  We present a novel Bayesian approach to analysing multiple time-series with
the aim of detecting abnormal regions. These are regions where the properties
of the data change from some normal or baseline behaviour. We allow for the
possibility that such changes will only be present in a, potentially small,
subset of the time-series. We develop a general model for this problem, and
show how it is possible to accurately and efficiently perform Bayesian
inference, based upon recursions that enable independent sampling from the
posterior distribution. A motivating application for this problem comes from
detecting copy number variation (CNVs), using data from multiple individuals.
Pooling information across individuals can increase the power of detecting
CNVs, but often a specific CNV will only be present in a small subset of the
individuals. We evaluate the Bayesian method on both simulated and real CNV
data, and give evidence that this approach is more accurate than a recently
proposed method for analysing such data.


  For general antiferromagnetic 2-spin systems, including the hardcore model
and the antiferromagnetic Ising model, there is an $\mathsf{FPTAS}$ for the
partition function on graphs of maximum degree $\Delta$ when the infinite
regular tree lies in the uniqueness region by Li et al. (2013). Moreover, in
the tree non-uniqueness region, Sly (2010) showed that there is no
$\mathsf{FPRAS}$ to estimate the partition function unless
$\mathsf{NP}=\mathsf{RP}$. The algorithmic results follow from the correlation
decay approach due to Weitz (2006) or the polynomial interpolation approach
developed by Barvinok (2016). However the running time is only polynomial for
constant $\Delta$. For the hardcore model, recent work of Anari et al. (2020)
establishes rapid mixing of the simple single-site Markov chain known as the
Glauber dynamics in the tree uniqueness region. Our work simplifies their
analysis of the Glauber dynamics by considering the total pairwise influence of
a fixed vertex $v$ on other vertices, as opposed to the total influence on $v$,
thereby extending their work to all 2-spin models and improving the mixing
time.
  More importantly our proof ties together the three disparate algorithmic
approaches: we show that contraction of the tree recursions with a suitable
potential function, which is the primary technique for establishing efficiency
of Weitz's correlation decay approach and Barvinok's polynomial interpolation
approach, also establishes rapid mixing of the Glauber dynamics. We emphasize
that this connection holds for all 2-spin models (both antiferromagnetic and
ferromagnetic), and existing proofs for correlation decay or polynomial
interpolation immediately imply rapid mixing of Glauber dynamics. Our proof
utilizes that the graph partition function divides that of Weitz's
self-avoiding walk trees, leading to new tools for analyzing influence of
vertices.


  In this paper we present results on the disordered Holstein-Double Exchange
model, explicitly in three dimension and `metallic' densities, obtained by
using a recently developed Monte Carlo approach. Following up on our earlier
paper, cond-mat/0406085, here we provide a detailed microscopic picture of the
thermally driven metal-insulator transition (MIT) that arises close to the
ferromagnet to paramagnet transition in this problem. This paper is focused
mainly on the `diagnostics', clarifying the origin of the effective disorder
that drives the MIT in this system. To that effect, we provide results on the
thermal evolution of the distributions of (i) lattice distortions, (ii) the net
`structural disorder' and (iii) the `hopping disorder' arising from spin
randomness feeding back through the Hunds coupling. We suggest a phenomenology
for the thermally driven MIT, viewing it as an `Anderson-Holstein' transition.


  A search for the rare decay $\eta \rightarrow e^+e^-$ is performed using the
inverse process $e^+e^- \rightarrow \eta$ in the decay mode
$\eta\to\pi^0\pi^0\pi^0 $. We analyze data with an integrated luminosity of 654
nb$^{-1} $ accumulated at the VEPP-2000 $e^+e^-$ collider with the SND detector
at the center-of-mass energy $E=m_\eta c ^2\approx 548 $ MeV, and set the upper
limit $ {\cal B} (\eta \rightarrow e^+ e^-) < 7 \times 10^{-7} $ at the 90\%
confidence level.


  Attention mechanism plays a more and more important role in point cloud
analysis and channel attention is one of the hotspots. With so much channel
information, it is difficult for neural networks to screen useful channel
information. Thus, an adaptive channel encoding mechanism is proposed to
capture channel relationships in this paper. It improves the quality of the
representation generated by the network by explicitly encoding the
interdependence between the channels of its features. Specifically, a
channel-wise convolution (Channel-Conv) is proposed to adaptively learn the
relationship between coordinates and features, so as to encode the channel.
Different from the popular attention weight schemes, the Channel-Conv proposed
in this paper realizes adaptability in convolution operation, rather than
simply assigning different weights for channels. Extensive experiments on
existing benchmarks verify our method achieves the state of the arts.


  Physiological sensing deep in tissue, remains a clinical challenge. Here a
flexible miniaturised sensing optrode providing a platform to perform minimally
invasive in vivo in situ measurements is reported. Silica microspheres
covalently coupled with a high density of ratiometrically configured
fluorophores were deposited into etched pits on the distal end of a 150 micron
diameter multicore optical fibre. With this platform, multiplexed photonic
measurements of pH and oxygen concentration with high precision in the distal
alveolar space of the lung is reported. We demonstrated the phenomenon that
high-density deposition of carboxyfluorescein covalently coupled to silica
microspheres shows an inverse shift in fluorescence in response to varying pH.
This platform delivered fast and accurate measurements, near instantaneous
response time, no photobleaching, immunity to power fluctuations and a flexible
architecture for addition of multiple sensors.


  We develop one-zone galaxy formation models in the early Universe, taking
into account dust formation and evolution by supernova (SN) explosions.
Especially we focus on the time evolution of dust size distribution, because
${\rm H}_{2}$ formation on the dust surface plays a critical role in the star
formation process in the early Universe. In the model we assume that star
formation rate (SFR) is proportional to the total amount of ${\rm H}_{2}$. We
consistently treat (i) the formation and size evolution of dust, (ii) the
chemical reaction networks including ${\rm H}_{2}$ formation both on the
surface of dust and in gas phase, and (iii) the SFR in the model. First, we
find that, because of dust destruction due to both reverse and forward shocks
driven by SNe, H$_{2}$ formation is more suppressed than that without dust
destruction. At the galaxy age of $sim0.8 {\rm Gyr}$, for galaxy models with
virial mass $M_{\rm vir}=10^{9} M_{\odot}$ and formation redshift $z_{\rm
vir}=10$, the molecular fraction is 2.5 orders of magnitude less in the model
with dust destruction by both shocks than that in the model without dust
destruction. Second, we show that the H$_{2}$ formation rate strongly depends
on the ISM density around SN progenitors. The SFR in higher ISM density is
lower, since dust destruction by reverse shocks is more effective in higher ISM
density. We conclude that not only the amount but also the size distribution of
dust being related with the star formation activity strongly affects the
evolution of galaxies in the early Universe.


  The fixed-point logic LREC= was developed by Grohe et al. (CSL 2011) in the
quest for a logic to capture all problems decidable in logarithmic space. It
extends FO+C, first-order logic with counting, by an operator that formalises a
limited form of recursion. We show that for every LREC=-definable property on
relational structures, there is a constant k such that the k-variable fragment
of first-order logic with counting quantifiers expresses the property via
formulae of logarithmic quantifier depth. This yields that any pair of graphs
separable by the property can be distinguished with the k-dimensional
Weisfeiler-Leman algorithm in a logarithmic number of iterations. In
particular, it implies that a constant dimension of the algorithm identifies
every interval graph and every chordal claw-free graph in logarithmically many
iterations, since every such graph admits LREC=-definable canonisation.


  The Remodeling Conjecture proposed by Bouchard-Klemm-Mari\~{n}o-Pasquetti
(BKMP) [arXiv:0709.1453, arXiv:0807.0597] relates the A-model open and closed
topological string amplitudes (the all genus open and closed Gromov-Witten
invariants) of a semi-projective toric Calabi-Yau 3-manifold/3-orbifold to the
Eynard-Orantin invariants of its mirror curve. It is an all genus open-closed
mirror symmetry for toric Calabi-Yau 3-manifolds/3-orbifolds. In this paper, we
present a proof of the BKMP Remodeling Conjecture for all genus open-closed
orbifold Gromov-Witten invariants of an arbitrary semi-projective toric
Calabi-Yau 3-orbifold relative to an outer framed Aganagic-Vafa Lagrangian
brane. We also prove the conjecture in the closed string sector at all genera.


  This paper is devoted to finding solutions of polynomial equations in roots
of unity. It was conjectured by S. Lang and proved by M. Laurent that all such
solutions can be described in terms of a finite number of parametric families
called maximal torsion cosets. We obtain new explicit upper bounds for the
number of maximal torsion cosets on an algebraic subvariety of the complex
algebraic $n$-torus ${\mathbb G}_{\rm m}^n$. In contrast to earlier works that
give the bounds of polynomial growth in the maximum total degree of defining
polynomials, the proofs of our results are constructive. This allows us to
obtain a new algorithm for determining maximal torsion cosets on an algebraic
subvariety of ${\mathbb G}_{\rm m}^n$.


  In a recent article [M. Olshanii, Phys. Rev. Lett. {\bf 81}, 938 (1998)], an
analytic solution of atom-atom scattering with a delta-function pseudopotential
interaction in the presence of transverse harmonic confinement yielded an
effective coupling constant that diverged at a `confinement induced resonance.'
In the present work, we report numerical results that corroborate this
resonance for more realistic model potentials. In addition, we extend the
previous theoretical discussion to include two-atom bound states in the
presence of transverse confinement, for which we also report numerical results
here


  It is shown that codimension one parabolic foliations of complex manifolds
are holomorphic. This is proved using the fact that codimension one foliations
of complex manifolds are necessarily locally Monge-Amp\`ere foliations and that
parabolic leaves cannot have hyperbolic behavior. The result holds true also
for locally Monge-Amp\`ere foliations with parabolic leaves of arbitrary
codimension.


  Dual encoders have been used for retrieval tasks and representation learning
with good results. A standard way to train dual encoders is using a contrastive
loss with in-batch negatives. In this work, we propose an improved contrastive
learning objective by adding queries or documents from the same encoder towers
to the negatives, for which we name it as "contrastive loss with SAMe TOwer
NEgatives" (SamToNe). By evaluating on question answering retrieval benchmarks
from MS MARCO and MultiReQA, and heterogenous zero-shot information retrieval
benchmarks (BEIR), we demonstrate that SamToNe can effectively improve the
retrieval quality for both symmetric and asymmetric dual encoders. By directly
probing the embedding spaces of the two encoding towers via the t-SNE algorithm
(van der Maaten and Hinton, 2008), we observe that SamToNe ensures the
alignment between the embedding spaces from the two encoder towers. Based on
the analysis of the embedding distance distributions of the top-$1$ retrieved
results, we further explain the efficacy of the method from the perspective of
regularisation.


  Malaria is one of the most common mosquito-borne diseases widespread in
tropical and subtropical regions, causing thousands of deaths every year in the
world. In a previous paper, we formulated an age-structured model containing
three structural variables: (i) the chronological age of human and mosquito
populations, (ii) the time since they are infected, and (iii) humans waning
immunity (i.e. the progressive loss of protective antibodies after recovery).
In the present paper, we expand the analysis of this age-structured model and
focus on the derivation of entomological and epidemiological results commonly
used in the literature, following the works of Smith and McKenzie. We
generalize their results to the age-structured case. In order to quantify the
impact of neglecting structuring variables such as chronological age, we
assigned values from the literature to our model parameters. While some
parameters values are readily accessible from the literature, at least those
about the human population, the parameters concerning mosquitoes are less
commonly documented and the values of a number of them (e.g. mosquito survival
in the presence or in absence of infection) can be discussed extensively.


  The cross section for prompt neutral and charged three pion production in pp
interactions was measured at excess energies in the range 160 - 217 MeV. That
comprises the first measurement of the pp->pp pi0pi0pi0 reaction and the
comparison with the pp->pp pi+pi-pi0 reaction, in a very direct way. The
experiment was performed above the eta meson production threshold and the cross
section normalization was obtained from a concurrent measurement of the
reaction pp->pp eta with the eta decaying into 3 pions. Since the same final
states are selected, the measurement has a low systematical error. The measured
cross section ratio sigma(pp->pp pi+pi-pi0)/sigma(pp->pp pi0\pi0\pi0) is
compared to predictions of dominance of different isobars in the intermediate
state.


  We show that cosmic censorship takes an exceptionally complex and interesting
form in the case of five-dimensional AdS-Kerr black holes, due to the unusually
distant relation obtaining in that case between the black hole parameters and
the physical mass and angular momentum. One finds that, in this case,
censorship is less restrictive than one might hope: it apparently allows some
rather bizarre behaviour, and in particular does not prohibit arbitrarily large
angular momenta. We find however that most of the unwelcome geometries
permitted by censorship can be eliminated by requiring stability against
pair-production of branes. We suggest that the small set of surviving
problematic cases can be eliminated in a natural way by imposing a certain
(holographic) bound on the physical mass.


  We give a new realization of $Y(sl_3)$ via Cartan-Weyl elements. An algebraic
description of Yangian Double $DY(sl_3)$, explicit comultiplication formulas
and universal R-matrix are obtained in these terms.


  We study logarithmic conformal field theory (LogCFT) in four dimensions using
conformal bootstrap techniques in the large spin limit. We focus on the
constraints imposed by conformal symmetry on the four point function of certain
logarithmic scalar operators and compute the leading correction to the
anomalous dimension of double trace operators in the large spin limit. There
exist certain holographic duals to such LogCFTs, which involve higher
derivative equations of motion. The anomalous dimension is related to the
binding energy of a state where two scalars rotate around each other with a
large angular momentum. We compute this energy shift and compare it to the
anomalous dimension of the large spin double trace operators due to stress
tensor exchange in the LogCFT. Our result shows that the cluster decomposition
principle is satisfied for LogCFTs as long as the dimensions of the operators
are positive.


  The potential occurrence of variable returns to size in research activity is
a factor to be considered in choices about the size of research organizations
and also in the planning of national research assessment exercises, so as to
avoid favoring those organizations that would benefit from such occurrence. The
aim of the current work is to improve on weaknesses in past inquiries
concerning returns to size through application of a research productivity
measurement methodology that is more accurate and robust. The method involves
field-standardized measurements that are free of the typical distortions of
aggregate measurement by discipline or organization. The analysis is conducted
for 183 hard science fields in all 77 Italian universities (time period
2004-2008) and allows detection of potential differences by field.


  In this paper we analyze the problem of the geodesic connectedness of subsets
of Riemannian manifolds. By using variational methods, the geodesic
connectedness of open domains (whose boundaries can be not differentiable and
not convex) of a smooth Riemannian manifold is proved. In some cases also the
convexity of the domain is obtained. Moreover we present examples of the
applicability and of the independence of the assumptions. Finally we give an
application to the existence of trajectories with fixed energy of dynamical
systems.


  We investigate self-similar solutions to the inverse mean curvature flow in
Euclidean space. In the case of one dimensional planar solitons, we explicitly
classify all homothetic solitons and translators. Generalizing Andrews' theorem
that circles are the only compact homothetic planar solitons, we apply the
Hsiung-Minkowski integral formula to prove the rigidity of the hypersphere in
the class of compact expanders of codimension one. We also establish that the
moduli space of compact expanding surfaces of codimension two is big. Finally,
we update the list of Huisken-Ilmanen's rotational expanders by constructing
new examples of complete expanders with rotational symmetry, including
topological hypercylinders, called infinite bottles, that interpolate between
two concentric round hypercylinders.


  Although the security benefits of domain name encryption technologies such as
DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are
clear, their positive impact on user privacy is weakened by--the still
exposed--IP address information. However, content delivery networks, DNS-based
load balancing, co-hosting of different websites on the same server, and IP
address churn, all contribute towards making domain-IP mappings unstable, and
prevent straightforward IP-based browsing tracking.
  In this paper, we show that this instability is not a roadblock (assuming a
universal DoT/DoH and ECH deployment), by introducing an IP-based website
fingerprinting technique that allows a network-level observer to identify at
scale the website a user visits. Our technique exploits the complex structure
of most websites, which load resources from several domains besides their
primary one. Using the generated fingerprints of more than 200K websites
studied, we could successfully identify 84% of them when observing solely
destination IP addresses. The accuracy rate increases to 92% for popular
websites, and 95% for popular and sensitive websites. We also evaluated the
robustness of the generated fingerprints over time, and demonstrate that they
are still effective at successfully identifying about 70% of the tested
websites after two months. We conclude by discussing strategies for website
owners and hosting providers towards hindering IP-based website fingerprinting
and maximizing the privacy benefits offered by DoT/DoH and ECH.


  The $\textit{BABAR}$ Collaboration data of the $D^0 \to K^0_S K^+ K^-$
process are analyzed within a quasi two-body factorization framework. In
earlier studies, assuming $D^0$ transitions to two kaons and the transitions
between one kaon and two kaons to proceed through the dominant intermediate
resonances, we approximated them as being proportional to the kaon form
factors. To obtain good fits, one has to multiply the scalar-kaon form factors,
derived from unitary relativistic coupled-channel models or in a dispersion
relation approach, by phenomenological energy-dependent functions. The final
state kaon-kaon interactions in the $S$-, $P$- and $D$- waves are taken into
account. All $S$-wave channels are treated in a unitary way. The $K^+K^-$ and
$\bar K^0 K^+$ $S$-wave effective mass squared distributions, corrected for
phase space, are shown, in a model-independent manner, to be significantly
different. Then the $f_0(980)$ resonance must be included at variance with the
BABAR analysis. The best fit has 19 free parameters and indicates i) the
dominance of annihilation amplitudes, ii) a large dominance of the $f_0(980)$
meson in the near threshold $K^+K^-$ invariant mass distribution and iii) a
sizable branching fraction to the $K_S^0 \ [\rho(770)^+ + \rho(1450)^+ +
\rho(1700)^+] $ final states. An appendix provides an update of the
determination of the isoscalar-scalar meson-meson amplitudes based on an
enlarged set of data. A second appendix proposes two alternative fits based on
scalar-kaon form factors calculated from the Muskhelishvili-Omn\`es dispersion
relation approach. These fits have $\chi^2$ quite close to that of the best fit
but they show important contributions from both the $f_0$ and $a_0^0$ mesons
and a weaker role of the $\rho^+$ mesons.


  In the cascaded approach to spoken language translation (SLT), the ASR output
is typically punctuated and segmented into sentences before being passed to MT,
since the latter is typically trained on written text. However, erroneous
segmentation, due to poor sentence-final punctuation by the ASR system, leads
to degradation in translation quality, especially in the simultaneous (online)
setting where the input is continuously updated. To reduce the influence of
automatic segmentation, we present a sliding window approach to translate raw
ASR outputs (online or offline) without needing to rely on an automatic
segmenter. We train translation models using parallel windows (instead of
parallel sentences) extracted from the original training data. At test time, we
translate at the window level and join the translated windows using a simple
approach to generate the final translation. Experiments on English-to-German
and English-to-Czech show that our approach improves 1.3--2.0 BLEU points over
the usual ASR-segmenter pipeline, and the fixed-length window considerably
reduces flicker compared to a baseline retranslation-based online SLT system.


  We prove that arithmetic quantum unique ergodicity holds on compact
arithmetic quotients of $GL(2,\mathbb{Q}_p)$ for automorphic forms belonging to
the principal series. We interpret this conclusion in terms of the
equidistribution of eigenfunctions on covers of a fixed regular graph or along
nested sequences of regular graphs. Our results are the first of their kind on
any p-adic arithmetic quotient. They may be understood as analogues of
Lindenstrauss's theorem on the equidistribution of Maass forms on a compact
arithmetic surface. The new ingredients here include the introduction of a
representation-theoretic notion of "p-adic microlocal lifts" with favorable
properties, such as diagonal invariance of limit measures, the proof of
positive entropy of limit measures in a p-adic aspect, following the method of
Bourgain--Lindenstrauss, and some analysis of local Rankin--Selberg integrals
involving the microlocal lifts introduced here as well as classical newvectors.
An important input is a measure-classification result of
Einsiedler--Lindenstrauss.


  It is commonly believed that melting occurs when mean square displacement
(MSD) of a particle of crystalline solid exceeds a threshold value. This is
known as the Lindemann criterion, first introduced in the year of 1910 by
Lindemann. However, Chakravarty et al. demonstrated that this common wisdom is
inadequate because the MSD at melting can be temperature dependent when
pressure is also allowed to vary along the coexistence line of the phase
diagram [Chakravarty C, Debenedetti P G and Stillinger F H 2007 J. Chem.
Phys.126 204508]. We show here by extensive molecular dynamics simulation of
both two and three dimensional polydisperse Lennard-Jones solids that particles
on the small and large limits of size distribution exhibit substantially
different Lindemann ratio at melting. Despite all the dispersion in MSD,
melting is found to be first order in both the dimensions at 5-10% dispersity
in size.


  We calculate a class of two-point boundary correlators in 2D quantum gravity
using its microscopic realization as loop gas on a random surface. We find a
perfect agreement with the two-point boundary correlation function in Liouville
theory, obtained by V. Fateev, A. Zamolodchikov and Al. Zamolodchikov. We also
give a geometrical meaning of the functional equation satisfied by this
two-point function.


  Fashion is now among the largest industries worldwide, for it represents
human history and helps tell the worlds story. As a result of the Fourth
Industrial Revolution, the Internet has become an increasingly important source
of fashion information. However, with a growing number of web pages and social
data, it is nearly impossible for humans to manually catch up with the ongoing
evolution and the continuously variable content in this domain. The proper
management and exploitation of big data can pave the way for the substantial
growth of the global economy as well as citizen satisfaction. Therefore,
computer scientists have found it challenging to handle e-commerce fashion
websites by using big data and machine learning technologies. This paper first
proposes a scalable focused Web Crawler engine based on the distributed
computing platforms to extract and process fashion data on e-commerce websites.
The role of the proposed platform is then described in developing a
disentangled feature extraction method by employing deep convolutional
generative adversarial networks (DCGANs) for content-based image indexing and
retrieval. Finally, the state-of-the-art solutions are compared, and the
results of the proposed approach are analyzed on a standard dataset. For the
real-life implementation of the proposed solution, a Web-based application is
developed on Apache Storm, Kafka, Solr, and Milvus platforms to create a
fashion search engine called SnapMode.


  In this paper we propose solving localized multiple kernel learning (LMKL)
using LMKL-Net, a feedforward deep neural network. In contrast to previous
works, as a learning principle we propose {\em parameterizing} both the gating
function for learning kernel combination weights and the multiclass classifier
in LMKL using an attentional network (AN) and a multilayer perceptron (MLP),
respectively. In this way we can learn the (nonlinear) decision function in
LMKL (approximately) by sequential applications of AN and MLP. Empirically on
benchmark datasets we demonstrate that overall LMKL-Net can not only outperform
the state-of-the-art MKL solvers in terms of accuracy, but also be trained
about {\em two orders of magnitude} faster with much smaller memory footprint
for large-scale learning.


  We discuss the importance of high-pT hadron and jet measurements in
nucleus-nucleus collisions at the CERN Large Hadron Collider.


  In this paper we present a detailed theory of the triplet states of
oligoacenes containing up to seven rings, i.e., starting from naphthalene all
the way up to heptacene. In particular, we present results on the optical
absorption from the first triplet excited state $1^{3}B_{2u}^{+}$ of these
oligomers, computed using the Pariser-Parr-Pople (PPP) model Hamiltonian, and a
correlated electron approach employing the configuration-interaction (CI)
methodology at various levels. Excitation energies of various triplets states
obtained by our calculations are in good agreement with the experimental
results, where available. The computed triplet spectra of oligoacenes exhibits
rich structure dominated by two absorption peaks of high intensities, which are
well separated in energy, and are caused by photons polarized along the
conjugation direction. This prediction of ours can be tested in future
experiments performed on oriented samples of oligoacenes.


  Bulk viscosity in cold dark matter is an appealing feature that introduces
distinctive phenomenological effects in the cosmological setting as compared to
the $\Lambda$CDM model. Under this view, we propose a general parametrization
of the bulk viscosity of the form $\xi\sim H^{1-2s} \rho_{m}^{s}$, that covers
intriguingly some well-known cases in the Eckart's theory. Some advantages of
this novel parametrization are: first, it allows to write the resulting
equations of cosmological evolution in the form of an autonomous system for any
value of $s$, so a general treatment of the fixed points and stability can be
done, and second, the bulk viscosity effect is consistently handled so that it
naturally turns off when matter density vanishes. As a main result we find,
based on detailed dynamical system analysis, one-parameter family of
de-Sitter-like asymptotic solutions with non-vanishing bulk viscosity
coefficient during different cosmological periods. Numerical computations are
performed jointly along with analytical phase space analysis in order to assess
more quantitatively the bulk viscosity effect on the cosmological background
evolution. Finally, as a first contact with observation we derive constraints
on the free parameters of some bulk viscosity models with specific
$s$-exponents from Supernovae Ia and observations of the Hubble parameter, by
performing a Bayesian statistical analysis thought the Markov Chain Monte Carlo
method.


  Perfectoid spaces are sophisticated objects in arithmetic geometry introduced
by Peter Scholze in 2012. We formalised enough definitions and theorems in
topology, algebra and geometry to define perfectoid spaces in the Lean theorem
prover. This experiment confirms that a proof assistant can handle complexity
in that direction, which is rather different from formalising a long proof
about simple objects. It also confirms that mathematicians with no computer
science training can become proficient users of a proof assistant in a
relatively short period of time. Finally, we observe that formalising a piece
of mathematics that is a trending topic boosts the visibility of proof
assistants amongst pure mathematicians.


  Given a similarity graph between items, correlation clustering (CC) groups
similar items together and dissimilar ones apart. One of the most popular CC
algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of
vertices, and obtains a 3-approximation ratio. Unfortunately, KwikCluster in
practice requires a large number of clustering rounds, a potential bottleneck
for large graphs.
  We present C4 and ClusterWild!, two algorithms for parallel correlation
clustering that run in a polylogarithmic number of rounds and achieve nearly
linear speedups, provably. C4 uses concurrency control to enforce
serializability of a parallel clustering process, and guarantees a
3-approximation ratio. ClusterWild! is a coordination free algorithm that
abandons consistency for the benefit of better scaling; this leads to a
provably small loss in the 3-approximation ratio.
  We provide extensive experimental results for both algorithms, where we
outperform the state of the art, both in terms of clustering accuracy and
running time. We show that our algorithms can cluster billion-edge graphs in
under 5 seconds on 32 cores, while achieving a 15x speedup.


  We propose a method to determine the power spectrum of \HI column density
fluctuations using radio-interferometric observations of 21-cm emission from
the ISM of galaxies. We have used this to estimate the power spectra of six
nearly face on nearby spiral galaxies. Earlier work has shown that these power
spectra are well fitted by power laws with slopes around -1.6 across
length-scales $\sim 1 \, {\rm kpc}$ to $\sim 10 \, {\rm kpc}$, the amplitude
however was undetermined. In the present work we have determined the amplitude
of the \HI column density power spectrum. We find that the \HI column density
$N_{\rm HI}$ expressed in units of $10^{20} \, {\rm cm}^{-2}$ has mean square
fluctuations in the range $\sim 0.03$ to $\sim 20$. The amplitude of the power
spectrum is found to be tightly correlated with the \HI mass fraction of the
galaxies. The physical process responsible for these scale-invariant
fluctuations is, however, at present not known.


  We study the spatial properties of the Casimir friction phenomenon for an
atom moving at a non-relativistic constant velocity parallel to a planar
graphene sheet. The coupling of the atom to the vacuum electromagnetic (EM)
field is implemented by an electric dipole term, plus a R\"ontgen term. We
study the fermion pair production, evaluating the angular dependence of the
fermion emission probability. The phenomenon exhibits a threshold: it only
exists when the speed of the sliding motion is larger than the Fermi velocity
of the medium.


  Photons are a natural resource in quantum information, and the last decade
showed significant progress in high-quality single photon generation and
detection. Furthermore, photonic qubits are easy to manipulate and do not
require particularly strongly sealed environments, making them an appealing
platform for quantum computing. With the one-way model, the vision of a
universal and large-scale quantum computer based on photonics becomes feasible.
In one-way computing, the input state is not an initial product state, but a
so-called cluster state. A series of measurements on the cluster state's
individual qubits and their temporal order, together with a feed-forward
procedure, determine the quantum circuit to be executed. We propose a pipeline
to convert a QASM circuit into a graph representation named measurement-graph
(m-graph), that can be directly translated to hardware instructions on an
optical one-way quantum computer. In addition, we optimize the graph using
ZX-Calculus before evaluating the execution on an experimental discrete
variable photonic platform.


  Conventional magneto-static finite element analysis of electrical machine
design is time-consuming and computationally expensive. Since each machine
topology has a distinct set of parameters, design optimization is commonly
performed independently. This paper presents a novel method for predicting Key
Performance Indicators (KPIs) of differently parameterized electrical machine
topologies at the same time by mapping a high dimensional integrated design
parameters in a lower dimensional latent space using a variational autoencoder.
After training, via a latent space, the decoder and multi-layer neural network
will function as meta-models for sampling new designs and predicting associated
KPIs, respectively. This enables parameter-based concurrent multi-topology
optimization.


  We first review why a strongly first-order phase transition needed for
baryogenesis is excluded in the electroweak standard model. We also comment on
some intriguing effects in the strongly interacting hot phase. In the MSSM with
a light stop a strongly first-order phase transition can be achieved. It
possibly proceeds in two stages.


  We present a photometrically accurate restitution of the K and F coronae from
white-light images obtained over 24 Years [1996--2019] by the Large-Angle
Spectrometric COronagraph "LASCO-C2" onboard the Solar and Heliospheric
Observatory (SOHO). The procedure starts with the data set coming from the
polarimetric separation of images of 512 x 512 pixels in which the F-corona and
the instrumental stray light are entangled. Disentangling these components
proceeds in three stages, each composed of several steps. Stage 1 establishes
the distinct variations of the radiance of these components with the Sun--SOHO
distance and generate a new data set of median images calculated for each
Carrington rotation. Stage 2 achieves the restitution of a set of 36 stray
light images reflecting its temporal variation and the periodic rolls of SOHO
which started in 2003. Stage 3 achieves the restitution of the F-corona and a
time series of daily images is generated. These results allowed us processing
the whole set of routine LASCO-C2 images of 1024 x 1024 pixels (approximately
626000 images) and producing calibrated, high resolution images of the
K-corona. We extend our past conclusions that the temporal variation of the
integrated radiance of the K-corona tracks the solar activity over two solar
cycles 23 and 24 and that it is highly correlated with the temporal variation
of the total magnetic field. The behaviours of the integrated radiance during
the last few years of the declining phases of solar cycles 23 and 24 are
remarkably similar, reaching the same floor level and leading to a duration of
11.0 year for the latter cycle, in agreement with the sunspot determination.


  Semi-supervised learning (SSL) has become important in current data analysis
applications, where the amount of unlabeled data is growing exponentially and
user input remains limited by logistics and expense. Constrained clustering, as
a subclass of SSL, makes use of user input in the form of relationships between
data points (e.g., pairs of data points belonging to the same class or
different classes) and can remarkably improve the performance of unsupervised
clustering in order to reflect user-defined knowledge of the relationships
between particular data points. Existing algorithms incorporate such user
input, heuristically, as either hard constraints or soft penalties, which are
separate from any generative or statistical aspect of the clustering model;
this results in formulations that are suboptimal and not sufficiently general.
In this paper, we propose a principled, generative approach to
probabilistically model, without ad hoc penalties, the joint distribution given
by user-defined pairwise relations. The proposed model accounts for general
underlying distributions without assuming a specific form and relies on
expectation-maximization for model fitting. For distributions in a standard
form, the proposed approach results in a closed-form solution for updated
parameters.


  We study the dynamics of two qubits separately sent through two coupled
resonators, each initially containing a coherent state field. We present
analytical arguments and numerical calculations for the qubit-field system
under different two-qubit initial states, photon hopping strengths, and
detunings. In far off-resonant regime, the maximal entanglement of two qubits
can be generated with the initial qubit state in which one qubit is in the
excited state and the other is in the ground state, and the initially maximal
two-qubit entanglement can be frozen and fully revived even for large mean
photon number. When the qubits are both initially in their excited states or
ground states, the qubit-qubit entanglement birth and death apparently appear
in the regime where the photon hopping strength is close to qubit-field
detuning, and its peaks do not decrease monotonically as the interaction time
increases. It is interesting to observe that when there is photon hopping
strength between two fields, the field-field entanglement can be larger than
one and increases as the initial amplitude of the coherent state grows. By
postselecting the fields both in their coherent states, the entanglement of two
initially unentangled qubits can be largely improved. Our present setup is
fundamental for the distributed quantum information processing and applicable
to different physical qubit-resonator systems.


  In this paper we extend work of Kawamura, see kawamura, for Cuntz-Krieger
algebras O_A for infinite matrices A. We generalize the definition of branching
systems, prove their existence for any given matrix A and show how they induce
some very concrete representations of O_A. We use these representations to
describe the Perron-Frobenius operator, associated to an nonsingular
transformation, as an infinite sum and under some hypothesis we find a matrix
representation for the operator. We finish the paper with a few examples.


  Measurements of the sphericity of primary charged particles in minimum bias
proton--proton collisions at $\sqrt{s}=0.9$, 2.76 and 7 TeV with the ALICE
detector at the LHC are presented. The observable is linearized to be collinear
safe and is measured in the plane perpendicular to the beam direction using
primary charged tracks with $p_{\rm T}\geq0.5$ GeV/c in $|\eta|\leq0.8$. The
mean sphericity as a function of the charged particle multiplicity at
mid-rapidity ($N_{\rm ch}$) is reported for events with different $p_{\rm T}$
scales ("soft" and "hard") defined by the transverse momentum of the leading
particle. In addition, the mean charged particle transverse momentum versus
multiplicity is presented for the different event classes, and the sphericity
distributions in bins of multiplicity are presented. The data are compared with
calculations of standard Monte Carlo event generators. The transverse
sphericity is found to grow with multiplicity at all collision energies, with a
steeper rise at low $N_{\rm ch}$, whereas the event generators show the
opposite tendency. The combined study of the sphericity and the mean $p_{\rm
T}$ with multiplicity indicates that most of the tested event generators
produce events with higher multiplicity by generating more back-to-back jets
resulting in decreased sphericity (and isotropy). The PYTHIA6 generator with
tune PERUGIA-2011 exhibits a noticeable improvement in describing the data,
compared to the other tested generators.


  Very loosely, $\mathbb{Z}_2^n$-manifolds are `manifolds' with
$\mathbb{Z}_2^n$-graded coordinates and their sign rule is determined by the
scalar product of their $\mathbb{Z}_2^n$-degrees. A little more carefully, such
objects can be understood within a sheaf-theoretical framework, just as
supermanifolds can, but with subtle differences. In this paper, we examine the
notion of a Riemannian $\mathbb{Z}_2^n$-manifold, i.e., a
$\mathbb{Z}_2^n$-manifold equipped with a Riemannian metric that may carry
non-zero $\mathbb{Z}_2^n$-degree. We show that the basic notions and tenets of
Riemannian geometry directly generalise to the setting of
$\mathbb{Z}_2^n$-geometry. For example, the Fundamental Theorem holds in this
higher graded setting. We point out the similarities and differences with
Riemannian supergeometry.


  We discuss out-of-equilibrium population imbalances between different orbital
states due to applied thermal gradients. This purely thermoelectric orbital
effect appears quite generically in nanostructures with a pseudospin degree of
freedom. We establish an orbital Seebeck coefficient that characterizes the
induced orbital bias in response to a temperature difference between reservoirs
coupled to a quantum conductor. We analyze a two-terminal strongly interacting
quantum dot with two orbital states and find that the orbital thermopower acts
as an excellent tool to describe the transition between SU(4) and SU(2) Kondo
physics. Our conclusions are reinforced from a detailed comparison with the
charge thermopower using numerical renormalization group calculations.


  Considered herein is a particular nonlinear dispersive stochastic equation.
It was introduced recently in [3], as a model describing surface water waves
under location uncertainty. The corresponding noise term is introduced through
a Hamiltonian formulation, which guarantees the energy conservation of the
flow. Here the initial-value problem is studied.


  The valence of a function $f$ at a point $w$ is the number of distinct,
finite solutions to $f(z) = w$. Let $f$ be a complex-valued harmonic function
in an open set $R \subseteq \mathbb{C}$. Let $S$ denote the critical set of $f$
and $C(f)$ the global cluster set of $f$. We show that $f(S) \cup C(f)$
partitions the complex plane into regions of constant valence. We give some
conditions such that $f(S) \cup C(f)$ has empty interior. We also show that a
component $R_0 \subseteq R \backslash f^{-1}(f(S) \cup C(f))$ is a $n_0$-fold
covering of some component $\Omega_0 \subseteq \mathbb{C} \backslash (f(S) \cup
C(f))$. If $\Omega_0$ is simply connected, then $f$ is univalent on $R_0$. We
explore conditions for combining adjacent components to form a larger region of
univalence. Those results which hold for $C^1$ functions on open sets in
$\mathbb{R}^2$ are first stated in that form and then applied to the case of
planar harmonic functions. If $f$ is a light, harmonic function in the complex
plane, we apply a structure theorem of Lyzzaik to gain information about the
difference in valence between components of $\mathbb{C} \backslash (f(S) \cup
C(f))$ sharing a common boundary arc in $f(S) \backslash C(f)$.


  Within the frame of quantum optics we analyze the properties of spontaneous
emission of two-level atom in media with indefinite permittivity tensor where
the geometry of the dispersion relation is characterized by an ellipsoid or a
hyperboloid(hyperbolic medium). The decay rate is explicitly given with the
orientation of the dipole transition matrix element taken into account. It
indicates that for the ellipsoid case the intensity of the photons coupled into
different modes can be tuned by changing the direction of the matrix element
and for the hyperboloid case it is found that spontaneous emission in
hyperbolic medium can be dramatically enhanced compared to the dielectric
background. Moreover, spontaneous emission exhibit the strong directivity and
get the maximum in the asymptote direction.


  Let $R=\oplus_{i\geq 0} R_i$ be an Artinian standard graded $K$-algebra
defined by quadrics. Assume that $\dim R_2\leq 3$ and that $K$ is algebraically
closed of characteristic $\neq 2$. We show that $R$ is defined by a Gr\"obner
basis of quadrics with, essentially, one exception. The exception is given by
$K[x,y,z]/I$ where $I$ is a complete intersection of 3 quadrics not containing
the square of a linear form.


  We finish the classification of equitable 2-partitions of the Johnson graphs
of diameter 3, $J(n,3)$, for $n>10$.


  We present a comprehensive phenomenological study of heavy flavor
distributions and correlations in longitudinally polarized proton-proton
collisions at BNL-RHIC. All results are obtained with a flexible parton-level
Monte Carlo program at next-to-leading order accuracy and include the
fragmentation into heavy mesons, their subsequent semi-leptonic decays, and
experimental cuts. Next-to-leading order QCD corrections are found to be
significant for both cross sections and double-spin asymmetries. The
sensitivity of heavy flavor measurements at BNL-RHIC to the gluon polarization
of the nucleon is assessed. Electron-muon and muon-muon correlations turn out
to be the most promising observables. Theoretical uncertainties are estimated
by varying renormalization and factorization scales, heavy quark masses, and
fragmentation parameters.


  The $SL(3)$ Kuznetsov formula exists in several versions, and has been
employed with some success to study automorphic forms on $SL(3)$. In each
version, the weight functions on the geometric side are given by multiple
integrals with complicated oscillating factors; this is the primary obstruction
to its use. By describing them as solutions to systems of differential
equations, we give power series and Mellin-Barnes integral representations of
minimal dimension for these weight functions. This completes the role of
harmonic analysis on symmetric spaces on the geometric side of the Kuznetsov
formula, so that further study may be done through classical analytic
techniques.


  In this paper we adopt the familiar sparse, high-dimensional linear
regression model and focus on the important but often overlooked task of
prediction. In particular, we consider a new empirical Bayes framework that
incorporates data in the prior in two ways: one is to center the prior for the
non-zero regression coefficients and the other is to provide some additional
regularization. We show that, in certain settings, the asymptotic concentration
of the proposed empirical Bayes posterior predictive distribution is very fast,
and we establish a Bernstein--von Mises theorem which ensures that the derived
empirical Bayes prediction intervals achieve the targeted frequentist coverage
probability. The empirical prior has a convenient conjugate form, so posterior
computations are relatively simple and fast. Finally, our numerical results
demonstrate the proposed method's strong finite-sample performance in terms of
prediction accuracy, uncertainty quantification, and computation time compared
to existing Bayesian methods.


  We present high angular resolution, high sensitivity 8.46 GHz (3.6 cm) radio
continuum observations made toward the core of the HH~92 outflow with the Very
Large Array in 2002-2003 and with the Expanded Very Large Array in 2011. We
detect a group of three compact sources distributed in a region 2$''$ in
extension and discuss their nature. We conclude that one of the objects (VLA 1)
is the exciting source of the giant outflow associated with HH~92. In the case
of HH~34 we present new 43.3 GHz (7 mm) observations that reveal the presence
of a structure associated with the exciting source and elongated perpendicular
to the highly collimated optical jet in the region. We propose that this 7 mm
source is a circumstellar disk with radius of $\sim$80 AU and mass of
$\sim$0.21 $M_\odot$.


  We study the localization properties of the two-dimensional Lieb lattice and
its extensions in the presence of disorder using transfer matrix method and
finite-size scaling. We find that all states in the Lieb lattice and its
extensions are localized for $W \geq 1$. Clear differences in the localization
properties between disordered flat band and disordered dispersive bands are
identified. Our results complement previous experimental studies of clean
photonic Lieb lattices and provide information about their stability with
respect to disorder.


  Recent high angular resolution observations of protoplanetary disks at
different wavelengths have revealed several kinds of structures, including
multiple bright and dark rings. Embedded planets are the most used explanation
for such structures, but there are alternative models capable of shaping the
dust in rings as it has been observed. We assume a disk around a Herbig star
and investigate the effect that ice lines have on the dust evolution, following
the growth, fragmentation, and dynamics of multiple dust size particles,
covering from 1 $\mu$m to 2 m sized objects. We use simplified prescriptions of
the fragmentation velocity threshold, which is assumed to change radially at
the location of one, two, or three ice lines. We assume changes at the radial
location of main volatiles, specifically H$_2$O, CO$_2$, and NH$_3$. Radiative
transfer calculations are done using the resulting dust density distributions
in order to compare with current multiwavelength observations. We find that the
structures in the dust density profiles and radial intensities at different
wavelengths strongly depend on the disk viscosity. A clear gap of emission can
be formed between ice lines and be surrounded by ring-like structures, in
particular between the H$_2$O and CO$_2$ (or CO). The gaps are expected to be
shallower and narrower at millimeter emission than at near-infrared, opposite
to model predictions of particle trapping. In our models, the total gas surface
density is not expected to show strong variations, in contrast to other
gap-forming scenarios such as embedded giant planets or radial variations of
the disk viscosity.


  Radio interferometers can potentially detect the sky-averaged signal from the
Cosmic Dawn (CD) and the Epoch of Reionisation (EoR) by studying the Moon as a
thermal block to the foreground sky. The first step is to mitigate the
Earth-based RFI reflections (Earthshine) from the Moon, which significantly
contaminate the FM band $\approx 88-110$ MHz, crucial to CD-EoR science. We
analysed MWA phase-I data from $72-180$ MHz at $40$ kHz resolution to
understand the nature of Earthshine over three observing nights. We took two
approaches to correct the Earthshine component from the Moon. In the first
method, we mitigated the Earthshine using the flux density of the two
components from the data, while in the second method, we used simulated flux
density based on an FM catalogue to mitigate the Earthshine. Using these
methods, we were able to recover the expected Galactic foreground temperature
of the patch of sky obscured by the Moon. We performed a joint analysis of the
Galactic foregrounds and the Moon's intrinsic temperature $(T_{\rm Moon})$
while assuming that the Moon has a constant thermal temperature throughout
three epochs. We found $T_{\rm Moon}$ to be at $184.4\pm{2.6}\rm ~K$ and
$173.8\pm{2.5}\rm ~K$ using the first and the second methods, respectively, and
the best-fit values of the Galactic spectral index $(\alpha)$ to be within the
$5\%$ uncertainty level when compared with the global sky models. Compared with
our previous work, these results improved constraints on the Galactic spectral
index and the Moon's intrinsic temperature. We also simulated the Earthshine at
MWA between November and December 2023 to find suitable observing times less
affected by the Earthshine. Such observing windows act as Earthshine avoidance
and can be used to perform future global CD-EoR experiments using the Moon with
the MWA.


  The McEliece public-key encryption scheme has become an interesting
alternative to cryptosystems based on number-theoretical problems. Differently
from RSA and ElGa- mal, McEliece PKC is not known to be broken by a quantum
computer. Moreover, even tough McEliece PKC has a relatively big key size,
encryption and decryption operations are rather efficient. In spite of all the
recent results in coding theory based cryptosystems, to the date, there are no
constructions secure against chosen ciphertext attacks in the standard model -
the de facto security notion for public-key cryptosystems. In this work, we
show the first construction of a McEliece based public-key cryptosystem secure
against chosen ciphertext attacks in the standard model. Our construction is
inspired by a recently proposed technique by Rosen and Segev.


  In this paper we investigate the $\eta_c$ production by photon - photon and
photon - hadron interactions in $pp$ and $pA$ collisions at the LHC energies.
The inclusive and diffractive contributions for the $\eta_c$ photoproduction
are estimated using the nonrelativistic quantum chromodynamics (NRQCD)
formalism. We estimate the rapidity and transverse momentum distributions for
the $\eta_c$ photoproduction in hadronic collisions at the LHC and present our
estimate for the total cross sections at the Run 2 energies. A comparison with
the predictions for the exclusive $\eta_c$ photoproduction, which is a direct
probe of the Odderon, also is presented.


  Multi-agent reinforcement learning is a standard framework for modeling
multi-agent interactions applied in real-world scenarios. Inspired by
experience sharing in human groups, learning knowledge parallel reusing between
agents can potentially promote team learning performance, especially in
multi-task environments. When all agents interact with the environment and
learn simultaneously, how each independent agent selectively learns from other
agents' behavior knowledge is a problem that we need to solve. This paper
proposes a novel knowledge transfer framework in MARL, PAT (Parallel
Attentional Transfer). We design two acting modes in PAT, student mode and
self-learning mode. Each agent in our approach trains a decentralized student
actor-critic to determine its acting mode at each time step. When agents are
unfamiliar with the environment, the shared attention mechanism in student mode
effectively selects learning knowledge from other agents to decide agents'
actions. PAT outperforms state-of-the-art empirical evaluation results against
the prior advising approaches. Our approach not only significantly improves
team learning rate and global performance, but also is flexible and
transferable to be applied in various multi-agent systems.


  Continuous observations were obtained of active region 10953 with the Solar
Optical Telescope (SOT) on board the \emph{Hinode} satellite during 2007 April
28 to May 9. A prominence was located over the polarity inversion line (PIL) in
the south-east of the main sunspot. These observations provided us with a time
series of vector magnetic fields on the photosphere under the prominence. We
found four features: (1) The abutting opposite-polarity regions on the two
sides along the PIL first grew laterally in size and then narrowed. (2) These
abutting regions contained vertically-weak, but horizontally-strong magnetic
fields. (3) The orientations of the horizontal magnetic fields along the PIL on
the photosphere gradually changed with time from a normal-polarity
configuration to a inverse-polarity one. (4) The horizontal-magnetic field
region was blueshifted. These indicate that helical flux rope was emerging from
below the photosphere into the corona along the PIL under the pre-existing
prominence. We suggest that this supply of a helical magnetic flux into the
corona is associated with evolution and maintenance of active-region
prominences.


  Graphene and carbon nanotubes have extraordinary mechanical and electronic
properties. Intrinsic line defects such as local non-hexagonal reconstructions
or grain boundaries, however, significantly reduce the tensile strength, but
feature exciting electronic properties. Here, we address the properties of line
defects in graphene from first-principles on the level of full-potential
density-functional theory, and assess doping as one strategy to strengthen such
materials. We carefully disentangle the global and local effect of doping by
comparing results from the virtual crystal approximation with those from local
substitution of chemical species, in order to gain a detailed understanding of
the breaking and stabilization mechanisms. We find that n-type doping or local
substitution with nitrogen increases the ultimate tensile strength
significantly. In particular, it can stabilize the defects beyond the ultimate
tensile strength of the pristine material. We therefore propose this as a key
strategy to strengthen graphenic materials. Furthermore, we find that doping
and/or applying external stress lead to tunable and technologically interesting
metal/semi-conductor transitions.


  The implementation of connected and automated vehicle (CAV) technologies
enables a novel computational framework for real-time control aimed at
optimizing energy consumption with associated benefits. In this paper, we
implement an optimal control framework, developed previously, in an Audi A3
etron plug-in hybrid electric vehicle, and demonstrate that we can improve the
vehicle's efficiency and travel time in a corridor including an on-ramp
merging, a speed reduction zone, and a roundabout. Our exposition includes the
development, integration, implementation and validation of the proposed
framework in (1) simulation, (2) hardware-in-the-loop (HIL) testing, (3)
connectivity enabled virtual reality based bench-test, and (4) field test in
Mcity. We show that by adopting such inexpensive, yet effective process, we can
efficiently integrate and test the controller framework, ensure proper
connectivity and data transmission between different modules of the system, and
reduce uncertainty. We evaluate the performance and effectiveness of the
control framework and observe significant improvement in terms of energy and
travel time compared to the baseline scenario.


  The use of sensors available through smart devices has pervaded everyday life
in several applications including human activity monitoring, healthcare, and
social networks. In this study, we focus on the use of smartwatch accelerometer
sensors to recognize eating activity. More specifically, we collected sensor
data from 10 participants while consuming pizza. Using this information, and
other comparable data available for similar events such as smoking and
medication-taking, and dissimilar activities of jogging, we developed a
LSTM-ANN architecture that has demonstrated 90% success in identifying
individual bites compared to a puff, medication-taking or jogging activities.


  We show that the damping rate of elementary excitations of hybrid systems
close to a phase transition can undergo a remarkable resonance like enhancement
before mode softening takes place. In particular, we consider the friction of a
collective density wave in a homogeneous superfluid of weakly interacting
bosonic atoms coupled to the electromagnetic field of a single mode optical
resonator. Here the Beliaev damping can thus be controlled by an external laser
drive and be enhanced by several orders of magnitude.


  The electron and hole g factors are the key quantities for the spin
manipulations in semiconductor quantum nanostructures. However, for the
individual nanostructures, the separate determination including the signs of
those g factors is difficult by using some methods adopted conventionally in
bulks and quantum wells. We report a convenient optical method for the sign
identification of out-of-plane g factors in the individual quantum
nanostructures, which utilizes the optically-induced nuclear spin switch. The
method is demonstrated in typical single self-assembled
In$_{0.75}$Al$_{0.25}$As/Al$_{0.3}$Ga$_{0.7}$As quantum dots and InAs/GaAs
quantum rings, where the g factors with the opposite sign for electron and the
same sign for hole are proved.


  A photon can encode several bits of information based on an alphabet of its
time of arrival, energy, and polarization. Heisenberg's uncertainty principle
places a limit on measuring pairs of physical properties of a particle,
limiting the maximal information efficiency to <59 bits per photon in practice,
and <171 bits per photon at Planck energy, at a data rate of one photon per
second.


  Explainable AI (XAI) systems are sociotechnical in nature; thus, they are
subject to the sociotechnical gap--divide between the technical affordances and
the social needs. However, charting this gap is challenging. In the context of
XAI, we argue that charting the gap improves our problem understanding, which
can reflexively provide actionable insights to improve explainability.
Utilizing two case studies in distinct domains, we empirically derive a
framework that facilitates systematic charting of the sociotechnical gap by
connecting AI guidelines in the context of XAI and elucidating how to use them
to address the gap. We apply the framework to a third case in a new domain,
showcasing its affordances. Finally, we discuss conceptual implications of the
framework, share practical considerations in its operationalization, and offer
guidance on transferring it to new contexts. By making conceptual and practical
contributions to understanding the sociotechnical gap in XAI, the framework
expands the XAI design space.


  Exploring spatial-temporal dependencies from observed motions is one of the
core challenges of human motion prediction. Previous methods mainly focus on
dedicated network structures to model the spatial and temporal dependencies.
This paper considers a new direction by introducing a model learning framework
with auxiliary tasks. In our auxiliary tasks, partial body joints' coordinates
are corrupted by either masking or adding noise and the goal is to recover
corrupted coordinates depending on the rest coordinates. To work with auxiliary
tasks, we propose a novel auxiliary-adapted transformer, which can handle
incomplete, corrupted motion data and achieve coordinate recovery via capturing
spatial-temporal dependencies. Through auxiliary tasks, the auxiliary-adapted
transformer is promoted to capture more comprehensive spatial-temporal
dependencies among body joints' coordinates, leading to better feature
learning. Extensive experimental results have shown that our method outperforms
state-of-the-art methods by remarkable margins of 7.2%, 3.7%, and 9.4% in terms
of 3D mean per joint position error (MPJPE) on the Human3.6M, CMU Mocap, and
3DPW datasets, respectively. We also demonstrate that our method is more robust
under data missing cases and noisy data cases. Code is available at
https://github.com/MediaBrain-SJTU/AuxFormer.


  Saturn's moon Enceladus offers a unique opportunity in the search for life
and habitable environments beyond Earth, a key theme of the National Research
Council's 2013-2022 Decadal Survey. A plume of water vapor and ice spews from
Enceladus's south polar region. Cassini data suggest that this plume, sourced
by a liquid reservoir beneath the moon's icy crust, contain organics, salts,
and water-rock interaction derivatives. Thus, the ingredients for life as we
know it-- liquid water, chemistry, and energy sources-- are available in
Enceladus's subsurface ocean. We have only to sample the plumes to investigate
this hidden ocean environment. We present a New Frontiers class, solar-powered
Enceladus orbiter that would take advantage of this opportunity, Testing the
Habitability of Enceladus's Ocean (THEO). Developed by the 2015 Jet Propulsion
Laboratory Planetary Science Summer School student participants under the
guidance of TeamX, this mission concept includes remote sensing and in situ
analyses with a mass spectrometer, a sub-mm radiometer-spectrometer, a camera,
and two magnetometers. These instruments were selected to address four key
questions for ascertaining the habitability of Enceladus's ocean within the
context of the moon's geological activity: (1) How are the plumes and ocean
connected? (2) Are the abiotic conditions of the ocean suitable for
habitability? (3) How stable is the ocean environment? (4) Is there evidence of
biological processes? By taking advantage of the opportunity Enceladus's plumes
offer, THEO represents a viable, solar-powered option for exploring a
potentially habitable ocean world of the outer solar system.


  This paper introduces SPOT, a Secure and Privacy-preserving prOximity based
protocol for e-healthcare systems. It relies on a distributed proxy-based
approach to preserve users' privacy and a semi-trusted computing server to
ensure data consistency and integrity. The proposed protocol ensures a balance
between security, privacy and scalability. As far as we know, in terms of
security, SPOT is the first one to prevent malicious users from colluding and
generating false positives. In terms of privacy, SPOT supports both anonymity
of users being in proximity of infected people and unlinkability of contact
information issued by the same user. A concrete construction based on
structure-preserving signatures and NIWI proofs is proposed and a detailed
security and privacy analysis proves that SPOT is secure under standard
assumptions. In terms of scalability, SPOT's procedures and algorithms are
implemented to show its efficiency and practical usability with acceptable
computation and communication overhead.


  In non-classical linear transport the chord length distribution between
collisions is non-exponential and attenuation does not respect Beer's law.
Generalized radiative transfer (GRT) extends the classical theory to account
for such two-point correlation between collisions and neglects all higher order
correlations. For this form of transport, we derive the exact time-independent
Green's functions for the isotropic point source in infinite 3D homogeneous
media with general anisotropic scattering. Green's functions for both collision
rate density, which characterizes absorption and reaction rates in the system,
and radiance/flux, which characterizes displacement of radiation/particles, are
solved in Fourier space. We validate the derivations using gamma random flights
to produce the first anisotropic scattering benchmark solutions for the
generalized linear Boltzmann equation. For gamma-4 flights with linearly
anisotropic scattering and gamma-6 flights with Rayleigh scattering the
collision rate density is found explicitly in real space as a sum of diffusion
modes.


  We present a detailed survey of recent developments in the study of the
finite Hilbert transform and its corresponding inversion problem in
rearrangement invariant spaces on $(-1,1)$.


  We study the effect of a long-range DM-nuclei interaction occurring via the
exchange of a light mediator. We consider the main direct detection
experiments: DAMA, CoGeNT, CRESST, CDMS and XENON100. We find that a long-range
force is a viable mechanism, which can provide full agreement between the
various experiments, especially for masses of the mediator in the 10-30 MeV
range and a light DM with a mass around 10 GeV. The relevant bounds on the
light mediator mass and scattering cross section are then derived.


  The recently discovered coherent X-ray pulsations at a frequency of ~400 Hz
in SAX J1808.4-3658, together with a measure of the source luminosity in
quiescence, allow us to put an upper limit on the neutron star magnetic field,
that is B < 5 10^8 Gauss, using simple considerations on the position of the
magnetospheric radius during quiescent periods. Combined with the lower limit
inferred from the presence of X-ray pulsations, this constrains the SAX
J1808.4-3658 neutron star magnetic field in the quite narrow range (1-5) 10^8
Gauss. Similar considerations applied to the case of Aql X-1 give a neutron
star magnetic field lower than ~10^9 Gauss.


  WE propose a new unified evolution equation for parton distribution functions
appropriate for both large and small Bjorken x. Compared with the Ciafaloni-
Catani-Fiorani-Marchesini equation, the cancellation of soft poles between
virtual and real gluon emissions is made explicitly without introducing
infrared cutoffs, next-to-leading contributions to the Sudakov resummation can
be included systematically, and the scales of the running coupling constants
are determined unambiguously.


  Magnetic resonance imaging (MRI) is an effective imaging modality for
identifying and localizing breast lesions in women. Accurate and precise lesion
segmentation using a computer-aided-diagnosis (CAD) system, is a crucial step
in evaluating tumor volume and in the quantification of tumor characteristics.
However, this is a challenging task, since breast lesions have sophisticated
shape, topological structure, and high variance in their intensity distribution
across patients. In this paper, we propose a novel marker-controlled watershed
transformation-based approach, which uses the brightest pixels in a region of
interest (determined by experts) as markers to overcome this challenge, and
accurately segment lesions in breast MRI. The proposed approach was evaluated
on 106 lesions, which includes 64 malignant and 42 benign cases. Segmentation
results were quantified by comparison with ground truth labels, using the Dice
similarity coefficient (DSC) and Jaccard index (JI) metrics. The proposed
method achieved an average Dice coefficient of 0.7808$\pm$0.1729 and Jaccard
index of 0.6704$\pm$0.2167. These results illustrate that the proposed method
shows promise for future work related to the segmentation and classification of
benign and malignant breast lesions.


  Detecting text located on the torsos of marathon runners and sports players
in video is a challenging issue due to poor quality and adverse effects caused
by flexible/colorful clothing, and different structures of human bodies or
actions. This paper presents a new unified method for tackling the above
challenges. The proposed method fuses gradient magnitude and direction
coherence of text pixels in a new way for detecting candidate regions.
Candidate regions are used for determining the number of temporal frame
clusters obtained by K-means clustering on frame differences. This process in
turn detects key frames. The proposed method explores Bayesian probability for
skin portions using color values at both pixel and component levels of temporal
frames, which provides fused images with skin components. Based on skin
information, the proposed method then detects faces and torsos by finding
structural and spatial coherences between them. We further propose adaptive
pixels linking a deep learning model for text detection from torso regions. The
proposed method is tested on our own dataset collected from marathon/sports
video and three standard datasets, namely, RBNR, MMM and R-ID of marathon
images, to evaluate the performance. In addition, the proposed method is also
tested on the standard natural scene datasets, namely, CTW1500 and MS-COCO text
datasets, to show the objectiveness of the proposed method. A comparative study
with the state-of-the-art methods on bib number/text detection of different
datasets shows that the proposed method outperforms the existing methods.


  We present new evolutionary stellar models suitable for old Population I
clusters, discussing both the consequences of the most recent improvements in
the input physics and the effect of element diffusion within the stellar
structures. Theoretical cluster isochrones are presented, covering the range of
ages from 1 to 9 Gyr for the four selected choices on the metallicity Z= 0.007,
0.010, 0.015 and 0.020. Theoretical uncertainties on the efficiency of
superadiabatic convection are discussed in some details. Isochrone fitting to
the CM diagrams of the two well observed galactic clusters NGC2420 and M67
indicates that a mixing length parameter alpha = 1.9 appears adequate for
reproducing the observed color of cool giant stars. The problems in matching
theoretical preditions to the observed slope of MS stars are discussed.


  We consider positronium-like bound states of doubly charged fermions.We
consider also $P(CP)$-parity violation in positronium like system (including
quarkonium and lepton antilepton bound states and mesoatoms) which take place
due to $Z^0$-bosons or Higgs bosons exchange.By model independent way we
consider also vector bosons bound states in the Coulomb and Higgs potential and
have shown that in the presence of the Higgs potential effective potential
become less singular and in this case fall down on the center is absent.Vector
bosons energy levels in the magnetic field are considered. Considered magnetism
of electron gas in the finite volume.


  We study the sharp interface limit of a non-mass-conserving
Cahn--Hilliard--Darcy system with the weak compactness method developed in Chen
(J. Differential Geometry, 1996). The source term present in the Cahn--Hilliard
component is a product of the order parameter and a prescribed function with
zero spatial mean, leading to non-conservation of mass. Furthermore, the
divergence of the velocity field is given by another prescribed function with
zero spatial mean, which yields a coupling of the Cahn--Hilliard equation to a
non-solenoidal Darcy flow. New difficulties arise in the derivation of uniform
estimates due to the presence of the source terms, which can be circumvented if
we consider the above specific structures. Moreover, due to the lack of
mass-conservation, the analysis is valid as long as one phase does not vanish
completely, leading to a local-in-time result. The sharp interface limit for a
Cahn--Hilliard--Brinkman system is also discussed.


  In this article, we study the asymptotic solutions of the generalized
Fermat-type equation of signature $(p,p,3)$ over totally real number fields
$K$, i.e., $Ax^p+By^p=Cz^3$ with prime exponent $p$ and $A,B,C \in
\mathcal{O}_K \setminus \{0\}$. For certain class of fields $K$, we prove that
$Ax^p+By^p=Cz^3$ has no asymptotic solutions over $K$ (resp., solutions of
certain type over $K$) with restrictions on $A,B,C$ (resp., for all $A,B,C \in
\mathcal{O}_K \setminus \{0\}$). Finally, we present several local criteria
over $K$.


  We report a neutron diffraction and muon spin relaxation muSR study of static
and dynamical magnetic properties of BaCo2V2O8, a quasi-one-dimensional
spin-chain system. A proposed model for the antiferromagnetic structure
includes: a propagation vector k_AF = (0, 0, 1), independent of external
magnetic fields for fields below a critical value H_c(T). The ordered moments,
of 2.18 \mu_B per Co ion, are aligned along the crystallographic c-axis. Within
the screw chains, along the c axis, the moments are arranged
antiferromagnetically. In the basal planes the spins are arranged
ferromagnetically (forming zig-zags paths) along one of the axis and
antiferromagnetically along the other. The temperature dependence of the
sub-lattice magnetization is consistent with the expectations of the 3D Ising
model. A similar behavior is observed for the internal static fields at
different muon stopping sites. Muon time spectra measured at weak longitudinal
fields and temperatures much higher than T_N can be well described using a
single muon site with an exponential muon spin relaxation that gradually
changes into an stretched exponential on approaching T_N. The
temperature-induced changes of the relaxation suggest that the Co fluctuations
dramatically slow down and the system becomes less homogeneous as it approaches
the antiferromagnetic state.


  A fast leading-order Monte Carlo generator for the process
$e^+e^-\to\mu^+\mu^-\gamma$ is described. In fact, using the
$e^+e^-\to\mu^+\mu^-\gamma $ process as an example, we provide a pedagogical
demonstration of how a Monte Carlo generator can be created from scratch. The
$e^+ e^- \to \mu^+ \mu^- \gamma$ process was chosen, since in this case we are
not faced with either too trivial or too difficult a task. Matrix elements are
calculated using the helicity amplitude method. Monte Carlo algorithm uses the
acceptance-rejection method with an appropriately chosen simplified
distribution that can be generated using an efficient algorithm. We provide a
detailed pedagogical exposition of both the helicity amplitude method and the
Monte Carlo technique, which we hope will be useful for high energy physics
students.


  We linearize the field equations for higher order theories of gravity that
contain scalar invariants other than the Ricci scalar. We find that besides a
massless spin-2 field (the standard graviton), the theory contains also spin-0
and spin-2 massive modes with the latter being, in general, ghost modes. The
rate at which such particles would emit gravitational Cherenkov radiation is
calculated for some interesting physical cases.


  This paper continues previous work on a novel alternative model of gravity,
based on the theory of fractional-dimension spaces applied to Newton's law of
gravitation. In particular, our Newtonian Fractional-Dimension Gravity is now
applied to axially-symmetric structures, such as thin/thick disk galaxies
described by exponential, Kuzmin, or other similar mass distributions.
  As in the case of spherically-symmetric structures, which was studied in
previous work on the subject, we examine a possible connection between our
model and Modified Newtonian Dynamics, a leading alternative gravity model,
which accounts for the observed properties of galaxies and other astrophysical
structures without requiring the dark matter hypothesis.
  By relating the MOND acceleration constant $a_{0} \simeq 1.2 \times 10^{
-10}\mbox{m}\thinspace \mbox{s}^{ -2}$ to a natural scale length $l_{0}$ of our
model, namely $a_{0} \approx GM/l_{0}^{2}$ for a galaxy of mass $M$, and by
using the empirical Radial Acceleration Relation, we are able to explain the
connection between the observed radial acceleration $g_{obs}$ and the baryonic
radial acceleration $g_{bar}$ in terms of a variable local dimension $D$. As an
example of this methodology, we provide a detailed rotation curve fitting for
the case of the field dwarf spiral galaxy NGC 6503.


  Given a dynamical simplex $K$ on a Cantor space $X$, we consider the set
$G_K^*$ of all homeomorphisms of $X$ which preserve all elements of $K$ and
have no nontrivial clopen invariant subset. Generalising a theorem of Yingst,
we prove that for a generic element $g$ of $G_K^*$ the set of invariant
measures of $g$ is equal to $K$. We also investigate when there exists a
generic conjugacy class in $G_K^*$ and prove that this happens exactly when $K$
has only one element, which is the unique invariant measure associated to some
odometer; and that in that case the conjugacy class of this odometer is generic
in $G_K^*$.


  This report summarizes our findings regarding a severe weakness in
implementations of the Open Mobile API deployed on several Android devices. The
vulnerability allows arbitrary code coming from a specially crafted Android
application package (APK) to be injected into and executed by the smartcard
system service component (the middleware component of the Open Mobile API
implementation). This can be exploited to gain elevated capabilities, such as
privileges protected by signature- and system-level permissions assigned to
this service. The affected source code seems to originate from the
SEEK-for-Android open-source project and was adopted by various vendor-specific
implementations of the Open Mobile API, including the one that is used on the
Nexus 6 (as of Android version 5.1).


  Consider the braid group $B_3=< a,b| aba=bab>$ and the nearest neighbor
random walk defined by a probability $\nu$ with support
$\{a,a^{-1},b,b^{-1}\}$. The rate of escape of the walk is explicitly expressed
in function of the unique solution of a set of eight polynomial equations of
degree three over eight indeterminates. We also explicitly describe the
harmonic measure of the induced random walk on $B_3$ quotiented by its center.
The method and results apply, mutatis mutandis, to nearest neighbor random
walks on dihedral Artin groups.


  The verification of safety properties for concurrent systems often reduces to
the coverability problem for Petri nets. This problem was shown to be
ExpSpace-complete forty years ago. Driven by the concurrency revolution, it has
regained a lot of interest over the last decade. In this paper, we propose a
generic and simple approach to solve this problem. Our method is inspired from
the recent approach of Blondin, Finkel, Haase and Haddad. Basically, we combine
forward invariant generation techniques for Petri nets with backward
reachability for well- structured transition systems. An experimental
evaluation demonstrates the efficiency of our approach.


  We study collective light scattering from linear crystals with up to twelve
$^{40}\text{Ca}^+$ ions, acting as coherent single photon emitters.
Light-scattering is induced by two-photon laser excitation, starting from the
S$_{1/2}$ to D$_{5/2}$ quadrupole transition at 729~nm in combination with
laser excitation of the D$_{5/2}$ to P$_{3/2}$ dipole transition at 854~nm,
followed by a decay back to S$_{1/2}$ via a single photon emission near 393~nm.
The scattered intensity is recorded in the far field, featuring the
interference of emitted light fields of the entire crystal. Furthermore, we
demonstrate spin-dependent coherent scattering and unveil the time evolution of
a previously encoded spin texture in a crystal employing the recorded dynamics
of the spatial frequency components of the fringe pattern.


  We compute the amplitude of the non-Gaussianities in inflationary models with
multiple, uncoupled scalar fields. This calculation thus applies to all models
of assisted inflation, including N-flation, where inflation is driven by
multiple axion fields arising from shift symmetries in a flux stabilized string
vacuum. The non-Gaussianities are associated with nonlinear evolution of the
field (and density) perturbations, characterized by the parameter $f_{NL}$. We
derive a general expression for the nonlinear parameter, incorporating the
evolution of perturbations after horizon-crossing. This is valid for arbitrary
separable potentials during slow roll. To develop an intuitive understanding of
this system and to demonstrate the applicability of the formalism we examine
several cases with quadratic potentials: two-field models with a wide range of
mass ratios, and a general N-field model with a narrow mass spectrum. We
uncover that $f_{NL}$ is suppressed as the number of e-foldings grows, and that
this suppression is increased in models with a broad spectrum of masses. On the
other hand, we find no enhancement to $f_{NL}$ that increases with the number
of fields. We thus conclude that the production of a large non-Gaussian signal
in multi-field models of inflation is very unlikely as long as fields are
slowly rolling and potentials are of simple, quadratic form. Finally, we
compute a spectrum for the scalar spectral index that incorporates the
nonlinear corrections to the fields' evolution.


  Machine learning has endless applications in the health care industry. White
blood cell classification is one of the interesting and promising area of
research. The classification of the white blood cells plays an important part
in the medical diagnosis. In practise white blood cell classification is
performed by the haematologist by taking a small smear of blood and careful
examination under the microscope. The current procedures to identify the white
blood cell subtype is more time taking and error-prone. The computer aided
detection and diagnosis of the white blood cells tend to avoid the human error
and reduce the time taken to classify the white blood cells. In the recent
years several deep learning approaches have been developed in the context of
classification of the white blood cells that are able to identify but are
unable to localize the positions of white blood cells in the blood cell image.
Following this, the present research proposes to utilize YOLOv3 object
detection technique to localize and classify the white blood cells with
bounding boxes. With exhaustive experimental analysis, the proposed work is
found to detect the white blood cell with 99.2% accuracy and classify with 90%
accuracy.


  We present diffraction-limited bispectrum speckle interferometry observations
of four well-known Herbig Ae/Be (HAeBe) stars, LkHa 198, Elias 1, HK Ori and
V380 Ori. For two of these, LkHa 198 and Elias 1, we present the first
unambiguous detection of close companions. The plane of the orbit of the new
LkHa 198 companion appears to be significantly inclined to the plane of the
circumprimary disk, as inferred from the orientation of the outflow. We show
that the Elias 1 companion may be a convective star, and suggest that it could
therefore be the true origin of the X-ray emission from this object. In the
cases of HK Ori and V380 Ori, we present new measurements of the relative
positions of already-known companions, indicating orbital motion. For HK Ori,
photometric measurements of the brightness of the individual components in four
bands allowed us to decompose the system spectral energy distribution (SED)
into the two separate component SEDs. The primary exhibits a strong infrared
excess which suggests the presence of circumstellar material, whereas the
companion can be modelled as a naked photosphere. The infrared excess of HK Ori
A was found to contribute around two thirds of the total emission from this
component, suggesting that accretion power contributes significantly to the
flux. Submillimetre constraints mean that the circumstellar disk cannot be
particularly massive, whilst the near-infrared data indicates a high accretion
rate. Either the disk lifetime is very short, or the disk must be seen in an
outburst phase.


  Artificial intelligence (AI) technologies have emerged as pivotal enablers
across a multitude of industries largely due to their significant resurgence
over the past decade. The transformative power of AI is primarily derived from
the utilization of deep neural networks (DNNs), which require extensive data
for training and substantial computational resources for processing.
Consequently, DNN models are typically trained and deployed on resource-rich
cloud servers. However, due to potential latency issues associated with cloud
communications, deep learning (DL) workflows are increasingly being
transitioned to wireless edge networks in proximity to end-user devices (EUDs).
This shift is designed to support latency-sensitive applications and has given
rise to a new paradigm of edge AI, which will play a critical role in upcoming
sixth-generation (6G) networks to support ubiquitous AI applications. Despite
its considerable potential, edge AI faces substantial challenges, mostly due to
the dichotomy between the resource limitations of wireless edge networks and
the resource-intensive nature of DL. Specifically, the acquisition of
large-scale data, as well as the training and inference processes of DNNs, can
rapidly deplete the battery energy of EUDs. This necessitates an
energy-conscious approach to edge AI to ensure both optimal and sustainable
performance. In this paper, we present a contemporary survey on green edge AI.
We commence by analyzing the principal energy consumption components of edge AI
systems to identify the fundamental design principles of green edge AI. Guided
by these principles, we then explore energy-efficient design methodologies for
the three critical tasks in edge AI systems, including training data
acquisition, edge training, and edge inference. Finally, we underscore
potential future research directions to further enhance the energy efficiency
of edge AI.


  We study networks of interacting queues governed by utility-maximising
service-rate allocations in both discrete and continuous time. For {\em finite}
networks we establish stability and some steady-state moment bounds under
natural conditions and rather weak assumptions on utility functions. These
results are obtained using direct applications of Lyapunov-Foster-type
criteria, and apply to a wide class of systems, including those for which fluid
limit-based approaches are not applicable.
  We then establish stability and some steady-state moment bounds for two
classes of {\em infinite} networks, with single-hop and multi-hop message
routes. These results are proved by considering the infinite systems as limits
of their truncated finite versions. The uniform moment bounds for the finite
networks play a key role in these limit transitions.


  We study the difference between the level of systemic risk that is
empirically measured on an interbank network and the risk that can be deduced
from the balance sheets composition of the participating banks. Using
generalised DebtRank dynamics, we measure observed systemic risk on e-MID
network data (augmented by BankFocus information) and compare it with the
expected systemic risk of a null model network, obtained through an appropriate
maximum-entropy approach constraining relevant balance sheet variables. We show
that the aggregate levels of observed and expected systemic risks are usually
compatible but differ significantly during turbulent times (in our case, after
the default of Lehman Brothers and the VLTRO implementation by the ECB). At the
individual level instead, banks are typically more or less risky than what
their balance sheet prescribes due to their position in the network. Our
results confirm on one hand that balance sheet information used within a proper
maximum-entropy network model provides good aggregate estimates of systemic
risk, and on the other hand the importance of knowing the empirical details of
the network for conducting precise stress tests on individual banks, especially
after systemic events.


  Many machine learning systems rely on data collected in the wild from
untrusted sources, exposing the learning algorithms to data poisoning.
Attackers can inject malicious data in the training dataset to subvert the
learning process, compromising the performance of the algorithm producing
errors in a targeted or an indiscriminate way. Label flipping attacks are a
special case of data poisoning, where the attacker can control the labels
assigned to a fraction of the training points. Even if the capabilities of the
attacker are constrained, these attacks have been shown to be effective to
significantly degrade the performance of the system. In this paper we propose
an efficient algorithm to perform optimal label flipping poisoning attacks and
a mechanism to detect and relabel suspicious data points, mitigating the effect
of such poisoning attacks.


  We revisit the problem of online learning with sleeping experts/bandits: in
each time step, only a subset of the actions are available for the algorithm to
choose from (and learn about). The work of Kleinberg et al. (2010) showed that
there exist no-regret algorithms which perform no worse than the best ranking
of actions asymptotically. Unfortunately, achieving this regret bound appears
computationally hard: Kanade and Steinke (2014) showed that achieving this
no-regret performance is at least as hard as PAC-learning DNFs, a notoriously
difficult problem. In the present work, we relax the original problem and study
computationally efficient no-approximate-regret algorithms: such algorithms may
exceed the optimal cost by a multiplicative constant in addition to the
additive regret. We give an algorithm that provides a no-approximate-regret
guarantee for the general sleeping expert/bandit problems. For several
canonical special cases of the problem, we give algorithms with significantly
better approximation ratios; these algorithms also illustrate different
techniques for achieving no-approximate-regret guarantees.


  Several numerical schemes are proposed for the solution of Nonequilibrium
Langevin Dynamics (NELD), and the rate of convergence is analyzed. Due to the
special deforming boundary conditions used, care must be taken when using
standard stochastic integration schemes, and we demonstrate a loss of
convergence for a naive implementation. We then present several first and
second order schemes, in the sense of strong convergence.


  The Hill Restricted 4-Body Problem (HR4BP) is a coherent time-periodic model
that can be used to represent motion in the Sun-Earth-Moon (SEM) system.
Periodic orbits were computed in this model to better understand the periodic
orbit family structures that exist in these types of systems. First, periodic
orbits in the Circular Restricted 3-Body Problem (CR3BP) representation of the
Earth-Moon (EM) system were identified. A Melnikov-type function was used to
identify a set of candidate points on the EM CR3BP periodic orbits to start a
continuation algorithm. A pseudo-arclength continuation scheme was then used to
obtain the corresponding periodic orbit families in the HR4BP when including
the effect of the Sun. Bifurcation points were identified in the computed
families to obtain additional orbit families.


  The phenomenon of jet quenching provides essential information about the
properties of hot and dense matter created in ultra-relativistic heavy-ion
collisions. Recent results from experiments at the Large Hadron Collider (LHC)
show evidence for an unexpectedly similar suppression of both light and heavy
flavor jets. Furthermore, the role of radiative energy loss of heavy quarks is
still under active discussion within the theoretical community. By employing
the parton cascade Boltzmann Approach to Multi-Parton Scatterings (BAMPS),
which numerically solves the 3+1D Boltzmann equation both for light and heavy
flavor partons, we calculate the nuclear modification factor of inclusive and
b-tagged reconstructed jets in 0-10% central $\sqrt{s_{\rm LHC}}$ = 2.76 ATeV
Pb+Pb collisions. Based on perturbative QCD cross sections we find a
suppression of both light and heavy flavor jets. While the inclusive jets are
slightly too strong suppressed within BAMPS in comparison with data, both
elastic+radiative and only elastic interactions lead to a realistic b-tagged
jet suppression. To further investigate light and heavy flavor energy loss we
predict the R dependence of inclusive and b-tagged jet suppression.
Furthermore, we propose the medium modification of b-tagged jet shapes as an
observable for discriminating between different heavy quark energy loss
scenarios.


  The paper introduces a general framework for derivation of continuum
equations governing meso-scale dynamics of large particle systems. The balance
equations for spatial averages such as density, linear momentum, and energy
were previously derived by a number of authors. These equations are not in
closed form because the stress and the heat flux cannot be evaluated without
the knowledge of particle positions and velocities. We propose a closure method
for approximating fluxes in terms of other meso-scale averages. The main idea
is to rewrite the non-linear averages as linear convolutions that relate micro-
and meso-scale dynamical functions. The convolutions can be approximately
inverted using regularization methods developed for solving ill-posed problems.
This yields closed form constitutive equations that can be evaluated without
solving the underlying ODEs. We test the method numerically on Fermi-Pasta-Ulam
chains with two different potentials: the classical Lennard-Jones, and the
purely repulsive potential used in granular materials modeling. The initial
conditions incorporate velocity fluctuations on scales that are smaller than
the size of the averaging window. The results show very good agreement between
the exact stress and its closed form approximation.


  Measurement-based quantum computing, a powerful alternative to the standard
circuit model, proceeds using only local adaptive measurements on a
highly-entangled resource state of many spins on a graph or lattice. Along with
the canonical cluster state, the valence-bond solid ground state on a chain of
spin-1 particles, studied by Affleck, Kennedy, Lieb, and Tasaki (AKLT), is such
a resource state. We propose a simulation of this AKLT state using linear
optics, wherein we can make use of the high-fidelity projective measurements
that are commonplace in quantum optical experiments, and describe how quantum
logic gates can be performed on this chain. In our proposed implementation, the
spin-1 particles comprizing the AKLT state are encoded on polarization
biphotons: three level systems consisting of pairs of polarized photons in the
same spatio-temporal mode. A logical qubit encoded on the photonic AKLT state
can be initialized, read out and have an arbitrary single qubit unitary applied
to it by performing projective measurements on the constituent biphotons. For
MBQC, biphoton measurements are required which cannot be deterministically
performed using only linear optics and photodetection.