  The self-consistent random phase approximation (RPA) based on a correlated
realistic nucleon-nucleon interaction is used to evaluate correlation energies
in closed-shell nuclei beyond the Hartree-Fock level. The relevance of
contributions associated with charge exchange excitations as well as the
necessity to correct for the double counting of the second order contribution
to the RPA ring summation are emphasized. Once these effects are properly
accounted for, the RPA ring summation provides an efficient tool to assess the
impact of long-range correlations on binding energies throughout the whole
nuclear chart, which is of particular importance when starting from realistic
interactions.


  Image Forgery is a problem of image forensics and its detection can be
leveraged using Deep Learning. In this paper we present an approach for
identification of authentic and tampered images done using image editing tools
with Error Level Analysis and Convolutional Neural Network. The process is
performed on CASIA ITDE v2 dataset and trained for 50 and 100 epochs
respectively. The respective accuracies of the training and validation sets are
represented using graphs.


  Some algebraic aspects of field quantization in space-time with boundaries
are discussed. We introduce an associative algebra, whose exchange properties
are inferred from the scattering processes in integrable models with reflecting
boundary conditions on the half line. The basic properties of this algebra are
established and the Fock representations associated with certain involutions
are derived. We apply these results for the construction of quantum fields and
for the study of scattering on the half line.


  We examine the effects of pure additive noise on spatially extended systems
with quadratic nonlinearities. We develop a general multiscale theory for such
systems and apply it to the Kuramoto-Sivashinsky equation as a case study. We
first focus on a regime close to the instability onset (primary bifurcation),
where the system can be described by a single dominant mode. We show
analytically that the resulting noise in the equation describing the amplitude
of the dominant mode largely depends on the nature of the stochastic forcing.
For a highly degenerate noise, in the sense that it is acting on the first
stable mode only, the amplitude equation is dominated by a pure multiplicative
noise, which in turn induces the dominant mode to undergo several critical
state transitions and complex phenomena, including intermittency and
stabilisation, as the noise strength is increased. The intermittent behaviour
is characterised by a power-law probability density and the corresponding
critical exponent is calculated rigorously by making use of the first-passage
properties of the amplitude equation. On the other hand, when the noise is
acting on the whole subspace of stable modes, the multiplicative noise is
corrected by an additive-like term, with the eventual loss of any stabilised
state. We also show that the stochastic forcing has no effect on the dominant
mode dynamics when it is acting on the second stable mode. Finally, in a regime
which is relatively far from the instability onset, so that there are two
unstable modes, we observe numerically that when the noise is acting on the
first stable mode, both dominant modes show noise-induced complex phenomena
similar to the single-mode case.


  We derive the general anomaly polynomial for a class of two-dimensional CFTs
arising as twisted compactifications of a higher-dimensional theory on compact
manifolds $\mathcal{M}_d$, including the contribution of the isometries of
$\mathcal{M}_d$. We then use the result to perform a counting of microstates
for electrically charged and rotating supersymmetric black strings in
AdS$_5\times S^5$ and AdS$_7\times S^4$ with horizon topology BTZ$ \ltimes S^2$
and BTZ$ \ltimes S^2 \times \Sigma_\mathfrak{g}$, respectively, where
$\Sigma_\mathfrak{g}$ is a Riemann surface. We explicitly construct the latter
class of solutions by uplifting a class of four-dimensional rotating black
holes. We provide a microscopic explanation of the entropy of such black holes
by using a charged version of the Cardy formula.


  Precise measurements of magnetic and electric dipole moments are important
tests of the Standard Model and beyond Standard Model physics, particularly for
the electron and the muon. However, the situation presents distinctive
challenges when dealing with the tau lepton due to its very short lifetime and
relatively high mass. Here, we review the theoretical predictions and
experimental measurements of both the anomalous magnetic and electric dipole
moments of the tau lepton.
  Note: Contribution to the proceedings of the 17th International Workshop on
Tau Lepton Physics, Louisville, USA, 4-8 December 2023


  We study the generation of planar quantum squeezed (PQS) states by quantum
non-demolition (QND) measurement of a cold ensemble of $^{87}$Rb atoms. Precise
calibration of the QND measurement allows us to infer the conditional
covariance matrix describing the $F_y$ and $F_z$ components of the PQS,
revealing the dual squeezing characteristic of PQS. PQS states have been
proposed for single-shot phase estimation without prior knowledge of the likely
values of the phase. We show that for an arbitrary phase, the generated PQS
gives a metrological advantage of at least 3.1 dB relative to classical states.
The PQS also beats traditional squeezed states generated with the same QND
resources, except for a narrow range of phase values. Using spin squeezing
inequalities, we show that spin-spin entanglement is responsible for the
metrological advantage.


  In the context of MDPs with high-dimensional states, downstream tasks are
predominantly applied on a compressed, low-dimensional representation of the
original input space. A variety of learning objectives have therefore been used
to attain useful representations. However, these representations usually lack
interpretability of the different features. We present a novel approach that is
able to disentangle latent features into a controllable and an uncontrollable
partition. We illustrate that the resulting partitioned representations are
easily interpretable on three types of environments and show that, in a
distribution of procedurally generated maze environments, it is feasible to
interpretably employ a planning algorithm in the isolated controllable latent
partition.


  Using data from IRAM's Plateau de Bure Interferometer and 30 m Telescope, we
discuss the mm-wave absorption lines of CS, SO, H2S and HCS+ which arise in
diffuse clouds occulting several extragalactic continuum sources. Typical
relative abundances are X(CS)/X(HCO+) ~ 2, X(CS)/X(SO) ~ 2, X(CS)/X(H2S) ~ 6
and X(CS)/X(HCS+) ~ 13.


  In this study, we propose using an over-the-air computation (OAC) scheme for
the federated k-means clustering algorithm to reduce the per-round
communication latency when it is implemented over a wireless network. The OAC
scheme relies on an encoder exploiting the representation of a number in a
balanced number system and computes the sum of the updates for the federated
k-means via signal superposition property of wireless multiple-access channels
non-coherently to eliminate the need for precise phase and time
synchronization. Also, a reinitialization method for ineffectively used
centroids is proposed to improve the performance of the proposed method for
heterogeneous data distribution. For a customer-location clustering scenario,
we demonstrate the performance of the proposed algorithm and compare it with
the standard k-means clustering. Our results show that the proposed approach
performs similarly to the standard k-means while reducing communication
latency.


  Analysis of structural and functional connectivity (FC) of human brains is of
pivotal importance for diagnosis of cognitive ability. The Human Connectome
Project (HCP) provides an excellent source of neural data across different
regions of interest (ROIs) of the living human brain. Individual specific data
were available from an existing analysis (Dai et al., 2017) in the form of time
varying covariance matrices representing the brain activity as the subjects
perform a specific task. As a preliminary objective of studying the
heterogeneity of brain connectomics across the population, we develop a
probabilistic model for a sample of covariance matrices using a scaled Wishart
distribution. We stress here that our data units are available in the form of
covariance matrices, and we use the Wishart distribution to create our
likelihood function rather than its more common usage as a prior on covariance
matrices. Based on empirical explorations suggesting the data matrices to have
low effective rank, we further model the center of the Wishart distribution
using an orthogonal factor model type decomposition. We encourage shrinkage
towards a low rank structure through a novel shrinkage prior and discuss
strategies to sample from the posterior distribution using a combination of
Gibbs and slice sampling. We extend our modeling framework to a dynamic setting
to detect change points. The efficacy of the approach is explored in various
simulation settings and exemplified on several case studies including our
motivating HCP data. We extend our modeling framework to a dynamic setting to
detect change points.


  We report on the demonstration of a deep sub-micron normally-off AlGaN/GaN
HEMT with high on-current and high threshold voltage (VTH). The
high-performance device was realized by utilizing a gate recess with length and
depth of 200 nm and 124 nm respectively. The recess etched region had a
roughness of 0.7 nm. Various recess etch depths and dielectric annealing
conditions were used to tune the VTH. The optimized device exhibited on-current
and VTH of 500 mA/mm and 5 V respectively. The measured breakdown
characteristics of the devices and their limitations were investigated using
2D-TCAD device simulation. The penetration of the residual electric field in
most of the recess region could be the reason for the premature breakdown of
deeply scaled recess-gate e-mode HEMTs.


  In synaptic molecular communication (MC), the activation of postsynaptic
receptors by neurotransmitters (NTs) is governed by a stochastic
reaction-diffusion process. This randomness of synaptic MC contributes to the
randomness of the electrochemical downstream signal in the postsynaptic cell,
called postsynaptic membrane potential (PSP). Since the randomness of the PSP
is relevant for neural computation and learning, characterizing the statistics
of the PSP is critical. However, the statistical characterization of the
synaptic reaction-diffusion process is difficult because the reversible
bi-molecular reaction of NTs with receptors renders the system nonlinear.
Consequently, there is currently no model available which characterizes the
impact of the statistics of postsynaptic receptor activation on the PSP. In
this work, we propose a novel statistical model for the synaptic
reaction-diffusion process in terms of the chemical master equation (CME). We
further propose a novel numerical method which allows to compute the CME
efficiently and we use this method to characterize the statistics of the PSP.
Finally, we present results from stochastic particle-based computer simulations
which validate the proposed models. We show that the biophysical parameters
governing synaptic transmission shape the autocovariance of the receptor
activation and, ultimately, the statistics of the PSP. Our results suggest that
the processing of the synaptic signal by the postsynaptic cell effectively
mitigates synaptic noise while the statistical characteristics of the synaptic
signal are preserved. The results presented in this paper contribute to a
better understanding of the impact of the randomness of synaptic signal
transmission on neuronal information processing.


  The research field of Information Retrieval (IR) has evolved significantly,
expanding beyond traditional search to meet diverse user information needs.
Recently, Large Language Models (LLMs) have demonstrated exceptional
capabilities in text understanding, generation, and knowledge inference,
opening up exciting avenues for IR research. LLMs not only facilitate
generative retrieval but also offer improved solutions for user understanding,
model evaluation, and user-system interactions. More importantly, the
synergistic relationship among IR models, LLMs, and humans forms a new
technical paradigm that is more powerful for information seeking. IR models
provide real-time and relevant information, LLMs contribute internal knowledge,
and humans play a central role of demanders and evaluators to the reliability
of information services. Nevertheless, significant challenges exist, including
computational costs, credibility concerns, domain-specific limitations, and
ethical considerations. To thoroughly discuss the transformative impact of LLMs
on IR research, the Chinese IR community conducted a strategic workshop in
April 2023, yielding valuable insights. This paper provides a summary of the
workshop's outcomes, including the rethinking of IR's core values, the mutual
enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and
open challenges.


  The neutron structure function, $F_{2n}$, has historically been extracted
from measurements of the deuteron structure function, but our understanding of
the nuclear effects on the bound proton and neutron limits the extraction of
$F_{2n}$. The MARATHON collaboration recently extracted $F_{2n}$ from the
comparison of $^3$H and $^3$He targets, where the nuclear effects are larger
but nearly identical, yielding a precise extraction of $F_{2n}/F_{2p}$. This
precise extraction can then be compared to deuteron extractions, providing
important constraints on the nuclear effects in the deuteron. To ensure that
this comparison is not biased by the specific model of nuclear effects used by
MARATHON, we examine a range of models of the nuclear effects to obtain a more
conservative, but more model-independent, extraction of $F_{2n}/F_{2p}$ for
comparison with deuteron extractions. Even with the more conservative approach,
the comparison suggests the need for significant off-shell corrections or other
nuclear effects, beyond those include in most calculations, even for the
weakly-bound deuteron.


  Online garment shopping has gained many customers in recent years. Describing
a dress using keywords does not always yield the proper results, which in turn
leads to dissatisfaction of customers. A visual search based system will be
enormously beneficent to the industry. Hence, we propose a framework that can
retrieve similar clothes that can be found in an image. The first task is to
extract the garment from the input image (street photo). There are various
challenges for that, including pose, illumination, and background clutter. We
use a Generative Adversarial Network for the task of retrieving the garment
that the person in the image was wearing. It has been shown that GAN can
retrieve the garment very efficiently despite the challenges of street photos.
Finally, a siamese based matching system takes the retrieved cloth image and
matches it with the clothes in the dataset, giving us the top k matches. We
take a pre-trained inception-ResNet v1 module as a siamese network (trained
using triplet loss for face detection) and fine-tune it on the shopping dataset
using center loss. The dataset has been collected inhouse. For training the
GAN, we use the LookBook dataset, which is publically available.


  We give some general properties of good and bad reduction, and some recent
examples (worked out with Dipendra Prasad) of varieties having bad reduction
not accounted for by their cohomology. We include some consequences of our
remarks for varieties over number fields having good reduction everywhere.


  This paper introduces a novel computational method for mapping indoor
luminance values on the facade of an open workplace to improve its daylight
performance. 180-degree fisheye renderings from different indoor locations,
view positions, and times of the year are created. These renderings are then
transformed from two-dimensional (2D) images into three-dimensional (3D)
hemispheres. High luminance values are filtered and projected from the
hemisphere to the facade surface. This framework will highlight the areas of
the facade that allow too much light penetration into the interior environment.
The flexible workflow allows occupant centric lighting analysis that computes
multiple design parameters and synthesizes results for localized facade
optimization and daylight design.


  A mirror-extended standard model (MESM) is offered, where in the left-right
symmetric underlying action the sector of the standard model (SM) and its
mirror copy have the same SU(2)\times U(1) gauge structure and parameters; the
mirror fermion counter-partners have opposite chiralities. A theory is used
that allows one to obtain MESM in Minkowski space only if we start with an
accurate account of gravity and only at the end go to the limit of Minkowski
space. Spontaneous breaking of left-right symmetry and the "wrong" sign in the
mass terms of the Higgs fields potentials arise due to the initial conditions
imposed on the T-model inflation. MESM allows choosing the universal Yukawa
coupling constant $y$ in the underlying Lagrangian for all generations of
charged leptons and up-quarks. As an example, with y=10^{-3}, it is shown that
a set of additional dimensionless parameters in width range less than 0.5 is
sufficient to obtain the masses of all known charged leptons and up-quarks. In
this sense, MESM bypasses the problem of the fermion mass hierarchy. With this
choice of parameters, MESM predicts that vacuum expectation value for the
mirror Higgs field is 10^{14}GeV and masses of mirror particles are in the
range 10^{8}GeV-10^{14}GeV. There is only gravitational interaction between
particles of the SM and mirror sectors. Hence, mirror particles can constitute
dark matter. The vacuum is realized as a limiting state in the cosmological
evolution of the inflaton and classical Higgs fields, in which the energy
stored in them becomes minimal, providing the maximum possible contribution of
these fields to the entropy of the Universe. If further study of the model
reveals the existence of an effective mechanism for preheating the Universe,
this will mean that the cosmological constant problem is absent.


  We consider the holographic entanglement entropy of $(d+2)$-dimensional
semi-local quantum liquids, for which the dual gravity background in the deep
interior is $AdS_{2}\times\mathbb{R}^{d}$ multiplied by a warp factor which
depends on the radial coordinate. The entropy density of this geometry goes to
zero in the extremal limit. The thermodynamics associated with this semi-local
background is discussed via dimensional analysis and scaling arguments. For the
case of an asymptotically AdS UV completion of this geometry, we show that the
entanglement entropy of a strip and an annulus exhibits a phase transition as a
typical length of the different shapes is varied, while there is no sign of
such a transition for the entanglement entropy of a sphere. Moreover, for the
spherical entangling region, the leading order contribution to the entanglement
entropy in the IR is calculated analytically. It exhibits an area law behaviour
and agrees with the numerical result.


  The lineless power-law emission observed by ASCA from the northeastern rim of
the supernova remnant SN1006 has recently been interpreted as synchrotron
radiation of electrons with energies around 100 TeV. In this letter we
calculate the flux of inverse Compton emission at TeV photon energies that is a
natural consequence of the existence of such high energy electrons and the
cosmic microwave background. We find that the predicted flux is near the
present sensitivity limit of the southern \v Cerenkov telescope CANGAROO, and
should be detectable with the next performance improvements. The spectrum of
SN1006 at a few TeV will be very soft. The existence of such highest energy
electrons in SN1006 may not be a unique to this remnant. We can therefore
conclude that the detection of TeV $\gamma$-ray emission in any supernova
remnant does not necessarily provide evidence for a large number of cosmic ray
nucleons in these objects, and thus is no simple test of cosmic ray origin as
far as nucleons are concerned.


  Significant research has been conducted in recent years to extend Inductive
Logic Programming (ILP) methods to induce Answer Set Programs (ASP). These
methods perform an exhaustive search for the correct hypothesis by encoding an
ILP problem instance as an ASP program. Exhaustive search, however, results in
loss of scalability. In addition, the language bias employed in these methods
is overly restrictive too. In this paper we extend our previous work on
learning stratified answer set programs that have a single stable model to
learning arbitrary (i.e., non-stratified) ones with multiple stable models. Our
extended algorithm is a greedy FOIL-like algorithm, capable of inducing
non-monotonic logic programs, examples of which includes programs for
combinatorial problems such as graph-coloring and N-queens. To the best of our
knowledge, this is the first heuristic-based ILP algorithm to induce answer set
programs with multiple stable models.


  In this work we use the Slave Boson Mean Field Approximation at finite U to
study the effects of spin-spin correlations in the transport properties of two
quantum dots coupled in series to metallic leads. Different quantum regimes of
this system are studied in a wide range of parameter space. The main aspects
related to the interplay between the half-filling Kondo effect and the
antiferromagnetic correlation between the quantum dots are reviewed. Slave
boson results for conductance, local density of states in the quantum dots, and
the renormalized energy parameters, are presented. As a different approach to
the Kondo physics in a double dot system, the Kondo cloud extension inside the
metallic leads is calculated and its dependence with the inter-dot coupling is
analyzed. In addition, the cloud extension permits the calculation of the Kondo
temperature of the double quantum dot. This result is very similar to the
corresponding critical temperature $T_c$, as a function of the parameters of
the system, as obtained by using the finite temperature extension of the Slave
Boson Mean Field Approximation.


  This paper reports the results of a theoretical and experimental study of how
the initial circulation of ring-shaped Bose-Einstein condensates (BECs) can be
probed by time-of-flight (TOF) images. We have studied theoretically the
dynamics of a BEC after release from a toroidal trap potential by solving the
3D Gross-Pitaevskii (GP) equation. The trap and condensate characteristics
matched those of a recent experiment. The circulation, experimentally imparted
to the condensate by stirring, was simulated theoretically by imprinting a
linear azimuthal phase on the initial condensate wave function. The theoretical
TOF images were in good agreement with the experimental data. We find that upon
release the dynamics of the ring--shaped condensate proceeds in two distinct
phases. First, the condensate expands rapidly inward, filling in the initial
hole until it reaches a minimum radius that depends on the initial circulation.
In the second phase, the density at the inner radius increases to a maximum
after which the hole radius begins slowly to expand. During this second phase a
series of concentric rings appears due to the interference of ingoing and
outgoing matter waves from the inner radius. The results of the GP equation
predict that the hole area is a quadratic function of the initial circulation
when the condensate is released directly from the trap in which it was stirred
and is a linear function of the circulation if the trap is relaxed before
release. These scalings matched the data. Thus, hole size after TOF can be used
as a reliable probe of initial condensate circulation. This connection between
circulation and hole size after TOF will facilitate future studies of
atomtronic systems that are implemented in ultracold quantum gases.


  In this article we introduce a new type of cyclic contraction mapping on a
pair of subsets of a metric space with a graph and prove best proximity points
results for the same. Also, we demonstrate that the number of such points is
same with the number of connected subgraphs. Hereafter, we introduce a fixed
point mapping obtained from the aforesaid cyclic contraction and prove some
fixed point theorems which will be used to find a common solution for a system
of periodic boundary value problems. Our results unify and subsume many
existing results in the literature.


  We present a study of ro-vibrational OH and CO emission from 21 disks around
Herbig Ae/Be stars. We find that the OH and CO luminosities are proportional
over a wide range of stellar ultraviolet luminosities. The OH and CO line
profiles are also similar, indicating that they arise from roughly the same
radial region of the disk. The CO and OH emission are both correlated with the
far-ultraviolet luminosity of the stars, while the PAH luminosity is correlated
with the longer wavelength ultraviolet luminosity of the stars. Although disk
flaring affects the PAH luminosity, it is not a factor in the luminosity of the
OH and CO emission. These properties are consistent with models of
UV-irradiated disk atmospheres. We also find that the transition disks in our
sample, which have large optically thin inner regions, have lower OH and CO
luminosities than non-transition disk sources with similar ultraviolet
luminosities. This result, while tentative given the small sample size, is
consistent with the interpretation that transition disks lack a gaseous disk
close to the star.


  Consider a sum $S_n=v_i\varepsilon_1+\cdots+v_n\varepsilon_{n}$, where
$(v_i)^{n}_{i=1}$ are non-zero vectors in $\mathbb{R}^{d}$ and
$(\varepsilon_i)^{n}_{i=1}$ are independent Rademacher random variables (i.e.,
$~{\mathbb{P}(\varepsilon_{i}=\pm 1)=1/2}$). The classical Littlewood-Offord
problem asks for the best possible upper bound for $~{\sup_{x}\mathbb{P}(S_n =
x)}$. In this paper we consider a non-uniform version of this problem. Namely,
we obtain the optimal bound for $\mathbb{P}(S_n = x)$ in terms of the length of
the vector $x\in \mathbb{R}^d$.


  A quantile summary is a data structure that approximates to
$\varepsilon$-relative error the order statistics of a much larger underlying
dataset.
  In this paper we develop a randomized online quantile summary for the cash
register data input model and comparison data domain model that uses
$O(\frac{1}{\varepsilon} \log \frac{1}{\varepsilon})$ words of memory. This
improves upon the previous best upper bound of $O(\frac{1}{\varepsilon}
\log^{3/2} \frac{1}{\varepsilon})$ by Agarwal et. al. (PODS 2012). Further, by
a lower bound of Hung and Ting (FAW 2010) no deterministic summary for the
comparison model can outperform our randomized summary in terms of space
complexity. Lastly, our summary has the nice property that
$O(\frac{1}{\varepsilon} \log \frac{1}{\varepsilon})$ words suffice to ensure
that the success probability is $1 - e^{-\text{poly}(1/\varepsilon)}$.


  A weighted bicolored plane tree is a bicolored plane tree whose edges are
endowed with positive integral weights. The degree of a vertex is defined as
the sum of the weights of the edges incident to this vertex. Using the theory
of dessins d'enfants, which studies the action of the absolute Galois group on
graphs embedded into Riemann surfaces, we show that a weighted plane tree is a
graphical representation of a pair of coprime complex polynomials A,B such
that: (a) deg A = deg B, and A and B have the same leading coefficient; (b) the
multiplicities of the roots of A (respectively, of B) are equal to the degrees
of the black (respectively, white) vertices of the corresponding tree; (c) the
degree of the difference A-B attains the minimum which is possible for the
given multiplicities of the roots of A and B. Moreover, if a tree in question
is uniquely determined by the set of its black and white vertex degrees (we
call such trees unitrees), then the corresponding polynomials are defined over
Q.
  The pairs of polynomials A,B such that the degree of the difference A-B
attains the minimum, and especially those defined over Q, are related to some
important questions of number theory. Dozens of papers were dedicated to their
study. The main result of this paper is a complete classification of the
unitrees which provides us with the most massive class of such pairs defined
over Q. We also study combinatorial invariants of the Galois action on trees,
as well as on the corresponding polynomial pairs, which permit us to find yet
more examples defined over Q. In a subsequent paper we compute the polynomials
A,B corresponding to all the unitrees.


  Second-order transitive-closure logic, SO(TC), is an expressive declarative
language that captures the complexity class PSPACE. Already its monadic
fragment, MSO(TC), allows the expression of various NP-hard and even
PSPACE-hard problems in a natural and elegant manner. As SO(TC) offers an
attractive framework for expressing properties in terms of declaratively
specified computations, it is interesting to understand the expressivity of
different features of the language. This paper focuses on the fragment MSO(TC),
as well on the purely existential fragment SO(2TC)(E); in 2TC, the TC operator
binds only tuples of relation variables. We establish that, with respect to
expressive power, SO(2TC)(E) collapses to existential first-order logic. In
addition we study the relationship of MSO(TC) to an extension of MSO(TC) with
counting features (CMSO(TC)) as well as to order-invariant MSO. We show that
the expressive powers of CMSO(TC) and MSO(TC) coincide. Moreover we establish
that, over unary vocabularies, MSO(TC) strictly subsumes order-invariant MSO.


  Turing machine and decision tree have developed independently for a long
time. With the recent development of differentiable models, there is an
intersection between them. Neural turing machine(NTM) opens door for the memory
network. It use differentiable attention mechanism to read/write external
memory bank. Differentiable forest brings differentiable properties to
classical decision tree. In this short note, we show the deep connection
between these two models. That is: differentiable forest is a special case of
NTM. Differentiable forest is actually decision tree based neural turing
machine. Based on this deep connection, we propose a response augmented
differential forest (RaDF). The controller of RaDF is differentiable forest,
the external memory of RaDF are response vectors which would be read/write by
leaf nodes.


  A study of the gas pressure effect in the position resolution of an
interacting X- or gamma-ray photon in a gas medium is performed. The intrinsic
position resolution for pure noble gases (Argon and Xenon) and their mixtures
with CO2 and CH4 were calculated for several gas pressures (1-10bar) and for
photon energies between 5.4 and 60.0 keV, being possible to establish a linear
match between the intrinsic position resolution and the inverse of the gas
pressure in that energy range. In order to evaluate the quality of the method
here described, a comparison between the available experimental data and the
calculated one in this work, is done and discussed. In the majority of the
cases, a strong agreement is observed.


  We address the problem of managing historical data for large evolving
information networks like social networks or citation networks, with the goal
to enable temporal and evolutionary queries and analysis. We present the design
and architecture of a distributed graph database system that stores the entire
history of a network and provides support for efficient retrieval of multiple
graphs from arbitrary time points in the past, in addition to maintaining the
current state for ongoing updates. Our system exposes a general programmatic
API to process and analyze the retrieved snapshots. We introduce DeltaGraph, a
novel, extensible, highly tunable, and distributed hierarchical index structure
that enables compactly recording the historical information, and that supports
efficient retrieval of historical graph snapshots for single-site or parallel
processing. Along with the original graph data, DeltaGraph can also maintain
and index auxiliary information; this functionality can be used to extend the
structure to efficiently execute queries like subgraph pattern matching over
historical data. We develop analytical models for both the storage space needed
and the snapshot retrieval times to aid in choosing the right parameters for a
specific scenario. In addition, we present strategies for materializing
portions of the historical graph state in memory to further speed up the
retrieval process. Secondly, we present an in-memory graph data structure
called GraphPool that can maintain hundreds of historical graph instances in
main memory in a non-redundant manner. We present a comprehensive experimental
evaluation that illustrates the effectiveness of our proposed techniques at
managing historical graph information.


  Creating a standardized introduction course becomes more critical as the
field of human-robot interaction (HRI) becomes more established. This paper
outlines the key components necessary to provide an undergraduate with a
sufficient foundational understanding of the interdisciplinary nature of this
field and provides proposed course content. It emphasizes the importance of
creating a course with theoretical and experimental components to accommodate
all different learning preferences. This manuscript also advocates creating or
adopting a universal platform to standardize the hands-on component of
introductory HRI courses, regardless of university funding or size. Next, it
recommends formal training in how to read scientific articles and staying
up-to-date with the latest relevant papers. Finally, it provides detailed
lecture content and project milestones for a 15-week semester. By creating a
standardized course, researchers can ensure consistency and quality are
maintained across institutions, which will help students as well as industrial
and academic employers understand what foundational knowledge is expected.


  We consider a system of linear equations, whose coefficients depend linearly
on interval parameters. Its solution set is defined as the set of all solutions
of all admissible realizations of the parameters. We study unbounded directions
of the solution set and its relation with its kernel. The kernel of a matrix
characterizes unbounded direction in the real case and in the case of ordinary
interval systems. In the general parametric case, however, this is not
completely true. There is still a close relation preserved, which we discuss in
the paper. Nevertheless, we identify several special sub-classes, for which the
characterization remains valid. Next, we extend the results to the so called AE
parametric systems, which are defined by forall-exists quantification.


  Multi-vehicle coordinated motion planning has always been challenged to
safely and efficiently resolve conflicts under non-holonomic dynamic
constraints. Constructing spatial-temporal corridors for multi-vehicle can
decouple the high-dimensional conflicts and further reduce the difficulty of
obtaining feasible trajectories. Therefore, this paper proposes a novel
hierarchical method based on interactive spatio-temporal corridors (ISTCs). In
the first layer, based on the initial guidance trajectories, Mixed Integer
Quadratic Programming is designed to construct ISTCs capable of resolving
conflicts in generic multi-vehicle scenarios. And then in the second layer,
Non-Linear Programming is settled to generate in-corridor trajectories that
satisfy the vehicle dynamics. By introducing ISTCs, the multi-vehicle
coordinated motion planning problem is able to be decoupled into single-vehicle
trajectory optimization problems, which greatly decentralizes the computational
pressure and has great potential for real-world applications. Besides, the
proposed method searches for feasible solutions in the 3-D $(x,y,t)$
configuration space, preserving more possibilities than the traditional
velocity-path decoupling method. Simulated experiments in unsignalized
intersection and challenging dense scenarios have been conduced to verify the
feasibility and adaptability of the proposed framework.


  Consciousness will be introduced axiomatically, inspired by Buddhist insight
meditation and psychology, logic in computer science, and cognitive
neuroscience, as consisting of a stream of $configurations$ that is $compound$,
$discrete$, and (non-deterministically) $computable$. Within this context the
notions of self, concentration, mindfulness, and various forms of suffering can
be defined. As an application of this set up, it will be shown how a combined
development of concentration and mindfulness can attenuate and eventually
eradicate some of the forms of suffering.


  Crowd simulation holds crucial applications in various domains, such as urban
planning, architectural design, and traffic arrangement. In recent years,
physics-informed machine learning methods have achieved state-of-the-art
performance in crowd simulation but fail to model the heterogeneity and
multi-modality of human movement comprehensively. In this paper, we propose a
social physics-informed diffusion model named SPDiff to mitigate the above gap.
SPDiff takes both the interactive and historical information of crowds in the
current timeframe to reverse the diffusion process, thereby generating the
distribution of pedestrian movement in the subsequent timeframe. Inspired by
the well-known social physics model, i.e., Social Force, regarding crowd
dynamics, we design a crowd interaction module to guide the denoising process
and further enhance this module with the equivariant properties of crowd
interactions. To mitigate error accumulation in long-term simulations, we
propose a multi-frame rollout training algorithm for diffusion modeling.
Experiments conducted on two real-world datasets demonstrate the superior
performance of SPDiff in terms of macroscopic and microscopic evaluation
metrics. Code and appendix are available at
https://github.com/tsinghua-fib-lab/SPDiff.


  Unsupervised visible-infrared person re-identification (USVI-ReID) aims to
match specified people in infrared images to visible images without annotation,
and vice versa. USVI-ReID is a challenging yet under-explored task. Most
existing methods address the USVI-ReID problem using cluster-based contrastive
learning, which simply employs the cluster center as a representation of a
person. However, the cluster center primarily focuses on shared information,
overlooking disparity. To address the problem, we propose a Progressive
Contrastive Learning with Multi-Prototype (PCLMP) method for USVI-ReID. In
brief, we first generate the hard prototype by selecting the sample with the
maximum distance from the cluster center. This hard prototype is used in the
contrastive loss to emphasize disparity. Additionally, instead of rigidly
aligning query images to a specific prototype, we generate the dynamic
prototype by randomly picking samples within a cluster. This dynamic prototype
is used to retain the natural variety of features while reducing instability in
the simultaneous learning of both common and disparate information. Finally, we
introduce a progressive learning strategy to gradually shift the model's
attention towards hard samples, avoiding cluster deterioration. Extensive
experiments conducted on the publicly available SYSU-MM01 and RegDB datasets
validate the effectiveness of the proposed method. PCLMP outperforms the
existing state-of-the-art method with an average mAP improvement of 3.9%. The
source codes will be released.


  Tactile Internet is an emerging concept that focuses on supporting
high-fidelity, ultra-responsive, and widely available human-to-machine
interactions. To reduce the transmission latency and alleviate Internet
congestion, fog computing has been advocated as an important component of the
Tactile Internet. In this paper, we focus on energy-efficient design of fog
computing networks that support low-latency Tactile Internet applications. We
investigate two performance metrics: Service response time of end-users and
power usage efficiency of fog nodes. We quantify the fundamental tradeoff
between these two metrics and then extend our analysis to fog computing
networks involving cooperation between fog nodes. We introduce a novel
cooperative fog computing concept, referred to as offload forwarding, in which
a set of fog nodes with different computing and energy resources can cooperate
with each other. The objective of this cooperation is to balance the workload
processed by different fog nodes, further reduce the service response time, and
improve the efficiency of power usage. We develop a distributed optimization
framework based on dual decomposition to achieve the optimal tradeoff. Our
framework does not require fog nodes to disclose their private information nor
conduct back-and-forth negotiations with each other. Two distributed
optimization algorithms are proposed. One is based on the subgradient method
with dual decomposition and the other is based on distributed ADMM-VS. We prove
that both algorithms can achieve the optimal workload allocation that minimizes
the response time under the given power efficiency constraints of fog nodes.


  We prove an analogue of a result by Goldston, Pintz and Yildirim for small
gaps between primes that split completely in an abelian number field. We prove
both a conditional result assuming the Elliott-Halberstam conjecture, and an
unconditional result. We also give another proof of the same result in the
special case of a quadratic extension of class number 1, which relies on a
generalization of the Bombieri-Vinogradov theorem for quadratic number fields.


  Conventional extreme learning machines solve a Moore-Penrose generalized
inverse of hidden layer activated matrix and analytically determine the output
weights to achieve generalized performance, by assuming the same loss from
different types of misclassification. The assumption may not hold in
cost-sensitive recognition tasks, such as face recognition based access control
system, where misclassifying a stranger as a family member may result in more
serious disaster than misclassifying a family member as a stranger. Though
recent cost-sensitive learning can reduce the total loss with a given cost
matrix that quantifies how severe one type of mistake against another, in many
realistic cases the cost matrix is unknown to users. Motivated by these
concerns, this paper proposes an evolutionary cost-sensitive extreme learning
machine (ECSELM), with the following merits: 1) to our best knowledge, it is
the first proposal of ELM in evolutionary cost-sensitive classification
scenario; 2) it well addresses the open issue of how to define the cost matrix
in cost-sensitive learning tasks; 3) an evolutionary backtracking search
algorithm is induced for adaptive cost matrix optimization. Experiments in a
variety of cost-sensitive tasks well demonstrate the effectiveness of the
proposed approaches, with about 5%~10% improvements.


  In this paper, we consider the direct and inverse problem for isotropic
scatterers with two conductive boundary conditions. First, we show the
uniqueness for recovering the coefficients from the known far-field data at a
fixed incident direction for multiple frequencies. Then, we address the inverse
shape problem for recovering the scatterer for the measured far-field data at a
fixed frequency. Furthermore, we examine the direct sampling method for
recovering the scatterer by studying the factorization for the far-field
operator. The direct sampling method is stable with respect to noisy data and
valid in two dimensions for partial aperture data. The theoretical results are
verified with numerical examples to analyze the performance by the direct
sampling method.


  Optical and infrared observations have thus far detected more celestial
cataclysms than have been seen in gravity waves (GW). This argues that we
should search for gravity wave signatures that correspond to flux variability
seen at optical wavelengths, at precisely known positions. There is an unknown
time delay between the optical and gravitational transient, but knowing the
source location precisely specifies the corresponding time delays across the
gravitational antenna network as a function of the GW-to-optical arrival time
difference. Optical searches should detect virtually all supernovae that are
plausible gravitational radiation sources. The transient optical signature
expected from merging compact objects is not as well understood, but there are
good reasons to expect detectable transient optical/IR emission from most of
these sources as well. The next generation of deep wide-field surveys (for
example PanSTARRS and LSST) will be sensitive to subtle optical variability,
but we need to fill the ``blind spots'' that exist in the Galactic plane, and
for optically bright transient sources. In particular, a Galactic plane
variability survey at 2 microns seems worthwhile. Science would benefit from
closer coordination between the various optical survey projects and the gravity
wave community.


  Let $ \{P_{n}\}_{n\geq 0} $ be the sequence of Pell numbers defined by $
P_0=0 $, $ P_1 =1$ and $ P_{n+2}= 2P_{n+1} +P_n$ for all $ n\geq 0 $ and let $
\{Q_{n}\}_{n\geq 0} $ be its companion sequence, the Pell-Lucas numbers defined
by $ Q_0=Q_1 =2$ and $ Q_{n+2}= 2Q_{n+1} +Q_n$ for all $ n\geq 0 $ . In this
paper, we find all Pell and Pell-Lucas numbers which can be written as
difference of two repdigits. It is shown that the largest Pell and Pell-Lucas
numbers which can be written as difference of two repdigits are
  $$P_6=70= 77-7 \quad\quad \hbox{and} \quad\quad Q_7 = 478=555-77.$$


  We experimentally investigate the escape from a metastable state over a
fluctuating barrier of a physical system. The system is switching between two
states under electronic control of a dichotomous noise. We measure the escape
time and its probability density function as a function of the correlation rate
of the dichotomous noise in a frequency interval spanning more than 4 frequency
decades. We observe resonant activation, namely a minimum of the average escape
time as a function of the correlation rate. We detect two regimes in the study
of the shape of the escape time probability distribution: (i) a regime of
exponential and (ii) a regime of non-exponential probability distribution.


  In this paper, we study the nurse rostering problem that considers multiple
units and many soft time-related constraints. An efficient branch and price
solution approach that relies on a fast algorithm to solve the pricing
subproblem of the column generation process is presented. For the nurse
rostering problem, its pricing subproblem can be formulated as a shortest path
problem with resource constraints, which has been the backbone of several
solutions for several classical problems like vehicle routing problems.
However, approaches that perform well on these problems cannot be used since
most constraints in the nurse rostering problem are soft. Based on ideas
borrowed from global constraints in constraint programming to model rostering
problems, an efficient dynamic programming algorithm with novel label
definitions and dominating rules specific to soft time-related constraints is
proposed. In addition, several acceleration strategies are employed to improve
the branch and price algorithm. Computational results on instances of different
sizes indicate that the proposed algorithm is a promising solution for the
nurse rostering problem with multiple units.


  We consider the Wigner equation corresponding to a nonlinear Schroedinger
evolution of the Hartree type in the semiclassical limit $\hbar\to 0$. Under
appropriate assumptions on the initial data and the interaction potential, we
show that the Wigner function is close in $L^2$ to its weak limit, the solution
of the corresponding Vlasov equation. The strong approximation allows the
construction of semiclassical operator-valued observables, approximating their
quantum counterparts in Hilbert-Schmidt topology. The proof makes use of a
pointwise-positivity manipulation, which seems necessary in working with the
$L^2$ norm and the precise form of the nonlinearity. We employ the Husimi
function as a pivot between the classical probability density and the Wigner
function, which -- as it is well known -- is not pointwise positive in general.


  Imaginary-time evolution (ITE) on a quantum computer is a promising formalism
for obtaining the ground state of a quantum system. As a kind of it, the
probabilistic ITE (PITE) takes advantage of measurements to implement the
nonunitary operations. We propose a new approach of PITE which requires only a
single ancillary qubit. Under a practical approximation, the circuit is
constructed from the forward and backward real-time evolution (RTE) gates as
black boxes, generated by the original many-qubit Hamiltonian. All the
efficient unitary quantum algorithms for RTE proposed so far and those in the
future can thus be transferred to ITE exactly as they are. Our approach can
also be used for obtaining the Gibbs state at a finite temperature and the
partition function. We apply the approach to several systems as illustrative
examples to see its validity. We also discuss the application of our approach
to quantum chemistry by focusing on the scaling of computational cost, leading
to a novel framework denoted by first-quantized quantum eigensolver.


  We simulate scattering of electrons by a chain of antiferromagnetically
coupled quantum Heisenberg spins, to analyze spin-transfer effects not
described by the classical models of magnetism. Our simulations demonstrate
efficient excitation of dynamical states that would be forbidden by the
semiclassical symmetries, such as generation of multiple magnetic excitation
quanta by a single electron. Furthermore, quantum interference of spin
wavefunctions enables generation of magnetization dynamics with amplitudes
exceeding the transferred magnetic moment. The efficiency of excitation is
almost independent of the electron spin polarization, and is governed mainly by
the transfer of energy. Non-classical spin transfer may thus enable efficient
electronic control of antiferromagnets not limited by the classical
constraints.


  This is a survey article on the stable cohomotopy refinement of
Seiberg-Witten invariants containing also new results, for example:
  - Stable cohomotopy groups describe path components of certain mapping
spaces.
  - Relation of stable cohomotopy invariants to Seiberg-Witten invariants
without restriction on Betti numbers.
  - Theorem on connected sums along (-2)-spheres.


  Three fundamental laws of the physics of earthquakes, bearing the names of
their discoverers Omori, Gutenberg, Richter and Bath, are widely used in
original, review, monographic, and encyclopedic literature. In this paper, we
have tried to look at laws from an unusual point of view. The idea behind our
approach is as follows. Each of the three laws was established in the course of
work on solving a specific research problem, and the result was expressed in a
specific mathematical form. We asked ourselves whether it is possible to modify
the statement of the problem, and/or modify the form of expression of the law
in order to see new facets of a well-known scientific statement? The result was
successful in relation to Omori's law, according to which the frequency of
aftershocks hyperbolically decreases over time. The modified formulation of the
problem was to find a differential equation describing the time evolution of
aftershocks. We found a simple differential model, within the framework of
which the concept of deactivation of the earthquake source is introduced. The
question of dependence of the deactivation factor on the magnitude of main
shock is analyzed theoretically and experimentally. A monotonic decrease in the
deactivation factor with an increase in the magnitude of the main shock has
been reliably established. Other possible applications of the proposed
methodological technique are considered. In particular, it is indicated how to
overcome the contradiction between the Omori law and the law of energy
conservation. Keywords: Omori law, Gutenberg-Richter law, Bath law, main shock,
aftershock, deactivation factor, Omori epoch, differential equation of
evolution.


  Deep learning based semantic communication(DLSC) systems have shown great
potential of making wireless networks significantly more efficient by only
transmitting the semantics of the data. However, the open nature of wireless
channel and fragileness of neural models cause DLSC systems extremely
vulnerable to various attacks. Traditional wireless physical layer key (PLK),
which relies on reciprocal channel and randomness characteristics between two
legitimate users, holds the promise of securing DLSC. The main challenge lies
in generating secret keys in the static environment with ultra-low/zero rate.
Different from prior efforts that use relays or reconfigurable intelligent
surfaces (RIS) to manipulate wireless channels, this paper proposes a novel
physical layer semantic encryption scheme by exploring the randomness of
bilingual evaluation understudy (BLEU) scores in the field of machine
translation, and additionally presents a novel semantic obfuscation mechanism
to provide further physical layer protections. Specifically, 1) we calculate
the BLEU scores and corresponding weights of the DLSC system. Then, we generate
semantic keys (SKey) by feeding the weighted sum of the scores into a hash
function. 2) Equipped with the SKey, our proposed subcarrier obfuscation is
able to further secure semantic communications with a dynamic dummy data
insertion mechanism. Experiments show the effectiveness of our method,
especially in the static wireless environment.


  The interaction between metallic and biological nanoparticles (NPs) is widely
used in various biotechnology and biomedical applications. However, detailed
characterization of this type of interaction is challenging due to a lack of
high-throughput techniques that can quantify both size and composition of
suspended NP complexes. Here, we introduce a technique called ``twilight
nanoparticle tracking analysis'' (tNTA) and demonstrate that it provides a
quantitative relationship between the measured optical signal and the
composition of suspended dielectric-metal NP complexes. We assess the
performance of tNTA by analyzing the selective binding of tannic acid-modified
gold nanoparticles (TaAuNPs) to herpes simplex viruses (HSV). Our results show
that TaAuNPs bind specifically to HSV without causing substantial changes in
the size or refractive index of the virus, suggesting that the binding does not
cause virus disruption. Instead, the anti-viral properties of TaAuNPs appear to
stem from direct particle binding to the virus.


  Augmenting transformation knowledge onto a convolutional neural network's
weights has often yielded significant improvements in performance. For
rotational transformation augmentation, an important element to recent
approaches has been the use of a steerable basis i.e. the circular harmonics.
Here, we propose a scale-steerable filter basis for the locally scale-invariant
CNN, denoted as log-radial harmonics. By replacing the kernels in the locally
scale-invariant CNN \cite{lsi_cnn} with scale-steered kernels, significant
improvements in performance can be observed on the MNIST-Scale and FMNIST-Scale
datasets. Training with a scale-steerable basis results in filters which show
meaningful structure, and feature maps demonstrate which demonstrate visibly
higher spatial-structure preservation of input. Furthermore, the proposed
scale-steerable CNN shows on-par generalization to global affine transformation
estimation methods such as Spatial Transformers, in response to test-time data
distortions.


  We discuss fun problems, vaguely related to notions and theorems of a course
in differential geometry. This paper can be regarded as a weekend "treasure
chest" supplementing the course weekday lecture notes. The problems and
solutions are not original, while their relation to the course might be so.


  We consider frequency control of synchronous generator networks and study
transient performance under both primary and secondary frequency control. We
model random step changes in power loads and evaluate performance in terms of
expected deviations from a synchronous frequency over the synchronization
transient; what can be thought of as lack of frequency coherence. We compare a
standard droop control strategy to two secondary proportional integral (PI)
controllers: centralized averaging PI control (CAPI) and dis- tributed
averaging PI control (DAPI). We show that the performance of a power system
with DAPI control is always superior to that of a CAPI controlled system, which
in turn has the same transient performance as standard droop control.
Furthermore, for a large class of network graphs, performance scales
unfavorably with network size with CAPI and droop control, which is not the
case with DAPI control. We discuss optimal tuning of the DAPI controller and
describe how inter- nodal alignment of the integral states affects performance.
Our results are demonstrated through simulations of the Nordic power grid.


  The 2nd Fermi-LAT pulsar catalog includes 117 gamma-ray pulsars, of which
roughly one third are millisecond pulsars (MSPs) while the remaining two thirds
split evenly into young radio-loud and radio-quiet pulsars. Although this large
population will enable future, detailed studies of emission mechanisms and the
evolution of the underlying neutron star population, some nearly-universal
properties are already clear and unequivocal. We discuss some of these aspects
below, including the altitude of the gamma-ray emission site and the shape of
the gamma-ray spectrum and its implications for the radiation mechanism.


  Entangled quantum states, such as N00N states, are of major importance for
quantum technologies due to their quantum-enhanced performance. At the same
time, their quantum correlations are relatively vulnerable when they are
subjected to imperfections. Therefore, it is crucial to determine under which
circumstances their distinct quantum features can be exploited. In this paper,
we study the entanglement property of noisy N00N states. This class of states
is a generalization of N00N states including various attenuation effects, such
as mixing, constant or fluctuating losses, and dephasing. To verify their
entanglement, we pursue two strategies: detection-based entanglement witnesses
and entanglement quasiprobabilities. Both methods result from our solution of
so-called separability eigenvalue equations. In particular, the entanglement
quasiprobabilities allow for a full entanglement characterization. As examples
of our general treatment, the cases of N00N states subjected to Gaussian
dephasing and fluctuating atmospheric losses are explicitly studied. In any
correlated fluctuating loss channel, entanglement is found to survive for
non-zero transmissivity. In addition, an extension of our approach to
multipartite systems is given, and the relation to the quantum-optical
nonclassicality in phase-space is discussed.


  We consider eigenvalues of the Pauli operator in $\mathbb R^3$ embedded in
the continuous spectrum. In our main result we prove the absence of such
eigenvalues above a threshold which depends on the asymptotic behavior of the
magnetic and electric field at infinity. We show moreover that the decay
conditions on the magnetic and electric field are sharp. Analogous results are
obtained for purely magnetic Dirac operators.


  There is a second kind of light which does not interact with our electrons.
However it interacts with some of our protons (p) and some of our neutrons (n)
which are both of two kinds: protons (p, p`), neutrons (n`, n) differing in the
two kinds of charges (Q1, Q2) associated with the two kinds of light. p [p`]
and n` [n] have (Q1, Q2) values equal to (1, 1) [(1, 0)] and (0, 0) [(0, 1)]
respectively. There is also a second kind of electron (Q2 =1, Q1= 0), equal in
mass to our electron (Q1 = -1, Q2= 0), which does not interact with our (the
first) kind of light. Three major scenarios S1, S2 and X4 arise. In S1, matter
in the solar system on large scales is predominantly neutralized in both kinds
of charges and the weak forces of attraction among the sun and planets are due
to a fundamental force of nature. However in this scenario we must postulate
that human consciousness is locked on to chemical reactions in the retina
involving the first kind of light and the first kind of electrons only. It is
oblivious to the simultaneous parallel chemical reactions governed by a
chemistry which is based on the second kind of light and the second kind of
anti-electrons and involves the same physical atoms manifesting different
atomic numbers Z` (= Q2). In scenario S2, matter in the solar system on large
scales is predominantly neutralized in the first kind of charge only. In this
scenario human consciousness is not restricted in its awareness to a narrowly
>....... continued


  CP violation in semileptonic top-quark decay is investigated by exactly using
one charged- and three neutral-Higgs bosons obtained by solving the Higgs mass
matrix in two-Higgs doublet model. The CP-violating up-down asymmetry of
leptons from $W$ boson decays is shown to be $1 \times 10^{-4} - 4 \times
10^{-3}$ for the region of ${\rm tan} \beta \ll 1$, where ${\rm tan} \beta $ is
the ratio of vacuum expectation values for the two neutral Higgs bosons.


  We present results from VLBA+Effelsberg 1.7 GHz, VLBA 8.4 GHz and VLBA 15 GHz
observations of the JVAS gravitational lens system B1030+074. The
VLBA+Effelsberg 1.7 GHz data with 3 mas resolution reveal a detailed 60 mas jet
structure in the strong A component of the lens system and maybe a first hint
of the corresponding jet structure in the faint component B. As we go to the
higher resolution 8.4 GHz and 15 GHz data we can see more of the inner
structure of the A core, which is resolved, while the B core still remains
unresolved.


  We provide several new examples in dynamics on the $2$-sphere, with the
emphasis on better understanding the induced boundary dynamics of invariant
domains in parametrized families. First, motivated by a topological version of
the Poincar\'e-Bendixson Theorem obtained recently by Koropecki and Passeggi,
we show the existence of homeomorphisms of $\mathbb{S}^2$ with Lakes of Wada
rotational attractors, with an arbitrarily large number of complementary
domains, and with or without fixed points, that are arbitrarily close to the
identity. This answers a question of Le Roux. Second, from reduced Arnold's
family we construct a parametrised family of Birkhoff-like cofrontier
attractors, where at least for uncountably many choices of the parameters, two
distinct irrational prime ends rotation numbers are induced from the two
complementary domains. This example complements the resolution of Walker's
Conjecture by Koropecki, Le Calvez and Nassiri from 2015. Third, answering a
question of Boyland, we show that there exists a non-transitive Birkhoff-like
attracting cofrontier which is obtained from a BBM embedding of inverse limit
of circles, such that the interior prime ends rotation number belongs to the
interior of the rotation interval of the cofrontier dynamics. There exists
another BBM embedding of the same attractor so that the two induced prime ends
rotation numbers are exactly the two endpoints of the rotation interval.


  Time series forecasting plays a key role in a variety of domains. In a lot of
real-world scenarios, there exist multiple forecasting entities (e.g. power
station in the solar system, stations in the traffic system). A straightforward
forecasting solution is to mine the temporal dependency for each individual
entity through 1d-CNN, RNN, transformer, etc. This approach overlooks the
relations between these entities and, in consequence, loses the opportunity to
improve performance using spatial-temporal relation. However, in many
real-world scenarios, beside explicit relation, there could exist crucial yet
implicit relation between entities. How to discover the useful implicit
relation between entities and effectively utilize the relations for each entity
under various circumstances is crucial. In order to mine the implicit relation
between entities as much as possible and dynamically utilize the relation to
improve the forecasting performance, we propose an attentional multi-graph
neural network with automatic graph learning (A2GNN) in this work.
Particularly, a Gumbel-softmax based auto graph learner is designed to
automatically capture the implicit relation among forecasting entities. We
further propose an attentional relation learner that enables every entity to
dynamically pay attention to its preferred relations. Extensive experiments are
conducted on five real-world datasets from three different domains. The results
demonstrate the effectiveness of A2GNN beyond several state-of-the-art methods.


  We present the 2SXPS (Swift-XRT Point Source) catalog, containing 206,335
point sources detected by the Swift X-ray Telescope (XRT) in the 0.3--10 keV
energy range. This catalog represents a significant improvement over 1SXPS,
with double the sky coverage (now 3,790 deg$^2$), and several significant
developments in source detection and classification. In particular, we present
for the first time techniques to model the effect of stray light --
significantly reducing the number of spurious sources detected. These
techniques will be very important for future, large effective area X-ray
mission such as the forthcoming Athena X-ray observatory. We also present a new
model of the XRT point spread function, and a method for correctly localising
and characterising piled up sources. We provide light curves -- in four energy
bands, two hardness ratios and two binning timescales -- for every source, and
from these deduce that over 80,000 of the sources in 2SXPS are variable in at
least one band or hardness ratio. The catalog data can be queried or downloaded
via a bespoke web interface at https://www.swift.ac.uk/2SXPS, via HEASARC, or
in Vizier (IX/58).


  In this work, the branching ratio $\mathcal{B}[B\to K\chi_{c0}(2P)]$ is
extracted for the first time through the fit fractions in the newly measured
$B\to KD\bar{D}$ process by LHCb. With the rescattering mechanism, this
extracted branching ratio is reproduced well. Our study further enforces the
role of the newly measured $B\to KD\bar{D}$ process to establish the
$\chi_{c0}(2P)$ charmonium state, which is a crucial step when constructing
charmonium family.


  We introduce two classes of rotating solutions of Einstein-Maxwell gravity in
$n+1$ dimensions which are asymptotically anti-de Sitter type. They have no
curvature singularity and no horizons. The first class of solutions, which has
a conic singularity yields a spacetime with a longitudinal magnetic field and
$k$ rotation parameters. We show that when one or more of the rotation
parameters are non zero, the spinning brane has a net electric charge that is
proportional to the magnitude of the rotation parameters. The second class of
solutions yields a spacetime with an angular magnetic field and $% \kappa$
boost parameters. We find that the net electric charge of these traveling
branes with one or more nonzero boost parameters is proportional to the
magnitude of the velocity of the brane. We also use the counterterm method
inspired by AdS/CFT correspondence and calculate the conserved quantities of
the solutions. We show that the logarithmic divergencies associated to the Weyl
anomalies and matter field are zero, and the $r$ divergence of the action can
be removed by the counterterm method.


  The interpretation of the Meixner-Pollaczek, Meixner and Laguerre polynomials
as overlap coefficients in the positive discrete series representations of the
Lie algebra su(1,1) and the Clebsch-Gordan decomposition leads to
generalisations of the convolution identities for these polynomials. Using the
Racah coefficients convolution identities for continuous Hahn, Hahn and Jacobi
polynomials are obtained. From the quantised universal enveloping algebra for
su(1,1) convolution identities for the Al-Salam and Chihara polynomials and the
Askey-Wilson polynomials are derived by using the Clebsch-Gordan and Racah
coefficients. For the quantised universal enveloping algebra for su(2) q-Racah
polynomials are interpreted as Clebsch-Gordan coefficients, and the
linearisation coefficients for a two-parameter family of Askey-Wilson
polynomials are derived.


  We develop and extend a line of recent works on the design of mechanisms for
heterogeneous tasks assignment problem in 'crowdsourcing'. The budgeted market
we consider consists of multiple task requesters and multiple IoT devices as
task executers; where each task requester is endowed with a single distinct
task along with the publicly known budget. Also, each IoT device has valuations
as the cost for executing the tasks and quality, which are private. Given such
scenario, the objective is to select a subset of IoT devices for each task,
such that the total payment made is within the allotted quota of the budget
while attaining a threshold quality. For the purpose of determining the unknown
quality of the IoT devices, we have utilized the concept of peer grading. In
this paper, we have carefully crafted a truthful budget feasible mechanism;
namely TUBE-TAP for the problem under investigation that also allows us to have
the true information about the quality of the IoT devices. The simulations are
performed in order to measure the efficacy of our proposed mechanism.


  I review recent results of the high-redshift X-ray selected AGN clustering,
based on the XMM/2dF survey. Using the luminosity-dependent density evolution
luminosity function we find that the spatial clustering lengths, derived using
Limber's inversion equation, are ~16 and ~19 h-1 Mpc respectively (for the
comoving clustering evolution model) while the median redshifts of the soft and
hard X-ray sources are z~ 1.2 and 0.75, respectively. Within the framework of
flat cosmological models we find that these results support a model with
Omega_m=0.26, \sigma_8=0.75, w=-0.9 (in excellent agreement with the 3 year
WMAP results). We also find the present day bias of X-ray AGNs to be b_o=2.


  An important physical model describing the dynamics of dilute weakly ionized
plasmas in the collisional kinetic theory is the Vlasov-Poisson-Boltzmann
system for which the plasma responds strongly to the self-consistent
electrostatic force. This paper is concerned with the electron dynamics of
kinetic plasmas in the whole space when the positive charged ion flow provides
a spatially uniform background. We establish the global existence and optimal
convergence rates of solutions near a global Maxwellian to the Cauchy problem
on the Vlasov-Poisson-Boltzmann system for angular cutoff soft potentials with
$-2\leq \gamma<0$. The main idea is to introduce a time dependent weight
function in the velocity variable to capture the singularity of the
cross-section at zero relative velocity.


  It has been asserted in the literature that Mathisson's helical motions are
unphysical, with the argument that their radius can be arbitrarily large. We
revisit Mathisson's helical motions of a free spinning particle, and observe
that such statement is unfounded. Their radius is finite and confined to the
disk of centroids. We argue that the helical motions are perfectly valid and
physically equivalent descriptions of the motion of a spinning body, the
difference between them being the choice of the representative point of the
particle, thus a gauge choice. We discuss the kinematical explanation of these
motions, and we dynamically interpret them through the concept of hidden
momentum. We also show that, contrary to previous claims, the frequency of the
helical motions coincides, even in the relativistic limit, with the
zitterbewegung frequency of the Dirac equation for the electron.


  Fatgraphs are multigraphs enriched with a cyclic order of the edges incident
to a vertex. This paper presents algorithms to: (1) generate the set of all
fatgraphs having a given genus and number of boundary cycles; (2) compute
automorphisms of any given fatgraph; (3) compute the homology of the fatgraph
complex. The algorithms are suitable for effective computer implementation.
  In particular, this allows us to compute the rational homology of the moduli
space of Riemann surfaces with marked points. We thus compute the Betti numbers
of $M_{g,n}$ with $(2g + n) \leq 6$, corroborating known results.


  We investigate the universe evolution from inflation to late-time
acceleration in a unified way, using a two-component fluid constituted from
extended Chaplygin gas alongside a phantom scalar field. We extract solutions
for the various cosmological eras, focusing on the behavior of the scale
factor, the various density parameters and the equation-of-state parameter.
Furthermore, we extract and discuss bouncing solutions. Finally, we examine the
perturbations of the model, ensuring about their stability and extracting the
predictions for the tensor-to-scalar ratio.


  The impressive performance of large language models (LLMs) has attracted
considerable attention from the academic and industrial communities. Besides
how to construct and train LLMs, how to effectively evaluate and compare the
capacity of LLMs has also been well recognized as an important yet difficult
problem. Existing paradigms rely on either human annotators or model-based
evaluators to evaluate the performance of LLMs on different tasks. However,
these paradigms often suffer from high cost, low generalizability, and
inherited biases in practice, which make them incapable of supporting the
sustainable development of LLMs in long term. In order to address these issues,
inspired by the peer review systems widely used in academic publication
process, we propose a novel framework that can automatically evaluate LLMs
through a peer-review process. Specifically, for the evaluation of a specific
task, we first construct a small qualification exam to select "reviewers" from
a couple of powerful LLMs. Then, to actually evaluate the "submissions" written
by different candidate LLMs, i.e., the evaluatees, we use the reviewer LLMs to
rate or compare the submissions. The final ranking of evaluatee LLMs is
generated based on the results provided by all reviewers. We conducted
extensive experiments on text summarization tasks with eleven LLMs including
GPT-4. The results demonstrate the existence of biasness when evaluating using
a single LLM. Also, our PRE model outperforms all the baselines, illustrating
the effectiveness of the peer review mechanism.


  We are now moving into an era where multi-object wide-field surveys, which
traditionally use single fibres to observe many targets simultaneously, can
exploit compact integral field units in place of single fibres. Current
multi-object integral field instruments such as SAMI (Croom et al. 2012; Bryant
et al. 2012a) have driven the development of new imaging fibre bundles
(hexabundles) for multi-object spectrographs. We have characterised the
performance of hexabundles with different cladding thicknesses and compared
them to that of the same type of bare fibre, across the range of fill-fractions
and input f-ratios likely in an IFU instrument. Hexabundles with 7-cores and
61-cores were tested for focal ratio degradation (FRD), throughput and
cross-talk when fed with inputs from F/3.4 to >F/8. The five 7-core bundles
have cladding thickness ranging from 1 to 8 microns, and the 61-core bundles
have 5micron cladding. As expected, the FRD improves as the input focal ratio
decreases. We find that the FRD and throughput of the cores in the hexabundles
match the performance of single fibres of the same material at low input
f-ratios. The performance results presented can be used to set a limit on the
f-ratio of a system based on the maximum loss allowable for a planned
instrument. Our results confirm that hexabundles are a successful alternative
for fibre imaging devices for multi-object spectroscopy on wide-field
telescopes and have prompted further development of hexabundle designs with
hexagonal packing and square cores.


  In this note, it is proved the existence of an infinitely generated
multiplicative group consisting of entire functions that are, except for the
constant function 1, hypercyclic with respect to the convolution operator
associated to a given entire function of subexponential type. A certain
stability under multiplication is also shown for compositional hypercyclicity
on complex domains.


  Using high-speed photography coupled with optical interference, we
experimentally study the air entrapment during a liquid drop impacting a solid
substrate. We observe the formation of a compressed air film before the liquid
touches the substrate, with internal pressure considerably higher than the
atmospheric value. The degree of compression highly depends on the impact
velocity, as explained by balancing the liquid deceleration with the large
pressure of compressed air. After contact, the air film expands vertically at
the edge, reducing its pressure within a few tens of microseconds and producing
a thick rim on the perimeter. This thick-rimmed air film subsequently contracts
into an air bubble, governed by the complex interaction between surface
tension, inertia and viscous drag. Such a process is universally observed for
impacts above a few centimeters high.


  Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a
knowledge representation that is useful in mechanizing decision theoretic
planning in relational domains. GFODDs generalize function-free first order
logic and include numerical values and numerical generalizations of existential
and universal quantification. Previous work presented heuristic inference
algorithms for GFODDs and implemented these heuristics in systems for decision
theoretic planning. In this paper, we study the complexity of the computational
problems addressed by such implementations. In particular, we study the
evaluation problem, the satisfiability problem, and the equivalence problem for
GFODDs under the assumption that the size of the intended model is given with
the problem, a restriction that guarantees decidability. Our results provide a
complete characterization placing these problems within the polynomial
hierarchy. The same characterization applies to the corresponding restriction
of problems in first order logic, giving an interesting new avenue for
efficient inference when the number of objects is bounded. Our results show
that for $\Sigma_k$ formulas, and for corresponding GFODDs, evaluation and
satisfiability are $\Sigma_k^p$ complete, and equivalence is $\Pi_{k+1}^p$
complete. For $\Pi_k$ formulas evaluation is $\Pi_k^p$ complete, satisfiability
is one level higher and is $\Sigma_{k+1}^p$ complete, and equivalence is
$\Pi_{k+1}^p$ complete.


  We discuss the efficacy of evolutionary method for the purpose of structural
analysis of amorphous solids. At present ab initio molecular dynamics (MD)
based melt-quench technique is used and this deterministic approach has proven
to be successful to study amorphous materials. We show that a stochastic
approach motivated by Darwinian evolution can also be used to simulate
amorphous structures. Applying this method, in conjunction with density
functional theory (DFT) based electronic, ionic and cell relaxation, we
re-investigate two well known amorphous semiconductors, namely silicon and
indium gallium zinc oxide (IGZO). We find that characteristic structural
parameters like average bond length and bond angle are within $\sim$ 2% to
those reported by ab initio MD calculations and experimental studies.


  Magnetic resonance (MR) protocols rely on several sequences to assess
pathology and organ status properly. Despite advances in image analysis, we
tend to treat each sequence, here termed modality, in isolation. Taking
advantage of the common information shared between modalities (an organ's
anatomy) is beneficial for multi-modality processing and learning. However, we
must overcome inherent anatomical misregistrations and disparities in signal
intensity across the modalities to obtain this benefit. We present a method
that offers improved segmentation accuracy of the modality of interest (over a
single input model), by learning to leverage information present in other
modalities, even if few (semi-supervised) or no (unsupervised) annotations are
available for this specific modality. Core to our method is learning a
disentangled decomposition into anatomical and imaging factors. Shared
anatomical factors from the different inputs are jointly processed and fused to
extract more accurate segmentation masks. Image misregistrations are corrected
with a Spatial Transformer Network, which non-linearly aligns the anatomical
factors. The imaging factor captures signal intensity characteristics across
different modality data and is used for image reconstruction, enabling
semi-supervised learning. Temporal and slice pairing between inputs are learned
dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and
Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2
abdominal segmentation. Code is available at
https://github.com/vios-s/multimodal_segmentation.


  Negative sequential pattern mining (SPM) is an important SPM research topic.
Unlike positive SPM, negative SPM can discover events that should have occurred
but have not occurred, and it can be used for financial risk management and
fraud detection. However, existing methods generally ignore the repetitions of
the pattern and do not consider gap constraints, which can lead to mining
results containing a large number of patterns that users are not interested in.
To solve this problem, this paper discovers frequent one-off negative
sequential patterns (ONPs). This problem has the following two characteristics.
First, the support is calculated under the one-off condition, which means that
any character in the sequence can only be used once at most. Second, the gap
constraint can be given by the user. To efficiently mine patterns, this paper
proposes the ONP-Miner algorithm, which employs depth-first and backtracking
strategies to calculate the support. Therefore, ONP-Miner can effectively avoid
creating redundant nodes and parent-child relationships. Moreover, to
effectively reduce the number of candidate patterns, ONP-Miner uses pattern
join and pruning strategies to generate and further prune the candidate
patterns, respectively. Experimental results show that ONP-Miner not only
improves the mining efficiency, but also has better mining performance than the
state-of-the-art algorithms. More importantly, ONP mining can find more
interesting patterns in traffic volume data to predict future traffic.


  In this paper, we characterize the effects of turning and merging maneuvers
of connected and/or automated vehicles (CAVs or AVs) on network traffic
instability using the macroscopic or network fundamental diagram (MFD or NFD).
We revisit the two-ring system from a theoretical perspective and develop an
integrated modeling framework consisting of different microscopic traffic
models of human-driven vehicles (HVs), AVs, and CAVs. Results suggest that
network traffic instability due to turning and merging maneuvers is an
intrinsic property of road networks. When the turning probability is low, CAVs
do not significantly change the NFD bifurcation, but scatter in both the
simulated link fundamental diagrams (FDs) and NFDs reduces leading to higher
and more stable network flows. When the turning probability is high,
non-cooperative AVs worsen network traffic instability - the NFD undergoes
bifurcation long before the critical density is reached. Results highlight the
important impact of cooperative merging on network traffic stability when AVs
are widely deployed in road networks.


  We study ALP-photon-conversion within strong inhomogeneous electromagnetic
fields as provided by contemporary high-intensity laser systems. We observe
that probe photons traversing the focal spot of a superposition of Gaussian
beams of a single high-intensity laser at fundamental and frequency-doubled
mode can experience a frequency shift due to their intermittent propagation as
axion-like-particles. This process is strongly peaked for resonant masses on
the order of the involved laser frequencies. Purely laser-based experiments in
optical setups are sensitive to ALPs in the $\mathrm{eV}$ mass range and can
thus complement ALP searches at dipole magnets.


  Production of electron-positron pairs by a high-energy $\gamma$ photon and a
bichromatic laser wave is considered where the latter is composed of a strong
low-frequency and a weak high-frequency component, both with circular
polarization. An integral expression for the production rate is derived that
accounts for the strong laser mode to all orders and for the weak laser mode to
first order. The structure of this formula resembles the well-known expression
for the nonlinear Breit-Wheeler process in a strong laser field, but includes
the dynamical assistance from the weak laser mode. We analyze the dependence of
the dynamical rate enhancement on the applied field parameters and show, in
particular, that it is substantially higher when the two laser modes have
opposite helicity.


  Quantitative MRI enables direct quantification of contrast agent
concentrations in contrast-enhanced scans. However, the lengthy scan times
required by conventional methods are inadequate for tracking contrast agent
transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF)
method for simultaneous T1 and T2 mapping across the whole mouse brain with
4.3-min temporal resolution. We designed a 3D MRF sequence with variable
acquisition segment lengths and magnetization preparations on a 9.4T
preclinical MRI scanner. Model-based reconstruction approaches were employed to
improve the accuracy and speed of MRF acquisition. The method's accuracy for T1
and T2 measurements was validated in vitro, while its repeatability of T1 and
T2 measurements was evaluated in vivo (n=3). The utility of the 3D MRF sequence
for dynamic tracking of intracisternally infused Gd-DTPA in the whole mouse
brain was demonstrated (n=5). Phantom studies confirmed accurate T1 and T2
measurements by 3D MRF with an undersampling factor up to 48. Dynamic
contrast-enhanced (DCE) MRF scans achieved a spatial resolution of 192 x 192 x
500 um3 and a temporal resolution of 4.3 min, allowing for the analysis and
comparison of dynamic changes in concentration and transport kinetics of
intracisternally infused Gd-DTPA across brain regions. The sequence also
enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse
brain (192 x 192 x 250 um3) in 30 min. We present the first dynamic and
multi-parametric approach for quantitatively tracking contrast agent transport
in the mouse brain using 3D MRF.


  We introduce a new computer algebra system optimized for use in lattice
perturbation theory as well as continuum perturbation theory and a new
framework to perform automated perturbative calculations on top of said
computer algebra system. The new framework is used to tune the relativistic
heavy quark action in the Columbia formulation at one loop in
meanfield-improved perturbation theory. Preliminary results for the matching
and O(a)-improvement of heavy-light axial vector currents with light
domain-wall quarks are also presented.


  In a typical video rate allocation problem, the objective is to optimally
distribute a source rate budget among a set of (in)dependently coded data units
to minimize the total distortion of all units. Conventional Lagrangian
approaches convert the lone rate constraint to a linear rate penalty scaled by
a multiplier in the objective, resulting in a simpler unconstrained
formulation. However, the search for the "optimal" multiplier, one that results
in a distortion-minimizing solution among all Lagrangian solutions that satisfy
the original rate constraint, remains an elusive open problem in the general
setting. To address this problem, we propose a computation-efficient search
strategy to identify this optimal multiplier numerically. Specifically, we
first formulate a general rate allocation problem where each data unit can be
dependently coded at different quantization parameters (QP) using a previous
unit as predictor, or left uncoded at the encoder and subsequently interpolated
at the decoder using neighboring coded units. After converting the original
rate constrained problem to the unconstrained Lagrangian counterpart, we design
an efficient dynamic programming (DP) algorithm that finds the optimal
Lagrangian solution for a fixed multiplier. Finally, within the DP framework,
we iteratively compute neighboring singular multiplier values, each resulting
in multiple simultaneously optimal Lagrangian solutions, to drive the rates of
the computed Lagrangian solutions towards the bit budget. We terminate when a
singular multiplier value results in two Lagrangian solutions with rates below
and above the bit budget. In extensive monoview and multiview video coding
experiments, we show that our DP algorithm and selection of optimal multipliers
on average outperform comparable rate control solutions used in video
compression standards such as HEVC that do not skip frames in Y-PSNR.


  Reasoning with knowledge graphs (KGs) has primarily focused on triple-shaped
facts. Recent advancements have been explored to enhance the semantics of these
facts by incorporating more potent representations, such as hyper-relational
facts. However, these approaches are limited to \emph{atomic facts}, which
describe a single piece of information. This paper extends beyond \emph{atomic
facts} and delves into \emph{nested facts}, represented by quoted triples where
subjects and objects are triples themselves (e.g., ((\emph{BarackObama},
\emph{holds\_position}, \emph{President}), \emph{succeed\_by},
(\emph{DonaldTrump}, \emph{holds\_position}, \emph{President}))). These nested
facts enable the expression of complex semantics like \emph{situations} over
time and \emph{logical patterns} over entities and relations. In response, we
introduce NestE, a novel KG embedding approach that captures the semantics of
both atomic and nested factual knowledge. NestE represents each atomic fact as
a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix
that rotates the $1\times3$ atomic fact matrix through matrix multiplication.
Each element of the matrix is represented as a complex number in the
generalized 4D hypercomplex space, including (spherical) quaternions,
hyperbolic quaternions, and split-quaternions. Through thorough analysis, we
demonstrate the embedding's efficacy in capturing diverse logical patterns over
nested facts, surpassing the confines of first-order logic-like expressions.
Our experimental results showcase NestE's significant performance gains over
current baselines in triple prediction and conditional link prediction. The
code and pre-trained models are open available at
https://github.com/xiongbo010/NestE.


  We show that for a fixed positive integer k one can efficiently decide if a
finite algebra A admits a k-ary weak near unanimity operation by looking at the
local behavior of the terms of A. We also observe that the problem of deciding
if a given finite algebra has a quasi Taylor operation is solvable in
polynomial time by looking, essentially, for local quasi Siggers operations.


  The CH$^+$ ion is a key species in the initial steps of interstellar carbon
chemistry. Its formation in diverse environments where it is observed is not
well understood, however, because the main production pathway is so endothermic
(4280 K) that it is unlikely to proceed at the typical temperatures of
molecular clouds. We investigation CH$^+$ formation with the first
velocity-resolved spectral mapping of the CH$^+$ $J=1-0, 2-1$ rotational
transitions, three sets of CH $\Lambda$-doubled triplet lines, $^{12}$C$^+$ and
$^{13}$C$^+$, and CH$_3$OH 835~GHz E-symmetry Q branch transitions, obtained
with Herschel/HIFI over $\approx$12 arcmin$^2$ centered on the Orion BN/KL
source. We present the spatial morphologies and kinematics, cloud boundary
conditions, excitation temperatures, column densities, and $^{12}$C$^+$ optical
depths. Emission from C$^+$, CH$^+$, and CH is indicated to arise in the
diluted gas, outside of the explosive, dense BN/KL outflow. Our models show
that UV-irradiation provides favorable conditions for steady-state production
of CH$^+$ in this environment. Surprisingly, no spatial or kinematic
correspondences of these species are found with H$_2$ S(1) emission tracing
shocked gas in the outflow. We propose that C$^+$ is being consumed by rapid
production of CO to explain the lack of C$^+$ and CH$^+$ in the outflow, and
that fluorescence provides the reservoir of H$_2$ excited to higher
ro-vibrational and rotational levels. Hence, in star-forming environments
containing sources of shocks and strong UV radiation, a description of CH$^+$
formation and excitation conditions is incomplete without including the
important --- possibly dominant --- role of UV irradiation.


  We report on magnetic measurements carried out in a textured
YBa$_2$Cu$_3$O$_{7-\delta}$ and YBa$_2$(Cu$_{1-x}$Ag$_x$)$_3$O$_{7-\delta}$ (at
$x \approx$ 0.02) crystals. The so-called fishtail-effect (FE) or second
magnetization peak has been observed in a wide temperature range
0.4~$<T/T_c<$~0.8 for $\textbf{H}\parallel c$. The origin of the FE arises for
the competition between surface barrier and bulk pinning. This is confirmed in
a non-monotonically behavior of the relaxation rate $R$. The value $H_{max}$
for Ag-doped crystals is larger than for the pure one due to the presence of
additional pinning centers, above all on silver atoms.


  Spacetime in the vicinity of an event horizon can be probed using
observations which explore the dynamics of the accretion disc. Many high energy
theories of gravity lead to modifications of the near horizon regime,
potentially providing a testing ground for these theories. In this paper, we
explore the impact of braneworld gravity on this region by formulating a method
of deriving the general behaviour of the as yet unknown braneworld black hole
solution. We use simple bounds to constrain the solution close to the horizon.


  Recent PHENIX elliptic flow ($v_2$) measurements for identified particles
produced in Au+Au and Cu+Cu collisions at $\sqrt{s_{NN}}=200$ GeV are presented
and compared to other RHIC measurements. They indicate universal scaling of
$v_2$ compatible with partonic collectivity leading to the flow of light,
strange and heavy quarks with a common expansion velocity field.


  We study tropical line arrangements associated to a three-regular graph $G$
that we refer to as \emph{tropical graph curves}. Roughly speaking, the
tropical graph curve associated to $G$, whose genus is $g$, is an arrangement
of $2g-2$ lines in tropical projective space that contains $G$ (more precisely,
the topological space associated to $G$) as a deformation retract. We show the
existence of tropical graph curves when the underlying graph is a
three-regular, three-vertex-connected planar graph. Our method involves
explicitly constructing an arrangement of lines in projective space, i.e. a
graph curve whose tropicalisation yields the corresponding tropical graph curve
and in this case, solves a topological version of the tropical lifting problem
associated to canonically embedded graph curves. We also show that the set of
tropical graph curves that we construct are connected via certain local
operations. These local operations are inspired by Steinitz' theorem in
polytope theory.


  H.E.S.S. is a system of Imaging Atmospheric Cherenkov Telescopes (IACTs)
measuring cosmic gamma-rays with very high energies in Namibia. Extending the
array with a fifth telescope with a mirror area of 600 m$^2$ leads to a lower
energy threshold as well as an increased sensitivity of the system. Moreover,
it is now the first IACT array consisting of telescopes with different sizes.
Low-energetic gamma-rays detected by the telescopes can either be analyzed
monoscopically, allowing for a lower threshold, or stereoscopically, using
hybrid events only which leads to a better reconstruction performance. We
present the status of the monoscopic analysis of H.E.S.S. II events. In order
to cross-check the results, we use two independent analysis chains, based on
different reconstruction methods. The first method uses the second moments of
the cleaned camera image (Hillas parameters) in order to deduce the properties
of the primary particle. The background discrimination of this method can be
optimized with multi-variate analysis techniques. The second method is based on
the comparison of the camera image with the results of a semi-analytical model
of the air shower using a Loglikelihood-Maximization. We present the status of
these analysis efforts and their respective performances. One of the chains has
been applied on real data of the Crab Nebula. All results shown here have to be
considered preliminary.


  We study ordered configuration spaces of compact manifolds with boundary. We
show that for a large class of such manifolds, the real homotopy type of the
configuration spaces only depends on the real homotopy type of the pair
consisting of the manifold and its boundary. We moreover describe explicit real
models of these configuration spaces using three different approaches. We do
this by adapting previous constructions for configuration spaces of closed
manifolds which relied on Kontsevich's proof of the formality of the little
disks operads. We also prove that our models are compatible with the richer
structure of configuration spaces, respectively a module over the Swiss-Cheese
operad, a module over the associative algebra of configurations in a collar
around the boundary of the manifold, and a module over the little disks operad.