHz showed two shells. Recent high-resolution IR imaging revealed a
complex dust structure and ongoing star formation. New high-bandwidth and
high-resolution data of the RCW49 field were obtained to survey the radio
emission at arcsec scale and investigate the small-scale features and nature of
the HII region.- Data were collected with the new 2-GHz bandwidth receivers and
the CABB correlator of the ATCA, at 5.5 and 9.0 GHz. In addition, archival
observations at 1.4 and 2.4 GHz have been re-reduced and re-analyzed, together
with optical, IR, X-ray, and gamma-ray observations.- The CABB data result in
the most detailed radio continuum images of RCW49 to date. The radio emission
closely mimics the near-IR emission observed by Spitzer, showing pillars and
filaments. The brightest continuum emission comes from the region known as the
bridge. The overall flattish spectral index is consistent with a free-free
emission mechanism. However, hints of nonthermal components are also present in
the bridge. A jet-like structure surrounded by a bubble feature whose nature is
still unclear has been discovered close to the Westerlund~2 core. Two apparent
bow shocks and a number of discrete sources have been detected as well in the
surroundings of RCW49. We also report on and discuss the possible detection of
a H RRL.- The radio results support an association between the cm continuum and
molecular emission. The detection of the RRL kinematically favors a RCW49
distance of 6--7 kpc. If the negative spectral indices measured at the bridge
are caused by synchrotron emission, we propose a scenario where high-energy
emission could be produced. Finally, the newly discovered jet-like structure
appears to deserve a detailed study by itself.


  The development of personalized recommendation has significantly improved the
accuracy of information matching and the revenue of e-commerce platforms.
Recently, it has 2 trends: 1) recommender systems must be trained timely to
cope with ever-growing new products and ever-changing user interests from
online marketing and social network; 2) SOTA recommendation models introduce
DNN modules to improve prediction accuracy. Traditional CPU-based recommender
systems cannot meet these two trends, and GPU- centric training has become a
trending approach. However, we observe that GPU devices in training recommender
systems are underutilized, and they cannot attain an expected throughput
improvement as what it has achieved in CV and NLP areas. This issue can be
explained by two characteristics of these recommendation models: First, they
contain up to a thousand input feature fields, introducing fragmentary and
memory-intensive operations; Second, the multiple constituent feature
interaction submodules introduce substantial small-sized compute kernels. To
remove this roadblock to the development of recommender systems, we propose a
novel framework named PICASSO to accelerate the training of recommendation
models on commodity hardware. Specifically, we conduct a systematic analysis to
reveal the bottlenecks encountered in training recommendation models. We
leverage the model structure and data distribution to unleash the potential of
hardware through our packing, interleaving, and caching optimization.
Experiments show that PICASSO increases the hardware utilization by an order of
magnitude on the basis of SOTA baselines and brings up to 6x throughput
improvement for a variety of industrial recommendation models. Using the same
hardware budget in production, PICASSO on average shortens the walltime of
daily training tasks by 7 hours, significantly reducing the delay of continuous
delivery.


  In this paper, combining the $p$-capacity for $p\in (1, n)$ with the Orlicz
addition of convex domains, we develop the $p$-capacitary
Orlicz-Brunn-Minkowski theory. In particular, the Orlicz $L_{\phi}$ mixed
$p$-capacity of two convex domains is introduced and its geometric
interpretation is obtained by the $p$-capacitary Orlicz-Hadamard variational
formula. The $p$-capacitary Orlicz-Brunn-Minkowski and Orlicz-Minkowski
inequalities are established, and the equivalence of these two inequalities are
discussed as well. The $p$-capacitary Orlicz-Minkowski problem is proposed and
solved under some mild conditions on the involving functions and measures. In
particular, we provide the solutions for the normalized $p$-capacitary $L_q$
Minkowski problems with $q>1$ for both discrete and general measures.


  We study the phenomenology of the mu-tau and e-tau lepton flavour violation
(LFV) in a general two Higgs Doublet Model (2HDM) including the supersymmetric
case. We consider several LFV decay modes of the charged fermion tau, namely
tau-> l_jgamma, tau->l_j l_k l_k and tau-> l_jeta. The predictions and the
correlations among the rates of the above processes are computed. In
particular, it is shown that tau->l_jgamma processes are the most sensitive
channels to Higgs-mediated LFV specially if the splitting among the neutral
Higgs bosons masses is not below the 10% level.


  We report that metal-free phthalocyanine (H2Pc) molecules with a central
cavity are able to incorporate Ag atoms from an Ag(110) surface thus creating
silver-phthalocyanine (AgPc). The reaction was investigated by means of
scanning tunneling microscopy (STM) under ultrahigh vacuum, and the metalation
of \ce{H2Pc} at the interface was confirmed with X-ray photoelectron
spectroscopy. Three different kinds of molecules were found on the surface that
are assigned to H2Pc, the corresponding dehydrogenated molecules (Pc) and AgPc.
The relative amounts of Pc and AgPc increase with increasing annealing
temperature. We suggest that the reaction with Ag atoms from the steps of the
surface occurs favorably only for already dehydrogenated molecules; thus, the
metalation of H2Pc is likely limited by the heat-induced dehydrogenation.
Density functional theory simulations of the reaction path are presented to
corroborate this hypothesis.


  Combinatorial algorithms are widely used for decision-making and knowledge
discovery, and it is important to ensure that their output remains stable even
when subjected to small perturbations in the input. Failure to do so can lead
to several problems, including costly decisions, reduced user trust, potential
security concerns, and lack of replicability. Unfortunately, many fundamental
combinatorial algorithms are vulnerable to small input perturbations. To
address the impact of input perturbations on algorithms for weighted graph
problems, Kumabe and Yoshida (FOCS'23) recently introduced the concept of
Lipschitz continuity of algorithms. This work explores this approach and
designs Lipschitz continuous algorithms for covering problems, such as the
minimum vertex cover, set cover, and feedback vertex set problems.
  Our algorithm for the feedback vertex set problem is based on linear
programming, and in the rounding process, we develop and use a technique called
cycle sparsification, which may be of independent interest.


  Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a
labeled source domain to perform well on an unlabeled target domain with some
data distribution shift. While many methods have been proposed in the
literature, fair and realistic evaluation remains an open question,
particularly due to methodological difficulties in selecting hyperparameters in
the unsupervised setting. With SKADA-Bench, we propose a framework to evaluate
DA methods and present a fair evaluation of existing shallow algorithms,
including reweighting, mapping, and subspace alignment. Realistic
hyperparameter selection is performed with nested cross-validation and various
unsupervised model selection scores, on both simulated datasets with controlled
shifts and real-world datasets across diverse modalities, such as images, text,
biomedical, and tabular data with specific feature extraction. Our benchmark
highlights the importance of realistic validation and provides practical
guidance for real-life applications, with key insights into the choice and
impact of model selection approaches. SKADA-Bench is open-source, reproducible,
and can be easily extended with novel DA methods, datasets, and model selection
criteria without requiring re-evaluating competitors. SKADA-Bench is available
on GitHub at https://github.com/scikit-adaptation/skada-bench.


  This paper studies the canonical symmetric connection $\nabla$ associated to
any Lie group $G$. The salient properties of $\nabla$ are stated and proved.
The Lie symmetries of the geodesic system of a general linear connection are
formulated. The results are then applied to $\nabla$ in the special case where
the Lie algebra $\g$ of $G$, has a codimension one abelian nilradical. The
conditions that determine a Lie symmetry in such a case are completely
integrated. Finally the results obtained are compared with some
four-dimensional Lie groups whose Lie algebras have three-dimensional abelian
nilradicals, for which the calculations were performed by MAPLE.


  Recently, a mechanism for relaxing a large cosmological constant (CC) has
been proposed [arxiv:0902.2215], which permits solutions with low Hubble rates
at late times without fine-tuning. The setup is implemented in the LXCDM
framework, and we found a reasonable cosmological background evolution similar
to the LCDM model with a fine-tuned CC. In this work we analyse analytically
the perturbations in this relaxation model, and we show that their evolution is
also similar to the LCDM model, especially in the matter era. Some tracking
properties of the vacuum energy are discussed, too.


  We consider a zeroth-order distributed optimization problem, where the global
objective function is a black-box function and, as such, its gradient
information is inaccessible to the local agents. Instead, the local agents can
only use the values of the objective function to estimate the gradient and
update their local decision variables. In this paper, we also assume that these
updates are done asynchronously. To solve this problem, we propose an
asynchronous zeroth-order distributed optimization method that relies on a
one-point residual feedback to estimate the unknown gradient. We show that this
estimator is unbiased under asynchronous updating, and theoretically analyze
the convergence of the proposed method. We also present numerical experiments
that demonstrate that our method outperforms two-point methods under
asynchronous updating. To the best of our knowledge, this is the first
asynchronous zeroth-order distributed optimization method that is also
supported by theoretical guarantees.


  We have measured rapidity densities dN/dy of pions and kaons over a broad
rapidity range (-0.1 < y < 3.5) for central Au+Au collisions at sqrt(snn) = 200
GeV. These data have significant implications for the chemistry and dynamics of
the dense system that is initially created in the collisions. The full
phase-space yields are 1660 +/- 15 +/- 133 (pi+), 1683 +/- 16 +/- 135 (pi-),
286 +/- 5 +/- 23 (K+) and 242 +/- 4 +/- 19 (K-). The systematics of the strange
to non--strange meson ratios are found to track the variation of the
baryo-chemical potential with rapidity and energy. Landau--Carruthers
hydrodynamic is found to describe the bulk transport of the pions in the
longitudinal direction.


  Using a Mathematica code, we present a straightforward numerical analysis of
the 384-dimensional solution space of signed permutation 4x4 matrices, which in
sets of four provide representations of the GR(4,4) algebra, closely related to
the N=1 (simple) supersymmetry algebra in 4-dimensional spacetime. Following
after ideas discussed in previous papers about automorphisms and classification
of adinkras and corresponding supermultiplets, we make a new and alternative
proposal to use equivalence classes of the (unsigned) permutation group S4 to
define distinct representations of higher dimensional spin bundles within the
context of adinkras. For this purpose, the definition of a dual operator akin
to the well-known Hodge star is found to partition the space of these GR(4,4)
representations into three suggestive classes.


  Let $A$ be an irreducible (entrywise) nonnegative $n\times n$ matrix with
eigenvalues $$\rho, b+ic,b-ic, \lambda_4,\cdots,\lambda_n,$$ where $\rho$ is
the Perron eigenvalue. It is shown that for any $t \in [0, \infty)$ there is a
nonnegative matrix with eigenvalues $$\rho+ \tilde t,\lambda_2+t,\lambda_3+t,
\lambda_4 \cdots,\lambda_n,$$ whenever $\tilde t \ge \gamma_n t$ with
$\gamma_3=1, \gamma_4 = 2, \gamma_5=\sqrt 5$ and $\gamma_n = 2.25$ for $n \ge
6$. The result improves that of Guo et al. Our proof depends on an auxiliary
result in geometry asserting that the area of an $n$-sided convex polygon is
bounded by $\gamma_n$ times the maximum area of the triangle lying inside the
polygon.


  We have used ion-irradiation to damage the (001) surfaces of SmB_6 single
crystals to varying depths, and have measured the resistivity as a function of
temperature for each depth of damage. We observe a reduction in the residual
resistivity with increasing depth of damage. Our data are consistent with a
model in which the surface state is not destroyed by the ion-irradiation, but
instead the damaged layer is poorly conducting and the initial surface state is
reconstructed below the damage. This behavior is consistent with a surface
state that is topologically protected.


  With explosive growth in the number of mobile devices, the mobile malware is
rapidly spreading as well, and the number of encountered malware families is
increasing. Existing solutions, which are mainly based on one malware detector
running on the phone or in the cloud, are no longer effective. Main problem
lies in the fact that it might be impossible to create a unique mobile malware
detector that would be able to detect different malware families with high
accuracy, being at the same time lightweight enough not to drain battery
quickly and fast enough to give results of detection promptly. The proposed
approach to mobile malware detection is analogous to general practitioner
versus specialist approach to dealing with a medical problem. Similarly to a
general practitioner that, based on indicative symptoms identifies potential
illnesses and sends the patient to an appropriate specialist, our detection
system distinguishes among symptoms representing different malware families
and, once the symptoms are detected, it triggers specific analyses. A system
monitoring application operates in the same way as a general practitioner. It
is able to distinguish between different symptoms and trigger appropriate
detection mechanisms. As an analogy to different specialists, an ensemble of
detectors, each of which specifically trained for a particular malware family,
is used. The main challenge of the approach is to define representative
symptoms of different malware families and train detectors accordingly to them.
The main goal of the poster is to foster discussion on the most representative
symptoms of different malware families and to discuss initial results in this
area obtained by using Malware Genome project dataset.


  Is it possible to define what we could mean by chaos in a space-time metric
(even in the simplest toy-model studies)? Is it of importance for phenomena we
may search for in Nature?


  Starting from the von Neumann-Maxwell equations for the Wigner
quasi-probability distribution and for the self-consistent electric field, the
quantum analog of the classical single-wave model has been derived. The linear
stability of the quantum single-wave model has been studied, and periodic in
time patterns have been found both analytically and numerically. In addition,
some features of quantum chaos have been detected in the unstable region in
parameter space. Further, a class of standing-wave solutions of the quantum
single-wave model has also been found, which have been observed to behave as
stable solitary-wave structures. The analytical results have been finally
compared to the exact system dynamics obtained by solving the corresponding
equations in Schrodinger representation numerically.


  Traumatic brain injury is a global public health problem associated with
chronic neurological complications and long-term disability. Biomarkers that
map onto the underlying brain pathology driving these complications are
urgently needed to identify individuals at risk for poor recovery and to inform
design of clinical trials of neuroprotective therapies. Neuroinflammation and
neurodegeneration are two endophenotypes associated with increases in brain
extracellular water content after trauma. The objective of this study was to
describe the relationship between a neuroimaging biomarker of extracellular
free water content and the clinical features of patients with traumatic brain
injury. We analyzed a cohort of 64 adult patients requiring hospitalization for
non-penetrating traumatic brain injury of all severities as well as 32 healthy
controls. Patients underwent brain MRI and clinical neuropsychological
assessment in the subacute (2-weeks) and chronic (6-months) post-injury period,
and controls underwent a single MRI. For each subject, we derived a summary
score representing deviations in whole brain white matter (1) extracellular
free water volume fraction (VF) and (2) free water-corrected fractional
anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was
significantly higher in TBI patients in the subacute and chronic post-injury
period relative to controls. SAS for VF significantly correlated with
neuropsychological functioning in the subacute, but not chronic post-injury
period. These findings indicate abnormalities in whole brain white matter
extracellular water fraction in patients with TBI and are an important step
toward identifying and validating noninvasive biomarkers that map onto the
pathology driving disability after TBI.


  Discrete reinforcement learning (RL) algorithms have demonstrated exceptional
performance in solving sequential decision tasks with discrete action spaces,
such as Atari games. However, their effectiveness is hindered when applied to
continuous control problems due to the challenge of dimensional explosion. In
this paper, we present the Soft Decomposed Policy-Critic (SDPC) architecture,
which combines soft RL and actor-critic techniques with discrete RL methods to
overcome this limitation. SDPC discretizes each action dimension independently
and employs a shared critic network to maximize the soft $Q$-function. This
novel approach enables SDPC to support two types of policies: decomposed actors
that lead to the Soft Decomposed Actor-Critic (SDAC) algorithm, and decomposed
$Q$-networks that generate Boltzmann soft exploration policies, resulting in
the Soft Decomposed-Critic Q (SDCQ) algorithm. Through extensive experiments,
we demonstrate that our proposed approach outperforms state-of-the-art
continuous RL algorithms in a variety of continuous control tasks, including
Mujoco's Humanoid and Box2d's BipedalWalker. These empirical results validate
the effectiveness of the SDPC architecture in addressing the challenges
associated with continuous control.


  While the linear behavior of gravity in braneworld models is well understood,
much less is known about full non-linear gravitational effects. Even when they
agree at the linear level, these could be expected to distinguish braneworlds
from a lower-dimensional theory with no brane. Black holes are a good testing
ground for such studies, as they are nonlinear solutions that would be expected
to reflect the background geometry. In particular, we assess the role of black
hole quasinormal modes in gravitational experiments devised to be sensitive to
the existence of the brane, in a lower-dimensional setting where we have
analytical control. We compute quasinormal modes of brane-localized black holes
and find that they follow the entropy of the corresponding black hole. This
observation allows us to conclude that, surprisingly, the scattering problem we
consider, at least in some regimes, does not distinguish between non-linear
gravitational effects of black holes in AdS space with a brane and black holes
in a spacetime of one lower dimension.


  Recent planetary data and geophysical modelling suggest that hydrothermal
activity is ongoing under the ice crust of Enceladus, one of Saturn's moons.
According to these models, hydrothermal flow in the porous, rocky core of the
satellite is driven by tidal deformation that induces dissipation and
volumetric internal heating. Despite the effort in the modelling of Enceladus'
interior, systematic understanding---and even basic scaling laws---of
internally-heated porous convection and hydrothermal activity are still
lacking. In this article, using an idealised model of an internally-heated
porous medium, we explore numerically and theoretically the flows that develop
close and far from the onset of convection. In particular, we quantify
heat-transport efficiency by convective flows as well as the typical extent and
intensity of heat-flux anomalies created at the top of the porous layer. With
our idealised model, we derive simple and general laws governing the
temperature and hydrothermal velocity that can be driven in the oceans of icy
moons. In the future, these laws could help better constraining models of the
interior of Enceladus and other icy satellites.