  In prerecombination resolutions of the Hubble tension, such as early dark
energy, new physics before recombination shifts the values of relevant
cosmological parameters so that the models can fit with cosmic microwave
background and baryon acoustic oscillations observations as well as
$\Lambda$CDM does. In this paper, we clarify how the parameter shifts are
related with $\delta H_0$, particularly we find the shift of primordial scalar
spectral index scales as ${\delta n_s}\simeq 0.4{\delta H_0\over H_0}$ by
performing the Monte Carlo Markov chain analysis with the
Planck2018+BAO+Pantheon+R19+Keck Array/BICEP dataset. A novel point of our
result is that if the current $H_0$ measured locally is correct, complete
resolution of the Hubble tension seems to be pointing to a scale invariant
Harrison-Zeldovich spectrum, i.e. $n_s= 1$ for $H_0\sim 73$km/s/Mpc.


  To proactively offer social media users a safe online experience, there is a
need for systems that can detect harmful posts and promptly alert platform
moderators. In order to guarantee the enforcement of a consistent policy,
moderators are provided with detailed guidelines. In contrast, most
state-of-the-art models learn what abuse is from labelled examples and as a
result base their predictions on spurious cues, such as the presence of group
identifiers, which can be unreliable. In this work we introduce the concept of
policy-aware abuse detection, abandoning the unrealistic expectation that
systems can reliably learn which phenomena constitute abuse from inspecting the
data alone. We propose a machine-friendly representation of the policy that
moderators wish to enforce, by breaking it down into a collection of intents
and slots. We collect and annotate a dataset of 3,535 English posts with such
slots, and show how architectures for intent classification and slot filling
can be used for abuse detection, while providing a rationale for model
decisions.


  The measurement of very low reflection coefficients of anti-reflective coated
interfaces has become a key issue for the realization of precision instruments
such as the giant interferometers used for the detection of gravitational
waves. We propose in this paper a method, based on low coherence interferometry
and balanced detection, which not only allows to obtain the spectral dependence
of this reflection coefficient in amplitude and phase, with a sensitivity of
the order of 0.1 ppm and a spectral resolution of 0.2 nm, but also to eliminate
any spurious influence related to the possible presence of uncoated interfaces.
This method also implements a data processing similar to that used in Fourier
transform spectrometry. After establishing the formulas that control the
accuracy and the signal-to-noise ratio of this method, we present the results
that provide a complete demonstration of its successful operation in various
experimental conditions.


  The Tarski number of a non-amenable group G is the minimal number of pieces
in a paradoxical decomposition of G. In this paper we investigate how Tarski
numbers may change under various group-theoretic operations. Using these
estimates and known properties of Golod-Shafarevich groups, we show that the
Tarski numbers of 2-generated non-amenable groups can be arbitrarily large. We
also use the cost of group actions to show that there exist groups with Tarski
numbers 5 and 6. These provide the first examples of non-amenable groups
without free subgroups whose Tarski number has been computed precisely.


  Quantum bits, or qubits, are the fundamental building blocks of present
quantum computers. Hence, it is important to be able to characterize the state
of a qubit as accurately as possible. By evaluating the qubit characterization
problem from the viewpoint of quantum metrology, we are able to find optimal
measurements under the assumption of good prior knowledge. We implement these
measurements on a superconducting quantum computer. Our experiment produces
sufficiently low error to allow the saturation of the theoretical limits, given
by the Nagaoka--Hayashi bound. We also present simulations of adaptive
measurement schemes utilizing the proposed method. The results of the
simulations show the robustness of the method in characterizing arbitrary qubit
states with different amounts of prior knowledge.


  The inclusive spectra of secondaries produced in soft (minimum-bias) p+Pb
collisions at LHC energy are calculated in the frame of the Quark-Gluon String
Model, by including the inelastic screening corrections (percolation effects).
These effects are expected to be quite large at the very high energies, and
they should drive down the spectra in the midrapidity region more than 2 times,
at sqrt(sNN)=5 TeV.


  The NLP pipeline has evolved dramatically in the last few years. The first
step in the pipeline is to find suitable annotated datasets to evaluate the
tasks we are trying to solve. Unfortunately, most of the published datasets
lack metadata annotations that describe their attributes. Not to mention, the
absence of a public catalogue that indexes all the publicly available datasets
related to specific regions or languages. When we consider low-resource
dialectical languages, for example, this issue becomes more prominent. In this
paper we create \textit{Masader}, the largest public catalogue for Arabic NLP
datasets, which consists of 200 datasets annotated with 25 attributes.
Furthermore, We develop a metadata annotation strategy that could be extended
to other languages. We also make remarks and highlight some issues about the
current status of Arabic NLP datasets and suggest recommendations to address
them.


  In this paper we formulate a new N-state spin integrable model on a
three-dimensional lattice with spins interacting round each elementary cube of
the lattice. This model can be also reformulated as a vertex type model. Weight
functions of the model satisfy tetrahedron equations.


  Numerical multiscale methods usually rely on some coupling between a
macroscopic and a microscopic model. The macroscopic model is incomplete as
effective quantities, such as the homogenized material coefficients or fluxes,
are missing in the model. These effective data need to be computed by running
local microscale simulations followed by a local averaging of the microscopic
information. Motivated by the classical homogenization theory, it is a common
practice to use local elliptic cell problems for computing the missing
homogenized coefficients in the macro model. Such a consideration results in a
first order error $O(\varepsilon/\delta)$, where $\varepsilon$ represents the
wavelength of the microscale variations and $\delta$ is the size of the
microscopic simulation boxes. This error, called "resonance error", originates
from the boundary conditions used in the micro-problem and typically dominates
all other errors in a multiscale numerical method. Optimal decay of the
resonance error remains an open problem, although several interesting
approaches reducing the effect of the boundary have been proposed over the last
two decades. In this paper, as an attempt to resolve this problem, we propose a
computationally efficient, fully elliptic approach with exponential decay of
the resonance error.


  The paper describes an improved parallel MPI-based implementation of VBARMS,
a variable block variant of the pARMS preconditioner proposed by Li,~Saad and
Sosonkina [NLAA, 2003] for solving general nonsymmetric linear systems. The
parallel VBARMS solver can detect automatically exact or approximate dense
structures in the linear system, and exploits this information to achieve
improved reliability and increased throughput during the factorization. A novel
graph compression algorithm is discussed that finds these approximate dense
blocks structures and requires only one simple to use parameter. A complete
study of the numerical and parallel performance of parallel VBARMS is presented
for the analysis of large turbulent Navier-Stokes equations on a suite of
three-dimensional test cases.


  With the recent experimental advance in our precise knowledge of the neutrino
oscillation parameters, the correct form of the 3 X 3 neutrino mass matrix is
now approximately known. I discuss how this may be obtained from symmetry
principles, using as examples the finite groups A_4 and Z_4, predicting as a
result three nearly degenerate Majorana neutrino masses in the 0.2 eV range.


  We describe some general constructions on a real smooth projective 4-quadric
which provide analogues of the Willmore functional and conformal Gauss map in
both Lie sphere and projective differential geometry. Extrema of these
functionals are characterized by harmonicity of this Gauss map.


  We study thermal structure and evolution of magnetars as cooling neutron
stars with a phenomenological heat source in a spherical internal layer. We
explore the location of this layer as well as the heating rate that could
explain high observable thermal luminosities of magnetars and would be
consistent with the energy budget of neutron stars. We conclude that the heat
source should be located in an outer magnetar's crust, at densities rho < 5e11
g/cm^3, and should have the heat intensity of the order of 1e20 erg/s/cm^3.
Otherwise the heat energy is mainly emitted by neutrinos and cannot warm up the
surface.


  With the aim of the discovery of face-centered-cubic (fcc) high-entropy alloy
(HEA) superconductor, we have carried out materials research on Nb or
Pb-containing multi-component alloys. Although the X-ray diffraction (XRD)
patterns of some Nb-containing samples exhibited the dominant fcc phases, no
superconducting signals were observed down to 3 K. Examination with an energy
dispersive X-ray spectrometer revealed that all samples were multi-phase, but
the existence of several new Nb-containing HEA phases was found in them. It was
confirmed that the synthesis of Pb-containing an HEA or quaternary alloy would
be difficult, probably due to the large differences in the crystal structure
and atomic radius among constituent elements, low reaction temperature and the
lack of a rapid cooling process in the synthesis. Despite the negative results
in this research, some hints for an improved strategy for the search for an fcc
HEA superconductor are provided. Moreover, our results are useful as
fundamental data for future HEA predictions or for studies of phase relations
in Nb or Pb-containing multi-component alloys based on the CALPHAD (calculation
of phase diagram) method.


  Understanding the collisional outcomes of dust aggregates and dependence on
material properties of the constituting particles is of great importance toward
understanding planet formation. Recent numerical simulations have revealed that
interparticle tangential friction plays a crucial role in energy dissipation
during collisions between porous dust aggregates; however, the importance of
friction on the collisional growth of dust aggregates remains poorly
understood. Here we demonstrate the effects of interparticle tangential
friction on the collisional growth of dust aggregates. We performed numerical
simulations of collisions between equal-mass porous dust aggregates consisting
of cohesive and frictionless spheres. We changed the collision velocity and
impact angle systematically and calculated the collisional growth efficiency as
a function of the collision velocity. We found that the threshold velocity for
collisional growth decreases when dust aggregates are made of frictionless
spheres as compared to frictional spheres. Our results highlight the importance
of tangential interactions on the collisional behavior of dust aggregates and
indicate that the predictive equation for threshold velocity should be
reconstructed.


  We present a direct speech-to-speech translation (S2ST) model that translates
speech from one language to speech in another language without relying on
intermediate text generation. We tackle the problem by first applying a
self-supervised discrete speech encoder on the target speech and then training
a sequence-to-sequence speech-to-unit translation (S2UT) model to predict the
discrete representations of the target speech. When target text transcripts are
available, we design a joint speech and text training framework that enables
the model to generate dual modality output (speech and text) simultaneously in
the same inference pass. Experiments on the Fisher Spanish-English dataset show
that the proposed framework yields improvement of 6.7 BLEU compared with a
baseline direct S2ST model that predicts spectrogram features. When trained
without any text transcripts, our model performance is comparable to models
that predict spectrograms and are trained with text supervision, showing the
potential of our system for translation between unwritten languages. Audio
samples are available at
https://facebookresearch.github.io/speech_translation/direct_s2st_units/index.html .


  We calculate electron-hole generation and recombination rates for Coulomb
scattering (Auger recombination and impact ionization) in Graphene. The
conduction and valence band dispersion relation in Graphene together with
energy and momentum conservation requirements restrict the phase space for
Coulomb scattering so that electron-hole recombination times can be much longer
than 1 ps for electron-hole densities smaller than $10^{12}$ cm$^{-2}$.


  The way how recurrently connected networks of spiking neurons in the brain
acquire powerful information processing capabilities through learning has
remained a mystery. This lack of understanding is linked to a lack of learning
algorithms for recurrent networks of spiking neurons (RSNNs) that are both
functionally powerful and can be implemented by known biological mechanisms.
Since RSNNs are simultaneously a primary target for implementations of
brain-inspired circuits in neuromorphic hardware, this lack of algorithmic
insight also hinders technological progress in that area. The gold standard for
learning in recurrent neural networks in machine learning is back-propagation
through time (BPTT), which implements stochastic gradient descent with regard
to a given loss function. But BPTT is unrealistic from a biological
perspective, since it requires a transmission of error signals backwards in
time and in space, i.e., from post- to presynaptic neurons. We show that an
online merging of locally available information during a computation with
suitable top-down learning signals in real-time provides highly capable
approximations to BPTT. For tasks where information on errors arises only late
during a network computation, we enrich locally available information through
feedforward eligibility traces of synapses that can easily be computed in an
online manner. The resulting new generation of learning algorithms for
recurrent neural networks provides a new understanding of network learning in
the brain that can be tested experimentally. In addition, these algorithms
provide efficient methods for on-chip training of RSNNs in neuromorphic
hardware.


  We study the bootstrap for the maxima of the sums of independent random
variables, a problem of high relevance to many applications in modern
statistics. Since the consistency of bootstrap was justified by Gaussian
approximation in Chernozhukov et al. (2013), quite a few attempts have been
made to sharpen the error bound for bootstrap and reduce the sample size
requirement for bootstrap consistency. In this paper, we show that the sample
size requirement can be dramatically improved when we make the inference
slightly conservative, that is, to inflate the bootstrap quantile
$t_{\alpha}^*$ by a small fraction, e.g. by $1\%$ to $1.01\,t^*_\alpha$. This
simple procedure yields error bounds for the coverage probability of
conservative bootstrap at as fast a rate as $\sqrt{(\log p)/n}$ under suitable
conditions, so that not only the sample size requirement can be reduced to
$\log p \ll n$ but also the overall convergence rate is nearly parametric.
Furthermore, we improve the error bound for the coverage probability of the
standard non-conservative bootstrap to $[(\log (np))^3 (\log p)^2/n]^{1/4}$
under general assumptions on data. These results are established for the
empirical bootstrap and the multiplier bootstrap with third moment match. An
improved coherent Lindeberg interpolation method, originally proposed in Deng
and Zhang (2017), is developed to derive sharper comparison bounds, especially
for the maxima.


  We outline the science motivation for SGSO, the Southern Gamma-Ray Survey
Observatory. SGSO will be a next-generation wide field-of-view gamma-ray survey
instrument, sensitive to gamma-rays in the energy range from 100 GeV to
hundreds of TeV. Its science topics include unveiling galactic and
extragalactic particle accelerators, monitoring the transient sky at very high
energies, probing particle physics beyond the Standard Model, and the
characterization of the cosmic ray flux. SGSO will consist of an air shower
detector array, located in South America. Due to its location and large field
of view, SGSO will be complementary to other current and planned gamma-ray
observatories such as HAWC, LHAASO, and CTA.


  We consider the problem of deciding the satisfiability of quantifier-free
formulas in the theory of finite sets with cardinality constraints. Sets are a
common high-level data structure used in programming; thus, such a theory is
useful for modeling program constructs directly. More importantly, sets are a
basic construct of mathematics and thus natural to use when formalizing the
properties of computational systems. We develop a calculus describing a modular
combination of a procedure for reasoning about membership constraints with a
procedure for reasoning about cardinality constraints. Cardinality reasoning
involves tracking how different sets overlap. For efficiency, we avoid
considering Venn regions directly, as done in previous work. Instead, we
develop a novel technique wherein potentially overlapping regions are
considered incrementally as needed, using a graph to track the interaction
among the different regions. The calculus has been designed to facilitate its
implementation within SMT solvers based on the DPLL($T$) architecture. Our
experimental results demonstrate that the new techniques are competitive with
previous techniques and can scale much better on certain classes of problems.


  The constituent counting rules, i.e., the scaling behavior of amplitudes (in
terms of the number of fundamental constituents) for exclusive processes when
high energy scales are present, have been known for decades, and have been
borne out in a number of experiments. Such scaling would be sensitive, in
particular, to possible exotic multiquark content. Here we examine how one may
use the rules to test for pentaquarks in electroproduction, or for tetraquarks
in e^+ e^- annihilation. An interesting new type of scaling (separate
Mandelstam s and t behavior) arises in the forward scattering direction. The
correct scaling arises naturally in AdS/QCD, in which the amplitudes can be
computed explicitly.


  This paper examines signalling when the sender exerts effort and receives
benefits over time. Receivers only observe a noisy public signal about the
effort, which has no intrinsic value.
  The modelling of signalling in a dynamic context gives rise to novel
equilibrium outcomes. In some equilibria, a sender with a higher cost of effort
exerts strictly more effort than his low-cost counterpart. The low-cost type
can compensate later for initial low effort, but this is not worthwhile for a
high-cost type. The interpretation of a given signal switches endogenously over
time, depending on which type the receivers expect to send it.
  JEL classification: D82, D83, C73.
  Keywords: Dynamic games, signalling , incomplete information


  This paper studies the free group of rank two from the point of view of
Stallings core graphs. The first half of the paper examines primitive elements
in this group, giving new and self-contained proofs for various known results
about them. In particular, this includes the classification of bases of this
group. The second half of the paper is devoted to constructing a counterexample
to a conjecture by Miasnikov, Ventura and Weil, which seeks to characterize
algebraic extensions in free groups in terms of Stallings graphs.


  Autoregressive exogenous (ARX) systems are the general class of input-output
dynamical systems used for modeling stochastic linear dynamical systems (LDS)
including partially observable LDS such as LQG systems. In this work, we study
the problem of system identification and adaptive control of unknown ARX
systems. We provide finite-time learning guarantees for the ARX systems under
both open-loop and closed-loop data collection. Using these guarantees, we
design adaptive control algorithms for unknown ARX systems with arbitrary
strongly convex or convex quadratic regulating costs. Under strongly convex
cost functions, we design an adaptive control algorithm based on online
gradient descent to design and update the controllers that are constructed via
a convex controller reparametrization. We show that our algorithm has
$\tilde{\mathcal{O}}(\sqrt{T})$ regret via explore and commit approach and if
the model estimates are updated in epochs using closed-loop data collection, it
attains the optimal regret of $\text{polylog}(T)$ after $T$ time-steps of
interaction. For the case of convex quadratic cost functions, we propose an
adaptive control algorithm that deploys the optimism in the face of uncertainty
principle to design the controller. In this setting, we show that the explore
and commit approach has a regret upper bound of $\tilde{\mathcal{O}}(T^{2/3})$,
and the adaptive control with continuous model estimate updates attains
$\tilde{\mathcal{O}}(\sqrt{T})$ regret after $T$ time-steps.


  A typical goal of supervised dimension reduction is to find a low-dimensional
subspace of the input space such that the projected input variables preserve
maximal information about the output variables. The dependence maximization
approach solves the supervised dimension reduction problem through maximizing a
statistical dependence between projected input variables and output variables.
A well-known statistical dependence measure is mutual information (MI) which is
based on the Kullback-Leibler (KL) divergence. However, it is known that the KL
divergence is sensitive to outliers. On the other hand, quadratic MI (QMI) is a
variant of MI based on the $L_2$ distance which is more robust against outliers
than the KL divergence, and a computationally efficient method to estimate QMI
from data, called least-squares QMI (LSQMI), has been proposed recently. For
these reasons, developing a supervised dimension reduction method based on
LSQMI seems promising. However, not QMI itself, but the derivative of QMI is
needed for subspace search in supervised dimension reduction, and the
derivative of an accurate QMI estimator is not necessarily a good estimator of
the derivative of QMI. In this paper, we propose to directly estimate the
derivative of QMI without estimating QMI itself. We show that the direct
estimation of the derivative of QMI is more accurate than the derivative of the
estimated QMI. Finally, we develop a supervised dimension reduction algorithm
which efficiently uses the proposed derivative estimator, and demonstrate
through experiments that the proposed method is more robust against outliers
than existing methods.


  For a weight-set $A\subseteq \mathbb Z_n$, the $A$-weighted zero-sum constant
$C_A(n)$ is defined to be the smallest natural number $k$, such that any
sequence of $k$ elements in $\mathbb Z_n$ has an $A$-weighted zero-sum
subsequence of consecutive terms. A sequence of length $C_A(n)-1$ in $\mathbb
Z_n$ which does not have any $A$-weighted zero-sum subsequence of consecutive
terms will be called a $C$-extremal sequence for $A$.
  Let $\big(\frac{x}{n}\big)$ denote the Jacobi symbol of $x\in\mathbb Z_n$. We
characterize the $C$-extremal sequences for the weight-set $S(n)=\big\{\,x\in
U(n):\big(\frac{x}{n}\big)=1\,\big\}$ and for the weight-set
$L(n;p)=\big\{\,x\in U(n):\big(\frac{x}{n}\big)=\big(\frac{x}{p}\big)\,\big\}$
where $p$ is a prime divisor of $n$. We can define $D$-extremal sequences for
these weight-sets in a way analogous to the definition of $C$-extremal
sequences. We also characterize these sequences.


  Branched covering Riemann surfaces $(\mathbb{C},f)$ are studied, where $f$ is
the Euler Gamma function and the Riemann Zeta function. For both of them
fundamental domains are found and the group of covering transformations is
revealed. In order to find fundamental domains, pre-images of the real axis are
taken and a thorough study of their geometry is performed. The technique of
simultaneous continuation, introduced by the authors in previous papers, is
used for this purpose. Color visualization of the conformal mapping of the
complex plane by these functions is used for a better understanding of the
theory. For the Riemann Zeta function the outstanding question of the
multiplicity of its zeros, as well as of the zeros of its derivative is
answered.


  Generalizations of the Kovalevskaya, Chaplygin, Goryachev-Chaplygin and
Bogoyavlensky systems on a bundle are considered in this paper. Moreover, a
method of introduction of separating variables and action-angle variables is
described. Another integration method for the Kovalevskaya top on the bundle is
found. This method uses a coordinate transformation that reduces the
Kovalevskaya system to the Neumann system. The Kolosov analogy is considered. A
generalization of a recent Gaffet system to the bundle of Poisson brackets is
obtained at the end of the paper.


  We report a simple fiber-implemented technique for complete reconstruction of
intensity profile and phase of ultrashort laser pulses based on processing only
pulse spectrum and two self-phase modulated spectra measured after a short
piece of optical fiber. Its applicability is shown on an example of a fiber
optical system in the telecommunication range. A retrieval algorithm in a
dispersionless approximation and with considering dispersion effects is
developed. The obtained results are confirmed by independent measurements using
the second-harmonic generation frequency-resolved optical gating technique and
by reconstructing purposely introduced signal features. We also provide
estimates demonstrating great opportunities for implementing this technique in
all-waveguide optical systems ranging from optical communications to
nanophotonics with femtojoule pulses as well as to mid-IR photonics, where
specialty fibers with huge optical nonlinearities can be used.


  We exhibit a set of generating relations for the modular invariant ring of a
vector and a covector for the two-dimensional general linear group over a
finite field.


  Consider a connected network of agents endowed with local cost functions
representing private objectives. Agents seek to find an agreement on some
minimizer of the aggregate cost, by means of repeated communications between
neighbors. Consensus on the average over the network, usually addressed by
gossip algorithms, is a special instance of this problem, corresponding to
quadratic private objectives. Consensus on the median, or more generally
quantiles, is also a special instance, as many more consensus problems. In this
paper we show that optimizing the aggregate cost function regularized by a
total variation term has appealing properties. First, it can be done very
naturally in a distributed way, yielding algorithms that are efficient on
numerical simulations. Secondly, the optimum for the regularized cost is shown
to be also the optimum for the initial aggregate cost function under
assumptions that are simple to state and easily verifiable. Finally, these
algorithms are robust to unreliable agents that keep injecting some false value
in the network. This is remarkable enough, and is not the case, for instance,
of gossip algorithms, that are entirely ruled by unreliable agents as detailed
in the paper.


  The MINOS long baseline neutrino oscillation experiment is comprised of three
calorimeters, a Near Detector at FNAL, a Far Detector at the Soudan mine in
northern Minnesota, and a Calibration Detector in a test beam at CERN. The
light produced in the interactions of neutrinos in the Near Detector at FNAL
will be routed by optical fibres onto the pixels of Hamamatsu R5900-00-M64
multi-anode photomultiplier tubes. This article reports the measurements
performed on 15 of these tubes to evaluate them for their use in MINOS.


  We report on the recent results of the hypercentral Constituent Quark Model
(hCQM). The model contains a spin independent three-quark interaction which is
inspired by Lattice QCD calculations and reproduces the average energy values
of the SU(6) multiplets. The splittings within each multiplet are obtained with
a SU(6)-breaking interaction, which can include also an isospin dependent term.
All the 3- and 4-stars resonances are well reproduced. Moreover, as all the
Constituent Quark models, the hCQM predicts ``missing'' resonances ({\em e.g.}
extra $S11$ and $P13$ states) which can be of some help for the experimental
identification of new resonances. The model provides also a good description of
the medium $Q^2$-behavior of the electromagnetic transition form factors. In
particular the calculated helicity amplitude $A_{{1/2}}$ for the $S_{11}(1535)$
resonance agrees very well with the recent CLAS data. More recently, the
elastic nucleon form factors have been calculated using a relativistic version
of the hCQM and a relativistic quark current.


  Kostochka and Yancey proved that every $4$-critical graph $G$ has $e(G) \geq
\frac{5v(G) - 2}{3}$, and that equality holds if and only if $G$ is $4$-Ore. We
show that a question of Postle and Smith-Roberge implies that every
$4$-critical graph with no $(7,2)$-circular-colouring has $e(G) \geq
\frac{27v(G) -20}{15}$. We prove that every $4$-critical graph with no
$(7,2)$-colouring has $e(G) \geq \frac{17v(G)}{10}$ unless $G$ is isomorphic to
$K_{4}$ or the wheel on six vertices. We also show that if the Gallai Tree of a
$4$-critical graph with no $(7,2)$-colouring has every component isomorphic to
either an odd cycle, a claw, or a path. In the case that the Gallai Tree
contains an odd cycle component, then $G$ is isomorphic to an odd wheel. In
general, we show a $k$-critical graph with no $(2k-1,2)$-colouring that
contains a clique of size $k-1$ in it's Gallai Tree is isomorphic to $K_{k}$.


  We performed measurements of Quantum Shot Noise in order to determine the
quasiparticle charge in the Fractional Quantum Hall regime. The noise is
generated by a current flow through a partially transmitting Quantum Point
Contact in a 2DEG. The noise is directly proportional to the charge of the
quasiparticles, thus allowing direct determination of the charge. We measured
Quantum Shot Noise at a filling factor of 1/3 and found that the charge is e/3;
as predicted by Laughlin.


  We give an explicit algorithm and source code for extracting equity risk
factors from dead (a.k.a. "flatlined" or "hockey-stick") alphas and using them
to improve performance characteristics of good (tradable) alphas. In a
nutshell, we use dead alphas to extract directions in the space of stock
returns along which there is no money to be made (and/or those bets are too
volatile). In practice the number of dead alphas can be large compared with the
number of underlying stocks and care is required in identifying the aforesaid
directions.


  By means of numerical simulations, we demonstrate the innovative use of
computational ghost imaging in transmission electron microscopy to retrieve
images with a resolution that overcomes the limitations imposed by coherent
aberrations. The method requires measuring the intensity on a single pixel
detector with a series of structured illuminations. The success of the
technique is improved if the probes are made to resemble the sample and the
patterns cover the area of interest evenly. By using a simple 8 electrode
device as a specific example, a 2-fold increase in resolution beyond the
aberration limit is demonstrated to be possible under realistic experimental
conditions.


  Fisher (1934) argued that certain ancillary statistics form a relevant
subset, a subset of the sample space on which inference should be restricted,
and showed that conditioning on their observed value reduces the dimension of
the data without a loss of information. The use of ancillary statistics in
post-data inference has received significant attention; however, their role in
the design of the experiment has not been well characterized. Ancillary
statistics are unknown prior to data collection and as a result cannot be
incorporated into the design a priori. However, if the data are observed
sequentially then the ancillary statistics based on the data from the preceding
observations can be used to determine the design assignment for the current
observation. The main results of this work describe the benefits of
incorporating ancillary statistics, specifically, the ancillary statistic that
constitutes a relevant subset, into an adaptive design.


  Optical photometry is presented for the quadruple gravitational lens
PG1115+080. A preliminary reduction of data taken from November 1995 to June
1996 gives component ``C'' leading component ``B'' by 23.7+/-3.4 days and
components ``A1'' and ``A2'' by 9.4 days. A range of models has been fit to the
image positions, none of which gives an adequate fit. The best fitting and most
physically plausible of these, taking the lensing galaxy and the associated
group of galaxies to be singular isothermal spheres, gives a Hubble constant of
42 km/s/Mpc for Omega=1, with an observational uncertainty of 14%, as computed
from the B-C time delay measurement. Taking the lensing galaxy to have an
approximately E5 isothermal mass distribution yields H0=64 km/sec/Mpc while
taking the galaxy to be a point mass gives H0=84 km/sec/Mpc. The former gives a
particularly bad fit to the position of the lensing galaxy, while the latter is
inconsistent with measurements of nearby galaxy rotation curves. Constraints on
these and other possible models are expected to improve with planned HST
observations.


  The Kardar-Parisi-Zhang model of non-equilibrium critical behaviour (kinetic
surface roughening) with turbulent motion of the environment taken into account
is studied by the field theoretic renormalization group approach. The turbulent
motion is described by the stochastic Navier-Stokes equation with the random
stirring force whose correlation function includes two terms that allow one to
account both for a turbulent fluid and for a fluid in thermal equilibrium. The
renormalization group analysis performed in the leading order of perturbation
theory (one-loop approximation) reveals six possible types of scaling behaviour
(universality classes). The most interesting values of the spatial dimension
$d=2$ and~$3$ correspond to the universality class of a pure turbulent
advection where the nonlinearity of the Kardar--Parisi--Zhang model is
irrelevant.


  Humanoid Reaction Synthesis is pivotal for creating highly interactive and
empathetic robots that can seamlessly integrate into human environments,
enhancing the way we live, work, and communicate. However, it is difficult to
learn the diverse interaction patterns of multiple humans and generate
physically plausible reactions. The kinematics-based approaches face
challenges, including issues like floating feet, sliding, penetration, and
other problems that defy physical plausibility. The existing physics-based
method often relies on kinematics-based methods to generate reference states,
which struggle with the challenges posed by kinematic noise during action
execution. Constrained by their reliance on diffusion models, these methods are
unable to achieve real-time inference. In this work, we propose a Forward
Dynamics Guided 4D Imitation method to generate physically plausible human-like
reactions. The learned policy is capable of generating physically plausible and
human-like reactions in real-time, significantly improving the speed(x33) and
quality of reactions compared with the existing method. Our experiments on the
InterHuman and Chi3D datasets, along with ablation studies, demonstrate the
effectiveness of our approach.


  In this work, the simulation of optical photons is carried out in an
antineutrino detector module consisting of a plastic scintillator connected to
light guides and photomultipliers on both ends, which is considered to be used
for remote reactor monitoring in the field of nuclear safety. Using Monte Carlo
(MC) based GEANT4 simulation, numerous parameters influencing the light
collection and thereby the energy resolution of the antineutrino detector
module are studied: e.g., degrees of scintillator surface roughness, reflector
type, and its ap- plying method onto scintillator and light guide surface, the
reflectivity of the reflector, light guide geometries and diameter of the
photocathode. The impact of each parameter is inves- tigated by looking at the
detected spectrum, i.e. the number photoelectrons per depositing energy. In
addition, the average light collection efficiency of the detector module and
its spatial variation are calculated for each simulation setup. According to
the simulation re- sults, it is found that photocathode size, light guide
shape, reflectivity of reflecting material and wrapping method show a
significant impact on the light collection efficiency while scin- tillator
surface polishing level and the choose of reflector type show relatively less
impact. This study demonstrates that these parameters are very important in the
design of plastic scintillator included antineutrino detectors to improve the
energy resolution efficiency.


  In this work we propose a novel approach to solve the Schr\"{o}dinger
equation which combines projection onto the ground state with a path-filtering
truncation scheme. The resulting projector configuration interaction (PCI)
approach realizes a deterministic version of the full configuration interaction
quantum Monte Carlo (FCIQMC) method [Booth, G. H.; Thom, A. J. W.; Alavi, A. J.
Chem. Phys. 2009, 131, 054106]. To improve upon the linearized imaginary-time
propagator, we develop an optimal projector scheme based on an exponential
Chebyshev expansion in the limit of an infinite imaginary time step. After
writing the exact projector as a path integral in determinant space, we
introduce a path filtering procedure that truncates the size of the
determinantal basis and approximates the Hamiltonian. The path filtering
procedure is controlled by one real threshold that determines the accuracy of
the PCI energy and is not biased towards any determinant. Therefore, the PCI
approach can equally well describe static and dynamic electron correlation.
This point is illustrated in benchmark computation on N$_2$ at both equilibrium
and stretched geometries. In both cases, the PCI achieves chemical accuracy
with wave functions that contain less than 0.5% of the full CI space. We also
report computations on the ground state of C$_2$ with up to quaduple-$\zeta$
basis sets and wave functions as large as 200 million determinants, which allow
a direct comparison of the PCI, FCIQMC, and density matrix renormalization
group (DMRG) methods. The size of the PCI wave function grows modestly with the
number of unoccupied orbitals and its accuracy may be tuned to match that of
FCIQMC and DMRG.


  It was pointed out by Paczynski (1987) that the X-ray luminosity of accreting
neutron stars is very sensitive to the physical properties of the accretion
flow close to the innermost stable circular orbit. The X-ray radiation is
dominated by that emitted in the boundary layer, where accreted matter hits a
star surface. The X-ray luminosity of the boundary layer is proportional to the
local accretion rate. In this note, we estimate local accretion rate variations
from the disk that undergoes non-stationary axisymmetric perturbations. The
perturbations are given by the poloidal-velocity potential. We obtain a simple
formula describing the modulation of the accretion rate for the particular case
of global vertical disk oscillations that have been recently studied by
Abramowicz et al. (2005).


  Numerical models allow the investigation of phenomena that cannot exist in a
laboratory. Computational simulations are therefore essential for advancing our
knowledge of astrophysics, however, the very nature of simulation requires
making assumptions that can substantially affect their outcome. Here, we
present the challenges faced when simulating dim thermonuclear explosions, Type
Iax supernovae. This class of dim events produce a slow moving, sparse ejecta
that presents challenges for simulation. We investigate the limitations of the
equation of state and its applicability to the expanding, cooling ejecta. We
also discuss how the "fluff", i.e. the low-density gas on the grid in lieu of
vacuum, inhibits the ejecta as it expands. We explore how the final state of
the simulation changes as we vary the character of the burning, which
influences the outcome of the explosion. These challenges are applicable to a
wide range of astrophysical simulations, and are important to discuss and
overcome as a community.


  We prove that the moduli space of stable sheaves of rank 2 with a certain
Chern classes on a smooth quadric $Q$ in $\PP_3$, is isomorphic to $\PP_3$.
Using this identification, we give a new proof that a certain Brill-Noether
locus on a non-hyperelliptic curve of genus 4, is isomorphic to the
Donagi-Izadi cubic threefold.


  A rotating particle in electrorheological (ER) fluid leads to a displacement
of its polarization charges on the surface which relax towards the external
applied field ${\bf E}_0$, resulting in a steady-state polarization at an angle
with respect to ${\bf E}_0$. This dynamic effect has shown to affect the ER
fluids properties dramatically. In this paper, we develop a dynamic effective
medium theory (EMT) for a system containing rotating particles of finite volume
fraction. This is a generalization of established EMT to account for the
interactions between many rotating particles. While the theory is valid for
three dimensions, the results in a special two dimensional configuration show
that the system exhibits an off-diagonal polarization response, in addition to
a diagonal polarization response, which resembles the classic Hall effect. The
diagonal response monotonically decreases with an increasing rotational speed,
whereas the off-diagonal response exhibits a maximum at a reduced rotational
angular velocity $\omega_0$ comparing to the case of isolated rotating
particles. This implies a way of measurement on the interacting relaxation
time. The dependencies of the diagonal and off-diagonal responses on various
factors, such as $\omega_0$, the volume fraction, and the dielectric contrast,
are discussed.


  Ghost imaging is a non local imaging technology, which can obtain target
information by measuring the second-order intensity correlation between the
reference light field and the target detection light field. However, the
current imaging environment requires a large number of measurement data, and
the imaging results also have the problems of low image resolution and long
reconstruction time. Therefore, using orthogonal methods such as QR
decomposition, a variety of optimization methods for speckle patterns are
designed combined with Kronecker product,which can help to shorten the imaging
time, improve the imaging quality and image noise resistance.


  Event detection is a critical task for timely decision-making in graph
analytics applications. Despite the recent progress towards deep learning on
graphs, event detection on dynamic graphs presents particular challenges to
existing architectures. Real-life events are often associated with sudden
deviations of the normal behavior of the graph. However, existing approaches
for dynamic node embedding are unable to capture the graph-level dynamics
related to events. In this paper, we propose DyGED, a simple yet novel deep
learning model for event detection on dynamic graphs. DyGED learns correlations
between the graph macro dynamics -- i.e. a sequence of graph-level
representations -- and labeled events. Moreover, our approach combines
structural and temporal self-attention mechanisms to account for
application-specific node and time importances effectively. Our experimental
evaluation, using a representative set of datasets, demonstrates that DyGED
outperforms competing solutions in terms of event detection accuracy by up to
8.5% while being more scalable than the top alternatives. We also present case
studies illustrating key features of our model.


  We adopt a bottom-up Effective Field Theory (EFT) approach to derive a
model-independent Veltman condition to cancel the quadratic divergences in the
Higgs mass. We show using the equivalence theorem that all deviations in the
Higgs couplings to the W and Z from the SM predictions should vanish. We argue
based on tree-level unitarity, that any new physics that naturally cancels the
quadratic divergences should be ~19 TeV. We show that the level of fine-tuning
required is O(0.1- 1%) unless the UV sector has a symmetry that forces the
satisfaction of the model-independent Veltman condition, in which case all
fine-tuning is eliminated. We also conjecture that if no new physics that
couples to the Higgs is found up to ~ 19 TeV, or if the Higgs couplings to the
SM particles conform to the SM predictions, then either the Higgs does not
couple to any UV sector or is fine-tuned.


  In traditional thermodynamical and statistical-mechanical approaches one has
(some) detailed knowledge of the principles governing the microdynamics of a
system. However in many instances we may not have a Hamiltonian or good
information about the degrees of freedom (or, in a roughly complementary sense,
the redundancies in observed data), but on the other hand we have access to a
preponderance of raw data. The development outlined here is an attempt to apply
the principles of thermodynamics and statistical mechanics generically to such
cases.


  Search engines are crucial as they provide an efficient and easy way to
access vast amounts of information on the internet for diverse information
needs. User queries, even with a specific need, can differ significantly. Prior
research has explored the resilience of ranking models against typical query
variations like paraphrasing, misspellings, and order changes. Yet, these works
overlook how diverse demographics uniquely formulate identical queries. For
instance, older individuals tend to construct queries more naturally and in
varied order compared to other groups. This demographic diversity necessitates
enhancing the adaptability of ranking models to diverse query formulations. To
this end, in this paper, we propose a framework that integrates a novel
rewriting pipeline that rewrites queries from various demographic perspectives
and a novel framework to enhance ranking robustness. To be specific, we use
Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as
agents to emulate various demographic profiles, then use them for efficient
query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE)
architecture coupled with a hybrid loss function, collectively strengthening
the ranking models' robustness. Our extensive experimentation on both public
and industrial datasets assesses the efficacy of our query rewriting approach
and the enhanced accuracy and robustness of the ranking model. The findings
highlight the sophistication and effectiveness of our proposed model.


  We use the method of synthetic differential geometry to revisit the geometric
reasoning employed by Lie, Klein and others in their study of partial
differential equations.


  Spatiotemporal modelling of infectious diseases such as COVID-19 involves
using a variety of epidemiological metrics such as regional proportion of cases
or regional positivity rates. Although observing their changes over time is
critical to estimate the regional disease burden, the dynamical properties of
these measures as well as cross-relationships are not systematically explained.
Here we provide a spatiotemporal framework composed of six commonly used and
newly constructed epidemiological metrics and conduct a case study evaluation.
We introduce a refined risk model that is biased neither by the differences in
population sizes nor by the spatial heterogeneity of testing. In particular,
the proposed methodology is useful for the unbiased identification of time
periods with elevated COVID-19 risk, without sensitivity to spatial
heterogeneity of neither population nor testing. Our results also provide
insights regarding regional prioritization of testing and the consequences of
potential synchronization of epidemics between regions.


  We initiate a quantitative study of measure equivalence (and orbit
equivalence) between finitely generated groups, which extends the classical
setting of $\mathrm L^p$ measure equivalence. In this paper, our main focus
will be on amenable groups, for which we prove both rigidity and flexibility
results.
  On the rigidity side, we prove a general monotonicity property satisfied by
the isoperimetric profile, which implies in particular its invariance under
$\mathrm L^1$ measure equivalence. This yields explicit "lower bounds" on how
integrable a measure coupling between two amenable groups can be. This result
also has an unexpected application to geometric group theory: the isoperimetric
profile turns out to be monotonous under coarse embedding between amenable
groups. This has various applications, among which the existence of an
uncountable family of $3$-solvable groups which pairwise do not coarsely embed
into one another.
  On the flexibility side, we construct explicit orbit equivalences between
amenable groups with prescribed integrability conditions. Our main tool is a
new notion of F{\o}lner tiling sequences. We show in a number of instances that
the bounds derived from the isoperimetric profile are sharp up to a logarithmic
factor. We also deduce from this study that two important quasi-isometry
invariants are not preserved under $\mathrm L^1$ orbit equivalence: the
asymptotic dimension and finite presentability.


  Hydrogels which are hydrophilic and porous materials have recently emerged as
promising systems for filtration applications. In our study, we prepare
hydrogel membranes by the photopolymerization of a mixture of poly (ethylene
glycol) diacrylate (PEGDA) and large poly(ethylene glycol) (PEG) chains of 300
000 g.mol-1 in the presence of a photoinitiator. We find that this addition of
free PEG chains induces a large and non-linear increase of the water
permeability. Indeed, by changing the content of PEG chains added, we obtain
variations of the hydrogel water permeability over two orders of magnitude. The
highest water permeability values are obtained for the membranes when the PEG
concentration is equal to its critical overlap concentration C*. Moreover, we
find that the flow rate of water through the membranes varies non-linearly with
the pressure. We relate this result to the deformability of the membranes as
the applied pressure leads to a compression of the pores. This study provides
new perspectives for the design of flexible hydrogel membranes with controlled
permeability and their application in water treatment and bioseparation.


  Modal crosstalk is the main bottleneck in MMF-enabled optical datacenter
networks with direct detection. A novel time-slicing-based crosstalk-mitigated
MDM scheme is first proposed, then theoretically analyzed and experimentally
demonstrated.


  Open source software is becoming crucial in the design and testing of quantum
algorithms. Many of the tools are backed by major commercial vendors with the
goal to make it easier to develop quantum software: this mirrors how
well-funded open machine learning frameworks enabled the development of complex
models and their execution on equally complex hardware. We review a wide range
of open source software for quantum computing, covering all stages of the
quantum toolchain from quantum hardware interfaces through quantum compilers to
implementations of quantum algorithms, as well as all quantum computing
paradigms, including quantum annealing, and discrete and continuous-variable
gate-model quantum computing. The evaluation of each project covers
characteristics such as documentation, licence, the choice of programming
language, compliance with norms of software engineering, and the culture of the
project. We find that while the diversity of projects is mesmerizing, only a
few attract external developers and even many commercially backed frameworks
have shortcomings in software engineering. Based on these observations, we
highlight the best practices that could foster a more active community around
quantum computing software that welcomes newcomers to the field, but also
ensures high-quality, well-documented code.


  In this paper, we first prove that a quadratic form from $\mathbb{S}^m$ to
$\mathbb{S}^n$ is non-harmonic biharmonic if and only if it has constant energy
density $(m+1)/2$. Then, we give a positive answer to an open problem
concerning the structure of non-harmonic biharmonic quadratic forms. As a
direct application, using classification results for harmonic quadratic forms,
we infer classification results for non-harmonic biharmonic quadratic forms.


  The paper is another step towards a realisation of the goal, advanced in
articles 1706.05682 [hep-th] and 1808.04470 [hep-th], of a systematic
supersymmetry-equivariant geometrisation of physically distinguished
Green-Schwarz super-(p+2)-cocycles defining classes in the
supersymmetry-invariant refinement of the de Rham cohomology of homogeneous
spaces of (supersymmetry) Lie supergroups, associated with reductive
decompositions of their Lie superalgebras. It deals with a correlated
geometrisation of a pair of such super-(p+2)-cocycles on spaces in
correspondence under a blow-up transformation dual to the \.In\"on\"u-Wigner
contraction that relates the respective (supersymmetry) Lie superalgebras, the
latter correspondence being taken as the organising principle of the
geometrisation procedure that exploits the link between the Cartan-Eilenberg
cohomology of the supersymmetry group and the Chevalley-Eilenberg cohomology of
its Lie superalgebra, alongside a cohomological classification of central
extensions thereof. A general scheme of a correlated geometrisation compatible
with the contraction is laid out and illustrated on the nontrivial example of a
pair of consistent super-0-brane backgrounds: the super-Minkowski space
$sMink^{3,1|8}$ with the standard N=2 Green-Schwarz super-2-cocycle on it and
the super-$AdS_2 \times S^2$ space with Zhou's super-2-cocycle on it,
asymptoting to the former in the limit of an infinite common radius of the
$AdS_2$ and the $S^2$ in the body $AdS_2\times S^2$ of the supertarget. The
geometrisation yields the respective supersymmetry-equivariant super-0-gerbes,
i.e., the prequantum bundles of the associated Green-Schwarz
super-$\sigma$-models in the Nambu-Goto formulation. Upon passing to the
equivalent Hughes-Polchinski formulation of the models, the relevant extended
super-0-gerbes are verified to possess a weak $\kappa$-equivariant structure.


  The problem of path planning has been studied for years. Classic planning
pipelines, including perception, mapping, and path searching, can result in
latency and compounding errors between modules. While recent studies have
demonstrated the effectiveness of end-to-end learning methods in achieving high
planning efficiency, these methods often struggle to match the generalization
abilities of classic approaches in handling different environments. Moreover,
end-to-end training of policies often requires a large number of labeled data
or training iterations to reach convergence. In this paper, we present a novel
Imperative Learning (IL) approach. This approach leverages a differentiable
cost map to provide implicit supervision during policy training, eliminating
the need for demonstrations or labeled trajectories. Furthermore, the policy
training adopts a Bi-Level Optimization (BLO) process, which combines network
update and metric-based trajectory optimization, to generate a smooth and
collision-free path toward the goal based on a single depth measurement. The
proposed method allows task-level costs of predicted trajectories to be
backpropagated through all components to update the network through direct
gradient descent. In our experiments, the method demonstrates around 4x faster
planning than the classic approach and robustness against localization noise.
Additionally, the IL approach enables the planner to generalize to various
unseen environments, resulting in an overall 26-87% improvement in SPL
performance compared to baseline learning methods.


  This short communication shows that in some cases scalar elliptic finite
element matrices cannot be approximated well by an SDD matrix. We also give a
theoretical analysis of a simple heuristic method for approximating an element
by an SDD matrix.


  Cluster dynamical mean field methods are used to calculate the
superconductivity-induced changes in the interplane conductivity and Raman
scattering cross section of the two dimensional Hubbard model. When
superconductivity emerges from the pseudogap, the superconducting response is
found to be diminished in amplitude, broadened and, in the case of the
interplane conductivity, shifted to higher frequency. The results are in
agreement with data on high temperature copper-oxide superconductors indicating
that the Hubbard model contains the essential low energy physics of the
pseudogap and its interplay with superconductivity in the cuprates.


  We investigate large-angle scale temperature anisotropy in the Cosmic
Microwave Background (CMB) with the Wilkinson Microwave Anisotropy Probe (WMAP)
data and model the large-angle anomalies as the effect of the CMB quadrupole
anisotropies caused by the local density inhomogeneities. The quadrupole caused
by the local density inhomogeneities is different from the special relativity
kinematic quadrupole. If the observer inhabits a strong inhomogeneous region,
the local quadrupole should not be neglected. We calculate such local
quadrupole under the assumption that there is a huge density fluctuation field
in direction $(284^{\circ},74^{\circ})$, where the density fluctuation is
$10^{-3}$, and its center is $\sim 112h^{-1} \rm {Mpc}$ away from us. After
removing such mock signals from WMAP data, the power in quadrupole, $C_2$,
increases from the range $(200\sim260\mu \rm{K^2})$ to $\sim1000\mu \rm{K^2}$.
The quantity S, which is used to estimate the alignment between the quadrupole
and the octopole, decreases from $(0.7\sim0.74)$ to $(0.31\sim0.37)$, while the
model predict that $C_2=1071.5\mu \rm{K^2}$, $S=0.412$. So our local density
inhomogeneity model can, in part, explain the WMAP low-$\ell$ anomalies.


  The off-shell version of the c-map is presented, based on a systematic
off-shell reduction from four to three space-time dimensions for supergravity
theories with eight supercharges. In the reduction, the R-symmetry group is
enhanced to local [SU(2) x SU(2)]/Z2 =SO(4) and the c-map is effected by a
parity transformation in the internal space that interchanges the two SU(2)
factors. Vector and tensor supermultiplets are each others conjugate under the
c-map and both can be dualized in three dimensions to (on-shell)
hypermultiplets. As shown in this paper the off-shell formulation indeed leads
to a clarification of many of the intricate issues that play a role in the
c-map. The results for off-shell Lagrangians quadratic in space-time
derivatives are analyzed in detail and compared to the literature. The
underlying reasons are identified why not all of the four-dimensional tensor
multiplet Lagrangians can be in the image of the c-map. The advantage of the
off-shell approach is, that it also enables a systematic analysis of theories
with higher-derivative couplings. This is demonstrated for a particular
subclass of such theories, which, under certain conditions, are consistent
under the c-map. In principle, explicit results for realistic four-dimensional
type-II string compactifications can be explored in this way.


  Unlike language tasks, where the output space is usually limited to a set of
tokens, the output space of visual tasks is more complicated, making it
difficult to build a unified visual model for various visual tasks. In this
paper, we seek to unify the output space of visual tasks, so that we can also
build a unified model for visual tasks. To this end, we demonstrate a single
unified model that simultaneously handles two typical visual tasks of instance
segmentation and depth estimation, which have discrete/fixed-length and
continuous/varied-length outputs, respectively. We propose several new
techniques that take into account the particularity of visual tasks: 1) Soft
token. We employ soft token to represent the task output. Unlike hard tokens in
the common VQ-VAE which are assigned one-hot to discrete
codebooks/vocabularies, the soft token is assigned softly to the codebook
embeddings. Soft token can improve the accuracy of both the next token
inference and decoding of the task output; 2) Mask augmentation. Many visual
tasks have corruption, undefined or invalid values in label annotations, i.e.,
occluded area of depth maps. We show that a mask augmentation technique can
greatly benefit these tasks. With these new techniques and other designs, we
show that the proposed general-purpose task-solver can perform both instance
segmentation and depth estimation well. Particularly, we achieve 0.279 RMSE on
the specific task of NYUv2 depth estimation, setting a new record on this
benchmark. The general-purpose task-solver, dubbed AiT, is available at
\url{https://github.com/SwinTransformer/AiT}.


  Fresnel Zone Plates (FZP) are to date very successful focusing optics for
X-rays. Established methods of fabrication are rather complex and based on
electron beam lithography (EBL). Here, we show that ion beam lithography (IBL)
may advantageously simplify their preparation. A FZP operable from the extreme
UV to the limit of the hard X-ray was prepared and tested from 450 eV to 1500
eV. The trapezoidal profile of the FZP favorably activates its 2nd order focus.
The FZP with an outermost zone width of 100 nm allows the visualization of
features down to 61, 31 and 21 nm in the 1st, 2nd and 3rd order focus
respectively. Measured efficiencies in the 1st and 2nd order of diffraction
reach the theoretical predictions.


  In various topological phases, nontrivial states appear at the boundaries of
the system. In this paper, we investigate anomalous dielectric response caused
by such states caused by the pi Zak phase. First, by using the one-dimensional
Su-Schrieffer-Heeger model, we show that, when the system is insulating and the
Zak phase is pi, the polarization suddenly rises to a large value close to e/2,
by application of an external electric field. The pi Zak phase indicates
existence of half-filled edge states, and we attribute this phenomenon to
charge transfer between the edge states at the two ends of the system. We
extend this idea to two- and three-dimensional insulators with the pi Zak phase
over the Brillouin zone, and find similar anomalous dielectric response. We
also show that diamond and silicon slabs with (111) surfaces have the pi Zak
phase by ab intio calculations, and show that this anomalous response survives
even surface reconstruction involving an odd number of original surface unit
cells. Another material example with an anomalous dielectric response is
polytetrafluoroethylene (PTFE), showing plateaus of polarization at +e by ab
initio calculation, in agreement with our theory.


  The algebraic curve for the psu (2,2|4) quantum spin chain is determined from
the thermodynamic limit of the algebraic Bethe ansatz. The Hamiltonian of this
spin chain has been identified with the planar 1-loop dilatation operator of
N=4 SYM. In the dual AdS_5 x S^5 string theory, various properties of the data
defining the curve for the gauge theory are compared to the ones obtained from
semiclassical spinning-string configurations, in particular for the case of
strings on AdS_5 x S^1 and the su(2,2) spin chain agreement of the curves is
shown.


  We study a model Dyson-Schwinger equation for the quark propagator closed
using an {\it Ansatz} for the gluon propagator of the form \mbox{$D(q) \sim
q^2/[(q^2)^2 + b^4]$} and two {\it Ans\"{a}tze} for the quark-gluon vertex: the
minimal Ball-Chiu and the modified form suggested by Curtis and Pennington.
Using the quark condensate as an order parameter, we find that there is a
critical value of $b=b_c$ such that the model does not support dynamical chiral
symmetry breaking for $b>b_c$. We discuss and apply a confinement test which
suggests that, for all values of $b$, the quark propagator in the model {\bf is
not} confining. Together these results suggest that this Ansatz for the gluon
propagator is inadequate as a model since it does not yield the expected
behaviour of QCD.


  In the early 1990's, Lionel Schwartz gave a lovely characterization of the
Krull filtration of U, the category of unstable modules over the mod p Steenrod
algebra. Soon after, this filtration was used by the author as an
organizational tool in posing and studying some topological nonrealization
conjectures. In recent years the Krull filtration of U has been similarly used
by Castellana, Crespo, and Scherer in their study of H--spaces with finiteness
conditions, and Gaudens and Schwartz have given a proof of some of my
conjectures. In light of these topological applications, it seems timely to
better expose the algebraic properties of the Krull filtration.


  While frictionless spheres at jamming are isostatic, frictional spheres at
jamming are not. As a result, frictional spheres near jamming do not
necessarily exhibit an excess of soft modes. However, a generalized form of
isostaticity can be introduced if fully mobilized contacts at the Coulomb
friction threshold are considered as slipping contacts. We show here that, in
this framework, the vibrational density of states (DOS) of frictional discs
exhibits a plateau when the generalized isostaticity line is approached. The
crossover frequency to elastic behavior scales linearly with the distance from
this line. Moreover, we show that the frictionless limit, which appears
singular when fully mobilized contacts are treated elastically, becomes smooth
when fully mobilized contacts are allowed to slip.


  Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is
gaining popularity due to recent breakthroughs in ML and Internet of Things
(IoT). Most tinyML research focuses on model compression techniques that trade
accuracy (and model capacity) for compact models to fit into the KB-sized
tiny-edge devices. In this paper, we show how such models can be enhanced by
the addition of an early exit intermediate classifier. If the intermediate
classifier exhibits sufficient confidence in its prediction, the network exits
early thereby, resulting in considerable savings in time. Although early exit
classifiers have been proposed in previous work, these previous proposals focus
on large networks, making their techniques suboptimal/impractical for tinyML
applications. Our technique is optimized specifically for tiny-CNN sized
models. In addition, we present a method to alleviate the effect of network
overthinking by leveraging the representations learned by the early exit. We
evaluate T-RecX on three CNNs from the MLPerf tiny benchmark suite for image
classification, keyword spotting and visual wake word detection tasks. Our
results show that T-RecX 1) improves the accuracy of baseline network, 2)
achieves 31.58% average reduction in FLOPS in exchange for one percent accuracy
across all evaluated models. Furthermore, we show that our methods consistently
outperform popular prior works on the tiny-CNNs we evaluate.


  Spiking Neural Networks (SNNs) have shown capabilities for solving diverse
machine learning tasks with ultra-low-power/energy computation. To further
improve the performance and efficiency of SNN inference, the Compute-in-Memory
(CIM) paradigm with emerging device technologies such as resistive random
access memory is employed. However, most of SNN architectures are developed
without considering constraints from the application and the underlying CIM
hardware (e.g., memory, area, latency, and energy consumption). Moreover, most
of SNN designs are derived from the Artificial Neural Networks, whose network
operations are different from SNNs. These limitations hinder SNNs from reaching
their full potential in accuracy and efficiency. Toward this, we propose
HASNAS, a novel hardware-aware spiking neural architecture search (NAS)
framework for neuromorphic CIM systems that finds an SNN that offers high
accuracy under the given memory, area, latency, and energy constraints. To
achieve this, HASNAS employs the following key steps: (1) optimizing SNN
operations to achieve high accuracy, (2) developing an SNN architecture that
facilitates an effective learning process, and (3) devising a systematic
hardware-aware search algorithm to meet the constraints. The experimental
results show that our HASNAS quickly finds an SNN that maintains high accuracy
compared to the state-of-the-art by up to 11x speed-up, and meets the given
constraints: 4x10^6 parameters of memory, 100mm^2 of area, 400ms of latency,
and 120uJ energy consumption for CIFAR10 and CIFAR100; while the
state-of-the-art fails to meet the constraints. In this manner, our HASNAS can
enable efficient design automation for providing high-performance and
energy-efficient neuromorphic CIM systems for diverse applications.


  A rising topic in computational journalism is how to enhance the diversity in
news served to subscribers to foster exploration behavior in news reading.
Despite the success of preference learning in personalized news recommendation,
their over-exploitation causes filter bubble that isolates readers from
opposing viewpoints and hurts long-term user experiences with lack of
serendipity. Since news providers can recommend neither opposite nor
diversified opinions if unpopularity of these articles is surely predicted,
they can only bet on the articles whose forecasts of click-through rate involve
high variability (risks) or high estimation errors (uncertainties). We propose
a novel Bayesian model of uncertainty-aware scoring and ranking for news
articles. The Bayesian binary classifier models probability of success (defined
as a news click) as a Beta-distributed random variable conditional on a vector
of the context (user features, article features, and other contextual
features). The posterior of the contextual coefficients can be computed
efficiently using a low-rank version of Laplace's method via thin Singular
Value Decomposition. Efficiencies in personalized targeting of exceptional
articles, which are chosen by each subscriber in test period, are evaluated on
real-world news datasets. The proposed estimator slightly outperformed existing
training and scoring algorithms, in terms of efficiency in identifying
successful outliers.


  We define a theory of parameterized algebraic complexity classes in analogy
to parameterized Boolean counting classes. We define the classes VFPT and
VW[t], which mirror the Boolean counting classes #FPT and #W[t], and define
appropriate reductions and completeness notions. Our main contribution is the
VW[1]-completeness proof of the parameterized clique family. This proof is far
more complicated than in the Boolean world. It requires some new concepts like
composition theorems for bounded exponential sums and Boolean-arithmetic
formulas. In addition, we also look at two polynomials linked to the permanent
with vastly different parameterized complexity.


  We present significant differences in the simulated atmospheric flow for
warm, tidally-locked small Neptunes and super Earths (based on a nominal GJ
1214b) when solving the simplified, and commonly used, primitive dynamical
equations or the full Navier-Stokes equations. The dominant prograde,
superrotating zonal jet is markedly different between the simulations which are
performed using practically identical numerical setups, within the same model.
The differences arise due to the breakdown of the so-called `shallow-fluid' and
traditional approximations, which worsens when rotation rates are slowed, and
day-night temperature contrasts are increased. The changes in the zonal
advection between simulations solving the full and simplified equations, give
rise to significant differences in the atmospheric redistribution of heat,
altering the position of the hottest part of the atmosphere and temperature
contrast between the day and night sides. The implications for the atmospheric
chemistry and, therefore, observations need to be studied with a model
including a more detailed treatment of the radiative transfer and chemistry.
Small Neptunes and super Earths are extremely abundant and important,
potentially bridging the structural properties (mass, radius, composition) of
terrestrial and gas giant planets. Our results indicate care is required when
interpreting the output of models solving the primitive equations of motion for
such planets.


  The Internet of Things (IoT) is emerging as a multi-purpose technology with
enormous potential for improving the quality of life in several areas. In
particular, IoT has been applied in agriculture to make it more sustainable
ecologically. For instance, electronic traps have the potential to perform pest
control without any pesticide. In this paper, a smart trap with IoT
capabilities that uses computer vision to identify the insect of interest is
proposed. The solution includes 1) an embedded system with camera, GPS sensor
and motor actuators; 2) an IoT middleware as database service provider, and 3)
a Web application to present data by a configurable heat map. The demonstration
of proposed solution is exposed and the main conclusions are the perception
about pest concentration at the plantation and the viability as alternative
pest control over traditional control based on pesticides.


  $\DeclareMathOperator{\chicen}{\chi_{\mathrm{cen}}}\DeclareMathOperator{\chilin}{\chi_{\mathrm{lin}}}$
A centred colouring of a graph is a vertex colouring in which every connected
subgraph contains a vertex whose colour is unique and a \emph{linear colouring}
is a vertex colouring in which every (not-necessarily induced) path contains a
vertex whose colour is unique. For a graph $G$, the centred chromatic number
$\chicen(G)$ and the linear chromatic number $\chilin(G)$ denote the minimum
number of distinct colours required for a centred, respectively, linear
colouring of $G$. From these definitions, it follows immediately that
$\chilin(G)\le \chicen(G)$ for every graph $G$. The centred chromatic number is
equivalent to treedepth and has been studied extensively. Much less is known
about linear colouring. Kun et al [Algorithmica 83(1)] prove that $\chicen(G)
\le \tilde{O}(\chilin(G)^{190})$ for any graph $G$ and conjecture that
$\chicen(G)\le 2\chilin(G)$. Their upper bound was subsequently improved by
Czerwinski et al [SIDMA 35(2)] to $\chicen(G)\le\tilde{O}(\chilin(G)^{19})$.
The proof of both upper bounds relies on establishing a lower bound on the
linear chromatic number of pseudogrids, which appear in the proof due to their
critical relationship to treewidth. Specifically, Kun et al prove that $k\times
k$ pseudogrids have linear chromatic number $\Omega(\sqrt{k})$. Our main
contribution is establishing a tight bound on the linear chromatic number of
pseudogrids, specifically $\chilin(G)\ge \Omega(k)$ for every $k\times k$
pseudogrid $G$. As a consequence we improve the general bound for all graphs to
$\chicen(G)\le \tilde{O}(\chilin(G)^{10})$. In addition, this tight bound gives
further evidence in support of Kun et al's conjecture (above) that the centred
chromatic number of any graph is upper bounded by a linear function of its
linear chromatic number.


  The IIB matrix model is one of the candidates for nonperturbative formulation
of string theory, and it is believed that the model contains gravitational
degrees of freedom in some manner. In some preceding works, it was proposed
that the matrix model describes the curved space where the matrices represent
differential operators that are defined on a principal bundle. In this paper,
we study the dynamics of the model in this interpretation, and point out the
necessity of the principal bundle from the viewpoint of the stability and
diffeomorphism invariance. We also compute the one-loop correction which
possibly yields a mass term for each field due to the principal bundle. We find
that the correction does generate some mass terms with the supersymmetry
broken, while fields in the original IIB matrix model remain massless. The
positivity is not violated as long as the number of bosonic degrees of freedom
is larger than the fermionic counterpart. The generation of mass terms means
that the new mass scale emerges through the loop correction.


  The problem of selecting optimal backdoor adjustment sets to estimate causal
effects in graphical models with hidden and conditioned variables is addressed.
Previous work has defined optimality as achieving the smallest asymptotic
estimation variance and derived an optimal set for the case without hidden
variables. For the case with hidden variables there can be settings where no
optimal set exists and currently only a sufficient graphical optimality
criterion of limited applicability has been derived. In the present work
optimality is characterized as maximizing a certain adjustment information
which allows to derive a necessary and sufficient graphical criterion for the
existence of an optimal adjustment set and a definition and algorithm to
construct it. Further, the optimal set is valid if and only if a valid
adjustment set exists and has higher (or equal) adjustment information than the
Adjust-set proposed in Perkovi{\'c} et al. [Journal of Machine Learning
Research, 18: 1--62, 2018] for any graph. The results translate to minimal
asymptotic estimation variance for a class of estimators whose asymptotic
variance follows a certain information-theoretic relation. Numerical
experiments indicate that the asymptotic results also hold for relatively small
sample sizes and that the optimal adjustment set or minimized variants thereof
often yield better variance also beyond that estimator class. Surprisingly,
among the randomly created setups more than 90\% fulfill the optimality
conditions indicating that also in many real-world scenarios graphical
optimality may hold. Code is available as part of the python package
\url{https://github.com/jakobrunge/tigramite}.


  The starting point for this work is the family of functions
$\overline{p}_{-t}(n)$ which counts the number of $t$--colored overpartitions
of $n.$ In recent years, several infinite families of congruences satisfied by
$\overline{p}_{-t}(n)$ for specific values of $t\geq 1$ have been proven. In
particular, in his 2023 work, Saikia proved a number of congruence properties
modulo powers of 2 for $\overline{p}_{-t}(n)$ for $t=5,7,11,13$. He also
included the following conjecture in that paper: \newline \ %\newline \noindent
Conjecture: For all $n\geq 0$ and primes $t$, we have \begin{eqnarray*}
\overline{p}_{-t}(8n+1) &\equiv & 0 \pmod{2}, \\ \overline{p}_{-t}(8n+2)
&\equiv & 0 \pmod{4}, \\ \overline{p}_{-t}(8n+3) &\equiv & 0 \pmod{8}, \\
\overline{p}_{-t}(8n+4) &\equiv & 0 \pmod{2}, \\ \overline{p}_{-t}(8n+5)
&\equiv & 0 \pmod{8}, \\ \overline{p}_{-t}(8n+6) &\equiv & 0 \pmod{8}, \\
\overline{p}_{-t}(8n+7) &\equiv & 0 \pmod{32}. \end{eqnarray*} Using a truly
elementary approach, relying on classical generating function manipulations and
dissections, as well as proof by induction, we show that Saikia's conjecture
holds for {\bf all} odd integers $t$ (not necessarily prime).


  Computing differences between tree-structured data is a critical but
challenging problem in software analysis. In this paper, we propose a novel
tree diffing approach called SatDiff, which reformulates the structural diffing
problem into a MaxSAT problem. By encoding the necessary transformations from
the source tree to the target tree, SatDiff generates correct, minimal, and
type safe low-level edit scripts with formal guarantees. We then synthesize
concise high-level edit scripts by effectively merging low-level edits in the
appropriate topological order. Our empirical results demonstrate that SatDiff
outperforms existing heuristic-based approaches by a significant margin in
terms of conciseness while maintaining a reasonable runtime.


  We present results from the KMOS Lens-Amplified Spectroscopic Survey (KLASS),
an ESO Very Large Telescope (VLT) large program using gravitational lensing to
study the spatially resolved kinematics of 44 star-forming galaxies at
0.6<z<2.3 with a stellar mass of 8.1<log(M$_\star$/M$_{\odot}$)<11.0. These
galaxies are located behind six galaxy clusters selected from the HST Grism
Lens-Amplified Survey from Space (GLASS). We find that the majority of the
galaxies show a rotating disk, but most of the rotation-dominated galaxies only
have a low $\upsilon_{rot}/\sigma_0$ ratio (median of
$\upsilon_{rot}/\sigma_0\sim2.5$). We explore the Tully-Fisher relation by
adopting the circular velocity,
$V_{circ}=(\upsilon_{rot}^2+3.4\sigma_0^2)^{1/2}$, to account for pressure
support. We find that our sample follows a Tully-Fisher relation with a
positive zero-point offset of +0.18 dex compared to the local relation,
consistent with more gas-rich galaxies that still have to convert most of their
gas into stars. We find a strong correlation between the velocity dispersion
and stellar mass in the KLASS sample. When combining our data to other surveys
from the literature, we also see an increase of the velocity dispersion with
stellar mass at all redshift. We obtain an increase of
$\upsilon_{rot}/\sigma_0$ with stellar mass at 0.5<z<1.0. This could indicate
that massive galaxies settle into regular rotating disks before the low-mass
galaxies. For higher redshift (z>1), we find a weak increase or flat trend. We
investigate the relation between the rest-frame UV clumpiness of galaxies and
their global kinematic properties. We find no clear trend between the
clumpiness and the velocity dispersion and $\upsilon_{rot}/\sigma_0$. This
could suggest that the kinematic properties of galaxies evolve after the clumps
formed in the galaxy disk or that the clumps can form in different physical
conditions.


  Intensity inhomogeneities in images constitute a considerable challenge in
image segmentation. In this paper we propose a novel biconvex variational model
to tackle this task. We combine a total variation approach for multi class
segmentation with a multiplicative model to handle the inhomogeneities. Our
method assumes that the image intensity is the product of a smoothly varying
part and a component which resembles important image structures such as edges.
Therefore, we penalize in addition to the total variation of the label
assignment matrix a quadratic difference term to cope with the smoothly varying
factor. A critical point of our biconvex functional is computed by a modified
proximal alternating linearized minimization method (PALM). We show that the
assumptions for the convergence of the algorithm are fulfilled by our model.
Various numerical examples demonstrate the very good performance of our method.
Particular attention is paid to the segmentation of 3D FIB tomographical images
which was indeed the motivation of our work.


  Some partial orderings which compare probability distributions with the expo-
nential distribution, are found to be very useful to understand the phenomenon
of ageing. Here, we introduce some new generalized partial orderings which de-
scribe the same kind of characterization of some generalized ageing classes. We
give some equivalent conditions for each of the orderings. Inter-relations
among the generalized orderings have also been discussed.


  We show how the choice of an inflationary state that entangles scalar and
tensor fluctuations affects the angular two-point correlation functions of the
$T$, $E$, and $B$ modes of the cosmic microwave background. The propagators for
a state starting with some general quadratic entanglement are solved exactly,
leading to predictions for the primordial scalar-scalar, tensor-tensor, and
scalar-tensor power spectra. These power spectra are expressed in terms of
general functions that describe the entangling structure of the initial state
relative to the standard Bunch-Davies vacuum. We illustrate how such a state
would modify the angular correlations in the CMB with a simple example where
the initial state is a small perturbation away from the Bunch-Davies state.
Because the state breaks some of the rotational symmetries, the angular power
spectra no longer need be strictly diagonal.


  Point-cloud based 3D object detectors recently have achieved remarkable
progress. However, most studies are limited to the development of network
architectures for improving only their accuracy without consideration of the
computational efficiency. In this paper, we first propose an autoencoder-style
framework comprising channel-wise compression and decompression via interchange
transfer-based knowledge distillation. To learn the map-view feature of a
teacher network, the features from teacher and student networks are
independently passed through the shared autoencoder; here, we use a compressed
representation loss that binds the channel-wised compression knowledge from
both student and teacher networks as a kind of regularization. The decompressed
features are transferred in opposite directions to reduce the gap in the
interchange reconstructions. Lastly, we present an head attention loss to match
the 3D object detection information drawn by the multi-head self-attention
mechanism. Through extensive experiments, we verify that our method can train
the lightweight model that is well-aligned with the 3D point cloud detection
task and we demonstrate its superiority using the well-known public datasets;
e.g., Waymo and nuScenes.


  We explore terrestrial planet formation with a focus on the supply of
solid-state organics as the main source of volatile carbon. For the water-poor
Earth, the water ice line, or ice sublimation front, within the planet-forming
disk has long been a key focal point. We posit that the soot line, the location
where solid-state organics are irreversibly destroyed, is also a key location
within the disk. The soot line is closer to the host star than the water
snowline and overlaps with the location of the majority of detected exoplanets.
In this work, we explore the ultimate atmospheric composition of a body that
receives a major portion of its materials from the zone between the soot line
and water ice line. We model a silicate-rich world with 0.1% and 1% carbon by
mass with variable water content. We show that as a result of geochemical
equilibrium, the mantle of these planets would be rich in reduced carbon but
have relatively low water (hydrogen) content. Outgassing would naturally yield
the ingredients for haze production when exposed to stellar UV photons in the
upper atmosphere. Obscuring atmospheric hazes appear common in the exoplanetary
inventory based on the presence of often featureless transmission spectra
(Kreidberg et al. 2014, Knutson et al. 2014, Libby-Roberts et al. 2020). Such
hazes may be powered by the high volatile content of the underlying
silicate-dominated mantle. Although this type of planet has no solar system
counterpart, it should be common in the galaxy with potential impact on
habitability.


  Cognitive Radio Networks (CRNs) are being studied intensively and gaining
importance as spectrum is the heavily underutilized. CRN has the capability to
exploit smartly the unutilized frequency spectrum. Recently, the research
community started to work in the area of cognitive radio routing. In a flat
topology, all nodes are of the same level and functionality, thus making it
simple and efficient for smaller networks. However, when the network is large
with sparse nodes, the routing information becomes more complex making
cluster-based techniques really relevant to tackle such situations. In a
cluster-based routing, all nodes in the network are dynamically organized into
partitions called groups or clusters. In each cluster, a cluster head is chosen
to help in the data transmission management and to maintain cluster membership
information. This paper proposes a novel routing protocol for cognitive radio
ad hoc networks (CRAHNs) based on clustering model which amends swiftly to the
topological changes and establishes the routing efficiently. Our proposed
approach is thoroughly evaluated through simulation study. The results state
the suitability of the proposed protocol for cognitive radio ad hoc networks
and demonstrate that it has better performance in terms of finding the
source-destination route, reducing the amount of messages that are transmitted
all over the network and minimizing the routing delay.


  While monocular depth estimation (MDE) is an important problem in computer
vision, it is difficult due to the ambiguity that results from the compression
of a 3D scene into only 2 dimensions. It is common practice in the field to
treat it as simple image-to-image translation, without consideration for the
semantics of the scene and the objects within it. In contrast, humans and
animals have been shown to use higher-level information to solve MDE: prior
knowledge of the nature of the objects in the scene, their positions and likely
configurations relative to one another, and their apparent sizes have all been
shown to help resolve this ambiguity.
  In this paper, we present a novel method to enhance MDE performance by
encouraging use of known-useful information about the semantics of objects and
inter-object relationships within a scene. Our novel ObjCAViT module sources
world-knowledge from language models and learns inter-object relationships in
the context of the MDE problem using transformer attention, incorporating
apparent size information. Our method produces highly accurate depth maps, and
we obtain competitive results on the NYUv2 and KITTI datasets. Our ablation
experiments show that the use of language and cross-attention within the
ObjCAViT module increases performance. Code is released at
https://github.com/DylanAuty/ObjCAViT.


  Alonso de la Veracruz conducted a physical study of image, regarding the
activities of Soul in which image is produced, as organic operations. This
research was particularly important since, at that time, image was
systematically used to propagate the European culture in New Spain. Besides,
Alonso uses the visual radius to criticize the magical attitude popular in
Renaissance, denying the far sight and fascinations abilities attributed to
witches. Also, Alonso Guti\'errez applies the Aristotelian physics to deny the
healing powers attributed to the kings of France and England, secularizing this
way the gallic monarchies, and several other superstitions of that time. This
way, the physics of image developed by Alonso can be considered as close to the
rationalism of Descartes inasmuch as Guti\'errez criticizes the magical view of
Renaissance and introduces geometrical elements to elucidate physical problems.


  The session-based recommendation (SBR) garners increasing attention due to
its ability to predict anonymous user intents within limited interactions.
Emerging efforts incorporate various kinds of side information into their
methods for enhancing task performance. In this survey, we thoroughly review
the side information-driven session-based recommendation from a data-centric
perspective. Our survey commences with an illustration of the motivation and
necessity behind this research topic. This is followed by a detailed
exploration of various benchmarks rich in side information, pivotal for
advancing research in this field. Moreover, we delve into how these diverse
types of side information enhance SBR, underscoring their characteristics and
utility. A systematic review of research progress is then presented, offering
an analysis of the most recent and representative developments within this
topic. Finally, we present the future prospects of this vibrant topic.


  We establish an optimal C^{1,\alpha}-regularity for viscosity solutions of
degenerate/singular fully nonlinear elliptic equations by finding minimal
regularity requirements on the associated operator.


  A key task in the emerging field of bioelectronics is the transduction
between ionic/protonic and electronic signals at high fidelity. This is a
considerable challenge since the two carrier types exhibit intrinsically
different physics and are best supported by very different materials types --
electronic signals in inorganic semiconductors and ionic/protonic signals in
organic or bio-organic polymers, gels or electrolytes. Here we demonstrate a
new class of organic-inorganic transducing interface featuring semiconducting
nanowires electrostatically gated using a solid proton-transporting hygroscopic
polymer. This model platform allows us to study the basic transducing
mechanisms as well as deliver high fidelity signal conversion by tapping into
and drawing together the best candidates from traditionally disparate realms of
electronic materials research. By combining complementary n- and p-type
transducers we demonstrate functional logic with significant potential for
scaling towards high-density integrated bioelectronic circuitry.


  The quantum Yang-Baxter equation is a braiding condition on vector spaces
which is of high relevance in several fields of mathematics, such as knot
theory and quantum group theory. Their combinatorial counterpart are
set-theoretic solutions to the Yang--Baxter equation, whose investigation is
strongly driven by the study of algebraic objects called (skew) braces. In this
article, we focus on indecomposable involutive non-degenerate set-theoretic
solutions to the Yang-Baxter equation. More specifically, through a thorough
analysis of their associated braces, we give a full classification of those
which are of size $p^2$, for $p$ a prime.


  In this paper,firstly,to overcome the shortcoming of traditional ARX model, a
new operator between an interval number and a real matrix is defined, and then
it is applied to the traditional ARX model to get a new type of structure
interval ARX model that can deal with interval data, which is defined as
interval ARX model (IARX). Secondly,the IARX model is applied to moving
pattern-based modeling. Finally,to verify the validity of the proposed modeling
method,it is applied to a sintering process. The simulation results show the
moving pattern-based modeling using the new type of interval ARX model is
robust to variation in parameters of the model, and the performance of the
modeling using the proposed IARX is superior to that of the previous work.


  Within the framework of the effective Lagrangian approach, we perform a
thorough analysis of the $J/\psi \to P\gamma(\gamma^*)$, $J/\psi \to VP$, $V\to
P\gamma(\gamma^*)$, $P\to V\gamma(\gamma^*)$ and $P\to\gamma\gamma(\gamma^*)$
processes, where $V$ stand for light vector resonances, $P$ stand for light
pseudoscalar mesons, and $\gamma^*$ subsequently decays into lepton pairs. The
processes with light pseudoscalar mesons $\eta$ and $\eta'$ are paid special
attention to and the two-mixing-angle scheme is employed to describe their
mixing. The four mixing parameters both in singlet-octet and quark-flavor bases
are updated in this work. We confirm that the $J/\psi \to
\eta(\eta^{\prime})\gamma^{(*)}$ processes are predominantly dominated by the
$J/\psi\to \eta_c \gamma^{*} \to \eta(\eta^{\prime})\gamma^{(*)}$ mechanism.
Predictions for the $J/\psi \to P \mu^+\mu^-$ are presented. A detailed
discussion on the interplay between electromagnetic and strong transitions in
the $J/\psi \to VP$ decays is given.


  Given a metric space on n points, an {\alpha}-approximate universal algorithm
for the Steiner tree problem outputs a distribution over rooted spanning trees
such that for any subset X of vertices containing the root, the expected cost
of the induced subtree is within an {\alpha} factor of the optimal Steiner tree
cost for X. An {\alpha}-approximate differentially private algorithm for the
Steiner tree problem takes as input a subset X of vertices, and outputs a tree
distribution that induces a solution within an {\alpha} factor of the optimal
as before, and satisfies the additional property that for any set X' that
differs in a single vertex from X, the tree distributions for X and X' are
"close" to each other. Universal and differentially private algorithms for TSP
are defined similarly. An {\alpha}-approximate universal algorithm for the
Steiner tree problem or TSP is also an {\alpha}-approximate differentially
private algorithm. It is known that both problems admit O(logn)-approximate
universal algorithms, and hence O(log n)-approximate differentially private
algorithms as well. We prove an {\Omega}(logn) lower bound on the approximation
ratio achievable for the universal Steiner tree problem and the universal TSP,
matching the known upper bounds. Our lower bound for the Steiner tree problem
holds even when the algorithm is allowed to output a more general solution of a
distribution on paths to the root.


  Bladder cancer is widely spread in the world. Many adequate diagnosis
techniques exist. Video-endoscopy remains the standard clinical procedure for
visual exploration of the bladder internal surface. However, video-endoscopy
presents the limit that the imaged area for each image is about nearly 1cm2.
And, lesions are, typically, spread over several images. The aim of this
contribution is to assess the performance of two mosaicing algorithms leading
to the construction of panoramic maps (one unique image) of bladder walls. The
quantitative comparison study is performed on a set of real endoscopic exam
data and on simulated data relative to bladder phantom.