  Graphic Processing Units (GPUs) have become ubiquitous in scientific
computing. However, writing efficient GPU kernels can be challenging due to the
need for careful code tuning. To automatically explore the kernel optimization
space, several auto-tuning tools - like Kernel Tuner - have been proposed.
Unfortunately, these existing auto-tuning tools often do not concern themselves
with integration of tuning results back into applications, which puts a
significant implementation and maintenance burden on application developers. In
this work, we present Kernel Launcher: an easy-to-use C++ library that
simplifies the creation of highly-tuned CUDA applications. With Kernel
Launcher, programmers can capture kernel launches, tune the captured kernels
for different setups, and integrate the tuning results back into applications
using runtime compilation. To showcase the applicability of Kernel Launcher, we
consider a real-world computational fluid dynamics code and tune its kernels
for different GPUs, input domains, and precisions.


  We describe a novel technique that renders theories of $N$ axions tractable,
and more generally can be used to efficiently analyze a large class of periodic
potentials of arbitrary dimension. Such potentials are complex energy
landscapes with a number of local minima that scales as $\sqrt{N!}$, and so for
large $N$ appear to be analytically and numerically intractable. Our method is
based on uncovering a set of approximate symmetries that exist in addition to
the $N$ periods. These approximate symmetries, which are exponentially close to
exact, allow us to locate the minima very efficiently and accurately and to
analyze other characteristics of the potential. We apply our framework to
evaluate the diameters of flat regions suitable for slow-roll inflation, which
unifies, corrects and extends several forms of "axion alignment" previously
observed in the literature. We find that in a broad class of random theories,
the potential is smooth over diameters enhanced by $N^{3/2}$ compared to the
typical scale of the potential. A Mathematica implementation of our framework
is available online.


  We study zero temperature phase diagram of the three-dimensional
Kugel-Khomskii model on a cubic lattice using the cluster mean field theory and
different perturbative expansions in the orbital sector. The phase diagram is
rich, goes beyond the single-site mean field theory due to spin-orbital
entanglement. In addition to the antiferromagnetic (AF) and ferromagnetic (FM)
phases, one finds also a plaquette valence-bond phase with singlets ordered
either on horizontal or vertical bonds. More importantly, for increasing Hund's
exchange we identify three phases with exotic magnetic order stabilized by
orbital fluctuations in between the AF and FM order: (i) an AF phase with two
mutually orthogonal antiferromagnets on two sublattices in each $ab$ plane and
AF order along the c axis (ortho-$G$-type phase), (ii) a canted-$A$-type AF
phase with a non-trivial canting angle between nearest neighbor FM layers along
the c axis, and (iii) a striped-AF phase with anisotropic AF order in the $ab$
planes. We elucidate the mechanism responsible for each of the above phases by
deriving effective spin models which involve second and third neighbor
Heisenberg interactions as well as four-site spin interactions going beyond
Heisenberg physics, and explain how the entangled nearest neighbor spin-orbital
superexchange generates spin interactions between more distant spins.


  High-temperature gas reactors rely on TRIstructural-ISOtropic (TRISO) fuel
for enhanced fission product retention. Accurate fuel characterization would
improve monitoring of efficient fuel usage and accountability. We developed a
new neutron multiplicity counter (NMC) based on boron coated straw (BCS)
detectors and used it in coincidence mode for 235U assay in TRISO fuel. In this
work, we demonstrate that a high-efficiency version of the NMC encompassing 396
straws is able to estimate the 235U in used TRISO-fueled pebbles or compacts
with a relative uncertainty below 2.5% in 100 s. We performed neutronics and
fuel depletion calculation of the HTR-10 pebble bed reactor to estimate the
neutron and gamma-ray source strengths of used TRISO-fueled pebbles with burnup
between 9 and 90 GWd/t. Then, we measured a gamma-ray intrinsic efficiency of
10^-12 at an exposure rate of 340.87 R/h. The low gamma-ray sensitivity and
high neutron detection efficiency enable the inspection of used fuel.


  Time integration methods for solving initial value problems are an important
component of many scientific and engineering simulations. Implicit time
integrators are desirable for their stability properties, significantly
relaxing restrictions on timestep size. However, implicit methods require
solutions to one or more systems of nonlinear equations at each timestep, which
for large simulations can be prohibitively expensive. This paper introduces a
new family of linearly implicit multistep methods (LIMM), which only requires
the solution of one linear system per timestep. Order conditions and stability
theory for these methods are presented, as well as design and implementation
considerations. Practical methods of order up to five are developed that have
similar error coefficients, but improved stability regions, when compared to
the widely used BDF methods. Numerical testing of a self-starting variable
stepsize and variable order implementation of the new LIMM methods shows
measurable performance improvement over a similar BDF implementation.


  In this paper, we propose a multiscale empirical interpolation
  method for solving nonlinear multiscale partial differential
  equations. The proposed method combines empirical interpolation
  techniques and local multiscale methods, such as the Generalized
  Multiscale Finite Element Method (GMsFEM). To solve nonlinear
  equations, the GMsFEM is used to represent the solution on a coarse
  grid with multiscale basis functions computed offline. Computing
  the GMsFEM solution involves calculating the residuals on the fine
  grid. We use empirical interpolation concepts to evaluate the
  residuals and the Jacobians of the multiscale system with a
  computational cost which is proportional to the coarse scale problem
  rather than the fully-resolved fine scale one. Empirical
  interpolation methods use basis functions and an inexpensive
  inversion which are computed in the offline stage for finding the
  coefficients in the expansion based on a limited number of nonlinear
  function evaluations. The proposed multiscale empirical
  interpolation techniques: (1) divide computing the nonlinear
  function into coarse regions; (2) evaluate contributions of
  nonlinear functions in each coarse region taking advantage of a
  reduced-order representation of the solution; and (3) introduce
  multiscale proper-orthogonal-decomposition techniques to find
  appropriate interpolation vectors. We demonstrate the effectiveness
  of the proposed methods on several examples of nonlinear multiscale
  PDEs that are solved with Newton's methods and fully-implicit time
  marching schemes. Our numerical results show that the proposed
  methods provide a robust framework for solving nonlinear multiscale
  PDEs on a coarse grid with bounded error.


  The superextension $\lambda(X)$ of a set $X$ consists of all maximal linked
families on $X$. Any associative binary operation $*: X\times X \to X$ can be
extended to an associative binary operation $*:
\lambda(X)\times\lambda(X)\to\lambda(X)$. In the paper we study isomorphisms of
superextensions of groups and prove that two groups are isomorphic if and only
if their superextensions are isomorphic. Also we describe the automorphism
groups of superextensions of all groups of cardinality $\leq 5$.


  Robotic systems need advanced mobility capabilities to operate in complex,
three-dimensional environments designed for human use, e.g., multi-level
buildings. Incorporating some level of autonomy enables robots to operate
robustly, reliably, and efficiently in such complex environments, e.g.,
automatically "returning home" if communication between an operator and robot
is lost during deployment. This work presents a novel method that enables
mobile robots to robustly operate in multi-level environments by making it
possible to autonomously locate and climb a range of different staircases. We
present results wherein a wheeled robot works together with a quadrupedal
system to quickly detect different staircases and reliably climb them. The
performance of this novel staircase detection algorithm that is able to run on
the heterogeneous platforms is compared to the current state-of-the-art
detection algorithm. We show that our approach significantly increases the
accuracy and speed at which detections occur.


  We give the first detailed study of the arrival time distribution of nucleons
in UHECR air showers. We analyze in detail the influence of late arriving
particles on the energy determination of the AGASA experiment, as well as how
the arrival time distribution changes with distance from shower core. Our
calculations are consistent with experimental observations of the AGASA group.
Crucial to obtaining agreement, is the correct implementation of the energy
loss for low-energy protons. We confirm AGASA's estimation of the error in
their energy determination associated with late-arriving particles, assuming
primary protons.


  There were two famous conjectures on complete affine maximal surfaces, one
due to E. Calabi, the other to S.S. Chern. Both were solved with different
methods about one decade ago by studying the associated Euler-Lagrange
equation. Here we survey two proofs of Chern's conjecture in our recent
monograph [L-X-S-J], in particular we add some details of the proofs of
auxiliary material that were omitted in [L-X-S-J]. We describe the related
background in our Introduction. Our survey is suitable as a report about recent
developments and techniques in the study of certain Monge-Ampere equations.


  We exemplarily investigate how optical properties of single scatterers in
interacting multi-particle systems influence measurable structure factors. Both
particles with linear gradients of their scattering length density and
core-shell structures evoke characteristic deviations between the weighted sum
$\langle S(Q)\rangle$ of partial structure factors in a multicomponent system
and experimentally accessible, measurable structure factors
$S_{\mathrm{M}}(Q)$. While $\langle S(Q)\rangle$ contains only structural
information of self-organising systems, $S_{\mathrm{M}}(Q)$ additionally is
influenced by optical properties of their constituents resulting in features
such as changing amplitudes, additional peaks in the low wavevector region or
splitting of higher-order maxima which are not related to structural reasons.
Hence, a careful data analysis regarding size-distribution and optical
properties of single scatters is mandatory to avoid a misinterpretation of
measurable structure factors.


  In this survey, we discuss whether the complex projective space can be
characterized by its integral cohomology ring among compact complex manifolds.


  Self-organized criticality (SOC) refers to the ability of complex systems to
evolve towards a 2nd-order phase transition at which interactions between
system components lead to scale-invariant events beneficial for system
performance. For the last two decades, considerable experimental evidence
accumulated that the mammalian cortex with its diversity in cell types and
connections might exhibit SOC. Here we review experimental findings of
isolated, layered cortex preparations to self-organize towards four dynamical
motifs identified in the cortex in vivo: up-states, oscillations, neuronal
avalanches, and coherence potentials. During up-states, the synchronization
observed for nested theta/gamma-oscillations embeds scale-invariant neuronal
avalanches that exhibit robust power law scaling in size with a slope of -3/2
and a critical branching parameter of 1. This dynamical coordination, tracked
in the local field potential (nLFP) and pyramidal neuron activity using
2-photon imaging, emerges autonomously in superficial layers of organotypic
cortex cultures and acute cortex slices, is homeostatically regulated, displays
separation of time scales, and reveals unique size vs. quiet time dependencies.
A threshold operation identifies coherence potentials; avalanches that in
addition maintain the precise time course of propagated synchrony. Avalanches
emerge under conditions of external driving. Control parameters are established
by the balance of excitation and inhibition (E/I) and the neuromodulator
dopamine. This rich dynamical repertoire is not observed in dissociated cortex
cultures, which lack cortical layers and exhibit dynamics similar to a
1st-order phase transition. The precise interactions between up-states, nested
oscillations, avalanches, and coherence potentials in superficial cortical
layers provide compelling evidence for SOC in the brain.


  We propose a new model for speaker naming in movies that leverages visual,
textual, and acoustic modalities in an unified optimization framework. To
evaluate the performance of our model, we introduce a new dataset consisting of
six episodes of the Big Bang Theory TV show and eighteen full movies covering
different genres. Our experiments show that our multimodal model significantly
outperforms several competitive baselines on the average weighted F-score
metric. To demonstrate the effectiveness of our framework, we design an
end-to-end memory network model that leverages our speaker naming model and
achieves state-of-the-art results on the subtitles task of the MovieQA 2017
Challenge.


  In this work, a two-fluid interacting model in a flat FLRW universe has been
studied considering particle creation mechanism with a particular form of
particle creation rate $\Gamma=\Gamma_0 H+\frac{\Gamma_1}{H}$ from different
aspects. Statistical analysis with a combined data set of SNe Ia (Supernovae
Type Ia) and Hubble data is performed to achieve the best-fit values of the
model parameters, and the model is compatible with current observational data.
We also perform a dynamical analysis of this model to get an overall
qualitative description of the cosmological evolution by converting the
governing equations into a system of ordinary differential equations
considering a proper transformation of variables. We find some non-isolated
sets of critical points, among which some usually are normally hyperbolic sets
of points that describe the present acceleration of the universe dominated by
dark energy mimicking cosmological constant or phantom fluid. Scaling solutions
are also obtained from this analysis, and they can alleviate the coincidence
problem successfully. Statefinder diagnosis is also carried out for this model
to compare it with the $\Lambda$CDM, and any other dark energy models by
finding various statefinder parameters. Finally, the thermodynamic analysis
shows that the generalized second law of thermodynamics is valid in an
irreversible thermodynamic context.


  In this paper we propose a workflow to detect and track mitotic cells in
time-lapse microscopy image sequences. In order to avoid the requirement for
cell lines expressing fluorescent markers and the associated phototoxicity,
phase contrast microscopy is often preferred over fluorescence microscopy in
live-cell imaging. However, common specific image characteristics complicate
image processing and impede use of standard methods. Nevertheless, automated
analysis is desirable due to manual analysis being subjective, biased and
extremely time-consuming for large data sets. Here, we present the following
workflow based on mathematical imaging methods. In the first step, mitosis
detection is performed by means of the circular Hough transform. The obtained
circular contour subsequently serves as an initialisation for the tracking
algorithm based on variational methods. It is sub-divided into two parts: in
order to determine the beginning of the whole mitosis cycle, a backwards
tracking procedure is performed. After that, the cell is tracked forwards in
time until the end of mitosis. As a result, the average of mitosis duration and
ratios of different cell fates (cell death, no division, division into two or
more daughter cells) can be measured and statistics on cell morphologies can be
obtained. All of the tools are featured in the user-friendly
MATLAB$^{\circledR}$ Graphical User Interface MitosisAnalyser.


  We develop a semi-analytic model to explore different prescriptions of
supermassive black hole (SMBH) fuelling. This model utilises a merger-triggered
burst mode in concert with two possible implementations of a long-lived steady
mode for assembling the mass of the black hole in a galactic nucleus. We
improve modelling of the galaxy-halo connection in order to more realistically
determine the evolution of a halo's velocity dispersion. We use four model
variants to explore a suite of observables: the M-sigma relation, mass
functions of both the overall and broad-line quasar population, and luminosity
functions as a function of redshift. We find that "downsizing" is a natural
consequence of our improved velocity dispersion mappings, and that high-mass
SMBHs assemble earlier than low-mass SMBHs. The burst mode of fuelling is
sufficient to explain the assembly of SMBHs to z=2, but an additional steady
mode is required to both assemble low-mass SMBHs and reproduce the low-redshift
luminosity function. We discuss in detail the trade-offs in matching various
observables and the interconnected modelling components that govern them. As a
result, we demonstrate the utility as well as the limitations of these
semi-analytic techniques.


  The increasing relevance of energy storage technologies demands high-capacity
cathode materials for Li-ion batteries. Recently, Li-rich defect anti-fluorite
Li$_5$FeO$_4$ has emerged as a high-capacity cathode material exhibiting
simultaneous anionic and cationic redox without significant oxygen release. But
suffers from irreversible structural change and capacity fade during cycling.
Herein we investigate the suppressed capacity fade reported in Co substituted
Li$_5$FeO$_4$ and establish the optimal performance through tuning the
concentration and oxidation state of Co. We have substituted Co at the Fe site
and carried out a detailed analysis of structural, magnetic, electronic, and
electrochemical properties using first-principles density functional theory
calculations. The extended stability and suppressed capacity fading are found
only in specific compositions depending on the Co/Li concentration and the
oxidation state of Co. The reasons behind suppressed capacity fading in Co
substituted systems have been unveiled on the basis of bonding analyses and
proposed as a strategy to suppress the voltage fading reported in Li-rich
materials due to transition metal migration. From the evaluation of
thermodynamic stability and electronic structure,
Li$_{5.5}$Fe$_{0.5}$Co$_{0.5}$O$_4$, Li$_5$Fe$_{0.25}$Co$_{0.75}$O$_4$, and
Li$_{4.5}$Fe$_{0.5}$Co$_{0.5}$O$_4$ are found to exhibit better electrical
conductivity than Li$_5$FeO$_4$. All these systems have voltage in the range of
3 to 5 V and exhibit three dimensional Li diffusion pathway with a diffusion
barrier height of around 0.3 eV. From Li$_5$Fe$_{0.25}$Co$_{0.75}$O$_4$, one
can delithiate three Li-ions without structural change and oxygen release,
therefore expected to acquire a reversible capacity of around 513 mAh/g.
Moreover, as in pristine Li$_5$FeO$_4$, the selected Co substituted systems
also exhibit simultaneous anionic and cationic redox without significant O2
release.


  We present a dynamical approach to the classical Perron-Frobenius theory by
using some elementary knowledge on linear ODEs. It is completely self-contained
and significantly different from those in the literature. As a result, we
develop a complex version of the Perron-Frobenius theory and prove some
generalized Krein-Rutman type theorems.


  We consider the fermionic SU($3$) Hubbard model on the triangular lattice in
the presence of a three-sublattice staggered potential which provides the
possibility to investigate the competition of charge and magnetic order in
three-component systems. We show that depending on the strength of the
staggered potential $\Delta$, the Hubbard interaction $U$ destabilizes the band
insulator (BI) at small $U$ into the Mott insulator (MI) at large $U$ in three
different ways with different intermediate phases. This leads to a rich phase
diagram in the $U$-$\Delta$ plane. Our results indicate that multi-component
systems show not only exotic states in the Mott regime as has been considered
previously, but also interesting competition between charge and magnetic
orders.


  The features of turbulence modulation produced by a heavy loaded suspension
of small solid particles or liquid droplets are discussed by using a
physically-based regularisation of particle-fluid interactions. The approach
allows a robust description of the small scale properties of the system
exploiting the convergence of the statistics with respect to the regularisation
parameter. It is shown that sub-Kolmogorov particles/droplets modify the energy
spectrum leading to a scaling law, $E(k)\propto k^{-4}$, that emerges at small
scales where the particle forcing balances the viscous dissipation. This regime
is confirmed by Direct Numerical Simulation data of a particle-laden
statistically steady homogeneous shear flow, demonstrating the ability of the
regularised model to capture the relevant small-scale physics. The energy
budget in spectral space, extended to account for the inter-phase momentum
exchange, highlights how the particle provide an energy sink in the production
range that turns into a source at small scales. Overall, the dissipative
fluid-particle interaction is found to stall the energy cascade processes
typical of Newtonian turbulent flows. In terms of particle statistics,
clustering at small scale is depleted, with potential consequences for
collision models.


  Origin and properties of non-Lorentzian spectral lines in linear chains of
nanospheres are discussed. The lines are shown to be super-exponentially narrow
with the characteristic width proportional to exp[-C(h/a)^3] where C is a
numerical constant, h the spacing between the nanospheres in the chain and a
the sphere radius. The fine structure of these spectral lines is also
investigated.


  Chemical abundance data for the Galaxy show a wide dispersion. We try to
check if this dispersion may also be found with chemical evolution models as an
effect of variations of an Initial Mass Function (IMF) which follows a
Poisson's distribution


  The strong recurrence is equivalent to the Riemann hypothesis. On the other
hand, the generalized strong recurrence holds for any irrational number. In
this paper, we show the generalized strong recurrence for all non-zero rational
numbers. Moreover, we prove that the generalized strong recurrence in the
region of absolute convergence holds for any real number.


  Canard-induced phenomena have been extensively studied in the last three
decades, both from the mathematical and from the application viewpoints.
Canards in slow-fast systems with (at least) two slow variables, especially
near folded-node singularities, give an essential generating mechanism for
Mixed-Mode oscillations (MMOs) in the framework of smooth multiple timescale
systems. There is a wealth of literature on such slow-fast dynamical systems
and many models displaying canard-induced MMOs, in particular in neuroscience.
In parallel, since the late 1990s several papers have shown that the canard
phenomenon can be faithfully reproduced with piecewise-linear (PWL) systems in
two dimensions although very few results are available in the three-dimensional
case. The present paper aims to bridge this gap by analysing canonical PWL
systems that display folded singularities, primary and secondary canards, with
a similar control of the maximal winding number as in the smooth case. We also
show that the singular phase portraits are compatible in both frameworks.
Finally, we show on an example how to construct a (linear) global return and
obtain robust PWL MMOs.


  The purpose of this study is to establish whether the use of information
technology (IT) by elected municipal representatives, for constituency work,
emboldens the adoption of e-service in municipals of a developing country. The
research data was obtained through the completion of a questionnaire by a
sample of respondents who serve as authorities of IT in South African
municipals. The findings from both descriptive and inferential data analysis of
collected data confirm that the use of IT by elected municipal representatives
for constituency work impacts the adoption of e-service in municipals.
Furthermore, the use of IT by elected municipal representatives for
constituency work correlated with both e-service laws and e-service security.
This study contributes to a better understanding of choices needed when
planning for the adoption of e-service initiatives in municipals of developing
countries. Given that 87.2% of respondents are aware of a high access to
telephone mobile, a further research is needed to clarify why most elected
municipal representatives of a developing country choose not to exploit IT for
their constituency work, and similarly why municipals of a developing country
do not exploit IT to provide services.


  The present paper is on a new result on so-called realization problems of
graphs as Reeb graphs of Morse functions with prescribed preimages. The Reeb
graph of a smooth function is a graph obtained by identifying two points in the
manifold of the domain if and only if they are in a same connected component of
some preimage. In considerable cases such as cases where functions are
Morse-Bott functions on closed manifolds we obtain such graphs.
  Reeb graphs represent the manifolds of the domains compactly. They are not
only fundamental tools in geometry using differentiable functions and maps as
fundamental tools, but also in applications of mathematics such as
visualizations. Realization problems are also fundamental and important.
Construction of functions with prescribed preimages was considered first by the
author essentially and before the present study, the author has obtained a
result.


  We study the global and local existence and uniqueness of solutions to the
Navier-Stokes equations with anisotropic viscosity in a bounded cylindrical
domain $Q=\Omega\times (0,1)$, where $\Omega$ is a star-shaped domain in $R^2$.
In this paper, we consider the case of homogeneous Dirichlet boundary
conditions on the lateral boundary and vanishing normal trace on the top and
the bottom.


  An equation is derived that expresses the thermodynamic scaling exponent, g,
which superposes relaxation times and other measures of molecular mobility
determined over a range of temperatures and densities, in terms of static,
physical quantities. The latter are available in the literature or can be
measured at ambient pressure. We show for 13 materials, both molecular liquids
and polymers, that the calculated g are equivalent to the scaling exponents
obtained directly by superpositioning. The assumptions of the analysis are that
the glass transition is isochronal and that the first Ehrenfest relation is
valid; the first assumption is true by definition, while the second has been
corroborated for many glass-forming materials at ambient pressure. However, we
find that the Ehrenfest relation breaks down at elevated pressure, although
this limitation is of no consequence herein, since the appeal of the new
equation is its applicability to ambient pressure data.


  In this paper we describe the efficient numerical implementation of
Fractional HBVMs, a class of methods recently introduced for solving systems of
fractional differential equations. The reported arguments are implemented in
the Matlab code fhbvm, which is made available on the web. An extensive
experimentation of the code is reported, to give evidence of its effectiveness.


  We propose two new approaches for regulating spin polarization and spin
inversion in a conducting junction within a tight-binding framework based on
wave-guide theory. The system comprises a magnetic quantum ring with finite
modulation in site potential is coupled to two non-magnetic electrodes. Due to
close proximity an additional tunneling is established between the electrodes
which regulates electronic transmission significantly. At the same time the
phase associated with site potential, which can be tuned externally yields
controlled transmission probabilities. Our results are valid for a wide range
of parameter values which demonstrates the robustness of our proposition. We
strongly believe that the proposed model can be realized in the laboratory.


  Differential cross sections for $\gamma p \to K^+\Lambda(1405)$ and $\gamma p
\to K^+\Sigma^0(1385)$ reactions have been measured in the photon energy range
from 1.5 to 2.4 GeV and the angular range of $0.8<\cos(\Theta)<1.0$ for the
$K^+$ scattering angle in the center-of-mass system. This data is the first
measurement of the $\Lambda(1405)$ photoproduction cross section. The
lineshapes of \LamS measured in $\Sigma^+\pi^-$ and $\Sigma^-\pi^+$ decay modes
were different with each other, indicating a strong interference of the isospin
0 and 1 terms of the $\Sigma\pi$ scattering amplitudes. The ratios of \LamS
production to \SigS production were measured in two photon energy ranges: near
the production threshold ($1.5<E_\gamma<2.0$ GeV) and far from it ($2.0
<E_\gamma<2.4$ GeV). The observed ratio decreased in the higher photon energy
region, which may suggest different production mechanisms and internal
structures for these hyperon resonances.


  We study contributions to $K_L \rightarrow \mu^+\mu^-$ from anomalous $WWZ$
interactions. There are, in general, seven anomalous couplings. Among the seven
anomalous couplings, only two of them contribute significantly. The others are
suppressed by factors like $m_s^2/M_W^2$, $m_d^2$, or $m_K^2/M_W^2$. Using the
experimental data on $K_L\rightarrow \mu^+\mu^-$, we obtain strong bounds on
the two anomalous couplings.


  A procedure of implementing QCD corrections in Monte Carlo programs by a
reweighting method is described for the photoproduction of W bosons at HERA.
Tables for W boson production in LO and NLO are given in bins of the transverse
momentum of the W boson and its rapidity.


  Training models that perform well under distribution shifts is a central
challenge in machine learning. In this paper, we introduce a modeling framework
where, in addition to training data, we have partial structural knowledge of
the shifted test distribution. We employ the principle of minimum
discriminating information to embed the available prior knowledge, and use
distributionally robust optimization to account for uncertainty due to the
limited samples. By leveraging large deviation results, we obtain explicit
generalization bounds with respect to the unknown shifted distribution. Lastly,
we demonstrate the versatility of our framework by demonstrating it on two
rather distinct applications: (1) training classifiers on systematically biased
data and (2) off-policy evaluation in Markov Decision Processes.


  Epidemic situations typically demand intensive data collection and management
from different locations/entities within a strict time constraint. Such demand
can be fulfilled by leveraging the intensive and easy deployment of the
Internet of Things (IoT) devices. The management and containment of such
situations also rely on cross-organizational and national collaboration. Thus,
this paper proposes an Intelligent-Health (I-Health) system that aims to
aggregate diverse e-health entities in a unique national healthcare system by
enabling swift, secure exchange and storage of medical data. In particular, we
design an automated patients monitoring scheme, at the edge, which enables the
prompt discovery, remote monitoring, and fast emergency response for critical
medical events, such as emerging epidemics. Furthermore, we develop a
blockchain optimization model that aims to optimize medical data sharing
between different health entities to provide effective and secure health
services. Finally, we show the effectiveness of our system, in adapting to
different critical events, while highlighting the benefits of the proposed
I-Health system.


  We describe the inclusive Racah matrices for the first non-(anti)symmetric
rectangular representation R=[2,2] for quantum groups U_q(sl_N). Most of them
have sizes 2, 3, and 4 and are fully described by the eigenvalue hypothesis. Of
two 6x6 matrices, one is also described in this way, but the other one
corresponds to the case of degenerate eigenvalues and is evaluated by the
highest weight method. Together with the much harder calculation for R=[3,1] in
arXiv:1605.02313 and with the new method to extract exclusive matrices S and
\bar S from the inclusive ones, this completes the story of Racah matrices for
|R|\leq 4 and allows one to calculate and investigate the corresponding colored
HOMFLY polynomials for arbitrary 3-strand and arborescent knots.


  We show that there are exactly eight MMIK (minor minimal intrinsically
knotted) graphs of order nine.


  In the present paper, experimental and numerical studies of interaction
between different liquid droplets with different hot metal surfaces had been
carried out and the obtained results were interpreted using graphs and
pictures. Droplet impact, spreading, break-up, and leidenfrost phenomenon were
observed with the help of video camera at 100 fps. The interaction studies were
done at different surface temperatures and different droplet impinging
velocities. Three metals and three liquids were selected for the present study.
Copper, Aluminium and Steel were selected as metals with different roughness
factors and at different temperatures, while in liquid entities, Methanol,
Ethanol and Kerosene (Water was also used in some portion) were selected. The
selections of the metals were based on thethermal diffusivity property as it
influences the thermal interaction between droplet and the hot metal surface.
The temperature range of metal surface was kept from 150{\deg} C to 425{\deg}C
and the impinging velocity range of droplets were in the range of 2.4 - 4.9
m/s. For computational studies Ansys Fluent R3-2019 version was used. Numerical
study for the interaction process was carried out using Volume of Fluid (VOF)
method, solving Navier-Stokes and energy equations in a 2D-geometry transient
simulation. Parameters like surface temperature, heat flux, and spreading ratio
with respect to Weber number change were investigated and discussed in the
present paper. The output of this study will be useful to understand the
physical mechanisms better and to develop novel models for effective spray
cooling system designs. The results show that for a lower value of Weber
number, after impact the droplets spread less wide compare to high Weber
number. The spreading ratio of droplets was almost constant or uniform for
different surface temperature and it depends only on the surface roughness.


  We study numerical integration over bounded regions in $\mathbb{R}^s, s\ge1$
with respect to some probability measure. We replace random sampling with
quasi-Monte Carlo methods, where the underlying point set is derived from
deterministic constructions that aim to fill the space more evenly than random
points. Such quasi-Monte Carlo point sets are ordinarily designed for the
uniform measure, and the theory only works for product measures when a
coordinate-wise transformation is applied. Going beyond this setting, we first
consider the case where the target density is a mixture distribution where each
term in the mixture comes from a product distribution. Next we consider target
densities which can be approximated with such mixture distributions. We require
the approximation to be a sum of coordinate-wise products and the approximation
to be positive everywhere (so that they can be re-scaled to probability density
functions). We use tensor product hat function approximations for this purpose
here, since a hat function approximation of a positive function is itself
positive.
  We also study more complex algorithms, where we first approximate the target
density with a general Gaussian mixture distribution and approximate the
mixtures with an adaptive hat function approximation on rotated intervals. The
Gaussian mixture approximation allows us to locate the essential parts of the
target density, whereas the adaptive hat function approximation allows us to
approximate the finer structure of the target density. We prove convergence
rates for each of the integration techniques based on quasi-Monte Carlo
sampling for integrands with bounded partial mixed derivatives. The employed
algorithms are based on digital $(t,s)$-sequences over the finite field
$\mathbb{F}_2$ and an inversion method. Numerical examples illustrate the
performance of the algorithms for some target densities and integrands.


  Surveys of diffuse $\gamma$-ray in the interstellar medium (ISM) can be used
to probe hydrogen-antihydrogen oscillations, by detecting the $\gamma$-ray
emission from antihydrogen annihilation. A bound on the oscillation parameter
$\delta$ was originally derived by Feinberg, Goldhaber and Steigman (1978). In
this paper, we re-visit the original derivation by performing a more detailed
analysis that (1) incorporates suppression effects from additional elastic and
inelastic processes, (2) treats the ISM as a multi-phase medium, and (3)
utilises more recent $\gamma$-ray data from the Fermi Large Area Telescope. We
find that suppression from elastic scattering plays a more important role than
previously thought, while the multi-phase nature of the ISM affects how the
$\gamma$-ray data should be utilised. We derive a more accurate bound on the
oscillation period that is about an order of magnitude weaker than the older
bound.


  Our work focuses on tackling the challenging but natural visual recognition
task of long-tailed data distribution (i.e., a few classes occupy most of the
data, while most classes have rarely few samples). In the literature, class
re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent
and effective methods proposed to alleviate the extreme imbalance for dealing
with long-tailed problems. In this paper, we firstly discover that these
re-balancing methods achieving satisfactory recognition accuracy owe to that
they could significantly promote the classifier learning of deep networks.
However, at the same time, they will unexpectedly damage the representative
ability of the learned deep features to some extent. Therefore, we propose a
unified Bilateral-Branch Network (BBN) to take care of both representation
learning and classifier learning simultaneously, where each branch does perform
its own duty separately. In particular, our BBN model is further equipped with
a novel cumulative learning strategy, which is designed to first learn the
universal patterns and then pay attention to the tail data gradually. Extensive
experiments on four benchmark datasets, including the large-scale iNaturalist
ones, justify that the proposed BBN can significantly outperform
state-of-the-art methods. Furthermore, validation experiments can demonstrate
both our preliminary discovery and effectiveness of tailored designs in BBN for
long-tailed problems. Our method won the first place in the iNaturalist 2019
large scale species classification competition, and our code is open-source and
available at https://github.com/Megvii-Nanjing/BBN.


  Given $d\ge 1$, we provide a construction of the random measure - the
critical Gaussian Multiplicative Chaos - formally defined
$e^{\sqrt{2d}X}\mathrm{d} \mu$ where $X$ is a $\log$-correlated Gaussian field
and $\mu$ is a locally finite measure on $\mathbb R^d$. Our construction
generalizes the one performed in the case where $\mu$ is the Lebesgue measure.
It requires that the measure $\mu$ is sufficiently spread out, namely that for
$\mu$ almost every $x$ we have $$ \int_{B(0,1)}\frac{\mu(\mathrm{d}
y)}{|x-y|^{d}e^{\rho\left(\log \frac{1}{|x-y|} \right)}}<\infty, $$ for any
compact set where $\rho:\mathbb R_+\to \mathbb R_+$ can be chosen to be any
lower envelope function for the $3$-Bessel process (this includes
$\rho(x)=x^{\alpha}$ with $\alpha\in (0,1/2)$). We prove that three distinct
random objects converge to a common limit which defines the critical GMC: the
derivative martingale, the critical martingale, and the exponential of the
mollified field. We also show that the above criterion for the measure $\mu$ is
in a sense optimal.


  We study Tate-Vogel and relative cohomologies of complexes by applying the
model structure induced by a complete hereditary cotorsion pair ($\A$, $\B$) of
modules. We show first that the class of complexes admitting a complete $\A$
resolution is exactly the class of complexes with finite Gorenstein $\A$
dimension. This lets us give general techniques for computing Tate-Vogel
cohomoloies of complexes with finite Gorenstein $\A$ dimension. As a
consequence, relative cohomology groups for complexes with finite Gorenstein
$\A$ dimension are investigated. Finally, the relationships between Gorenstein
$\A$ dimensions and $\A$ dimensions for complexes are given.


  KIC8264293 is a fast rotating B-type pulsator observed by Kepler satellite.
Its photometric variability is mainly due to pulsations in high-order g modes.
Besides, we detected a weak H$\alpha$ emission. Thus, the second source of
variability is the fluctuation in a disk around the star. The pulsational
spectrum of KIC8264293 reveals a frequency grouping and period spacing pattern.
Here, we present the thorough seismic analysis of the star based on these
features. Taking into account the position of the star in the HR diagram and
fitting the 14 frequencies that form the period spacing we constrain the
internal structure of the star. We conclude that the star barely left the ZAMS
and the best seismic model has $M = 3.54\,\mathrm{M}_\odot$,
$V_\mathrm{rot}=248\,\mathrm{km\,s}^{-1}$ and $Z = 0.0112$. We found the upper
limit on the mixing at the edge of the convective core, with the overshooting
parameter up to $f_\mathrm{ov} = 0.03$. On the other hand, we were not able to
constrain the envelope mixing for the star. To excite the modes in the observed
frequency range, we had to modify the opacity data. Our best seismic model with
an opacity increase by 100% at the "nickel" bump $\log T=5.46$ explains the
whole instability. KIC8264293 is the unique, very young star pulsating in
high-order g modes with the Be feature. However, it is not obvious whether the
source of this circumstellar matter is the ejection of mass from the underlying
star or whether the star has retained its protostellar disk.


  Understanding the spectral and flavor composition of the astrophysical
neutrino flux responsible for the recently observed ultra-high energy events at
IceCube is of great importance for both astrophysics and particle physics. We
perform a statistical likelihood analysis to the 3-year IceCube data and derive
the allowed range of the spectral index and flux normalization for various
well-motivated physical flavor compositions at source. While most of the
existing analyses so far assume the flavor composition of the neutrinos at an
astrophysical source to be (1:2:0), it seems rather unnatural to assume only
one type of source, once we recognize the possibility of at least two physical
sources. Bearing this in mind, we entertain the possibility of a two-component
source for the analysis of IceCube data. It appears that our two component
hypothesis explains some key features of the data better than a
single-component scenario, i.e it addresses the apparent energy gap between 400
TeV to about 1 PeV and easily accommodates the observed track to shower ratio.
Given the extreme importance of the flavor composition for the correct
interpretation of the underlying astrophysical processes as well as for the
ramification for particle physics, this two-component flux should be tested as
more data is accumulated.


  We prove that the results in scattering theory that involve resonances are
still valid for non-analytic potentials, even if the notion of resonance is not
defined in this setting. More precisely, we show that if the potential of a
semiclassical Schr\"odinger operator is supposed to be smooth and to decrease
at infinity, the usual formulas relating scattering quantities and resonances
still hold. The main ingredient for the proofs is a resolvent estimate of a new
type, relating the resolvent of an operator with the resolvent of its cut-off
counterpart.


  The linear response of a nonrelativistic superfluid baryon system on an
external weak field is investigated while taking into account of the
Fermi-liquid interactions. We generalize the theory developed by Leggett for a
superfluid Fermi-liquid at finite temperature to the case of timelike momentum
transfer typical of the problem of neutrino emission from neutron stars. A
space-like kinematics is also analysed for completeness and compared with known
results. We use the obtained response functions to derive the neutrino energy
losses caused by recombination of broken pairs in the electrically neutral
superfluid baryon matter. We find that the dominant neutrino radiation occurs
through the axial-vector neutral currents. The emissivity is found to be of the
same order as in the BCS approximation, but the details of its temperature
dependence are modified by the Fermi-liquid interactions. The role of
electromagnetic correlations in the pairing case of protons interacting with
the electron background is discussed in the conclusion.


  How to accurately probe chemically reactive flows with essential
thermodynamic nonequilibrium effects is an open issue. Via the Chapman-Enskog
analysis, the local nonequilibrium particle velocity distribution function is
derived from the gas kinetic theory. It is demonstrated theoretically and
numerically that the distribution function depends on the physical quantities
and derivatives, and is independent of the chemical reactions directly. Based
on the simulation results of the discrete Boltzmann model, the departure
between equilibrium and nonequilibrium distribution functions is obtained and
analyzed around the detonation wave. Besides, it has been verified for the
first time that the kinetic moments calculated by summations of the discrete
distribution functions are close to those calculated by integrals of their
original forms.


  We study the end-behavior of integer-valued FI-modules. Our first result
describes the high degrees of an FI-module in terms of newly defined tail
invariants. Our main result provides an equivalence of categories between
FI-tails and finitely supported modules for a new category that we call FJ.
Objects of FJ are natural numbers, and morphisms are infinite series with
summands drawn from certain modules of Lie brackets.


  Increasingly complex and diverse deep neural network (DNN) models necessitate
distributing the execution across multiple devices for training and inference
tasks, and also require carefully planned schedules for performance. However,
existing practices often rely on predefined schedules that may not fully
exploit the benefits of emerging diverse model-aware operator placement
strategies. Handcrafting high-efficiency schedules can be challenging due to
the large and varying schedule space. This paper presents Tessel, an automated
system that searches for efficient schedules for distributed DNN training and
inference for diverse operator placement strategies. To reduce search costs,
Tessel leverages the insight that the most efficient schedules often exhibit
repetitive pattern (repetend) across different data inputs. This leads to a
two-phase approach: repetend construction and schedule completion. By exploring
schedules for various operator placement strategies, Tessel significantly
improves both training and inference performance. Experiments with
representative DNN models demonstrate that Tessel achieves up to 5.5x training
performance speedup and up to 38% inference latency reduction.


  We present a precision gravimeter based on coherent Bragg diffraction of
freely falling cold atoms. Traditionally, atomic gravimeters have used
stimulated Raman transitions to separate clouds in momentum space by driving
transitions between two internal atomic states. Bragg interferometers utilize
only a single internal state, and can therefore be less susceptible to
environmental perturbations. Here we show that atoms extracted from a
magneto-optical trap using an accelerating optical lattice are a suitable
source for a Bragg atom interferometer, allowing efficient beamsplitting and
subsequent separation of momentum states for detection. Despite the inherently
multi-state nature of atom diffraction, we are able to build a Mach-Zehnder
interferometer using Bragg scattering which achieves a sensitivity to the
gravitational acceleration of $\Delta g/g = 2.7\times10^{-9}$ with an
integration time of 1000s. The device can also be converted to a gravity
gradiometer by a simple modification of the light pulse sequence.


  Transition metal sulfides (Fe, V)3S4 and (Fe, Ti)3S4, with the monoclinic
Cr3S4 type structure have been studied for a long time, itinerant magnetism in
form of Spin density waves (SDW) have been found in these systems with
different features as evidenced by 57Fe Mossbauer Spectroscopy, there is an
intricate relationship between the proportion of Fe, V and Ti atoms, the degree
of commensurability of the SDW and the magnetic transition temperature. These
sulfides have no natural occurrence on Earth and some of these phases were
detected as minerals in meteorites; the mineral Heideite in the Bustee and
Kaidun meteorites, with minor proportion of Cr atoms leading to the general
formula (Fe, Cr)1+x (Ti, Fe)2S4, and the mineral Brezinaite in the Tucson
meteorite, with minor proportion of Fe atoms and traces of V, Ti and Mn atoms,
leading to the formula (Cr2.65Fe0.20V0.09Ti0.06Mn0.04)3.04S4. In this critical
review of the experimental literature, we discuss the issues that these
meteoritic minerals are structurally sensitive to the method of synthesis, and
so is their magnetic behavior, especially in the presence of minor and trace
elements. This discussion could shed light on our knowledge in Solid State
Physics and Planetary Science; these meteoritic minerals are promising
candidates for iron-based superconductors because of three clues: they are
layered structures, they undergo a transition to SDW with variable degree of
commensurability and the minor and trace elements could act as dopants and
hence suppress the SDW giving rise to superconductivity. On the other side, the
genesis of these meteoritic minerals could require controlled and sophisticated
process not easily found in nature. So, it is important to be open-minded and
even provocative to consider the following question: Are these meteoritic
minerals samples of Extraterrestrial Technosignatures?


  The paper presents two examples of non-traditional using of program
specialization by Turchin's supercompilation method. In both cases we are
interested in syntactical properties of residual programs produced by
supercompilation. In the first example we apply supercompilation to a program
encoding a word equation and as a result we obtain a program representing a
graph describing the solution set of the word equation. The idea of the second
example belongs to Alexandr V. Korlyukov. He considered an interpreter
simulating the dynamic of the well known missionaries-cannibals puzzle.
Supercompilation of the interpreter allows us to solve the puzzle. The
interpreter may also be seen as an encoding of a non-deterministic protocol.


  Let G be a topological compact group acting on some space Y. We study a
decomposition of Y-indexed stochastic processes, based on the orthogonality
relations between the characters of the irreducible representations of G. In
the particular case of a Gaussian process with a G-invariant law, such a
decomposition gives a very general explanation of a classic identity in law -
between quadratic functionals of a Brownian bridge - due to Watson (1961).
Several relations with Karhunen-Lo\`{e}ve expansions are discussed, and some
applications and extensions are given - in particular related to Gaussian
processes indexed by a torus.


  Using bona-fide black hole (BH) mass estimates from reverberation mapping and
the line ratio [SiVI] 1.963$\mu$m/Br$\gamma_{\rm broad}$ as tracer of the AGN
ionising continuum, a novel BH-mass scaling relation of the form log($M_{\rm
BH}) = (6.40\pm 0.17) - (1.99\pm 0.37) \times$ log ([SiVI]/Br$\gamma_{\rm
broad})$, dispersion 0.47 dex, over the BH mass interval, $10^6 - 10^8$
M$_{\odot}$ is found. Following on the geometrically thin accretion disc
approximation and after surveying a basic parameter space for coronal lines
production, we believe one of main drivers of the relation is the effective
temperature of the disc, which is effectively sampled by the [SiVI] 1.963$\mu$m
coronal line for the range of BH masses considered. By means of CLOUDY
photoionisation models, the observed anti-correlation appears to be formally in
line with the thin disc prediction T_disc $\propto {M_{\rm BH}}^{-1/4}$.


  Accurate text segmentation results are crucial for text-related generative
tasks, such as text image generation, text editing, text removal, and text
style transfer. Recently, some scene text segmentation methods have made
significant progress in segmenting regular text. However, these methods perform
poorly in scenarios containing artistic text. Therefore, this paper focuses on
the more challenging task of artistic text segmentation and constructs a real
artistic text segmentation dataset. One challenge of the task is that the local
stroke shapes of artistic text are changeable with diversity and complexity. We
propose a decoder with the layer-wise momentum query to prevent the model from
ignoring stroke regions of special shapes. Another challenge is the complexity
of the global topological structure. We further design a skeleton-assisted head
to guide the model to focus on the global structure. Additionally, to enhance
the generalization performance of the text segmentation model, we propose a
strategy for training data synthesis, based on the large multi-modal model and
the diffusion model. Experimental results show that our proposed method and
synthetic dataset can significantly enhance the performance of artistic text
segmentation and achieve state-of-the-art results on other public datasets.


  Extraction of adverse drug events from biomedical literature and other
textual data is an important component to monitor drug-safety and this has
attracted attention of many researchers in healthcare. Existing works are more
pivoted around entity-relation extraction using bidirectional long short term
memory networks (Bi-LSTM) which does not attain the best feature
representations. In this paper, we introduce a question answering framework
that exploits the robustness, masking and dynamic attention capabilities of
RoBERTa by a technique of domain adaptation and attempt to overcome the
aforementioned limitations. Our model outperforms the prior work by 9.53%
F1-Score.


  Animating portraits using speech has received growing attention in recent
years, with various creative and practical use cases. An ideal generated video
should have good lip sync with the audio, natural facial expressions and head
motions, and high frame quality. In this work, we present SPACE, which uses
speech and a single image to generate high-resolution, and expressive videos
with realistic head pose, without requiring a driving video. It uses a
multi-stage approach, combining the controllability of facial landmarks with
the high-quality synthesis power of a pretrained face generator. SPACE also
allows for the control of emotions and their intensities. Our method
outperforms prior methods in objective metrics for image quality and facial
motions and is strongly preferred by users in pair-wise comparisons. The
project website is available at https://deepimagination.cc/SPACE/


  We attempt to systematically derive tree-level scattering amplitudes in
four-dimensional, planar, maximally supersymmetric Yang-Mills theory from
integrability. We first review the connections between integrable spin chains,
Yangian invariance, and the construction of such invariants in terms of
Grassmannian contour integrals. Building upon these results, we equip a class
of Grassmannian integrals for general symmetry algebras with unitary
integration contours. These contours emerge naturally by paying special
attention to the proper reality conditions of the algebras. Specializing to
psu(2,2|4) and thus to maximal superconformal symmetry in Minkowski space, we
find in a number of examples expressions similar to, but subtly different from
the perturbative physical scattering amplitudes. Our results suggest a subtle
breaking of Yangian invariance for the latter, with curious implications for
their construction from integrability.


  Multi-task learning has recently become a very active field in deep learning
research. In contrast to learning a single task in isolation, multiple tasks
are learned at the same time, thereby utilizing the training signal of related
tasks to improve the performance on the respective machine learning tasks.
Related work shows various successes in different domains when applying this
paradigm and this thesis extends the existing empirical results by evaluating
multi-task learning in four different scenarios: argumentation mining,
epistemic segmentation, argumentation component segmentation, and
grapheme-to-phoneme conversion. We show that multi-task learning can, indeed,
improve the performance compared to single-task learning in all these
scenarios, but may also hurt the performance. Therefore, we investigate the
reasons for successful and less successful applications of this paradigm and
find that dataset properties such as entropy or the size of the label inventory
are good indicators for a potential multi-task learning success and that
multi-task learning is particularly useful if the task at hand suffers from
data sparsity, i.e. a lack of training data. Moreover, multi-task learning is
particularly effective for long input sequences in our experiments. We have
observed this trend in all evaluated scenarios. Finally, we develop a highly
configurable and extensible sequence tagging framework which supports
multi-task learning to conduct our empirical experiments and to aid future
research regarding the multi-task learning paradigm and natural language
processing.


  It is shown that subclasses of separable binary Goppa codes, $\Gamma(L,G)$ -
codes, with $L=\{\alpha \in GF(2^{2l}):G(\alpha)\neq 0 \}$ and special Goppa
polynomials G(x) can be presented as a chain of embedded codes. The true
minimal distance has been obtained for all codes of the chain.


  As a byproduct of our search for Galactic stellar systems with gamma-ray
emission, we have identified an unrelated cool and evolved star (IRC-10412)
that attracted our attention due to its strong radio emission level with a
spectral index matching, almost perfectly, the canonical +0.6 value expected
from an ionized stellar wind. A follow-up observational analysis was undertaken
given that these two properties are hard to reconcile as originating in the
same stellar object. As a result, IRC-10412 has been classified as a new
semiregular variable of SRb type in the asymptotic giant branch, and different
but consistent estimates of its mass-loss parameter are reported. We propose
that its unusually high radio emission arises from a ~ 1E-5 Msun/yr stellar
wind exposed to an external source of ionizing photons, possibly coming from
nearby OB associations.


  In this paper we analyze the dispersion for one dimensional wave and
Schrodinger equations with BV coefficients. In the case of the wave equation we
give a complete answer in terms of the variation of the logarithm of the
coefficient showing that dispersion occurs if this variation is small enough
but it may fail when the variation goes beyond a sharp threshold. For the
Schrodigner equation we prove that the dispersion holds under the same
smallness assumption on the variation of the coefficient. But, whether
dispersion may fail for larger coefficients is unknown for the Schrodinger
equation.


  This paper presents a new protocol for Internet voting based on implicit data
security. This protocol allows recasting of votes, which permits a change of
mind by voters either during the time window over which polling is open or
during a shorter period over which recasting is permitted. The security of
votes depends on multiple servers such that each vote is divided into
partitions and these partitions are distributed among the servers, all of which
need to be brought together to reconstruct the votes. Such a protocol has
potential applications in bargaining and electronic commerce.


  We present BARTpho with two versions, BARTpho-syllable and BARTpho-word,
which are the first public large-scale monolingual sequence-to-sequence models
pre-trained for Vietnamese. BARTpho uses the "large" architecture and the
pre-training scheme of the sequence-to-sequence denoising autoencoder BART,
thus it is especially suitable for generative NLP tasks. We conduct experiments
to compare our BARTpho with its competitor mBART on a downstream task of
Vietnamese text summarization and show that: in both automatic and human
evaluations, BARTpho outperforms the strong baseline mBART and improves the
state-of-the-art. We further evaluate and compare BARTpho and mBART on the
Vietnamese capitalization and punctuation restoration tasks and also find that
BARTpho is more effective than mBART on these two tasks. We publicly release
BARTpho to facilitate future research and applications of generative Vietnamese
NLP tasks. Our BARTpho models are available at
https://github.com/VinAIResearch/BARTpho


  Aspects of screening and confinement are reassessed for a $B \wedge F$ model
with topological mass with the gauging of spin. Our discussion is carried out
using the gauge-invariant, but path-dependent, variables formalism. We
explicitly show that the static potential profile is the sum of a Yukawa and a
linear potential, leading to the confinement of static external charges.
Interestingly enough, similar results are obtained in a theory of antisymmetric
tensor fields that results from the condensation of topological defects as a
consequence of the Julia-Toulouse mechanism.


  Sub-resolution porosity (SRP) is an ubiquitous, yet often ignored, feature in
Digital Rock Physics. It embodies the trade-off between image resolution and
field-of-view, and it is a direct result of choosing an imaging resolution that
is larger than the smallest pores in a heterogeneous rock sample. In this
study, we investigate the impacts of SRP on multiphase flow in porous rocks. To
do so, we use our newly developed Multiphase Micro-Continuum model to perform
first-of-a-kind direct numerical simulations of two-phase flow in porous
samples containing SRP. We show that SRP properties (porosity, permeability,
wettability) can impact predicted absolute permeabilities, fluid breakthrough
times, residual saturations, and relative permeabilities by factors of up to 2,
1.5, 3, and 20, respectively. In particular, our results reveal that SRP can
function as a persistent connector preventing the formation of isolated wetting
fluid domains during drainage, thus dramatically increasing relative
permeabilities to both fluids at low saturations. Overall, our study confirms
previous evidence that the influence of SRP cannot be disregarded without
incurring significant errors in numerical predictions or experimental analyses
of multiphase flow in heterogeneous porous media.


  A path cover is a decomposition of the edges of a graph into edge-disjoint
simple paths. Gallai conjectured that every connected $n$-vertex graph has a
path cover with at most $\lceil n/2 \rceil$ paths. We prove Gallai's conjecture
for series-parallel graphs. For the class of planar 3-trees we show how to
construct a path cover with at most $\lfloor 5n/8 \rfloor$ paths, which is an
improvement over the best previously known bound of $\lfloor 2n/3 \rfloor$.


  An extension of the Erd\H{o}s-Renyi random graph model $G_{n,p}$ is the model
of perturbed graphs introduced by Bohman, Frieze and Martin (Bohman, Frieze,
Martin 2003). This is a special case of the model of randomly augmented graphs
studied in this paper. An augmented graph denoted by $pert_{H,p}$ is the union
of a deterministic host graph and a random graph $G_{n,p}$. Among the first
problems in perturbed graphs has been the question how many random edges are
needed to ensure Hamiltonicity of the graph. This question was answered in the
paper by Bohman, Frieze and Martin. The host graph is often chosen to be a
dense graph. In recent years several papers on combinatorial problems in
perturbed graphs were published, e.g. on the emergence of powers of Hamiltonian
cycles (Dudek, Reiher, Ruci\'nski, Schacht 2020), some positional games played
on perturbed graphs (Clemens, Hamann, Mogge, Parczyk, 2020) and the behavior of
multiple invariants e.g. fixed clique size (Bohman, Frieze, Krivelevich,
Martin, 2004). In this paper we study the chromatic number of randomly
augmented graphs. We concentrate on a host graph $H$ with chromatic number
$o(n)$, augmented by a $G_{n,p}$ with $n^{-\frac{1}{3} + \delta}\leq p(n) \leq
1-\delta$ for some $\delta \in (0,1)$. Our main result is an upper bound for
the chromatic number: we show that asymptotically almost surely
$\chi(pert_{H,p}) \leq (1+o(1)) \cdot \frac{n \log(b)}{2 (\log(n) -
\log(\chi(H))}$ where $b = (1-p)^{-1}$. This result collapses to the famous
theorem of Bollob\'as (1988), when $H$ is the empty host graph, thus our result
can be regarded as a generalization of the latter. Our proof is not
constructive. Further, we give a constructive coloring algorithm, when the
chromatic number of the host graph is at most $\frac{n}{\log(n)^{\alpha}},$
$\alpha>\frac{1}{2}.$


  The explicit expression of all the WZW effective actions for a simple group G
broken down to a subgroup H is established in a simple and direct way, and the
formal similarity of these actions to the Chern-Simons forms is explained.
Applications are also discussed.


  We have re-investigated Cosmology involving interaction between Dark matter
and Dark Energy in the light of a new parametrization. The new parametrization
is based on the hypothesis that when Dark matter and Dark Energy will interact,
Dark matter will dilute in a different manner than standard non-interacting
scenario. We re-built the Cosmological equations with this new parametrization.
Observational constraints on the traditional Cosmological parameters and new
parameters has also been obtained by using supernova data from Pantheon and
Hubble data. The parameter values obtained are $H_0$ = 69.023 $\pm$ 0.722, $M$
= -19.385 $\pm$ 0.019, $I$ = 2.901 $\pm$ 0.092 and $\Omega_{dm0}(1 -
\frac{3}{I})\frac{1}{\kappa}$ = 0.254 $\pm$ 0.023 where $H_0$, $M$,
$\Omega_{dm0}$ and $\kappa$ are Hubble constant, absolute magnitude of type 1a
supernova, present day dark matter density and coupling parameter between dark
matter and dark energy respectively while $I$ is a new parameter, dubbed
dilution parameter which we introduced in the model representing the modified
dilution of dark matter in the interacting scenario. The physical features of
the model in regard to evolution of the Universe, deceleration parameter, age
of Universe, particle physics implications of interacting scenario has also
been explored in depth and conclusions has been drawn.


  Many applications in Bayesian statistics are extremely computationally
intensive. However, they are often inherently parallel, making them prime
targets for modern massively parallel processors. Multi-core and distributed
computing is widely applied in the Bayesian community, however, very little
attention has been given to fine-grain parallelisation using single instruction
multiple data (SIMD) operations that are available on most modern commodity
CPUs and is the basis of GPGPU computing. In this work, we practically
demonstrate, using standard programming libraries, the utility of the SIMD
approach for several topical Bayesian applications. We show that SIMD can
improve the floating point arithmetic performance resulting in up to $6\times$
improvement in serial algorithm performance. Importantly, these improvements
are multiplicative to any gains achieved through multi-core processing. We
illustrate the potential of SIMD for accelerating Bayesian computations and
provide the reader with techniques for exploiting modern massively parallel
processing environments using standard tools.


  In conventional finite element simulations, foil windings with a thin foil
and many turns require many mesh elements. This renders models quickly
computationally infeasible. With the use of homogenization approaches, the
finite element mesh does not need to resolve the small-scale structure of the
foil winding domain. Present homogenization approaches take resistive and
inductive effects into account. With an increase of the operation frequency of
foil windings, however, capacitive effects between adjacent turns in the foil
winding become relevant. This paper presents an extension to the standard foil
winding model that covers the capacitive behavior of foil windings.


  We have investigated the formation of vortices by rotating the purely
magnetic potential confining a Bose-Einstein condensate. We modified the bias
field of an axially symmetric TOP trap to create an elliptical potential that
rotates in the radial plane. This enabled us to study the conditions for vortex
nucleation over a wide range of eccentricities and rotation rates.


  Let $K$ be a commutative field and let $V$ be a subspace of $P^{n}(K)$. Let
$\Gamma$ be a subgroup of $\operatorname{Aut}(K)$ and let $\Gamma$ act on
$P^{n}(K)$ by $\sigma((x_{i})_{0\leq i\leq n})=(\sigma(x_{i}))_{0\leq i\leq n}$
for $\sigma\in\Gamma$ and $(x_{i})_{0\leq i\leq n}\in P^{n}(K)$. In this paper,
we ask `how much' unstable $V$ is under $\Gamma$ by asking how much higher (or
lower) dimension the join (or the meet) of $\sigma(V)$ ($\sigma \in\Gamma$) has
than $V$, and answer it in terms of the Pl\"{u}cker coordinates of $V$ and the
invariant field $k$ of $\Gamma$, through presenting dual `irrationality'
measures of $V$ over $k$.


  Let $m$ denote the number of quasielectrons (QEs) in a quantum Hall system
containing $N$ particles altogether. We show in several general cases that for
systems containing $m$ QEs in a single angular momentum shell above $N-m$
Fermions in an incompressible quantum liquid (IQL) state having filling factor
$\nu=\frac{1}{3}$ that there always exists a configuration whose symmetric
correlation function $G$ is nonzero. This extends recent comparable results
concerning the IQL state. As a consequence, one can obtain (explicitly) a
configuration having a nonzero $G$ for all $N=8$ particle systems containing
any number of QEs. To establish our result, we construct a family of
multi-graphs on $N$ vertices satisfying certain restraints on the degrees of
the vertices and possessing the property that whenever one computes the linear
symmetrization of the graph monomial of any member of the family, the result is
always nonzero. The nonzero linear symmetrization that is obtained in each case
is in fact an example of what is called a relative semi-invariant of a
(generic) binary form of degree $N$. Thus, in addition to providing new
correlation functions for systems of interacting Fermions containing QEs, our
construction could be of interest from both the invariant and graph theoretic
standpoints.


  We develop a theoretical framework to describe the dielectric response of
live cells in suspensions when placed in low external electric fields. The
treatment takes into account the presence of the cell's membrane and of the
charge movement at the membrane's surfaces. For spherical cells suspended in
aqueous solutions, we give an analytic solution for the dielectric function,
which is shown to account for the alpha and beta plateaus seen in many
experimental data. The effect of different physical parameters on the
dielectric curves is methodically analyzed.


  In this paper, we investigate recommender systems from a network perspective
and investigate recommendation networks, where nodes are items (e.g., movies)
and edges are constructed from top-N recommendations (e.g., related movies). In
particular, we focus on evaluating the reachability and navigability of
recommendation networks and investigate the following questions: (i) How well
do recommendation networks support navigation and exploratory search? (ii) What
is the influence of parameters, in particular different recommendation
algorithms and the number of recommendations shown, on reachability and
navigability? and (iii) How can reachability and navigability be improved in
these networks? We tackle these questions by first evaluating the reachability
of recommendation networks by investigating their structural properties.
Second, we evaluate navigability by simulating three different models of
information seeking scenarios. We find that with standard algorithms,
recommender systems are not well suited to navigation and exploration and
propose methods to modify recommendations to improve this. Our work extends
from one-click-based evaluations of recommender systems towards multi-click
analysis (i.e., sequences of dependent clicks) and presents a general,
comprehensive approach to evaluating navigability of arbitrary recommendation
networks.


  The well know radiogalaxy Cen A has been recently detected as a source of
very high energy (VHE) gamma-rays by the HESS experiment just before Fermi/LAT
detected it at high energies (HE). The detection, together with that of M87,
establishes radiogalaxies as VHE gamma-ray emitters. The aim of this work is to
present a lepto-hadronic model for the VHE emission from the relativistic jets
in FR I radiogalaxies. We consider that protons and electrons are accelerated
in a compact region near the base of the jet, and they cool emitting multi
wavelength radiation as they propagate along the jet. The proton and electron
distributions are obtained through an inhomogeneous steady-state transport
equation taking into account acceleration, radiative and non-radiative cooling
processes, as well as particle transport by convection. Considering the effects
of photon absorption at different wavelengths, we calculate the radiation
emitted by the primary protons and electrons, as well as the contribution of
secondaries particles (e+/-, pions and muons). The expected high-energy
neutrino signal is also obtained and the possibility of detections with KM3Net
and IceCube is discussed. The spectral energy distribution obtained in our
model with an appropriate set of parameters for an extended emission zone can
account for much of the observed spectrum for both AGNs.


  Research on multi-agent planning has been popular in recent years. While
previous research has been motivated by the understanding that, through
cooperation, multi-agent systems can achieve tasks that are unachievable by
single-agent systems, there are no formal characterizations of situations where
cooperation is required to achieve a goal, thus warranting the application of
multi-agent systems. In this paper, we provide such a formal discussion from
the planning aspect. We first show that determining whether there is required
cooperation (RC) is intractable is general. Then, by dividing the problems that
require cooperation (referred to as RC problems) into two classes -- problems
with heterogeneous and homogeneous agents, we aim to identify all the
conditions that can cause RC in these two classes. We establish that when none
of these identified conditions hold, the problem is single-agent solvable.
Furthermore, with a few assumptions, we provide an upper bound on the minimum
number of agents required for RC problems with homogeneous agents. This study
not only provides new insights into multi-agent planning, but also has many
applications. For example, in human-robot teaming, when a robot cannot achieve
a task, it may be due to RC. In such cases, the human teammate should be
informed and, consequently, coordinate with other available robots for a
solution.


  This paper studies an adaptive approach for probabilistic wind power
forecasting (WPF) including offline and online learning procedures. In the
offline learning stage, a base forecast model is trained via inner and outer
loop updates of meta-learning, which endows the base forecast model with
excellent adaptability to different forecast tasks, i.e., probabilistic WPF
with different lead times or locations. In the online learning stage, the base
forecast model is applied to online forecasting combined with incremental
learning techniques. On this basis, the online forecast takes full advantage of
recent information and the adaptability of the base forecast model. Two
applications are developed based on our proposed approach concerning
forecasting with different lead times (temporal adaptation) and forecasting for
newly established wind farms (spatial adaptation), respectively. Numerical
tests were conducted on real-world wind power data sets. Simulation results
validate the advantages in adaptivity of the proposed methods compared with
existing alternatives.


  We present a new rigorous approach based on Orlicz spaces for the description
of the statistics of large regular statistical systems, both classical and
quantum. This approach has the advantage that statistical mechanics is much
better settled. In particular, a new kind of renormalization leading to states
having a well defined entropy function is presented.


  We present high-resolution spectroscopic mid-infrared observations of the
circumstellar disk around the Herbig Ae star HD97048 obtained with the VLT
Imager and Spectrometer for the mid-InfraRed (VISIR). We conducted observations
of mid-infrared pure rotational lines of molecular hydrogen (H2) as a tracer of
warm gas in the disk surface layers. In a previous paper, we reported the
detection of the S(1) pure rotational line of H2 at 17.035 microns and argued
it is arising from the inner regions of the disk around the star. We used VISIR
on the VLT for a more comprehensive study based on complementary observations
of the other mid-infrared molecular transitions, namely S(2) and S(4) at 12.278
microns and 8.025 microns respectively, to investigate the physical properties
of the molecular gas in the circumstellar disk around HD97048. We do not detect
neither the S(2) line nor the S(4) H2 line from the disk of HD97048, but we
derive upper limits on the integrated line fluxes which allows us to estimate
an upper limit on the gas excitation temperature, T_ex < 570 K. This limit on
the temperature is consistent with the assumptions previously used in the
analysis of the S(1) line, and allows us to set stronger contraints on the mass
of warm gas in the inner regions of the disk. Indeed, we estimate the mass of
warm gas to be lower than 0.1 M_Jup. We also discuss the probable physical
mechanisms which could be responsible of the excitation of H2 in the disk of
HD97048.


  Let $f(x,y)=0$ and $l(x,y)=0$ be respectively a singular and a regular
analytic curve defined in the neighborhood of the origin of the complex plane.
We study the family of analytic curves $f(x,y)-tl(x,y)^M=0$, where $t$ is a
complex parameter. For all but a finite number of parameters the curves of this
family have the same embedded topological type. The exceptional parameters are
called special values.
  We show that the number of nonzero special values does not exceed the number
of components of the curve $f(x,y)=0$ counted without multiplicities. Then we
apply this result to estimate the number of critical values at infinity of
complex polynomials in two variables.


  It is shown that generalized measurements, required for optimally
discriminating between nonorthogonal joint polarization states of two
indistinguishable photons, can be realized with the help of
polarization-dependent two-photon absorption and by means of sum-frequency
generation. Optimization schemes are investigated with respect to minimizing
the error probability in inferring the states, as well as with respect to
maximizing the probability of success for unambiguous discrimination. Moreover,
an implementation of error-minimizing discrimination between N symmetric
single-photon states is studied. The latter can be used to extract information
from the inconclusive results occurring in unambiguous discrimination between
three symmetric two-photon polarization states.


  In the context of a dynamical Ginzburg-Landau model it is shown numerically
that under the influence of a homogeneous external current J the vortex drifts
against the current with velocity $V= -J$ in agreement to earlier analytical
predictions. In the presence of dissipation the vortex undergoes skew
deflection at an angle $90^{\circ} < \delta < 180^{\circ}$ with respect to the
external current. It is shown analytically and verified numerically that the
angle $\delta$ and the speed of the vortex are linked through a simple
mathematical relation.


  We consider a stationary linear AR($p$) model with observations subject to
gross errors (outliers). The autoregression parameters are unknown as well as
the distribution and moments of innoovations. The distribution of outliers
$\Pi$ is unknown and arbitrary, their intensity is $\gamma n^{-1/2}$ with an
unknown $\gamma$, $n$ is the sample size. The autoregression parameters are
estimated by any estimator which is $n^{1/2}$-consistent uniformly in
$\gamma\leq \Gamma<\infty$. Using the residuals from the estimated
autoregression, we construct a kind of empirical distribution function
(e.d.f.), which is a counterpart of the (inaccessible) e.d.f. of the
autoregression innovations. We obtain a stochastic expansion of this e.d.f.,
which enables us to construct the symmetrized test of Pearson's chi-square type
for the normality of distribution of innovations. We establish qualitative
robustness of these tests in terms of uniform equicontinuity of the limiting
levels (as functions of $\gamma$ and $\Pi$) with respect to $\gamma$ in a
neighborhood of $\gamma=0$.


  Vehicular edge computing (VEC) is a promising technology to support real-time
applications through caching the contents in the roadside units (RSUs), thus
vehicles can fetch the contents requested by vehicular users (VUs) from the RSU
within short time. The capacity of the RSU is limited and the contents
requested by VUs change frequently due to the high-mobility characteristics of
vehicles, thus it is essential to predict the most popular contents and cache
them in the RSU in advance. The RSU can train model based on the VUs' data to
effectively predict the popular contents. However, VUs are often reluctant to
share their data with others due to the personal privacy. Federated learning
(FL) allows each vehicle to train the local model based on VUs' data, and
upload the local model to the RSU instead of data to update the global model,
and thus VUs' privacy information can be protected. The traditional synchronous
FL must wait all vehicles to complete training and upload their local models
for global model updating, which would cause a long time to train global model.
The asynchronous FL updates the global model in time once a vehicle's local
model is received. However, the vehicles with different staying time have
different impacts to achieve the accurate global model. In this paper, we
consider the vehicle mobility and propose an Asynchronous FL based
Mobility-aware Edge Caching (AFMC) scheme to obtain an accurate global model,
and then propose an algorithm to predict the popular contents based on the
global model. Experimental results show that AFMC outperforms other baseline
caching schemes.


  We investigate the periodic Anderson-Holstein model by using the dynamical
mean-field theory combined with the exact diagonalization method. For the
strong electron-phonon coupling $g\simg g_c$, the system shows an anomalous
heavy-fermion behaviour which is accompanied by a large lattice fluctuation and
an extreme phonon softening. We also calculate an effective potential for the
ions and find that a simple harmonic potential for $g\siml g_c$ changes into a
double-well potential for $g\simg g_c$. The effective pairing interaction
between the conduction electrons shows a maximum at $g \approx g_c$ where the
superconducting transition temperature is expected to be maximum.


  We describe, test, and apply a technique to incorporate full-sun, surface
flux evolution into an MHD model of the global solar corona. Requiring only
maps of the evolving surface flux, our method is similar to that of Lionello et
al. (2013), but we introduce two ways to correct the electric field at the
lower boundary to mitigate spurious currents. We verify the accuracy of our
procedures by comparing to a reference simulation, driven with known flows and
electric fields. We then present a thermodynamic MHD calculation lasting one
solar rotation driven by maps from the magnetic flux evolution model of
Schrijver & DeRosa (2003). The dynamic, time-dependent nature of the model
corona is illustrated by examining the evolution of the open flux boundaries
and forward modeled EUV emission, which evolve in response to surface flows and
the emergence and cancellation flux. Although our main goal is to present the
method, we briefly investigate the relevance of this evolution to properties of
the slow solar wind, examining the mapping of dipped field lines to the
topological signatures of the "S-Web" and comparing charge state ratios
computed in the time-dependently driven run to a steady state equivalent.
Interestingly, we find that driving on its own does not significantly improve
the charge states ratios, at least in this modest resolution run that injects
minimal helicity. Still, many aspects of the time-dependently driven model
cannot be captured with traditional steady-state methods, and such a technique
may be particularly relevant for the next generation of solar wind and CME
models.


  We have performed fully relativistic first-principles density functional
calculations for non-magnetic B20-type CoSi. The spin-orbit interaction has
crucial effects on the electronic structures of a chiral crystal. The
calculated band structure around the Fermi energy shows Bloch vector $k$-linear
dispersion expressed by a $real$-$spin$ Weyl Hamiltonian, i.e., a mass-less
Dirac Hamiltonian. We found the hedgehog-like spin texture in Bloch
$\boldsymbol k$-vector space (momentum space) on the isoenergy surface around
the $\Gamma$ point. The Fermi velocity for $k$-linear dispersion is about
0.22$v^g_F$, where $v^g_F$ is the Fermi velocity of graphene.


  We introduce equivariant tree models in algebraic statistics, which unify and
generalise existing tree models such as the general Markov model, the strand
symmetric model, and group based models. We focus on the ideals of such models.
We show how the ideals for general trees can be determined from the ideals for
stars. The main novelty is our proof that this procedure yields the entire
ideal, not just an ideal defining the model set-theoretically. A corollary of
theoretical importance is that the ideal for a general tree is generated by the
ideals of its flattenings at vertices.


  As firms' pricing strategies increasingly rely on algorithms, two concerns
have received much attention: algorithmic tacit collusion and price
discrimination. This paper investigates the interaction between these two
issues through simulations. In each period, a new buyer arrives with
independently and identically distributed willingness to pay (WTP), and each
firm, observing private signals about WTP, adopts Q-learning algorithms to set
prices. We document two novel mechanisms that lead to collusive outcomes. Under
asymmetric information, the algorithm with information advantage adopts a
Bait-and-Restrained-Exploit strategy, surrendering profits on some signals by
setting higher prices, while exploiting limited profits on the remaining
signals by setting much lower prices. Under a symmetric information structure,
competition on some signals facilitates convergence to supra-competitive prices
on the remaining signals. Algorithms tend to collude more on signals with
higher expected WTP. Both uncertainty and the lack of correlated signals
exacerbate the degree of collusion, thereby reducing both consumer surplus and
social welfare. A key implication is that the over-usage of data, both
payoff-relevant and non-relevant, by AIs in competitive contexts will reduce
the degree of collusion and consequently lead to a decline in industry profits.


  Automatic speech recognition (ASR) technique is becoming increasingly popular
to improve the efficiency and safety of air traffic control (ATC) operations.
However, the conversation between ATC controllers and pilots using multilingual
speech brings a great challenge to building high-accuracy ASR systems. In this
work, we present a two-stage multilingual ASR framework. The first stage is to
train a language identifier (LID), that based on a recurrent neural network
(RNN) to obtain sentence language identification in the form of one-hot
encoding. The second stage aims to train an RNN-based end-to-end multilingual
recognition model that utilizes sentence language features generated by LID to
enhance input features. In this work, We introduce Featurewise Linear
Modulation (FiLM) to improve the performance of multilingual ASR by utilizing
sentence language identification. Furthermore, we introduce a new sentence
language identification learning module called SLIL, which consists of a FiLM
layer and a Squeeze-and-Excitation Networks layer. Extensive experiments on the
ATCSpeech dataset show that our proposed method outperforms the baseline model.
Compared to the vanilla FiLMed backbone model, the proposed multilingual ASR
model obtains about 7.50% character error rate relative performance
improvement.


  An index theory for projective families of elliptic pseudodifferential
operators is developed when the twisting, i.e. Dixmier-Douady, class is
decomposable. One of the features of this special case is that the
corresponding Azumaya bundle can be realized in terms of smoothing operators.
The topological and the analytic index of a projective family of elliptic
operators both take values in the twisted K-theory of the parameterizing space.
The main result is the equality of these two notions of index. The twisted
Chern character of the index class is then computed by a variant of Chern-Weil
theory.


  In this work, we exactly solve a Kondo lattice model in the thermodynamic
limit. The system consists of an electronic conduction band described by
unconstrained hopping matrix elements between the lattice sites. The conducting
electrons interact with a localized impurity spin at each lattice cell. We have
found the exact thermodynamics, the ground state energies of the system. At
T=0, we explicitly demonstrate that the system exhibits a metal-insulator phase
transition at half-filling. In the limit of strong coupling between the
impurity spin and the electrons, J=\infty, we have solved the system on a
lattice of any size L. The ground states are the resonating-valence-bond type
Jastrow product wavefunctions. Various correlation functions may be computed
for the impurity spins, and for the singlets formed by the electrons and
impurities.


  We consider an alternative explanation of newly found growth of neutral pion
transition form factor with virtuality of one of photon. It is based on Sudakov
suppression of quark-photon vertex. Some applications to scattering and
annihilation channels are considered including the relevant experiments with
lepton-proton scattering.


  3D generation has seen remarkable progress in recent years. Existing
techniques, such as score distillation methods, produce notable results but
require extensive per-scene optimization, impacting time efficiency.
Alternatively, reconstruction-based approaches prioritize efficiency but
compromise quality due to their limited handling of uncertainty. We introduce
GECO, a novel method for high-quality 3D generative modeling that operates
within a second. Our approach addresses the prevalent issues of uncertainty and
inefficiency in current methods through a two-stage approach. In the initial
stage, we train a single-step multi-view generative model with score
distillation. Then, a second-stage distillation is applied to address the
challenge of view inconsistency from the multi-view prediction. This two-stage
process ensures a balanced approach to 3D generation, optimizing both quality
and efficiency. Our comprehensive experiments demonstrate that GECO achieves
high-quality image-to-3D generation with an unprecedented level of efficiency.


  The mechanical vapor recompression is an efficient process to decrease energy
consumption of drying processes. In order to use the mechanical vapor
recompression (MVR) in residential clothe dryers, the volumetric Turbivo
technology is used to design a dry water vapor compressor. The Turbivo
volumetric machine is composed mainly of a rotor with one blade, a stator, and
a mobile oscillating thrust. The advantages of Turbivo(R) technology are the
absence of contact between rotor and stator as well as the oil-free operation.
A model of the Turbivo compressor, including kinematic, dynamic, and
thermodynamic analysis is presented. The compressor internal tightness is
ensured by a surface treatment of the compressor components. Using the model, a
water vapor Turbivo compressor of 12m3/h and compression ratio of 5 has been
sized and realized. The compressor prototype will be tested on a dedicated test
bench to characterize its volumetric and isentropic efficiencies.


  Despite their groundbreaking performance for many generative modeling tasks,
diffusion models have fallen short on discrete data domains such as natural
language. Crucially, standard diffusion models rely on the well-established
theory of score matching, but efforts to generalize this to discrete structures
have not yielded the same empirical gains. In this work, we bridge this gap by
proposing score entropy, a novel loss that naturally extends score matching to
discrete spaces, integrates seamlessly to build discrete diffusion models, and
significantly boosts performance. Experimentally, we test our Score Entropy
Discrete Diffusion models (SEDD) on standard language modeling tasks. For
comparable model sizes, SEDD beats existing language diffusion paradigms
(reducing perplexity by $25$-$75$\%) and is competitive with autoregressive
models, in particular outperforming GPT-2. Furthermore, compared to
autoregressive mdoels, SEDD generates faithful text without requiring
distribution annealing techniques like temperature scaling (around
$6$-$8\times$ better generative perplexity than un-annealed GPT-2), can trade
compute and quality (similar quality with $32\times$ fewer network
evaluations), and enables controllable infilling (matching nucleus sampling
quality while enabling other strategies besides left to right prompting).


  Pure gravity and gauge theories in two dimensions are shown to be special
cases of a much more general class of field theories each of which is
characterized by a Poisson structure on a finite dimensional target space. A
general scheme for the quantization of these theories is formulated. Explicit
examples are studied in some detail. In particular gravity and gauge theories
with equivalent actions are compared. Big gauge transformations as well as the
condition of metric nondegeneracy in gravity turn out to cause significant
differences in the structure of the corresponding reduced phase spaces and the
quantum spectra of Dirac observables. For $R^2$ gravity coupled to SU(2) Yang
Mills the question of quantum dynamics (`problem of time') is addressed. [This
article is a contribution to the proceedings (to appear in LNP) of the 3rd
Baltic RIM Student Seminar (1993). Importance is attached to concrete examples.
A more abstract presentation of the ideas underlying this article (including
new developments) is found in hep-th/9405110.]


  Given an unlabeled dataset, wherein we have access only to pairwise
similarities (or distances), how can we effectively (1) detect outliers, and
(2) annotate/tag the outliers by type? Outlier detection has a large
literature, yet we find a key gap in the field: to our knowledge, no existing
work addresses the outlier annotation problem. Outliers are broadly classified
into 3 types, representing distinct patterns that could be valuable to
analysts: (a) global outliers are severe yet isolate cases that do not repeat,
e.g., a data collection error; (b) local outliers diverge from their peers
within a context, e.g., a particularly short basketball player; and (c)
collective outliers are isolated micro-clusters that may indicate coalition or
repetitions, e.g., frauds that exploit the same loophole. This paper presents
C-AllOut: a novel and effective outlier detector that annotates outliers by
type. It is parameter-free and scalable, besides working only with pairwise
similarities (or distances) when it is needed. We show that C-AllOut achieves
on par or significantly better performance than state-of-the-art detectors when
spotting outliers regardless of their type. It is also highly effective in
annotating outliers of particular types, a task that none of the baselines can
perform.


  The AMADEUS experiment aims to provide unique quality data of $K^-$ hadronic
interactions in light nuclear targets, in order to solve fundamental open
questions in the non-perturbative strangeness QCD sector, like the
controversial nature of the $\Lambda(1405)$ state, the yield of hyperon
formation below threshold, the yield and shape of multi-nucleon $K^-$
absorption, processes which are intimately connected to the possible existence
of exotic antikaon multi-nucleon clusters. AMADEUS takes advantage of the
DA$\Phi$NE collider, which provides a unique source of monochromatic
low-momentum kaons and exploits the KLOE detector as an active target, in order
to obtain excellent acceptance and resolution data for $K^-$ nuclear capture on
H, ${}^4$He, ${}^{9}$Be and ${}^{12}$C, both at-rest and in-flight. During the
second half of 2012 a successful data taking was performed with a dedicated
pure carbon target implemented in the central region of KLOE, providing a high
statistic sample of pure at-rest $K^-$ nuclear interactions. For the future
dedicated setups involving cryogenic gaseous targets are under preparation.