  We discuss a global anomaly associated with the coupling of chiral Weyl
fermions to gravity. The Standard Model based upon
$SU(3){\times}SU(2){\times}{U(1)}$ which has 15 fermions per generation is
shown to be inconsistent if all background spin manifolds with signature
invariant $\tau=8k$ are allowed. Similarly, GUTs based on odd number of Weyl
fermions are inconsistent. Consistency can be achieved by adding an extra Weyl
fermion which needs to couple only to gravity. For arbitrary $\tau$'s,
generalized spin structures are needed, and the global anomaly cancellation
requires that the net index of the total Dirac operator with spin and internal
gauge connections be even. As a result GUTs with fundamental multiplets which
contain multiples of 16 Weyls per generation are selected. The simplest
consistent GUT is the SO(10) model with a multiplet of 16 Weyls per generation.
The combined gravity and internal symmetry gauge group of the theory is then
$[Spin(3,1){\times}Spin(10)]/Z_2$. Physical implications of these results are
commented on.


  Accessing high-quality video content can be challenging due to insufficient
and unstable network bandwidth. Recent advances in neural enhancement have
shown promising results in improving the quality of degraded videos through
deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach
into video streaming, allowing users to download low-quality video segments and
then enhance them to obtain high-quality content without violating the playback
of the video stream. We introduce BONES, an NES control algorithm that jointly
manages the network and computational resources to maximize the quality of
experience (QoE) of the user. BONES formulates NES as a Lyapunov optimization
problem and solves it in an online manner with near-optimal performance, making
it the first NES algorithm to provide a theoretical performance guarantee.
Comprehensive experimental results indicate that BONES increases QoE by 5\% to
20\% over state-of-the-art algorithms with minimal overhead. Our code is
available at https://github.com/UMass-LIDS/bones.


  The aura of mystery surrounding quantum physics makes it difficult to advance
quantum technologies. Demystification requires methodological techniques that
explain the basics of quantum technologies without metaphors and abstract
mathematics. The article provides an example of such an explanation for the
BB84 quantum key distribution protocol based on phase coding. This allows you
to seamlessly get acquainted with the real cryptographic installation QRate,
used at the WorldSkills competition in the competence of "Quantum
Technologies".


  Enlightening our understanding of the first galaxies responsible for driving
reionisation requires detecting the 21-cm signal from neutral hydrogen.
Interpreting the wealth of information embedded in this signal requires
Bayesian inference. Parameter inference from the 21-cm signal is primarily
restricted to the spherically averaged power spectrum (1D PS) owing to its
relatively straightforward derivation of an analytic likelihood function
enabling traditional Monte-Carlo Markov-Chain (MCMC) approaches. However, in
recent years, simulation-based inference (SBI) has become feasible which
removes the necessity of having an analytic likelihood, enabling more complex
summary statistics of the 21-cm signal to be used for Bayesian inference. In
this work, we use SBI, specifically marginal neural ratio estimation to learn
the likelihood-to-evidence ratio with Swyft, to explore parameter inference
using the cylindrically averaged 2D PS. Since the 21-cm signal is anisotropic,
the 2D PS should yield more constraining information compared to the 1D PS
which isotropically averages the signal. For this, we consider a mock 1000 hr
observation of the 21-cm signal using the SKA and compare the performance of
the 2D PS relative to the 1D PS. Additionally, we explore two separate
foreground mitigation strategies, perfect foreground removal and wedge
avoidance. We find the 2D PS outperforms the 1D PS by improving the
marginalised uncertainties on individual astrophysical parameters by up to
$\sim30-40$ per cent irrespective of the foreground mitigation strategy.
Primarily, these improvements stem from how the 2D PS distinguishes between the
transverse, $k_{\perp}$, and redshift dependent, $k_{\parallel}$ information
which enables greater sensitivity to the complex reionisation morphology.


  An essentially "complete" description of the low-energy nuclear structure of
the superfluid nucleus $^{120}$Sn and of its odd-$A$ neighbors is provided by
the observations carried out with the help of Coulomb excitation and of one--
and of two-- particle transfer reactions, specific probes of vibrations,
quasiparticle and pairing degrees of freedom respectively, and of their mutual
couplings. These experimental findings are used to stringently test the
predictions of a similarly "complete" description of $^{119,120,121}$Sn carried
out in terms of elementary modes of excitation which, through their
interweaving, melt together into effective fields, each displaying properties
reflecting that of all others, there individuality resulting from the actual
relative importance of each one. Its implementation is done by solving the
Nambu-Gor'kov equations including, for the first time, all medium polarization
effects resulting from the interweaving of quasiparticles, spin and surface
vibrations, taking into account, within the framework of nuclear field theory
(NFT), the variety of processes leading to self-energy, vertex and Pauli
principle corrections, and to the induced pairing interaction. Theory provides
an overall quantitative account of the experimental findings. From these
results one can, not only obtain strong circumstantial evidence for the
inevitability for the dual origin of pairing in nuclei but also, extract
information which can be used at profit to quantitatively disentangle the
contributions to pairing correlations in general and to the pairing gap in
particular, arising from the bare and from the induced pairing interactions.


  We design and implement a distributed algorithm for balanced $k$-way
hypergraph partitioning that minimizes fanout, a fundamental hypergraph
quantity also known as the communication volume and ($k-1$)-cut metric, by
optimizing a novel objective called probabilistic fanout. This choice allows a
simple local search heuristic to achieve comparable solution quality to the
best existing hypergraph partitioners.
  Our algorithm is arbitrarily scalable due to a careful design that controls
computational complexity, space complexity, and communication. In practice, we
commonly process hypergraphs with billions of vertices and hyperedges in a few
hours. We explain how the algorithm's scalability, both in terms of hypergraph
size and bucket count, is limited only by the number of machines available. We
perform an extensive comparison to existing distributed hypergraph partitioners
and find that our approach is able to optimize hypergraphs roughly $100$ times
bigger on the same set of machines.
  We call the resulting tool Social Hash Partitioner (SHP), and accompanying
this paper, we open-source the most scalable version based on recursive
bisection.


  BL Lac objects are known to have compact jets inclined to our line of sight
at a small angle, showing prominent radio emission. Two radio-weak BL Lac
candidates with no counterparts in current radio surveys were recently reported
by Massaro et al. (2017). Both sources were selected as candidate low-energy
counterparts of unassociated Fermi {\gamma}-ray sources. We carried out very
long baseline interferometry (VLBI) observations with the European VLBI Network
(EVN) at 5 GHz to explore their radio properties at milli-arcsecond (mas)
scale. One target, J1410+7405, is clearly detected with the EVN. Its measured
5-GHz flux density, 2.4 mJy, is consistent with recent interferometric
measurements with the Karl G. Jansky Very Large Array, suggesting that the
radio emission is confined to the inner < ~10 mas region. J1410+7405 is
therefore identified as a radio-loud jetted active galactic nucleus, and its
brightness temperature exceeds ~10^9 K. Its properties are similar to those of
other {\gamma}-ray detected BL Lac objects. On the other hand, the second
target, J0644+6031, remains undetected with the EVN with a 6-{\sigma}
brightness upper limit of 0.12 mJy/beam. This source is thus radio-quiet,
confirming its peculiarity, or possibly questioning its BL Lac nature.


  We study the centrality dependence of the mid-rapidity (|y|<0.5) yield
(dN/dy) and transverse momentum distributions of K*(892)0 and {\phi}(1020)
resonances produced in Pb+Pb collisions at root(sNN) = 2.76 TeV. The
mid-rapidity density (dN/dy) and the shape of the transverse momentum spectra
are well reproduced by our earlier proposed Unified Statistical Thermal
Freeze-out Model (USTFM) which incorporates the effects of both longitudinal as
well as transverse hydrodynamic flow. The freeze-out properties in terms of
kinetic freeze-out temperature and transverse flow velocity parameter are
extracted from the model fits to the ALICE data. The extracted kinetic
freeze-out temperature is found to increase with decrease in centrality while
as the transverse flow velocity parameter shows a mild decrease towards
peripheral collisions. Moreover the centrality dependence of the mid-rapidity
system size at freeze-out has also been studied in terms of transverse radius
parameter.


  We generalize the so-called density approach to Stein characterizations of
probability distributions. We prove an elementary factorization property of the
resulting Stein operator in terms of a generalized (standardized) score
function. We use this result to connect Stein characterizations with
information distances such as the generalized (standardized) Fisher
information.


  Working to all orders in dimensionally-regularized QCD, we study the
radiation of a photon whose energy is much lower than that of external partons,
but much larger than the masses of some quarks. We argue that the conventional
soft photon theorem receives corrections at leading power in the photon energy,
associated with soft virtual loops of massless fermions. These additive
corrections give an overall factor times the non-radiative amplitude that is
infrared finite and real to all orders in $\alpha_s$. Based on recent
calculations of the three-loop soft gluon current, we identify the lowest-order
three-loop correction.


  We present deep HST imaging, as well as ground-based imaging and
spectroscopy, of the optical afterglow associated with the long-duration
gamma-ray burst GRB 990712 and its host galaxy. The data were obtained 48--123
days after the burst occurred. The magnitudes of the host (R = 21.9, V = 22.5)
and optical afterglow (R = 25.4, V = 25.8, 47.7 days after the burst) favor a
scenario where the optical light follows a pure power-law decay with an index
of alpha ~ -1.0. We find no evidence for a contribution from a supernova like
SN1998bw. This suggests that either there are multiple classes of long-duration
gamma-ray bursts, or that the peak luminosity of the supernova was > 1.5 mag
fainter than SN1998bw. The HST images and EFOSC2 spectra indicate that the
gamma-ray burst was located in a bright, extended feature (possibly a
star-forming region) 1.4 kpc from the nucleus of a 0.2 L^*_B galaxy at z =
0.434, possibly a Seyfert 2 galaxy. The late-time afterglow and host galaxy of
GRB 990712 bear some resemblance to those of GRB 970508.


  We calculate the real time non-equilibrium dynamics of quantum spin systems
at finite temperatures. The mathematical framework originates from the
$C^*$-approach to quantum statistical mechanics and is applied to samples
investigated by means of spin-polarized scanning tunneling microscopy. Quantum
fluctuations around thermal equilibrium are analyzed and calculated. The time
averaged expectation values agree with the time averaged experimental data for
magnetization curves. The method is used to investigate the dynamics of a
sample for shorter times than the resolution time of the experimental setup.
Furthermore, predictions for relaxation times of single spins on metallic and
semiconductor surfaces are made. To check the validity of our model we compare
our results with experimental data obtained from Fe adatoms on InSb and Co
adatoms on Pt(111) and find good agreement. Approximated thermalization is
found numerically for the expectation values of the spin operators.


  Network science investigates methodologies that summarise relational data to
obtain better interpretability. Identifying modular structures is a fundamental
task, and assessment of the coarse-grain level is its crucial step. Here, we
propose principled, scalable, and widely applicable assessment criteria to
determine the number of clusters in modular networks based on the leave-one-out
cross-validation estimate of the edge prediction error.


  Cloned voices of popular singers sound increasingly realistic and have gained
popularity over the past few years. They however pose a threat to the industry
due to personality rights concerns. As such, methods to identify the original
singer in synthetic voices are needed. In this paper, we investigate how singer
identification methods could be used for such a task. We present three
embedding models that are trained using a singer-level contrastive learning
scheme, where positive pairs consist of segments with vocals from the same
singers. These segments can be mixtures for the first model, vocals for the
second, and both for the third. We demonstrate that all three models are highly
capable of identifying real singers. However, their performance deteriorates
when classifying cloned versions of singers in our evaluation set. This is
especially true for models that use mixtures as an input. These findings
highlight the need to understand the biases that exist within singer
identification systems, and how they can influence the identification of voice
deepfakes in music.


  IoT has enabled the rapid growth of smart remote healthcare applications.
These IoT-based remote healthcare applications deliver fast and preventive
medical services to patients at risk or with chronic diseases. However,
ensuring data security and patient privacy while exchanging sensitive medical
data among medical IoT devices is still a significant concern in remote
healthcare applications. Altered or corrupted medical data may cause wrong
treatment and create grave health issues for patients. Moreover, current remote
medical applications' efficiency and response time need to be addressed and
improved. Considering the need for secure and efficient patient care, this
paper proposes a lightweight Blockchain-based and Fog-enabled remote patient
monitoring system that provides a high level of security and efficient response
time. Simulation results and security analysis show that the proposed
lightweight blockchain architecture fits the resource-constrained IoT devices
well and is secure against attacks. Moreover, the augmentation of Fog computing
improved the responsiveness of the remote patient monitoring system by 40%.


  We have introduced a semi-automated quantitative method to estimate the age
and reddening of 1072 star clusters in the Large Magellanic Cloud (LMC) using
the Optical Gravitational Lensing Experiment (OGLE) III survey data. This study
brings out 308 newly parameterised clusters. In a first of its kind, the LMC
clusters are classified into groups based on richness/mass as very poor, poor,
moderate and rich clusters, similar to the classification scheme of open
clusters in the Galaxy. A major cluster formation episode is found to happen at
125 +- 25 Myr in the inner LMC. The bar region of the LMC appears prominently
in the age range 60 - 250 Myr and is found to have a relatively higher
concentration of poor and moderate clusters. The eastern and the western ends
of the bar are found to form clusters initially, which later propagates to the
central part. We demonstrate that there is a significant difference in the
distribution of clusters as a function of mass, using a movie based on the
propagation (in space and time) of cluster formation in various groups. The
importance of including the low mass clusters in the cluster formation history
is demonstrated. The catalog with parameters, classification, and cleaned and
isochrone fitted CMDs of 1072 clusters, which are available as online material,
can be further used to understand the hierarchical formation of clusters in
selected regions of the LMC.


  Transverse mode-coupling instability (TMCI) with a high-frequency resonator
wake is examined by the Nested Head-Tail Vlasov solver (NHT), where a Gaussian
bunch in a parabolic potential (GP model) is represented by concentric rings in
the longitudinal phase space. It is shown that multiple mode couplings and
decouplings make impossible an unambiguous definition of the threshold, unless
Landau damping is taken into account. To address this problem, instead of a
single instability threshold, an interval of thresholds is suggested, bounded
by the low and high intensity ones. For the broadband impedance model, the high
intensity threshold is shown to follow Zotter's scaling, but smaller by about a
factor of two. The same scaling, this time smaller than Zotter's by a factor of
four, is found for the ABS model (Air Bag Square well).


  A fake quadric is a smooth projective surface that has the same rational
cohomology as a smooth quadric surface but is not biholomorphic to one. We
provide an explicit classification of all irreducible fake quadrics according
to the commensurability class of their fundamental group. To accomplish this
task, we develop a number of new techniques that explicitly bound the
arithmetic invariants of a fake quadric and more generally of an arithmetic
manifold of bounded volume arising from a form of SL_2 over a number field.


  A new variant of Newton's method - named Backtracking New Q-Newton's method
(BNQN) - which has strong theoretical guarantee, is easy to implement, and has
good experimental performance, was recently introduced by the third author.
  Experiments performed previously showed some remarkable properties of the
basins of attractions for finding roots of polynomials and meromorphic
functions using BNQN. In particular, it seems that for finding roots of
polynomials of degree 2, the basins of attraction of the dynamics for BNQN are
the same as that for Newton's method (the latter is the classical Schr\"oder's
result in Complex Dynamics).
  In this paper, we show that indeed the picture we obtain when finding roots
of polynomials of degree 2 is the same as that in Sch\"oder's result, with a
remarkable difference: on the boundary line of the basins, the dynamics of
Newton's method is chaotic, while the dynamics of BNQN is more smooth. On the
way to proving the result, we show that BNQN (in any dimension) is invariant
under conjugation by linear operators of the form $A=cR$, where $R$ is unitary
and $c>0$ a constant. This again illustrates the similarity-difference relation
between BNQN and Newton's method.


  We present experimental results demonstrating that, relative to continuous
illumination, an increase of a factor of 3-10 in the photon efficiency of algal
photo-synthesis is attainable via the judicious application of pulsed light for
light intensities of practical interest (e.g., average-to-peak solar photon
flux). We also propose a simple model that can account for all the
measurements. The model (1) reflects the essential rate-limiting elements in
bio-productivity, (2) incorporates the impact of photon arrival-time statistics
and (3) accounts for how the enhancement in photon efficiency depends on the
timescales of light pulsing and photon flux density. The key is avoiding
clogging of the photosynthetic pathway by properly timing the light-dark cycles
experienced by algal cells. We show how this can be realized with pulsed light
sources, or by producing pulsed-light effects from continuous illumination via
turbulent mixing in dense algal cultures in thin photo-bioreactors.


  The paper is devoted to the exact controllability of a system of coupled
abstract wave equations when the control is exerted on a part of the boundary
by means of one control. We give a Kalman type condition and give a description
of the attainable set.


  There exists a growing body of observational evidence supporting a
non-vanishing cosmological constant at the present epoch. We examine the
possibility that such a term may arise directly from the potential energy which
drove an inflationary expansion of the very early universe. To avoid arbitrary
alterations in the shape of this potential at various epochs it is necessary to
introduce a time-dependent viscosity into the system. The evolution of the
effective Planck mass in scalar-tensor theories is a natural candidate for such
an effect. In these models there are observational constraints arising from
anisotropies in the cosmic microwave background, large-scale galactic
structure, observations of the primordial Helium abundance and solar system
tests of general relativity. Decaying power law and exponential potentials are
considered, but for these models it is very difficult to simultaneously satisfy
all of the limits. This may have implications for the joint evolution of the
gravitational and cosmological constants.


  Impurity-induced quasiparticle bound states on a pair-breaking surface of a
d-wave superconductor are theoretically described, taking into account
hybridization of impurity- and surface-induced Andreev states. Further a theory
for effects of surface disorder (of thin impurity surface layer) on the
low-bias conductance of tunnel junctions is developed. We find a threshold
$n_c$ for surface impurity concentration $n_S$, which separates the two regimes
for surface impurity effects on the zero-bias conductance peak (ZBCP). Below
the threshold, surface impurities do not broaden the ZBCP, but effectively
reduce its weight and generate impurity bands. For low $n_S$ impurity bands can
be, in principle, resolved experimentally, being centered at energies of bound
states induced by an isolated impurity on the surface. For larger $n_S$
impurity bands are distorted, move to lower energies and, beginning with the
threshold concentration $n_S=n_c$, become centered at zero energy. With
increasing $n_S$ above the threshold, the ZBCP is quickly destroyed in the case
of strong scatterers, while it is gradually suppressed and broaden in the
presence of weak impurity potentials. More realistic cases, taking into account
additional broadening, not related to the surface disorder, are also
considered.


  Zero-shot learning has been actively studied for image classification task to
relieve the burden of annotating image labels. Interestingly, semantic
segmentation task requires more labor-intensive pixel-wise annotation, but
zero-shot semantic segmentation has only attracted limited research interest.
Thus, we focus on zero-shot semantic segmentation, which aims to segment unseen
objects with only category-level semantic representations provided for unseen
categories. In this paper, we propose a novel Context-aware feature Generation
Network (CaGNet), which can synthesize context-aware pixel-wise visual features
for unseen categories based on category-level semantic representations and
pixel-wise contextual information. The synthesized features are used to
finetune the classifier to enable segmenting unseen objects. Furthermore, we
extend pixel-wise feature generation and finetuning to patch-wise feature
generation and finetuning, which additionally considers inter-pixel
relationship. Experimental results on Pascal-VOC, Pascal-Context, and
COCO-stuff show that our method significantly outperforms the existing
zero-shot semantic segmentation methods. Code is available at
https://github.com/bcmi/CaGNetv2-Zero-Shot-Semantic-Segmentation.


  A continuous-time quantum walk on a graph $G$ is given by the unitary matrix
$U(t) = \exp(-itA)$, where $A$ is the Hermitian adjacency matrix of $G$. We say
$G$ has pretty good state transfer between vertices $a$ and $b$ if for any
$\epsilon > 0$, there is a time $t$, where the $(a,b)$-entry of $U(t)$
satisfies $|U(t)_{a,b}| \ge 1-\epsilon$. This notion was introduced by Godsil
(2011). The state transfer is perfect if the above holds for $\epsilon = 0$. In
this work, we study a natural extension of this notion called universal state
transfer. Here, state transfer exists between every pair of vertices of the
graph. We prove the following results about graphs with this stronger property:
(1) Graphs with universal state transfer have distinct eigenvalues and flat
eigenbasis (where each eigenvector has entries which are equal in magnitude).
(2) The switching automorphism group of a graph with universal state transfer
is abelian and its order divides the size of the graph. Moreover, if the state
transfer is perfect, then the switching automorphism group is cyclic. (3) There
is a family of prime-length cycles with complex weights which has universal
pretty good state transfer. This provides a concrete example of an infinite
family of graphs with the universal property. (4) There exists a class of
graphs with real symmetric adjacency matrices which has universal pretty good
state transfer. In contrast, Kay (2011) proved that no graph with real-valued
adjacency matrix can have universal perfect state transfer. We also provide a
spectral characterization of universal perfect state transfer graphs that are
switching equivalent to circulants.


  Considerable efforts to measure and mitigate gender bias in recent years have
led to the introduction of an abundance of tasks, datasets, and metrics used in
this vein. In this position paper, we assess the current paradigm of gender
bias evaluation and identify several flaws in it. First, we highlight the
importance of extrinsic bias metrics that measure how a model's performance on
some task is affected by gender, as opposed to intrinsic evaluations of model
representations, which are less strongly connected to specific harms to people
interacting with systems. We find that only a few extrinsic metrics are
measured in most studies, although more can be measured. Second, we find that
datasets and metrics are often coupled, and discuss how their coupling hinders
the ability to obtain reliable conclusions, and how one may decouple them. We
then investigate how the choice of the dataset and its composition, as well as
the choice of the metric, affect bias measurement, finding significant
variations across each of them. Finally, we propose several guidelines for more
reliable gender bias evaluation.


  Visible light positioning(VLP) has gained prominence as a highly accurate
indoor positioning technique. Few techniques consider the practical limitations
of implementing VLP systems for indoor positioning. These limitations range
from having a single LED in the field of view(FoV) of the image sensor to not
having enough images for training deep learning techniques. Practical
implementation of indoor positioning techniques needs to leverage the ubiquity
of smartphones, which is the case with VLP using complementary metal oxide
semiconductor(CMOS) sensors. Images for VLP can be gathered only after the
lights in question have been installed making it a cumbersome process. These
limitations are addressed in the proposed technique, which uses simulated data
of a single LED to train machine learning models and test them on actual images
captured from a similar experimental setup. Such testing produced mean three
dimensional(3D) positioning error of 2.88 centimeters while training with real
images achieves accuracy of less than one centimeter compared to 6.26
centimeters of the closest competitor.


  In this paper, we present STAR, a new distributed in-memory database with
asymmetric replication. By employing a single-node non-partitioned architecture
for some replicas and a partitioned architecture for other replicas, STAR is
able to efficiently run both highly partitionable workloads and workloads that
involve cross-partition transactions. The key idea is a new phase-switching
algorithm where the execution of single-partition and cross-partition
transactions is separated. In the partitioned phase, single-partition
transactions are run on multiple machines in parallel to exploit more
concurrency. In the single-master phase, mastership for the entire database is
switched to a single designated master node, which can execute these
transactions without the use of expensive coordination protocols like two-phase
commit. Because the master node has a full copy of the database, this
phase-switching can be done at negligible cost. Our experiments on two popular
benchmarks (YCSB and TPC-C) show that high availability via replication can
coexist with fast serializable transaction execution in distributed in-memory
databases, with STAR outperforming systems that employ conventional concurrency
control and replication algorithms by up to one order of magnitude.


  Using longer spectra we re-analyze spectral properties of the two-body random
ensemble studied thirty years ago. At the center of the spectra the old results
are largely confirmed, and we show that the non-ergodicity is essentially due
to the variance of the lowest moments of the spectra. The longer spectra allow
to test and reach the limits of validity of French's correction for the number
variance. At the edge of the spectra we discuss the problems of unfolding in
more detail. With a Gaussian unfolding of each spectrum the nearest neighbour
spacing distribution between ground state and first exited state is shown to be
stable. Using such an unfolding the distribution tends toward a semi-Poisson
distribution for longer spectra. For comparison with the nuclear table ensemble
we could use such unfolding obtaining similar results as in the early papers,
but an ensemble with realistic splitting gives reasonable results if we just
normalize the spacings in accordance with the procedure used for the data.


  Twenty-four cores have been mapped in CS J = 7 -> 6 at the CSO. From the
spectra we determine core sizes and virial masses. Combining results from the
CS and dust continuum studies for M8E, we use Monte Carlo simulations for the
CS emission to determine radial profiles.


  Using the 2D Jordan-Wigner transformation we reformulate the square-lattice
s=1/2 XY (XZ) model in terms of noninteracting spinless fermions and examine
the ground-state and thermodynamic properties of this spin system. We consider
the model with two types of anisotropy: the spatial anisotropy interpolating
between 2D and 1D lattices and the anisotropy of the exchange interaction
interpolating between isotropic XY and Ising interactions. We compare the
obtained (approximate) results with exact ones (1D limit, square-lattice Ising
model) and other approximate ones (linear spin-wave theory and exact
diagonalization data for finite lattices of up to N=36 sites supplemented by
finite-size scaling). We discuss the ground-state and thermodynamic properties
in dependence on the spatial and exchange interaction anisotropies. We pay
special attention to the quantum phase transition driven by the exchange
interaction anisotropy as well as to the appearance/disappearance of the
zero-temperature magnetization in the quasi-1D limit.


  We report on the characteristics and design details of the Medium Scale
Anisotropy Measurement (MSAM), a millimeter-wave, balloon-borne telescope that
has been used to observe anisotropy in the Cosmic Microwave Background
Radiation (CMBR) on 0\fdg5 angular scales. The gondola is capable of
determining and maintaining absolute orientation to a few arcminutes during a
one-night flight. Emphasis is placed on the optical and pointing performance as
well as the weight and power budgets. We also discuss the total balloon/gondola
mechanical system. The pendulation from this system is a ubiquitous
perturbation on the pointing system. A detailed understanding in these areas is
needed for developing the next generation of balloon-borne instruments.


  We report determinations of the molybdenum abundances in five mildly to
extremely metal-poor turnoff stars using five Mo II lines near 2000A. In two of
the stars, the abundance of molybdenum is found to be extremely enhanced, as
high or higher than the neighboring even-Z elements ruthenium and zirconium. Of
the several nucleosynthesis scenarios envisioned for the production of nuclei
in this mass range in the oldest stars, a high-entropy wind acting in a
core-collapse supernova seems uniquely capable of the twin aspects of a high
molybdenum overproduction confined to a narrow mass range. Whatever the details
of the nucleosynthesis mechanism, however, this unusual excess suggests that
very few individual nucleosynthesis events were responsible for the synthesis
of the light trans-Fe heavy elements in these cases, an unexpected result given
that both are only moderately metal-poor.


  The relative Gromov seminorm is a finer invariant than stable commutator
length where a relative homology class is fixed. We show a duality result
between bounded cohomology and the relative Gromov seminorm, analogously to
Bavard duality for scl. We give an application to computations of scl in graphs
of groups. We also explain how our duality result can be given a purely
algebraic interpretation via a relative version of the Hopf formula. Moreover,
we show that this leads to a natural generalisation of a result of Calegari on
a connection between scl and the rotation quasimorphism.


  In this article, we study the ${1\over 2}^{\pm}$ and ${3\over 2}^{\pm}$
triply heavy baryon states in an systematic way by subtracting the
contributions from the corresponding ${1\over 2}^{\mp}$ and ${3\over 2}^{\mp}$
triply heavy baryon states with the QCD sum rules, and make reasonable
predictions for their masses.


  We construct a family of polynomials with real coefficients that contains as
a particular case the Fej\'er and Suffridge polynomials. These polynomials
allow us to suggest a robust algorithm to search for cycles of arbitrary length
in non-linear autonomous discrete dynamical systems. Numeric examples are
included.


  We calculate the normal state Nernst signal in the cuprates resulting from a
reconstruction of the Fermi surface due to spin density wave order. An order
parameter consistent with the reconstruction of the Fermi surface detected in
electron-doped materials is shown to sharply enhance the Nernst signal close to
optimal doping. Within a semiclassical treatment, the obtained magnitude and
position of the enhanced Nernst signal agrees with Nernst measurements in
electron-doped cuprates.


  An unconstrained nonlinear binary optimization problem of selecting a maximum
expected value subset of items is considered. Each item is associated with a
profit and probability. Each of the items succeeds or fails independently with
the given probabilities, and the profit is obtained in the event that all
selected items succeed. The objective is to select a subset that maximizes the
total value times the product of probabilities of the chosen items. The problem
is proven NP-hard by a nontrivial reduction from subset sum. Then we develop a
fully polynomial time approximation scheme (FPTAS) for this problem.


  We study the dynamical response of ultracold fermion-boson mixture in the
Bogoliubov regime, where the interactions between fermionic impurities and
bosonic excitations (phonons) are described by an effective Frohlich model
under the Bogoliubov approximation. A characteristic suppression of the
single-particle spectral weight is found in the small momentum region where the
impurity band and phonon mode intersect. Using diagrammatic technique we
compute the Bragg spectra as well as the momentum dependent force-force
correlation function. We fnd that both of them are heavily affected by the
spectral weight suppression effect at low impurity densities in both 1D and 2D
systems. We show that the the spectral weight suppression feature in Bragg
spectra, which was previously found in the quantum Monte Carlo simulations and
which cannot be recovered by the random phase approximation, can be accurately
reproduced with the help of vertex corrections.


  End-to-end ASR models trained on large amount of data tend to be implicitly
biased towards language semantics of the training data. Internal language model
estimation (ILME) has been proposed to mitigate this bias for autoregressive
models such as attention-based encoder-decoder and RNN-T. Typically, ILME is
performed by modularizing the acoustic and language components of the model
architecture, and eliminating the acoustic input to perform log-linear
interpolation with the text-only posterior. However, for CTC-based ASR, it is
not as straightforward to decouple the model into such acoustic and language
components, as CTC log-posteriors are computed in a non-autoregressive manner.
In this work, we propose a novel ILME technique for CTC-based ASR models. Our
method iteratively masks the audio timesteps to estimate a pseudo
log-likelihood of the internal LM by accumulating log-posteriors for only the
masked timesteps. Extensive evaluation across multiple out-of-domain datasets
reveals that the proposed approach improves WER by up to 9.8% and OOV F1-score
by up to 24.6% relative to Shallow Fusion, when only text data from target
domain is available. In the case of zero-shot domain adaptation, with no access
to any target domain data, we demonstrate that removing the source domain bias
with ILME can still outperform Shallow Fusion to improve WER by up to 9.3%
relative.


  We theoretically investigate Rydberg atom pair potentials of Rb atoms in
front of a perfectly conducting plate. The pair potentials are perturbed by
both the Casimir--Polder potential acting on a single atom and the scattering
contribution to the interatomic interaction. In contrast to the pair potentials
in free space, at atom-surface distances $d_s \lesssim 4\,\mu$m, avoided
crossings appear. In the associated potential wells that are entirely due to
dispersion interactions with the surface, there exist vibrational bound states,
i.e. Rydberg macrodimers, with groundstate energies of up to $E = -68\,$MHz and
radial expectation values of the order of several $\mu$m.


  We compute the effective action and correlators of the Polyakov loop operator
in the Schwinger model at finite temperature and discuss the realization of the
discrete symmetries that occur there. We show that, due to nonlocal effects of
massless fermions in two spacetime dimensions, the discrete symmetry which
governs the screening of charges is spontaneously broken even in an effective
one-dimensional model, when the volume is infinite. In this limit, the thermal
state of the Schwinger model screens an arbitrary external charge; consequently
the model is in the deconfined phase, with the charge of the deconfined
fermions completely screened. In a finite volume we show that the Schwinger
model is always confining.


  We review the current status of the facility instrumentation for the Large
Binocular Telescope (LBT). The LBT has 2x 8.4m primary mirrors on a single
mount with an effective collecting area of 11.8m or 23m when
interferometrically combined. The facility instruments are: 1) the Large
Binocular Cameras (LBCs), each with a 23'x25' field of view (FOV). The blue and
red optimized optical LBCs are mounted at the prime focus of the left and right
primary mirrors, respectively. The filter suite of the two LBCs covers
0.3-1.1{\mu}m, including the new TiO (0.78{\mu}m) and CN (0.82{\mu}m) filters;
2) the Multi-Object Double Spectrograph (MODS), two identical optical
spectrographs each mounted at a straight through f/15 Gregorian mount. MODS-1 &
-2 can do imaging with Sloan filters and medium resolution (R~2000)
spectroscopy, each with 24 interchangeable masks (multi-object or longslit)
over a 6'x6' FOV. Each MODS is capable of blue (0.32-0.6{\mu}m) and red
(0.5-1.05{\mu}m) wavelength only coverage or, using a dichroic, 0.32-1.05{\mu}m
coverage; and 3) the two LBT Utility Camera in the Infrared instruments
(LUCIs), each mounted at a bent-front Gregorian f/15 port. LUCI-1 & 2 are
designed for seeing-limited (4'x4'FOV) and AO (0.5'x0.5' FOV) imaging &
spectroscopy over 0.95-2.5{\mu}m with spectroscopic resolutions of R~400-11000,
including 32 interchangeable cryogenically cooled masks. All facility
instruments are on the LBT and, for the first time, have been on-sky for
science. We also report on the first science use of "mixed-mode" (differently
paired instruments). While both primary mirrors reside on a single fixed mount,
they are capable of operating independently within a defined "co-pointing"
limit. This provides users with the additional capability to independently
dither each mirror or center observations on two different sets of spatial
coordinates within this limit. (ABRIDGED)


  We present a greedy-based approach to construct an efficient single hidden
layer neural network with the ReLU activation that approximates a target
function. In our approach we obtain a shallow network by utilizing a greedy
algorithm with the prescribed dictionary provided by the available training
data and a set of possible inner weights. To facilitate the greedy selection
process we employ an integral representation of the network, based on the
ridgelet transform, that significantly reduces the cardinality of the
dictionary and hence promotes feasibility of the greedy selection. Our approach
allows for the construction of efficient architectures which can be treated
either as improved initializations to be used in place of random-based
alternatives, or as fully-trained networks in certain cases, thus potentially
nullifying the need for backpropagation training. Numerical experiments
demonstrate the tenability of the proposed concept and its advantages compared
to the conventional techniques for selecting architectures and initializations
for neural networks.


  We study the effect of multiple parton radiation to Higgs boson plus jet
production at the LHC, by applying the transverse momentum dependent (TMD)
factorization formalism to resum large logarithmic contributions to all orders
in the expansion of the strong interaction coupling. We show that the
appropriate resummation scale should be the jet transverse momentum, rather
than the partonic center of mass energy which has been normally used in the TMD
resummation formalism. Furthermore, the transverse momentum distribution of the
Higgs boson, particularly near the lower cut-off applied on the jet transverse
momentum, can only be reliably predicted by the resummation calculation which
is free of the so-called Sudakov-shoulder singularity problem, present in
fixed-order calculations.


  Many computer vision applications require robust and efficient estimation of
camera geometry. The robust estimation is usually based on solving camera
geometry problems from a minimal number of input data measurements, i.e.
solving minimal problems in a RANSAC framework. Minimal problems often result
in complex systems of polynomial equations. Many state-of-the-art efficient
polynomial solvers to these problems are based on Gr\"obner bases and the
action-matrix method that has been automatized and highly optimized in recent
years. In this paper we study an alternative algebraic method for solving
systems of polynomial equations, i.e., the sparse resultant-based method and
propose a novel approach to convert the resultant constraint to an eigenvalue
problem. This technique can significantly improve the efficiency and stability
of existing resultant-based solvers. We applied our new resultant-based method
to a large variety of computer vision problems and show that for most of the
considered problems, the new method leads to solvers that are the same size as
the the best available Gr\"obner basis solvers and of similar accuracy. For
some problems the new sparse-resultant based method leads to even smaller and
more stable solvers than the state-of-the-art Gr\"obner basis solvers. Our new
method can be fully automatized and incorporated into existing tools for
automatic generation of efficient polynomial solvers and as such it represents
a competitive alternative to popular Gr\"obner basis methods for minimal
problems in computer vision.


  In this paper we show how in the most natural extension of gravity with
torsion, fermion fields are endowed with running coupling in spinorial
interaction, that are shown to reproduce the strength and structure of the
leptonic weak forces.


  We propose a novel search-and-rescue management method that relies on the
aerial deployment of Wireless Sensor Network (WSN) for locating victims after
floods. The sensor nodes will collect vital information such as heat signatures
for detecting human presence and location, the flow of flood. The sensor
modules are packed in a portable floating buoy with a user interface to convey
emergency messages to the base station. Sensor nodes are designed based on
disaster conditions, cost-effectiveness and deployed in the affected region by
a centrifugal dispersion system from a helicopter.
  A mobile ad-hoc network is set up by modifying the Low Energy Adaptive
Cluster Hierarchy (LEACH) protocol for greater efficiency and adoption of
multi-hop of Cluster Heads for long-distance communication to Base Station. The
model metrics have been defined considering previous rural floods in India. The
efficiency and power characteristics of the network are compared to other
protocols via simulations. The sensor data from the network makes resource
management, rescue planning and emergency priority more efficient, thus saving
more lives from floods.


  The renormalization group transformation for extreme value statistics of
independent, identically distributed variables, recently introduced to describe
finite size effects, is presented here in terms of a partial differential
equation (PDE). This yields a flow in function space and gives rise to the
known family of Fisher-Tippett limit distributions as fixed points, together
with the universal eigenfunctions around them. The PDE turns out to handle
correctly distributions even having discontinuities. Remarkably, the PDE admits
exact solutions in terms of eigenfunctions even farther from the fixed points.
In particular, such are unstable manifolds emanating from and returning to the
Gumbel fixed point, when the running eigenvalue and the perturbation strength
parameter obey a pair of coupled ordinary differential equations. Exact
renormalization trajectories corresponding to linear combinations of
eigenfunctions can also be given, and it is shown that such are all solutions
of the PDE. Explicit formulas for some invariant manifolds in the Fr\'echet and
Weibull cases are also presented. Finally, the similarity between
renormalization flows for extreme value statistics and the central limit
problem is stressed, whence follows the equivalence of the formulas for Weibull
distributions and the moment generating function of symmetric L\'evy stable
distributions.


  The temperature and magnetic field dependent activation energy, U(T, H), is
one of the most important parameter in the field of applied superconductivity
as it primarily determines both the crtical current density and the
irreversibility field. Previously, we have determined the doping dependent U(T,
H) from the analysis of field dependent resistive transitions in high-quality
c-axis oriented crytalline thin films of Y123 (arXiv:1207.4312). In this short
communication, we have showed a direct link between the characteristic field Ho
that sets the magnitude of U(T, H) and the pseudogap temperature, T*. The
strong dependence of Ho on the in-plane hole content, p, seems to follow from
the p-dependent evolution of the pseudogap energy scale (T*, when expressed in
temperature) which reduces the superconducting condensation energy as hole
concentration decreases.


  We study upper bounds on the growth of operator entropy $S_K$ in operator
growth. Using uncertainty relation, we first prove a dispersion bound on the
growth rate $|\partial_t S_K|\leq 2b_1 \Delta S_K$, where $b_1$ is the first
Lanczos coefficient and $\Delta S_K$ is the variance of $S_K$. However, for
irreversible process, this bound generally turns out to be too loose at long
times. We further find a tighter bound in the long time limit using a universal
logarithmic relation between Krylov complexity and operator entropy. The new
bound describes the long time behavior of operator entropy very well for
physically interesting cases, such as chaotic systems and integrable models.


  In this work, we aim to analyze how attitude, self-efficacy, and altruism
influence effort and active contributions on Wikipedia. We propose a new
conceptual model based on the theory of planned behavior and findings from the
literature on online communities. This model differs from other models that
have been previously proposed by considering altruism in its various facets
(identification, reciprocity, and reputation), and by treating effort as a
factor prior to performance results, which is measured in terms of active
contributions, according to the organizational literature. To fulfill the study
specific objectives, Wikipedia surveyed community members and collected
secondary data. After excluding outliers, we obtained a final sample with 212
participants. We applied exploratory factor analysis and structural equation
modeling, which resulted in a model with satisfactory fit indices. The results
indicate that effort influences active contributions, and attitude, altruism by
reputation, and altruism by identification influence effort. None of the
proposed factors are directly related to active contributions. Experience
directly influences self-efficacy while it positively moderates the relation
between effort and active contributions. Finally, we present the conclusions
via several implications for the literature as well as suggestions for future
research.


  The purpose of this paper is to describe the relation between the Legendre
and the Lenstra constants. Indeed we show that they are equal whenever the
Legendre constant exists; in particular, this holds for both Rosen continued
fractions and $\alpha$-continued fractions. We also give the explicit value of
the entropy of the Rosen map with respect to the absolutely continuous
invariant probability measure.


  We extend the notions of "$R_\infty$-property" and "full (extended)
Reidemeister spectrum" to finite groups in a meaningful way. We provide
examples of finite groups admitting these properties, if they exist, by looking
at groups of small order as well as (quasi)simple groups.


  Data are often sampled irregularly in time. Dealing with this using Recurrent
Neural Networks (RNNs) traditionally involved ignoring the fact, feeding the
time differences as additional inputs, or resampling the data. All these
methods have their shortcomings. We propose an elegant straightforward
alternative approach where instead the RNN is in effect resampled in time to
match the time of the data or the task at hand. We use Echo State Network (ESN)
and Gated Recurrent Unit (GRU) as the basis for our solution. Such RNNs can be
seen as discretizations of continuous-time dynamical systems, which gives a
solid theoretical ground to our approach. Our Task-Synchronized ESN (TSESN) and
GRU (TSGRU) models allow for a direct model time setting and require no
additional training, parameter tuning, or computation (solving differential
equations or interpolating data) compared to their regular counterparts, thus
retaining their original efficiency. We confirm empirically that our models can
effectively compensate for the time-non-uniformity of the data and demonstrate
that they compare favorably to data resampling, classical RNN methods, and
alternative RNN models proposed to deal with time irregularities on several
real-world nonuniform-time datasets. We open-source the code at
https://github.com/oshapio/task-synchronized-RNNs .


  Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced
(LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity
arising from accumulation of contrast agent in infarcted myocardium. In this
paper, we present a comprehensive framework for automatic 3D segmentation of
the LV in LGE CMR images. Given myocardial contours in cine images as a priori
knowledge, the framework initially propagates the a priori segmentation from
cine to LGE images via 2D translational registration. Two meshes representing
respectively endocardial and epicardial surfaces are then constructed with the
propagated contours. After construction, the two meshes are deformed towards
the myocardial edge points detected in both short-axis and long-axis LGE images
in a unified 3D coordinate system. Taking into account the intensity
characteristics of the LV in LGE images, we propose a novel parametric model of
the LV for consistent myocardial edge points detection regardless of
pathological status of the myocardium (infarcted or healthy) and of the type of
the LGE images (short-axis or long-axis). We have evaluated the proposed
framework with 21 sets of real patient and 4 sets of simulated phantom data.
Both distance- and region-based performance metrics confirm the observation
that the framework can generate accurate and reliable results for myocardial
segmentation of LGE images. We have also tested the robustness of the framework
with respect to varied a priori segmentation in both practical and simulated
settings. Experimental results show that the proposed framework can greatly
compensate variations in the given a priori knowledge and consistently produce
accurate segmentations.


  Non-parametric reconstruction methods, such as Gaussian process (GP)
regression, provide a model-independent way of estimating an underlying
function and its uncertainty from noisy data. We demonstrate how
GP-reconstruction can be used as a consistency test between a given data set
and a specific model by looking for structures in the residuals of the data
with respect to the model's best-fit. Applying this formalism to the Planck
temperature and polarisation power spectrum measurements, we test their global
consistency with the predictions of the base $\Lambda$CDM model. Our results do
not show any serious inconsistencies, lending further support to the
interpretation of the base $\Lambda$CDM model as cosmology's gold standard.


  We present a new metric of link cohesion for measuring the strength of edges
in complex, highly connected graphs. Link cohesion accounts for local small hop
connections and associated node degrees and can be used to support edge scoring
and graph simplification. We also present a novel graph density measure to
estimate the average cohesion across nodes. Link cohesion and the density
measure are employed to demonstrate community detection through graph
sparsification by maximizing graph density. Link cohesion is also shown to be
loosely correlated with edge betweenness centrality.


  A natural composition $\odot$ on all pages of the lower central series
spectral sequence for spheres is defined. Moreover, it is defined for $p$-lower
central series spectral sequence of a simplicial group. It is proved that $r$th
differential satisfies a "Leibiz rule with suspension": $d^r(a\odot \sigma
b)=\pm d^ra\odot b+a\odot d^r\sigma b,$ where $\sigma$ is the suspension
homomorphism.


  We generalize recent results on tachyon condensation in boundary string field
theory to the superstring.


  We consider the exclusive pseudoscalar heavy-quarkonium (eta_{b,c})
production in association with a photon at future lepton colliders where the
collider energies of O(10^2) GeV are far greater than the quarkonium mass. At
these energies, the logarithm of mass to collision energy becomes increasingly
large hence its resummation becomes particularly important. By making use of
the light-cone-distribution factorization formula, we resum the logarithms up
to next-to-leading-logarithmic accuracy (NLL) that corresponds to order-alpha_s
accuracy. We combine the resummed result with a known fixed-order result at
next-to-leading order (NLO) such that both resummed-logarithmic terms and
non-logarithmic terms are included at the same order in alpha_s. This allowed
us to provide reliable predictions at accuracies of order alpha_s ranging from
relatively low energies near quarkonium mass to the collider energies of
O(10^2) GeV. We also include the leading relativistic corrections resummed at
leading-logarithmic accuracy. Our prediction at the Belle energy is comparable
with fixed-order predictions in literatures while it shows a large deviation
from a recent Belle's upper limit by about 4 sigma. Finally, we make
predictions for the energies of future Z and Higgs factories.


  We study radial vibrations of spherically symmetric skyrmions in the BPS
Skyrme model. Concretely, we numerically solve the linearised field equations
for small fluctuations in a skyrmion background, both for linearly stable
oscillations and for (unstable) resonances. This is complemented by numerical
solutions of the full nonlinear system, which confirm all the results of the
linear analysis. In all cases, the resulting fundamental excitation provides a
rather accurate value for the Roper resonance, supporting the hypothesis that
the BPS Skyrme model already gives a reasonable approximate description of this
resonance. Further, for many potentials additional higher resonances appear,
again in agreement with known experimental results.


  We study the influence of degree correlations or network mixing in
interdependent security. We model the interdependence in security among agents
using a dependence graph and employ a population game model to capture the
interaction among many agents when they are strategic and have various security
measures they can choose to defend themselves. The overall network security is
measured by what we call the average risk exposure (ARE) from neighbors, which
is proportional to the total (expected) number of attacks in the network.
  We first show that there exists a unique pure-strategy Nash equilibrium of a
population game. Then, we prove that as the agents with larger degrees in the
dependence graph see higher risks than those with smaller degrees, the overall
network security deteriorates in that the ARE experienced by agents increases
and there are more attacks in the network. Finally, using this finding, we
demonstrate that the effects of network mixing on ARE depend on the (cost)
effectiveness of security measures available to agents; if the security
measures are not effective, increasing assortativity of dependence graph
results in higher ARE. On the other hand, if the security measures are
effective at fending off the damages and losses from attacks, increasing
assortativity reduces the ARE experienced by agents.


  We present a concept for a 4000-fibre positioner for DESpec, based on the
Echidna 'tilting spine' technology. The DESpec focal plane is 450mm across and
curved, and the required pitch is ~6.75mm. The size, number of fibers and
curvature are all comparable with various concept studies for similar
instruments already undertaken at the AAO, but present new challenges in
combination. A simple, low-cost, and highly modular design is presented,
consisting of identical modules populated by identical spines. No show-stopping
issues in accommodating either the curvature or the smaller pitch have been
identified, and the actuators consist largely of off-the-shelf components. The
actuators have been prototyped at AAO, and allow reconfiguration times of ~15s
to reach position errors 7 microns or less. Straightforward designs for
metrology, acquisition, and guiding are also proposed. The throughput losses of
the entire positioner system are estimated to be ~15%, of which 6.3% is
attributable to the tilting-spine technology.


  We have designed, fabricated and operated a scalable system for applying
independently programmable time-independent, and limited time-dependent flux
biases to control superconducting devices in an integrated circuit. Here we
report on the operation of a system designed to supply 64 flux biases to
devices in a circuit designed to be a unit cell for a superconducting adiabatic
quantum optimization system. The system requires six digital address lines, two
power lines, and a handful of global analog lines.


  The utility of a continuous beam of He droplets for the assembly and surface
deposition of Ag clusters, <N(Ag)> ~ 300 - 6 000, is studied with transmission
electron microscopy. Images of the clusters on amorphous carbon substrates
obtained at short deposition times have provided for a measure of the size
distribution of the metal clusters. The average sizes of the deposited clusters
are in good agreement with an energy balance based estimate of Ag cluster
growth in He droplets. Measurements of the deposition rate indicate that upon
impact with the surface the He-embedded cluster is attached with high
probability. The stability of the deposited clusters on the substrate is
discussed.


  Using a spontaneous parametric-downconversion source of photon pairs, we are
working towards the creation of arbitrary 2-qubit quantum states with high
fidelity. Currently, all physically allowable combinations of polarization
entanglement and mixture can be produced, including maximally-entangled mixed
states. The states are experimentally measured and refined via
computer-automated quantum-state tomography, and this system has also been used
to perform single-qubit and ancilla-assisted quantum process tomography.


  Convolutional neural networks (CNNs) have gained widespread usage across
various fields such as weather forecasting, computer vision, autonomous
driving, and medical image analysis due to its exceptional ability to extract
spatial information, share parameters, and learn local features. However, the
practical implementation and commercialization of CNNs in these domains are
hindered by challenges related to model sizes, overfitting, and computational
time. To address these limitations, our study proposes a groundbreaking
approach that involves tensorizing the dense layers in the CNN to reduce model
size and computational time. Additionally, we incorporate attention layers into
the CNN and train it using Contrastive self-supervised learning to effectively
classify cloud information, which is crucial for accurate weather forecasting.
We elucidate the key characteristics of tensorized neural network (TNN),
including the data compression rate, accuracy, and computational speed. The
results indicate how TNN change their properties under the batch size setting.


  We investigate properties of a bootstrap-based methodology for testing
hypotheses about equality of certain characteristics of the distributions
between different populations in the context of functional data. The suggested
testing methodology is simple and easy to implement. It resamples the original
dataset in such a way that the null hypothesis of interest is satisfied and it
can be potentially applied to a wide range of testing problems and test
statistics of interest. Furthermore, it can be utilized to the case where more
than two populations of functional data are considered. We illustrate the
bootstrap procedure by considering the important problems of testing the
equality of mean functions or the equality of covariance functions (resp.
covariance operators) between two populations. Theoretical results that justify
the validity of the suggested bootstrap-based procedure are established.
Furthermore, simulation results demonstrate very good size and power
performances in finite sample situations, including the case of testing
problems and/or sample sizes where asymptotic considerations do not lead to
satisfactory approximations. A real-life dataset analyzed in the literature is
also examined.


  We search for hidden mirror symmetries at large angular scales in the WMAP 7
year Internal Linear Combination map of CMB temperature anisotropies using
global pixel based estimators introduced for this aim. Two different axes are
found for which the CMB intensity pattern is anomalously symmetric (or
anti-symmetric) under reflection with respect to orthogonal planes at the
99.84(99.96)% CL (confidence level), if compared to a result for an arbitrary
axis in simulations without the symmetry. We have verified that our results are
robust to the introduction of the galactic mask. The direction of such axes is
close to the CMB kinematic dipole and nearly orthogonal to the ecliptic plane,
respectively. If instead the real data are compared to those in simulations
taken with respect to planes for which the maximal mirror symmetry is generated
by chance, the confidence level decreases to 92.39 (76.65)%. But when the
effect in question translates into the anomalous alignment between normals to
planes of maximal mirror (anti)-symmetry and these natural axes mentioned. We
also introduce the representation of the above estimators in the harmonic
domain, confirming the results obtained in the pixel one. The symmetry anomaly
is shown to be almost entirely due to low multipoles, so it may have a
cosmological and even primordial origin. Contrary, the anti-symmetry one is
mainly due to intermediate multipoles that probably suggests its
non-fundamental nature. We have demonstrated that these anomalies are not
connected to the known issue of the low variance in WMAP observations and we
have checked that axially symmetric parts of these anomalies are small, so that
the axes are not the symmetry ones.


  In an era of increasing pressure to achieve sustainable agriculture, the
optimization of livestock feed for enhancing yield and minimizing environmental
impact is a paramount objective. This study presents a pioneering approach
towards this goal, using rumen microbiome data to predict the efficacy of feed
additives in dairy cattle.
  We collected an extensive dataset that includes methane emissions from 2,190
Holstein cows distributed across 34 distinct sites. The cows were divided into
control and experimental groups in a double-blind, unbiased manner, accounting
for variables such as age, days in lactation, and average milk yield. The
experimental groups were administered one of four leading commercial feed
additives: Agolin, Kexxtone, Allimax, and Relyon. Methane emissions were
measured individually both before the administration of additives and over a
subsequent 12-week period. To develop our predictive model for additive
efficacy, rumen microbiome samples were collected from 510 cows from the same
herds prior to the study's onset. These samples underwent deep metagenomic
shotgun sequencing, yielding an average of 15.7 million reads per sample.
Utilizing innovative artificial intelligence techniques we successfully
estimated the efficacy of these feed additives across different farms. The
model's robustness was further confirmed through validation with independent
cohorts, affirming its generalizability and reliability.
  Our results underscore the transformative capability of using targeted feed
additive strategies to both optimize dairy yield and milk composition, and to
significantly reduce methane emissions. Specifically, our predictive model
demonstrates a scenario where its application could guide the assignment of
additives to farms where they are most effective. In doing so, we could achieve
an average potential reduction of over 27\% in overall emissions.


  Kerr lens-mode-locked Ti:Sapphire lasers are known to display three
coexistent modes of operation, that can be described as: continuous wave (CW),
transform limited pulses (P1) and positive chirped pulses (P2). Optical rogue
waves, in the form of pulses of high energy appearing much often than expected
in a Gaussian distribution, are observed in the chaotic regime of the mode P2,
but not of P1. These high energy pulses appear in an unpredictable way, but it
is observed that their separation (if measured in number of round trips) can
take only some definite values, which received the name of "magic numbers". The
existence of optical rogue waves in P2 and not in P1, and also of the magic
numbers, are correctly reproduced by a numerical simulation based on a
five-variables iterative map. But, a successful numerical simulation provides
limited insight on the physical causes of the observed phenomena. We present
evidence that optical rogue waves in this laser follow a modulational
instability, and that an initial condition in P1 rapidly evolves into P2 if the
parameters' values are beyond that instability's threshold. The magic numbers
are residuals of the periodic orbits of the "cold" cavity when it is perturbed
by the opposite effects of a dissipative term, due to the presence of
transversal apertures, and an expansive term, due to the Kerr effect.


  Silver films were deposited at room temperature onto H-passivated Si(111)
surfaces. Their electronic properties have been analyzed by angle-resolved
photoelectron spectroscopy. Submonolayer films were semiconducting and the
onset of metallization was found at a Ag coverage of $\sim$0.6 monolayers. Two
surface states were observed at $\bar{\Gamma}$-point in the metallic films,
with binding energies of 0.1 and 0.35 eV. By measurements of photoelectron
angular distribution at the Fermi level in these films, a cross-sectional cut
of the Fermi surface was obtained. The Fermi vector determined along different
symmetry directions and the photoelectron lifetime of states at the Fermi level
are quite close to those expected for Ag single crystal. In spite of this
concordance, the Fermi surface reflects a sixfold symmetry rather than the
threefold symmetry of Ag single crystal. This behavior was attributed to the
fact that these Ag films are composed by two domains rotated 60$^o$.


  As an extension to the paper by Breuer, Grinshpon, and White \cite{B}, we
study the linear statistics for the eigenvalues of the Schr\"odinger operator
with random decaying potential with order ${\cal O}(x^{-\alpha})$ ($\alpha>0$)
at infinity. We first prove similar statements as in \cite{B} for the trace of
$f(H)$, where $f$ belongs to a class of analytic functions : there exists a
critical exponent $\alpha_c$ such that the fluctuation of the trace of $f(H)$
converges in probability for $\alpha > \alpha_c$, and satisfies a CLT statement
for $\alpha \le \alpha_c$, where $\alpha_c$ differs depending on $f$.
Furthermore we study the asymptotic behavior of its expectation value.


  4U 1954+319 was discovered 25 years ago, but only recently has a clear
picture of its nature begun to emerge. We present for the first time a
broad-band spectrum of the source and a detailed timing study using more than
one year of monitoring data. The timing and spectral analysis was done using
publicly available Swift, INTEGRAL, BeppoSAX, and RXTE/ASM data in the 0.7-150
keV energy band. The source spectrum is described well by a highly absorbed
(N_H~10^23 cm^-2) power law with a high-energy exponential cutoff around 15
keV. An additional black body component is needed below 3 keV to account for a
soft excess. The derived ~5 hr periodicity, with a spin-up timescale of ~25
years, could be identified as the neutron star spin period. The spectral and
timing characteristics indicate that we are dealing both with the slowest
established wind-accreting X-ray pulsar and with the second confirmed member of
the emerging class dubbed "symbiotic low mass X-ray binaries" to host a neutron
star.


  A group of infinite products over low-order rational polynomials evaluated at
the sequence of prime numbers is loosely called the Hardy-Littlewood constants.
In this manuscript we look at them as factors embedded in a super-product over
primes, semiprimes, 3-almost primes etc. Numerical tables are derived by
transformation into series over k-almost prime zeta functions. Alternative
product representations in a basis of k-almost prime products associated with
Euler's formula for the Riemann zeta function are also pointed out.


  Deep neural networks (DNN) trained in a supervised way suffer from two known
problems. First, the minima of the objective function used in learning
correspond to data points (also known as rubbish examples or fooling images)
that lack semantic similarity with the training data. Second, a clean input can
be changed by a small, and often imperceptible for human vision, perturbation,
so that the resulting deformed input is misclassified by the network. These
findings emphasize the differences between the ways DNN and humans classify
patterns, and raise a question of designing learning algorithms that more
accurately mimic human perception compared to the existing methods.
  Our paper examines these questions within the framework of Dense Associative
Memory (DAM) models. These models are defined by the energy function, with
higher order (higher than quadratic) interactions between the neurons. We show
that in the limit when the power of the interaction vertex in the energy
function is sufficiently large, these models have the following three
properties. First, the minima of the objective function are free from rubbish
images, so that each minimum is a semantically meaningful pattern. Second,
artificial patterns poised precisely at the decision boundary look ambiguous to
human subjects and share aspects of both classes that are separated by that
decision boundary. Third, adversarial images constructed by models with small
power of the interaction vertex, which are equivalent to DNN with rectified
linear units (ReLU), fail to transfer to and fool the models with higher order
interactions. This opens up a possibility to use higher order models for
detecting and stopping malicious adversarial attacks. The presented results
suggest that DAM with higher order energy functions are closer to human visual
perception than DNN with ReLUs.


  The lack of accurate low-energy electron scattering cross sections for liquid
water is a substantial source of uncertainty in the modelling of radiation
chemistry and biology. The use of existing amorphous ice scattering cross
sections for lack of liquid data has been discussed controversially over
decades. Here, we compare experimental photoemission data of liquid water with
corresponding predictions using amorphous ice cross sections, with the aim of
resolving the debate regarding the difference of electron scattering in liquid
water and amorphous ice. We find very similar scattering properties in the
liquid and the ice for electron kinetic energies up to a few hundred electron
volts. The scattering cross sections recommended here for liquid water are an
extension of the amorphous ice cross sections. Within the framework of
currently available experimental data, our work answers one of the most debated
questions regarding electron scattering in liquid water.


  We find a new formula for the orthonormal polynomials corresponding to a
measure mu on the unit circle whose Verblunsky coefficients are periodic. The
formula is presented using the Chebyshev polynomials of the second kind and the
discriminant of the periodic sequence. We present several applications
including a resolution of a problem suggested by Simon in 2006 regarding the
existence of singular points in the bands of the support of the measure and a
universality result at all points of the essential support of mu.


  A new method, 'Configurationally-Resolved-Super-Transition-Arrays', for
calculation of the spectral absorption coefficient in hot plasmas is presented.
In the new method, the spectrum of each Super-Transition-Array is evaluated as
the Fourier transform of a single Complex Pseudo Partition Function, which
represents the exact analytical sum of the contributions of all constituting
unresolved transition arrays sharing the same set of one-electron solutions.
Thus, in the new method, the spectrum of each Super-Transition-Array is
resolved down to the level of the (unresolved) transition arrays. It is shown
that the corresponding spectrum, evaluated by the traditional
Super-Transition-Arrays (STA) method [A. Bar Shalom, J. Oreg, W.H. Goldstein,
D. Shvarts and A. Zigler, Phys. Rev. A 40, 3183 (1989)], is just the coarse
grained Gaussian approximation of the
Configurationally-Resolved-Super-Transition-Array. A new computer program is
presented, capable of evaluating the absorption coefficient by both the new
configurationally resolved and the traditional Gaussian Super-Transition-Arrays
methods. A numerical example of gold at temperature 1keV and density 0.5
gr/cm^{3}, is presented, demonstrating the simplicity, efficiency and accuracy
of the new method.


  Energy resolution, alpha/beta ratio, pulse-shape discrimination for gamma
rays and alpha particles, temperature dependence of scintillation properties,
and radioactive contamination were studied with CaMoO4 crystal scintillators. A
high sensitivity experiment to search for neutrinoless double beta decay of
100-Mo by using CaMoO4 scintillators is discussed.


  Learning algorithms for energy based Boltzmann architectures that rely on
gradient descent are in general computationally prohibitive, typically due to
the exponential number of terms involved in computing the partition function.
In this way one has to resort to approximation schemes for the evaluation of
the gradient. This is the case of Restricted Boltzmann Machines (RBM) and its
learning algorithm Contrastive Divergence (CD). It is well-known that CD has a
number of shortcomings, and its approximation to the gradient has several
drawbacks. Overcoming these defects has been the basis of much research and new
algorithms have been devised, such as persistent CD. In this manuscript we
propose a new algorithm that we call Weighted CD (WCD), built from small
modifications of the negative phase in standard CD. However small these
modifications may be, experimental work reported in this paper suggest that WCD
provides a significant improvement over standard CD and persistent CD at a
small additional computational cost.


  We investigate error propagation in sliding window decoding of braided
convolutional codes (BCCs). Previous studies of BCCs have focused on iterative
decoding thresholds, minimum distance properties, and their bit error rate
(BER) performance at small to moderate frame length. Here, we consider a
sliding window decoder in the context of large frame length or one that
continuously outputs blocks in a streaming fashion. In this case, decoder error
propagation, due to the feedback inherent in BCCs, can be a serious problem.In
order to mitigate the effects of error propagation, we propose several schemes:
a \emph{window extension algorithm} where the decoder window size can be
extended adaptively, a resynchronization mechanism where we reset the encoder
to the initial state, and a retransmission strategy where erroneously decoded
blocks are retransmitted. In addition, we introduce a soft BER stopping rule to
reduce computational complexity, and the tradeoff between performance and
complexity is examined. Simulation results show that, using the proposed window
extension algorithm, resynchronization mechanism, and retransmission strategy,
the BER performance of BCCs can be improved by up to four orders of magnitude
in the signal-to-noise ratio operating range of interest, and in addition the
soft BER stopping rule can be employed to reduce computational complexity.


  In 2001, Rama Cont introduced a now-widely used set of 'stylized facts' to
synthesize empirical studies of financial price changes (returns), resulting in
11 statistical properties common to a large set of assets and markets. These
properties are viewed as constraints a model should be able to reproduce in
order to accurately represent returns in a market. It has not been established
whether the characteristics Cont noted in 2001 still hold for modern markets
following significant regulatory shifts and technological advances. It is also
not clear whether a given time series of financial returns for an asset will
express all 11 stylized facts. We test both of these propositions by attempting
to replicate each of Cont's 11 stylized facts for intraday returns of the
individual stocks in the Dow 30, using the same authoritative data as that used
by the U.S. regulator from October 2018 - March 2019. We find conclusive
evidence for eight of Cont's original facts and no support for the remaining
three. Our study represents the first test of Cont's 11 stylized facts against
a consistent set of stocks, therefore providing insight into how these stylized
facts should be viewed in the context of modern stock markets.


  The perturbative calculation of the lifetime of charged excitations in
ultrarelativistic plasmas is plagued with infrared divergences which are not
eliminated by the screening corrections. The physical processes responsible for
these divergences are the collisions involving the exchange of longwavelength,
quasistatic, magnetic gluons (or photons), which are not screened by plasma
effects. In QED, the leading divergences can be resummed in a non-perturbative
treatement based on a generalization of the Bloch-Nordsieck model at finite
temperature. The resulting expression of the fermion propagator is free of
infrared problems, and exhibits a ``non-exponential'' damping at large times:
$S_R(t)\sim exp {-\alpha T t ln\omega_pt}$, where $\omega_p=eT/3$ is the plasma
frequency and $\alpha=e^2/4\pi$.


  We investigate the structure of the configuration space of gauge theories and
its description in terms of the set of absolute minima of certain Morse
functions on the gauge orbits. The set of absolute minima that is obtained when
the background connection is a pure gauge is shown to be isomorphic to the
orbit space of the pointed gauge group. We also show that the stratum of
irreducible orbits is geodesically convex, i.e. there are no geometrical
obstructions to the classical motion within the main stratum. An explicit
description of the singularities of the configuration space of SU(2) theories
on a topologically simple space-time and on the lattice is obtained; in the
continuum case we find that the singularities are conical and that the singular
stratum is isomorphic to a Z_2 orbifold of the configuration space of
electrodynamics.


  Investigations of quantum mechanical effects in macroscopic systems are of
great interest to shed light onto the question where and how the transition to
the classical world appears. It is also of practical relevance to determine if
a bath dephasing a qubit can be described classically or requires a quantum
mechanical treatment. We propose a measurement scheme to detect quantum
backaction via correlation measurements to answer this question for a bath
coupled to a single qubit. The presence of backaction leads to a dependence of
correlations of subsequent initialization-evolution-readout cycles on how the
qubit is manipulated in between. We compute the autocorrelation function for
both an instructive spin model and the realistic case of an electron spin
coupled to a bath of $10^6$ nuclear spins, as found in gated GaAs quantum dots,
and show that backaction from the qubit onto the nuclear spin bath should be
detectable even in such a large system.


  To reduce the manpower consumption on box-level annotations, many weakly
supervised object detection methods which only require image-level annotations,
have been proposed recently. The training process in these methods is
formulated into two steps. They firstly train a neural network under weak
supervision to generate pseudo ground truths (PGTs). Then, these PGTs are used
to train another network under full supervision. Compared with fully supervised
methods, the training process in weakly supervised methods becomes more complex
and time-consuming. Furthermore, overwhelming negative proposals are involved
at the first step. This is neglected by most methods, which makes the training
network biased towards to negative proposals and thus degrades the quality of
the PGTs, limiting the training network performance at the second step. Online
proposal sampling is an intuitive solution to these issues. However, lacking of
adequate labeling, a simple online proposal sampling may make the training
network stuck into local minima. To solve this problem, we propose an Online
Active Proposal Set Generation (OPG) algorithm. Our OPG algorithm consists of
two parts: Dynamic Proposal Constraint (DPC) and Proposal Partition (PP). DPC
is proposed to dynamically determine different proposal sampling strategy
according to the current training state. PP is used to score each proposal,
part proposals into different sets and generate an active proposal set for the
network optimization. Through experiments, our proposed OPG shows consistent
and significant improvement on both datasets PASCAL VOC 2007 and 2012, yielding
comparable performance to the state-of-the-art results.


  We show that the requirements of renormalizability and physical consistency
imposed on perturbative interactions of massive vector mesons fix the theory
essentially uniquely. In particular physical consistency demands the presence
of at least one additional physical degree of freedom which was not part of the
originally required physical particle content. In its simplest realization
(probably the only one) these are scalar fields as envisaged by Higgs but in
the present formulation without the ``symmetry-breaking Higgs condensate''. The
final result agrees precisely with the usual quantization of a classical gauge
theory by means of the Higgs mechanism. Our method proves an old conjecture of
Cornwall, Levin and Tiktopoulos stating that the renormalization and
consistency requirements of spin=1 particles lead to the gauge theory structure
(i.e. a kind of inverse of 't Hooft's famous renormalizability proof in
quantized gauge theories) which was based on the on-shell unitarity of the
$S$-matrix. We also speculate on a possible future ghostfree formulation which
avoids ''field coordinates'' altogether and is expected to reconcile the
on-shell S-matrix point of view with the off-shell field theory structure.


  We adapt a previous model and analysis method (the {\it master stability
function}), extensively used for studying the stability of the synchronous
state of networks of identical chaotic oscillators, to the case of oscillators
that are similar but not exactly identical. We find that bubbling induced
desynchronization bursts occur for some parameter values. These bursts have
spatial patterns, which can be predicted from the network connectivity matrix
and the unstable periodic orbits embedded in the attractor. We test the
analysis of bursts by comparison with numerical experiments. In the case that
no bursting occurs, we discuss the deviations from the exactly synchronous
state caused by the mismatch between oscillators.


  In this paper, we present rapid follow-up observations of the short GRB
201006A, consistent with being a compact binary merger, using the LOw Frequency
ARray (LOFAR). We have detected a candidate 5.6$\sigma$, short, coherent radio
flash at 144 MHz at 76.6 mins post-GRB with a 3$\sigma$ duration of 38 seconds.
This radio flash is 27 arcsec offset from the GRB location, which has a
probability of occurring by chance of $\sim$0.05% (3.8$\sigma$) when accounting
for measurement uncertainties. Despite the offset, we show that the probability
of finding an unrelated transient within 40 arcsec of the GRB location is
$<10^{-6}$ and conclude that this is a candidate radio counterpart to GRB
201006A. We performed image plane dedispersion and the radio flash is
tentatively (2.4$\sigma$) shown to be highly dispersed, allowing a distance
estimate, corresponding to a redshift of $0.58\pm0.06$. The corresponding
luminosity of the event at this distance is $6.7^{+6.6}_{-4.4} \times 10^{32}$
erg s$^{-1}$ Hz$^{-1}$. If associated with GRB 201006A, this emission would
indicate prolonged activity from the central engine that is consistent with
being a newborn, supramassive, likely highly magnetised, millisecond spin
neutron star (a magnetar).


  Sequential Recommender Systems (SRSs) have emerged as a highly efficient
approach to recommendation systems. By leveraging sequential data, SRSs can
identify temporal patterns in user behaviour, significantly improving
recommendation accuracy and relevance.Ensuring the reproducibility of these
models is paramount for advancing research and facilitating comparisons between
them. Existing works exhibit shortcomings in reproducibility and replicability
of results, leading to inconsistent statements across papers. Our work fills
these gaps by standardising data pre-processing and model implementations,
providing a comprehensive code resource, including a framework for developing
SRSs and establishing a foundation for consistent and reproducible
experimentation. We conduct extensive experiments on several benchmark
datasets, comparing various SRSs implemented in our resource. We challenge
prevailing performance benchmarks, offering new insights into the SR domain.
For instance, SASRec does not consistently outperform GRU4Rec. On the contrary,
when the number of model parameters becomes substantial, SASRec starts to
clearly dominate all the other SRSs. This discrepancy underscores the
significant impact that experimental configuration has on the outcomes and the
importance of setting it up to ensure precise and comprehensive results.
Failure to do so can lead to significantly flawed conclusions, highlighting the
need for rigorous experimental design and analysis in SRS research. Our code is
available at https://github.com/antoniopurificato/recsys_repro_conf.


  Interior penalty discontinuous Galerkin discretisations (IPDG) and especially
the symmetric variant (SIPG) for time-domain wave propagation problems are
broadly accepted and widely used due to their advantageous properties. Linear
systems with block structure arise by applying space-time discretisations and
reducing the global system to time-slab problems. The design of efficient and
robust iterative solvers for linear systems from interior penalty
discretisations for hyperbolic wave equations is still a challenging task and
relies on understanding the properties of the systems. In this work the
numerical properties such as the condition number and the distribution of
eigenvalues of different representations of the linear systems coming from
space-time discretisations for elastic wave propagation are numerically
studied. These properties for interior penalty discretisations depend on the
penalisation and on the time interval length.


  In theories with flat directions containing vortices, such as supersymmetric
QED, there is a vacuum selection effect in the allowed asymptotic
configurations. We explain the role played by gauge fields in this effect and
give a simple criterion for determining what vacua will be chosen, namely those
that minimise the vector mass. We then consider the effect of vacuum selection
on stable (BPS) non-topological vortices in a simple Abelian model with N=2
supersymmetry which occurs as a low energy limit of Calabi-Yau
compactifications of type II superstrings. In this case the magnetic flux
spreads over an arbitrarily large area. We discuss the implications for
cosmology and for superstring inspired magnetic confinement scenarios.


  The effects of Co-substitution on Ba(Ni$_{1-x}$Co$_x$)$_2$As$_2$ ($0\leq
x\leq 0.251$) single crystals grown out of Pb flux are investigated via
transport, magnetic, and thermodynamic measurements. BaNi$_2$As$_2$ exhibits a
first order tetragonal to triclinic structural phase transition at $T_s=137 K$
upon cooling, and enters a superconducting phase below $T_c=0.7 K$. The
structural phase transition is sensitive to cobalt content and is suppressed
completely by $x\geq0.133$. The superconducting critical temperature, $T_c$,
increases continuously with $x$, reaching a maximum of $T_c=2.3 K$ at the
structural critical point $x=0.083$ and then decreases monotonically until
superconductivity is no longer observable well into the tetragonal phase. In
contrast to similar BaNi$_2$As$_2$ substitutional studies, which show an abrupt
change in $T_c$ at the triclinic-tetragonal boundary that extends far into the
tetragonal phase, Ba(Ni$_{1-x}$Co$_x$)$_2$As$_2$ exhibits a dome-like phase
diagram centered around the first-order critical point. Together with an
anomalously large heat capacity jump $\Delta C_e/\gamma T\sim 2.2$ at optimal
doping, the smooth evolution of $T_c$ in the Ba(Ni$_{1-x}$Co$_x$)$_2$As$_2$
system suggests a mechanism for pairing enhancement other than phonon
softening.


  Event cameras do not produce images, but rather a continuous flow of events,
which encode changes of illumination for each pixel independently and
asynchronously. While they output temporally rich information, they lack any
depth information which could facilitate their use with other sensors. LiDARs
can provide this depth information, but are by nature very sparse, which makes
the depth-to-event association more complex. Furthermore, as events represent
changes of illumination, they might also represent changes of depth;
associating them with a single depth is therefore inadequate. In this work, we
propose to address these issues by fusing information from an event camera and
a LiDAR using a learning-based approach to estimate accurate dense depth maps.
To solve the "potential change of depth" problem, we propose here to estimate
two depth maps at each step: one "before" the events happen, and one "after"
the events happen. We further propose to use this pair of depths to compute a
depth difference for each event, to give them more context. We train and
evaluate our network, ALED, on both synthetic and real driving sequences, and
show that it is able to predict dense depths with an error reduction of up to
61% compared to the current state of the art. We also demonstrate the quality
of our 2-depths-to-event association, and the usefulness of the depth
difference information. Finally, we release SLED, a novel synthetic dataset
comprising events, LiDAR point clouds, RGB images, and dense depth maps.


  A general challenge in statistics is prediction in the presence of multiple
candidate models or learning algorithms. Model aggregation tries to combine all
predictive distributions from individual models, which is more stable and
flexible than single model selection. In this article we describe when and how
to aggregate models under the lens of Bayesian decision theory. Among two
widely used methods, Bayesian model averaging (BMA) and Bayesian stacking, we
compare their predictive performance, and review their theoretical optimality,
probabilistic interpretation, practical implementation, and extensions in
complex models.


  Reinforcement Learning (RL) methods have emerged as a popular choice for
training an efficient and effective dialogue policy. However, these methods
suffer from sparse and unstable reward signals returned by a user simulator
only when a dialogue finishes. Besides, the reward signal is manually designed
by human experts, which requires domain knowledge. Recently, a number of
adversarial learning methods have been proposed to learn the reward function
together with the dialogue policy. However, to alternatively update the
dialogue policy and the reward model on the fly, we are limited to
policy-gradient-based algorithms, such as REINFORCE and PPO. Moreover, the
alternating training of a dialogue agent and the reward model can easily get
stuck in local optima or result in mode collapse. To overcome the listed
issues, we propose to decompose the adversarial training into two steps. First,
we train the discriminator with an auxiliary dialogue generator and then
incorporate a derived reward model into a common RL method to guide the
dialogue policy learning. This approach is applicable to both on-policy and
off-policy RL methods. Based on our extensive experimentation, we can conclude
the proposed method: (1) achieves a remarkable task success rate using both
on-policy and off-policy RL methods; and (2) has the potential to transfer
knowledge from existing domains to a new domain.


  The change of the probability of proton emission in nuclear photoeffect due
to an intense coherent (laser) field is discussed near the threshold, where the
hindering effect of the Coulomb field of the remainder nucleus is essential.
The ratio of laser-assisted and laser free differential cross section is
deduced and found to be independent of the polarization state of the $\gamma $
field and the two types of initial nuclear state considered. The numerical
values of this ratio are given at some characteristic parameters of the intense
field.


  Person re-identification (ReID) is an important task in wide area video
surveillance which focuses on identifying people across different cameras.
Recently, deep learning networks with a triplet loss become a common framework
for person ReID. However, the triplet loss pays main attentions on obtaining
correct orders on the training set. It still suffers from a weaker
generalization capability from the training set to the testing set, thus
resulting in inferior performance. In this paper, we design a quadruplet loss,
which can lead to the model output with a larger inter-class variation and a
smaller intra-class variation compared to the triplet loss. As a result, our
model has a better generalization ability and can achieve a higher performance
on the testing set. In particular, a quadruplet deep network using a
margin-based online hard negative mining is proposed based on the quadruplet
loss for the person ReID. In extensive experiments, the proposed network
outperforms most of the state-of-the-art algorithms on representative datasets
which clearly demonstrates the effectiveness of our proposed method.


  This paper introduces an Online Localisation and Colored Mesh Reconstruction
(OLCMR) ROS perception architecture for ground exploration robots aiming to
perform robust Simultaneous Localisation And Mapping (SLAM) in challenging
unknown environments and provide an associated colored 3D mesh representation
in real time. It is intended to be used by a remote human operator to easily
visualise the mapped environment during or after the mission or as a
development base for further researches in the field of exploration robotics.
The architecture is mainly composed of carefully-selected open-source ROS
implementations of a LiDAR-based SLAM algorithm alongside a colored surface
reconstruction procedure using a point cloud and RGB camera images projected
into the 3D space. The overall performances are evaluated on the Newer College
handheld LiDAR-Vision reference dataset and on two experimental trajectories
gathered on board of representative wheeled robots in respectively urban and
countryside outdoor environments. Index Terms: Field Robots, Mapping, SLAM,
Colored Surface Reconstruction