  ML(n)BiCGStab is a Krylov subspace method for the solution of large, sparse
and non-symmetric linear systems. In theory, it is a method that lies between
the well-known BiCGStab and GMRES/FOM. In fact, when n = 1, ML(1)BiCGStab is
BiCGStab and when n = N, ML(N)BiCGStab is GMRES/FOM where N is the size of the
linear system. Therefore, ML(n)BiCGStab is a bridge that connects the
Lanczos-based BiCGStab and the Arnoldi-based GMRES/FOM. In computation,
ML(n)BiCGStab can be much more stable and converge much faster than BiCGStab
when a problem with ill-condition is solved. We have tested ML(n)BiCGStab on
the standard oil reservoir simulation test data called SPE9 and found that
ML(n)BiCGStab reduced the total computational time by more than 60% when
compared to BiCGStab. Tests made on the data from Matrix Market also support
the superiority of ML(n)BiCGStab over BiCGStab. Because of the O(N^2) storage
requirement in the full GMRES, one has to adopt a restart strategy to get the
storage under control when GMRES is implemented. In comparison, ML(n)BiCGStab
is a method with only O(nN) storage requirement and therefore it does not need
a restart strategy. In this paper, we introduce ML(n)BiCGStab (in particular, a
new algorithm involving A-transpose), its relations to some existing methods
and its implementations.


  A large-scale dataset is essential for learning good features in 3D shape
understanding, but there are only a few datasets that can satisfy deep learning
training. One of the major reasons is that current tools for annotating
per-point semantic labels using polygons or scribbles are tedious and
inefficient. To facilitate segmentation annotations in 3D shapes, we propose an
effective annotation tool, named iSeg for 3D shape. It can obtain a satisfied
segmentation result with minimal human clicks (< 10). Under our observation,
most objects can be considered as the composition of finite primitive shapes,
and we train iSeg3D model on our built primitive-composed shape data to learn
the geometric prior knowledge in a self-supervised manner. Given human
interactions, the learned knowledge can be used to segment parts on arbitrary
shapes, in which positive clicks help associate the primitives into the
semantic parts and negative clicks can avoid over-segmentation. Besides, We
also provide an online human-in-loop fine-tuning module that enables the model
perform better segmentation with less clicks. Experiments demonstrate the
effectiveness of iSeg3D on PartNet shape segmentation. Data and codes will be
made publicly available.


  We study a large-scale instability in a sheared nonhelical turbulence that
causes generation of large-scale vorticity. Three types of the background
large-scale flows are considered, i.e., the Couette and Poiseuille flows in a
small-scale homogeneous turbulence, and the "log-linear" velocity shear in an
inhomogeneous turbulence. It is known that laminar plane Couette flow and
antisymmetric mode of laminar plane Poiseuille flow are stable with respect to
small perturbations for any Reynolds numbers. We demonstrate that in a
small-scale turbulence under certain conditions the large-scale Couette and
Poiseuille flows are unstable due to the large-scale instability. This
instability causes formation of large-scale vortical structures stretched along
the mean sheared velocity. The growth rate of the large-scale instability for
the "log-linear" velocity shear is much larger than that for the Couette and
Poiseuille background flows. We have found a turbulent analogue of the
Tollmien-Schlichting waves in a small-scale sheared turbulence. A mechanism of
excitation of turbulent Tollmien-Schlichting waves is associated with a
combined effect of the turbulent Reynolds stress-induced generation of
perturbations of the mean vorticity and the background sheared motions. These
waves can be excited even in a plane Couette flow imposed on a small-scale
turbulence when perturbations of mean velocity depend on three spatial
coordinates. The energy of these waves is supplied by the small-scale sheared
turbulence.


  We consider results for the master integrals of the kite family, given in
terms of ELi-functions which are power series in the nome $q$ of an elliptic
curve. The analytic continuation of these results beyond the Euclidean region
is reduced to the analytic continuation of the two period integrals which
define $q.$ We discuss the solution to the latter problem from the perspective
of the Picard-Lefschetz formula.


  Personalized Intelligence (PI) is the problem of providing customized AI
experiences tailored to each individual user. In many applications, PI is
preferred or even required. Existing personalization approaches involve
fine-tuning pre-trained models to create new customized models. However, these
approaches require a significant amount of computation to train, scaling with
model size and the number of users, inhibiting PI to be realized widely. In
this work, we introduce a novel model architecture and training/inference
framework to enable Personalized Intelligence at scale. We achieve this by
attaching a Personalization Head (PH) to pre-trained language models (LM).
During training, the base LMs are frozen and only the parameters in PH are
updated and are unique per user. This results in significantly smaller overall
model sizes and training cost than traditional fine-tuning approaches when
scaled across many users. We evaluate PHs on academia and industry-focused
datasets and show that the PHs outperform zeroshot baseline in F1 score and are
significantly more scalable than traditional fine-tuning approaches. We
identify key factors required for effective PH design and training.


  A detailed examination of current data on S_17 (as opposed to an examination
of S_17(0) only) excludes quoting S_17(0) with sufficiently small uncertainty.
In contrast to suggestions that S_17(0) is now known with the accuracy of \pm
3%, the exact value of S_17(0) is dependent on the choice of the data and the
choice of theory used for extrapolation. In addition recent high precision
results (including the Seattle data) on S_17 which are in good agreement, still
differ on the measured slopes, as do theoretical models that predict different
d-wave contribution, precluding an accurate extrapolation to zero energy of the
consistent data. Using a common extrapolation of only the consistent high
precision data, suggests a value of S_17(0) = 21.2 \pm 0.5 eV-b, but a value
equal to or smaller than 19.0 eV-b can not be excluded due to the uncertainty
in the extrapolation, leading to an additional error of +0.0 -3.0 eV-b. This
(unacceptable) situation must be cleared by future experiments.


  We address a quantization mechanism that can allow us to understand why the
cosmological constant is not large under the quantum corrections from studying
the circle compactification solution of the Standard Model coupled to Einstein
gravity which is subject to the constraint of the Swampland conjectures. A
novel result in the present work compared to the previous investigations in the
literature is that the radius of the compactified dimension and the 4D
cosmological constant $\Lambda_4$ must in fact be quantized. The quantization
rule of the cosmological constant is given by $\Lambda_4\propto n^2$ with
$n=1,2,3,4,\ldots$, which means that the values of $\Lambda_4$ are not
arbitrary but only its specific values are allowed. In general, the quantum
corrections as well as other effects would break this quantization rule. Hence,
it could prevent the quantum fluctuations from generating zero-point energy
contributions to the cosmological constant.


  We prove in ZFC that there is a MAD family of functions in omega^omega which
is also maximal with respect to infinite partial functions. This solves a 20
year old question of Van Douwen. We also strengthen a result of J. Steprans
stating that strongly MAD families of functions cannot be analytic. We show
that analytic MAD families of functions, if they exist, must satisfy some
strong constraints.


  Starting from a Hilbert cusp form of weight $2\kap$, we will construct a
Hilbert-Siegel cusp form of weight $\kap+\frac{m}{2}$ and degree $m$ and its
transfer to inner forms of symplectic groups.


  This paper is devoted to the construction of a solution for the "Inhomogenous
skew Brownian motion" equation, which first appeared in a seminal paper by
Sophie Weinryb, and recently, studied by \'{E}tor\'e and Martinez. Our method
is based on the use of the Balayage formula. At the end of this paper we study
a limit theorem of solutions.


  We study a family of mean field games with a state variable evolving as a
multivariate jump diffusion process. The jump component is driven by a Poisson
process with a time-dependent intensity function. All coefficients, i.e. drift,
volatility and jump size, are controlled. Under fairly general conditions, we
establish existence of a solution in a relaxed version of the mean field game
and give conditions under which the optimal strategies are in fact Markovian,
hence extending to a jump-diffusion setting previous results established in
[30]. The proofs rely upon the notions of relaxed controls and martingale
problems. Finally, to complement the abstract existence results, we study a
simple illiquid inter-bank market model, where the banks can change their
reserves only at the jump times of some exogenous Poisson processes with a
common constant intensity, and provide some numerical results.


  J.Bella\"iche and M.Dimitrov have shown that the $p$-adic eigencurve is
smooth but not etale over the weight space at $p$-regular theta series attached
to a character of a real quadratic field $F$ in which $p$ splits. We proof in
this paper the existence of an isomorphism between the subring of the completed
local ring of the eigencurve at these points fixed by the Atkin-Lehner
involution and an universal ring representing a pseudo-deformation problem, and
one gives also a precise criterion for which the ramification index is exactly
$2$. We finish this paper by proving the smoothness of the nearly ordinary and
ordinary Hecke algebras for Hilbert modular forms over $F$ at the
cuspidal-overconvergent Eisenstein points which are the base change lift for
$\mathrm{GL}(2)_{/F}$ of these theta series.


  This paper characterizes the performance of cellular networks employing
multiple antenna open-loop spatial multiplexing techniques. We use a stochastic
geometric framework to model distance depended inter cell interference. Using
this framework, we analyze the coverage and rate using two linear receivers,
namely, partial zero-forcing (PZF) and minimum-mean-square-estimation (MMSE)
receivers. Analytical expressions are obtained for coverage and rate
distribution that are suitable for fast numerical computation.
  In the case of the PZF receiver, we show that it is not optimal to utilize
all the receive antenna for canceling interference. With $\alpha$ as the path
loss exponent, $N_t$ transmit antenna, $N_r$ receive antenna, we show that it
is optimal to use $N_t\left\lceil\left(1-\frac{2}{\alpha}\right) \left(
\frac{N_r}{N_t} -\frac{1}{2}\right)\right\rceil$ receive antennas for
interference cancellation and the remaining antennas for signal enhancement
(array gain). For both PZF and MMSE receivers, we observe that increasing the
number of data streams provides an improvement in the mean data rate with
diminishing returns. Also transmitting $N_r$ streams is not optimal in terms of
the mean sum rate. We observe that increasing the SM rate with a PZF receiver
always degrades the cell edge data rate while the performance with MMSE
receiver is nearly independent of the SM rate.


  Pop III stars are the first stars in the universe. They do not contain metals
and their formation and evolution may be different from that of stars of later
generations. In fact, according to the theory of star formation, Pop III stars
might have very massive components ($\sim 100 - 10000M_\odot$). In this paper,
we compute the spherically symmetric gravitational collapse of these Pop III
massive stars. We solve the general relativistic hydrodynamics and neutrino
transfer equations simultaneously, treating neutrino reactions in detail.
Unlike supermassive stars ($\gtrsim 10^5 M_\odot$), the stars of concern in
this paper become opaque to neutrinos. The collapse is simulated until after an
apparent horizon is formed. We confirm that the neutrino transfer plays a
crucial role in the dynamics of gravitational collapse, and find also that the
$\beta$-equilibration leads to a somewhat unfamiliar evolution of electron
fraction. Contrary to the naive expectation, the neutrino spectrum does not
become harder for more massive stars. This is mainly because the neutrino
cooling is more efficient and the outer core is more massive as the stellar
mass increases. Here the outer core is the outer part of the iron core falling
supersonically. We also evaluate the flux of relic neutrino from Pop III
massive stars. As expected, the detection of these neutrinos is difficult for
the currently operating detectors. However, if ever observed, the spectrum will
enable us to obtain the information on the formation history of Pop III stars.
We investigate 18 models covering the mass range of $300 - 10^4 M_\odot$,
making this study the most detailed numerical exploration of spherical
gravitational collapse of Pop III massive stars. This will also serve as an
important foundation for multi-dimensional investigations.


  Rajchman measures of locally compact Abelian groups are studied for almost a
century now, and they play an important role in the study of trigonometric
series. Eymard's influential work allowed generalizing these measures to the
case of \emph{non-Abelian} locally compact groups $G$. The Rajchman algebra of
$G$, which we denote by $B_0(G)$, is the set of all elements of the
Fourier-Stieltjes algebra that vanish at infinity.
  In the present article, we characterize the locally compact groups that have
amenable Rajchman algebras. We show that $B_0(G)$ is amenable if and only if
$G$ is compact and almost Abelian. On the other extreme, we present many
examples of locally compact groups, such as non-compact Abelian groups and
infinite solvable groups, for which $B_0(G)$ fails to even have an approximate
identity.


  We report the second complete molecular line data release from the {\em
Census of High- and Medium-mass Protostars} (CHaMP), a large-scale, unbiased,
uniform mapping survey at sub-parsec resolution, of mm-wave line emission from
303 massive, dense molecular clumps in the Milky Way. This release is for all
$^{12}$CO $J$=1$\rightarrow$0 emission associated with the dense gas, the first
from Phase II of the survey, which includes $^{12}$CO, $^{13}$CO, and
C$^{18}$O. The observed clump emission traced by both $^{12}$CO and HCO$^+$
(from Phase I) shows very similar morphology, indicating that, for dense
molecular clouds and complexes of all sizes, parsec-scale clumps contain $\Xi$
~ 75% of the mass, while only 25% of the mass lies in extended (>~ 10 pc) or
"low density" components in these same areas. The mass fraction of all gas
above a density 10$^9$ m$^{-3}$ is $\xi_9$ >~ 50%. This suggests that
parsec-scale clumps may be the basic building blocks of the molecular ISM,
rather than the standard GMC concept. Using $^{12}$CO emission, we derive
physical properties of these clumps in their entirety, and compare them to
properties from HCO$^+$, tracing their denser interiors. We compare the
standard X-factor converting $I_{CO}$ to $N_{H_2}$ with alternative
conversions, and show that only the latter give whole-clump properties that are
physically consistent with those of their interiors. We infer that the clump
population is systematically closer to virial equilibrium than when considering
only their interiors, with perhaps half being long-lived (10s of Myr),
pressure-confined entities which only terminally engage in vigorous massive
star formation, supporting other evidence along these lines previously
published.


  We demonstrate the potential of few-shot translation systems, trained with
unpaired language data, for both high and low-resource language pairs. We show
that with only 5 examples of high-quality translation data shown at inference,
a transformer decoder-only model trained solely with self-supervised learning,
is able to match specialized supervised state-of-the-art models as well as more
general commercial translation systems. In particular, we outperform the best
performing system on the WMT'21 English - Chinese news translation task by only
using five examples of English - Chinese parallel data at inference. Moreover,
our approach in building these models does not necessitate joint multilingual
training or back-translation, is conceptually simple and shows the potential to
extend to the multilingual setting. Furthermore, the resulting models are two
orders of magnitude smaller than state-of-the-art language models. We then
analyze the factors which impact the performance of few-shot translation
systems, and highlight that the quality of the few-shot demonstrations heavily
determines the quality of the translations generated by our models. Finally, we
show that the few-shot paradigm also provides a way to control certain
attributes of the translation -- we show that we are able to control for
regional varieties and formality using only a five examples at inference,
paving the way towards controllable machine translation systems.


  Cactus group is the fundamental group of the real locus of the
Deligne-Mumford moduli space of stable rational curves. This group appears
naturally as an analog of the braid group in coboundary monoidal categories. We
define an action of the cactus group on the set of Bethe vectors of the Gaudin
magnet chain corresponding to arbitrary semisimple Lie algebra $\mathfrak{g}$.
Cactus group appears in our construction as a subgroup in the Galois group of
Bethe Ansatz equations. Following the idea of Pavel Etingof, we conjecture that
this action is isomorphic to the action of the cactus group on the tensor
product of crystals coming from the general coboundary category formalism. We
prove this conjecture in the case $\mathfrak{g}=\mathfrak{s}\mathfrak{l}_2$ (in
fact, for this case the conjecture almost immediately follows from the results
of Varchenko on asymptotic solutions of the KZ equation and crystal bases). We
also present some conjectures generalizing this result to Bethe vectors of
shift of argument subalgebras and relating the cactus group with the
Berenstein-Kirillov group of piecewise-linear symmetries of the Gelfand-Tsetlin
polytope.


  We present a Non-parametric Network for 3D point cloud analysis, Point-NN,
which consists of purely non-learnable components: farthest point sampling
(FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric
functions. Surprisingly, it performs well on various 3D tasks, requiring no
parameters or training, and even surpasses existing fully trained models.
Starting from this basic non-parametric model, we propose two extensions.
First, Point-NN can serve as a base architectural framework to construct
Parametric Networks by simply inserting linear layers on top. Given the
superior non-parametric foundation, the derived Point-PN exhibits a high
performance-efficiency trade-off with only a few learnable parameters. Second,
Point-NN can be regarded as a plug-and-play module for the already trained 3D
models during inference. Point-NN captures the complementary geometric
knowledge and enhances existing methods for different 3D benchmarks without
re-training. We hope our work may cast a light on the community for
understanding 3D point clouds with non-parametric methods. Code is available at
https://github.com/ZrrSkywalker/Point-NN.


  A unified theory is advanced to describe both the lateral Goos-H\"{a}nchen
(GH) effect and the transverse Imbert-Fedorov (IF) effect, through representing
the vector angular spectrum of a 3-dimensional light beam in terms of a 2-form
angular spectrum consisting of its 2 orthogonal polarized components. From this
theory, the quantization characteristics of the GH and IF displacements are
obtained, and the Artmann formula for the GH displacement is derived. It is
found that the eigenstates of the GH displacement are the 2 orthogonal linear
polarizations in this 2-form representation, and the eigenstates of the IF
displacement are the 2 orthogonal circular polarizations. The theoretical
predictions are found to be in agreement with recent experimental results.


  Application programming interfaces (APIs) offer a plethora of functionalities
for developers to reuse without reinventing the wheel. Identifying the
appropriate APIs given a project requirement is critical for the success of a
project, as many functionalities can be reused to achieve faster development.
However, the massive number of APIs would often hinder the developers' ability
to quickly find the right APIs. In this light, we propose a new, automated
approach called WebAPIRec that takes as input a project profile and outputs a
ranked list of {web} APIs that can be used to implement the project. At its
heart, WebAPIRec employs a personalized ranking model that ranks web APIs
specific (personalized) to a project. Based on the historical data of {web} API
usages, WebAPIRec learns a model that minimizes the incorrect ordering of web
APIs, i.e., when a used {web} API is ranked lower than an unused (or a
not-yet-used) web API. We have evaluated our approach on a dataset comprising
9,883 web APIs and 4,315 web application projects from ProgrammableWeb with
promising results. For 84.0% of the projects, WebAPIRec is able to successfully
return correct APIs that are used to implement the projects in the top-5
positions. This is substantially better than the recommendations provided by
ProgrammableWeb's native search functionality. WebAPIRec also outperforms
McMillan et al.'s application search engine and popularity-based
recommendation.


  The Fibonacci number is the residue of a rational function, from which
follows that Fibonacci number summation identities can be derived with the
integral representation method, a method also used to derive combinatorial
identities. A number of weighted generalized Fibonacci number summation
identities are derived this way. From these identities some infinite series,
generating functions and convolution identities are obtained. In addition, some
weighted generalized Fibonacci number summation identities with binomial
coefficients are derived. Many examples of both types of summation identities
are provided.


  Code clone detection is involved with detecting duplicated fragments of code
within a code base. Detecting these clones is useful for maintenance operations
which require editing the clones. The tools developed are expected to be robust
enough to identify clones even when they have been modified, whilst preserving
reasonable recall and precision rates. It is also expected that these tools be
easily adaptable to different programming languages. The major approaches to
this problem has involve the use of direct string matching, token comparison or
comparison using abstract syntax trees. It is difficult to compare detection
tools due to the absence of a standardized framework for measurement. More work
should be done to make the existing tools useful for other practical/industrial
purposes.


  We propose a simple seedless approach to assemble millimeter sized monolayer
single colloidal crystals with desired orientations at predetermined locations
on an unstructured charged substrate. This approach utilizes the
millimeter-ranged fluid flow on the bottom glass substrate induced by an ion
exchange resin (IEX) fixed on top of the closed sample cell. This fluid flow
increases with decreasing height of the sample cell and increasing radius R of
the IEX. For a single inverted pump, millimeter sized monolayer single crystals
of hexagonal close packing can be obtained. For two closely spaced (D ~ 4R)
pumps, the formed crystals have a predefined orientation along the line
connecting the two IEX. By patterning IEX into different structures, colloidal
crystals of different complex patterns form. The present method paves a
convenient way for fabricating high quality monolayer colloidal crystals for a
variety of applications.


  Nowadays, the new network architectures with respect to the traditional
network topologies must manage more data, which entails having increasingly
robust and scalable network structures. As there is a process of growth,
adaptability, and change in traditional data networks, faced with the
management of large volumes of information, it is necessary to incorporate the
virtualization of network functions in the context of information content
networks, in such a way that there is a balance between the user and the
provider at cost level and profit, the functions on the network. In turn, NFVs
(Network Functions Virtualization) are considered network structures designed
based on IT virtualization technologies, which allow virtualizing of the
functions that can be found in the network nodes, which are connected through
routing tables, which allows offering communication services for various types
of customers. Therefore, information-centric networks (IC, unlike traditional
data networks which proceed to exchange information between hosts using data
packets and TCP/IP communication protocols, use the content of the data for
this purpose where the data travels through the network is stored in a routing
table located in the CR (Content Router) of the router temporality to be reused
later, which allows for reducing operation costs and capital costs . The
purpose of this work is to analyze how the virtualization of network functions
is integrated into the field of information-centric networks. Also, the
advantages and disadvantages of both architectures are considered and presented
as a critical analysis when considering the current difficulties and future
trends of both network topologies.


  We study heat and charge transport through a normal diffusive wire coupled
with a superconducting wire over the region smaller than the coherence length.
Due to partial Andreev reflection of quasiparticles from the interface, the
subgap thermal flow is essentially suppressed and approaches zero along with
energy, which is specific for diffusive structures. Whereas the electric
conductance shows conventional reentrance effect, the thermal conductance
rapidly decreases with temperature which qualitatively explains the results of
recent experiments. In the Andreev interferometer geometry, the thermal
conductance experiences full-scale oscillations with the order parameter phase
difference.


  The Jahn-Teller effect of C$_{60}$ anions in the first electronically excited
states was theoretically investigated. The orbital vibronic coupling parameters
for the $t_{1g}$ next lowest unoccupied molecular orbitals were derived from
the Kohn-Sham orbital levels with hybrid B3LYP functional by using the frozen
phonon approach. With the use of these coupling parameters, the vibronic states
of the first excited C$_{60}^-$ were derived by exactly diagonalizing the
dynamical Jahn-Teller Hamiltonian. The dynamical Jahn-Teller stabilization
energy of the first excited C$_{60}^-$ is stronger than that of the ground
electronic states.


  Shapiro put forth a conjecture stating that neutron stars in head-on
collisions (infalling from infinity) will not collapse to black holes before
neutrino cooling, independent of the mass of the neutron stars. In a previous
paper we carried out a numerical simulation showing a counter example based on
1.4 $M_{\odot}$ neutron stars, and provided an analysis explaining why
Shapiro's argument was not applicable for this case. A recent paper by Shapiro
put forth an argument suggesting that numerical simulations of the 1.4
$M_{\odot}$ collisions could not disprove the conjecture with the accuracy that
is presently attainable. We show in this paper that this argument is not
applicable for the same reason that the Shapiro conjecture is not applicable to
the 1.4 $M_{\odot}$ neutron star collision, namely, the collision is too
dynamical to be treated by quasi-equilibrium arguments.


  Space-time measurements and gravitational experiments are made by using
objects, matter fields or particles and their mutual relationships. As a
consequence, any operationally meaningful assertion about space-time is in fact
an assertion about the degrees of freedom of the matter (\emph{i.e} non
gravitational) fields; those, say for definiteness, of the Standard Model of
particle physics. As for any quantum theory, the dynamics of the matter fields
can be described in terms of a unitary evolution of a state vector in a Hilbert
space. By writing the Hilbert space as a generic tensor product of "subsystems"
we analyse the evolution of a state vector on an information theoretical basis
and attempt to recover the usual space-time relations from the information
exchanges between these subsystems. We consider generic interacting second
quantized models with a finite number of fermionic degrees of freedom and
characterize on physical grounds the tensor product structure associated with
the class of "localized systems" and therefore with "position". We find that in
the case of free theories no space-time relation is operationally definable. On
the contrary, by applying the same procedure to the simple interacting model of
a one-dimensional Heisenberg spin chain we recover the tensor product structure
usually associated with "position". Finally, we discuss the possible role of
gravity in this framework.


  We present M3P, a Multitask Multilingual Multimodal Pre-trained model that
combines multilingual pre-training and multimodal pre-training into a unified
framework via multitask pre-training. Our goal is to learn universal
representations that can map objects occurred in different modalities or texts
expressed in different languages into a common semantic space. In addition, to
explicitly encourage fine-grained alignment between images and non-English
languages, we also propose Multimodal Code-switched Training (MCT) to combine
monolingual pre-training and multimodal pre-training via a code-switch
strategy. Experiments are performed on the multilingual image retrieval task
across two benchmark datasets, including MSCOCO and Multi30K. M3P can achieve
comparable results for English and new state-of-the-art results for non-English
languages.


  We describe a method to implement finite group global and gauged $q$-form
symmetries into the axiomatic structure of $d$-dimensional Topological Quantum
Field Theory (TQFT) in terms of bordisms decorated by cohomology classes.
Namely, on a manifold with a boundary, the gauge field is considered as a class
in an appropriate relative cohomology group. It is defined in a way that allows
self-consistent cutting and gluing of the manifolds and involves a choice of a
$(d-q-2)$-skeleton in the boundary. The method, in a sense, generalizes to
arbitrary $d$ and $q$ a method that has been considered in the literature in
the case of $d=3,\;q=0,1$.


  We use asymptotic methods to study the early time stability of isotropic and
homogeneous solutions filled with radiation which are close initially to the
exact, flat, radiation solution in quadratic lagrangian theories of gravity.
For such models, we analyze all possible modes of approach to the initial
singularity and prove the essential uniqueness and stability of the resulting
asymptotic scheme in all cases except perhaps that of the conformally invariant
Bach-Weyl gravity. We also provide a formal series representation valid near
the initial singularity of the general solution of these models and show that
this is dominated at early times by a form in which both curvature and
radiation play a subdominant role. We also discuss the implications of these
results for the generic initial state of the theory.


  Many important properties of multi-agent systems refer to the participants'
ability to achieve a given goal, or to prevent the system from an undesirable
event. Among intelligent agents, the goals are often of epistemic nature, i.e.,
concern the ability to obtain knowledge about an important fact \phi. Such
properties can be e.g. expressed in ATLK, that is, alternating-time temporal
logic ATL extended with epistemic operators. In many realistic scenarios,
however, players do not need to fully learn the truth value of \phi. They may
be almost as well off by gaining some knowledge; in other words, by reducing
their uncertainty about \phi. Similarly, in order to keep \phi secret, it is
often insufficient that the intruder never fully learns its truth value.
Instead, one needs to require that his uncertainty about \phi never drops below
a reasonable threshold.
  With this motivation in mind, we introduce the logic ATLH, extending ATL with
quantitative modalities based on the Hartley measure of uncertainty. The new
logic enables to specify agents' abilities w.r.t. the uncertainty of a given
player about a given set of statements. It turns out that ATLH has the same
expressivity and model checking complexity as ATLK. However, the new logic is
exponentially more succinct than ATLK, which is the main technical result of
this paper.


  The state-of-the-art performance for several real-world problems is currently
reached by convolutional neural networks (CNN). Such learning models exploit
recent results in the field of deep learning, typically leading to highly
performing, yet very large neural networks with (at least) millions of
parameters. As a result, the deployment of such models is not possible when
only small amounts of RAM are available, or in general within resource-limited
platforms, and strategies to compress CNNs became thus of paramount importance.
In this paper we propose a novel lossless storage format for CNNs based on
source coding and leveraging both weight pruning and quantization. We
theoretically derive the space upper bounds for the proposed structures,
showing their relationship with both sparsity and quantization levels of the
weight matrices. Both compression rates and excution times have been tested
against reference methods for matrix compression, and an empirical evaluation
of state-of-the-art quantization schemes based on weight sharing is also
discussed, to assess their impact on the performance when applied to both
convolutional and fully connected layers. On four benchmarks for classification
and regression problems and comparing to the baseline pre-trained uncompressed
network, we achieved a reduction of space occupancy up to 0.6% on fully
connected layers and 5.44% on the whole network, while performing at least as
competitive as the baseline.


  We find an enumeration formula for a $(t,q)$-Euler number which is a
generalization of the $q$-Euler number introduced by Han, Randrianarivony, and
Zeng. We also give a combinatorial expression for the $(t,q)$-Euler number and
find another formula when $t=\pm q^r$ for any integer $r$. Special cases of our
latter formula include the formula of the $q$-Euler number recently found by
Josuat-Verg\`es and Touchard-Riordan's formula.


  The asymptotic probability theory of conjugacy classes of the finite general
linear and unitary groups leads to a probability measure on the set of all
partitions of natural numbers. A simple method of understanding these measures
in terms of Markov chains is given and compared with work on the uniform
measure. Elementary probabilistic proofs of the Rogers-Ramanujan identities
follow. As a corollary, the main case of Bailey's lemma is interpreted as
finding eigenvectors of the transition matrix of the Markov chain. It is shown
that the viewpoint of Markov chains extends to quivers.


  We undertake a detailed study of the lowest weight modules for the Hermitian
symmetric pair (G,K), where G=Sp_4(R) and K is its maximal compact subgroup. In
particular, we determine K-types and composition series, and write down
explicit differential operators that navigate all the highest weight vectors of
such a module starting from the unique lowest-weight vector. By rewriting these
operators in classical language, we show that the automorphic forms on G that
correspond to the highest weight vectors are exactly those that arise from
nearly holomorphic vector-valued Siegel modular forms of degree 2.
  Further, by explicating the algebraic structure of the relevant space of
n-finite automorphic forms, we are able to prove a structure theorem for the
space of nearly holomorphic vector-valued Siegel modular forms of (arbitrary)
weight $det^\ell$ sym^m with respect to an arbitrary congruence subgroup of
Sp_4(Q). We show that the cuspidal part of this space is the direct sum of
subspaces obtained by applying explicit differential operators to holomorphic
vector-valued cusp forms of weight $det^{\ell'} sym^{m'}$ with $(\ell', m')$
varying over a certain set. The structure theorem for the space of all modular
forms is similar, except that we may now have an additional component coming
from certain nearly holomorphic forms of weight $det^{3}sym^{m'}$ that cannot
be obtained from holomorphic forms.
  As an application of our structure theorem, we prove several arithmetic
results concerning nearly holomorphic modular forms that improve previously
known results in that direction.


  Previous studies on music style transfer have mainly focused on one-to-one
style conversion, which is relatively limited. When considering the conversion
between multiple styles, previous methods required designing multiple modes to
disentangle the complex style of the music, resulting in large computational
costs and slow audio generation. The existing music style transfer methods
generate spectrograms with artifacts, leading to significant noise in the
generated audio. To address these issues, this study proposes a music style
transfer framework based on diffusion models (DM) and uses spectrogram-based
methods to achieve multi-to-multi music style transfer. The GuideDiff method is
used to restore spectrograms to high-fidelity audio, accelerating audio
generation speed and reducing noise in the generated audio. Experimental
results show that our model has good performance in multi-mode music style
transfer compared to the baseline and can generate high-quality audio in
real-time on consumer-grade GPUs.


  The first author explicitly describes the set of Fourier--Mukai partners of
elliptic ruled surfaces over the complex number field in \cite{Ue17}. In this
article, we generalize it over arbitrary characteristic fields. We also obtain
a partial evidence of the Popa--Schnell conjecture in the proof.


  We study the effects of noise in two models of spiny dendrites. Through the
introduction of different types of noise to both the Spike-diffuse-spike (SDS)
and Baer-Rinzel (BR) models we investigate the change in behaviour of the
travelling wave solutions present in the deterministic systems, as noise
intensity increases. We show that the speed of wave propagation in the SDS and
BR models respectively decreases and increases as the noise intensity in the
spine heads increases. Interestingly the discrepancy between the models does
not seem to arise from the type of active spine head dynamics employed by the
model but rather by the form of the spine density used. In contrast the cable
is very robust to noise and as such the speed shows very little variation from
the deterministic system. We look at the effect of the noise interpretation
used to evaluate the stochastic integral; Ito or Statonovich and discuss which
may be appropriate. We also show that the correlation time and length scales of
the noise can enhance propagation of travelling wave solutions where the white
noise dominates the signal and produces noise induced phenomena.


  Using geodesic currents, we provide a theoretical justification for some of
the experimental results regarding the behavior of Whitehead's algorithm on
non-minimal inputs, that were obtained by Haralick, Miasnikov and Myasnikov via
pattern recognition methods. In particular we prove that the images of "random"
elements of a free group $F$ under the automorphisms of $F$ form "clusters"
that share similar normalized Whitehead graphs and similar behavior with
respect to Whitehead's algorithm.


  Optimal prediction methods compensate for a lack of resolution in the
numerical solution of complex problems through the use of prior statistical
information. We know from previous work that in the presence of strong
underresolution a good approximation needs a non-Markovian "memory", determined
by an equation for the "orthogonal", i.e., unresolved, dynamics. We present a
simple approximation of the orthogonal dynamics, which involves an ansatz and a
Monte-Carlo evaluation of autocorrelations. The analysis provides a new
understanding of the fluctuation-dissipation formulas of statistical physics.
An example is given.


  The couplings of pions with lowest three doublets (0^-,1^-), (0^+,1^+) and
(1^+,2^+) of heavy mesons are studied with ligh cone QCD sum rules in the
leading order of heavy quark effective theory. The ambiguity due to the
presence of two distinct 1^+ states are solved.


  Increased availability of data and accessibility of computational tools in
recent years have created unprecedented opportunities for scientific research
driven by statistical analysis. Inherent limitations of statistics impose
constrains on reliability of conclusions drawn from data but misuse of
statistical methods is a growing concern. Significance, hypothesis testing and
the accompanying P-values are being scrutinized as representing most widely
applied and abused practices. One line of critique is that P-values are
inherently unfit to fulfill their ostensible role as measures of scientific
hypothesis's credibility. It has also been suggested that while P-values may
have their role as summary measures of effect, researchers underappreciate the
degree of randomness in the P-value. High variability of P-values would suggest
that having obtained a small P-value in one study, one is, nevertheless, likely
to obtain a much larger P-value in a similarly powered replication study. Thus,
"replicability of P-value" is itself questionable. To characterize P-value
variability one can use prediction intervals whose endpoints reflect the likely
spread of P-values that could have been obtained by a replication study.
Unfortunately, the intervals currently in use, the P-intervals, are based on
unrealistic implicit assumptions. Namely, P-intervals are constructed with the
assumptions that imply substantial chances of encountering large values of
effect size in an observational study, which leads to bias. As an alternative
to P-intervals, we develop a method that gives researchers flexibility by
providing them with the means to control these assumptions. Unlike endpoints of
P-intervals, endpoints of our intervals are directly interpreted as
probabilistic bounds for replication P-values and are resistant to selection
bias contingent upon approximate prior knowledge of the effect size
distribution.


  Chlorine isotopologues of polychlorinated organic compounds are usually
recognized to follow binomial distribution. Is this recognition exactly true?
This study presents a solid theoretical derivation to prove whether the
isotopologue distributions of organochlorines are binomial or not, and
investigates the implications of the distributions to relevant experimental
studies. During synthetic reactions, the C-Cl bonds with stronger strengths are
more readily to be formed with heavy isotopes. The chlorine KIEs are higher
during the breakages of the stronger C-Cl bonds than during the breaking of the
weaker ones. For a synthetic organochlorine, if the rate-limiting step during a
chlorination reaction is the formation of C-Cl bonds, then chlorine KIE takes
effect in the reaction, resulting in always higher chlorine isotope ratio on
the reaction position than that of the initial chlorine atoms from the chlorine
source. Chlorine KIEs vary during different chlorination reactions, leading to
inconsistent chlorine isotope ratios on different reaction positions of
asymmetric reaction intermediates and final products. If the rate-determining
step is the generation of reactive chlorine atoms and the initial isotope ratio
of the chlorine source is constant, then the chlorine isotope ratios on the
reaction positions of all intermediates and final products are equivalent,
demonstrating that the isotopologues follow binomial distribution. After
physical changes and dechlorination reactions, organochlorines in the
environment are unlikely binomially distributed. Experimental data show that
the detected isotopologues of all the investigated organochlorines disobeyed
binomial distribution. This study may bring a new perspective about the
chlorine isotopologue distributions of organochlorines, and the proposed
solutions may help to obtain better data for quantification and CSIA-Cl of
organochlorines.


  We introduce a framework to describe probabilistic models in Bell
experiments, and more generally in contextuality scenarios. Such a scenario is
a hypergraph whose vertices represent elementary events and hyperedges
correspond to measurements. A probabilistic model on such a scenario associates
to each event a probability, in such a way that events in a given measurement
have a total probability equal to one. We discuss the advantages of this
framework, like the unification of the notions of contexuality and nonlocality,
and give a short overview of results obtained elsewhere.


  We show that a geometrical notion of entropy, definable in flat space,
governs the first quantum correction to the Bekenstein-Hawking black hole
entropy. We describe two methods for calculating this entropy -- a
straightforward Hamiltonian approach, and a less direct but more powerful
Euclidean (heat kernel) method. The entropy diverges in quantum field theory in
the absence of an ultraviolet cutoff. Various related finite quantities can be
extracted with further work. We briefly discuss the corresponding question in
string theory.


  The conformalClassification package implements Transductive Conformal
Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification
problems. Conformal Prediction (CP) is a framework that complements the
predictions of machine learning algorithms with reliable measures of
confidence. TCP gives results with higher validity than ICP, however ICP is
computationally faster than TCP. The package conformalClassification is built
upon the random forest method, where votes of the random forest for each class
are considered as the conformity scores for each data point. Although the main
aim of the conformalClassification package is to generate CP errors (p-values)
for classification problems, the package also implements various diagnostic
measures such as deviation from validity, error rate, efficiency, observed
fuzziness and calibration plots. In future releases, we plan to extend the
package to use other machine learning algorithms, (e.g. support vector
machines) for model fitting.


  Imbalanced classification problems are extremely common in natural language
processing and are solved using a variety of resampling and filtering
techniques, which often involve making decisions on how to select training data
or decide which test examples should be labeled by the model. We examine the
tradeoffs in model performance involved in choices of training sample and
filter training and test data in heavily imbalanced token classification task
and examine the relationship between the magnitude of these tradeoffs and the
base rate of the phenomenon of interest. In experiments on sequence tagging to
detect rare phenomena in English and Arabic texts, we find that different
methods of selecting training data bring tradeoffs in effectiveness and
efficiency. We also see that in highly imbalanced cases, filtering test data
using first-pass retrieval models is as important for model performance as
selecting training data. The base rate of a rare positive class has a clear
effect on the magnitude of the changes in performance caused by the selection
of training or test data. As the base rate increases, the differences brought
about by those choices decreases.


  Currently, there is renewed interest in the problem, raised by Shafer in
1985, of updating probabilities when observations are incomplete (or
set-valued). This is a fundamental problem, and of particular interest for
Bayesian networks. Recently, Grunwald and Halpern have shown that commonly used
updating strategies fail here, except under very special assumptions. We
propose a new rule for updating probabilities with incomplete observations. Our
approach is deliberately conservative: we make no or weak assumptions about the
so-called incompleteness mechanism that produces incomplete observations. We
model our ignorance about this mechanism by a vacuous lower prevision, a tool
from the theory of imprecise probabilities, and we derive a new updating rule
using coherence arguments. In general, our rule produces lower posterior
probabilities, as well as partially determinate decisions. This is a logical
consequence of the ignorance about the incompleteness mechanism. We show how
the new rule can properly address the apparent paradox in the 'Monty Hall'
puzzle. In addition, we apply it to the classification of new evidence in
Bayesian networks constructed using expert knowledge. We provide an exact
algorithm for this task with linear-time complexity, also for multiply
connected nets.


  Fast Radio Bursts (FRBs) are bright millisecond-duration radio transients
that appear about 1,000 times per day, all-sky, for a fluence threshold 5 Jy ms
at 600 MHz. The FRB radio-emission physics and the compact objects involved in
these events are subjects of intense active debate. To better constrain source
models, the Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) is
optimized to discover and localize a large sample of rare, high-fluence, nearby
FRBs. This is the population most amenable to multi-messenger, multi-wavelength
follow-up, allowing deeper understanding of source mechanisms. BURSTT will
provide horizon-to-horizon sky coverage with a half power field-of-view (FoV)
of $\sim$10$^{4}$ deg$^{2}$, a 400 MHz effective bandwidth between 300-800 MHz,
and sub-arcsecond localization, made possible using outrigger stations hundreds
to thousands of km from the main array. Initially, BURSTT will employ 256
antennas. After tests of various antenna designs and optimization of system
performance we plan to expand to 2048 antennas. We estimate that BURSTT-256
will detect and localize $\sim$100 bright ($\geq$100 Jy ms) FRBs per year.
Another advantage of BURSTT's large FoV and continuous operation will be
greatly enhanced monitoring of FRBs for repetition. The current lack of
sensitive all-sky observations likely means that many repeating FRBs are
currently cataloged as single-event FRBs.


  Physical design and production of Integrated Circuits (IC) is becoming
increasingly more challenging as the sophistication in IC technology is
steadily increasing. Placement has been one of the most critical steps in IC
physical design. Through decades of research, partition-based, analytical-based
and annealing-based placers have been enriching the placement solution toolbox.
However, open challenges including long run time and lack of ability to
generalize continue to restrict wider applications of existing placement tools.
We devise a learning-based placement tool based on cyclic application of
Reinforcement Learning (RL) and Simulated Annealing (SA) by leveraging the
advancement of RL. Results show that the RL module is able to provide a better
initialization for SA and thus leads to a better final placement design.
Compared to other recent learning-based placers, our method is majorly
different with its combination of RL and SA. It leverages the RL model's
ability to quickly get a good rough solution after training and the heuristic's
ability to realize greedy improvements in the solution.


  A framework integrating information theory and network science is proposed,
giving rise to a potentially new area. By incorporating and integrating
concepts such as complexity, coding, topological projections and network
dynamics, the proposed network-based framework paves the way not only to
extending traditional information science, but also to modeling, characterizing
and analyzing a broad class of real-world problems, from language communication
to DNA coding. Basically, an original network is supposed to be transmitted,
with or without compaction, through a sequence of symbols or time-series
obtained by sampling its topology by some network dynamics, such as random
walks. We show that the degree of compression is ultimately related to the
ability to predict the frequency of symbols based on the topology of the
original network and the adopted dynamics. The potential of the proposed
approach is illustrated with respect to the efficiency of transmitting several
types of topologies by using a variety of random walks. Several interesting
results are obtained, including the behavior of the Barab\'asi-Albert model
oscillating between high and low performance depending on the considered
dynamics, and the distinct performances obtained for two geographical models.


  Given a smooth surface $X$ over a field and an effective Cartier divisor $D$,
we provide an exact sequence connecting $CH_0(X,D)$ and the relative $K$-group
$K_0(X,D)$. We use this exact sequence to answer a question of Kerz and Saito
whenever $X$ is a resolution of singularities of a normal surface. This exact
sequence is used to show that the localization sequence for ordinary Chow
groups does not extend to Chow groups with modulus.


  Many practical problems can be understood as the search for a state of
affairs that extends a fixed partial state of affairs, the \emph{environment},
while satisfying certain conditions that are formally specified. Such problems
are found in, e.g., engineering, law or economics.
  We study this class of problems in a context where some of the relevant
information about the environment is not known by the user at the start of the
search. During the search, the user may consider tentative solutions that make
implicit hypotheses about these unknowns. To ensure that the solution is
appropriate, these hypotheses must be verified by observing the environment.
Furthermore, we assume that, in addition to knowledge of what constitutes a
solution, knowledge of general laws of the environment is also present. We
formally define partial solutions with enough verified facts to guarantee the
existence of complete and appropriate solutions.
  Additionally, we propose an interactive system to assist the user in their
search by determining 1) which hypotheses implicit in a tentative solution must
be verified in the environment, and 2) which observations can bring useful
information for the search. We present an efficient method to over-approximate
the set of relevant information, and evaluate our implementation.


  Left braces, introduced by Rump, have turned out to provide an important tool
in the study of set theoretic solutions of the quantum Yang-Baxter equation. In
particular, they have allowed to construct several new families of solutions. A
left brace $(B,+,\cdot )$ is a structure determined by two group structures on
a set $B$: an abelian group $(B,+)$ and a group $(B,\cdot)$, satisfying certain
compatibility conditions. The main result of this paper shows that every finite
abelian group $A$ is a subgroup of the additive group of a finite simple left
brace $B$ with metabelian multiplicative group with abelian Sylow subgroups.
This result complements earlier unexpected results of the authors on an
abundance of finite simple left braces.


  A Lorentz non-invariant higher derivative effective action in flat spacetime,
characterised by a constant vector, can be made invariant under infinitesimal
Lorentz transformations by restricting the allowed field configurations. These
restricted fields are defined as functions of the background vector in such a
way that background dependance of the dynamics of the physical system is no
longer manifest. We show here that they also provide a field basis for the
realisation of Lorentz algebra and allow the construction of a Poincar\'e
invariant symplectic two form on the covariant phase space of the theory.


  Star formation proceeds via the collapse of a molecular cloud core over
multiple dynamical timescales. Turbulence within cores results in a spatially
non-uniform angular momentum of the cloud, causing a stochastic variation in
orientation of the disk forming from the collapsing material. In the absence of
star-disk angular momentum coupling, such disk-tilting would provide a natural
mechanism for production of primordial spin-orbit misalignments in the
resulting planetary systems. However, owing to high accretion rates in the
embedded phase of star formation, the inner edge of the circumstellar disk
extends down to the stellar surface, resulting in efficient gravitational and
accretional angular momentum transfer between the star and the disk. Here, we
demonstrate that the resulting gravitational coupling is sufficient to suppress
any significant star-disk misalignment, with accretion playing a secondary
role. The joint tilting of the star-disk system leads to a stochastic wandering
of star-aligned bipolar outflows. Such wandering widens the effective opening
angle of stellar outflows, allowing for more efficient clearing of the
remainder of the protostar's gaseous envelope. Accordingly, the processes
described in this work provide an additional mechanism responsible for
sculpting the stellar Initial Mass Function (IMF).


  We present an extension of Voronoi diagrams where when considering which site
a client is going to use, in addition to the site distances, other site
attributes are also considered (for example, prices or weights). A cell in this
diagram is then the locus of all clients that consider the same set of sites to
be relevant. In particular, the precise site a client might use from this
candidate set depends on parameters that might change between usages, and the
candidate set lists all of the relevant sites. The resulting diagram is
significantly more expressive than Voronoi diagrams, but naturally has the
drawback that its complexity, even in the plane, might be quite high.
Nevertheless, we show that if the attributes of the sites are drawn from the
same distribution (note that the locations are fixed), then the expected
complexity of the candidate diagram is near linear.
  To this end, we derive several new technical results, which are of
independent interest. In particular, we provide a high-probability,
asymptotically optimal bound on the number of Pareto optima points in a point
set uniformly sampled from the $d$-dimensional hypercube. To do so we revisit
the classical backward analysis technique, both simplifying and improving
relevant results in order to achieve the high-probability bounds.


  The integration of semantic information in a map allows robots to understand
better their environment and make high-level decisions. In the last few years,
neural networks have shown enormous progress in their perception capabilities.
However, when fusing multiple observations from a neural network in a semantic
map, its inherent overconfidence with unknown data gives too much weight to the
outliers and decreases the robustness. To mitigate this issue we propose a
novel robust fusion method to combine multiple Bayesian semantic predictions.
Our method uses the uncertainty estimation provided by a Bayesian neural
network to calibrate the way in which the measurements are fused. This is done
by regularizing the observations to mitigate the problem of overconfident
outlier predictions and using the epistemic uncertainty to weigh their
influence in the fusion, resulting in a different formulation of the
probability distributions. We validate our robust fusion strategy by performing
experiments on photo-realistic simulated environments and real scenes. In both
cases, we use a network trained on different data to expose the model to
varying data distributions. The results show that considering the model's
uncertainty and regularizing the probability distribution of the observations
distribution results in a better semantic segmentation performance and more
robustness to outliers, compared with other methods. Video -
https://youtu.be/5xVGm7z9c-0


  We give two new characterizations of ($\F_2$-linear) locally testable
error-correcting codes in terms of Cayley graphs over $\F_2^h$:
  \begin{enumerate} \item A locally testable code is equivalent to a Cayley
graph over $\F_2^h$ whose set of generators is significantly larger than $h$
and has no short linear dependencies, but yields a shortest-path metric that
embeds into $\ell_1$ with constant distortion. This extends and gives a
converse to a result of Khot and Naor (2006), which showed that codes with
large dual distance imply Cayley graphs that have no low-distortion embeddings
into $\ell_1$.
  \item A locally testable code is equivalent to a Cayley graph over $\F_2^h$
that has significantly more than $h$ eigenvalues near 1, which have no short
linear dependencies among them and which "explain" all of the large
eigenvalues. This extends and gives a converse to a recent construction of
Barak et al. (2012), which showed that locally testable codes imply Cayley
graphs that are small-set expanders but have many large eigenvalues.
\end{enumerate}


  In this article, we consider the mollified stochastic heat equation with flat
initial data and the corresponding Kardar-Parisi-Zhang (KPZ) equation in
dimension $d \geq 3$ when the mollification parameter is turned off. In
addition, we assume that the strength of the space-time white noise is small
enough, such that the solution of the mollified stochastic heat equation has a
finite limiting second moment. In this case, with regard to the mollified KPZ
equation, we show that the fluctuation of the mollified KPZ equation about its
stationary solution converges in the sense of finite dimensional distributions
towards a Gaussian free field. On the other hand, about the mollified
stochastic heat equation, we show that the fluctuation of the mollified
stochastic heat equation about its stationary solution converges in the sense
of finite dimensional convergence towards the product of an i.i.d random field
and an independent Gaussian free field. Here the i.i.d random field is
generated by the limiting partition function of the continuous directed
polymer. As an application, we prove the limiting fluctuations of the partition
function and the free energy of the continuous directed polymer.


  BL Lac objects are the most numerous class of extragalactic TeV-detected
sources. One of the biggest difficulties in investigating their TeV emission
resides in their limited number, since only 47 BL Lacs are known as TeV
emitters. In this paper, we propose new criteria to select TeV BL Lac
candidates based on the infrared (IR) and X-ray observations. We apply our
selection criteria to the BL Lac objects listed in the ROMA-BZCAT catalog so
identifying 41 potential TeV emitters. We then consider a search over a more
extended sample combining the ROSAT bright source catalog and the WISE all-sky
survey revealing 54 additional candidates for TeV observations. Our
investigation also led to a tentative classification of 16 unidentified X-ray
sources as BL Lac candidates. This analysis provides new interesting BL Lac
targets for future observations with ground based Cherenkov telescopes.


  Nonlinear optical microscopy techniques have emerged as a set of successful
tools for biological imaging. Stimulated emission microscopy belongs to a small
subset of pump-probe techniques which can image non-fluorescent samples without
requiring fluorescent labelling. However, its sensitivity has been shown to be
ultimately limited by the quantum fluctuations in the probe beam. We propose
and experimentally implement sub-shot-noise limited stimulated emission
microscopy by preparing the probe pulse in an intensity-squeezed state. This
technique paves the way for imaging delicate biological samples that have no
detectable fluorescence with sensitivity beyond standard quantum fluctuations.


  The dynamics of a glass-forming material slow greatly near the glass
transition, and molecular motion becomes inhibited. We use confocal microscopy
to investigate the motion of colloidal particles near the colloidal glass
transition. As the concentration in a dense colloidal suspension is increased,
particles become confined in transient cages formed by their neighbors. This
prevents them from diffusing freely throughout the sample. We quantify the
properties of these cages by measuring temporal anticorrelations of the
particles' displacements. The local cage properties are related to the
subdiffusive rise of the mean square displacement: over a broad range of time
scales, the mean square displacement grows slower than linearly in time.


  We consider subleading power corrections to event shape variables in $e^+e^-$
collisions at the first order in the QCD coupling $\alpha_s$. We start from the
jettiness variable $\tau_2$ and the $y_{23}$ resolution variable for the $k_T$
jet clustering algorithm and we analytically compute the corresponding
cumulative cross section. We investigate the origin of the different power
suppressed contributions in the two-jet limit and trace it back to their
different coverage of the phase space. We extend our analysis to the case of
thrust and of the $C$-parameter, and we finally discuss a class of observables
that depend on a continuous parameter giving different weight to central and
forward emissions and we evaluate the corresponding subleading power
corrections.


  Massive brute-force compute campaigns relying on demanding ab initio
calculations routinely search for novel materials in chemical compound space,
the vast virtual set of all conceivable stable combinations of elements and
structural configurations which form matter. Here we demonstrate that
4-dimensional chirality, arising from anti-symmetry of alchemical
perturbations, dissects that space and defines approximate ranks which
effectively reduce its formal dimensionality, and enable us to break down its
combinatorial scaling. The resulting distinct `alchemical' enantiomers must
share the exact same electronic energy up to third order -- independent of
respective covalent bond topology, and imposing relevant constraints on
chemical bonding. Alchemical chirality deepens our understanding of chemical
compound space and enables the `on-the-fly' establishment of new trends without
empiricism for any materials with fixed lattices. We demonstrate its efficacy
for three such cases: i) new formulas for estimating electronic energy
contributions to chemical bonding; ii) analysis of the perturbed electron
density of BN doped benzene; and iii) ranking stability estimates for BN doping
in over 2,000 naphthalene and over 400 million picene derivatives.


  Recently, Bartelmann et al. presented a 'Kinetic Field Theory' (KFT)
formalism to tackle the difficulties of large scale structure formation. In
this approach, the dynamics of a non-equilibrium ensemble of classical
particles are examined based on methods of statistical field theory. So far,
only contributions coming from dark matter were considered, which is assumed to
pose an accurate description of our universe on very large scales.
Nevertheless, going to smaller scales, also baryonic contributions have to be
taken into account. Building on the ideas of Viermann et al. we present an
effective particle model of hydrodynamics to describe baryonic matter in a
cosmological framework. Using this model, the baryonic density contrast power
spectrum is computed to lowest perturbative order within the resummed KFT
framework of Lilow et al. We discuss the qualitative differences to the dark
matter case and perform a quantitative comparison to the baryonic spectrum
obtained from Eulerian perturbation theory. A subsequent paper will resolve the
problem of coupling both theories describing dark and baryonic matter,
respectively, to gain a full model of cosmic matter. Even though our focus is
on cosmological systems only, we want to emphasize that all methods presented
here are of a quite general fashion, making it applicable also to other fields.


  Knowledge graphs contain rich semantic relationships related to items and
incorporating such semantic relationships into recommender systems helps to
explore the latent connections of items, thus improving the accuracy of
prediction and enhancing the explainability of recommendations. However, such
explainability is not adapted to users' contexts, which can significantly
influence their preferences. In this work, we propose CA-KGCN (Context-Aware
Knowledge Graph Convolutional Network), an end-to-end framework that can model
users' preferences adapted to their contexts and can incorporate rich semantic
relationships in the knowledge graph related to items. This framework captures
users' attention to different factors: contexts and features of items. More
specifically, the framework can model users' preferences adapted to their
contexts and provide explanations adapted to the given context. Experiments on
three real-world datasets show the effectiveness of our framework: modeling
users' preferences adapted to their contexts and explaining the recommendations
generated.


  Motivated by the dark energy issue, a minisuperspace approach to the
stability for modified gravitational models in a four dimensional cosmological
setting are investigated. Specifically, after revisiting the $f(R)$ case, $R$
being the Ricci curvature, we present a stability condition around a de Sitter
solution valid for modified gravitational models of generalized Gauss-Bonnet
type $F(R,G,Q)$, $G$ and $Q$ being the Gauss-Bonnet and quadratic Riemann
invariants respectively. A generalization to higher order invariants is
mentioned.


  To circumvent the FLP impossibility result in a deterministic way several
protocols have been proposed on top of an asynchronous distributed system
enriched with additional assumptions. In the context of Byzantine failures for
systems where at most t processes may exhibit a Byzantine behavior, two
approaches have been investigated to solve the consensus problem.The first,
relies on the addition of synchrony, called Timer-Based, but the second is
based on the pattern of the messages that are exchanged, called Time-Free. This
paper shows that both types of assumptions are not antagonist and can be
combined to solve authenticated Byzantine consensus. This combined assumption
considers a correct process pi, called 2t-BW, and a set X of 2t processes such
that, eventually, for each query broadcasted by a correct process pj of X, pj
receives a response from pi 2 X among the (n- t) first responses to that query
or both links connecting pi and pj are timely. Based on this combination, a
simple hybrid authenticated Byzantine consensus protocol,benefiting from the
best of both worlds, is proposed. Whereas many hybrid protocols have been
designed for the consensus problem in the crash model, this is, to our
knowledge, the first hybrid deterministic solution to the Byzantine consensus
problem.


  RPL, the routing protocol proposed by IETF for IPv6/6LoWPAN Low Power and
Lossy Networks has significant complexity. Another protocol called LOADng, a
lightweight variant of AODV, emerges as an alternative solution. In this paper,
we compare the performance of the two protocols in a Home Automation scenario
with heterogenous traffic patterns including a mix of multipoint-to-point and
point-to-multipoint routes in realistic dense non-uniform network topologies.
We use Contiki OS and Cooja simulator to evaluate the behavior of the
ContikiRPL implementation and a basic non-optimized implementation of LOADng.
Unlike previous studies, our results show that RPL provides shorter delays,
less control overhead, and requires less memory than LOADng. Nevertheless,
enhancing LOADng with more efficient flooding and a better route storage
algorithm may improve its performance.


  We present an analytical model of the self-consistent equilibrium of a
magnetic flux rope which is obtained in cylindrical geometry. The equilibrium
azimuthal magnetic field and plasma pressure are determined in a
self-consistent way through the current density which is derived as a solution
of a nonlinear equation. By minimizing the energy functional, it was shown that
the constrained equilibrium state is stable. The obtained results are also
applicable to the cylindrical tokamak magnetic configurations. It is shown that
the analytically predicted radial profiles of equilibrium quantities are in
good agreement with the experimental data.


  Computational codes for direct numerical simulations of Rayleigh-B\'enard
(RB) convection are compared in terms of computational cost and quality of the
solution. As a benchmark case, RB convection at $Ra=10^8$ and $Pr=1$ in a
periodic domain, in cubic and cylindrical containers is considered. A dedicated
second-order finite-difference code (AFID/RBflow) and a specialized
fourth-order finite-volume code (Goldfish) are compared with a general purpose
finite-volume approach (OpenFOAM) and a general purpose spectral-element code
(Nek5000). Reassuringly, all codes provide predictions of the average heat
transfer that converge to the same values. The computational costs, however,
are found to differ considerably. The specialized codes AFID/RBflow and
Goldfish are found to excel in efficiency, outperforming the general purpose
flow solvers Nek5000 and OpenFOAM by an order of magnitude with an error on the
Nusselt number $Nu$ below $5\%$. However, we find that $Nu$ alone is not
sufficient to assess the quality of the numerical results: in fact,
instantaneous snapshots of the temperature field from a near wall region
obtained for deliberately under-resolved simulations using Nek5000 clearly
indicate inadequate flow resolution even when $Nu$ is converged. Overall,
dedicated special purpose codes for RB convection are found to be more
efficient than general purpose codes.


  We consider the problem of optical tomographic imaging in the mesoscopic
regime where the photon mean free path is of order of the system size. Within
the accuracy of the single-scattering approximation to the radiative transport
equation, we show that it is possible to recover the extinction coefficient of
an inhomogeneous medium from angularly-resolved measurements. Applications to
biomedical imaging are described and illustrated with numerical simulations.


  The ubiquitous appearance of regions of localized deformation (shear bands)
in different kinds of disordered materials under shear is studied in the
context of a mesoscopic model of plasticity. The model may or may not include
relaxational (aging) effects. In the absence of relaxational effects the model
displays a monotonously increasing dependence of stress on strain-rate, and
stationary shear bands do not occur. However, in start up experiments transient
(although long lived) shear bands occur, that widen without bound in time. I
investigate this transient effect in detail, reproducing and explaining a t^1/2
law for the thickness increase of the shear band that has been obtained in
atomistic numerical simulations. Relaxation produces a negative sloped region
in the stress vs. strain-rate curve that stabilizes the formation of shear
bands of a well defined width, which is a function of strain-rate. Simulations
at very low strain-rates reveal a non-trivial stick-slip dynamics of very thin
shear bands that has relevance in the study of seismic phenomena. In addition,
other non-stationary processes, such as stop-and-go, or strain-rate inversion
situations display a phenomenology that matches very well the results of recent
experimental studies.


  In an instrumental variable model, the score statistic can be bounded for any
alternative in parts of the parameter space. These regions involve a constraint
on the first-stage regression coefficients and the reduced-form covariance
matrix. Consequently, the Lagrange Multiplier test can have power close to
size, despite being efficient under standard asymptotics. This information loss
limits the power of conditional tests which use only the Anderson-Rubin and the
score statistic. The conditional quasi-likelihood ratio test also suffers
severe losses because it can be bounded for any alternative.
  A necessary condition for drastic power loss to occur is that the Hermitian
of the reduced-form covariance matrix has eigenvalues of opposite signs. These
cases are denoted impossibility designs (ID). We show this happens in practice,
by applying our theory to the problem of inference on the intertemporal
elasticity of substitution (IES). Of eleven countries studied by Yogo (2004}
and Andrews (2016), nine are consistent with ID at the 95\% level.


  We investigate the pseudo-hyperk\"ahler geometry of higher degree rational
curves in the twistor space of a hyperk\"ahler $4$-manifold.


  Metric Ramsey theory is concerned with finding large well-structured subsets
of more complex metric spaces. For finite metric spaces this problem was first
studies by Bourgain, Figiel and Milman \cite{bfm}, and studied further in depth
by Bartal et. al \cite{BLMN03}. In this paper we provide deterministic
constructions for this problem via a novel notion of \emph{metric Ramsey
decomposition}. This method yields several more applications, reflecting on
some basic results in metric embedding theory.
  The applications include various results in metric Ramsey theory including
the first deterministic construction yielding Ramsey theorems with tight
bounds, a well as stronger theorems and properties, implying appropriate
distance oracle applications.
  In addition, this decomposition provides the first deterministic
Bourgain-type embedding of finite metric spaces into Euclidean space, and an
optimal multi-embedding into ultrametrics, thus improving its applications in
approximation and online algorithms.
  The decomposition presented here, the techniques and its consequences have
already been used in recent research in the field of metric embedding for
various applications.


  In the light of the recent Daya Bay result the reactor angle is about 9
degrees, we reconsider the model presented in arXiv:1005.3482 showing that,
when all neutrino oscillation parameters are taken at their best fit values of
Schwetz et al and the reactor angle to be the central value of Daya Bay, the
predicted value of the CP phase is approximately 45 degrees.


  We present VLT eclipse photometry for the giant planet CoRoT-1b. We observed
a transit in the R-band filter and an occultation in a narrow filter centered
on 2.09 microns. Our analysis of this new photometry and published radial
velocities, in combination with stellar-evolutionary modeling, leads to a
planetary mass and radius of 1.07 (+0.13,-0.18) M_Jup and 1.45 (+0.07,-0.13)
R_Jup, confirming the very low density previously deduced from CoRoT
photometry. The large occultation depth that we measure at 2.09 microns (0.278
(+0.043,-0.066) %) is consistent with thermal emission and is better reproduced
by an atmospheric model with no redistribution of the absorbed stellar flux to
the night side of the planet.


  Sound event detection (SED) is typically posed as a supervised learning
problem requiring training data with strong temporal labels of sound events.
However, the production of datasets with strong labels normally requires
unaffordable labor cost. It limits the practical application of supervised SED
methods. The recent advances in SED approaches focuses on detecting sound
events by taking advantages of weakly labeled or unlabeled training data. In
this paper, we propose a joint framework to solve the SED task using
large-scale unlabeled in-domain data. In particular, a state-of-the-art general
audio tagging model is first employed to predict weak labels for unlabeled
data. On the other hand, a weakly supervised architecture based on the
convolutional recurrent neural network (CRNN) is developed to solve the strong
annotations of sound events with the aid of the unlabeled data with predicted
labels. It is found that the SED performance generally increases as more
unlabeled data is added into the training. To address the noisy label problem
of unlabeled data, an ensemble strategy is applied to increase the system
robustness. The proposed system is evaluated on the SED dataset of DCASE 2018
challenge. It reaches a F1-score of 21.0%, resulting in an improvement of 10%
over the baseline system.


  We discuss memory effects in the conductance of hopping insulators due to
slow rearrangements of many-electron clusters leading to formation of polarons
close to the electron hopping sites. An abrupt change in the gate voltage and
corresponding shift of the chemical potential change populations of the hopping
sites, which then slowly relax due to rearrangements of the clusters. As a
result, the density of hopping states becomes time dependent on a scale
relevant to rearrangement of the structural defects leading to the excess time
dependent conductivity.


  In this work, a new energy-efficiency performance metric is proposed for MIMO
(multiple input multiple output) point-to-point systems. In contrast with
related works on energy-efficiency, this metric translates the effects of using
finite blocks for transmitting, using channel estimates at the transmitter and
receiver, and considering the total power consumed by the transmitter instead
of the radiated power only. The main objective pursued is to choose the best
pre-coding matrix used at the transmitter in the following two scenarios~: 1)
the one where imperfect channel state information (CSI) is available at the
transmitter and receiver~; 2) the one where no CSI is available at the
transmitter. In both scenarios, the problem of optimally tuning the total used
power is shown to be non-trivial. In scenario 2), the optimal fraction of
training time can be characterized by a simple equation. These results and
others provided in the paper, along with the provided numerical analysis, show
that the present work can therefore be used as a good basis for studying power
control and resource allocation in energy-efficient multiuser networks.


  We show that the GBW saturation model provides an exact solution to the
one-dimensional linear transport equation. We also show that it is motivated by
the BK equation considered in the saturated regime when the diffusion and the
splitting term in the diffusive approximation are balanced by the nonlinear
term.


  Motivated by mapping from a stochastic system with spatially random rates, we
consider disordered non-conserving free-fermion systems using a scaling
procedure for the equations of motion. This approach demonstrates
disorder-induced localization acting in competition with the asymmetric
driving. We discuss the resulting implications for the original stochastic
system.


  Quantum metrology is the state-of-the-art measurement technology. It uses
quantum resources to enhance the sensitivity of phase estimation beyond what
reachable within classical physics. While single parameter estimation theory
has been widely investigated, much less is known about the simultaneous
estimation of multiple phases, which finds key applications in imaging and
sensing. In this manuscript we provide conditions of useful entanglement (among
multimode particles, qudits) for multiphase estimation and adapt them to
multiarm Mach-Zehnder interferometry. We discuss benchmark multimode Fock
states containing useful qudit entanglement and overcoming the sensitivity of
separable qudit states in three and four arm Mach-Zehnder-like interferometers
- currently within the reach of integrated photonics technology.


  We present a new approach for generating 3D house wireframes with semantic
enrichment using an autoregressive model. Unlike conventional generative models
that independently process vertices, edges, and faces, our approach employs a
unified wire-based representation for improved coherence in learning 3D
wireframe structures. By re-ordering wire sequences based on semantic meanings,
we facilitate seamless semantic integration during sequence generation. Our
two-phase technique merges a graph-based autoencoder with a transformer-based
decoder to learn latent geometric tokens and generate semantic-aware
wireframes. Through iterative prediction and decoding during inference, our
model produces detailed wireframes that can be easily segmented into distinct
components, such as walls, roofs, and rooms, reflecting the semantic essence of
the shape. Empirical results on a comprehensive house dataset validate the
superior accuracy, novelty, and semantic fidelity of our model compared to
existing generative models. More results and details can be found on
https://vcc.tech/research/2024/3DWire.


  The sparse version of the Sachdev-Ye-Kitaev (SYK) model reproduces essential
features of the original SYK model while reducing the number of disorder
parameters. In this paper, we propose a further simplification of the model
which we call the binary-coupling sparse SYK model. We set the nonzero
couplings to be $\pm 1$, rather than being sampled from a continuous
distribution such as Gaussian. Remarkably, this simplification turns out to be
an improvement: the binary-coupling model exhibits strong correlations in the
spectrum, which is the important feature of the original SYK model that leads
to the quick onset of the random-matrix universality, more efficiently in terms
of the number of nonzero terms. This model is better suited for analog or
digital quantum simulations of quantum chaotic behavior and holographic metals
due to its simplicity and scaling properties.


  In the present work we investigate the largest possible gaps between
consecutive numbers which can be written as the difference of two primes. The
best known upper bounds are the same as those concerning the largest possible
difference of Goldbach numbers (that is, numbers which can be written as the
sum of two primes). Thus, we know that any interval of the form [X, X+X^c]
contains numbers which are the difference (or sum, respectively) of two primes,
where c=21/800. It is announced in our work that there is a constant C such
that for sufficiently large X all intervals of the form [X, X+(logX)^C] contain
an even integer which can be written as the difference of two primes. The work
contains, as an illustration of the method, the proof of the weaker result that
given an arbitrarily small c>0, the interval [X, X+X^c] contains the difference
of two primes if X is large enough. Some conditional results are announced too,
which are valid under the deep unproved hypothesis that primes have an
admissible level of distribution larger than 1/2. The above hypothesis implies,
for example, the existence of a large constant C (depending on the admissible
distribution level of the primes) such that for sufficiently large values of X
the interval [X, X+C] contains at least one even number which can be written as
the difference of two consecutive primes in infinitely many ways.


  We matched spectroscopically identified C stars (from low resolution
objective prism surveys) in the Magellanic Clouds with 2MASS sources. We
confirm that C stars show a large spread in absolute magnitudes, even in the Ks
band. We show that the I and Ks magnitude distributions of a population of C
stars (in the LMC) have a similar narrow dispersion if the C stars are selected
in a well defined color range. Using magnitude and color criteria, we employ
the 2MASS data to identify 26 C stars in the the Fornax dwarf spheroidal
galaxy.
  The mean Ks magnitude and the mean bolometric magnitude of C stars are found
to be slightly brighter in the LMC and SMC when compared to those of the Fornax
dwarf spheroidal galaxy. The difference could be explained by ages or/and
abundance differences.


  The observed density of dark matter is of the magnitude expected for a
thermal relic weakly-interacting massive particle (WIMP). In addition, the
observed baryon density is within an order of magnitude of the dark matter
density. This suggests that the baryon density is physically related to a
typical thermal relic WIMP dark matter density. We present a model which
simultaneously generates thermal relic WIMP-like densities for both baryons and
dark matter by modifying a large initial baryon asymmetry. Production of
unstable scalars carrying baryon number at the LHC would be a clear signature
of the model.


  We prove that a curious generating series identity implies Faber's
intersection number conjecture (by showing that it implies a combinatorial
identity already given in arXiv:1902.02742) and give a new proof of Faber's
conjecture by directly proving this identity.


  We report the results of a molecular dynamics simulation of a supercooled
binary Lennard-Jones mixture. By plotting the self intermediate scattering
functions vs. rescaled time, we find a master curve in the $\beta$-relaxation
regime. This master curve can be fitted well by a power-law for almost three
decades in rescaled time and the scaling time, or relaxation time, has a
power-law dependence on temperature. Thus the predictions of
mode-coupling-theory on the existence of a von Schweidler law are found to hold
for this system; moreover, the exponents in these two power-laws are very close
to satisfying the exponent relationship predicted by the mode-coupling-theory.
At low temperatures, the diffusion constants also show a power-law behavior
with the same critical temperature. However, the exponent for diffusion differs
from that of the relaxation time, a result that is in disagreement with the
theory.


  We report our 110 ks Chandra observations of the supernova remnant (SNR)
0104-72.3 in the Small Magellanic Cloud (SMC). The X-ray morphology shows two
prominent lobes along the northwest-southeast direction and a soft faint arc in
the east. Previous low resolution X-ray images attributed the unresolved
emission from the southeastern lobe to a Be/X-ray star. Our high resolution
Chandra data clearly shows that this emission is diffuse, shock-heated plasma,
with negligible X-ray emission from the Be star. The eastern arc is
positionally coincident with a filament seen in optical and infrared
observations. Its X-ray spectrum is well fit by plasma of normal SMC
abundances, suggesting that it is from shocked ambient gas. The X-ray spectra
of the lobes show overabundant Fe, which is interpreted as emission from the
reverse-shocked Fe-rich ejecta. The overall spectral characteristics of the
lobes and the arc are similar to those of Type Ia SNRs, and we propose that SNR
0104-72.3 is the first case for a robust candidate Type Ia SNR in the SMC. On
the other hand, the remnant appears to be interacting with dense clouds toward
the east and to be associated with a nearby star-forming region. These features
are unusual for a standard Type Ia SNR. Our results suggest an intriguing
possibility that the progenitor of SNR 0104-72.3 might have been a white dwarf
of a relatively young population.


  Functionalization of polyoxotungstates with organoarsonate co-ligands
enabling surface decoration was explored for the triangular cluster
architectures of the composition
[CoII9(H2O)6(OH)3(p-RC6H4AsVO3)2({\alpha}-PV2WVI15O56)3]25-({Co9(P2W15)3}, R =
H or NH2), isolated as Na25[Co9(OH)3(H2O)6(C6H5AsO3)2(P2W15O56)3]86H2O (Na-1)
and Na25[Co9(OH)3(H2O)6(H2NC6H4AsO3)2(P2W15O56)3]86H2O (Na-2). The axially
oriented para-aminophenyl groups in 2 facilitate the formation of
self-assembled monolayers on gold surfaces, and thus provide a viable molecular
platform for charge transport studies of magnetically functionalized
polyoxometalates. The title systems were isolated and characterized in the
solid state and in aqueous solutions, and on metal surfaces. Using conducting
tip atomic force microscopy (C-AFM), the energies of {Co9(P2W15)3} frontier
molecular orbitals in the surface-bound state were found to directly correlate
with cyclic voltammetry data in aqueous solution.


  Previous work mainly focuses on improving cross-lingual transfer for NLU
tasks with a multilingual pretrained encoder (MPE), or improving the
performance on supervised machine translation with BERT. However, it is
under-explored that whether the MPE can help to facilitate the cross-lingual
transferability of NMT model. In this paper, we focus on a zero-shot
cross-lingual transfer task in NMT. In this task, the NMT model is trained with
parallel dataset of only one language pair and an off-the-shelf MPE, then it is
directly tested on zero-shot language pairs. We propose SixT, a simple yet
effective model for this task. SixT leverages the MPE with a two-stage training
schedule and gets further improvement with a position disentangled encoder and
a capacity-enhanced decoder. Using this method, SixT significantly outperforms
mBART, a pretrained multilingual encoder-decoder model explicitly designed for
NMT, with an average improvement of 7.1 BLEU on zero-shot any-to-English test
sets across 14 source languages. Furthermore, with much less training
computation cost and training data, our model achieves better performance on 15
any-to-English test sets than CRISS and m2m-100, two strong multilingual NMT
baselines.


  In self-tuning brane-world models with extra dimensions, large contributions
to the cosmological constant are absorbed into the curvature of extra
dimensions and consistent with flat 4d geometry. In models with conventional
Lagrangians fine-tuning is needed nevertheless to ensure a finite effective
Planck mass. Here, we consider a class of models with non conventional
Lagrangian in which known problems can be avoided. Unfortunately these models
are found to suffer from tachyonic instabilities. An attempt to cure these
instabilities leads to the prediction of a positive cosmological constant,
which in turn needs a fine-tuning to be consistent with observations.


  Using the CLEO~II detector we measure ${\cal B}(D_s^+\to \eta e^+\nu)/{\cal
B}(D_s^+\to \phi e^+\nu) =1.24\pm0.12\pm0.15$, ${\cal B}(D_s^+\to \eta'
e^+\nu)/{\cal B}(D_s^+\to \phi e^+\nu) =0.43\pm0.11\pm0.07$ and ${\cal
B}(D_s^+\to \eta' e^+\nu)/{\cal B}(D_s^+\to \eta e^+\nu) =0.35\pm0.09\pm0.07$.
We find the vector to pseudoscalar ratio, ${\cal B}(D_s^+\to \phi e^+\nu)/{\cal
B}(D_s^+\to (\eta+\eta') e^+\nu) =0.60\pm0.06\pm0.06$, which is similar to the
ratio found in non strange $D$ decays.


  This paper extends the canonical model of epidemiology, the SIRD model, to
allow for time-varying parameters for real-time measurement and prediction of
the trajectory of the Covid-19 pandemic. Time variation in model parameters is
captured using the generalized autoregressive score modeling structure designed
for the typical daily count data related to the pandemic. The resulting
specification permits a flexible yet parsimonious model with a low
computational cost. The model is extended to allow for unreported cases using a
mixed-frequency setting. Results suggest that these cases' effects on the
parameter estimates might be sizeable. Full sample results show that the
flexible framework accurately captures the successive waves of the pandemic. A
real-time exercise indicates that the proposed structure delivers timely and
precise information on the pandemic's current stance. This superior
performance, in turn, transforms into accurate predictions of the confirmed and
death cases.


  Graph Attention Network (GAT) and GraphSAGE are neural network architectures
that operate on graph-structured data and have been widely studied for link
prediction and node classification. One challenge raised by GraphSAGE is how to
smartly combine neighbour features based on graph structure. GAT handles this
problem through attention, however the challenge with GAT is its scalability
over large and dense graphs. In this work, we proposed a new architecture to
address these issues that is more efficient and is capable of incorporating
different edge type information. It generates node representations by attending
to neighbours sampled from weighted multi-step transition probabilities. We
conduct experiments on both transductive and inductive settings. Experiments
achieved comparable or better results on several graph benchmarks, including
the Cora, Citeseer, Pubmed, PPI, Twitter, and YouTube datasets.


  The Colour and Stereo Surface Science Imaging System (CaSSIS) of the ExoMars
Trace Gas Orbiter returns on average twenty images per day of the Martian
surface, most of them in 3 or 4 colours and some of them in stereo. CaSSIS uses
a push-frame approach to acquire colour images, with four bandpass filters
deposited directly above the sensor and an imaging cadence synchronized with
the ground track velocity to cover the imaged area with tens of small,
partially overlapping images. These "framelets" are later map-projected and
mosaicked to build the final image. This approach offers both advantages and
challenges in terms of radiometric calibration. While the collection of dark
and flatfield frames is considerably enhanced by the frequent and fast
acquisition of tens of successive images, mosaics assembled from the adjacent
framelets highlight the straylight and changes in the bias of the detector.
Both issues have been identified on CaSSIS images, with low intensities overall
(up to a few percents), but sufficient to generate prominent artefacts on the
final assembled colour images. We have therefore developed methods to correct
these artefacts that are now included into the radiometric calibration
pipeline. We detail here the different steps of the calibration procedure and
the generation of the products used for calibration, and discuss the efficacy
of the corrections. The relative uncertainties on the bias and flatfield frames
are low, of the order of 0.2 and 0.1 percents, respectively. The uncertainty on
the absolute radiometric calibration is of 3 percents, which is quite low for
such an instrument. The straylight adds an estimated about 1 percent error to
the absolute calibration. The residuals after corrections of the straylight and
bias offsets are of the order of a few DNs to tens of DNs.


  This paper has been withdrawn by the author, due to the crucial mistake of
the discussion in Sec IV.


  Synchronization, which is caused by mutual coupling, and turnover, which is
the replacement of old components with new ones, are observed in various open
systems consisting of many components. Although these phenomena can co-occur,
the interplay of coupling and turnover has been overlooked. Here, we analyze
coupled phase oscillators with turnover and reveal that two distinct
transitions occur, depending on both coupling and turnover: desynchronization
and what we name stochastic oscillation quenching. Importantly, the latter
requires both the turnover and coupling to be sufficiently intense.