  Electronic structure simulation (ESS) has been used for decades to provide
quantitative scientific insights on an atomistic scale, enabling advances in
chemistry, biology, and materials science, among other disciplines. Following
standard practice in scientific computing, the software packages driving these
studies have been implemented in compiled languages such as FORTRAN and C.
However, the recent introduction of machine learning (ML) into these domains
has meant that ML models must be coded in these languages, or that complex
software bridges have to be built between ML models in Python and these large
compiled software systems. This is in contrast with recent progress in modern
ML frameworks which aim to optimise both ease of use and high performance by
harnessing hardware acceleration of tensor programs defined in Python. We
introduce MESS: a modern electronic structure simulation package implemented in
JAX; porting the ESS code to the ML world. We outline the costs and benefits of
following the software development practices used in ML for this important
scientific workload. MESS shows significant speedups n widely available
hardware accelerators and simultaneously opens a clear pathway towards
combining ESS with ML. MESS is available at
https://github.com/graphcore-research/mess.


  This report describes the computer program GKINT, which was developed to
plan, analyze and interpret the first High Velocity Transient Field (HVTF)
g-factor measurements on radioactive beams produced as fast fragments. The
computer program and these notes were written in September 2004. Minor
corrections and updates have been added to these notes since then. The
experiment, NSCL experiment number 02020, 'Excited-state configurations in S-38
and S-40 through transient-field g-factor measurements on fast fragments', was
performed in October 2004; results have been published in Physical Review
Letters 96, 112503 (2006).


  The polarization controlled optical signal routing has many important
applications in photonics such as polarization beam splitter. By using
two-dimensional transmission lines with lumped elements, we experimentally
demonstrate the selective excitation of guided modes in waveguides composed of
two kinds of single-negative metamaterials. A localized, circularly polarized
emitter placed near the interface of the two kinds of single-negative
metamaterials only couples with one guided mode with a specific propagating
direction determined by the polarization handedness of the source. Moreover,
this optical spin-orbit locking phenomenon, also called the photonic spin Hall
effect, is robust against interface fluctuations, which may be very useful in
the manipulation of electromagnetic signals.


  Data Science research is undergoing a revolution fueled by the transformative
power of technology, the Internet, and an ever increasing computational
capacity. The rate at which sophisticated algorithms can be developed is
unprecedented, yet they remain outpaced by the massive amounts of data that are
increasingly available to researchers. Here we argue for the need to creatively
leverage the scientific research and algorithm development community as an axis
of robust innovation. Engaging these communities in the scientific discovery
enterprise by critical assessments, community experiments, and/or crowdsourcing
will multiply opportunities to develop new data driven, reproducible and well
benchmarked algorithmic solutions to fundamental and applied problems of
current interest. Coordinated community engagement in the analysis of highly
complex and massive data has emerged as one approach to find robust
methodologies that best address these challenges. When community engagement is
done in the form of competitions, also known as challenges, the validation of
the analytical methodology is inherently addressed, establishing performance
benchmarks. Finally, challenges foster open innovation across multiple
disciplines to create communities that collaborate directly or indirectly to
address significant scientific gaps. Together, participants can solve important
problems as varied as health research, climate change, and social equity.
Ultimately, challenges can catalyze and accelerate the synthesis of complex
data into knowledge or actionable information, and should be viewed a powerful
tool to make lasting social and research contributions.


  We consider the problem of regularization by noise for the three dimensional
magnetohydrodynamical (3D MHD) equations. It is shown that, in a suitable
scaling limit, multiplicative noise of transport type gives rise to bounds on
the vorticity fields of the fluid velocity and magnetic fields. As a result, if
the noise intensity is big enough, then the stochastic 3D MHD equations admit a
pathwise unique global solution for large initial data, with high probability.


  A strengthened Lindblad inequality has been proved. We have applied this
result for proving a generalized $H$-theorem in non equilibrium thermodynamics.
Information processing also can be considered as some thermodynamic process.
  From this point of view we have proved a strengthened data processing
inequality in quantum information theory.


  This article is devoted to results on pion-pion -Bose-Einstein correlations
in central Pb+Pb collisions measured by the NA49 experiment at the CERN SPS.
Rapidity as well as transverse momentum dependences of the correlation lengths
will be shown for collisions at 20A, 30A, 40A, 80A, and 158A GeV beam energy.
Only a weak energy dependence of the radii is observed at SPS energies. The
kt-dependence of the correlation lengths as well as the single particle
mt-spectra will be compared to model calculations. The rapidity dependence is
analysed in a range of 2.5 units of rapidity starting at the center of mass
rapidity at each beam energy. The correlation lengths measured in the
longitudinally comoving system show only a weak dependence on rapidity.


  We have studied two types of meshwork models by using the canonical Monte
Carlo simulation technique. The first meshwork model has elastic junctions,
which are composed of vertices, bonds, and triangles, while the second model
has rigid junctions, which are hexagonal (or pentagonal) rigid plates.
Two-dimensional elasticity is assumed only at the elastic junctions in the
first model, and no two-dimensional bending elasticity is assumed in the second
model. Both of the meshworks are of spherical topology. We find that both
models undergo a first-order collapsing transition between the smooth spherical
phase and the collapsed phase. The Hausdorff dimension of the smooth phase is
H\simeq 2 in both models as expected. It is also found that H\simeq 2 in the
collapsed phase of the second model, and that H is relatively larger than 2 in
the collapsed phase of the first model, but it remains in the physical bound,
i.e., H<3. Moreover, the first model undergoes a discontinuous surface
fluctuation transition at the same transition point as that of the collapsing
transition, while the second model undergoes a continuous transition of surface
fluctuation. This indicates that the phase structure of the meshwork model is
weakly dependent on the elasticity at the junctions.


  We calculate at two-loop order in chiral perturbation theory the
electromagnetic corrections to the dominant two-pion exchange nucleon-nucleon
interaction that is generated by the isoscalar $\pi N$ contact-vertex
proportional to the large low-energy constant $c_3$. We find that the
respective $2\pi\gamma$-exchange potential contains sizeable isospin-breaking
components which amount to about -1% of the strongly attractive isoscalar
central $2\pi$-exchange potential. The typical value of these novel
charge-independence and charge-symmetry breaking central potentials is $0.3
$MeV at a nucleon distance of $r= m_\pi^{-1} = 1.4 $fm. Our analytical result
for this presumably dominant $2\pi\gamma$-exchange interaction is in a form
such that it can be easily implemented into phase-shift analyses and few-body
calculations.


  Social media platforms such as Twitter (now known as X) have revolutionized
how the public engage with important societal and political topics. Recently,
climate change discussions on social media became a catalyst for political
polarization and the spreading of misinformation. In this work, we aim to
understand how real world events influence the opinions of individuals towards
climate change related topics on social media. To this end, we extracted and
analyzed a dataset of 13.6 millions tweets sent by 3.6 million users from 2006
to 2019. Then, we construct a temporal graph from the user-user mentions
network and utilize the Louvain community detection algorithm to analyze the
changes in community structure around Conference of the Parties on Climate
Change~(COP) events. Next, we also apply tools from the Natural Language
Processing literature to perform sentiment analysis and topic modeling on the
tweets. Our work acts as a first step towards understanding the evolution of
pro-climate change communities around COP events. Answering these questions
helps us understand how to raise people's awareness towards climate change thus
hopefully calling on more individuals to join the collaborative effort in
slowing down climate change.


  We introduce a fermionic formula associated with any quantum affine algebra
U_q(X^{(r)}_N). Guided by the interplay between corner transfer matrix and
Bethe ansatz in solvable lattice models, we study several aspects related to
representation theory, most crucially, the crystal basis theory. They include
one dimensional sums over both finite and semi-infinite paths, spinon character
formulae, Lepowski-Primc type conjectural formula for vacuum string functions,
dilogarithm identities, Q-systems and their solution by characters of various
classical subalgebras and so forth. The results expand [HKOTY1] including the
twisted cases and more details on inhomogeneous paths consisting of non-perfect
crystals. As a most intriguing example, certain inhomogeneous one dimensional
sums conjecturally give rise to branching functions of an integrable
G^{(1)}_2-module related to the embedding G^{(1)}_2 \hookrightarrow B^{(1)}_3
\hookrightarrow D^{(1)}_4.


  This paper presents Mechanistic Neural Networks, a neural network design for
machine learning applications in the sciences. It incorporates a new
Mechanistic Block in standard architectures to explicitly learn governing
differential equations as representations, revealing the underlying dynamics of
data and enhancing interpretability and efficiency in data modeling. Central to
our approach is a novel Relaxed Linear Programming Solver (NeuRLP) inspired by
a technique that reduces solving linear ODEs to solving linear programs. This
integrates well with neural networks and surpasses the limitations of
traditional ODE solvers enabling scalable GPU parallel processing. Overall,
Mechanistic Neural Networks demonstrate their versatility for scientific
machine learning applications, adeptly managing tasks from equation discovery
to dynamic systems modeling. We prove their comprehensive capabilities in
analyzing and interpreting complex scientific data across various applications,
showing significant performance against specialized state-of-the-art methods.


  The field of artificial intelligence (AI) is witnessing a recent upsurge in
research, tools development, and deployment of applications. Multiple software
companies are shifting their focus to developing intelligent systems; and many
others are deploying AI paradigms to their existing processes. In parallel, the
academic research community is injecting AI paradigms to provide solutions to
traditional engineering problems. Similarly, AI has evidently been proved
useful to software engineering (SE). When one observes the SE phases
(requirements, design, development, testing, release, and maintenance), it
becomes clear that multiple AI paradigms (such as neural networks, machine
learning, knowledge-based systems, natural language processing) could be
applied to improve the process and eliminate many of the major challenges that
the SE field has been facing. This survey chapter is a review of the most
commonplace methods of AI applied to SE. The review covers methods between
years 1975-2017, for the requirements phase, 46 major AI-driven methods are
found, 19 for design, 15 for development, 68 for testing, and 15 for release
and maintenance. Furthermore, the purpose of this chapter is threefold;
firstly, to answer the following questions: is there sufficient intelligence in
the SE lifecycle? What does applying AI to SE entail? Secondly, to measure,
formulize, and evaluate the overlap of SE phases and AI disciplines. Lastly,
this chapter aims to provide serious questions to challenging the current
conventional wisdom (i.e., status quo) of the state-of-the-art, craft a call
for action, and to redefine the path forward.


  Hypotheses: Quantitative molecular-thermodynamic theory of the growth of
giant wormlike micelles in mixed nonionic surfactant solutions can be developed
on the basis of a generalized model, which includes the classical phase
separation and mass action models as special cases. The generalized model
describes spherocylindrical micelles, which are simultaneously multicomponent
and polydisperse in size. Theory: The model is based on explicit analytical
expressions for the four components of the free energy of mixed nonionic
micelles: interfacial-tension, headgroup-steric, chain-conformation components
and free energy of mixing. The radii of the cylindrical part and the spherical
endcaps, as well as the chemical composition of the endcaps, are determined by
minimization of the free energy. Findings: In the case of multicomponent
micelles, an additional term appears in the expression for the micelle growth
parameter (scission free energy), which takes into account the fact that the
micelle endcaps and cylindrical part have different compositions. The model
accurately predicts the mean mass aggregation number of wormlike micelles in
mixed nonionic surfactant solutions without using any adjustable parameters.
The endcaps are enriched in the surfactant with smaller packing parameter that
is better accommodated in regions of higher mean surface curvature. The model
can be further extended to mixed solutions of nonionic, ionic and zwitterionic
surfactants used in personal-care and house-hold detergency.


  We run numerical simulations of molecular clouds (MCs), adopting properties
similar to those found in the Central Molecular Zone (CMZ) of the Milky Way.
For this, we employ the moving mesh code Arepo and perform simulations which
account for a simplified treatment of time-dependent chemistry and the
non-isothermal nature of gas and dust. We perform simulations using an initial
density of n_0 = 10^3 cm^{-3} and a mass of 1.3x10^5 M_sun. Furthermore, we
vary the virial parameter, defined as the ratio of kinetic and potential
energy, alpha = E_{kin} / |E_{pot}|, by adjusting the velocity dispersion. We
set it to alpha = 0.5, 2.0 and 8.0, in order to analyze the impact of the
kinetic energy on our results. We account for the extreme conditions in the CMZ
and increase both the interstellar radiation field (ISRF) and the cosmic-ray
flux (CRF) by a factor of 1000 compared to the values found in the solar
neighbourhood. We use the radiative transfer code RADMC-3D to compute synthetic
images in various diagnostic lines. These are [CII] at 158 micron, [OI] (145
micron), [OI] (63 micron), 12CO (J = 1 -> 0) and 13CO (J = 1 -> 0) at 2600
micron and 2720 micron, respectively. When alpha is large, the turbulence
disperses much of the gas in the cloud, reducing its mean density and allowing
the ISRF to penetrate more deeply into the cloud's interior. This significantly
alters the chemical composition of the cloud, leading to the dissociation of a
significant amount of the molecular gas. On the other hand, when alpha is
small, the cloud remains compact, allowing more of the molecular gas to
survive. We show that in each case the atomic tracers accurately reflect most
of the physical properties of both the H2 and the total gas of the cloud and
that they provide a useful alternative to molecular lines when studying the ISM
in the CMZ.


  We report on the temperature and electric field driven evolution of the
magnetoresistance lineshape at an interface between Ni/AlO$_x$ and Nb-doped
SrTiO$_3$. This is manifested as a superposition of the Lorentzian lineshape
due to spin accumulation and a parabolic background related to tunneling
anisotropic magnetoresistance (TAMR). The characteristic Lorentzian line shape
of the spin voltage is retrieved only at low temperatures and large positive
applied bias. This is caused by the reduction of electric field at large
positive applied bias which results in a simultaneous reduction of the
background TAMR and a sharp enhancement in spin injection. Such mechanisms to
tune magnetoresistance are uncommon in conventional semiconductors.


  We propose and study generic multi-Weyl semimetal (mWSM) lattice Hamiltonians
that break particle-hole symmetry. These models fall into two categories: model
I (model II) where the gap and tilt terms are coupled (decoupled) can host
type-I and type-II Weyl nodes simultaneously (separately) in a hybrid phase
(type-I and type-II phases, respectively). We concentrate on the question of
how anisotropy and non-linearity in the dispersions, gaps and tilt terms
influence diffusive second order transport quantities namely, the circular
photogalvanic effect (CPGE) and the Berry curvature dipole (BCD) as well as
first order Magnus Hall effect (MHE) in the ballistic limit. The signatures of
topological charges are clearly imprinted in the quantized CPGE response for
the hybrid mWSM phase in model I. Such a quantization is also found in the
type-I WSM phase for model II, however, the frequency profiles of the CPGE in
these two cases is distinctively different owing to their different band
dispersion irrespective of the identical topological properties. The
contributions from the vicinity of Weyl nodes and away from the WNs are clearly
manifested in the BCD response, respectively, for model I model II. The Fermi
surface properties for the activated momentum lead to a few hallmark features
on the MHE for both the models. Furthermore, we identify distinguishing
signatures of the above responses for type-I, type-II and hybrid phases to
provide an experimentally viable probe to differentiate these WSMs phases.


  Proceeding by way of examples, we update the combinatorics of the treatment
of Feynman diagrams with subdivergences in differential renormalization from
more recent viewpoints in Epstein--Glaser renormalization in $x$-space.


  Subhalo abundance matching (SHAM) inserts galaxies into dark matter only
simulations of the growth of cosmic structure in a way that requires minimal
assumptions about galaxy formation. A galaxy is placed at the potential minimum
of each distinct self-bound subhalo with a luminosity which is a monotonically
increasing function of the maximum mass (or circular velocity) attained over
the subhalo's earlier history. Galaxy and subhalo properties are linked by
matching model and observed luminosity functions. Simulated structures can then
be compared in detail with observation, for example, through galaxy correlation
statistics, group catalogues, or galaxy-galaxy lensing. Robust astrophysical
conclusions can be drawn from such a comparison only on scales which are
unaffected by the numerical limitations of the simulation. Here we compare
results for the Millennium Simulation (MS) with subhalos defined using the
SUBFIND to those obtained applying identical analysis to the much higher
resolution Millennium-II. Correlation statistics on scales between 200 kpc and
2 Mpc converge to within 20% only for subhalos with masses at infall
corresponding to at least 1000 simulation particles in the MS. Numerically
converged results can be obtained to much lower infall particle number, if
galaxies are followed even after their associated subhalos have been tidally
disrupted, as in most recent semi-analytic galaxy formation simulations. This
allows robust comparison between simulation and observation over a wider
dynamic range in mass than for a SHAM analysis which ignores such "orphan"
galaxies.


  We perform the canonical analysis of the Holst action for general relativity
with a cosmological constant without introducing second-class constraints. Our
approach consists in identifying the dynamical and nondynamical parts of the
involved variables from the very outset. After integrating out the nondynamical
variables associated with the connection, we obtain the description of phase
space in terms of manifestly $SO(3,1)$ [or $SO(4)$, depending on the signature]
covariant canonical variables and first-class constraints only. We impose the
time gauge on them and show that the Ashtekar-Barbero formulation of general
relativity emerges. Later, we discuss a family of canonical transformations
that allows us to construct new $SO(3,1)$ [or $SO(4)$] covariant canonical
variables for the phase space of the theory and compare them with the ones
already reported in the literature, pointing out the presence of a set of
canonical variables not considered before. Finally, we resort to the time gauge
again and find that the theory, when written in terms of the new canonical
variables, either collapses to the $SO(3)$ ADM formalism or to the
Ashtekar-Barbero formalism with a rescaled Immirzi parameter.


  The study of dynamics of single active particles plays an important role in
the development of artificial or hybrid micro-systems for bio-medical and other
applications at micro-scale. Here, we utilize the results of these studies to
better understand their implications for the specific application of drug
delivery. We analyze the variations in the capture efficiency for different
types of motion dynamics without inter-particle interactions and compare the
results. We also discuss the reasons for the same and describe the specific
parameters that affect the capture efficiency, which in turn helps in both
hardware and control design of a micro-bot swarm system for drug delivery.


  The nonrelativistic limit of the centrally extended Poincar\'e group is
considered and their consequences in the modal Hamiltonian interpretation of
quantum mechanics are discussed [ O. Lombardi and M. Castagnino, Stud. Hist.
Philos. Mod. Phys 39, 380 (2008) ; J. Phys, Conf. Ser. 128, 012014 (2008) ].
Through the assumption that in quantum field theory the Casimir operators of
the Poincar\'e group actualize, the nonrelativistic limit of the latter group
yields to the actualization of the Casimir operators of the Galilei group,
which is in agreement with the actualization rule of previous versions of modal
Hamiltonian interpretation [ Ardenghi et al., Found. Phys. (submitted) ]


  The ability to record high-fidelity videos at high acquisition rates is
central to the study of fast moving phenomena. The difficulty of imaging fast
moving scenes lies in a trade-off between motion blur and underexposure noise:
On the one hand, recordings with long exposure times suffer from motion blur
effects caused by movements in the recorded scene. On the other hand, the
amount of light reaching camera photosensors decreases with exposure times so
that short-exposure recordings suffer from underexposure noise. In this paper,
we propose to address this trade-off by treating the problem of high-speed
imaging as an underexposed image denoising problem. We combine recent advances
on underexposed image denoising using deep learning and adapt these methods to
the specificity of the high-speed imaging problem. Leveraging large external
datasets with a sensor-specific noise model, our method is able to speedup the
acquisition rate of a High-Speed Camera over one order of magnitude while
maintaining similar image quality.


  3D die stacking has often been proposed to build large-scale DRAM-based
caches. Unfortunately, the power and performance overheads of DRAM limit the
efficiency of high-bandwidth memories. Also, DRAM is facing serious scalability
challenges that make alternative technologies more appealing. This paper
examines Monarch, a resistive 3D stacked memory based on a novel reconfigurable
crosspoint array called XAM. The XAM array is capable of switching between
random access and content-addressable modes, which enables Monarch (i) to
better utilize the in-package bandwidth and (ii) to satisfy both the random
access memory and associative search requirements of various applications.
Moreover, the Monarch controller ensures a given target lifetime for the
resistive stack. Our simulation results on a set of parallel memory-intensive
applications indicate that Monarch outperforms an ideal DRAM caching by 1.21x
on average. For in-memory hash table and string matching workloads, Monarch
improves performance up to 12x over the conventional high bandwidth memories.


  In recent years, AI models that mine intrinsic patterns from molecular
structures and protein sequences have shown promise in accelerating drug
discovery. However, these methods partly lag behind real-world pharmaceutical
approaches of human experts that additionally grasp structured knowledge from
knowledge bases and unstructured knowledge from biomedical literature. To
bridge this gap, we propose KEDD, a unified, end-to-end, and multimodal deep
learning framework that optimally incorporates both structured and unstructured
knowledge for vast AI drug discovery tasks. The framework first extracts
underlying characteristics from heterogeneous inputs, and then applies
multimodal fusion for accurate prediction. To mitigate the problem of missing
modalities, we leverage multi-head sparse attention and a modality masking
mechanism to extract relevant information robustly. Benefiting from integrated
knowledge, our framework achieves a deeper understanding of molecule entities,
brings significant improvements over state-of-the-art methods on a wide range
of tasks and benchmarks, and reveals its promising potential in assisting
real-world drug discovery.


This paper has been withdrawn due to its publication


  Learning abstract and systematic relations has been an open issue in neural
network learning for over 30 years. It has been shown recently that neural
networks do not learn relations based on identity and are unable to generalize
well to unseen data. The Relation Based Pattern (RBP) approach has been
proposed as a solution for this problem. In this work, we extend RBP by
realizing it as a Bayesian prior on network weights to model the identity
relations. This weight prior leads to a modified regularization term in
otherwise standard network learning. In our experiments, we show that the
Bayesian weight priors lead to perfect generalization when learning identity
based relations and do not impede general neural network learning. We believe
that the approach of creating an inductive bias with weight priors can be
extended easily to other forms of relations and will be beneficial for many
other learning tasks.


  Mobile device users can now easily capture and socially share video clips in
a timely manner by uploading them wirelessly to a server. When attending
crowded events, such as an exhibition or the Olympic Games, however, timely
sharing of videos becomes difficult due to choking bandwidth in the network
infrastructure, preventing like-minded attendees from easily sharing videos
with each other through a server. One solution to alleviate this problem is to
use direct device-to-device communication to share videos among nearby
attendees. Contact capacity between two devices, however, is limited, and thus
a recommendation algorithm, such as collaborative filtering, is needed to
select and transmit only videos of potential interest to an attendee. In this
paper, we address the question: which video clip should be transmitted to which
user. We proposed an video transmission scheduling algorithm, called CoFiGel,
that runs in a distributed manner and aims to improve both the prediction
coverage and precision of the collaborative filtering algorithm. At each
device, CoFiGel transmits the video that would increase the estimated number of
positive user-video ratings the most if this video is transferred to the
destination device. We evaluated CoFiGel using real-world traces and show that
substantial improvement can be achieved compared to baseline schemes that do
not consider rating or contact history.


  The paper is devoted to representation theoretic and algebraic geometric
aspects of the theory of orthogonal decompositions of Lie algebra sl(n) into
Cartan subalgebras orthogonal with respect to Killing form and the relevant
theory of mutually unbiased bases. We describe the main steps of our proof for
the existence of 4-dimensional family of orthogonal pairs of Cartan subalgebras
in sl(6). As an application, we prove existence of a real 4-dimensional family
of pairs of mutually unbiased bases in C^6, thus solving the long standing
problem in Quantum Information Theory.


  In this paper we present a dynamical model of SUSY breaking with a hybrid
messenger sector. SUSY is broken dynamically at a scale of order $10^9$ GeV via
strong SU(2) gauge interactions. SUSY breaking is then transmitted to the
observable sector via two distinct sources: (1) messengers, carrying Standard
Model gauge quantum numbers, with the messenger mass of order
  $10^{15}$ GeV, and (2) the D term of an anomalous U(1)$_X$. The model is
quite constrained. The messenger scale is fixed by the Fayet-Iliopoulos term
for the anomalous U(1)$_X$ interaction. In addition, we show that the D term
SUSY breaking contributions to squark and slepton masses are "naturally" the
same order as those coming from the messengers.


  We show that H-phase transition metal dichalcogenides (TMDs) monolayers such
as MoS$_2$ and WSe$_2$, are orbital Hall insulators. They present very large
orbital Hall conductivity plateaus in their semiconducting gap, where the spin
Hall conductivity vanishes. Our results open the possibility of using TMDs for
orbital current injection and orbital torque transfers that surpass their
spin-counterparts in spin-orbitronics devices. The orbital Hall effect (OHE) in
TMD monolayers occurs even in the absence of spin-orbit coupling. It can be
linked to exotic momentum-space Dresselhaus-like orbital textures, analogous to
the spin-momentum locking in 2D Dirac fermions that arise from a combination of
orbital attributes and lattice symmetry.


  E. Maillet proved that the set of Liouville numbers is preserved under
rational functions with rational coefficients. Based on this result, a problem
posed by Kurt Mahler is to investigate whether there exist entire
transcendental functions with this property or not. For large parametrized
classes of Liouville numbers, we construct such functions and moreover we show
that it can be constructed such that all their derivatives share this property.
We use a completely different approach than in a recent paper, where functions
with a different invariant subclass of Liouville numbers were constructed
(though with no information on derivatives). More generally, we study the image
of Liouville numbers under analytic functions, with particular attention to
$f(z)=z^{q}$ where $q$ is a rational number.


  The phenomenon of coherence resonance (CR) is investigated in an unijunction
transistor relaxation oscillator (UJT-RO) and quantified by estimating the
normal variance (NV). Depending upon the measuring points two types of NV
curves have been obtained. We have observed that the degradations in coherency
at higher noise amplitudes in our system is probably the result of direct
interference of coherent oscillations and the stochastic perturbation.
Degradation of coherency may be minimal if this direct interference of noise
and coherent oscillations is eliminated.


  Quadrotors can provide services such as infrastructure inspection and
search-and-rescue, which require operating autonomously in cluttered
environments. Autonomy is typically achieved with receding-horizon planning,
where a short plan is executed while a new one is computed, because sensors
receive limited information at any time. To ensure safety and prevent robot
loss, plans must be verified as collision free despite uncertainty (e.g,
tracking error). Existing spline-based planners dilate obstacles uniformly to
compensate for uncertainty, which can be conservative. On the other hand,
reachability-based planners can include trajectory-dependent uncertainty as a
function of the planned trajectory. This work applies Reachability-based
Trajectory Design (RTD) to plan quadrotor trajectories that are safe despite
trajectory-dependent tracking error. This is achieved by using zonotopes in a
novel way for online planning. Simulations show aggressive flight up to 5 m/s
with zero crashes in 500 cluttered, randomized environments.


  We start out by demonstrating that an elementary learning task, corresponding
to the training of a single linear neuron in a convolutional neural network,
can be solved for feature spaces of very high dimensionality. In a second step,
acknowledging that such high-dimensional learning tasks typically benefit from
some form of regularization and arguing that the problem of scale has not been
taken care of in a very satisfactory manner, we come to a combined resolution
of both of these shortcomings by proposing a form of scale regularization.
Moreover, using variational method, this regularization problem can also be
solved rather efficiently and we demonstrate, on an artificial filter learning
problem, the capabilities of our basic linear neuron. From a more general
standpoint, we see this work as prime example of how learning and variational
methods could, or even should work to their mutual benefit.


  The promise of e-Science will only be realized when data is discoverable,
accessible, and comprehensible within distributed teams, across disciplines,
and over the long-term--without reliance on out-of-band (non-digital) means. We
have developed the open-source Tupelo semantic content management framework and
are employing it to manage a wide range of e-Science entities (including data,
documents, workflows, people, and projects) and a broad range of metadata
(including provenance, social networks, geospatial relationships, temporal
relations, and domain descriptions). Tupelo couples the use of global
identifiers and resource description framework (RDF) statements with an
aggregatable content repository model to provide a unified space for securely
managing distributed heterogeneous content and relationships.


  We study the satisfiability problem of symbolic tree automata and decompose
it into the satisfiability problem of the existential first-order theory of the
input characters and the existential monadic second-order theory of the indices
of the accepted words. We use our decomposition to obtain tight computational
complexity bounds on the decision problem for this automata class and an
extension that considers linear arithmetic constraints on the underlying
effective Boolean algebra


  Morphology mediates the interplay between the structure and electronic
transport in atomically thin nanoribbons such as graphene as the relaxation of
edge stresses occurs preferentially via out-of-plane deflections. In the case
of end-supported suspended nanoribbons that we study here, past experiments and
computations have identified a range of equilibrium morphologies, in particular
for graphene flakes, yet a unified understanding of their relative stability
remains elusive. Here, we employ atomic-scale simulations and a composite
framework based on isotropic elastic plate theory to chart out the
morphological stability space of suspended nanoribbons with respect to
intrinsic (ribbon elasticity) and engineered (ribbon geometry) parameters, and
the combination of edge and body actuation. The computations highlight a rich
morphological shape space that can be naturally classified into two competing
shapes, bending-like and twist-like, depending on the distribution of ripples
across the interacting edges. The linearized elastic framework yields exact
solutions for these rippled shapes. For compressive edge stresses, the body
strain emerges as a key variable that controls their relative stability and in
extreme cases stabilizes co-existing transverse ripples. Tensile edge stresses
lead to dimples within the ribbon core that decay into the edges, a feature of
obvious significance for stretchable nanoelectronics. The interplay between
geometry and mechanics that we report should serve as a key input for
quantifying the transport along these ribbons.


  This paper presents the DYON simulations of the plasma burn-through phase at
Joint European Torus (JET) with the ITER-like wall. The main purpose of the
study is to validate the simulations with the ITER-like wall, made of
beryllium. Without impurities, the burn-through process of a pure deuterium
plasma is described using DYON simulations, and the criterion for deuterium
burn-through is derived analytically. The plasma burn-through with impurities
are simulated using wall-sputtering models in the DYON code, which are modified
for the ITER-like wall. The wall-sputtering models and the validation against
JET data are presented. The impact of the assumed plasma parameters in DYON
simulations are discussed by means of parameter scans. As a result, the
operation space of prefill gas pressure and toroidal electric field for plasma
burn-through in JET is compared to the Townsend avalanche criterion.


  We present a model of optically thin, two-temperature, accretion flows using
an exact Monte Carlo treatment of global Comptonization, with seed photons from
synchrotron and bremsstrahlung emission, as well as with a fully general
relativistic description of both the radiative and hydrodynamic processes. We
consider accretion rates for which the luminosities of the flows are between
~0.001 and 0.01 of the Eddington luminosity. The black hole spin parameter
strongly affects the flow structure within the innermost 10 gravitational
radii. The resulting large difference between the Coulomb heating in models
with a non-rotating and a rapidly rotating black hole is, however, outweighed
by a strong contribution of compression work, much less dependent on spin. The
consequent reduction of effects related to the value of the black spin is more
significant at smaller accretion rates. For a non-rotating black hole, the
compressive heating of electrons dominates over their Coulomb heating, and
results in an approximately constant radiative efficiency of approximately 0.4
per cent in the considered range of luminosities. For a rapidly rotating black
hole, the Coulomb heating dominates, the radiative efficiency is ~1 per cent
and it slightly increases (but less significantly than estimated in some
previous works) with increasing accretion rate. We find an agreement between
our model, in which the synchrotron emission is the main source of seed
photons, and observations of black-hole binaries in their hard states and AGNs
at low luminosities. In particular, our model predicts a hardening of the X-ray
spectrum with increasing luminosity, as indeed observed below ~0.01 of the
Eddington luminosity in both black-hole binaries and AGNs. Also, our model
approximately reproduces the luminosity and the slope of the X-ray emission in
Cen A.


  In this paper we analyze the structure, phase transitions and some transport
properties of the vortex system when the external magnetic field lies parallel
to the planes in layered superconductors. We show that experimental results for
resistivity are qualitatively consistent with numerical simulations that
describe the melting of a commensurate rotated lattice. However for some
magnetic fields, the structure factor indicates the occurrence of smectic peaks
at an intermediate temperature regime.


  Self-similar solutions of the equation that describes spreading of a liquid
layer due to intermolecular forces are found. It is supposed that, when the
thickness of the layer reaches some magnitude of the order of the molecular
size, it turns into a monomolecular layer, which can be liquid or gaseous. To
describe the spreading of a drop, the solutions of the equations that describe
evolution of liquid or gaseous monomolecular layers are matched with the
self-similar solutions with using relevant boundary conditions.


  We propose a general framework for increasing local stability of Artificial
Neural Nets (ANNs) using Robust Optimization (RO). We achieve this through an
alternating minimization-maximization procedure, in which the loss of the
network is minimized over perturbed examples that are generated at each
parameter update. We show that adversarial training of ANNs is in fact
robustification of the network optimization, and that our proposed framework
generalizes previous approaches for increasing local stability of ANNs.
Experimental results reveal that our approach increases the robustness of the
network to existing adversarial examples, while making it harder to generate
new ones. Furthermore, our algorithm improves the accuracy of the network also
on the original test data.


  Aims: We derive the age and metallicity of circumnuclear star formation
regions (CNSFRs) located in the spiral galaxies NGC6951 and NGC1097, and
investigate the cause of the very low equivalent widths of emission lines found
for these regions.
  Methods: We used optical two-dimensional spectroscopic data obtained with
Gemini GMOS-IFUs and a grid of photoionization models to derive the the
metallicities and ages of CNSFRs.
  Results: We find star formation rates in the range 0.002-0.14 Msun/yr and
oxygen abundance of 12+log(O/H)~8.8 dex, similar to those of most metal-rich
nebulae located in the inner region of galactic disks.
  Conclusions: We conclude that the very low emission-line equivalent widths
observed in CNSFRs are caused by the ``contamination'' of the continuum by (1)
contribution of the underlying bulge continuum combined with (2) contribution
from previous episodes of star formation at the CNSFRs.


  These notes focus on the Lipschitz geometry of sets that are definable in
o-minimal structures (expanding the real field). We show that every set which
is definable in a polynomially bounded o-minimal structure admits a
stratification which is locally definably bi-Lipschitz trivial along the
strata. This result is obtained as a byproduct of two foregoing results of the
author. The first one asserts that, given a family definable in an o-minimal
structure, there is a regular vector, up to a definable family of bi-Lipschitz
homeomorphisms. The second one is a bi-Lipschitz version of the famous Hardt's
theorem. We give proofs of these two theorems that avoid the use of the real
spectrum. The article recalls the basic facts and the results about Lipschitz
geometry that are needed to understand the proofs, providing references.


  We show that the space of all bounded derivations from the disc algebra into
its dual can be identified with the Hardy space $H^1$; using this, we infer
that all such derivations are compact. Also, given a fixed derivation $D$, we
construct a finite, positive Borel measure $\mu_D$ on the closed disc, such
that $D$ factors through $L^2(\mu_D)$. Such a measure is known to exist, for
any bounded linear map from the disc algebra to its dual, by results of
Bourgain and Pietsch, but these results are highly non-constructive.


  Video surveillance systems are increasingly used in different fields, from
the domestic to the commercial environment. Current systems are being improved
and complemented with new elements and functionalities. This paper proposes the
design of a video surveillance robot based on Raspberry Pi with the abilities
to perform tasks of motion detection, send video on real time, fire detection
and also, the possibility of control it remotely from the Internet. In order to
check the information received from the robot, as well as the video sent, a
client application has been developed to any device with an Internet
connection. In addition to this, in order to protect the information obtained
by the robot, a secure system is proposed, which uses different security
mechanisms to achieve this goal.


  Emission of hard microscopic string (graviton) by an excited macroscopic
string may be viewed as a model of branching of a $(1+1)$-dimensional baby
universe off large parent one. We show that, apart from a trivial factor, the
total emission rate is not suppressed by the size of the macroscopic string.
This implies unsuppressed loss of quantum coherence in $(1+1)$-dimensional
parent universe.


  In this work, we use the notion of ``symmetry'' of functions for an extension
$K/L$ of finite fields to produce extensions of a function field $F/K$ in which
almost all places of degree one split completely. Then we introduce the notion
of ``quasi-symmetry'' of functions for $K/L$, and demonstrate its use in
producing extensions of $F/K$ in which all places of degree one split
completely. Using these techniques, we are able to restrict the ramification
either to one chosen rational place, or entirely to non-rational places. We
then apply these methods to the related problem of building asymptotically good
towers of function fields. We construct examples of towers of function fields
in which all rational places split completely throughout the tower. We
construct Abelian towers with this property also.
  Furthermore, all of the above are done explicitly, ie., we give generators
for the extensions, and equations that they satisfy.
  We also construct an integral basis for a set of places in a tower of
function fields meeting the Drinfeld-Vladut bound using the discriminant of the
tower localized at each place. Thus we are able to obtain a basis for a
collection of functions that contains the set of regular functions in this
tower. Regular functions are of interest in the theory of error-correcting
codes as they lead to an explicit description of the code associated to the
tower by providing the code's generator matrix.


  Antinuclei are considered to be one of the most promising probes in the
indirect search for dark matter (DM) annihilation in space. However, in light
of recent results on the production of light antinuclei in pp collisions at the
LHC, an abundant production of light (anti)nuclei is also expected from
Standard Model (SM) collisions of primary cosmic rays with the interstellar
medium. Hence further precise measurements are required to constrain the
production models of antinuclei in SM collisions to be sensitive to additional
DM annihilation events. The most recent results of the ALICE collaboration on
the production of antideuterons ($\overline{\mathrm{d}}$) and antihelium-3
($^3\overline{\text{He}}$) in proton-proton (pp) and proton-lead (p--Pb)
collisions are shown. These results provide valuable input for state-of-the-art
calculations of the production models currently used to estimate the SM
background to DM searches.


  We study the shadow of a rotating squashed Kaluza-Klein (KK) black hole and
the shadow is found to possess distinct properties from those of usual rotating
black holes. It is shown that the shadow for a rotating squashed KK black hole
is heavily influenced by the specific angular momentum of photon from the fifth
dimension. Especially, as the parameters lie in a certain special range, there
is no any shadow for a black hole, which does not emerge for the usual black
holes. In the case where the black hole shadow exists, the shadow shape is a
perfect black disk and its radius decreases with the rotation parameter of the
black hole. Moreover, the change of the shadow radius with extra dimension
parameter also depends on the rotation parameter of black hole. Finally, with
the latest observation data, we estimate the angular radius of the shadow for
the supermassive black hole Sgr $A^{*}$ at the centre of the Milky Way galaxy
and the supermassive black hole in $M87$.


  Magnetization manipulation is becoming an indispensable tool for both basic
and applied research. Theory predicts two types of ultrafast demagnetization
dynamics classified as type I and type II. In type II materials, a second
slower process takes place after the initial fast drop of magnetization. In
this letter we investigate this behavior for FePt recording materials with
perpendicular anisotropy. The magnetization dynamics have been simulated using
a thermal micromagnetic model based on the Landau-Lifshitz-Bloch equation. We
identify a transition to type II behavior and relate it to the electron
temperatures reached by the laser heating. This slowing down is a fundamental
limit to reconding speeds in heat assisted reversal.


  National statistical systems are the enterprises tasked with collecting,
validating and reporting societal attributes. These data serve many purposes -
they allow governments to improve services, economic actors to traverse
markets, and academics to assess social theories. National statistical systems
vary in quality, especially in developing countries. This study examines
determinants of national statistical capacity in developing countries, focusing
on the impact of general purpose technologies (GPTs). Just as technological
progress helps to explain differences in economic growth, states with markets
with greater technological attainment (specifically, general purpose
technologies) arguably have greater capacity for gathering and processing
quality data. Analysis using panel methods shows a strong, statistically
significant positive linear relationship between GPTs and national statistical
capacity. There is no evidence to support a non-linear function in this
relationship. Which is to say, there does not appear to be a marginal
depreciating National Statistical Capacity benefit associated with increases in
GPTs.


  In this paper, we present CharacterGLM, a series of models built upon
ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM
is designed for generating Character-based Dialogues (CharacterDial), which
aims to equip a conversational AI system with character customization for
satisfying people's inherent social desires and emotional needs. On top of
CharacterGLM, we can customize various AI characters or social agents by
configuring their attributes (identities, interests, viewpoints, experiences,
achievements, social relationships, etc.) and behaviors (linguistic features,
emotional expressions, interaction patterns, etc.). Our model outperforms most
mainstream close-source large langauge models, including the GPT series,
especially in terms of consistency, human-likeness, and engagement according to
manual evaluations. We will release our 6B version of CharacterGLM and a subset
of training data to facilitate further research development in the direction of
character-based dialogue generation.


  Eye tracking is handled as one of the key technologies for applications that
assess and evaluate human attention, behavior, and biometrics, especially using
gaze, pupillary, and blink behaviors. One of the challenges with regard to the
social acceptance of eye tracking technology is however the preserving of
sensitive and personal information. To tackle this challenge, we employ a
privacy-preserving framework based on randomized encoding to train a Support
Vector Regression model using synthetic eye images privately to estimate the
human gaze. During the computation, none of the parties learn about the data or
the result that any other party has. Furthermore, the party that trains the
model cannot reconstruct pupil, blinks or visual scanpath. The experimental
results show that our privacy-preserving framework is capable of working in
real-time, with the same accuracy as compared to non-private version and could
be extended to other eye tracking related problems.


  This paper deals with the Neumann eigenvalue problem for the Hermite operator
defined in a convex, possibly unbounded, planar domain $\Omega$, having one
axis of symmetry passing through the origin. We prove a sharp lower bound for
the first eigenvalue $\mu_1^{odd}(\Omega)$ with an associated eigenfunction odd
with respect to the axis of symmetry. Such an estimate involves the first
eigenvalue of the corresponding one-dimensional problem. As an immediate
consequence, in the class of domains for which
$\mu_1(\Omega)=\mu_1^{odd}(\Omega)$, we get an explicit lower bound for the
difference between $\mu(\Omega)$ and the first Neumann eigenvalue of any strip.


  Infrared Dark Clouds (IRDCs) represent the earliest observed stages of
clustered star formation, characterized by large column densities of cold and
dense molecular material observed in silhouette against a bright background of
mid-IR emission. Up to now, IRDCs were predominantly known toward the inner
Galaxy where background infrared emission levels are high. We present Spitzer
observations with the Infrared Camera Array toward object G111.80+0.58 (G111)
in the outer Galactic Plane, located at a distance of ~3 kpc from us and ~10
kpc from the Galactic center. Earlier results show that G111 is a massive, cold
molecular clump very similar to IRDCs. The mid-IR Spitzer observations
unambiguously detect object G111 in absorption. We have identified for the
first time an IRDC in the outer Galaxy, which confirms the suggestion that
cluster-forming clumps are present throughout the Galactic Plane. However,
against a low mid-IR back ground such as the outer Galaxy it takes some effort
to find them.


  The black hole rigidity theorem asserts that a rotating stationary black hole
must be axisymmetric. This theorem holds for General Relativity with suitable
matter fields, in four or more dimensions. We show that the theorem can be
extended to any diffeomorphism invariant theory of vacuum gravity, assuming
that this is interpreted in the sense of effective field theory, with coupling
constants determined in terms of a ``UV scale'', and that the black hole
solution can locally be expanded as a power series in this scale.


  A clear gate voltage tunable weak antilocalization and a giant
magnetoresistance of 400 percent are observed at 1.9 K in single layer graphene
with an out-of-plane field. A large magnetoresistance value of 275 percent is
obtained even at room temperature implying potential applications of graphene
in magnetic sensors. Both the weak antilocalization and giant magnetoresistance
persists far away from the charge neutrality point in contrast to previous
reports, and both effects are originated from charged impurities.
Interestingly, the signatures of Shubnikov-de Haas oscillations and the quantum
Hall effect are also observed for the same sample.


  Growing attention has been paid in Conversational Recommendation System
(CRS), which works as a conversation-based and recommendation task-oriented
tool to provide items of interest and explore user preference. However,
existing work in CRS fails to explicitly show the reasoning logic to users and
the whole CRS still remains a black box. Therefore we propose a novel
end-to-end framework named Explanation Generation for Conversational
Recommendation (EGCR) based on generating explanations for conversational
agents to explain why they make the action. EGCR incorporates user reviews to
enhance the item representation and increase the informativeness of the whole
conversation. To the best of our knowledge, this is the first framework for
explainable conversational recommendation on real-world datasets. Moreover, we
evaluate EGCR on one benchmark conversational recommendation datasets and
achieve better performance on both recommendation accuracy and conversation
quality than other state-of-the art models. Finally, extensive experiments
demonstrate that generated explanations are not only having high quality and
explainability, but also making CRS more trustworthy. We will make our code
available to contribute to the CRS community


  We show that C^2 conformally compact Riemannian Einstein metrics have
conformal compactifications that are smooth up to the boundary in dimension 3
and all even dimensions, and polyhomogeneous in odd dimensions greater than 3.


  Neutrinos propagate in astrophysical and cosmological environments modifying
their flavor in intriguing ways. The study of neutrino propagation in media is
based on the mean-field, extended mean-field and Boltzmann equations. We
summarise salient features of these evolution equations and the methods
employed so far to derive them. We emphasize applications to situations of
observational interest.


  We present images showing the first detections of polarization on parsec
scales in the nuclei of four Fanaroff-Riley type I (low-luminosity) radio
galaxies. Observations with Very Long Baseline Interferometry at \lambda 3.6cm
reveal the presence of ordered magnetic fields within ~1650 Schwarzchild radii
of the putative central supermassive black hole. The relatively high fractional
polarization in the pc-scale jets of these galaxies is consistent with the
standard scheme unifying low-luminosity radio galaxies with BL Lac objects.
This result also suggests that these radio galaxies lack the obscuring tori
that apparently depolarize the nuclear emission in the more powerful FRII radio
galaxies, and that their supermassive blackholes are poorly fed and/or
inefficient radiators.


  For a quasi-Fuchsian group $\Ga$ with ordinary set $\Omega$, and $\Delta_{n}$
the Laplacian on \n differentials on $\Ga\bk\Omega$, we define a notion of a
Bers dual basis $\phi_{1},...c,\phi_{2d}$ for $\ker\Delta_{n}$. We prove that
$\det\Delta_{n}/\det <\phi_{j},\phi_{k}>$, is, up to an anomaly computed by
Takhtajan and the second author in \cite{TT1}, the modulus squared of a
holomorphic function F(n), where F(n) is a quasi-Fuchsian analogue of the
Selberg zeta Z(n). This generalizes the D'Hoker-Phong formula
$\det\Delta_{n}=c_{g,n}Z(n)$, and is a quasi-Fuchsian counterpart of the result
for Schottky groups proved by Takhtajan and the first author in \cite{MT}.


  We construct dual supergravity descriptions of field theories and little
string theories with light-like non-commutativity. The field theories are
realized on the world-volume of Dp branes with light-like NS $B$ field and M5
branes with light-like $C$ field. The little string theories are realized on
the world-volume of NS5 branes with light-like RR $A$ fields. The supergravity
backgrounds are closely related to the $A=0,B=0,C=0$ backgrounds. We discuss
the implications of these results. We also construct dual supergravity
descriptions of ODp theories realized on the worldvolume of NS5 branes with RR
backgrounds.


  Usually the boundary map in K-theory localization only gives the tame symbol
at $K_{2}$. It sees the tamely ramified part of the Hilbert symbol, but no wild
ramification. Gillet has shown how to prove Weil reciprocity using such
boundary maps. This implies Hilbert reciprocity for curves over finite fields.
However, phrasing Hilbert reciprocity for number fields in a similar way fails
because it crucially hinges on wild ramification effects. We resolve this
issue, except at p=2. Our idea is to pinch singularities near the ramification
locus. This fattens up K-theory and makes the wild symbol visible as a boundary
map.


  We study the behavior of hysteresis loops in Finemet
Fe$_{73.5}$Cu$_1$Nb$_3$Si$_{18.5}$B$_4$ thin films by using a fluxometric setup
based on a couple of well compensated pickup coils. The presence of scaling
laws of the hysteresis area is investigated as a function of the amplitude and
frequency of the applied field, considering sample thickness from about 20 nm
to 5 $\mu$m. We do not observe any scaling predicted by theoretical models,
while dynamic loops show a logarithmic dependence on the frequency.


  We introduce a non-autonomous generalization of spatial-temporal
differentiations, and prove results about probabilistically and topologically
generic behaviors of certain spatial-temporal differentiations generated by
endomorphisms of compact abelian metrizable groups.


  Meditation is a family of mental practices that encompasses a wide array of
techniques employing distinctive mental strategies. We systematically reviewed
78 functional neuroimaging (fMRI and PET) studies of meditation, and used
activation likelihood estimation to meta-analyze 257 peak foci from 31
experiments involving 527 participants. We found reliably dissociable patterns
of brain activation and deactivation for four common styles of meditation
(focused attention, mantra recitation, open monitoring, and
compassion/loving-kindness), and suggestive differences for three others
(visualization, sense-withdrawal, and non-dual awareness practices). Overall,
dissociable activation patterns are congruent with the psychological and
behavioral aims of each practice. Some brain areas are recruited consistently
across multiple techniques - including insula, pre/supplementary motor
cortices, dorsal anterior cingulate cortex, and frontopolar cortex - but
convergence is the exception rather than the rule. A preliminary effect-size
meta-analysis found medium effects for both activations (d = .59) and
deactivations (d = -.74), suggesting potential practical significance. Our
meta-analysis supports the neurophysiological dissociability of meditation
practices, but also raises many methodological concerns and suggests avenues
for future research.


  In this paper we study a vanishing pressure process for highly compressible
Navier-Stokes equations as the Mach number tends to infinity. We first prove
the global existence of weak solutions for the pressureless system in the
framework [Li-Xin, arXiv:1504.06826v2], where the weak solutions are
established for compressible Navier-Stokes equations with degenerate viscous
coefficients. Furthermore, a rate of convergence of the density in
$L^{\infty}\left(0,T;L^{2}(\rr)\right)$ is obtained, in case when the velocity
corresponds to the gradient of density at initial time.


  One of the biggest challenges in contemporary physics is understanding the
origin and dynamics of the internal structure of hadrons which, at a
fundamental level, is described by quantum chromodynamics (QCD). Taking great
prominence amongst hadrons are pions and kaons which, despite being the
lightest hadrons in nature, their very existence is intimately connected to
those mechanisms responsible for almost all of the mass of the visible matter.
In this manuscript we discuss many aspects of the pion and kaon structure via
light front wave functions and generalized parton distributions, and a
collection of other distributions and structural properties that are inferred
therefrom.


  Understanding the nature of the electronic nematic phase in iron pnictide
superconductors is important for elucidating its impact on high-temperature
superconductivity. Here we use transport and inelastic neutron scattering to
study spin excitations and in-plane resistivity anisotropy in uniaxial pressure
detwinned BaFe$_2$As$_2$ and SrFe$_2$As$_2$, the parent compounds of iron
pnictide superconductors. While BaFe$_2$As$_2$ exhibits weakly first order
tetragonal-to-orthorhombic structural and antiferromagnetic (AF) phase
transitions below $T_s > T_N\approx 138$ K, SrFe$_2$As$_2$ has strongly coupled
first order structural and AF transitions below $T_s= T_N\approx 210$ K. We
find that the direct signatures of the nematic phase persist to lower
temperatures above the phase transition in the case of SrFe$_2$As$_2$ compared
to BaFe$_2$As$_2$. Our findings support the conclusion that the strongly
first-order nature of the magnetic transition in SrFe$_2$As$_2$ weakens the
nematic phase and resistivity anisotropy in the system.


  Security issue of distributed state estimation (DSE) is an important prospect
for the rapidly growing smart grid ecosystem. Any coordinated cyberattack
targeting the distributed system of state estimators can cause unrestrained
estimation errors and can lead to a myriad of security risks, including failure
of power system operation. This article explores the security threats of a
smart grid arising from the exploitation of DSE vulnerabilities. To this aim,
novel adversarial strategies based on two-stage data availability and integrity
attacks are proposed towards a distributed industrial Internet of Things-based
smart grid. The former's attack goal is to prevent boundary data exchange among
distributed control centers, while the latter's attack goal is to inject a
falsified data to cause local and global system unobservability. The proposed
framework is evaluated on IEEE standard 14-bus system and benchmarked against
the state-of-the-art research. Experimental results show that the proposed
two-stage cyberattack results in an estimated error of approximately 34.74%
compared to an error of the order of 10^-3 under normal operating conditions.


  Computer-assisted prostate biopsies became a very active research area during
the last years. Prostate tracking makes it possi- ble to overcome several
drawbacks of the current standard transrectal ultrasound (TRUS) biopsy
procedure, namely the insufficient targeting accuracy which may lead to a
biopsy distribution of poor quality, the very approximate knowledge about the
actual location of the sampled tissues which makes it difficult to implement
focal therapy strategies based on biopsy results, and finally the difficulty to
precisely reach non-ultrasound (US) targets stemming from different modalities,
statistical atlases or previous biopsy series. The prostate tracking systems
presented so far are limited to rigid transformation tracking. However, the
gland can get considerably deformed during the intervention because of US probe
pres- sure and patient movements. We propose to use 3D US combined with
image-based elastic registration to estimate these deformations. A fast elastic
registration algorithm that copes with the frequently occurring US shadows is
presented. A patient cohort study was performed, which yielded a statistically
significant in-vivo accuracy of 0.83+-0.54mm.


  The Chandra Multiwavength Plane (ChaMPlane) Survey of the galactic plane
incorporates serendipitous sources from selected Chandra pointings in or near
the galactic plane (b < 12deg; >20 ksec; lack of bright diffuse or point
sources) to measure or constrain the luminosity function of low-luminosity
accretion sources in the Galaxy. The primary goal is to detect and identify
accreting white dwarfs (cataclysmic variables, with space density still
uncertain by a factor of >10-100), neutron stars and black holes (quiescent low
mass X-ray binaries) to constrain their space densities and thus origin and
evolution. Secondary objectives are to identify Be stars in high mass X-ray
binaries and constrain their space densities, and to survey the H-R diagram for
stellar coronal sources. A parallel optical imaging under the NOAO Long Term
Survey program provides deep optical images using the Mosaic imager on the CTIO
and KPNO 4-m telescopes. The 36arcmin X 36arcmin optical images (Halpha, R, V
and I) cover ~5X the area of each enclosed Chandra ACIS FOV, providing an
extended survey of emission line objects for comparison with Chandra.
Spectroscopic followup of optical counterparts is then conducted, thus far with
WIYN and Magellan. The X-ray preliminary results from both the Chandra and
optical surveys will be presented, including logN-logS vs. galactic position
(l,b) and optical idenifications.


  In this paper, we present a closed computable formula for the cellular
homology coefficients for real flag manifolds of split real forms of type A. We
illustrate the process through movements of a type of Young diagram for
permutations. Furthermore, we compute the third and fourth homology groups and
provide generators for the free part up to the 6-homology groups.


  We re-evaluate prospects for supersymmetry at the proposed International
Linear $e^+e^-$ Collider (ILC) in light of the first year of serious data
taking at LHC with $\sqrt{s}=7$ TeV and $\sim 5$ fb$^{-1}$ of $pp$ collisions
(LHC7). Strong new limits from LHC SUSY searches, along with a hint of a Higgs
boson signal around $m_h\sim 125$ GeV, suggest a paradigm shift from previously
popular models to ones with new and compelling signatures. We present a variety
of new ILC benchmark models, including: natural SUSY, hidden SUSY, NUHM2 with
low $m_A$, non-universal gaugino mass (NUGM) model, pMSSM, Kallosh-Linde model,
Br\"ummer-Buchm\"uller model, normal scalar mass hierarchy (NMH) plus one
surviving case from mSUGRA/CMSSM in the far focus point region. While all these
models at present elude the latest LHC limits, they do offer intriguing case
study possibilities for ILC operating at $\sqrt{s}\sim 0.25-1$ TeV, and present
a view of some of the diverse SUSY phenomena which might be expected at both
LHC and ILC in the post LHC7 era.


  In this paper we consider some aspects of the relativistic dynamics of a
cylindrical shell of counter rotating particles. In some sense these are the
simplest systems with a physically acceptable matter content that display in a
well defined sense an interaction with the radiative modes of the gravitational
field. These systems have been analyzed previously, but in most cases resorting
to approximations, or considering a particular form for the initial value data.
Here we show that there exists a family of solutions where the space time
inside the shell is flat and the equation of motion of the shell decouples
completely from the gravitational modes. The motion of the shell is governed by
an equation of the same form as that of a particle in a time independent one
dimensional potential. We find that under appropriate initial conditions one
can have collapsing, bounded periodic, and unbounded motions. We analyze and
solve also the linearized equations that describe the dynamics of the system
near a stable static solutions, keeping a regular interior. The surprising
result here is that the motion of the shell is completely determined by the
configuration of the radiative modes of the gravitational field. In particular,
there are oscillating solutions for any chosen period, in contrast with the
"approximately Newtonian plus small radiative corrections" motion expectation.
We comment on the physical meaning of these results and provide some explicit
examples. We also discuss the relation of our results to the initial value
problem for the linearized dynamics of the shell.


  We develop a method for computing the free-energy of a canonical ensemble of
quantum fields near the horizon of a rotating black hole. We show that the
density of energy levels of a quantum field on a stationary background can be
related to the density of levels of the same field on a fiducial static
space-time. The effect of the rotation appears in the additional interaction of
the "static" field with a fiducial abelian gauge-potential. The fiducial static
space-time and the gauge potential are universal, i.e., they are determined by
the geometry of the given physical space-time and do not depend on the spin of
the field. The reduction of the stationary axially symmetric problem to the
static one leads to a considerable simplification in the study of statistical
mechanics and we use it to draw a number of conclusions. First, we prove that
divergences of the entropy of scalar and spinor fields at the horizon in the
presence of rotation have the same form as in the static case and can be
removed by renormalization of the bare black hole entropy. Second, we
demonstrate that statistical-mechanical representation of the
Bekenstein-Hawking entropy of a black hole in induced gravity is universal and
does not depend on the rotation.


  Let $\I$ be a coherent subsheaf of a locally free sheaf $\Ok(E_0)$ and
suppose that $\F=\Ok(E_0)/\I$ has pure codimension. Starting with a residue
current $R$ obtained from a locally free resolution of $\F$ we construct a
vector-valued Coleff-Herrera current $\mu$ with support on the variety
associated to $\F$ such that $\phi$ is in $\I$ if and only if $\mu\phi=0$. Such
a current $\mu$ can also be derived algebraically from a fundamental theorem of
Roos about the bidualizing functor, and the relation between these two
approaches is discussed. By a construction due to Bj\"ork one gets Noetherian
operators for $\I$ from the current $\mu$. The current $R$ also provides an
explicit realization of the Dickenstein-Sessa decomposition and other related
canonical isomorphisms.


  In many practical applications of supervised learning the task involves the
prediction of multiple target variables from a common set of input variables.
When the prediction targets are binary the task is called multi-label
classification, while when the targets are continuous the task is called
multi-target regression. In both tasks, target variables often exhibit
statistical dependencies and exploiting them in order to improve predictive
accuracy is a core challenge. A family of multi-label classification methods
address this challenge by building a separate model for each target on an
expanded input space where other targets are treated as additional input
variables. Despite the success of these methods in the multi-label
classification domain, their applicability and effectiveness in multi-target
regression has not been studied until now. In this paper, we introduce two new
methods for multi-target regression, called Stacked Single-Target and Ensemble
of Regressor Chains, by adapting two popular multi-label classification methods
of this family. Furthermore, we highlight an inherent problem of these methods
- a discrepancy of the values of the additional input variables between
training and prediction - and develop extensions that use out-of-sample
estimates of the target variables during training in order to tackle this
problem. The results of an extensive experimental evaluation carried out on a
large and diverse collection of datasets show that, when the discrepancy is
appropriately mitigated, the proposed methods attain consistent improvements
over the independent regressions baseline. Moreover, two versions of Ensemble
of Regression Chains perform significantly better than four state-of-the-art
methods including regularization-based multi-task learning methods and a
multi-objective random forest approach.


  The FRS-ESR facility at GSI provides unique conditions for precision
measurements of large areas on the nuclear mass surface in a single experiment.
Values for masses of 604 neutron-deficient nuclides (30<=Z<=92) were obtained
with a typical uncertainty of 30 microunits. The masses of 114 nuclides were
determined for the first time. The odd-even staggering (OES) of nuclear masses
was systematically investigated for isotopic chains between the proton shell
closures at Z=50 and Z=82. The results were compared with predictions of modern
nuclear models. The comparison revealed that the measured trend of OES is not
reproduced by the theories fitted to masses only. The spectral pairing gaps
extracted from models adjusted to both masses, and density related observables
of nuclei agree better with the experimental data.


  Metric-affine theories of gravity provide an interesting alternative to
General Relativity: in such an approach, the metric and the affine (not
necessarily symmetric) connection are independent quantities. Furthermore, the
action should include covariant derivatives of the matter fields, with the
covariant derivative naturally defined using the independent connection. As a
result, in metric-affine theories a direct coupling involving matter and
connection is also present. The role and the dynamics of the connection in such
theories is explored. We employ power counting in order to construct the action
and search for the minimal requirements it should satisfy for the connection to
be dynamical. We find that for the most general action containing lower order
invariants of the curvature and the torsion the independent connection does not
carry any dynamics. It actually reduces to the role of an auxiliary field and
can be completely eliminated algebraically in favour of the metric and the
matter field, introducing extra interactions with respect to general
relativity. However, we also show that including higher order terms in the
action radically changes this picture and excites new degrees of freedom in the
connection, making it (or parts of it) dynamical. Constructing actions that
constitute exceptions to this rule requires significant fine tuned and/or extra
a priori constraints on the connection. We also consider f(R) actions as a
particular example in order to show that they constitute a distinct class of
metric-affine theories with special properties, and as such they cannot be used
as representative toy theories to study the properties of metric-affine
gravity.


  The fusion rings associated to affine Kac-Moody algebras appear in several
different contexts in math and mathematical physics. In this paper we find all
automorphisms of all affine fusion rings, or equivalently the symmetries of the
corresponding fusion coefficients. Most of these are directly related to
symmetries of the corresponding Coxeter-Dynkin diagram. We also find all pairs
of isomorphic affine fusion rings.


  Alongside the effort underway to build quantum computers, it is important to
better understand which classes of problems they will find easy and which
others even they will find intractable. We study random ensembles of the
QMA$_1$-complete quantum satisfiability (QSAT) problem introduced by Bravyi.
QSAT appropriately generalizes the NP-complete classical satisfiability (SAT)
problem. We show that, as the density of clauses/projectors is varied, the
ensembles exhibit quantum phase transitions between phases that are satisfiable
and unsatisfiable. Remarkably, almost all instances of QSAT for any hypergraph
exhibit the same dimension of the satisfying manifold. This establishes the
QSAT decision problem as equivalent to a, potentially new, graph theoretic
problem and that the hardest typical instances are likely to be localized in a
bounded range of clause density.


  The primary aim of the present paper is to attract the attention of particle
physicists to new developments in studying squeezed and correlated states of
the electromagnetic field as well as of those working on the latest topic to
new findings about multiplicity distributions in quantum chromodynamics. The
new types of nonclassical states used in quantum optics as squeezed states,
correlated states, even and odd coherent states (Schr\"odinger cat states) for
one--mode and multimode interaction are reviewed. Their distribution functions
are analyzed according to the method used first for multiplicity distributions
in high energy particle interactions. The phenomenon of oscillations of
particle distribution functions of the squeezed fields is described and
confronted to the phenomenon of oscillations of cumulant moments of some
distributions for squeezed and correlated field states. Possible extension of
the method to fields different from the electromagnetic field (gluons, pions,
etc.) is speculated.


  In the context of the so-called Gauss-Bonnet gravity, where the gravitational
action includes function of the Gauss-Bonnet invariant, we study cosmological
solutions, especially the well-known $\Lambda$CDM model. It is shown that the
dark energy contribution and even the inflationary epoch can be explained in
the frame of this kind of theories with no need of any other kind of component.
Other cosmological solutions are constructed and the rich properties that this
kind of theories provide are explored.


  We present a search for the Standard Model Higgs boson produced in
association with a W boson in $p\bar{p}$ collisions at a center-of-mass energy
of $\sqrt{s} = 1.96$ TeV. The search is performed in the $WH\to \ell\nu
b\bar{b}$ channel using 2.7 fb$^{-1}$ of data collected by both the CDF
detector and by the $D\O$ detector, at the Fermilab Tevatron. The searches
employ artificial neural network, matrix element and boosted decision tree
techniques to improve the separation between signal and background. Additional
techniques used to improve the Higgs sensitivity include the use of optimized
b-quark jet energy corrections and improved algorithms for identifying
b-quarks. In the absence of an observed excess in data, upper limits are set by
both experiments on the Higgs production rate times branching ratio for a range
of possible Higgs masses between 100 and 150 GeV.


  In framework of the chiral perturbation theory we obtain the phenomenological
relations between decay branches of rare radiative kaon to pion and leptons
$K^+ \to \pi^+ l^+ l^-$ and $K^0_S \to \pi^0 l^+ l^-$ and meson form factors.
The comparison of these results with the present day experimental data shows us
that the ChPT relations for a charge kaon can determine meson form factors from
already measured decay rates at high precision level. However, in the case of
the neutral kaon decays $K^0 \to \pi^0 e^+ e^- (\mu^+ \mu^-)$ the formfactor
data are known to a high precision than data on the differential rates of
radiative kaon decay $K^0 \to \pi^0 e^+ e^- (\mu^+ \mu^-)$.


  This article provides an introduction to the ideas behind the multilevel
blocking (MLB) approach to the fermion sign problem in path-integral Monte
Carlo simulations, and also gives a detailed discussion of MLB results for
quantum dots. MLB can turn the exponential severity of the sign problem into an
algebraic one, thereby enabling numerically exact studies of otherwise
inaccessible systems. Low-temperature simulation results for up to eight
strongly correlated electrons in a parabolic 2D quantum dot are presented.


  Given two arbitrary vector bundles on the Fargues-Fontaine curve, we
completely classify all vector bundles which arise as their extensions.


  At the heart of what drives the bulk of innovation and activity in Silicon
Valley and elsewhere is scalability. This unwavering commitment to scalability
-- to identify strategies for efficient growth -- is at the heart of what we
refer to as "scale thinking." Whether people are aware of it or not, scale
thinking is all-encompassing. It is not just an attribute of one's product,
service, or company, but frames how one thinks about the world (what
constitutes it and how it can be observed and measured), its problems (what is
a problem worth solving versus not), and the possible technological fixes for
those problems. This paper examines different facets of scale thinking and its
implication on how we view technology and collaborative work. We argue that
technological solutions grounded in scale thinking are unlikely to be as
liberatory or effective at deep, systemic change as their purveyors imagine.
Rather, solutions which resist scale thinking are necessary to undo the social
structures which lie at the heart of social inequality. We draw on recent work
on mutual aid networks and propose questions to ask of collaborative work
systems as a means to evaluate technological solutions and guide designers in
identifying sites of resistance to scale thinking.


  We present a thorough tight-binding analysis of the band structure of a wide
variety of lattices belonging to the class of honeycomb and Kagome systems
including several mixed forms combining both lattices. The band structure of
these systems are made of a combination of dispersive and flat bands. The
dispersive bands possess Dirac cones (linear dispersion) at the six corners (K
points) of the Brillouin zone although in peculiar cases Dirac cones at the
center of the zone $(\Gamma$ point) appear. The flat bands can be of different
nature. Most of them are tangent to the dispersive bands at the center of the
zone but some, for symmetry reasons, do not hybridize with other states. The
objective of our work is to provide an analysis of a wide class of so-called
ligand-decorated honeycomb Kagome lattices that are observed in 2D
metal-organic framework (MOF) where the ligand occupy honeycomb sites and the
metallic atoms the Kagome sites. We show that the $p_x$-$p_y$ graphene model is
relevant in these systems and there exists four types of flat bands: Kagome
flat (singly degenerate) bands, two kinds of ligand-centered flat bands (A$_2$
like and E like, respectively doubly and singly degenerate) and metal-centered
(three fold degenerate) flat bands.


  Vision Transformers (ViT) have been shown to attain highly competitive
performance for a wide range of vision applications, such as image
classification, object detection and semantic image segmentation. In comparison
to convolutional neural networks, the Vision Transformer's weaker inductive
bias is generally found to cause an increased reliance on model regularization
or data augmentation ("AugReg" for short) when training on smaller training
datasets. We conduct a systematic empirical study in order to better understand
the interplay between the amount of training data, AugReg, model size and
compute budget. As one result of this study we find that the combination of
increased compute and AugReg can yield models with the same performance as
models trained on an order of magnitude more training data: we train ViT models
of various sizes on the public ImageNet-21k dataset which either match or
outperform their counterparts trained on the larger, but not publicly available
JFT-300M dataset.


  Machine learning is often used in competitive scenarios: Participants learn
and fit static models, and those models compete in a shared platform. The
common assumption is that in order to win a competition one has to have the
best predictive model, i.e., the model with the smallest out-sample error. Is
that necessarily true? Does the best theoretical predictive model for a target
always yield the best reward in a competition? If not, can one take the best
model and purposefully change it into a theoretically inferior model which in
practice results in a higher competitive edge? How does that modification look
like? And finally, if all participants modify their prediction models towards
the best practical performance, who benefits the most? players with inferior
models, or those with theoretical superiority? The main theme of this paper is
to raise these important questions and propose a theoretical model to answer
them. We consider a study case where two linear predictive models compete over
a shared target. The model with the closest estimate gets the whole reward,
which is equal to the absolute value of the target. We characterize the reward
function of each model, and using a basic game theoretic approach, demonstrate
that the inferior competitor can significantly improve his performance by
choosing optimal model coefficients that are different from the best
theoretical prediction. This is a preliminary study that emphasizes the fact
that in many applications where predictive machine learning is at the service
of competition, much can be gained from practical (back-testing) optimization
of the model compared to static prediction improvement.


  The closed string model in the background gravity field is considered as the
bi-Hamiltonian system in assumption that string model is the integrable model
for particular kind of the background fields. The dual nonlocal Poisson
brackets, de pending of the background fields and of there derivatives, are
obtained. The integrability condition is formulated as compatibility of the
bi-Hamiltonity con dition and the Jacobi identity of the dual Poisson bracket.
Two types of the non local brackets are introduced. Constant curvature and
time-dependent metrics are considered, as example. It is, shown that the Jacobi
identities for the non local brackets have particular solution for the
space-time coordinates, as matrix representation of the simple Lie group.


  The soft Dice loss (SDL) has taken a pivotal role in numerous automated
segmentation pipelines in the medical imaging community. Over the last years,
some reasons behind its superior functioning have been uncovered and further
optimizations have been explored. However, there is currently no implementation
that supports its direct utilization in scenarios involving soft labels. Hence,
a synergy between the use of SDL and research leveraging the use of soft
labels, also in the context of model calibration, is still missing. In this
work, we introduce Dice semimetric losses (DMLs), which (i) are by design
identical to SDL in a standard setting with hard labels, but (ii) can be
employed in settings with soft labels. Our experiments on the public QUBIQ,
LiTS and KiTS benchmarks confirm the potential synergy of DMLs with soft labels
(e.g. averaging, label smoothing, and knowledge distillation) over hard labels
(e.g. majority voting and random selection). As a result, we obtain superior
Dice scores and model calibration, which supports the wider adoption of DMLs in
practice. The code is available at https://github.com/zifuwanggg/JDTLosses


  Recent superconducting transition temperatures (Tc) over 100 K for monolayer
FeSe on SrTiO3 have renewed interest in the bulk parent compound. In KCl:AlCl3
flux-transport-grown crystals of FeSe0.94Be0.06, FeSe0.97Be0.03 and, for
comparison, FeSe, this work reports doping of FeSe using Be, among the smallest
of possible dopants, corresponding to an effective chemical pressure. According
to lattice parameter measurements, 6% Be doping shrank the tetragonal FeSe
lattice equivalent to a physical pressure of 0.75 GPa. Using this
flux-transport method of sample preparation, 6% of Be was the maximum amount of
dopant achievable. At this maximal composition of FeSe0.94Be0.06, the lattice
unit cell shrinks by 2.4%, Tc - measured in the bulk via specific heat -
increases by almost 10%, the Tc vs pressure behavior shifts its peak Tconset
downwards by ~1 GPa, the high temperature structural transition around TS = 89
K increases by 1.9 K (in contrast to other dopants in FeSe which uniformly
depress TS), and the low temperature specific heat gamma increases by 10 %
compared to pure FeSe. Also, upon doping by 6% Be the residual resistivity
ratio, rho(300K)/rho(T->0), increases by almost a factor of four, while
rho(300K)/rho(T=Tc+) increases by 50%.


  We present an experimental study of the time of flight properties of a gas of
ultra-cold fermions in the BEC-BCS crossover. Since interactions can be tuned
by changing the value of the magnetic field, we are able to probe both non
interacting and strongly interacting behaviors. These measurements allow us to
characterize the momentum distribution of the system as well as its equation of
state. We also demonstrate the breakdown of superfluid hydrodynamics in the
weakly attractive region of the phase diagram, probably caused by pair breaking
taking place during the expansion.


  We characterize the combinatorial types of stacked d-polytopes that are
inscribable. Equivalently, we identify the triangulations of a simplex by
stellar subdivisions that can be realized as Delaunay triangulations.


  The emergence of an epidemic evokes the need to monitor its spread and assess
and validate any mitigation measures enacted by governments and administrative
bodies in real time. We present here a method to observe and quantify this
spread and the response of affected populations and governing bodies and apply
it to COVID-19 as a case study. This method provides means to simultaneously
track in real time quantities such as the mortality and the recovery rates as
well as the number of new infections caused by an infected person. With
sufficient data, this method enables thorough monitoring and assessment of an
epidemic without assumptions regarding the evolution of the pandemic in the
future.


  The energy spectra of the lepton(s) in eebar --> ttbar --> l^{+-}X/ l^+l^- X'
at next linear colliders (NLC) are analyzed a model-independent way for
arbitrary longitudinal beam polarizations as a general test of possible
anomalous top-quark couplings.


  We express the classical r-matrix of AdS/CFT in terms of tensor products
involving an infinite family of generators, which takes a form suggestive of
the universal expression obtained from a Yangian double. This should provide an
insight into the structure of the infinite dimensional symmetry algebra
underlying the integrability of the model, and give a clue to the construction
of its universal R-matrix. We derive the commutation relations under which the
algebra of these new generators close.


  Wholesale electricity markets are increasingly integrated via high voltage
interconnectors, and inter-regional trade in electricity is growing. To model
this, we consider a spatial equilibrium model of price formation, where
constraints on inter-regional flows result in three distinct equilibria in
prices. We use this to motivate an econometric model for the distribution of
observed electricity spot prices that captures many of their unique empirical
characteristics. The econometric model features supply and inter-regional trade
cost functions, which are estimated using Bayesian monotonic regression
smoothing methodology. A copula multivariate time series model is employed to
capture additional dependence -- both cross-sectional and serial-- in regional
prices. The marginal distributions are nonparametric, with means given by the
regression means. The model has the advantage of preserving the heavy
right-hand tail in the predictive densities of price. We fit the model to
half-hourly spot price data in the five interconnected regions of the
Australian national electricity market. The fitted model is then used to
measure how both supply and price shocks in one region are transmitted to the
distribution of prices in all regions in subsequent periods. Finally, to
validate our econometric model, we show that prices forecast using the proposed
model compare favorably with those from some benchmark alternatives.


  Particle production at the end of a first-order electroweak phase transition
may be rather generic in theories beyond the standard model. Dark matter may
then be abundantly produced by this mechanism if it has a sizable coupling to
the Higgs field. For an electroweak phase transition occuring at a temperature
T_EW ~ 50-100 GeV, non-thermally generated dark matter with mass M_X > TeV will
survive thermalization after the phase transition, and could then potentially
account for the observed dark matter relic density in scenarios where a thermal
dark matter component is either too small or absent. Dark matter in these
scenarios could then either be multi-TeV WIMPs whose relic abundace is mostly
generated at the electroweak phase transition, or "Baby-Zillas" with mass M_GUT
>> M_X >> v_EW that never reach thermal equilibrium in the early universe.


  A set of vertices $S$ is a \emph{determining set} of a graph $G$ if every
automorphism of $G$ is uniquely determined by its action on $S$. The
\emph{determining number} of $G$ is the minimum cardinality of a determining
set of $G$. This paper studies determining sets of Kneser graphs from a
hypergraph perspective. This new technique lets us compute the determining
number of a wide range of Kneser graphs, concretely $K_{n:k}$ with $n\geq
\frac{k(k+1)}{2}+1$. We also show its usefulness by giving shorter proofs of
the characterization of all Kneser graphs with fixed determining number 2, 3 or
4, going even further to fixed determining number 5. We finally establish for
which Kneser graphs $K_{n:k}$ the determining number is equal to $n-k$,
answering a question posed by Boutin.


  Affective computing, which aims to recognize, interpret, and understand human
emotions, provides benefits in healthcare, such as improving patient care and
enhancing doctor-patient communication. However, there is a noticeable absence
of a comprehensive summary of recent advancements in affective computing for
healthcare, which could pose difficulties for researchers entering this field.
To address this, our paper aims to provide an extensive literature review of
related studies published in the last five years. We begin by analyzing trends,
benefits, and limitations of recent datasets and affective computing methods
devised for healthcare. Subsequently, we highlight several healthcare
application hotspots of current technologies that could be promising for
real-world deployment. Through our analysis, we identify and discuss some
ongoing challenges in the field as evidenced by the literature. Concluding with
a thorough review, we further offer potential future research directions and
hope our findings and insights could guide related researchers to make better
contributions to the evolution of affective computing in healthcare.


  The optimal success probability of a communication game sets fundamental
limitations on an operational theory. Quantum advantage of parity oblivious
random access code (PORAC), a communication game, over classical resources
reveals the preparation contextuality of quantum theory [Phys. Rev. Lett. 102,
010401 (2009)]. Optimal quantum advantage in the N-dit PORAC game for finite
dimensions is an open problem. Here, we show that the degree of uncertainty
allowed in an operational theory determines the amount of preparation
contextuality. We connect the upper bound of fine-grained uncertainty relation
to the success probability of PORAC game played with the quantum resource.
Subsequently, we find the optimal success probability for the 2-dit PORAC game
using MUBs for the decoding strategy. Finally, we also derive an upper bound on
quantum advantage for the N-dit PORAC game.


  Retrieval augmentation enhances performance of traditional language models by
incorporating additional context. However, the computational demands for
retrieval augmented large language models (LLMs) pose a challenge when applying
them to real-time tasks, such as composition assistance. To address this
limitation, we propose the Hybrid Retrieval-Augmented Generation (HybridRAG)
framework, a novel approach that efficiently combines a cloud-based LLM with a
smaller, client-side, language model through retrieval augmented memory. This
integration enables the client model to generate effective responses,
benefiting from the LLM's capabilities and contextual information.
Additionally, through an asynchronous memory update mechanism, the client model
can deliver real-time completions swiftly to user inputs without the need to
wait for responses from the cloud. Our experiments on five benchmark datasets
demonstrate that HybridRAG significantly improves utility over client-only
models while maintaining low latency.