  Fisheye lens, which is suitable for panoramic imaging, has the prominent
advantage of a large field of view and low cost. However, the fisheye image has
a severe geometric distortion which may interfere with the stage of image
registration and stitching. Aiming to resolve this drawback, we devise a
stronger stitching algorithm for fisheye images by combining the traditional
image processing method with deep learning. In the stage of fisheye image
correction, we propose the Attention-based Nonlinear Activation Free Network
(ANAFNet) to deblur fisheye images corrected by Zhang calibration method.
Specifically, ANAFNet adopts the classical single-stage U-shaped architecture
based on convolutional neural networks with soft-attention technique and it can
restore a sharp image from a blurred image effectively. In the part of image
registration, we propose the ORB-FREAK-GMS (OFG), a comprehensive image
matching algorithm, to improve the accuracy of image registration. Experimental
results demonstrate that panoramic images of superior quality stitching by
fisheye images can be obtained through our method.


  We investigate the domain of coupling constants which achieve binding for a
3-body system, while none of the 2-body subsystems is bound. We derive some
general properties of the shape of the domain, and rigorous upper bounds on its
size, using a Hall--Post decomposition of the Hamiltonian. Numerical
illustrations are provided in the case of a Yukawa potential, using a simple
variational method.


  By means of the COSY-11 detection system, using a stochastically cooled
proton beam of the cooler synchrotron COSY and a hydrogen cluster target, we
have performed a high statistics measurement of the pp --> pp eta reaction at
an excess energy of Q = 15.5 MeV.
  The experiment was based on the four-momentum determination of both outgoing
protons. This permits to identify pp --> pp eta events and to derive the
complete kinematical information of the eta-p-p-system allowing for subsequent
investigations of the eta-p interaction.
  The observed spectrum of the invariant mass of the proton-proton system
deviates strongly from the phase-space distribution revealing the influence of
the final-state-interaction among the outgoing particles or nontrivial features
of the primary production mechanism.


  We study a class of integrable non-linear differential equations related to
the A.III-type symmetric spaces. These spaces are realized as factor groups of
the form SU(N)/S(U(N-k) x U(k)). We use the Cartan involution corresponding to
this symmetric space as an element of the reduction group and restrict generic
Lax operators to this symmetric space. The symmetries of the Lax operator are
inherited by the fundamental analytic solutions and give a characterization of
the corresponding Riemann-Hilbert data.


  Hypervelocity stars (HVSs) travel so fast that they may leave the Galaxy. The
tidal disruption of a binary system by the supermassive black hole in the
Galactic center is widely assumed to be their ejection mechanism. To test the
hypothesis of an origin in the Galactic center using kinematic investigations,
the current space velocities of the HVSs need to be determined. With the advent
of Gaia's second data release, accurate radial velocities from spectroscopy are
complemented by proper motion measurements of unprecedented quality. Based on a
new spectroscopic analysis method, we provide revised distances and stellar
ages, both of which are crucial to unravel the nature of the HVSs. We
reanalyzed low-resolution optical spectra of 14 HVSs from the MMT HVS survey
using a new grid of synthetic spectra, which account for deviations from local
thermodynamic equilibrium, to derive effective temperatures, surface gravities,
radial velocities, and projected rotational velocities. Stellar masses, radii,
and ages were then determined by comparison with stellar evolutionary models
that account for rotation. Finally, these results were combined with
photometric measurements to obtain spectroscopic distances. The resulting
atmospheric parameters are consistent with those of main sequence stars with
masses in the range 2.5 - 5.0 $M_\odot$. The majority of the stars rotate at
fast speeds, providing further evidence for their main sequence nature. Stellar
ages range from 90 to 400 Myr and distances (with typical
$1\sigma$-uncertainties of about 10-15%) from 30 to 100 kpc. Except for one
object (B711), which we reclassify as A-type star, all stars are of spectral
type B. The spectroscopic distances and stellar ages derived here are key
ingredients for upcoming kinematic studies of HVSs based on Gaia proper
motions.


  In this work, we explore the dependencies between speaker recognition and
emotion recognition. We first show that knowledge learned for speaker
recognition can be reused for emotion recognition through transfer learning.
Then, we show the effect of emotion on speaker recognition. For emotion
recognition, we show that using a simple linear model is enough to obtain good
performance on the features extracted from pre-trained models such as the
x-vector model. Then, we improve emotion recognition performance by fine-tuning
for emotion classification. We evaluated our experiments on three different
types of datasets: IEMOCAP, MSP-Podcast, and Crema-D. By fine-tuning, we
obtained 30.40%, 7.99%, and 8.61% absolute improvement on IEMOCAP, MSP-Podcast,
and Crema-D respectively over baseline model with no pre-training. Finally, we
present results on the effect of emotion on speaker verification. We observed
that speaker verification performance is prone to changes in test speaker
emotions. We found that trials with angry utterances performed worst in all
three datasets. We hope our analysis will initiate a new line of research in
the speaker recognition community.


  The usual vertex algebras have as underlying symmetry the Hopf algebra
$H_D=\mathbb C[D]$ of infinitesimal translations. We show that it is possible
to replace $H_D$ by another symmetry algebra $H_T=\mathbb C[T,T\inv]$, the
group algebra of the Abelian group generated by $T$. $H_T$ is the algebra of
symmetries of a lattice of rank 1, and the construction gives a class of vertex
algebras related to the Infinite Toda Lattice in the same way as the usual
$H_D$-vertex algebras are related to Korteweg-de Vries hierarchies.


  Assuming that the lightest neutrino mass $ m_0 $ is measured, we study the
influence of error of the measured $ m_0 $ on the uncertainty of the predicted
value of the neutrinoless double beta decay ($0 \, \nu \beta \beta$) effective
Majorana mass $ \vert m_{ee} \vert $. The error of the predicted value of
effective Majorana mass $ \sigma ( \vert m_{ee} \vert ) $ due to the error $
\sigma (m_0) $ is obtained by use of the propagation of errors. We investigate
in detail how $ \sigma ( \vert m_{ee} \vert ) $ behaves when the Majorana
phases $ \beta $ and $ \alpha $ change, and also when the size of the lightest
neutrino mass changes. It is shown that $ \sigma ( \vert m_{ee} \vert ) $ does
not exceed $ \sigma (m_0) $, and that the minimum value of $ \sigma ( \vert
m_{ee} \vert ) $ in the $ \beta - \alpha $ plane can be zero if the size of $
m_0 $ is rather small. In the normal mass ordering case, the maximum value of $
\sigma ( \vert m_{ee} \vert ) $ in the $ \beta - \alpha $ plane becomes about $
0.68 \, \sigma (m_0) $ if $ m_0 (= m_1 ) $ is very small, while in the inverted
mass ordering case, it becomes about $ 0.02 \, \sigma (m_0) $ if $ m_0 (= m_3 )
$ is very small. By use of the calculated $ \sigma ( \vert m_{ee} \vert ) $, we
also discuss the minimum condition for the relative error of the lightest
neutrino mass to raise the possibility of obtaining the information on $ \beta
$ and $ \alpha $.


  We investigate strained heteroepitaxial crystal growth in the framework of a
simplifying (1+1)-dimensional model by use of off-lattice Kinetic Monte Carlo
simulations. Our modified Lennard-Jones system displays the so-called
Stranski-Krastanov growth mode: initial pseudomorphic growth ends by the sudden
appearance of strain induced multilayer islands upon a persisting wetting
layer.


  Gauss-Bonnet formula is used to derive a new and simple theorem of
nonexistence of vacuum static nonsingular lorentzian wormholes. We also derive
simple proofs for the nonexistence of lorentzian wormhole solutions for some
classes of static matter such as, for instance, real scalar fields with a
generic potential obeying $\phi V'(\phi) \ge 0$ and massless fermions fields.


  In case of the KP hierarchy where the dependent variable takes values in an
(arbitrary) associative algebra A, it is known that there are solutions which
can be expressed in terms of quasideterminants of a Wronski matrix which solves
the linear heat hierarchy. We obtain these solutions without the help of
quasideterminants in a simple way via solutions of matrix KP hierarchies (over
A) and by use of a Cole-Hopf transformation. For this class of exact solutions
we work out a correspondence with 'weakly nonassociative' algebras.


  A strong-coupling-perturbation theory around the Atomic Limit of the Anderson
model with large $U$ for a localized $f$-orbital coupled to a
conduction-electron band is presented. Although an auxiliary-particle
representation is {\em not} used, application of the canonical Wick's theorem
is possible and yields an expansion in the hybridization $V$ via dressed
skeleton-Feynman diagrams. The Self-Consistent T-Approximation is constructed
as a $\Phi$-derivable approximation. From a numerical solution of
self-consistency equations the $f$-electron-excitation spectrum is
investigated. Comparison to the Non-Crossing Approximation is made in virtue of
exact formal relations and numerical results. An extension of this
Feynman-diagram approach to the Anderson-lattice model is indicated, and
application within the Local-Approximation scheme (limit of infinite spatial
dimension) is given.


  Single object tracking aims to locate one specific target in video sequences,
given its initial state. Classical trackers rely solely on visual cues,
restricting their ability to handle challenges such as appearance variations,
ambiguity, and distractions. Hence, Vision-Language (VL) tracking has emerged
as a promising approach, incorporating language descriptions to directly
provide high-level semantics and enhance tracking performance. However, current
VL trackers have not fully exploited the power of VL learning, as they suffer
from limitations such as heavily relying on off-the-shelf backbones for feature
extraction, ineffective VL fusion designs, and the absence of VL-related loss
functions. Consequently, we present a novel tracker that progressively explores
target-centric semantics for VL tracking. Specifically, we propose the first
Synchronous Learning Backbone (SLB) for VL tracking, which consists of two
novel modules: the Target Enhance Module (TEM) and the Semantic Aware Module
(SAM). These modules enable the tracker to perceive target-related semantics
and comprehend the context of both visual and textual modalities at the same
pace, facilitating VL feature extraction and fusion at different semantic
levels. Moreover, we devise the dense matching loss to further strengthen
multi-modal representation learning. Extensive experiments on VL tracking
datasets demonstrate the superiority and effectiveness of our methods.


  Camera image sensors can be used to detect ionizing radiation in addition to
optical photons. In particular, cosmic-ray muons are detected as long, straight
tracks passing through multiple pixels. The distribution of track lengths can
be related to the thickness of the active (depleted) region of the camera image
sensor through the known angular distribution of muons at sea level. We use a
sample of cosmic-ray muon tracks recorded by the Distributed Electronic
Cosmic-ray Observatory to measure the thickness of the depletion region of the
camera image sensor in a commercial smart phone, the HTC Wildfire S. The track
length distribution prefers a cosmic-ray muon angular distribution over an
isotropic distribution. Allowing either distribution, we measure the depletion
thickness to be between 13.9~$\mu$m and 27.7~$\mu$m. The same method can be
applied to additional models of image sensor. Once measured, the thickness can
be used to convert track length to incident polar angle on a per-event basis.
Combined with a determination of the incident azimuthal angle directly from the
track orientation in the sensor plane, this enables direction reconstruction of
individual cosmic-ray events.


  The importance of digital identity as a foundation for digital public
services is considered. As the classical, centralised model digital identity
has proven to be subject to several limitations, self-sovereign identities are
proposed as replacement, especially in the context of e-government platforms
and direct participation to policymaking (e.g. through e-voting tools).


  In this paper, a new offline actor-critic learning algorithm is introduced:
Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an
approximated policy gradient by using the critic to evaluate the samples. This
sampling allows SPG to search the action-Q-value space more globally than
deterministic policy gradient (DPG), enabling it to theoretically avoid more
local optima. SPG is compared to Q-learning and the actor-critic algorithms
CACLA and DPG in a pellet collection task and a self play environment in the
game Agar.io. The online game Agar.io has become massively popular on the
internet due to intuitive game design and the ability to instantly compete
against players around the world. From the point of view of artificial
intelligence this game is also very intriguing: The game has a continuous input
and action space and allows to have diverse agents with complex strategies
compete against each other. The experimental results show that Q-Learning and
CACLA outperform a pre-programmed greedy bot in the pellet collection task, but
all algorithms fail to outperform this bot in a fighting scenario. The SPG
algorithm is analyzed to have great extendability through offline exploration
and it matches DPG in performance even in its basic form without extensive
sampling.


  Dynamical studies of local ETGs and the Fundamental Plane point to a strong
dependence of M/L ratio on luminosity (and stellar mass) with a relation of the
form $M/L \propto L^{\gamma}$. The "tilt" $\gamma$ may be caused by various
factors, including stellar population properties, IMF, rotational support,
luminosity profile non-homology and dark matter (DM) fraction. We evaluate the
impact of all these factors using a large uniform dataset of local ETGs from
Prugniel & Simien (1997). We take particular care in estimating the stellar
masses, using a general star formation history, and comparing different
population synthesis models. We find that the stellar M/L contributes little to
the tilt. We estimate the total M/L using simple Jeans dynamical models, and
find that adopting accurate luminosity profiles is important but does not
remove the need for an additional tilt component, which we ascribe to DM. We
survey trends of the DM fraction within one effective radius, finding it to be
roughly constant for galaxies fainter than $M_B \sim -20.5$, and increasing
with luminosity for the brighter galaxies; we detect no significant differences
among S0s and fast- and slow-rotating ellipticals. We construct simplified
cosmological mass models and find general consistency, where the DM transition
point is caused by a change in the relation between luminosity and effective
radius. A more refined model with varying galaxy star formation efficiency
suggests a transition from total mass profiles (including DM) of faint galaxies
distributed similarly to the light, to near-isothermal profiles for the bright
galaxies. These conclusions are sensitive to various systematic uncertainties
which we investigate in detail, but are consistent with the results of dynamics
studies at larger radii.


  We present a new method which uses Feynman-like diagrams to calculate the
statistical quantities of embedded many-body random matrix problems. The method
provides a promising alternative to existing techniques and offers many
important simplifications. We use it here to find the fourth, sixth and eighth
moments of the level density for k fermions or bosons interacting through a
random hermitian potential in the limit where the number of possible
single-particle states is taken to infinity. All share the same transition,
starting immediately after 2k = m, from moments arising from a semi-circular
level density to gaussian moments. The results also reveal a striking feature;
the domain of the 2n'th moment is naturally divided into n subdomains specified
by the points 2k = m, 3k = m, ..., nk = m.


  Solar spicules are the fundamental magnetic structures in the chromosphere
and considered to play a key role in channelling the chromosphere and corona.
Recently, it was suggested by De Pontieu et al. that there were two types of
spicules with very different dynamic properties, which were detected by space-
time plot technique in the Ca ii H line (3968 A) wavelength from Hinode/SOT
observations. 'Type I' spicule, with a 3-7 minute lifetime, undergoes a cycle
of upward and downward motion; in contrast, 'Type II' spicule fades away within
dozens of seconds, without descending phase. We are motivated by the fact that
for a spicule with complicated 3D motion, the space-time plot, which is made
through a slit on a fixed position, could not match the spicule behavior all
the time and might lose its real life story. By revisiting the same data sets,
we identify and trace 105 and 102 spicules in quiet sun (QS) and coronal hole
(CH), respectively, and obtain their statistical dynamic properties. First, we
have not found a single convincing example of 'Type II' spicules. Secondly,
more than 60% of the identified spicules in each region show a complete cycle,
i.e., majority spicules are 'Type I'. Thirdly, the lifetime of spicules in QS
and CH are 148 s and 112 s, respectively, but there is no fundamental lifetime
difference between the spicules in QS and CH reported earlier. Therefore, the
suggestion of coronal heating by 'Type II' spicules should be taken with
cautions. Subject headings: Sun: chromosphere Sun:transition region Sun:corona


  We consider N x N matrices with complex entries that are perturbed by a
complex Gaussian matrix with small variance. We prove that if the unperturbed
matrix satisfies certain local laws then the bulk correlation functions are
universal in the large N limit. Assuming the entries are independent and
identically distributed with a common distribution that has finite moments, the
Gaussian component is removed by the four moment theorem of Tao and Vu.


  As an extension of earlier work on QED, we construct a BRST-invariant
Lagrangian for SU(N) Yang-Mills theory with fundamental matter, regulated by
the inclusion of massive Pauli-Villars (PV) gluons and PV quarks. The
underlying gauge symmetry for massless PV gluons is generalized to accommodate
the PV-index-changing currents that are required by the regularization.
Auxiliary adjoint scalars are used, in a mechanism due to Stueckelberg, to
attribute mass to the PV gluons and the PV quarks. The addition of
Faddeev--Popov ghosts then establishes a residual BRST symmetry. Although there
are drawbacks to the approach, in particular the computational load of a large
number of PV fields and a nonlocal interaction of the ghost fields, this
formulation could provide a foundation for renormalizable nonperturbative
solutions of light-front QCD in an arbitrary covariant gauge.


  In his breakthrough paper, Raz showed that any parity learning algorithm
requires either quadratic memory or an exponential number of samples [FOCS'16,
JACM'19]. A line of work that followed extended this result to a large class of
learning problems. Until recently, all these results considered learning in the
streaming model, where each sample is drawn independently, and the learner is
allowed a single pass over the stream of samples. Garg, Raz, and Tal [CCC'19]
considered a stronger model, allowing multiple passes over the stream. In the
$2$-pass model, they showed that learning parities of size $n$ requires either
a memory of size $n^{1.5}$ or at least $2^{\sqrt{n}}$ samples. (Their result
also generalizes to other learning problems.)
  In this work, for any constant $q$, we prove tight memory-sample lower bounds
for any parity learning algorithm that makes $q$ passes over the stream of
samples. We show that such a learner requires either $\Omega(n^{2})$ memory
size or at least $2^{\Omega(n)}$ samples. Beyond establishing a tight lower
bound, this is the first non-trivial lower bound for $q$-pass learning for any
$q\ge 3$. Similar to prior work, our results extend to any learning problem
with many nearly-orthogonal concepts.
  We complement the lower bound with an upper bound, showing that parity
learning with $q$ passes can be done efficiently with $O(n^2/\log q)$ memory.


  High-energy particles traversing a medium experience modified dispersion. In
the Quark-Gluon Plasma, such dispersion affects jet propagation and transport
properties and should be determined better. Above $\sim 2T_c$ we expect
strongly coupled infrared behavior and perturbative ultraviolet behavior,
allowing a perturbative matching to an effective theory called EQCD, which can
be studied non-perturbatively. We study the relevant non-local operator in EQCD
at next-to-leading order which allows for a complete EQCD-to-lattice match and
prepares the groundwork for a matching between EQCD and full QCD. Our results
in EQCD show remarkable agreement between perturbation theory and the lattice
in the expected regime.


  Using \emph{ab initio} band structure and DFT+dynamical mean-field theory
methods we examine the effects of electron-electron interactions on the normal
state electronic structure, Fermi surface, and magnetic correlations of the
recently discovered double-layer perovskite superconductor La$_3$Ni$_2$O$_7$
under pressure. Our results suggest the formation of a negative charge transfer
mixed-valence state with the Ni valence close to 1.75+. We find a remarkable
orbital-selective renormalization of the Ni $3d$ bands, with $m^*/m \sim 3$ and
2.3 for the Ni $3z^2-r^2$ and $x^2-y^2$ orbitals, respectively, in agreement
with experimental estimates. Our results for the {\bf k}-dependent spectral
functions and Fermi surfaces show significant incoherence of the Ni $3z^2-r^2$
states, implying the proximity of the Ni $3d$ states to orbital-dependent
localization. Our analysis of the static magnetic susceptibility suggests the
possible formation of the spin and charge (or bond) density wave stripe states.


  Divergence estimators based on direct approximation of density-ratios without
going through separate approximation of numerator and denominator densities
have been successfully applied to machine learning tasks that involve
distribution comparison such as outlier detection, transfer learning, and
two-sample homogeneity test. However, since density-ratio functions often
possess high fluctuation, divergence estimation is still a challenging task in
practice. In this paper, we propose to use relative divergences for
distribution comparison, which involves approximation of relative
density-ratios. Since relative density-ratios are always smoother than
corresponding ordinary density-ratios, our proposed method is favorable in
terms of the non-parametric convergence speed. Furthermore, we show that the
proposed divergence estimator has asymptotic variance independent of the model
complexity under a parametric setup, implying that the proposed estimator
hardly overfits even with complex models. Through experiments, we demonstrate
the usefulness of the proposed approach.


  Boson-Sampling is a classically computationally hard problem that can - in
principle - be efficiently solved with quantum linear optical networks. Very
recently, a rush of experimental activity has ignited with the aim of
developing such devices as feasible instances of quantum simulators. Even
approximate Boson-Sampling is believed to be hard with high probability if the
unitary describing the optical network is drawn from the Haar measure. In this
work we show that in this setup, with probability exponentially close to one in
the number of bosons, no symmetric algorithm can distinguish the Boson-Sampling
distribution from the uniform one from fewer than exponentially many samples.
This means that the two distributions are operationally indistinguishable
without detailed a priori knowledge. We carefully discuss the prospects of
efficiently using knowledge about the implemented unitary for devising
non-symmetric algorithms that could potentially improve upon this. We conclude
that due to the very fact that Boson-Sampling is believed to be hard, efficient
classical certification of Boson-Sampling devices seems to be out of reach.


  Early stopping of iterative algorithms is a widely-used form of
regularization in statistics, commonly used in conjunction with boosting and
related gradient-type algorithms. Although consistency results have been
established in some settings, such estimators are less well-understood than
their analogues based on penalized regularization. In this paper, for a
relatively broad class of loss functions and boosting algorithms (including
L2-boost, LogitBoost and AdaBoost, among others), we exhibit a direct
connection between the performance of a stopped iterate and the localized
Gaussian complexity of the associated function class. This connection allows us
to show that local fixed point analysis of Gaussian or Rademacher complexities,
now standard in the analysis of penalized estimators, can be used to derive
optimal stopping rules. We derive such stopping rules in detail for various
kernel classes, and illustrate the correspondence of our theory with practice
for Sobolev kernel classes.


  We propose a novel physical realization of a quantum computer. The qubits are
electric dipole moments of ultracold diatomic molecules, oriented along or
against an external electric field. Individual molecules are held in a 1-D trap
array, with an electric field gradient allowing spectroscopic addressing of
each site. Bits are coupled via the electric dipole-dipole interaction. Using
technologies similar to those already demonstrated, this design can plausibly
lead to a quantum computer with $\gtrsim 10^4$ qubits, which can perform $\sim
10^5$ CNOT gates in the anticipated decoherence time of $\sim 5$ s.


  We show that the phase imprinting method is capable of generating vortices in
a one-component gas of neutral fermionic atoms at zero and finite temperatures.
We find qualitative differences in dynamics of vortices in comparision with the
case of Bose-Einstein condensate. The results of the imprinting strongly depend
on the geometry of the trap, e.g., in asymmetric traps no single vortex state
exists. The observations could be considered as a signature of Cooper-pair
based superfluidity in a Fermi gas.


  The applications of effective lagrangians to the determination of the effects
of physics beyond the Standard Model are briefly described. Emphasis is given
to those effective operators which generate the largest deviations form the
Standard Model; some applications are described. of the paper (or other short
description) [Talk given at "Beyond the Standard Model IV", Lake Tahoe, CA,
Dec. 13-18, 1994]


  Configuration interaction (CI) calculations in atoms with two valence
electrons, carried out in the V(N-2) Hartree-Fock potential of the core, are
corrected for core-valence interactions using many-body perturbation theory
(MBPT). Two variants of the mixed CI+MBPT theory are described and applied to
obtain energy levels and transition amplitudes for Be, Mg, Ca, and Sr.


  We show that the counter-rotating terms of the dispersive qubit-cavity Rabi
model can produce relatively large and nonmonotonic Bloch-Siegert shifts in the
cavity frequency as the system is driven through a quantum-to-classical
transition. Using a weak microwave probe tone, we demonstrate experimentally
this effect by monitoring the resonance frequency of a microwave cavity coupled
to a transmon and driven by a microwave field with varying power. In the weakly
driven regime (quantum phase), the Bloch-Siegert shift appears as a small
constant frequency shift, while for strong drive (classical phase) it presents
an oscillatory behaviour as a function of the number of photons in the cavity.
The experimental results are in agreement with numerical simulations based on
the quasienergy spectrum.


  Suppose $\alpha, \beta$ are Lipschitz strongly concave functions from $[0,
1]$ to $\mathbb{R}$ and $\gamma$ is a concave function from $[0, 1]$ to
$\mathbb{R}$, such that $\alpha(0) = \gamma(0) = 0$, and $\alpha(1) = \beta(0)
= 0$ and $\beta(1) = \gamma(1) = 0.$ For an $n \times n$ Hermitian matrix $W$,
let $spec(W)$ denote the vector in $\mathbb{R}^n$ whose coordinates are the
eigenvalues of $W$ listed in non-increasing order. Let $\lambda = \partial^-
\alpha$, $\mu = \partial^- \beta$ on $(0, 1]$ and $\nu = \partial^- \gamma,$ at
all points of $(0, 1]$, where $\partial^-$ is the left derivative, which is
monotonically decreasing. Let $\lambda_n(i) :=
n^2(\alpha(\frac{i}{n})-\alpha(\frac{i-1}{n}))$, for $i \in [n]$, and
similarly, $\mu_n(i) := n^2(\beta(\frac{i}{n})-\beta(\frac{i-1}{n}))$, and
$\nu_n(i) := n^2(\gamma(\frac{i}{n})-\gamma(\frac{i-1}{n}))$.
  Let $X_n, Y_n$ be independent random Hermitian matrices from unitarily
invariant distributions with spectra $\lambda_n$, $\mu_n$ respectively. We
define norm $\|\cdot\|_\mathcal{I}$ to correspond in a certain way to the sup
norm of an antiderivative. For suitable $\lambda$ and $\mu$, we prove that the
following limit exists. \begin{equation} \lim\limits_{n \rightarrow
\infty}\frac{\ln \mathbb{P}\left[\|spec(X_n + Y_n) - \nu_n\|_{\mathcal{I}} <
n^2 \epsilon\right]}{n^2}.\end{equation} We interpret this limit in terms of
the surface tension $\sigma$ of continuum limits of the discrete hives defined
by Knutson and Tao.


  In a radiatively heated and cooled medium, the thermal instability is a
plausible mechanism for forming clouds, while the radiation force provides a
natural acceleration, especially when ions recombine and opacity increases.
Here we extend Field's theory to self-consistently account for a radiation
force resulting from bound-free and bound-bound transitions in the optically
thin limit. We present physical arguments for clouds to be significantly
accelerated by a radiation force due to lines during a nonlinear phase of the
instability. To qualitatively illustrate our main points, we perform both one
and two-dimensional (1-D/2-D) hydrodynamical simulations that allow us to study
the nonlinear outcome of the evolution of thermally unstable gas subjected to
this radiation force. Our 1-D simulations demonstrate that the thermal
instability can produce long-lived clouds that reach a thermal equilibrium
between radiative processes and thermal conduction, while the radiation force
can indeed accelerate the clouds to supersonic velocities. However, our 2-D
simulations reveal that a single cloud with a simple morphology cannot be
maintained due to destructive processes, triggered by the Rayleigh-Taylor
instability and followed by the Kelvin-Helmholtz instability. Nevertheless, the
resulting cold gas structures are still significantly accelerated before they
are ultimately dispersed.


  In this work, we consider the identifiability assumption of Gaussian linear
structural equation models (SEMs) in which each variable is determined by a
linear function of its parents plus normally distributed error. It has been
shown that linear Gaussian structural equation models are fully identifiable if
all error variances are the same or known. Hence, this work proves the
identifiability of Gaussian SEMs with both homogeneous and heterogeneous
unknown error variances. Our new identifiability assumption exploits not only
error variances, but edge weights; hence, it is strictly milder than prior work
on the identifiability result. We further provide a structure learning
algorithm that is statistically consistent and computationally feasible, based
on our new assumption. The proposed algorithm assumes that all relevant
variables are observed, while it does not assume causal minimality and
faithfulness. We verify our theoretical findings through simulations and real
multivariate data, and compare our algorithm to state-of-the-art PC, GES and
GDS algorithms.


  We present XMM-Newton observations and spectral fitting of two highly
redshift, [OIII]-luminous, narrow-line radio galaxies, B3 0731+438 and 3C 257.
Their X-ray continua are well fitted by a partial covering model with
intrinsically unabsorbed and absorbed powerlaw components. The spectral models
indicate that both objects harbour highly obscured nuclei, with N_H ~ 0.5 - 2 x
10^23 cm^-2. Correcting for this absorption we find large intrinsic
luminosities in the range L_X ~ 0.2 - 1 x 10^45 erg/s. Thus, both sources are
type-2 quasars.


  We consider the asymptotic limit of a diffuse interface model for
tumor-growth when a parameter $\varepsilon$ proportional to the thickness of
the diffuse interface goes to zero. An approximate solution which shows
explicitly the behavior of the true solution for small $\varepsilon$ will be
constructed by using matched expansion method. Based on the energy method, a
spectral condition in particular, we establish a smallness estimate of the
difference between the approximate solution and the true solution.


  Denote by P(K, k) the members of the field K which are sums of kth powers of
field elements, by P+(K, k) the set of members of K which are sums of kth
powers of totally positive elements of K. We are interested in deciding whether
or not there exist integers w(K, k) and g(K, k) such that: a in P(K, k) implies
that a is the sum of at most w(K, k) kth powers; a in P+(K, k) implies that a
is the sum of at most g(K, k) totally positive kth powers. We will show that if
w(K, 2) is finite and provided that the kth powers are dense (in a sense
described explicitly in theorem 3) in K, then w(K, k) is also finite for k > 2.
The proofs are constructive, but the implied upper bounds for w(K, k) are
large. This is to be expected since the method of proof does not use any deep
arithmetical or algebraic properties of the field K.


  We report the discovery of an expanding cylinder of HI gas of radius 1 kpc
and vertical extent 800 pc by analyzing the 21-cm line survey data from the
literature. The cylinder is expanding at 150 km s$^{-1}$ and rotating at 100 km
s$^{-1}$, and is interpreted as due to a high-velocity conical wind at $\sim
180$ km s$^{-1}$ from the Galactic Center. The total mass of the cylinder is
estimated to be $\sim 8.5\times 10^5 M_\odot$ and kinetic energy $\sim 3\times
10^{53}$ ergs.


  We introduce Riesz Logic, whose models are abelian lattice ordered groups,
which generalise Riesz spaces (vector lattices), and show soundness and
completeness. Our motivation is to provide a logic for distributional semantics
of natural language, where words are typically represented as elements of a
vector space whose dimensions correspond to contexts in which words may occur.
This basis provides a lattice ordering on the space, and this ordering may be
interpreted as "distributional entailment". Several axioms of Riesz Logic are
familiar from Basic Fuzzy Logic, and we show how the models of these two logics
may be related; Riesz Logic may thus be considered a new fuzzy logic. In
addition to applications in natural language processing, there is potential for
applying the theory to neuro-fuzzy systems.


  By reduction to a generalized Sturm Liouville problem, we establish spectral
stability of hydraulic shock profiles of the Saint-Venant equations for
inclined shallow-water flow, over the full parameter range of their existence,
for both smooth-type profiles and discontinuous-type profiles containing
subshocks. Together with work of Mascia-Zumbrun and Yang-Zumbrun, this yields
linear and nonlinear $H^2\cap L^1 \to H^2$ stability with sharp rates of decay
in $L^p$, $p\geq 2$, the first complete stability results for large-amplitude
shock profiles of a hyperbolic relaxation system.


  Gamma-ray burst (GRB) dust echoes were first proposed as an alternative
explanation for the supernova-like (SN-like) components to the afterglows of
GRB 980326 and GRB 970228. However, the spectroscopic identification of Type Ic
SN 2003dh associated with GRB 030329, as well as the identification of SN-like
components to the afterglows of other GRBs, appears to have confirmed the
GRB/SN paradigm. However, the likely progenitors of Type Ic SNe are Wolf-Rayet
WC stars, and late-type WC stars have been observed to be surrounded by dust,
at a distance of 10^14 -- 10^15 cm from the star. Consequently, we revisit the
possibility of GRB dust echoes, not on a timescale of weeks after the burst but
on a timescale of minutes to hours. We find that if the optical flash is
sufficiently bright and the jet sufficiently wide, GRB afterglows may be
accompanied by chromatic variations on this timescale. From these signatures,
such model parameters as the inner radius of the dust distribution, the initial
opening angle of the jet, etc., may be deduced. With rapid and regular
localizations of GRBs by HETE-2, Integral, and now Swift, and new and improved
robotic telescope systems, these early-time GRB dust echoes may soon be
detected. We describe one such robotic telescope system, called PROMPT, that
the University of North Carolina at Chapel Hill is building at the Cerro Tololo
Inter-American Observatory in greater detail.


  In this letter we discuss a method for generating synchrony-optimized
coupling architectures of Kuramoto oscillators with a heterogeneous
distribution of native frequencies. The method allows us to relate the
properties of the coupling network to its synchronizability. These relations
were previously only established from a linear stability analysis of the
identical oscillator case. We further demonstrate that the heterogeneity in the
oscillator population produces heterogeneity in the optimal coupling network as
well. Two rules for enhancing the synchronizability of a given network by a
suitable placement of oscillators are given: (i) native frequencies of adjacent
oscillators must be anti-correlated and (ii) frequency magnitudes should
positively correlate with the degree of the node they are placed at.


  This article presents a novel approach for efficient operation of non-smart
household appliances under smart grid environment using the proposed smart load
node (SLN). In real world scenario, there are so many non-smart loads currently
in use and embedding appliance specific intelligence into them to make them as
smart loads will be more expensive compared to the proposed SLN, which is a
common solution for all types of non-smart loads. This makes the proposed
low-cost SLN, which neither requires any infrastructural change in the
electrical wiring of a house nor any constructional change in home appliances
at the manufacturing stage and at the consumer end, as a feasible solution for
intelligent operation of non-smart home appliances under smart grid
environment. The SLNs, which are placed in a home like distributed wireless
sensor nodes, form a home area network (HAN). The HAN includes a load
management unit (LMU) which acts as master for all distributed SLNs. Wi-Fi is
chosen as a medium of communication in the HAN. The LMU incorporates load
management algorithm which is written in Python script.


  We study the entanglement cost under quantum operations preserving the
positivity of the partial transpose (PPT-operations). We demonstrate that this
cost is directly related to the logarithmic negativity, thereby providing the
operational interpretation for this easily computable entanglement measure. As
examples we discuss general Werner states and arbitrary bi-partite Gaussian
states. Equipped with this result we then prove that for the anti-symmetric
Werner state PPT-cost and PPT-entanglement of distillation coincide giving the
first example of a truly mixed state for which entanglement manipulation is
asymptotically reversible.


  We have recently calculated shot noise for entangled and spin-polarized
electrons in novel beam-splitter geometries with a local Rashba s-o interaction
in the incoming leads. This interaction allows for a gate-controlled rotation
of the incoming electron spins. Here we present an alternate simpler route to
the shot noise calculation in the above work and focus on only electron pairs.
Shot noise for these shows continuous bunching and antibunching behaviors. In
addition, entangled and unentangled triplets yield distinctive shot noise
oscillations. Besides allowing for a direct way to identify triplet and singlet
states, these oscillations can be used to extract s-o coupling constants
through noise measurements. Incoming leads with spin-orbit interband mixing
give rise an additional modulation of the current noise. This extra rotation
allows the design of a spin transistor with enhanced spin control.


  Generative probabilistic forecasting produces future time series samples
according to the conditional probability distribution given past time series
observations. Such techniques are essential in risk-based decision-making and
planning under uncertainty with broad applications in grid operations,
including electricity price forecasting, risk-based economic dispatch, and
stochastic optimizations. Inspired by Wiener and Kallianpur's innovation
representation, we propose a weak innovation autoencoder architecture and a
learning algorithm to extract independent and identically distributed
innovation sequences from nonparametric stationary time series. We show that
the weak innovation sequence is Bayesian sufficient, which makes the proposed
weak innovation autoencoder a canonical architecture for generative
probabilistic forecasting. The proposed technique is applied to forecasting
highly volatile real-time electricity prices, demonstrating superior
performance across multiple forecasting measures over leading probabilistic and
point forecasting techniques.


  In this paper, we propose a novel iterative convolution-thresholding method
(ICTM) that is applicable to a range of variational models for image
segmentation. A variational model usually minimizes an energy functional
consisting of a fidelity term and a regularization term. In the ICTM, the
interface between two different segment domains is implicitly represented by
their characteristic functions. The fidelity term is then usually written as a
linear functional of the characteristic functions and the regularized term is
approximated by a functional of characteristic functions in terms of heat
kernel convolution. This allows us to design an iterative
convolution-thresholding method to minimize the approximate energy. The method
is simple, efficient and enjoys the energy-decaying property. Numerical
experiments show that the method is easy to implement, robust and applicable to
various image segmentation models.


  The Birkhoff--Kakutani Theorem asserts that a topological group is metrizable
if and only if it has countable character. We develop and apply tools for the
estimation of the character for a wide class of nonmetrizable topological
groups.
  We consider abelian groups whose topology is determined by a countable
cofinal family of compact sets. These are the closed subgroups of
Pontryagin--van Kampen duals of \emph{metrizable} abelian groups, or
equivalently, complete abelian groups whose dual is metrizable. By
investigating these connections, we show that also in these cases, the
character can be estimated, and that it is determined by the weights of the
\emph{compact} subsets of the group, or of quotients of the group by compact
subgroups. It follows, for example, that the density and the local density of
an abelian metrizable group determine the character of its dual group. Our main
result applies to the more general case of closed subgroups of Pontryagin--van
Kampen duals of abelian \v{C}ech-complete groups.
  In the special case of free abelian topological groups, our results extend a
number of results of Nickolas and Tkachenko, which were proved using
combinatorial methods.
  In order to obtain concrete estimations, we establish a natural bridge
between the studied concepts and pcf theory, that allows the direct application
of several major results from that theory. We include an introduction to these
results and their use.


  The nearby TRAPPIST-1 planetary system is an exciting target for
characterizing the atmospheres of terrestrial planets. The planets e, f and g
lie in the circumstellar habitable zone and could sustain liquid water on their
surfaces. During the extended pre-main sequence phase of TRAPPIST-1, however,
the planets may have experienced extreme water loss, leading to a desiccated
mantle. The presence or absence of an ocean is challenging to determine with
current and next generation telescopes. Therefore, we investigate whether
indirect evidence of an ocean and/or a biosphere can be inferred from
observations of the planetary atmosphere. We introduce a newly developed
photochemical model for planetary atmospheres, coupled to a
radiative-convective model and validate it against modern Earth, Venus and
Mars. The coupled model is applied to the TRAPPIST-1 planets e and f, assuming
different surface conditions and varying amounts of CO$_2$ in the atmosphere.
As input for the model we use a constructed spectrum of TRAPPIST-1, based on
near-simultaneous data from X-ray to optical wavelengths. We compute cloud-free
transmission spectra of the planetary atmospheres and determine the
detectability of molecular features using the Extremely Large Telescope (ELT)
and the James Webb Space Telescope (JWST). We find that under certain
conditions, the existence or non-existence of a biosphere and/or an ocean can
be inferred by combining 30 transit observations with ELT and JWST within the
K-band. A non-detection of CO could suggest the existence of an ocean, whereas
significant CH$_4$ hints at the presence of a biosphere.


  We prove an approximation result for Lipschitz functions on the quantum
sphere $S_q^2$, from which we deduce that the two natural quantum metric
structures on $S_q^2$ have quantum Gromov-Hausdorff distance zero.


  The discrepancy BR for an $m \times n$ $0,1$-matrix from Brualdi and
Sanderson \cite{Brualdi1998} counts the minimum number of $1$'s which need to
be shifted in each row to the left to achieve its Ferrers matrix, i.e. each row
consists of consecutive $1$'s followed by consecutive $0$'s. For ecological
bipartite networks BR describes how nested a set of relationships is. Since
different labeled matrices can be isomorphic but possess different
discrepancies, we define a metric determining the minimum discrepancy in an
isomorphic class. We give a reduction to $k\leq n$ minimum weighted perfect
matching problems.


  Outcome phenotype measurement error is rarely corrected in comparative effect
estimation studies in observational pharmacoepidemiology. Quantitative bias
analysis (QBA) is a misclassification correction method that algebraically
adjusts person counts in exposure-outcome contingency tables to reflect the
magnitude of misclassification. The extent QBA minimizes bias is unclear
because few systematic evaluations have been reported. We empirically evaluated
QBA impact on odds ratios (OR) in several comparative effect estimation
scenarios. We estimated non-differential and differential phenotype errors with
internal validation studies using a probabilistic reference. Further, we
synthesized an analytic space defined by outcome incidence, uncorrected ORs,
and phenotype errors to identify which combinations produce invalid results
indicative of input errors. We evaluated impact with relative bias
[(OR-ORQBA)]/OR*100%]. Results were considered invalid if any contingency table
cell was corrected to a negative number. Empirical bias correction was greatest
in lower incidence scenarios where uncorrected ORs were larger. Similarly,
synthetic bias correction was greater in lower incidence settings with larger
uncorrected estimates. The invalid proportion of synthetic scenarios increased
as uncorrected estimates increased. Results were invalid in common, low
incidence scenarios indicating problematic inputs. This demonstrates the
importance of accurately and precisely estimating phenotype errors before
implementing QBA in comparative effect estimation studies.


  We formulate Lorentz covariance of a quantum field theory in terms of
covariance of time-ordered products (or other Green's functions). This
formulation of Lorentz covariance implies spacelike local commutativity or
anticommutativity of fields, sometimes called microscopic causality or
microcausality. With this formulation microcausality does not have to be taken
as a separate assumption.


  Multi-class classification problems are fundamental in many varied domains in
research and industry. To solve multi-class classification problems, heuristic
strategies such as One-vs-One or One-vs-All can be employed. However, these
strategies require the number of binary classification models developed to grow
with the number of classes. Recent work in quantum machine learning has seen
the development of multi-class quantum classifiers that circumvent this growth
by learning a mapping between the data and a set of label states. This work
presents the first multi-class SWAP-Test classifier inspired by its binary
predecessor and the use of label states in recent work. With this classifier,
the cost of developing multiple models is avoided. In contrast to previous
work, the number of qubits required, the measurement strategy, and the topology
of the circuits used is invariant to the number of classes. In addition, unlike
other architectures for multi-class quantum classifiers, the state
reconstruction of a single qubit yields sufficient information for multi-class
classification tasks. Both analytical results and numerical simulations show
that this classifier is not only effective when applied to diverse
classification problems but also robust to certain conditions of noise.


  The Kerr-Newman metric describes a very special rotating, charged mass and is
the most general of the asymptotically flat stationary 'black hole' solutions
to the Einstein-Maxwell equations of general relativity. We review the
derivation of this metric from the Reissner-Nordstrom solution by means of a
complex transformation algorithm and provide a brief overview of its basic
geometric properties. We also include some discussion of interpretive issues,
related metrics, and higher-dimensional analogues.


  The phase diagram in temperature and magnetic field of the metal-organic,
two-leg, spin-ladder compound (C5H12N)2CuBr4 is studied by measurements of the
specific heat and the magnetocaloric effect. We demonstrate the presence of an
extended spin Luttinger-liquid phase between two field-induced quantum critical
points and over a broad range of temperature. Based on an ideal spin-ladder
Hamiltonian, comprehensive numerical modelling of the ladder specific heat
yields excellent quantitative agreement with the experimental data across the
complete phase diagram.


  By year 2020, the number of smartphone users globally will reach 3 Billion
and the mobile data traffic (cellular + WiFi) will exceed PC Internet traffic
the first time. As the number of smartphone users and the amount of data
transferred per smartphone grow exponentially, limited battery power is
becoming an increasingly critical problem for mobile devices which heavily
depend on network I/O. Despite the growing body of research in power management
techniques for the mobile devices at the hardware layer as well as the lower
layers of the networking stack, there has been little work focusing on saving
energy at the application layer for the mobile systems during network I/O. In
this paper, we show that significant energy savings can be achieved with
application-layer solutions at the mobile systems during data transfer with no
performance penalty. In many cases, performance increase and energy savings can
be achieved simultaneously.


  We prove a strong Symmetry of Information relation for random strings (in the
sense of Kolmogorov complexity) and establish tight bounds on the amount on
nonuniformity that is necessary for extracting a string with randomness rate 1
from a single source of randomness. More precisely, as instantiations of more
general results, we show: (1) For all n-bit random strings x and y, x is random
conditioned by y if and only if y is random conditioned by x, and (2) while
O(1) amount of advice regarding the source is not enough for extracting a
string with randomness rate 1 from a source string with constant random rate,
\omega(1) amount of advice is. The proofs use Kolmogorov extractors as the main
technical device.


  A set of N three component unit scalar fields in (3+1) Minkowski space-time
is investigated. The highly nonlinear coupling between them is chosen to omit
the scaling instabilities. The multi-soliton static configurations with
arbitrary Hopf numbers are found. Moreover, the generalized version of the
Vakulenko-Kapitansky inequality is obtained. The possibility of attractive,
repulsing and noninteracting channels is discussed.


  Human evaluation is a critical component in machine translation system
development and has received much attention in text translation research.
However, little prior work exists on the topic of human evaluation for speech
translation, which adds additional challenges such as noisy data and
segmentation mismatches. We take first steps to fill this gap by conducting a
comprehensive human evaluation of the results of several shared tasks from the
last International Workshop on Spoken Language Translation (IWSLT 2023). We
propose an effective evaluation strategy based on automatic resegmentation and
direct assessment with segment context. Our analysis revealed that: 1) the
proposed evaluation strategy is robust and scores well-correlated with other
types of human judgements; 2) automatic metrics are usually, but not always,
well-correlated with direct assessment scores; and 3) COMET as a slightly
stronger automatic metric than chrF, despite the segmentation noise introduced
by the resegmentation step systems. We release the collected human-annotated
data in order to encourage further investigation.


  Prior work on training generative Visual Dialog models with reinforcement
learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown
that this 'self-talk' approach can lead to improved performance at the
downstream dialog-conditioned image-guessing task. However, this improvement
saturates and starts degrading after a few rounds of interaction, and does not
lead to a better Visual Dialog model. We find that this is due in part to
repeated interactions between Qbot and Abot during self-talk, which are not
informative with respect to the image. To improve this, we devise a simple
auxiliary objective that incentivizes Qbot to ask diverse questions, thus
reducing repetitions and in turn enabling Abot to explore a larger state space
during RL ie. be exposed to more visual concepts to talk about, and varied
questions to answer. We evaluate our approach via a host of automatic metrics
and human studies, and demonstrate that it leads to better dialog, ie. dialog
that is more diverse (ie. less repetitive), consistent (ie. has fewer
conflicting exchanges), fluent (ie. more human-like),and detailed, while still
being comparably image-relevant as prior work and ablations.


  Bayesian reinforcement learning (RL) offers a principled and elegant approach
for sequential decision making under uncertainty. Most notably, Bayesian agents
do not face an exploration/exploitation dilemma, a major pathology of
frequentist methods. However theoretical understanding of model-free approaches
is lacking. In this paper, we introduce a novel Bayesian model-free formulation
and the first analysis showing that model-free approaches can yield
Bayes-optimal policies. We show all existing model-free approaches make
approximations that yield policies that can be arbitrarily Bayes-suboptimal. As
a first step towards model-free Bayes optimality, we introduce the Bayesian
exploration network (BEN) which uses normalising flows to model both the
aleatoric uncertainty (via density estimation) and epistemic uncertainty (via
variational inference) in the Bellman operator. In the limit of complete
optimisation, BEN learns true Bayes-optimal policies, but like in variational
expectation-maximisation, partial optimisation renders our approach tractable.
Empirical results demonstrate that BEN can learn true Bayes-optimal policies in
tasks where existing model-free approaches fail.


  We present, as a pure Prolog program, the first executable specification of
the 3 + 3 dose-escalation protocol commonly used in early-phase oncology drug
development. In this program, the imperative operations of the protocol emerge
as consequences of clinically meaningful anticipatory-regret scenarios that are
declared as CLP(Z) constraints. This 'regret-constrained' (RC) specification
yields a robust formulation which can be used to prove clinically meaningful
safety and liveness properties of the protocol before incorporating it into a
trial, and then as an on-line decision support system while the trial is
underway. Our RC specification also readily accommodates certain pragmatic
modifications to trial enrollment which severely strain traditionally
imperative formulations. The features of modern Prolog systems let us describe
the 3 + 3 protocol with a short and general program that has desirable
algebraic properties and can therefore be used, tested and reasoned about in
several different ways.


  Computational resource provisioning that is closer to a user is becoming
increasingly important, with a rise in the number of devices making continuous
service requests and with the significant recent take up of latency-sensitive
applications, such as streaming and real-time data processing. Fog computing
provides a solution to such types of applications by bridging the gap between
the user and public/private cloud infrastructure via the inclusion of a "fog"
layer. Such approach is capable of reducing the overall processing latency, but
the issues of redundancy, cost-effectiveness in utilizing such computing
infrastructure and handling services on the basis of a difference in their
characteristics remain. This difference in characteristics of services because
of variations in the requirement of computational resources and processes is
termed as service heterogeneity. A potential solution to these issues is the
use of Osmotic Computing -- a recently introduced paradigm that allows division
of services on the basis of their resource usage, based on parameters such as
energy, load, processing time on a data center vs. a network edge resource.
Service provisioning can then be divided across different layers of a
computational infrastructure, from edge devices, in-transit nodes, and a data
center, and supported through an Osmotic software layer. In this paper, a
fitness-based Osmosis algorithm is proposed to provide support for osmotic
computing by making more effective use of existing Fog server resources. The
proposed approach is capable of efficiently distributing and allocating
services by following the principle of osmosis. The results are presented using
numerical simulations demonstrating gains in terms of lower allocation time and
a higher probability of services being handled with high resource utilization.


  NASA's Science Mission Directorate (SMD) has initiated a program to enhance
the participation of historically underrepresented institutions and communities
in NASA's mission. Currently known as the NASA SMD Bridge Program, its goal is
to establish enduring partnerships among these institutions, research-intensive
universities, and NASA centers. There are concerns about using "Bridge" in the
program's name, with stakeholders suggesting that it might stigmatize students
and mislead applicants about its focus. In this white paper, we address these
concerns and conclude that a name change that better reflects the mission of
this SMD effort is necessary to address these concerns.


  The classical limit of quantum mechanics is discussed for closed quantum
systems in terms of observational aspects. Initially, the failure of the limit
h->0 is explicitly demonstrated in a model of two quantum mechanically
interacting oscillators by showing that neither quantum expectations reduce to
Newtonian trajectories nor entanglement vanishes. This result suggests that the
quantum-to-classical transition occurs only at an approximative level, which is
regulated by the low accuracy of the measurements. In order to verify the
consistence of these ideas we take into account the experimental resolution of
physical measurements by introducing a discretized formulation for the quantum
structure of wave functions. As a result, in the low-resolution limit the
quasi-determinism is recovered and hence the quantum-to-classical transition is
shown to occur adequately. Other puzzling problems, such as the classical limit
of quantum superpositions and nonlocal correlations, are naturally address as
well.


  Spin-orbit coupling and breaking of inversion symmetry are necessary
ingredients to enable a pure spin current-based manipulation of the
magnetization via the spin-orbit torque effect. Currently, magnetic insulator
oxides with non-dissipative characteristics are being explored. When combined
with non-magnetic heavy metals, known for their large spin-orbit coupling, they
offer promising potential for energy-efficient spin-orbitronics applications.
The intrinsic electronic correlations characterizing those strongly correlated
oxides hold the promises to add extra control-knobs to the desired efficient
spin-wave propagation and abrupt magnetization switching phenomena. Spinel
vanadate FeV2O4 (FVO) exhibits several structural phase transitions which are
accompanied by an intricate interplay of magnetic, charge and orbital
orderings. When grown as a thin film onto SrTiO3, the compressive strain state
induces a perpendicular magnetic anisotropy, making FVO-based heterostructures
desirable for spin-orbitronics applications. In this study, we have optimised
the deposition of stoichiometric and epitaxial Pt/FVO heterostructures by
Pulsed Laser Deposition and examined their spin-related phenomena. From
angle-dependent magnetotransport measurements, we observed both Anisotropic
Magnetoresistance (AMR) and Spin Hall Magnetoresistance (SMR) effects. Our
findings show the SMR component as the primary contributor to the overall
magnetoresistance, whose high value of 0.12% is only comparable to properly
optimized oxide-based systems.


  There has been growing interest in developing accurate models that can also
be explained to humans. Unfortunately, if there exist multiple distinct but
accurate models for some dataset, current machine learning methods are unlikely
to find them: standard techniques will likely recover a complex model that
combines them. In this work, we introduce a way to identify a maximal set of
distinct but accurate models for a dataset. We demonstrate empirically that, in
situations where the data supports multiple accurate classifiers, we tend to
recover simpler, more interpretable classifiers rather than more complex ones.


  Effective Field Theories are an established framework to bridge the gap
between UV and low energy theories. In the context of the Standard Model, the
bottom-up approach extends its operator set and thus equips us to astutely
probe its observables while encapsulating indirect evidence of unknown high
scale theories. While the top-down approach, on the other hand, employs
functional techniques to integrate out the heavy fields from a BSM Lagrangian
leading to a set of SMEFT operators. An intricate interplay of the two
approaches enhances the efficacy of the SMEFT in making meaningful predictions
while providing a platform for conducting a coherent comparison of new physics
scenarios. However, while the bottom-up approach fails to indicate the origin
of the effective operators, the top-down approach is highly dependent on the
specific model assumptions of the UV theory. We, for the first time, are
proposing a diagrammatic approach to establish selection criteria for the
allowed heavy field representations corresponding to each SMEFT operator. This,
in turn, paves the way to construct observable driven new physics models. While
we take a predominantly minimalistic approach, we also highlight the necessity
for non-minimal interactions for certain operators.


  The ladder operators for one dimensional quantum harmonic oscillator were
constructed by Schr\"odinger in 1940s. We extend this method to a two
dimensional uniform magnetic field and establish the ladder operators which
depend on all spatial variables of quantum system. The Hamiltonian of quantum
system can also be written by the velocity of the particle.


  X-ray bursts from bright sources in globular clusters of our galaxy show that
at least 11 (of a total 13) of these sources are neutron stars. One of the
low-luminosity X-ray sources in omega Cen is a neutron star accreting at a low
rate. Together with the discoveries that M~15 contains two bright X-ray
sources, and that 47 Tuc and NGC 6440 contain 2 and 5 low-luminosity X-ray
sources with a neutron star, this indicates that the total number of binaries
with neutron stars in globular clusters is higher than previously suspected.
The discovery of very bright X-ray sources in globular clusters near other
galaxies indicates that these clusters contain binaries with accreting black
holes, and multiple bright X-ray sources. Dozens of low-luminosity X-ray
sources have been discovered in omega Cen, NGC 6397, NGC 6752, NGC 6440, and a
hundred in 47 Tuc. Accurate Chandra positions of these sources combined with
HST observations have lead to unambiguous identifications with cataclysmic
variables, RS CVn binaries, and millisecond radio pulsars.


  This dissertation answers some of the questions raised in Borcherds' papers
on Moonshine and Lorentzian reflection groups. We prove (assuming an open
conjecture of Burger, Li and Sarnak) that a Lorentzian reflection group with
Weyl vector is associated to a vector-valued modular form. This result allows
us to establish a folklore conjecture that the maximal dimension of a
Lorentzian reflection group with Weyl vector is 26. In the case of elementary
lattices, we show that these vector-valued forms can be obtained by inducing
scalar-valued forms. This allows us to explain the critical signatures which
occur in Borcherds' work. Many of the structures which occur at these critical
signatures are especially beautiful and have appeared independently throughout
the literature. We investigate Borcherds-Kac-Moody (BKM) algebras with
denominator formulas that are singular weight automorphic forms. The results of
these investigations suggest that all such BKM algebras are related to orbifold
constructions of vertex algebras and elements of the Monster sporadic group.
Finally, we show how work in this dissertation combined with results of
Bruinier gives a new insight into the arithmetic mirror symmetry conjecture of
Gritsenko and Nikulin.


  In this paper we have investigated some old issues concerning four color map
problem. We have given a general method for constructing counter-examples to
Kempe's proof of the four color theorem and then show that all counterexamples
can be rule out by re-constructing special 2-colored two paths decomposition in
the form of a double-spiral chain of the maximal planar graph. In the second
part of the paper we have given an algorithmic proof of the four color theorem
which is based only on the coloring faces (regions) of a cubic planar maps. Our
algorithmic proof has been given in three steps. The first two steps are the
maximal mono-chromatic and then maximal dichromatic coloring of the faces in
such a way that the resulting uncolored (white) regions of the incomplete
two-colored map induce no odd-cycles so that in the (final) third step four
coloring of the map has been obtained almost trivially.


  The large rate of multiple simultaneous proton--proton interactions, or
pile-up, generated by the Large Hadron Collider in Run 1 required the
development of many new techniques to mitigate the adverse effects of these
conditions. This paper describes the methods employed in the ATLAS experiment
to correct for the impact of pile-up on jet energy and jet shapes, and for the
presence of spurious additional jets, with a primary focus on the large 20.3
fb$^{-1}$ data sample collected at a centre-of-mass energy of $\sqrt{s} = 8$
TeV. The energy correction techniques that incorporate sophisticated estimates
of the average pile-up energy density and tracking information are presented.
Jet-to-vertex association techniques are discussed and projections of
performance for the future are considered. Lastly, the extension of these
techniques to mitigate the effect of pile-up on jet shapes using subtraction
and grooming procedures is presented.


  The MAGIC Cherenkov telescope has observed very high energy (VHE) gamma-ray
emission from the Active Galactic Nucleus 1ES1959+650 during six hours in
September and October 2004. The observations were carried out alternated with
the Crab Nebula, whose data were used as reference source for optimizing
gamma/hadron separation and for flux comparison. The data analysis shows VHE
gamma-ray emission of 1ES1959+650 with ~ 8 sigma significance, at a time of low
activity in both optical and X-ray wavelengths. An integral flux above ~ 180
GeV of about 20% of the Crab was obtained. The light curve, sampled over 7
days, shows no significant variations. The differential energy spectrum between
180 GeV and 2 TeV can be fitted with a power law of index -2.72 +/- 0.14. The
spectrum is consistent with the slightly steeper spectrum seen by HEGRA at
higher energies, also during periods of low X-ray activity.


  A dispersive analysis of $\eta\to 3\pi$ decays has been performed in the past
by many authors. The numerical analysis of the pertinent integral equations is
hampered by two technical difficulties: i) The angular averages of the
amplitudes need to be performed along a complicated path in the complex plane.
ii) The averaged amplitudes develop singularities along the path of integration
in the dispersive representation of the full amplitudes. It is a delicate
affair to handle these singularities properly, and independent checks of the
obtained solutions are demanding and time consuming. In the present article, we
propose a solution method that avoids these difficulties. It is based on a
simple deformation of the path of integration in the dispersive representation
(not in the angular average). Numerical solutions are then obtained rather
straightforwardly. We expect that the method also works for $\omega\to 3\pi$.


  We report a discovery of shocked gas from the supernova remnant (SNR)
G357.7+0.3. Our millimeter and submillimeter observations reveal broad
molecular lines of CO(2-1), CO(3-2), CO(4-3), 13CO (2-1) and 13CO (3-2), HCO^+
and HCN using HHSMT, Arizona 12-Meter Telescope, APEX and MOPRA Telescope. The
widths of the broad lines are 15-30 kms, and the detection of such broad lines
is unambiguous, dynamic evidence showing that the SNR G357.7+0.3 is interacting
with molecular clouds. The broad lines appear in extended regions (>4.5'x5').
We also present detection of shocked H2 emission in mid-infrared but lacking
ionic lines using the Spitzer IRS observations to map a few arcmin area. The H2
excitation diagram shows a best-fit with a two-temperature LTE model with the
temperatures of ~200 and 660 K. We observed [C II] at 158um and high-J
CO(11-10) with the GREAT on SOFIA. The GREAT spectrum of [C II], a 3 sigma
detection, shows a broad line profile with a width of 15.7 km/s that is similar
to those of broad CO molecular lines. The line width of [C~II] implies that
ionic lines can come from a low-velocity C-shock. Comparison of H2 emission
with shock models shows that a combination of two C-shock models is favored
over a combination of C- and J-shocks or a single shock. We estimate the CO
density, column density, and temperature using a RADEX model. The best-fit
model with n(H2) = 1.7x10^{4} cm^{-3}, N(CO) = 5.6x10^{16} cm^{-2}, and T = 75
K can reproduce the observed millimeter CO brightnesses.


  August 1 to November 15, 2016 period was characterized by the presence of
Corotating Interaction Regions (CIRs) and a few weak Coronal Mass Ejections
(CMEs) in the heliosphere. In this study we show recurrent energetic electron
and proton enhancements observed near Earth (1 AU) and Mars (1.43-1.38 AU)
during this period. The observations near Earth are using data from instruments
aboard ACE, SOHO, and SDO whereas those near Mars are by the SEP, SWIA, and MAG
instruments aboard MAVEN. During this period, the energetic electron fluxes
observed near Earth and Mars showed prominent periodic enhancements over four
solar rotations, with major periodicities of ~27 days and ~13 days. Periodic
radar blackout/weakening of radar signals at Mars are observed by MARSIS/MEX,
associated with these solar energetic electron enhancements. During this
period, a weak CME and a High Speed Stream (HSS)-related interplanetary shock
could interact with the CIR and enhance energetic proton fluxes near 1.43-1.38
AU, and as a result, ~27 day periodicity in proton fluxes is significantly
diminished at 1.43-1.38 AU. These events also cause unexpected impact on the
Martian topside ionosphere, such as topside ionospheric depletion and
compression observed by LPW and NGIMS onboard MAVEN. These observations are
unique not only because of the recurring nature of electron enhancements seen
at two vantage points, but also because they reveal unexpected impact of the
weak CME and interplanetary shock on the Martian ionosphere, which provide new
insight into the impact of CME-HSS interactions on Martian plasma environment.


  In this paper, we consider the normalized least squares estimator of the
parameter in a mildly-explosive first-order autoregressive model with dependent
errors which are modeled as a mildly-explosive AR(1) process. We prove that the
estimator has a Cauchy limit law which provides a bridge between moderate
deviation asymptotics and the earlier results on the local to unity and
explosive autoregressive models. In particular, the results can be applied to
understand the near-integrated second order autoregressive processes.
Simulation studies are also carried out to assess the performance of least
squares estimation in finite samples.


  The collective variable representation (CV) of classical statistical systems
such as simple liquids has been intensively developed by the Ukrainian school
after seminal works by Prof. Ihor Yukhnovskii. The basis and the structure of
the CV representation are reexamined here from the point of view of statistical
field theory and compared with another exact statistical field representation
of liquids based upon a Hubbard-Stratonovich transform. We derive a two-loop
expansion for the grand potential and free energy of a simple fluid in both
version of the theory. The results obtained by the two approaches are shown to
coincide at each order of the loop expansion. The one-loop results are
identical to those obtained in the framework of the random phase approximation
of the theory of liquids. However, at the second-loop level, new expressions
for the pressure and the free energy are obtained, yielding a new type of
approximation.


  We study the concept of the fourth skein module of 3-manifolds, that is a
skein module based on the skein relation b_0L_0 + b_1L_1 + b_2L_2 + b_3L_3 = 0
and a framing relation L^{(1)} = aL, where a, b_0, b_3 are invertible. We
introdule the concept of n-algebraic tangles (and links) and analyze the skein
module for 3-algebraic links. As a byproduct we prove the Montesinos-Nakanishi
3-move conjecture for 3-algebraic links (including 3-bridge links).


  We study Campana points on biequivariant compactifications of the Heisenberg
group and confirm the log Manin conjecture introduced by Pieropan, Smeets,
Tanimoto and V\'{a}rilly-Alvarado.


  Conformational properties of star-shaped polymer aggregates that carry
attractive end-groups, called telechelic star polymers, are investigated by
simulation and analytical variational theory. We focus on the case of low
telechelic star polymer functionalities, $f \leq 5$, a condition which allows
aggregation of all attractive monomers on one site. We establish the
functionality- and polymerization-number dependence of the transition
temperature from the "star burst'' to the "water melon" macroparticle
structure. Extensions to telechelic stars featuring partially collapsed
configurations are also discussed.


  We show that the class of representable substitution algebras is
characterized by a set of universal first order sentences. In addition, it is
shown that a necessary and sufficient condition for a substitution algebra to
be representable is that it is embeddable in a substitution algebra in which
elements are distinguished. Furthermore, conditions in terms of neat embeddings
are shown to be equivalent to representability.


  We introduce a new parameterization of $B\rightarrow D \pi \ell \nu$ form
factors using a partial-wave expansion and derive bounds on the series
coefficients using analyticity and unitarity. This is the first generalization
of the model-independent formalism developed by Boyd, Grinstein, and Lebed for
$B \to D \ell \nu$ to semileptonic decays with multi-hadron final states, and
enables data-driven form factor determinations with robust,
systematically-improvable uncertainties. Using this formalism, we extract the
form-factor parameters for $B \to D_2^\ast(\to D\pi) \ell \nu$ decays in a
model-independent way from fits of data from the Belle Experiment, and, for the
first time, study the two-pole structure in the $D\pi$ S-wave in semileptonic
decays employing lineshapes from unitarized chiral perturbation theory.


  Cross-domain CTR (CDCTR) prediction is an important research topic that
studies how to leverage meaningful data from a related domain to help CTR
prediction in target domain. Most existing CDCTR works design implicit ways to
transfer knowledge across domains such as parameter-sharing that regularizes
the model training in target domain. More effectively, recent researchers
propose explicit techniques to extract user interest knowledge and transfer
this knowledge to target domain. However, the proposed method mainly faces two
issues: 1) it usually requires a super domain, i.e. an extremely large source
domain, to cover most users or items of target domain, and 2) the extracted
user interest knowledge is static no matter what the context is in target
domain. These limitations motivate us to develop a more flexible and efficient
technique to explicitly transfer knowledge. In this work, we propose a
cross-domain augmentation network (CDAnet) being able to perform explicit
knowledge transfer between two domains. Specifically, CDAnet contains a
designed translation network and an augmentation network which are trained
sequentially. The translation network computes latent features from two domains
and learns meaningful cross-domain knowledge of each input in target domain by
using a designed cross-supervised feature translator. Later the augmentation
network employs the explicit cross-domain knowledge as augmented information to
boost the target domain CTR prediction. Through extensive experiments on two
public benchmarks and one industrial production dataset, we show CDAnet can
learn meaningful translated features and largely improve the performance of CTR
prediction. CDAnet has been conducted online A/B test in image2product
retrieval at Taobao app, bringing an absolute 0.11 point CTR improvement, a
relative 0.64% deal growth and a relative 1.26% GMV increase.


  This is the first of the series of papers which present a precision method of
the day-by-day monitoring of the absolute LHC luminosity. The method is based
on the measurement of the rate of coplanar lepton pairs produced in peripheral
collisions of the beams' particles. In the present paper we evaluate the
modeling precision of the lepton pair production processes in proton-proton
collisions, optimize the measurement region to achieve better than 1% accuracy
of the predicted rates, and discuss the experimental challenges to filter out
the luminosity monitoring lepton pairs at LHC.


  A theorem due to D. Bernstein states that Euler characteristic of a
hypersurface defined by a polynomial f in (C\{0})^n is equal (upto a sign) to
n! times volume of the Newton polyhedron of f. This result is related to
algebaric torus actions and toric varieties. In this thesis, I prove that one
can generalize the above result to actions of reductive groups with spherical
orbits. That is, if a reductive group acts linearly on a vector space such that
generic orbits are spherical, one can compute the Euler characteristic of
generic hyperplane sections of a generic orbit in terms of combinatorial data.
  Our main tool is Morse theory. We begin with developing a variant of
classical Morse theory for algebraic submanifolds of R^n and linear
functionals. This will become related to stratification theory of Thom and
Whitney as well as Palais-Smale generalized Morse theory.


  This paper presents a comprehensive study of post-mortem human iris
recognition carried out for 1,200 near-infrared and 1,787 visible-light samples
collected from 37 deceased individuals kept in the mortuary conditions. We used
four independent iris recognition methods (three commercial and one academic)
to analyze genuine and impostor comparison scores and check the dynamics of
iris quality decay over a period of up to 814 hours after death. This study
shows that post-mortem iris recognition may be close-to-perfect approximately 5
to 7 hours after death and occasionally is still viable even 21 days after
death. These conclusions contradict the statements present in past literature
that the iris is unusable as a biometrics shortly after death, and show that
the dynamics of post-mortem changes to the iris that are important for
biometric identification are more moderate than previously hypothesized. The
paper contains a thorough medical commentary that helps to understand which
post-mortem metamorphoses of the eye may impact the performance of automatic
iris recognition. We also show that post-mortem iris recognition works equally
well for images taken in near-infrared and when the red channel of
visible-light sample is used. However, cross-wavelength matching presents
significantly worse performance. This paper conforms to reproducible research
and the database used in this study is made publicly available to facilitate
research of post-mortem iris recognition. To our knowledge, this paper offers
the most comprehensive evaluation of post-mortem iris recognition and the
largest database of post-mortem iris images.


  In this paper we have investigated an LRS Bianchi I anisotropic cosmological
model of the universe by taking time varying $G$ and $\Lambda$ in the presence
of bulk viscous fluid source described by full causal non-equilibrium
thermodynamics. We obtain a cosmological constant as a decreasing function of
time and for $m, n > 0$, the value of cosmological ``constant'' for this model
is found to be small and positive which is supported by the results from recent
supernovae observations.


  The holographic duals of higher spin theories on AdS_3 are described by the
large N limit of a family of minimal model CFTs, whose symmetry algebra is
equivalent to W(infinity)[lambda]. We study perturbations of these limit
theories, and show that they possess a marginal symmetry-preserving
perturbation that describes switching on the 1/N corrections. We also test our
general results for the specific cases of lambda=0,1, where free field
realisations are available.


  Experiment{Fabre_1983} shows that Rydberg atoms do not pass through 1
micronmeter width slits if their principal quantum number is rather large(n >
60). Thus, the particle density measured after the slits is null while the wave
function calculated after the slits is not. This experiment is in contradiction
with the Born interpretation (the square of the wave function is proportional
to the probability density for the particle to be found at each point in
space). The classical interpretation of this experiment, which removes the
contradiction, is to suppose that if the particles do not pass, the wave
function does not pass either (classical assumption).
  An alternative interpretation of this experiment is to suppose that the wave
function passes through the slits, but that the Born interpretation is not
valid any more in this case (alternative assumption).
  The aim of this paper is to present an experiment testing this alternative
assumption compared to the classical assumption.


  We address a question and a conjecture on the expected length of the longest
common subsequences of two i.i.d.$\ $random permutations of
$[n]:=\{1,2,...,n\}$. The question is resolved by showing that the minimal
expectation is not attained in the uniform case. The conjecture asserts that
$\sqrt{n}$ is a lower bound on this expectation, but we only obtain
$\sqrt[3]{n}$ for it.


  In this article we study Weinstein structures endowed with a Lefschetz
fibration in terms of the Legendrian front projection. First we provide a
systematic recipe for translating from a Weinstein Lefschetz bifibration to a
Legendrian handlebody. Then we present several applications of this technique
to symplectic topology. This includes the detection of flexibility and rigidity
for several families of Weinstein manifolds and the existence of closed exact
Lagrangian submanifolds. In addition, we prove that the Koras--Russell cubic is
Stein deformation equivalent to affine complex 3-space and verify the affine
parts of the algebraic mirrors of two Weinstein 4-manifolds.


  We propose a perturbative method to estimate the normalization constant in
exponential random graph models as the weighting parameters approach infinity.
As an application, we give evidence of discontinuity in natural parametrization
along the critical directions of the edge-triangle model.


  It has been experimentally established that magnetic adatoms on surfaces can
be arranged to form antiferromagnetic quantum spin chains with strong uniaxial
anisotropy and Neel states in such spin systems can be used to realize
information storage. Here, we investigate eigen states, quantum spin dynamics,
and life times of Neel states in short antiferromagnetic quantum spin chains
with strong uniaxial anisotropy on the basis of numerical exact diagonalization
method. We show rigorously that as long as the uniaxial anisotropy is very
strong, the ground state and the first excitation state, being nearly
degenerate, are safely separated from the other states and thus dominate the
quantum dynamics of the Neel states. Through further numerical analysis, we
achieve a powerful life-time expression of the Neel states for arbitrary spin
and model parameters. It is interesting that for the famous Fe adatom chains on
Cu$_2$N surface, 14 or 16 Fe adatoms are enough to obtain a practical long
life-time for Neel state storage of information. These should be applicable to
other similar antiferromagnetic spin systems for atomic-scale antiferromagnetic
spintronics.


  We comment on the puzzling status of the proton magnetic radius
determinations.


  Let $(G,\tau)$ be a finite-dimensional Lie group with an involutive
automorphism $\tau$ of $G$ and let $\mathfrak g = \mathfrak h \oplus \mathfrak
q $ be its corresponding Lie algebra decomposition. We show that every
non-degenerate strongly continuous representation on a complex Hilbert space
$\mathcal H$ of an open $*$-subsemigroup $S \subset G$, where $s^* =
\tau(s)^{-1}$, has an analytic extension to a strongly continuous unitary
representation of the 1-connected Lie group $G_1^c$ with Lie algebra
$[\mathfrak q,\mathfrak q] \oplus i\mathfrak q$.
  We further examine the minimal conditions under which an analytic extension
to the 1-connected Lie group $G^c$ with Lie algebra $\mathfrak h \oplus
i\mathfrak q$ exists. This result generalizes the L\"uscher-Mack Theorem and
the extensions of the L\"uscher-Mack Theorem for $*$-subsemigroups satisfying
$S = S(G^\tau)_0$ by Merigon, Neeb, and \'Olafsson.
  Finally, we prove that non-degenerate strongly continuous representations of
certain $*$-subsemigroups $S$ can even be extended to representations of a
generalized version of an Olshanski semigroup.


  Pd$_{81}$Ge$_{19}$ metallic glass was investigated by neutron diffraction,
X-ray diffraction and extended X-ray absorption fine structure spectroscopy
(EXAFS) at the Ge K-edge. Large scale structural models were obtained by
fitting the three measurements simultaneously in the framework of the reverse
Monte Carlo simulation technique. It was found that the experimental data sets
can be adequately fitted without Ge-Ge nearest neighbours. Mean Pd-Pd and Pd-Ge
distances are 2.80$\pm$0.02 {\AA} and 2.50$\pm$0.02 {\AA}, respectively. The
total average coordination number of Pd is 12.1$\pm$0.5 while Ge is surrounded
by 10.6$\pm$1.1 Pd atoms. The coordination numbers calculated from partial pair
correlation functions were compared to those obtained by Voronoi tessellation
method. It was found that the latter technique overestimates the number of
nearest neighbours by about 20% due to the significant contribution of distant
pairs.


  Tor is a popular low-latency anonymous communication system that focuses on
usability and performance: a faster network will attract more users, which in
turn will improve the anonymity of everyone using the system. The standard
practice for previous research attempting to enhance Tor performance is to draw
conclusions from the observed results of a single simulation for standard Tor
and for each research variant. But because the simulations are run in sampled
Tor networks, it is possible that sampling error alone could cause the observed
effects. Therefore, we call into question the practical meaning of any
conclusions that are drawn without considering the statistical significance of
the reported results.
  In this paper, we build foundations upon which we improve the Tor
experimental method. First, we present a new Tor network modeling methodology
that produces more representative Tor networks as well as new and improved
experimentation tools that run Tor simulations faster and at a larger scale
than was previously possible. We showcase these contributions by running
simulations with 6,489 relays and 792k simultaneously active users, the largest
known Tor network simulations and the first at a network scale of 100%. Second,
we present new statistical methodologies through which we: (i) show that
running multiple simulations in independently sampled networks is necessary in
order to produce informative results; and (ii) show how to use the results from
multiple simulations to conduct sound statistical inference. We present a case
study using 420 simulations to demonstrate how to apply our methodologies to a
concrete set of Tor experiments and how to analyze the results.


  Obtaining large pre-trained models that can be fine-tuned to new tasks with
limited annotated samples has remained an open challenge for medical imaging
data. While pre-trained deep networks on ImageNet and vision-language
foundation models trained on web-scale data are prevailing approaches, their
effectiveness on medical tasks is limited due to the significant domain shift
between natural and medical images. To bridge this gap, we introduce LVM-Med,
the first family of deep networks trained on large-scale medical datasets. We
have collected approximately 1.3 million medical images from 55 publicly
available datasets, covering a large number of organs and modalities such as
CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art
self-supervised algorithms on this dataset and propose a novel self-supervised
contrastive learning algorithm using a graph-matching formulation. The proposed
approach makes three contributions: (i) it integrates prior pair-wise image
similarity metrics based on local and global information; (ii) it captures the
structural constraints of feature embeddings through a loss function
constructed via a combinatorial graph-matching objective; and (iii) it can be
trained efficiently end-to-end using modern gradient-estimation techniques for
black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream
medical tasks ranging from segmentation and classification to object detection,
and both for the in and out-of-distribution settings. LVM-Med empirically
outperforms a number of state-of-the-art supervised, self-supervised, and
foundation models. For challenging tasks such as Brain Tumor Classification or
Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models
trained on 1 billion masks by 6-7% while using only a ResNet-50.


  In many applications of natural language processing (NLP) it is necessary to
determine the likelihood of a given word combination. For example, a speech
recognizer may need to determine which of the two word combinations ``eat a
peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine
the likelihood of a word combination from its frequency in a training corpus.
However, the nature of language is such that many word combinations are
infrequent and do not occur in any given corpus. In this work we propose a
method for estimating the probability of such previously unseen word
combinations using available information on ``most similar'' words.
  We describe probabilistic word association models based on distributional
word similarity, and apply them to two tasks, language modeling and pseudo-word
disambiguation. In the language modeling task, a similarity-based model is used
to improve probability estimates for unseen bigrams in a back-off language
model. The similarity-based method yields a 20% perplexity improvement in the
prediction of unseen bigrams and statistically significant reductions in
speech-recognition error.
  We also compare four similarity-based estimation methods against back-off and
maximum-likelihood estimation methods on a pseudo-word sense disambiguation
task in which we controlled for both unigram and bigram frequency to avoid
giving too much weight to easy-to-disambiguate high-frequency configurations.
The similarity-based methods perform up to 40% better on this particular task.


  A basis of Bloch waves, distorted locally by the random potential, is
introduced for electrons in the Anderson model. Matrix elements of the
Hamiltonian between these distorted waves are averages over infinite numbers of
independent site-energies, and so take definite values rather than
distributions of values. The transformed Hamiltonian is ordered, and may be
interpreted as an itinerant electron interacting with a spin on each site. In
this new basis, the distinction between extended and localized states is clear,
and edges of the bands of extended states, the mobility edges, are calculated
as a function of disorder. In two dimensions these edges have been found in
both analytic and numerical applications of tridiagonalization, but they have
not been found in analytic approaches based on perturbation theory, or the
single-parameter scaling hypothesis; nor have they been detected in numerical
approaches based on scaling or critical distributions of level spacing. In both
two and three dimensions the mobility edges in this work are found to separate
with increasing disorder for all disorders, in contrast with the results of
calculation using numerical scaling for three dimensions. The analytic
trajectories are compared with recent results of numerical tridiagonalization
on samples of over 10^9 sites. This representation of the Anderson model as an
ordered interacting system implies that in addition to transitions at mobility
edges, the Anderson model contains weaker transitions characterized by critical
disorders where the band of extended states decouples from individual sites;
and that singularities in the distribution of site energies, rather than its
second moment, determine localization properties of the Anderson model.


  Birefringence of light induced not by matter, but by the gradient of an
electric field, has been predicted in 1955 and observed in 2008. Here we
replace the electric field by the gravitional field of our expanding universe.


  Circadian rhythmicity lies at the center of various important physiological
and behavioral processes in mammals, such as sleep, metabolism, homeostasis,
mood changes and more. It has been shown that this rhythm arises from
self-sustained biomolecular oscillations of a neuronal network located in the
Suprachiasmatic Nucleus (SCN). Under normal circumstances, this network remains
synchronized to the day-night cycle due to signaling from the retina.
Misalignment of these neuronal oscillations with the external light signal can
disrupt numerous physiological functions and take a long-lasting toll on health
and well-being. In this work, we study a modern computational neuroscience
model to determine the limits of circadian synchronization to external light
signals of different frequency and duty cycle. We employ a matrix-free approach
to locate periodic steady states of the high-dimensional model for various
driving conditions. Our algorithmic pipeline enables numerical continuation and
construction of bifurcation diagrams w.r.t. forcing parameters. We
computationally explore the effect of heterogeneity in the circadian neuronal
network, as well as the effect of corrective therapeutic interventions, such as
that of the drug molecule Longdaysin. Lastly, we employ unsupervised learning
to construct a data-driven embedding space for representing neuronal
heterogeneity.