  Zero queueing delay is highly desirable in large-scale computing systems.
Existing work has shown that it can be asymptotically achieved by using the
celebrated Power-of-$d$-choices (pod) policy with a probe overhead $d =
\omega\left(\frac{\log N}{1-\lambda}\right)$, and it is impossible when $d =
O\left(\frac{1}{1-\lambda}\right)$, where $N$ is the number of servers and
$\lambda$ is the load of the system. However, these results are based on the
model where each job is an indivisible unit, which does not capture the
parallel structure of jobs in today's predominant parallel computing paradigm.
  This paper thus considers a model where each job consists of a batch of
parallel tasks. Under this model, we propose a new notion of zero (asymptotic)
queueing delay that requires the job delay under a policy to approach the job
delay given by the max of its tasks' service times, i.e., the job delay
assuming its tasks entered service right upon arrival. This notion quantifies
the effect of queueing on a job level for jobs consisting of multiple tasks,
and thus deviates from the conventional zero queueing delay for single-task
jobs in the literature.
  We show that zero queueing delay for parallel jobs can be achieved using the
batch-filling policy (a variant of the celebrated pod policy) with a probe
overhead $d = \omega\left(\frac{1}{(1-\lambda)\log k}\right)$ in the
sub-Halfin-Whitt heavy-traffic regime, where $k$ is the number of tasks in each
job { and $k$ properly scales with $N$ (the number of servers)}. This result
demonstrates that for parallel jobs, zero queueing delay can be achieved with a
smaller probe overhead. We also establish an impossibility result: we show that
zero queueing delay cannot be achieved if $d = \exp\left({o\left(\frac{\log
N}{\log k}\right)}\right)$.


  We study the existence and uniqueness for weak solutions to some classes of
anisotropic elliptic Dirichlet problems with data belonging to the natural dual
space.


  In introductory physics laboratory instruction, students often expect to
confirm or demonstrate textbook physics concepts (Wilcox & Lewandowski, 2017;
Hu & Zwickl, 2017; Hu & Zwickl, 2018). This expectation is largely undesirable:
labs that emphasize confirmation of textbook physics concepts are unsuccessful
at teaching those concepts (Wieman & Holmes, 2015; Holmes et al., 2017) and
even in contexts that don't emphasize confirmation, such expectations can lead
to students disregarding or manipulating their data in order to obtain the
expected result (Smith et al., 2020). In other words, when students expect
their lab activities to confirm a known result, they may relinquish epistemic
agency and violate disciplinary practices. We claim that, in other cases,
confirmatory expectations can actually support productive disciplinary
engagement. In particular, when an expected result is not confirmed, students
may enter a productive "troubleshooting" mode (Smith et al., 2020). We analyze
the complex dynamics of students' epistemological framing in a lab where
student's confirmatory expectations support and even generate epistemic agency
and disciplinary practices, including developing original ideas, measures, and
apparatuses to apply to the material world.


  The latest advances in deep learning have facilitated the development of
highly accurate monocular depth estimation models. However, when training a
monocular depth estimation network, practitioners and researchers have observed
not a number (NaN) loss, which disrupts gradient descent optimization. Although
several practitioners have reported the stochastic and mysterious occurrence of
NaN loss that bothers training, its root cause is not discussed in the
literature. This study conducted an in-depth analysis of NaN loss during
training a monocular depth estimation network and identified three types of
vulnerabilities that cause NaN loss: 1) the use of square root loss, which
leads to an unstable gradient; 2) the log-sigmoid function, which exhibits
numerical stability issues; and 3) certain variance implementations, which
yield incorrect computations. Furthermore, for each vulnerability, the
occurrence of NaN loss was demonstrated and practical guidelines to prevent NaN
loss were presented. Experiments showed that both optimization stability and
performance on monocular depth estimation could be improved by following our
guidelines.


  We study the transport of charge carriers through finite graphene structures.
The use of numerical exact kernel polynomial and Green function techniques
allows us to treat actual sized samples beyond the Dirac-cone approximation.
Particularly we investigate disordered nanoribbons, normal-conductor/graphene
interfaces and normal-conductor/graphene/normal-conductor junctions with a
focus on the behavior of the local density of states, single-particle spectral
function, optical conductivity and conductance. We demonstrate that the
contacts and bulk disorder will have a major impact on the electronic
properties of graphene-based devices.


  This teaching article describes a simple and low-cost methodology for
studying electrical transport and constructing basic sensor devices using
everyday stationery items, including pencils, paper, and a handheld multimeter.
The approach is designed for high school and undergraduate teachers and offers
an easy-to-implement, hands-on method for teaching fundamental concepts in
physical electronics. The materials and experiments outlined in this article
are widely accessible and can be easily replicated in various teaching labs,
even with limited budgets.


  We study $3$-dimensional digital nets over $\mathbb{F}_2$ generated by
matrices $(I,B,B^2)$ where $I$ is the identity matrix and $B$ is a square
matrix. We give a characterization of $B$ for which the $t$-value of the
digital net is $0$. As a corollary, we prove that such $B$ satisfies $B^3=I$.


  Kolmogorov complexity has inspired several alignment-free distance measures,
based on the comparison of lengths of compressions, which have been applied
successfully in many areas. One of these measures, the so-called Universal
Similarity Metric (USM), has been used by Krasnogor and Pelta to compare simple
protein contact maps, showing that it yielded good clustering on four small
datasets. We report an extensive test of this metric using a much larger and
representative protein dataset: the domain dataset used by Sierk and Pearson to
evaluate seven protein structure comparison methods and two protein sequence
comparison methods. One result is that Krasnogor-Pelta method has less domain
discriminant power than any one of the methods considered by Sierk and Pearson
when using these simple contact maps. In another test, we found that the USM
based distance has low agreement with the CATH tree structure for the same
benchmark of Sierk and Pearson. In any case, its agreement is lower than the
one of a standard sequential alignment method, SSEARCH. Finally, we manually
found lots of small subsets of the database that are better clustered using
SSEARCH than USM, to confirm that Krasnogor-Pelta's conclusions were based on
datasets that were too small.


  High-energy scattering in pQCD in the Regge limit is described by the
evolution of Wilson lines governed by the BK equation. In the leading order,
the BK equation is conformally invariant and the eigenfunctions of the
linearized BFKL equation are powers. It is a common belief that at $d\neq 4$
the BFKL equation is useless since unlike $d=4$ case it cannot be solved by
usual methods. However, we demonstrate that at critical Wilson-Fisher point of
QCD the relevant part of NLO BK restores the conformal invariance so the
solutions are again powers. As a check of our approach to high-energy
amplitudes at the Wilson-Fisher point, we calculate the anomalous dimensions of
twist-2 light-ray operators in the Regge limit $j\rightarrow 1$.}


  If the geometry of space-time is $\nc$, i.e. $[x_{\mu},x_{\nu}]=i \theta_{\mu
\nu}$, then $\nc$ $\cpviolng$ effects may be manifest at low energies. For a
$\nc$ scale $\Lambda \equiv \theta^{-1/2} \leq 2 TeV$, $\cpviol$ from $\ncg$ is
comparable to that from the Standard Model (SM) alone: the $\nc$ contributions
to $\epsilon$ and $\epsilon'/\epsilon$ in the $K$-system, may actually dominate
over the Standard Model contributions. Present data permit $\ncg$ to be the
only source of $\cpviol$. Furthermore the most recent findings for g-2 of the
muon are consistent with predictions from $\ncg$. If the geometry of space-time
is $\nc$, $i.e.$ $[x_{\mu},x_{\nu}]=i \theta_{\mu \nu}$, then $\nc$ $\cpviolng$
effects may be manifest at low energies. For a $\nc$ scale $\Lambda \equiv
\theta^{-1/2} \leq 2 TeV$, $\cpviol$ from $\ncg$ is comparable to that from the
Standard Model (SM) alone: the $\nc$ contributions to $\epsilon$ and
$\epsilon'/\epsilon$ in the K-system, may actually dominate over the Standard
Model contributions. Present data permit $\ncg$ to be the only source of
$\cpviol$. Furthermore the most recent findings for g-2 of the muon are
consistent with predictions from $\ncg$.


  We study synchronization in disordered arrays of Josephson junctions. In the
first half of the paper, we consider the relation between the coupled
resistively- and capacitively shunted junction (RCSJ) equations for such arrays
and effective phase models of the Winfree type. We describe a multiple-time
scale analysis of the RCSJ equations for a ladder array of junctions
\textit{with non-negligible capacitance} in which we arrive at a second order
phase model that captures well the synchronization physics of the RCSJ
equations for that geometry. In the second half of the paper, motivated by
recent work on small world networks, we study the effect on synchronization of
random, long-range connections between pairs of junctions. We consider the
effects of such shortcuts on ladder arrays, finding that the shortcuts make it
easier for the array of junctions in the nonzero voltage state to synchronize.
In 2D arrays we find that the additional shortcut junctions are only marginally
effective at inducing synchronization of the active junctions. The differences
in the effects of shortcut junctions in 1D and 2D can be partly understood in
terms of an effective phase model.


  As far as the renormalization in perturbative QCD is concerned the n-point
one particle irreducible (1PI) proper vertex function is the basic building
block where the ultra-violet (UV) divergence occurs when the loop momentum
integration limit goes to infinity. In this paper we express the S-matrix
element for the $gg \rightarrow ggg$ scattering process at all orders in
coupling constant in terms of 5-point, 4-point, 3-point 1PI proper vertex
functions and the (full) propagator by using the path integral formulation of
QCD.


  We study Bayesian optimization (BO) in high-dimensional and non-stationary
scenarios. Existing algorithms for such scenarios typically require extensive
hyperparameter tuning, which limits their practical effectiveness. We propose a
framework, called BALLET, which adaptively filters for a high-confidence region
of interest (ROI) as a superlevel-set of a nonparametric probabilistic model
such as a Gaussian process (GP). Our approach is easy to tune, and is able to
focus on local region of the optimization space that can be tackled by existing
BO methods. The key idea is to use two probabilistic models: a coarse GP to
identify the ROI, and a localized GP for optimization within the ROI. We show
theoretically that BALLET can efficiently shrink the search space, and can
exhibit a tighter regret bound than standard BO without ROI filtering. We
demonstrate empirically the effectiveness of BALLET on both synthetic and
real-world optimization tasks.


  Due to long chemical equilibration times within standard hadronic reactions
during the hadron gas phase in relativistic heavy ion collisions it has been
suggested that the hadrons are "born" into equilibrium after the quark gluon
plasma phase. Here we develop a dynamical scheme in which possible Hagedorn
states contribute to fast chemical equilibration times of baryon anti-baryon
pairs (as well as kaon anti-kaon pairs) inside a hadron gas and just below the
critical temperature. Within this scheme, we use master equations and derive
various analytical estimates for the chemical equilibration times. Applying a
Bjorken picture to the expanding fireball, the kaons and baryons as well as the
bath of pions and Hagedorn resonances can indeed quickly chemically equilibrate
for both an initial overpopulation or underpopulation of Hagedorn resonances.
Moreover, a comparison of our results to $(B+\bar{B})/\pi^{+}$ and $K/\pi^{+}$
ratios at RHIC, indeed, shows a close match.


  A new seemingly weak axiomatic formulation of information algebras is given.
It is shown how such information algebras can be embedded into set
(information) algebras. In set algebras there is a natural relation of
conditional independence between partitions. Via the embedding of information
algebras this relation carries over to information algebras. The new axiomatic
formulation is thereby shown to be equivalent to the one given in
arXiv:1701.02658. In this way the abstract concept of conditional independence
in information algebras gets a concrete interpretation in terms of set
theoretical relations.


  A remarkable connection between BCFW recursion relations and constraints on
the S-matrix was made by Benincasa and Cachazo in 0705.4305, who noted that
mutual consistency of different BCFW constructions of four-particle amplitudes
generates non-trivial (but familiar) constraints on three-particle coupling
constants --- these include gauge invariance, the equivalence principle, and
the lack of non-trivial couplings for spins >2. These constraints can also be
derived with weaker assumptions, by demanding the existence of four-point
amplitudes that factorize properly in all unitarity limits with complex
momenta. From this starting point, we show that the BCFW prescription can be
interpreted as an algorithm for fully constructing a tree-level S-matrix, and
that complex factorization of general BCFW amplitudes follows from the
factorization of four-particle amplitudes. The allowed set of BCFW deformations
is identified, formulated entirely as a statement on the three-particle sector,
and using only complex factorization as a guide. Consequently, our analysis
based on the physical consistency of the S-matrix is entirely independent of
field theory. We analyze the case of pure Yang-Mills, and outline a proof for
gravity. For Yang-Mills, we also show that the well-known scaling behavior of
BCFW-deformed amplitudes at large z is a simple consequence of factorization.
For gravity, factorization in certain channels requires asymptotic behavior
~1/z^2.


  For a root of unity $\zeta$ of odd prime order, we restrict coefficients of
non-semisimple quantum representations of mapping class groups associated with
the small quantum group $\mathfrak{u}_\zeta \mathfrak{sl}_2$ from
$\mathbb{Q}(\zeta)$ to $\mathbb{Z}[\zeta]$. We do this by exhibiting explicit
bases of states spaces that span $\mathbb{Z}[\zeta]$-lattices that are
invariant under projective actions of mapping class groups.


  Strain localization and resulting plasticity and failure play an important
role in the evolution of the lithosphere. These phenomena are commonly modeled
by Stokes flows with viscoplastic rheologies. The nonlinearities of these
rheologies make the numerical solution of the resulting systems challenging,
and iterative methods often converge slowly or not at all. Yet accurate
solutions are critical for representing the physics. Moreover, for some
rheology laws, aspects of solvability are still unknown. We study a basic but
representative viscoplastic rheology law. The law involves a yield stress that
is independent of the dynamic pressure, referred to as von Mises yield
criterion. Two commonly used variants, perfect/ideal and composite
viscoplasticity, are compared. We derive both variants from energy minimization
principles, and we use this perspective to argue when solutions are unique. We
propose a new stress-velocity Newton solution algorithm that treats the stress
as an independent variable during the Newton linearization but requires
solution only of Stokes systems that are of the usual velocity-pressure form.
To study different solution algorithms, we implement 2D and 3D finite element
discretizations, and we generate Stokes problems with up to 7 orders of
magnitude viscosity contrasts, in which compression or tension results in
significant nonlinear localization effects. Comparing the performance of the
proposed Newton method with the standard Newton method and the Picard
fixed-point method, we observe a significant reduction in the number of
iterations and improved stability with respect to problem nonlinearity, mesh
refinement, and the polynomial order of the discretization.


  Quantum interference, like Hong-Ou-Mandel interference, has played an
important role to test fundamental concepts in quantum physics. We
experimentally show that the multiple quantum interference effects enable the
generation of high-performance polarization entangled photons. These photons
have a high-emission rate, are degenerate, have a broadband distribution, and
are postselection free. A quantum interferometric scheme, based on a round-trip
configuration of a double-pass polarization Sagnac interferometer, makes it
possible to use the large generation efficiency of polarization entangled
photons in the process of parametric down-conversion and to separate degenerate
photon pairs into different optical modes with no requirement of postselection.
We demonstrate experimentally that multiple quantum interference is not only an
interesting fundamental quantum optical phenomenon but can be used for novel
photonic quantum information technologies.


  Experimental and empirical observations on cell metabolism cannot be
understood as a whole without their integration into a consistent systematic
framework. However, the characterization of metabolic flux phenotypes is
typically reduced to the study of a single optimal state, like maximum biomass
yield that is by far the most common assumption. Here we confront optimal
growth solutions to the whole set of feasible flux phenotypes (FFP), which
provides a benchmark to assess the likelihood of optimal and high-growth states
and their agreement with experimental results. In addition, FFP maps are able
to uncover metabolic behaviors, such as aerobic fermentation accompanying
exponential growth on sugars at nutrient excess conditions, that are
unreachable using standard models based on optimality principles. The
information content of the full FFP space provides us with a map to explore and
evaluate metabolic behavior and capabilities, and so it opens new avenues for
biotechnological and biomedical applications.


  The PYTHIA program is a standard tool for the generation of high-energy
collisions, comprising a coherent set of physics models for the evolution from
a few-body hard process to a complex multihadronic final state. It contains a
library of hard processes and models for initial- and final-state parton
showers, multiple parton-parton interactions, beam remnants, string
fragmentation and particle decays. It also has a set of utilities and
interfaces to external programs. While previous versions were written in
Fortran, PYTHIA 8 represents a complete rewrite in C++. The current release is
the first main one after this transition, and does not yet in every respect
replace the old code. It does contain some new physics aspects, on the other
hand, that should make it an attractive option especially for LHC physics
studies.


  This paper proposes an end-to-end framework, namely fully convolutional
recurrent network (FCRN) for handwritten Chinese text recognition (HCTR).
Unlike traditional methods that rely heavily on segmentation, our FCRN is
trained with online text data directly and learns to associate the pen-tip
trajectory with a sequence of characters. FCRN consists of four parts: a
path-signature layer to extract signature features from the input pen-tip
trajectory, a fully convolutional network to learn informative representation,
a sequence modeling layer to make per-frame predictions on the input sequence
and a transcription layer to translate the predictions into a label sequence.
The FCRN is end-to-end trainable in contrast to conventional methods whose
components are separately trained and tuned. We also present a refined beam
search method that efficiently integrates the language model to decode the FCRN
and significantly improve the recognition results.
  We evaluate the performance of the proposed method on the test sets from the
databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition
competition, and both achieve state-of-the-art performance with correct rates
of 96.40% and 95.00%, respectively.


  We study the existence of weak solutions to the two-phase model of crowd
motion. The model encompasses the flow in the uncongested regime (compressible)
and the congested one (incompressible) with the free boundary separating the
two phases. The congested regime appears when the density in the uncongested
regime $\varrho^*(t,x)$ achieves a threshold value $\varrho^*(t,x)$ that
describes the comfort zone of individuals. This quantity is prescribed
initially and transported along with the flow. We prove that this system can be
approximated by the fully compressible Navier-Stokes system with a singular
pressure, supplemented with transport equation for the congestion density. We
also present the application of this approximation for the purposes of
numerical simulations in the one-dimensional domain.


  Motivated by recent experiments involving the non-destructive imaging of
magnetization of a spin-1 87Rb Bose gas (Higbie et al., cond-mat/0502517), we
address the question of how the spontaneous magnetization of a ferromagnetic
BEC occurs in a spin-conserving system. Due to competition between the
ferromagnetic interaction and the total spin conservation, various spin
structures such as staggered magnetic domains, and helical and concentric ring
structures are formed, depending on the geometry of the trapping potential.


  The storage and processing of quantum information are susceptible to external
noise, resulting in computational errors that are inherently continuous A
powerful method to suppress these effects is to use quantum error correction.
Typically, quantum error correction is executed in discrete rounds where errors
are digitized and detected by projective multi-qubit parity measurements. These
stabilizer measurements are traditionally realized with entangling gates and
projective measurement on ancillary qubits to complete a round of error
correction. However, their gate structure makes them vulnerable to errors
occurring at specific times in the code and errors on the ancilla qubits. Here
we use direct parity measurements to implement a continuous quantum bit-flip
correction code in a resource-efficient manner, eliminating entangling gates,
ancilla qubits, and their associated errors. The continuous measurements are
monitored by an FPGA controller that actively corrects errors as they are
detected. Using this method, we achieve an average bit-flip detection
efficiency of up to 91%. Furthermore, we use the protocol to increase the
relaxation time of the protected logical qubit by a factor of 2.7 over the
relaxation times of the bare comprising qubits. Our results showcase
resource-efficient stabilizer measurements in a multi-qubit architecture and
demonstrate how continuous error correction codes can address challenges in
realizing a fault-tolerant system.


  We argue that a field theory defined on noncommutative (NC) spacetime should
be regarded as a theory of gravity, which we refer to as the emergent gravity.
A whole point of the emergent gravity is essentially originated from the basic
property: A NC spacetime is a (NC) phase space. This fact leads to two
important consequences: (I) A NC field theory can basically be identified with
a matrix model or a large N field theory where NC fields can be regarded as
master fields of large N matrices. (II) NC fields essentially define vector
(tetrad) fields. So they define a gravitational metric of some manifold as an
emergent geometry from NC gauge fields. Of course, the pictures (I) and (II)
should refer to the same physics, which should be familiar with the large N
duality in string theory. The 1/N corrections in the picture (I) correspond to
the derivative corrections in terms of the noncommutativity \theta for the
picture (II).


  This paper investigates properties of realizations of linear or group codes
on general graphs that lead to local reducibility.
  Trimness and properness are dual properties of constraint codes. A linear or
group realization with a constraint code that is not both trim and proper is
locally reducible. A linear or group realization on a finite cycle-free graph
is minimal if and only if every local constraint code is trim and proper.
  A realization is called observable if there is a one-to-one correspondence
between codewords and configurations, and controllable if it has independent
constraints. A linear or group realization is observable if and only if its
dual is controllable. A simple counting test for controllability is given. An
unobservable or uncontrollable realization is locally reducible. Parity-check
realizations are controllable if and only if they have independent parity
checks. In an uncontrollable tail-biting trellis realization, the behavior
partitions into disconnected subbehaviors, but this property does not hold for
non-trellis realizations. On a general graph, the support of an unobservable
configuration is a generalized cycle.


  This monograph develops a comprehensive statistical learning framework that
is robust to (distributional) perturbations in the data using Distributionally
Robust Optimization (DRO) under the Wasserstein metric. Beginning with
fundamental properties of the Wasserstein metric and the DRO formulation, we
explore duality to arrive at tractable formulations and develop finite-sample,
as well as asymptotic, performance guarantees. We consider a series of learning
problems, including (i) distributionally robust linear regression; (ii)
distributionally robust regression with group structure in the predictors;
(iii) distributionally robust multi-output regression and multiclass
classification, (iv) optimal decision making that combines distributionally
robust regression with nearest-neighbor estimation; (v) distributionally robust
semi-supervised learning, and (vi) distributionally robust reinforcement
learning. A tractable DRO relaxation for each problem is being derived,
establishing a connection between robustness and regularization, and obtaining
bounds on the prediction and estimation errors of the solution. Beyond theory,
we include numerical experiments and case studies using synthetic and real
data. The real data experiments are all associated with various health
informatics problems, an application area which provided the initial impetus
for this work.


  In this proceedings, we present our first measurements of $\phi$ meson and
$\Xi^-$ hyperon production in Au+Au collisions at $\sqrt{s_{NN}}$ = 3 GeV.
Various models including thermal and transport model calculations are compared
to data, these results imply that the matter produced in the 3 GeV Au+Au
collisions is considerably different from that at higher energies.


  Open-world detection poses significant challenges, as it requires the
detection of any object using either object class labels or free-form texts.
Existing related works often use large-scale manual annotated caption datasets
for training, which are extremely expensive to collect. Instead, we propose to
transfer knowledge from vision-language models (VLMs) to enrich the
open-vocabulary descriptions automatically. Specifically, we bootstrap dense
synthetic captions using pre-trained VLMs to provide rich descriptions on
different regions in images, and incorporate these captions to train a novel
detector that generalizes to novel concepts. To mitigate the noise caused by
hallucination in synthetic captions, we also propose a novel hyperbolic
vision-language learning approach to impose a hierarchy between visual and
caption embeddings. We call our detector ``HyperLearner''. We conduct extensive
experiments on a wide variety of open-world detection benchmarks (COCO, LVIS,
Object Detection in the Wild, RefCOCO) and our results show that our model
consistently outperforms existing state-of-the-art methods, such as GLIP,
GLIPv2 and Grounding DINO, when using the same backbone.


  It is proven that the usual quadratic general-covariant Lagrangian for the
Dirac field leads to a symmetric, divergence-free energy-momentum tensor in the
standard Riemannian framework of space-time without torsion, provided the
tetrad field components are the only quantities related to gravitation that are
varied independently.


  We have studied theoretically the space-time evolution of the thermal and
electromagnetic perturbation in a superconductor with the linear
current-voltage characteristic in the flux flow regime. On the basis of a
linear analysis of a set of differential equations describing small
perturbations of temperature and magnetic field we will found that under some
conditions an instability may occur in the sample, which takes into account an
inertial properties of the vortices mas


  We show that the Dean--Kawasaki equation does not admit nontrivial solutions
in the space of tempered measures. More specifically, we consider martingale
solutions taking values, and with initial conditions, in the subspace of
measures admitting infinite mass and satisfying some integrability conditions.
Following work by the first author, Lehmann and von Renesse [arXiv:1806.05018],
we show that the equation only admits solutions if the initial measure is a
discrete measure. Our result extends the previously mentioned works by allowing
measures with infinite mass.


  The Savage-Dickey ratio is known as a specialised representation of the Bayes
factor (O'Hagan and Forster, 2004) that allows for a functional plugging
approximation of this quantity. We demonstrate here that the Savage-Dickey
representation is in fact a generic representation of the Bayes factor that
relies on specific measure-theoretic versions of the densities involved in the
ratio, instead of a special identity imposing the above constraints on the
prior distributions. We completely clarify the measure-theoretic foundations of
the representation as well as the generalisation of Verdinelli and Wasserman
(1995) and propose a comparison of this new approximation with their version,
as well as with bridge sampling and Chib's approaches.


  Runtime monitoring provides a more realistic and applicable alternative to
verification in the setting of real neural networks used in industry. It is
particularly useful for detecting out-of-distribution (OOD) inputs, for which
the network was not trained and can yield erroneous results. We extend a
runtime-monitoring approach previously proposed for classification networks to
perception systems capable of identification and localization of multiple
objects. Furthermore, we analyze its adequacy experimentally on different kinds
of OOD settings, documenting the overall efficacy of our approach.


  We investigate the merits of a massive forward modeling of ground-based
optical imaging as a diagnostic for the strong lensing nature of Early-Type
Galaxies, in the light of which blurred and faint Einstein rings can hide. We
simulate several thousand mock strong lenses under ground- and space-based
conditions as arising from the deflection of an exponential disk by a
foreground de Vaucouleurs light profile whose lensing potential is described by
a Singular Isothermal Ellipsoid. We then fit for the lensed light distribution
with sl_fit after having subtracted the foreground light emission off (ideal
case) and also after having fitted the deflector's light with galfit. By
setting thresholds in the output parameter space, we can decide the
lens/not-a-lens status of each system. We finally apply our strategy to a
sample of 517 lens candidates present in the CFHTLS data to test the
consistency of our selection approach. The efficiency of the fast modeling
method at recovering the main lens parameters like Einstein radius, total
magnification or total lensed flux, is quite comparable under CFHT and HST
conditions when the deflector is perfectly subtracted off (only possible in
simulations), fostering a sharp distinction between the good and the bad
candidates. Conversely, for a more realistic subtraction, a substantial
fraction of the lensed light is absorbed into the deflector's model, which
biases the subsequent fitting of the rings and then disturbs the selection
process. We quantify completeness and purity of the lens finding method in both
cases. This suggests that the main limitation currently resides in the
subtraction of the foreground light. Provided further enhancement of the
latter, the direct forward modeling of large numbers of galaxy-galaxy strong
lenses thus appears tractable and could constitute a competitive lens finder in
the next generation of wide-field imaging surveys.


  We study families $\Phi$ of coverings which are faithful for the Hausdorff
dimension calculation on a given set $E$ (i. e., special relatively narrow
families of coverings leading to the classical Hausdorff dimension of an
arbitrary subset of $E$) and which are natural generalizations of comparable
net-coverings. They are shown to be very useful for the determination or
estimation of the Hausdorff dimension of sets and probability measures.
  We give general necessary and sufficient conditions for a covering family to
be faithful and new techniques for proving faithfulness/non-faithfulness for
the family of cylinders generated by expansions of real numbers. Motivated by
applications in the multifractal analysis of infinite Bernoulli convolutions,
we study in details the Cantor series expansion and prove necessary and
sufficient conditions for the corresponding net-coverings to be faithful. To
the best of our knowledge this is the first known sharp condition of the
faithfulness for a class of covering families containing both faithful and
non-faithful ones.
  Applying our results, we characterize fine fractal properties of probability
measures with independent digits of the Cantor series expansion and show that a
class of faithful net-coverings essentially wider that the class of comparable
ones. We construct, in particular, rather simple examples of faithful families
$\mathcal{A}$ of net-coverings which are "extremely non-comparable" to the
Hausdorff measure.


  Data augmentation (DA) is fundamental against overfitting in large
convolutional neural networks, especially with a limited training dataset. In
images, DA is usually based on heuristic transformations, like geometric or
color transformations. Instead of using predefined transformations, our work
learns data augmentation directly from the training data by learning to
transform images with an encoder-decoder architecture combined with a spatial
transformer network. The transformed images still belong to the same class but
are new, more complex samples for the classifier. Our experiments show that our
approach is better than previous generative data augmentation methods, and
comparable to predefined transformation methods when training an image
classifier.


  The use of large, X-ray selected galaxy cluster catalogues for cosmological
analyses requires a thorough understanding of the X-ray mass estimates. Weak
gravitational lensing is an ideal method to shed light on such issues, due to
its insensitivity to the cluster dynamical state. We perform a weak lensing
calibration of 166 galaxy clusters from the REFLEX and BCS cluster catalogue
and compare our results to the X-ray masses based on scaled luminosities from
that catalogue. To interpret the weak lensing signal in terms of cluster
masses, we compare the lensing signal to simple theoretical Navarro-Frenk-White
models and to simulated cluster lensing profiles, including complications such
as cluster substructure, projected large-scale structure, and Eddington bias.
We find evidence of underestimation in the X-ray masses, as expected, with
$\langle M_{\mathrm{X}}/M_{\mathrm{WL}}\rangle = 0.75 \pm 0.07$ stat. $\pm
0.05$ sys. for our best-fit model. The biases in cosmological parameters in a
typical cluster abundance measurement that ignores this mass bias will
typically exceed the statistical errors.


  Monocular SLAM has received a lot of attention due to its simple RGB inputs
and the lifting of complex sensor constraints. However, existing monocular SLAM
systems are designed for bounded scenes, restricting the applicability of SLAM
systems. To address this limitation, we propose MoD-SLAM, the first monocular
NeRF-based dense mapping method that allows 3D reconstruction in real-time in
unbounded scenes. Specifically, we introduce a Gaussian-based unbounded scene
representation approach to solve the challenge of mapping scenes without
boundaries. This strategy is essential to extend the SLAM application.
Moreover, a depth estimation module in the front-end is designed to extract
accurate priori depth values to supervise mapping and tracking processes. By
introducing a robust depth loss term into the tracking process, our SLAM system
achieves more precise pose estimation in large-scale scenes. Our experiments on
two standard datasets show that MoD-SLAM achieves competitive performance,
improving the accuracy of the 3D reconstruction and localization by up to 30%
and 15% respectively compared with existing state-of-the-art monocular SLAM
systems.


  Preserving differential privacy has been well studied under centralized
setting. However, it's very challenging to preserve differential privacy under
multiparty setting, especially for the vertically partitioned case. In this
work, we propose a new framework for differential privacy preserving multiparty
learning in the vertically partitioned setting. Our core idea is based on the
functional mechanism that achieves differential privacy of the released model
by adding noise to the objective function. We show the server can simply
dissect the objective function into single-party and cross-party sub-functions,
and allocate computation and perturbation of their polynomial coefficients to
local parties. Our method needs only one round of noise addition and secure
aggregation. The released model in our framework achieves the same utility as
applying the functional mechanism in the centralized setting. Evaluation on
real-world and synthetic datasets for linear and logistic regressions shows the
effectiveness of our proposed method.


  We report the detection of [CII]158um emission from a system of three
closely-separated sources in the COSMOS field at z~4.56, as part of the ALMA
Large Program to INvestigate CII at Early times (ALPINE). The two dominant
sources are closely associated, both spatially (1.6"~11kpc) and in velocity
(~100km/s), while the third source is slightly more distant (2.8"~18kpc,
~300km/s). The second strongest source features a slight velocity gradient,
while no significant velocity gradient is seen in the other two sources. Using
the observed [CII] luminosities, we derive a total
log(SFR_[CII]/[Msol/year])=2.8+/-0.2, which may be split into contributions of
59%, 31%, and 10% from the central, east, and west sources, respectively.
Comparison of these [CII] detections to recent zoom-in cosmological simulations
suggests an ongoing major merger. We are thus witnessing a system in a major
phase of mass build-up by merging, including an on-going major merger and an
upcoming minor merger, which is expected to end up in a single massive galaxy
by z~2.5.


  The principle components of the Standard Model and the status of their
experimental verification are reviewed. Lectures given at TASI97:
Supersymmetry, Supergravity, and Supercolliders}, Boulder, CO, June 1997.


  We report the properties of molecular gas in a sample of six host galaxies of
fast radio bursts (FRBs) obtained from CO observations with the Atacama Large
Millimeter/submillimeter Array (FRBs 20180924B, 20190102C, and 20190711A) and
results of one non-detection in a dwarf galaxy (FRB20121102A) and two events
detected in M81 (FRB20200120E) and the Milky Way (FRB20200428A). The CO
observations resulted in the detection of CO(3-2) emission in the FRB20180924B
host and non-detections of CO(3-2) and CO(2-1) emission in the hosts of
FRB20190102C and FRB20190711A, respectively. The derived molecular gas mass and
3$\sigma$ upper limit is $(2.4 \pm 0.2) \times 10^9$ $M_{\odot}$, $<3.8 \times
10^8$ $M_{\odot}$, and $<6.7 \times 10^9$ $M_{\odot}$ for the hosts of
FRB20180924B, FRB20190102C, and FRB20190711A, respectively. We found diversity
in molecular gas properties (gas mass, gas depletion time, and gas fraction to
stellar mass) in the sample. Compared to other star-forming galaxies, the
FRB20180924B host is gas-rich (the larger molecular gas fraction), and the
hosts of FRB20190102C and FRB20200120E are gas-poor with a shorter depletion
time for their stellar mass and star-formation rate. Our findings suggest that
FRBs arise from multiple progenitors or single progenitors that can exist in a
wide range of galaxy environments. Statistical analysis shows a significant
difference in the distribution of molecular gas fraction between the FRB hosts
and local star-forming galaxies. However, the difference is not substantial
when an outlier, the FRB20200120E host, is excluded, and analysis with a larger
sample is needed.


  We consider interference between the Higgs signal and QCD background in
$gg\rightarrow h \rightarrow \gamma\gamma$ and its effect on the on-shell Higgs
rate. The existence of sizable strong phases leads to destructive interference
of about 2% of the on-shell cross section in the Standard Model. This effect
can be enhanced by beyond the standard model physics. In particular, since it
scales differently from the usual rates, the presence of interference allows
indirect limits to be placed on the Higgs width in a novel way, using on-shell
rate measurements. Our study motivates further QCD calculations to reduce
uncertainties. We discuss potential width-sensitive observables, both using
total and differential rates and find that the HL-LHC can potentially
indirectly probe widths of order tens of MeV.


  Judging a gymnastics routine is a noisy process, and the performance of
judges varies widely. In collaboration with the F\'ed\'eration Internationale
de Gymnastique (FIG) and Longines, we are designing and implementing an
improved statistical engine to analyze the performance of gymnastics judges
during and after major competitions like the Olympic Games and the World
Championships. The engine, called the Judge Evaluation Program (JEP), has three
objectives: (1) provide constructive feedback to judges, executive committees
and national federations; (2) assign the best judges to the most important
competitions; (3) detect bias and outright cheating.
  Using data from international gymnastics competitions held during the
2013-2016 Olympic cycle, we first develop a marking score evaluating the
accuracy of the marks given by gymnastics judges. Judging a gymnastics routine
is a random process, and we can model this process very accurately using
heteroscedastic random variables. The marking score scales the difference
between the mark of a judge and the theoretical performance of a gymnast as a
function of the standard deviation of the judging error estimated from data for
each apparatus. This dependence between judging variability and performance
quality has never been properly studied. We then study ranking scores assessing
to what extent judges rate gymnasts in the correct order, and explain why we
ultimately chose not to implement them. We also study outlier detection to
pinpoint gymnasts who were poorly evaluated by judges. Finally, we discuss
interesting observations and discoveries that led to recommendations and rule
changes at the FIG.


  The Hosoya index $Z (G)$ of a graph $G$ is defined as the total number of
edge independent sets of $G$. In this paper, we extend the research of [J. Ou,
On extremal unicyclic molecular graphs with maximal Hosoya index,
\textit{Discrete Appl. Math.} 157 (2009) 391--397.] and [Y. Ye, X. Pan, H. Liu,
Ordering unicyclic graphs with respect to Hosoya indices and Merrifield-Simmons
indices, \textit{MATCH Commun. Math. Comput. Chem.} 59 (2008) 191--202.] and
order the largest $n-1$ unicyclic graphs with respect to the Hosoya index.


  We discuss relation between lattice phenomenology of confining fields in the
vacuum state of Yang-Mills theories (mostly SU(2) case) and continuum theories.
In the continuum, understanding of the confinement is most straightforward in
the dual formulation which involves higher dimensions. We try to bridge these
two approaches to the confinement, let it be on a rudimentary level. We review
lattice data on low-dimensional defects, that is monopoles, center vortices,
topological defects. There is certain resemblance to dual strings, domain
walls, introduced in large-N Yang-Mills theories.


  We present a large deviations theory of the spin-spin correlation functions
in the Random Field Ising Model on the Bethe lattice, both at finite and zero
temperature. Rare events of atypically correlated variables are particularly
important at the critical point: the phase transition is driven by few pairs of
strongly correlated spins, while the majority remains basically uncorrelated.
At the zero temperature critical point the number of spin pairs correlated over
a distance L is shown to be no longer exponential, but only linear in the spins
separation.


  We give improved algorithms for maintaining edge-orientations of a
fully-dynamic graph, such that the out-degree of each vertex is bounded. On one
hand, we show how to orient the edges such that the out-degree of each vertex
is proportional to the arboricity $\alpha$ of the graph, in, either, an
amortised update time of $O(\log^2 n \log \alpha)$, or a worst-case update time
of $O(\log^3 n \log \alpha)$. On the other hand, motivated by applications
including dynamic maximal matching, we obtain a different trade-off, namely
either $O(\log n \log \alpha)$, amortised, or $O(\log ^2 n \log \alpha)$,
worst-case time, for the problem of maintaining an edge-orientation with at
most $O(\alpha + \log n)$ out-edges per vertex. Since our algorithms have
update times with worst-case guarantees, the number of changes to the solution
(i.e. the recourse) is naturally limited. Our algorithms adapt to the current
arboricity of the graph, and yield improvements over previous work: Firstly, we
obtain an $O(\varepsilon^{-6}\log^3 n \log \rho)$ worst-case update time
algorithm for maintaining a $(1+\varepsilon)$ approximation of the maximum
subgraph density, $\rho$.
  Secondly, we obtain an $O(\varepsilon^{-6}\log^3 n \log \alpha)$ worst-case
update time algorithm for maintaining a $(1 + \varepsilon) \cdot OPT + 2$
approximation of the optimal out-orientation of a graph with adaptive
arboricity $\alpha$. This yields the first worst-case polylogarithmic dynamic
algorithm for decomposing into $O(\alpha)$ forests.Thirdly, we obtain
arboricity-adaptive fully-dynamic deterministic algorithms for a variety, of
problems including maximal matching, $\Delta+1$ coloring, and matrix vector
multiplication. All update times are worst-case $O(\alpha+\log^2n \log
\alpha)$, where $\alpha$ is the current arboricity of the graph.


  In this work, the univariant two-phase coexistence line of the hydrate of
tetrahydrofuran (THF) is determined from 100 to 1000 bar by molecular dynamics
simulations. The study is carried out by putting in contact a THF hydrate phase
with a stoichiometric aqueous solution phase. Following the direct coexistence
technique, the pressure has been fixed, and the coexistence line has been
determined by analyzing if the hydrate phase grows or melts at different values
of temperature. The model of water used is the well-known TIP4P/Ice model. We
have used two different models of THF based on the transferable parameters for
phase equilibria-united atom approach (TraPPE-UA), the original (flexible)
TraPPe-UA model as well as a rigid and planar version of it. Overall, at high
pressures, small differences have been observed in the results obtained by both
models. Also, large differences have been observed in the computational efforts
required by the simulations performed using both models, being the rigid and
planar version much faster than the original one. The effect of the unlike
dispersive interactions between the water and THF molecules has been also
analyzed at 250 bar using the rigid and planar THF model. In particular, we
have modified the Berthelot combining rule by adding a factor ({\xi}O-THF) that
modifies the unlike water-THF dispersive interactions and we have analyzed the
effect on the dissociation temperature when {\xi}O-THF is modified from 1.0
(original Berthelot combining rule) to 1.4 (modified Berthelot combining rule).
We have extended the study using {\xi}O-THF = 1.4 and the rigid THF model to
the rest of the pressures considered in this work, finding an excellent
agreement with the scarce experimental data taken from the literature.


  In this work we introduce symmetry-preserving, physics-informed neural
networks (S-PINNs) motivated by symmetries that are ubiquitous to solutions of
nonlinear dynamical lattices. Although the use of PINNs have recently attracted
much attention in data-driven discovery of solutions chiefly to partial
differential equations, we demonstrate that they fail at enforcing important
physical laws including symmetries of solutions and conservation laws. Through
the correlation of parity symmetries in both space and time of solutions to
differential equations with their group equivariant representation, we
construct group-equivariant NNs which respect spatio-temporal parity symmetry.
Moreover, we adapt the proposed architecture to enforce different types of
periodicity (or localization) of solutions to nonlinear dynamical lattices. We
do so by applying S-PINNs to the completely integrable Ablowitz-Ladik model,
and performing numerical experiments with a special focus on waveforms that are
related to rogue structures. These include the Kuznetsov-Ma soliton, and
Akhmediev breather as well as the Peregrine soliton. Our numerical results
demonstrate the superiority and robustness of the proposed architecture over
standard PINNs.


  When learning to behave in a stochastic environment where safety is critical,
such as driving a vehicle in traffic, it is natural for human drivers to plan
fallback strategies as a backup to use if ever there is an unexpected change in
the environment. Knowing to expect the unexpected, and planning for such
outcomes, increases our capability for being robust to unseen scenarios and may
help prevent catastrophic failures. Control of Autonomous Vehicles (AVs) has a
particular interest in knowing when and how to use fallback strategies in the
interest of safety. Due to imperfect information available to an AV about its
environment, it is important to have alternate strategies at the ready which
might not have been deduced from the original training data distribution.
  In this paper we present a principled approach for a model-free Reinforcement
Learning (RL) agent to capture multiple modes of behaviour in an environment.
We introduce an extra pseudo-reward term to the reward model, to encourage
exploration to areas of state-space different from areas privileged by the
optimal policy. We base this reward term on a distance metric between the
trajectories of agents, in order to force policies to focus on different areas
of state-space than the initial exploring agent. Throughout the paper, we refer
to this particular training paradigm as learning fallback strategies.
  We apply this method to an autonomous driving scenario, and show that we are
able to learn useful policies that would have otherwise been missed out on
during training, and unavailable to use when executing the control algorithm.


  We compute the $L^p$-cohomology spaces of some negatively curved manifolds.
We deal with two cases: manifolds with finite volume and sufficiently pinched
negative curvature, and conformally compact manifolds.


  In this paper we investigate the theory of cuspidalisation of sections of
arithmetic fundamental groups of hyperbolic curves to cuspidally i-th and
2/p-th step prosolvable arithmetic fundamental groups. As a consequence we
exhibit two, necessary and sufficient, conditions for sections of arithmetic
fundamental groups of hyperbolic curves over p-adic local fields to arise from
rational points. We also exhibit a class of sections of arithmetic fundamental
groups of p-adic curves which are orthogonal to Pic, and which satisfy
(unconditionally) one of the above conditions.


  We show that in the Ho\v{r}ava-Lifshitz theory at the kinetic-conformal
point, in the low energy regime, a wave zone for asymptotically flat fields can
be consistently defined. In it, the physical degrees of freedom, the transverse
traceless tensorial modes, satisfy a linear wave equation. The Newtonian
contributions, among which there are terms which manifestly break the
relativistic invariance, are non-trivial but do not obstruct the free
propagation (radiation) of the physical degrees of freedom. For an appropriate
value of the couplings of the theory, the wave equation becomes the
relativistic one in agreement with the propagation of the gravitational
radiation in the wave zone of General Relativity. Previously to the wave zone
analysis, and in general grounds, we obtain the physical Hamiltonian of the
Ho\v{r}ava-Lifshitz theory at the kinetic-conformal point in the constrained
submanifold. We determine the canonical physical degrees of freedom in a
particular coordinate system. They are well defined fuctions of the
transverse-traceless modes of the metric and coincide with them in the wave
zone and also at linearized level.


  We show that for a suitable class of ``Dirac-like'' operators there holds a
Gluing Theorem for connected sums. More precisely, if $M_1$ and $M_2$ are
closed Riemannian manifolds of dimension $n\ge 3$ together with such operators,
then the connected sum $M_1 # M_2$ can be given a Riemannian metric such that
the spectrum of its associated operator is close to the disjoint union of the
spectra of the two original operators. As an application, we show that in
dimension $n\equiv 3$ mod 4 harmonic spinors for the Dirac operator of a spin,
$\Spinc$, or $\Spinh$ manifold are not topologically obstructed.


  Some sufficient conditions on a simplicial space $X$ guaranteeing that
$X_1\simeq \Omega|X|$ were given by Segal. We give a generalization of this
result for multisimplicial spaces. This generalization is appropriate for the
reduced bar construction, providing an $n$-fold delooping of the classifying
space of a category.


  Context: Flare kernels brighten simultaneously in all SDO/AIA channels making
it difficult to determine their temperature structure. IRIS is able to
spectrally resolve Fe xxi emission from cold chromospheric brightenings, so can
be used to infer the amount of Fe xxi emission in 131 channel. Aims: We use
observations of two small solar flares seen by IRIS and SDO to compare the EMs
deduced from the IRIS Fe xxi line and the AIA 131 channel to determine the
fraction of Fe xxi emission in flare kernels in the 131 channel of AIA.
Methods: Cotemporal and cospatial pseudo-raster AIA images are compared with
the IRIS results.We use multi-Gaussian line fitting to separate the blending
chromospheric emission so as to derive Fe xxi intensities and Doppler shifts in
IRIS spectra. Results: We define loop and kernel regions based on the
brightness of the 131 and 1600 {\AA} intensities. In the loop regions the Fe
xxi EMs are typically 80% of the 131 ones, and range from 67% to 92%. Much of
the scatter is due to small misalignments but the largest site with low Fe xxi
contributions was probably affected by a recent injection of cool plasma into
the loop. In flare kernels the contribution of Fe xxi increases from less than
10% at the low intensity 131 sites to 40-80% in the brighter kernels. Here the
Fe xxi is superimposed on bright chromospheric emission and the Fe xxi line
shows blue shifts, sometimes extending up to the edge of the spectral window,
200 km/s. Conclusions: The AIA 131 emission in flare loops is due to Fe xxi
emission with a 10-20% contribution from continuum, Fe xxiii, and cooler
background plasma emission. In bright flare kernels up to 52% of the 131 is
from cooler plasma. The wide range seen in the kernels is caused by significant
structure in the kernels which is seen as sharp gradients in Fe xxi EM at sites
of molecular and transition region emission.


  We present three multi-scale similarity learning architectures, or DeepSim
networks. These models learn pixel-level matching with a contrastive loss and
are agnostic to the geometry of the considered scene. We establish a middle
ground between hybrid and end-to-end approaches by learning to densely allocate
all corresponding pixels of an epipolar pair at once. Our features are learnt
on large image tiles to be expressive and capture the scene's wider context. We
also demonstrate that curated sample mining can enhance the overall robustness
of the predicted similarities and improve the performance on radiometrically
homogeneous areas. We run experiments on aerial and satellite datasets. Our
DeepSim-Nets outperform the baseline hybrid approaches and generalize better to
unseen scene geometries than end-to-end methods. Our flexible architecture can
be readily adopted in standard multi-resolution image matching pipelines.


  We present a simple algorithm for finding eigenmodes of the Laplacian for
arbitrary compact hyperbolic 3-manifolds. We apply our algorithm to a sample of
twelve manifolds and generate a list of the lowest eigenvalues. We also display
a selection of eigenmodes taken from the Weeks space.


  The energy-momentum tensor, which is coordinate independent, is used to
calculate energy, momentum and angular-momentum of two different tetrad fields.
Although, the two tetrad fields reproduce the same space-time their energies
are different. Therefore, a regularized expression of the gravitational
energy-momentum tensor of the teleparallel equivalent of general relativity,
(TEGR), is used to make the energies of the two tetrad fields equal. The
definition of the gravitational energy-momentum is used to investigate the
energy within the external event horizon. The components of angular-momentum
associated with these space-times are calculated. In spite that we use a static
space-times, we get a non-zero component of angular-momentum! Therefore, we
derive the killing vectors associated with these space-times using the
definition of the Lie derivative of a second rank tensor in the framework of
the TEGR to make the picture more clear.


  The luminous, oxygen-rich supernova remnant, J1228+441, is located in the
irregular galaxy NGC 4449 and has been observed at radio wavelengths for thirty
years. An analysis of recent VLA observations of NGC 4449, combined with VLA
archive data and previously published VLA and WSRT observations, yields light
curves at 6 and 20 cm from 1972 to 2002. The light curves at all radio
frequencies exhibit a marked decline in radio emission, confirming past
findings. This paper presents and discusses the radio light curves and spectral
index, alpha, variations from 1972 to 2002 where nu propto nu^(-alpha) and
compares J1228+441 with other radio supernovae. The spectral index of J1228+441
appears to have steepened in the last five years at higher frequencies from
alpha=0.64 +- 0.02 in 1996 to alpha=1.01 +- 0.02 in 2001-2002.


  We introduce InFusionSurf, a novel approach to enhance the fidelity of neural
radiance field (NeRF) frameworks for 3D surface reconstruction using RGB-D
video frames. Building upon previous methods that have employed feature
encoding to improve optimization speed, we further improve the reconstruction
quality with minimal impact on optimization time by refining depth information.
Our per-frame intrinsic refinement scheme addresses frame-specific blurs caused
by camera motion in each depth frame. Furthermore, InFusionSurf utilizes a
classical real-time 3D surface reconstruction method, the truncated signed
distance field (TSDF) Fusion, as prior knowledge to pretrain the feature grid
to support reconstruction details while accelerating the training. The
quantitative and qualitative experiments comparing the performances of
InFusionSurf against prior work indicate that our method is capable of
accurately reconstructing a scene without sacrificing optimization speed. We
also demonstrate the effectiveness of our per-frame intrinsic refinement and
TSDF Fusion prior learning techniques via an ablation study.


  It is well-known (cf. K.-Pudl\'ak 1989) that a polynomial time algorithm
finding tautologies hard for a propositional proof system $P$ exists iff $P$ is
not optimal. Such an algorithm takes $1^{(k)}$ and outputs a tautology $\tau_k$
of size at least $k$ such that $P$ is not p-bounded on the set of all
$\tau_k$'s.
  We consider two more general search problems involving finding a hard
formula, {\bf Cert} and {\bf Find}, motivated by two hypothetical situations:
that one can prove that $\np \neq co\np$ and that no optimal proof system
exists. In {\bf Cert} one is asked to find a witness that a given
non-deterministic circuit with $k$ inputs does not define $TAUT \cap \kk$. In
{\bf Find}, given $1^{(k)}$ and a tautology $\alpha$ of size at most $k^{c_0}$,
one should output a size $k$ tautology $\beta$ that has no size $k^{c_1}$
$P$-proof from substitution instances of $\alpha$.
  We shall prove, assuming the existence of an exponentially hard one-way
permutation, that {\bf Cert} cannot be solved by a time $2^{O(k)}$ algorithm.
Using a stronger hypothesis about the proof complexity of Nisan-Wigderson
generator we show that both problems {\bf Cert} and {\bf Find} are actually
only partially defined for infinitely many $k$ (i.e. there are inputs
corresponding to $k$ for which the problem has no solution). The results are
based on interpreting the Nisan-Wigderson generator as a proof system.


  Single electron tunneling into small superconducting islands is sensitive to
the gap energy of the excitations created in the process and, hence, depends on
the parity of the electron number in the island. We study these effects by
analyzing the kinetics of the system. Since the interplay of Coulomb blockade
and parity effects leads to a blocking of single electron tunneling, higher
order tunneling processes become important. This is well-established for
two-electron tunneling. Here we study processes of third and fourth order. We
estimate the order of magnitude for these processes and discuss their relevance
for recent experiments.


  Integral field spectroscopy can map astronomical objects spatially and
spectroscopically. Due to instrumental and atmospheric effects, it is common
for integral field instruments to yield a sampling of the sky image that is
both irregular and wavelength-dependent. Most subsequent analysis procedures
require a regular, wavelength independent sampling (for example a fixed
rectangular grid), and thus an initial step of fundamental importance is to
resample the data onto a new grid. The best possible resampling would produce a
well-sampled image, with a resolution equal to that imposed by the intrinsic
spatial resolution of the instrument, telescope, and atmosphere, and with no
statistical correlations between neighboring pixels. A standard method in the
field to produce a regular set of samples from an irregular set of samples is
Shepard's method, but Shepard's method typically yields images with a degraded
resolution and large statistical correlations between pixels. Here we introduce
a new method, which improves on Shepard's method in both these respects. We
apply this method to data from the Mapping Nearby Galaxies at Apache Point
Observatory survey, part of Sloan Digital Sky Survey IV, demonstrating a
full-width half maximum close to that of the intrinsic resolution (and ~ 16%
better than Shepard's method) and low statistical correlations between pixels.
These results nearly achieve the ideal resampling. This method can have broader
applications to other integral field data sets and to other astronomical data
sets (such as dithered images) with irregular sampling.


  We review a construction of a new class of algebraic curves, called
super-A-polynomials, and their quantum generalizations. The super-A-polynomial
is a two-parameter deformation of the A-polynomial known from knot theory or
Chern-Simons theory with SL(2,C) gauge group. The two parameters of the
super-A-polynomial encode, respectively, the t-deformation which leads to the
"refined A-polynomial", and the Q-deformation which leads to the augmentation
polynomial of knot contact homology. For a given knot, the super-A-polynomial
encodes the asymptotics of the corresponding S^r-colored HOMFLY homology for
large r, while the quantum super-A-polynomial provides recursion relations for
such homology theories for each r. The super-A-polynomial also admits a simple
physical interpretation as the defining equation for the space of SUSY vacua in
a circle compactification of the effective 3d N=2 theory associated to a given
knot (complement). We discuss properties of super-A-polynomials and illustrate
them in many examples.


  The stochastic differential equations for a model of dissipative particle
dynamics, with both total energy and total momentum conservation at every
time-step, are presented. The algorithm satisfies detailed balance as well as
the fluctuation-dissipation theorems that ensure that the proper thermodynamic
equilibrium can be reached. Macroscopic equilibrium probability distributions
as well as equations of state for the model are also derived, and an
appropriate definition of the free energy of the system is consistently
proposed. Several simulations results of equilibrium as well as transport
properties, including heat transport and thermal convection in a box, are shown
as proof of the internal consistency of the model.


  In this paper we present a hierarchical scheme to detect cyber-attacks in a
hierarchical control architecture for large-scale interconnected systems (LSS).
We consider the LSS as a network of physically coupled subsystems, equipped
with a two-layer controller: on the local level, decentralized controllers
guarantee overall stability and reference tracking; on the supervisory level, a
centralized coordinator sets references for the local regulators. We present a
scheme to detect attacks that occur at the local level, with malicious agents
capable of affecting the local control. The detection scheme is computed at the
supervisory level, requiring only limited exchange of data and model knowledge.
We offer detailed theoretical analysis of the proposed scheme, highlighting its
detection properties in terms of robustness, detectability and stealthiness
conditions.


  Score based approaches to sampling have shown much success as a generative
algorithm to produce new samples from a target density given a pool of initial
samples. In this work, we consider if we have no initial samples from the
target density, but rather $0^{th}$ and $1^{st}$ order oracle access to the log
likelihood. Such problems may arise in Bayesian posterior sampling, or in
approximate minimization of non-convex functions. Using this knowledge alone,
we propose a Monte Carlo method to estimate the score empirically as a
particular expectation of a random variable. Using this estimator, we can then
run a discrete version of the backward flow SDE to produce samples from the
target density. This approach has the benefit of not relying on a pool of
initial samples from the target density, and it does not rely on a neural
network or other black box model to estimate the score.


  We present $NuSTAR$ X-ray observations of the active galactic nucleus (AGN)
in NGC 7674. The source shows a flat X-ray spectrum, suggesting that it is
obscured by Compton-thick gas columns. Based upon long-term flux dimming,
previous work suggested the alternate possibility that the source is a recently
switched-off AGN with the observed X-rays being the lagged echo from the torus.
Our high-quality data show the source to be reflection-dominated in hard
X-rays, but with a relatively weak neutral Fe K$\alpha$ emission line
(equivalent width [EW] of $\approx$ 0.4 keV) and a strong Fe XXVI ionised line
(EW $\approx$ 0.2 keV). We construct an updated long-term X-ray light curve of
NGC 7674 and find that the observed 2-10 keV flux has remained constant for the
past $\approx$ 20 years, following a high flux state probed by $Ginga$. Light
travel time arguments constrain the minimum radius of the reflector to be
$\sim$ 3.2 pc under the switched-off AGN scenario, $\approx$ 30 times larger
than the expected dust sublimation radius, rendering this possibility unlikely.
A patchy Compton-thick AGN (CTAGN) solution is plausible, requiring a minimum
line-of-sight column density ($N_{\rm H}$) of 3 $\times$ 10$^{24}$ cm$^{-2}$ at
present, and yields an intrinsic 2-10 keV luminosity of (3-5) $\times$
10$^{43}$ erg s$^{-1}$. Realistic uncertainties span the range of $\approx$
(1-13) $\times$ 10$^{43}$ erg s$^{-1}$. The source has one of the weakest
fluorescence lines amongst {\em bona fide} CTAGN, and is potentially a local
analogue of bolometrically luminous systems showing complex neutral and ionised
Fe emission. It exemplifies the difficulty of identification and proper
characterisation of distant CTAGN based on the strength of the neutral Fe
K$\alpha$ line.


  We consider the problem of providing valid inference for a selected parameter
in a sparse regression setting. It is well known that classical regression
tools can be unreliable in this context due to the bias generated in the
selection step. Many approaches have been proposed in recent years to ensure
inferential validity. Here, we consider a simple alternative to data splitting
based on randomising the response vector, which allows for higher selection and
inferential power than the former and is applicable with an arbitrary selection
rule. We provide a theoretical and empirical comparison of both methods and
derive a Central Limit Theorem for the randomisation approach. Our
investigations show that the gain in power can be substantial.


  We study quantum measurement with preselection and postselection, and derive
the precise expressions of the measurement results without any restriction on
the coupling strength between the system and the measuring device. For a qubit
system, we derive the maximum pointer shifts by choosing appropriate initial
and finial states. A significant amplification effect is obtained when the
interaction between the system and the measuring device is very weak, and
typical ideal quantum measurement results are obtained when the interaction is
strong. The improvement of the signal-tonoise ratio (SNR) and the enhancement
of the measurement sensitivity (MS) by weak measurements are studied. Without
considering the probability decrease due to postselection, the SNR and the MS
can be both significantly improved by weak measurements; however, neither SNR
nor MS can be effectively improved when the probability decrease is considered.


  The definition of the intersection number of a map with a closed manifold can
be extended to the case of a closed stratified set such that the difference
between dimensions of its two biggest strata is greater than $1$. The set Sigma
of matrices of positive corank is an example of such a set. It turns out that
the intersection number of a map from an (n-k+1)--dimensional manifold with
boundary into the set of (n x k) real matrices with Sigma coincides with a
homotopy invariant associated with a map going to the Stiefel manifold. In a
polynomial case, we present an effective method to compute this intersection
number. We also show how to use it to count the number mod 2 or the algebraic
sum of cross--cap singularities of a map from an m--dimensional manifold with
boundary to R^{2m-1}.


  Results are presented from a search for supersymmetric particles in scenarios
with a compressed mass spectrum. The data sample corresponds to 19.7 inverse
femtobarns of proton-proton collisions recorded by the CMS experiment at
sqrt(s) = 8 TeV. The search targets top squark pair production in scenarios
with mass differences Delta m = m(top squark) - m(neutralino) below the W-boson
mass and with top-squark decays in the four-body mode (top squark to b l nu
neutralino), where the neutralino is assumed to be the lightest supersymmetric
particle (LSP). The signature includes a high transverse momentum (pt) jet
associated with initial-state radiation, one or two low-pt leptons, and
significant missing transverse energy. The event yields observed in data are
consistent with the expected background contributions from standard model
processes. Limits are set on the cross section for top squark pair production
as a function of the top squark and LSP masses. Assuming a 100% branching
fraction for the four-body decay mode, top-squark masses below 316 GeV are
excluded for Delta m = 25 GeV at 95% CL. The dilepton data are also interpreted
under the assumption of chargino-neutralino production, with subsequent decays
to sleptons or sneutrinos. Assuming a difference between the common lightest
chargino/next lightest neutralino mass and the LSP mass of 20 GeV and a
tau-enriched decay scenario, masses in the range m(chargino) < 307 GeV are
excluded at 95% CL.


  Squeezing of atomic resonance fluorescence is shown to be optimized by a
properly designed environment, which can be realized by a quasi-resonant
cavity. Optimal squeezing is achieved if the atomic coherence is maximized,
corresponding to a pure atomic quantum state. The atomic-state purification is
achieved by the backaction of the cavity field on the atom, which increases the
atomic coherence and decreases the atomic excitation. For realistic cavities,
the coupling of the atom to the cavity field yields a purity of the atomic
state of more than 99%. The fragility of squeezing against dephasing is
substantially reduced in this scenario, which may be important for various
applications.


  This paper presents the design and development of a low cost Data Acquisition
System (DAS) using PIC12F675 microcontroller for real time temperature and
humidity monitoring. The designed DAS has 4 analog input channels having 10-bit
resolution and was interfaced through the serial port of the PC. A precision
integrated temperature sensor and an instrumentation-quality RH (Relative
Humidity) sensor were used for sensing the temperature and humidity
respectively. The firmware was written in Basic and compiled using Oshonsoft
PIC IDE and downloaded to the microcontroller by using PICkit2 programmer. An
application program was also developed using Visual Basic 6, which allows
displaying the waveform of the signal(s) in real time and the data can be saved
into the hard disk of the computer for future use and analysis. It can also be
interfaced to the USB port of the PC or laptop using USB to serial adapter BAFO
BF-810. Thus, the designed low cost device works with the legacy hardware as
well as the modern USB interface.


  We study chiral symmetry breaking in QED when a uniform external magnetic
field is present. We calculate higher order corrections to the dynamically
generated fermion mass and find them to be small. In so doing we correct an
error in the literature regarding the matrix structure of the fermion
self-energy.


  Tavis-Cummings (TC) cavity quantum electrodynamical effects, describing the
interaction of $N$ atoms with an optical resonator, are at the core of atomic,
optical and solid state physics. The full numerical simulation of TC dynamics
scales exponentially with the number of atoms. By restricting the open quantum
system to a single excitation, typical of experimental realizations in quantum
optics, we analytically solve the TC model with an arbitrary number of atoms
with linear complexity. This solution allows us to devise the Quantum Mapping
Algorithm of Resonator Interaction with $N$ Atoms (Q-MARINA), an intuitive TC
mapping to a quantum circuit with linear space and time scaling, whose $N+1$
qubits represent atoms and a lossy cavity, while the dynamics is encoded
through $2N$ entangling gates. Finally, we benchmark the robustness of the
algorithm on a quantum simulator and superconducting quantum processors against
the quantum master equation solution on a classical computer.


  The increasing pervasive and ubiquitous presence of devices at the edge of
the Internet is creating new scenarios for the emergence of novel services and
applications. This is particularly true for location- and context-aware
services. These services call for new decentralized, self-organizing
communication schemes that are able to face issues related to demanding
resource consumption constraints, while ensuring efficient locality-based
information dissemination and querying. Voronoi-based communication techniques
are among the most widely used solutions in this field. However, when used for
forwarding messages inside closed areas of the network (called Areas of
Interest, AoIs), these solutions generally require a significant overhead in
terms of redundant and/or unnecessary communications. This fact negatively
impacts both the devices' resource consumption levels, as well as the network
bandwidth usage. In order to eliminate all unnecessary communications, in this
paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing
over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward
information within an AoI in a Voronoi network using only local information,
reaching all the devices in the area, and using the lowest possible number of
messages, i.e., just one message for each node included in the AoI. The paper
presents the mathematical and algorithmic descriptions of MABRAVO, as well as
experimental findings of its performance, showing its ability to reduce
communication costs to the strictly minimum required.


  This work revisits the physics giving rise to the carbon nanotubes phonon
bands in the mid- infrared. Our measurements of doped and undoped samples of
single-walled carbon nanotubes in Fourier transform infrared spectroscopy show
that the phonon bands exhibit an asymmetric lineshape and that their effective
cross-section is enhanced upon doping. We relate these observations to
electron-phonon coupling or, more specifically, to a Fano resonance phenomenon.
We note that only the dopant-induced intraband continuum couples to the phonon
modes and that defects induced in the sidewall increase the resonance
probabilities.


  The theory of quaternionic operators has applications in several different
fields such as quantum mechanics, fractional evolution problems, and
quaternionic Schur analysis, just to name a few. The main difference between
complex and quaternionic operator theory is based on the definition of
spectrum. In fact, in quaternionic operator theory the classical notion of
resolvent operator and the one of spectrum need to be replaced by the two
$S$-resolvent operators and the $S$-spectrum. This is a consequence of the
non-commutativity of the quaternionic setting. Indeed, the $S$-spectrum of a
quaternionic linear operator $T$ is given by the non invertibility of a second
order operator. This presents new challenges which makes our approach to
perturbation theory of quaternionic operators different from the classical
case. In this paper we study the problem of perturbation of a quaternionic
normal operator in a Hilbert space by making use of the concepts of
$S$-spectrum and of slice hyperholomorphicity of the $S$-resolvent operators.
For this new setting we prove results on the perturbation of quaternionic
normal operators by operators belonging to a Schatten class and give conditions
which guarantee the existence of a nontrivial hyperinvariant subspace of a
quaternionic linear operator.


  Work on the chemical evolution of pre-biotic molecules remains incomplete
since the major obstacle is the lack of adequate knowledge of rate coefficients
of various reactions which take place in interstellar conditions. In this work,
we study the possibility of forming three pyrimidine bases, namely, cytosine,
uracil and thymine in interstellar regions. Our study reveals that the
synthesis of uracil from cytosine and water is quite impossible under
interstellar circumstances. For the synthesis of thymine, reaction between
uracil and :CH2 is investigated. Since no other relevant pathways for the
formation of uracil and thymine were available in the literature, we consider a
large gas-grain chemical network to study the chemical evolution of cytosine in
gas and ice phases. Our modeling result shows that cytosine would be produced
in cold, dense interstellar conditions. However, presence of cytosine is yet to
be established. We propose that a new molecule, namely, C4N3OH5 could be
observable in the interstellar region. C4N3OH5 is a precursor (Z isomer of
cytosine) of cytosine and far more abundant than cytosine. We hope that
observation of this precursor molecule would enable us to estimate the
abundance of cytosine in interstellar regions. We also carry out quantum
chemical calculations to find out the vibrational as well as rotational
transitions of this precursor molecule along with three pyrimidine bases.


  We give Harnack inequalities for the hitting distributions of a large family
of symmetric random walks on $\Z^2$, and their projections onto the lattice
torus $\Z^2_K$. This extends a framework for the simple random walk in Dembo,
et al. (2006), and generalizes the results in Bass, Rosen (2007) to the toral
projection.


  As a step toward clarification of the power of supersymmetry (SUSY) in Matrix
theory, a complete calculation, including all the spin effects, is performed of
the effective action of a probe D-particle, moving along an arbitrary
trajectory in interaction with a large number of coincident source D-particles,
at one loop at order 4 in the derivative expansion. Furthermore, exploiting the
SUSY Ward identity developed previously, the quantum-corrected effective
supersymmetry transformation laws are obtained explicitly to the relevant order
and are used to verify the SUSY-invariance of the effective action. Assuming
that the agreement with 11-dimensional supergravity persists, our result can be
regarded as a prediction for supergravity calculation, which, yet unavailable,
is known to be highly non-trivial.


  In this paper we discuss the use of implicit Runge-Kutta schemes for the time
discretization of optimal control problems with evolution equations. The
specialty of the considered discretizations is that the discretizations schemes
for the state and adjoint state are chosen such that discretization and
optimization commute. It is well known that for Runge-Kutta schemes with this
property additional order conditions are necessary. We give sufficient
conditions for which class of schemes these additional order condition are
automatically fulfilled. The focus is especially on implicit Runge-Kutta
schemes of Gauss, Radau IA, Radau IIA, Lobatto IIIA, Lobatto IIIB and Lobatto
IIIC collocation type up to order six. Furthermore we also use a SDIRK (singly
diagonally implicit Runge-Kutta) method to demonstrate, that for general
implicit Runge-Kutta methods the additional order conditions are not
automatically fulfilled.
  Numerical examples illustrate the predicted convergence rates.


  Recently, neural networks trained as optimizers under the "learning to learn"
or meta-learning framework have been shown to be effective for a broad range of
optimization tasks including derivative-free black-box function optimization.
Recurrent neural networks (RNNs) trained to optimize a diverse set of synthetic
non-convex differentiable functions via gradient descent have been effective at
optimizing derivative-free black-box functions. In this work, we propose
RNN-Opt: an approach for learning RNN-based optimizers for optimizing
real-parameter single-objective continuous functions under limited budget
constraints. Existing approaches utilize an observed improvement based
meta-learning loss function for training such models. We propose training
RNN-Opt by using synthetic non-convex functions with known (approximate)
optimal values by directly using discounted regret as our meta-learning loss
function. We hypothesize that a regret-based loss function mimics typical
testing scenarios, and would therefore lead to better optimizers compared to
optimizers trained only to propose queries that improve over previous queries.
Further, RNN-Opt incorporates simple yet effective enhancements during training
and inference procedures to deal with the following practical challenges: i)
Unknown range of possible values for the black-box function to be optimized,
and ii) Practical and domain-knowledge based constraints on the input
parameters. We demonstrate the efficacy of RNN-Opt in comparison to existing
methods on several synthetic as well as standard benchmark black-box functions
along with an anonymized industrial constrained optimization problem.


  We generalize the Bochner technique to foliations with non-negative
transverse Ricci curvature. In particular, we obtain a new vanishing theorem
for basic cohomology. Subsequently, we provide two natural applications, namely
to degenerate 3-$(\alpha,\delta) $-Sasaki and certain Sasaki-$ \eta $-Einstein
manifolds, which arise for example as Boothby-Wang bundles over hyperk\"ahler
and Calabi-Yau manifolds, respectively.


  The recent proliferation of research into transformer based natural language
processing has led to a number of studies which attempt to detect the presence
of human-like cognitive behavior in the models. We contend that, as is true of
human psychology, the investigation of cognitive behavior in language models
must be conducted in an appropriate population of an appropriate size for the
results to be meaningful. We leverage work in uncertainty estimation in a novel
approach to efficiently construct experimental populations. The resultant tool,
PopulationLM, has been made open source. We provide theoretical grounding in
the uncertainty estimation literature and motivation from current cognitive
work regarding language models. We discuss the methodological lessons from
other scientific communities and attempt to demonstrate their application to
two artificial population studies. Through population based experimentation we
find that language models exhibit behavior consistent with typicality effects
among categories highly represented in training. However, we find that language
models don't tend to exhibit structural priming effects. Generally, our results
show that single models tend to over estimate the presence of cognitive
behaviors in neural models.


  A consistent picture on the mean-field level of the magnetic properties and
electronic structure of the superconducting itinerant ferromagnet UGe$_2$ is
shown to require inclusion of correlation effects beyond the local density
approximation (LDA). The "LDA+U" approach reproduces both the magnitude of the
observed moment, composed of strongly opposing spin and orbital parts, and the
magnetocrystalline anisotropy. The largest Fermi surface sheet is comprised
primarily of spin majority states with orbital projection $m_{\ell}$=0,
suggesting a much simpler picture of the pairing than is possible for general
strong spin-orbit coupled materials. This occurrence, and the
quasi-two-dimensional geometry of the Fermi surface, support the likelihood of
magnetically mediated p-wave triplet pairing.


  We review standard holographic derivations of the scalar glueball spectra and
emphasize computational assumptions which rely strongly on the original AdS/CFT
correspondence, and those which are more arbitrary. In the soft-wall model, we
show explicitly that the dual of the spectral mass constraint in the
Kallen-Lehmann representation of the 2-point scalar gluonic correlator is an
eigenvalue constraint on the motion equation of the bulk field corresponding
this correlator.


  This paper considers the problem of communication over a memoryless
classical-quantum wiretap channel subject to the constraint that the
eavesdropper on the channel should not be able to learn whether the legitimate
parties are using the channel to communicate or not. Specifically, the relative
entropy between the output quantum states at the eavesdropper when a codeword
is transmitted and when no input is provided must be sufficiently small.
Extending earlier works, this paper proves the "square-root law" for a broad
class of classical-quantum channels: the maximum amount of information that can
be reliably and covertly transmitted over $n$ uses of such a channel scales
like $\sqrt{n}$. The scaling constant is also determined.


  In a first paper (Forni & Aghanim 1999), we developed several statistical
discriminators to test the non-gaussian nature of a signal. These tests are
based on the study of the coefficients in a wavelet decomposition basis. In
this paper, we apply them in a cosmological context, to the study of the nature
of the Cosmic Microwave Background (CMB) anisotropies. The latter represent the
superposition of primary anisotropy imprints of the initial density
perturbations and secondary ones due to photon interactions after
recombination. In an inflationary scenario (standard Cold Dark Matter) with
gaussian distributed fluctuations, we study the statistical signature of the
secondary effects. More specifically, we investigate the dominant effects
arising from the Compton scattering of CMB photons in ionised regions of the
Universe: the Sunyaev-Zel'dovich effect of galaxy clusters and the effects of a
spatially inhomogeneous re-ionisation of the Universe. Our study specifies and
predicts the non-gaussian signature of the secondary anisotropies induced by
these scattering effects. We find that our statistical discriminators allow us
to distinguish and highlight the non-gaussian signature of a process even if it
is combined with a larger gaussian one. We investigate the detectability of the
secondary anisotropy non-gaussian signature in the context of the future CMB
satellites (MAP and Planck Surveyor).


  We report the direct observation of quantum coupling in individual quantum
dot molecules and its manipulation using static electric fields. A pronounced
anti-crossing of different excitonic transitions is observed as the electric
field is tuned. Comparison of our experimental results with theory shows that
the observed anti-crossing occurs between excitons with predominant spatially
\emph{direct} and \emph{indirect} character. The electron component of the
exciton wavefunction is shown to have molecular character at the anti-crossing
and the quantum coupling strength is deduced optically. In addition, we
determine the dependence of the coupling strength on the inter-dot separation
and identify a field driven transition of the nature of the molecular ground
state.


  Large language models (LLMs) encode a large amount of world knowledge.
However, as such knowledge is frozen at the time of model training, the models
become static and limited by the training data at that time. In order to
further improve the capacity of LLMs for knowledge-intensive tasks, we consider
augmenting LLMs with the large-scale web using search engine. Unlike previous
augmentation sources (e.g., Wikipedia data dump), the web provides broader,
more comprehensive and constantly updated information. In this paper, we
present a web-augmented LLM UNIWEB, which is trained over 16
knowledge-intensive tasks in a unified text-to-text format. Instead of simply
using the retrieved contents from web, our approach has made two major
improvements. Firstly, we propose an adaptive search engine assisted learning
method that can self-evaluate the confidence level of LLM's predictions, and
adaptively determine when to refer to the web for more data, which can avoid
useless or noisy augmentation from web. Secondly, we design a pretraining task,
i.e., continual knowledge learning, based on salient spans prediction, to
reduce the discrepancy between the encoded and retrieved knowledge. Experiments
on a wide range of knowledge-intensive tasks show that our model significantly
outperforms previous retrieval-augmented methods.


  The large and saturating differential elliptic flow v2(pT) observed in Au+Au
reactions at RHIC so far could only be explained assuming an order of magnitude
denser initial parton system than estimated from perturbative QCD.
Hadronization via parton coalescence can resolve this ``opacity puzzle''
because it enhances hadron elliptic flow at large pT relative to that of
partons at the same transverse momentum. An experimentally testable consequence
of the coalescence scenario is that v2(pT) saturates at about 50% higher values
for baryons than for mesons. In addition, if strange quarks have weaker flow
than light quarks, hadron v2 at high pT decreases with relative strangeness
content.


  In the Bayesian paradigm for presenting forensic evidence to court, it is
recommended that the weight of the evidence be summarized as a likelihood ratio
(LR) between two opposing hypotheses of how the evidence could have been
produced. Such LRs are necessarily based on probabilistic models, the
parameters of which may be uncertain. It has been suggested by some authors
that the value of the LR, being a function of the model parameters should
therefore also be considered uncertain and that this uncertainty should be
communicated to the court. In this tutorial, we consider a simple example of a
'fully Bayesian' solution, where model uncertainty is integrated out to produce
a value for the LR which is not uncertain. We show that this solution agrees
with common sense. In particular, the LR magnitude is a function of the amount
of data that is available to estimate the model parameters.


  A nutrient-limited model for avascular cancer growth including cell
proliferation, motility and death is presented. The model qualitatively
reproduces commonly observed morphologies for primary tumors, and the simulated
patterns are characterized by its gyration radius, total number of cancer
cells, and number of cells on tumor periphery. These very distinct
morphological patterns follow Gompertz growth curves, but exhibit different
scaling laws for their surfaces. Also, the simulated tumors incorporate a
spatial structure composed of a central necrotic core, an inner rim of
quiescent cells and a narrow outer shell of proliferating cells in agreement
with biological data. Finally, our results indicate that the competition for
nutrients among normal and cancer cells may be a determinant factor in
generating papillary tumor morphology.


  Conditional 3D generation is undergoing a significant advancement, enabling
the free creation of 3D content from inputs such as text or 2D images. However,
previous approaches have suffered from low inference efficiency, limited
generation categories, and restricted downstream applications. In this work, we
revisit the impact of different 3D representations on generation quality and
efficiency. We propose a progressive generation method through Voxel-Point
Progressive Representation (VPP). VPP leverages structured voxel representation
in the proposed Voxel Semantic Generator and the sparsity of unstructured point
representation in the Point Upsampler, enabling efficient generation of
multi-category objects. VPP can generate high-quality 8K point clouds within
0.2 seconds. Additionally, the masked generation Transformer allows for various
3D downstream tasks, such as generation, editing, completion, and pre-training.
Extensive experiments demonstrate that VPP efficiently generates high-fidelity
and diverse 3D shapes across different categories, while also exhibiting
excellent representation transfer performance. Codes will be released at
\url{https://github.com/qizekun/VPP}.


  We report an apparent detection of the C VI 4p to 1s transition line at 459
eV, during a long-term enhancement (LTE) in the Suzaku north ecliptic pole
(NEP) observation of 2005 September 2. The observed intensity of the line is
comparable to that of the C VI 2p to 1s line at 367 eV. This is strong evidence
for the charge-exchange process. In addition to the C VI lines, emission lines
from O VII, O VIII, Ne X, and Mg XI lines showed clear enhancements. There are
also features in the 750 to 900 eV range that could be due to some combination
of Fe XVII and XVIII L-lines, higher order transitions of O VIII (3p to 1s and
6p to 1s), and a Ne IX line. From the correlation of the X-ray intensity with
solar-wind flux on time scales of about half a day, and from the short-term
(~10 minutes) variations of the X-ray intensity, these lines most likely arise
from solar-wind heavy ions interacting with neutral material in the Earth's
magnetosheath. A hard power-law component is also necessary to explain the LTE
spectrum. The origin of this component is not yet known. Our results indicate
that solar activity can significantly contaminate Suzaku cosmic X-ray spectra
below ~1 keV. Recommendations are provided for recognizing such contamination
in observations of extended sources.


  A detailed diffraction study of Ca10Cr7O28 is presented which adds
significant new insights into the structural and magnetic properties of this
compound. A new crystal structure type was used where the a and b axes are
doubled compared to previous models providing a more plausible structure where
all crystallographic sites are fully occupied. The presence of two different
valences of chromium was verified and the locations of the magnetic Cr5+ and
non-magnetic Cr6+ ions were identified. The Cr5+ ions have spin-1/2 and form
distorted kagome bilayers which are stacked in an ABC arrangement along the c
axis. These results lay the foundation for understanding of the quantum spin
liquid behavior in Ca10Cr7O28 which has recently been reported in [C. Balz et
al., Nature Physics, 12, 942 (2016)].