  Since the introduction of Vision Transformers, the landscape of many computer
vision tasks (e.g., semantic segmentation), which has been overwhelmingly
dominated by CNNs, recently has significantly revolutionized. However, the
computational cost and memory requirement renders these methods unsuitable on
the mobile device. In this paper, we introduce a new method squeeze-enhanced
Axial Transformer (SeaFormer) for mobile visual recognition. Specifically, we
design a generic attention block characterized by the formulation of squeeze
Axial and detail enhancement. It can be further used to create a family of
backbone architectures with superior cost-effectiveness. Coupled with a light
segmentation head, we achieve the best trade-off between segmentation accuracy
and latency on the ARM-based mobile devices on the ADE20K, Cityscapes, Pascal
Context and COCO-Stuff datasets. Critically, we beat both the mobilefriendly
rivals and Transformer-based counterparts with better performance and lower
latency without bells and whistles. Furthermore, we incorporate a feature
upsampling-based multi-resolution distillation technique, further reducing the
inference latency of the proposed framework. Beyond semantic segmentation, we
further apply the proposed SeaFormer architecture to image classification and
object detection problems, demonstrating the potential of serving as a
versatile mobile-friendly backbone. Our code and models are made publicly
available at https://github.com/fudan-zvg/SeaFormer.


  Our measurements of $^{125}$Te NMR relaxations reveal an enhancement of
electronic spin fluctuations above $\mu_0H^*\sim15$ T, leading to their
divergence in the vicinity of the metamagnetic transition at
$\mu_0H_m\approx35$ T, below which field-reinforced superconductivity appears
when a magnetic field ($H$) is applied along the crystallographic $b$ axis. The
NMR data evidence that these fluctuations are dominantly longitudinal,
providing a key to understanding the peculiar superconducting phase diagram in
$H\|b$, where such fluctuations enhance the pairing interactions.


  $ $The aim of this thesis is to lower the bound on square-free primitive
roots modulo primes. Let $g^{\Box}(p)$ be the least square-free primitive root
modulo $p$. We have proven the following two theorems.
  Theorem 0.1. $$g^{\Box}(p) < p^{0.88}\quad\text{for all primes }p.$$ Theorem
0.2. $$g^{\Box}(p) < p^{0.63093}\quad\text{for all primes } p <
2.5\times10^{15} \text{ and }p > 9.63\times10^{65}.$$
  Theorem 0.1 shows an improvement in the best known bound while Theorem 0.2
shows for which primes we can prove the theoretical lower bound.
  After some introductory information in Chapter 1, we will start to prove the
above theorems in Chapter 2. We will introduce an indicator function for
primitive roots of primes in {\S}2.1 and together with results from {\S}1.2.1,
{\S}1.2.3 and {\S}1.2.4 we will outline the first step in proving a general
theorem of the above form. The next two stages in the proof will be outlined in
Chapter 3. These two stages require the introduction of the prime sieve. Before
defining the sieve in {\S}3.2 we will introduce the $e-$free integers which
will play an important role in defining the sieve.
  In {\S}3.2 we will obtain results that do not require computation, including
Theorem 0.2. An algorithm is then introduced in {\S}3.3 which is the last stage
of the proof. There we will complete the proof of Theorem 0.1.


  In the context of the sensational results concerning superluminal velocities,
announced recently by the OPERA Collaboration, we have proposed a classical
model yielding a statistically calculated measured velocity of a beam, higher
than the velocity of the particles constituting the beam. The two key elements
of our model, necessary and sufficient to obtain this curious result, are a
time-dependent "transmission" function and statistical method of the
maximum-likelihood estimation.


  We present a high time resolution study of the two brightest $\gamma$-ray
outbursts from a blazar PKS 1222+216 observed by the \textit{Fermi} Large Area
Telescope (LAT) in 2010. The $\gamma$-ray light-curves obtained in four
different energy bands: 0.1--3, 0.1--0.3, 0.3--1 and 1--3 GeV, with time bin of
6 hr, show asymmetric profiles with a similar rise time in all the bands but a
rapid decline during the April flare and a gradual one during the June. The
light-curves during the April flare show $\sim 2$ days long plateau in 0.1--0.3
GeV emission, erratic variations in 0.3--1 GeV emission, and a daily recurring
feature in 1--3 GeV emission until the rapid rise and decline within a day. The
June flare shows a monotonic rise until the peak, followed by a gradual decline
powered mainly by the multi-peak 0.1--0.3 GeV emission. The peak fluxes during
both the flares are similar except in the 1--3 GeV band in April which is twice
the corresponding flux during the June flare. Hardness ratios during the April
flare indicate spectral hardening in the rising phase followed by softening
during the decay. We attribute this behavior to the development of a shock
associated with an increase in acceleration efficiency followed by its decay
leading to spectral softening. The June flare suggests hardening during the
rise followed by a complicated energy dependent behavior during the decay.
Observed features during the June flare favor multiple emission regions while
the overall flaring episode can be related to jet dynamics.


  Deep Learning (DL) algorithms have gained popularity owing to their practical
problem-solving capacity. However, they suffer from a serious integrity threat,
i.e., their vulnerability to adversarial attacks. In the quest for DL
trustworthiness, recent works claimed the inherent robustness of Spiking Neural
Networks (SNNs) to these attacks, without considering the variability in their
structural spiking parameters. This paper explores the security enhancement of
SNNs through internal structural parameters. Specifically, we investigate the
SNNs robustness to adversarial attacks with different values of the neuron's
firing voltage thresholds and time window boundaries. We thoroughly study SNNs
security under different adversarial attacks in the strong white-box setting,
with different noise budgets and under variable spiking parameters. Our results
show a significant impact of the structural parameters on the SNNs' security,
and promising sweet spots can be reached to design trustworthy SNNs with 85%
higher robustness than a traditional non-spiking DL system. To the best of our
knowledge, this is the first work that investigates the impact of structural
parameters on SNNs robustness to adversarial attacks. The proposed
contributions and the experimental framework is available online to the
community for reproducible research.


  We construct a mininal Lefschetz decomposition of the bounded derived
category of coherent sheaves on the isotropic Grassmannian $\mathsf{IGr}(3,7)$.
Moreover, we show that $\mathsf{IGr}(3, 7)$ admits a full exceptional
collection consisting of equivariant vector bundles.


  Two languages are separable by a piecewise testable language if and only if
there exists no infinite tower between them. An infinite tower is an infinite
sequence of strings alternating between the two languages such that every
string is a subsequence (scattered substring) of all the strings that follow.
For regular languages represented by nondeterministic finite automata, the
existence of an infinite tower is decidable in polynomial time. In this paper,
we investigate the complexity of a particular method to compute a piecewise
testable separator. We show that it is closely related to the height of maximal
finite towers, and provide the upper and lower bounds with respect to the size
of the given nondeterministic automata. Specifically, we show that the upper
bound is polynomial with respect to the number of states with the cardinality
of the alphabet in the exponent. Concerning the lower bound, we show that
towers of exponential height with respect to the cardinality of the alphabet
exist. Since these towers mostly turn out to be sequences of prefixes, we also
provide a comparison with towers of prefixes.


  In this paper we establish $L^p$-boundedness properties for variation
operators defined by semigroups associated with Fourier-Bessel expansions.


  Consider $\Gamma$, a non-degenerate lattice in $\R^2$ and a constant magnetic
field $B$ with a flux though a cell of $\Gamma$ that is a rational multiple of
$2\pi$. We prove that for a generic $\Gamma$-periodic potential $V$, the
spectrum of the Landau Hamiltonian with magnetic field $B$ and periodic
potential $V$ is purely absolutely continuous.


  We study radiative corrections to deeply virtual Compton scattering in the
kinematics of HERA collider experiments to next--to--leading and
next--to--next--to--leading order. In the latter case the radiative corrections
are evaluated in a special scheme that allows us to employ the predictive power
of conformal symmetry. As observed before, the size of next--to--leading order
corrections strongly depends on the gluonic input, as gluons start to
contribute at this order. Beyond next--to--leading order we find, in contrast,
that the corrections for an input scale of few GeV^2 are small enough to
justify the uses of perturbation theory. For $\xi > 5 10^{-3}$ the modification
of the scale dependence is also small. However, with decreasing $\xi$ it
becomes moderate or even large, in particular for the phase.


  Supervised training of optical flow predictors generally yields better
accuracy than unsupervised training. However, the improved performance comes at
an often high annotation cost. Semi-supervised training trades off accuracy
against annotation cost. We use a simple yet effective semi-supervised training
method to show that even a small fraction of labels can improve flow accuracy
by a significant margin over unsupervised training. In addition, we propose
active learning methods based on simple heuristics to further reduce the number
of labels required to achieve the same target accuracy. Our experiments on both
synthetic and real optical flow datasets show that our semi-supervised networks
generally need around 50% of the labels to achieve close to full-label
accuracy, and only around 20% with active learning on Sintel. We also analyze
and show insights on the factors that may influence active learning
performance. Code is available at
https://github.com/duke-vision/optical-flow-active-learning-release.


  Classes of $G$-Hom-associative algebras are constructed as deformations of
$G$-associative algebras along algebra endomorphisms. As special cases, we
obtain Hom-associative and Hom-Lie algebras as deformations of associative and
Lie algebras, respectively, along algebra endomorphisms. Chevalley-Eilenberg
type homology for Hom-Lie algebras are also constructed.


  The von Neumann theory of measurement, based on an entanglement of the
quantum observable with a classical machine followed by decoherence or
collapse, does not readily apply to most measurements of momentum. Indeed, how
we measure the momentum of a quantum particle is not even discussed in most
quantum mechanics textbooks. Instead, we often teach the lore that position and
momentum cannot be measured at the same time. Yet, most ways to measure
momentum actually involve measuring position to infer momentum. In this
tutorial review, I examine real experiments that measure momentum and describe
how one can improve our teaching of the theory of measurement when we focus on
real experiments, rather than abstract mathematical models of measurement.


  In this work, we investigated resonant production of spin-3/2 color octet
electron at the Large Hadron electron Collider (LHeC). Signal and background
analysis have been performed and discovery, observation and exclusion limits
are determined for spin 3/2 color octet electron masses. Reachable values of
the compositeness scale are presented as a function of the spin-3/2 color octet
electron masses.


  Orc is a theory of orchestration of services that allows structured
programming of distributed and timed computations. Several formal semantics
have been proposed for Orc, including a rewriting logic semantics developed by
the authors. Orc also has a fully fledged implementation in Java with
functional programming features. However, as with descriptions of most
distributed languages, there exists a fairly substantial gap between Orc's
formal semantics and its implementation, in that: (i) programs in Orc are not
easily deployable in a distributed implementation just by using Orc's formal
semantics, and (ii) they are not readily formally analyzable at the level of a
distributed Orc implementation. In this work, we overcome problems (i) and (ii)
for Orc. Specifically, we describe an implementation technique based on
rewriting logic and Maude that narrows this gap considerably. The enabling
feature of this technique is Maude's support for external objects through TCP
sockets. We describe how sockets are used to implement Orc site calls and
returns, and to provide real-time timing information to Orc expressions and
sites. We then show how Orc programs in the resulting distributed
implementation can be formally analyzed at a reasonable level of abstraction by
defining an abstract model of time and the socket communication infrastructure,
and discuss the assumptions under which the analysis can be deemed correct.
Finally, the distributed implementation and the formal analysis methodology are
illustrated with a case study.


  In this paper we introduce a novel way of estimating prediction uncertainty
in deep networks through the use of uncertainty surrogates. These surrogates
are features of the penultimate layer of a deep network that are forced to
match predefined patterns. The patterns themselves can be, among other
possibilities, a known visual symbol. We show how our approach can be used for
estimating uncertainty in prediction and out-of-distribution detection.
Additionally, the surrogates allow for interpretability of the ability of the
deep network to learn and at the same time lend robustness against adversarial
attacks. Despite its simplicity, our approach is superior to the
state-of-the-art approaches on standard metrics as well as computational
efficiency and ease of implementation. A wide range of experiments are
performed on standard datasets to prove the efficacy of our approach.


  Person search is to detect all persons and identify the query persons from
detected persons in the image without proposals and bounding boxes, which is
different from person re-identification. In this paper, we propose a fusing
multi-task convolutional neural network(FMT-CNN) to tackle the correlation and
heterogeneity of detection and re-identification with a single convolutional
neural network. We focus on how the interplay of person detection and person
re-identification affects the overall performance. We employ person labels in
region proposal network to produce features for person re-identification and
person detection network, which can improve the accuracy of detection and
re-identification simultaneously. We also use a multiple loss to train our
re-identification network. Experiment results on CUHK-SYSU Person Search
dataset show that the performance of our proposed method is superior to
state-of-the-art approaches in both mAP and top-1.


  A linear time approximate maximum likelihood decoding algorithm on
tail-biting trellises is prsented, that requires exactly two rounds on the
trellis. This is an adaptation of an algorithm proposed earlier with the
advantage that it reduces the time complexity from O(mlogm) to O(m) where m is
the number of nodes in the tail-biting trellis. A necessary condition for the
output of the algorithm to differ from the output of the ideal ML decoder is
reduced and simulation results on an AWGN channel using tail-biting rrellises
for two rate 1/2 convoluational codes with memory 4 and 6 respectively are
reported


  Three experiments, among the LHC project, are getting ready to explore the b
quark flavour sector. While ATLAS and CMS are general purpose experiments,
where the study of B mesons is going to proceed in parallel with the Higgs
boson and supersymmetry searches, the LHCb experiment is devoted to B physics
studies. The key parameters entering the physics analyses and the performances
achieved in all the three experiments are presented. Given the large B physics
program foreseen in the LHC experiments, the studies reported in this paper
have been selected as those with higher likelihood to provide solid and
interesting new results on Standard Model validation and New Physics processes
search with early data.


  Languages have diverse characteristics that have emerged through evolution.
In modern English grammar, the perfect is formed with \textit{have}+PP (past
participle), but in earlier English the \textit{be}+PP form also existed. It is
widely recognised that the auxiliary verb BE was replaced by HAVE throughout
evolution, except for some special cases. However, whether this evolution was
caused by natural selection or random drift is still unclear. Here we examined
directional forces in the evolution of the English perfect with intransitives
by combining three large-scale data sources: EEBO (Early English Books Online),
COHA (Corpus of Historical American English), and Google Books. We found that
most intransitive verbs exhibited an apparent transition from \textit{be}+PP to
\textit{have}+PP, most of which were classified as `selection' by a deep neural
network-based model. These results suggest that the English perfect could have
evolved through natural selection rather than random drift, and provide
insights into the cultural evolution of grammar.


  We introduce a notion of quasimorphism between two arbitrary groups,
generalizing the classical notion of Ulam. We then define and study the
category of homogeneous quasigroups, whose objects are groups and whose
morphisms are equivalence classes of quasimorphisms in our sense. We call the
automorphism group QOut(G) of a group G in this category the quasioutomorphism
group. It acts on the space of real-valued homogeneous quasimorphisms on G
extending the natural action of Out(G). We discuss various classes of examples
of quasioutomorphisms of free groups. We use these examples to show that the
orbit of Hom(F_n, R) under QOut(F_n) spans a dense subspace. This is contrast
to the classical fact that the corresponding Out(F_n)-orbit is closed and of
uncountable codimension. We also show that QOut(Z^n) = GL_n(R).


  The 2021 Snowmass Energy Frontier panel wrote in its final report "The
realization of a Higgs factory will require an immediate, vigorous and targeted
detector R&D program". Both linear and circular $e^+e^-$ collider efforts have
developed a conceptual design for their detectors and are aggressively pursuing
a path to formalize these detector concepts. The U.S. has world-class expertise
in particle detectors, and is eager to play a leading role in the next
generation $e^+e^-$ collider, currently slated to become operational in the
2040s. It is urgent that the U.S. organize its efforts to provide leadership
and make significant contributions in detector R&D. These investments are
necessary to build and retain the U.S. expertise in detector R&D and future
projects, enable significant contributions during the construction phase and
maintain its leadership in the Energy Frontier regardless of the choice of the
collider project. In this document, we discuss areas where the U.S. can and
must play a leading role in the conceptual design and R&D for detectors for
$e^+e^-$ colliders.


  This study considers the expressiveness (that is the expressive power or
expressivity) of different types of Knowledge Organization Systems (KOS) and
discusses its potential to be machine-processable in the context of the
Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed
based on conceptualizations introduced by the Functional Requirements for
Subject Authority Data (FRSAD) and the Simple Knowledge Organization System
(SKOS); natural language processing techniques are also implemented. Applying a
comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject
headings system (LCSH) and a classification scheme (DDC). These are compared
with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts
and relations. It was observed that LCSH and DDC focus on the formalism of
character strings (nomens) rather than on the modelling of semantics; their
definition of what constitutes a concept is quite fuzzy, and they comprise a
large number of complex concepts. By contrast, thesauri have a coherent
definition of what constitutes a concept, and apply a systematic approach to
the modelling of relations. Ontologies explicitly define diverse types of
relations, and are by their nature machine-processable. The paper concludes
that the potential of both the expressiveness and machine processability of
each KOS is extensively regulated by its structural rules. It is harder to
represent subject headings and classification schemes as semantic networks with
nodes and arcs, while thesauri are more suitable for such a representation. In
addition, a paradigm shift is revealed which focuses on the modelling of
relations between concepts, rather than the concepts themselves.


  Time-energy high-dimensional quantum key distribution (HD-QKD) leverages the
high-dimensional nature of time-energy entangled biphotons and the loss
tolerance of single-photon detection to achieve long-distance key distribution
with high photon information efficiency. To date, the general-attack security
of HD-QKD has only been proven in the asymptotic regime, while HD-QKD's
finite-key security has only been established for a limited set of attacks.
Here we fill this gap by providing a rigorous HD-QKD security proof for general
attacks in the finite-key regime. Our proof relies on a novel entropic
uncertainty relation that we derive for time and conjugate-time measurements
using dispersive optics, and our analysis includes an efficient decoy-state
protocol in its parameter estimation. We present numerically-evaluated
secret-key rates illustrating the feasibility of secure and composable HD-QKD
over metropolitan-area distances when the system is subjected to the most
powerful eavesdropping attack.


  We classify all two-dimensional, unitary, rational conformal field theories
with two primaries, central charge $c<25$, and arbitrary Wronskian index. In
mathematical parlance, we classify all strongly regular vertex operator
algebras (VOAs) with central charge $c<25$ and exactly two simple modules. We
find that any such theory is either one of the Mathur-Mukhi-Sen (MMS) theories
$\mathsf{A}_{1,1}$, $\mathsf{G}_{2,1}$, $\mathsf{F}_{4,1}$, or
$\mathsf{E}_{7,1}$, or it is a coset of a chiral algebra with one primary
operator (also known as a holomorphic VOA) by such an MMS theory. By leveraging
existing results on the classification of holomorphic VOAs, we are able to
explicitly enumerate all of the aforementioned cosets and compute their
characters. This leads to 123 theories, most of which are new. We emphasize
that our work is a bona fide classification of RCFTs, not just of characters.
Our techniques are general, and we argue that they offer a promising strategy
for classifying chiral algebras with low central charge beyond two primaries.


  We report the discovery of the eighth and ninth known Trojans in stable
orbits around Neptune's leading Lagrange point, L4. The objects 2014 QO$_{441}$
and 2014 QP$_{441}$ were detected in data obtained during the 2013-14 and
2014-15 observing seasons by the Dark Energy Survey, using the Dark Energy
Camera (DECam) on the 4-meter Blanco telescope at Cerro Tololo Inter-American
Observatory. Both are in high-inclination orbits (18.8$^{\circ}$ and
19.4$^{\circ}$ respectively). With an eccentricity of 0.104, 2014 QO$_{441}$
has the most eccentric orbit of the eleven known stable Neptune Trojans. Here
we describe the search procedure and investigate the objects' long-term
dynamical stability and physical properties.


  The conditional copula model arises when the dependence between random
variables is influenced by another covariate. Despite its importance in
modelling complex dependence structures, there are very few fully nonparametric
approaches to estimate the conditional copula function. In the bivariate
setting, the only nonparametric estimator for the conditional copula is based
on Sklar's Theorem and proposed by Gijbels \textit{et al.} (2011). In this
paper, we propose an alternative nonparametric approach %based on functional
principal component analysis. We to construct an estimator for the bivariate
conditional copula from the Karhunen-Lo\`eve representation of a suitably
defined conditional copula process. We establish its consistency and weak
convergence to a limit Gaussian process with explicit covariance function.


  Based on the experimental results, all concepts drift types have their
respective hyperparameter configurations. Simple and gradual concept drift have
similar pattern which requires a smaller {\alpha} value than recurring concept
drift because, in this type of drift, a new concept appear continuously, so it
needs a high-frequency model adaptation. However, in recurring concepts, the
new concept may repeat in the future, so the lower frequency adaptation is
better. Furthermore, high-frequency model adaptation could lead to an
overfitting problem. Implementing CMGMM component pruning mechanism help to
control the number of the active component and improve model performance.


  Drawing on the idea that brain development is a Darwinian process of
``evolution + selection'' and the idea that the current state is a local
equilibrium state of many bodies with self-organization and evolution processes
driven by the temperature and gravity in our universe, in this work, we
describe an artificial intelligence system called the ``Synergetic Learning
Systems''. The system is composed of two or more subsystems (models, agents or
virtual bodies), and it is an open complex giant system. Inspired by natural
intelligence, the system achieves intelligent information processing and
decision-making in a given environment through cooperative/competitive
synergetic learning. The intelligence evolved by the natural law of ``it is not
the strongest of the species that survives, but the one most responsive to
change,'' while an artificial intelligence system should adopt the law of
``human selection'' in the evolution process. Therefore, we expect that the
proposed system architecture can also be adapted in human-machine synergy or
multi-agent synergetic systems. It is also expected that under our design
criteria, the proposed system will eventually achieve artificial general
intelligence through long term coevolution.


  Large language models augmented with task-relevant documents have
demonstrated impressive performance on knowledge-intensive tasks. However,
regarding how to obtain effective documents, the existing methods are mainly
divided into two categories. One is to retrieve from an external knowledge
base, and the other is to utilize large language models to generate documents.
We propose an iterative retrieval-generation collaborative framework. It is not
only able to leverage both parametric and non-parametric knowledge, but also
helps to find the correct reasoning path through retrieval-generation
interactions, which is very important for tasks that require multi-step
reasoning. We conduct experiments on four question answering datasets,
including single-hop QA and multi-hop QA tasks. Empirical results show that our
method significantly improves the reasoning ability of large language models
and outperforms previous baselines.


  We study the dipole picture of high-energy virtual-photon-proton scattering.
It is shown that different choices for the energy variable in the dipole cross
section used in the literature are not related to each other by simple
arguments equating the typical dipole size and the inverse photon virtuality,
contrary to what is often stated. We argue that the good quality of fits to
structure functions that use Bjorken-x as the energy variable - which is
strictly speaking not justified in the dipole picture - can instead be
understood as a consequence of the sign of scaling violations that occur for
increasing Q^2 at fixed small x. We show that the dipole formula for massless
quarks has the structure of a convolution. From this we obtain derivative
relations between the structure function F_2 at large and small Q^2 and the
dipole-proton cross section at small and large dipole size r, respectively.


  We present a spectroscopic analysis of MWC 314, a luminous blue variable
(LBV) candidate with an extended bipolar nebula. The detailed spectroscopic
variability is investigated to determine if MWC 314 is a massive binary system
with a supersonically accelerating wind or a low-mass B[e] star. We compare the
spectrum and spectral energy distribution to other LBVs (such as P Cyg) and
find very similar physical wind properties, indicating strong kinship. We
combined long-term high-resolution optical spectroscopic monitoring and V-band
photometric observations to determine the orbital elements and stellar
parameters and to investigate the spectral variability with the orbital phases.
We developed an advanced model of the large-scale wind-velocity and
wind-density structure with 3-D radiative transfer calculations that fit the
orbitally modulated P Cyg profile of He I lam5876, showing outflow velocities
above 1000 km/s. We find that MWC 314 is a massive semi-detached binary system
of ~1.22 AU, observed at an inclination angle of i=72.8 deg. with an orbital
period of 60.8 d and e=0.23. The primary star is a low-vsini LBV candidate of
m1=39.6 Msun and R1=86.8 Rsun. The detailed radiative transfer fits show that
the geometry of wind density is asymmetric around the primary star with
increased wind density by a factor of 3.3, which leads the orbit of the
primary. The variable orientation causes the orbital modulation that is
observed in absorption portions of P Cyg wind lines. Wind accretion in the
system produces a circumbinary disc. MWC 314 is in a crucial evolutionary phase
of close binary systems, when the massive primary star has its H envelope being
stripped and is losing mass to a circumbinary disc. MWC 314 is a key system for
studying the evolutionary consequences of these effects.


  We show that a weighted shift on a directed tree is related to a multiplier
algebra of coefficients of analytic functions. We use this relation to study
spectral properties of the operators in question.


  Unless another thing is stated one works in the $C^\infty$ category and
manifolds have empty boundary. Let $X$ and $Y$ be vector fields on a manifold
$M$. We say that $Y$ tracks $X$ if $[Y,X]=fX$ for some continuous function
$f\colon M\rightarrow\mathbb R$. A subset $K$ of the zero set ${\mathsf Z}(X)$
is an essential block for $X$ if it is non-empty, compact, open in ${\mathsf
Z}(X)$ and its Poincar\'e-Hopf index does not vanishes. One says that $X$ is
non-flat at $p$ if its $\infty$-jet at $p$ is non-trivial. A point $p$ of
${\mathsf Z}(X)$ is called a primary singularity of $X$ if any vector field
defined about $p$ and tracking $X$ vanishes at $p$. This is our main result:
Consider an essential block $K$ of a vector field $X$ defined on a surface $M$.
Assume that $X$ is non-flat at every point of $K$. Then $K$ contains a primary
singularity of $X$. As a consequence, if $M$ is a compact surface with non-zero
characteristic and $X$ is nowhere flat, then there exists a primary singularity
of $X$.


  Azzalini (1985) introduced a skew-normal distribution of which normal
distribution is a special case. Recently Kundu (2014) introduced a geometric
skew-normal distribution and showed that it has certain advantages over
Azzalini's skew-normal distribution. In this paper we discuss about the
multivariate geometric skew-normal distribution. It can be used as an
alternative to Azzalini's skew normal distribution. We discuss different
properties of the proposed distribution. It is observed that the joint
probability density function of the multivariate geometric skew normal
distribution can take variety of shapes. Several characterization results have
been established. Generation from a multivariate geometric skew normal
distribution is quite simple, hence the simulation experiments can be performed
quite easily. The maximum likelihood estimators of the unknown parameters can
be obtained quite conveniently using expectation maximization (EM) algorithm.
We perform some simulation experiments and it is observed that the performances
of the proposed EM algorithm are quite satisfactory. Further, the analyses of
two data sets have been performed, and it is observed that the proposed methods
and the model work very well.


  We examine the constraints on models of weakly interacting massive particle
(WIMP) dark matter from the recent observations of the Galactic center by the
High Energy Spectroscopic System (HESS) telescope. We analyze canonical WIMP
annihilation into Standard Model particle final states, including b/b-bar,
t/t-bar and W^+ W^-. The constraints on annihilation into b/b-bar is within an
order of magnitude of the thermal cross section at ~3 TeV, while the
tau^+/tau^- channel is within a factor of ~2 of thermal. We also study
constraints on Sommerfeld-enhanced dark matter annihilation models, and find
that the gamma-ray observational constraints here rule out all of the parameter
space consistent with dark matter annihilation interpretations of PAMELA and
the Fermi-LAT e^+/e^- spectrum, in specific classes of models, and strongly
constrains these interpretations in other classes. The gamma-ray constraints we
find are more constraining on these models, in many cases, than current relic
density, cosmic microwave background, halo shape and naturalness constraints.


  I argue that cosmological data from the epoch of primordial inflation is
catalyzing the maturation of quantum gravity from speculation into a hard
science. I explain why quantum gravitational effects from primordial inflation
are observable. I then review what has been done, both theoretically and
observationally, and what the future holds. I also discuss what this tells us
about quantum gravity.


  The second pulsar catalogue of the Fermi Large Area Telescope (LAT) will
contain in excess of 100 gamma-ray pulsars. The light curves (LCs) of these
pulsars exhibit a variety of shapes, and also different relative phase lags
with respect to their radio pulses, hinting at distinct underlying emission
properties (e.g., inclination and observer angles) for the individual pulsars.
Detailed geometric modelling of the radio and gamma-ray LCs may provide
constraints on the B-field structure and emission geometry. We used different
B-field solutions, including the static vacuum dipole and the retarded vacuum
dipole, in conjunction with an existing geometric modelling code, and
constructed radiation sky maps and LCs for several different pulsar parameters.
Standard emission geometries were assumed, namely the two-pole caustic (TPC)
and outer gap (OG) models. The sky maps and LCs of the various B-field and
radiation model combinations were compared to study their effect on the
resulting LCs. As an application, we compared our model LCs with Fermi LAT data
for the Vela pulsar, and inferred the most probable configuration in this case,
thereby constraining Vela's high-altitude magnetic structure and system
geometry.


  The pressure dependence of the critical temperature $T_c$ and upper critical
field $H_{c2}(T)$ has been measured up to 19 GPa in the layered superconducting
material 2H-NbSe$_2$. Relating the behavior of $H_{c2}(T)$ to Fermi surface
parameters, we find that the electron phonon coupling of the 2D Nb 4d derived
bands shows a peak at 5 GPa when the charge density wave (CDW) order is
suppressed. On the other hand, $T_c(P)$ shows a bell shaped curve with a
maximum at 10.5 GPa, well above the pressure for the suppression of the CDW
order. Changes in the band structure produce this shift in the maximum of
$T_c(P)$, demonstrating that 2H-NbSe$_2$ shows important differences with
respect to other compounds where $T_c$ has a maximum in the temperature-density
phase diagram shaped by the suppression of another, non-superconducting, ground
state.


  Social reinforcement and modular structure are two salient features observed
in the spreading of behavior through social contacts. In order to investigate
the interplay between these two features, we study the generalized epidemic
process on modular networks with equal-sized finite communities and adjustable
modularity. Using the analytical approach originally applied to clique-based
random networks, we show that the system exhibits a bond-percolation type
continuous phase transition for weak social reinforcement, whereas a
discontinuous phase transition occurs for sufficiently strong social
reinforcement. Our findings are numerically verified using the finite-size
scaling analysis and the crossings of the bimodality coefficient.


  Modular self-reconfigurable robots (MSRRs) offer enhanced task flexibility by
constructing various structures suitable for each task. However, conventional
terrestrial MSRRs equipped with wheels face critical challenges, including
limitations in the size of constructible structures and system robustness due
to elevated wrench loads applied to each module. In this work, we introduce an
Aerial MSRR (A-MSRR) system named BEATLE, capable of merging and separating
in-flight. BEATLE can merge without applying wrench loads to adjacent modules,
thereby expanding the scalability and robustness of conventional terrestrial
MSRRs. In this article, we propose a system configuration for BEATLE, including
mechanical design, a control framework for multi-connected flight, and a motion
planner for reconfiguration motion. The design of a docking mechanism and
housing structure aims to balance the durability of the constructed structure
with ease of separation. Furthermore, the proposed flight control framework
achieves stable multi-connected flight based on contact wrench control.
Moreover, the proposed motion planner based on a finite state machine (FSM)
achieves precise and robust reconfiguration motion. We also introduce the
actual implementation of the prototype and validate the robustness and
scalability of the proposed system design through experiments and simulation
studies.


  Following a theoretical proposal on multi-impurity charge Kondo circuits [T.
K. T. Nguyen and M. N. Kiselev, Phys. Rev. B {\bf 97}, 085403 (2018)] and the
experimental breakthrough in fabrication of the two-site Kondo simulator [W.
Pouse {\it et al}, Nat. Phys. (2023)] we investigate a thermoelectric transport
through a double-dot charge Kondo quantum nano-device in the strong coupling
operational regime. We focus on the fingerprints of the non-Fermi liquid and
its manifestation in the charge and heat quantum transport. We construct a
full-fledged quantitative theory describing crossovers between different
regimes of the multi-channel charge Kondo quantum circuits and discuss possible
experimental realizations of the theory.


  Moufang loops are one of the best-known generalizations of groups. There is
only one countable family of nonassociative finite simple Moufang loops,
arising from the split octonion algebras. We prove that every member of this
family is generated by three elements, using the classical results on
generators of unimodular groups.


  This note gives a simple proof that certain values of Artin's $L$-function,
for a representation $\rho$ with character $\chi_\rho$, are stable under
twisting by an even Dirichlet character $\chi$, up to an element generated over
$\mathbb Q$ by the values of $\chi$ and $\chi_\rho$, and a product with a power
of the Gauss sum $\tau(\chi)$ equal to the dimension of $\rho$. This extends a
result due to J. Coates and S. Lichtenbaum.


  M-SPARC is a Matlab code for performing ab initio Kohn-Sham Density
Functional Theory simulations. Version 2.0.0 of the software further extends
its capability to include relativistic effects, dispersion interactions, and
advanced semilocal/nonlocal exchange-correlation functionals. These features
significantly increase the fidelity of first principles calculations that can
be performed using M-SPARC.


  Machine/deep-learning (ML/DL) based techniques are emerging as a driving
force behind many cutting-edge technologies, achieving high accuracy on
computer vision workloads such as image classification and object detection.
However, training these models involving large parameters is both
time-consuming and energy-hogging. In this regard, several prior works have
advocated for sparsity to speed up the of DL training and more so, the
inference phase. This work begins with the observation that during training,
sparsity in the forward and backward passes are correlated. In that context, we
investigate two types of sparsity (input and output type) inherent in gradient
descent-based optimization algorithms and propose a hardware micro-architecture
to leverage the same. Our experimental results use five state-of-the-art CNN
models on the Imagenet dataset, and show back propagation speedups in the range
of 1.69$\times$ to 5.43$\times$, compared to the dense baseline execution. By
exploiting sparsity in both the forward and backward passes, speedup
improvements range from 1.68$\times$ to 3.30$\times$ over the sparsity-agnostic
baseline execution. Our work also achieves significant reduction in training
iteration time over several previously proposed dense as well as sparse
accelerator based platforms, in addition to achieving order of magnitude energy
efficiency improvements over GPU based execution.


  Social network alignment shows fundamental importance in a wide spectrum of
applications. To the best of our knowledge, existing studies mainly focus on
network alignment at the individual user level, requiring abundant common
information between shared individual users. For the networks that cannot meet
such requirements, social community structures actually provide complementary
and critical information at a slightly coarse-grained level, alignment of which
will provide additional information for user alignment. In turn, user alignment
also reveals more clues for community alignment. Hence, in this paper, we
introduce the problem of joint social network alignment, which aims to align
users and communities across social networks simultaneously. Key challenges lie
in that 1) how to learn the representations of both users and communities, and
2) how to make user alignment and community alignment benefit from each other.
To address these challenges, we first elaborate on the characteristics of
real-world networks with the notion of delta-hyperbolicity, and show the
superiority of hyperbolic space for representing social networks. Then, we
present a novel hyperbolic embedding approach for the joint social network
alignment, referred to as PERFECT, in a unified optimization. Extensive
experiments on real-world datasets show the superiority of PERFECT in both user
alignment and community alignment.


  In this paper we develop a notion of Gibbs measure for the geodesic flow
tangent to a foliated bundle over a compact and negatively curved basis. We
also develop a notion of $F$-harmonic measure and prove that there exists a
natural bijective correspondence between the two.
  For projective foliated bundles with $\mathbb{C}\mathbb{P}^1$-fibers without
transverse invariant measure, we show the uniqueness of these measures for any
H\"older potential on the basis. In that case we also prove that $F$-harmonic
measures are realized as weighted limits of large balls tangent to the leaves
and that their conditional measures on the fibers are limits of weighted
averages on the orbits of the holonomy group.


  We consider cycles for graded $C^{*,r}$-algebras (Real $C^{*}$-algebras)
which are compatible with the $*$-structure and the real structure. Their
characters are cyclic cocycles. We define a Connes type pairing between such
characters and elements of the van Daele $K$-groups of the $C^{*,r}$-algebra
and its real subalgebra. This pairing vanishes on elements of finite order. We
define a second type of pairing between characters and $K$-group elements which
is derived from a unital inclusion of $C^{*}$-algebras. It is potentially
non-trivial on elements of order two and torsion valued. Such torsion valued
pairings yield topological invariants for insulators. The two-dimensional
Kane-Mele and the three-dimensional Fu-Kane-Mele strong invariant are special
cases of torsion valued pairings. We compute the pairings for a simple class of
periodic models and establish structural results for two dimensional aperiodic
models with odd time reversal invariance.


  Irrespective of the class they belong to, all the hole doped high-Tc cuprate
superconductors show an anti-correlation between the superconducting transition
temperature and the characteristic pseudogap energy in the underdoped region.
The doping dependent pseudogap in the quasiparticle spectral density is
believed to remove low-energy electronic states and thereby reduce the
superconducting condensate. Impurities within the CuO2 plane, on the other
hand, break Cooper pairs in the unitarity limit and diminish superfluid
density. Both pseudogap in pure cuprates and impurity scattering in disordered
cuprates reduces Tc very effectively. In this study we have compared and
contrasted the mechanisms of Tc degradation due to pseudogap and impurity
scattering in hole doped cuprates. We have suggested a framework where both
these factors can be treated on somewhat equal footing. Beside impurity and
pseudogap dependent superconducting transition temperature, the proposed
scenario has been employed to investigate the disorder and hole content
dependent isotope exponent in high-Tc cuprates in this work.


  Recently, several array radar structures combined with sub-Nyquist techniques
and corresponding algorithms have been extensively studied. Carrier frequency
and direction-of-arrival (DOA) estimations of multiple narrow-band signals
received by array radars at the sub-Nyquist rates are considered in this paper.
We propose a new sub-Nyquist array radar architecture (a binary array radar
separately connected to a multi-coset structure with M branches) and an
efficient joint estimation algorithm which can match frequencies up with
corresponding DOAs. We further come up with a delay pattern augmenting method,
by which the capability of the number of identifiable signals can increase from
M-1 to Q-1 (Q is extended degrees of freedom). We further conclude that the
minimum total sampling rate 2MB is sufficient to identify $ {K \leq Q-1}$
narrow-band signals of maximum bandwidth $B$ inside. The effectiveness and
performance of the estimation algorithm together with the augmenting method
have been verified by simulations.


  Compositional zero-shot learning (CZSL) task aims to recognize unseen
compositional visual concepts, e.g., sliced tomatoes, where the model is
learned only from the seen compositions, e.g., sliced potatoes and red
tomatoes. Thanks to the prompt tuning on large pre-trained visual language
models such as CLIP, recent literature shows impressively better CZSL
performance than traditional vision-based methods. However, the key aspects
that impact the generalization to unseen compositions, including the diversity
and informativeness of class context, and the entanglement between visual
primitives, i.e., state and object, are not properly addressed in existing
CLIP-based CZSL literature. In this paper, we propose a model by prompting the
language-informed distribution, aka., PLID, for the CZSL task. Specifically,
the PLID leverages pre-trained large language models (LLM) to (i) formulate the
language-informed class distributions which are diverse and informative, and
(ii) enhance the compositionality of the class embedding. Moreover, a
visual-language primitive decomposition (VLPD) module is proposed to
dynamically fuse the classification decisions from the compositional and the
primitive space. Orthogonal to the existing literature of soft, hard, or
distributional prompts, our method advocates prompting the LLM-supported class
distributions, leading to a better zero-shot generalization. Experimental
results on MIT-States, UT-Zappos, and C-GQA datasets show the superior
performance of the PLID to the prior arts. Our code and models are released:
https://github.com/Cogito2012/PLID.


  Pretraining on large natural image classification datasets such as ImageNet
has aided model development on data-scarce 2D medical tasks. 3D medical tasks
often have much less data than 2D medical tasks, prompting practitioners to
rely on pretrained 2D models to featurize slices. However, these 2D models have
been surpassed by 3D models on 3D computer vision benchmarks since they do not
natively leverage cross-sectional or temporal information. In this study, we
explore whether natural video pretraining for 3D models can enable higher
performance on smaller datasets for 3D medical tasks. We demonstrate video
pretraining improves the average performance of seven 3D models on two chest CT
datasets, regardless of finetuning dataset size, and that video pretraining
allows 3D models to outperform 2D baselines. Lastly, we observe that
pretraining on the large-scale out-of-domain Kinetics dataset improves
performance more than pretraining on a typically-sized in-domain CT dataset.
Our results show consistent benefits of video pretraining across a wide array
of architectures, tasks, and training dataset sizes, supporting a shift from
small-scale in-domain pretraining to large-scale out-of-domain pretraining for
3D medical tasks. Our code is available at:
https://github.com/rajpurkarlab/chest-ct-pretraining


  In this paper we analyze a dark matter model inspired by theories with extra
dimensions. The dark matter candidate corresponds to the first Kaluza-Klein
mode of a real scalar added to the Standard Model. The tower of new particles
enriches the calculation of the relic abundance. For large mass splitting, the
model converges to the predictions of the inert singlet dark matter model. For
nearly degenerate mass spectrum, coannihilations increase the cross-sections
used for direct and indirect dark matter searches. Moreover, the Kaluza-Klein
zero mode can mix with the SM higgs and further constraints can be applied.


  Disgust is a basic emotion that serves to avoid contaminants, and is central
to the behavioural immune system. While disgust-motivated avoidance occurs in
bonobos and chimpanzees, humans show uniquely high levels of contamination
sensitivity. Current theory postulates that human disgust is primarily a
genetic adaptation, side-lining social transmission and denying parental
modelling. Here, we test whether this strong view is warranted by simulating
100000 years of cultural and biological evolution in various scenarios. We
modelled disgust as a trait that governed the extent to which individuals
forewent potentially contaminated nutrition. This lowered their risk of
gastrointestinal illness, at the cost of increasing starvation risk and
birth-interval. As environmental levels and costs of contamination rose, so did
relative fitness of high-disgust individuals. Evolutionary shifts in disgust as
polygenic trait occurred as a consequence of germ-cell mutations, but were most
prominent in populations with high initial genetic variance. Crucially,
cultural transmission between generations operated at a higher rate, even if
parental modelling was eliminated. This study serves not only as evidence of
cultural evolution shaping the behavioural immune system, but also as an
illustration of emerging theories that paint affective and cognitive mechanisms
as socially transmitted rather than biologically determined functions.


  A new data-driven method for operator learning of stochastic differential
equations(SDE) is proposed in this paper. The central goal is to solve forward
and inverse stochastic problems more effectively using limited data. Deep
operator network(DeepONet) has been proposed recently for operator learning.
Compared to other neural networks to learn functions, it aims at the problem of
learning nonlinear operators. However, it can be challenging by using the
original model to learn nonlinear operators for high-dimensional stochastic
problems. We propose a new multi-resolution autoencoder DeepONet model referred
to as MultiAuto-DeepONet to deal with this difficulty with the aid of
convolutional autoencoder. The encoder part of the network is designed to
reduce the dimensionality as well as discover the hidden features of
high-dimensional stochastic inputs. The decoder is designed to have a special
structure, i.e. in the form of DeepONet. The first DeepONet in decoder is
designed to reconstruct the input function involving randomness while the
second one is used to approximate the solution of desired equations. Those two
DeepONets has a common branch net and two independent trunk nets. This
architecture enables us to deal with multi-resolution inputs naturally. By
adding $L_1$ regularization to our network, we found the outputs from the
branch net and two trunk nets all have sparse structures. This reduces the
number of trainable parameters in the neural network thus making the model more
efficient. Finally, we conduct several numerical experiments to illustrate the
effectiveness of our proposed MultiAuto-DeepONet model with uncertainty
quantification.


  A first-order expansion of the $\mathbb{R}$-vector space structure on
$\mathbb{R}$ does not define every compact subset of every $\mathbb{R}^n$ if
and only if topological and Hausdorff dimension coincide on all closed
definable sets. Equivalently, if $A \subseteq \mathbb{R}^k$ is closed and the
Hausdorff dimension of $A$ exceeds the topological dimension of $A$, then every
compact subset of every $\mathbb{R}^n$ can be constructed from $A$ using
finitely many boolean operations, cartesian products, and linear operations.
The same statement fails when Hausdorff dimension is replaced by packing
dimension.


  The introduction of emojis (or emoticons) in social media platforms has given
the users an increased potential for expression. We propose a novel method
called Classification of Emojis using Siamese Network Architecture (CESNA) to
learn emoji-based representations of resource-poor languages by jointly
training them with resource-rich languages using a siamese network.
  CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent
Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive
loss function based on a similarity metric. The model learns the
representations of resource-poor and resource-rich language in a common emoji
space by using a similarity metric based on the emojis present in sentences
from both languages. The model, hence, projects sentences with similar emojis
closer to each other and the sentences with different emojis farther from one
another. Experiments on large-scale Twitter datasets of resource-rich languages
- English and Spanish and resource-poor languages - Hindi and Telugu reveal
that CESNA outperforms the state-of-the-art emoji prediction approaches based
on distributional semantics, semantic rules, lexicon lists and deep neural
network representations without shared parameters.


  Hydrogen and/or helium accreted by a neutron star from a binary companion may
undergo thermonuclear fusion. At different mass accretion rates different
burning regimes are discerned. Theoretical models predict helium fusion to
proceed as a thermonuclear runaway for accretion rates below the Eddington
limit and as stable burning above this limit. Observations, however, place the
boundary close to 10% of the Eddington limit. We study the effect of
rotationally induced transport processes on the stability of helium burning.
For the first time detailed calculations of thin helium shell burning on
neutron stars are performed using a hydrodynamic stellar evolution code
including rotation and rotationally induced magnetic fields. We find that in
most cases the instabilities from the magnetic field provide the dominant
contribution to the chemical mixing, while Eddington-Sweet circulations become
important at high rotation rates. As helium is diffused to greater depths, the
stability of the burning is increased, such that the critical accretion rate
for stable helium burning is found to be lower. Combined with a higher heat
flux from the crust, as suggested by recent studies, turbulent mixing could
explain the observed critical accretion rate. Furthermore, close to this
boundary we find oscillatory burning, which previous studies have linked to mHz
QPOs. In models where we continuously lower the heat flux from the crust, the
period of the oscillations increases by up to several tens of percents, similar
to the observed frequency drift, suggesting that this drift could be caused by
the cooling of deeper layers.


  The mass of the central black hole in the giant elliptical galaxy M84 has
previously been measured by two groups using the same observations of
emission-line gas with the Space Telescope Imaging Spectrograph (STIS) on the
Hubble Space Telescope, giving strongly discrepant results: Bower et al. (1998)
found M_BH = (1.5^{+1.1}_{-0.6}) x 10^9 M_sun, while Maciejewski & Binney
(2001) estimated M_BH = 4 x 10^8 M_sun. In order to resolve this discrepancy,
we have performed new measurements of the gas kinematics in M84 from the same
archival data, and carried out comprehensive gas-dynamical modeling for the
emission-line disk within ~70 pc from the nucleus. In comparison with the two
previous studies of M84, our analysis includes a more complete treatment of the
propagation of emission-line profiles through the telescope and STIS optics, as
well as inclusion of the effects of an intrinsic velocity dispersion in the
emission-line disk. We find that an intrinsic velocity dispersion is needed in
order to match the observed line widths, and we calculate gas-dynamical models
both with and without a correction for asymmetric drift. Including the effect
of asymmetric drift improves the model fit to the observed velocity field. Our
best-fitting model with asymmetric drift gives M_BH = (8.5^{+0.9}_{-0.8}) x
10^8 M_sun (68% confidence). This is a factor of ~2 smaller than the mass often
adopted in studies of the M_BH - sigma and M_BH - L relationships. Our result
provides a firmer basis for the inclusion of M84 in the correlations between
black hole mass and host galaxy properties.


  The one-dimensional quantum harmonic oscillator problem is examined via the
Laplace transform method. The stationary states are determined by requiring
definite parity and good behaviour of the eigenfunction at the origin and at
infinity.


  In algebraic geometry, Gromov--Witten invariants are enumerative invariants
that count the number of complex curves in a smooth projective variety
satisfying some incidence conditions. In 2001, A. Givental and Y.P. Lee defined
new invariants, called $K$-theoretical Gromov--Witten invariants. These
invariants are obtained by replacing cohomological constructions used in the
definition of the usual Gromov--Witten invariants by their $K$-theoretical
analogues. Then, an essential question is to understand how these two
invariants are related. In 2013, Iritani-Givental-Milanov-Tonita show that
$K$-theoretical Gromov--Witten invariants can be embedded in a function which
satisfies a $q$-difference equation. In general, these equations verify a
property called "confluence", which guarantees that we can take some limit of
these functional equations to obtain differential equations. In this thesis, we
propose to compare the two Gromov--Witten theories through the confluence of
$q$-difference equation. We show that, in the case of complex projective
spaces, the confluence of Givental's small $K$-theoretical $J$-function as a
solution of a $q$-difference equations outputs its cohomological analogue.


  We commence the evaluation of the one- and two-loop binding corrections to
the $g factor for an electron in a hydrogenlike system of order alpha^2 (Z
alpha)^5 and consider diagrams with closed fermion loops. The one-loop
vacuum-polarization correction is rederived and confirmed. For the two-loop
vacuum-polarization correction, due to a specific gauge-invariant set of
diagrams with closed fermion loops, we find a correction delta g = 7.442
(alpha/pi)^2 (Z alpha)^5. Based on the numerical trend of the coefficients
inferred from the gauge-invariant subset, we obtain a numerically large
tentative estimate for the complete two-loop binding correction to the g factor
(sum of self-energy and vacuum polarization).


  This work proposes a moving target defense (MTD) strategy to detect
coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists
of a physical attack, such as disconnecting a transmission line, followed by a
coordinated cyber attack that injects false data into the sensor measurements
to mask the effects of the physical attack. Such attacks can lead to
undetectable line outages and cause significant damage to the grid. The main
idea of the proposed approach is to invalidate the knowledge that the attackers
use to mask the effects of the physical attack by actively perturbing the
grid's transmission line reactances using distributed flexible AC transmission
system (D-FACTS) devices. We identify the MTD design criteria in this context
to thwart CCPAs. The proposed MTD design consists of two parts. First, we
identify the subset of links for D-FACTS device deployment that enables the
defender to detect CCPAs against any link in the system. Then, in order to
minimize the defense cost during the system's operational time, we use a
game-theoretic approach to identify the best subset of links (within the
D-FACTS deployment set) to perturb which will provide adequate protection.
Extensive simulations performed using the MATPOWER simulator on IEEE bus
systems verify the effectiveness of our approach in detecting CCPAs and
reducing the operator's defense cost.


  We study the class of groups generated by automata that act essentially
freely on the boundary of a rooted tree. In the process we establish and
discuss some general tools for determining if a group belongs to this class,
and explore the connections of this class to the classes of just-infinite and
scale-invariant groups. Our main application is a complete classification of
groups generated by 3-state automata over 2-letter alphabet that are in this
class.


  A space X is selectively sequentially pseudocompact if for every sequence
(U_n) of non-empty open subsets of X, one can choose a point x_n in each U_n in
such a way that the sequence (x_n) has a convergent subsequence. Let G be a
group from one of the following three classes: (i) V-free groups, where V is an
arbitrary variety of Abelian groups; (ii) torsion Abelian groups; (iii)
torsion-free Abelian groups. Under the Singular Cardinal Hypothesis SCH, we
prove that if G admits a pseudocompact group topology, then it can also be
equipped with a selectively sequentially pseudocompact group topology. Since
selectively sequentially pseudocompact spaces are strongly pseudocompact in the
sense of Garc\'ia-Ferreira and Ortiz-Castillo, this provides a strong positive
(albeit partial) answer to a question of Garc\'ia-Ferreira and Tomita.


  We present a novel framework for jointly modelling the weak lensing source
galaxy redshift distribution and the intrinsic alignment of galaxies via a
shared luminosity function (LF). Considering this framework within the context
of a Rubin Observatory's Legacy Survey of Space and Time (LSST) Year 1 and Year
10 cosmic shear analysis, we first demonstrate the substantial impact of the LF
on both source galaxy redshift distributions and the intrinsic alignment
contamination. We establish how the individual parameters of a Schechter LF
model impact the redshift distribution of a magnitude-limited sample, and we
demonstrate the effect of marginalising over the LF parameters as incorporated
in the intrinsic alignment modelling of a standard cosmic shear analysis
set-up. We forecast the impact of our joint modelling framework on cosmological
parameter constraints. Our preliminary results are promising, indicating that
this framework can yield cosmological constraints consistent with those
expected from standard analyses, enhanced by the flexibility of not fixing LF
parameters. We plan to further validate these findings with comprehensive
Markov chain Monte Carlo simulations to robustly quantify bias avoidance and
underscore the framework's efficacy. Taking advantage of our forecasting
results and the parameter degeneracies, we identify the specific impact of the
shape of the LF of source galaxies on the cosmic shear data vector. We also
discuss the potential of this method in providing a way to model generic
selection functions in redshift distribution estimation, as well as its
possibilities for extension to a 3x2pt analysis, particularly with respect to
incorporating galaxy bias in this luminosity-function-based framework. Although
we consider the context of LSST cosmic shear in this work, the proposed joint
modelling framework is generically applicable to weak lensing surveys.


  We investigate several Pb$(n,n'\gamma$) and Ge$(n,n'\gamma$) reactions. We
measure $\gamma$-ray production from Pb$(n,n'\gamma$) reactions that can be a
significant background for double-beta decay experiments which use lead as a
massive inner shield. Particularly worrisome for Ge-based double-beta decay
experiments are the 2041-keV and 3062-keV $\gamma$ rays produced via
Pb$(n,n'\gamma$). The former is very close to the ^{76}Ge double-beta decay
endpoint energy and the latter has a double escape peak energy near the
endpoint. Excitation $\gamma$-ray lines from Ge$(n,n'\gamma$) reactions are
also observed. We consider the contribution of such backgrounds and their
impact on the sensitivity of next-generation searches for neutrinoless
double-beta decay using enriched germanium detectors.


  We show that for a positive proportion of Laplace eigenvalues $\lambda_j$ the
associated Hecke-Maass $L$-functions $L(s,u_j)$ approximate with arbitrary
precision any target function $f(s)$ on a closed disc with center in $3/4$ and
radius $r<1/4$. The main ingredients in the proof are the spectral large sieve
of Deshouillers-Iwaniec and Sarnak's equidistribution theorem for Hecke
eigenvalues.


  The k-d tree is a classic binary space-partitioning tree used to organize
points in k-dimensional space. While used in computational geometry and
graphics, the data structure has a long history of application in nearest
neighbor search. The objective of the nearest neighbor search problem is to
efficiently find the closest point(s) to a given query point, and is the basis,
in turn, of common machine learning techniques. We present in this paper a case
study in the certified implementation, using the Coq proof assistant, of k-d
tree construction from a set of data and the accompanying K-nearest neighbors
search algorithm. Our experience demonstrates an intuitive method for
specifying properties of these algorithms using the notion of list
permutations.


  We develop an analytical framework to characterize the set of optimal ReLU
neural networks by reformulating the non-convex training problem as a convex
program. We show that the global optima of the convex parameterization are
given by a polyhedral set and then extend this characterization to the optimal
set of the non-convex training objective. Since all stationary points of the
ReLU training problem can be represented as optima of sub-sampled convex
programs, our work provides a general expression for all critical points of the
non-convex objective. We then leverage our results to provide an optimal
pruning algorithm for computing minimal networks, establish conditions for the
regularization path of ReLU networks to be continuous, and develop sensitivity
results for minimal ReLU networks.


  The magnetosphere of Mercury is studied using an implicit full particle in
cell simulation (PIC). We use a hybrid simulation where ions are full particles
and electrons are considered as a fluid to start a full PIC simulation where
electrons are also particles and follow their distribution function. This
approach allows us to estimate the changes introduced by the electron kinetic
physics. We find that the overall macroscopic state of the magnetosphere of
Mercury is little affected but several physical processes are significantly
modified in the full PIC simulation: the foreshock region is more active with
more intense shock reformation, the Kelvin-Helmholtz rippling effects on the
nightside magnetopause are sharper, and the magnetotail current sheet becomes
thinner than those predicted by the hybrid simulation. The greatest effect of
the electron physics, comes from the processes of particle energization. Both
species, not just the electrons, are found to gain more energy when kinetic
electron processes are taken into account. The region with the most energetic
plasma is found on the dusk side of the tail where magnetic flux ropes are
formed due to reconnection. We find that the ion and electron energization is
associated with the regions of reconnection and the development of kinetic
instabilities caused by counter-streaming electron populations. The resulting
electron distributions are highly non Maxwellian, a process that neither MHD
nor hybrid models can describe.


  We present a Bayesian reconstruction method which maps a galaxy distribution
from redshift-space to real-space inferring the distances of the individual
galaxies. The method is based on sampling density fields assuming a lognormal
prior with a likelihood given by the negative binomial distribution function
modelling stochastic bias. We assume a deterministic bias given by a power law
relating the dark matter density field to the expected halo or galaxy field.
Coherent redshift-space distortions are corrected in a Gibbs-sampling procedure
by moving the galaxies from redshift-space to real-space according to the
peculiar motions derived from the recovered density field using linear theory
with the option to include tidal field corrections from second order Lagrangian
perturbation theory. The virialised distortions are corrected by sampling
candidate real-space positions (being in the neighbourhood of the observations
along the line of sight), which are compatible with the bulk flow corrected
redshift-space position adding a random dispersion term in high density
collapsed regions. The latter are defined according to the eigenvalues of the
Hessian. This approach presents an alternative method to estimate the distances
to galaxies using the three dimensional spatial information, and assuming
isotropy. Hence the number of applications is very broad. In this work we show
the potential of this method to constrain the growth rate up to $k$ ~ 0.3 $h$
Mpc$^{-1}$. Furthermore it could be useful to correct for photo-metric redshift
errors, and to obtain improved BAO reconstructions.


  We investigate a system of two-dimensional interacting Brownian particles at
finite density. In the continuum limit, we uncover a hidden symmetry under
area-preserving diffeomorphism transformations. This symmetry leads to the
conservation of local vorticity. By calculating the generating functional
within the saddle-point plus Gaussian fluctuations approximation, we reveal the
emergence of a $U(1)$ gauge symmetry. This emergent symmetry enables us to
describe the dynamics of density fluctuations as a gauge theory. We solve the
corresponding equations of motion for various local and non-local two-body
potentials, demonstrating the presence of multiple dynamical regimes and
associated dynamical phase transitions.


  Emergence, the phenomena where a system's micro-scale dynamics facilitate the
development of non-trivial, informative higher scales, has become a
foundational concept in modern sciences, tying together fields as diverse as
physics, biology, economics, and ecology. Despite it's apparent universality
and the considerable interest, historically researchers have struggled to
provide a rigorous, formal definition of emergence that is applicable across
fields. Recent theoretical work using information theory and network science to
formalize emergence in state-transition networks (causal emergence) has
provided a promising way forward, however the relationship between this new
framework and other well-studied system dynamics is unknown. In this study, we
apply causal emergence analysis to two well-described dynamical systems: the 88
unique elementary cellular automata and the continuous Rossler system in
periodic, critical, and chaotic regimes. We find that emergence, as well as its
component elements (determinism, degeneracy, and effectiveness) vary
dramatically in different dynamical regimes in sometimes unexpected ways. We
conclude that the causal emergence framework provides a rich new area of
research to explore both to theoreticians and natural scientists in many
fields.


  Automated program repair (APR) has attracted great research attention, and
various techniques have been proposed. Search-based APR is one of the most
important categories among these techniques. Existing researches focus on the
design of effective mutation operators and searching algorithms to better find
the correct patch. Despite various efforts, the effectiveness of these
techniques are still limited by the search space explosion problem. One of the
key factors attribute to this problem is the quality of fault spaces as
reported by existing studies. This motivates us to study the importance of the
fault space to the success of finding a correct patch. Our empirical study aims
to answer three questions. Does the fault space significantly correlate with
the performance of search-based APR? If so, are there any indicative
measurements to approximate the accuracy of the fault space before applying
expensive APR techniques? Are there any automatic methods that can improve the
accuracy of the fault space? We observe that the accuracy of the fault space
affects the effectiveness and efficiency of search-based APR techniques, e.g.,
the failure rate of GenProg could be as high as $60\%$ when the real fix
location is ranked lower than 10 even though the correct patch is in the search
space. Besides, GenProg is able to find more correct patches and with fewer
trials when given a fault space with a higher accuracy. We also find that the
negative mutation coverage, which is designed in this study to measure the
capability of a test suite to kill the mutants created on the statements
executed by failing tests, is the most indicative measurement to estimate the
efficiency of search-based APR. Finally, we confirm that automated generated
test cases can help improve the accuracy of fault spaces, and further improve
the performance of search-based APR techniques.


  The problem of optimizing affine feedback laws that explicitly steer the mean
and covariance of an uncertain system state in the presence of a Gaussian
random field is considered. Spatially-dependent disturbances are successively
approximated with respect to a nominal trajectory by a sequence of jointly
Gaussian random vectors. Sequential updates to the nominal control inputs are
computed via convex optimization that includes the effect of affine state
feedback, the perturbing effects of spatial disturbances, and chance
constraints on the closed-loop state and control. The developed method is
applied to solve for an affine feedback law to minimize the 99th percentile of
$\Delta v$ required to complete an aerocapture mission around a planet with a
randomly disturbed atmosphere.


  This paper is devoted to study the asymptotic properties for the solution of
decoupled forward backward stochastic differential equations with delayed
generator. As an application, we establish a large deviation principe for
solution of the backward equation.


  We study the Schroedinger operator with a constant magnetic field in the
exterior of a compact domain in $\mathbb{R}^{2d}$, $d\geq 1$. The spectrum of
this operator consists of clusters of eigenvalues around the Landau levels. We
give asymptotic formulas for the rate of accumulation of eigenvalues in these
clusters. When the compact is a Reinhart domain we are able to show a more
precise asymptotic formula.


  This note attempts to interpret some of the recent findings about a downtrend
in the mean azimuthal velocity of low [alpha/Fe] thin disc stars with
increasing metallicity. The presence of such a trend was predicted in the model
of Schoenrich & Binney (2009), albeit with a slightly steeper slope. We show
that in a simple picture a Galactic disc without mixing in angular momenta
would display an exceedingly steep trend, while in the case of complete mixing
of all stars the trend has to vanish. The difference between model and
observational data can hence be interpreted as the consequence of the radial
abundance gradient in the model being too high resulting in an underestimate of
the migration strength. We shortly discuss the value of eccentricity
distributions in constraining structure and history of the Galactic disc.


  While rich medical datasets are hosted in hospitals distributed across the
world, concerns on patients' privacy is a barrier against using such data to
train deep neural networks (DNNs) for medical diagnostics. We propose Dopamine,
a system to train DNNs on distributed datasets, which employs federated
learning (FL) with differentially-private stochastic gradient descent (DPSGD),
and, in combination with secure aggregation, can establish a better trade-off
between differential privacy (DP) guarantee and DNN's accuracy than other
approaches. Results on a diabetic retinopathy~(DR) task show that Dopamine
provides a DP guarantee close to the centralized training counterpart, while
achieving a better classification accuracy than FL with parallel DP where DPSGD
is applied without coordination. Code is available at
https://github.com/ipc-lab/private-ml-for-health.


  Now a days, Internet plays a major role in our day to day activities e.g.,
for online transactions, online shopping, and other network related
applications. Internet suffers from slow convergence of routing protocols after
a network failure which becomes a growing problem. Multiple Routing
Configurations [MRC] recovers network from single node/link failures, but does
not support network from multiple node/link failures. In this paper, we propose
Enhanced MRC [EMRC], to support multiple node/link failures during data
transmission in IP networks without frequent global re-convergence. By
recovering these failures, data transmission in network will become fast.


  Based on his 50-year career in static high-pressure research, P. W. Bridgman
(PWB) is the father of modern high-pressure physics. What is not generally
recognized is that Bridgman was also intimately connected with establishing
shock compression as a scientific tool and he predicted major events in shock
research that occurred up to 40 years after his death. In 1956 the first phase
transition under shock compression was reported in Fe at 13 GPa (130 kbar). PWB
said a phase transition could not occur in a ~microsec, thus setting off a
controversy. The scientific legitimacy of shock compression resulted 5 years
later when static high-pressure researchers confirmed with x-ray diffraction
the existence of epsilon-Fe. Once PWB accepted the fact that shock waves
generated with chemical explosives were a valid scientific tool, he immediately
realized that substantially higher pressures would be achieved with nuclear
explosives. He included his ideas for achieving higher pressures in articles
published a few years after his death. L. V. Altshuler eventually read Bridgman
articles and pursued the idea of using nuclear explosives to generate super
high pressures, which subsequently morphed today into giant lasers. PWB also
anticipated combining static and shock methods, which today is done with
pre-compression of a soft sample in a diamond anvil cell followed by
laser-driven shock compression. One variation of that method is the
reverberating-shock technique, in which the first shock pre-compresses a soft
sample and subsequent reverberations isentropically compress the first-shocked
state.


  We study the role of the lattice artifacts associated with the Coulomb
binding effects in the analysis of the heavy quarkonium within lattice NRQCD.
We find that a "naive" perturbative matching generates spurious linear Coulomb
artifacts, which result in a large systematic error in the lattice predictions
for the heavy quarkonium spectrum. This effect is responsible, in particular,
for the discrepancy between the recent determinations of the bottomonium
hyperfine splitting in the radiatively improved lattice NRQCD [1, 2]. We show
that the correct matching procedure which provides full control over
discretization errors is based on the asymptotic expansion of the lattice
theory about the continuum limit, which gives
$M_{\Upsilon(1S)}-M_{\eta_b(1S)}=52.9\pm 5.5~{\rm MeV}$ [1].


  Spoken language processing requires speech and natural language integration.
Moreover, spoken Korean calls for unique processing methodology due to its
linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic
spoken Korean processing engine, which emphasizes that: 1) connectionist and
symbolic techniques must be selectively applied according to their relative
strength and weakness, and 2) the linguistic characteristics of Korean must be
fully considered for phoneme recognition, speech and language integration, and
morphological/syntactic processing. The design and implementation of SKOPE
demonstrates how connectionist/symbolic hybrid architectures can be constructed
for spoken agglutinative language processing. Also SKOPE presents many novel
ideas for speech and language processing. The phoneme recognition,
morphological analysis, and syntactic analysis experiments show that SKOPE is a
viable approach for the spoken Korean processing.


  Explicit general constructions of paragrassmann calculus with one and many
variables are given. Relations of the paragrassmann calculus to quantum groups
are outlined and possible physics applications are briefly discussed. This
paper is the same as the original 9210075 except added Appendix and minor
changes in Acknowledgements and References. IMPORTANT NOTE: This paper bears
the same title as the Dubna preprint E5-92-392 but is NOT identical to it,
containing new results, extended discussions, and references.


  A reinforcement learning algorithm accomplishes the task of synthesizing a
set-theoretical formula that evaluates to given truth values for given
assignments.


  We tackle human image synthesis, including human motion imitation, appearance
transfer, and novel view synthesis, within a unified framework. It means that
the model, once being trained, can be used to handle all these tasks. The
existing task-specific methods mainly use 2D keypoints to estimate the human
body structure. However, they only express the position information with no
abilities to characterize the personalized shape of the person and model the
limb rotations. In this paper, we propose to use a 3D body mesh recovery module
to disentangle the pose and shape. It can not only model the joint location and
rotation but also characterize the personalized body shape. To preserve the
source information, such as texture, style, color, and face identity, we
propose an Attentional Liquid Warping GAN with Attentional Liquid Warping Block
(AttLWB) that propagates the source information in both image and feature
spaces to the synthesized reference. Specifically, the source features are
extracted by a denoising convolutional auto-encoder for characterizing the
source identity well. Furthermore, our proposed method can support a more
flexible warping from multiple sources. To further improve the generalization
ability of the unseen source images, a one/few-shot adversarial learning is
applied. In detail, it firstly trains a model in an extensive training set.
Then, it finetunes the model by one/few-shot unseen image(s) in a
self-supervised way to generate high-resolution (512 x 512 and 1024 x 1024)
results. Also, we build a new dataset, namely iPER dataset, for the evaluation
of human motion imitation, appearance transfer, and novel view synthesis.
Extensive experiments demonstrate the effectiveness of our methods in terms of
preserving face identity, shape consistency, and clothes details. All codes and
dataset are available on
https://impersonator.org/work/impersonator-plus-plus.html.


  We numerically investigate atomic interferometry based on spin-exchange
collisions in $F=1$ spinor Bose-Einstein condensates in the regime of long
evolution times $t\gg h/c$, where c is the spin-dependent interaction energy.
We show that the sensitivity of spin-mixing interferometry can be enhanced by
using classically seeded initial states with a small population prepared in the
$m_F=\pm1$ states.


  In many fields, and especially in the medical and social sciences and in
recommender systems, data are gathered through clinical studies or targeted
surveys. Participants are generally reluctant to respond to all questions in a
survey or they may lack information to respond adequately to some questions.
The data collected from these studies tend to lead to linear regression models
where the regression vectors are only known partially: some of their entries
are either missing completely or replaced randomly by noisy values. In this
work, assuming missing positions are replaced by noisy values, we examine how a
connected network of agents, with each one of them subjected to a stream of
data with incomplete regression information, can cooperate with each other
through local interactions to estimate the underlying model parameters in the
presence of missing data. We explain how to adjust the distributed diffusion
through (de)regularization in order to eliminate the bias introduced by the
incomplete model. We also propose a technique to recursively estimate the
(de)regularization parameter and examine the performance of the resulting
strategy. We illustrate the results by considering two applications: one
dealing with a mental health survey and the other dealing with a household
consumption survey.


  Complex behaviors are often driven by an internal model, which integrates
sensory information over time and facilitates long-term planning. Inferring an
agent's internal model is a crucial ingredient in social interactions (theory
of mind), for imitation learning, and for interpreting neural activities of
behaving agents. Here we describe a generic method to model an agent's behavior
under an environment with uncertainty, and infer the agent's internal model,
reward function, and dynamic beliefs. We apply our method to a simulated agent
performing a naturalistic foraging task. We assume the agent behaves rationally
--- that is, they take actions that optimize their subjective utility according
to their understanding of the task and its relevant causal variables. We model
this rational solution as a Partially Observable Markov Decision Process
(POMDP) where the agent may make wrong assumptions about the task parameters.
Given the agent's sensory observations and actions, we learn its internal model
and reward function by maximum likelihood estimation over a set of
task-relevant parameters. The Markov property of the POMDP enables us to
characterize the transition probabilities between internal belief states and
iteratively estimate the agent's policy using a constrained
Expectation-Maximization (EM) algorithm. We validate our method on simulated
agents performing suboptimally on a foraging task currently used in many
neuroscience experiments, and successfully recover their internal model and
reward function. Our work lays a critical foundation to discover how the brain
represents and computes with dynamic beliefs.


  We present our early results of the mapping of the nucleus of the starburst
galaxy NGC 253 and its immediate surroundings using the Infrared Spectrograph
onboard the Spitzer Space Telescope. The map is centered on the nucleus of the
galaxy and spans the inner 800 x 688 pc2. The spatial distribution of the
\neiii line at 15.55 microns and the polycyclic aromatic hydrocarbons feature
at 11.3 microns peaks at the center while the purely rotationnal transition of
molecular hydrogen at 17.03 microns is strong over several slit positions. We
perform a brief investigation of the implications of these measurement on the
properties of the star formation in this region using theories developped to
explain the deficiency of massive stars in starbursts.


  We explore the prospects for the detection of giant circumbinary exoplanets
and brown dwarfs (BDs) orbiting Galactic double white dwarfs binaries (DWDs)
with the Laser Interferometer Space Antenna (LISA). By assuming an occurrence
rate of 50%, motivated by white dwarf pollution observations, we built a
Galactic synthetic population of P-type giant exoplanets and BDs orbiting DWDs.
We carried this out by injecting different sub-stellar populations, with
various mass and orbital separation characteristics, into the DWD population
used in the LISA mission proposal. We then performed a Fisher matrix analysis
to measure how many of these three-body systems show a periodic Doppler-shifted
gravitational wave perturbation detectable by LISA. We report the number of
circumbinary planets (CBPs) and (BDs) that can be detected by LISA for various
combinations of mass and semi-major axis distributions. We identify pessimistic
and optimistic scenarios corresponding, respectively, to 3 and 83 (14 and 2218)
detections of CBPs (BDs), observed during the length of the nominal LISA
mission. These detections are distributed all over the Galaxy following the
underlying DWD distribution, and they are biased towards DWDs with higher LISA
signal-to-noise ratio and shorter orbital period. Finally, we show that if LISA
were to be extended for four more years, the number of systems detected will be
more than doubled in both the optimistic and pessimistic scenarios. Our results
present promising prospects for the detection of post-main sequence exoplanets
and BDs, showing that gravitational waves can prove the existence of these
populations over the totality of the Milky Way. Detections by LISA will deepen
our knowledge on the life of exoplanets subsequent to the most extreme
evolution phases of their hosts, clarifying whether new phases of planetary
formation take place later in the life of the stars.


  The evolution of the web can be characterized as an emergence of frameworks
paving the way from static websites to dynamic web applications. As the scope
of web applications has grown, new technical challenges have emerged, leading
to the need for new solutions. The latest of these developments is the rise of
so-called disappearing web frameworks that question the axioms of earlier
generations of web frameworks, providing benefits of the early web and simple
static sites.


  The plethora of comparison shopping agents (CSAs) in today's markets enables
buyers to query more than a single CSA when shopping, and an inter-CSAs
competition naturally arises. We suggest a new approach, termed "selective
price disclosure", which improves the attractiveness of a CSA by removing some
of the prices in the outputted list. The underlying idea behind this approach
is to affect the buyer's beliefs regarding the chance of obtaining more
attractive prices. The paper presents two methods, which are suitable for
fully-rational buyers, for deciding which prices among those known to the CSA
should be disclosed. The effectiveness and efficiency of the methods are
evaluated using real data collected from five CSAs. The methods are also
evaluated with human subjects, showing that selective price disclosure can be
highly effective in this case as well, however, the disclosed subset of prices
should be extracted in a different (simplistic) manner.


  Humanitarian aid-distribution programs help bring physical goods to people in
need. Traditional paper-based solutions to support aid distribution do not
scale to large populations and are hard to secure. Existing digital solutions
solve these issues, at the cost of collecting large amount of personal
information. This lack of privacy can endanger recipients' safety and harm
their dignity. In collaboration with the International Committee of the Red
Cross, we build a safe digital aid-distribution system. We first systematize
the requirements such a system should satisfy. We then propose a decentralized
solution based on the use of tokens that fulfills the needs of humanitarian
organizations. It provides scalability and strong accountability, and, by
design, guarantees the recipients' privacy. We provide two instantiations of
our design, on a smart card and on a smartphone. We formally prove the security
and privacy properties of these solutions, and empirically show that they can
operate at scale.


  A search for squarks and gluinos in final states containing jets, missing
transverse momentum and no electrons or muons is presented. The data were
recorded by the ATLAS experiment in sqrt(s) = 7 TeV proton-proton collisions at
the Large Hadron Collider. No excess above the Standard Model background
expectation was observed in 35 inverse picobarns of analysed data. Gluino
masses below 500 GeV are excluded at the 95% confidence level in simplified
models containing only squarks of the first two generations, a gluino octet and
a massless neutralino. The exclusion increases to 870 GeV for equal mass
squarks and gluinos. In MSUGRA/CMSSM models with tan(beta)= 3, A_0=0 and mu>0,
squarks and gluinos of equal mass are excluded below 775 GeV. These are the
most stringent limits to date.


  Stochastic inhomogeneous oxidation is an inherent characteristic of copper
(Cu), often hindering color tuning and bandgap engineering of oxides. Coherent
control of the interface between metal and metal oxide remains unresolved. We
demonstrate coherent propagation of an oxidation front in single-crystal Cu
thin film to achieve a full-color spectrum for Cu by precisely controlling its
oxide-layer thickness. Grain boundary-free and atomically flat films prepared
by atomic-sputtering epitaxy allow tailoring of the oxide layer with an abrupt
interface via heat treatment with a suppressed temperature gradient. Color
tuning of nearly full-color RGB indices is realized by precise control of
oxide-layer thickness; our samples covered ~50.4% of the sRGB color space. The
color of copper/copper oxide is realized by the reconstruction of the
quantitative yield color from oxide pigment (complex dielectric functions of
Cu2O) and light-layer interference (reflectance spectra obtained from the
Fresnel equations) to produce structural color. We further demonstrate
laser-oxide lithography with micron-scale linewidth and depth through local
phase transformation to oxides embedded in the metal, providing spacing
necessary for semiconducting transport and optoelectronics functionality.


  Biomedical taxonomies, thesauri and ontologies in the form of the
International Classification of Diseases (ICD) as a taxonomy or the National
Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in
acquiring, representing and processing information about human health. With
increasing adoption and relevance, biomedical ontologies have also
significantly increased in size. For example, the 11th revision of the ICD,
which is currently under active development by the WHO contains nearly 50,000
classes representing a vast variety of different diseases and causes of death.
This evolution in terms of size was accompanied by an evolution in the way
ontologies are engineered. Because no single individual has the expertise to
develop such large-scale ontologies, ontology-engineering projects have evolved
from small-scale efforts involving just a few domain experts to large-scale
projects that require effective collaboration between dozens or even hundreds
of experts, practitioners and other stakeholders. Understanding how these
stakeholders collaborate will enable us to improve editing environments that
support such collaborations. We uncover how large ontology-engineering
projects, such as the ICD in its 11th revision, unfold by analyzing usage logs
of five different biomedical ontology-engineering projects of varying sizes and
scopes using Markov chains. We discover intriguing interaction patterns (e.g.,
which properties users subsequently change) that suggest that large
collaborative ontology-engineering projects are governed by a few general
principles that determine and drive development. From our analysis, we identify
commonalities and differences between different projects that have implications
for project managers, ontology editors, developers and contributors working on
collaborative ontology-engineering projects and tools in the biomedical domain.


  In 1970 Baxter considered the statistical three-coloring lattice model for
the case of toroidal boundary conditions. He used the Bethe ansatz and found
the partition function of the model in the thermodynamic limit. We consider the
same model but use other boundary conditions for which one can prove that the
partition function satisfies some functional equations similar to the
functional equations satisfied by the partition function of the six-vertex
model for a special value of the crossing parameter.


  To bridge the digital skills gap, we need to train more people in Software
Engineering techniques. This paper reports on a project exploring the way
students solve tasks using collaborative development platforms and version
control systems, such as GitLab, to find patterns and evaluation metrics that
can be used to improve the course content and reflect on the most common issues
the students are facing. In this paper, we explore Learning Analytics
approaches that can be used with GitLab and similar tools, and discuss the
challenges raised when applying those approaches in Software Engineering
Education, with the objective of building a pipeline that supports the full
Learning Analytics cycle, from data extraction to data analysis. We focus in
particular on the data anonymisation step of the proposed pipeline to explore
the available alternatives to satisfy the data protection requirements when
handling personal information in academic environments for research purposes.