  In this paper, we introduce restricted products for families of locally
convex spaces and formulate criteria ensuring that mappings into such products
are continuous or smooth. As a special case, can define restricted products of
weighted function spaces and obtain results concerning continuity and
differentiability properties of natural non-linear mappings between such
spaces. These concepts and results are the basis for the study of weighted
vector fields on Riemannian manifolds in a subsequent work (see [B. Walter,
"Weighted diffeomorphism groups of Riemannian manifolds", arXiv: 1601.02834]),
which serve as modelling spaces for suitable infinite-dimensional Lie groups of
diffeomorphisms.


  Electron interactions are usually probed indirectly, through their impact on
transport coefficients. Here we describe a direct scheme that, in principle,
gives access to the full angle dependence of carrier scattering in 2D Fermi
gases. The latter is particularly interesting, because, due to the dominant
role of head-on collisions, carrier scattering generates tightly focused
fermionic jets. We predict a jet-dominated signal for the magnetic steering
geometry, that appears at classically weak $B$-fields, much lower than the
free-particle focusing fields. The effect is "anti-Lorentz" in sign, producing
a peak at the field polarity for which the free-particle focusing does not
occur. The steering signal measured vs. $B$ yields detailed information on the
angular structure of fermionic jets.


  The covariogram g_K of a convex body K in E^d is the function which
associates to each x in E^d the volume of the intersection of K with K+x. In
1986 G. Matheron conjectured that for d=2 the covariogram g_K determines K
within the class of all planar convex bodies, up to translations and
reflections in a point. This problem is equivalent to some problems in
stochastic geometry and probability as well as to a particular case of the
phase retrieval problem in Fourier analysis. It is also relevant for the
inverse problem of determining the atomic structure of a quasicrystal from its
X-ray diffraction image. In this paper we confirm Matheron's conjecture
completely.


  The ability to learn and refine behavior after deployment has become ever
more important for robots as we design them to operate in unstructured
environments like households. In this work, we design a new learning system
based on large language model (LLM), OLAF, that allows everyday users to teach
a robot using verbal corrections when the robot makes mistakes, e.g., by saying
"Stop what you're doing. You should move closer to the cup." A key feature of
OLAF is its ability to update the robot's visuomotor neural policy based on the
verbal feedback to avoid repeating mistakes in the future. This is in contrast
to existing LLM-based robotic systems, which only follow verbal commands or
corrections but not learn from them. We demonstrate the efficacy of our design
in experiments where a user teaches a robot to perform long-horizon
manipulation tasks both in simulation and on physical hardware, achieving on
average 20.0% improvement in policy success rate. Videos and more results are
at https://ut-austin-rpl.github.io/olaf/


  Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit
unique electrical characteristics, such as tri-state operation and
bi-directionality, enabling systems with complex and reconfigurable computing.
In this paper, AP-CNFETs are used to design a mixed-signal machine learning
(ML) classifier. The classifier is designed in SPICE with feature size of 15 nm
and operates at 250 MHz. The system is demonstrated based on MNIST digit
dataset, yielding 90% accuracy and no accuracy degradation as compared with the
classification of this dataset in Python. The system also exhibits lower power
consumption and smaller physical size as compared with the state-of-the-art
CMOS and memristor based mixed-signal classifiers.


  This work proposes the first strategy to make distributed training of neural
networks resilient to computing errors, a problem that has remained unsolved
despite being first posed in 1956 by von Neumann. He also speculated that the
efficiency and reliability of the human brain is obtained by allowing for low
power but error-prone components with redundancy for error-resilience. It is
surprising that this problem remains open, even as massive artificial neural
networks are being trained on increasingly low-cost and unreliable processing
units. Our coding-theory-inspired strategy, "CodeNet," solves this problem by
addressing three challenges in the science of reliable computing: (i) Providing
the first strategy for error-resilient neural network training by encoding each
layer separately; (ii) Keeping the overheads of coding
(encoding/error-detection/decoding) low by obviating the need to re-encode the
updated parameter matrices after each iteration from scratch. (iii) Providing a
completely decentralized implementation with no central node (which is a single
point of failure), allowing all primary computational steps to be error-prone.
We theoretically demonstrate that CodeNet has higher error tolerance than
replication, which we leverage to speed up computation time. Simultaneously,
CodeNet requires lower redundancy than replication, and equal computational and
communication costs in scaling sense. We first demonstrate the benefits of
CodeNet in reducing expected computation time over replication when accounting
for checkpointing. Our experiments show that CodeNet achieves the best
accuracy-runtime tradeoff compared to both replication and uncoded strategies.
CodeNet is a significant step towards biologically plausible neural network
training, that could hold the key to orders of magnitude efficiency
improvements.


  We present Atacama Large Millimeter/Sub-millimeter Array observations of the
candidate jet-ISM interaction zones near the black hole X-ray binaries GRS
1758$-$258 and 1E 1740.7$-$2942. Using these data, we map the molecular line
emission in the regions, detecting emission from the HCN [$J=1-0$], HCO$^+$
[$J=1-0$], SiO [$J=2-1$], CS [$J=2-1$], $^{13}$CO [$J=1-0$], C$^{18}$O
[$J=1-0$], HNCO [$J=4_{0,4}-3_{0,3}$], HNCO [$J=5_{0,5}-4_{0,4}$], and CH$_3$OH
[$J=2_{1,1}-1_{1,0}$] molecular transitions. Through examining the
morphological, spectral, and kinematic properties of this emission, we identify
molecular structures that may trace jet-driven cavities in the gas surrounding
these systems. Our results from the GRS 1758$-$258 region in particular, are
consistent with recent work, which postulated the presence of a jet-blown
cocoon structure in deep radio continuum maps of the region. Using these newly
discovered molecular structures as calorimeters, we estimate the time averaged
jet power from these systems, finding $(1.1-5.7)\times10^{36}{\rm erg\,s}^{-1}$
over $0.12-0.31$ Myr for GRS 1758$-$258 and $(0.7-3.5)\times10^{37}{\rm
erg\,s}^{-1}$ over $0.10-0.26$ Myr for 1E 1740.7$-$2942. Additionally, the
spectral line characteristics of the detected emission place these molecular
structures in the central molecular zone of our Galaxy, thereby constraining
the distances to the black hole X-ray binaries to be $8.0\pm1.0$ kpc. Overall,
our analysis solidifies the diagnostic capacity of molecular lines, and
highlights how astro-chemistry can both identify jet-ISM interaction zones and
probe jet feedback from Galactic X-ray binaries.


  A weakly distance-regular digraph is quasi-thin if the maximum value of its
intersection numbers is 2. In this paper, we show that the valency of any
commutative quasi-thin weakly distance-regular digraph is at most 6.


  Infrared, visible and near-UV reflectivity measurements are used to obtain
conductivity as a function of temperature and frequency in YbInCu_4, which
exhibits an isostructural phase-transition into a mixed-valent phase below
T_v=42 K. In addition to a gradual loss of spectral weight with decreasing
temperature extending up to 1.5 eV, a sharp resonance appears at 0.25 eV in the
mixed-valent phase. This feature can be described in terms of excitations into
the Kondo (Abrikosov-Suhl) resonance, and, like the sudden reduction of
resistivity, provides a direct reflection of the onset of coherence in this
strongly correlated electron system.


  We study SLE$_\kappa(\rho)$ curves, with $\kappa$ and $\rho$ chosen so that
the curves hit the boundary. More precisely, we study the sets on which the
curves collide with the boundary at a prescribed "angle" and determine the
almost sure Hausdorff dimension of these sets. This is done by studying the
moments of the spatial derivatives of the conformal maps $g_t$, by employing
the Girsanov theorem and using imaginary geometry techniques to derive a
correlation estimate.


  One of the products of the National Solar Observatory's Integrated Synoptic
Program (NISP) is the farside seismic map which shows the magnetic activity on
the unobserved side of the Sun. The production of these rudimentary maps began
in 2006, and they have since proven to be a valuable tool in tracking solar
activity which cannot be directly observed from the earth's surface. The
continuous tracking of solar active regions allows space weather forecasters to
monitor critical solar events which may have larger economic and societal
impacts here on Earth. In an effort to improve these maps, several steps are
underway through the Windows on the Universe project (WoU) funded by the NSF.
One of these steps is to improve the quality assurance measures for the images
collected at individual sites throughout the GONG network and is used to
develop the farside maps. To this end, we have designed a binary classification
neural network to determine which of these site images should and should not be
included in the farside pipeline that produces the end product maps. This
convolutional neural network is a highly effective and computationally
efficient method of significantly improving the quality of the farside maps
currently produced by the NISP program.


  Telescope slew and settle time markedly reduces the efficiency of wide-field
multi-epoch surveys for sensitive interferometers with small fields of view.
The overheads can be mitigated through the use of On-the-Fly Mosaicing (OTFM),
where the the antennas are driven at a non-sidereal rate and visibilities are
recorded continuously. Here we introduce the OTFM technique for the VLA, and
describe its implementation for the Caltech-NRAO Stripe 82 Survey (CNSS), a
dedicated 5-epoch survey for slow transients at S band (2-4 GHz). We also
describe the OTFSim tool for planning dynamically-scheduled OTFM observations
on the VLA, the latest imaging capabilities for OTFM in CASA, and present a
comparison of OTFM observations with pointed observations. Using the subset of
our observations from the CNSS pilot and final surveys, we demonstrate that the
wide-band and wide-field OTFM observations with the VLA can be imaged
accurately, and that this technique offers a more efficient alternative to
standard mosaicing for multi-epoch shallow surveys such as the CNSS and the VLA
Sky Survey (VLASS). We envisage that the new OTFM mode will facilitate new
synoptic surveys and high-frequency mapping experiments on the VLA.


  We study degree bounds on rational but not necessarily polynomial generators
for the field $\mathbf{k}(V)^G$ of rational invariants of a linear action of a
finite abelian group. We show that lattice-theoretic methods used recently by
the author and collaborators to study polynomial generators for the same field
largely carry over, after minor modifications to the arguments. It then
develops that the specific degree bounds found in that setting also carry over.


  Since batch algorithms suffer from lack of proficiency in confronting model
mismatches and disturbances, this contribution proposes an adaptive scheme
based on continuous Lyapunov function for online robot dynamic identification.
This paper suggests stable updating rules to drive neural networks inspiring
from model reference adaptive paradigm. Network structure consists of three
parallel self-driving neural networks which aim to estimate robot dynamic terms
individually. Lyapunov candidate is selected to construct energy surface for a
convex optimization framework. Learning rules are driven directly from Lyapunov
functions to make the derivative negative. Finally, experimental results on
3-DOF Phantom Omni Haptic device demonstrate efficiency of the proposed method.


  One may probe intrinsic coherence of a qubit by periodically sweeping its
control parameter. The qubit is then excited by Landau-Zener (LZ) mechanism.
The interference between multiple LZ transition leads to the unusual
oscillatory dependence of the energy absorption rate on sweeping amplitude and
period. This interference pattern allows to determine the decoherence time of
the qubit. We introduce a simple phenomenological model describing this
"interferometer", and find the form of the interference pattern.


  The atmospheric dispersion in the mid-infrared transparency windows presents
and important albeit frequently neglected factor when developing ultra-short
pulsed lasers. We show that it can amount to hundreds of fs^2 in 2-3 micron
window with typical laser round-trip path lengths. Using the Cr:ZnS
ultrashort-pulsed laser as a test-bed, we demonstrate the atmospheric
dispersion influence on a femtosecond and chirped-pulse oscillator performance
and show that the humidity fluctuations can be compensated by active dispersion
control, greatly improving stability of mid-IR few-optical cycle laser sources.
The approach can be readily extended to any ultrafast source in the mid-IR
transparency windows.


  We studied magnetization dynamics of a geometrically confined domain wall
under dc current by solving simultaneously the Landau-Lifshitz-Gilbert equation
and diffusion equation for spin accumulation. We showed that the oscillation
motion of the domain wall is driven by the spin-transfer torque and the dc
current is converted to the ac voltage signal. The results means that the
geometrically confined domain wall is applicable as a source of microwave
oscillator.


  Despite its importance in both industrial and service robotics, mobile
manipulation remains a significant challenge as it requires a seamless
integration of end-effector trajectory generation with navigation skills as
well as reasoning over long-horizons. Existing methods struggle to control the
large configuration space, and to navigate dynamic and unknown environments. In
previous work, we proposed to decompose mobile manipulation tasks into a
simplified motion generator for the end-effector in task space and a trained
reinforcement learning agent for the mobile base to account for kinematic
feasibility of the motion. In this work, we introduce Neural Navigation for
Mobile Manipulation (N$^2$M$^2$) which extends this decomposition to complex
obstacle environments and enables it to tackle a broad range of tasks in real
world settings. The resulting approach can perform unseen, long-horizon tasks
in unexplored environments while instantly reacting to dynamic obstacles and
environmental changes. At the same time, it provides a simple way to define new
mobile manipulation tasks. We demonstrate the capabilities of our proposed
approach in extensive simulation and real-world experiments on multiple
kinematically diverse mobile manipulators. Code and videos are publicly
available at http://mobile-rl.cs.uni-freiburg.de.


  We consider the design of sublinear space and query complexity algorithms for
estimating the cost of a minimum spanning tree (MST) and the cost of a minimum
traveling salesman (TSP) tour in a metric on $n$ points. We first consider the
$o(n)$-space regime and show that, when the input is a stream of all
$\binom{n}{2}$ entries of the metric, for any $\alpha \ge 2$, both MST and TSP
cost can be $\alpha$-approximated using $\tilde{O}(n/\alpha)$ space, and that
$\Omega(n/\alpha^2)$ space is necessary for this task. Moreover, we show that
even if the streaming algorithm is allowed $p$ passes over a metric stream, it
still requires $\tilde{\Omega}(\sqrt{n/\alpha p^2})$ space.
  We next consider the semi-streaming regime, where computing even the exact
MST cost is easy and the main challenge is to estimate TSP cost to within a
factor that is strictly better than $2$. We show that, if the input is a stream
of all edges of the weighted graph that induces the underlying metric, for any
$\varepsilon > 0$, any one-pass $(2-\varepsilon)$-approximation of TSP cost
requires $\Omega(\varepsilon^2 n^2)$ space; on the other hand, there is an
$\tilde{O}(n)$ space two-pass algorithm that approximates the TSP cost to
within a factor of 1.96.
  Finally, we consider the query complexity of estimating metric TSP cost to
within a factor that is strictly better than $2$, when the algorithm is given
access to a matrix that specifies pairwise distances between all points. For
MST estimation in this model, it is known that a
$(1+\varepsilon)$-approximation is achievable with
$\tilde{O}(n/\varepsilon^{O(1)})$ queries. We design an algorithm that performs
$\tilde{O}(n^{1.5})$ distance queries and achieves a strictly better than
$2$-approximation when either the metric is known to contain a spanning tree
supported on weight-$1$ edges or the algorithm is given access to a minimum
spanning tree of the graph.


  The Industrial Internet of Things drastically increases connectivity of
devices in industrial applications. In addition to the benefits in efficiency,
scalability and ease of use, this creates novel attack surfaces. Historically,
industrial networks and protocols do not contain means of security, such as
authentication and encryption, that are made necessary by this development.
Thus, industrial IT-security is needed. In this work, emulated industrial
network data is transformed into a time series and analysed with three
different algorithms. The data contains labeled attacks, so the performance can
be evaluated. Matrix Profiles perform well with almost no parameterisation
needed. Seasonal Autoregressive Integrated Moving Average performs well in the
presence of noise, requiring parameterisation effort. Long Short Term
Memory-based neural networks perform mediocre while requiring a high training-
and parameterisation effort.


  Over recent years Value of Information analysis has become more widespread in
health-economic evaluations, specifically as a tool to perform Probabilistic
Sensitivity Analysis. This is largely due to methodological advancements
allowing for the fast computation of a typical summary known as the Expected
Value of Partial Perfect Information (EVPPI). A recent review discussed some
estimations method for calculating the EVPPI but as the research has been
active over the intervening years this review does not discuss some key
estimation methods. Therefore, this paper presents a comprehensive review of
these new methods. We begin by providing the technical details of these
computation methods. We then present a case study in order to compare the
estimation performance of these new methods. We conclude that the most recent
development based on non-parametric regression offers the best method for
calculating the EVPPI efficiently. This means that the EVPPI can now be used
practically in health economic evaluations, especially as all the methods are
developed in parallel with R


  We study boundary value problems with measure data in smooth bounded domains
$\Omega$, for semilinear equations involving Hardy type potentials.
Specifically we consider problems of the form $-L_V u + f(u) = \tau$ in
$\Omega$ and $\mathrm{tr}^*u=\nu$ on $\partial \Omega$, where $L_V= \Delta+V$,
$f\in C(\mathbb{R})$ is monotone increasing with $f(0)=0$ and $\mathrm{tr}^*u$
denotes the normalized boundary trace of $u$ associated with $L_V$. The
potential $V$ is typically a H\"older continuous function in $\Omega$ that
explodes as $\mathrm{dist}(x,F)^{-2}$ for some $F \subset \partial \Omega$. In
general the above boundary value problem may not have a solution. We are
interested in questions related to the concept of 'reduced measures',
introduced by Brezis, Marcus and Ponce for $V=0$. For positive measures, the
reduced measures $\tau^*, \nu^*$ are the largest measures dominated by $\tau$
and $\nu$ respectively such that the boundary value problem with data
$(\tau^*,\nu^*)$ has a solution. Our results extend results for the case $V=0$,
including a relaxation of the conditions on $f$. In the case of signed
measures, some of the present results are new even for $V=0$.


  This work completes the classification of the imprimitive irreducible
modules, over algebraically closed fields of characteristic 0, of the finite
quasisimple groups.


  The problem of bounding the "complexity" of a polynomial ideal in terms of
the degrees of its generators has attracted considerable interest, brought into
focus by the influential survey of Bayer and Mumford. The present paper
examines some of these results and questions from a geometric perspective.
Specifically, motivated by work of Paoletti we introduce an invariant s(I) that
measures the "positivity" of an ideal sheaf I. Degree bounds on generators of I
yield bounds on this s-invariant, but s(I) may be small even when the degrees
of its generators are large. We prove that s(I) computes the asymptotic
Castelnuovo-Mumford regularity of large powers of I, and bounds the asymptotic
behavior of several other measures of complexity. We also show that this
s-invariant behaves very well with respect to natural geometric and algebraic
operations, which leads to considerably simplified constructions of varieties
with irrational asymptotic regularity. The main tools are intersection theory
and vanishing theorems.


  Two methods of constraining the properties of dark energy are weak lensing
tomography and cluster counting. Uncertainties in mass calibration of clusters
can be reduced by using the properties of halo clustering (the clustering of
clusters). However, within a single survey, weak lensing and halo clustering
probe the same density fluctuations. We explore the question of whether this
information can be used twice -- once in weak lensing and then again in halo
clustering to calibrate cluster masses -- or whether the combined dark energy
constraints are weaker than the sum of the individual constraints. For a survey
like the Dark Energy Survey (DES), we find that the cosmic shearing of source
galaxies at high redshifts is indeed highly correlated with halo clustering at
lower redshifts. Surprisingly, this correlation does not degrade cosmological
constraints for a DES-like survey, and in fact, constraints are marginally
improved since the correlations themselves act as additional observables. This
considerably simplifies the analysis for a DES-like survey: when weak lensing
and halo clustering are treated as independent experiments, the combined dark
energy constraints (cluster counts included) are accurate if not slightly
conservative. Our findings mirror those of Takada and Bridle, who investigated
correlations between the cosmic shear and cluster counts.


  Based on the picture that the $ f_0(1370), f_0(1710), f_2(1270), f'_2(1525),
\bar{K}^{*0}_2(1430)$ resonances are dynamically generated from the
vector-vector interaction, we study the decays $J/\psi \to \phi (\omega)
f_0(1370) [f_0(1710)]$, $J/\psi \to \phi (\omega) f_2(1270) [f'_2(1525)]$, and
$J/\psi \to K^{*0} \bar{K}^{*0}_2(1430)$ and make predictions for seven
independent ratios that can be done among them. The starting mechanism is that
the $J/\psi$ decays into three vectors, followed by the final state interaction
of a pair of them. The weights of the different three vector primary channels
are obtained from the basic assumption that the $J/\psi$ ($c \bar c$) is an
SU(3) singlet. By means of only one free parameter we predict four ratios in
good agreement with experiment, make two extra predictions for rates yet
unmeasured, and disagree on one data for which only upper bounds are reported.
Further measurements are most welcome to complete the information required for
these ratios that are testing the nature of these resonances as dynamically
generated.


  Transformer-based language models (LMs) are at the core of modern NLP, but
their internal prediction construction process is opaque and largely not
understood. In this work, we make a substantial step towards unveiling this
underlying prediction process, by reverse-engineering the operation of the
feed-forward network (FFN) layers, one of the building blocks of transformer
models. We view the token representation as a changing distribution over the
vocabulary, and the output from each FFN layer as an additive update to that
distribution. Then, we analyze the FFN updates in the vocabulary space, showing
that each update can be decomposed to sub-updates corresponding to single FFN
parameter vectors, each promoting concepts that are often human-interpretable.
We then leverage these findings for controlling LM predictions, where we reduce
the toxicity of GPT2 by almost 50%, and for improving computation efficiency
with a simple early exit rule, saving 20% of computation on average.


  We continue to study the squared Frobenius norm of a submatrix of a $n \times
n$ random unitary matrix. When the choice of the submatrix is deterministic and
its size is $[ns] \times [nt]$, we proved in a previous paper that, after
centering and without any rescaling, the two-parameter process converges in
distribution to a bivariate Brownian bridge. Here, we consider Bernoulli
independent choices of rows and columns with respective parameters $s$ and $t$.
We prove by subordination that after centering and rescaling by $n^{-1/2}$, the
process converges to another Gaussian process.


  Spectral line widths are often observed to be larger than can be accounted
for by thermal and instrumental broadening alone. This excess broadening is a
key observational constraint for both nanoflare and wave dissipation models of
coronal heating. Here we present a survey of non-thermal velocities measured in
the high temperature loops (1--5MK) often found in the cores of solar active
regions. This survey of $\textit{Hinode}$ Extreme Ultraviolet Imaging
Spectrometer (EIS) observations covers 15 non-flaring active regions that span
a wide range of solar conditions. We find relatively small non-thermal
velocities, with a mean value of 17km s$^{-1}$, and no significant trend with
temperature or active region magnetic flux. These measurements appear to be
inconsistent with those expected from reconnection jets in the corona,
chromospheric evaporation induced by coronal nanoflares, and Alfv\'en wave
turbulence models. Furthermore, because the observed non-thermal widths are
generally small their measurements are difficult and susceptible to systematic
effects.


  Soliton solutions of the KP equation have been studied since 1970, when
Kadomtsev and Petviashvili proposed a two-dimensional nonlinear dispersive wave
equation now known as the KP equation. It is well-known that the Wronskian
approach to the KP equation provides a method to construct soliton solutions.
The regular soliton solutions that one obtains in this way come from points of
the totally non-negative part of the Grassmannian. In this paper we explain how
the theory of total positivity and cluster algebras provides a framework for
understanding these soliton solutions to the KP equation. We then use this
framework to give an explicit construction of certain soliton contour graphs,
and solve the inverse problem for soliton solutions coming from the totally
positive part of the Grassmannian.


  We investigate a high-order, fully explicit, asymptotic-preserving scheme for
a kinetic equation with linear relaxation, both in the hydrodynamic and
diffusive scalings in which a hyperbolic, resp. parabolic, limiting equation
exists. The scheme first takes a few small (inner) steps with a simple,
explicit method (such as direct forward Euler) to damp out the stiff components
of the solution and estimate the time derivative of the slow components. These
estimated time derivatives are then used in an (outer) Runge-Kutta method of
arbitrary order. We show that, with an appropriate choice of inner step size,
the time-step restriction on the outer time step is similar to the stability
condition for the limiting macroscopic equation. Moreover, the number of inner
time steps is also independent of the scaling parameter. We analyse stability
and consistency, and illustrate with numerical results.


  Neural task-oriented dialogue systems often struggle to smoothly interface
with a knowledge base. In this work, we seek to address this problem by
proposing a new neural dialogue agent that is able to effectively sustain
grounded, multi-domain discourse through a novel key-value retrieval mechanism.
The model is end-to-end differentiable and does not need to explicitly model
dialogue state or belief trackers. We also release a new dataset of 3,031
dialogues that are grounded through underlying knowledge bases and span three
distinct tasks in the in-car personal assistant space: calendar scheduling,
weather information retrieval, and point-of-interest navigation. Our
architecture is simultaneously trained on data from all domains and
significantly outperforms a competitive rule-based system and other existing
neural dialogue architectures on the provided domains according to both
automatic and human evaluation metrics.


  In the functional approach to quantum chromodynamics, the properties of
hadronic bound states are accessible via covariant integral equations, e.g. the
Bethe-Salpeter equations for mesons. In particular, one has to deal with
linear, homogeneous integral equations which, in sophisticated model setups,
use numerical representations of the solutions of other integral equations as
part of their input. Analogously, inhomogeneous equations can be constructed to
obtain off-shell information in addition to bound-state masses and other
properties obtained from the covariant analogue to a wave function of the bound
state. These can be solved very efficiently using well-known matrix algorithms
for eigenvalues (in the homogeneous case) and the solution of linear systems
(in the inhomogeneous case). We demonstrate this by solving the homogeneous and
inhomogeneous Bethe-Salpeter equations and find, e.g. that for the calculation
of the mass spectrum it is more efficient to use the inhomogeneous equation.
This is valuable insight, in particular for the study of baryons in a
three-quark setup and more involved systems.


  Existing deep learning-based depth completion methods generally employ
massive stacked layers to predict the dense depth map from sparse input data.
Although such approaches greatly advance this task, their accompanied huge
computational complexity hinders their practical applications. To accomplish
depth completion more efficiently, we propose a novel lightweight deep network
framework, the Long-short Range Recurrent Updating (LRRU) network. Without
learning complex feature representations, LRRU first roughly fills the sparse
input to obtain an initial dense depth map, and then iteratively updates it
through learned spatially-variant kernels. Our iterative update process is
content-adaptive and highly flexible, where the kernel weights are learned by
jointly considering the guidance RGB images and the depth map to be updated,
and large-to-small kernel scopes are dynamically adjusted to capture
long-to-short range dependencies. Our initial depth map has coarse but complete
scene depth information, which helps relieve the burden of directly regressing
the dense depth from sparse ones, while our proposed method can effectively
refine it to an accurate depth map with less learnable parameters and inference
time. Experimental results demonstrate that our proposed LRRU variants achieve
state-of-the-art performance across different parameter regimes. In particular,
the LRRU-Base model outperforms competing approaches on the NYUv2 dataset, and
ranks 1st on the KITTI depth completion benchmark at the time of submission.
Project page: https://npucvr.github.io/LRRU/.


  The issue of single range based observability analysis and observer design
for the kinematics model of a 3D vehicle subject to a constant unknown drift
velocity is addressed. The proposed method departs from alternative solutions
to the problem and leads to the definition of a linear time invariant state
equation with a linear time varying output that can be used to globally solve
the original nonlinear state estimation problem with a standard Kalman filter.
Simple necessary and sufficient observability conditions are derived. Numerical
simulation examples are described to illustrate the performance of the method.


  Numerical simulations are performed for isolated cyclic chains with excluded
volume. Data are reported for the form factor, S(x), where x is the reduced
scattering variable, and also for averages and distributions of the distance
between intramolecular units. The averages of distances are compared with two
alternative expressions describing their dependence with the number of segments
separating the units. The distribution function results are compared with the
des Cloizeaux form. Finally the S(x) data are compared with theoretical
functions also derived from the des Cloizeaux expression for the distribution
function. Moreover, the low x and asymptotic expansions of these functions are
obtained. Based on these expansions, simple formulas are proposed to give a
good description of the simulation data in the whole range of values of x. A
comparison with similar results for linear chains is also included.


  We discuss a method based on sampling minimally entangled typical thermal
states (METTS) that can simulate finite temperature quantum systems with a
computational cost comparable to ground state DMRG. Detailed implementations of
each step of the method are presented, along with efficient algorithms for
working with matrix product states and matrix product operators. We furthermore
explore how properties of METTS can reveal characteristic order and excitations
of systems and discuss why METTS form an efficient basis for sampling. Finally,
we explore the extent to which the average entanglement of a METTS ensemble is
minimal.


  The maps $x \mapsto ax^{2^k}+b$ defined over finite fields of characteristic
two can be related to the duplication map over binary supersingular elliptic
curves. Relying upon the structure of the group of rational points of such
curves we can describe the possible cycle lengths of the maps. Then we extend
our investigation to the maps $x \mapsto (ax^{2^k}+b)^{-1}$. We also notice
some relations between these latter maps and the polynomials $x^{2^k+1} + x
+a$, which have been extensively studied in literature.


  We consider the sound ranging, or source localization, problem --- find the
unknown source-point from known moments when the spherical wave of linearly,
with time, increasing radius reaches known sensor-points --- in some non-proper
metric spaces (closed ball is not always compact). Under certain conditions we
approximate the solution to arbitrary precision by the iterative processes with
and without a stopping criterion. We also consider this problem in normed
spaces with a strictly convex norm when the sensors are dense on the unit
sphere.
  Appended is the implementation of the approximation algorithm in Julia
language.


  Simulated Quantum Annealing (SQA) is a Markov Chain Monte-Carlo algorithm
that samples the equilibrium thermal state of a Quantum Annealing (QA)
Hamiltonian. In addition to simulating quantum systems, SQA has also been
proposed as another physics-inspired classical algorithm for combinatorial
optimization, alongside classical simulated annealing. However, in many cases
it remains an open challenge to determine the performance of both QA and SQA.
One piece of evidence for the strength of QA over classical simulated annealing
comes from an example by Farhi, Goldstone and Gutmann . There a bit-symmetric
cost function with a thin, high energy barrier was designed to show an
exponential seperation between classical simulated annealing, for which thermal
fluctuations take exponential time to climb the barrier, and quantum annealing
which passes through the barrier and reaches the global minimum in poly time,
arguably by taking advantage of quantum tunneling. In this work we apply a
comparison method to rigorously show that the Markov chain underlying SQA
efficiently samples the target distribution and finds the global minimum of
this spike cost function in polynomial time. Our work provides evidence for the
growing consensus that SQA inherits at least some of the advantages of
tunneling in QA, and so QA is unlikely to achieve exponential speedups over
classical computing solely by the use of quantum tunneling. Since we analyze
only a particular model this evidence is not decisive. However, techniques
applied here---including warm starts from the adiabatic path and the use of the
quantum ground state probability distribution to understand the stationary
distribution of SQA---may be valuable for future studies of the performance of
SQA on cost functions for which QA is efficient.


  Centrality metrics are a popular tool in Network Science to identify
important nodes within a graph. We introduce the Potential Gain as a centrality
measure that unifies many walk-based centrality metrics in graphs and captures
the notion of node navigability, interpreted as the property of being reachable
from anywhere else (in the graph) through short walks. Two instances of the
Potential Gain (called the Geometric and the Exponential Potential Gain) are
presented and we describe scalable algorithms for computing them on large
graphs. We also give a proof of the relationship between the new measures and
established centralities. The geometric potential gain of a node can thus be
characterized as the product of its Degree centrality by its Katz centrality
scores. At the same time, the exponential potential gain of a node is proved to
be the product of Degree centrality by its Communicability index. These formal
results connect potential gain to both the "popularity" and "similarity"
properties that are captured by the above centralities.


  We transpose work by K.Yajima and by T.Mizumachi to prove dispersive and
smoothing estimates for dispersive solutions of the linearization at a ground
state of a Nonlinear Schr\"odinger equation (NLS) in 2D. As an application we
extend to dimension 2D a result on asymptotic stability of ground states of NLS
proved in the literature for all dimensions different from 2.


  With the help of the functional integration method, the formation of scalar,
pseudoscalar condensates, and dynamical symmetry breaking in the U(3)
four-fermion model has been investigated. We show the possibility of
spontaneous CP symmetry violation in the model under consideration. The
bosonization procedures of the model are performed; the propagators of quarks,
scalar and pseudoscalar fields are calculated in one loop approximation. The
masses of the scalar and pseudoscalar mesons are evaluated.


  We consider the Stokes eigenvalue problem in a bounded domain of R3 with
Dirich- let boundary conditions. The aim of this paper is to advance the
development of high-order terms in the asymptotic expansions of the boundary
perturbations of eigen- values, eigenfunctions and eigenpressures for the
Stokes operator caused by small per- turbations of the boundary. Our derivation
is rigorous and proved by layer potential techniques.


  Data aggregation in the setting of local differential privacy (LDP)
guarantees strong privacy by providing plausible deniability of sensitive data.
Existing works on this issue mostly focused on discovering heavy hitters,
leaving the task of frequent itemset mining (FIM) as an open problem. To the
best of our knowledge, the-state-of-the-art LDP solution to FIM is the SVSM
protocol proposed recently. The SVSM protocol is mainly based on the padding
and sampling based frequency oracle (PSFO) protocol, and regarded an itemset as
an independent item without considering the frequency consistency among
itemsets.
  In this paper, we propose a novel LDP approach to FIM called LDP-FPMiner
based on frequent pattern tree (FP-tree). Our proposal exploits frequency
consistency among itemsets by constructing and optimizing a noisy FP-tree with
LDP. Specifically, it works as follows. First, the most frequent items are
identified, and the item domain is cut down accordingly. Second, the maximum
level of the FP-tree is estimated. Third, a noisy FP-tree is constructed and
optimized by using itemset frequency consistency, and then mined to obtain the
k most frequent itemsets. Experimental results show that the LDP-FPMiner
significantly improves over the state-of-the-art approach, SVSM, especially in
the case of a high privacy level.


  Several active organisms in nature tend to reside as a community in a viscous
fluid medium. We analyze the variation of swimming characteristics of an active
swimmer present in a dilute and disperse suspension, modeled as an effective
Brinkman medium. This idealized representation of a collection of active
swimmers allows one to distinguish the impact of the interior domain available
to an individual swimmer as well as the contribution of its neighbors. Darcy's
law along with the analytical solution enables the effective resistivity to be
predicted as a function of the volume fraction which is in close agreement with
the well-known Carman-Kozeny equation. This facilitates the successive analysis
of the propulsion speed, power dissipation, and swimming efficiency of the
targeted swimmer as a function of the volume fraction which is decisive in
nutrient transport and uptake or reproduction in a collective environment. A
stress-jump condition is also imposed across a cell to indicate a mean
effective force due to the nearby swimmers. At suitable values of this
stress-jump coefficient, the relative increase of the migration velocity and
swimming efficiency is noticeably higher at an optimum occupancy.


  We show that the quantum relaxation process in a classically chaotic open
dynamical system is characterized by a quantum relaxation time scale t_q. This
scale is much shorter than the Heisenberg time and much larger than the
Ehrenfest time: t_q ~ g^alpha where g is the conductance of the system and the
exponent alpha is close to 1/2. As a result, quantum and classical decay
probabilities remain close up to values P ~ exp(-sqrt(g)) similarly to the case
of open disordered systems.


  Multi-objective optimization (MOO) is a well-studied problem for several
important recommendation problems. While multiple approaches have been
proposed, in this work, we focus on using constrained optimization formulations
(e.g., quadratic and linear programs) to formulate and solve MOO problems. This
approach can be used to pick desired operating points on the trade-off curve
between multiple objectives. It also works well for internet applications which
serve large volumes of online traffic, by working with Lagrangian duality
formulation to connect dual solutions (computed offline) with the primal
solutions (computed online).
  We identify some key limitations of this approach -- namely the inability to
handle user and item level constraints, scalability considerations and variance
of dual estimates introduced by sampling processes. We propose solutions for
each of the problems and demonstrate how through these solutions we
significantly advance the state-of-the-art in this realm. Our proposed methods
can exactly handle user and item (and other such local) constraints, achieve a
$100\times$ scalability boost over existing packages in R and reduce variance
of dual estimates by two orders of magnitude.


  On 20 August 2010 an energetic disturbance triggered large-amplitude
longitudinal oscillations in a nearby filament. The triggering mechanism
appears to be episodic jets connecting the energetic event with the filament
threads. In the present work we analyze this periodic motion in a large
fraction of the filament to characterize the underlying physics of the
oscillation as well as the filament properties. The results support our
previous theoretical conclusions that the restoring force of large-amplitude
longitudinal oscillations is solar gravity, and the damping mechanism is the
ongoing accumulation of mass onto the oscillating threads. Based on our
previous work, we used the fitted parameters to determine the magnitude and
radius of curvature of the dipped magnetic field along the filament, as well as
the mass accretion rate onto the filament threads. These derived properties are
nearly uniform along the filament, indicating a remarkable degree of
cohesiveness throughout the filament channel. Moreover, the estimated mass
accretion rate implies that the footpoint heating responsible for the thread
formation, according to the thermal nonequilibrium model, agrees with previous
coronal heating estimates. We estimate the magnitude of the energy released in
the nearby event by studying the dynamic response of the filament threads, and
discuss the implications of our study for filament structure and heating.


  An enabling technology for future sea transports is safe and energy-efficient
autonomous maritime navigation in narrow environments with other marine vessels
present. This requires that the algorithm controlling the ship is able to
account for the vessel's dynamics, and obeys the rules specified in the
international regulations for preventing collision at sea (COLREGs). To account
for these properties, this work proposes a two-step optimization-based and
COLREGs-compliant motion planner tailored for large vessels. In the first step,
a lattice-based motion planner is used to compute a suboptimal trajectory based
on a library of precomputed motion primitives. To comply with the rules
specified in COLREGs, the lattice-based planner is augmented with discrete
states that represent what type of COLREGs situations that are active with
respect to other nearby vessels. The trajectory computed by the lattice-based
planner is then used as warm-start for the second receding-horizon step based
on direct optimal control techniques. The aim of the second step is to solve
the problem to local optimality with the discrete COLREGs states remained
fixed. The applicability of the proposed motion planner is demonstrated in
simulations, where it computes energy-efficient and COLREGs-compliant
trajectories. The results also show that the motion planner is able to prevent
complex collision situations from occurring.


  State space modelling is an efficient and flexible method for statistical
inference of a broad class of time series and other data. This paper describes
an R package KFAS for state space modelling with the observations from an
exponential family, namely Gaussian, Poisson, binomial, negative binomial and
gamma distributions. After introducing the basic theory behind Gaussian and
non-Gaussian state space models, an illustrative example of Poisson time series
forecasting is provided. Finally, a comparison to alternative R packages
suitable for non-Gaussian time series modelling is presented.


  In arXiv:1905.08311, the author and Rohatgi proved a shuffling theorem for
doubly-dented hexagons. In particular, we showed that shuffling removed unit
triangles along a horizontal axis in a hexagon only changes the tiling number
by a simple multiplicative factor. In this paper, we consider a similar
phenomenon for a symmetry class of tilings, the reflectively symmetric tilings,
of the doubly-dented hexagons. We also prove several shuffling theorems for
halved hexagons. These theorems generalize a number of known results in the
enumeration of halved hexagons.


  We investigate the coalescence of surfactant-laden water droplets by using
several different surfactant types and a wide range of concentrations by means
of a coarse-grained model obtained by the statistical associating fluid theory.
Our results demonstrate in detail a universal mass transport mechanism of
surfactant across many concentrations and several surfactant types during the
process. Coalescence initiation is seen to occur via a single pinch due to
aggregation of surface surfactant, and its remnants tend to become engulfed in
part inside the forming bridge. Across the board we confirm the existence of an
initial thermal regime with constant bridge width followed by a later inertial
regime with bridge width scaling roughly as the square root of time, but see no
evidence of an intermediate viscous regime. Coalescence becomes slower as
surfactant concentration grows, and we see evidence of the appearance of a
further slowdown of a different nature for several times the critical
concentration. We anticipate that our results provide further insights in the
mechanisms of coalescence of surfactant-laden droplets.


  Deriving acceptance tests from high-level, natural language requirements that
achieve full coverage is a major manual challenge at the interface between
requirements engineering and testing. Conditional requirements (e.g., "If A or
B then C.") imply causal relationships which - when extracted - allow to
generate these acceptance tests automatically. This paper presents a tool from
the CiRA (Causality In Requirements Artifacts) initiative, which automatically
processes conditional natural language requirements and generates a minimal set
of test case descriptions achieving full coverage. We evaluate the tool on a
publicly available data set of 61 requirements from the requirements
specification of the German Corona-Warn-App. The tool infers the correct test
variables in 84.5% and correct variable configurations in 92.3% of all cases,
which corroborates the feasibility of our approach.


  Alkali halides $MX$, have been viewed as typical ionic compounds,
characterized by 1:1 ratio necessary for charge balance between M$^+$ and
X$^-$. It was proposed that group I elements like Cs can be oxidized further
under high pressure. Here we perform a comprehensive study for the CsF-F system
at pressures up to 100 GPa, and find extremely versatile chemistry. A series of
CsF$_n$ ($n$ $\geq$ 1) compounds are predicted to be stable already at ambient
pressure. Under pressure, 5$p$ electrons in Cs atoms become active, with
growing tendency to form Cs$^{3+}$ and Cs$^{5+}$ valence states at
fluorine-rich conditions. Although Cs$^{2+}$ and Cs$^{4+}$ are not
energetically favoured, the interplay between two mechanisms (polyfluoride
anions and polyvalent Cs cations) allows CsF$_2$ and CsF$_4$ compounds to be
stable under pressure. The estimated defluorination temperatures of CsF$_n$
(n=2,3,5) compounds at atmospheric pressure (218 $^\circ$C, 150 $^\circ$C, -15
$^\circ$C, respectively), are attractive for fluorine storage applications.


  In a wide variety of fields, analysis of images involves defining a region
and measuring its inherent properties. Such measurements include a region's
surface area, curvature, volume, average gray and/or color scale, and so on.
Furthermore, the subsequent subdivision of these regions is sometimes
performed. These subdivisions are then used to measure local information, at
even finer scales. However, simple griding or manual editing methods are
typically used to subdivide a region into smaller units. The resulting
subdivisions can therefore either not relate well to the actual shape or
property of the region being studied (i.e., gridding methods), or be time
consuming and based on user subjectivity (i.e., manual methods). The method
discussed in this work extracts subdivisional units based on a region's general
shape information. We present the results of applying our method to the medical
image analysis of nested regions-of-interest of myocardial wall, where the
subdivisions are used to study temporal and/or spatial heterogeneity of
myocardial perfusion. This method is of particular interest for creating
subdivision regions-of-interest (SROIs) when no variable intensity or other
criteria within a region need be used to separate a particular region into
subunits.


  The pion structure is represented by Generelazed parton distribution
functions (GPDs). The momentum transfer dependence of GPDs of the pion was
obtained on the basis of the form of GPDs of the nucleon in the framework of
the high energy generalized structure (HEGS) model. To this end, different
forms of PDFs of the pion of various Collaborations were examined with taking
into account the available experimental data on the pion form factors. As a
result, the electromagnetic and gravitomagnetic form factors of the pion were
calculated. They were used in the framework of the HEGS model with the
electromagnetic and gravitomagnetic form factors of the proton for describing
pion-nucleon elastic scattering in a wide energy and momentum transfer region
with a minimum of fitting parameters. The properties of the obtained scattering
amplitude were analyzed.


  Some PARI programs have bringed out a property for the non-genus part of the
class number of the imaginary quadratic fields, with respect to $(\sqrt
D\,)^{\varepsilon}$, where $D$ is the absolute value of the discriminant and
$\varepsilon \in ]0, 1[$, in relation with the $\varepsilon$-conjecture. The
general Conjecture 3.1, restricted to quadratic fields, states that, for
$\varepsilon \in ]0, 1[$, the successive maxima, as $D$ increases, of
$\frac{H}{2^{N-1} \cdot (\sqrt D\,)^{\varepsilon}}$, where $H$ is the class
number and $N$ the number of ramified primes, occur only for prime
discriminants (i.e., $H$ odd); we perform computations giving some obviousness
in the selected intervals. For degree $p>2$ cyclic fields, we define a "mean
value" of the non-genus parts of the class numbers of the fields having the
same conductor and obtain an analogous property on the successive maxima. In
Theorem 2.5 we prove, under an assumption (true for $p=2, 3$), that the
sequence of successive maxima of $\frac{H}{p^{N-1} \cdot (\sqrt
D\,)^{\varepsilon}}$ is infinite. Finally we consider cyclic or non-cyclic
abelian fields of degrees $4, 8, 6, 9, 10, 30$ to test the Conjecture 3.1. The
successive maxima of $\frac{H}{(\sqrt D\,)^{\varepsilon}}$ are also analyzed.


  The Somos-4 equation defines the sequences with this name. Looking at these
sequences with an additional property we get a quartic polynomial in 4
variables. This polynomial defines a rational, projective surface in
$\mathbb{RP}^{3}$. Here some generators of the subgroup of $Cr_3 (\mathbb{R})$
are determined, whose birational maps are automorphisms of the quartic surface.


  Plasmodium of \emph{Physarum polycephalum} is a single cell visible by
unaided eye. The plasmodium's foraging behaviour is interpreted in terms of
computation. Input data is a configuration of nutrients, result of computation
is a network of plasmodium's cytoplasmic tubes spanning sources of nutrients.
Tsuda et al (2004) experimentally demonstrated that basic logical gates can be
implemented in foraging behaviour of the plasmodium. We simplify the original
designs of the gates and show --- in computer models --- that the plasmodium is
capable for computation of two-input two-output gate $<x, y> \to <xy, x+y>$ and
three-input two-output $<x, y, z> \to < \bar{x}yz, x+y+z>$. We assemble the
gates in a binary one-bit adder and demonstrate validity of the design using
computer simulation.


  We introduce the quaternionic Mahler measure for non-commutative polynomials,
extending the classical complex Mahler measure. We establish the existence of
quaternionic Mahler measure for slice regular polynomials in one and two
variables. We study the quaternionic Mahler measure for real and slice regular
polynomials, and consider the associated Lehmer problem. Various formulas of
quaternionic Mahler measures are proved.


  Randomized quadratures for integrating functions in Sobolev spaces of order
$\alpha \ge 1$, where the integrability condition is with respect to the
Gaussian measure, are considered. In this function space, the optimal rate for
the worst-case root-mean-squared error (RMSE) is established. Here, optimality
is for a general class of quadratures, in which adaptive non-linear algorithms
with a possibly varying number of function evaluations are also allowed. The
optimal rate is given by showing matching bounds. First, a lower bound on the
worst-case RMSE of $O(n^{-\alpha-1/2})$ is proven, where $n$ denotes an upper
bound on the expected number of function evaluations. It turns out that a
suitably randomized trapezoidal rule attains this rate, up to a logarithmic
factor. A practical error estimator for this trapezoidal rule is also
presented. Numerical results support our theory.


  We develop an alternative systematic approach to the AKNS hierarchy based on
elementary algebraic methods. In particular, we recursively construct Lax pairs
for the entire AKNS hierarchy by introducing a fundamental polynomial formalism
and establish the basic algebro-geometric setting including associated
Burchnall-Chaundy curves, Baker-Akhiezer functions, trace formulas,
Dubrovin-type equations for analogs of Dirichlet and Neumann divisors, and
theta function representations for algebro-geometric solutions.


  Visual information extraction (VIE) plays an important role in Document
Intelligence. Generally, it is divided into two tasks: semantic entity
recognition (SER) and relation extraction (RE). Recently, pre-trained models
for documents have achieved substantial progress in VIE, particularly in SER.
However, most of the existing models learn the geometric representation in an
implicit way, which has been found insufficient for the RE task since geometric
information is especially crucial for RE. Moreover, we reveal another factor
that limits the performance of RE lies in the objective gap between the
pre-training phase and the fine-tuning phase for RE. To tackle these issues, we
propose in this paper a multi-modal framework, named GeoLayoutLM, for VIE.
GeoLayoutLM explicitly models the geometric relations in pre-training, which we
call geometric pre-training. Geometric pre-training is achieved by three
specially designed geometry-related pre-training tasks. Additionally, novel
relation heads, which are pre-trained by the geometric pre-training tasks and
fine-tuned for RE, are elaborately designed to enrich and enhance the feature
representation. According to extensive experiments on standard VIE benchmarks,
GeoLayoutLM achieves highly competitive scores in the SER task and
significantly outperforms the previous state-of-the-arts for RE (\eg, the F1
score of RE on FUNSD is boosted from 80.35\% to 89.45\%). The code and models
are publicly available at
https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/GeoLayoutLM


  Finite groups with given systems of permuteral and strongly permuteral
subgroups are studied. New characterizations of w-supersoluble and supersoluble
groups are received.


  In this paper, using a (2+1)-dimensional field theory approach, we study the
Aharonov-Bohm (AB) scattering with Lorentz symmetry breaking. We obtain the
modified scattering amplitude to the AB effect due to the small Lorentz
violation correction in the breaking parameter and prove that up to one loop
the model is free from ultraviolet divergences.


  In a supersymmetric axion model where the scale for both supersymmetry
breaking and Peccei-Quinn symmetry breaking is around $10^{11}$ GeV, we find
that there is a reasonable parameter space for a strongly first order phase
transition at the scale.


  We analyze the data on the total cross sections for the $dd \to \eta{}^4{\rm
He}$ reaction close to threshold and look for possible $\eta ^4 {\rm He}$ bound
states. We develop a framework in which the $\eta ^4{\rm He}$ optical potential
is the key ingredient, rather than parameterizing the scattering matrix, as is
usually done. The strength of this potential, together with some production
parameters, are fitted to the available experimental data. The relationship of
the scattering matrix to the optical potential is established using the
Bethe-Salpeter equation and the $\eta ^4{\rm He}$ loop function incorporates
the range of the interaction given by the experimental $^4 {\rm He}$ density.
However, when we look for poles of the scattering matrix, we get poles in the
bound region, poles in the positive energy region or no poles at all. If we
further restrict the results with constraints from a theoretical model with all
its uncertainties the bound states are not allowed. However, we find a bump
structure in $|T|^2$ of the $\eta ^4{\rm He} \to \eta ^4{\rm He}$ amplitude
below threshold for the remaining solutions.


  Accurate flight delay prediction is crucial for the secure and effective
operation of the air traffic system. Recent advances in modeling inter-airport
relationships present a promising approach for investigating flight delay
prediction from the multi-airport scenario. However, the previous prediction
works only accounted for the simplistic relationships such as traffic flow or
geographical distance, overlooking the intricate interactions among airports
and thus proving inadequate. In this paper, we leverage causal inference to
precisely model inter-airport relationships and propose a self-corrective
spatio-temporal graph neural network (named CausalNet) for flight delay
prediction. Specifically, Granger causality inference coupled with a
self-correction module is designed to construct causality graphs among airports
and dynamically modify them based on the current airport's delays.
Additionally, the features of the causality graphs are adaptively extracted and
utilized to address the heterogeneity of airports. Extensive experiments are
conducted on the real data of top-74 busiest airports in China. The results
show that CausalNet is superior to baselines. Ablation studies emphasize the
power of the proposed self-correction causality graph and the graph feature
extraction module. All of these prove the effectiveness of the proposed
methodology.


  In recent years, some traffic information prediction methods have been
proposed to provide the precise information of travel time, vehicle speed, and
traffic flow for highways. However, big errors may be obtained by these methods
for urban roads or the alternative roads of highways. Therefore, this study
proposes a travel time prediction method based on convolutional neural networks
to extract important factors for the improvement of traffic information
prediction. In practical experimental environments, the travel time records of
No. 5 Highway and the alternative roads of its were collected and used to
evaluate the proposed method. The results showed that the mean absolute
percentage error of the proposed method was about 5.69%. Therefore, the
proposed method based on deep learning techniques can improve the accuracy of
travel time prediction.


  There have been many advances in the artificial intelligence field due to the
emergence of deep learning. In almost all sub-fields, artificial neural
networks have reached or exceeded human-level performance. However, most of the
models are not interpretable. As a result, it is hard to trust their decisions,
especially in life and death scenarios. In recent years, there has been a
movement toward creating explainable artificial intelligence, but most work to
date has concentrated on image processing models, as it is easier for humans to
perceive visual patterns. There has been little work in other fields like
natural language processing. In this paper, we train a convolutional model on
textual data and analyze the global logic of the model by studying its filter
values. In the end, we find the most important words in our corpus to our
models logic and remove the rest (95%). New models trained on just the 5% most
important words can achieve the same performance as the original model while
reducing training time by more than half. Approaches such as this will help us
to understand NLP models, explain their decisions according to their word
choices, and improve them by finding blind spots and biases.


  The standard Halo Occupation Distribution (HOD) models were originally
developed based on results from semi-analytic and hydrodynamical galaxy
formation models. Those models have since progressed, in particular to include
AGN feedback to match the galaxy luminosity function in a universe with the
observed baryon fraction. AGN feedback affects the relationship between galaxy
stellar mass and luminosity, in particular making the relationship
non-monotonic. For matched number density samples, galaxies in
luminosity-threshold samples occupy a different range of halo masses from those
in stellar-mass-threshold samples. We find that the shapes of the HODs of
luminosity-threshold samples are slightly more complicated in semi-analytic
galaxy formation models that include AGN feedback than are assumed by standard
HOD models. We also find that subhalo abundance matching (SHAM) does not
preserve these non-standard shapes. We show that catalogues created using SHAM
and the semi-analytic model Galform that have the same large-scale 2-point
clustering by construction have different void probability functions (VPFs) in
both real and redshift space. We find that these differences arise from the
different HOD shapes, as opposed to assembly bias, which indicates that the VPF
could be used to test the suitability of an HOD model with real data.


  Motivated by the question of finding a type B analogue of the bijection
between oscillating tableaux and matchings, we find a correspondence between
oscillating m-rim hook tableaux and m-colored matchings, where m is a positive
integer. An oscillating m-rim hook tableau is defined as a sequence
$(\lambda^0,\lambda^1,...,\lambda^{2n})$ of Young diagrams starting with the
empty shape and ending with the empty shape such that $\lambda^{i}$ is obtained
from $\lambda^{i-1}$ by adding an m-rim hook or by deleting an m-rim hook. Our
bijection relies on the generalized Schensted algorithm due to White. An
oscillating 2-rim hook tableau is also called an oscillating domino tableau.
When we restrict our attention to two column oscillating domino tableaux of
length 2n, we are led to a bijection between such tableaux and noncrossing
2-colored matchings on $\{1, 2,..., 2n\}$, which are counted by the product
$C_nC_{n+1}$ of two consecutive Catalan numbers. A 2-colored matching is
noncrossing if there are no two arcs of the same color that are intersecting.
We show that oscillating domino tableaux with at most two columns are in
one-to-one correspondence with Dyck path packings. A Dyck path packing of
length 2n is a pair (D, E), where D is a Dyck path of length 2n, and E is a
dispersed Dyck path of length 2n that is weakly covered by D. So we deduce that
Dyck path packings of length 2n are counted by $C_nC_{n+1}$.


  We consider an algebraic treatment of a three-body system. In particular, we
develop the formalism for a system of three identical objects and discuss an
application to nonstrange baryon resonances which are interpreted as
vibrational and rotational excitations of an oblate symmetric top. We derive
closed expressions for a set of elementary form factors that appear in the
calculation of both electromagnetic, strong and weak couplings of baryons.


  The Square Kilometre Array (SKA) is an international initiative for
developing the world's largest radio telescope with a total collecting area of
over a million square meters. The scale of the operation, combined with the
remote location of the telescope, requires the use of energy-efficient
computational algorithms. This, along with the extreme data rates that will be
produced by the SKA and the requirement for a real-time observing capability,
necessitates in-situ data processing in an edge style computing solution. More
generally, energy efficiency in the modern computing landscape is becoming of
paramount concern. Whether it be the power budget that can limit some of the
world's largest supercomputers, or the limited power available to the smallest
Internet-of-Things devices. In this paper, we study the impact of hardware
frequency scaling on the energy consumption and execution time of the Fast
Fourier Transform (FFT) on NVIDIA GPUs using the cuFFT library. The FFT is used
in many areas of science and it is one of the key algorithms used in radio
astronomy data processing pipelines. Through the use of frequency scaling, we
show that we can lower the power consumption of the NVIDIA V100 GPU when
computing the FFT by up to 60% compared to the boost clock frequency, with less
than a 10% increase in the execution time. Furthermore, using one common core
clock frequency for all tested FFT lengths, we show on average a 50% reduction
in power consumption compared to the boost core clock frequency with an
increase in the execution time still below 10%. We demonstrate how these
results can be used to lower the power consumption of existing data processing
pipelines. These savings, when considered over years of operation, can yield
significant financial savings, but can also lead to a significant reduction of
greenhouse gas emissions.


  We study the statistical distribution of satellites around star-forming and
quiescent central galaxies at 1<z<3 using imaging from the FourStar Galaxy
Evolution Survey (ZFOURGE) and the Cosmic Assembly Near-IR Deep Extragalactic
Legacy Survey (CANDELS). The deep near-IR data select satellites down to
$\log(M/M_\odot)>9$ at z<3. The radial satellite distribution around centrals
is consistent with a projected NFW profile. Massive quiescent centrals,
$\log(M/M_\odot)>10.78$, have $\sim$2 times the number of satellites compared
to star-forming centrals with a significance of 2.7$\sigma$ even after
accounting for differences in the centrals' stellar-mass distributions. We find
no statistical difference in the satellite distributions of intermediate-mass
quiescent and star-forming centrals, $10.48<\log(M/M_\odot)<10.78$. Comparing
to the Guo2011 semi-analytic model, the excess number of satellites indicates
that quiescent centrals have halo masses 0.3 dex larger than star-forming
centrals, even when the stellar-mass distributions are fixed. We use a simple
toy model that relates halo mass and quenching, which roughly reproduces the
observed quenched fractions and the differences in halo mass between
star-forming and quenched galaxies only if galaxies have a quenching
probability that increases with halo mass from $\sim$0 for
$\log(M_h/M_\odot)\sim$11 to $\sim$1 for $\log(M_h/M_\odot)\sim$13.5. A single
halo-mass quenching threshold is unable to reproduce the quiescent fraction and
satellite distribution of centrals. Therefore, while halo quenching may be an
important mechanism, it is unlikely to be the only factor driving quenching. It
remains unclear why a high fraction of centrals remain star-forming even in
relatively massive halos.


  DevSecOps is a software development paradigm that places a high emphasis on
the culture of collaboration between developers (Dev), security (Sec) and
operations (Ops) teams to deliver secure software continuously and rapidly.
Adopting this paradigm effectively, therefore, requires an understanding of the
challenges, best practices and available solutions for collaboration among
these functional teams. However, collaborative aspects related to these teams
have received very little empirical attention in the DevSecOps literature.
Hence, we present a study focusing on a key security activity, Application
Security Testing (AST), in which practitioners face difficulties performing
collaborative work in a DevSecOps environment. Our study made novel use of 48
systematically selected webinars, technical talks and panel discussions as a
data source to qualitatively analyse software practitioner discussions on the
most recent trends and emerging solutions in this highly evolving field. We
find that the lack of features that facilitate collaboration built into the AST
tools themselves is a key tool-related challenge in DevSecOps. In addition, the
lack of clarity related to role definitions, shared goals, and ownership also
hinders Collaborative AST (CoAST). We also captured a range of best practices
for collaboration (e.g., Shift-left security), emerging communication methods
(e.g., ChatOps), and new team structures (e.g., hybrid teams) for CoAST.
Finally, our study identified several requirements for new tool features and
specific gap areas for future research to provide better support for CoAST in
DevSecOps.


  The Hohenberg-Mermin-Wagner (HMW) theorem states that infrared (IR)
fluctuations prevent long-range order which breaks continuous symmetries in two
dimensions (2D), at finite temperatures. We note that the theorem becomes
physically effective for superconductivity (SC) only for astronomical sample
sizes, so it does not prevent 2D SC in practice. We systematically explore the
sensitivity of the magnetic and SC versions of the theorem to finite-size and
disorder effects. For magnetism, finite-size effects, disorder, and
perpendicular coupling can all restore the order parameter at a non-negligible
value of $T_c$ equally well, making the physical reason for finite $T_c$
sample-dependent. For SC, an alternative version of the HMW theorem is
presented, in which the temperature cutoff is set by Cooper pairing, in place
of the Fermi energy in the standard version. It still allows 2D SC at $2$--$3$
times the room temperature when the interaction scale is large and Cooper pairs
are small, the case with high-$T_c$ SC in the cuprates. Thus IR fluctuations do
not prevent 2D SC at room temperatures in samples of any reasonable size, by
any known version of the HMW argument. A possible approach to derive
mechanism-dependent upper bounds for SC $T_c$ is pointed out.


  It is well-known that chiral symmetry breaking ($\chi$SB) in QCD with
$N_{f}=2$ light quark flavours can be described by orthogonal groups as $O(4)
\to O(3)$, due to local isomorphisms. Here we discuss the question how specific
this property is. We consider generalised forms of $\chi$SB involving an
arbitrary number of light flavours of continuum or lattice fermions, in various
representations. We search systematically for isomorphic descriptions by
non-unitary, compact Lie groups. It turns out that there are a few alternative
options in terms of orthogonal groups, while we did not find any description
entirely based on symplectic or exceptional Lie groups. If we adapt such an
alternative as the symmetry breaking pattern for a generalised Higgs mechanism,
we may consider a Higgs particle composed of bound fermions and trace back the
mass generation to $\chi$SB. In fact, some of the patterns that we encounter
appear in technicolour models. In particular if one observes a Higgs mechanism
that can be expressed in terms of orthogonal groups, we specify in which cases
it could also represent some kind of $\chi$SB of techniquarks.


  Recent years have seen substantial advances in the development of
biofunctional materials using synthetic polymers. The growing problem of
elusive sequence-functionality relations for most biomaterials has driven
researchers to seek more effective tools and analysis methods. In this study,
statistical models are used to study sequence features of the recently reported
random heteropolymers (RHP), which transport protons across lipid bilayers
selectively and rapidly like natural proton channels. We utilized the
probabilistic graphical model framework and developed a generalized hidden
semi-Markov model (GHSMM-RHP) to extract the function-determining sequence
features, including the transmembrane segments within a chain and the sequence
heterogeneity among different chains. We developed stochastic variational
methods for efficient inference on parameter estimation and predictions, and
empirically studied their computational performance from a comparative
perspective on Bayesian (i.e., stochastic variational Bayes) versus frequentist
(i.e., stochastic variational expectation-maximization) frameworks that have
been studied separately before. The real data results agree well with the
laboratory experiments, and suggest GHSMM-RHP's potential in predicting
protein-like behavior at the polymer-chain level.


  Antibiotic resistant bacteria are a serious threat to public health system.
In order to be able to model the spread of this pathogens within healthcare
system network, it is necessary to obtain information on patient movements and
the structure of the network. To accomplish that we need informations about
patient population and their exchange within the network. In this paper, we
analysed hospitalisation records which were provided for the EMerGE-Net project
to the Martin Luther University Halle-Wittenberg. We studied and discussed
properties of the data with respect to German states. Moreover, we investigated
the patterns of patients movements between different states. Then, we took a
closer look at resulting hospital network structure, which can be further used
to numerically model spread of pathogens.


  SPARK 2014 is a modern programming language and a new state-of-the-art tool
set for development and verification of high-integrity software. In this paper,
we explore the capabilities and limitations of its latest version in the
context of building a flight stack for a high-altitude unmanned glider. Towards
that, we deliberately applied static analysis early and continuously during
implementation, to give verification the possibility to steer the software
design. In this process we have identified several limitations and pitfalls of
software design and verification in SPARK, for which we give workarounds and
protective actions to avoid them. Finally, we give design recommendations that
have proven effective for verification, and summarize our experiences with this
new language.


  Mild traumatic brain injury (mTBI) is a growing public health problem with an
estimated incidence of one million people annually in US. Neurocognitive tests
are used to both assess the patient condition and to monitor the patient
progress. This work aims to directly use MR images taken shortly after injury
to detect whether a patient suffers from mTBI, by incorporating machine
learning and computer vision techniques to learn features suitable
discriminating between mTBI and normal patients. We focus on 3 regions in
brain, and extract multiple patches from them, and use bag-of-visual-word
technique to represent each subject as a histogram of representative patterns
derived from patches from all training subjects. After extracting the features,
we use greedy forward feature selection, to choose a subset of features which
achieves highest accuracy. We show through experimental studies that BoW
features perform better than the simple mean value features which were used
previously.


  Instead of using expensive manual annotations, researchers have proposed to
train named entity recognition (NER) systems using heuristic labeling rules.
However, devising labeling rules is challenging because it often requires a
considerable amount of manual effort and domain expertise. To alleviate this
problem, we propose \textsc{GLaRA}, a graph-based labeling rule augmentation
framework, to learn new labeling rules from unlabeled data. We first create a
graph with nodes representing candidate rules extracted from unlabeled data.
Then, we design a new graph neural network to augment labeling rules by
exploring the semantic relations between rules. We finally apply the augmented
rules on unlabeled data to generate weak labels and train a NER model using the
weakly labeled data. We evaluate our method on three NER datasets and find that
we can achieve an average improvement of +20\% F1 score over the best baseline
when given a small set of seed rules.


  The primary difficulty in understanding the sources and processes that
powered cosmic reionization is that it is not possible to directly probe the
ionizing Lyman Continuum (LyC) radiation at that epoch as those photons have
been absorbed by the intervening neutral hydrogen in the IGM on their way to
us. It is therefore imperative to build a model to accurately predict LyC
emission using other properties of galaxies in the reionization era.
  In recent years, studies have shown that the LyC emission from galaxies may
be correlated to their Lya emission. Here, we study this correlation by
analyzing thousands of galaxies at high-z in the SPHINX cosmological
simulation. We post-process these galaxies with the Lya radiative transfer code
RASCAS and analyze the Lya - LyC connection.
  We find that the Lya and LyC luminosities are strongly correlated with each
other, although with dispersion. There is a positive correlation between Lya
and LyC escape fractions in the brightest Lya emitters (>$10^{41}$ erg/s),
similar to the recent observational studies. However, when we also include
fainter Lya emitters (LAEs), the correlation disappears, which suggests that
the observed relationship may be driven by selection effects. We also find that
bright LAEs are dominant contributors to reionization ($> 10^{40}$ erg/s
galaxies contribute $> 90\%$ of LyC emission). Finally, we build predictive
models using multivariate linear regression where we use the physical and the
Lya properties of simulated galaxies to predict their intrinsic and escaping
LyC luminosities with a high degree of accuracy. We find that the most
important galaxy properties to predict the escaping LyC luminosity of a galaxy
are its escaping Lya luminosity, gas mass, gas metallicity, and SFR.
  These models can be very useful to predict LyC emissions from galaxies and
can help us identify the sources of reionization.


  From the viewpoint of quantum walks, the Ihara zeta function of a finite
graph can be said to be closely related to its evolution matrix. In this note
we introduce another kind of zeta function of a graph, which is closely related
to, as to say, the square of the evolution matrix of a quantum walk. Then we
give to such a function two types of determinant expressions and derive from it
some geometric properties of a finite graph. As an application, we illustrate
the distribution of poles of this function comparing with those of the usual
Ihara zeta function.


  A linear polyomial non-negative on the non-negativity domain of finitely many
linear polynomials can be expressed as their non-negative linear combination.
Recently, under several additional assumptions, Helton, Klep, and McCullough
extended this result to matrix polynomials. The aim of this paper is to study
which of these additional assumptions are really necessary.


  We first show that every quasisimple sporadic group possesses an unmixed
strongly real Beauville structure aside from the Mathieu groups M11 and M23
(and possibly 2B and M). We go on to show that no almost simple sporadic group
possesses a mixed Beauville structure. We then go on to use the exceptional
nature of the alternating group A6 to give a strongly real Beauville structure
for this group explicitly correcting an earlier error of Fuertes and
Gonzalez-Diez. In doing so we complete the classification of alternating groups
that possess strongly real Beauville structures. We conclude by discussing
mixed Beauville structures of the groups A6:2 and A6:2^2.


  The measurement of quarkonia production in relativistic heavy ion collisions
provides a powerful tool for studying the properties of the hot and dense
matter created in these collisions. To be really useful, however, such
measurements must cover a wide range of quarkonia states and colliding species.
The PHENIX experiment at RHIC has successfully measured J/psi, psi-prime, chi_c
and Upsilon production in different colliding systems at various energies. In
this talk I will present recent results from the PHENIX collaboration on
charmonium production in d+Au, Cu+Au and U+U collisions at 200 GeV/c.


  We show that there exist scalar field theories with plausible one-particle
states in general $D$ dimensional nonstationary curved spacetimes whose
propagating modes are localized on $d\le D$ dimensional hypersurfaces, and the
corresponding stress tensor resembles the bare cosmological constant
$\lambda_B$ in the $D$ dimensional bulk. We show that nontrivial $d=1$
dimensional solutions correspond to $\lambda_B< 0$. Considering free scalar
theories we find that for $d=2$ the symmetry of the parameter space of
classical solutions corresponding to $\lambda_B\neq 0$ is $O(1,1)$ which
enhances to $\mathbb{Z}_2\times{\rm Diff}(\mathbb{R}^1)$ at $\lambda_B=0$. For
$d>2$ we obtain $O(d-1,1)$, $O(d-1)\times {\rm Diff}(\mathbb{R}^1)$ and
$O(d-1,1)\times O(d-2)\times {\rm Diff}(\mathbb{R}^1)$ corresponding to,
respectively, $\lambda_B<0$, $\lambda_B=0$ and $\lambda_B>0$.


  In this note we prove an abstract version of a result from 2002 due to
Delgado and Pi\~{n}ero on absolutely summing operators. Several applications
are presented; some of them in the multilinear framework and some in a
completely nonlinear setting. In a final section we investigate the size of the
set of non uniformly dominated sets of linear operators under the point of view
of lineability.


  Action anticipation, which aims to recognize the action with a partial
observation, becomes increasingly popular due to a wide range of applications.
In this paper, we investigate the problem of 3D action anticipation from
streaming videos with the target of understanding best practices for solving
this problem. We first introduce several complementary evaluation metrics and
present a basic model based on frame-wise action classification. To achieve
better performance, we then investigate two important factors, i.e., the length
of the training clip and clip sampling method. We also explore multi-task
learning strategies by incorporating auxiliary information from two aspects:
the full action representation and the class-agnostic action label. Our
comprehensive experiments uncover the best practices for 3D action
anticipation, and accordingly we propose a novel method with a multi-task loss.
The proposed method considerably outperforms the recent methods and exhibits
the state-of-the-art performance on standard benchmarks.


  There has been a lot of work in question generation where different methods
to provide target answers as input, have been employed. This experimentation
has been mostly carried out for RNN based models. We use three different
methods and their combinations for incorporating answer information and explore
their effect on several automatic evaluation metrics. The methods that are used
are answer prompting, using a custom product method using answer embeddings and
encoder outputs, choosing sentences from the input paragraph that have answer
related information, and using a separate cross-attention attention block in
the decoder which attends to the answer. We observe that answer prompting
without any additional modes obtains the best scores across rouge, meteor
scores. Additionally, we use a custom metric to calculate how many of the
generated questions have the same answer, as the answer which is used to
generate them.


  We study the XXZ chain with a boundary at massless regime $-1<\Delta<1$.
  We give the free field realizations of the boundary vacuum state and it's
dual. Using these realizations, we give the integrable representations of the
correlation functions.


  Human motion prediction is key to understand social environments, with direct
applications in robotics, surveillance, etc. We present a simple yet effective
pedestrian trajectory prediction model aimed at pedestrians positions
prediction in urban-like environments conditioned by the environment: map and
surround agents. Our model is a neural-based architecture that can run several
layers of attention blocks and transformers in an iterative sequential fashion,
allowing to capture the important features in the environment that improve
prediction. We show that without explicit introduction of social masks,
dynamical models, social pooling layers, or complicated graph-like structures,
it is possible to produce on par results with SoTA models, which makes our
approach easily extendable and configurable, depending on the data available.
We report results performing similarly with SoTA models on publicly available
and extensible-used datasets with unimodal prediction metrics ADE and FDE.


  We have used the Swedish ESO Submillimetre Telescope (SEST) to search for new
class II methanol maser transitions towards the southern source G345.01+1.79.
Over a period of 5 days we observed 11 known or predicted class II methanol
maser transitions. Emission with the narrow line width and characteristic
velocity of class II methanol masers (in this source) was detected in 8 of
these transitions, two of which have not previously been reported as masers.
The new class II methanol maser transitions are the 13(-3)-12(-4)E transition
at 104.1 GHz and the 5(1)-4(2)E transition at 216.9 GHz. Both of these are from
transition series for which there are no previous known class II methanol maser
transitions. This takes the total number of known class II methanol maser
series to 10, and the total number of transitions (or transition groups) to 18.
The observed 104.1 GHz maser suggests the presence of two or more regions of
masing gas with similar line of sight velocities, but quite different physical
conditions. Although these newly discovered transitions are likely to be
relatively rare, where they are observed combined studies using the Australia
Telescope Compact Array and the Atacama Large Millimeter Array offer the
prospect to be able to undertake multi-transition methanol maser studies with
unprecedented detail.


  We perform a joint fit to differential number counts from Spitzer's MIPS and
Herschel's SPIRE instruments, and angular power spectra of cosmic infrared
background (CIB) anisotropies from SPIRE, Planck, the Atacama Cosmology
Telescope, and the South Pole Telescope, which together span 220 < \nu / GHz <
4300 (70 < \lambda / \mu m < 1400). We simultaneously constrain the dust
luminosity function, thermal dust spectral energy distribution (SED) and
clustering properties of CIB sources, and the evolution of these quantities
over cosmic time. We find that the data strongly require redshift evolution in
the thermal dust SED. In our adopted parametrization, this evolution takes the
form of an increase in graybody dust temperature at high redshift, but it may
also be related to a temperature - dust luminosity correlation or evolution in
dust opacity. The counts and spectra together constrain the evolution of the
thermal dust luminosity function up to z ~ 2.5-3, complementing approaches
relying on rest-frame mid-infrared observations of the rarest bright objects.
We are able to fit the power spectra without requiring a complex halo model
approach, and show that neglecting scale-dependent halo bias may be impairing
analyses that do use this framework.


  When translating from speech, special consideration for conversational speech
phenomena such as disfluencies is necessary. Most machine translation training
data consists of well-formed written texts, causing issues when translating
spontaneous speech. Previous work has introduced an intermediate step between
speech recognition (ASR) and machine translation (MT) to remove disfluencies,
making the data better-matched to typical translation text and significantly
improving performance. However, with the rise of end-to-end speech translation
systems, this intermediate step must be incorporated into the
sequence-to-sequence architecture. Further, though translated speech datasets
exist, they are typically news or rehearsed speech without many disfluencies
(e.g. TED), or the disfluencies are translated into the references (e.g.
Fisher). To generate clean translations from disfluent speech, cleaned
references are necessary for evaluation. We introduce a corpus of cleaned
target data for the Fisher Spanish-English dataset for this task. We compare
how different architectures handle disfluencies and provide a baseline for
removing disfluencies in end-to-end translation.


  Let $f: X \to Y$ be a regular covering of a surface $Y$ of finite type with
nonempty boundary, with finitely-generated (possibly infinite) deck group $G$.
We give necessary and sufficient conditions for an integral homology class on
$X$ to admit a representative as a connected component of the preimage of a
nonseparating simple closed curve on $Y$, possibly after passing to a
"stabilization", i.e. a $G$-equivariant embedding of covering spaces $X
\hookrightarrow X^+$.


  The concept of spectrum for a class of non-linear wave equations is studied.
Instead of looking for stability, the key to the spectral structure is found in
the instability phenomena (bifurcations). This aspect is best seen in the
`classical model' of the non-linear wave mechanics. The solitons
(macro-localizations) are a part of the non-linear spectral problem; their
bifurcations reflect the dynamical symmetry breaking. The computer simulations
suggest that the bifurcations of the asymptotic behaviour occur also for the
general, non-stationary states. A~phenomenon of the soliton splitting is
observed.


  We prove that it is PPAD-hard to compute a Nash equilibrium in a tree
polymatrix game with twenty actions per player. This is the first PPAD hardness
result for a game with a constant number of actions per player where the
interaction graph is acyclic. Along the way we show PPAD-hardness for finding
an $\epsilon$-fixed point of a 2D LinearFIXP instance, when $\epsilon$ is any
constant less than $(\sqrt{2} - 1)/2 \approx 0.2071$. This lifts the hardness
regime from polynomially small approximations in $k$-dimensions to constant
approximations in two-dimensions, and our constant is substantial when compared
to the trivial upper bound of $0.5$.


  The Cherenkov Telescope Array (CTA) is a planned observatory for very-high
energy gamma-ray astronomy. It will consist of several tens of telescopes of
different sizes, with a total mirror area of up to 10,000 square meters. Most
mirrors of current installations are either polished glass mirrors or
diamond-turned aluminium mirrors, both labour intensive technologies. For CTA,
several new technologies for a fast and cost-efficient production of
light-weight and reliable mirror substrates have been developed and industrial
pre-production has started for most of them. In addition, new or improved
aluminium-based and dielectric surface coatings have been developed to increase
the reflectance over the lifetime of the mirrors compared to those of current
Cherenkov telescope instruments.


  We present a simple laboratory experiment to illustrate some aspects of the
soliton theory in discrete lattices with a system that models the dynamics of
dislocations in a crystal or the properties of adsorbed atomic layers. The
apparatus not only shows the role of the Peierls-Nabarro potential but also
illustrates the hierarchy of depinning transitions and the importance of the
collective motion in mass transport.


  We present the results of Smoothed Particle Hydrodynamics (SPH) simulations
of the formation of a massive counterrotating disk in a spiral galaxy. The
current study revisits and extends (with SPH) previous work carried out with
sticky particle gas dynamics, in which adiabatic gas infall and a retrograde
gas-rich dwarf merger were tested as the two most likely processes for
producing such a counterrotating disk. We report on experiments with a cold
primary similar to our Galaxy, as well as a hot, compact primary modeled after
NGC 4138. We have also conducted numerical experiments with varying amounts of
prograde gas in the primary disk, and an alternative infall model (a spherical
shell with retrograde angular momentum). The structure of the resulting
counterrotating disks is dramatically different with SPH. The disks we produce
are considerably thinner than the primary disks and those produced with sticky
particles. The time-scales for counterrotating disk formation are shorter with
SPH because the gas loses kinetic energy and angular momentum more rapidly.
Spiral structure is evident in most of the disks, but an exponential radial
profile is not a natural byproduct of these processes. The infalling gas shells
that we tested produce counterrotating bulges and rings rather than disks. The
presence of a considerable amount of preexisting prograde gas in the primary
causes, at least in the absence of star formation, a rapid inflow of gas to the
center and a subsequent hole in the counterrotating disk. In general, our SPH
experiments yield stronger evidence to suggest that the accretion of massive
counterrotating disks drives the evolution of the host galaxies towards earlier
(S0/Sa) Hubble types.


  The Zeeman hyperfine state dependent force in a Stern-Gerlach (SG) experiment
has been exploited to separate and detect atoms having different Zeeman
hyperfine states in a cold atom cloud. Utilizing this SG technique, we have
made the quantitative estimate of atoms in different Zeeman hyperfine states in
an atom cloud, which has been helpful in optimizing the optical pumping of
atoms for efficient preparation of atomic state. Employing an optimized optical
pumping, nearly $92 \% $ of cold $^{87}Rb$ atoms from a grey magneto-optical
trap (G-MOT) on atom-chip have been optically pumped to the trappable Zeeman
hyperfine state $\ket{F=2, \, m_{F} = +2}$. These optically pumped atoms have
been trapped in an Ioffe-Pritchard magnetic trap near the atom-chip surface.


  Classical particle mechanics on curved spaces is related to the flow of ideal
fluids, by a dual interpretation of the Hamilton-Jacobi equation. As in second
quantization, the procedure relates the description of a system with a finite
number of degrees of freedom to one with infinitely many degrees of freedom. In
some two-dimensional fluid mechanics models a duality transformation between
the velocity potential and the stream function can be performed relating
sources and sinks in one model to vortices in the other. The particle mechanics
counterpart of the dual theory is reconstructed. In the quantum theory the
strength of sources and sinks, as well as vorticity are quantized; for the
duality between theories to be preserved these quantization conditions must be
related.