  We purpose a study a Lorentz-breaking extension of the scalar QED. We
calculate the contributions in the Lorentz-violating parameters to the
two-point functions of scalar and gauge fields. We found that the two
background tensors, coming from the two sectors (scalar and gauge) are mixed in
the one-loop corrections. This shows that these two Lorentz-breaking terms
cannot be studied in an isolated form.


  Many skin lesion analysis (SLA) methods recently focused on developing a
multi-modal-based multi-label classification method due to two factors. The
first is multi-modal data, i.e., clinical and dermoscopy images, which can
provide complementary information to obtain more accurate results than
single-modal data. The second one is that multi-label classification, i.e.,
seven-point checklist (SPC) criteria as an auxiliary classification task can
not only boost the diagnostic accuracy of melanoma in the deep learning (DL)
pipeline but also provide more useful functions to the clinical doctor as it is
commonly used in clinical dermatologist's diagnosis. However, most methods only
focus on designing a better module for multi-modal data fusion; few methods
explore utilizing the label correlation between SPC and skin disease for
performance improvement. This study fills the gap that introduces a Graph
Convolution Network (GCN) to exploit prior co-occurrence between each category
as a correlation matrix into the DL model for the multi-label classification.
However, directly applying GCN degraded the performances in our experiments; we
attribute this to the weak generalization ability of GCN in the scenario of
insufficient statistical samples of medical data. We tackle this issue by
proposing a Graph-Ensemble Learning Model (GELN) that views the prediction from
GCN as complementary information of the predictions from the fusion model and
adaptively fuses them by a weighted averaging scheme, which can utilize the
valuable information from GCN while avoiding its negative influences as much as
possible. To evaluate our method, we conduct experiments on public datasets.
The results illustrate that our GELN can consistently improve the
classification performance on different datasets and that the proposed method
can achieve state-of-the-art performance in SPC and diagnosis classification.


  We solve the word problem of the identity $x(yz) = (xy)(yz)$ by investigating
a certain group describing the geometry of that identity. We also construct a
concrete realization of the free system of rank~1 relative to the above
identity


  We define a notion which contains numerous basic notions of Analysis as
special cases, for example limit, continuity, differential, Riemann and
Lebesgue integral, root and exponential functions. Properties like additivity
or linearity of all mentioned notions follow from one single theorem. A
generalisation of Definition 1 contains basic ideas of other theories such as
topology, group and measure theory.


  In this paper, we present a polynomial dynamic programming algorithm that
tests whether a $n$-vertex directed tree $T$ has an upward planar embedding
into a convex point-set $S$ of size $n$. Further, we extend our approach to the
class of outerplanar digraphs. This nontrivial and surprising result implies
that any given digraph can be efficiently tested for an upward planar embedding
into a given convex point set.


  I report the analysis performed on fermion masses and mixing, including
neutrino mixing, within the context of a model with hierarchical radiative mass
generation mechanism for light charged fermions, mediated by exotic scalar
particles at one and two loops, respectively, meanwhile the neutrinos get
Majorana mass terms at tree level through the Yukawa couplings with two SU(2)_L
Higgs triplets. All the resulting mass matrices in the model, for the u, d, and
e fermion charged sectors, the neutrinos and the exotic scalar particles, are
diagonalized in exact analytical form. Quantitative analysis shows that this
model is successful to accommodate the hierarchical spectrum of masses and
mixing in the quark sector as well as the charged lepton masses. The lepton
mixing matrix, V_{PMNS}, is written completely in terms of the neutrino masses
m_1, m_2, and m_3. Large lepton mixing for \theta_{12} and \theta_{23} is
predicted in the range of values 0.7 \lesssim \sin^22\theta_{12}\lesssim 0.7772
and 0.87 \lesssim \sin^22\theta_{23} \lesssim 0.9023 by using 0.033 \lesssim
s_{13}^2 \lesssim 0.04. These values for lepton mixing are consistent with 3
\sigma allowed ranges provided by recent global analysis of neutrino data
oscillation. From \Delta m_{\text{sol}}^2 bounds, neutrino masses are predicted
in the range of values m_1 \thickapprox (1.706 - 2.494) x 10^{- 3} eV, m_2
\thickapprox (6.675 - 12.56) x 10^{- 3} eV, and m_3 \thickapprox (1.215 -
2.188) x 10^{- 2} eV, respectively. The above allowed lepton mixing leads to
the quak-lepton complementary relations \theta_{12}^{CKM} + \theta_{12}^{PMNS}
\thickapprox 41.543^{\circ} - 44.066^{\circ} and
\theta_{23}^{CKM}+\theta_{23}^{PMNS}\thickapprox 36.835^{\circ} -
38.295^{\circ}.


  Collective scattering of spatially coherent radiation by separated point
emitters induces inter-particle forces. For particles close to nano-photonic
structures as, for example, nano-fibers, hollow core fibers or photonic
waveguides, this pair-interaction induced by monochromatic light is periodic
and virtually of infinite range. Here we show that the shape and range of the
optical interaction potential can be precisely controlled by spectral design of
the incoming illumination. If each particle is only weakly coupled to the
confined guided modes the forces acting within a particle ensemble can be
decomposed to pairwise interactions. These forces can be tailored to almost
arbitrary spatial dependence as they are related to Fourier transforms with
coefficients controlled by the intensities and frequencies of the illuminating
lasers. We demonstrate the versatility of the scheme by highlighting some
examples of unconventional pair potentials. Implementing these interactions in
a chain of trapped quantum particles could be the basis of a versatile quantum
simulator with almost arbitrary all-to-all interaction control.


  We investigate the Jacobian decomposition of some algebraic curves over
finite fields with genus $4$, $5$ and $10$. As a corollary, explicit equations
for curves that are either maximal or minimal over the finite field with $p^2$
elements are obtained for infinitely many $p$'s. Lists of small $p$'s for which
maximality holds are provided. In some cases we describe the automorphism group
of the curve.


  This paper introduces VALL-E 2, the latest advancement in neural codec
language models that marks a milestone in zero-shot text-to-speech synthesis
(TTS), achieving human parity for the first time. Based on its predecessor,
VALL-E, the new iteration introduces two significant enhancements: Repetition
Aware Sampling refines the original nucleus sampling process by accounting for
token repetition in the decoding history. It not only stabilizes the decoding
but also circumvents the infinite loop issue. Grouped Code Modeling organizes
codec codes into groups to effectively shorten the sequence length, which not
only boosts inference speed but also addresses the challenges of long sequence
modeling. Our experiments on the LibriSpeech and VCTK datasets show that VALL-E
2 surpasses previous systems in speech robustness, naturalness, and speaker
similarity. It is the first of its kind to reach human parity on these
benchmarks. Moreover, VALL-E 2 consistently synthesizes high-quality speech,
even for sentences that are traditionally challenging due to their complexity
or repetitive phrases. The advantages of this work could contribute to valuable
endeavors, such as generating speech for individuals with aphasia or people
with amyotrophic lateral sclerosis. See https://aka.ms/valle2 for demos of
VALL-E 2.


  We experimentally investigated the rotationally resolved polarization
characteristics of N$_2^+$ lasing at 391 and 428 nm using a pump-seed scheme.
By varying the relative angle between the linear polarizations of the pump and
seed, it is found that the polarizations of the P and R branches of 391-nm
lasing are counter-rotated. By contrast, both branches of 428-nm lasing remain
polarized along the pump. The origin of the puzzled abnormal polarization
characteristics is found based on a complete physical model that simultaneously
includes the transient photoionization and the subsequent coupling among the
electronic, vibrational and rotational quantum states of ions.It suggests that
the cascaded resonant Raman processes following ionization create negative
coherence between the rotational states of $J$ and $J$+2 in the ionic ground
state X$^2\Sigma_g^+(\nu=0)$, which leads to mirror-symmetrical polarization
for the P and R branches of 391-nm lasing. Both the experiment and theory
indicate that the demonstrated rotational coherence plays an extremely pivotal
role in clarifying the gain mechanism of N$_2^+$ lasing and opens up the route
toward quantum optics under ultrafast strong fields.


  In this paper we characterize magnitude-dependent systematics in the proper
motions of the Gaia EDR3 catalog and provide a prescription for their removal.
The reference frame of bright stars (G<13) in EDR3 is known to rotate with
respect to extragalactic objects, but this rotation has proven difficult to
characterize and correct. We employ a sample of binary stars and a sample of
open cluster members to characterize this proper motion bias as a
magnitude-dependent spin of the reference frame. We show that the bias varies
with G magnitude, reaching up to 80 {\mu}as/yr for sources in the range G = 11
- 13, several times the formal EDR3 proper motion uncertainties. We also show
evidence for an additional dependence on the color of the source, with a
magnitude up to 10 {\mu}as/yr. However, a color correction proportional to the
effective wavenumber is unsatisfactory for very red or very blue stars and we
do not recommend its use. We provide a recipe for a magnitude-dependent
correction to align the proper motion of the Gaia EDR3 sources brighter than
G=13 with the International Celestial Reference Frame.


  In this work, we introduce a novel framework which combines physics and
machine learning methods to analyse acoustic signals. Three methods are
developed for this task: a Bayesian inference approach for inferring the
spectral acoustics characteristics, a neural-physical model which equips a
neural network with forward and backward physical losses, and the non-linear
least squares approach which serves as benchmark. The inferred propagation
coefficient leads to the room impulse response (RIR) quantity which can be used
for relocalisation with uncertainty. The simplicity and efficiency of this
framework is empirically validated on simulated data.


  In this paper, we study end-to-end service reliability in Data Center
Networks (DCN) with flow and Service Function Chains (SFCs) parallelism. In our
approach, we consider large flows to i) be split into multiple parallel smaller
sub-flows; ii) SFC along with their VNFs are replicated into at least as many
VNF instances as there are sub-flows, resulting in parallel sub-SFCs; and iii)
all sub-flows are distributed over multiple shortest paths and processed in
parallel by parallel sub-SFCs. We study service reliability as a function of
flow and SFC parallelism and placement of parallel active and backup sub-SFCs
within DCN. Based on the probability theory and by considering both server and
VNF failures, we analytically derive for each studied VNF placement method the
probability that all sub-flows can be successfully processed by the
parallelized SFC without service interruption. We evaluate the amount of backup
VNFs required to protect the parallelized SFC with a certain level of service
reliability. The results show that the proposed flow and SFC parallelism in DCN
can significantly increase end-to-end service reliability, while reducing the
amount of backup VNFs required, as compared to traditional SFCs with serial
traffic flows.


  In this paper, we analyze electroencephalograms (EEG) which are recordings of
brain electrical activity. We develop new clustering methods for identifying
synchronized brain regions, where the EEGs show similar oscillations or
waveforms according to their spectral densities. We treat the estimated
spectral densities from many epochs or trials as functional data and develop
clustering algorithms based on functional data ranking. The two proposed
clustering algorithms use different dissimilarity measures: distance of the
functional medians and the area of the central region. The performance of the
proposed algorithms is examined by simulation studies. We show that, when
contaminations are present, the proposed methods for clustering spectral
densities are more robust than the mean-based methods. The developed methods
are applied to two stages of resting state EEG data from a male college
student, corresponding to early exploration of functional connectivity in the
human brain.


  In the present work, we consider 2-person zero-sum stochastic differential
games with a nonlinear pay-off functional which is defined through a backward
stochastic differential equation. Our main objective is to study for such a
game the problem of the existence of a value without Isaacs condition. Not
surprising, this requires a suitable concept of mixed strategies which, to the
authors' best knowledge, was not known in the context of stochastic
differential games. For this, we consider nonanticipative strategies with a
delay defined through a partition $\pi$ of the time interval $[0,T]$. The
underlying stochastic controls for the both players are randomized along $\pi$
by a hazard which is independent of the governing Brownian motion, and knowing
the information available at the left time point $t_{j-1}$ of the subintervals
generated by $\pi$, the controls of Players 1 and 2 are conditionally
independent over $[t_{j-1},t_j)$. It is shown that the associated lower and
upper value functions $W^{\pi}$ and $U^{\pi}$ converge uniformly on compacts to
a function $V$, the so-called value in mixed strategies, as the mesh of $\pi$
tends to zero. This function $V$ is characterized as the unique viscosity
solution of the associated Hamilton-Jacobi-Bellman-Isaacs equation.


  We investigate continuous-time quantum walks of two indistinguishable anyons
in one-dimensional lattices with both on-site and nearest-neighbor interactions
based on the fractional Jordan-Wigner transformation. It is shown that the
two-body correlations in position space are symmetric about the initial sites
of two quantum walkers in the Bose limit ($\chi=0$ ) and Fermi limit (
$\chi=1$), while in momentum space this happens only in the Bose limit. An
interesting asymmetry arises in the correlation for most cases with the
statistical parameter $\chi$ varying in between. It turns out that the origin
of this asymmetry comes from the fractional statistics that anyons obey. On the
other hand, the two-body correlations of hard-core anyons in position spaceshow
uniform behaviors from anti-bunching to co-walking regardless of the
statistical parameter. The momentum correlations in the case of strong
interaction undergo a smooth process of two stripes smoothly merging into a
single one, i.e. the evolution of fermions into hard-core bosons.


  Text is by far the most ubiquitous source of knowledge and information and
should be made easily accessible to as many people as possible; however, texts
often contain complex words that hinder reading comprehension and
accessibility. Therefore, suggesting simpler alternatives for complex words
without compromising meaning would help convey the information to a broader
audience. This paper proposes mTLS, a multilingual controllable
Transformer-based Lexical Simplification (LS) system fined-tuned with the T5
model. The novelty of this work lies in the use of language-specific prefixes,
control tokens, and candidates extracted from pre-trained masked language
models to learn simpler alternatives for complex words. The evaluation results
on three well-known LS datasets -- LexMTurk, BenchLS, and NNSEval -- show that
our model outperforms the previous state-of-the-art models like LSBert and
ConLS. Moreover, further evaluation of our approach on the part of the recent
TSAR-2022 multilingual LS shared-task dataset shows that our model performs
competitively when compared with the participating systems for English LS and
even outperforms the GPT-3 model on several metrics. Moreover, our model
obtains performance gains also for Spanish and Portuguese.


  We study the Casimir pressure for a dielectric-diamagnetic cylinder subject
to light velocity conservation and with a dispersion law analogous to
Sellmeir's rule. Similarities to and differences from the spherical case are
pointed out.


  We investigate the absorption and transmission properties of a weak probe
field under the influence of a strong control field in a hybrid cavity
magnomechanical system in the microwave regime. This hybrid system consists of
two ferromagnetic material yttrium iron garnet (YIG) spheres strongly coupled
to a single cavity mode. In addition to two magnon-induced transparency (MIT)
that arise due to strong photon-magnon interactions, we observe a
magnomechanically induced transparency (MMIT) due to the presence of nonlinear
phonon-magnon interaction. In addition, we discuss the emergence and tunability
of the multiple Fano resonances in our system. We find that due to strong
photon-magnon coupling the group delay of the probe field can be enhanced
significantly. The subluminal or superluminal propagation depends on the
frequency of the magnons, which can be easily tuned by an external bias
magnetic field. Besides, the group delay of the transmitted field can also be
controlled with the control field power.


  In this article we characterize the complex hyperbolic groups that leave
invariant a copy of the Veronese curve in $\Bbb{P}^2_{\Bbb{C}}$. As a corollary
we get that every discrete compact surface group in $\PO^+(2,1)$ admits a
deformation in $\PSL(3,\Bbb{C})$ with a non-empty region of discontinuity which
is not conjugate to a complex hyperbolic subgroup. This provides a way to
construct new examples of Kleinian groups acting on $\Bbb{P}^2_\Bbb{C}$.


  Understanding the dynamics and evolution of climate change and associated
uncertainties is key for designing robust policy actions. Computer models are
key tools in this scientific effort, which have now reached a high level of
sophistication and complexity. Model auditing is needed in order to better
understand their results, and to deal with the fact that such models are
increasingly opaque with respect to their inner workings. Current techniques
such as Global Sensitivity Analysis (GSA) are limited to dealing either with
multivariate outputs, stochastic ones, or finite-change inputs. This limits
their applicability to time-varying variables such as future pathways of
greenhouse gases. To provide additional semantics in the analysis of a model
ensemble, we provide an extension of GSA methodologies tackling the case of
stochastic functional outputs with finite change inputs. To deal with finite
change inputs and functional outputs, we propose an extension of currently
available GSA methodologies while we deal with the stochastic part by
introducing a novel, domain-selective inferential technique for sensitivity
indices. Our method is explored via a simulation study that shows its
robustness and efficacy in detecting sensitivity patterns. We apply it to real
world data, where its capabilities can provide to practitioners and
policymakers additional information about the time dynamics of sensitivity
patterns, as well as information about robustness.


  We present a quantum embedding method that allows for the calculation of
local excited states embedded in a Kohn-Sham density functional theory (DFT)
environment. Projection-based quantum embedding methodologies provide a
rigorous framework for performing DFT-in-DFT and wave function in DFT
(WF-in-DFT) calculations. The use of absolute localization, where the density
of each subsystem is expanded in only the basis functions associated with the
atoms of that subsystem, provide improved computationally efficiency for
WF-in-DFT calculations by reducing the number of orbitals in the WF
calculation. In this work, we extend absolutely localized projection-based
quantum embedding to study localized excited states using EOM-CCSD-in-DFT and
TDDFT-in-DFT. The embedding results are highly accurate compared to the
corresponding canonical EOM-CCSD and TDDFT results on the full system, with
TDDFT-in-DFT frequently more accurate than canonical TDDFT. The absolute
localization method is shown to eliminate the spurious low-lying excitation
energies for charge transfer states and prevent over delocalization of excited
states. Additionally, we attempt to recover the environment response caused by
the electronic excitations in the high-level subsystem using different schemes
and compare their accuracy. Finally, we apply this method to the calculation of
the excited state energy of green fluorescent protein and show that we
systematically converge to the full system results. Here we demonstrate how
this method can be useful in understanding excited states, specifically which
chemical moieties polarize to the excitation. This work shows absolutely
localized projection-based quantum embedding can treat local electronic
excitations accurately, and make computationally expensive WF methods
applicable to systems beyond current computational limits.


  Motivated by understanding the scattering of gravitons and their
superpartners from extended $(p, q)$-strings in type IIB string theory via
AdS/CFT, we study an integrated two-point function of stress tensor multiplet
operators in the presence of a half-BPS line defect in ${\cal N} = 4$ $SU(N)$
super-Yang-Mills theory. We determine this integrated correlator at the five
lowest non-trivial orders in $1/\sqrt{N}$ at fixed Yang-Mills coupling and
$\theta$ angle. Our calculations are performed explicitly when the line defect
is a Wilson line, in which case we find a finite number of perturbative
contributions at each order in $1/\sqrt{N}$, as well as instanton
contributions. Using $SL(2, \mathbb{Z})$ transformations, our results can also
be applied to Wilson-'t Hooft line defects dual to extended $(p, q)$-strings in
the bulk. We analyze features of these integrated correlators in the weak
coupling expansion by comparing with open-closed amplitudes of type IIB string
theory on $\text{AdS}_5\times \text{S}^5$, as well as in its flat space limit.
We predict new higher-derivative interaction vertices on the D1-brane and, more
generally, on $(p,q)$-strings.


  Medical image processing is one of the most important topics in the field of
the Internet of Medical Things (IoMT). Recently, deep learning methods have
carried out state-of-the-art performances on medical image tasks. However,
conventional deep learning have two main drawbacks: 1) insufficient training
data and 2) the domain mismatch between the training data and the testing data.
In this paper, we propose a distant domain transfer learning (DDTL) method for
medical image classification. Moreover, we apply our methods to a recent issue
(Coronavirus diagnose). Several current studies indicate that lung Computed
Tomography (CT) images can be used for a fast and accurate COVID-19 diagnosis.
However, the well-labeled training data cannot be easily accessed due to the
novelty of the disease and a number of privacy policies. Moreover, the proposed
method has two components: Reduced-size Unet Segmentation model and Distant
Feature Fusion (DFF) classification model. It is related to a not
well-investigated but important transfer learning problem, termed Distant
Domain Transfer Learning (DDTL). DDTL aims to make efficient transfers even
when the domains or the tasks are entirely different. In this study, we develop
a DDTL model for COVID-19 diagnose using unlabeled Office-31, Catech-256, and
chest X-ray image data sets as the source data, and a small set of COVID-19
lung CT as the target data. The main contributions of this study: 1) the
proposed method benefits from unlabeled data collected from distant domains
which can be easily accessed, 2) it can effectively handle the distribution
shift between the training data and the testing data, 3) it has achieved 96\%
classification accuracy, which is 13\% higher classification accuracy than
"non-transfer" algorithms, and 8\% higher than existing transfer and distant
transfer algorithms.


  We gather some classical results and examples that show strict inclusion
between the families of unital rings, rings with enough idempotents, rings with
sets of local units, locally unital rings, s-unital rings and idempotent rings.


  We present limits to the amplitude of non-Gaussian primordial fluctuations in
the WMAP 1-year cosmic microwave background sky maps. A non-linear coupling
parameter, f_NL, characterizes the amplitude of a quadratic term in the
primordial potential. We use two statistics: one is a cubic statistic which
measures phase correlations of temperature fluctuations after combining all
configurations of the angular bispectrum. The other uses the Minkowski
functionals to measure the morphology of the sky maps. Both methods find the
WMAP data consistent with Gaussian primordial fluctuations and establish
limits, -58<f_NL<134, at 95% confidence. There is no significant frequency or
scale dependence of f_NL. The WMAP limit is 30 times better than COBE, and
validates that the power spectrum can fully characterize statistical properties
of CMB anisotropy in the WMAP data to high degree of accuracy. Our results also
validate the use of a Gaussian theory for predicting the abundance of clusters
in the local universe. We detect a point-source contribution to the bispectrum
at 41 GHz, b_src = (9.5+-4.4) X 1e-5 uK^3 sr^2, which gives a power spectrum
from point sources of c_src = (15+-6) X 1e-3 uK^2 sr in thermodynamic
temperature units. This value agrees well with independent estimates of source
number counts and the power spectrum at 41 GHz, indicating that b_src directly
measures residual source contributions.


  We write $S_{\leq n}(A)$ and $\Part_{\fin}(A)$ for the set of permutations
with at most $n$ non-fixed points, where $n$ is a natural number, and the set
of partitions whose members are finite, respectively, of a set $A$. Among our
results, we show, in the Zermelo-Fraenkel set theory, that $|\Part_{\fin}(A)|
\nleq |S_{\leq n}(A)|$ for any infinite set $A$ and if $A$ can be linearly
ordered, then $|S_{\leq n}(A)| < |\Part_{\fin}(A)|$ while the statement
``$|S_{\leq n}(A)|\leq|\Part_{\fin}(A)|$ for all infinite sets $A$" is not
provable for $n\geq 3$.


  With the development of lattice QCD in recent years, a determination of decay
rates for inclusive semileptonic decays from lattice correlators now seems
viable. We report on the calculation of the inclusive semileptonic decay of the
$D_s$ meson on the lattice. We simulate the $D_s \rightarrow X_s\ell\nu_\ell$
process with M\"obius domain-wall charm and strange quarks, whose masses were
approximately tuned to the physical values. We cover the whole kinematical
region. We present a general overview on how the inclusive decays are treated
on the lattice and discuss ongoing challenges, such as the estimation of
systematic errors and future prospects of the project.


  While both outdoor and indoor localization methods are flourishing, how to
properly marry them to offer pervasive localizability in urban areas remains
open. Recently proposals on indoor-outdoor detection make the first step
towards such an integration, yet complicated urban environments render such a
binary classification incompetent. In this paper, we intend to fully explore
raw GNSS measurements in order to better characterize the diversified urban
environments. Essentially, we tackle the challenges introduced by the complex
GNSS data and apply a deep learning model to identify representations for
respective location contexts. We further develop two preliminary applications
of our deep profiling. On one hand, we offer a more fine-grained semantic
classification than binary indoor-outdoor detection. On the other hand, we
derive a GPS error indicator more meaningful than that provided by Google Maps.
These results are all corroborated by our extensive data collection and
trace-driven evaluations.


  The dispersion of electrons and phonons near the K point of bilayer graphene
was investigated in a resonant Raman study using different laser excitation
energies in the near infrared and visible range. The electronic structure was
analyzed within the tight-binding approximation, and the
Slonczewski-Weiss-McClure (SWM) parameters were obtained from the analysis of
the dispersive behavior of the Raman features. A softening of the phonon
branches was observed near the K point, and results evidence the Kohn anomaly
and the importance of considering electron-phonon and electron-electron
interactions to correctly describe the phonon dispersion in graphene systems.


  We obtain an explicit formula for the bilateral counterparty valuation
adjustment of a credit default swaps portfolio referencing an asymptotically
large number of entities. We perform the analysis under a doubly stochastic
intensity framework, allowing for default correlation through a common jump
process. The key insight behind our approach is an explicit characterization of
the portfolio exposure as the weak limit of measure-valued processes associated
to survival indicators of portfolio names. We validate our theoretical
predictions by means of a numerical analysis, showing that counterparty
adjustments are highly sensitive to portfolio credit risk volatility as well as
to default correlation.


  We present the reduced dynamics of a bead in a Rouse chain which is submerged
in a bath containing a driving agent that renders it out-of-equilibrium. We
first review the generalized Langevin equation of the middle bead in an
equilibrated bath. Thereafter, we introduce two driving forces. Firstly, we add
a constant force that is applied to the first bead of the chain. We investigate
how the generalized Langevin equation changes due to this perturbation for
which the system evolves towards a new equilibrium state after some time.
Secondly, we consider the case of stochastic active forces which will drive the
system to a nonequilibrium state. Including these active forces results in a
frenetic contribution to the second fluctuation-dissipation relation, in accord
with a recent extension of the fluctuation-dissipation relation to
nonequilibrium. The form of the frenetic term is analysed for the specific case
of Gaussian, exponentially correlated active forces. We also discuss the
resulting rich dynamics of the middle bead in which various regimes of normal
diffusion, subdiffusion and superdiffusion can be present.


  Because diffusion typically involves symmetric interactions, scant attention
has been focused on studying asymmetric cases. However, important networked
systems underlain by diffusion (e.g. cortical networks and WWW) are inherently
directed. In the case of undirected diffusion, it can be shown that the
steady-state probability of the random walk dynamics is fully correlated with
the degree, which no longer holds for directed networks. We investigate the
relationship between such probability and the inward node degree, which we call
efficiency, in modular networks. Our findings show that the efficiency of a
given community depends mostly on the balance between its ingoing and outgoing
connections. In addition, we derive analytical expressions to show that the
internal degree of the nodes do not play a crucial role in their efficiency,
when considering the Erd\H{o}s-R\'enyi and Barab\'asi-Albert models. The
results are illustrated with respect to the macaque cortical network, providing
subsidies for improving transportation and communication systems.


  We present follow-up X-ray observations of the candidate massive black hole
(BH) in the nucleus of the low-mass, compact starburst galaxy Henize 2-10.
Using new high-resolution observations from the Chandra X-ray Observatory
totaling 200 ks in duration, as well as archival Chandra observations from
2001, we demonstrate the presence of a previously unidentified X-ray point
source that is spatially coincident with the known nuclear radio source in
Henize 2-10 (i.e., the massive BH). We show that the hard X-ray emission
previously identified in the 2001 observation is dominated by a source that is
distinct from the nucleus, with the properties expected for a high-mass X-ray
binary. The X-ray luminosity of the nuclear source suggests the massive BH is
radiating significantly below its Eddington limit (~10^-6 L_Edd), and the soft
spectrum resembles other weakly accreting massive BHs including Sagittarius A*.
Analysis of the X-ray light curve of the nucleus reveals the tentative
detection of a ~9-hour periodicity, although additional observations are
required to confirm this result. Our study highlights the need for sensitive
high-resolution X-ray observations to probe low-level accretion, which is the
dominant mode of BH activity throughout the Universe.


  We report on observations of the two newly-identified reconnection geometries
involving erupting flux ropes. In 3D, a flux rope can reconnect either with a
surrounding coronal arcade (recently named "ar-rf" reconnection) or with itself
("rr-rf" reconnection), and both kinds of reconnection create a new flux rope
field line and a flare loop. For the first time, we identify all four
constituents of both reconnections in a solar eruptive event, the filament
eruption of 2011 June 07 observed by SDO/AIA. The ar-rf reconnection manifests
itself as shift of one leg of the filament by more than 25" northward. At its
previous location, a flare arcade is formed, while the new location of the
filament leg previously corresponded to a footpoint of a coronal loop in 171 A.
In addition, the evolution of the flare ribbon hooks is also consistent with
the occurrence of ar--rf reconnection as predicted by MHD simulations.
Specifically, the growing hook sweeps footpoints of preeruptive coronal
arcades, and these locations become inside the hook. Furthermore, the rr-rf
reconnection occurs during the peak phase above the flare arcade, in an
apparently X-type geometry involving a pair of converging bright filament
strands in the erupting filament. A new flare loop forms near the leg of one of
the strands, while a bright blob, representing a remnant of the same strand, is
seen ascending into the erupting filament. All together, these observations
vindicate recent predictions of the 3D standard solar flare model.


  This work investigates the gapped interfaces of 3+1d fracton phases of matter
using foliated gauge theories and lattice models. We analyze the gapped
boundaries and gapped interfaces in X cube model, and the gapped interfaces
between the X-cube model and the toric code. The gapped interfaces are either
"undecorated" or "decorated", where the "decorated" interfaces have additional
Chern-Simons like actions for foliated gauge fields. We discover many new
gapped boundaries and interfaces, such as (1) a gapped boundary for X-cube
model where the electric lineons orthogonal to the interface become the
magnetic lineons, the latter are the composite of magnetic planons; (2) a
Kramers-Wannier-duality type gapped interface between the X-cube model and the
toric code model from gauging planar subsystem one-form symmetry; and (3) an
electromagnetic duality interface in the X-cube model that exchanges the
electric and magnetic lineons.


  A microscopic theory of spin excitations in strongly-correlated electronic
systems within the t-J model is discussed. An exact representation for the
dynamic spin susceptibility is derived. In the normal state, the excitation
spectrum reveals a crossover from spin-wave-like excitations at low doping to
overdamped paramagnons above the optimal doping. At low temperatures, the
resonance mode at the antiferromagnetic wave vector Q = \pi(1,1) emerges which
is explained by a strong suppression of the spin excitation damping caused by a
spin gap at Q rather than by opening of a superconducting gap. A major role of
spin excitations in the d-wave superconducting pairing in cuprates is stressed
in discussing mechanisms of high-Tc superconductivity within the Hubbard model
in the limit of strong correlations, while electron-phonon interaction and a
well-screened weak Coulomb interaction are not essential.


  The independence polynomial of a graph is the generating polynomial for the
number of independent sets of each size, and its roots are called {\em
independence roots}. We investigate the stability of such polynomials, that is,
conditions under which the roots lie in the left half-plane (all of the real
roots of independence polynomial are negative and hence lie in this
half-plane). We show stability for all independence polynomials of graphs with
independence number at most three, but for larger independence number we show
that the independence polynomials can have roots arbitrarily far to the right.
We provide families of graphs whose independence polynomials are stable and
ones that are not, utilizing various graph operations.


  We show that it is consistent to have an uncountable sequential group of
intermediate sequential order while no countable such groups exist. This is
proved by adding $\omega_2$ Cohen reals to a model of $\diamondsuit$.


  Recently, dimensionality testing of a quantum state has received extensive
attention (Ac{\'i}n et al. Phys. Rev. Letts. 2006, Scarani et al. Phys. Rev.
Letts. 2006). Security proofs of existing quantum information processing
protocols rely on the assumption about the dimension of quantum states in which
logical bits are encoded. However, removing such assumption may cause security
loophole. In the present paper, we show that this is indeed the case. We choose
two players' quantum private query protocol by Yang et al. (Quant. Inf.
Process. 2014) as an example and show how one player can gain an unfair
advantage by changing the dimension of subsystem of a shared quantum system. To
resist such attack we propose dimensionality testing in a different way. Our
proposal is based on CHSH like game. As we exploit CHSH like game, it can be
used to test if the states are product states for which the protocol becomes
completely vulnerable.


  Gauge Theory plays a crucial role in many areas in science, including high
energy physics, condensed matter physics and quantum information science. In
quantum simulations of lattice gauge theory, an important step is to construct
a wave function that obeys gauge symmetry. In this paper, we have developed
gauge equivariant neural network wave function techniques for simulating
continuous-variable quantum lattice gauge theories in the Hamiltonian
formulation. We have applied the gauge equivariant neural network approach to
find the ground state of 2+1-dimensional lattice gauge theory with U(1) gauge
group using variational Monte Carlo. We have benchmarked our approach against
the state-of-the-art complex Gaussian wave functions, demonstrating improved
performance in the strong coupling regime and comparable results in the weak
coupling regime.


  In this paper are defined cohomology-like groups that classify loop
extensions satisfying a given identity in three variables for association
identities, and in two variables for the case of commutativity. It is
considered a large amount of identities. This groups generalize those defined
in works of Nishigori [2] and of Jhonson and Leedham-Green [4]. It is computed
the number of metacyclic extensions for trivial action of the quotient on the
kernel in one particular case for left Bol loops and in general for commutative
loops.


  Web is increasingly becoming the primary platform to deliver AI services onto
edge devices, making in-browser deep learning (DL) inference more prominent.
Nevertheless, the heterogeneity of edge devices, combined with the
underdeveloped state of Web hardware acceleration practices, hinders current
in-browser inference from achieving its full performance potential on target
devices. To address this issue, this paper presents the pioneering inbrowser
inference system, nnJIT, which enables just-in-time (JIT) auto-generation of
optimized computing kernels for edge devices. nnJIT is built upon two novel
techniques that significantly reduce kernel search and compilation overhead
while improving performance firmly: Tensor-Web Compiling Co-Design lowers
compiling costs by around 100X through eliminating redundant and ineffective
compiling passes; Web-Specific Lite Kernel Optimization Space reduces kernel
tuning costs by focusing on Web programming requirements and efficient device
resource utilization, pruning the optimization space from millions to only
dozens. nnJIT is evaluated for modern models, e.g., BART, T5, and Llama 2, on a
range of edge devices including laptops and smartphones using different
browsers and hardware from ARM, Intel, AMD and Nvidia. The results show that
nnJIT can achieve up to 8.2X faster within 30 seconds compared to the existing
baselines.


  We are able to characterize a 2--dimensional classical fluid sharing some of
the same thermodynamic state functions as the Schwarzschild black hole. This
phenomenological correspondence between black holes and fluids is established
by means of the model liquid's pair-correlation function and the two-body
atomic interaction potential. These latter two functions are calculated exactly
in terms of the black hole internal (quasilocal) energy and the isothermal
compressibility. We find the existence of a ``screening" like effect for the
components of the liquid.


  Many real world practical problems can be formulated as
$\ell_{0}$-minimization problems with nonnegativity constraints, which seek the
sparsest nonnegative signals to underdetermined linear systems. They have been
widely applied in signal and image processing, machine learning, pattern
recognition and computer vision. Unfortunately, this $\ell_{0}$-minimization
problem with nonnegativity constraint is computational and NP-hard because of
the discrete and discontinuous nature of the $\ell_{0}$-norm. In this paper, we
replace the $\ell_{0}$-norm with a non-convex fraction function, and study the
minimization problem of this non-convex fraction function in recovering the
sparse nonnegative signals from an underdetermined linear system. Firstly, we
discuss the equivalence between $(P_{0}^{\geq})$ and $(FP_{a}^{\geq})$, and the
equivalence between $(FP_{a}^{\geq})$ and $(FP_{a,\lambda}^{\geq})$. It is
proved that the optimal solution of the problem $(P_{0}^{\geq})$ could be
approximately obtained by solving the regularization problem
$(FP_{a,\lambda}^{\geq})$ if some specific conditions satisfied. Secondly, we
propose a nonnegative iterative thresholding algorithm to solve the
regularization problem $(FP_{a,\lambda}^{\geq})$ for all $a>0$. Finally, some
numerical experiments on sparse nonnegative siganl recovery problems show that
our method performs effective in finding sparse nonnegative signals compared
with the linear programming.


  In this paper, we define a generalization of the Brauer groups by using
Bloch's cycle complex on etale site. We prove the Gersten conjecture of
generalized Brauer group on some cases. As an application we prove the Gersten
conjecture of the logarithmic Hodge-Witt cohomology for a two dimensional
regular local ring which is smooth over a spectrum of some discrete valuation
ring of characteristic $p>0$. Moreover we consider a generalization of Artin's
theorem on Brauer groups.


  In this paper, we introduce our work of building a Streaming Multilingual
Speech Model (SM2), which can transcribe or translate multiple spoken languages
into texts of the target language. The backbone of SM2 is Transformer
Transducer, which has high streaming capability. Instead of human labeled
speech translation (ST) data, SM2 models are trained using weakly supervised
data generated by converting the transcriptions in speech recognition corpora
with a machine translation service. With 351 thousand hours of anonymized
speech training data from 25 languages, SM2 models achieve comparable or even
better ST quality than some recent popular large-scale non-streaming speech
models. More importantly, we show that SM2 has the truly zero-shot capability
when expanding to new target languages, yielding high quality ST results for
{source-speech, target-text} pairs that are not seen during training.


  Using the classical universal coefficient theorem of Rosenberg-Schochet, we
prove a simple classification of all localizing subcategories of the Bootstrap
category of separable complex C*-algebras. Namely, they are in bijective
correspondence with subsets of the Zariski spectrum of the integers --
precisely as for the localizing subcategories of the derived category of
complexes of abelian groups. We provide corollaries of this fact and put it in
context with similar classifications available in the literature.


  The detection of gravity plays a fundamental role during the growth and
evolution of plants. Although progress has been made in our understanding of
the molecular, cellular and physical mechanisms involved in the gravity
detection, a coherent scenario consistent with all the observations is still
lacking. In this perspective paper we discuss recent experiments showing that
the response to inclination of shoots is independent of the gravity intensity,
meaning that the gravity sensor detects an inclination and not a force. This
result questions some of the commonly accepted hypotheses and leads to propose
a new "position sensor hypothesis". The implications of this new scenario are
discussed in the light of different observations available in the literature.


  We have demonstrated the fabrication of a monolithic, 5-meter diameter,
aluminum reflector with 17.4 $\mu$m RMS surface error. The reflector was
designed to avoid the problem of pickup due to scattering from panel gaps in a
large, millimeter-wavelength telescope that will be used for measurements on
the cosmic microwave background.


  Graphs can be associated with a matrix according to some rule and we can find
the spectrum of a graph with respect to that matrix. Two graphs are cospectral
if they have the same spectrum. Constructions of cospectral graphs help us
establish patterns about structural information not preserved by the spectrum.
We generalize a construction for cospectral graphs previously given for the
distance Laplacian matrix to a larger family of graphs. In addition, we show
that with appropriate assumptions this generalized construction extends to the
adjacency matrix, combinatorial Laplacian matrix, signless Laplacian matrix,
normalized Laplacian matrix, and distance matrix.


  A measurement of the $ZZ$ production in the $\ell^{-}\ell^{+}\ell^{\prime
-}\ell^{\prime +}$ and $\ell^{-}\ell^{+}\nu\bar{\nu}$ channels $(\ell = e,
\mu)$ in proton--proton collisions at $\sqrt{s} = 8$ TeV at the Large Hadron
Collider at CERN, using data corresponding to an integrated luminosity of 20.3
fb$^{-1}$ collected by the ATLAS experiment in 2012 is presented. The fiducial
cross sections for $ZZ\to\ell^{-}\ell^{+}\ell^{\prime -}\ell^{\prime +}$ and
$ZZ\to \ell^{-}\ell^{+}\nu\bar{\nu}$ are measured in selected phase-space
regions. The total cross section for $ZZ$ events produced with both $Z$ bosons
in the mass range 66 to 116 GeV is measured from the combination of the two
channels to be
$7.3\pm0.4\textrm{(stat)}\pm0.3\textrm{(syst)}\pm0.2\textrm{(lumi)}$ pb, which
is consistent with the Standard Model prediction of $6.6^{+0.7}_{-0.6}$ pb. The
differential cross sections in bins of various kinematic variables are
presented. The differential event yield as a function of the transverse
momentum of the leading $Z$ boson is used to set limits on anomalous neutral
triple gauge boson couplings in $ZZ$ production.


  We present results from a wide-field imaging campaign at the
Canada-France-Hawaii Telescope to study the spectacular outburst of comet
17P/Holmes in late 2007. Using image-processing techniques we probe inside the
spherical dust coma and find sixteen fragments having both spatial distribution
and kinematics consistent with isotropic ejection from the nucleus. Photometry
of the fragments is inconsistent with scattering from monolithic, inert bodies.
Instead, each detected fragment appears to be an active cometesimal producing
its own dust coma. By scaling from the coma of the primary nucleus of
17P/Holmes, assumed to be 1.7 km in radius, we infer that the sixteen fragments
have maximum effective radii between ~ 10 m and ~ 100 m on UT 2007 Nov. 6. The
fragments subsequently fade at a common rate of ~ 0.2 mag/day, consistent with
steady depletion of ices from these bodies in the heat of the Sun. Our
characterization of the fragments supports the hypothesis that a large piece of
material broke away from the nucleus and crumbled, expelling smaller, icy
shards into the larger dust coma around the nucleus.


  In a galaxy merger, the stars tidally stripped from the satellite and
accreted onto the host galaxy undergo phase mixing and form finely-grained
structures in the phase space. However, these fragile structures may be
destroyed in the subsequent galaxy evolution, in particular, by a rotating bar
that appears well after the merger is completed. In this work, we investigate
the survivability of phase-space structures in the presence of a bar. We find
that a bar with amplitude and pattern speed similar to those of the Milky Way
would blur and destroy a substantial amount of the substructure that consists
of particles with pericentre radii comparable to the bar length. While this
appears to be in tension with the recent discovery of phase-space chevrons in
\textit{Gaia} DR3 data, the most prominent chevrons in our simulations can
still be recovered when applying the same analysis procedure as in
observations. Moreover, the smoothing effect is less pronounced in the
population of stars whose angular momenta have the opposite sign to the bar
pattern speed.


  We show that in supersymmetric models with a gauged flavor symmetry
explaining the hierarchy of fermion masses and mixings, the electron, muon,
neutron and the deuteron acquire sizable electric dipole moments (EDM) through
loop diagrams involving the flavor gaugino/gauge boson near the Planck scale.
These EDMs are proportional to the phases in the fermion Yukawa couplings and
are typically much larger than the neutrino seesaw induced EDM for the leptons.
In a popular class of models based on anomalous U(1) flavor symmetry of string
origin, we find d_e ~ (10^{-26}-10^{-27}) e cm, d_\mu ~ (10^{-24}-10^{-26}) e
cm, d_n ~ 10^{-27} e cm, and d_D ~ (10^{-26}-10^{-27}) e cm, which are within
reach of next generation experiments.


  Laparoscopic surgery uses a thin tube with a camera called a laparoscope,
which is inserted into the abdomen through a small incision in the skin during
surgery. This allows to a surgeon to see inside of the body without causing
significant injury to the patient. These characteristics make laparoscopy a
widely used technique. In laparoscopic surgery, image quality can be severely
degraded by surgical smoke caused by the use of tissue dissection tools which
reduces the visibility of the observed organs and tissues. This lack of
visibility increases the possibility of errors and surgery time with the
consequences that this may have on the patient's health. In this paper, we
introduce a novel hybrid approach for computational smoke removal which is
based on the combination of a widely dehazing method used: the dark channel
prior (DCP) and a pixel-to-pixel neural network approach: Generative Adversary
Network (GAN). The experimental results have proven that the proposed method
achieves a better performance than the individual results of the DCP and GAN in
terms of restoration quality, obtaining a PSNR value of 25 and SSIM index of
0.88 over a test set of synthetic images.


  Although entanglement is a key resource for quantum-enhanced metrology, not
all entanglement is useful. For example in the process of many-body
thermalisation, bipartite entanglement grows rapidly, naturally saturating to a
volume law. This type of entanglement generation is ubiquitous in nature but
has no known application in most quantum technologies. The generation,
stabilisation and exploitation of genuine multipartite entanglement, on the
other hand, is far more elusive yet highly desirable for metrological
applications. Recently it has been shown that quantum many-body scars can have
extensive multipartite entanglement. However the accessibility of this
structure for real application has been so far unclear. In this work, we show
how systems containing quantum many-body scars can be used to dynamically
generate stable multipartite entanglement, and describe how to exploit this
structure for phase estimation with a precision that beats the standard quantum
limit. Key to this is a physically motivated modification of a Hamiltonian that
generates a variety of multipartite entangled states through the dynamics in
the scar subspace.


  Let $A$ be a countable and discrete subset of ${\Bbb R}^d$, $d \ge 2$, of
positive upper Beurling density. Let $K$ denote a bounded symmetric convex set
with a smooth boundary and everywhere non-vanishing Gaussian curvature. It is
known that ${\mathcal E}(A)=\{e^{2 \pi i x \cdot a}\}_{a \in A}$ cannot serve
as an orthogonal basis for $L^2(K)$ \cite{IKT01}. In this paper, we prove that
even approximate average orthogonality is an obstacle to the existence of an
exponential frame in the following sense. Let $A$ be as above and $\phi \ge 0$
be a continuous monotonically nonincreasing function on $[0, \infty)$ such that
the approximate orthogonality condition holds \begin{align}\notag {\left(
\frac{1}{2^j} \int_{2^j}^{2^{j+1}} \phi^p(t) dt \right)}^{1/p} \leq c_j
2^{-j\frac{d+1}{2}} \quad \text{and} \quad |\widehat{\chi}_K(a-a')| \leq
\phi(\rho^*(a-a')) \ \forall a \not=a , a,a' \in A, \end{align} where $\rho^*$
is the Minkowski functional on $K^*$, the dual body of $K$. Then, if
$$\limsup_{j \to \infty} c_j=0,$$ then the upper density of $A$ is equal to
$0$, hence ${\mathcal E}(A)$ is not a frame for $L^2(K)$. The case $p=\infty$
was previously established by the authors of this paper in \cite{IM2020}. The
point is that if ${\mathcal E}(A)$ is a frame for $L^2(K)$, then very few pairs
of distinct exponentials $e^{2 \pi i x.a}, e^{2 \pi i x.a'}$ from ${\mathcal
E}(A)$ come anywhere near being orthogonal. Our proof uses a generalization of
Bourgain's result on pinned distances determined by sets of positive Lebesgue
upper density in ${\Bbb R}^d$, $d \ge 2$. We also improve the $L^{\infty}$
version of this result originally established in \cite{IM2020}. By using an
extension of the combinatorial idea from \cite{IR03}, we prove that under the
$L^{\infty}$ hypothesis, $A$ is finite if $d \not=1 \mod 4$. If $d=1$ mod $4,
A$ may be infinite, but if it is, then it must be a subset of a line.


  We describe a simple realization of Fizeau's "aether-drag" experiment. Using
an inexpensive setup, we measure the phase shift induced by moving water in a
laser interferometer and find good agreement with the relativistic prediction
or, in the terms of nineteenth century physics, with Fresnel's partial-drag
theory. This appealing experiment, particularly suited for an undergraduate
laboratory project, not only allows a quantitative measurement of a
relativistic effect on a macroscopic system, but also constitutes a practical
application of important concepts of optics, data acquisition and processing,
and fluid mechanics.


  The mass of axion dark matter is only weakly bounded by cosmological
observations, necessitating a variety of detection techniques over several
orders of magnitude of mass ranges. Axions haloscopes based on resonant
cavities have become the current standard to search for dark matter axions.
Such structures are inherently narrowband and for low masses the volume of the
required cavity becomes prohibitively large. Broadband low-mass detectors have
already been proposed using inductive magnetometer sensors and a gapped
toroidal solenoid magnet. In this work we propose an alternative, which uses
electric sensors in a conventional solenoidal magnet aligned in the laboratory
z-axis, as implemented in standard haloscope experiments. In the presence of
the DC magnetic field, the inverse Primakoff effect causes a time varying
permanent electric vacuum polarization in the z-direction to oscillate at the
axion Compton frequency, which induces an oscillating electromotive force. We
propose non-resonant techniques to detect this oscillating elctromotive force
by implementing a capacitive sensor or an electric dipole antenna coupled to a
low noise amplifier. We present the first experimental results and discuss the
foundations and potential of this proposal. Preliminary results constrain
$g_{a\gamma\gamma} >\sim2.35\times10^{-12}$ $\text{GeV}^{-1}$ in the mass range
of $2.08\times10^{-11}$ to $2.2\times10^{-11}$ eV, and demonstrate potential
sensitivity to axion-like dark matter with masses in the range of $10^{-12}$ to
$10^{-8}$ eV.


  The paper is devoted to the study of the twice epi-differentiablity of
extended-real-valued functions, with an emphasis on functions satisfying a
certain composite representation. This will be conducted under the parabolic
regularity, a second-order regularity condition that was recently utilized in
[13] for second-order variational analysis of constraint systems. Besides
justifying the twice epi-differentiablity of composite functions, we obtain
precise formulas for their second subderivatives under the metric subregularity
constraint qualification. The latter allows us to derive second-order
optimality conditions for a large class of composite optimization problems.


  In this paper we explore few-shot imitation learning for control problems,
which involves learning to imitate a target policy by accessing a limited set
of offline rollouts. This setting has been relatively under-explored despite
its relevance to robotics and control applications. State-of-the-art methods
developed to tackle few-shot imitation rely on meta-learning, which is
expensive to train as it requires access to a distribution over tasks (rollouts
from many target policies and variations of the base environment). Given this
limitation we investigate an alternative approach, fine-tuning, a family of
methods that pretrain on a single dataset and then fine-tune on unseen
domain-specific data. Recent work has shown that fine-tuners outperform
meta-learners in few-shot image classification tasks, especially when the data
is out-of-domain. Here we evaluate to what extent this is true for control
problems, proposing a simple yet effective baseline which relies on two stages:
(i) training a base policy online via reinforcement learning (e.g. Soft
Actor-Critic) on a single base environment, (ii) fine-tuning the base policy
via behavioral cloning on a few offline rollouts of the target policy. Despite
its simplicity this baseline is competitive with meta-learning methods on a
variety of conditions and is able to imitate target policies trained on unseen
variations of the original environment. Importantly, the proposed approach is
practical and easy to implement, as it does not need any complex meta-training
protocol. As a further contribution, we release an open source dataset called
iMuJoCo (iMitation MuJoCo) consisting of 154 variants of popular OpenAI-Gym
MuJoCo environments with associated pretrained target policies and rollouts,
which can be used by the community to study few-shot imitation learning and
offline reinforcement learning.


  Using Planck satellite data, we construct SZ gas pressure profiles for a
large, volume-complete sample of optically selected clusters. We have defined a
sample of over 8,000 redMaPPer clusters from the Sloan Digital Sky Survey
(SDSS), within the volume-complete redshift region 0.100 < z < 0.325, for which
we construct Sunyaev-Zel'dovich (SZ) effect maps by stacking Planck data over
the full range of richness. Dividing the sample into richness bins we
simultaneously solve for the mean cluster mass in each bin together with the
corresponding radial pressure profile parameters, employing an MCMC analysis.
These profiles are well detected over a much wider range of cluster mass and
radius than previous work, showing a clear trend towards larger break radius
with increasing cluster mass. Our SZ-based masses fall ~24% below the
mass-richness relations from weak lensing, in a similar fashion as the
"hydrostatic bias" related with X-ray derived masses. We correct for this bias
to derive an optimal mass-richness relation finding a slope 1.22 +/- 0.04 and a
pivot mass log(M_500/M_0)= 14.432 +/- 0.041, evaluated at a richness lambda=60.
Finally, we derive a tight Y_500-M_500 relation over a wide range of cluster
mass, with a power law slope equal to 1.72 +/- 0.07, that agrees well with the
independent slope obtained by the Planck team with an SZ-selected cluster
sample, but extends to lower masses with higher precision.


  We prove some sharp systolic inequalities for compact $3$-manifolds with
boundary. They relate the (relative) homological systoles of the manifold to
its scalar curvature and mean curvature of the boundary. In the equality case,
the universal cover of the manifold is isometric to a cylinder over a disk of
nonnegative constant curvature.


  We study the process $D^0 \to \pi^+ \pi^- f_0(980),\ f_0 \to \pi^+ \pi^-$ by
introducing the triangle mechanism, in which $f_0(980)$ is considered to be
dynamically generated from the meson-meson interaction. For the total
contribution of this process, the contribution of the triangular loop formed by
$K^{*} \bar{K} K$ particles could generate a triangular singularity of about
1418 MeV. We calculate the differential decay width of this process and show a
narrow peak of about 980 MeV in the $\pi^+ \pi^-$ invariant mass distribution,
which comes from $f_0$ decay. For the $M_{inv}(\pi f_0)$ invariant mass
distribution, we obtain a finite peak at 1418 MeV, which is consistent with the
triangle singularity.


  In this paper, we report an interesting kinematic phenomenon around the
halos' edge related to the splashback radius. After the shell-crossing, cosmic
flow exhibits various rotational morphologies via stream-mixing. Vorticity is
generated in a particular way that coincides with the large-scale structure.
Notably, one specific flow morphology, which is spiraling inward and
compressing in the third direction, concentrates around halos. A detailed
examination that reveals a sharp change in the logarithmic derivative of its
volume fraction, coincides with the location of the splashback radius defined
as the outermost caustic structure. Such a feature encodes valuable phase space
information and provides a new perspective on understanding the dynamical
evolution of halos. As a volume-weighted quantity, the profile of flow
morphology is purely kinematic. And unlike other related studies, the
rotational flow morphologies capture the anisotropic phase structure in the
multi-stream region.


  We give an exposition of Delzant's ideas extending the notion of Scott
complexity of finitely generated groups to surjective homomorphisms of finitely
presented groups to finitely generated groups.


  In this paper the shortest path and the minimum spanning tree problems in a
graph with $n$ nodes and $K$ cost scenarios (objectives) are discussed. In
order to choose a solution the min-max criterion is applied. The minmax
versions of both problems are hard to approximate within $O(\log^{1-\epsilon}
K)$ for any $\epsilon>0$. The best approximation algorithm for the min-max
shortest path problem, known to date, has approximation ratio of $K$. On the
other hand, for the min-max spanning tree, there is a randomized algorithm with
approximation ratio of $O(\log^2 n)$. In this paper a deterministic
$O(\sqrt{n\log K/\log\log K})$-approximation algorithm for min-max shortest
path is constructed. For min-max spanning tree a deterministic $O(\log n \log
K/\log\log K)$-approximation algorithm is proposed, which works for a large
class of graphs and a randomized $O(\log n)$-approximation algorithm, which can
be applied to all graphs, is constructed. It is also shown that the
approximation ratios obtained are close to the integrality gaps of the
corresponding LP relaxations.


  Strong nonlinear elastic effects arise due to the Lifshitz transition
associated with a change in the Fermi surface topology. It is shown that an
electronic contribution to the elastic characteristics of cuprates becomes
important if the chemical potential is near but not too near the Van Hove
critical energy $\epsilon_c$. In the case of a saddle point singularity in the
energy spectrum an enhancement factor arises for the "electronic" anharmonic
constants of the 3rd and 4th order, that is proportional to
$\epsilon_F/(\epsilon_F-\epsilon_c)$ to the 1st and 2nd power, respectively.
The behaviour of the Grueneisen coefficient in the vicinity of the Lifshitz
transition is considered.


  We discuss the problem of designing unambiguous programmable discriminators
for any n unknown quantum states in an m-dimensional Hilbert space. The
discriminator is a fixed measurement that has two kinds of input registers: the
program registers and the data register. The quantum state in the data register
is what users want to identify, which is confirmed to be among the n states in
program registers. The task of the discriminator is to tell the users which
state stored in the program registers is equivalent to that in the data
register. First, we give a necessary and sufficient condition for judging an
unambiguous programmable discriminator. Then, if $m=n$, we present an optimal
unambiguous programmable discriminator for them, in the sense of maximizing the
worst-case probability of success. Finally, we propose a universal unambiguous
programmable discriminator for arbitrary n quantum states.


  Effective Lagrangians with dimension-six operators are widely used to analyse
Higgs and other electroweak data. We show how to build a basis of operators
such that each operator corresponds to a coupling which is well measured or
will be in the future. We choose a set of couplings such that the
correspondence is one-to-one. In our framework, some important features of the
Lagrangian are transparent. For example, one can clearly see the presence or
absence of correlations among measurable quantities. This may be a useful guide
when searching for physics beyond the Standard Model.


  In this paper, we extend the work of Pimentel et al. (2015) and propose an
adjusted estimator of Kendall's $\tau$ for bivariate zero-inflated count data.
We provide achievable lower and upper bounds of our proposed estimator and show
its relationship with current literature. In addition, we also suggest an
estimator of the achievable bounds, thereby helping practitioners interpret the
results while working with real data. The performance of the proposed estimator
for Kendall's $\tau$ is unbiased with smaller mean squared errors compared to
the unadjusted estimator of Pimentel et al. (2015). Our results also show that
the bound estimator can be used when knowledge of the marginal distributions is
lacking.


  To any metric space it is possible to associate the cardinal invariant
corresponding to the least number of rectifiable curves in the space whose
union is not meagre. It is shown that this invariant can vary with the metric
space considered, even when restricted to the class of convex subspaces of
separable Banach spaces. As a corollary it is obtained that it is consistent
with set theory that that any set of reals of size $\aleph_1$ is meagre yet
therer are $\aleph_1$ rectifiable curves in $\Reals^3$ whose union is not
meagre. The consistency of this statement when the phrase ``rectifiable
curves'' is replaced by ``straight lines'' remains open.


  Owing to its unparalleled tractability, the Poisson point process (PPP) has
emerged as a popular model for the analysis of cellular networks. Considering a
stationary point process of users, which is independent of the base station
(BS) point process, it is well known that the typical user does not lie in the
typical cell and thus it may not truly represent the typical cell performance.
Inspired by this observation, we present a construction that allows a direct
characterization of the downlink performance of the typical cell. For this, we
present an exact downlink analysis for the 1-D case and a remarkably accurate
approximation for the 2-D case. Several useful insights about the differences
and similarities in the two viewpoints (typical user vs. typical cell) are also
provided.


  We consider Gribov's equation for inverse quark Green function with and
without pion correction. With polar parametrization of inverse quark Green
function, we relate the dynamical mass function without pion correction,
$M_{0}(q^2)$ and with pion correction, $M(q^2)$ at low momentum. A graph is
plotted for $M(q^2)$ and $M_{0}(q^2)$ with q for low momentum. It is found that
at low momenta pion corrections are small.


  Annotators' sociodemographic backgrounds (i.e., the individual compositions
of their gender, age, educational background, etc.) have a strong impact on
their decisions when working on subjective NLP tasks, such as toxic language
detection. Often, heterogeneous backgrounds result in high disagreements. To
model this variation, recent work has explored sociodemographic prompting, a
technique, which steers the output of prompt-based models towards answers that
humans with specific sociodemographic profiles would give. However, the
available NLP literature disagrees on the efficacy of this technique - it
remains unclear for which tasks and scenarios it can help, and the role of the
individual factors in sociodemographic prompting is still unexplored. We
address this research gap by presenting the largest and most comprehensive
study of sociodemographic prompting today. We analyze its influence on model
sensitivity, performance and robustness across seven datasets and six
instruction-tuned model families. We show that sociodemographic information
affects model predictions and can be beneficial for improving zero-shot
learning in subjective NLP tasks. However, its outcomes largely vary for
different model types, sizes, and datasets, and are subject to large variance
with regards to prompt formulations. Most importantly, our results show that
sociodemographic prompting should be used with care for sensitive applications,
such as toxicity annotation or when studying LLM alignment. Code and data:
https://github.com/UKPLab/arxiv2023-sociodemographic-prompting


  We present an algorithm for detecting basepoints of linear series of curves
in the plane. Moreover, we give an algorithm for constructing a linear series
of curves in the plane for given basepoints. The underlying method of these
algorithms is the classical procedure of blowing up points in the plane. We
motivate the algorithmic version of this procedure with several applications.


  This paper deals with the formulation, calibration, and validation of a
Lattice Discrete Particle Model (LDPM) for the simulation of the
pressure-dependent inelastic response of granular rocks. LDPM is formulated in
the framework of discrete mechanics and it simulates the heterogeneous
deformation of cemented granular systems by means of discrete
compatibility/equilibrium equations defined at the grain scale. A numerical
strategy is proposed to generate a realistic microstructure based on the actual
grain size distribution of a sandstone and the capabilities of the method are
illustrated with reference to the particular case of Bleurswiller sandstone,
i.e. a granular rock that has been extensively studied at the laboratory scale.
LDPM micromechanical parameters are calibrated based on evidences from triaxial
experiments, such as hydrostatic compression, brittle failure at low
confinement and plastic behavior at high confinement. Results show that LDPM
allows exploring the effect of fine-scale heterogeneity on the inelastic
response of rock cores, achieving excellent quantitative performance across a
wide range of stress conditions. In addition, LDPM simulations demonstrate its
capability of capturing different modes of strain localization within a unified
mechanical framework, which makes this approach applicable for a wide variety
of geomechanical settings. Such promising performance suggests that LDPM may
constitute a viable alternative to existing discrete numerical methods for
granular rocks, as well as a versatile tool for the interpretation of their
complex deformation/failure patterns and for the development of continuum
models capturing the effect of micro-scale heterogeneity.


  We propose a classification of the solutions of the graded reflection
equations to the $U_{q}[spo(2n|2m)]$ vertex model. We find twelve distinct
classes of reflection matrices such that four of them are diagonal. In the
non-diagonal matrices the number of free parameters depending on the number of
bosonic ($2n$) and fermionic ($2m$) degrees of freedom while in the diagonal
ones we find solutions with at most one free parameter.


  Optimal resource allocation (RA) in massive carrier aggregation scenarios is
a challenging combinatorial optimization problem whose dimension is
proportional to the number of users, component carriers (CCs), and OFDMA
resource blocks per CC. Towards scalable, near-optimal RA in massive CA
settings, an iterative RA algorithm is proposed for joint assignment of CCs and
OFDMA resource blocks to users. The algorithm is based on the principle of
successive geometric programming approximations and has a complexity that
scales only linearly with the problem dimension. Although its derivation is
based on a relaxed formulation of the RA problem, the algorithm is shown to
converge to integer-valued RA variables with probability 1 under mild
assumptions on the distribution of user utilities. Simulations demonstrate
improved performance of the proposed algorithm compared to commonly considered
heuristic RA procedures of comparable complexity.


  We present i and z photometry for 25 T dwarfs and one L dwarf. Combined with
published photometry, the data show that the i - z, z - Y and z - J colors of T
dwarfs are very red, and continue to increase through to the late-type T
dwarfs, with a hint of a saturation for the latest types with T_eff ~ 600 K. We
present new 0.7-1.0 um and 2.8-4.2 um spectra for the very late-type T dwarf
UGPS J072227.51-054031.2, as well as improved astrometry for this dwarf.
Examination of the spectral energy distribution using the new and published
data, with Saumon & Marley models, shows that the dwarf has T_eff = 505 +/- 10
K, a mass of 3-11 M_Jupiter and an age between 60 Myr and 1 Gyr. This young age
is consistent with the thin disk kinematics of the dwarf. The mass range
overlaps with that usually considered to be planetary, despite this being an
unbound object discovered in the field near the Sun. This apparently young
rapid rotator is also undergoing vigorous atmospheric mixing, as determined by
the IRAC and WISE-2 4.5 um photometry and the Saumon & Marley models. The
optical spectrum for this 500 K object shows clearly detected lines of the
neutral alkalis Cs and Rb, which are emitted from deep atmospheric layers with
temperatures of 900-1200 K.


  Large Multimodal Models (LMMs) such as GPT-4V and LLaVA have shown remarkable
capabilities in visual reasoning with common image styles. However, their
robustness against diverse style shifts, crucial for practical applications,
remains largely unexplored. In this paper, we propose a new benchmark,
BenchLMM, to assess the robustness of LMMs against three different styles:
artistic image style, imaging sensor style, and application style, where each
style has five sub-styles. Utilizing BenchLMM, we comprehensively evaluate
state-of-the-art LMMs and reveal: 1) LMMs generally suffer performance
degradation when working with other styles; 2) An LMM performs better than
another model in common style does not guarantee its superior performance in
other styles; 3) LMMs' reasoning capability can be enhanced by prompting LMMs
to predict the style first, based on which we propose a versatile and
training-free method for improving LMMs; 4) An intelligent LMM is expected to
interpret the causes of its errors when facing stylistic variations. We hope
that our benchmark and analysis can shed new light on developing more
intelligent and versatile LMMs.


  We propose the use of microcanonical analyses for numerical studies of
peptide aggregation transitions. Performing multicanonical Monte Carlo
simulations of a simple hydrophobic-polar continuum model for interacting
heteropolymers of finite length, we find that the microcanonical entropy
behaves convex in the transition region, leading to a negative microcanonical
specific heat. As this effect is also seen in first-order-like transitions of
other finite systems, our results provide clear evidence for recent hints that
the characterisation of phase separation in first-order-like transitions of
finite systems profits from this microcanonical view.


  In this paper, we investigate the strong cosmic censorship conjecture (SCC)
for charged black holes in the de Sitter space by considering the weak gravity
conjecture (WGC). Using analytical methods, we find that the SCC is preserved
for dS-charged black holes with respect to some restriction $qQ\gg1$ and
$r_+\geq Q$ with the help of the WGC condition viz $\frac{q}{m}\geq 1$ for
scalar fields. Where q, m are the charge and mass of the scalar field, and
$r_+$, Q determine the radius of the outer event horizon and the charge of the
black hole, respectively. In that case, when the (WGC) is valid, SCC will
definitely be satisfied for the dS-charged black holes. On the other hand, the
SCC is violated when the WGC is not satisfied. Also, we examined the RN-dS
charged black hole in the extremality state and found that SCC can be violated
with the condition $\Lambda r_+^2=1$.


  We present multi-instruments and multi-wavelengths observations of the famous
LBV star Pistol Star. These observations are part of a larger program about
early O stars at different metallicities. The Pistol star has been claimed as
the most massive star known, with 250 solar masses. We present the preliminary
results based on X-Shooter spectra, as well as the observations performed with
the VLTI-AMBER and the VLT-NACO adaptive optics. The X-shooter spectrograph
allows to obtain simultaneously a spectrum from the UV to the K-band with a
resolving power of $\sim$15000. The preliminary results obtained indicate that
Pistol Star has similar properties of Eta Car, including shells of matter, but
also the binarity. Other objects of the program, here briefly presented, were
selected for their particular nature: early O stars with mass discrepancies
between stellar evolution models and observations, discrepancies with the wind
momentum luminosity relation.


  We study open string mirror symmetry for one-parameter Calabi-Yau
hypersurfaces in weighted projective space. We identify mirror pairs of D-brane
configurations, derive the corresponding inhomogeneous Picard-Fuchs equations,
and solve for the domainwall tensions as analytic functions over moduli space.
Our calculations exemplify several features that had not been seen in previous
work on the quintic or local Calabi-Yau manifolds. We comment on the
calculation of loop amplitudes.


  We show that the two molecular mechanics models, the stick-spiral and the
beam models, predict considerably different mechanical properties of materials
based on energy equivalence. The difference between the two models is
independent of the materials since all parameters of the beam model are
obtained from the harmonic potentials. We demonstrate this difference for
finite width graphene nanoribbons and a single polyethylene chain comparing
results of the molecular dynamics (MD) simulations with harmonic potentials and
the finite element method with the beam model. We also find that the difference
strongly depends on the loading modes, chirality and width of the graphene
nanoribbons, and it increases with decreasing width of the nanoribbons under
pure bending condition. The maximum difference of the predicted mechanical
properties using the two models can exceed 300% in different loading modes.
Comparing the two models with the MD results of AIREBO potential, we find that
the stick-spiral model overestimates and the beam model underestimates the
mechanical properties in narrow armchair graphene nanoribbons under pure
bending condition.


  On the night of Oct 18/19, 2012 at 00:23 UT a -14.7 mag Orionid fireball
occurred over northeastern Poland. The precise orbit and atmospheric trajectory
of the event is presented, based on the data collected by five video and one
photographic Polish Fireball Network (PFN) stations. The beginning height of
the meteor is 168.4 +\- 0.6 km which makes the PF191012 Myszyniec fireball the
highest ever observed, well documented meteor not belonging to the Leonid
shower. The ablation became the dominant source of light of the meteor at a
height of around 115 km. The thermalization of sputtered particles is suggested
to be the source of radiation above that value. The transition height of 115 km
is 10-15 km below the transition heights derived for Leonids and it might
suggest that the material of Leonids should be more fragile and have probably
smaller bulk density than in case of Orionids.


  On the basis of our previous studies of magnetoelectric ordering of BiFeO3,
TbMnO3, TbMn2O5 and BiMn2O5 we formulate the crystal chemistry criteria for the
search of multiferroics and reveal potential multiferroics Pb2Cu(OH)4Cl2,
Pb5Cr3F19, Mn(SeO3){\dot}H2O and BiPbSr2MnO6 each containing the ion with a
lone pair.


  We resum the leading logarithms $\alpha_s^n \ln^{2 n-1}(1-z)$, $n=1,2,\ldots$
near the kinematic threshold $z=Q^2/\hat{s}\to 1$ of the Drell-Yan process at
next-to-leading power in the expansion in $(1-z)$. The derivation of this
result employs soft-collinear effective theory in position space and the
anomalous dimensions of subleading-power soft functions, which are computed.
Expansion of the resummed result leads to the leading logarithms at fixed loop
order, in agreement with exact results at NLO and NNLO and predictions from the
physical evolution kernel at N$^3$LO and N$^4$LO, and to new results at the
five-loop order and beyond.


  Concept map is a graphical tool for representing knowledge. They have been
used in many different areas, including education, knowledge management,
business and intelligence. Constructing of concept maps manually can be a
complex task; an unskilled person may encounter difficulties in determining and
positioning concepts relevant to the problem area. An application that
recommends concept candidates and their position in a concept map can
significantly help the user in that situation. This paper gives an overview of
different approaches to automatic and semi-automatic creation of concept maps
from textual and non-textual sources. The concept map mining process is
defined, and one method suitable for the creation of concept maps from
unstructured textual sources in highly inflected languages such as the Croatian
language is described in detail. Proposed method uses statistical and data
mining techniques enriched with linguistic tools. With minor adjustments, that
method can also be used for concept map mining from textual sources in other
morphologically rich languages.


  We consider a system of N point vortices in a bounded domain with null total
circulation, whose statistics are given by the Canonical Gibbs Ensemble at
inverse temperature $\beta\geq 0$. We prove that the space-time fluctuation
field around the (constant) Mean Field limit satisfies when $N\to\infty$ a
generalized version of 2-dimensional Euler dynamics preserving the Gaussian
Energy-Enstrophy ensemble.


  An important first step when deploying a wireless ad hoc network is neighbor
discovery in which every node attempts to determine the set of nodes it can
communicate with in one wireless hop. In the recent years, cognitive radio (CR)
technology has gained attention as an attractive approach to alleviate spectrum
congestion. A cognitive radio transceiver can operate over a wide range of
frequencies, possibly scanning multiple frequency bands. A cognitive radio node
can opportunistically utilize unused wireless spectrum without interference
from other wireless devices in its vicinity. Due to spatial variations in
frequency usage and hardware variations in radio transceivers, different nodes
in the network may perceive different subsets of frequencies available to them
for communication. This heterogeneity in the available channel sets across the
network increases the complexity of solving the neighbor discovery problem in a
cognitive radio network. In this work, we design and analyze several randomized
algorithms for neighbor discovery in such a (heterogeneous) network under a
variety of assumptions (e.g. maximum node degree known or unknown) for both
synchronous and asynchronous systems under minimal knowledge. We also show that
our randomized algorithms are naturally suited to tolerate unreliable channels
and adversarial attacks.


  We prove that if ${\mathcal E} \subset {\Bbb R}^{2d}$, $d \ge 2$, is an
Ahlfors-David regular product set of sufficiently large Hausdorff dimension,
denoted by $dim_{{\mathcal H}}({\mathcal E})$, and $\phi$ is a sufficiently
regular function, then the upper Minkowski dimension of the set $$ \{w \in
{\mathcal E}: \phi_l(w)=t_l; 1 \leq l \leq m \}$$ does not exceed
$dim_{{\mathcal H}}({\mathcal E})-m$, in line with the regular value theorem
from the elementary differential geometry. Our arguments are based on the
mapping properties of the underlying Fourier Integral Operators and are
intimately connected with the Falconer distance conjecture in geometric measure
theory. We shall see that our results are in general sharp in the sense that if
the Hausdorff dimension is smaller than a certain threshold, then the
dimensional inequality fails in a quantifiable way. The constructions used to
demonstrate this are based on the distribution of lattice points on convex
surfaces and have connections with combinatorial geometry.


  In hypercube approach to correlation functions in Chern-Simons theory (knot
polynomials) the central role is played by the numbers of cycles, in which the
link diagram is decomposed under different resolutions. Certain functions of
these numbers are further interpreted as dimensions of graded spaces,
associated with hypercube vertices. Finding these functions is, however, a
somewhat non-trivial problem. In arXiv:1506.07516 it was suggested to solve it
with the help of the matrix model technique, in the spirit of AMM/EO
topological recursion. In this paper we further elaborate on this idea and
provide a vast collection of non-trivial examples, related both to ordinary and
virtual links and knots. Remarkably, most powerful versions of the formalism
freely convert ordinary knots/links to virtual and back -- moreover, go beyond
the knot-related set of the (2,2)-valent graphs.


  We study the problem of finding a small subset of items that is
\emph{agreeable} to all agents, meaning that all agents value the subset at
least as much as its complement. Previous work has shown worst-case bounds,
over all instances with a given number of agents and items, on the number of
items that may need to be included in such a subset. Our goal in this paper is
to efficiently compute an agreeable subset whose size approximates the size of
the smallest agreeable subset for a given instance. We consider three
well-known models for representing the preferences of the agents: ordinal
preferences on single items, the value oracle model, and additive utilities. In
each of these models, we establish virtually tight bounds on the approximation
ratio that can be obtained by algorithms running in polynomial time.


  Hugs are complex affective interactions that often include gestures like
squeezes. We present six new guidelines for designing interactive hugging
robots, which we validate through two studies with our custom robot. To achieve
autonomy, we investigated robot responses to four human intra-hug gestures:
holding, rubbing, patting, and squeezing. Thirty-two users each exchanged and
rated sixteen hugs with an experimenter-controlled HuggieBot 2.0. The robot's
inflated torso's microphone and pressure sensor collected data of the subjects'
demonstrations that were used to develop a perceptual algorithm that classifies
user actions with 88\% accuracy. Users enjoyed robot squeezes, regardless of
their performed action, they valued variety in the robot response, and they
appreciated robot-initiated intra-hug gestures. From average user ratings, we
created a probabilistic behavior algorithm that chooses robot responses in real
time. We implemented improvements to the robot platform to create HuggieBot 3.0
and then validated its gesture perception system and behavior algorithm with
sixteen users. The robot's responses and proactive gestures were greatly
enjoyed. Users found the robot more natural, enjoyable, and intelligent in the
last phase of the experiment than in the first. After the study, they felt more
understood by the robot and thought robots were nicer to hug.


  The $\eta$-$\eta^\prime$ mixing angle is deduced from an updated
phenomenological analysis of $J/\psi$ decays into a vector and a pseudoscalar
meson. Corrections due to non-ideal $\omega$-$\phi$ mixing are confirmed to be
crucial to find $\theta_P=(-16.9\pm 1.7)^\circ$, in agreement with most recent
analyses. The newly reported values of $B(J/\psi\to\rho\pi)$ by the BABAR and
BES Collaborations make more difficult a reasonable description of data.


  We perform the potential analysis for the holographic Schwinger effect in a
rotating deformed AdS black-hole background. We calculate the total potential
of a quark-antiquark ($Q\bar{Q}$) pair in an external electric field and
evaluate the critical electric field from Dirac-Born-Infeld (DBI) action. It is
shown that the inclusion of angular velocity decreases the potential barrier
thus enhancing the Schwinger effect, opposite to the effect of the confining
scale. Moreover, by increasing angular velocity decreases the critical electric
field above which the pairs are produced freely without any suppression.
Furthermore, we conclude that producing $Q\bar{Q}$ pairs would be easier in
rotating medium.


  We show that there exists a maximal possible accretion rate, beyond which
global slim disk solutions cannot be constructed because in the vertical
direction the gravitational force would be unable to balance the pressure force
to gather the accreted matter. The principle for this restriction is the same
as that for the Eddington luminosity and the corresponding critical accretion
rate, which were derived for spherical accretion by considering the same force
balance in the radial direction. If the assumption of hydrostatic equilibrium
is waived and vertical motion is included, this restriction may become even
more serious as the value of the maximal possible accretion rate becomes
smaller. Previous understanding in the literature that global slim disk
solutions could stand for any large accretion rates is due to the
overestimation of the vertical gravitational force by using an approximate
potential. For accretion flows with large accretion rates at large radii,
outflows seem unavoidable in order for the accretion flow to reduce the
accretion rate and follow a global solution till the central black hole.


  We propose UnMixMatch, a semi-supervised learning framework which can learn
effective representations from unconstrained unlabelled data in order to scale
up performance. Most existing semi-supervised methods rely on the assumption
that labelled and unlabelled samples are drawn from the same distribution,
which limits the potential for improvement through the use of free-living
unlabeled data. Consequently, the generalizability and scalability of
semi-supervised learning are often hindered by this assumption. Our method aims
to overcome these constraints and effectively utilize unconstrained unlabelled
data in semi-supervised learning. UnMixMatch consists of three main components:
a supervised learner with hard augmentations that provides strong
regularization, a contrastive consistency regularizer to learn underlying
representations from the unlabelled data, and a self-supervised loss to enhance
the representations that are learnt from the unlabelled data. We perform
extensive experiments on 4 commonly used datasets and demonstrate superior
performance over existing semi-supervised methods with a performance boost of
4.79%. Extensive ablation and sensitivity studies show the effectiveness and
impact of each of the proposed components of our method.


  We consider a molecule described by the Hartree-Fock model without the
exchange term. We prove that nuclei of total charge $Z$ can bind at most $Z+C$
electrons, where $C$ is a constant independent of $Z$.


  We show connection formulae of local solutions of the Ramanujan equation
between the origin and the infinity. These solutions are given by the Ramanujan
function, the $q$-Airy function and the divergent basic hypergeometric series
${}_2\varphi_0(0,0;-;q,x)$. We use two different $q$-Borel-Laplace resummation
methods to obtain our connection formulae. We also introduce the
$q$-Borel-Laplace transformation of level $r-1$, which are higher order
extension of these transformations. These methods are useful to obtain an
asymptotic formula of a divergent series ${}_r\varphi_0(0,0,\dots ,0;-;q,x)$.


  In this paper we describe how imitative logit dynamic (rather popular in
population games theory) can be interpreted in terms of mirror descent method
(well known in convex optimization). We demonstrate the connection of this two
type dynamics on the Shortest Path Problem.


  In this paper, we study the Kelly criterion in the continuous time framework
building on the work of E.O. Thorp and others. The existence of an optimal
strategy is proven in a general setting and the corresponding optimal wealth
process is found. A simple formula is provided for calculating the optimal
portfolio for a set of price processes satisfying some simple conditions.
Properties of the optimal investment strategy for assets governed by multiple
Ornstein-Uhlenbeck processes are studied. The paper ends with a short
discussion of the implications of these ideas for financial markets.


  In recent years, multi-view multi-label learning has aroused extensive
research enthusiasm. However, multi-view multi-label data in the real world is
commonly incomplete due to the uncertain factors of data collection and manual
annotation, which means that not only multi-view features are often missing,
and label completeness is also difficult to be satisfied. To deal with the
double incomplete multi-view multi-label classification problem, we propose a
deep instance-level contrastive network, namely DICNet. Different from
conventional methods, our DICNet focuses on leveraging deep neural network to
exploit the high-level semantic representations of samples rather than
shallow-level features. First, we utilize the stacked autoencoders to build an
end-to-end multi-view feature extraction framework to learn the view-specific
representations of samples. Furthermore, in order to improve the consensus
representation ability, we introduce an incomplete instance-level contrastive
learning scheme to guide the encoders to better extract the consensus
information of multiple views and use a multi-view weighted fusion module to
enhance the discrimination of semantic features. Overall, our DICNet is adept
in capturing consistent discriminative representations of multi-view
multi-label data and avoiding the negative effects of missing views and missing
labels. Extensive experiments performed on five datasets validate that our
method outperforms other state-of-the-art methods.


  Intermediate rings of real valued continuous functions with countable range
on a Hausdorff zero-dimensional space $X$ are introduced in this article. Let
$\Sigma_c(X)$ be the family of all such intermediate rings $A_c(X)$'s which lie
between $C_c^*(X)$ and $C_c(X)$. It is shown that the structure space of each
$A_c(X)$ is $\beta_0X$, the Banaschewski compactification of $X$. $X$ is shown
to be a $P$-space if and only if each ideal in $C_c(X)$ is closed in the
$m_c$-topology on it. Furthermore $X$ is realized to be an almost $P$-space
when and only when each maximal ideal/ $z$-ideal in $C_c(X)$ becomes a
$z^0$-ideal. Incidentally within the family of almost $P$-spaces, $C_c(X)$ is
characterized among all the members of $\Sigma_c(X)$ by virtue of either of
these two properties. Equivalent descriptions of pseudocompact condition on $X$
are given via $U_c$-topology, $m_c$-topology and norm on $C_c(X)$. The article
ends with a result which essentially says that $z^0$-ideals in a typical
$A_c(X)$ $\in$ $\Sigma_c(X)$ are precisely the contraction of $z^0$-ideals in
$C_c(X)$.


  In the model of Arkani-Hamed, Dimopoulos, and Dvali where gravity is allowed
to propagate in the extra dimensions of very large size, virtual graviton
exchange between the standard model particles can give rise to signatures that
can be tested in collider experiments. We study these effects in dilepton and
diphoton production at hadron colliders. Specifically, we examine the double
differential cross-section in the invariant mass and scattering angle, which is
found to be useful in separating the gravity effects from the standard model.
In this work, sensitivity obtained using the double differential cross-section
is higher than that in previous studies based on single differential
distributions. Assuming no excess of events over the standard model
predictions, we obtain the following 95% confidence level lower limits on the
effective Planck scale: $0.9-1.5$ TeV in the Tevatron Run I, $1.3-2.5$ TeV in
Run IIa, $1.7-3.5$ TeV in Run IIb, and $6.5-12.8$ TeV at the LHC. The range of
numbers corresponds to the number of extra dimensions $n=7-2$.