  In this study, we investigate the attentiveness exhibited by participants
sourced through Amazon Mechanical Turk (MTurk), thereby discovering a
significant level of inattentiveness amongst the platform's top crowd workers
(those classified as 'Master', with an 'Approval Rate' of 98% or more, and a
'Number of HITS approved' value of 1,000 or more). A total of 564 individuals
from the United States participated in our experiment. They were asked to read
a vignette outlining one of four hypothetical technology products and then
complete a related survey. Three forms of attention check (logic, honesty, and
time) were used to assess attentiveness. Through this experiment we determined
that a total of 126 (22.3%) participants failed at least one of the three forms
of attention check, with most (94) failing the honesty check - followed by the
logic check (31), and the time check (27). Thus, we established that
significant levels of inattentiveness exist even among the most elite MTurk
workers. The study concludes by reaffirming the need for multiple forms of
carefully crafted attention checks, irrespective of whether participant quality
is presumed to be high according to MTurk criteria such as 'Master', 'Approval
Rate', and 'Number of HITS approved'. Furthermore, we propose that researchers
adjust their proposals to account for the effort and costs required to address
participant inattentiveness.


  We present an analysis of the spectator mechanism of $\Delta $-isobar
production in the pion photoproduction on nuclei with the emission of two
nucleons. The reaction mechanism is studied within the framework of the $\Delta
N$-correlation model, which considers the isobar and nucleon of the $\Delta
N$-system produced in the nucleus at the virtual $NN \to \Delta N$ transition,
to be in a dynamic relationship. The two-particle transition operator for
nuclei is obtained by the \textit{S}-matrix approach. We consider the
properties of the spectator mechanism of isobar production using the example of
the reaction $^{16}$O$ (\gamma ,\ \pi ^{ - }pn)^{14}$O. Numerical estimates of
the cross section are obtained in the kinematic region, where it is possible to
expect the manifestation of bound isobar-nuclear states.


  Conventional Domain Generalization (CDG) utilizes multiple labeled source
datasets to train a generalizable model for unseen target domains. However, due
to expensive annotation costs, the requirements of labeling all the source data
are hard to be met in real-world applications. In this paper, we investigate a
Single Labeled Domain Generalization (SLDG) task with only one source domain
being labeled, which is more practical and challenging than the CDG task. A
major obstacle in the SLDG task is the discriminability-generalization bias:
the discriminative information in the labeled source dataset may contain
domain-specific bias, constraining the generalization of the trained model. To
tackle this challenging task, we propose a novel framework called
Domain-Specific Bias Filtering (DSBF), which initializes a discriminative model
with the labeled source data and then filters out its domain-specific bias with
the unlabeled source data for generalization improvement. We divide the
filtering process into (1) feature extractor debiasing via k-means
clustering-based semantic feature re-extraction and (2) classifier
rectification through attention-guided semantic feature projection. DSBF
unifies the exploration of the labeled and the unlabeled source data to enhance
the discriminability and generalization of the trained model, resulting in a
highly generalizable model. We further provide theoretical analysis to verify
the proposed domain-specific bias filtering process. Extensive experiments on
multiple datasets show the superior performance of DSBF in tackling both the
challenging SLDG task and the CDG task.


  Based on the isospin-dependent quantum molecular dynamics model along with
the GEMINI model, heavy-ion collisions at intermediate energies are studied. We
calculate the production cross sections of different fragments for reactions of
112Sn+112Sn and 124Sn+124Sn at different beam energies. The species and
production cross sections of neutron-rich isotopes are generally dependent on
the isospin of the system and the incident energies. The nucleon 48Ca and 54Ca
are more productive for the neutron-rich system at 30 to 150 MeV/nucleon.


  Dark Matter is a necessary ingredient for a complete theory of Nature, which
has so far remained elusive in laboratory searches for new particles. Searches
at current and future colliders are in principle a promising way to search for
electroweak charged dark matter particles, but the sensitivity of experiments
at the LHC and future $pp$ colliders falls short to fully probe the whole mass
range allowed for electroweak charged dark matter particles, which extends in
principle up to the O(0.1) PeV. In this work we examine the effect of on-shell
and off-shell propagation of electroweak charged thermal dark matter particles
on integrated and differential rates of several Standard Model final states at
the muon collider, considering candidates from weak 2-plet at the TeV scale up
to 7-plet and 9-plet in the O(0.1) PeV ballpark. For fermionic WIMPs we find
that all dark matter candidates with $n\leq 5$, corresponding to a thermal mass
up to 14~TeV, can be excluded at the high-energy muon collider for some
center-of-mass energy at or below 14~TeV. For the $n>5$ WIMPs our results show
that higher energy muon colliders offer a route to conclusively probe both
scalar and fermionic WIMPs off-shell production all the way up to the
perturbativity bound for WIMP dark matter at O(0.1)~PeV. Our results bring
WIMPs over the whole allowed mass range in the realm of collider searches and
motivate research and development for the realization of a high energy muon
collider.


  The problem of cylindrically symmetric vacuum solutions of Brans-Dicke scalar
fields has been studied. Exact solutions have been obtained for the vacuum B-D
field equations for the cylindrically symmetric Einstein-Rosen metric. The
solutions obtained in the present work are generalized solutions of the problem
which has been studied by Rao et al. (Annals of Physics, Vol. 87, 1974).The
physical and kinematical behaviors of the solutions relevant to conformal space
is also discussed in details, these solutions will be beneficial in solving the
problems for investigating the different model of our universe.


  Although conceptualization has been widely studied in semantics and knowledge
representation, it is still challenging to find the most accurate concept
phrases to characterize the main idea of a text snippet on the fast-growing
social media. This is partly attributed to the fact that most knowledge bases
contain general terms of the world, such as trees and cars, which do not have
the defining power or are not interesting enough to social media app users.
Another reason is that the intricacy of natural language allows the use of
tense, negation and grammar to change the logic or emphasis of language, thus
conveying completely different meanings. In this paper, we present TAG, a
high-quality concept matching dataset consisting of 10,000 labeled pairs of
fine-grained concepts and web-styled natural language sentences, mined from the
open-domain social media. The concepts we consider represent the trending
interests of online users. Associated with TAG is a concept graph of these
fine-grained concepts and entities to provide the structural context
information. We evaluate a wide range of popular neural text matching models as
well as pre-trained language models on TAG, and point out their insufficiency
to tag social media content with the most appropriate concept. We further
propose a novel graph-graph matching method that demonstrates superior
abstraction and generalization performance by better utilizing both the
structural context in the concept graph and logic interactions between semantic
units in the sentence via syntactic dependency parsing. We open-source both the
TAG dataset and the proposed methods to facilitate further research.


  Deep Neural Networks (DNNs) can be represented as graphs whose links and
vertices iteratively process data and solve tasks sub-optimally. Complex
Network Theory (CNT), merging statistical physics with graph theory, provides a
method for interpreting neural networks by analysing their weights and neuron
structures. However, classic works adapt CNT metrics that only permit a
topological analysis as they do not account for the effect of the input data.
In addition, CNT metrics have been applied to a limited range of architectures,
mainly including Fully Connected neural networks. In this work, we extend the
existing CNT metrics with measures that sample from the DNNs' training
distribution, shifting from a purely topological analysis to one that connects
with the interpretability of deep learning. For the novel metrics, in addition
to the existing ones, we provide a mathematical formalisation for Fully
Connected, AutoEncoder, Convolutional and Recurrent neural networks, of which
we vary the activation functions and the number of hidden layers. We show that
these metrics differentiate DNNs based on the architecture, the number of
hidden layers, and the activation function. Our contribution provides a method
rooted in physics for interpreting DNNs that offers insights beyond the
traditional input-output relationship and the CNT topological analysis.


  In the landscape perspective, our Universe begins with a quantum tunneling
from an eternally-inflating parent vacuum, followed by a period of slow-roll
inflation. We investigate the tunneling process and calculate the probability
distribution for the initial conditions and for the number of e-folds of
slow-roll inflation, modeling the landscape by a small-field one-dimensional
random Gaussian potential. We find that such a landscape is fully consistent
with observations, but the probability for future detection of spatial
curvature is rather low, $P \sim 10^{-3}$.


  Recent work on red and blue quasi-stellar objects (QSOs) has identified
peculiar number distributions as a function of radio loudness that we explore
and attempt to explain from the perspective of a picture in which a subset of
the population of active galaxies evolves from the radio loud to the radio
quiet state. Because the time evolution is slowed down by an order of magnitude
or more for the radio quiet phase, the numbers of red and blue QSOs approach
each other at the extreme end of the radio quiet range of radio loudness with
larger numbers. The rapid time evolution of most radio loud phases, instead,
makes the numbers similar but lower at the far radio loud end. At the midpoint
of radio loudness, instead, the differences between red and blue QSOs
experience their largest values which results from accretion rapidly spinning
black holes down but subsequently spinning them up more slowly. Recovering
these basic features of the observations is evidence for rapid evolution away
from radio loudness and slow evolution in radio quiet states.


  Although learning in high dimensions is commonly believed to suffer from the
curse of dimensionality, modern machine learning methods often exhibit an
astonishing power to tackle a wide range of challenging real-world learning
problems without using abundant amounts of data. How exactly these methods
break this curse remains a fundamental open question in the theory of deep
learning. While previous efforts have investigated this question by studying
the data (D), model (M), and inference algorithm (I) as independent modules, in
this paper, we analyze the triplet (D, M, I) as an integrated system and
identify important synergies that help mitigate the curse of dimensionality. We
first study the basic symmetries associated with various learning algorithms
(M, I), focusing on four prototypical architectures in deep learning:
fully-connected networks (FCN), locally-connected networks (LCN), and
convolutional networks with and without pooling (GAP/VEC). We find that
learning is most efficient when these symmetries are compatible with those of
the data distribution and that performance significantly deteriorates when any
member of the (D, M, I) triplet is inconsistent or suboptimal.


  We explore the old idea that, in a theory containing several gauge groups,
the topological defects of one gauge group coincide with those of another gauge
group. This simple 'unification' constraint has deep consequences, the best
known of which is a natural explanation of the fractional electric charge of
quarks. Here we explore the consequences of this idea for the phase diagram, in
a toy model U(1)x U(1).


  There is evidence that soft gamma repeaters (SGRs) are neutron stars which
experience frequent starquakes, possibly driven by an evolving, ultra-strong
magnetic field. The empirical power-law distribution of SGR burst energies,
analogous to the Gutenberg-Richter law for earthquakes, exhibits a turn-over at
high energies consistent with a global limit on the crust fracture size. With
such large starquakes occurring, the significant excitation of global seismic
oscillations (GSOs) seems likely. Moreover, GSOs may be self-exciting in a
stellar crust that is strained by many, randomly-oriented stresses. We explain
why low-order toroidal modes, which preserve the shape of the star and have
observable frequencies as low as ~ 30 Hz, may be especially susceptible to
excitation. We estimate the eigenfrequencies as a function of stellar mass and
radius, and their magnetic and rotational shiftings/splittings. We also
describes ways in which these modes might be detected and damped. There is
marginal evidence for 23 ms oscillations in the hard initial pulse of the 1979
March 5th event. This could be due to the $_3t_0$ mode in a neutron star with B
~ 10^{14} G or less; or it could be the fundamental toroidal mode if the field
in the deep crust of SGR 0526-66 is ~ 4 X 10^{15} G, in agreement with other
evidence. If confirmed, GSOs would give corroborating evidence for
crust-fracturing magnetic fields in SGRs: B >~ 10^{14} G.


  We present a set of N-dimensional functions, based on generalized
SU(N)-symmetric coherent states, that represent finite-dimensional Wigner
functions, Q-functions, and P-functions. We then show the fundamental
properties of these functions and discuss their usefulness for analyzing
N-dimensional pure and mixed quantum states.


  Let $X$ be a locally compact non compact Hausdorff topological space.
Consider the algebras $C(X)$, $C_b(X)$, $C_0(X)$, and $C_{00}(X)$ of
respectively arbitrary, bounded, vanishing at infinity, and compactly supported
continuous functions on $X$. From these, the second and third are
$C^{*}$-algebras, the forth is a normed algebra, where as the first is only a
topological algebra. The interesting fact about these algebras is that if one
of them is given, the rest can be obtained using functional analysis tools. For
instance, given the $C^{*}$-algebra $C_0(X)$, one can get the other three
algebras by $C_{00}(X)=K(C_0(X))$, $C_b(X)=M(C_0(X))$,
$C(X)=\Gamma(K(C_0(X)))$, that is by forming the Pedersen's ideal, the
multiplier algebra, and the unbounded multiplier algebra of the Pedersen's
ideal, respectively.In this article we consider the possibility of these
transitions for general $C^{*}$-algebra . The difficult part is to start with a
pro_$C^{*}$-algebra $A$ and to construct a $C^{*}$-algebra $A_0$ such that
$A=\Gamma(K(A_0))$. The pro-$C^{*}$-algebras for which this is possible are
called {\it locally compact} and we have characterized them using a concept
similar to approximate identities.


  The Delone (Selling) scalars, which are used in unit cell reduction and in
lattice type determination, are studied in $\mathbb{C}^3$, the space of three
complex variables. The three complex coordinate planes are composed of the six
Delone scalars.


  The paper addresses the problem of sampling discretization of integral norms
of elements of finite-dimensional subspaces satisfying some conditions. We
prove sampling discretization results under two standard kinds of assumptions
-- conditions on the entropy numbers and conditions in terms of the
Nikol'skii-type inequalities. We prove some upper bounds on the number of
sample points sufficient for good discretization and show that these upper
bounds are sharp in a certain sense. Then we apply our general conditional
results to subspaces with special structures, namely, subspaces with the tensor
product structure. We demonstrate that applications of results based on the
Nikol'skii-type inequalities provide somewhat better results than applications
of results based on the entropy numbers conditions. Finally, we apply
discretization results to the problem of sampling recovery.


  We present a general formalism for incorporating the string corrections in
generalised geometry, which necessitates the extension of the generalised
tangent bundle. Not only are such extensions obstructed, string symmetries and
the existence of a well-defined effective action require a precise choice of
the (generalised) connection. The action takes a universal form given by a
generalised Lichnerowitz--Bismut theorem. As examples of this construction we
discuss the corrections linear in $\alpha'$ in heterotic strings and the
absence of such corrections for type II theories.


  In the low-energy limit, M-theory compactified on S1/Z2 is formulated in
terms of Bianchi identities with sources localized at orbifold singularities
and anomaly-cancelling counterterms to the Wilson effective Lagrangian.
Compactifying to four dimensions on a Calabi-Yau space leads to N=1 local
supersymmetry. We derive a formulation of the effective supergravity which
explicitly relates four-dimensional supergravity multiplets and field equations
with these fundamental M-theory aspects. This formulation proves convenient for
the introduction in the effective supergravity of non-perturbative M-theory
contributions. It also applies to the universal sector of generic
compactifications with N=1 supersymmetry.


  We present a model checking approach for the verification of data flow
correctness in networks during concurrent updates of the network configuration.
This verification problem is of great importance for software-defined
networking (SDN), where errors can lead to packet loss, black holes, and
security violations. Our approach is based on a specification of temporal
properties of individual data flows, such as the requirement that the flow is
free of cycles. We check whether these properties are simultaneously satisfied
for all active data flows while the network configuration is updated. To
represent the behavior of the concurrent network controllers and the resulting
evolutions of the configurations, we introduce an extension of Petri nets with
a transit relation, which characterizes the data flow caused by each transition
of the Petri net. For safe Petri nets with transits, we reduce the verification
of temporal flow properties to a circuit model checking problem that can be
solved with effective verification techniques like IC3, interpolation, and
bounded model checking. We report on encouraging experiments with a prototype
implementation based on the hardware model checker ABC.


  The [CII] 158$\mu$m fine-structure line is known to trace regions of active
star formation and is the main coolant of the cold, neutral atomic medium. In
this \textit{Letter}, we report a strong detection of the [CII] line in the
host galaxy of the brightest quasar known at $z>6.5$, the Pan-STARRS1 selected
quasar PSO J036.5078+03.0498 (hereafter P036+03), using the IRAM NOEMA
millimeter interferometer. Its [CII] and total far-infrared luminosities are
$(5.8 \pm 0.7) \times 10^9 \,L_\odot$ and $(7.6\pm1.5) \times
10^{12}\,L_\odot$, respectively. This results in a $L_{[CII]} /L_{TIR}$ ratio
of $\sim 0.8\times 10^{-3}$, which is at the high end for those found for
active galaxies, though it is lower than the average found in typical main
sequence galaxies at $z\sim 0$. We also report a tentative additional line
which we identify as a blended emission from the $3_{22} - 3_{13}$ and $5_{23}
- 4_{32}$ H$_2$O transitions. If confirmed, this would be the most distant
detection of water emission to date. P036+03 rivals the current prototypical
luminous J1148+5251 quasar at $z=6.42$, in both rest-frame UV and [CII]
luminosities. Given its brightness and because it is visible from both
hemispheres (unlike J1148+5251), P036+03 has the potential of becoming an
important laboratory for the study of star formation and of the interstellar
medium only $\sim 800\,$Myr after the Big Bang.


  The beginning of this work is the achievement of a design tool, which is a
multiple model software called " CODYRUN ", suitable for professionnals and
usable by researchers. The original aspect of this software is that the
designer has at his disposal a wide panel of choices between different heat
transfer models More precisely, it consists in a multizone software integrating
both natural ventilation and moisture tranfers . This software is developed on
PC micro computer and gets advantage of the Microsoft WINDOWS front-end. Most
of time, HVAC systems and specially domestic air conditioners, are taken into
account in a very simplified way, or in a elaborated one. On one side,they are
just supposed to supply the demand of cooling loads with an ideal control loop
(no delay between the sollicitations and the time response of the system), The
available outputs are initially the hourly cooling and heating consumptions
without integrating the real caracteristics of the HVAC system This paper is
also following the same multiple model approach than for the building modelling
by defining different modelling levels for the air conditionning systems, from
a very simplified one to a detailled one. An experimental validation is
achieved in order to compare the sensitivity of each defined model and to point
out the interaction between the thermal behaviour of the envelop and the
electrical system consumption. For validation purposes, we will describe the
data acquisition system. and the used real size test cell located in the
University of Reunion island, Indian Ocean.


  Gait recognition has a rapid development in recent years. However, gait
recognition in the wild is not well explored yet. An obvious reason could be
ascribed to the lack of diverse training data from the perspective of intrinsic
and extrinsic factors. To remedy this problem, we propose to construct a
large-scale gait dataset with the help of controllable computer simulation. In
detail, to diversify the intrinsic factors of gait, we generate numerous
characters with diverse attributes and empower them with various types of
walking styles. To diversify the extrinsic factors of gait, we build a
complicated scene with a dense camera layout. Finally, we design an automated
generation toolkit under Unity3D for simulating the walking scenario and
capturing the gait data automatically. As a result, we obtain an in-the-wild
gait dataset, called VersatileGait, which has more than one million silhouette
sequences of 10,000 subjects with diverse scenarios. VersatileGait possesses
several nice properties, including huge dataset size, diverse pedestrian
attributes, complicated camera layout, high-quality annotations, small domain
gap with the real one, good scalability for new demands, and no privacy issues.
Based on VersatileGait, we propose series of experiments and applications for
both research exploration of gait in the wild and practical applications. Our
dataset and its corresponding generation toolkit will be publicly available for
further studies.


  Prototypes of MICROMEGAS chambers, using bulk technology and analog readout,
with 1x1cm2 readout segmentation have been built and tested. Measurements in
Ar/iC4H10 (95/5) and Ar/CO2 (80/20) are reported. The dependency of the
prototypes gas gain versus pressure, gas temperature and amplification gap
thickness variations has been measured with an 55Fe source and a method for
temperature and pressure correction of data is presented. A stack of four
chambers has been tested in 200GeV/c and 7GeV/c muon and pion beams
respectively. Measurements of response uniformity, detection efficiency and hit
multiplicity are reported. A bulk MICROMEGAS prototype with embedded digital
readout electronics has been assembled and tested. The chamber layout and first
results are presented.


  Like classical fluids, quantum gases may suffer from hydrodynamic
instabilities. Our paper develops a quantum version of the classical stability
analysis in fluids, the Bogoliubov theory of elementary excitations in unstable
Bose-Einstein condensates. In unstable condensates the excitation modes have
complex frequencies. We derive the normalization conditions for unstable modes
such that they can serve in a mode decomposition of the non-condensed
component. Furthermore, we develop approximative techniques to determine the
spectrum and the mode functions. Finally, we apply our theory to a sonic white
hole and find that the spectrum of unstable modes is intrinsically discrete.


  Contraction coefficients give a quantitative strengthening of the data
processing inequality. As such, they have many natural applications whenever
closer analysis of information processing is required. However, it is often
challenging to calculate these coefficients. As a remedy we discuss a quantum
generalization of Doeblin coefficients. These give an efficiently computable
upper bound on many contraction coefficients. We prove several properties and
discuss generalizations and applications. In particular, we give additional
stronger bounds. One especially for PPT channels and one for general channels
based on a constraint relaxation. Additionally, we introduce reverse Doeblin
coefficients that bound certain expansion coefficients.


  We present the CFHTLS-Archive-Research Survey (CARS). It is a virtual
multi-colour survey based on public archive images from the CFHT-Legacy-Survey.
Our main scientific interests in CARS are optical searches for galaxy clusters
from low to high redshift and their subsequent study with photometric and
weak-gravitational lensing techniques. As a first step of the project we
present multi-colour catalogues from 37 sq. degrees of the CFHTLS-Wide
component. Our aims are to create astrometrically and photometrically well
calibrated co-added images. Second goal are five-band (u*, g', r', i', z')
multi-band catalogues with an emphasis on reliable estimates for object
colours. These are subsequently used for photometric redshift estimates. The
article explains in detail data processing, multi-colour catalogue creation and
photometric redshift estimation. Furthermore we apply a novel technique, based
on studies of the angular galaxy cross-correlation function, to quantify the
reliability of photo-z's. The accuracy of our high-confidence photo-z sample
(10-15 galaxies per sq. arcmin) is estimated to $\sigma_{\Delta_z/(1+z)}\approx
0.04-0.05$ up to i'<24 with typically only 1-3% outliers. Interested users can
obtain access to our data by request to the authors.


  In a 2004 paper, Totaro asked whether a G-torsor X that has a zero-cycle of
degree d > 0 will necessarily have a closed etale point of degree dividing d,
where G is a connected algebraic group. This question is closely related to
several conjectures regarding exceptional algebraic groups. Totaro gave a
positive answer to his question in the following cases: G simple, split, and of
type G_2, type F_4, or simply connected of type E_6. We extend the list of
cases where the answer is "yes" to all groups of type G_2 and some nonsplit
groups of type F_4 and E_6. No assumption on the characteristic of the base
field is made. The key tool is a lemma regarding linkage of Pfister forms.


  Training deep neural networks usually requires a large amount of labeled data
to obtain good performance. However, in medical image analysis, obtaining
high-quality labels for the data is laborious and expensive, as accurately
annotating medical images demands expertise knowledge of the clinicians. In
this paper, we present a novel relation-driven semi-supervised framework for
medical image classification. It is a consistency-based method which exploits
the unlabeled data by encouraging the prediction consistency of given input
under perturbations, and leverages a self-ensembling model to produce
high-quality consistency targets for the unlabeled data. Considering that human
diagnosis often refers to previous analogous cases to make reliable decisions,
we introduce a novel sample relation consistency (SRC) paradigm to effectively
exploit unlabeled data by modeling the relationship information among different
samples. Superior to existing consistency-based methods which simply enforce
consistency of individual predictions, our framework explicitly enforces the
consistency of semantic relation among different samples under perturbations,
encouraging the model to explore extra semantic information from unlabeled
data. We have conducted extensive experiments to evaluate our method on two
public benchmark medical image classification datasets, i.e.,skin lesion
diagnosis with ISIC 2018 challenge and thorax disease classification with
ChestX-ray14. Our method outperforms many state-of-the-art semi-supervised
learning methods on both single-label and multi-label image classification
scenarios.


  Deep learning techniques have led to remarkable breakthroughs in the field of
generic object detection and have spawned a lot of scene-understanding tasks in
recent years. Scene graph has been the focus of research because of its
powerful semantic representation and applications to scene understanding. Scene
Graph Generation (SGG) refers to the task of automatically mapping an image
into a semantic structural scene graph, which requires the correct labeling of
detected objects and their relationships. Although this is a challenging task,
the community has proposed a lot of SGG approaches and achieved good results.
In this paper, we provide a comprehensive survey of recent achievements in this
field brought about by deep learning techniques. We review 138 representative
works that cover different input modalities, and systematically summarize
existing methods of image-based SGG from the perspective of feature extraction
and fusion. We attempt to connect and systematize the existing visual
relationship detection methods, to summarize, and interpret the mechanisms and
the strategies of SGG in a comprehensive way. Finally, we finish this survey
with deep discussions about current existing problems and future research
directions. This survey will help readers to develop a better understanding of
the current research status and ideas.


  A theory of flexibility and rigidity is developed for general infinite
bar-joint frameworks (G,p). Determinations of nondeformability through
vanishing flexibility are obtained as well as sufficient conditions for
deformability. Forms of infinitesimal flexibility are defined in terms of the
operator theory of the associated infinite rigidity matrix R(G,p). The
matricial symbol function of an abstract crystal framework is introduced, being
the matrix-valued function on the $d$-torus representing R(G,p) as a Hilbert
space operator. The symbol function is related to infinitesimal flexibility,
deformability and isostaticity. Various generic abstract crystal frameworks
which are in Maxwellian equilibrium, such as certain 4-regular planar
frameworks, are proven to be square-summably infinitesimally rigid as well as
smoothly deformable in infinitely many ways. The symbol function of a
three-dimensional crystal framework determines the infinitesimal wave flexes in
models for the low energy vibrational modes (RUMs) in material crystals. For
crystal frameworks with inversion symmetry it is shown that the RUMS appear in
surfaces, generalising a result of F. Wegner for tetrahedral crystals.


  One aspect of intelligence is the ability to restructure your own environment
so that the world you live in becomes more beneficial to you. In this paper we
investigate how the information-theoretic measure of agent empowerment can
provide a task-independent, intrinsic motivation to restructure the world. We
show how changes in embodiment and in the environment change the resulting
behaviour of the agent and the artefacts left in the world. For this purpose,
we introduce an approximation of the established empowerment formalism based on
sparse sampling, which is simpler and significantly faster to compute for
deterministic dynamics. Sparse sampling also introduces a degree of randomness
into the decision making process, which turns out to beneficial for some cases.
We then utilize the measure to generate agent behaviour for different agent
embodiments in a Minecraft-inspired three dimensional block world. The
paradigmatic results demonstrate that empowerment can be used as a suitable
generic intrinsic motivation to not only generate actions in given static
environments, as shown in the past, but also to modify existing environmental
conditions. In doing so, the emerging strategies to modify an agent's
environment turn out to be meaningful to the specific agent capabilities, i.e.,
de facto to its embodiment.


  Euler proves that the sum of two 4th powers can't be a 4th power and that the
difference of two distinct non-zero 4th powers can't be a 4th power and
Fermat's theorem that the equation x(x+1)/2=y^4 can only be solved in integers
if x=1 and the final theorem y^3+1=x^2 can only be solves for x=3 and y=2 in
integers. The paper is translated from Euler's Latin original into German.


  Low-symmetry van der Waals materials have enabled strong confinement of
mid-infrared light through hyperbolic phonon polaritons (HPhPs) at the
nanoscale. Yet, the bottleneck persists in manipulating the intrinsic
polaritonic dispersion to drive further progress in phonon-polaritonics. Here,
we present a thermomechanical strategy to manipulate the HPhPs in
$\alpha$-MoO$_3$ using high pressure and temperature treatment. The hot
pressing engineers the stoichiometry of $\alpha$-MoO$_3$ by controllably
introducing oxygen vacancy defects (OVDs), which cause a
semiconductor-to-semimetal transition. Our density functional theory and
finite-difference time-domain results, combined with experimental studies show
that the OVDs induce a metastable metallic state by reducing the bandgap while
modifying the intrinsic dielectric permittivity of $\alpha$-MoO$_3$.
Photo-induced force microscopy confirms an average dielectric permittivity
tunability of $|\Delta\varepsilon/\varepsilon|\approx0.35$ within a
Reststrahlen band of $\alpha$-MoO$_3$, resulting in drastic shifts in the HPhP
dispersion. The polariton lifetimes for pristine and hot-pressed flakes were
measured as $0.92 \pm 0.06$ and $0.86 \pm 0.11$ ps, respectively, exhibiting a
loss of only 7%, while the group velocity exhibited an increase of $38.8 \pm
0.2$%. The OVDs in $\alpha$-MoO$_3$ provide a low-loss platform that enables
active tuning of mid-infrared HPhPs and have a profound impact on applications
in super-resolution imaging, nanoscale thermal manipulation, boosted molecular
sensing, and on-chip photonic circuits.


  Achieving people's well-being with AI systems requires that each user is
guided to a healthier lifestyle in a way that is appropriate for her or him.
Although well-being has diverse definitions~\cite{calvo2014positive}, leading a
healthy lifestyle is one of the most representative aspects of well-being. A
healthy lifestyle often varies from individual to individual and cannot be
defined in a top-down manner. For example, while moderate exercise is important
for almost everyone, how much exercise is needed and at what time of day varies
from person to person. A habit that is easy for one person may be very
difficult for another. Habits that are too difficult do not lead to a mentally
healthy lifestyle.


  With edge intelligence, AI models are increasingly pushed to the edge to
serve ubiquitous users. However, due to the drift of model, data, and task, AI
model deployed at the edge suffers from degraded accuracy in the inference
serving phase. Model retraining handles such drifts by periodically retraining
the model with newly arrived data. When colocating model retraining and model
inference serving for the same model on resource-limited edge servers, a
fundamental challenge arises in balancing the resource allocation for model
retraining and inference, aiming to maximize long-term inference accuracy. This
problem is particularly difficult due to the underlying mathematical
formulation being time-coupled, non-convex, and NP-hard. To address these
challenges, we introduce a lightweight and explainable online approximation
algorithm, named ORRIC, designed to optimize resource allocation for adaptively
balancing the accuracy of model training and inference. The competitive ratio
of ORRIC outperforms that of the traditional Inference-Only paradigm,
especially when data drift persists for a sufficiently lengthy time. This
highlights the advantages and applicable scenarios of colocating model
retraining and inference. Notably, ORRIC can be translated into several
heuristic algorithms for different resource environments. Experiments conducted
in real scenarios validate the effectiveness of ORRIC.


  We present our framework DKVF that enables one to quickly prototype and
evaluate new protocols for key-value stores and compare them with existing
protocols based on selected benchmarks. Due to limitations of CAP theorem, new
protocols must be developed that achieve the desired trade-off between
consistency and availability for the given application at hand. Hence, both
academic and industrial communities focus on developing new protocols that
identify a different (and hopefully better in one or more aspect) point on this
trade-off curve. While these protocols are often based on a simple intuition,
evaluating them to ensure that they indeed provide increased availability,
consistency, or performance is a tedious task. Our framework, DKVF, enables one
to quickly prototype a new protocol as well as identify how it performs
compared to existing protocols for pre-specified benchmarks. Our framework
relies on YCSB (Yahoo! Cloud Servicing Benchmark) for benchmarking. We
demonstrate DKVF by implementing four existing protocols --eventual
consistency, COPS, GentleRain and CausalSpartan-- with it. We compare the
performance of these protocols against different loading conditions. We find
that the performance is similar to our implementation of these protocols from
scratch. And, the comparison of these protocols is consistent with what has
been reported in the literature. Moreover, implementation of these protocols
was much more natural as we only needed to translate the pseudocode into Java
(and add the necessary error handling). Hence, it was possible to achieve this
in just 1-2 days per protocol. Finally, our framework is extensible. It is
possible to replace individual components in the framework (e.g., the storage
component).


  Statistical Mechanics deals with ensembles of microstates that are compatible
with fixed constraints and that on average define a thermodynamic macrostate.
The evolution of a small system is normally subjected to changing constraints
and involve a stochastic dependence on previous events. Here, we develop a
theory for reversible processes with memory that comprises equilibrium
statistics and that converges to the same physics in the limit of independent
events. This framework is based on the characterization of single phase-space
pathways and is used to derive ensemble-average dynamics in stochastic systems
driven by a protocol in the limit of no friction. We show that the state of a
system depends on its history to the extent of attaining a one-to-one
correspondence between states and pathways when memory covers all the previous
events. Equilibrium appears as the consequence of exploring all pathways that
connect two states by all procedures. This theory is useful to interpret
single-molecule experiments in Biophysics and other fields in Nanoscience and
an adequate platform for a general theory of irreversible processes.


  We consider a $D$-dimensional Einstein-Gauss-Bonnet model with a cosmological
term $\Lambda$ and two non-zero constants: $\alpha_1$ and $\alpha_2$. We
restrict the metrics to be diagonal ones and study a class of solutions with
exponential time dependence of three scale factors, governed by three
non-coinciding Hubble-like parameters: $H \neq 0$, $h_1$ and $h_2$, obeying $m
H + k_1 h_1 + k_2 h_2 \neq 0$ and corresponding to factor spaces of dimensions
$m > 1$, $k_1 > 1$ and $k_2 > 1$, respectively ($D = 1 + m + k_1 + k_2$). We
analyse two cases: i) $m < k_1 < k_2$ and ii) $1< k_1 = k_2 = k$, $k \neq m$.
We show that in both cases the solutions exist if $\alpha = \alpha_2 / \alpha_1
> 0$ and $\alpha \Lambda > 0$ satisfies certain restrictions, e.g. upper and
lower bounds. In case ii) explicit relations for exact solutions are found. In
both cases the subclasses of stable and non-stable solutions are singled out.
For $m > 3$ the case i) contains a subclass of solutions describing an
exponential expansion of $3$-dimensional subspace with Hubble parameter $H > 0$
and zero variation of the effective gravitational constant $G$. The case $H =
0$ is also considered.


  We apply the generalization of the Parikh-Wilczek method to the tunneling of
massive particles from noncommutative inspired Schwarzschild black holes. By
deriving the equation of radial motion of the tunneling particle directly, we
calculate the emission rate which is shown to be dependent on the
noncommutative parameter besides the energy and mass of the tunneling particle.
After equating the emission rate to the Boltzmann factor, we obtain the
modified Hawking temperature which relates to the noncommutativity and recovers
the standard Hawking temperature in the commutative limit. We also discuss the
entropy of the noncommutative inspired Schwarzschild black hole and its
difference after and before a massive particle's emission.


  Recently, there has been a growing availability of pre-trained text models on
various model repositories. These models greatly reduce the cost of training
new models from scratch as they can be fine-tuned for specific tasks or trained
on large datasets. However, these datasets may not be publicly accessible due
to the privacy, security, or intellectual property issues. In this paper, we
aim to develop a lightweight student network that can learn from multiple
teacher models without accessing their original training data. Hence, we
investigate Data-Free Knowledge Amalgamation (DFKA), a knowledge-transfer task
that combines insights from multiple pre-trained teacher models and transfers
them effectively to a compact student network. To accomplish this, we propose
STRATANET, a modeling framework comprising: (a) a steerable data generator that
produces text data tailored to each teacher and (b) an amalgamation module that
implements a self-regulative strategy using confidence estimates from the
teachers' different layers to selectively integrate their knowledge and train a
versatile student. We evaluate our method on three benchmark text
classification datasets with varying labels or domains. Empirically, we
demonstrate that the student model learned using our STRATANET outperforms
several baselines significantly under data-driven and data-free constraints.


  Incompatibility between patient and donor is a major barrier in kidney
transplantation (KT). The increasing shortage of kidney donors has driven the
development of desensitization techniques to overcome this immunological
challenge. Compared with compatible KT, patients undergoing incompatible KTs
are more likely to experience rejection, infection, malignancy, and graft loss.
We study the optimal acceptance of possibly incompatible kidneys for individual
end-stage kidney disease patients. To capture the effect of incompatibility, we
propose a Markov Decision Process (MDP) model that explicitly includes
compatibility as a state variable. The resulting higher-dimensional model makes
it more challenging to analyze, but under suitable conditions, we derive
structural properties including control limit-type optimal policies that are
easy to compute and implement. Numerical examples illustrate the behavior of
the optimal policy under different mismatch levels and highlight the importance
of explicitly incorporating the incompatibility level into the acceptance
decision when desensitization therapy is an option.


  We report on the influence of low gamma irradiation (10^4 Gy) on the noise
properties of individual carbon nanotube (CNT) field-effect transistors (FETs)
with different gate configurations and two different dielectric layers, SiO2
and Al2O3. Before treatment, strong generation-recombination (GR) noise
components are observed. These data are used to identify several charge traps
related to dielectric layers of the FETs by determining their activation
energy. Investigation of samples with a single SiO2 dielectric layer as well as
with two dielectric layers allows us to separate traps for each of the two
dielectric layers. We reveal that each charge trap level observed in the side
gate operation splits into two levels in top gate operation due to a different
potential profile along the CNT channel. After gamma irradiation, only reduced
flicker noise is registered in the noise spectra, which indicates a decrease of
the number of charge traps. The mobility, which is estimated to be larger than
2x10^4 cm2V-1s-1 at room temperature, decreases only slightly after radiation
treatment demonstrating high radiation hardness of the CNTs. Finally, we study
the influence of Schottky barriers at the metal-nanotube interface on the
transport properties of FETs analyzing the behavior of the flicker noise
component.


  As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to
in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM)
to predict the model parameters, which is difficult to find global optimal
solutions. This paper proposes an optimized Hidden Markov Model with Particle
Swarm Optimization (PSO) algorithm and so is called PSOHMM. In order to
overcome the statistical constraints in HMM, the paper develops
re-normalization and re-mapping mechanisms to ensure the constraints in HMM.
The experiments have shown that PSOHMM can search better solution than BWHMM,
and has faster convergence speed.


  The question of the universality of the longitudinal peak conductivity at the
integer quantum Hall transition is considered. For this purpose, a system of 2D
Dirac fermions with random mass characterised by variance $g$ is proposed as a
model which undergoes a quantum Hall transition. Whilst for some specific
models the longitudinal peak conductivity $\sigma_{xx}$ was found to be
universal (in agreement with the conjecture of Lee et al. as well as with some
numerical work), we find that $\sigma_{xx}$ is reduced by a factor
$(1+g/2\pi)^{-1}$, at least for small $g$. This provides some theoretical
evidence for the non-universality of $\sigma_{xx}$, as observed in a number of
experiments.


  One of the great pleasures of working with Imre Leader is to experience his
infectious delight on encountering a compelling combinatorial problem. This
collection of open problems in combinatorics has been put together by a subset
of his former PhD students and students-of-students for the occasion of his
60th birthday. All of the contributors have been influenced (directly or
indirectly) by Imre: his personality, enthusiasm and his approach to
mathematics. The problems included cover many of the areas of combinatorial
mathematics that Imre is most associated with: including extremal problems on
graphs, set systems and permutations, and Ramsey theory. This is a personal
selection of problems which we find intriguing and deserving of being better
known. It is not intended to be systematic, or to consist of the most
significant or difficult questions in any area. Rather, our main aim is to
celebrate Imre and his mathematics and to hope that these problems will make
him smile. We also hope this collection will be a useful resource for
researchers in combinatorics and will stimulate some enjoyable collaborations
and beautiful mathematics.


  Global sensitivity analysis (GSA) is used to quantify the influence of
uncertain variables in a mathematical model. Prior to performing GSA, the user
must specify (or implicitly assume), a probability distribution to model the
uncertainty, and possibly statistical dependencies, of the variables.
Determining this distribution is challenging in practice as the user has
limited and imprecise knowledge of the uncertain variables. This article
analyzes the robustness of the Sobol' indices, a commonly used tool in GSA, to
changes in the distribution of the uncertain variables. A method for assessing
such robustness is developed which requires minimal user specification and no
additional evaluations of the model. Theoretical and computational aspects of
the method are considered and illustrated through examples.


  Spatially indirect excitons in a coupled quantum well structure were studied
by means of polarization and time resolved photoluminescence. A strong degree
of circular polarization (> 50%) in emission was achieved when the excitation
energy was tuned into resonance with the direct exciton state. The indirect
transition remained polarized several tens of nanoseconds after the pumping
laser pulse, demonstrating directly a very long relaxation time of exciton
spin. The observed spin memory effect exceeds the radiative lifetime of the
indirect excitons.


  We study under the standpoint of integrable complex analytic 1-forms (complex
analytic foliations), a class of second order ordinary differential equations
with periodic coefficients. More precisely, we study Hill's equations of motion
of the moon, which are related to the dynamics of the system Sun-Earth-Moon. We
associate to the {\em complex Hill equation} an integrable complex analytic
one-form in dimension three. This defines a {\it Hill foliation}. The existence
of first integral for a Hill foliation is then studied. The simple cases
correspond to the existence of rational or Liouvillian first integrals. We then
prove the existence of a {\it Bessel type} first integral in a more general
case. We construct a standard two dimensional model for the foliation which we
call {\it Hill fundamental form}. This plane foliation is then studied also
under the standpoint of reduction of singularities and existence of first
integral. For the more general case of the Hill equation, we prove for the
corresponding Hill foliation, the existence of a Laurent-Fourier type formal
first integral. Our approach suggests that there may be a class of plane
foliations admitting Bessel type first integrals, in connection with the
classification of (holonomy) groups of germs of complex diffeomorphisms
associate to a certain class of second order ODEs.


  In this work, an integer linear programming (ILP) based model is proposed for
the computation of a minimal cost addition sequence for a given set of
integers. Since exponents are additive under multiplication, the minimal length
addition sequence will provide an economical solution for the evaluation of a
requested set of power terms. This is turn, finds application in, e.g.,
window-based exponentiation for cryptography and polynomial evaluation. Not
only is an optimal model proposed, the model is extended to consider different
costs for multipliers and squarers as well as controlling the depth of the
resulting addition sequence.


  This work investigates the downlink performance of a multi-cell massive
multiple-input multiple-output (MIMO) system that employs one-bit
analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) in
the receiving and transmitting radio frequency (RF) chains at each base station
(BS) in order to reduce the power consumption. We utilize Bussgang
decomposition to derive the minimum mean squared error (MMSE) channel estimates
at each BS based on the quantized received uplink training signals, and the
asymptotic closed-form expressions of the achievable downlink rates under
one-bit quantized zero-forcing (ZF) precoding implemented using the estimated
channels. The derived expressions explicitly show the impact of quantization
noise, thermal noise, pilot contamination, and interference, and are utilized
to study the number of additional antennas needed at each BS of the one-bit
MIMO system to perform as well as the conventional MIMO system. Numerical
results verify our analysis, and reveal that despite needing more antennas to
achieve the same sum average rate, the one-bit massive MIMO system is more
energy-efficient than the conventional system, especially at high sampling
frequencies.


  We present the results from simultaneous observations of the Narrow-Line
Seyfert 1 galaxy NGC 4051 with the Chandra High Energy Transmission Grating
Spectrometer and the HST Space Telescope Imaging Spectrograph. The X-ray
grating spectrum reveals absorption and emission lines from hydrogen-like and
helium-like ions of O, Ne, Mg and Si. We resolve two distinct X-ray absorption
systems: a high-velocity blueshifted system at -2340+/-130 km/s and a
low-velocity blueshifted system at -600+/-130 km/s. In the UV spectrum we
detect strong absorption, mainly from C IV, N V and Si IV, that is resolved
into as many as nine different intrinsic absorption systems with velocities
between -650 km/s and 30 km/s. Although the low-velocity X-ray absorption is
consistent in velocity with many of the UV absorption systems, the
high-velocity X-ray absorption seems to have no UV counterpart. In addition to
the absorption and emission lines, we also observe rapid X-ray variability and
a state of low X-ray flux during the last ~15 ks of the observation. NGC 4051
has a soft X-ray excess which we fit in both the high and low X-ray flux
states. The high-resolution X-ray spectrum directly reveals that the soft
excess is not composed of narrow emission lines and that it has significant
spectral curvature. A power-law model fails to fit it, while a blackbody
produces a nearly acceptable fit. We compare the observed spectral variability
with the results of previous studies of NGC 4051.


  We study steady state of the totally asymmetric simple exclusion process with
inhomogeneous hopping rates associated with sites (site-wise disorder). Using
the fact that the non-normalized steady-state weights which solve the master
equation are polynomials in all the hopping rates, we propose a general method
for calculating their first few lowest coefficients exactly. In case of binary
disorder where all slow sites share the same hopping rate r<1, we apply this
method to calculate steady-state current up to the quadratic term in r for some
particular disorder configurations. For the most general (non-binary) disorder,
we show that in the low-current regime the current is determined solely by the
current-minimizing subset of equal hopping rates, regardless of other hopping
rates. Our approach can be readily applied to any other driven diffusive system
with unidirectional hopping if one can identify a hopping rate such that the
current vanishes when this rate is set to zero.


  A simulation is performed of the recently reported data from PHOBOS at
energies of 56 and 130 A GeV using the relativistic heavy ion cascade LUCIFER
which had previously given a good description of the NA49 inclusive spectra at
E=17.2 A GeV. The results compare well with these early measurements at RHIC.


  In two recent Letters [Phys. Rev. Lett. 90, 046806; Phys. Rev. Lett. 94,
057408], Hirakawa et al. investigate the emission of coherent terahertz (THz)
radiation from optically excited superlattices. The authors interpret the
spectra of the THz transients in terms of a semiclassical transport model and
conclude that the data provide evidence for the existence of the so-called
Bloch gain. We show that the interpretation of the data in terms of a
conductivity is, however, not performed correctly, thus making the conclusions
of cited authors void.


  We propose a novel concurrent ranging technique for distance estimation with
ultra-wideband (UWB) radios. Conventional schemes assume that the necessary
packet exchanges occur in isolation, to avoid collisions. Concurrent ranging
relies on the overlapping of replies from nearby responders to the same ranging
request issued by an initiator node. As UWB transmissions rely on short pulses,
the individual times of arrival can be discriminated by examining the channel
impulse response (CIR) of the initiator transceiver. By ranging against N
responders with a single, concurrent exchange, our technique drastically abates
network overhead, enabling higher ranging frequency with lower latency and
energy consumption w.r.t. conventional schemes.
  Concurrent ranging can be implemented with a strawman approach requiring
minimal changes to standard schemes. Nevertheless, we empirically show that
this limits the attainable accuracy, reliability, and therefore applicability.
We identify the main challenges in realizing concurrent ranging without
dedicated hardware and tackle them by contributing several techniques, used in
synergy in our prototype based on the popular DW1000 transceiver. Our
evaluation, with static targets and a mobile robot, confirms that concurrent
ranging reliably achieves decimeter-level distance and position accuracy,
comparable to conventional schemes but at a fraction of the network and energy
cost.


  A method for construction of the multipartite Clauser-Horne-Shimony-Holt
(CHSH) type Bell inequalities, for the case of local binary observables, is
presented. The standard CHSH-type Bell inequalities can be obtained as special
cases. A unified framework to establish all kinds of CHSH-type Bell
inequalities by increasing step by step the number of observers is given. As an
application, compact Bell inequalities, for eight observers, involving just
four correlation functions are proposed. They require much less experimental
effort than standard methods and thus is experimentally friendly in
multi-photon experiments.


  We study an agent-based model of evolution of wealth distribution in a
macro-economic system. The evolution is driven by multiplicative stochastic
fluctuations governed by the law of proportionate growth and interactions
between agents. We are mainly interested in interactions increasing wealth
inequality that is in a local implementation of the accumulated advantage
principle. Such interactions destabilise the system. They are confronted in the
model with a global regulatory mechanism which reduces wealth inequality. There
are different scenarios emerging as a net effect of these two competing
mechanisms. When the effect of the global regulation (economic interventionism)
is too weak the system is unstable and it never reaches equilibrium. When the
effect is sufficiently strong the system evolves towards a limiting stationary
distribution with a Pareto tail. In between there is a critical phase. In this
phase the system may evolve towards a steady state with a multimodal wealth
distribution. The corresponding cumulative density function has a
characteristic stairway pattern which reflects the effect of economic
stratification. The stairs represent wealth levels of economic classes
separated by wealth gaps. As we show, the pattern is typical for macro-economic
systems with a limited economic freedom. One can find such a multimodal pattern
in empirical data, for instance, in the highest percentile of wealth
distribution for the population in urban areas of China.


  We present simulations of scattering phenomena which are important in pulsar
observations, but which are analytically intractable. The simulation code,
which has also been used for solar wind and atmospheric scattering problems, is
available from the authors. These simulations reveal an unexpectedly important
role of dispersion in combination with refraction. We demonstrate the effect of
analyzing observations which are shorter than the refractive scale. We examine
time-of-arrival fluctuations in detail: showing their correlation with
intensity and dispersion measure; providing a heuristic model from which one
can estimate their contribution to pulsar timing observations; and showing that
much of the effect can be corrected making use of measured intensity and
dispersion. Finally, we analyze observations of the millisecond pulsar
J0437$-$4715, made with the Parkes radio telescope, that show timing
fluctuations which are correlated with intensity. We demonstrate that these
timing fluctuations can be corrected, but we find that they are much larger
than would be expected from scattering in a homogeneous turbulent plasma with
isotropic density fluctuations. We do not have an explanation for these timing
fluctuations.


  We present a numerical method to plot the root locus of
Single-Input-Single-Output (SISO) dead-time systems with respect to the
controller gain or the system delay. We compute the trajectories of
characteristic roots of the closed-loop system on a prescribed complex right
half-plane. We calculate the starting, branch and boundary crossing roots of
root-locus branches inside the region. We compute the root locus of each
characteristic root based on a predictor-corrector type continuation method. To
avoid the high sensitivity of roots with respect to the locus parameter in the
neighborhood of branch points, the continuation method relies on a natural
parameterization of the root-locus trajectory in terms of a distance in the
(characteristic root, locus parameter)-space. The method is numerically stable
for high order SISO dead-time systems.


  A process based on particle evaporation, diffusion and redeposition is
applied iteratively to a two-dimensional object of arbitrary shape. The
evolution spontaneously transforms the object morphology, converging to
branched structures. Independently of initial geometry, the structures found
after long time present fractal geometry with a fractal dimension around 1.75.
The final morphology, which constantly evolves in time, can be considered as
the dynamic attractor of this evaporation-diffusion-redeposition operator. The
ensemble of these fractal shapes can be considered to be the {\em dynamical
equilibrium} geometry of a diffusion controlled self-transformation process.


  The compact object in quasar 3C 186 is one of the most promising recoiling
black-hole candidates, exhibiting both an astrometric displacement between the
quasar and the host galaxy as well as a spectroscopic shift between broad and
narrow lines. 3C 186 also presents a radio jet which, when projected onto the
plane of the sky, appears to be perpendicular to the quasar/galaxy
displacement. Assuming a gravitational-wave kick is indeed responsible for the
properties of 3C 186 and using state-of-the-art relativistic modeling, we show
that current observations allow for exquisite modeling of the recoiling black
hole. Most notably, we find that the kick velocity, the black-hole spin, and
the line of sight are almost collinear and the former appear perpendicular to
each other only because of a strong projection effect. The targeted
configuration requires substantial fine-tuning: while there exists a region in
the black-hole binary parameter space that is compatible with 3C 186, the
observed system appears to be a rare occurrence. Using archival radio
observations, we explore different strategies that could potentially confirm or
rule out our interpretation. In particular, we develop two observational tests
that rely on the brightness ratio between the approaching and receding jet as
well as the asymmetry of the jet lobes. While the available radio data provide
loose constraints, deeper observations have the unique potential of unveiling
the nature of 3C 186.


  Let $X$ be a mean zero Gaussian random vector in a separable Hilbert space
${\mathbb H}$ with covariance operator $\Sigma:={\mathbb E}(X\otimes X).$ Let
$\Sigma=\sum_{r\geq 1}\mu_r P_r$ be the spectral decomposition of $\Sigma$ with
distinct eigenvalues $\mu_1>\mu_2> \dots$ and the corresponding spectral
projectors $P_1, P_2, \dots.$ Given a sample $X_1,\dots, X_n$ of size $n$ of
i.i.d. copies of $X,$ the sample covariance operator is defined as $\hat
\Sigma_n := n^{-1}\sum_{j=1}^n X_j\otimes X_j.$ The main goal of principal
component analysis is to estimate spectral projectors $P_1, P_2, \dots$ by
their empirical counterparts $\hat P_1, \hat P_2, \dots$ properly defined in
terms of spectral decomposition of the sample covariance operator $\hat
\Sigma_n.$ The aim of this paper is to study asymptotic distributions of
important statistics related to this problem, in particular, of statistic
$\|\hat P_r-P_r\|_2^2,$ where $\|\cdot\|_2^2$ is the squared Hilbert--Schmidt
norm. This is done in a "high-complexity" asymptotic framework in which the so
called effective rank ${\bf r}(\Sigma):=\frac{{\rm
tr}(\Sigma)}{\|\Sigma\|_{\infty}}$ (${\rm tr}(\cdot)$ being the trace and
$\|\cdot\|_{\infty}$ being the operator norm) of the true covariance $\Sigma$
is becoming large simultaneously with the sample size $n,$ but ${\bf
r}(\Sigma)=o(n)$ as $n\to\infty.$ In this setting, we prove that, in the case
of one-dimensional spectral projector $P_r,$ the properly centered and
normalized statistic $\|\hat P_r-P_r\|_2^2$ with {\it data-dependent} centering
and normalization converges in distribution to a Cauchy type limit. The proofs
of this and other related results rely on perturbation analysis and Gaussian
concentration.


  We study the geodesic motion of massive and massless test particles in the
background of equally spinning Myers-Perry-anti-de Sitter (AdS) black holes in
five dimensions. By adopting a coordinate system that makes manifest the
cohomogeneity-1 property of these spacetimes, the equations of motion simplify
considerably. This allows us to easily separate the radial motion from the
angular part and to obtain solutions for angular trajectories in a compact
closed form. For the radial motion we focus our attention on spherical orbits.
In particular, we determine the timelike innermost stable circular orbits
(ISCOs) for these asymptotically AdS spacetimes, as well as the location of
null circular orbits. We find that the ISCO dives below the ergosurface for
black holes rotating close to extremality and merges with the event horizon
exactly at extremality, in analogy with the four-dimensional Kerr case. For
sufficiently massive black holes in AdS there exists a spin parameter range in
which the background spacetime is stable against superradiance and the ISCO
lies inside the ergoregion. Our results for massless geodesics show that there
are no stable circular null orbits outside the horizon, but there exist such
orbits inside the horizon, as well as around over-extremal spacetimes, i.e.,
naked singularities. We also discuss how these orbits deform from the static to
the rotating case.


  This paper presents the first ever approach for solving
\emph{continuous-observation} Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and their semi-Markovian counterparts,
Dec-POSMDPs. This contribution is especially important in robotics, where a
vast number of sensors provide continuous observation data. A
continuous-observation policy representation is introduced using Stochastic
Kernel-based Finite State Automata (SK-FSAs). An SK-FSA search algorithm titled
Entropy-based Policy Search using Continuous Kernel Observations (EPSCKO) is
introduced and applied to the first ever continuous-observation
Dec-POMDP/Dec-POSMDP domain, where it significantly outperforms
state-of-the-art discrete approaches. This methodology is equally applicable to
Dec-POMDPs and Dec-POSMDPs, though the empirical analysis presented focuses on
Dec-POSMDPs due to their higher scalability. To improve convergence, an entropy
injection policy search acceleration approach for both continuous and discrete
observation cases is also developed and shown to improve convergence rates
without degrading policy quality.


  The charge carrier dynamics of organic solar cells are strongly influenced by
trapping and allow to draw conclusions on the loss mechanisms limiting the
photovoltaic performance. In this study we derive the recombination order
\Delta\ of mobile charge carriers. For annealed P3HT:PCBM solar cells, it
allows us to pinpoint the dominant recombination of mobile with trapped charge
carriers in tail states. While the characteristic tail state energy of about 40
meV rises to about 100 meV for 30 h oxygen exposure under illumination, \Delta\
decreases only weakly from 1.70 to 1.62: This corresponds to a slight shift
towards trap-assisted recombination.


  The ultrahigh energy neutrino-nucleon cross sections are computed taking into
account different phenomenological implementations of the non-linear QCD
dynamic s. Based on the color dipole framework, the results for the saturation
model supplemented by DGLAP evolution as well as for the BFKL formalism in the
geometric scaling regime are presented. They are contrasted with recent
calculations using NLO DGLAP and unified BFKL-DGLAP formalisms.


  Purpose: Design of a preconditioner for fast and efficient parallel imaging
and compressed sensing reconstructions. Theory: Parallel imaging and compressed
sensing reconstructions become time consuming when the problem size or the
number of coils is large, due to the large linear system of equations that has
to be solved in l_1 and l_2-norm based reconstruction algorithms. Such linear
systems can be solved efficiently using effective preconditioning techniques.
Methods: In this paper we construct such a preconditioner by approximating the
system matrix of the linear system, which comprises the data fidelity and
includes total variation and wavelet regularization, by a matrix with the
assumption that is a block circulant matrix with circulant blocks. Due to its
circulant structure, the preconditioner can be constructed quickly and its
inverse can be evaluated fast using only two fast Fourier transformations. We
test the performance of the preconditioner for the conjugate gradient method as
the linear solver, integrated into the Split Bregman algorithm. Results: The
designed circulant preconditioner reduces the number of iterations required in
the conjugate gradient method by almost a factor of~5. The speed up results in
a total acceleration factor of approximately 2.5 for the entire reconstruction
algorithm when implemented in MATLAB, while the initialization time of the
preconditioner is negligible. Conclusion: The proposed preconditioner reduces
the reconstruction time for parallel imaging and compressed sensing in a Split
Bregman implementation and can easily handle large systems since it is
Fourier-based, allowing for efficient computations.
  Key words: preconditioning; compressed sensing; Split Bregman; parallel
imaging


  This paper proposes a novel approach to person re-identification, a
fundamental task in distributed multi-camera surveillance systems. Although a
variety of powerful algorithms have been presented in the past few years, most
of them usually focus on designing hand-crafted features and learning metrics
either individually or sequentially. Different from previous works, we
formulate a unified deep ranking framework that jointly tackles both of these
key components to maximize their strengths. We start from the principle that
the correct match of the probe image should be positioned in the top rank
within the whole gallery set. An effective learning-to-rank algorithm is
proposed to minimize the cost corresponding to the ranking disorders of the
gallery. The ranking model is solved with a deep convolutional neural network
(CNN) that builds the relation between input image pairs and their similarity
scores through joint representation learning directly from raw image pixels.
The proposed framework allows us to get rid of feature engineering and does not
rely on any assumption. An extensive comparative evaluation is given,
demonstrating that our approach significantly outperforms all state-of-the-art
approaches, including both traditional and CNN-based methods on the challenging
VIPeR, CUHK-01 and CAVIAR4REID datasets. Additionally, our approach has better
ability to generalize across datasets without fine-tuning.


  We develop a robust and efficient iterative method for hyper-elastodynamics
based on a novel continuum formulation recently developed. The numerical scheme
is constructed based on the variational multiscale formulation and the
generalized-$\alpha$ method. Within the nonlinear solution procedure, a block
factorization is performed for the consistent tangent matrix to decouple the
kinematics from the balance laws. Within the linear solution procedure, another
block factorization is performed to decouple the mass balance equation from the
linear momentum balance equations. A nested block preconditioning technique is
proposed to combine the Schur complement reduction approach with the fully
coupled approach. This preconditioning technique, together with the Krylov
subspace method, constitutes a novel iterative method for solving
hyper-elastodynamics. We demonstrate the efficacy of the proposed
preconditioning technique by comparing with the SIMPLE preconditioner and the
one-level domain decomposition preconditioner. Two representative examples are
studied: the compression of an isotropic hyperelastic cube and the tensile test
of a fully-incompressible anisotropic hyperelastic arterial wall model. The
robustness with respect to material properties and the parallel performance of
the preconditioner are examined.


  Query Expansion (QE) is a well established method for improving retrieval
metrics in image search applications. When using QE, the search is conducted on
a new query vector, constructed using an aggregation function over the query
and images from the database. Recent works gave rise to QE techniques in which
the aggregation function is learned, whereas previous techniques were based on
hand-crafted aggregation functions, e.g., taking the mean of the query's
nearest neighbors. However, most QE methods have focused on aggregation
functions that work directly over the query and its immediate nearest
neighbors. In this work, a hierarchical model, Graph Query Expansion (GQE), is
presented, which is learned in a supervised manner and performs aggregation
over an extended neighborhood of the query, thus increasing the information
used from the database when computing the query expansion, and using the
structure of the nearest neighbors graph. The technique achieves
state-of-the-art results over known benchmarks.


  The MACHO Collaboration's search for baryonic dark matter via its
gravitational microlensing signature has generated a massive database of time
ordered photometry of millions of stars in the LMC and the bulge of the Milky
Way. The search's experimental design and capabilities are reviewed and the
dark matter results are briefly noted. Preliminary analysis of the
approximately 39,000 variable stars discovered in the LMC database is presented
and examples of periodic variables are shown. A class of aperiodically variable
Be stars is described which is the closest background to microlensing which has
been found. Plans for future work on variable stars using the MACHO data are
described.


  Spin injection efficiency based on conventional ferromagnet (or half-metallic
ferromagnet) /semiconductor is greatly limited by the Schmidt obstacle due to
conductivity mismatch, here we proposed that by replacing the metallic
injectors with spin gapless semiconductors can significantly reduce the
conductive mismatch while conserve high spin polarization. By performing first
principles calculations based on superlattice structure, we have studied the
representative system of Mn2CoAl/semiconductor spin injector scheme. The
results showed that high spin polarization were maintained at the interface in
systems of Mn2CoAl/Fe2VAl constructed with (100) interface and Mn2CoAl/GaAs
with (110) interface, and the latter is expected to possess long spin diffusion
length. Inherited from the spin gapless feature of Mn2CoAl, a pronounced dip
was observed around the Fermi level in the majority-spin DOS in both systems,
suggesting fast transport of the low-density carriers.


  In June 2019 the Earth will approach within 5{\deg} mean anomaly of the
centre of the Taurid resonant swarm, its closest post-perihelion encounter with
Earth since 1975. This will be the best viewing geometry to detect and place
limits on the number of NEOs proposed to reside at the swarm centre until the
early 2030s. We present an analysis of the optimal times and pointing locations
to image NEOs associated with the swarm


  14N(p,gamma)15O is one of the key reactions of nuclear astrophysics playing a
role in various stellar processes and influencing energy generation of stars,
stellar evolution and nucleosynthesis. For a reliable reaction rate calculation
the low energy cross section of 14N(p,gamma)15O must be known with high
accuracy. Owing to the unmeasurable low cross sections, theoretical
calculations are unavoidable. High precision experimental cross section data
are needed in a wide energy range in order to provide the necessary basis for
low energy extrapolations. In the present work the total 14N(p,gamma)15O cross
section was measured with a method complementary to the available data sets.
The cross section was measured with activation, based on the detection of the
annihilation radiation following the beta+ decay of the reaction product 15O.
This method, which provides directly the astrophysically important total cross
section, was never used for the 14N(p,gamma)15O cross section measurement in
the studied energy range. The non-resonant cross section was measured between
550 keV and 1400 keV center-of-mass energies with total uncertainty of about
10%. The results were compared with literature data using an R-matrix analysis.
It is found that the cross sections measured in this work are in acceptable
agreement with the two recent measurements only if the weak transitions - not
measured in those works - are included. The present data set, being largely
independent from the other available data, can be used to constrain the
extrapolated cross sections to astrophysical energies and helps to make the
astrophysical model calculations more reliable.


  We build a new estimate for the normalized eigenfunctions of the operator
$-\partial_{xx}+\mathcal V(x)$ based on the oscillatory integrals and Langer's
turning point method, where $\mathcal V(x)\sim |x|^{2\ell}$ at infinity with
$\ell>1$. From it and an improved reducibility theorem we show that the
equation \[\textstyle
  {\rm i}\partial_t \psi =-\partial_x^2 \psi+\mathcal V(x) \psi+\epsilon
\langle x\rangle^{\mu} W(\nu x,\omega t)\psi,\quad \psi=\psi(t,x),~x\in\mathbb
R, ~\mu<\min\left\{\ell-\frac23,\frac{\sqrt{4\ell^2-2\ell+1}-1}2\right\},\] can
be reduced in $L^2(\mathbb R)$ to an autonomous system for most values of the
frequency vector $\omega$ and $\nu$, where $W(\varphi, \phi)$ is a smooth map
from $ \mathbb T^d\times \mathbb T^n$ to $\mathbb R$ and odd in $\varphi$.


  In this note, we construct simple stochastic toy models for holographic gauge
theories in which distributions of energy on a collection of sites evolve by a
master equation with some specified transition rates. We build in only energy
conservation, locality, and the standard thermodynamic requirement that all
states with a given energy are equally likely in equilibrium. In these models,
we investigate the qualitative behavior of the dynamics of the energy
distributions for different choices of the density of states for the individual
sites. For typical field theory densities of states (\log(\rho(E)) ~
E^{\alpha<1}), the model gives diffusive behavior in which initially localized
distributions of energy spread out relatively quickly. For large N gauge
theories with gravitational duals, the density of states for a finite volume of
field theory degrees of freedom typically includes a Hagedorn regime
(\log(\rho(E)) ~ E). We find that this gives rise to a trapping of energy in
subsets of degrees of freedom for parametrically long time scales before the
energy leaks away. We speculate that this Hagedorn trapping may be part of a
holographic explanation for long-lived gravitational bound states (black holes)
in gravitational theories.


  In this work, we explore a new approach to synchronization of coupled
oscillators. In contrast to the celebrated Kuramoto model we do not work in
polar coordinates and do not consider oscillations of fixed magnitude. We
propose a synchronizing feedback based on relative state information and local
measurements that induces consensus-like dynamics. We show that, under a mild
stability condition, the combination of the synchronizing feedback with a
decentralized magnitude control law renders the oscillators' almost globally
asymptotically stable with respect to set-points for the phase shift,
frequency, and magnitude. We apply these result to rigorously solve an open
problem in control of inverter-based AC power systems. In this context, the
proposed control strategy can be implemented using purely local information,
induces a grid-forming behavior, and ensures that a network of AC power
inverters is almost globally asymptotically stable with respect to a
pre-specified solution of the AC power-flow equations. Moreover, we show that
the controller exhibits a droop-like behavior around the standard operating
point thus making it backward-compatible with the existing power system
operation.


  Depression has proven to be a significant public health issue, profoundly
affecting the psychological well-being of individuals. If it remains
undiagnosed, depression can lead to severe health issues, which can manifest
physically and even lead to suicide. Generally, Diagnosing depression or any
other mental disorder involves conducting semi-structured interviews alongside
supplementary questionnaires, including variants of the Patient Health
Questionnaire (PHQ) by Clinicians and mental health professionals. This
approach places significant reliance on the experience and judgment of trained
physicians, making the diagnosis susceptible to personal biases. Given that the
underlying mechanisms causing depression are still being actively researched,
physicians often face challenges in diagnosing and treating the condition,
particularly in its early stages of clinical presentation. Recently,
significant strides have been made in Artificial neural computing to solve
problems involving text, image, and speech in various domains. Our analysis has
aimed to leverage these state-of-the-art (SOTA) models in our experiments to
achieve optimal outcomes leveraging multiple modalities. The experiments were
performed on the Extended Distress Analysis Interview Corpus Wizard of Oz
dataset (E-DAIC) corpus presented in the Audio/Visual Emotion Challenge (AVEC)
2019 Challenge. The proposed solutions demonstrate better results achieved by
Proprietary and Open-source Large Language Models (LLMs), which achieved a Root
Mean Square Error (RMSE) score of 3.98 on Textual Modality, beating the AVEC
2019 challenge baseline results and current SOTA regression analysis
architectures. Additionally, the proposed solution achieved an accuracy of
71.43% in the classification task. The paper also includes a novel audio-visual
multi-modal network that predicts PHQ-8 scores with an RMSE of 6.51.


  We unveil the relationship existing between the temperature of an atomic
ensemble of three-level atoms in a \Lambda-configuration, and the width of the
emission cone of Stokes photons that are spontaneously emitted when atoms are
excited by an optical beam. This relationship, which is based on the amount of
which-way information available about where the Stokes photon originated during
the interaction, allow us to put forward a scheme to determine the temperature
of atomic clouds by measuring the width of the emission cone. Unlike the
commonly used time-of-flight measurements, with this technique, the atomic
cloud is not destroyed during each measurement.


  This work presents a framework to synthesize structured gain-scheduled
controllers for structured plants whose dynamics change according to
time-varying scheduling parameters. Both the system and the controller are
assumed to admit descriptions in terms of a linear time-invariant system in
feedback with so-called scheduling blocks, which collect all scheduling
parameters into a static system. We show that such linear fractional
representations permit to exploit a so-called lifting technique in order to
handle several structured gain-scheduling design problems. These could arise
from a nested inner and outer loop control configuration with partial or full
dependence on the scheduling variables. Our design conditions are formulated in
terms of convex linear matrix inequalities and permit to handle multiple
performance objectives.


  We derive direct data-driven dissipativity analysis methods for Linear
Parameter-Varying (LPV) systems using a single sequence of
input-scheduling-output data. By means of constructing a semi-definite program
subject to linear matrix inequality constraints based on this data-dictionary,
direct data-driven verification of $(Q,S,R)$-type of dissipativity properties
of the data-generating LPV system is achieved. Multiple implementation methods
are proposed to achieve efficient computational properties and to even exploit
structural information on the scheduling, e.g., rate bounds. The effectiveness
and trade-offs of the proposed methodologies are shown in simulation studies of
academic and physically realistic examples.


  Hyperspectral imaging measures the amount of electromagnetic energy across
the instantaneous field of view at a very high resolution in hundreds or
thousands of spectral channels. This enables objects to be detected and the
identification of materials that have subtle differences between them. However,
the increase in spectral resolution often means that there is a decrease in the
number of photons received in each channel, which means that the noise linked
to the image formation process is greater. This degradation limits the quality
of the extracted information and its potential applications. Thus, denoising is
a fundamental problem in hyperspectral image (HSI) processing. As images of
natural scenes with highly correlated spectral channels, HSIs are characterized
by a high level of self-similarity and can be well approximated by low-rank
representations. These characteristics underlie the state-of-the-art methods
used in HSI denoising. However, where there are rarely occurring pixel types,
the denoising performance of these methods is not optimal, and the subsequent
detection of these pixels may be compromised. To address these hurdles, in this
article, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI
denoiser, which implements explicit low-rank representation, promotes
self-similarity, and, by using a form of collaborative sparsity, preserves rare
pixels. The denoising and detection effectiveness of the proposed robust HSI
denoiser is illustrated using semireal and real data.


  We explain a discontinuous drop in the exponential growth rate for certain
multivariate generating functions at a critical parameter value, in even
dimensions d at least 4. This result depends on computations in the homology of
the algebraic variety where the generating function has a pole. These
computations are similar to, and inspired by, a thread of research in
applications of complex algebraic geometry to hyperbolic PDEs, going back to
Leray, Petrowski, Atiyah, Bott and Garding. As a consequence, we give a
topological explanation for certain asymptotic phenomenon appearing in the
combinatorics and number theory literature. Furthermore, we show how to combine
topological methods with symbolic algebraic computation to determine explicitly
the dominant asymptotics for such multivariate generating functions, giving a
significant new tool to attack the so-called connection problem for asymptotics
of P-recursive sequences. This in turn enables the rigorous determination of
integer coefficients in the Morse-Smale complex, which are difficult to
determine using direct geometric methods.


  In this paper, we investigate the kinetic freeze-out properties in
relativistic heavy ion collisions at different collision energies. We present a
study of standard Boltzmann-Gibbs Blast-Wave (BGBW) fits and Tsallis Blast-Wave
(TBW) fits performed on the transverse momentum spectra of identified hadrons
produced in Au + Au collisions at collision energies of $\sqrt{s_{\rm{NN}}}=$
7.7 - 200 GeV at the Relativistic Heavy Ion Collider (RHIC), and in Pb + Pb
collisions at collision energies of $\sqrt{s_{\rm{NN}}}=$ 2.76 and 5.02 TeV at
the Large Hadron Collider (LHC). The behavior of strange and multi-strange
particles is also investigated. We found that the TBW model describes data
better than the BGBW one overall, and the contrast is more prominent as the
collision energy increases as the degree of non-equilibrium of the produced
system is found to increase. From TBW fits, the kinetic freeze-out temperature
at the same centrality shows a weak dependence of collision energy between 7.7
and 39 GeV, while it decreases as collision energy continues to increase up to
5.02 TeV. The radial flow is found to be consistent with zero in peripheral
collisions at RHIC energies but sizable at LHC energies and central collisions
at all RHIC energies. We also observed that the strange hadrons, with higher
temperature and similar radial flow, approach equilibrium more quickly from
peripheral to central collisions than light hadrons. The dependence of
temperature and flow velocity on non-equilibrium parameter ($q-1$) is
characterized by two second-order polynomials. Both $a$ and $d\xi$ from the
polynomials fit, related to the influence of the system bulk viscosity,
increase toward lower RHIC energies.


  The kernel truncation method (KTM) is a commonly-used algorithm to compute
the convolution-type nonlocal potential $\Phi(x)=(U\ast \rho)(x), ~x \in
{\mathbb R^d}$, where the convolution kernel $U(x)$ might be singular at the
origin and/or far-field and the density $\rho(x)$ is smooth and fast-decaying.
In KTM, in order to capture the Fourier integrand's oscillations that is
brought by the kernel truncation, one needs to carry out a zero-padding of the
density, which means a larger physical computation domain and a finer mesh in
the Fourier space by duality. The empirical fourfold zero-padding [ Vico et al
J. Comput. Phys. (2016) ] puts a heavy burden on memory requirement especially
for higher dimension problems. In this paper, we derive the optimal
zero-padding factor, that is, $\sqrt{d}+1$, for the first time together with a
rigorous proof. The memory cost is greatly reduced to a small fraction, i.e.,
$(\frac{\sqrt{d}+1}{4})^d$, of what is needed in the original fourfold
algorithm. For example, in the precomputation step, a double-precision
computation on a $256^3$ grid requires a minimum $3.4$ Gb memory with the
optimal threefold zero-padding, while the fourfold algorithm requires around
$8$ Gb where the reduction factor is $\frac{37}{64}\approx \frac{3}{5}$. Then,
we present the error estimates of the potential and density in $d$ dimension.
Next, we re-investigate the optimal zero-padding factor for the anisotropic
density. Finally, extensive numerical results are provided to confirm the
accuracy, efficiency, optimal zero-padding factor for the anisotropic density,
together with some applications to different types of nonlocal potential,
including the 1D/2D/3D Poisson, 2D Coulomb, quasi-2D/3D Dipole-Dipole
Interaction and 3D quadrupolar potential.


  A bivariant functor is defined on a category of *-algebras and a category of
operator ideals, both with actions of a second countable group $G$, into the
category of abelian monoids. The element of the bivariant functor will be
$G$-equivariant extensions of a *-algebra by an operator ideal under a suitable
equivalence relation. The functor is related with the ordinary $Ext$-functor
for $C^*$-algebras defined by Brown-Douglas-Fillmore. Invertibility in this
monoid is studied and characterized in terms of Toeplitz operators with
abstract symbol.


  In the last years the Space Science community was confronted to a continuous
increasing interest in Martian missions, extra-solar planet search and
multi-satellite missions. The presented T.I.P.O. mission is a proposal for a
research program dedicated to study, by space borne interferometric methods,
the radio emissions generated in the atmospheres and magnetospheres of planets,
both solar and extra-solar.


  Multilevel selection is an important organizing principle that crucially
underlies evolutionary processes from the emergence of cells to eusociality and
the economics of nations. Previous studies on multilevel selection assumed that
the effective higher-level selection emerges from lower-level reproduction.
This leads to selection among groups, although only individuals reproduce. We
introduce selective group extinction, where groups die with a probability
inversely proportional to their group fitness. When accounting for this the
critical benefit-to-cost ratio is substantially lowered. Because in game theory
and evolutionary dynamics the degree of cooperation crucially depends on this
ratio above which cooperation emerges previous studies may have substantially
underestimated the establishment and maintenance of cooperation.


  Agarwal and Tara, in 1991 introduced a new class of states defined as m times
application of creation operator to Coherent States known as Excited Coherent
States (ECS) or Photon Added Coherent States (PACS). They are neither
completely quantum nor completely classical. Here we present and develop these
Excited Coherent Sates from a basic and more approachable Wave-function
approach. We have derived the ECS wave function as a blend of Coherent States
and Fock States and thus established them as a result of Quantum fluctuations
(represented by Fock states) on Coherent States. We further derived and
analyzed basic relations such as wave packet width and uncertainty relation in
a more generalized form and presented their development with time. Another
important property of ECS is Quadrature Squeezing. Here we also present a
general analysis of squeezing in ECS and derived conditions on parameters for
squeezing.


  Building 3D perception systems for autonomous vehicles that do not rely on
high-density LiDAR is a critical research problem because of the expense of
LiDAR systems compared to cameras and other sensors. Recent research has
developed a variety of camera-only methods, where features are differentiably
"lifted" from the multi-camera images onto the 2D ground plane, yielding a
"bird's eye view" (BEV) feature representation of the 3D space around the
vehicle. This line of work has produced a variety of novel "lifting" methods,
but we observe that other details in the training setups have shifted at the
same time, making it unclear what really matters in top-performing methods. We
also observe that using cameras alone is not a real-world constraint,
considering that additional sensors like radar have been integrated into real
vehicles for years already. In this paper, we first of all attempt to elucidate
the high-impact factors in the design and training protocol of BEV perception
models. We find that batch size and input resolution greatly affect
performance, while lifting strategies have a more modest effect -- even a
simple parameter-free lifter works well. Second, we demonstrate that radar data
can provide a substantial boost to performance, helping to close the gap
between camera-only and LiDAR-enabled systems. We analyze the radar usage
details that lead to good performance, and invite the community to re-consider
this commonly-neglected part of the sensor platform.


  New BES and Belle data show a peak in the Y(4260) decay into J/psi plus one
charged pion. We point out that the peak might correspond to a charged
resonance at about 3880 MeV predicted time ago within a tetraquark model. The
same tetraquark model predicts another peak at about 100 MeV below the observed
one. We discuss the possibility of having it in present data. On the other hand
we expect that if the molecular picture were the correct one, a peak
corresponding to a D* D*bar state should appear at about 4020 MeV.


  Social networking and micro-blogging services, such as Twitter, play an
important role in sharing digital information. Despite the popularity and
usefulness of social media, they are regularly abused by corrupt users. One of
these nefarious activities is so-called fake news -- a "virus" that has been
spreading rapidly thanks to the hospitable environment provided by social media
platforms. The extensive spread of fake news is now becoming a major problem
with far-reaching negative repercussions on both individuals and society.
Hence, the identification of fake news on social media is a problem of utmost
importance that has attracted the interest not only of the research community
but most of the big players on both sides - such as Facebook, on the industry
side, and political parties on the societal one. In this work, we create a
model through which we hope to be able to offer a solution that will instill
trust in social network communities. Our model analyses the behaviour of 50,000
politicians on Twitter and assigns an influence score for each evaluated user
based on several collected and analysed features and attributes. Next, we
classify political Twitter users as either trustworthy or untrustworthy using
random forest and support vector machine classifiers. An active learning model
has been used to classify any unlabeled ambiguous records from our dataset.
Finally, to measure the performance of the proposed model, we used accuracy as
the main evaluation metric.


  Strain can affect the morphology of a crystal surface, and cause
modifications of its reconstruction even when weak, as in the case of
mechanical bending. We carried out calculations of strain-dependent surface
free energy and direct bending simulations demonstrating the change of
incommensurate reconstruction in Au(111) under strain, in good agreement with
recent data. Time-dependent strain should cause a sliding of the topmost layer
over the second, suggesting an interesting case of nanofriction. Bending strain
could also be used to fine tune the spacing of selectively absorbed
nanoclusters.