  Located 2 km underground in SNOLAB, Sudbury, Canada, SNO+ is a large scale
liquid scintillator experiment that primarily aims to search for neutrinoless
double beta decay. Whilst SNO+ has light and radioactive calibration sources
external to the inner volume, an internally deployed optical source is
necessary for the full characterization of the detector model. A laser diffuser
ball developed for SNO has previously demonstrated to be an effective optical
calibration device for both SNO and SNO+ water phase. Since the introduction of
liquid scintillator for SNO+, the material compatibility, cleanliness, and
radiopurity requirements of any materials in contact with the internal medium
have increased. Improving on the original SNO laserball design, a new laserball
calibration device has been developed for the SNO+ scintillator phase with the
goal of measuring the optical properties of the detector and performing routine
PMT gain and timing calibrations. Simulations have been written to model the
diffusion properties to optimise optical and temporal performance for
calibration. Prototype laserballs have been built and characterised,
demonstrating sub-ns timing resolution and a quasi-isotropic light distribution


  To evaluate a single cause of a binary effect, Dawid et al. (2014) defined
the probability of causation, while Pearl (2015) defined the probabilities of
necessity and sufficiency. For assessing the multiple correlated causes of a
binary effect, Lu et al. (2023) defined the posterior causal effects based on
post-treatment variables. In many scenarios, outcomes are continuous, simply
binarizing them and applying previous methods may result in information loss or
biased conclusions. To address this limitation, we propose a series of
posterior causal estimands for retrospectively evaluating multiple correlated
causes from a continuous effect, including posterior intervention effects,
posterior total causal effects, and posterior natural direct effects. Under the
assumptions of sequential ignorability, monotonicity, and perfect positive
rank, we show that the posterior causal estimands of interest are identifiable
and present the corresponding identification equations. We also provide a
simple but effective estimation procedure and establish the asymptotic
properties of the proposed estimators. An artificial hypertension example and a
real developmental toxicity dataset are employed to illustrate our method.


  Our research is part of a wider project that aims to investigate and reason
about the correctness of scheme-based source code transformations of Erlang
programs. In order to formally reason about the definition of a programming
language and the software built using it, we need a mathematically rigorous
description of that language.
  In this paper, we present our proof-assistant-based formalisation of a subset
of Erlang, intended to serve as a base for proving refactorings correct. After
discussing how we reused concepts from related work, we show the syntax and
semantics of our formal description, including the abstractions involved (e.g.
closures). We also present essential properties of the formalisation (e.g.
determinism) along with their machine-checked proofs. Finally, we prove the
correctness of some simple refactoring strategies.


  Serverless Function-as-a-Service (FaaS) platforms provide applications with
resources that are highly elastic, quick to instantiate, accounted at fine
granularity, and without the need for explicit runtime resource orchestration.
This combination of the core properties underpins the success and popularity of
the serverless FaaS paradigm. However, these benefits are not available to most
cloud applications because they are designed for networked virtual
machines/containers environments. Since such cloud applications cannot take
advantage of the highly elastic resources of serverless and require run-time
orchestration systems to operate, they suffer from lower resource utilization,
additional management complexity, and costs relative to their FaaS serverless
counterparts.
  We propose Imaginary Machines, a new serverless model for cloud applications.
This model (1.) exposes the highly elastic resources of serverless platforms as
the traditional network-of-hosts model that cloud applications expect, and (2.)
it eliminates the need for explicit run-time orchestration by transparently
managing application resources based on signals generated during cloud
application executions. With the Imaginary Machines model, unmodified cloud
applications become serverless applications. While still based on the
network-of-host model, they benefit from the highly elastic resources and do
not require runtime orchestration, just like their specialized serverless FaaS
counterparts, promising increased resource utilization while reducing
management costs.


  We study the number of real roots of a Kostlan random polynomial of degree
$d$ in one variable. More generally, we are interested in the distribution of
the counting measure of the set of real roots of such a polynomial. We compute
the asymptotics of the central moments of any order of these random variables,
in the large degree limit. As a consequence, we prove that these quantities
satisfy a strong Law of Large Numbers and a Central Limit Theorem. In
particular, the real roots of a Kostlan polynomial almost surely equidistribute
as the degree diverges. Moreover, the fluctuations of the counting measure of
this random set around its mean converge in distribution to the Gaussian White
Noise. More generally, our results hold for the real zeros of a random real
section of a line bundle of degree d over a real projective curve, in the
complex Fubini--Study model.


  Matrix elements of intertwining operators between $q$-Wakimoto modules
associated to the tensor product of representations of $U_q(\widehat{sl_2})$
with arbitrary spins are studied. It is shown that they coincide with the
Tarasov-Varchenko's formulae of the solutions of the qKZ equations. The result
generalizes that of the previous paper [Kuroki K., Nakayashiki A., SIGMA 4
(2008), 049, 13 pages, arXiv:0802.1776].


  Magnetic atoms on superconductors induce localized Yu-Shiba-Rusinov (YSR)
bound states. The proposal that topological superconductivity and Majorana
modes can be engineered in arrays of hybridizing YSR states has led to their
intense investigation. Here, we study Majorana modes emerging from bands of
hybridized YSR states in artificially constructed Manganese (Mn) chains on
superconducting Niobium (Nb). By controlling the chain geometry on the single
atom level, we can measure the interaction-induced energy splitting of Majorana
modes from both chain's ends with increasing chain length. We find periodic
lengths where their interaction is tuned to zero within the experimental energy
resolution. Our work unravels ways to manipulate and minimize interactions
between Majorana modes in finite-size systems as required for Majorana-based
storage and processing of quantum information.


  In this paper we characterize compactness of the canonical solution operator
to d-bar on weighted L^2 spaces on C. For this purpose we consider certain
Schr\"odinger operators with magnetic fields and use a condition which is
equivalent to the property that these operators have compact resolvents.


  We theoretically demonstrate a spontaneous spin superconductor (SC) state in
ABCA-stacked tetralayer graphene, under sequential effects of electron-electron
(e-e) and electron-hole (e-h) interactions. First of all, we examine the
ferromagnetic (FM) exchange instability and phase diagram of the system induced
by the long-range e-e interaction. At non- or low-doping levels, the
interaction trends to stabilize a FM phase with the coexisting electron and
hole carriers. Superior to bilayer and trilayer systems, tetralayer graphene
has a larger FM phase region and spin splitting, making it more advantageous to
realize the spin SC state. Subsequently, we prove that the FM phase becomes
unstable when attractive e-h interaction is considered. As a consequence, the
spin SC state can be spontaneously formed at low temperature, where
spin-triplet exciton pairs act as the equivalent of Cooper pairs. We further
develop a consistent BCS-type theory for the spin SC state in ABCA-stacked
graphene. The predicted spin superconducting gap can reach about $7.0$ meV,
with a critical temperature of about 45 K for non-doping system. At last, we
demonstrated a spin-current Josephson effect in the ABCA-stacked graphene spin
SC heterojunction. Our findings enrich the prospective spin SC candidate
materials, illuminating more possibilities for achieving non-dissipative
super-spintronics.


  The ability to form non-line-of-sight (NLOS) images of changing scenes could
be transformative in a variety of fields, including search and rescue,
autonomous vehicle navigation, and reconnaissance. Most existing active NLOS
methods illuminate the hidden scene using a pulsed laser directed at a relay
surface and collect time-resolved measurements of returning light. The
prevailing approaches include raster scanning of a rectangular grid on a
vertical wall opposite the volume of interest to generate a collection of
confocal measurements. These are inherently limited by the need for laser
scanning. Methods that avoid laser scanning track the moving parts of the
hidden scene as one or two point targets. In this work, based on more complete
optical response modeling yet still without multiple illumination positions, we
demonstrate accurate reconstructions of objects in motion and a 'map' of the
stationary scenery behind them. The ability to count, localize, and
characterize the sizes of hidden objects in motion, combined with mapping of
the stationary hidden scene, could greatly improve indoor situational awareness
in a variety of applications.


  A direct and exact method for calculating the density of states for systems
with localized potentials is presented. The method is based on explicit
inversion of the operator $E-H$. The operator is written in the discrete
variable representation of the Hamiltonian, and the Toeplitz property of the
asymptotic part of the obtained {\it infinite} matrix is used. Thus, the
problem is reduced to the inversion of a {\it finite} matrix.


  We discuss the seminal three-step model and the recollision picture in the
context of high harmonic generation in molecules. In particular, we stress the
importance of multi-electron correlation during the first and the third of the
three steps of the process: (1) the strong field ionization and (3) the
recombination. We point out how accurate account for multi-electron
correlations during the third, recombination, step allows one to gauge the
importance of pre-collision: the term coined by J. H. Eberly to describe
unusual pathways during the first, ionization, step.


  The dc-magnetization of the unique S=1/2 kagome antiferromagnet
Herbertsmithite has been measured down to 0.1K. No sign of spin freezing is
observed in agreement with former muSR and ac-susceptibility results. The low
temperature magnetic response is dominated by a defect contribution which
exhibits a new energy scale $\simeq 1$ K, likely reflecting the coupling of the
defects. The defect component is saturated at low temperature by H>8T applied
magnetic fields which enables us to estimate an upper bound for the non
saturated intrinsic kagome susceptibility at T=1.7K.


  It is known that greedy methods perform well for maximizing monotone
submodular functions. At the same time, such methods perform poorly in the face
of non-monotonicity. In this paper, we show - arguably, surprisingly - that
invoking the classical greedy algorithm $O(\sqrt{k})$-times leads to the
(currently) fastest deterministic algorithm, called Repeated Greedy, for
maximizing a general submodular function subject to $k$-independent system
constraints. Repeated Greedy achieves $(1 + O(1/\sqrt{k}))k$ approximation
using $O(nr\sqrt{k})$ function evaluations (here, $n$ and $r$ denote the size
of the ground set and the maximum size of a feasible solution, respectively).
We then show that by a careful sampling procedure, we can run the greedy
algorithm only once and obtain the (currently) fastest randomized algorithm,
called Sample Greedy, for maximizing a submodular function subject to
$k$-extendible system constraints (a subclass of $k$-independent system
constrains). Sample Greedy achieves $(k + 3)$-approximation with only $O(nr/k)$
function evaluations. Finally, we derive an almost matching lower bound, and
show that no polynomial time algorithm can have an approximation ratio smaller
than $ k + 1/2 - \varepsilon$. To further support our theoretical results, we
compare the performance of Repeated Greedy and Sample Greedy with prior art in
a concrete application (movie recommendation). We consistently observe that
while Sample Greedy achieves practically the same utility as the best baseline,
it performs at least two orders of magnitude faster.


  It has been shown that a magnetic field proportional to angular velocity of
rotation $\omega$ arises around a rotating conductor with the radial gradient
of temperature $\nabla_r T$. However, the theoretical value of the
proportionality coefficient $10^{-17} {\rm cm}^{5/2}\cdot {\rm g}^{1/2}\cdot
{\rm deg}^{-1}$ does not coincide with the experimental one $10^{-8} {\rm
cm}^{5/2}\cdot {\rm g}^{1/2}\cdot {\rm deg}^{-1}$ [1]. At least two additional
mechanisms act to produce an observable magnetic field in the experiment [1].
First, the fact that the rotation of the conductor is slowed down and second,
the nonstationarity of the temperature gradient during the measurements
contribute to the vertical magnetic field. The value of the magnetic flux
arising from the azimuthal current induced by the ``Coriolis force'' effect on
the thermoelectric radial current, is in good agreement with the experimental
data [1].


  We apply the worldline formalism and its numerical Monte-Carlo approach to
computations of fluctuation induced energy-momentum tensors. For the case of a
fluctuating Dirichlet scalar, we derive explicit worldline expressions for the
components of the canonical energy-momentum tensor that are straightforwardly
accessible to partly analytical and generally numerical evaluation. We present
several simple proof-of-principle examples, demonstrating that efficient
numerical evaluation is possible at low cost. Our methods can be applied to an
investigation of positive-energy conditions.


  Compressive sensing (CS) can effectively recover a signal when it is sparse
in some discrete atoms. However, in some applications, signals are sparse in a
continuous parameter space, e.g., frequency space, rather than discrete atoms.
Usually, we divide the continuous parameter into finite discrete grid points
and build a dictionary from these grid points. However, the actual targets may
not exactly lie on the grid points no matter how densely the parameter is
grided, which introduces mismatch between the predefined dictionary and the
actual one. In this article, a novel method, namely adaptive matching pursuit
with constrained total least squares (AMP-CTLS), is proposed to find actual
atoms even if they are not included in the initial dictionary. In AMP-CTLS, the
grid and the dictionary are adaptively updated to better agree with
measurements. The convergence of the algorithm is discussed, and numerical
experiments demonstrate the advantages of AMP-CTLS.


  Proton and neutron densities have been obtained for the even-even isotopes of
Sn from $^{100}$Sn to $^{176}$Sn using a Hartree-Fock-Bogoliubov model with a
Skyrme interaction. The matter densities so defined have been used with
realistic nucleon-nucleon interactions in a folding model to specify optical
potentials for the elastic scattering of protons with energies in the range 40
to 200 MeV. Those potentials have been used to make predictions of the
differential cross sections and spin observables for proton scattering. As the
target mass increases, the emergence of the neutron skin in the Sn isotopes is
revealed by marked effects in the differential cross section. Comparisons with
available data show how similar scattering data for the neutron-rich isotopes
may provide constraints for the model structures.


  Electron density irregularities in the ionosphere are known to be
magnetically anisotropic, preferentially elongated along the lines of force.
While many studies of their morphology have been undertaken by topside sounding
and whistler measurements, it is only recently that detailed regional-scale
reconstructions have become possible, enabled by the advent of widefield radio
telescopes. Here we present a new approach for visualising and studying
field-aligned irregularities (FAIs), which involves transforming
interferometric measurements of TEC gradients onto a magnetic shell tangent
plane. This removes the perspective distortion associated with the oblique
viewing angle of the irregularities from the ground, facilitating the
decomposition of dynamics along and across magnetic field lines. We apply this
transformation to the dataset of Loi et al. [2015a], obtained on 15 October
2013 by the Murchison Widefield Array (MWA) radio telescope and displaying
prominent FAIs. We study these FAIs in the new reference frame, quantifying
field-aligned and field-transverse behaviour, examining time and altitude
dependencies, and extending the analysis to FAIs on sub-array scales. We show
that the inclination of the plane can be derived solely from the data, and
verify that the best-fit value is consistent with the known magnetic
inclination. The ability of the model to concentrate the fluctuations along a
single spatial direction may find practical application to future calibration
strategies for widefield interferometry, by providing a compact representation
of FAI-induced distortions.


  The origin of collisionless cold dark matter has been traced back to its
origin to the supersolid model of quantum vacuum.


  We applied complex network analysis to ~27,000 tweets posted by the 2016
presidential election's principal participants in the USA. We identified the
stages of the election campaigns and the recurring topics addressed by the
candidates. Finally, we revealed the leader-follower relationships between the
candidates. We conclude that Secretary Hillary Clinton's Twitter performance
was subordinate to that of Donald Trump, which may have been one factor that
led to her electoral defeat.


  In the strictly periodic setting, the electric polarization of
inversion-symmetric solids with and without time-reversal symmetry and the
isotropic magneto-electric response function of time-reversal symmetric
insulators are known to be topological invariants displaying an exact $\mathbb
Z_2$ quantization. This quantization is stabilized by the symmetries. In the
present work, we investigate the fate of such symmetry-stabilized topological
invariants in the presence of a disorder which breaks the symmetries but
restores them on average. Using a rigorous analysis, we conclude that the
strict quantization still holds in these conditions. Numerical calculations
confirm this prediction.


  In this paper we analyze theoretically how the introduction of the third
component into the two-dimensional photonic crystal influences the photonic
band structure and the density-of-states of the system. We consider the
periodic array of cylindrical air rods in a dielectric, and the third medium is
introduced as a ring-shaped intermediate layer between the air pores and the
dielectric background. Using the plane wave method, we have obtained the band
structures for the 2D triangular lattice photonic crystals. The dependencies of
TE and TM band gaps' widths and gaps' edges position on the interlayer
dielectric constant and interlayer thickness were analyzed. In the framework of
this approach, we have estimated the influence of the surface oxide layer on
the band structure of macroporous silicon. We observed the shift of the gaps'
edges to the higher or lower frequencies, depending on the interlayer thickness
and dielectric constant. We have shown that the existence of a native oxide
surface layer should be taken into consideration to understand the optical
properties of 2D photonic crystals, particularly in macroporous silicon
structures.


  In this letter, we have investigated the third-order optical nonlinearities
of high-quality Ni doped ZnO nanorods crystallized in wurtzite lattice,
prepared by the wet chemical method. In our experiments, we found that the two
photon absorption coefficient beta increases by as much as 14 times i.e. 7.6
err 0.4 to 112 err 6 cm per GW, when the Ni doping is increased from 0 to 10
percent. The substantial enhancement in beta is discussed in terms of the
bandgap scaling and Ni doping. Furthermore, we also show that the optical
bandgap measured by UVVis and photo luminescence spectroscopic, continuously
red shift with increasing Ni doping concentration. We envision that the strong
nonlinear optical properties together with their dilute magnetic effects, they
form an important class of materials for potential applications in
magneto-optical and integrated optical chips.


  Answering complex questions that require making latent decisions is a
challenging task, especially when limited supervision is available. Recent
works leverage the capabilities of large language models (LMs) to perform
complex question answering in a few-shot setting by demonstrating how to output
intermediate rationalizations while solving the complex question in a single
pass. We introduce ``Successive Prompting'', where we iteratively break down a
complex task into a simple task, solve it, and then repeat the process until we
get the final solution. Successive prompting decouples the supervision for
decomposing complex questions from the supervision for answering simple
questions, allowing us to (1) have multiple opportunities to query in-context
examples at each reasoning step (2) learn question decomposition separately
from question answering, including using synthetic data, and (3) use bespoke
(fine-tuned) components for reasoning steps where a large LM does not perform
well. The intermediate supervision is typically manually written, which can be
expensive to collect. We introduce a way to generate a synthetic dataset which
can be used to bootstrap a model's ability to decompose and answer intermediate
questions. Our best model (with successive prompting) achieves an improvement
of ~5% absolute F1 on a few-shot version of the DROP dataset when compared with
a state-of-the-art model with the same supervision.


  Based on the distinction between the covariant and contravariant metric
tensor components in the framework of the affine geometry approach and also on
the choice of the contravariant components, it was shown that a wide variety of
third, fourth, fifth, sixth, seventh - degree algebraic equations exists in
gravity theory. This fact, together with the derivation of the algebraic
equations for a generally defined contravariant tensor components in this
paper, are important in view of finding new solutions of the Einstein's
equations, if they are treated as algebraic ones. Some important properties of
the introduced in hep-th/0107231 more general connection have been also proved
- it possesses affine transformation properties and it is an equiaffine one.
Basic and important knowledge about the affine geometry approach and about
gravitational theories with covariant and contravariant connections and metrics
is also given with the purpose of demonstrating when and how these theories can
be related to the proposed algebraic approach and to the existing theory of
gravity and relativistic hydrodynamics.


  Born in the aftermath of core collapse supernovae, neutron stars contain
matter under extraordinary conditions of density and temperature that are
difficult to reproduce in the laboratory. In recent years, neutron star
observations have begun to yield novel insights into the nature of strongly
interacting matter in the high-density regime where current theoretical models
are challenged. At the same time, chiral effective field theory has developed
into a powerful framework to study nuclear matter properties with quantified
uncertainties in the moderate-density regime for modeling neutron stars. In
this article, we review recent developments in chiral effective field theory
and focus on many-body perturbation theory as a computationally efficient tool
for calculating the properties of hot and dense nuclear matter. We also
demonstrate how effective field theory enables statistically meaningful
comparisons between nuclear theory predictions, nuclear experiments, and
observational constraints on the nuclear equation of state.


  In this article, tunable surface plasmon polaritons (SPPs) in graphene-based
elliptical waveguides containing gyro-electric layers are investigated. The
general structure has an elliptical cross-section, where each gyro-electric
layer is surrounded by two graphene layers. The DC magnetic bias is applied on
the z-axis. As a special case, a new plasma-based elliptical structure with
double-layer graphene is studied in this paper. The figure of merit (FOM) for
this waveguide can be varied by changing the magnetostatic bias and the
chemical doping. At the frequency of 40 THz, the FOM of 139 for this waveguide
is reported for the B= 1 T and {\mu}c=0.9 eV. Ability to adjust and tune the
propagating properties of SPPs in hybrid graphene-plasma elliptical structures
can be exploited for the design of new plasmonic components in the THz spectral
region.


  The fractional Laplacian $(-\Delta)^{\alpha/2}$ is a non-local operator which
depends on the parameter $\alpha$ and recovers the usual Laplacian as $\alpha
\to 2$. A numerical method for the fractional Laplacian is proposed, based on
the singular integral representation for the operator. The method combines
finite difference with numerical quadrature, to obtain a discrete convolution
operator with positive weights. The accuracy of the method is shown to be
$O(h^{3-\alpha})$. Convergence of the method is proven. The treatment of far
field boundary conditions using an asymptotic approximation to the integral is
used to obtain an accurate method. Numerical experiments on known exact
solutions validate the predicted convergence rates. Computational examples
include exponentially and algebraically decaying solution with varying
regularity. The generalization to nonlinear equations involving the operator is
discussed: the obstacle problem for the fractional Laplacian is computed.


  We report on preliminary results of our X-ray survey of the most massive
clusters currently identified from the Deep Lens Survey (DLS). The DLS cluster
sample is selected based on weak lensing shear, which makes it possible for the
first time to study clusters in a baryon-independent way. In this article we
present X-ray properties of a subset of the shear-selected cluster sample.


  Incomplete binary mixing of two components can form a heterogeneous
assemblage in space. The heterogeneity power spectrum of the assemblage can be
frequently obtained. However, it is unknown if a stochastic binary field exists
to generate the observed spectrum. We propose a novel and powerful constructive
procedure for this purpose. The procedure allows us not only to test whether
certain binary mixing is feasible but also to tightly constrain the properties
of the mixing components and the modal proportion. The method should find wide
applications in many branches of geosciences.


  We define a transverse Dolbeault cohomology associated to any almost complex
structure $j$ on a smooth manifold $M$. This we do by extending the notion of
transverse complex structure and by introducing a natural j-stable involutive
limit distribution with such a transverse complex structure. We relate this
transverse Dolbeault cohomology to the generalized Dolbeault cohomology of
(M,j) introduced by Cirici and Wilson, showing that the (p,0) cohomology spaces
coincide. This study of transversality leads us to suggest a notion of
minimally non-integrable almost complex structure.


  We give several different geometric characterizations of the situation in
which the parallel set $F_\epsilon$ of a self-similar set $F$ can be described
by the inner $\epsilon$-parallel set $T_{-\epsilon}$ of the associated
canonical tiling $\mathcal T$, in the sense of \cite{SST}. For example,
$F_\epsilon=T_{-\epsilon} \cup C_\epsilon$ if and only if the boundary of the
convex hull $C$ of $F$ is a subset of $F$, or if the boundary of $E$, the
unbounded portion of the complement of $F$, is the boundary of a convex set. In
the characterized situation, the tiling allows one to obtain a tube formula for
$F$, i.e., an expression for the volume of $F_\epsilon$ as a function of
$\epsilon$. On the way, we clarify some geometric properties of canonical
tilings.
  Motivated by the search for tube formulas, we give a generalization of the
tiling construction which applies to all self-affine sets $F$ having empty
interior and satisfying the open set condition. We also characterize the
relation between the parallel sets of $F$ and these tilings.


  Geometric algebra is a mathematical structure that is inherent in any metric
vector space, and defined by the requirement that the metric tensor is given by
the scalar part of the product of vectors. It provides a natural framework in
which to represent the classical groups as subgroups of rotation groups, and
similarly their Lie algebras. In this article we show how the geometric algebra
of a six-dimensional real Euclidean vector space naturally allows one to
construct the special unitary group on a two-qubit (quantum bit) Hilbert space,
in a fashion similar to that used in the well-established Bloch sphere model
for a single qubit. This is then used to illustrate the Cartan decompositions
and subalgebras of the four-dimensional special unitary group, which have
recently been used by J. Zhang, J. Vala, S. Sastry and K. B. Whaley [Phys. Rev.
A 67, 042313, 2003] to study the entangling capabilities of two-qubit
unitaries.


  In platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that
is responsible for managing the platoon's moving directions and velocity
periodically disseminates control messages to the vehicles that follow.
Securing wireless transmissions of the messages between the vehicles is
critical for privacy and confidentiality of platoon's driving pattern. However,
due to the broadcast nature of radio channels, the transmissions are vulnerable
to eavesdropping. In this paper, we propose a cooperative secret key agreement
(CoopKey) scheme for encrypting/decrypting the control messages, where the
vehicles in PVCPS generate a unified secret key based on the quantized fading
channel randomness. Channel quantization intervals are optimized by dynamic
programming to minimize the mismatch of keys. A platooning testbed is built
with autonomous robotic vehicles, where a TelosB wireless node is used for
onboard data processing and multi-hop dissemination. Extensive real-world
experiments demonstrate that CoopKey achieves significantly low secret bit
mismatch rate in a variety of settings. Moreover, the standard NIST test suite
is employed to verify randomness of the generated keys, where the p-values of
our CoopKey pass all the randomness tests. We also evaluate CoopKey with an
extended platoon size via simulations to investigate the effect of system
scalability on performance.


  First QCD results obtained from the CDF experiment using Run II data are
reported. The Run II physics program at the Tevatron started in the spring of
2001, with protons and anti-protons colliding at an energy of sqrt(s) = 1.96
TeV. The size of the data sample already compares to that of Run I. Results
presented here include the measurement of the inclusive jet cross section, a
search for new particles decaying to dijets, and a study of diffractive dijet
events.


  This paper studies the construction of symbolic abstractions for nonlinear
control systems via feedback refinement relation. Both the delay-free and
time-delay cases are addressed. For the delay-free case, to reduce the
computational complexity, we propose a new approximation approach for the state
and input sets based on a static quantizer, and then a novel symbolic model is
constructed such that the original system and the symbolic model satisfy the
feedback refinement relation. For the time-delay case, both static and dynamic
quantizers are combined to approximate the state and input sets. This leads to
a novel dynamic symbolic model for time-delay control systems, and a feedback
refinement relation is established between the original system and the symbolic
model. Finally, a numerical example is presented to illustrate the obtained
results.


  B2352+495 is a prototypical example of a Compact Symmetric Object (CSO). It
has a double radio lobe symmetrically located with respect to a central flat
spectrum radio core (the location of the AGN) and has a physical extent of less
than 200 pc. In this work we report VLBA observation of 21 cm HI absorption
toward B2352+495 to investigate the properties of this remarkable radio source,
in particular, to explore whether the radio emission can be confined by
circumnuclear material (frustration scenario) or whether the source is likely
to be young. We confirmed the two HI absorption features previously detected
toward B2352+495 - a broad line nearly centered at the systemic velocity of the
galaxy and a narrow redshifted component. The atomic gas from the broad
absorption component is likely associated with circumnuclear material,
consistent with the current paradigm of clumpy HI distribution in toroidal
structures around supermassive black holes.


  In this paper, we define the class of hourglass automata, which are timed
automata with bounded clocks that can be made to progress backwards as well as
forwards at a constant rate. We then introduce a new clock update for timed
automata that allows hourglass automata to be expressed. This allows us to show
that language emptiness remains decidable with this update when the number of
clocks is two or less. This is done by showing that we can construct a finite
untimed graph using clock regions from any timed automaton that use this new
update.


  Molecular cloud complexes exhibit both (i) an unfettered Larson-type spectrum
over much of their dynamic range, whilst (ii) still producing a much lower
star-formation rate than were this cascade to remain unfettered all the way
down to star-forming scales. Here we explain the compatibility of these
attributes with minimalist considerations of a mass-conserving fragmentation
cascade, combined with estimates of stellar feedback. Of importance is that the
amount of feedback needed to abate fragmentation and truncate the complex
decreases with decreasing scale. The scale at which the feedback momentum
matches the free-fall momentum marks a transition scale below most of the
cascade is truncated and the molecular cloud complex dissipated. For a
$10^6M_\odot$ GMC complex starting with radius of $\sim 50$pc, the combined
feedback from young stellar objects, supernovae, radiation, and stellar winds
for a GMC cloud complex can truncate the cascade within an outer free-fall time
but only after the cascade reaches parsec scales.


  Millimeter-wave (mmW) radar is widely applied to advanced autopilot
assistance systems. However, its small antenna aperture causes a low imaging
resolution. In this paper, a new distributed mmW radar system is designed to
solve this problem. It forms a large sparse virtual planar array to enlarge the
aperture, using multiple-input and multiple-output (MIMO) processing. However,
in this system, traditional imaging methods cannot apply to the sparse array.
Therefore, we also propose a 3D super-resolution imaging method specifically
for this system in this paper. The proposed method consists of three steps: (1)
using range FFT to get range imaging, (2) using 2D adaptive diagonal loading
iterative adaptive approach (ADL-IAA) to acquire 2D super-resolution imaging,
which can satisfy this sparsity under single-measurement, (3) using constant
false alarm (CFAR) processing to gain final 3D super-resolution imaging. The
simulation results show the proposed method can significantly improve imaging
resolution under the sparse array and single-measurement.


  Affleck-Kennedy-Lieb-Tasaki (AKLT) states are an important class of many-body
quantum states that are useful in quantum information processing, including
measurement-based quantum computation in particular. Here we propose a general
approach for constructing efficient verification protocols for AKLT states on
arbitrary graphs with local spin measurements. Our verification protocols build
on bond verification protocols and matching covers (including edge coloring) of
the underlying graphs, which have a simple geometric and graphic picture. We
also provide rigorous performance guarantee that is required for practical
applications. With our approach, most AKLT states of wide interest, including
those defined on 1D and 2D lattices, can be verified with a constant sample
cost, which is independent of the system size and is dramatically more
efficient than all previous approaches. As an illustration, we construct
concrete verification protocols for AKLT states on various lattices and on
arbitrary graphs up to five vertices.


  We study a two-level quantum system embedded in a dispersive environment and
coupled with the electromagnetic field. We expand the theory of light-matter
interactions to include the spatial extension of the system, taken into account
through its wavefunctions. This is a development beyond the point-dipole
approximation. This ingredient enables us to overcome the divergence problem
related to the Green tensor propagator. Hence, we can reformulate the
expressions for the spontaneous emission rate and the Lamb shift. In
particular, the inclusion of the spatial structure of the atomic system
clarifies the role of the asymmetry of atomic states with respect to spatial
inversion in these quantities.


  Many inequivalent approaches to study black holes yield identical results.
Any meaningful theory of gravity should explain the origin of this property.
Here we show that the basic holomorphic modes characterising the underlying two
dimensional conformal symmetry near the horizon bring about this universality.
Moreover these modes lead to a law of equipartition of energy for black holes
which suggests a statistical origin of gravity. This emergent nature of gravity
is further bolstered by showing the equivalence of entropy with the action
$\left(S=-\frac{i}{\hbar}\,I\right)$ and expressing the generalised Smarr
formula for mass as a thermodynamic relation, $S=\frac{E}{2T}$ where $S$, $E$
and $T$ are the entropy, energy and temperature, respectively, of a black hole.


  The superior performance of modern deep networks usually comes with a costly
training procedure. This paper presents a new curriculum learning approach for
the efficient training of visual backbones (e.g., vision Transformers). Our
work is inspired by the inherent learning dynamics of deep networks: we
experimentally show that at an earlier training stage, the model mainly learns
to recognize some 'easier-to-learn' discriminative patterns within each
example, e.g., the lower-frequency components of images and the original
information before data augmentation. Driven by this phenomenon, we propose a
curriculum where the model always leverages all the training data at each
epoch, while the curriculum starts with only exposing the 'easier-to-learn'
patterns of each example, and introduces gradually more difficult patterns. To
implement this idea, we 1) introduce a cropping operation in the Fourier
spectrum of the inputs, which enables the model to learn from only the
lower-frequency components efficiently, 2) demonstrate that exposing the
features of original images amounts to adopting weaker data augmentation, and
3) integrate 1) and 2) and design a curriculum learning schedule with a
greedy-search algorithm. The resulting approach, EfficientTrain, is simple,
general, yet surprisingly effective. As an off-the-shelf method, it reduces the
wall-time training cost of a wide variety of popular models (e.g., ResNet,
ConvNeXt, DeiT, PVT, Swin, and CSWin) by >1.5x on ImageNet-1K/22K without
sacrificing accuracy. It is also effective for self-supervised learning (e.g.,
MAE). Code is available at https://github.com/LeapLabTHU/EfficientTrain.


  Topological and geometrical properties and the associated topological defects
find a rapidly growing interest in studying the interplay between mechanics and
the collective behavior of cells on the tissue level. We here test if well
studied equilibrium laws for polydisperse passive systems such as the Lewis's
and the Aboav-Weaire's law are applicable also for active cellular structures.
Large scale simulations, which are based on a multi phase field active polar
gel model, indicate that these active cellular structures follow these laws. If
the system is in a state of collective motion also quantitative agreement with
typical values for passive systems is observed. If this state has not developed
quantitative differences can be found. We further compare the model with
discrete modeling approaches for cellular structures and show that essential
properties, such as T1 transitions and rosettes are naturally fulfilled.


  Gastric diffuse-type adenocarcinoma represents a disproportionately high
percentage of cases of gastric cancers occurring in the young, and its relative
incidence seems to be on the rise. Usually it affects the body of the stomach,
and presents shorter duration and worse prognosis compared with the
differentiated (intestinal) type adenocarcinoma. The main difficulty
encountered in the differential diagnosis of gastric adenocarcinomas occurs
with the diffuse-type. As the cancer cells of diffuse-type adenocarcinoma are
often single and inconspicuous in a background desmoplaia and inflammation, it
can often be mistaken for a wide variety of non-neoplastic lesions including
gastritis or reactive endothelial cells seen in granulation tissue. In this
study we trained deep learning models to classify gastric diffuse-type
adenocarcinoma from WSIs. We evaluated the models on five test sets obtained
from distinct sources, achieving receiver operator curve (ROC) area under the
curves (AUCs) in the range of 0.95-0.99. The highly promising results
demonstrate the potential of AI-based computational pathology for aiding
pathologists in their diagnostic workflow system.


  We extend the worldline measure for pocket formation in eternal inflation to
allow for time-ordered bubble formation. Such a time-ordering is equivalent to
imposing a preferred time-slicing on the "parent" de Sitter space. Using this
measure, we describe a covariant version of the youngness paradox and show that
the youngness paradox is a gauge artifact if the parent spacetime is an
unbroken de Sitter space, due to the lack of an explicit time-ordering for the
bubble nucleation events. We then show that one can add a "clock" to the de
Sitter space, in the form of a vector field with a spontaneously broken
symmetry that defines a unique timelike direction accessible to all observers.
Once this is done, the existence of a preferred slicing means that the
youngness paradox cannot be easily resolved. We use this to elucidate the
apparent "persistence of memory" discussed recently by Garriga, Guth and
Vilenkin, for inflationary universes produced by bubble nucleation.


  We construct a stochastic fluid process with an underlying piecewise
deterministic Markov process (PDMP) akin to the one used in the construction of
the rational arrival process (RAP), which we call the RAP-modulated fluid
process. As opposed to the classic stochastic fluid process driven by a Markov
jump process, the underlying PDMP of a RAP-modulated fluid process has a
continuous state space and is driven by matrix parameters which may not be
related to an intensity matrix. Through novel techniques we show how well-known
formulae associated to the classic stochastic fluid process, such as first
passage probabilities and the stationary distribution of its queue, translate
to its RAP-modulated counterpart.


  The competition between strength and correlation of coupling terms in a
Hamiltonian defines numerous phenomenological models exhibiting spectral
properties interpolating between those of Poisson (integrable) and Wigner-Dyson
(chaotic) ensembles. It is important to understand how the off-diagonal terms
of a Hamiltonian evolve as one or more symmetries of an integrable system are
explicitly broken. We introduce a deformed Poisson ensemble to demonstrate an
exact mapping of the coupling terms to the underlying symmetries of a
Hamiltonian. From the maximum entropy principle we predict a chaotic limit
which is numerically verified from the spectral properties and the survival
probability calculations.


  A frequency mixer is a nonlinear device that combines electromagnetic waves
to create waves at new frequencies. Mixers are ubiquitous components in modern
radio-frequency technology and are widely used in microwave signal processing.
The development of versatile frequency mixers for optical frequencies remains
challenging: such devices generally rely on weak nonlinear optical processes
and, thus, must satisfy phase matching conditions. In this work, we utilize a
GaAs-based dielectric metasurface to demonstrate an optical frequency mixer
that concurrently generates eleven new frequencies spanning the ultraviolet to
near-infrared (NIR) spectral range. Our approach combines strong intrinsic
material nonlinearities, enhanced electromagnetic fields, and relaxed
phase-matching requirements, to allow seven different nonlinear optical
processes to occur simultaneously. Specifically, when pumped by two femtosecond
NIR pulses, we observe second-, third- and fourth-harmonic generation,
sum-frequency generation, two-photon absorption induced photoluminescence,
four-wave mixing, and six-wave mixing. Such ultracompact optical mixers may
enable a plethora of applications in biology, chemistry, sensing,
communications and quantum optics.


  Optical properties of the weakly helimagnetic metal MnSi have been determined
in the photon energy range from 2 meV to 4.5 eV using the combination of
grazing incidence reflectance at 80 degrees (2 meV to 0.8 eV) and ellipsometry
(0.8 to 4.5 eV). As the sample is cooled below 100 K the effective mass becomes
strongly frequency dependent at low frequencies, while the scattering rate
developes a linear frequency dependence. The complex optical conductivity can
be described by the phenomenological relation \sigma(\omega,T) \propto
(\Gamma(T)+i\omega)^{-1/2} used for cuprates and ruthenates.


  We discuss a set of tests which confront observations of cooling compact
objects and theories of their thermal evolution. As an example we apply the
recently developed $\mathrm{Log N}$-$\mathrm{Log S}$ test of compact star
cooling theories to hybrid stars with a color superconducting quark matter
core, we also apply and discuss other existing tests. While there is not yet a
microscopically founded superconducting quark matter phase which would fulfill
constraints from cooling phenomenology, we explore the hypothetical 2SC+X phase
and show that the magnitude and density-dependence of the X-gap can be chosen
to satisfy a set of tests: the temperature -- age ($\mathrm{T}$-$\mathrm{t}$)
test, the $\mathrm{Log N}$-$\mathrm{Log S}$ test, the brightness constraint,
and the mass spectrum constraint. Some recent modifications of the population
synthesis model used to obtain the $\mathrm{Log N}$-$\mathrm{Log S}$
distribution are briefly discussed. In addition, we propose to use the
age-distance diagram as a new tool to study the local population of young
isolated neutron stars.


  This is a survey article on recent results on vector bundles on symmetric
product of non-singular projective curves.


  In this paper we construct, by means of a variational formulation, the
solutions of a problem of elastodynamics which includes the effect of damage
for the elastic material. The result is a wave equation with time dependent
operators which represents the elastic coefficients of the material undergoing
damage. The dynamics that we construct also satisfies a threshold condition
with the same threshold value that characterizes the quasi-static evolution of
damage (see \cite{GL}).


  Homological stability for sequences of groups is often proved by studying the
spectral sequence associated to the action of a typical group in the sequence
on a highly-connected simplicial complex whose stabilizers are related to
previous groups in the sequence. In the case of mapping class groups of
manifolds, suitable simplicial complexes can be made using isotopy classes of
various geometric objects in the manifold. In this paper we focus on the case
of surfaces and show that by using more refined geometric objects consisting of
certain configurations of curves with arcs that tether these curves to the
boundary, the stabilizers can be greatly simplified and consequently also the
spectral sequence argument. We give a careful exposition of this program and
its basic tools, then illustrate the method using braid groups before treating
mapping class groups of orientable surfaces in full detail.


  Young exoplanets are snapshots of the planetary evolution process. Planets
that orbit stars in young associations are particularly important because the
age of the planetary system is well constrained. We present the discovery of a
transiting planet larger than Neptune but smaller than Saturn in the 45 Myr
Tucana-Horologium young moving group. The host star is a visual binary, and our
follow-up observations demonstrate that the planet orbits the G6V primary
component, DS Tuc A (HD 222259A, TIC 410214986). We first identified transits
using photometry from the Transiting Exoplanet Survey Satellite (TESS; alerted
as TOI 200.01). We validated the planet and improved the stellar parameters
using a suite of new and archival data, including spectra from SOAR/Goodman,
SALT/HRS and LCO/NRES; transit photometry from Spitzer; and deep adaptive
optics imaging from Gemini/GPI. No additional stellar or planetary signals are
seen in the data. We measured the planetary parameters by simultaneously
modeling the photometry with a transit model and a Gaussian process to account
for stellar variability. We determined that the planetary radius is
$5.70\pm0.17$ Earth radii and that the orbital period is 8.1 days. The
inclination angles of the host star's spin axis, the planet's orbital axis, and
the visual binary's orbital axis are aligned within 15 degrees to within the
uncertainties of the relevant data. DS Tuc Ab is bright enough (V=8.5) for
detailed characterization using radial velocities and transmission
spectroscopy.


  A centerpiece of the ever-popular reinforcement learning from human feedback
(RLHF) approach to fine-tuning autoregressive language models is the explicit
training of a reward model to emulate human feedback, distinct from the
language model itself. This reward model is then coupled with policy-gradient
methods to dramatically improve the alignment between language model outputs
and desired responses. In this work, we adopt a novel perspective wherein a
pre-trained language model is itself simultaneously a policy, reward function,
and transition function. An immediate consequence of this is that reward
learning and language model fine-tuning can be performed jointly and directly,
without requiring any further downstream policy optimization. While this
perspective does indeed break the traditional agent-environment interface, we
nevertheless maintain that there can be enormous statistical benefits afforded
by bringing to bear traditional algorithmic concepts from reinforcement
learning. Our experiments demonstrate one concrete instance of this through
efficient exploration based on the representation and resolution of epistemic
uncertainty. In order to illustrate these ideas in a transparent manner, we
restrict attention to a simple didactic data generating process and leave for
future work extension to systems of practical scale.


  The problem of measuring an unbounded system attribute near a singularity has
been discussed. Lenses have been introduced as formal objects to study
increasingly precise measurements around the singularity and a specific family
of lenses called Exterior probabilities have been investigated. It has been
shown that under such probabilities, measurement variance of a measurable
function around a 1st order pole on a complex manifold, consists of two
separable parts - one that decreases with diminishing scale of the lenses, and
the other that increases. It has been discussed how this framework can lend
mathematical support to ideas of non-deterministic uncertainty prevalent at a
quantum scale. In fact, the aforementioned variance decomposition allows for a
minimum possible variance for such a system irrespective of how close the
measurements are. This inequality is structurally similar to Heisenberg
uncertainty relationship if one considers energy/momentum to be a meromorphic
function of a complex spacetime.


  Model-based reinforcement learning (MBRL) methods have shown strong sample
efficiency and performance across a variety of tasks, including when faced with
high-dimensional visual observations. These methods learn to predict the
environment dynamics and expected reward from interaction and use this
predictive model to plan and perform the task. However, MBRL methods vary in
their fundamental design choices, and there is no strong consensus in the
literature on how these design decisions affect performance. In this paper, we
study a number of design decisions for the predictive model in visual MBRL
algorithms, focusing specifically on methods that use a predictive model for
planning. We find that a range of design decisions that are often considered
crucial, such as the use of latent spaces, have little effect on task
performance. A big exception to this finding is that predicting future
observations (i.e., images) leads to significant task performance improvement
compared to only predicting rewards. We also empirically find that image
prediction accuracy, somewhat surprisingly, correlates more strongly with
downstream task performance than reward prediction accuracy. We show how this
phenomenon is related to exploration and how some of the lower-scoring models
on standard benchmarks (that require exploration) will perform the same as the
best-performing models when trained on the same training data. Simultaneously,
in the absence of exploration, models that fit the data better usually perform
better on the downstream task as well, but surprisingly, these are often not
the same models that perform the best when learning and exploring from scratch.
These findings suggest that performance and exploration place important and
potentially contradictory requirements on the model.


  The paper deals with a mixed boundary value problem for the Stokes system in
a polyhedral cone. Here different boundary conditions (in particular,
Dirichlet, Neumann, free surface conditions) are prescribed on the sides of the
polyhedron. The authors obtain regularity results for weak solutions in
weighted $L_2$ Sobolev spaces and prove point estimates of Green's matrix.


  We study the constraints that high-redshift structure formation in the
universe places on warm dark matter (WDM) dominated cosmological models. We
modify the extended Press-Schechter formalism to derive the halo mass function
in WDM models. We show that our predictions agree with recent numerical
simulations at low redshift over the halo masses of interest. Applying our
model to galaxy formation at high redshift, we find that the loss of power on
small scales, together with the delayed collapse of low-mass objects, results
in strong limits on the root-mean-square velocity dispersion v_rms of the WDM
particles at z=0. For fermions decoupling while relativistic, these limits are
equivalent to constraints on the mass m_X of the particles. The presence of a 4
billion solar mass black hole at z=5.8, believed to power the quasar SDSS
1044-1215, implies m_X > 0.5 keV (or v_rms < 0.10 km/s), assuming that the
quasar is unlensed and radiating at or below the Eddington limit. Reionization
by z=5.8 also implies a limit on m_X. If high-redshift galaxies produce
ionizing photons with an efficiency similar to their z=3 counterparts, we find
m_X > 1.2 keV (or v_rms < 0.03 km/s). However, given the uncertainties in
current measurements from the proximity effect of the ionizing background at
redshift 3, values of m_X as low as 0.75 keV (v_rms = 0.06 km/s) are not ruled
out. The limit weakens further if, instead, the ionizing-photon production
efficiency is greater at high z, but this limit will tighten considerably if
reionization is shown in the future to have occurred at higher redshifts. WDM
models with m_X < 1 keV (v_rms > 0.04 km/s) produce a low-luminosity cutoff in
the high-redshift galaxy luminosity function which is directly detectable with
the Next Generation Space Telescope (abridged).


  Emergent non-reciprocal interactions violating Newton's third law are
widespread in out-of-equilibrium systems. Phase separating mixtures with such
interactions exhibit travelling states with no equilibrium counterpart. Using
extensive Brownian dynamics simulations, we investigate the existence and
stability of such travelling states in a generic non-reciprocal particle
system. By varying a broad range of parameters including aggregate state of
mixture components, diffusivity, degree of non-reciprocity, effective spatial
dimension and density, we determine that travelling states do exist below the
predator-prey regime, but nonetheless are only found in a narrow region of the
parameter space. Our work also sheds light on the physical mechanisms for the
disappearance of travelling states when relevant parameters are being varied,
and has implications for a range of non-equilibrium systems including
non-reciprocal phase separating mixtures, non-equilibrium pattern formation and
predator-prey models.


  We calculate the next-to-leading order corrections to the $SU(2)\otimes
SU(2)$ and $SU(3)\otimes SU(3)$ Gell-Mann-Oakes-Renner relations. We use a
pseudoscalar correlator calculated from Perturbative QCD up to five loops and
use the QCD Finite Energy Sum Rules with integration kernels tuned to suppress
the importance of the hadronic resonances. This leads to a substantial
reduction in the systematic uncertainties from the experimentally unknown
resonance spectral function. We use the method of Fixed Order and Fixed
Renormalization Scale Perturbation Theory to compute the integrals. We
calculate these corrections to be $\delta_\pi = 0.060 \pm 0.014$ and $\delta _K
=0.64 \pm 0.24$. As a result of these new values, we predict the value of the
light quark condensate $\left\langle {0|\bar qq|0} \right\rangle = - \left(
{266 \pm 5{\text{ MeV}}} \right)^3$ and the Chiral Perturbation Theory low
energy constant $H_2^r = - \left( {4.9 \pm 1.8} \right) \times 10^{ - 3}$.
Results from this work have been published as: J. Bordes, C.A. Dominguez, P.
Moodley, J. Pe$\widetilde{\text{n}}$arrocha and K. Schilcher, J. High Ener.
Phys. 05 (2010) 064.


  This paper shows that orbital equations generated by iteration of polynomial
maps do not have necessarily a unique representation. Remarkably, they may be
represented in an infinity of ways, all interconnected by certain nonlinear
transformations. Five direct and five inverse transformations are established
explicitly between a pair of orbits defined by cyclic quintic polynomials with
real roots and minimum discriminant. In addition, infinite sequences of
transformations generated recursively are introduced and shown to produce
unlimited supplies of equivalent orbital equations. Such transformations are
generic, valid for arbitrary dynamics governed by algebraic equations of
motion.


  The idea of the modal expansion in electromagnetics is derived from the
research on electromagnetic resonators, which play an essential role in
developments in nanophotonics. All of the electromagnetic resonators share a
common property: they possess a discrete set of special frequencies that show
up as peaks in scattering spectra and are called resonant modes. These resonant
modes are soon recognized to dictate the interaction between electromagnetic
resonators and light. This leads to a hypothesis that the optical response of
resonators is the synthesis of the excitation of each physical-resonance-state
in the system: Under the excitation of external pulses, these resonant modes
are initially loaded, then release their energy which contributes to the total
optical responses of the resonators. These resonant modes with complex
frequencies are known in the literature as the Quasi-Normal Mode (QNM).
Mathematically, these QNMs correspond to solutions of the eigenvalue problem of
source-free Maxwell's equations. In the case where the optical structure of
resonators is unbounded and the media are dispersive (and possibly anisotropic
and non-reciprocal) this requires solving non-linear (in frequency) and
non-Hermitian eigenvalue problems. Thus, the whole problem boils down to the
study of the spectral theory for electromagnetic Maxwell operators. As a
result, modal expansion formalisms have recently received a lot of attention in
photonics because of their capabilities to model the physical properties in the
natural resonance-state basis of the considered system, leading to a
transparent interpretation of the numerical results. This manuscript is
intended to extend the study of QNM expansion formalism, in particular, and
nonlinear spectral theory, in general. At the same time, several numerical
modelings are provided as examples for the application of modal expansion in
computations.


  Inelastic neutron scattering in variable magnetic field and high-field
magnetization measurements, at the milikelvin temperature range, were performed
to gain insight into the low-energy magnetic excitation spectrum and the
field-induced level crossings in the molecular spin cluster \{Cr$_8$\}-cubane.
These complementary techniques provide consistent estimates of the lowest
level-crossing field. The overall features of the experimental data are
explained using an isotropic Heisenberg model, based on three distinct exchange
interactions linking the eight Cr$^{\text{III}}$ paramagnetic centers (spins $s
= 3/2$), that is supplemented with a relatively large molecular magnetic
anisotropy term for the lowest S=1 multiplet. It is noted that the existence of
the anisotropy is clearly evident from the magnetic field dependence of the
excitations in the INS measurements, while the magnetization measurements are
not sensitive to its effects.


  The chemical abundances of exoplanet atmospheres may provide valuable
information about the bulk compositions, formation pathways, and evolutionary
histories of planets. Exoplanets with large, relatively cloud-free atmospheres,
and which orbit bright stars provide the best opportunities for accurate
abundance measurements. For this reason, we measured the transmission spectrum
of the bright (V~10.2), large (1.37 R$_{J}$), sub-Saturn mass (0.19 M$_{J}$)
exoplanet WASP-127b across the near-UV to near-infrared wavelength range (0.3 -
5 $\mu$m), using the Hubble and Spitzer Space Telescopes. Our results show a
feature-rich transmission spectrum, with absorption from Na, H$_{2}$O, and
CO$_{2}$, and wavelength-dependent scattering from small-particle condensates.
We ran two types of atmospheric retrieval models: one enforcing chemical
equilibrium, and the other which fit the abundances freely. Our retrieved
abundances at chemical equilibrium for Na, O and C are all super-solar, with
abundances relative to solar values of 9$^{+15}_{-6}$, 16$^{+7}_{-5}$, and
26$^{+12}_{-9}$ respectively. Despite giving conflicting C/O ratios, both
retrievals gave super-solar CO$_{2}$ volume mixing ratios, which adds to the
likelihood that WASP-127b's bulk metallicity is super-solar, since CO$_{2}$
abundance is highly sensitive to atmospheric metallicity. We detect water at a
significance of 13.7 $\sigma$. Our detection of Na is in agreement with
previous ground-based detections, though we find a much lower abundance, and we
also do not find evidence for Li or K despite increased sensitivity. In the
future, spectroscopy with JWST will be able to constrain WASP-127b's C/O ratio,
and may reveal the formation history of this metal-enriched, highly observable
exoplanet.


  Transformer-based Large Language Models (LLMs) have been applied in diverse
areas such as knowledge bases, human interfaces, and dynamic agents, and
marking a stride towards achieving Artificial General Intelligence (AGI).
However, current LLMs are predominantly pretrained on short text snippets,
which compromises their effectiveness in processing the long-context prompts
that are frequently encountered in practical scenarios. This article offers a
comprehensive survey of the recent advancement in Transformer-based LLM
architectures aimed at enhancing the long-context capabilities of LLMs
throughout the entire model lifecycle, from pre-training through to inference.
We first delineate and analyze the problems of handling long-context input and
output with the current Transformer-based models. We then provide a taxonomy
and the landscape of upgrades on Transformer architecture to solve these
problems. Afterwards, we provide an investigation on wildly used evaluation
necessities tailored for long-context LLMs, including datasets, metrics, and
baseline models, as well as optimization toolkits such as libraries,
frameworks, and compilers to boost the efficacy of LLMs across different stages
in runtime. Finally, we discuss the challenges and potential avenues for future
research. A curated repository of relevant literature, continuously updated, is
available at https://github.com/Strivin0311/long-llms-learning.


  Strategic decisions rely heavily on non-scientific instrumentation to
forecast emerging technologies and leading companies. Instead, we build a fast
quantitative system with a small computational footprint to discover the most
important technologies and companies in a given field, using generalisable
methods applicable to any industry. With the help of patent data from the US
Patent and Trademark Office, we first assign a value to each patent thanks to
automated machine learning tools. We then apply network science to track the
interaction and evolution of companies and clusters of patents (i.e.
technologies) to create rankings for both sets that highlight important or
emerging network nodes thanks to five network centrality indices. Finally, we
illustrate our system with a case study based on the cybersecurity industry.
Our results produce useful insights, for instance by highlighting (i) emerging
technologies with a growing mean patent value and cluster size, (ii) the most
influential companies in the field and (iii) attractive startups with few but
impactful patents. Complementary analysis also provides evidence of decreasing
marginal returns of research and development in larger companies in the
cybersecurity industry.


  We discuss self-gravitating global O(3) monopole solutions associated with
the spontaneous breaking of O(3) down to a global O(2) in an extended Gauss
Bonnet theory of gravity in (3+1)-dimensions, in the presence of a non-trivial
scalar field $\Phi$ that couples to the Gauss-Bonnet higher curvature
combination with a coupling parameter $\alpha$. We obtain a range of values for
$\alpha < 0$ (in our notation and conventions), which are such that a global
(Israel type) matching is possible of the space time exterior to the monopole
core $\delta$ with a de-Sitter interior, guaranteeing the positivity of the ADM
mass of the monopole, which, together with a positive core radius $\delta > 0$,
are both dynamically determined as a result of this matching. It should be
stressed that in the General Relativity (GR) limit, where $\alpha \to 0$, and
$\Phi \to $ constant, such a matching yields a negative ADM monopole mass,
which might be related to the stability issues the (Barriola-Vilenkin (BV))
global monopole of GR faces. Thus, our global monopole solution, which shares
many features with the BV monopole, such as an asymptotic-space-time deficit
angle, of potential phenomenological/cosmological interest, but has, par
contrast, a positive ADM mass, has a chance of being a stable configuration,
although a detailed stability analysis is pending.


  In the field of robotics, the point cloud has become an essential map
representation. From the perspective of downstream tasks like localization and
global path planning, points corresponding to dynamic objects will adversely
affect their performance. Existing methods for removing dynamic points in point
clouds often lack clarity in comparative evaluations and comprehensive
analysis. Therefore, we propose an easy-to-extend unified benchmarking
framework for evaluating techniques for removing dynamic points in maps. It
includes refactored state-of-art methods and novel metrics to analyze the
limitations of these approaches. This enables researchers to dive deep into the
underlying reasons behind these limitations. The benchmark makes use of several
datasets with different sensor types. All the code and datasets related to our
study are publicly available for further development and utilization.


  Deep learning (DL) models are being deployed at medical centers to aid
radiologists for diagnosis of lung conditions from chest radiographs. Such
models are often trained on a large volume of publicly available labeled
radiographs. These pre-trained DL models' ability to generalize in clinical
settings is poor because of the changes in data distributions between publicly
available and privately held radiographs. In chest radiographs, the
heterogeneity in distributions arises from the diverse conditions in X-ray
equipment and their configurations used for generating the images. In the
machine learning community, the challenges posed by the heterogeneity in the
data generation source is known as domain shift, which is a mode shift in the
generative model. In this work, we introduce a domain-shift detection and
removal method to overcome this problem. Our experimental results show the
proposed method's effectiveness in deploying a pre-trained DL model for
abnormality detection in chest radiographs in a clinical setting.


  In this paper, we propose a novel truncation method for determining the trace
distance between two Gaussian states in fermionic systems. For two fermionic
Gaussian states, characterized by their correlation matrices, we consider the
von Neumann entropies and dissimilarities between their correlation matrices
and truncate the correlation matrices to facilitate trace distance
calculations. Our method exhibits notable efficacy in two distinct scenarios.
In the first scenario, the states have small von Neumann entropies, indicating
finite or logarithmic-law entropy, while their correlation matrices display
near-commuting behavior, characterized by a finite or gradual nonlinear
increase in the trace norm of the correlation matrix commutator relative to the
system size. The second scenario encompasses situations where the two states
are nearly orthogonal, with a maximal canonical value difference approaching 2.
To evaluate the performance of our method, we apply it to various compelling
examples. Notably, we successfully compute the subsystem trace distances
between low lying eigenstates of Ising and XX spin chains, even for
significantly large subsystem sizes. This is in stark contrast to existing
literature, where subsystem trace distances are limited to subsystems of
approximately ten sites. With our truncation method, we extend the analysis to
subsystems comprising several hundred sites, thus expanding the scope of
research in this field.


  We consider a general approach for the process of Lagrangian and Hamiltonian
reduction by symmetries in chiral gauge models. This approach is used to show
the complete integrability of several one dimensional texture equations arising
in liquid Helium phases and neutron stars.


  In the local density approximation (LDA) for electronic time-dependent
current-density functional theory (TDCDFT) many-body effects are described in
terms of the visco-elastic constants of the homogeneous three-dimensional
electron gas. In this paper we critically examine the applicability of the
three-dimensional LDA to the calculation of the viscous damping of
1-dimensional collective oscillations of angular frequency $\omega$ in a quasi
2-dimensional quantum well. We calculate the effective viscosity
$\zeta(\omega)$ from perturbation theory in the screened Coulomb interaction
and compare it with the commonly used three-dimensional LDA viscosity
$Y(\omega)$. Significant differences are found. At low frequency $Y(\omega)$ is
dominated by a shear term, which is absent in $\zeta(\omega)$. At high
frequency $\zeta(\omega)$ and $Y(\omega)$ exhibit different power law behaviors
($\omega^{-3}$ and $\omega^{-5/2}$ respectively), reflecting different spectral
densities of electron-hole excitations in two and three dimensions. These
findings demonstrate the need for better approximations for the
exchange-correlation stress tensor in specific systems where the use of the
three-dimensional functionals may lead to unphysical results.


  In this paper, we investigate existence and stability of solitary waves to
the rotation-Camassa-Holm equation which can be considered as a model in the
shallow water for the long-crested waves propagating near the equator with
effect of the Coriolis force due to the Earths rotation. We prove existence of
solitary waves by performing a phase plane analysis. Moreover, utilizing the
approach proposed by Grillakis-Shatah-Strauss, we prove stability of solitary
waves.


  The amplitude for $\gamma p \to V p$, where $V$ is a $J/\psi$ or $\Upsilon$
ground state or excited vector meson, is calculated in a pQCD
$k_{T}$-factorization approach. We use this amplitude to predict the cross
section for exclusive photoproduction of $J/\psi, \psi', \Upsilon$ mesons in
proton-proton collisions. Calculations are performed for a variety of
unintegrated gluon distributions, and we compare our results to LHCb data. Here
we especially focus on the possibilty of constraining saturation effects.
Compared to earlier calculations we include both Dirac and Pauli
electromagnetic form factors. We discuss the role of the $Q\bar Q$ light-cone
wave functions for differential distributions for ratios such as
$\sigma(\psi')/\sigma(J/\psi)$. Absorption effects are always taken into
account.
  We also discuss the related diffractive production in proton dissociative
events. Here we concentrate on electromagnetic dissociation , which is
calculable without additional free parameters. Besides being of interest in
their own right, dissociative events constitute an important experimental
background to exclusive production.


  Deep Neural Networks (DNNs) have shown great promise in various domains.
Alongside these developments, vulnerabilities associated with DNN training,
such as backdoor attacks, are a significant concern. These attacks involve the
subtle insertion of triggers during model training, allowing for manipulated
predictions. More recently, DNNs for tabular data have gained increasing
attention due to the rise of transformer models. Our research presents a
comprehensive analysis of backdoor attacks on tabular data using DNNs, mainly
focusing on transformers. We also propose a novel approach for trigger
construction: an in-bounds attack, which provides excellent attack performance
while maintaining stealthiness. Through systematic experimentation across
benchmark datasets, we uncover that transformer-based DNNs for tabular data are
highly susceptible to backdoor attacks, even with minimal feature value
alterations. We also verify that our attack can be generalized to other models,
like XGBoost and DeepFM. Our results demonstrate up to 100% attack success rate
with negligible clean accuracy drop. Furthermore, we evaluate several defenses
against these attacks, identifying Spectral Signatures as the most effective.
Nevertheless, our findings highlight the need to develop tabular data-specific
countermeasures to defend against backdoor attacks.


  We study underlying-event observables in inelastic proton-proton (pp)
collisions at a centre-of-mass energy of $\sqrt{s} = 13$ TeV with identified
light and heavy-flavor triggers using the PYTHIA 8 event generator. The study
is performed as a function of the transverse momentum of the leading particle
($p_\mathrm{T}^{\rm trigger}$). While at high $p_\mathrm{T}^{\rm trigger}$
($>10$ GeV/$c$) the underlying-event activity is independent of the leading
particle species, at intermediate $p_\mathrm{T}^{\rm trigger}$
($2<p_\mathrm{T}^{\rm trigger}<8$ GeV/$c$) it is larger in pion-triggered
events than in events triggered with B mesons. Moreover, the underlying event
in pion-triggered events, the majority of which are initiated by gluons, shows
a stronger effect of color reconnection than events triggered with B-hadrons,
that are mostly initiated by quark jets. The effect is observed at both
hadronic and partonic level. Given that color reconnection affects the
interaction among final partons before the hadronization, and that in the
string model quarks (gluons) are connected to one (two) string piece(s), we
conclude that the observed effect can be attributed to differences in the
interactions of gluon and quark jets with the underlying event.


  A novel low cost, near equi-atomic alloy comprising of Al, Cu, Fe and Mn is
synthesized using arc-melting technique. The cast alloy possesses a dendritic
microstructure where the dendrites consist of disordered FCC and ordered FCC
phases. The inter-dendritic region is comprised of ordered FCC phase and
spinodally decomposed BCC phases. A Cu segregation is observed in the
inter-dendritic region while dendritic region is rich in Fe. The bulk hardness
of the alloy is ~ 380 HV, indicating significant yield strength.


  The modern treatment of any disease is heavily dependent on the medical
diagnosis. Clinical data obtained through the diagnostics tests need to be
collected and entered into the computer database in order to make a clinical
data repository. In most of the cases, manual entry is an absolute necessity.
However, manual entry can cause errors also, leading to wrong diagnosis. This
paper explains how data could be entered free of error to reduce the chances of
wrong diagnosis by designing and implementation of a simple database driven
application.


  In this chapter we take a closer look at the distribution of symbolic
regression models generated by genetic programming in the search space. The
motivation for this work is to improve the search for well-fitting symbolic
regression models by using information about the similarity of models that can
be precomputed independently from the target function. For our analysis, we use
a restricted grammar for uni-variate symbolic regression models and generate
all possible models up to a fixed length limit. We identify unique models and
cluster them based on phenotypic as well as genotypic similarity. We find that
phenotypic similarity leads to well-defined clusters while genotypic similarity
does not produce a clear clustering. By mapping solution candidates visited by
GP to the enumerated search space we find that GP initially explores the whole
search space and later converges to the subspace of highest quality expressions
in a run for a simple benchmark problem.


  As a typical immiscible binary system, copper (Cu) and lithium (Li) show no
alloying and chemical intermixing under normal circumstances. A notable example
that takes advantages of the immiscibility between Cu and Li is the widespread
utilization of Cu foils as the anodic current collector in Li-ion batteries.
Here we show that the nanoscale size effect can play a subtle yet critical role
in mediating the chemical activity of Cu and therefore its miscibility with Li,
such that the electrochemical alloying and solid-state amorphization will occur
in such an immiscible system when decreasing Cu nanoparticle sizes into
ultrasmall range. This unusual observation was accomplished by performing
in-situ studies of the electrochemical lithiation processes of individual CuO
nanowires inside a transmission electron microscopy (TEM). Upon lithiation, CuO
nanowires are first electrochemically reduced to form discrete ultrasmall Cu
nanocrystals that, unexpectedly, can in turn undergo further electrochemical
lithiation to form amorphous CuLix nanoalloys. Real-time dynamic observations
by in-situ TEM unveil that there is a critical grain size (ca. 6 nm), below
which the crystalline Cu nanoparticles can be continuously lithiated and
amorphized. Electron energy loss spectra indicate that there is a net charge
transfer from Li to Cu in the amorphous CuLix nanoalloys. Another intriguing
finding is that the amorphous alloying phenomena in Cu-Li system is reversible,
as manifested by the in-situ observation of electron-beam-induced delithiation
of the as-formed amorphous CuLix nanoalloys.


  Genuine tests of an asymmetry under T and/or CPT transformations imply the
interchange between in-states and out-states. I explain a methodology to
perform model-indepedent separate measurements of the three CP, T and CPT
symmetry violations for transitions involving the decay of the neutral meson
systems in B- and {\Phi}-factories. It makes use of the quantum-mechanical
entanglement only, for which the individual state of each neutral meson is not
defined before the decay of its orthogonal partner. The final proof of the
independence of the three asymmetries is that no other theoretical ingredient
is involved and that the event sample corresponding to each case is different
from the other two. The experimental analysis for the measurements of these
three asymmetries as function of the time interval {\Delta}t > 0 between the
first and second decays is discussed, as well as the significance of the
expected results. In particular, one may advance a first observation of true,
direct, evidence of Time-Reserval-Violation in B-factories by many standard
deviations from zero, without any reference to, and independent of,
CP-Violation. In some quantum gravity framework the CPT-transformation is
ill-defined, so there is a resulting loss of particle-antiparticle identity.
This mechanism induces a breaking of the EPR correlation in the entanglement
imposed by Bose statistics to the neutral meson system, the so-called
{\omega}-effect. I present results and prospects for the {\omega}-parameter in
the correlated neutral meson-antimeson states.


  We study the problem of finding structured low-rank matrices using nuclear
norm regularization where the structure is encoded by a linear map. In contrast
to most known approaches for linearly structured rank minimization, we do not
(a) use the full SVD, nor (b) resort to augmented Lagrangian techniques, nor
(c) solve linear systems per iteration. Instead, we formulate the problem
differently so that it is amenable to a generalized conditional gradient
method, which results in a practical improvement with low per iteration
computational cost. Numerical results show that our approach significantly
outperforms state-of-the-art competitors in terms of running time, while
effectively recovering low rank solutions in stochastic system realization and
spectral compressed sensing problems.


  We present the c-axis optical reflectance measurement on single crystals of
BaFe$_2$As$_2$ and SrFe$_2$As$_2$, the parent compounds of FeAs based
superconductors. Different from the ab-plane optical response where two
distinct energy gaps were observed in the SDW state, only the smaller energy
gap could be seen clearly for \textbf{E}$\parallel$c-axis. The very pronounced
energy gap structure seen at a higher energy scale for
\textbf{E}$\parallel$ab-plane is almost invisible. We propose a novel picture
for the band structure evolution across the SDW transition and suggest
different driving mechanisms for the formation of the two energy gaps.


  We study two-flavor collective neutrino oscillations in the dense-neutrino
region above the neutrino sphere in a supernova (SN). The angular dependence of
the neutrino-neutrino interaction potential causes "multi-angle" effects that
can lead either to complete kinematical decoherence in flavor space or only to
small differences between different trajectories. This nonlinear system
switches abruptly between "self-maintained coherence" and "self-induced
decoherence" among the angular modes, depending on the strength of the
deleptonization flux. For a realistic SN the quasi single-angle behavior is
probably typical, simplifying the numerical treatment and probably allowing for
the survival of observational features of flavor oscillations.


  Zaslavsky proved in 2012 that, up to switching isomorphism, there are six
different signed Petersen graphs and that they could be told apart by their
chromatic polynomials, by showing that the latter give distinct results when
evaluated at 3. He conjectured that the six different signed Petersen graphs
also have distinct zero-free chromatic polynomials, and that both types of
chromatic polynomials have distinct evaluations at \emph{any} positive integer.
We developed and executed a computer program (running in {\tt SAGE}) that
efficiently determines the number of proper $k$-colorings for a given signed
graph; our computations for the signed Petersen graphs confirm Zaslavsky's
conjecture. We also computed the chromatic polynomials of all signed complete
graphs with up to five vertices.


  Several years ago, Stefan Pokorski, Manfred Muenz and us outlined a program
for calculation of the NLO QCD corrections to the weak radiative B meson decay
B -> X_s gamma. Very recently, just before the 60th birthday of Stefan
Pokorski, this program has been formally completed. In the present paper, we
summarize the existing results and discuss perspectives for further improvement
of the accuracy of the Standard Model prediction for BR[ B -> X_s gamma].


  (1) Background: The success of physiotherapy depends on the regular and
correct performance of movement exercises. A system that automatically
evaluates these could support the therapy. Previous approaches in this area
rarely rely on Deep Learning methods and do not yet fully use their potential.
(2) Methods: Using a measurement system consisting of 17 IMUs, a dataset of
four Functional Movement Screening (FMS) exercises is recorded. Exercise
execution is evaluated by physiotherapists using the FMS criteria. This dataset
is used to train a neural network that assigns the correct FMS score to an
exercise repetition. We use an architecture consisting of CNN, LSTM and Dense
layers. Based on this framework, we apply various methods to optimize the
performance of the network. For the optimization, we perform a extensive
hyperparameter optimization. In addition, we are comparing different CNN
structures that have been specifically adapted for use with IMU data. Finally,
the developed network is trained with the data of different FMS exercises and
the performance is compared. (3) Results: The evaluation shows that the
presented approach achieves a convincing performance in the classification of
unknown repetitions of already known subjects. However, the trained network is
yet unable to achieve consistent performance on the data of a previously
unknown subjects. Additionally, it can be seen that the performance of the
network differs significantly depending on the exercise it is trained for.


  We obtain sufficient conditions ensuring the topological equivalence of two
perturbed difference linear systems whose linear part has a property of
generalized exponential dichotomy. When the exponential dichotomy is verified,
we obtain a strongly and H\"older topological equivalence.


  Self-attention-based networks have achieved remarkable performance in
sequential recommendation tasks. A crucial component of these models is
positional encoding. In this study, we delve into the learned positional
embedding, demonstrating that it often captures the distance between tokens.
Building on this insight, we introduce novel attention models that directly
learn positional relations. Extensive experiments reveal that our proposed
models, \textbf{PARec} and \textbf{FPARec} outperform previous
self-attention-based approaches.Our code is available at the link for anonymous
review: https://anonymous.4open.science/ r/FPARec-2C55/


  In the oracle identification problem we have oracle access to bits of an
unknown string $x$ of length $n$, with the promise that it belongs to a known
set $C\subseteq\{0,1\}^n$. The goal is to identify $x$ using as few queries to
the oracle as possible. We develop a quantum query algorithm for this problem
with query complexity $O\left(\sqrt{\frac{n\log M }{\log(n/\log M)+1}}\right)$,
where $M$ is the size of $C$. This bound is already derived by Kothari in 2014,
for which we provide a more elegant simpler proof.


  We present the first observational study of pulsars performed with the
second-generation precursor stations to the low-frequency component of the
Square Kilometre Array (SKA-Low): the Aperture Array Verification System 2
(AAVS2) and the Engineering Development Array 2 (EDA2). Using the SKA-Low
stations, we have observed 100 southern-sky pulsars between 70-350 MHz,
including follow-up observations at multiple frequencies for a selected sample
of bright pulsars. These observations have yielded detections of 22 pulsars,
including the lowest-frequency detections ever published for 6 pulsars, despite
the modest sensitivity of initial system where the recording bandwidth is
limited to ~1 MHz. By comparing simultaneous flux density measurements obtained
with the SKA-Low stations and performing rigorous electromagnetic simulations,
we verify the accuracy of the SKA-Low sensitivity simulation code presented in
Sokolowski et al. (2022). Furthermore, we perform model fits to the radio
spectra of the detected pulsars using the method developed by Jankowski et al.
(2018), including 9 pulsars which were not fitted in the original work. We
robustly classify the spectra into 5 morphological classes and find that all
but one pulsar exhibit deviations from simple power-law behaviour. These
findings suggest that pulsars with well-determined spectra are more likely to
show spectral flattening or turn-over than average. Our work demonstrates how
SKA-Low stations can be meaningfully used for scientifically useful
measurements and analysis of pulsar radio spectra, which are important inputs
for informing pulsar surveys and science planned with the SKA-Low.


  How cooperation can evolve between players is an unsolved problem of biology.
Here we use Hamiltonian dynamics of models of the Ising type to describe
populations of cooperating and defecting players to show that the equilibrium
fraction of cooperators is given by the expectation value of a thermal
observable akin to a magnetization. We apply the formalism to the Public Goods
game with three players, and show that a phase transition between cooperation
and defection occurs that is equivalent to a transition in one-dimensional
Ising crystals with long-range interactions. We then investigate the effect of
punishment on cooperation and find that punishment plays the role of a magnetic
field that leads to an "alignment" between players, thus encouraging
cooperation. We suggest that a thermal Hamiltonian picture of the evolution of
cooperation can generate other insights about the dynamics of evolving groups
by mining the rich literature of critical dynamics in low-dimensional spin
systems.


  We investigate the performance of the standard Greedy algorithm for
cardinality constrained maximization of non-submodular nondecreasing set
functions. While there are strong theoretical guarantees on the performance of
Greedy for maximizing submodular functions, there are few guarantees for
non-submodular ones. However, Greedy enjoys strong empirical performance for
many important non-submodular functions, e.g., the Bayesian A-optimality
objective in experimental design. We prove theoretical guarantees supporting
the empirical performance. Our guarantees are characterized by a combination of
the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In
particular, we prove that Greedy enjoys a tight approximation guarantee of
$\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained
maximization. In addition, we bound the submodularity ratio and curvature for
several important real-world objectives, including the Bayesian A-optimality
objective, the determinantal function of a square submatrix and certain linear
programs with combinatorial constraints. We experimentally validate our
theoretical findings for both synthetic and real-world applications.


  We investigate the effect of high order radiative corrections in unpolarized
electron proton elastic scattering and compare with the calculations at lowest
order, which are usually applied to experimental data. Particular attention is
devoted to the $\epsilon$ dependence of radiative corrections, which is
directly related to the electric proton form factor. We consider in particular
the effects of the interference terms for soft and hard photon emission. Both
quadratic amplitude describing the collinear emission along the scattered
electron as well as the interference with the amplitudes of emission from the
initial electron and the emission from protons are important in leading and
next to leading approximation and they may compensate in particular kinematical
conditions.


  3D object detection using LiDAR data remains a key task for applications like
autonomous driving and robotics. Unlike in the case of 2D images, LiDAR data is
almost always collected over a period of time. However, most work in this area
has focused on performing detection independent of the temporal domain. In this
paper we present 3D-FCT, a Siamese network architecture that utilizes temporal
information to simultaneously perform the related tasks of 3D object detection
and tracking. The network is trained to predict the movement of an object based
on the correlation features of extracted keypoints across time. Calculating
correlation across keypoints only allows for real-time object detection. We
further extend the multi-task objective to include a tracking regression loss.
Finally, we produce high accuracy detections by linking short-term object
tracklets into long term tracks based on the predicted tracks. Our proposed
method is evaluated on the KITTI tracking dataset where it is shown to provide
an improvement of 5.57% mAP over a state-of-the-art approach.


  The Hubbard model is the simplest model that is believed to exhibit
superconductivity arising from purely repulsive interactions, and has been
extensively applied to explore a variety of unconventional superconducting
systems. Here we study the evolution of the leading superconducting
instabilities of the single-orbital Hubbard model on a two-dimensional square
lattice as a function of onsite Coulomb repulsion $U$ and band filling by
calculating the irreducible particle-particle scattering vertex obtained from
dynamical cluster approximation (DCA) calculations, and compare the results to
both perturbative Kohn-Luttinger (KL) theory as well as the widely used random
phase approximation (RPA) spin-fluctuation pairing scheme. Near half-filling we
find remarkable agreement of the hierarchy of the leading pairing states
between these three methods, implying adiabatic continuity between weak- and
strong-coupling pairing solutions of the Hubbard model. The $d_{x^2-y^2}$-wave
instability is robust to increasing $U$ near half-filling as expected. Away
from half filling, the predictions of KL and RPA at small $U$ for transitions
to other pair states agree with DCA at intermediate $U$ as well as recent
diagrammatic Monte Carlo calculations. RPA results fail only in the very dilute
limit, where it yields a $d_{xy}$ ground state instead of a $p$-wave state
established by diagrammatic Monte Carlo and low-order perturbative methods, as
well as our DCA calculations. We discuss the origins of this discrepancy,
highlighting the crucial role of the vertex corrections neglected in the RPA
approach. Overall, comparison of the various methods over the entire phase
diagram strongly suggests a smooth crossover of the superconducting interaction
generated by local Hubbard interactions between weak and strong coupling.


  Pervasive sensors have become essential in research for gathering real-world
data. However, current studies often focus solely on objective data, neglecting
subjective human contributions. We introduce an approach and system for
collecting big-thick data, combining extensive sensor data (big data) with
qualitative human feedback (thick data). This fusion enables effective
collaboration between humans and machines, allowing machine learning to benefit
from human behavior and interpretations. Emphasizing data quality, our system
incorporates continuous monitoring and adaptive learning mechanisms to optimize
data collection timing and context, ensuring relevance, accuracy, and
reliability. The system comprises three key components: a) a tool for
collecting sensor data and user feedback, b) components for experiment planning
and execution monitoring, and c) a machine-learning component that enhances
human-machine interaction.