  Motivated by the ubiquity of turbulent flows in realistic conditions, effects
of turbulent advection on two models of classical non-linear systems are
investigated. In particular, we analyze model A (according to the
Hohenberg-Halperin classification [1]) of a non-conserved order parameter and a
model of the direct bond percolation process. Having two paradigmatic
representatives of distinct stochastic dynamics, our aim is to elucidate to
what extent velocity fluctuations affect their scaling behavior. The main
emphasis is put on an interplay between anisotropy and compressibility of the
velocity flow on their respective scaling regimes. Velocity fluctuations are
generated by means of the Kraichnan rapid-change model, in which the anisotropy
is due to a distinguished spatial direction n and a correlator of the velocity
field obeys the Gaussian distribution law with prescribed statistical
properties. As the main theoretical tool, the field-theoretic perturbative
renormalization group is adopted. Actual calculations are performed in the
leading (one-loop) approximation. Having obtained infra-red stable asymptotic
regimes, we have found four possible candidates for macroscopically observable
behavior for each model. In contrast to the isotropic case, anisotropy brings
about enhancement of non-linearities and non-trivial regimes are proved to be
more stable.


  We discuss the influence of a helicity imbalance on the phase diagram of
dense QCD at finite temperature. We argue that the helical chemical potential
is a thermodynamically relevant quantity in theories with the mass gap
generation. Using the linear sigma model coupled to quarks, we show that the
presence of the helical density substantially affects the phase diagram of
dense quark matter. A moderate helical density makes the chiral phase
transition softer while shifting the critical endpoint towards lower
temperatures and higher baryon chemical potentials. As the helical density
increases, the segment of the first-order transition disappears, and the chiral
transition becomes a soft crossover. At even higher helical chemical
potentials, the first-order transition reappears again at the zero-density
finite-temperature transition and extends into the interior of the phase
diagram. This evolution of the chiral transition reflects the existence of a
thermodynamic duality between helical and vector (baryonic) chemical
potentials. We also show that the presence of the helicity imbalance of quark
matter increases the curvature of the chiral pseudocritical line in QCD.


  We study gradient models for spins taking values in the integers (or an
integer lattice), which interact via a general potential depending only on the
differences of the spin values at neighboring sites, located on a regular tree
with d + 1 neighbors. We first provide general conditions in terms of the
relevant p-norms of the associated transfer operator Q which ensure the
existence of a countable family of proper Gibbs measures. Next we prove
existence of delocalized gradient Gibbs measures, under natural conditions on
Q. This implies coexistence of both types of measures for large classes of
models including the SOS-model, and heavy-tailed models arising for instance
for potentials of logarithmic growth.


  We consider the decay problem for global solutions of the Skyrme and
Adkins-Nappi equations. We prove that the energy associated to any bounded
energy solution of the Skyrme (or Adkins-Nappi) equation decays to zero outside
the light cone (in the radial coordinate). Furthermore, we prove that suitable
polynomial weighted energies of any small solution decays to zero when these
energies are bounded. The proof consists of finding three new virial type
estimates, one for the exterior of the light cone, based on the energy of the
solution, and a more subtle virial identity for the weighted energies, based on
a modification of momentum type quantities.


  The sources and fluxes of superGZK neutrinos, $E>10^{20}$ eV, are discussed.
The fluxes of {\em cosmogenic neutrinos}, i.e. those produced by ultra-high
energy cosmic rays (UHECR) interacting with CMB photons, are calculated in the
models, which give the good fit to the observed flux of UHECR. The best fit
given in no-evolutionary model with maximum acceleration energy $E_{\rm
max}=1\times 10^{21}$ eV results in very low flux of superGZK neutrinos an
order of magnitude lower than the observed flux of UHECR. The predicted
neutrino flux becomes larger and observable by next generation detectors at
energies $10^{20} - 10^{21}$ eV in the evolutionary models with $E_{\rm
max}=1\times 10^{23}$ eV. The largest cosmogenic neutrino flux is given in
models with very flat generation spectrum, e.g. $\propto E^{-2}$. The neutrino
energies are naturally high in the models of {\em superheavy dark matter and
topological defects}. Their fluxes can also be higher than those of cosmogenic
neutrinos. The largest fluxes are given by {\em mirror neutrinos}, oscillating
into ordinary neutrinos. Their fluxes obey some theoretical upper limit which
is very weak, and in practice these fluxes are most efficiently limited now by
observations of radio emission from neutrino-induced showers.


  Detailed knowledge of the primordial power spectrum of curvature
perturbations is essential both in order to elucidate the physical mechanism
(`inflation') which generated it, and for estimating the cosmological
parameters from observations of the cosmic microwave background and large-scale
structure. Hence it ought to be extracted from such data in a model-independent
manner, however this is difficult because relevant cosmological observables are
given by a convolution of the primordial perturbations with some smoothing
kernel which depends on both the assumed world model and the matter content of
the universe. Moreover the deconvolution problem is ill-conditioned so a
regularisation scheme must be employed to control error propagation. We
demonstrate that `Tikhonov regularisation' can robustly reconstruct the
primordial spectrum from multiple cosmological data sets, a significant
advantage being that both its uncertainty and resolution are then quantified.
Using Monte Carlo simulations we investigate several regularisation parameter
selection methods and find that generalised cross-validation and Mallow's $C_p$
method give optimal results. We apply our inversion procedure to data from the
Wilkinson Microwave Anisotropy Probe, other ground-based small angular scale
CMB experiments, and the Sloan Digital Sky Survey. The reconstructed spectrum
(assuming the standard $\Lambda$CDM cosmology) is \emph{not} scale-free but has
an infrared cutoff at $k \lesssim 5 \times 10^{-4}\; \mathrm{Mpc}^{-1}$ (due to
the anomalously low CMB quadrupole) and several features with $\sim 2 \sigma$
significance at $k/\mathrm{Mpc}^{-1} \sim$ 0.0013--0.0025, 0.0362--0.0402 and
0.051--0.056, reflecting the `WMAP glitches'. To test whether these are indeed
real will require more accurate data, such as from the Planck satellite and new
ground-based experiments.


  Cell migration is important in many biological processes, including embryonic
development, cancer metastasis, and wound healing. In these tissues, a cell's
motion is often strongly constrained by its neighbors, leading to glassy
dynamics. While self-propelled particle models exhibit a density-driven glass
transition, this does not explain liquid-to-solid transitions in confluent
tissues, where there are no gaps between cells and therefore the density is
constant. Here we demonstrate the existence of a new type of rigidity
transition that occurs in the well-studied vertex model for confluent tissue
monolayers at constant density. We find the onset of rigidity is governed by a
model parameter that encodes single-cell properties such as cell-cell adhesion
and cortical tension, providing an explanation for a liquid-to-solid
transitions in confluent tissues and making testable predictions about how
these transitions differ from those in particulate matter.


  To operate safely and efficiently alongside human workers, collaborative
robots (cobots) require the ability to quickly understand the dynamics of
manipulated objects. However, traditional methods for estimating the full set
of inertial parameters rely on motions that are necessarily fast and unsafe (to
achieve a sufficient signal-to-noise ratio). In this work, we take an
alternative approach: by combining visual and force-torque measurements, we
develop an inertial parameter identification algorithm that requires slow or
'stop-and-go' motions only, and hence is ideally tailored for use around
humans. Our technique, called Homogeneous Part Segmentation (HPS), leverages
the observation that man-made objects are often composed of distinct,
homogeneous parts. We combine a surface-based point clustering method with a
volumetric shape segmentation algorithm to quickly produce a part-level
segmentation of a manipulated object; the segmented representation is then used
by HPS to accurately estimate the object's inertial parameters. To benchmark
our algorithm, we create and utilize a novel dataset consisting of realistic
meshes, segmented point clouds, and inertial parameters for 20 common workshop
tools. Finally, we demonstrate the real-world performance and accuracy of HPS
by performing an intricate 'hammer balancing act' autonomously and online with
a low-cost collaborative robotic arm. Our code and dataset are open source and
freely available.


  In this paper we will attempt to show that the Dirac theory lends itself to
an interpretation in terms of a unified sub-quantum mechanical field theory
where, the fundamental force fields are weak electric and weak magnetic fields.
We present an electron model based on some of the predictions of the Dirac
theory and justify our arguments by showing that the model not only yields the
correct value for the g-factor but also gives us the very small correction to
the magnetic moment as obtained in the renormalization programme of quantum
electrodynamics. The model also allows us to give a new meaning to the negative
energy state of an electron. The general outlines of the unified field theory
are given and the connection between fluctuations at the Planck and Compton
scales is presented in terms of concrete physical events.


  Dirac's hole theory and quantum field theory are generally considered to be
equivalent to each other. However, it has recently been shown that this is not
necessarily the case. In this article we will discuss the reason for this lack
of equivalence and suggest a possible solution.


  Let U be a given function defined on R^d and \pi(x) be a density function
proportional to \exp -U(x). The following diffusion X(t) is often used to
sample from \pi(x), dX(t)=-\nabla U(X(t)) dt+\sqrt2 dW(t),\qquad X(0)=x_0. To
accelerate the convergence, a family of diffusions with \pi(x) as their common
equilibrium is considered, dX(t)=\bigl(-\nabla U(X(t))+C(X(t))\bigr) dt+\sqrt2
dW(t),\qquad X(0)=x_0. Let L_C be the corresponding infinitesimal generator.
The spectral gap of L_C in L^2(\pi) (\lambda (C)), and the convergence exponent
of X(t) to \pi in variational norm (\rho(C)), are used to describe the
convergence rate, where \lambda(C)= Sup{real part of \mu\dvtx\mu is in the
spectrum of L_C, \mu is not zero}, {-2.8cm}\rho(C) = Inf\biggl{\rho\dvtx\int |
p(t,x,y) -\pi(y)| dy \le g(x) e^{\rho t}\biggr}.Roughly speaking, L_C is a
perturbation of the self-adjoint L_0 by an antisymmetric operator C\cdot\nabla,
where C is weighted divergence free. We prove that \lambda (C)\le \lambda (0)
and equality holds only in some rare situations. Furthermore, \rho(C)\le
\lambda (C) and equality holds for C=0. In other words, adding an extra drift,
C(x), accelerates convergence. Related problems are also discussed.


  Helicity-dependent incoherent pion photoproduction in the reaction \vec
\gamma \vec d\to \pi NN is studied in the framework of the diagrammatic
approach. Contributions to the reaction amplitude from diagrams corresponding
to impulse approximation as well as NN and \pi N interactions in the final
state have been evaluated. The elementary \gamma N\to \pi N operator is taken
from the MAID and SAID models. A detailed comparison of the predictions with
recent experimental data by the GDH and A2 collaborations at energies below 500
MeV is presented. Reasonable agreement with the data on the yields and cross
sections for polarized beam and polarized target has been achieved in all
channels. The unpolarized data of the GDH and A2 collaborations have also been
analyzed within the approach. A strong overestimation for the neutral channel
has been found. At the same time, the model provides a quite satisfactory
description of the unpolarized data for the charged channels. The sensitivity
of the obtained results to the choice of the elementary \gamma N\to \pi N
operator is discussed in detail. The contribution of the \gamma d\to \pi NN
reaction to the GDH sum rule for the deuteron up to a photon energy of 1.65 GeV
has been evaluated with the result of 235\pm 25 \mub.


  In a recent volume of Mathematics Magazine (Vol. 90, No. 3, June 2017) there
is an interesting article by Seth Zimmerman, titled Detecting Deficiencies: An
Optimal Group Testing Algorithm. The claim in the summary is contradictory to
well-known facts reported in the group- testing literature, which is easily
verified, beginning with the work by Sobel and Groll (1959), which was cited by
S. Zimmerman himself. Therefore, I feel compelled to offer a number of comments
and clarifications. In addition, I have made some correction of mistaken claim
made by Zimmerman (2017).


  Training of Generative Adversarial Networks (GANs) is notoriously fragile,
requiring to maintain a careful balance between the generator and the
discriminator in order to perform well. To mitigate this issue we introduce a
new regularization technique - progressive augmentation of GANs (PA-GAN). The
key idea is to gradually increase the task difficulty of the discriminator by
progressively augmenting its input or feature space, thus enabling continuous
learning of the generator. We show that the proposed progressive augmentation
preserves the original GAN objective, does not compromise the discriminator's
optimality and encourages a healthy competition between the generator and
discriminator, leading to the better-performing generator. We experimentally
demonstrate the effectiveness of PA-GAN across different architectures and on
multiple benchmarks for the image synthesis task, on average achieving ~3 point
improvement of the FID score.


  The complex-field approach is developed to derive analytical expressions of
the magnetic field distributions around superconducting strips on ferromagnetic
substrates (SC/FM strips). We consider the ferromagnetic substrates as ideal
soft magnets with an infinite magnetic permeability, neglecting the
ferromagnetic hysteresis. On the basis of the critical state model for a
superconducting strip, the ac susceptibility $\chi_1'+i\chi_1''$ of a SC/FM
strip exposed to a perpendicular ac magnetic field is theoretically
investigated, and the results are compared with those for superconducting
strips on nonmagnetic substrates (SC/NM strips). The real part $\chi_1'$ for
$H_0/j_cd_s\to 0$ (where $H_0$ is the amplitude of the ac magnetic field, $j_c$
is the critical current density, and $d_s$ is the thickness of the
superconducting strip) of a SC/FM strip is 3/4 of that of a SC/NM strip. The
imaginary part $\chi_1''$ (or ac loss $Q$) for $H_0/j_cd_s<0.14$ of a SC/FM
strip is larger than that of a SC/NM strip, even when the ferromagnetic
hysteresis is neglected, and this enhancement of $\chi_1''$ (or $Q$) is due to
the edge effect of the ferromagnetic substrate.


  We develop a stochastic whole-brain and body simulator of the nematode
roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently
regularizing to allow imputation of latent membrane potentials from partial
calcium fluorescence imaging observations. This is the first attempt we know of
to "complete the circle," where an anatomically grounded whole-connectome
simulator is used to impute a time-varying "brain" state at single-cell
fidelity from covariates that are measurable in practice. The sequential Monte
Carlo (SMC) method we employ not only enables imputation of said latent states
but also presents a strategy for learning simulator parameters via variational
optimization of the noisy model evidence approximation provided by SMC. Our
imputation and parameter estimation experiments were conducted on distributed
systems using novel implementations of the aforementioned techniques applied to
synthetic data of dimension and type representative of that which are measured
in laboratories currently.


  We consider a class of infinite-dimensional singular stochastic control
problems. These can be thought of as spatial monotone follower problems and
find applications in spatial models of production and climate transition. Let
$(D,\mathcal{M},\mu)$ be a finite measure space and consider the Hilbert space
$H:=L^2(D,\mathcal{M},\mu; \mathbb{R})$. Let then $X$ be an $H$-valued
stochastic process on a suitable complete probability space, whose evolution is
determined through an SPDE driven by a self-adjoint linear operator
$\mathcal{A}$ and affected by a cylindrical Brownian motion. The evolution of
$X$ is controlled linearly via an $H$-valued control consisting of the
direction and the intensity of action, a real-valued nondecreasing
right-continuous stochastic process, adapted to the underlying filtration. The
goal is to minimize a discounted convex cost-functional over an infinite
time-horizon. By combining properties of semiconcave functions and techniques
from viscosity theory, we first show that the value function of the problem $V$
is a $C^{1,Lip}(H)$-viscosity solution to the corresponding dynamic programming
equation, which here takes the form of a variational inequality with gradient
constraint. Then, by allowing the decision maker to choose only the intensity
of the control and requiring that the given control direction $\hat{n}$ is an
eigenvector of the linear operator $\mathcal{A}$, we establish that the
directional derivative $V_{\hat{n}}$ is of class $C^1(H)$, hence a second-order
smooth-fit principle in the controlled direction holds for $V$. This result is
obtained by exploiting a connection to optimal stopping and combining results
and techniques from convex analysis and viscosity theory.


  Predictive theory to geometrically engineer devices and materials in
continuum systems to have desired topological-like effects is developed here by
bridging the gap between quantum and continuum mechanical descriptions. A
platonic crystal, a bosonic-like system in the language of quantum mechanics,
is shown to exhibit topological valley modes despite the system having no
direct physical connection to quantum effects. We emphasise a predictive,
first-principle, approach, the strength of which is demonstrated by the ability
to design well-defined broadband edge states, resistant to backscatter, using
geometric differences; the mechanism underlying energy transfer around gentle
and sharp corners is described. Using perturbation methods and group theory,
several distinct cases of symmetry-induced Dirac cones which when gapped yield
non-trivial band-gaps are identified and classified. The propagative behavior
of the edge states around gentle or sharp bends depends strongly upon the
symmetry class of the bulk media and we illustrate this via numerical
simulations.


  We address points recently discussed in Georgoulis (2011) in reference to
Leka et al. (2009b). Most importantly, we find that the results of Georgoulis
(2011) support a conclusion of Leka et al. (2009b): that limited spatial
resolution and the presence of unresolved magnetic structures can challenge
ambiguity- resolution algorithms. Moreover, the findings of both Metcalf et al.
(2006) and Leka et al. (2009b) are confirmed in Georgoulis (2011): a method's
performance can be diminished when the observed field fails to conform to that
method's assumptions. The implication of boundaries in models of solar magnetic
structures is discussed; we confirm that the distribution of the field
components in the model used in Leka et al. (2009b) is closer to what is
observed on the Sun than what is proposed in Georgoulis (2011). It is also
shown that method does matter with regards to simulating limited spatial
resolution and avoiding an inadvertent introduction of bias. Finally, the
assignment of categories to data- analysis algorithms is revisited; we argue
that assignments are only useful and elucidating when used appropriately.


  This paper gives sharp rates of convergence for natural versions of the
Metropolis algorithm for sampling from the uniform distribution on a convex
polytope. The singular proposal distribution, based on a walk moving locally in
one of a fixed, finite set of directions, needs some new tools. We get useful
bounds on the spectrum and eigenfunctions using Nash and Weyl-type
inequalities. The top eigenvalues of the Markov chain are closely related to
the Neuman eigenvalues of the polytope for a novel Laplacian.


  In cellular vortical flows, namely arrays of counter-rotating vortices, short
but flexible filaments can show simple random walks through their stretch-coil
interactions with flow stagnation points. Here, we study the dynamics of
semi-rigid filaments long enough to broadly sample the vortical field. Using
simulation, we find a surprising variety of long-time transport behavior --
random walks, ballistic transport, and trapping -- depending upon the
filament's relative length and effective flexibility. Moreover, we find that
filaments execute L\'evy walks whose diffusion exponents generally decrease
with increasing filament length, until transitioning to Brownian walks.
Lyapunov exponents likewise increase with length. Even completely rigid
filaments, whose dynamics is finite-dimensional, show a surprising variety of
transport states and chaos. Fast filament dispersal is related to an underlying
geometry of ``conveyor belts''. Evidence for these various transport states are
found in experiments using arrays of counter-rotating rollers, immersed in a
fluid and transporting a flexible ribbon.


  In this paper a classification of tetravalent edge-transitive metacirculants
is given. It is shown that a tetravalent edge-transitive metacirculant $\Gamma$
is a normal graph except for four known graphs. If further, $\Gamma$ is a
Cayley graph of a non-abelian metacyclic group, then $\Gamma$ is
half-transitive.


  We have obtained high resolution spectra of $\sim$40 members of M35,
determined the Lithium-T$_{\rm{eff}}$ morphology and the distribution of the
rotational velocity for G and K stars, and compared them to those of the
Pleiades and other well-known open clusters.


  Let $U_c(H)={u: u is unitary and u-1 is compact}$ stand for the unitary
Fredholm group. We prove the following convexity result. Denote by $d_\infty$
the rectifiable distance induced by the Finsler metric given by the operator
norm in $U_c(H)$. If $u_0,u_1,u\in U_c(H)$ and the geodesic $\beta$ joining
$u_0$ and $u_1$ in $U_c(H)$ verifies $d_\infty(u,\beta)<\pi/2$, then the map
$f(s)=d_\infty(u,\beta(s))$ is convex for $s\in[0,1]$. In particular the
convexity radius of the geodesic balls in $U_c(H)$ is $\pi/4$. The same
convexity property holds in the $p$-Schatten unitary groups $U_p(H)={u: u is
unitary and u-1 is in the p-Schatten class}$, for $p$ an even integer, $p\ge 4$
(in this case, the distance is strictly convex). The same results hold in the
unitary group of a $C^*$-algebra with a faithful finite trace. We apply this
convexity result to establish the existence of curves of minimal length with
given initial conditions, in the unitary orbit of an operator, under the action
of the Fredholm group. We characterize self-adjoint operators $A$ such that
this orbit is a submanifold (of the affine space $A+K(H)$, where $K(H)$=compact
operators).


  Named entities in text documents are the names of people, organization,
location or other types of objects in the documents that exist in the real
world. A persisting research challenge is to use computational techniques to
identify such entities in text documents. Once identified, several text mining
tools and algorithms can be utilized to leverage these discovered named
entities and improve NLP applications. In this paper, a method that clusters
prominent names of people and organizations based on their semantic similarity
in a text corpus is proposed. The method relies on common named entity
recognition techniques and on recent word embeddings models. The semantic
similarity scores generated using the word embeddings models for the named
entities are used to cluster similar entities of the people and organizations
types. Two human judges evaluated ten variations of the method after it was run
on a corpus that consists of 4,821 articles on a specific topic. The
performance of the method was measured using three quantitative measures. The
results of these three metrics demonstrate that the method is effective in
clustering semantically similar named entities.


  For the offline safety assessment of automated vehicles, the most challenging
and critical scenarios must be identified efficiently. Therefore, we present a
new approach to define challenging scenarios based on a sensor setup model of
the ego-vehicle. First, a static optimal approaching path of a road user to the
ego-vehicle is calculated using an A* algorithm. We consider a poor perception
of the road user by the automated vehicle as optimal, because we want to define
scenarios that are as critical as possible. The path is then transferred to a
dynamic scenario, where the trajectory of the road user and the road layout are
determined. The result is an optimal road geometry, so that the ego-vehicle can
perceive an approaching object as poorly as possible. The focus of our work is
on the highway as the Operational Design Domain (ODD).


  For stochastic non-equilibrium dynamics like a Langevin equation for a
colloidal particle or a master equation for discrete states, entropy production
along a single trajectory is studied. It involves both genuine particle entropy
and entropy production in the surrounding medium. The integrated sum of both
$\Delta s\t$is shown to obey a fluctuation theorem $<\exp[-\Delta s\t]> =1$ for
arbitrary initial conditions and arbitrary time-dependent driving over a finite
time interval.


  Let $(M,g)$ be a compact Riemann surface, $h$ be a positive smooth function
on $M$. It is well known the functional $$J(u)=\frac{1}{2}\int_M|\nabla
u|^2dv_g+8\pi\int_M udv_g-8\pi\log\int_Mhe^{u}dv_g$$ achieves its minimum under
Ding-Jost-Li-Wang condition. This result was generalized to nonnegative $h$ by
Yang and the author. Later, Sun and Zhu (arXiv:2012.12840) showed
Ding-Jost-Li-Wang condition is also sufficient for $J$ achieves its minimum
when $h$ changes sign, which was reproved later by Wang and Yang (J. Funct.
Anal. 282: Paper No. 109449, 2022) and Li and Xu (Calc. Var. 61: Paper No. 143,
2022) respectively using flow approach. The aim of this note is to give a new
proof of Sun and Zhu's result. Our proof is based on the variational method and
the maximum principle.


  We present linear ensembles of dangling bond chains on a hydrogen terminated
Si(100) surface, patterned in the closest spaced arrangement allowed by the
surface lattice. Local density of states maps over a range of voltages
extending spatially over the close-coupled entities reveal a rich energetic and
spatial variation of electronic states. These artificial molecules exhibit
collective electronic states resulting from covalent interaction of the
constituent atoms. A pronounced electrostatic perturbation of dangling bond
chain structure is induced by close placement of a negatively dangling bond.
The electronic changes so induced are entirely removed, paradoxically, by
addition of a second dangling bond.


  Nonperturbative and perturbative interaction mechanisms of Wilson loops in
gluodynamics are studied within the background field formalism. The first one
operates when distance between minimal surfaces of the loops is small and may
be important for sea quark effects and strong decay processes. The second
mechanism -- perturbative interaction in nonperturbative confining background
is found to be physically dominant for all loop configurations characteristic
of scattering process. It reduces to perturbative gluon exchanges at small
distances, while at larger distances it corresponds to the t-channel exchange
of (reggeized) glueball states.
  Comparison to other approaches is made and possible physical applications are
discussed.


  Multilingual Machine Comprehension (MMC) is a Question-Answering (QA)
sub-task that involves quoting the answer for a question from a given snippet,
where the question and the snippet can be in different languages. Recently
released multilingual variant of BERT (m-BERT), pre-trained with 104 languages,
has performed well in both zero-shot and fine-tuned settings for multilingual
tasks; however, it has not been used for English-Hindi MMC yet. We, therefore,
present in this article, our experiments with m-BERT for MMC in zero-shot,
mono-lingual (e.g. Hindi Question-Hindi Snippet) and cross-lingual (e.g.
English QuestionHindi Snippet) fine-tune setups. These model variants are
evaluated on all possible multilingual settings and results are compared
against the current state-of-the-art sequential QA system for these languages.
Experiments show that m-BERT, with fine-tuning, improves performance on all
evaluation settings across both the datasets used by the prior model, therefore
establishing m-BERT based MMC as the new state-of-the-art for English and
Hindi. We also publish our results on an extended version of the recently
released XQuAD dataset, which we propose to use as the evaluation benchmark for
future research.


  We study recent observations of propagating fluctuations in a prominence foot
with Hinode Solar Optical Telescope (SOT) high-resolution observations in Ca~II
and H alpha emission which we identify as nonlinear fast magnetosnic waves.
Here we analyze further the observations of propagating waves and flows with
Interface Region Imaging Spectrograph (IRIS) Mg~II slit jaw images, in addition
to Hinode/SOT Ca~II images. We find that the waves have typical periods in the
range of 5 - 11 minutes and wavelengths in the plane of the sky (POS) of about
2000 km, while the flows in narrow threads have typical speed in the POS of
~16-46 km/s. We also detect apparent kink oscillations in the threads with
flowing material, and apply coronal seismology to estimate the magnetic field
strength in the range 5-17 G. Using 2.5D MHD we model the combined effects of
nonlinear waves and flows on the observed dynamics of the prominence material,
and reproduce the propagating and refracting fast magnetosonic waves, as well
as standing kink-mode waves in flowing material along the magnetic field. The
modeling results are in good qualitative agreements with the observations of
the various waves and flows in the prominence foot, further confirming coronal
seismology analysis and improving the understanding of the fine scale dynamics
of the prominence material.


  We discuss observations of the first galaxies, within cosmic reionization, at
centimeter and millimeter wavelengths. We present a summary of current
observations of the host galaxies of the most distant QSOs ($z \sim 6$). These
observations reveal the gas, dust, and star formation in the host galaxies on
kpc-scales. These data imply an enriched ISM in the QSO host galaxies within 1
Gyr of the big bang, and are consistent with models of coeval supermassive
black hole and spheroidal galaxy formation in major mergers at high redshift.
Current instruments are limited to studying truly pathologic objects at these
redshifts, meaning hyper-luminous infrared galaxies ($L_{FIR} \sim 10^{13}$
L$_\odot$). ALMA will provide the one to two orders of magnitude improvement in
millimeter astronomy required to study normal star forming galaxies (ie.
Ly-$\alpha$ emitters) at $z \sim 6$. ALMA will reveal, at sub-kpc spatial
resolution, the thermal gas and dust -- the fundamental fuel for star formation
-- in galaxies into cosmic reionization.


  We summarize the current status on constraining the density dependence of the
symmetry energy from terrestrial laboratory measurements and astrophysical
observations. While the value $E_{sym}({\rho_{0}})$ and density slope $L$ of
the symmetry energy at saturation density $\rho_{0}$ can vary largely depending
on the data or methods, all the existing constraints are essentially consistent
with $E_{sym}({\rho_{0}}) = 31 \pm 2$ MeV and $L = 50 \pm 20$ MeV. The
determination of the supra-saturation density behavior of the symmetry energy
remains a big challenge.


  In order to study the axiomatization of the if-then-else construct over
possibly non-halting programs and tests, this paper introduces the notion of
$C$-sets by considering the tests from an abstract $C$-algebra. When the
$C$-algebra is an ada, the axiomatization is shown to be complete by obtaining
a subdirect representation of $C$-sets. Further, this paper considers the
equality test with the if-then-else construct and gives a complete
axiomatization through the notion of agreeable $C$-sets.


  Modern methods often formulate the counting of cells from microscopic images
as a regression problem and more or less rely on expensive, manually annotated
training images (e.g., dot annotations indicating the centroids of cells or
segmentation masks identifying the contours of cells). This work proposes a
supervised learning framework based on classification-oriented convolutional
neural networks (CNNs) to count cells from greyscale microscopic images without
using annotated training images. In this framework, we formulate the cell
counting task as an image classification problem, where the cell counts are
taken as class labels. This formulation has its limitation when some cell
counts in the test stage do not appear in the training data. Moreover, the
ordinal relation among cell counts is not utilized. To deal with these
limitations, we propose a simple but effective data augmentation (DA) method to
synthesize images for the unseen cell counts. We also introduce an ensemble
method, which can not only moderate the influence of unseen cell counts but
also utilize the ordinal information to improve the prediction accuracy. This
framework outperforms many modern cell counting methods and won the data
analysis competition (Case Study 1: Counting Cells From Microscopic Images
https://ssc.ca/en/case-study/case-study-1-counting-cells-microscopic-images) of
the 47th Annual Meeting of the Statistical Society of Canada (SSC). Our code is
available at https://github.com/anno2020/CellCount_TinyBBBC005.


  Localization of radio frequency sources over multipath channels is a
difficult problem arising in applications such as outdoor or indoor gelocation.
Common approaches that combine ad-hoc methods for multipath mitigation with
indirect localization relying on intermediary parameters such as
time-of-arrivals, time difference of arrivals or received signal strengths,
provide limited performance. This work models the localization of known
waveforms over unknown multipath channels in a sparse framework, and develops a
direct approach in which multiple sources are localized jointly, directly from
observations obtained at distributed sources. The proposed approach exploits
channel properties that enable to distinguish line-of-sight (LOS) from non-LOS
signal paths. Theoretical guarantees are established for correct recovery of
the sources' locations by atomic norm minimization. A second-order cone-based
algorithm is developed to produce the optimal atomic decomposition, and it is
shown to produce high accuracy location estimates over complex scenes, in which
sources are subject to diverse multipath conditions, including lack of LOS.


  We present the Sunyaev-Zeldovich (SZ) signal-to-richness scaling relation
(Y500-N200) for the MaxBCG cluster catalogue. Employing a multi-frequency
matched filter on the Planck sky maps, we measure the SZ signal for each
cluster by adapting the filter according to weak-lensing calibrated
mass-richness relations (N200-M500). We bin our individual measurements and
detect the SZ signal down to the lowest richness systems (N200=10) with high
significance, achieving a detection of the SZ signal in systems with mass as
low as M500~5e13 Msolar. The observed Y500-N200 relation is well modeled by a
power law over the full richness range. It has a lower normalisation at given
N200 than predicted based on X-ray models and published mass-richness
relations. An X-ray subsample, however, does conform to the predicted scaling,
and model predictions do reproduce the relation between our measured
bin-average SZ signal and measured bin-average X-ray luminosities. At fixed
richness, we find an intrinsic dispersion in the Y500-N200 relation of 60%
rising to of order 100% at low richness. Thanks to its all-sky coverage, Planck
provides observations for more than 13,000 MaxBCG clusters and an unprecedented
SZ/optical data set, extending the list of known cluster scaling laws to
include SZ-optical properties. The data set offers essential clues for models
of galaxy formation. Moreover, the lower normalisation of the SZ-mass relation
implied by the observed SZ-richness scaling has important consequences for
cluster physics and cosmological studies with SZ clusters.


  The spin Seebeck effect (SSE) refers to the generation of a spin current as a
result of a temperature gradient in a magnetic material, which can be detected
electrically via the inverse spin Hall effect in a metallic contact. Since the
discovery of SSE in 2008, intensive studies on SSE have been conducted to
elucidate its origin. SSEs appear in a wide range of magnetic materials
including ferro-, ferri-, and antiferro-magnets and also paramagnets with
classical or quantum spin fluctuation. SSE voltage reflects fundamental
properties of a magnet, such as elementary excitation, static magnetic order,
spin correlation, and spin transport. In this article, we review recent
progress on SSEs in various systems, with particular emphasis on its emerging
role as a probe of these magnetic properties in solids. We also briefly discuss
the recently-discovered nuclear SSE.


  Speech enhancement model is used to map a noisy speech to a clean speech. In
the training stage, an objective function is often adopted to optimize the
model parameters. However, in most studies, there is an inconsistency between
the model optimization criterion and the evaluation criterion on the enhanced
speech. For example, in measuring speech intelligibility, most of the
evaluation metric is based on a short-time objective intelligibility (STOI)
measure, while the frame based minimum mean square error (MMSE) between
estimated and clean speech is widely used in optimizing the model. Due to the
inconsistency, there is no guarantee that the trained model can provide optimal
performance in applications. In this study, we propose an end-to-end
utterance-based speech enhancement framework using fully convolutional neural
networks (FCN) to reduce the gap between the model optimization and evaluation
criterion. Because of the utterance-based optimization, temporal correlation
information of long speech segments, or even at the entire utterance level, can
be considered when perception-based objective functions are used for the direct
optimization. As an example, we implement the proposed FCN enhancement
framework to optimize the STOI measure. Experimental results show that the STOI
of test speech is better than conventional MMSE-optimized speech due to the
consistency between the training and evaluation target. Moreover, by
integrating the STOI in model optimization, the intelligibility of human
subjects and automatic speech recognition (ASR) system on the enhanced speech
is also substantially improved compared to those generated by the MMSE
criterion.


  The ubiquitous reliance on software systems increases the need for ensuring
that systems behave correctly and are well protected against security risks.
Runtime enforcement is a dynamic analysis technique that utilizes software
monitors to check the runtime behaviour of a software system with respect to a
correctness specification. Whenever the runtime behaviour of the monitored
system is about to deviate from the specification (either due to a programming
bug or a security hijack attack), the monitors apply enforcement techniques to
prevent this deviation.
  Current Runtime Enforcement techniques require that the correctness
specification defines the behaviour of the enforcement monitor itself. This
burdens the specifier with not only having to define property that needs to be
enforced, but also with having to specify how this should be enforced at
runtime; we thus relieve the specifier from this burden by resorting to a
highly expressive logic. Using a logic we allow the specifier to define the
correctness specification as a logic formula from which we can automatically
synthesise the appropriate enforcement monitor.
  Highly expressive logics can, however, permit for defining a wide variety of
formulae, some of which cannot actually be enforced correctly at runtime. We
thus study the enforceability of Hennessy Milner Logic with Recursion for which
we identify a subset that allows for defining enforceable formulae. This allows
us to define a synthesis function that translates enforceable formulae into
enforcement monitors. As our monitors are meant to ensure the correct behaviour
of the monitored system, it is imperative that they work correctly themselves.
We thus study formal definitions that allow us to ensure that our enforcement
monitors behave correctly.


  Learning the underlying patterns in data goes beyond instance-based
generalization to external knowledge represented in structured graphs or
networks. Deep learning that primarily constitutes neural computing stream in
AI has shown significant advances in probabilistically learning latent patterns
using a multi-layered network of computational nodes (i.e., neurons/hidden
units). Structured knowledge that underlies symbolic computing approaches and
often supports reasoning, has also seen significant growth in recent years, in
the form of broad-based (e.g., DBPedia, Yago) and domain, industry or
application specific knowledge graphs. A common substrate with careful
integration of the two will raise opportunities to develop neuro-symbolic
learning approaches for AI, where conceptual and probabilistic representations
are combined. As the incorporation of external knowledge will aid in
supervising the learning of features for the model, deep infusion of
representational knowledge from knowledge graphs within hidden layers will
further enhance the learning process. Although much work remains, we believe
that knowledge graphs will play an increasing role in developing hybrid
neuro-symbolic intelligent systems (bottom-up deep learning with top-down
symbolic computing) as well as in building explainable AI systems for which
knowledge graphs will provide scaffolding for punctuating neural computing. In
this position paper, we describe our motivation for such a neuro-symbolic
approach and framework that combines knowledge graph and neural networks.


  G-formula is a popular approach for estimating treatment or exposure effects
from longitudinal data that are subject to time-varying confounding. G-formula
estimation is typically performed by Monte-Carlo simulation, with
non-parametric bootstrapping used for inference. We show that G-formula can be
implemented by exploiting existing methods for multiple imputation (MI) for
synthetic data. This involves using an existing modified version of Rubin's
variance estimator. In practice missing data is ubiquitous in longitudinal
datasets. We show that such missing data can be readily accommodated as part of
the MI procedure when using G-formula, and describe how MI software can be used
to implement the approach. We explore its performance using a simulation study
and an application from cystic fibrosis.


  In this note, we will discuss how to relate an operator ideal on Banach
spaces to the sequential structures it defines. Concrete examples of ideals of
compact, weakly compact, completely continuous, Banach-Saks and weakly
Banach-Saks operators will be demonstrated.


  We have developed a Hauser-Feshbach fission fragment decay model, HF$^3$D,
which can be applied to the statistical decay of more than 500 primary fission
fragment pairs (1,000 nuclides) produced by the neutron induced fission of
$^{235}$U. The fission fragment yield $Y(A)$ and the total kinetic energy TKE
are model inputs, and we estimate them from available experimental data for the
$^{235}$U(n$\rm_{th}$,f) system. The model parameters in the statistical decay
calculation are adjusted to reproduce some fission observables, such as the
neutron emission multiplicity $\overline{\nu}$, its distribution $P(\nu)$, and
the mass dependence $\overline\nu(A)$. The calculated fission product yield and
isomeric ratio are compared with experimental data. We show that the calculated
independent fission product yield $Y_I(A)$ at the thermal energy reproduces the
experimental data well, while the calculated isomeric ratios tend to be lower
than the Madland-England model prediction. The model is extended to higher
incident neutron energies up to the second chance fission threshold. We
demonstrate for the first time that most of the isomeric ratios stay constant,
although the production of isomeric state itself changes as the incident energy
increases.


  The shift current responses of two-dimensional systems in the gapless limit
are investigated. As the energy gap becomes smaller, the interband transition
probability becomes larger at the band edges. We found the divergence of the
shift current response in a manner proportional to the inverse of the gap size
when the system becomes a two-dimensional Weyl semimetal in the gapless limit.
This behavior is different from that for electric polarization, which has a
jump across the gapless case. It means that the quantitative relationship
between electric polarizations and shift currents is broken in this gapless
limit.


  Vanadium sesquioxide, V2O3, is a prototypical metal-to-insulator system
where, in temperature-dependent studies, the transition always coincides with a
corundum-to-monoclinic structural transition. As a function of pressure, V2O3
follows the expected behavior of increased metallicity due to a larger
bandwidth for pressures up to 12.5 GPa. Surprisingly, for higher pressures when
the structure becomes unstable, the resistance starts to increase. Around 32.5
GPa at 300 K, we observe a novel pressure-induced corundum-to-monoclinic
transition between two metallic phases, showing that the structural phase
transition can be decoupled from the metal-insulator transition. Using X-ray
Raman scattering, we find that screening effects, which are strong in the
corundum phase, become weakened at high pressures. Theoretical calculations
indicate that this can be related to a decrease in coherent quasiparticle
strength, suggesting that the high-pressure phase is likely a critical
correlated metal, on the verge of Mott-insulating behavior.


  Detecting objects in a video is a compute-intensive task. In this paper we
propose CaTDet, a system to speedup object detection by leveraging the temporal
correlation in video. CaTDet consists of two DNN models that form a cascaded
detector, and an additional tracker to predict regions of interests based on
historic detections. We also propose a new metric, mean Delay(mD), which is
designed for latency-critical video applications. Experiments on the KITTI
dataset show that CaTDet reduces operation count by 5.1-8.7x with the same mean
Average Precision(mAP) as the single-model Faster R-CNN detector and incurs
additional delay of 0.3 frame. On CityPersons dataset, CaTDet achieves 13.0x
reduction in operations with 0.8% mAP loss.


  It is well known that the flat radio spectrum is a common property in the
spectral energy distribution of blazars. Although the one-zone leptonic models
are generally successful in explaining the multi-wave band emission, they are
problematic in reproducing the radio spectrum. In the study of Mrk 421,
one-zone models suggest that in order to avoid overproducing the radio flux,
the minimum electron Lorentz factor should be larger than a few hundred at
least, even considering the synchrotron self-absorption effect. This result
suggests that the model predicted spectral index in the radio band of Mrk 421
should be -1/3. On the basis of this result, by assuming there is a neglected
region that will also contribute the radio emission and its electron energy
index is naturally originate from the simplest first-order Fermi acceleration
mechanism, we can get a superimposed flat radio spectrum. In this paper, a
two-zone model is proposed to reproduce the quiescent state spectral energy
distribution of Mrk 421. In addition to taking into account the emission from a
conventional radiation zone, we further consider the emission from the
acceleration zone in which particles are accelerated at a shock front. With the
present model, our fitting result suggests that the low frequency flat radio
spectrum of Mrk 421 might be explained as a superposition of the synchrotron
emission from acceleration zone and radiation zone.


  An inexpensive, easily programmed microcontroller is demonstrated for the
fast frequency stabilization of an infrared fiber laser. The microcontroller
manages all digitalization and processing, with external circuitry only
providing buffering, amplification and overvoltage protection. Several
convenient features are included to recover from common failure modes of a
laser lock system, and a final Nyquist frequency of 200 kHz is obtained,
corresponding to a usable bandwidth of 40-60 kHz.


  We study supersymmetric Wilson loops from a geometrical perspective. To this
end, we propose a new formulation of these operators in terms of an integral
form associated to the immersion of the loop into a supermanifold. This
approach provides a unifying description of Wilson loops preserving different
sets of supercharges, and clarifies the flow between them. Moreover, it allows
to exploit the powerful techniques of super-differential calculus for
investigating their symmetries. As remarkable examples, we discuss
supersymmetry and kappa-symmetry invariance.


  To confirm previously reported evidence of high-temperature superconductivity
in laser processed Sr-Ru-O, we performed simultaneous two-probe and four-probe
resistive measurements using bar-geometry samples. A superconducting-type
transition with an onset at about 250K was recorded in one of the samples,
consistent with our previously reported measurements in the X-bridge geometry.
Some compositional details of the samples are also provided which were not
known at the time of previous web-publication.


  We present the activities of the "New Physics" working group for the "Physics
at TeV Colliders" workshop (Les Houches, France, 3--21 June, 2013). Our report
includes new computational tool developments, studies of the implications of
the Higgs boson discovery on new physics, important signatures for searches for
natural new physics at the LHC, new studies of flavour aspects of new physics,
and assessments of the interplay between direct dark matter searches and the
LHC.


  Publishing open-source academic video recordings is an emergent and prevalent
approach to sharing knowledge online. Such videos carry rich multimodal
information including speech, the facial and body movements of the speakers, as
well as the texts and pictures in the slides and possibly even the papers.
Although multiple academic video datasets have been constructed and released,
few of them support both multimodal content recognition and understanding
tasks, which is partially due to the lack of high-quality human annotations. In
this paper, we propose a novel multimodal, multigenre, and multipurpose
audio-visual academic lecture dataset (M$^3$AV), which has almost 367 hours of
videos from five sources covering computer science, mathematics, and medical
and biology topics. With high-quality human annotations of the slide text and
spoken words, in particular high-valued name entities, the dataset can be used
for multiple audio-visual recognition and understanding tasks. Evaluations
performed on contextual speech recognition, speech synthesis, and slide and
script generation tasks demonstrate that the diversity of M$^3$AV makes it a
challenging dataset.


  Hyperspectral image super-resolution addresses the problem of fusing a
low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral
image (HR-MSI) to produce a high-resolution hyperspectral image (HR-HSI).
Tensor analysis has been proven to be an efficient method for hyperspectral
image processing. However, the existing tensor-based methods of hyperspectral
image super-resolution like the tensor train and tensor ring decomposition only
establish an operation between adjacent two factors and are highly sensitive to
the permutation of tensor modes, leading to an inadequate and inflexible
representation. In this paper, we propose a novel method for hyperspectral
image super-resolution by utilizing the specific properties of high-order
tensors in fully-connected tensor network decomposition. The proposed method
first tensorizes the target HR-HSI into a high-order tensor that has multiscale
spatial structures. Then, a coupled fully-connected tensor network
decomposition model is proposed to fuse the corresponding high-order tensors of
LR-HSI and HR-MSI. Moreover, a weighted-graph regularization is imposed on the
spectral core tensors to preserve spectral information. In the proposed model,
the superiorities of the fully-connected tensor network decomposition lie in
the outstanding capability for characterizing adequately the intrinsic
correlations between any two modes of tensors and the essential invariance for
transposition. Experimental results on three data sets show the effectiveness
of the proposed approach as compared to other hyperspectral image
super-resolution methods.


  It is argued from geometrical, group-theoretical and physical points of view
that in the framework of QCD it is not only necessary but also possible to
modify the Dirac equation so that correspondence principle holds valid. The
Dirac wave equation for a quark is proposed and some consequences are
considered. In particular, it is shown that interquark potential expresses the
Coulomb law for the quarks and, in fact, coincides with the known Cornell
potential.


  Model-free reinforcement learning algorithms such as Deep Deterministic
Policy Gradient (DDPG) often require additional exploration strategies,
especially if the actor is of deterministic nature. This work evaluates the use
of model-based trajectory optimization methods used for exploration in Deep
Deterministic Policy Gradient when trained on a latent image embedding. In
addition, an extension of DDPG is derived using a value function as critic,
making use of a learned deep dynamics model to compute the policy gradient.
This approach leads to a symbiotic relationship between the deep reinforcement
learning algorithm and the latent trajectory optimizer. The trajectory
optimizer benefits from the critic learned by the RL algorithm and the latter
from the enhanced exploration generated by the planner. The developed methods
are evaluated on two continuous control tasks, one in simulation and one in the
real world. In particular, a Baxter robot is trained to perform an insertion
task, while only receiving sparse rewards and images as observations from the
environment.


  We include a resummation of large transverse momentum logarithms in the
next-to-leading order (NLO) Balitsky-Kovchegov equation. The resummed evolution
equation is shown to be stable, the evolution speed being significantly reduced
by NLO corrections. The contributions from NLO terms that are not enhanced by
large logarithms are found to be numerically important close to
phenomenologically relevant initial conditions. We numerically determine the
value for the constant in the resummed logarithm that includes a maximal part
of the full NLO terms in the resummation.


  This is a study of singular solutions of the problem of traveling gravity
water waves on flows with vorticity. We show that, for a certain class of
vorticity functions, a sequence of regular waves converges to an extreme wave
with stagnation points at its crests. We also show that, for any vorticity
function, the profile of an extreme wave must have either a corner of
$120^\circ$ or a horizontal tangent at any stagnation point about which it is
supposed symmetric. Moreover, the profile necessarily has a corner of
$120^\circ$ if the vorticity is nonnegative near the free surface.


  Albeit the standard model is the most successful model of particles physics,
it still has some theoretical shortcomings, for instance the hierarchy problem,
the absence of dark matter, etc. Supersymmetric extensions of the standard
model could be a possible solution to these problems. One of the building
blocks of these supersymmetric models are supersymmetric gauge theories. It is
expected that they exhibit interesting features like confinement, chiral
symmetry breaking, magnetic monopoles and the like. We present new results on
N=2 Super Yang Mills theory in two dimensions. The lattice action is derived by
a dimensional reduction of the N=1 Super Yang Mills theory in four dimensions.
By preserving the R symmetry of the four dimensional model we can exploit Ward
identities to fine tune our parameters of the model to obtain the chiral and
supersymmetric continuum limit. This allows us to calculate the mass spectrum
at the physical point and compare these results with effective field theories.


  We report the absence of spin effects such as spin-galvanic effect, spin
polarization and spin current under static electric field and
inter-spin-subband absorption in 1D system with spin-orbit interaction of
arbitrary form. It was also shown that the accounting for the direct
interaction of electron spin with magnetic field violates this statement.


  In models of Asymmetric Dark Matter (ADM) the relic density is set by a
particle asymmetry in an analogous manner to the baryons. Here we explore the
scenario in which ADM decouples from the Standard Model thermal bath during an
early period of matter domination. We first present a model independent
analysis for a generic ADM candidate with s-wave annihilation cross section
with fairly general assumptions regarding the origin of the early matter
dominated period. We contrast our results to those from conventional ADM models
which assume radiation domination during decoupling. Subsequently, we examine
an explicit example of this scenario in the context of an elegant SO(10)
implementation of ADM in which the matter dominated era is due to a long lived
heavy right-handed neutrino. In the concluding remarks we discuss the prospects
for superheavy ADM in this setting.


  We study the relativistic quantum mechanical problem of a Dirac particle
tunneling through two successive electrostatic barriers. Our aim is to study
the emergence of the so-called \emph{Generalized Hartman Effect}, an effect
observed in the context of nonrelativistic tunneling as well as in its
electromagnetic counterparts, and which is often associated with the
possibility of superluminal velocities in the tunneling process. We discuss the
behavior of both the phase (or group) tunneling time and the dwell time, and
show that in the limit of opaque barriers the relativistic theory also allows
the emergence of the Generalized Hartman Effect. We compare our results with
the nonrelativistic ones and discuss their interpretation.


  We give a general Gaussian bound for the first chaos (or innovation) of point
processes with stochastic intensity constructed by embedding in a bivariate
Poisson process. We apply the general result to nonlinear Hawkes processes,
providing quantitative central limit theorems.


  The presence of submicron grains has been inferred in several debris discs,
despite the fact that these particles should be blown out by stellar radiation
pressure on very short timescales. So far, no fully satisfying explanation has
been found for this apparent paradox. We investigate the possibility that the
observed abundances of submicron grains could be "naturally" produced in bright
debris discs, where the high collisional activity produces them at a rate high
enough to partially compensate for their rapid removal. We also investigate to
what extent this potential presence of small grains can affect our
understanding of some debris disc characteristics. We use a code following the
collisional evolution of a debris disc down to submicron grains far below the
limiting blow-out size $s_{blow}$. We explore different configurations: A and G
stars, cold and warm discs, "bright" and "very bright" systems. We find that,
in bright discs (fractional luminosity $>10^{-3}$) around A stars, there is
always a high-enough amount of submicron grains to leave detectable signatures,
both in scattered-light, where the disc's color becomes blue, and in the mid-IR
($10<\lambda<20\mu$m), where it boosts the disc's luminosity by at least a
factor of 2 and induces a pronounced silicate solid-state band around $10\mu$m.
We also show that, with this additional contribution of submicron grains, the
SED can mimic that of two debris belts separated by a factor of 2 in radial
distance. For G stars, the effect of $s<s_{blow}$ grains remains limited in the
spectra, in spite of the fact that they dominate the system's geometrical cross
section. We also find that, for all considered cases, the halo of small (bound
and unbound) grains that extends far beyond the main disc contributes to
$\sim50$% of the flux up to $\lambda\sim50\mu$m wavelengths.


  In this {\sl Letter} we construct the Hubble diagram (HD) for the standard
Friedmann-Lema\^itre-Robertson-Walker (FLRW) cosmological model after enforcing
it with the general relativistic energy conditions, heeding to investigate
whether it still stands on as the leading scenario for cosmology in face of the
distance modulus-redshift relation of a sample of GRBs that had their redshifts
properly estimated and corrected upon applying on the data analysis the {\sl
Ghirlanda relation} (Ghirlanda et al. 2004) and the recently discovered {\sl
Firmani et al. relation} (Firmani et al. 2006a). Our $\chi^2$ analysis support
the view that FLRW plus the strong energy condition (SEC) is what better fits
the GRB data. But this is not the whole story, since for a cosmological
constant $\Lambda \neq 0$ the FLRW+SEC analysis with undefined $p=p(\rho)$
suggests that $\Lambda$ is not constant anyhow, because it does not follows the
$p= \omega \rho$ HD, with $\omega=-1$. Thenceforth, one concludes that either
the cosmological constant does not exist at all, and consequently one is left
with the FLRW+SEC which fits properly the GRBs HD and there is no late-time
acceleration, or it does exist but should be time-varying. This all is contrary
to current views based on SNIa observations that advogate for an actual
constant $\Lambda$ and an accelerating universe. In connection to the results
above, this last argumentation would imply that either there is something wrong
with the SNIa interpretation regarding cosmic late-time acceleration
(Middleditch 2006) or we will have to move away from general relativity. We
would better to think that general relativity is still the most correct theory
of gravity.


  The nineteenth century Russian author Leo Tolstoy based his egalitarian views
on sociology and history on mathematical and probabilistic views, and he also
proposed a mathematical theory of waging war.


  We present an approach to the computation of the non-Abelian statistics of
quasiholes in quantum Hall states, such as the Pfaffian state, whose
wavefunctions are related to the conformal blocks of minimal model conformal
field theories. We use the Coulomb gas construction of these conformal field
theories to formulate a plasma analogy for the quantum Hall states. A number of
properties of the Pfaffian state follow immediately, including the Berry phases
which demonstrate the quasiholes' fractional charge, the abelian statistics of
the two-quasihole state, and equal-time ground state correlation functions. The
non-Abelian statistics of multi-quasihole states follows from an additional
assumption.


  The HRT (Heil-Ramanathan-Topiwala) conjecture stipulates that the set of any
finitely many time-frequency shifts of a non-zero square Lebesgue integrable
function is linearly independent. The present work settles two special cases of
this conjecture, namely, the cases where the set of time-frequency shifts has
cardinality $N+1$ such that either $N$ of the points lie on some integer
lattice and the last point is arbitrary, or $N$ of the points are on a line,
while the last point does not belong this line. In both cases, we prove that
the HRT conjecture holds appealing mainly to various forms of the ergodic
theorem. We note that, in recent years, the latter case has been the subject of
many investigations -- notably, the subcase where $N=3$ -- and our work
completely resolves it.


  Spin textures such as magnetic domain walls and skyrmions have the potential
to revolutionize electronic devices by encoding information bits. Although
recent advancements in ferromagnetic films have led to promising device
prototypes, their widespread implementation has been hindered by the
material-related drawbacks. Antiferromagnetic spin textures, however, offer a
solution to many of these limitations, paving the way for faster, smaller, more
energy-efficient, and more robust electronics. The functionality of synthetic
antiferromagnets, comprised of two or more magnetic layers separated by
spacers, may be easily manipulated by making use of different materials as well
as interface engineering. In this Perspective article, we examine the
challenges and opportunities presented by spin textures in synthetic
antiferromagnets and propose possible directions and prospects for future
research in this burgeoning field.


  This paper studies the market viability with proportional transaction costs.
Instead of requiring the existence of strictly consistent price systems (SCPS)
as in the literature, we show that strictly consistent local martingale systems
(SCLMS) can successfully serve as the dual elements such that the market
viability can be verified. We introduce two weaker notions of no arbitrage
conditions on market models named no unbounded profit with bounded risk (NUPBR)
and no local arbitrage with bounded portfolios (NLABP). In particular, we show
that the NUPBR and NLABP conditions in the robust sense for the smaller bid-ask
spreads is the equivalent characterization of the existence of SCLMS for
general market models. We also discuss the implications for the utility
maximization problem.


  We present an exact diagonalisation study of bilayer quantum Hall systems at
a filling factor of two in the spherical geometry. We find the
high-Zeeman-coupling phase boundary of the broken symmetry canted
antiferromagnet is given exactly by previous Hartree-Fock mean-field theories,
but that the state's stability at weak Zeeman coupling has been qualitatively
overestimated. In the absence of interlayer tunneling, degeneracies occur
between total spin multiplets due to the Hamiltonian's invariance under
independent spin-rotations in top and bottom two-dimensional electron layers.


  We discuss how one could study asymptotics of cyclotomic quantities via the
mean values of certain multiplicative functions and their Dirichlet series
using a theorem of Delange. We show how this could provide a new approach to
Artin's conjecture on primitive roots. We focus on whether a fixed prime has a
certain order modulo infinitely many other primes. We also give an estimate for
the mean value of one such Dirichlet series.


  This paper investigates the recent stellar merger transient M31LRN 2015 in
the Andromeda galaxy. We analyze published optical photometry and spectroscopy
along with a Hubble Space Telescope detection of the color and magnitude of the
pre-outburst source. The transient outburst is consistent with dynamically
driven ejecta at the onset of a common envelope episode, which eventually leads
to the complete merger of a binary system. The light curve appears to contain
two components: first $\sim10^{-2} M_\odot$ of fast ejecta driven by shocks at
the onset of common envelope, and later, $\sim0.3 M_\odot$ of further ejecta as
the secondary becomes more deeply engulfed within the primary. Just prior to
merger, we find that the primary star is a $3-5.5 M_\odot$ sub-giant branch
primary star with radius of $30-40R_\odot$. Its position in the color-magnitude
diagram shows that it is growing in radius, consistent with a picture where it
engulfs its companion. By matching the properties of the primary star to the
transient outburst, we show that the optical transient lasts less than ten
orbits of the original binary, which had a pre-merger period of $\sim 10$ days.
We consider the possible orbital dynamics leading up to the merger, and argue
that if the system merged due to the Darwin tidal instability it implies a
lower mass main sequence companion of $0.1-0.6M_\odot$. This analysis
represents a promising step toward a more detailed understanding of flows in
common envelope episodes through direct observational constraints.


  We consider the singular phases of the smooth finite-gap integrable systems
arising in the context of Seiberg-Witten theory. These degenerate limits
correspond to the weak and strong coupling regimes of SUSY gauge theories. The
spectral curves in such limits acquire simpler forms: in most cases they become
rational, and the corresponding expressions for coupling constants and
superpotentials can be computed explicitly. We verify that in accordance with
the computations from quantum field theory, the weak-coupling limit gives rise
to precisely the "trigonometric" family of Calogero-Moser and open Toda models,
while the strong-coupling limit corresponds to the solitonic degenerations of
the finite-gap solutions. The formulae arising provide some new insights into
the corresponding phenomena in SUSY gauge theories. Some open conjectures have
been proven.


  The paper proposes an approach to transcend multicultural and multilingual
barriers in the use and reuse of geographical data at the European level. The
approach aims at sharing scientific terms in the field of nature conservation
with the goal of assisting different user communities with metadata compilation
and information discovery. A multi-thesauri solution is proposed, based on a
Common Thesaurus Framework for Nature Conservation, where different well-known
Knowledge Organization Systems are assembled and shared. It has been designed
according to semantic web and W3C recommendations employing SKOS standard
models and Linked Data to publish the thesauri as a whole in
machine-understandable format. The outcome is a powerful framework satisfying
the requirements of modularity and openness for further thesaurus extension and
updating, interlinking among thesauri, and exploitability from other systems.
The paper supports the employment of Linked Data to deal with terminologies in
complex domains such as nature conservation and it proposes a hands-on recipe
to publish thesauri in the framework.


  Based on a thorough numerical analysis of the spectrum of Harper's operator,
which describes, e.g., an electron on a two-dimensional lattice subjected to a
magnetic field perpendicular to the lattice plane, we make the following
conjecture: For any value of the incommensurability parameter sigma of the
operator its spectrum can be covered by the bands of the spectrum for every
rational approximant of sigma after stretching them by factors with a common
upper bound. We show that this conjecture has the following important
consequences: For all irrational values of sigma the spectrum is (i) a zero
measure Cantor set and has (ii) a Hausdorff dimension less or equal to 1/2. We
propose that our numerical approach may be a guide in finding a rigorous proof
of these results.


  The development of annotated datasets over the 21st century has helped us
truly realize the power of deep learning. Most of the datasets created for the
named-entity-recognition (NER) task are not domain specific. Finance domain
presents specific challenges to the NER task and a domain specific dataset
would help push the boundaries of finance research. In our work, we develop the
first high-quality NER dataset for the finance domain. To set the benchmark for
the dataset, we develop and test a weak-supervision-based framework for the NER
task. We extend the current weak-supervision framework to make it employable
for span-level classification. Our weak-ner framework and the dataset are
publicly available on GitHub and Hugging Face.


  We present results from SMA observations of the star forming region Cepheus
A-East at ~340 GHz (875 micron) with 0.7'' - 2'' resolution. At least four
compact submm continuum sources have been detected, as well as a rich forest of
hot core line emission. Two kinematically, chemically, and thermally distinct
regions of molecular emission are present in the vicinity of the HW2 thermal
jet, both spatially distinct from the submm counterpart to HW2. We propose that
this emission is indicative of multiple protostars rather than a massive disk
as reported by Patel et al. (2005).


  Although supervised deep-learning has achieved promising performance in
medical image segmentation, many methods cannot generalize well on unseen data,
limiting their real-world applicability. To address this problem, we propose a
deep learning-based Bayesian framework, which jointly models image and label
statistics, utilizing the domain-irrelevant contour of a medical image for
segmentation. Specifically, we first decompose an image into components of
contour and basis. Then, we model the expected label as a variable only related
to the contour. Finally, we develop a variational Bayesian framework to infer
the posterior distributions of these variables, including the contour, the
basis, and the label. The framework is implemented with neural networks, thus
is referred to as deep Bayesian segmentation. Results on the task of
cross-sequence cardiac MRI segmentation show that our method set a new state of
the art for model generalizability. Particularly, the BayeSeg model trained
with LGE MRI generalized well on T2 images and outperformed other models with
great margins, i.e., over 0.47 in terms of average Dice. Our code is available
at https://zmiclab.github.io/projects.html.


  In contrast to the standard quantum state tomography, the direct tomography
seeks the direct access to the complex values of the wave function at
particular positions (i.e., the expansion coefficient in a fixed basis).
Originally put forward as a special case of weak measurement, it can be
extended to arbitrary measurement setup. We generalize the idea of "quantum
metrology," where a real-valued phase is estimated, to the estimation of
complex-valued phase, and apply it for the direct tomography of the wave
function. It turns out that the reformulation can help us easily find the
optimal measurements for efficient estimation. We further propose two different
measurement schemes that eventually approach the Heisenberg limit. In the first
scheme, the ensemble of measured system is duplicated and the replica ensemble
is time-reversal transformed before the start of the measurement. In the other
method, the pointers are prepared in special entangled states, either GHZ-like
maximally entangled state or the symmetric Dicke state. In both methods, the
real part of the parameter is estimated with a Ramsey-type interferometry while
the imaginary part is estimated by amplitude measurements.


  The effect of electric field, applied on systems in the nanoscale regime has
attracted a lot of research in recent times. We highlight some of the recent
results in the field of single molecule electronics and then move on to focus
on some of our own results in this area. We have first shown how important it
is to obtain the spatial profile of the external bias potential across the
system, and how this would change in the presence of electron-electron
interactions. We have also studied different kinds of insulators in the
presence of the spatially varying external bias, and have explicitly shown that
a two sublattice structure, caused either by a lattice distortion, or by the
presence of substituents with strong dipolar nature, can result in negative
differential resistance (NDR) in the transport characteristics. We also find
this to be true in case of correlated insulators. Additionally, we have shown
clear NDR behavior in a correlated double quantum dot by tuning the
electron-electron interaction strength in the system.


  Product Bundling and offering products to customers is of critical importance
in retail marketing. In general, product bundling and offering products to
customers involves two main issues, namely identification of product taste
according to demography and product evaluation and selection to increase sales.
The former helps to identify, analyze and understand customer needs according
to the demo-graphical characteristics and correspondingly transform them into a
set of specifications and offerings for people. The latter, concerns with how
to determine the best product strategy and offerings for the customer in
helping the retail market to improve their sales. Existing research has focused
only on identifying patterns for a particular dataset and for a particular
setting. This work aims to develop an explicit decision support for the
retailers to improve their product segmentation for different settings based on
the people characteristics and thereby promoting sales by efficient knowledge
discovery from the existing sales and product records. The work presents a
framework, which models an association relation mapping between the customers
and the clusters of products they purchase in an existing location and helps in
finding rules for a new location. The methodology is based on the integration
of popular data mining approaches such as clustering and association rule
mining. It focuses on the discovery of rules that vary according to the
economic and demographic characteristics and concentrates on marketing of
products based on the population.


  We consider the group isomorphism problem: given two finite groups G and H
specified by their multiplication tables, decide if G and H are isomorphic. The
n^(log n) barrier for group isomorphism has withstood all attacks --- even for
the special cases of p-groups and solvable groups --- ever since the n^(log n +
O(1)) generator-enumeration algorithm. In this work, we present the first
significant improvement over n^(log n) by showing that group isomorphism is
n^((1 / 2) log_p n + O(1)) Turing reducible to composition-series isomorphism
where p is the smallest prime dividing the order of the group. Combining our
reduction with an n^(O(p / log p)) algorithm for p-group composition-series
isomorphism, we obtain an n^((1 / 2) log n + O(1)) algorithm for p-group
isomorphism. We then generalize our techniques from p-groups using Sylow bases
to derive an n^((1 / 2) log n + O(log n / log log n)) algorithm for
solvable-group isomorphism. Finally, we relate group isomorphism to the
collision problem which allows us replace the 1 / 2 in the exponents with 1 / 4
using randomized algorithms and 1 / 6 using quantum algorithms.


  This paper explores special conditions on the starting value of a Collatz
sequence which imply that the Collatz conjecture is true. This is the result of
the collaboration of a retired mathematics professor (Koelzer) and a retired
physics professor (Welling).


  In this paper we study the effect of positional randomness on transmissional
properties of a two dimensional photonic crystal as a function of a randomness
parameter $\alpha$ ($\alpha=0$ completely ordered, $\alpha=1$ completely
disordered). We use finite-difference time-domain~(FDTD) method to solve the
Maxwell's equations in such a medium numerically. We consider two situations:
first a $90\degr$ bent photonic crystal wave-guide and second a centrally
pulsed photonic crystal micro-cavity. We plot various figures for each case
which characterize the effect of randomness quantitatively. More specifically,
in the wave-guide situation, we show that the general shape of the normalized
total output energy is a Gaussian function of randomness with
wavelength-dependent width. For centrally pulsed PC, the output energy curves
display extremum behavior both as a function of time as well as randomness. We
explain these effects in terms of two distinct but simultaneous effects which
emerge with increasing randomness, namely the creation of semi-localized modes
and the shrinking (and eventual destruction) of the photonic band-gaps.
Semi-localized (i.e. Anderson localized) modes are seen to arise as a
synchronization of internal modes within a cluster of randomly positioned
dielectric nano-particles. The general trend we observe shows a sharp change of
behavior in the intermediate randomness regime (i.e. $\alpha \approx 0.5$)
which we attribute to a similar behavior in the underlying overlap probability
of nano-particles


  Quantum models based on the mathematics of quantum mechanics (QM) have been
developed in cognitive sciences, game theory and econophysics. In this work a
generalization of credit loans is introduced by using the vector space
formalism of QM. Operators for the debt, amortization, interest and periodic
installments are defined and its mean values in an arbitrary orthonormal basis
of the vectorial space give the corresponding values at each period of the
loan. Endowing the vector space of dimension M, where M is the loan duration,
with a SO(M) symmetry, it is possible to rotate the eigenbasis to obtain better
schedule periodic payments for the borrower, by using the rotation angles of
the SO(M) transformation. Given that a rotation preserves the length of the
vectors, the total amortization, debt and periodic installments are not
changed. For a general description of the formalism introduced, the loan
operator relations are given in terms of a generalized Heisenberg algebra,
where finite dimensional representations are considered and commutative
operators are defined for the specific loan types. The results obtained are an
improvement of the usual financial instrument of credit because introduce
several degrees of freedom through the rotation angles, which allows to select
superposition states of the corresponding commutative operators that enables
the borrower to tune the periodic installments in order to obtain better
benefits without changing what the lender earns.


  The Baryon-Lepton difference ($B-L$) is increasingly emerging as a possible
new symmetry of the weak interactions of quarks and leptons as a way to
understand the small neutrino masses. There is the possibility that current and
future searches at colliders and in low energy rare processes may provide
evidence for this symmetry. This paper provides a brief overview of the early
developments that led to B-L as a possible symmetry beyond the standard model,
and also discusses some recent developments.


  The phase transition "Coulomb-confinement" in the U(1) regularized gauge
theory is considered in the framework of dual Abelian Higgs model of scalar
monopoles (shortly: Higgs monopole model). The effective potential analogous to
the Coleman-Weinberg's one, improved by the renormalization group equation, is
constructed, using beta-functions in the two-loop approximation. The value of
QED critical fine structure constant was calculated: $\alpha_{crit}\approx
0.208$, in correspondence with lattice result: $\alpha_{crit}^{lat}\approx
0.20\pm 0.015$.


  A novel method to differentiate the effects of Dirac and Majorana (D-M)
neutrinos in four-body decays has been discussed in arXiv:2106.11785. There, it
is concluded that the back-to-back kinematic scenario seems to avoid the
constraint imposed by the "practical Dirac-Majorana confusion theorem", as one
does not need to fully integrate over neutrino and antineutrino momenta. In
this paper, we propose to analyse radiative leptonic lepton-decays
($\ell\to\ell'\nu\bar{\nu}\gamma$), as an independent alternative process to
study the possible Majorana nature of neutrinos. Our approach demonstrates
that, in the back-to-back kinematic configuration (for the $\ell'- \gamma$ and
$\nu-\bar{\nu}$ systems, respectively), the distinction between Dirac and
Majorana cases disappears when the inaccessible neutrino angle is integrated
out, which might appear unexpected considering the claims in arXiv:2106.11785.
Our results apply in absence of non-standard interactions, which can enhance
generally the sensitivity to the neutrino nature.


  Realizing an optimal Schottky interface of graphene on Si is challenging, as
the electrical transport strongly depends on the graphene quality and the
fabrication processes. Such interfaces are of increasing research interest for
integration in diverse electronic devices as they are thermally and chemically
stable in all environments, unlike standard metal/semiconductor interfaces. We
fabricate such interfaces with n-type Si at ambient conditions and find their
electrical characteristics to be highly rectifying, with minimal reverse
leakage current ($<$10$^{-10}$ A) and rectification of more than $10^6$. We
extract Schottky barrier height of 0.69 eV for the exfoliated graphene and 0.83
eV for the CVD graphene devices at room temperature. The temperature dependent
electrical characteristics suggest the influence of inhomogeneities at the
graphene/n-Si interface. A quantitative analysis of the inhomogeneity in
Schottky barrier heights is presented using the potential fluctuation model
proposed by Werner and G\"{u}ttler.


  A pseudo ternary diffusion couple technique in a multicomponent system by
simplifying the mathematical complications of Onsager formalism is proposed for
the estimation of composition dependent values of the interdiffusion
coefficients. This is otherwise impossible following the conventional method in
a system with more than three components. Other alternative methods estimate
the average diffusion coefficients over a composition range of random choice
and lack physical significance. This method can be followed in a multicomponent
system with any number of components on the condition that only three
components develop diffusion profiles keeping others as constant.


  The algebraic properties of flattenings and subflattenings provide direct
methods for identifying edges in the true phylogeny -- and by extension the
complete tree -- using pattern counts from a sequence alignment. The relatively
small number of possible internal edges among a set of taxa (compared to the
number of binary trees) makes these methods attractive, however more could be
done to evaluate their effectiveness for inferring phylogenetic trees. This is
the case particularly for subflattenings, and our work makes progress in this
area. We introduce software for constructing and evaluating subflattenings for
splits, utilising a number of methods to make computing subflattenings more
tractable. We then present the results of simulations we have performed in
order to compare the effectiveness of subflattenings to that of flattenings in
terms of split score distributions, and susceptibility to possible biases. We
find that subflattenings perform similarly to flattenings in terms of the
distribution of split scores on the trees we examined, but may be less affected
by bias arising from both split size/balance and long branch attraction. These
insights are useful for developing effective algorithms to utilise these tools
for the purpose of inferring phylogenetic trees.


  Probabilistic circuits (PCs) are a powerful modeling framework for
representing tractable probability distributions over combinatorial spaces. In
machine learning and probabilistic programming, one is often interested in
understanding whether the distributions learned using PCs are close to the
desired distribution. Thus, given two probabilistic circuits, a fundamental
problem of interest is to determine whether their distributions are close to
each other.
  The primary contribution of this paper is a closeness test for PCs with
respect to the total variation distance metric. Our algorithm utilizes two
common PC queries, counting and sampling. In particular, we provide a poly-time
probabilistic algorithm to check the closeness of two PCs when the PCs support
tractable approximate counting and sampling. We demonstrate the practical
efficiency of our algorithmic framework via a detailed experimental evaluation
of a prototype implementation against a set of 475 PC benchmarks. We find that
our test correctly decides the closeness of all 475 PCs within 3600 seconds.


  The generalised deformed su_{q}(2) model is applied to 79 superdeformed bands
in the region A~190. The transition energies and the moments of inertia are
calculated within the model, Its validity is investigated by comparing it with
the experimental data. Both the standard su_{q}(2) and the generalized one fail
to account for the uprising and the downturn of the dynamic moment of inertia.
Both models, however, show remarkable agreement with the available experimental
data at low angular frequancy (hw <= 0.25MeV).


  The evolution and growing automation of collaborative robots introduce more
complexity and unpredictability to systems, highlighting the crucial need for
robot's adaptability and flexibility to address the increasing complexities of
their environment. In typical industrial production scenarios, robots are often
required to be re-programmed when facing a more demanding task or even a few
changes in workspace conditions. To increase productivity, efficiency and
reduce human effort in the design process, this paper explores the potential of
using digital twin combined with Reinforcement Learning (RL) to enable robots
to generate self-improving collision-free trajectories in real time. The
digital twin, acting as a virtual counterpart of the physical system, serves as
a 'forward run' for monitoring, controlling, and optimizing the physical system
in a safe and cost-effective manner. The physical system sends data to
synchronize the digital system through the video feeds from cameras, which
allows the virtual robot to update its observation and policy based on real
scenarios. The bidirectional communication between digital and physical systems
provides a promising platform for hardware-in-the-loop RL training through
trial and error until the robot successfully adapts to its new environment. The
proposed online training framework is demonstrated on the Unfactory Xarm5
collaborative robot, where the robot end-effector aims to reach the target
position while avoiding obstacles. The experiment suggest that proposed
framework is capable of performing policy online training, and that there
remains significant room for improvement.


  We investigated the theoretical possibility of accurately determining the
helium-to-metal enrichment ratio $\Delta Y/\Delta Z$ from precise observations
of double lined eclipsing binary systems. Using Monte Carlo simulations, we
drew synthetic binary systems with masses between 0.85 and 1.00 $M_{\odot}$
from a grid of stellar models with $\Delta Y/\Delta Z = 2.0$ [...].
Subsequently, a broader grid with $\Delta Y/\Delta Z$ from 1.0 to 3.0 was used
in the fitting process. To account for observational uncertainties, two
scenarios were explored: S1 with realistic uncertainties of 100 K in
temperature and 0.1 dex in [Fe/H], and S2 with halved uncertainties. We
repeated the simulation at two baseline metallicities: [Fe/H] = 0.0 and -0.3.
The posterior distributions of $\Delta Y/\Delta Z$ were severely biased towards
the edge of the allowable range in the S1 errors scenario. The situation only
marginally improved when considering the S2 scenario. The effect is due to the
impact of changing $\Delta Y/\Delta Z$ in the stellar effective temperature and
its interplay with [Fe/H] observational error, and it is therefore not
restricted to the specific fitting method. Despite the presence of these
systematic discrepancies, the age of the systems were recovered unbiased with
10% precision. Our findings indicate that the observational uncertainty in
effective temperature and metallicity significantly hinders the accurate
determination of the $\Delta Y/\Delta Z$ parameter from main sequence binary
systems.


  Electronic cooling in hybrid normal metal-insulator-superconductor junctions
is a promising technology for the manipulation of thermal loads in solid state
nanosystems. One of the main bottlenecks for efficient electronic cooling is
the electron-phonon coupling, as it represents a thermal leakage channel to the
phonon bath. Graphene is a two-dimensional material that exhibits a weaker
electron-phonon coupling compared to standard metals. For this reason, we study
the electron cooling in graphene-based systems consisting of a graphene sheet
contacted by two insulator/superconductor junctions. We show that, by properly
biasing the graphene, its electronic temperature can reach base values lower
than those achieved in similar systems based on metallic ultra-thin films.
Moreover, the lower electron-phonon coupling is mirrored in a lower heat power
pumped into the superconducting leads, thus avoiding their overheating and
preserving the cooling mechanisms. Finally, we analyze the possible application
of cooled graphene as a bolometric radiation sensor. We study its main figures
of merit, i.e. responsivity, noise equivalent power and response time. In
particular, we show that the built-in electron refrigeration allows reaching a
responsivity of the order of 50 nA/pW and a noise equivalent power of order of
$\rm 10^{-18}\, W\, Hz^{-1/2}$ while the response speed is about 10 ns,
corresponding to a thermal bandwidth in the order of 20MHz.


  We demonstrate a mesoscopic spin polarizer/analyzer system that allows the
spin polarization of current from a quantum point contact in an in-plane
magnetic field to be measured. A transverse focusing geometry is used to couple
current from an emitter point contact into a collector point contact. At large
in-plane fields, with the point contacts biased to transmit only a single spin
(g < e^2/h), the voltage across the collector depends on the spin polarization
of the current incident on it. Spin polarizations of greater than 80% are found
for both emitter and collector at 300mK and 7T in-plane field.


  The extraordinary performance of large language models (LLMs) heightens the
importance of detecting whether the context is generated by an AI system. More
importantly, while more and more companies and institutions release their LLMs,
the origin can be hard to trace. Since LLMs are heading towards the time of
AGI, similar to the origin tracing in anthropology, it is of great importance
to trace the origin of LLMs. In this paper, we first raise the concern of the
origin tracing of LLMs and propose an effective method to trace and detect
AI-generated contexts. We introduce a novel algorithm that leverages the
contrastive features between LLMs and extracts model-wise features to trace the
text origins. Our proposed method works under both white-box and black-box
settings therefore can be widely generalized to detect various LLMs.(e.g. can
be generalized to detect GPT-3 models without the GPT-3 models). Also, our
proposed method requires only limited data compared with the supervised
learning methods and can be extended to trace new-coming model origins. We
construct extensive experiments to examine whether we can trace the origins of
given texts. We provide valuable observations based on the experimental
results, such as the difficulty level of AI origin tracing, and the AI origin
similarities, and call for ethical concerns of LLM providers. We are releasing
all codes and data as a toolkit and benchmark for future AI origin tracing and
detecting studies. \footnote{We are releasing all available resource at
\url{https://github.com/OpenLMLab/}.}


  We report on our study of supernova (SN) 2022xxf based on observations
obtained during the first four months of its evolution. The light curves (LCs)
display two humps of similar maximum brightness separated by 75 days,
unprecedented for a broad-lined (BL) Type Ic supernova (SN IcBL). SN 2022xxf is
the most nearby SN IcBL to date (in NGC 3705, $z = 0.0037$, at a distance of
about 20 Mpc). Optical and near-infrared photometry and spectroscopy are used
to identify the energy source powering the LC. Nearly 50 epochs of high
signal-to-noise-ratio spectroscopy were obtained within 130 days, comprising an
unparalleled dataset for a SN IcBL, and one of the best-sampled SN datasets to
date. The global spectral appearance and evolution of SN 2022xxf points to
typical SN Ic/IcBL, with broad features (up to $\sim14000$ km s$^{-1}$) and a
gradual transition from the photospheric to the nebular phase. However, narrow
emission lines (corresponding to $\sim1000-2500$ km s$^{-1}$) are present in
the spectra from the time of the second rise, suggesting slower-moving
circumstellar material (CSM). These lines are subtle, in comparison to the
typical strong narrow lines of CSM-interacting SNe, for example, Type IIn, Ibn,
and Icn, but some are readily noticeable at late times such as in Mg I
$\lambda$5170 and [O I] $\lambda$5577. Unusually, the near-infrared spectra
show narrow line peaks in a number of features formed by ions of O and Mg. We
infer the presence of CSM that is free of H and He. We propose that the
radiative energy from the ejecta-CSM interaction is a plausible explanation for
the second LC hump. This interaction scenario is supported by the color
evolution, which progresses to the blue as the light curve evolves along the
second hump, and the slow second rise and subsequent rapid LC drop. (Abstract
abridged)