  We study an extended 2 Higgs doublet model (2HDM) in which the Standard Model
(SM) Yukawa interactions are forbidden due to a global $U(1)^\prime$ symmetry,
but may arise via mixing with vector-like families. In this model, the
hierarchical structure of Yukawa couplings of quarks and leptons in the SM
arises from the heavy masses of the fourth and fifth vector-like families.
Within this model, we consider various non-standard contributions to the
electron and muon anomalous magnetic moments. We first consider the $W$
exchange at one-loop level, consistent with the $\mu \rightarrow e \gamma$
constraint, and show that it yields a negligible contribution to both electron
and muon anomalous magnetic moments. We then consider Higgs scalar exchange,
together with vector-like leptons, at one-loop level and show that it is
possible to have non-standard contributions to the electron and muon anomalous
magnetic moments within the $1\sigma$ constraint of certain experiments. We
present some benchmark points for both the muon and the electron anomalies,
together with some numerical scans around these points, which indicate the mass
regions of the Higgs scalars of the 2HDM in this scenario.


  PSR J1907+0602 is a radio-faint, 107-ms GeV gamma-ray pulsar that was
discovered with the Fermi LAT in a blind pulsar search. PSR J1907+0602 is
located near the bright, extended TeV gamma-ray source MGRO J1908+06 which may
be an associated pulsar wind nebula. We present an analysis of XMM-Newton X-ray
data and EVLA radio data of the pulsar. We detect a faint X-ray source
coincident with the gamma-ray pulsar and investigate its spectral and timing
properties. We also find marginal evidence for a bow shock in the X-ray images.
The pulsar was not detected with the EVLA, and we derive upper limits on the
time-averaged radio flux in multiple frequency bands.


  Within the context of the usual semi classical investigation of Planck scale
Schwarzchild Black Holes, as in Quantum Gravity, and later attempts at a full
Quantum Mechanical description in terms of a Kerr-Newman metric including the
spinorial behaviour, we attempt to present a formulation that extends from the
Planck scale to the Hubble scale. In the process the so called large number
coincidences as also the hitherto inexplicable relations between the pion mass
and the Hubble Constant, pointed out by Weinberg, turn out to be natural
consequences in a consistent description.


  Aspect-level sentiment classification (ALSC) and aspect oriented opinion
words extraction (AOWE) are two highly relevant aspect-based sentiment analysis
(ABSA) subtasks. They respectively aim to detect the sentiment polarity and
extract the corresponding opinion words toward a given aspect in a sentence.
Previous works separate them and focus on one of them by training neural models
on small-scale labeled data, while neglecting the connections between them. In
this paper, we propose a novel joint model, Opinion Transmission Network (OTN),
to exploit the potential bridge between ALSC and AOWE to achieve the goal of
facilitating them simultaneously. Specifically, we design two tailor-made
opinion transmission mechanisms to control opinion clues flow bidirectionally,
respectively from ALSC to AOWE and AOWE to ALSC. Experiment results on two
benchmark datasets show that our joint model outperforms strong baselines on
the two tasks. Further analysis also validates the effectiveness of opinion
transmission mechanisms.


  A Boltzmann machine whose effective "temperature" can be dynamically "cooled"
provides a stochastic neural network realization of simulated annealing, which
is an important metaheuristic for solving combinatorial or global optimization
problems with broad applications in machine intelligence and operations
research. However, the hardware realization of the Boltzmann stochastic element
with "cooling" capability has never been achieved within an individual
semiconductor device. Here we demonstrate a new memristive device concept based
on two-dimensional material heterostructures that enables this critical
stochastic element in a Boltzmann machine. The dynamic cooling effect in
simulated annealing can be emulated in this multi-terminal memristive device
through electrostatic bias with sigmoidal thresholding distributions. We also
show that a machine-learning-based method is efficient for device-circuit
co-design of the Boltzmann machine based on the stochastic memristor devices in
simulated annealing. The experimental demonstrations of the tunable stochastic
memristors combined with the machine-learning-based device-circuit
co-optimization approach for stochastic-memristor-based neural-network circuits
chart a pathway for the efficient hardware realization of stochastic neural
networks with applications in a broad range of electronics and computing
disciplines.


  We determine the mass of the charm quark ($m_c$) from lattice QCD with two
flavors of dynamical quarks with a mass around the strange quark. We compare
this to a determination in quenched QCD which has the same lattice spacing (0.1
fm). We investigate different formulations of the quark mass, based on the
Vector Ward Identity, PCAC relation and the FNAL heavy quark formalism. Based
on these preliminary results we find no effects due to sea quarks with a mass
around strange.


  Highly multiplexed, fiber-fed spectroscopy is enabling surveys of millions of
stars and galaxies. The performance of these surveys depends on accurately
positioning fibers in the focal plane to capture target light. We describe a
technique to measure the positioning accuracy of fibers by dithering fibers
slightly around their ideal locations. This approach also enables measurement
of the total system throughput and point spread function delivered to the focal
plane. We then apply this technique to observations from the Dark Energy Survey
Instrument (DESI), and demonstrate that DESI positions fibers to within 0.08"
of their targets (5% of a fiber diameter) and achieves a system throughput
within about 5% of expectations.


  The robustness of neural network classifiers is becoming important in the
safety-critical domain and can be quantified by robustness verification.
However, at present, efficient and scalable verification techniques are always
sound but incomplete. Therefore, the improvement of certified robustness bounds
is the key criterion to evaluate the superiority of robustness verification
approaches. In this paper, we present a Tight Linear approximation approach for
robustness verification of Convolutional Neural Networks(Ti-Lin). For general
CNNs, we first provide a new linear constraints for S-shaped activation
functions, which is better than both existing Neuron-wise Tightest and
Network-wise Tightest tools. We then propose Neuron-wise Tightest linear bounds
for Maxpool function. We implement Ti-Lin, the resulting verification method.
We evaluate it with 48 different CNNs trained on MNIST, CIFAR-10, and Tiny
ImageNet datasets. Experimental results show that Ti-Lin significantly
outperforms other five state-of-the-art methods(CNN-Cert, DeepPoly, DeepCert,
VeriNet, Newise). Concretely, Ti-Lin certifies much more precise robustness
bounds on pure CNNs with Sigmoid/Tanh/Arctan functions and CNNs with Maxpooling
function with at most 63.70% and 253.54% improvement, respectively.


  The problem of high-speed transport for cold atoms with minimal heating has
received considerable attention in theory and experiment. Much theoretical work
has focused on solutions of general problems, often assuming a harmonic
trapping potential or a 1D geometry. However in the case of optical conveyor
belts these assumptions are not always valid. Here we present experimental and
numerical studies of the effects of various motional parameters on heating and
retention of atoms transported in an optical conveyor. Our numerical model is
specialized to the geometry of a moving optical lattice and uses dephasing in
the density matrix formalism to account for effects of motion in the transverse
plane. We verify the model by a comparison with experimental measurements, and
use it to gain further insight into the relationship between the conveyor's
performance and the various parameters of the system.


  Photoionization spectroscopy and mass spectrometry of doped helium (He)
nanodroplets rely on the ability to efficiently detect ions and/or electrons.
Using a commercial quadrupole mass spectrometer and a photoelectron-photoion
coincidence (PEPICO) spectrometer, we systematically measure yields of ions and
electrons created in pure and doped He nanodroplets in a wide size range and in
two ionization regimes -- direct ionization and secondary ionization after
resonant photoexcitation of the droplets. For two different types of dopants
(oxygen molecules, O$_2$, and lithium atoms, Li), we infer the optimal droplet
size to maximize the yield of ejected ions. When dopants are ionized by
charge-transfer to photoionized He nanodroplets, the highest yield of O$_2$ and
Li ions is detected for a mean size of $\sim5\times10^4$ He atoms per
nanodroplet. When dopants are Penning ionized via photoexcitation of the He
droplets, the highest yield of O$_2$ and Li ions is detected for $\sim10^3$ and
$\sim10^5$ He atoms per droplet, respectively. At optimum droplet sizes, the
detection efficiency of dopant ions in proportion to the number of primary
photoabsorption events is up to 20\,\% for charge-transfer ionization of O$_2$
and 2\,\% for Li, whereas for Penning ionization it is 1\,\% for O$_2$ and
4\,\% for Li. Our results are instrumental in determining optimal conditions
for mass spectrometric studies and photoionization spectroscopy of molecules
and complexes isolated in He nanodroplets.


  This volume contains the proceedings of FOCLASA 2014, the 13th International
Workshop on the Foundations of Coordination Languages and Self-Adaptive
Systems. FOCLASA 2014 was held in Rome, Italy, on September 9, 2014 as a
satellite event of CONCUR 2014, the 25th International Conference on
Concurrency Theory.
  Modern software systems are distributed, concurrent, mobile, and often
involve composition of heterogeneous components and stand-alone services.
Service coordination and self-adaptation constitute the core characteristics of
distributed and service-oriented systems. Coordination languages and formal
approaches to modelling and reasoning about self-adaptive behaviour help to
simplify the development of complex distributed service-based systems, enable
functional correctness proofs and improve reusability and maintainability of
such systems. The goal of the FOCLASA workshop is to put together researchers
and practitioners of the aforementioned fields, to share and identify common
problems, and to devise general solutions in the context of coordination
languages and self-adaptive systems.


  We calculate the Sheikholeslami-Wohlert coefficient of the O(a) improvement
term for Wilson fermions in any representation of the gauge group SU(N)
perturbatively at the one-loop level. The result applies to QCD with adjoint
quarks and to N=1 supersymmetric Yang-Mills theory on the lattice.


  A Markovian battery model is proposed to provide the variation of energy
states for energy harvesting (EH) secondary users (SUs) in the EH cognitive
radio networks (CRN). Based on the proposed battery model, we derive the packet
loss probability in the EH SUs due to sensing inaccuracy and energy outage.
With the proposed analysis, the packet loss probability can easily be predicted
and utilized to optimize the transmission policy (i.e., opportunities for
successful transmission and EH) of EH SUs to improve their throughput.
Especially, the proposed method can be applied to upper layer (scheduling and
routing) optimization. To this end, we validate the proposed analysis through
Monte-Carlo simulation and show an agreement between the analysis and
simulations results.


  We present an approach to unsupervised audio representation learning. Based
on a triplet neural network architecture, we harnesses semantically related
cross-modal information to estimate audio track-relatedness. By applying Latent
Semantic Indexing (LSI) we embed corresponding textual information into a
latent vector space from which we derive track relatedness for online triplet
selection. This LSI topic modelling facilitates fine-grained selection of
similar and dissimilar audio-track pairs to learn the audio representation
using a Convolution Recurrent Neural Network (CRNN). By this we directly
project the semantic context of the unstructured text modality onto the learned
representation space of the audio modality without deriving structured
ground-truth annotations from it. We evaluate our approach on the Europeana
Sounds collection and show how to improve search in digital audio libraries by
harnessing the multilingual meta-data provided by numerous European digital
libraries. We show that our approach is invariant to the variety of annotation
styles as well as to the different languages of this collection. The learned
representations perform comparable to the baseline of handcrafted features,
respectively exceeding this baseline in similarity retrieval precision at
higher cut-offs with only 15\% of the baseline's feature vector length.


  A Heisenberg spin-$s$ chain with alternating ferromagnetic ($-J_1^F<0$) and
antiferromagnetic ($J_1^A>0$) nearest-neighbor (NN) interactions, exhibits the
Dimer and spin-$2s$ Haldane phases in the limits $J_1^F/J_1^A \rightarrow 0$
and $J_1^F/J_1^A \rightarrow \infty$ respectively. These two phases are
understood to be topologically equivalent. Induction of the frustration through
the next nearest-neighbor ferromagnetic interaction ($-J_2^F<0$) produces a
very rich quantum phase diagram. With frustration, the whole phase diagram is
divided into a ferromagnetic (FM) and a nonmagnetic (NM) phase. For $s=1/2$,
the full NM phase is seen to be of Haldane-Dimer type, but for $s>1/2$, a
spiral phase comes between the FM and the Haldane-Dimer phases. The study of a
suitably defined string-order parameter and spin-gap at the phase boundary
indicates that the Haldane-Dimer and spiral phases have different topological
characters. We also find that, along the $J_2^F=\frac 12 J_1^F$ line in the NM
phase, an NN dimer state is the {\it exact} groundstate, provided
$J_1^A>J_C=\kappa J_1^F$ where $\kappa \le s + h$ for applied magnetic field
$h$. Without magnetic field, the position of $J_C$ is on the FM-NM phase
boundary when $s=1/2$, but for $s>1/2$, the location of $J_C$ is on the phase
separation line between the Haldane-Dimer and spiral phases.


  We identify two Cabibbo suppressed $D^+$ decay modes with anomalously high
branching ratios which are not simply explained by any model. All standard
model diagrams that can contribute to these decays are related by symmetries to
diagrams for other decays that do not show any such enhancement. If these high
branching ratios are confirmed by more precise experiments, they may require
new physics to explain them. Anomalies in $D_s$ decays and tests for possible
violation of G-parity are discussed.


  Plastic waste production is a global challenging problem since its
accumulation in the environment is causing devastating effects on the planet's
ecosystem. Sustainable and green solutions are urgently needed, and this pairs
with increasingly stronger regulations combined with improved ecological
awareness. This study proposes a simple, scalable and water-based process to
produce free-standing, transparent and flexible bioplastic films by combining
amyloid fibrils with biodegradable polymers as two main building blocks.
Amyloid fibrils can be obtained through denaturation and self-assembly from a
broad class of food proteins found in milk, soy, and egg, for example. Whey is
used here as a model protein, since it is the major by-product of dairy
industries, and its valorization creates a valuable opportunity to produce
sustainable, biodegradable, and environmentally friendly bioplastics perfectly
integrated within a circular economy. Against this background, we highlight the
sustainability superiority of these bioplastics over common plastics and
bioplastic via a detailed life cycle assessment, anticipating an important role
of this new class of bioplastics in mitigating the pressing plastic pollution
challenge.


  Based on a recent work on traveling waves in spatially nonlocal
reaction-diffusion equations, we investigate the existence of traveling fronts
in reaction-diffusion equations with a memory term. We will explain how such
memory terms can arise from reduction of reaction-diffusion systems if the
diffusion constants of the other species can be neglected. In particular, we
show that two-scale homogenization of spatially periodic systems can induce
spatially homogeneous systems with temporal memory.
  The existence of fronts is proved using comparison principles as well as a
reformulation trick involving an auxiliary speed that allows us to transform
memory terms into spatially nonlocal terms. Deriving explicit bounds and
monotonicity properties of the wave speed of the arising travelingfront, we are
able to establish the existence of true traveling fronts for the original
problem with memory. Our results are supplemented by numerical simulations.


  We study the problem of estimating the leading eigenvectors of a
high-dimensional population covariance matrix based on independent Gaussian
observations. We establish lower bounds on the rates of convergence of the
estimators of the leading eigenvectors under $l^q$-sparsity constraints when an
$l^2$ loss function is used. We also propose an estimator of the leading
eigenvectors based on a coordinate selection scheme combined with PCA and show
that the proposed estimator achieves the optimal rate of convergence under a
sparsity regime. Moreover, we establish that under certain scenarios, the usual
PCA achieves the minimax convergence rate.


  Requirement prioritization is recognized as an important decision-making
activity in requirements engineering and software development. Requirement
prioritization is applied to determine which requirements should be implemented
and released. In order to prioritize requirements, there are several
approaches/techniques/tools that use different requirements prioritization
criteria, which are often identified by gut feeling instead of an in-depth
analysis of which criteria are most important to use. Therefore, in this study
we investigate which requirements prioritization criteria are most important to
use in industry when determining which requirements are implemented and
released, and if the importance of the criteria change depending on how far a
requirement has reached in the development process. We conducted a quantitative
study of one completed project from one software developing company by
extracting 32,139 requirements prioritization decisions based on eight
requirements prioritization criteria for 11,110 requirements. The results show
that not all requirements prioritization criteria are equally important, and
this change depending on how far a requirement has reached in the development
process.


  Let E be an arbitrary directed graph with no restrictions on the number of
vertices and edges and let K be any field. We give necessary and sufficient
conditions for the Leavitt path algebra L_K(E) to be of countable irreducible
representation type, that is, we determine when L_K(E)has at most countably
many distinct isomorphism classes of simple left L_K(E-modules. It is also
shown that L_K(E) has dinitely many isomorphism classes of simple left modules
if and only if L_K(E) is a semi-artinian von Neumann regular ring with at most
finitely many ideals. Equivalent conditions on the graph E are also given.
Examples are constructed showing that for each (finite or infinite) cardinal m
there exists a Leavitt path algebra L having exactly m distinct isomorphism
classes of simple left modules.


  The inclusive production of the charm-strange baryon $\rm \Omega_c^0$ is
measured for the first time via its hadronic decay into $\rm \Omega^-\pi^+$ at
midrapidity ($|y|<0.5$) in proton-proton (pp) collisions at the centre-of-mass
energy $\sqrt{s}=13$ TeV with the ALICE detector at the LHC. The transverse
momentum ($p_{\rm T}$) differential cross section multiplied by the branching
ratio is presented in the interval $2<p_{\rm T}<12~{\rm GeV}/c$. The $p_{\rm
T}$ dependence of the $\rm \Omega_c^0$-baryon production relative to the prompt
$\rm D^0$-meson and to the prompt $\rm \Xi_c^0$-baryon production is compared
to various models that take different hadronisation mechanisms into
consideration. In the measured $p_{\rm T}$ interval, the ratio of the $p_{\rm
T}$-integrated cross sections of $\rm \Omega_c^0$ and prompt $\Lambda_{\rm
c}^{+}$ baryons multiplied by the $\rm \Omega^-\pi^+$ branching ratio is found
to be larger by a factor of about 20 with a significance of about 4$\sigma$
when compared to $\rm e^+e^-$ collisions.


  We bring cocycle enhancement theory to the case of psyquandles. Analogously
to our previous work on virtual biquandle cocycle enhancements, we define
enhancements of the psyquandle counting invariant via pairs of a biquandle
2-cocycle and a new function satisfying some conditions. As an application we
define new single-variable and two-variable polynomial invariants of oriented
pseudoknots and singular knots and links. We provide examples to show that the
new invariants are proper enhancements of the counting invariant are are not
determined by the Jablan polynomial.


  We study deformations of certain crepant resolutions of isolated rational
Gorenstein singularities. After a general discussion of the deformation theory,
we specialize to dimension three and consider examples which are good (log)
resolutions as well as the case of small resolutions. We obtain some partial
results on the classification of canonical threefold singularities that admit
good crepant resolutions. Finally, we study a non-crepant example, the blowup
of a small resolution whose exceptional set is a smooth curve.


  The large scale binary black hole effort in numerical relativity has led to
an increasing distinction between numerical and mathematical relativity. This
note discusses this situation and gives some examples of succesful interactions
between numerical and mathematical methods is general relativity.


  One important approach to the study of galaxy evolution is to identify those
galaxies whose spectral and/or morphological characteristics suggest that they
are in transition. For example, ``E+A'' galaxies, which have strong Balmer
absorption lines and no significant [OII] emission, are generally interpreted
as post-starburst galaxies in which the star formation ceased within the last
Gyr. This transition between a star forming and non-star forming state is a
critical link in any galaxy evolution model in which a blue, star forming disk
galaxy evolves into a S0 or elliptical. Another possible evolutionary track is
that the star formation in an ``E+A'' resumes at some later time, if enough gas
remains in the galaxy after its starburst ends. Given this ambiguity, it is
important to investigate (1) the environment's role in ``E+A'' evolution, (2)
the stellar and gas morphologies of ``E+A''s, (3) the likely progenitors of
``E+A''s, and (4) how common the ``E+A'' phase is in the evolution of galaxies.
This proceeding summarizes recent results from several inter-related projects
designed to address these questions. These projects focus on a sample of 21
nearby ``E+A'' galaxies (0.05 < z < 0.15) drawn from the Las Campanas Redshift
Survey. These studies include VLA and HST observations, in addition to
comparisons of these data with galaxy-galaxy interaction simulations and
stellar population synthesis models.


  This paper is concerned with constructive and structural aspects of euclidean
field theory. We present a C*-algebraic approach to lattice field theory.
Concepts like block spin transformations, action, effective action, and
continuum limits are generalized and reformulated within the C*-algebraic
setup. Our approach allows to relate to each family of lattice models a set of
continuum limits which satisfies reflexion positivity and translation
invariance which suggests a guideline for constructing euclidean field theory
models. The main purpose of the present paper is to combine the concepts of
constructive field theory with the axiomatic framework of algebraic euclidean
field theory in order to separate model independent aspects from model specific
properties.


  We have computed branching ratios of two-body nonleptonic B_c \to
X_{c\bar{c}}M decays, where X_{c\bar{c}} is the radially excited charmonium
\eta_c(2S) or \psi(2S) meson, and M is a pseudoscalar (P) or a vector (V) or an
axial-vector (A(^3P_1)) meson. We have assumed factorization hypothesis and
calculated the form factors in the nonrelativistic ISGW2 quark model. We have
found that some of these decays have branching ratios of the order of 10^{-3} -
10^{-4}.


  A study of strange hadron production associated with hard scattering
processes and with the underlying event is conducted to investigate the origin
of the enhanced production of strange hadrons in small collision systems
characterised by large charged-particle multiplicities. For this purpose, the
production of the single-strange meson ${\rm K^0_S}$ and the double-strange
baryon $\Xi^{\pm}$ is measured, in each event, in the azimuthal direction of
the highest-$p_{\rm T}$ particle (``trigger" particle), related to hard
scattering processes, and in the direction transverse to it in azimuth,
associated with the underlying event, in pp collisions at $\sqrt{s}=5.02$ TeV
and $\sqrt{s}=13$ TeV using the ALICE detector at the LHC. The per-trigger
yields of ${\rm K^0_S}$ and $\Xi^{\pm}$ are dominated by the
transverse-to-leading production (i.e., in the direction transverse to the
trigger particle), whose contribution relative to the toward-leading production
is observed to increase with the event charged-particle multiplicity. The
transverse-to-leading and the toward-leading $\Xi^{\pm}$/${\rm K^0_S}$ yield
ratios increase with the multiplicity of charged particles, suggesting that
strangeness enhancement with multiplicity is associated with both hard
scattering processes and the underlying event. The relative production of
$\Xi^{\pm}$ with respect to ${\rm K^0_S}$ is higher in transverse-to-leading
processes over the whole multiplicity interval covered by the measurement. The
${\rm K}^{0}_{\rm{S}}$ and $\Xi^{\pm}$ per-trigger yields and yield ratios are
compared with predictions of three different phenomenological models, namely
PYTHIA 8.2 with the Monash tune, PYTHIA 8.2 with ropes and EPOS LHC. The
comparison shows that none of them can quantitatively describe either the
transverse-to-leading or the toward-leading yields of ${\rm K}^{0}_{\rm{S}}$
and $\Xi^{\pm}$.


  Recently, there has been a significant growth of interest in applying
software engineering techniques for the quality assurance of deep learning (DL)
systems. One popular direction is deep learning testing, where adversarial
examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided
search with the help of certain testing metrics. However, recent studies have
revealed that the commonly used neuron coverage metrics by existing DL testing
approaches are not correlated to model robustness. It is also not an effective
measurement on the confidence of the model robustness after testing. In this
work, we address this gap by proposing a novel testing framework called
Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative
measurement on 1) the value of each test case in improving model robustness
(often via retraining), and 2) the convergence quality of the model robustness
improvement. RobOT utilizes the proposed metric to automatically generate test
cases valuable for improving model robustness. The proposed metric is also a
strong indicator on how well robustness improvement has converged through
testing. Experiments on multiple benchmark datasets confirm the effectiveness
and efficiency of RobOT in improving DL model robustness, with 67.02% increase
on the adversarial robustness that is 50.65% higher than the state-of-the-art
work DeepGini.


  Advanced deep Convolutional Neural Networks (CNNs) have shown great success
in video-based person Re-Identification (Re-ID). However, they usually focus on
the most obvious regions of persons with a limited global representation
ability. Recently, it witnesses that Transformers explore the inter-patch
relations with global observations for performance improvements. In this work,
we take both sides and propose a novel spatial-temporal complementary learning
framework named Deeply-Coupled Convolution-Transformer (DCCT) for
high-performance video-based person Re-ID. Firstly, we couple CNNs and
Transformers to extract two kinds of visual features and experimentally verify
their complementarity. Further, in spatial, we propose a Complementary Content
Attention (CCA) to take advantages of the coupled structure and guide
independent features for spatial complementary learning. In temporal, a
Hierarchical Temporal Aggregation (HTA) is proposed to progressively capture
the inter-frame dependencies and encode temporal information. Besides, a gated
attention is utilized to deliver aggregated temporal information into the CNN
and Transformer branches for temporal complementary learning. Finally, we
introduce a self-distillation training strategy to transfer the superior
spatial-temporal knowledge to backbone networks for higher accuracy and more
efficiency. In this way, two kinds of typical features from same videos are
integrated mechanically for more informative representations. Extensive
experiments on four public Re-ID benchmarks demonstrate that our framework
could attain better performances than most state-of-the-art methods.


  We investigate asymptotic safety of a toy model of a singlet-scalar extension
of the Higgs sector including two real scalar fields under the impact of
quantum-gravity fluctuations. Employing functional renormalization group
techniques, we search for fixed points of the system which provide a tentative
ultraviolet completion of the system. We find that in a particular regime of
the gravitational parameter space the canonically marginal and relevant
couplings in the scalar sector -- including the mass parameters -- become
irrelevant at the ultraviolet fixed point. The infrared potential for the two
scalars that can be reached from that fixed point is fully predicted and
features no free parameters. In the remainder of the gravitational parameter
space, the values of the quartic couplings in our model are predicted. In light
of these results, we discuss whether the singlet-scalar could be a dark-matter
candidate. Furthermore, we highlight how "classical scale invariance" in the
sense of a flat potential of the scalar sector at the Planck scale could arise
as a consequence of asymptotic safety.


  We study the quenching of galaxies in different environments and its
evolution in the redshift range $0.43 \leq z \leq 0.89$. For this purpose, we
identify galaxies inhabiting filaments, the isotropic infall region of groups,
the field, and groups in the VIMOS Public Extragalactic Redshift Survey
(VIPERS). We classify galaxies as quenched (passive), through their $NUV-r$
versus $r-K$ colours. We study the fraction of quenched galaxies ($F_{r}$) as a
function of stellar mass and environment at two redshift intervals. Our results
confirm that stellar mass is the dominant factor determining galaxy quenching
over the full redshift range explored. We find compelling evidence of evolution
in the quenching of intermediate mass galaxies $(9.3 \leq \log(M_{*}/M_{\odot})
\leq 10.5)$ for all environments. For this mass range, $F_{r}$ is largest for
galaxies in groups and smallest for galaxies in the field, while galaxies in
filaments and in the isotropic infall regions appear to have intermediate
values with the exception of the high redshift bin, where the latter show
similar fraction of quenched galaxies as in the field. Galaxies in filaments
are systematically more quenched than their counterparts infalling from other
directions, in agreement to similar results found at low redshift. The least
massive galaxies in our samples do not show evidence of internal or
environmental quenching.


  The excitation spectrum of light mesons, which are composed of up, down, and
strange quarks, is studied since decades. However, it still holds a number of
puzzles and surprises that provide new insights into the nature of the strong
interaction. Recent high-quality data samples from several experiments allow us
to not only study the properties of established mesons with unprecedented
precision but to also search for new states. These searches aim in particular
at resolving the question of the existence of so-called exotic states, such as
four-quark states or states with excited gluon fields. Since light mesons have
often large widths and are overlapping, the mapping of their spectrum is
challenging and requires large quantities of data on different production and
decay modes. The data are analyzed using a framework of interfering quantum
mechanical amplitudes known as partial-wave analysis (PWA). Most excited meson
states decay into multi-particle final states, for which the PWA requires
extensive modeling of the dynamics of the final-state hadrons. I will give an
overview on ongoing experimental studies of light mesons and discuss possible
interpretations. I will also touch on novel analysis techniques and the
prospects for future progress.


  A second order relativistic hydrodynamic theory has been derived using
momentum dependent relaxation time in the relativistic transport equation. In
order to do that, an iterative technique of gradient expansion approach, namely
Chapman-Enskog (CE) expansion of the particle distribution function has been
employed. The key findings of this work are, (i) momentum dependent relaxation
time in collision term results in an extended Landau matching condition for the
thermodynamic variables, (ii) the result from numerical solution of Boltzmann
equation lies somewhere in between the two popular extreme limits : linear and
quadratic ansatz, indicating a fractional power of momentum dependence in
relaxation time to be appropriate, (ii) an equivalence has been established
between the iterative gradient expansion method like CE and the well known
moment approach like Grad's 14-moment method.


  This paper focuses on Watson-Crick languages inspired by DNA computing, their
models, and algorithms for deciding the language membership. It analyzes a
recently introduced algorithm called WK-CYK and introduces a state space search
algorithm that is based on regular Breadth-first search but uses a number of
optimizations and heuristics to be efficient in practical use and able to
analyze longer inputs. The key parts are the heuristics for pruning the state
space (detecting dead ends) and heuristics for choosing the most promising
branches to continue the search.
  These two algorithms have been tested with 20 different Watson-Crick grammars
(40 including their Chomsky normal form versions). While WK-CYK is able to
decide the language membership in a reasonable time for inputs of the length of
roughly 30-50 symbols and its performance is very consistent for all kinds of
grammars and inputs, the state space search is usually (89-98 % of cases) more
efficient and able to do the computation for inputs with lengths of hundreds or
even thousands of symbols. Thus, the state space search has the potential to be
a good tool for practical Watson-Crick membership testing and is a good basis
for improvement the efficiency of the algorithm in the future.


  We study quantum invariant Z(M) for cusped hyperbolic 3-manifold M. We
construct this invariant based on oriented ideal triangulation of M by
assigning to each tetrahedron the quantum dilogarithm function, which is
introduced by Faddeev in studies of the modular double of the quantum group.
Following Thurston and Neumann-Zagier, we deform a complete hyperbolic
structure of M, and correspondingly we define quantum invariant Z(M_u). This
quantum invariant is shown to give the Neumann--Zagier potential function in
the classical limit, and the A-polynomial can be derived from the potential
function. We explain our construction by taking examples of 3-manifolds such as
complements of hyperbolic knots and punctured torus bundle over the circle.


  We show that a singularity can occur at a finite future time in an expanding
Friedmann universe even when the density is positive and the density plus the
sum of the principal pressures is positive. Explicit examples are constructed
and a simple condition is given which can be used to eliminate behaviour of
this sort if it is judged to be unphysical.


  For solving a wide class of nonconvex and nonsmooth problems, we propose a
proximal linearized iteratively reweighted least squares (PL-IRLS) algorithm.
We first approximate the original problem by smoothing methods, and second
write the approximated problem into an auxiliary problem by introducing new
variables. PL-IRLS is then built on solving the auxiliary problem by utilizing
the proximal linearization technique and the iteratively reweighted least
squares (IRLS) method, and has remarkable computation advantages. We show that
PL-IRLS can be extended to solve more general nonconvex and nonsmooth problems
via adjusting generalized parameters, and also to solve nonconvex and nonsmooth
problems with two or more blocks of variables. Theoretically, with the help of
the Kurdyka- Lojasiewicz property, we prove that each bounded sequence
generated by PL-IRLS globally converges to a critical point of the approximated
problem. To the best of our knowledge, this is the first global convergence
result of applying IRLS idea to solve nonconvex and nonsmooth problems. At
last, we apply PL-IRLS to solve three representative nonconvex and nonsmooth
problems in sparse signal recovery and low-rank matrix recovery and obtain new
globally convergent algorithms.


  We address the problem of distributed uncon- strained convex optimization
under separability assumptions, i.e., the framework where each agent of a
network is endowed with a local private multidimensional convex cost, is
subject to communication constraints, and wants to collaborate to compute the
minimizer of the sum of the local costs. We propose a design methodology that
combines average consensus algorithms and separation of time-scales ideas. This
strategy is proved, under suitable hypotheses, to be globally convergent to the
true minimizer. Intuitively, the procedure lets the agents distributedly
compute and sequentially update an approximated Newton- Raphson direction by
means of suitable average consensus ratios. We show with numerical simulations
that the speed of convergence of this strategy is comparable with alternative
optimization strategies such as the Alternating Direction Method of
Multipliers. Finally, we propose some alternative strategies which trade-off
communication and computational requirements with convergence speed.


  In the paper, we consider the initial value problem to the Camassa-Holm
equation in the real-line case. Based on the local well-posedness result and
the lifespan, we proved that the data-to-solution map of this problem is not
uniformly continuous in nonhomogeneous Besov spaces in the sense of Hadamard.
Our obtained result improves considerably the result in \cite{H-K}.


  In this work we investigate electron-impurity binding energy in GaN/HfO$_2$
quantum wells. The calculation considers simultaneously all energy
contributions caused by the dielectric mismatch: (i) image self-energy (i.e.,
interaction between electron and its image charge), (ii) the direct Coulomb
interaction between the electron-impurity and (iii) the interactions among
electron and impurity image charges. The theoretical model account for the
solution of the time-dependent Schr\"odinger equation and the results shows how
the magnitude of the electron-impurity binding energy depends on the position
of impurity in the well-barrier system. The role of the large dielectric
constant in the barrier region is exposed with the comparison of the results
for GaN/HfO$_2$ with those of a more typical GaN/AlN system, for two different
confinement regimes: narrow and wide quantum wells.


  The electronic structure of pyrite-type cobalt phosphosulfide (CoSP) has been
studied using density-functional theory. The calculated band structure reveals
the non-magnetic semiconducting character of the compound. The electronic
structure is described through the electronic band structure and the densities
of states. A band gap of 1.14 eV has been computed within standard GGA, a value
which is enhanced using hybrid functional. It separates the upper part of the
valence band dominated by Co-3d-t2g states from the lower part of the
conduction band made exclusively of Co-3d-eg , above of which lie S-3p and P-3p
ones. The obtained values are suitable for applications in solar cells,
according to Shockley-Queisser theory of light to electric conversion
efficiency. The origin of the larger CoSP band gap, with respect to the one of
the promising FeS2 compound, is explained and the chemical bonding properties
are addressed. A comparative picture is established where several similarities
have been found, suggesting that CoSP could be for a great practical interest
in photovoltaics.


  Anomalous Hall-like signals in platinum in contact with magnetic insulators
are common observations that could be explained by either proximity
magnetization or spin Hall magnetoresistance. In this work, longitudinal and
transverse magnetoresistances are measured in a pure gold thin film on the
ferrimagnetic insulator Y$_3$Fe$_5$O$_{12}$ (Yttrium Iron Garnet, YIG). We show
that both the longitudinal and transverse magnetoresistances have
quantitatively consistent scaling in YIG/Au and in a YIG/Pt reference system
when applying the Spin Hall magnetoresistance framework. No contribution of an
anomalous Hall effect due to the magnetic proximity effect is evident.


  We consider normal almost contact structures on a Riemannian manifold and,
through their associated sections of an ad-hoc twistor bundle, study their
harmonicity, as sections or as maps. We rewrite these harmonicity equations in
terms of the curvature tensor and find conditions relating the harmonicity of
the almost contact metric and almost complex structures of the total and base
spaces of the Morimoto fibration. We apply these results to homogeneous
principal circle bundles over generalised flag manifolds, in particular
Aloff-Wallach spaces, to mass-produce harmonic almost contact metric
structures.


  Optical images of the nearby star Fomalhaut show a ring of dust orbiting the
central star. This dust is in many respects expected to be similar to the
zodiacal dust in the solar system. The ring displays a clear brightness
asymmetry, attributed to asymmetric scattering of the central starlight by the
circumstellar dust grains. Recent measurements show that the bright side of the
Fomalhaut ring is oriented away from us. This implies that the grains in this
system scatter most of the light in the backward direction, in sharp contrast
to the forward-scattering nature of the grains in the solar system. In this
letter, we show that grains considerably larger than those dominating the solar
system zodiacal dust cloud provide a natural explanation for the apparent
backward scattering behavior. In fact, we see the phases of the dust grains in
the same way as we can observe the phases of the Moon and other large solar
system bodies. We outline how the theory of the scattering behavior of
planetesimals can be used to explain the Fomalhaut dust properties. This
indicates that the Fomalhaut dust ring is dominated by very large grains. The
material orbiting Fomalhaut, which is at the transition between dust and
planetesimals, can, with respect to their optical behavior, best be described
as micro-asteroids.


  The goal of finding and characterizing nearby Earth-like planets is driving
many NASA high-contrast flagship mission concepts, the latest of which is known
as the Advanced Technology Large-Aperture Space Telescope (ATLAST). In this
article, we calculate the optimal spectral resolution $R=\lambda/\delta\lambda$
and minimum signal-to-noise ratio per spectral bin (SNR), two central design
requirements for a high-contrast space mission, in order to detect signatures
of water, oxygen, and chlorophyll on an Earth twin. We first develop a
minimally parametric model and demonstrate its ability to fit synthetic and
observed Earth spectra; this allows us to measure the statistical evidence for
each component's presence. We find that water is the easiest to detect,
requiring a resolution $R \gtrsim 20$, while the optimal resolution for oxygen
is likely to be closer to $R = 150$, somewhat higher than the canonical value
in the literature. At these resolutions, detecting oxygen will require $\sim$2
times the SNR as water. Chlorophyll requires $\sim$6 times the SNR as oxygen
for an Earth twin, only falling to oxygen-like levels of detectability for a
low cloud cover and/or a large vegetation covering fraction. This suggests
designing a mission for sensitivity to oxygen and adopting a multi-tiered
observing strategy, first targeting water, then oxygen on the more favorable
planets, and finally chlorophyll on only the most promising worlds.


  Mating preferences of many biological species are not constant but
season-dependent. Within the framework of evolutionary game theory this can be
modeled with two finite opposite-sex populations playing against each other
following the rules that are periodically changing. By combining Floquet theory
and the concept of quasi-stationary distributions, we reveal existence of
metastable time-periodic states in the evolution of finite game-driven
populations. The evolutionary Floquet states correspond to time-periodic
probability flows in the strategy space which cannot be resolved within the
mean-field framework. The lifetime of metastable Floquet states increases with
the size $N$ of populations so that they become attractors in the limit $N
\rightarrow \infty$.


  Statistical methods are proposed to select homogeneous locations when
analyzing spatial block maxima data, such as in extreme event attribution
studies. The methods are based on classical hypothesis testing using Wald-type
test statistics, with critical values obtained from suitable parametric
bootstrap procedures and corrected for multiplicity. A large-scale Monte Carlo
simulation study finds that the methods are able to accurately identify
homogeneous locations, and that pooling the selected locations improves the
accuracy of subsequent statistical analyses. The approach is illustrated with a
case study on precipitation extremes in Western Europe. The methods are
implemented in an R package that allows easy application in future extreme
event attribution studies.


  We present a $40\,$ks Chandra observation of the IRAS$\,$20126+4104 core
region. In the inner $6^{\prime\prime}$ two X-ray sources were detected, which
are coincident with the radio jet source I20S and the variable radio source
I20Var. No X-ray emission was detected from the nearby massive protostar I20N.
The spectra of both detected sources are hard and highly absorbed, with no
emission below $3\,$keV.
  For I20S, the measured $0.5-8\,$keV count rate was $4.3\,$cts$\,$ks$^{-1}$.
The X-ray spectrum was fit with an absorbed 1T APEC model with an energy of
kT$\,=10\,$keV and an absorbing column of N$_H = 1.2\times 10^{23}\,$cm$^{-2}$.
An unabsorbed X-ray luminosity of about $1.4\times 10^{32}\,$erg$\,$s$^{-1}$
was estimated. The spectrum shows broad line emission between 6.4 and 6.7\,
keV, indicative of emission from both neutral and highly ionized iron. The
X-ray lightcurve indicates that I20S is marginally variable; however, no flare
emission was observed.
  The variable radio source I20Var was detected with a count rate of
$0.9\,$cts$\,$ks$^{-1}$ but there was no evidence of X-ray variability. The
best fit spectral model is a 1T APEC model with an absorbing hydrogen column of
N$_H = 1.1\times 10^{23}\,$cm$^{-2}$ and a plasma energy of kT = 6.0$\,$keV.
The unabsorbed X-ray luminosity is about $3\times 10^{31}\,$erg$\,$s$^{-1}$.


  Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale
of billions of atoms has been achieved for the heterogeneous catalytic system
of H$_2$/Pt(111) using the FLARE Bayesian force field. This achievement
provides accelerated time-to-solution from first principles, with Bayesian
active learning enabling efficient and autonomous training of the machine
learning model. The resulting model is then deployed in LAMMPS on GPUs using
the Kokkos performance portability library. The Bayesian force field provides
quantitative uncertainty of predictions on every atomic environment, critical
for detecting configurations in large reactive simulations that are outside of
the training set. Scaling benchmarks were performed using real-application MD
of the H$_2$/Pt(111) heterogeneous catalysis on the Summit supercomputer, with
simulations reaching 0.5 trillion atoms on 4556 GPU nodes.


  This paper explores the geometry of putting in the limiting case of a planar
putting surface. Putts equidistant from the hole originating on an arc spanning
\pm30 degrees are shown to share a common target point. Moving around the
circle of all equidistant putts, the ensemble of target points map out a small,
diamond-shaped structure centered on the fall line directly above the hole. The
position and size of this target diamond for any length putt on a putting
surface of any grade and speed is reasonably approximated by a single universal
curve. This understanding suggests a practical methodology for reading putts.
Instead of lining up only the putt-at-hand, the golfer should line up all putts
known to share a common target point. This methodology will increase the
probability of choosing the correct target line.


  Event extraction is an important work of medical text processing. According
to the complex characteristics of medical text annotation, we use the
end-to-end event extraction model to enhance the output formatting information
of events. Through pre training and fine-tuning, we can extract the attributes
of the four dimensions of medical text: anatomical position, subject word,
description word and occurrence state. On the test set, the accuracy rate was
0.4511, the recall rate was 0.3928, and the F1 value was 0.42. The method of
this model is simple, and it has won the second place in the task of mining
clinical discovery events (task2) in the Chinese electronic medical record of
the seventh China health information processing Conference (chip2021).


  In this study, we executed a genomic analysis with the objective of selecting
a set of genes (possibly small) that would help in the detection and
classification of samples from patients affected by Parkinson Disease. We
performed a complete data analysis and during the exploratory phase, we
selected a list of differentially expressed genes. Despite their association
with the diseased state, we could not use them as a biomarker tool. Therefore,
our research was extended to include a multivariate analysis approach resulting
in the identification and selection of a group of 20 genes that showed a clear
potential in detecting and correctly classify Parkinson Disease samples even in
the presence of other neurodegenerative disorders.


  Even pruned by the state-of-the-art network compression methods, Graph Neural
Networks (GNNs) training upon non-Euclidean graph data often encounters
relatively higher time costs, due to its irregular and nasty density
properties, compared with data in the regular Euclidean space. Another natural
property concomitantly with graph is class-imbalance which cannot be alleviated
by the massive graph data while hindering GNNs' generalization. To fully tackle
these unpleasant properties, (i) theoretically, we introduce a hypothesis about
what extent a subset of the training data can approximate the full dataset's
learning effectiveness. The effectiveness is further guaranteed and proved by
the gradients' distance between the subset and the full set; (ii) empirically,
we discover that during the learning process of a GNN, some samples in the
training dataset are informative for providing gradients to update model
parameters. Moreover, the informative subset is not fixed during training
process. Samples that are informative in the current training epoch may not be
so in the next one. We also notice that sparse subnets pruned from a
well-trained GNN sometimes forget the information provided by the informative
subset, reflected in their poor performances upon the subset. Based on these
findings, we develop a unified data-model dynamic sparsity framework named
Graph Decantation (GraphDec) to address challenges brought by training upon a
massive class-imbalanced graph data. The key idea of GraphDec is to identify
the informative subset dynamically during the training process by adopting
sparse graph contrastive learning. Extensive experiments on benchmark datasets
demonstrate that GraphDec outperforms baselines for graph and node tasks, with
respect to classification accuracy and data usage efficiency.


  Goal: Squamous cell carcinoma of cervix is one of the most prevalent cancer
worldwide in females. Traditionally, the most indispensable diagnosis of cervix
squamous carcinoma is histopathological assessment which is achieved under
microscope by pathologist. However, human evaluation of pathology slide is
highly depending on the experience of pathologist, thus big inter- and
intra-observer variability exists. Digital pathology, in combination with deep
learning provides an opportunity to improve the objectivity and efficiency of
histopathologic slide analysis. Methods: In this study, we obtained 800
haematoxylin and eosin stained slides from 300 patients suffered from cervix
squamous carcinoma. Based on information from morphological heterogeneity in
the tumor and its adjacent area, we established deep learning models using
popular convolution neural network architectures (inception-v3,
InceptionResnet-v2 and Resnet50). Then random forest was introduced to feature
extractions and slide-based classification. Results: The overall performance of
our proposed models on slide-based tumor discrimination were outstanding with
an AUC scores > 0.94. While, location identifications of lesions in whole slide
images were mediocre (FROC scores > 0.52) duo to the extreme complexity of
tumor tissues. Conclusion: For the first time, our analysis workflow
highlighted a quantitative visual-based slide analysis of cervix squamous
carcinoma. Significance: This study demonstrates a pathway to assist
pathologist and accelerate the diagnosis of patients by utilizing new
computational approaches.


  We provide a detailed analysis on the acoustic radiation force and torque
exerted on a homogeneous viscoelastic particle in the long-wave limit (the
particle radius is much smaller than the incident wavelength) by an arbitrary
wave. We assume that the particle behaves as a linear viscoelastic solid, which
obeys the fractional Kelvin-Voigt model. Simple analytical expressions for the
radiation force and torque are obtained considering the low- and high-frequency
approximation in the viscoelastic model. The developed theory is used to
describe the interaction of acoustic waves (traveling and standing plane waves,
and zero- and first-order Bessel beams) with a low- and high-density
polyethylene particle chosen as examples. Negative axial radiation force and
torque are predicted when the ratio of the longitudinal to shear relaxation
times is smaller than a constant that depends on the speed of sound in the
particle. In addition, a full 3D tractor Bessel vortex beam acting on the
high-density polyethylene is depicted. These predictions may enable new
possibilities of particle handling in acoustophoretic techniques.


  The CPL parametrization is very important for investigating the property of
dark energy with observational data. However, the CPL parametrization only
respects the past evolution of dark energy but does not care about the future
evolution of dark energy, since $w(z)$ diverges in the distant future. In a
recent paper [J.Z. Ma and X. Zhang, Phys.\ Lett.\ B {\bf 699}, 233 (2011)], a
robust, novel parametrization for dark energy, $w(z)=w_0+w_1({\ln (2+z)\over
1+z}-\ln2)$, has been proposed, successfully avoiding the future divergence
problem in the CPL parametrization. On the other hand, an oscillating
parametrization (motivated by an oscillating quintom model) can also avoid the
future divergence problem. In this Letter, we use the two divergence-free
parametrizations to probe the dynamics of dark energy in the whole evolutionary
history. In light of the data from 7-year WMAP temperature and polarization
power spectra, matter power spectrum of SDSS DR7, and SN Ia Union2 sample, we
perform a full Markov Chain Monte Carlo exploration for the two dynamical dark
energy models. We find that the best-fit dark energy model is a quintom model
with the EOS across -1 during the evolution. However, though the quintom model
is more favored, we find that the cosmological constant still cannot be
excluded.


  We consider the recent argument by Higuchi, Marolf and Morrison [1] that a
nonlocal gauge transformation can be used to eliminate the infrared divergence
of the graviton propagator, when evaluated in Bunch-Davies vacuum on the open
coordinate submanifold of de Sitter space in transverse-traceless-synchronous
gauge. Because the transformation is not local, the equal time commutator of
undifferentiated fields no longer vanishes. From explicit examination of the
Wightman function we demonstrate that the transformation adds anti-sources in
the far future which cancel the bad infrared behavior but also change the
propagator equation. The same problem exists in the localized version of the
recent argument. Adding such anti-sources does not seem to be legitimate and
could be used to eliminate the infrared divergence of the massless, minimally
coupled scalar. The addition of such anti-sources in flat space QED could be
effected by an almost identical gauge transformation, and would seem to
eliminate the well known infrared divergences which occur in loop corrections
to exclusive amplitudes.


  Wavelet frames for $L^2({\mathbb R})$ can be characterized by means of
spectral techniques. This work uses spectral formulas to determine all the
tight wavelet frames for $L^2({\mathbb R})$ with a fixed finite number of
generators of minimal support. The method associates wavelet frames of this
type with certain inner operator-valued functions in Hardy spaces. The cases
with one and two generators are completely solved.


  This note presents several results in graph theory inspired by the author's
work in the proof theory of linear logic; these results are purely
combinatorial and do not involve logic.
  We show that trails avoiding forbidden transitions, properly arc-colored
directed trails and rainbow paths for complete multipartite color classes can
be found in linear time, whereas finding rainbow paths is NP-complete for any
other restriction on color classes. For the tractable cases, we also state new
structural properties equivalent to Kotzig's theorem on the existence of
bridges in unique perfect matchings.
  Another result on graphs equipped with unique perfect matchings that we prove
here is the combinatorial counterpart of a theorem due to Bellin in linear
logic: a connection between blossoms and bridge deletion orders.


  We present the first 7.5'x11.5' velocity-resolved map of the [CII]158um line
toward the Orion molecular cloud-1 (OMC-1) taken with the Herschel/HIFI
instrument. In combination with far-infrared (FIR) photometric images and
velocity-resolved maps of the H41alpha hydrogen recombination and CO J=2-1
lines, this data set provides an unprecedented view of the intricate
small-scale kinematics of the ionized/PDR/molecular gas interfaces and of the
radiative feedback from massive stars. The main contribution to the [CII]
luminosity (~85%) is from the extended, FUV-illuminated face of the cloud
G_0>500, n_H>5x10^3 cm^-3) and from dense PDRs (G_0~10^4, n_H~10^5 cm^-3) at
the interface between OMC-1 and the HII region surrounding the Trapezium
cluster. Around 15% of the [CII] emission arises from a different gas component
without CO counterpart. The [CII] excitation, PDR gas turbulence, line opacity
(from [13CII]) and role of the geometry of the illuminating stars with respect
to the cloud are investigated. We construct maps of the [CII]/FIR and FIR/M_Gas
ratios and show that [CII]/FIR decreases from the extended cloud component
(10^-2-10^-3) to the more opaque star-forming cores (10^-3-10^-4). The lowest
values are reminiscent of the "[CII] deficit" seen in local ultra-luminous IR
galaxies hosting vigorous star formation. Spatial correlation analysis shows
that the decreasing [CII]/FIR ratio correlates better with the column density
of dust through the molecular cloud than with FIR/M_Gas. We conclude that the
[CII] emitting column relative to the total dust column along each line of
sight is responsible for the observed [CII]/FIR variations through the cloud.


  Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/.


  The disorder operator is often designed to reveal the conformal field theory
information in quantum many-body systems. By using large-scale quantum Monte
Carlo simulation, we study the scaling behavior of disorder operators on the
boundary in the two-dimensional Heisenberg model on the square-octagon lattice
with gapless topological edge state. In the Affleck-Kennedy-Lieb-Tasaki phase,
the disorder operator is shown to hold the perimeter scaling with a logarithmic
term associated with the Luttinger liquid parameter K. This effective Luttinger
liquid parameter K reflects the low-energy physics and CFT for (1+1)D boundary.
At bulk critical point, the effective K is suppressed but keeps finite value,
indicating the coupling between the gapless edge state and bulk fluctuation.
The logarithmic term numerically captures this coupling picture, which reveals
the (1+1)D SU(2)1 CFT and (2+1)D O(3) CFT at boundary criticality. Our Letter
paves a new way to study the exotic boundary state and boundary criticality.


  This paper proposes a transformer-based learned image compression system. It
is capable of achieving variable-rate compression with a single model while
supporting the region-of-interest (ROI) functionality. Inspired by prompt
tuning, we introduce prompt generation networks to condition the
transformer-based autoencoder of compression. Our prompt generation networks
generate content-adaptive tokens according to the input image, an ROI mask, and
a rate parameter. The separation of the ROI mask and the rate parameter allows
an intuitive way to achieve variable-rate and ROI coding simultaneously.
Extensive experiments validate the effectiveness of our proposed method and
confirm its superiority over the other competing methods.


  Some instances of creative thinking require an agent to build and test
hypothetical theories. Such a reasoner needs to explore the space of not only
those situations that have occurred in the past, but also those that are
rationally conceivable. In this paper we present a formalism for exploring the
space of conceivable situation-models for those domains in which the knowledge
is primarily probabilistic in nature. The formalism seeks to construct
consistent, minimal, and desirable situation-descriptions by selecting suitable
domain-attributes and dependency relationships from the available domain
knowledge.


  We propose a generalization of Hopf fibrations to quotient the streamwise
translation symmetry of turbulent pipe flows viewed as dynamical systems. In
particular, we exploit the geometric structure of the associate high
dimensional state space, which is that of a principal fiber bundle. The
relation between the comoving frame velocity $U_{d}$ associated with the
dynamical phase of an orbit in the bundle and the Taylor's hypothesis is
investigated. As an application, Laser-Induced-Fluorescence techniques are
exploited to capture planar fluorescent dye concentration fields tracing a
turbulent pipe flow at the bulk Reynolds number $\mathfrak{\mathsf{Re}}=3200$.
The symmetry reduction analysis of the experimental data reveals that the speed
$u$ of dye concentration bursts is associated with the dynamical and geometric
phases of the corresponding orbits in the fiber bundle. In particular, in the
symmetry-reduced frame we unveil a pattern-changing dynamics of the passive
scalar structures, which explains the observed speed $u\approx U_{d}+U_{g}$ of
intense bursting events in terms of the geometric phase velocity
$U_{g}\approx0.43U_{d}$ associated with the orbits in the bundle.


  Several results obtained within the SUSY group of the ECFA/DESY linear
collider study are presented: (i) a possibility to determine tan beta and the
trilinear couplings A_f via polarisation in sfermion decays, (ii) the impact of
complex MSSM parameters on the third generation sfermion decays, (iii)
determination of CP violation in the complex MSSM via T-odd asymmetries in
neutralino production and decay, and (iv) an analysis of the chargino and
neutralino mass parameters at one-loop level.


  Although Casimir, or quantum vacuum, forces between distinct bodies, or
self-stresses of individual bodies, have been calculated by a variety of
different methods since 1948, they have always been plagued by divergences.
Some of these divergences are associated with the volume, and so may be more or
less unambiguously removed, while other divergences are associated with the
surface. The interpretation of these has been quite controversial. Particularly
mysterious is the contradiction between finite total self-energies and surface
divergences in the local energy density. In this paper we clarify the role of
surface divergences.


  We consider the classical Cauchy problem for the linear heat equation and
integrable initial data in the Euclidean space $\mathbb{R}^N$. In the case
$N=1$ we show that given a weighted $L^p$-space $L_w^p(\mathbb{R})$ with $1
\leq p < \infty$ and a fast growing weight $w$, there is a Schauder basis
$(e_n)_{n=1}^\infty$ in $L_ w^p(\mathbb{R})$ with the following property: given
a positive integer $m $ there exists $n_m > 0$ such that, if the initial data
$f$ belongs to the closed linear space of $e_n$ with $n \geq n_m$, then the
decay rate of the solution of the heat equation is at least $t^{-m}$. The
result is also generalized to the case $N >1$ with a slightly weaker
formulation. The proof is based on a construction of a Schauder basis of
$L_w^p( \mathbb{R}^N)$, which annihilates an infinite sequence of bounded
functionals.


  Self-organization on crystal surface is studied as a two dimensional spinodal
decomposition in presence of a surface stress. The elastic Green function is
calculated for a $(001)$ cubic crystal surface taking into account the crystal
anisotropy. Numerical calculations show that the phase separation is driven by
the interplay between domain boundary energy and long range elastic
interactions. At late stage of the phase separation process, a steady state
appears with different nanometric patterns according to the surface coverage
and the crystal elastic constants.


  We discuss in detail very little known results obtained by Majorana as early
as 1931, regarding the quantum theory of the chemical bond in homopolar
molecules, based on the key concept of exchange interaction. After a brief
historical overview of the quantum homopolar valence theory, we address the
intriguing issues of the formation of the helium molecular ion, He2+, and of
the accurate description of the hydrogen molecule, H2. For the first case, the
group theory-inspired approach used by Majorana is contrasted with that more
known followed by Pauling (and published few months after that of Majorana),
while for the second case we focus on his proposal concerning the possible
existence of ionic structures in homopolar compounds, just as in the hydrogen
molecule. The novelty and relevance of Majorana's results in the modern
research on molecular and chemical physics is emphasized as well.


  In the infinite regular tree $\mathbb{T}_{q+1}$ with $q \in \mathbb{Z}_{\ge
2}$, we consider families $\{\mu_u^n\}$, indexed by vertices $u$ and
nonnegative integers ("discrete time steps") $n$, of probability measures such
that $\mu_u^n(v) = \mu_{u'}^n(v')$ if the distances $\operatorname{dist}(u,v)$
and $\operatorname{dist}(u',v')$ are equal. Let $d$ be a positive integer, and
let $X$ and $Y$ be two vertices in the tree which are at distance $d$ apart. We
compute a formula for the transportation distance $W_1\!\left( \mu_X^n, \mu_Y^n
\right)$ in terms of generating functions. In the special case where $\mu_u^n =
\mathfrak{m}_u^n$ are measures from simple random walks after $n$ time steps,
we establish the linear asymptotic formula $W_1\!\left( \mathfrak{m}_X^n,
\mathfrak{m}_Y^n \right) = An + B + o(1)$, as $n \to \infty$, and give the
formulas for the coefficients $A$ and $B$ in closed forms. We also obtain
linear asymptotic formulas in the cases of spheres and uniform balls as the
radii tend to infinity. We show that these six coefficients (two from simple
random walks, two from spheres, and two from uniform balls) are related by
inequalities.


  A graph $G$ is called $H$-saturated if it does not contain any copy of $H$,
but for any edge $e$ in the complement of $G$ the graph $G+e$ contains some
$H$. The minimum size of an $n$-vertex $H$-saturated graph is denoted by
$\sat(n,H)$. We prove $$\sat(n,C_k) = n + n/k + O((n/k^2) + k^2)$$ holds for
all $n\geq k\geq 3$, where $C_k$ is a cycle with length $k$. We have a similar
result for semi-saturated graphs $$\ssat(n,C_k) = n + n/(2k) + O((n/k^2) +
k).$$ We conjecture that our three constructions are optimal.


  We study the thermodynamics and thermodynamic geometry of a five-dimensional
Reissner-Nordstr\"om-AdS black hole in the extended phase space by treating the
cosmological constant as being related to the number of colors in the boundary
gauge theory and its conjugate quantity as the associated chemical potential.
It is found that the contribution of the charge of the black hole to the
chemical potential is always positive and the existence of charge make the
chemical potential become positive more easily. We calculate the scalar
curvatures of the thermodynamical Weinhold metric, Ruppeiner metric and Quevedo
metric, respectively, in the fixed $N^2$ case and the fixed $q$ case. It is
found that in the fixed $N^2$ case the divergence of the scalar curvature is
related to the divergence of the specific heat with fixed electric potential in
the Weinhold metric and Ruppeiner metric, and the divergence of the scalar
curvature in the Quevedo metric corresponds to the divergence of the specific
heat with fixed electric charge density. In the fixed $q$ case, however, the
divergence of the scalar curvature is related to the divergence of the specific
heat with fixed chemical potential in the Weinhold metric and Ruppeiner metric,
while in the Quevedo metric the divergence of the scalar curvature corresponds
to the divergence of the specific heat with fixed number of colors and the
vanishing of the specific heat with fixed chemical potential.


  Capturing the essence of a textile image in a robust way is important to
retrieve it in a large repository, especially if it has been acquired in the
wild (by taking a photo of the textile of interest). In this paper we show that
a texel-based representation fits well with this task. In particular, we refer
to Texel-Att, a recent texel-based descriptor which has shown to capture fine
grained variations of a texture, for retrieval purposes. After a brief
explanation of Texel-Att, we will show in our experiments that this descriptor
is robust to distortions resulting from acquisitions in the wild by setting up
an experiment in which textures from the ElBa (an Element-Based texture
dataset) are artificially distorted and then used to retrieve the original
image. We compare our approach with existing descriptors using a simple ranking
framework based on distance functions. Results show that even under extreme
conditions (such as a down-sampling with a factor of 10), we perform better
than alternative approaches.


  We investigate the two-terminal nonlinear conductance of a Coulomb-blockaded
quantum dot attached to chiral edge states. Reversal of the applied magnetic
field inverts the system chirality and leads to a different polarization
charge. As a result, the current--voltage characteristic is not an even
function of the magnetic field. We show that the corresponding magnetic-field
asymmetry arises from single-charge effects and vanishes in the limit of high
temperature.


  In this letter, we introduce several algorithms for geometry inpainting of 3D
point clouds with large holes. The algorithms are examplar-based: hole filling
is performed iteratively using templates near the hole boundary to find the
best matching regions elsewhere in the cloud, from where existing points are
transferred to the hole. We propose two improvements over the previous work on
exemplar-based hole filling. The first one is adaptive template size selection
in each iteration, which simultaneously leads to higher accuracy and lower
execution time. The second improvement is a non-rigid transformation to better
align the candidate set of points with the template before the point transfer,
which leads to even higher accuracy. We demonstrate the algorithms' ability to
fill holes that are difficult or impossible to fill by existing methods.


  The object of this paper is to describe an explicit two--parameter family of
logarithmic flat connections over the complex projective plane. These
connections have dihedral monodromy and their polar locus is a prescribed
quintic composed of a circle and three tangent lines. By restricting them to
generic lines we get an algebraic family of isomonodromic deformations of the
five--punctured sphere. This yields new algebraic solutions of a Garnier
system. Finally, we use the associated Riccati one--forms to construct an
interesting non--generic family of transversally projective Lotka--Volterra
foliations.


  We explore the dynamical consequences of switching the coupling form in a
system of coupled oscillators. We consider two types of switching, one where
the coupling function changes periodically and one where it changes
probabilistically. We find, through bifurcation diagrams and Basin Stability
analysis, that there exists a window in coupling strength where the
oscillations get suppressed. Beyond this window, the oscillations are revived
again. A similar trend emerges with respect to the relative predominance of the
coupling forms, with the largest window of fixed point dynamics arising where
there is balance in the probability of occurrence of the coupling forms.
Further, significantly, more rapid switching of coupling forms yields large
regions of oscillation suppression. Lastly, we propose an effective model for
the dynamics arising from switched coupling forms and demonstrate how this
model captures the basic features observed in numerical simulations and also
offers an accurate estimate of the fixed point region through linear stability
analysis.


  One major goal of upcoming large-scale-structure surveys is to constrain dark
energy and modified gravity theories. In particular, galaxy clustering and
gravitational lensing convergence are probes sensitive to modifications of
general relativity. While the standard analysis for these surveys typically
includes power spectra or 2-point correlation functions, it is known that the
bispectrum contains more information, and could offer improved constraints on
parameters when combined with the power spectra. However, the use of bispectra
has been limited so far to one single probe, e.g. the lensing convergence
bispectrum or the galaxy bispectrum. In this paper, we extend the formalism to
explore the power of cross-bispectra between different probes, and exploit
their ability to break parameter degeneracies and improve constraints. We study
this on a test case of lensing convergence and galaxy density auto- and
cross-bispectra, for a particular sub-class of Horndeski theories parametrized
by $c_M$ and $c_B$. Using the 2000 deg$^2$ notional survey of the Nancy Grace
Roman Space Telescope with overlapping photometry from the Rubin Observatory
Legacy Survey of Space and Time, we find that a joint power spectra and
bispectra analysis with three redshift bins at $l_{\rm max} = 1000$ yields
$\sigma_{c_M} = 1.0$ and $\sigma_{c_B} = 0.3$, both a factor of $\sim$1.2
better than the power spectra results; this would be further improved to
$\sigma_{c_M} = 0.7$ and $\sigma_{c_B} = 0.2$ if $l_{\rm max} = 3000$ is taken.
Furthermore, we find that using all possible cross-bispectra between the two
probes in different tomographic bins improves upon auto-bispectra results by a
factor of 1.3 in $\sigma_{c_M}$, 1.1 in $\sigma_{c_B}$ and 1.3 in
$\sigma_{\Omega_m}$. We expect that similar benefits of using cross-bispectra
between probes could apply to other science cases and surveys.


  A contribution is presented to the study of hadron spectroscopy through the
use of fractals and discrete scale invariance implying log-periodic corrections
to continuous scaling. The masses of mesons and baryons, reported by the
Particle Data Group (PDG), are properly fitted with help of the equation
derived from the discrete-scale invariance (DSI) model. The same property is
observed for the mass ratios between different particle species. This is also
the case for total widths of several hadronic species.
  Each fitted parameter, as a function of the hadronic masses, displays the
same distribution for all hadronic species. Several masses of still unobserved
mesons and baryons are tentatively predicted.


  Images obtained with the Gemini Multi-Object Spectrograph on Gemini South are
used to examine the photometric properties and spatial distributions of main
sequence (MS) and pre-main sequence (PMS) objects in the star cluster NGC 2401.
The data sample several magnitudes fainter than previous studies, and a large
population of candidate PMS (cPMS) stars are identified. The cPMS stars are
traced out to 2.4 arcmin from the cluster center, and have a flatter spatial
distribution than the brightest MS stars near the cluster center. The
luminosity function of all MS and candidate PMS stars can be matched by a model
that assumes a solar neighborhood mass function, suggesting that NGC 2401 has
not yet shed significant numbers of members with masses in excess of 0.5 solar.
The frequency of wide binaries among the MS stars is ~3 times higher than among
the cPMS stars. It is argued that the difference in the spatial distributions
of MS and PMS objects is not the consequence of secular dynamical evolution or
structural evolution driven by near-catastrophic mass loss. Rather, it is
suggested that the different spatial distributions of these objects is the
fossil imprint of primordial sub-clustering that arises naturally if massive
stars form preferentially in the highest density central regions of a
protocluster.


  ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning,
i.e., applying pre-trained transformer models to modalities beyond their
training data. The technique consists primarily of training an embedder and
fine-tuning the embedder and model. Despite its high performance on a variety
of downstream tasks, we do not understand precisely how each of these
components contribute to ORCA's success. Therefore, we run a series of
ablations and find that embedder training does not help 2D tasks at all,
contrary to what the original paper posits. In 1D tasks, some amount of
embedder training is necessary but more is not better. In 4 out of 6 datasets
we experiment with, it is model fine-tuning that makes the biggest difference.
Through our ablations and baselines, we contribute a better understanding of
the individual components of ORCA.


  Based on the standard gravitational lensing formalism with its effective,
projected lensing potential in a given background cosmology, we investigate
under which transformations of the source position and of the deflection angle
the observable properties of the multiple images, i.e. the time delay
differences, the relative image positions, relative shapes, and magnification
ratios, remain invariant. As these observables only constrain local lens
properties, we derive general, local invariance transformations in the areas
covered by the multiple images. We show that the known global invariance
transformations, e.g. the mass sheet transformation or the source position
transformation, are contained in our invariance transformations, when they are
restricted to the areas covered by the multiple images and when
lens-model-based degeneracies are ignored, like the freedom to add or subtract
masses in unconstrained regions without multiple images. Hence, we have
identified the general class of invariance transformations that can occur, in
particular in our model-independent local characterisation of strong
gravitational lenses.


  Using the Mellin transform approach, it is shown that, in contrast with
integer-order derivatives, the fractional-order derivative of a periodic
function cannot be a function with the same period. The three most widely used
definitions of fractional-order derivatives are taken into account, namely, the
Caputo, Riemann-Liouville and Grunwald-Letnikov definitions. As a consequence,
the non-existence of exact periodic solutions in a wide class of
fractional-order dynamical systems is obtained. As an application, it is
emphasized that the limit cycle observed in numerical simulations of a simple
fractional-order neural network cannot be an exact periodic solution of the
system.


  We present new experimental data on directed flow in collisions of Au+Au,
Xe+CsI and Ni+Ni at incident energies from 90 to 400A MeV. We study the
centrality and system dependence of integral and differential directed flow for
particles selected according to charge. All the features of the experimental
data are compared with Isospin Quantum Molecular Dynamics (IQMD) model
calculations in an attempt to extract information about the nuclear matter
equation of state (EoS). We show that the combination of rapidity and
transverse momentum analysis of directed flow allow to disentangle various
parametrizations in the model. At 400A MeV, a soft EoS with momentum dependent
interactions is best suited to explain the experimental data in Au+Au and
Xe+CsI, but in case of Ni+Ni the model underpredicts flow for any EoS. At 90A
MeV incident beam energy, none of the IQMD parametrizations studied here is
able to consistently explain the experimental data.


This paper has been withdrawn due to an error in the proof of Theorem 5.3.


  The vertex-cover problem is studied for random graphs $G_{N,cN}$ having $N$
vertices and $cN$ edges. Exact numerical results are obtained by a
branch-and-bound algorithm. It is found that a transition in the coverability
at a $c$-dependent threshold $x=x_c(c)$ appears, where $xN$ is the cardinality
of the vertex cover. This transition coincides with a sharp peak of the typical
numerical effort, which is needed to decide whether there exists a cover with
$xN$ vertices or not. For small edge concentrations $c\ll 0.5$, a cluster
expansion is performed, giving very accurate results in this regime. These
results are extended using methods developed in statistical physics. The so
called annealed approximation reproduces a rigorous bound on $x_c(c)$ which was
known previously. The main part of the paper contains an application of the
replica method. Within the replica symmetric ansatz the threshold $x_c(c)$ and
the critical backbone size $b_c(c)$ can be calculated. For $c<e/2$ the results
show an excellent agreement with the numerical findings. At average vertex
degree $2c=e$, an instability of the simple replica symmetric solution occurs.


  Let $T$ be a tournament with nondecreasing score sequence $R$ and $A$ be its
tournament matrix. An upset of $T$ corresponds to an entry above the main
diagonal of $A$. Given a feasible score sequence $R$, Fulkerson~(1965) gave a
simple recursive construction for a tournament with score sequence $R$ and the
minimum number of upsets, and Hacioglu et al. (2019) provided a construction
for all of such tournament matrices. Let $U_{\min}(R)$ denote the set of
tournament matrices with score sequence $R$ that have minimum number of upsets.
Brauldi and Li~(1983) characterized the strong score sequences $R$ ($R$ is
strong if a tournament $T$ with score sequence $R$ is strongly connected) with
$|U_{\min}(R)|=1$. In this article, we characterize all feasible score
sequences $R$ with $|U_{\min}(R)|=1$ and give an explicit formula for the
number of the feasible score sequences $R$ with $|U_{\min}(R)|=1$.


  Relativistic shocks that accompany supernovae (SNe) produce X-ray burst
emissions as they break out in the dense circumstellar medium around the
progenitors. This phenomenon is sometimes associated with peculiar
low-luminosity gamma-ray bursts (LL GRBs). Here, we investigate the high energy
neutrino and gamma-ray counterparts of such a class of SNe. Just beyond the
shock breakout radius, particle acceleration in the collisionless shock starts
to operate in the presence of breakout photons. We show that protons may be
accelerated to sufficiently high energies and produce high energy neutrinos and
gamma rays via the photomeson interaction. These neutrinos and gamma rays may
be detectable from 10 Mpc away by IceCube/KM3Net as multi-TeV transients almost
simultaneously with the X-ray burst emission, and even from 100 Mpc away with
follow-up observations by CTA using a wide-field sky monitor like Swift as a
trigger. A statistical technique using a stacking approach could also be
possible for the detection, with the aid of the SN optical/infrared
counterparts. Such multi-messenger observations offer the possibility to probe
the transition of relativistic shocks from radiation-mediated to collisionless
ones, and would also constrain the mechanisms of particle acceleration and
emission in LL GRBs.


  We propose a single-shot approach to determining 6-DoF pose of an object with
available 3D computer-aided design (CAD) model from a single RGB image. Our
method, dubbed MRC-Net, comprises two stages. The first performs pose
classification and renders the 3D object in the classified pose. The second
stage performs regression to predict fine-grained residual pose within class.
Connecting the two stages is a novel multi-scale residual correlation (MRC)
layer that captures high-and-low level correspondences between the input image
and rendering from first stage. MRC-Net employs a Siamese network with shared
weights between both stages to learn embeddings for input and rendered images.
To mitigate ambiguity when predicting discrete pose class labels on symmetric
objects, we use soft probabilistic labels to define pose class in the first
stage. We demonstrate state-of-the-art accuracy, outperforming all competing
RGB-based methods on four challenging BOP benchmark datasets: T-LESS, LM-O,
YCB-V, and ITODD. Our method is non-iterative and requires no complex
post-processing.


  The efficient computation of viewpoints under consideration of various system
and process constraints is a common challenge that any robot vision system is
confronted with when trying to execute a vision task. Although fundamental
research has provided solid and sound solutions for tackling this problem, a
holistic framework that poses its formal description, considers the
heterogeneity of robot vision systems, and offers an integrated solution
remains unaddressed. Hence, this publication outlines the generation of
viewpoints as a geometrical problem and introduces a generalized theoretical
framework based on Feature-Based Constrained Spaces ($\mathcal{C}$-spaces) as
the backbone for solving it. A $\mathcal{C}$-space can be understood as the
topological space that a viewpoint constraint spans, where the sensor can be
positioned for acquiring a feature while fulfilling the regarded constraint.
The present study demonstrates that many viewpoint constraints can be
efficiently formulated as $\mathcal{C}$-spaces providing geometric,
deterministic, and closed solutions. The introduced $\mathcal{C}$-spaces are
characterized based on generic domain and viewpoint constraints models to ease
the transferability of the present framework to different applications and
robot vision systems. The effectiveness and efficiency of the concepts
introduced are verified on a simulation-based scenario and validated on a real
robot vision system comprising two different sensors.


  The nonlinear magneto-optical effect has significantly impacted modern
society with prolific applications ranging from precision mapping of the
Earth's magnetic field to bio-magnetic sensing. Pioneering works on collisional
spin-exchange effects have led to ultra-high magnetic field detection
sensitivities at the level of $fT/\sqrt{Hz}$ using a single linearly-polarized
probe light field. Here we demonstrate a nonlinear Zeeman-coherence parametric
wave-mixing optical-atomic magnetometer using room temperature rubidium vapor
that results in more than a three-order-of-magnitude optical signal-to-noise
ratio (SNR) enhancement for extremely weak magnetic field sensing. This
unprecedented enhancement was achieved with nearly a two-order-of-magnitude
reduction in laser power while preserving the sensitivity of the widely-used
single-probe beam optical-atomic magnetometry method. This new method opens a
myriad of applications ranging from bio-magnetic imaging to precision
measurement of the magnetic properties of subatomic particles.


  In this paper, we introduce a metric ansatz for describing spherically
symmetric quantum corrected black hole (BH) space-time within an AdS
background, incorporating both ordinary and phantom global monopoles.
Afterwards, we focus into the thermodynamic properties of this BH, calculating
essential parameters such as the Hawking temperature and the specific heat
capacity. Moving forward, we analyze the effective potential of the system for
both null and time-like geodesics, as well as the shadow radius of the BH.
Additionally, we compute the emission rate of particles from this BH. Finally,
we explore the geodesic equations of motion and visualize the trajectories of
massive particles within the BH. Throughout our investigation, we examine how
the presence of ordinary and phantom global monopoles, alongside the quantum
corrected parameter, influences various thermal properties, the effective
potential of the system, the BH shadow radius, energy emission rate, and the
trajectories of massive particles. Importantly, through the generation of
figures depicting these phenomena, we highlight the distinctions between
results obtained with ordinary global monopoles and phantom ones, across a
range of quantum corrected parameter values considering small values of the
energy scale parameter.


  We generalize the KPZ equation to an O(3) $N=2j+1$ component model. In the
limit $N \to \infty$ we show that the mode coupling equations become exact.
Solving these approximately we find that the dynamic exponent $z$ increases
from $3/2$ for $d=1$ to $2$ at the dimension $d\approx3.6$. For $d=1$ it can be
shown analytically that $z=3/2$ for all $j$. The case $j=2$ for $d=2$ is
investigated by numerical integration of the KPZ equation.


  The Hamiltonian cycle problem is to decide whether a given graph has a
Hamiltonian cycle. Bertossi and Bonuccelli (1986, Information Processing
Letters, 23, 195-200) proved that the Hamiltonian Cycle Problem is NP-Complete
even for undirected path graphs and left the Hamiltonian cycle problem open for
directed path graphs. Narasimhan (1989, Information Processing Letters, 32,
167-170) proved that the Hamiltonian Cycle Problem is NP-Complete even for
directed path graphs and left the Hamiltonian cycle problem open for rooted
directed path graphs. In this paper we resolve this open problem by proving
that the Hamiltonian Cycle Problem is also NP-Complete for rooted directed path
graphs.


  We present detailed numerical calculations of the mechanical torque induced
by quantum fluctuations on two parallel birefringent plates with in plane
optical anisotropy, separated by either vacuum or a liquid (ethanol). The
torque is found to vary as $\sin(2\theta)$, where $\theta$ represents the angle
between the two optical axes, and its magnitude rapidly increases with
decreasing plate separation $d$. For a 40 $\mu$m diameter disk, made out of
either quartz or calcite, kept parallel to a Barium Titanate plate at $d\simeq
100$ nm, the maximum torque (at $\theta={\pi\over 4}$) is of the order of
$\simeq 10^{-19}$ N$\cdot$m. We propose an experiment to observe this torque
when the Barium Titanate plate is immersed in ethanol and the other
birefringent disk is placed on top of it. In this case the retarded van der
Waals (or Casimir-Lifshitz) force between the two birefringent slabs is
repulsive. The disk would float parallel to the plate at a distance where its
net weight is counterbalanced by the retarded van der Waals repulsion, free to
rotate in response to very small driving torques.


  Given the amino acid sequence of a protein, researchers often infer its
structure and function by finding homologous, or evolutionarily-related,
proteins of known structure and function. Since structure is typically more
conserved than sequence over long evolutionary distances, recognizing remote
protein homologs from their sequence poses a challenge.
  We first consider all proteins of known three-dimensional structure, and
explore how they cluster according to different levels of homology. An
automatic computational method reasonably approximates a human-curated
hierarchical organization of proteins according to their degree of homology.
  Next, we return to homology prediction, based only on the one-dimensional
amino acid sequence of a protein. Menke, Berger, and Cowen proposed a Markov
random field model to predict remote homology for beta-structural proteins, but
their formulation was computationally intractable on many beta-strand
topologies.
  We show two different approaches to approximate this random field, both of
which make it computationally tractable, for the first time, on all protein
folds. One method simplifies the random field itself, while the other retains
the full random field, but approximates the solution through stochastic search.
Both methods achieve improvements over the state of the art in remote homology
detection for beta-structural protein folds.


  Search systems are often used to support learning-oriented goals. This trend
has given rise to the "search-as-learning" movement, which proposes that search
systems should be designed to support learning. To this end, an important
research question is: How does a searcher's \emph{type} of learning objective
influence their trajectory (or \emph{pathway}) towards that objective? We
report on a lab study ($N=36$) in which participants gathered information to
meet a specific type of learning objective. To characterize learning objectives
\emph{and pathways}, we leveraged Anderson and Krathwohl's (A\&K's) taxonomy
\cite{anderson2001taxonomy}. Participants completed learning-oriented search
tasks that varied along three cognitive processes (apply, evaluate, create) and
three knowledge types (factual, conceptual, procedural knowledge). A
\emph{pathway} is defined as a sequence of \emph{learning instances} (e.g.,
subgoals) that were also each classified into cells from A\&K's taxonomy. Our
study used a think-aloud protocol, and pathways were generated through a
qualitative analysis of participants' think-aloud comments and recorded screen
activities. We investigate three research questions. First, in RQ1, we study
the impact of the learning objective on pathway characteristics (e.g., pathway
length). Second, in RQ2, we study the impact of the learning objective on the
types of A\&K cells traversed along the pathway. Third, in RQ3, we study common
and uncommon \emph{transitions} between A\&K cells along pathways conditioned
on the knowledge type of the objective. We discuss implications of our results
for designing search systems to support learning.