  We unveil the multifractal behavior of Ising spin glasses in their
low-temperature phase. Using the Janus II custom-built supercomputer, the
spin-glass correlation function is studied locally. Dramatic fluctuations are
found when pairs of sites at the same distance are compared. The scaling of
these fluctuations, as the spin-glass coherence length grows with time, is
characterized through the computation of the singularity spectrum and its
corresponding Legendre transform. A comparatively small number of site pairs
controls the average correlation that governs the response to a magnetic field.
We explain how this scenario of dramatic fluctuations (at length scales smaller
than the coherence length) can be reconciled with the smooth, self-averaging
behavior that has long been considered to describe spin-glass dynamics.


  We present a viewpoint of the transport process in quantum cascade laser
structures in which spatial transport of charge through the structure is a
property of coherent quantum-mechanical wavefunctions. In contrast, scattering
processes redistribute particles in energy and momentum but do not directly
cause spatial motion of charge.


  We report the results of a large-scale modelling study of radiation damage
effects in the nuclear waste form zirconolite. We particularly focus on the
effects of radiation damage in amorphous zirconolite and have developed a new
way to analyse the damaged structure in terms of local coordination statistics.
On the basis of this analysis, we find that the amorphous structure responds to
radiation damage differently from the crystal. Amorphous zirconolite is found
to be "softer" than crystalline zirconolite with a much larger number of atoms
becoming displaced and changing coordination during a 70 keV cascade. The local
coordination and connectivity analysis shows that the amorphous structure
continues to evolve as a result of repeated radiation damage, changes which
cannot be identified from globally averaged properties such as pair
distribution functions. We also find large density inhomogeneities at the local
level which we suggest may play an important role for future developments in
nuclear waste storage. Finally, we find a correlation between the changes in
enthalpy and local coordination, suggesting that measurements of enthalpy
change can be linked quantitatively to structural radiation damage. Our results
raise an interesting possibility of whether an evolution of the amorphous
structure due to radiation damage can converge to a new equilibrium amorphous
regime, posing the fundamental question of what that structure may be.


  We present the X-UDS survey, a set of wide and deep Chandra observations of
the Subaru-XMM Deep/UKIDSS Ultra Deep Survey (SXDS/UDS) field. The survey
consists of 25 observations that cover a total area of 0.33 deg$^{2}$. The
observations are combined to provide a nominal depth of ~600 ksec in the
central 100 arcmin$^{2}$ region of the field that has been imaged with
Hubble/WFC3 by the CANDELS survey and $\sim$200 ksec in the remainder of the
field. In this paper, we outline the survey's scientific goals, describe our
observing strategy, and detail our data reduction and point source detection
algorithms. Our analysis has resulted in a total of 868 band-merged point
sources detected with a false-positive Poisson probability of
$<1\times10^{-4}$. In addition, we present the results of an X-ray spectral
analysis and provide best-fitting neutral hydrogen column densities, $N_{\rm
H}$, as well as a sample of 51 Compton-thick active galactic nucleus
candidates. Using this sample, we find the intrinsic Compton-thick fraction to
be 30-35% over a wide range in redshift ($z=0.1-3$), suggesting the obscured
fraction does not evolve very strongly with epoch. However, if we assume that
the Compton-thick fraction is dependent on luminosity, as is seen for
Compton-thin sources, then our results are consistent with a rise in the
obscured fraction out to $z\sim3$. Finally, an examination of the host
morphologies of our Compton-thick candidates shows a high fraction of
morphological disturbances, in agreement with our previous results. All data
products described in this paper are made available via a public website.


  In this paper we give local and global parametric classifications of a class
of Einstein submanifolds of Euclidean space. The highlight is for submanifolds
of codimension two since in this case our assumptions are only of intrinsic
nature.


  Multi-scale deep CNN architecture [1, 2, 3] successfully captures both fine
and coarse level image descriptors for visual similarity task, but they come up
with expensive memory overhead and latency. In this paper, we propose a
competing novel CNN architecture, called MILDNet, which merits by being vastly
compact (about 3 times). Inspired by the fact that successive CNN layers
represent the image with increasing levels of abstraction, we compressed our
deep ranking model to a single CNN by coupling activations from multiple
intermediate layers along with the last layer. Trained on the famous
Street2shop dataset [4], we demonstrate that our approach performs as good as
the current state-of-the-art models with only one third of the parameters,
model size, training time and significant reduction in inference time. The
significance of intermediate layers on image retrieval task has also been shown
to be performing on popular datasets Holidays, Oxford, Paris [5]. So even
though our experiments are done on ecommerce domain, it is applicable to other
domains as well. We further did an ablation study to validate our hypothesis by
checking the impact on adding each intermediate layer. With this we also
present two more useful variants of MILDNet, a mobile model (12 times smaller)
for on-edge devices and a compactly featured model (512-d feature embeddings)
for systems with less RAMs and to reduce the ranking cost. Further we present
an intuitive way to automatically create a tailored in-house triplet training
dataset, which is very hard to create manually. This solution too can also be
deployed as an all-inclusive visual similarity solution. Finally, we present
our entire production level architecture which currently powers visual
similarity at Fynd.


  Recent global fit analyses of oscillation data show a preference for normal
mass ordering (NMO) at 2.5$\sigma$ and provide 1.6$\sigma$ indications for
lower $\theta_{23}$ octant and leptonic CP violation. A high-precision
measurement of $\theta_{23}$ is pivotal to convert these hints into
discoveries. In this work, we study in detail the capabilities of DUNE to
establish the deviation from maximal $\theta_{23}$ and to resolve its octant at
high confidence levels. We exhibit the possible correlations and degeneracies
among $\sin^2\theta_{23}$, $\Delta m^2_{31}$, and $\delta_{CP}$ in
disappearance and appearance oscillation channels at the probability and event
levels. Introducing for the first time, a bi-events plot in the plane of total
$\nu$ and $\bar\nu$ disappearance events, we discuss the impact of
$\sin^2\theta_{23}$ - $\Delta m^2_{31}$ degeneracy in establishing non-maximal
$\theta_{23}$ and show how this degeneracy can be resolved by exploiting the
spectral shape information in $\nu$ and $\bar\nu$ disappearance events. A
3$\sigma$ (5$\sigma$) determination of non-maximal $\theta_{23}$ is possible in
DUNE in total 7 years if $\sin^2\theta_{23} \lesssim 0.465~(0.450)$ or
$\sin^2\theta_{23} \gtrsim 0.554~(0.572)$ for any value of $\delta_{CP}$ and
NMO. We study the individual contributions from appearance and disappearance
channels, impact of systematic uncertainties and marginalization over
oscillation parameters, importance of spectral analysis and data from both
$\nu$ and $\bar\nu$ runs, while analyzing DUNE's sensitivity to establish
non-maximal $\theta_{23}$. DUNE can resolve the octant of $\theta_{23}$ at
4.2$\sigma$ (5$\sigma$) using 7 (10) years of run assuming $\sin^2\theta_{23}$
= 0.455, $\delta_{CP}$ = $223^\circ$, and NMO. DUNE can improve the current
relative 1$\sigma$ precision on $\sin^2\theta_{23}$ ($\Delta m^2_{31}$) by a
factor of 4.4 (2.8) using 7 years of run.


  Self-supervised monocular depth estimation has been widely studied recently.
Most of the work has focused on improving performance on benchmark datasets,
such as KITTI, but has offered a few experiments on generalization performance.
In this paper, we investigate the backbone networks (e.g. CNNs, Transformers,
and CNN-Transformer hybrid models) toward the generalization of monocular depth
estimation. We first evaluate state-of-the-art models on diverse public
datasets, which have never been seen during the network training. Next, we
investigate the effects of texture-biased and shape-biased representations
using the various texture-shifted datasets that we generated. We observe that
Transformers exhibit a strong shape bias and CNNs do a strong texture-bias. We
also find that shape-biased models show better generalization performance for
monocular depth estimation compared to texture-biased models. Based on these
observations, we newly design a CNN-Transformer hybrid network with a
multi-level adaptive feature fusion module, called MonoFormer. The design
intuition behind MonoFormer is to increase shape bias by employing Transformers
while compensating for the weak locality bias of Transformers by adaptively
fusing multi-level representations. Extensive experiments show that the
proposed method achieves state-of-the-art performance with various public
datasets. Our method also shows the best generalization ability among the
competitive methods.


  Mean Field Control Games (MFCG), introduced in [Angiuli et al., 2022a],
represent competitive games between a large number of large collaborative
groups of agents in the infinite limit of number and size of groups. In this
paper, we prove the convergence of a three-timescale Reinforcement Q-Learning
(RL) algorithm to solve MFCG in a model-free approach from the point of view of
representative agents. Our analysis uses a Q-table for finite state and action
spaces updated at each discrete time-step over an infinite horizon. In [Angiuli
et al., 2023], we proved convergence of two-timescale algorithms for MFG and
MFC separately highlighting the need to follow multiple population
distributions in the MFC case. Here, we integrate this feature for MFCG as well
as three rates of update decreasing to zero in the proper ratios. Our technique
of proof uses a generalization to three timescales of the two-timescale
analysis in [Borkar, 1997]. We give a simple example satisfying the various
hypothesis made in the proof of convergence and illustrating the performance of
the algorithm.


  Riccati's differential equation is formulated as abstract equation in finite
or infinite dimensional Banach spaces. Since the Riccati's differential
equation with the Cole-Hopf transform shows a relation between the first order
evolution equations and the second order evolution equations, its
generalization suggests the existence of recurrence formula leading to a
sequence of differential equations with different order. %%% In conclusion, by
means of the logarithmic representation of operators, a transform between the
first order evolution equations and the higher order evolution equation is
presented. Several classes of evolution equations with different orders are
given, and some of them are shown as examples.


  Recently, a surge of interest in visual transformers is to reduce the
computational cost by limiting the calculation of self-attention to a local
window. Most current work uses a fixed single-scale window for modeling by
default, ignoring the impact of window size on model performance. However, this
may limit the modeling potential of these window-based models for multi-scale
information. In this paper, we propose a novel method, named Dynamic Window
Vision Transformer (DW-ViT). The dynamic window strategy proposed by DW-ViT
goes beyond the model that employs a fixed single window setting. To the best
of our knowledge, we are the first to use dynamic multi-scale windows to
explore the upper limit of the effect of window settings on model performance.
In DW-ViT, multi-scale information is obtained by assigning windows of
different sizes to different head groups of window multi-head self-attention.
Then, the information is dynamically fused by assigning different weights to
the multi-scale window branches. We conducted a detailed performance evaluation
on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with related
state-of-the-art (SoTA) methods, DW-ViT obtains the best performance.
Specifically, compared with the current SoTA Swin Transformers
\cite{liu2021swin}, DW-ViT has achieved consistent and substantial improvements
on all three datasets with similar parameters and computational costs. In
addition, DW-ViT exhibits good scalability and can be easily inserted into any
window-based visual transformers.


  Several different ways exist for approaching hard optimization problems.
Mathematical programming techniques, including (integer) linear
programming-based methods and metaheuristic approaches, are two highly
successful streams for combinatorial problems. These two have been established
by different communities more or less in isolation from each other. Only over
several years ago, a larger number of researchers recognized the advantages and
huge potentials of building hybrids of mathematical programming methods and
metaheuristics.


  We investigated a system based solely on a single molecular species,
Cu(tmdt)$_{2}$, accommodating $d$ and ${\pi}$ orbitals within the molecule.
$^{13}$C nuclear magnetic resonance measurements captured singlet-triplet
excitations of ${\pi}$ spins indicating the existence of a
${\pi}$-electron-based spin-gapped Mott insulating subsystem, which has been
hidden by the large magnetic susceptibility exhibited by the $d$ spins forming
antiferromagnetic chains. The present results demonstrate a unique hybrid Mott
insulator composed of antiferromagnetic and spin-singlet Mott subsystems with
distinctive dimensionalities.


  We consider a family {P} of determinantal point processes arising in
representation theory and random matrix theory. The processes live on the
one-dimensional lattice and their correlation kernels correspond to projection
operators in the l^2 Hilbert space on the lattice. Moreover, these projections
are spectral projections associated to certain selfadjoint second order
difference operators on the lattice. The aim of the note is to demonstrate that
the difference operators in question can be efficiently employed in the study
of limit transitions inside the family {P}.


  An important issue in the study of cities is defining a metropolitan area, as
different definitions affect the statistical distribution of urban activity. A
commonly employed method of defining a metropolitan area is the Metropolitan
Statistical Areas (MSA), based on rules attempting to capture the notion of
city as a functional economic region, and is constructed using experience. The
MSA is time-consuming and is typically constructed only for a subset (few
hundreds) of the most highly populated cities. Here, we introduce a new method
to designate metropolitan areas, denoted the "City Clustering Algorithm" (CCA).
The CCA is based on spatial distributions of the population at a fine
geographic scale, defining a city beyond the scope of its administrative
boundaries. We use the CCA to examine Gibrat's law of proportional growth,
postulating that the mean and standard deviation of the growth rate of cities
are constant, independent of city size. We find that the mean growth rate of a
cluster utilizing the CCA exhibits deviations from Gibrat's law, and that the
standard deviation decreases as a power-law with respect to the city size. The
CCA allows for the study of the underlying process leading to these deviations,
shown to arise from the existence of long-range spatial correlations in the
population growth. These results have socio-political implications, such as
those pertaining to the location of new economic development in cities of
varied size.


  BL Lacertae was the target of an extensive multiwavelength monitoring
campaign in the second half of 2000. In this paper, we are using leptonic and
hadronic jet models to fit the observed broadband spectra and spectral
variability patterns. We start out with global spectral models. Subsequently,
we investigate various flaring scenarios for comparison with the observed
short-term variability. For our leptonic jet model, we find that the short-term
variability, in particular the optical and X-ray spectral variability, can be
best represented with a flaring scenario dominated by a spectral-index change
of the spectrum of ultrarelativistic electrons injected into the jet. Based on
this result, a detailed model simulation, reproducing the observed optical and
X-ray spectral variability and broadband SED of BL Lacertae simultaneously, is
presented. Our leptonic modeling results are compared to fits using the
hadronic synchrotron-proton blazar (SPB) model. That model can reproduce the
observed SEDs of BL Lacertae in a scenario with muon-synchrotron dominated
high-energy emission. It requires a significantly higher magnetic field than
the leptonic model (~ 40 G vs. ~ 2 G in the leptonic model) and a lower Doppler
factor associated with the bulk motion of the emission region (D ~ 8 vs. D ~ 18
in the leptonic model). The hadronic model predicts a significantly larger >
100 GeV flux than the leptonic models, well within the anticipated capabilities
of VERITAS and MAGIC.


  Multiple-input multiple-output (MIMO) mmWave devices broadcast multiple
spatially-separated data streams simultaneously in order to increase data
transfer rates. Data transfer can, however, be compromised by interference.
Conventional techniques for mitigating interference require additional space
and power not generally available in handheld mobile devices. Here, we propose
a photonic mmWave MIMO receiver architecture capable of interference
cancellation with greatly reduced space and power needs. We demonstrate
real-time photonic interference cancellation with an integrated FPGA-photonic
system that executes a novel zero-calibration micro-ring resonator control
algorithm. The system achieves sub-second cancellation weight determination
latency with sub-Nyquist sampling. We evaluate the impact of canceller design
parameters on performance, establishing that effective photonic cancellation is
possible in handheld devices with less than 30 ms weight determination latency.


  We provide a generalization of the notion of Dirac system by using Morse
families to intrinsically embrace the dynamics associated with different
physical systems such as constrained variational calculus, optimal control,
Lagrangian mechanics, etc., from both the Lagrangian and the Hamiltonian
viewpoint. Besides, we introduce a geometric construction of Port-Dirac systems
and use it to describe the interconnection of Dirac systems. Special emphasis
on theoretical and applied examples is given throughout the paper to show the
applicability of the proposed framework.


  In this letter we present a simple relation, that allows one to predict the
Josephson current between two unconventional superconductors with arbitrary
band-structure and pairing symmetry. We illustrate this relation with examples
of s-wave and d-wave junctions and show a detailed numerical comparison with
the phenomenological Sigrist and Rice relation commonly used to interpret
tunneling experiments between d-wave superconductors. Our relation
automatically accounts for `mid-gap states' that occur in d-wave
superconductors and clearly shows how these states lead to a
temperature-sensitive contribution to the Josephson current for certain
orientations of the junction. This relation should be useful in exploring the
effects of disordered junctions, multiple atomic orbitals and complicated order
parameters.


  Associated to a convex integral polygon $N$ is a cluster integrable system
$\mathcal X_N$ constructed from the dimer model. We compute the group $G_N$ of
symmetries of $\mathcal X_N$, called the (2-2) cluster modular group, showing
that it is a certain abelian group conjectured by Fock and Marshakov.
Combinatorially, non-torsion elements of $G_N$ are ways of shuffling the
underlying bipartite graph, generalizing domino-shuffling.
Algebro-geometrically, $G_N$ is a subgroup of the Picard group of a certain
algebraic surface associated to $N$.


  Recently, various studies have leveraged Large Language Models (LLMs) to help
decision-making and planning in environments, and try to align the LLMs'
knowledge with the world conditions. Nonetheless, the capacity of LLMs to
continuously acquire environmental knowledge and adapt in an open world remains
uncertain. In this paper, we propose an approach to spur LLMs to explore the
open world, gather experiences, and learn to improve their task-solving
capabilities. In this approach, a multi-round feedback-revision mechanism is
utilized to encourage LLMs to actively select appropriate revision actions
guided by feedback information from the environment. This facilitates
exploration and enhances the model's performance. Besides, we integrate
sub-task relabeling to assist LLMs in maintaining consistency in sub-task
planning and help the model learn the combinatorial nature between tasks,
enabling it to complete a wider range of tasks through training based on the
acquired exploration experiences. By evaluation in Minecraft, an open-ended
sandbox world, we demonstrate that our approach LLaMA-Rider enhances the
efficiency of the LLM in exploring the environment, and effectively improves
the LLM's ability to accomplish more tasks through fine-tuning with merely 1.3k
instances of collected data, showing minimal training costs compared to the
baseline using reinforcement learning.


  The arguments presented demonstrate that the Mean Normalized Citation Score
(MNCS) and other size-independent indicators based on the ratio to publications
are not indicators of research performance. The article provides examples of
the distortions when rankings by MNCS are compared to those based on indicators
of productivity. The authors propose recommendations for the scientometric
community to switch to ranking by research efficiency, instead of MNCS and
other size-independent indicators.


  Recent high resolution near infrared (HST-NICMOS) and mm-interferometric
imaging have revealed dense gas and dust accretion disks in nearby
ultra-luminous galactic nuclei. In the best studied ultraluminous IR galaxy,
Arp 220, the 2 micron imaging shows dust disks in both of the merging galactic
nuclei and mm-CO line imaging indicates molecular gas masses approx. 10^9 M_sun
for each disk. The two gas disks in Arp 220 are counterrotating and their
dynamical masses are approx. 2x10^9 M_sun, that is, only slightly larger than
the gas masses. These disks have radii approx 100 pc and thickness 10-50 pc.
The high brightness temperatures of the CO lines indicate that the gas in the
disks has area filling factors of approx. 25-50% and mean densities of >~ 10^4
cm^(-3). Within these nuclear disks, the rate of massive star formation is
undoubtedly prodigious and, given the high viscosity of the gas, there will
also be high radial accretion rates, perhaps >~ 10 M_sun/yr. If this inflow
persists to very small radii, it is enough to feed even the highest luminosity
AGNs.


  The recent detections of two transit events attributed to the super-Earth
candidate K2-18b have provided the unprecedented prospect of spectroscopically
studying a habitable-zone planet outside the Solar System. Orbiting a nearby
M2.5 dwarf and receiving virtually the same stellar insolation as Earth, K2-18b
would be a prime candidate for the first detailed atmospheric characterization
of a habitable-zone exoplanet using HST and JWST. Here, we report the detection
of a third transit of K2-18b near the predicted transit time using the Spitzer
Space Telescope. The Spitzer detection demonstrates the periodic nature of the
two transit events discovered by K2, confirming that K2-18 is indeed orbited by
a super-Earth in a 33-day orbit and ruling out the alternative scenario of two
similarly-sized, long-period planets transiting only once within the 75-day K2
observation. We also find, however, that the transit event detected by Spitzer
occurred 1.85 hours (7-sigma) before the predicted transit time. Our joint
analysis of the Spitzer and K2 photometry reveals that this early occurrence of
the transit is not caused by transit timing variations (TTVs), but the result
of an inaccurate K2 ephemeris due to a previously undetected data anomaly in
the K2 photometry likely caused by a cosmic ray hit. We refit the ephemeris and
find that K2-18b would have been lost for future atmospheric characterizations
with HST and JWST if we had not secured its ephemeris shortly after the
discovery. We caution that immediate follow-up observations as presented here
will also be critical in confirming and securing future planets discovered by
TESS, in particular if only two transit events are covered by the relatively
short 27-day TESS campaigns.


  In this paper, it is shown that for any two non-empty closed (resp., open)
and spherical convex subsets $\mathcal{W}_1, \mathcal{W}_2$ of $S^n$, the
intersection $\mathcal{W}_1\cap \mathcal{W}_2$ is empty if and only if the
subset $\{P\in S^n\; |\; P\cdot Q>0 \mbox{ for any } Q\in \mathcal{W}_1 \mbox{
and } P\cdot R<0 \mbox{ for any } R\in \mathcal{W}_2\}$ is non-empty, open
(resp., closed) and spherical convex.


  We construct the Poincare polynomials for Landau-Ginzburg orbifolds with
projection operators.Using them we show that special types of dualities
including Poincare duality are realized under certain conditions. When
Calabi-Yau interpretation exists, two simple formulae for Hodge numbers
$h^{2,1}$ and $h^{1,1}$ are obtained.


  It is well-known that the problem of recognizing an ESS in a symmetric
bimatrix game is coNP-complete. In this paper, we show that recognizing an ESS
even in doubly symmetric bimatrix games is also coNP-complete. Our result
further implies that recognizing asymptotically stable equilibria of the
replicator dynamic in this class of games is also a coNP-complete problem.


  Hybrid multiple-input multiple-output (MIMO) is an attractive technology for
realizing extreme massive MIMO systems envisioned for future wireless
communications in a scalable and power-efficient manner. However, the fact that
hybrid MIMO systems implement part of their beamforming in analog and part in
digital makes the optimization of their beampattern notably more challenging
compared with conventional fully digital MIMO. Consequently, recent years have
witnessed a growing interest in using data-aided artificial intelligence (AI)
tools for hybrid beamforming design. This article reviews candidate strategies
to leverage data to improve real-time hybrid beamforming design. We discuss the
architectural constraints and characterize the core challenges associated with
hybrid beamforming optimization. We then present how these challenges are
treated via conventional optimization, and identify different AI-aided design
approaches. These can be roughly divided into purely data-driven deep learning
models and different forms of deep unfolding techniques for combining AI with
classical optimization.We provide a systematic comparative study between
existing approaches including both numerical evaluations and qualitative
measures. We conclude by presenting future research opportunities associated
with the incorporation of AI in hybrid MIMO systems.


  We have performed high field magnetotransport measurements to investigate the
interface electron gas in LaAlO3/SrTiO3 heterostructures. Shubnikov-de Haas
oscillations reveal several 2D conduction subbands with carrier effective
masses between 1 and 3 m_e, quantum mobilities of order 3000 cm^2/V s, and band
edges only a few millielectronvolts below the Fermi energy. Measurements in
tilted magnetic fields confirm the 2D character of the electron gas, and show
evidence of inter-subband scattering.


  New results from NA49 on the production of strangeness in elementary and
nuclear reactions are presented. New measurements of charged kaon and pion
production have been obtained from 40 AGeV Pb+Pb collisions. The evolution of
strange meson yields in nucleus-nucleus interactions is studied as a function
of collision energy and system size. Cascade baryon yields are presented for a
wide range of hadronic interactions including first measurements in p+p and
centrality controlled p+Pb collisions.


  Using the Multipolar post-Minskowskian formalism, we compute the linear
momentum flux from black-hole binaries in circular orbits and having no spins.
The total linear momentum flux contains various types of instantaneous (which
are functions of the retarded time) and hereditary (which depends on the
dynamics of the binary in the past) terms both of which are analytically
computed. In addition to the inspiral contribution, we use a simple model of
plunge to compute the kick or recoil accumulated during this phase.


  We introduce a systematic method for constructing a class of lattice
structures that we call ``partial line graphs''.In tight-binding models on
partial line graphs, energy bands with flat energy dispersions emerge.This
method can be applied to two- and three-dimensional systems. We show examples
of partial line graphs of square and cubic lattices. The method is useful in
providing a guideline for synthesizing materials with flat energy bands, since
the tight-binding models on the partial line graphs provide us a large room for
modification, maintaining the flat energy dispersions.


  First-principles density-functional calculations reveal a large spin-phonon
coupling in cubic SrMnO$_{3}$, with ferromagnetic ordering producing a polar
instability. Through combination of this coupling with the strain-polarization
coupling characteristic of perovskites, the bulk antiferromagnetic paraelectric
ground state of SrMnO$_3$ is shown to be driven to a previously unreported
multiferroic ferroelectric-ferromagnetic state by increasing epitaxial strain,
both tensile and compressive. This state has a computed polarization and
estimated Curie temperature above 54 $\mu$C/cm$^2$ and 92 K. Large mixed
magnetic-electric-elastic responses are predicted in the vicinity of the phase
boundaries.


  A geometrical approach in the non-symmetric connection framework is employed
to examine the issue of higher order $\alpha'$ corrections to D=10 type IIB
superstring backgrounds with a covariantly constant null Killing isometry and
non-zero Ramond-Ramond field content. These describe generalized supersymmetric
string waves and were obtained recently by us through the S-duality
transformation of purely NS-NS plane wave backgrounds. We find that the
backgrounds are exact subject to the existence of certain field redefinitions
and provided certain restrictive conditions are satisfied.


  Francis Castro, et al [2] computed the exact divisibility of families of
exponential sums associated to binomials $F(X) = aX^{d_1} + bX^{d_2}$ over
$\mathbb{F}_p$, and a conjecture is presented for related work. Here we study
this question.


  We present the high resolution spectra of the youn and compact planerary
nebula NGC 6790 obtained with the echelle spectrograph at Bohyunsan Optical
Astronomy Observatory and report the discovery of Raman scattered He II 6545 in
this object. This line feature is formed in a thick neutral region surrounding
the hot central star, where He II 1025 line photons are scattered inelastically
by hydrogen atoms. A Monte Carlo technique is adopted to compute the line
profile with a simple geometric model, in which the neutral region is in the
form of a cylindrical shell that is expanding from the central star. From our
line profile analysis, the expansion velocity of the HI region lies in the
range V_exp = 15- 19 Km/s. Less stringent constraints are put on the HI column
density N_HI and covering factor C, where the total flux of Raman He II 6545 is
consistent with the product of N_HI and C being CN_HI ~ 0.5 X 10^20 cm^-2. The
Monte Carlo profiles from stationary emission models exhibit deficit in the
wing parts. A much better fit is obtained when the He II emission region is
assumed to take the form of a ring that slowly rotates with a rotation speed ~
18Km/s. Brief discussions are presented regarding the mass loss processes and
future observations.


  This paper examines the uniqueness of weak solutions to the d-dimensional
magnetohydrodynamic (MHD) equations with the fractional dissipation
$(-\Delta)^\alpha u$ and without the magnetic diffusion. Important progress has
been made on the standard Laplacian dissipation case $\alpha=1$. This paper
discovers that there are new phenomena with the case $\alpha<1$. The approach
for $\alpha=1$ can not be directly extended to $\alpha<1$. We establish that,
for $\alpha<1$, any initial data $(u_0, b_0)$ in the inhomogeneous Besov space
$B^\sigma_{2,\infty}(\mathbb R^d)$ with $\sigma> 1+\frac{d}{2}-\alpha$ leads to
a unique local solution. For the case $\alpha\ge 1$, $u_0$ in the homogeneous
Besov space $\mathring B^{1+\frac{d}{2}-2\alpha}_{2,1}(\mathbb R^d)$ and $b_0$
in $ \mathring B^{1+\frac{d}{2}-\alpha}_{2,1}(\mathbb R^d)$ guarantees the
existence and uniqueness. These regularity requirements appear to be optimal.


  The lack of extensive research in the application of inexpensive wireless
sensor nodes for the early detection of wildfires motivated us to investigate
the cost of such a network. As a first step, in this paper we present several
results which relate the time to detection and the burned area to the number of
sensor nodes in the region which is protected. We prove that the probability
distribution of the burned area at the moment of detection is approximately
exponential, given that some hypotheses hold: the positions of the sensor nodes
are independent random variables uniformly distributed and the number of sensor
nodes is large. This conclusion depends neither on the number of ignition
points nor on the propagation model of the fire.


  Revised version, a few typing errors that might cause confusion of physics
argument in the article have been corrected.


  In [14], Gueant, Lasry and Lions considered the model problem ``What time
does meeting start?'' as a prototype for a general class of optimization
problems with a continuum of players, called Mean Field Games problems. In this
paper we consider a similar model, but with the dynamics of the agents defined
on a network. We discuss appropriate transition conditions at the vertices
which give a well posed problem and we present some numerical results.


  Sum rules for the total- and scissors-mode M1 strength in odd-A nuclei are
derived within the single-j interacting boson-fermion model. We discuss the
physical content and geometric interpretation of these sum rules and apply them
to ^{167}Er and ^{161}Dy. We find consistency with the former measurements but
not with the latter.


  We present magnetotransport measurements in HgTe quantum well with inverted
band structure, which expected to be a two-dimensional topological insulator
having the bulk gap with helical gapless states at the edge. The negative
magnetoresistance is observed in the local and nonlocal resistance
configuration followed by the periodic oscillations damping with magnetic
field. We attribute such behaviour to Aharonov-Bohm effect due to magnetic flux
through the charge carrier puddles coupled to the helical edge states. The
characteristic size of these puddles is about 100 nm.


  While extracting a subset of a commit history, specifying the necessary
portion is a time-consuming task for developers. Several commit-based history
slicing techniques have been proposed to identify dependencies between commits
and to extract a related set of commits using a specific commit as a slicing
criterion. However, the resulting subset of commits become large if commits for
systematic edits whose changes do not depend on each other exist. We
empirically investigated the impact of systematic edits on history slicing. In
this study, commits in which systematic edits were detected are split between
each file so that unnecessary dependencies between commits are eliminated. In
several histories of open source systems, the size of history slices was
reduced by 13.3-57.2% on average after splitting the commits for systematic
edits.


  Recent resonant inelastic x-ray scattering (RIXS) experiments have detected a
significant high-energy spin-excitation anisotropy in the nematic phase of the
enigmatic iron-based superconductor FeSe, whose origin remains controversial.
We apply an itinerant model previously used to describe the spin-excitation
anisotropy as measured by neutron scattering measurements, with magnetic
fluctuations included within the RPA approximation. The calculated RIXS cross
section exhibits overall agreement with the RIXS data, including the high
energy spin-excitation anisotropy.


  Text summarization is a fundamental task in natural language processing that
aims to condense large amounts of textual information into concise and coherent
summaries. With the exponential growth of content and the need to extract key
information efficiently, text summarization has gained significant attention in
recent years. In this study, LSTM and pre-trained T5, Pegasus, BART and
BART-Large model performances were evaluated on the open source dataset (Xsum,
CNN/Daily Mail, Amazon Fine Food Review and News Summary) and the prepared
resume dataset. This resume dataset consists of many information such as
language, education, experience, personal information, skills, and this data
includes 75 resumes. The primary objective of this research was to classify
resume text. Various techniques such as LSTM, pre-trained models, and
fine-tuned models were assessed using a dataset of resumes. The BART-Large
model fine-tuned with the resume dataset gave the best performance.


  To enhance low-light images to normally-exposed ones is highly ill-posed,
namely that the mapping relationship between them is one-to-many. Previous
works based on the pixel-wise reconstruction losses and deterministic processes
fail to capture the complex conditional distribution of normally exposed
images, which results in improper brightness, residual noise, and artifacts. In
this paper, we investigate to model this one-to-many relationship via a
proposed normalizing flow model. An invertible network that takes the low-light
images/features as the condition and learns to map the distribution of normally
exposed images into a Gaussian distribution. In this way, the conditional
distribution of the normally exposed images can be well modeled, and the
enhancement process, i.e., the other inference direction of the invertible
network, is equivalent to being constrained by a loss function that better
describes the manifold structure of natural images during the training. The
experimental results on the existing benchmark datasets show our method
achieves better quantitative and qualitative results, obtaining better-exposed
illumination, less noise and artifact, and richer colors.


  Recent observations have resulted in the detection of chemical gradients on
ultra-hot gas giants. Notwithstanding their high temperature, chemical
reactions in ultra-hot atmospheres may occur in disequilibrium, due to vigorous
day-night circulation and intense UV radiation from their stellar hosts. The
goal of this work is to explore whether photochemistry is affecting the
composition of ultra-hot giant planets, and if it can introduce horizontal
chemical gradients. In particular, we focus on hydrogen cyanide (HCN) on
WASP-76 b, as it is a photochemically active molecule with a reported detection
on only one side of this planet. We use a pseudo-2D chemical kinetics code to
model the chemical composition of WASP-76 b along its equator. Our approach
improves on chemical equilibrium models by computing vertical mixing,
horizontal advection, and photochemistry. We find that production of HCN is
initiated through thermal and photochemical dissociation of CO and N$_2$ on the
day side of WASP-76 b. The resulting radicals are subsequently transported to
the night side via the equatorial jet stream, where they recombine into
different molecules. This process results in an HCN gradient with a maximal
abundance on the planet's morning limb. We verified that photochemical
dissociation is a necessary condition for this mechanism, as thermal
dissociation alone proves insufficient. Other species produced via night-side
disequilibrium chemistry are SO$_2$ and S$_2$. Our model acts as a proof of
concept for chemical gradients on ultra-hot exoplanets. We demonstrate that
even ultra-hot planets can exhibit disequilibrium chemistry and recommend that
future studies do not neglect photochemistry in their analyses of ultra-hot
planets.


  After reviewing techniques for extracting clean information on CP-violating
phase angles from $B$ decays, we explain the rules for finding decay modes that
can probe the phase angle $\gamma$ of the unitarity triangle. We identify the
more promising of these ``$\gamma$ modes,'' estimate their branching ratios,
and examine the degree to which they are theoretically clean. We then show that
when the quark mixing matrix is not approximated as usual, but is treated
exactly, none of the ``$\gamma$ modes'' actually measures $\gamma$. Rather,
each of them measures $\gamma$ plus some correction. In all modes, the
correction is small enough to be disregarded in first-generation experiments,
but in some of them, it may be large enough to be observed in second-generation
experiments. Our treatment of the $\gamma$ modes calls attention to the fact
that when the quark mixing matrix is treated exactly, there are six unitarity
triangles, rather than just one triangle. However, only four of the angles in
these six triangles are independent. Examining the role played by these four
angles, we discover that, in principle at least, measurements of nothing but
CP-violating asymmetries in $B$ decays are sufficient to determine the entire
quark mixing matrix.


  We obtain contraints on three Universal Extra Dimensional models utilizing
limits from the CMS Collaboration on W' production and decay into a
single-top-quark final state. We find a weak constraint on the Minimal
Universal Extra Dimensions model due to small Kaluza-Klein number violating
terms. In contrast, the W' search puts a strong limit on the size of the Dirac
mass term of the quarks in Split Universal Extra Dimension models. In
Non-minimal Universal Extra Dimension models the W' search constrains the
splitting between the boundary localized kinetic terms of the gauge bosons and
the quarks. Each of these bounds can be translated into constraints on the mass
splitting between the Kaluza-Klein excitations of the SU(2) charged quarks and
the Klauza-Klein excitations of the W boson.


  We consider a nonzero-sum Markov game on an abstract measurable state space
with compact metric action spaces. The goal of each player is to maximize his
respective discounted payoff function under the condition that some constraints
on a discounted payoff are satisfied. We are interested in the existence of a
Nash or noncooperative equilibrium. Under suitable conditions, which include
absolute continuity of the transitions with respect to some reference
probability measure, additivity of the payoffs and the transition probabilities
(ARAT condition), and continuity in action of the payoff functions and the
density function of the transitions of the system, we establish the existence
of a constrained stationary Markov Nash equilibrium, that is, the existence of
stationary Markov strategies for each of the players yielding an optimal
profile within the class of all history-dependent profiles.


  We exemplify the way the rigged Hilbert space deals with the
Lippmann-Schwinger equation by way of the spherical shell potential. We
explicitly construct the Lippmann-Schwinger bras and kets along with their
energy representation, their time evolution and the rigged Hilbert spaces to
which they belong. It will be concluded that the natural setting for the
solutions of the Lippmann-Schwinger equation--and therefore for scattering
theory--is the rigged Hilbert space rather than just the Hilbert space.


  Large text-to-image models have revolutionized the ability to generate
imagery using natural language. However, particularly unique or personal visual
concepts, such as pets and furniture, will not be captured by the original
model. This has led to interest in how to personalize a text-to-image model.
Despite significant progress, this task remains a formidable challenge,
particularly in preserving the subject's identity. Most researchers attempt to
address this issue by modifying model architectures. These methods are capable
of keeping the subject structure and color but fail to preserve identity
details. Towards this issue, our approach takes a data-centric perspective. We
introduce a novel regularization dataset generation strategy on both the text
and image level. This strategy enables the model to preserve fine details of
the desired subjects, such as text and logos. Our method is
architecture-agnostic and can be flexibly applied on various text-to-image
models. We show on established benchmarks that our data-centric approach forms
the new state of the art in terms of identity preservation and text alignment.


  Sub-nanovolt resolution longitudinal and Hall voltages are measured in an
ultra pure YBa2Cu3O7-d single crystal. The Hall anomaly and the first-order
vortex-lattice melting transition are observed simultaneously. Changes in the
dynamic behavior of the vortex solid and liquid are correlated with features of
the Hall conductivity sxy. With the magnetic field oriented at an angle from
the twin-boundaries, the Hall conductivity sharply decreases toward large
negative values at the vortex-lattice melting transition.


  We give a path integral expression for the quantum amplitude to produce a
black hole from particle collisions. When expanded about an appropriate
classical solution it yields the leading order contribution to the production
amplitude in a curvature expansion. Classical solutions describing black hole
production resulting from two particle scattering at non-zero impact parameter,
combined with our formalism, indicate a geometric cross section for the quantum
process. In TeV gravity scenarios these solutions may exhibit large curvatures,
but (modulo a mild assumption about quantum gravity) corrections to the
semi-classical cross section are small.


  Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) have
been employed to investigate the dynamics of finite-size spherical particles,
slightly heavier than the carrier fluid, in a horizontal turbulent square duct
flow. Interface resolved Direct Numerical Simulations (DNS) have also been
performed with the Immersed Boundary Method (IBM) at the same experimental
conditions, bulk Reynolds number $Re_{2H}$ = 5600, duct height to particle size
ratio $2H/d_p$ = 14.5, particle volume fraction $\Phi$ = 1% and particle to
fluid density ratio $\rho_p/\rho_f$ = 1.0035. A good agreement has been
observed between experiments and simulations in terms of the overall pressure
drop, concentration distribution and turbulent statistics of the two phases.
Additional experimental results considering two particle sizes, $2H/d_p$ = 14.5
and 9 and multiple $\Phi$ = 1, 2, 3, 4 and 5% are reported at the same
$Re_{2H}$. The pressure drop monotonically increases with the volume fraction,
almost linearly and nearly independently of the particle size for the above
parameters. However, despite the similar pressure drop, the microscopic
picture, the fluid velocity statistics, differs significantly with the particle
size. This one-to-one comparison between simulations and experiments extends
the validity of interface resolved DNS in complex turbulent multiphase flows
and highlights the ability of experiments to investigate such flows in
considerable details, even in regions where the local volume fraction is
relatively high.


  We study boundary traces of shift-invariant diffusions: two-dimensional
diffusions in the upper half-plane $\mathbb{R} \times [0, \infty)$ (or in
$\mathbb{R} \times [0, R)$) invariant under horizontal translations. We prove
that the corresponding trace processes are L\'evy processes with completely
monotone jumps, and, conversely, every L\'evy process with completely monotone
jumps is a boundary trace of some shift-invariant diffusion. Up to some natural
transformations of space and time, this correspondence is bijective. We also
reformulate this result in the language of additive functionals of the Brownian
motion in $[0, \infty)$ (or in $[0, R)$), and Brownian excursions. Our main
tool is the recent extension of Krein's spectral theory of strings, due to
Eckhardt and Kostenko.


  We study the propagation of the scalar modes around a
Friedmann-Lemaitre-Robertson-Walker universe for general modifications of
gravity in the presence of a real scalar field. In general, there will be two
propagating scalar perturbation fields, which will have in total four degrees
of freedom. Two of these degrees will have a superluminal propagation--with
k-dependent speed of propagation--whereas the other two will travel with the
speed of light. Therefore, the scalar degrees of freedom do not modify the
general feature of modified gravity models: the appearance of modes whose
frequency depends on the second power of the modulus of the wave vector.
Constraints are given and special cases are discussed.


  We propose an algebraic setup for end-to-end physical-layer network coding
based on submodule transmission. We introduce a distance function between
modules, describe how it relates to information loss and errors, and show how
to compute it. Then we propose a definition of submodule error-correcting code,
and investigate bounds and constructions for such codes.


  The PHENIX Experiment at RHIC has conducted a beam energy scan at several
collision energies in order to search for signatures of the QCD critical point
and the onset of deconfinement. PHENIX has conducted measurements of transverse
energy production, muliplicity fluctuations, the skewness and kurtosis of net
charge distributions, Hanbury-Brown Twiss correlations, charged hadron flow,
and energy loss. The data analyzed to date show no significant indications of
the presence of the critical point.


  A regular language $L$ is said to be prime, if it is not the product of two
non-trivial languages. Martens et al. settled the exact complexity of deciding
primality for deterministic finite automata in 2010. For finite languages,
Mateescu et al. and Wieczorek suspect the $\mathrm{NP}\text{ - }completeness$
of primality, but no actual bounds are given. Using techniques of Martens et
al., we prove the $\mathrm{NP}$ lower bound and give a $\Pi_{2}^{\mathrm{P}}$
upper bound for deciding primality of finite languages given as deterministic
finite automata.


  In a monogamy-of-entanglement (MoE) game, two players who do not communicate
try to simultaneously guess a referee's measurement outcome on a shared quantum
state they prepared. We study the prototypical example of a game where the
referee measures in either the computational or Hadamard basis and informs the
players of her choice.
  We show that this game satisfies a rigidity property similar to what is known
for some nonlocal games. That is, in order to win optimally, the players'
strategy must be of a specific form, namely a convex combination of four
unentangled optimal strategies generated by the Breidbart state. We extend this
to show that strategies that win near-optimally must also be near an optimal
state of this form. We also show rigidity for multiple copies of the game
played in parallel.
  We give three applications: (1) We construct for the first time a weak string
erasure (WSE) scheme where the security does not rely on limitations on the
parties' hardware. Instead, we add a prover, which enables security via the
rigidity of this MoE game. (2) We show that the WSE scheme can be used to
achieve bit commitment in a model where it is impossible classically. (3) We
achieve everlasting-secure randomness expansion in the model of trusted but
leaky measurement and untrusted preparation and measurements by two isolated
devices, while relying only on the temporary assumption of pseudorandom
functions. This achieves randomness expansion without the need for shared
entanglement.


  We propose a method of generating a vortex ring in a Bose-Einstein condensate
by means of electromagnetically-induced atomic transitions. Some remnant
population of atoms in a second internal state remains within the toroidal trap
formed by the mean field repulsion of the vortex ring. This population can be
removed, or it can be made to flow around the torus (i.e. within the vortex
ring). If this flow has unit topological winding number, the entire structure
formed by the two condensates is an example of a three-dimensional skyrmion
texture.


  A dynamical resolution to the cosmological constant fine-tuning problem has
been previously put forward, based on a scalar-tensor gravitational theory
possessing de Sitter attractor solutions characterized by a small Hubble
expansion rate, irrespective of an initially large vacuum energy. We show that
a technically natural subregion of the parameter space yields a cosmological
evolution through radiation- and matter-dominated eras that is essentially
indistinguishable from that predicted by General Relativity. Similarly, the
proposed model automatically satisfies the observational constraints on a fifth
force mediated by the new scalar degree of freedom.


  We consider a novel adversarial shock/protection problem for a class of
network equilibria models emerging from a variety of different fields as
continuous network games, production networks, opinion dynamic models. The
problem is casted into a min-max problem and analytically solved for two
particular cases of aggregate performances: the mean square of the equilibrium
or of its arithmetic mean. The main result is on the shape of the solutions,
typically exhibiting a waterfilling type structure with the optimal protection
concentrated in a proper subset of the nodes, depending significantly on the
aggregate performance considered. The relation of the optimal protection with
the Bonacich centrality is also considered.


  Numerical solution of nonlinear eigenvalue problems (NEPs) is frequently
encountered in computational science and engineering. The applicability of most
existing methods is limited by matrix structures, property of eigen-solutions,
size of the problem, etc. This paper aims to break those limitations and to
develop robust and universal NEP solvers for large-scale engineering
applications. The novelty lies in two aspects. First, a rational interpolation
approach (RIA) is proposed based on the Keldysh theorem for holomorphic matrix
functions. Comparing with the existing contour integral approach (CIA), the RIA
provides the possibility to select sampling points in more general regions and
has advantages in improving accuracy and reducing computational cost. Second, a
resolvent sampling scheme using the RIA is proposed for constructing reliable
search spaces for the Rayleigh-Ritz procedure, based on which a robust
eigen-solver, denoted by RSRR, is developed for solving general NEPs. RSRR can
be easily implemented and parallelized. The advantages of the RIA and the
performance of RSRR are demonstrated by a variety of benchmark and practical
problems.


  We present the results of a spectral analysis of a large sample of subdwarf B
stars selected from follow-up observations of candidates from the Hamburg
Quasar Survey. Fundamental parameters (effective temperature, gravity, and
helium abundance) were determined by matching synthetic line profiles
calculated from model atmospheres to all hydrogen and helium absorption lines
present in the observed optical spectra. The derived helium abundances are
compared with the atmospheric parameters to search forpossible trends. We
discovered a correlation between the helium abundance and the effective
temperature: the larger the temperature, the larger the photospheric helium
content of sdB stars. Additionally, a separation into two sequences of sdB
stars in the effective temperature - helium abundance plane is detected. We
compared our analysis results with data from the literature. The stars from our
sample are found to be somewhat more luminous. This can only partlybe explained
by NLTE effects. Three apparently normal B stars were discovered, which could
be massive stars far away from the galactic plane (7-19kpc). Radial velocities
were measured for 23 stars from which we discovered a new radial velocity
variable sdB star.


  We show that for smooth and proper varieties over local fields with no
non-trivial vector fields, good reduction descends over purely inseparable
extensions. We use this to extend the Neron-Ogg-Shafarevich criterion for K3
surfaces to the equicharacteristic $p>0$ case.


  The November 2014 Australian State of Victoria election was the first
statutory political election worldwide at State level which deployed an
end-to-end verifiable electronic voting system in polling places. This was the
first time blind voters have been able to cast a fully secret ballot in a
verifiable way, and the first time a verifiable voting system has been used to
collect remote votes in a political election. The code is open source, and the
output from the election is verifiable. The system took 1121 votes from these
particular groups, an increase on 2010 and with fewer polling places.


  To investigate the consequences of component confinement such as at a glass
transition and the well-known energy or enthalpy gap (between the glass and the
perfect crystal at absolute zero, see text), we follow our previous approach
[Phys. Rev. E 81, 051130 (2010)] of using the second law applied to an isolated
system {\Sigma}_0 consisting of the homogeneous system {\Sigma} and the medium
{\Sigma}. We establish on general grounds the continuity of the Gibbs free
energy G(t) of {\Sigma} as a function of time at fixed temperature and pressure
of the medium. It immediately follows from this and the observed continuity of
the enthalpy during component confinement that the entropy S of the open system
{\Sigma} must remain continuous during a component confinement such as at a
glass transition. We use these continuity properties and the recently developed
non-equilibrium thermodynamics to formulate thermodynamic principles of
additivity, reproducibility, continuity and stability that must also apply to
non-equilibrium systems in internal equilibrium. We find that the
irreversibility during a glass transition only justifies the residual entropy
S_{R} to be at least as much as that determined by disregarding the
irreversibility, a common practice in the field. This justifies a non-zero
residual entropy S_{R} in glasses, which is also in accordance with the energy
or enthalpy gap at absolute zero. We develop a statistical formulation of the
entropy of a non-equilibrium system, which results in the continuity of entropy
during component confinement in accordance with the second law and sheds light
on the mystery behind the residual entropy, which is consistent with the recent
conclusion [Symmetry 2, 1201 (2010)] drawn by us.


  We study the evolution of magnetic fields in accretion flows around six
different sink particles in a global star formation simulation. Using the
adaptive mesh refinement capabilities of \ramses, the vicinity of the sinks is
resolved with a resolution down to $2$ AU. The statistical properties of the
magnetic field are tracked as a function of radius, height and time around each
sink particle. All six systems are strongly magnetised with plasma beta being
unity or below and we know that at least three of the sinks host an accretion
disc. One of the discs is studied at a higher resolution of $0.06$ AU and we
report its magnetic properties. We find that the angular momentum transport is
dominated by large scale radial-azimuthal Maxwell stress. Furthermore, contrary
to the weakly magnetised case studied in shearing box simulations, the large
scale azimuthal field does not show smooth cycle periods but instead forms a
banded structure. We conclude by speculating what this result might mean for
observations and whether it will hold with highly resolved simulations
including turbulence or non-ideal MHD effects.


  Software issues contain units of work to fix, improve, or create new threads
during the development and facilitate communication among the team members.
Assigning an issue to the most relevant team member and determining a category
of an issue is a tedious and challenging task. Wrong classifications cause
delays and rework in the project and trouble among the team members. This paper
proposes a set of carefully curated linguistic features for shallow machine
learning methods and compares the performance of shallow and ensemble methods
with deep language models. Unlike the state-of-the-art, we assign issues to
four roles (designer, developer, tester, and leader) rather than to specific
individuals or teams to contribute to the generality of our solution. We also
consider the level of experience of the developers to reflect the industrial
practices in our solution formulation. We collect and annotate five industrial
data sets from one of the top three global television producers to evaluate our
proposal and compare it with deep language models. Our data sets contain 5324
issues in total. We show that an ensemble classifier of shallow techniques
achieves 0.92 for issue assignment in accuracy which is statistically
comparable to the state-of-the-art deep language models. The contributions
include the public sharing of five annotated industrial issue data sets, the
development of a clear and comprehensive feature set, the introduction of a
novel label set, and the validation of the efficacy of an ensemble classifier
of shallow machine learning techniques.


  Various astrophysical observations point towards an as-of-yet unexplained,
mainly gravitationally interacting type of matter. If this matter, called Dark
Matter, is an elementary particle, it could be produced in particle collisions
at the Large Hadron Collider. Given its weak interaction with ordinary matter,
however, it would not be directly observable with the general-purpose detectors
at the Large Hadron Collider. Its production would therefore manifest as events
in which detector-visible objects recoil against the detector-invisible Dark
Matter, giving rise to missing transverse energy. This thesis focuses on final
states in which these visible objects are jets.
  A measurement of the final state of large missing transverse energy and at
least one jet in 139 fb$^{-1}$ of proton-proton collisions at 13 TeV recorded
with the ATLAS detector at the Large Hadron Collider is performed in this
thesis. Good agreement between measured data and Standard-Model prediction is
found in a statistical fit, corresponding to a reduced chi-square of 1.37. The
measurement is corrected for detector effects to facilitate later
reinterpretation. Measurements prepared in such a way can, for example, be
exploited by the CONTUR toolkit to set constraints on new theories. Both, the
results of the measurement and the CONTUR toolkit making use of existing
measurements at the Large Hadron Collider, are employed to set exclusion limits
on a model able to explain Dark Matter, the two-Higgs-doublet model with a
pseudoscalar mediator to Dark Matter. At $\tan\beta=1$, masses of the
pseudoscalar $A$ up to 425 GeV and larger than 1600 GeV are excluded at 95 %
confidence level. At $m_H\equiv m_A\equiv m_{H^\pm}=$ 600 GeV, masses of the
pseudoscalar $a$ up to 550 GeV and values of $\tan\beta$ up to 1.5 as well as
larger than 20 are excluded at 95 % confidence level.


  Galaxies broadly fall into two categories: star-forming (blue) galaxies and
quiescent (red) galaxies. In between, one finds the less populated ``green
valley". Some of these galaxies are suspected to be in the process of ceasing
their star-formation through a gradual exhaustion of gas supply or already dead
and are experiencing a rejuvenation of star-formation through fuel injection.
We use the Galaxy And Mass Assembly database and the Galaxy Zoo citizen science
morphological estimates to compare the morphology of galaxies in the green
valley against those in the red sequence and blue cloud.
  Our goal is to examine the structural differences within galaxies that fall
in the green valley, and what brings them there. Previous results found disc
features such as rings and lenses are more prominently represented in the green
valley population. We revisit this with a similar sized data set of galaxies
with morphology labels provided by the Galaxy Zoo for the GAMA fields based on
new KiDS images. Our aim is to compare qualitatively the results from expert
classification to that of citizen science.
  We observe that ring structures are indeed found more commonly in green
valley galaxies compared to their red and blue counterparts. We suggest that
ring structures are a consequence of disc galaxies in the green valley actively
exhibiting characteristics of fading discs and evolving disc morphology of
galaxies. We note that the progression from blue to red correlates with
loosening spiral arm structure.


  Generalized Brown-Resnick processes form a flexible class of stationary
max-stable processes based on Gaussian random fields. With regard to
applications fast and accurate simulation of these processes is an important
issue. In fact, Brown-Resnick processes that are generated by a dissipative
flow do not allow for good finite approximations using the definition of the
processes. On large intervals we get either huge approximation errors or very
long operating times. Looking for solutions of this problem, we give different
representations of the generalized Brown-Resnick processes - including random
shifting and a mixed moving maxima representation - and derive various kinds of
finite approximations that can be used for simulation purposes. Furthermore,
error bounds are calculated in the case of the original process by Brown and
Resnick (1977).


  The Moran model with recombination is considered, which describes the
evolution of the genetic composition of a population under recombination and
resampling. There are $n$ sites (or loci), a finite number of letters (or
alleles) at every site, and we do not make any scaling assumptions. In
particular, we do not assume a diffusion limit. We consider the following
marginal ancestral recombination process. Let $S = \{1,...,n\}$ and $\mathcal
A=\{A_1, ..., A_m\}$ be a partition of $S$. We concentrate on the joint
probability of the letters at the sites in $A_1$ in individual $1$, $...$, and
at the sites in $A_m$ in individual $m$, where the individuals are sampled from
the current population without replacement. Following the ancestry of these
sites backwards in time yields a process on the set of partitions of $S$,
which, in the diffusion limit, turns into a marginalised version of the
$n$-locus ancestral recombination graph. With the help of an
inclusion-exclusion principle, we show that the type distribution corresponding
to a given partition may be represented in a systematic way, with the help of
so-called recombinators and sampling functions. The same is true of correlation
functions (known as linkage disequilibria in genetics) of all orders.
  We prove that the partitioning process (backward in time) is dual to the
Moran population process (forward in time), where the sampling function plays
the role of the duality function. This sheds new light on the work of
Bobrowski, Wojdyla, and Kimmel (2010). The result also leads to a closed system
of ordinary differential equations for the expectations of the sampling
functions, which can be translated into expected type distributions and
expected linkage disequilibria.


  The efficiency for multi-strange hyperon reconstruction and the detection
strategy for basic kinematic parameters are described using experimental data
of p+C interaction at 10 GeV/c. The efficiency for $\Lambda$ hyperon
reconstruction is equal to 16%. The $\Sigma^{*+}$(1385)and $\Sigma^+$(1620)
resonances are observed in the reconstructed $\Lambda \pi^+$ mass spectrum.


  We present a method for computing reduced-order models of parameterized
partial differential equation solutions. The key analytical tool is the
singular value expansion of the parameterized solution, which we approximate
with a singular value decomposition of a parameter snapshot matrix. To evaluate
the reduced-order model at a new parameter, we interpolate a subset of the
right singular vectors to generate the reduced-order model's coefficients. We
employ a novel method to select this subset that uses the parameter gradient of
the right singular vectors to split the terms in the expansion yielding a mean
prediction and a prediction covariance---similar to a Gaussian process
approximation. The covariance serves as a confidence measure for the reduce
order model. We demonstrate the efficacy of the reduced-order model using a
parameter study of heat transfer in random media. The high-fidelity simulations
produce more than 4TB of data; we compute the singular value decomposition and
evaluate the reduced-order model using scalable MapReduce/Hadoop
implementations. We compare the accuracy of our method with a scalar response
surface on a set of temperature profile measurements and find that our model
better captures sharp, local features in the parameter space.


  Due to the need for higher reliability and performance from RF circuits,
multi-port reflectometers are increasingly used as low-overhead impedance
monitors. In this work, using periodic structures as multi-ports is proposed.
Periodic structures impose a new constraint on the multi-port theory and
simplify it significantly. This simplification leads to closed form solution
for calibration and measurement procedures. The closed-form solution also shows
that any arbitrary periodic structure will always have a unique solution for
both procedures. Therefore, the proposed technique does not rely on
frequency-dependent behavior of devices, such as directivity and phase shift to
measure impedance. This fact leads to increased bandwidth and simplified design
procedure. In addition, proposed multi-port structures can be calibrated by
using fewer known loads than existing multi-port techniques. This fact, coupled
with the closed-form solution, reduces computation overhead and test time. The
theory and its robustness against non-idealities, such as part-to-part
variation, are verified with Monte-Carlo simulations. A practical embodiment of
the technique is demonstrated with EM simulations and hardware experiments. In
this embodiment, the multi-port structure is embedded into an LC matching
network. Hardware experiments show that the embedded multi-port structure can
measure test loads with high accuracy from 1.5 GHz to 3.5 GHz, without
degrading matching network performance.


  A common issue for classification in scientific research and industry is the
existence of imbalanced classes. When sample sizes of different classes are
imbalanced in training data, naively implementing a classification method often
leads to unsatisfactory prediction results on test data. Multiple resampling
techniques have been proposed to address the class imbalance issues. Yet, there
is no general guidance on when to use each technique. In this article, we
provide a paradigm-based review of the common resampling techniques for binary
classification under imbalanced class sizes. The paradigms we consider include
the classical paradigm that minimizes the overall classification error, the
cost-sensitive learning paradigm that minimizes a cost-adjusted weighted type I
and type II errors, and the Neyman-Pearson paradigm that minimizes the type II
error subject to a type I error constraint. Under each paradigm, we investigate
the combination of the resampling techniques and a few state-of-the-art
classification methods. For each pair of resampling techniques and
classification methods, we use simulation studies and a real data set on credit
card fraud to study the performance under different evaluation metrics. From
these extensive numerical experiments, we demonstrate under each classification
paradigm, the complex dynamics among resampling techniques, base classification
methods, evaluation metrics, and imbalance ratios. We also summarize a few
takeaway messages regarding the choices of resampling techniques and base
classification methods, which could be helpful for practitioners.


  We show that a non-minimal coupling of electromagnetism with background
torsion can produce birefringence of the electromagnetic waves. This
birefringence gives rise to a B-mode polarization of the CMB. From the bounds
on B-mode from WMAP and BOOMERanG data, one can put limits on the background
torsion at $\xi_{1}T_{1}=(-3.35 \pm 2.65) \times 10^{-22} GeV^{-1}$.


  Let $\mathbf a=(a_1,\ldots,a_r)$ be a vector of positive integers. In
continuation of a previous paper we present other formulas for the restricted
partition function $p_{\mathbf a}(n): = $ the number of integer solutions
$(x_1,\dots,x_r)$ to $\sum_{j=1}^r a_jx_j=n$ with $x_1\geq 0, \ldots, x_r\geq
0$.


  Heavy ion collisions at RHIC and at the LHC can create the quark-gluon
plasma, a state of matter at very high temperatures. Among a plethora of
particles that are produced in these collisions, also light is emitted
throughout the evolution of the plasma.
  In this talk, the properties of this light are discussed and related to
recent efforts towards shorter and more energetic photon pulses in laser
physics. In particular, the time evolution of high-energy photons is studied.
These photons originate from Compton scattering of gluons and quark-antiquark
annihilation in the plasma. Due to the internal dynamics of the plasma, double
pulses at the yoctosecond time scale can be generated under certain conditions.
Such double pulses may be utilized for novel pump-probe experiments at nuclear
time scales.


  Differentially private synthetic data provide a powerful mechanism to enable
data analysis while protecting sensitive information about individuals.
However, when the data lie in a high-dimensional space, the accuracy of the
synthetic data suffers from the curse of dimensionality. In this paper, we
propose a differentially private algorithm to generate low-dimensional
synthetic data efficiently from a high-dimensional dataset with a utility
guarantee with respect to the Wasserstein distance. A key step of our algorithm
is a private principal component analysis (PCA) procedure with a near-optimal
accuracy bound that circumvents the curse of dimensionality. Unlike the
standard perturbation analysis, our analysis of private PCA works without
assuming the spectral gap for the covariance matrix.


  We have studied a sample of 296 faint (> 0.5 mJy) radio sources selected from
an area of the Tenth Cambridge (10C) survey at 15.7 GHz in the Lockman Hole. By
matching this catalogue to several lower frequency surveys (e.g. including a
deep GMRT survey at 610 MHz, a WSRT survey at 1.4 GHz, NVSS, FIRST and WENSS)
we have investigated the radio spectral properties of the sources in this
sample; all but 30 of the 10C sources are matched to one or more of these
surveys. We have found a significant increase in the proportion of flat
spectrum sources at flux densities below approximately 1 mJy - the median
spectral index between 15.7 GHz and 610 MHz changes from 0.75 for flux
densities greater than 1.5 mJy to 0.08 for flux densities less than 0.8 mJy.
This suggests that a population of faint, flat spectrum sources is emerging at
flux densities below 1 mJy.
  The spectral index distribution of this sample of sources selected at 15.7
GHz is compared to those of two samples selected at 1.4 GHz from FIRST and
NVSS. We find that there is a significant flat spectrum population present in
the 10C sample which is missing from the samples selected at 1.4 GHz. The 10C
sample is compared to a sample of sources selected from the SKADS Simulated Sky
by Wilman et al. and we find that this simulation fails to reproduce the
observed spectral index distribution and significantly underpredicts the number
of sources in the faintest flux density bin. It is likely that the observed
faint, flat spectrum sources are a result of the cores of FRI sources becoming
dominant at high frequencies. These results highlight the importance of
studying this faint, high frequency population.


  The long-time behavior of many complex molecular systems is often governed by
slow relaxation dynamics that can be described by a few reaction coordinates
referred to as collective variables (CVs). However, identifying CVs hidden in a
high-dimensional configuration space poses a fundamental challenge in chemical
physics. To address this problem, we expand on a recently introduced
deep-learning technique called spectral map [Rydzewski, J. Phys. Chem. Lett.
2023, 14, 22, 5216-5220]. Spectral map learns CVs by maximizing a spectral gap
between slow and fast eigenvalues of a Markov transition matrix describing
anisotropic diffusion. An introduced modification in the learning algorithm
allows spectral map to represent multiscale free-energy landscapes. Through a
Markov state model analysis, we validate that spectral map learns slow CVs
related to the dominant relaxation timescales and discerns between long-lived
metastable states.


  Image restoration is a long-standing task that seeks to recover the latent
sharp image from its deteriorated counterpart. Due to the robust capacity of
self-attention to capture long-range dependencies, transformer-based methods or
some attention-based convolutional neural networks have demonstrated promising
results on many image restoration tasks in recent years. However, existing
attention modules encounters limited receptive fields or abundant parameters.
In order to integrate contextual information more effectively and efficiently,
in this paper, we propose a dilated strip attention network (DSAN) for image
restoration. Specifically, to gather more contextual information for each pixel
from its neighboring pixels in the same row or column, a dilated strip
attention (DSA) mechanism is elaborately proposed. By employing the DSA
operation horizontally and vertically, each location can harvest the contextual
information from a much wider region. In addition, we utilize multi-scale
receptive fields across different feature groups in DSA to improve
representation learning. Extensive experiments show that our DSAN outperforms
state-of-the-art algorithms on several image restoration tasks.


  A four-dimensional regularization of Lovelock-Lanczos gravity up to an
arbitrary curvature order is considered. We show that Lovelock-Lanczos terms
can provide a non-trivial contribution to the Einstein field equations in four
dimensions, for spherically symmetric and
Friedmann-Lema\^{i}tre-Robertson-Walker spacetimes, as well as at first order
in perturbation theory around (anti) de Sitter vacua. We will discuss the
cosmological and black hole solutions arising from these theories, focusing on
the presence of attractors and their stability. Although curvature
singularities persist for any finite number of Lovelock terms, it is shown that
they disappear in the non-perturbative limit of a theory with a unique vacuum.


  Learned image compression has a problem of non-bit-exact reconstruction due
to different calculations of floating point arithmetic on different devices.
This paper shows a method to achieve a deterministic reconstructed image by
quantizing only the decoder of the learned image compression model. From the
implementation perspective of an image codec, it is beneficial to have the
results reproducible when decoded on different devices. In this paper, we study
quantization of weights and activations without overflow of accumulator in all
decoder subnetworks. We show that the results are bit-exact at the output, and
the resulting BD-rate loss of quantization of decoder is 0.5 % in the case of
16-bit weights and 16-bit activations, and 7.9 % in the case of 8-bit weights
and 16-bit activations.


  We derive a formula for the Coxeter polynomial of the s-fold tensor product
F[A_{n_1-1}] x ... x F[A_{n_s-1}] of path algebras of linearly oriented quivers
of Dynkin type A_{n_i-1}, in terms of the weights n_1, ..., n_s > 1, and show
that conversely the weights can be recovered from the Coxeter polynomial of the
tensor product. Our results have applications in singularity theory, in
particular these algebras occur as endomorphism algebras of tilting objects in
certain stable categories of vector bundles.


  Nies and Scholz defined quantum Martin-L\"of randomness (q-MLR) for states
(infinite qubitstrings). We define a notion of quantum Solovay randomness and
show it to be equivalent to q-MLR using purely linear algebraic methods.
Quantum Schnorr randomness is then introduced. A quantum analogue of the law of
large numbers is shown to hold for quantum Schnorr random states. We introduce
quantum-K, ($QK$) a measure of the descriptive complexity of density matrices
using classical prefix-free Turing machines and show that the initial segments
of weak Solovay random and quantum Schnorr random states are incompressible in
the sense of $QK$. Several connections between Solovay randomness and $K$ carry
over to those between weak Solovay randomness and $QK$. We then define $QK_C$,
using computable measure machines and connect it to quantum Schnorr randomness.
We then explore a notion of `measuring' a state. We formalize how `measurement'
of a state induces a probability measure on the space of infinite bitstrings. A
state is `measurement random' ($mR$) if the measure induced by it, under any
computable basis, assigns probability one to the set of Martin-L\"of randoms.
I.e., measuring a $mR$ state produces a Martin-L\"of random bitstring almost
surely. While quantum-Martin-L\"of random states are $mR$, the converse fails:
there is a $mR$ state, $\rho$ which is not quantum-Martin-L\"of random. In
fact, something stronger is true. While $\rho$ is computable and can be easily
constructed, measuring it in any computable basis yields an arithmetically
random sequence with probability one. So, classical randomness can be generated
from a computable state which is not quantum random. We conclude by studying
the asymptotic von Neumann entropy of computable states.


  We present switching field distributions of spin-transfer assisted
magnetization reversal in perpendicularly magnetized Co/Ni multilayer
spin-valve nanopillars at room temperature. Switching field measurements of the
Co/Ni free layer of spin-valve nanopillars with a 50 nm x 300 nm ellipse cross
section were conducted as a function of current. The validity of a model that
assumes a spin-current dependent effective barrier for thermally activated
reversal is tested by measuring switching field distributions under applied
direct currents. We show that the switching field distributions deviate
significantly from the double exponential shape predicted by the effective
barrier model, beginning at applied currents as low as half of the zero field
critical current. Barrier heights extracted from switching field distributions
for currents below this threshold are a monotonic function of the current.
However, the thermally-induced switching model breaks down for currents
exceeding the critical threshold.


  The Covid pandemic is a clarion call for increased sensitivity to the
interconnected nature of social problems facing our world today. A
future-oriented education on critical issues, such as those outlined in the
United Nations Sustainable Development Goals (UN SDGs) and designing potential
solutions for such problems is an imperative skill that must be imparted to
children to help them navigate their future in today's unpredictable world.
Towards this goal, we have been conducting 3.5 month-long mentoring programs
for pre-university students in India to participate in a STEAM for Social Good
innovation challenge conducted annually by the Government of India. Using
digital and physical computing skills, we helped children explore creative
solutions for social problems through a constructionist approach to learning,
wherein they ideated and reflected upon the problems in their communities. The
children learnt the Engineering Design Thinking process and worked in online
groups of two or three, from concept to completion. Despite the constraints
posed by the pandemic, they explored creative ways to think about design and
innovation. They completed a variety of tasks by making, tinkering,
engineering, assembling, and programming to grasp the intricate relationship
between software and hardware. Subsequently, the children showcased their
creative abilities through video storytelling to a panel of domain experts. In
this paper, we present the children's perspective of their experiences through
this journey, the evaluation metrics based on IEEE design principles, and our
learnings from conducting this initiative as a university-school partnership
model for 84 middle and high school students. The aspirational intent of this
initiative is to make the children better social problem solvers and help them
perceive social problems as opportunities to enhance life for themselves and
their communities.


  Few-shot learning aims to learn a classifier using a few labelled instances
for each class. Metric-learning approaches for few-shot learning embed
instances into a high-dimensional space and conduct classification based on
distances among instance embeddings. However, such instance embeddings are
usually shared across all episodes and thus lack the discriminative power to
generalize classifiers according to episode-specific features. In this paper,
we propose a novel approach, namely \emph{Episode Adaptive Embedding Network}
(EAEN), to learn episode-specific embeddings of instances. By leveraging the
probability distributions of all instances in an episode at each channel-pixel
embedding dimension, EAEN can not only alleviate the overfitting issue
encountered in few-shot learning tasks, but also capture discriminative
features specific to an episode. To empirically verify the effectiveness and
robustness of EAEN, we have conducted extensive experiments on three widely
used benchmark datasets, under various combinations of different generic
embedding backbones and different classifiers. The results show that EAEN
significantly improves classification accuracy about $10\%$ to $20\%$ in
different settings over the state-of-the-art methods.


  Non-congruent gas-liquid phase transition (NCPT) have been studied in
modified Coulomb model of a binary ionic mixture C(+6) + O(+8) on a
\textit{uniformly compressible} ideal electronic background /BIM($\sim$)/. The
features of NCPT in improved version of the BIM($\sim$) model for the same
mixture on background of \textit{non-ideal} electronic Fermi-gas and comparison
it with the previous calculations are the subject of present study. Analytical
fits for Coulomb corrections to EoS of electronic and ionic subsystems were
used in present calculations within the Gibbs--Guggenheim conditions of
non-congruent phase equilibrium.Parameters of critical point-line (CPL) were
calculated on the entire range of proportions of mixed ions $0<X<1$. Strong
"distillation" effect was found for NCPT in present BIM($\sim$) model. Just
similar distillation was obtained in variant of NCPT in dense nuslear matter.
The absence of azeotropic compositions was revealed in studied variants of
BIM($\sim$) in contrast to explicit existence of azeotropic compositions for
the NCPT in chemically reacting plasmas and in astrophysical applications.


  Artificial intelligence applications enable farmers to optimize crop growth
and production while reducing costs and environmental impact. Computer
vision-based algorithms in particular, are commonly used for fruit
segmentation, enabling in-depth analysis of the harvest quality and accurate
yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based
model for semantic segmentation of on-plant tomatoes. When evaluated against
other competitive methods, our model demonstrates state-of-the-art (SOTA)
performance, even in challenging environments with highly occluded fruits.
Additionally, we introduce Tomatopia, a new, large and challenging dataset of
greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and
pixel-level annotations of the fruits.


  Methods to detect the spectral sensitivity of an object using undulator
radiation without monochromators or any other spectral devices are developed.
The spectral transmission function of the object is calculated from its
response to the spectrum-integral undulator radiation with the known spectral
distribution. This response is measured as a function of the electron energy.


  Condon and Lipton (FOCS 1989) showed that the class of languages having a
space-bounded interactive proof system (IPS) is a proper subset of decidable
languages, where the verifier is a probabilistic Turing machine. In this paper,
we show that if we use architecturally restricted verifiers instead of
restricting the working memory, i.e. replacing the working tape(s) with a
single counter, we can define some IPS's for each decidable language. Such
verifiers are called two-way probabilistic one-counter automata (2pca's). Then,
we show that by adding a fixed-size quantum memory to a 2pca, called a two-way
one-counter automaton with quantum and classical states (2qcca), the protocol
can be space efficient. As a further result, if the 2qcca can use a quantum
counter instead of a classical one, then the protocol can even be public, also
known as Arthur-Merlin games.
  We also investigate the computational power of 2pca's and 2qcca's as language
recognizers. We show that bounded-error 2pca's can be more powerful than their
deterministic counterparts by giving a bounded-error simulation of their
nondeterministic counterparts. Then, we present a new programming technique for
bounded-error 2qcca's and show that they can recognize a language which seems
not to be recognized by any bounded-error 2pca. We also obtain some interesting
results for bounded-error 1-pebble quantum finite automata based on this new
technique. Lastly, we prove a conjecture posed by Ravikumar (FSTTCS 1992)
regarding 1-pebble probabilistic finite automata, i.e. they can recognize some
nonstochastic languages with bounded error.


  Printed mathematical expression recognition (MER) models are usually trained
and tested using LaTeX-generated mathematical expressions (MEs) as input and
the LaTeX source code as ground truth. As the same ME can be generated by
various different LaTeX source codes, this leads to unwanted variations in the
ground truth data that bias test performance results and hinder efficient
learning. In addition, the use of only one font to generate the MEs heavily
limits the generalization of the reported results to realistic scenarios. We
propose a data-centric approach to overcome this problem, and present
convincing experimental results: Our main contribution is an enhanced LaTeX
normalization to map any LaTeX ME to a canonical form. Based on this process,
we developed an improved version of the benchmark dataset im2latex-100k,
featuring 30 fonts instead of one. Second, we introduce the real-world dataset
realFormula, with MEs extracted from papers. Third, we developed a MER model,
MathNet, based on a convolutional vision transformer, with superior results on
all four test sets (im2latex-100k, im2latexv2, realFormula, and InftyMDB-1),
outperforming the previous state of the art by up to 88.3%.


  Adaptation-relevant predictions of climate change are often derived by
combining climate model simulations in a multi-model ensemble. Model evaluation
methods used in performance-based ensemble weighting schemes have limitations
in the context of high-impact extreme events. We introduce a locally
time-invariant method for evaluating climate model simulations with a focus on
assessing the simulation of extremes. We explore the behaviour of the proposed
method in predicting extreme heat days in Nairobi and provide comparative
results for eight additional cities.


  There exists a homomorphism from the affine super Yangian to the completion
of the universal enveloping algebra of $\widehat{\mathfrak{gl}}(m|n)$, called
the evaluation map. In this paper, we show that this homomorphism is
surjective. Via the homomorphism, we obtain irreducible representations of the
affine super Yangian.


  The task of Outside Knowledge Visual Question Answering (OKVQA) requires an
automatic system to answer natural language questions about pictures and images
using external knowledge. We observe that many visual questions, which contain
deictic referential phrases referring to entities in the image, can be
rewritten as "non-grounded" questions and can be answered by existing
text-based question answering systems. This allows for the reuse of existing
text-based Open Domain Question Answering (QA) Systems for visual question
answering. In this work, we propose a potentially data-efficient approach that
reuses existing systems for (a) image analysis, (b) question rewriting, and (c)
text-based question answering to answer such visual questions. Given an image
and a question pertaining to that image (a visual question), we first extract
the entities present in the image using pre-trained object and scene
classifiers. Using these detected entities, the visual questions can be
rewritten so as to be answerable by open domain QA systems. We explore two
rewriting strategies: (1) an unsupervised method using BERT for masking and
rewriting, and (2) a weakly supervised approach that combines adaptive
rewriting and reinforcement learning techniques to use the implicit feedback
from the QA system. We test our strategies on the publicly available OKVQA
dataset and obtain a competitive performance with state-of-the-art models while
using only 10% of the training data.


  We analyze the obstruction to metrics of positive scalar curvature within a
given bounded distortion class of metrics. This obstruction lives in a
non-Hausdorff cohomology group Poincare dual to the uniformly finite homology
studied by Block and Weinberger. One of the applications is a converse to their
theorem on infinite connected sums, giving a complete picture of when such
manifolds have metrics of positive scalar curvature.


  We exhibit a singularly perturbed parabolic problems for which the asymptotic
behavior can be described by an one-dimensional ordinary differential equation.
We estimate the continuity of attractors in the Hausdorff metric by rate of
convergence of resolvent operator.


  We study a class of stochastic exchangeable teams comprising a finite number
of decision makers (DMs) as well as their mean-field limits involving infinite
numbers of DMs. In the finite population regime, we study exchangeable teams
under the centralized information structure. For the infinite population
setting, we study exchangeable teams under the decentralized mean-field
information sharing. The paper makes the following main contributions: i) For
finite population exchangeable teams, we establish the existence of a
randomized optimal policy that is exchangeable (permutation invariant) and
Markovian; ii) As our main result in the paper, we show that a sequence of
exchangeable optimal policies for finite population settings converges to a
conditionally symmetric (identical), independent, and decentralized randomized
policy for the infinite population problem, which is globally optimal for the
infinite population problem. This result establishes the existence of a
symmetric, independent, decentralized optimal randomized policy for the
infinite population problem. Additionally, this proves the optimality of the
limiting measure-valued MDP for the representative DM; iii) Finally, we show
that symmetric, independent, decentralized optimal randomized policies are
approximately optimal for the corresponding finite-population team with a large
number of DMs under the centralized information structure. Our paper thus
establishes the relation between the controlled McKean-Vlasov dynamics and the
optimal infinite population decentralized stochastic control problem (without
an apriori restriction of symmetry in policies of individual agents), for the
first time, to our knowledge.


  Computational effects are commonly modelled by monads, but often a monad can
be presented by an algebraic theory of operations and equations. This talk is
about monads and algebraic theories for languages for inference, and their
connections to semirings and tensors.
  A basic class of examples of algebraic theories comes from considering the
theory of modules for a semiring, e.g. the theory of unnormalized
distributions, where the semiring is that of the non-negative real numbers. We
propose that an interesting perspective is given by studying theories via
semirings, and to this end explore several examples of subtheories of module
theories, mostly relating to probability. Our main contribution concerns the
commutative combination of effects, as studied by Hyland, Plotkin and Power: we
observe that while the semiring tensor does not in general determine the tensor
of subtheories of module theories, it still does in several fundamental
probabilistic examples.