  Effective potentials are an essential ingredient of classical molecular
dynamics (MD) simulations. Little is understood of the consequences of
representing the complex energy landscape of an atomic configuration by an
effective potential or force field containing considerably fewer parameters.
The probabilistic potential ensemble method has been implemented in the potfit
force matching code. This introduces uncertainty quantification into the
interatomic potential generation process. Uncertainties in the effective
potential are propagated through MD to obtain uncertainties in quantities of
interest, which are a measure of the confidence in the model predictions.
  We demonstrate the technique using three potentials for nickel: two simple
pair potentials, Lennard-Jones and Morse, and a local density dependent
embedded atom method (EAM) potential. A potential ensemble fit to density
functional theory (DFT) reference data is constructed for each potential to
calculate the uncertainties in lattice constants, elastic constants and thermal
expansion. We quantitatively illustrate the cases of poor model selection and
fit, highlighted by the uncertainties in the quantities calculated. This shows
that our method can capture the effects of the error incurred in quantities of
interest resulting from the potential generation process without resorting to
comparison with experiment or DFT, which is an essential part to assess the
predictive power of MD simulations.


  The explicit realizations of quantum field theory (QFT) admitted by a
revision to the Wightman axioms for the vacuum expectation values (VEV) of
fields includes massless particles when there are four or more spacetime
dimensions.


  A method is proposed to compute robust inner-approximations to the backward
reachable set for uncertain nonlinear systems. It also produces a robust
control law that drives trajectories starting in these sets to the target set.
The method merges dissipation inequalities and integral quadratic constraints
(IQCs) with both hard and soft IQC factorizations. Computational algorithms are
presented using the generalized S-procedure and sum-of-squares techniques. The
use of IQCs in backward reachability analysis allows for a variety of
perturbations including parametric uncertainty, unmodeled dynamics,
nonlinearities, and uncertain time delays. The method is demonstrated on two
examples, including a 6-state quadrotor with actuator uncertainties.


  On social network platforms, a user's behavior is based on his/her personal
interests, or influenced by his/her friends. In the literature, it is common to
model either users' personal preference or their socially influenced
preference. In this paper, we present a novel deep learning model SocialTrans
for social recommendations to integrate these two types of preferences.
SocialTrans is composed of three modules. The first module is based on a
multi-layer Transformer to model users' personal preference. The second module
is a multi-layer graph attention neural network (GAT), which is used to model
the social influence strengths between friends in social networks. The last
module merges users' personal preference and socially influenced preference to
produce recommendations. Our model can efficiently fit large-scale data and we
deployed SocialTrans to a major article recommendation system in China.
Experiments on three data sets verify the effectiveness of our model and show
that it outperforms state-of-the-art social recommendation methods.


  In this paper, we shall prove that space-like surfaces with bounded mean
curvature functions in real analytic Lorentzian 3-manifolds can change their
causality to time-like surfaces only if the mean curvature functions tend to
zero. Moreover, we shall show the existence of such surfaces with non-vanishing
mean curvature and investigate their properties.


  With the great success of text-conditioned diffusion models in creative
text-to-image generation, various text-driven image editing approaches have
attracted the attentions of many researchers. However, previous works mainly
focus on discreteness-sensitive instructions such as adding, removing or
replacing specific objects, background elements or global styles (i.e., hard
editing), while generally ignoring subject-binding but semantically
fine-changing continuity-sensitive instructions such as actions, poses or
adjectives, and so on (i.e., soft editing), which hampers generative AI from
generating user-customized visual contents. To mitigate this predicament, we
propose a spatio-temporal guided adaptive editing algorithm AdapEdit, which
realizes adaptive image editing by introducing a soft-attention strategy to
dynamically vary the guiding degree from the editing conditions to visual
pixels from both temporal and spatial perspectives. Note our approach has a
significant advantage in preserving model priors and does not require model
training, fine-tuning, extra data, or optimization. We present our results over
a wide variety of raw images and editing instructions, demonstrating
competitive performance and showing it significantly outperforms the previous
approaches.


  Inspired by the truncated Euler-Maruyama method developed in Mao (J. Comput.
Appl. Math. 2015), we propose the truncated Milstein method in this paper. The
strong convergence rate is proved to be close to 1 for a class of highly
non-linear stochastic differential equations. Numerical examples are given to
illustrate the theoretical results.


  This paper introduces a novel data-driven approach to address challenges
faced by city policymakers concerning the distribution of public funds.
Providing budgeting processes for improving quality of life based on objective
(data-driven) evidence has been so far a missing element in policy-making. This
paper focuses on a case study of 1,204 citizens in the city of Aarau,
Switzerland, and analyzes survey data containing insightful indicators that can
impact the legitimacy of decision-making. Our approach is twofold. On the one
hand, we aim to optimize the legitimacy of policymakers' decisions by
identifying the level of investment in neighborhoods and projects that offer
the greatest return in legitimacy. To do so, we introduce a new
context-independent legitimacy metric for policymakers. This metric allows us
to distinguish decisive vs. indecisive collective preferences for neighborhoods
or projects on which to invest, enabling policymakers to prioritize impactful
bottom-up consultations and participatory initiatives (e.g., participatory
budgeting). The metric also allows policymakers to identify the optimal number
of investments in various project sectors and neighborhoods (in terms of
legitimacy gain). On the other hand, we aim to offer guidance to policymakers
concerning which satisfaction and participation factors influence citizens'
quality of life through an accurate classification model and an evaluation of
relocations. By doing so, policymakers may be able to further refine their
strategy, making targeted investments with significant benefits to citizens'
quality of life. These findings are expected to provide transformative insights
for practicing direct democracy in Switzerland and a blueprint for
policy-making to adopt worldwide.


  As is well known, self-similar solutions to the mean curvature flow,
including self-shrinkers, translating solitons and self-expanders, arise
naturally in the singularity analysis of the mean curvature flow. Recently, Guo
\cite{Guo} proved that $n$-dimensional compact self-shrinkers in
$\mathbb{R}^{n+1}$ with scalar curvature bounded from above or below by some
constant are isometric to the round sphere $\mathbb{S}^n(\sqrt{n})$, which
implies that $n$-dimensional compact self-shrinkers in $\mathbb{R}^{n+1}$ with
constant scalar curvature are isometric to the round sphere
$\mathbb{S}^n(\sqrt{n})$(see also \cite{Hui1}). Complete classifications of
$n$-dimensional translating solitons in $\mathbb{R}^{n+1}$ with nonnegative
constant scalar curvature and of $n$-dimensional self-expanders in
$\mathbb{R}^{n+1}$ with nonnegative constant scalar curvature were given by
Mart\'{i}n, Savas-Halilaj and Smoczyk\cite{MSS} and Ancari and Cheng\cite{AC},
respectively. In this paper we give complete classifications of $n$-dimensional
complete self-shrinkers in $\mathbb{R}^{n+1}$ with nonnegative constant scalar
curvature. We will also give alternative proofs of the classification theorems
due to Mart\'{i}n, Savas-Halilaj and Smoczyk \cite{MSS} and Ancari and
Cheng\cite{AC}.


  In this paper we describe graded automorphisms and antiautomorphisms of
finite order on matrix algebras endowed with a group gradings by a finite
abelian group over an arbitrary algebraically closed field of charcteristic
different from 2.


  We demonstrate quantum key distribution (QKD) with classical signals in a
seven-core fiber using dense wavelength division multiplexing. Quantum signals
are transmitted in an outer core separately and intercore crosstalk (IC-XT) is
the main impairment of them. In order to alleviate IC-XT, we propose a
quantum-classical interleave scheme. Then the properties of IC-XT are analyzed
based on the measurement results which indicate counter-propagation is a better
co-existence method than co-propagation. Finally, we perform QKD experiments in
the presence of two classical channels with a channel spacing of 100 GHz
between quantum channel and the nearest classical channels. The experiment
results prove counter-propagation almost immune to IC-XT, which is consistent
with our analysis. Also, the feasibility of the transmission over the range of
metropolitan area networks is validated with our scheme.


  Recently H.Georgi has introduced the concept of unparticles in order to
describe the low energy physics of a nontrivial scale invariant sector of an
effective theory. We investigate its physical effects on the neutrino flux to
be detected in a kilometer cubic neutrino telescope such as IceCube. We study
the effects, on different observables, of the survival neutrino flux after
through the Earth and the regeneration originated in the neutral currents. We
calculate the contribution of unparticle physics to the neutrino-nucleon
interaction and, then, to the observables in order to evaluate detectable
effects in IceCUbe. Our results are compared with the bounds obtained by other
non-underground experiments. Finally, the results are presented as an exclusion
plot in the relevant parameters of the new physics stuff.


  Unmanned aerial vehicles (UAVs) are powerful Internet-of-Things components to
provide sensing and communications in the air due to their advantages in
mobility and flexibility. As aerial users, UAVs are envisioned to support
various sensing applications in the next generation cellular systems, which
have been studied by the Third Generation Partnership Project (3GPP). However,
the Quality-of-Services (QoS) of the cellular link between the UAV and the base
station may not be guaranteed when UAVs are at the cell edge or experiencing
deep fading. In this article, we first introduce the non-cooperative cellular
Internet of UAVs. Then we propose a cooperative sense-and-send protocol, in
which a UAV can upload sensory data with the help of a UAV relay, to provide a
better communication QoS for the sensing tasks. Key techniques including
trajectory design and radio resource management that support the cooperative
cellular Internet of UAVs are presented in detail. Finally, the extended
cooperative cellular Internet of UAVs is discussed for QoS improvement with
some open issues, such as massive multiple-input multiple-output systems,
millimeter-wave, and cognitive communications.


  We exhibit examples of projective varieties with degenerate Gauss mappings
and determine numerical invariants of such varieties. Our examples provide
counter-examples to an asserted structure theorem of Griffiths and Harris (Ann.
Sci. ENS 1979).


  We introduce a continuous family of frustration-free Hamiltonians with
exactly solvable ground states. We prove that the {ground state of our model is
non-degenerate and exhibits} a novel quantum phase transition from bounded
entanglement entropy to a massively entangled state with volume entropy
scaling. The ground state may be interpreted as a deformation away from the
uniform superposition of colored Motzkin paths, showed by Movassagh and Shor to
have a large (square-root) but sub-extensive scaling of entanglement into a
state with an extensive entropy.


  Supply Chain Agility is important for organisations to stay competitive in
today's dynamic business environment. There is increasing interest in deploying
Business Intelligence (BI) in the Supply Chain Management (SCM) context to
improve Supply Chain (SC) Agility. However, there is limited research exploring
BI contributions to SC Agility. In this research-in-progress paper we propose a
model based on a conceptual analysis of the literature showing how BI can help
organisations achieve SC Agility by supporting the key areas of SCM (Plan,
Source, Make, Deliver and Return). In the next stage of this project, we will
conduct a series of case studies investigating how organisations use BI when
managing their SC activities and how BI contributes to SC Agility. The result
of the study will help organizations deploy BI effectively to support SCM and
improve SC Agility.


  To achieve a highly agile and flexible production, it is envisioned that
industrial production systems gradually become more decentralized,
interconnected, and intelligent. Within this vision, production assets
collaborate with each other, exhibiting a high degree of autonomy. Furthermore,
knowledge about individual production assets is readily available throughout
their entire life-cycles. To realize this vision, adequate use of information
technology is required. Two commonly applied software paradigms in this context
are Software Agents (referred to as Agents) and Digital Twins (DTs). This work
presents a systematic comparison of Agents and DTs in industrial applications.
The goal of the study is to determine the differences, similarities, and
potential synergies between the two paradigms. The comparison is based on the
purposes for which Agents and DTs are applied, the properties and capabilities
exhibited by these software paradigms, and how they can be allocated within the
Reference Architecture Model Industry 4.0. The comparison reveals that Agents
are commonly employed in the collaborative planning and execution of production
processes, while DTs typically play a more passive role in monitoring
production resources and processing information. Although these observations
imply characteristic sets of capabilities and properties for both Agents and
DTs, a clear and definitive distinction between the two paradigms cannot be
made. Instead, the analysis indicates that production assets utilizing a
combination of Agents and DTs would demonstrate high degrees of intelligence,
autonomy, sociability, and fidelity. To achieve this, further standardization
is required, particularly in the field of DTs.


  Antisite defects play a critical role in Nb$_3$Sn superconducting radio
frequency (SRF) cavity physics. Such defects are the primary form of disorder
in Nb$_3$Sn, and are responsible for stoichiometry variations, including
experimentally observed tin-depleted regions within grains and tin-rich regions
around grain boundaries. But why they cluster to form regions of different
stoichiometries and how they affect the SRF properties of Nb$_3$Sn cavities are
not fully understood. Using $\textit{ab initio}$ techniques, we calculate the
A15 region of the Nb-Sn phase diagram, discuss a possible modification to the
phase diagram near grain boundaries, and calculate $T_c$ as a function of
stoichiometry, including experimentally inaccessible tin-rich stoichiometry. We
find that the impact of antisite defects on the density of states near the
Fermi-level of Nb$_3$Sn plays a key role in determining many of their
properties. These results improve our understanding of the obstacles facing
Nb$_3$Sn SRF systems, and how modified growth processes might overcome them.


  The development of ultra-light pixelated ladders is motivated by the
requirements of the ILD vertex detector at ILC. This paper summarizes three
projects related to system integration. The PLUME project tackles the issue of
assembling double-sided ladders. The SERWIETE project deals with a more
innovative concept and consists in making single-sided unsupported ladders
embedded in an extra thin plastic enveloppe. AIDA, the last project, aims at
building a framework reproducing the experimental running conditions where sets
of ladders could be tested.


  This document describes the linking of data discovery metadata to access to
the data itself, further detailed metadata, related resources, and to services
that perform operations on the data. The web service capability supports a
drill-down into the details of a specific dataset and provides a set of links
to the dataset file(s) and related resources. This specification also includes
a VOTable-specific method of providing descriptions of one or more services and
their input(s), usually using parameter values from elsewhere in the VOTable
document. Providers are able to describe services that are relevant to the
records (usually datasets with identifiers) by including service descriptors in
a result document.


  We observed the post-common-envelope eclipsing binary with a white dwarf
component, QS Vir, using the 1.88 m telescope of Kotammia Observatory in Egypt.
The new observations were analyzed together with all multicolor light curves
available online (sampling a period of 25 yr), using a full-feature binary
system modeling software based on Roche geometry. This is the first time
complete photometric modeling was done with most of these data. QS Vir is a
detached system, with the red dwarf component underfilling its Roche lobe by a
small margin. All light curves feature out-of-eclipse variability that is
associated with ellipsoidal variation, mutual irradiation and irregularities in
surface brightness of the tidally distorted and magnetically active red dwarf.
We tested models with one, two, and three dark spots and found that one spot is
sufficient to account for the light curve asymmetry in all data sets, although
this does not rule out the presence of multiple spots. We also found that a
single spotted model cannot fit light curves observed simultaneously in
different filters. Instead, each filter requires a different spot
configuration. To thoroughly explore the parameter space of spot locations, we
devised a grid-search procedure and used it to find consistent solutions. Based
on this, we conclude that the dark spot responsible for light curve distortions
has been stable for the past 15 yr, after a major migration that happened
between 1993 and 2002, possibly due to a flip-flop event.


  Metasurfaces have been investigated and its numerous exotic functionalities
and the potentials to arbitrarily control of the electromagnetic fields have
been extensively explored. However, only limited types of metasurface have
finally entered into real products. Here, we introduce a concept of a
metasurface-based smart table for wirelessly charging portable devices and
report its first prototype. The proposed metasurface can efficiently transform
evanescent fields into propagating waves which significantly improves the near
field coupling to charge a receiving device arbitrarily placed on its surface
wirelessly through magnetic resonance coupling. In this way, power transfer
efficiency of 80$\%$ is experimentally obtained when the receiver is placed at
any distances from the transmitter. The proposed concept enables a variety of
important applications in the fields of consumer electronics, electric
automobiles, implanted medical devices, etc. The further developed
metasurface-based smart table may serve as an ultimate 2-dimensional platform
and support charging multiple receivers.


  Motivated by a question of Rota, this paper studies the relationship between
Rota--Baxter algebras and symmetric related functions. The starting point is
the fact that the space of quasi-symmetric functions is spanned by monomial
quasi-symmetric functions which are indexed by compositions. When composition
is replaced by left weak composition (LWC), we obtain the concept of LWC
monomial quasi-symmetric functions and the resulting space of LWC
quasi-symmetric functions. In line with the question of Rota, the latter is
shown to be isomorphic to the free commutative nonunitary Rota--Baxter algebra
on one generator. The combinatorial interpretation of quasi-symmetric functions
by $P$-partitions from compositions is extended to the context of left weak
compositions, leading to the concept of LWC fundamental quasi-symmetric
functions. The transformation formulas for LWC monomial and LWC fundamental
quasi-symmetric functions are obtained, generalizing the corresponding results
for quasi-symmetric functions. Extending the close relationship between
quasi-symmetric functions and multiple zeta values, weighted multiple zeta
values and a $q$-analog of multiple zeta values are investigated and a
decomposition formula is established.


  Deep hashing has recently received attention in cross-modal retrieval for its
impressive advantages. However, existing hashing methods for cross-modal
retrieval cannot fully capture the heterogeneous multi-modal correlation and
exploit the semantic information. In this paper, we propose a novel
\emph{Fusion-supervised Deep Cross-modal Hashing} (FDCH) approach. Firstly,
FDCH learns unified binary codes through a fusion hash network with paired
samples as input, which effectively enhances the modeling of the correlation of
heterogeneous multi-modal data. Then, these high-quality unified hash codes
further supervise the training of the modality-specific hash networks for
encoding out-of-sample queries. Meanwhile, both pair-wise similarity
information and classification information are embedded in the hash networks
under one stream framework, which simultaneously preserves cross-modal
similarity and keeps semantic consistency. Experimental results on two
benchmark datasets demonstrate the state-of-the-art performance of FDCH.


  We compare and generalise the various geometric constructions (due to Ringel,
Lusztig, Schofield, Bozec, Davison...) of the unipotent generalised Kac-Moody
algebra associated with an arbitrary quiver. These constructions are
interconnected through several geometric operations, including the stalk Euler
characteristic of constructible complexes, the characteristic cycle, the Euler
obstruction map, and the intersection multiplicities of Lagrangian
subvarieties. We provide a proof that these geometric realisations hold for the
integral form of the Lie algebra. Furthermore, by modifying the generators of
the enveloping algebra, we ensure compatibility with the natural coproducts
that can be defined in terms of restriction diagrams.
  As a result, we establish that the top cohomological Hall algebra of the
strictly seminilpotent stack is isomorphic to the positive part of the
enveloping algebra of the generalised Kac-Moody algebra associated with the
quiver. This appears to be one of the cornerstones needed to describe the BPS
algebra of very general $2$-Calabi-Yau categories, which is the subject of the
author's work on BPS Lie algebras for $2$-Calabi-Yau categories with Davison
and Schlegel Mejia.


  We propose to develop deep learning models that can predict Pareto optimal
dose distributions by using any given set of beam angles, along with patient
anatomy, as input to train the deep neural networks. We implement and compare
two deep learning networks that predict with two different beam configuration
modalities. We generated Pareto optimal plans for 70 patients with prostate
cancer. We used fluence map optimization to generate 500 IMRT plans that
sampled the Pareto surface for each patient, for a total of 35,000 plans. We
studied and compared two different models, Model I and Model II. Model I
directly uses beam angles as a second input to the network as a binary vector.
Model II converts the beam angles into beam doses that are conformal to the
PTV. Our deep learning models predicted voxel-level dose distributions that
precisely matched the ground truth dose distributions. Quantitatively, Model I
prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50),
2.80% (D95), 3.90% (D98), 0.6% (D50), 1.10% (D2) was lower than that of Model
II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50),
7.10% (D95), 6.50% (D98), 8.40% (D50), 6.30% (D2). Treatment planners who use
our models will be able to use deep learning to control the tradeoffs between
the PTV and OAR weights, as well as the beam number and configurations in real
time. Our dose prediction methods provide a stepping stone to building
automatic IMRT treatment planning.


  This study discusses acoustic dissipation, which contributes to inaccuracies
in impedance tube measurements. To improve the accuracy of these measurements,
this paper introduces a transfer function model that integrates diverse
dissipation prediction models. Bayesian inference is used to estimate the
important parameters included in these models, describing dissipation
originating from various mechanisms, sound speed, and microphone positions. By
using experimental measurements and considering a hypothetical air layer in
front of a rigid termination as the material under test, Bayesian parameter
estimation allows a substantial enhancement in characterization accuracy by
incorporating the dissipation and sound speed estimates. This approach
effectively minimizes residual absorption coefficients attributed to both
boundary-layer effects and air medium relaxation. Incorporation of dissipation
models leads to a substantial reduction (to 1%) in residual absorption
coefficients. Moreover, the use of accurately estimated parameters further
enhances the accuracy of actual tube measurements of materials using the
two-microphone transfer function method.


  In a recent paper we have proposed the possibility that the lightest massive
string states could be identified with open strings living at intersections of
D-branes forming small angles. In this note, we reconsider the relevant
twist-field correlation functions and perform the analysis of the sub-dominant
physical poles in the various channels. Our derivation is new in that it is
based on the algebraic procedure for the construction of open string models
starting from their closed-string `parents' rather than on the stress-tensor
method. We also indicate possible generalizations and diverse applications of
our approach.


  We present HARP (HAnd Reconstruction and Personalization), a personalized
hand avatar creation approach that takes a short monocular RGB video of a human
hand as input and reconstructs a faithful hand avatar exhibiting a
high-fidelity appearance and geometry. In contrast to the major trend of neural
implicit representations, HARP models a hand with a mesh-based parametric hand
model, a vertex displacement map, a normal map, and an albedo without any
neural components. As validated by our experiments, the explicit nature of our
representation enables a truly scalable, robust, and efficient approach to hand
avatar creation. HARP is optimized via gradient descent from a short sequence
captured by a hand-held mobile phone and can be directly used in AR/VR
applications with real-time rendering capability. To enable this, we carefully
design and implement a shadow-aware differentiable rendering scheme that is
robust to high degree articulations and self-shadowing regularly present in
hand motion sequences, as well as challenging lighting conditions. It also
generalizes to unseen poses and novel viewpoints, producing photo-realistic
renderings of hand animations performing highly-articulated motions.
Furthermore, the learned HARP representation can be used for improving 3D hand
pose estimation quality in challenging viewpoints. The key advantages of HARP
are validated by the in-depth analyses on appearance reconstruction, novel-view
and novel pose synthesis, and 3D hand pose refinement. It is an AR/VR-ready
personalized hand representation that shows superior fidelity and scalability.


  The classical and quantum dynamics of a particle trapped in a one-dimensional
infinite square well with a time periodic pulsed field is investigated. This is
a two-parameter non-KAM generalization of the kicked rotor, which can be seen
as the standard map of particles subjected to both smooth and hard potentials.
The virtue of the generalization lies in the introduction of an extra parameter
R which is the ratio of two length scales, namely the well width and the field
wavelength. If R is a non-integer the dynamics is discontinuous and non-KAM. We
have explored the role of R in controlling the localization properties of the
eigenstates. In particular the connection between classical diffusion and
localization is found to generalize reasonably well. In unbounded chaotic
systems such as these, while the nearest neighbour spacing distribution of the
eigenvalues is less sensitive to the nature of the classical dynamics, the
distribution of participation ratios of the eigenstates proves to be a
sensitive measure; in the chaotic regimes the latter being lognormal. We find
that the tails of the well converged localized states are exponentially
localized despite the discontinuous dynamics while the bulk part shows
fluctuations that tend to be closer to Random Matrix Theory predictions. Time
evolving states show considerable R dependence and tuning R to enhance
classical diffusion can lead to significantly larger quantum diffusion for the
same field strengths, an effect that is potentially observable in present day
experiments.


  Makar-Limanov's conjecture states that if a division ring D is finitely
generated and infinite dimensional over its center k then D contains a free
k-subalgebra of rank 2. In this work, we will investigate the existence of such
structures in D, the division ring of fractions of the skew polynomial ring
L[t;\sigma], where t is a variable and $\sigma$ is a k-automorphism of L. For
instance, we prove Makar-Limanov's conjecture when either L is the function
field of an abelian variety or the function field of the n-dimensional
projective space.


  3D speech-driven facial animation generation has received much attention in
both industrial applications and academic research. Since the non-verbal facial
cues that exist across the face in reality are non-deterministic, the generated
results should be diverse. However, most recent methods are deterministic
models that cannot learn a many-to-many mapping between audio and facial motion
to generate diverse facial animations. To address this problem, we propose
GLDiTalker, which introduces a motion prior along with some stochasticity to
reduce the uncertainty of cross-modal mapping while increasing non-determinacy
of the non-verbal facial cues that reside throughout the face. Particularly,
GLDiTalker uses VQ-VAE to map facial motion mesh sequences into latent space in
the first stage, and then iteratively adds and removes noise to the latent
facial motion features in the second stage. In order to integrate different
levels of spatial information, the Spatial Pyramidal SpiralConv Encoder is also
designed to extract multi-scale features. Extensive qualitative and
quantitative experiments demonstrate that our method achieves the
state-of-the-art performance.


  While Transformer-based pre-trained language models and their variants
exhibit strong semantic representation capabilities, the question of
comprehending the information gain derived from the additional components of
PLMs remains an open question in this field. Motivated by recent efforts that
prove Multilayer-Perceptrons (MLPs) modules achieving robust structural capture
capabilities, even outperforming Graph Neural Networks (GNNs), this paper aims
to quantify whether simple MLPs can further enhance the already potent ability
of PLMs to capture linguistic information. Specifically, we design a simple yet
effective probing framework containing MLPs components based on BERT structure
and conduct extensive experiments encompassing 10 probing tasks spanning three
distinct linguistic levels. The experimental results demonstrate that MLPs can
indeed enhance the comprehension of linguistic structure by PLMs. Our research
provides interpretable and valuable insights into crafting variations of PLMs
utilizing MLPs for tasks that emphasize diverse linguistic structures.


  Light neutral mediators, with mass $\lesssim 1$ GeV, are common features of
extensions to the Standard Model (SM). Current astrophysical and terrestrial
experiments have constrained the model parameter space, and planned experiments
around the world promise continued improvement in sensitivity. In this paper we
study the prospects for probing light neutral mediators using terrestrial
stopped pion and reactor sources in combination with ultra-low threshold
nuclear and electron recoil detectors. We show that the coherent
neutrino-nucleus and neutrino-electron scattering channels provide
complementary sensitivity to light mediators. With low threshold detectors, we
show that most stringent bounds on models arise from the nuclear scattering
process, improving upon previous bounds from electron scattering of solar
neutrinos by nearly an order of magnitude for mediator masses $\gtrsim 0.1$
GeV.


  Proof competence, i.e. the ability to write and check (mathematical) proofs,
is an important skill in Computer Science, but for many students it represents
a difficult challenge. The main issues are the correct use of formal language
and the ascertainment of whether proofs, especially the students' own, are
complete and correct. Many authors have suggested using proof assistants to
assist in teaching proof competence, but the efficacy of the approach is
unclear. To improve the state of affairs, we introduce ProofBuddy: a web-based
tool using the Isabelle proof assistant which enables researchers to conduct
studies of the efficacy of approaches to using proof assistants in education by
collecting fine-grained data about the way students interact with proof
assistants. We have performed a preliminary usability study of ProofBuddy at
the Technical University of Denmark.


  We describe advice derived from one of the largest development programs in
Norway, where twelve Scrum teams combined agile practices with traditional
project management. The Perform program delivered 12 releases over a four-year
period, and finished on budget and on time. In this article, we summarize 12
key lessons on five crucial topics, relevant to other large development
projects seeking to combine Scrum with traditional project management.


  We propose a new kind of optical vortex called the Hermite-Gaussian-like
optical vortex (HGOV) inspired by the crossphase (CP). Theoretically, we
investigate how the CP is decoupled from the phase of a cylindrical lens. We
also investigate the propagation characteristics of an HGOV, which has a
Hermitian-Gaussian-like intensity distribution but still retains the orbital
angular momentum. Furthermore, we derive the Fresnel diffraction integral of an
HGOV and study the purity at infinity. Besides, we show a novel function of the
self-measurement of the HGOV. Finally, we show that we can change the relative
positions of singularities and the direction of an HGOV precisely, which
facilitates applications in optical micromanipulation.


  Blockchain, which is a technology for distributedly managing ledger
information over multiple nodes without a centralized system, has elicited
increasing attention. Performing experiments on actual blockchains are
difficult because a large number of nodes in wide areas are necessary. In this
study, we developed a blockchain network simulator SimBlock for such
experiments. Unlike the existing simulators, SimBlock can easily change
behavior of node, so that it enables to investigate the influence of nodes'
behavior on blockchains. We compared some simulation results with the measured
values in actual blockchains to demonstrate the validity of this simulator.
Furthermore, to show practical usage, we conducted two experiments which
clarify the influence of neighbor node selection algorithms and relay networks
on the block propagation time. The simulator could depict the effects of the
two techniques on block propagation time. The simulator will be publicly
available in a few months.


  Given a set of alternatives to be ranked, and some pairwise comparison data,
ranking is a least squares computation on a graph. The vertices are the
alternatives, and the edge values comprise the comparison data. The basic idea
is very simple and old: come up with values on vertices such that their
differences match the given edge data. Since an exact match will usually be
impossible, one settles for matching in a least squares sense. This formulation
was first described by Leake in 1976 for rankingfootball teams and appears as
an example in Professor Gilbert Strang's classic linear algebra textbook. If
one is willing to look into the residual a little further, then the problem
really comes alive, as shown effectively by the remarkable recent paper of
Jiang et al. With or without this twist, the humble least squares problem on
graphs has far-reaching connections with many current areas ofresearch. These
connections are to theoretical computer science (spectral graph theory, and
multilevel methods for graph Laplacian systems); numerical analysis (algebraic
multigrid, and finite element exterior calculus); other mathematics (Hodge
decomposition, and random clique complexes); and applications (arbitrage, and
ranking of sports teams). Not all of these connections are explored in this
paper, but many are. The underlying ideas are easy to explain, requiring only
the four fundamental subspaces from elementary linear algebra. One of our aims
is to explain these basic ideas and connections, to get researchers in many
fields interested in this topic. Another aim is to use our numerical
experiments for guidance on selecting methods and exposing the need for further
development.


  This paper studies higher-order inference properties of nonparametric local
polynomial regression methods under random sampling. We prove Edgeworth
expansions for $t$ statistics and coverage error expansions for interval
estimators that (i) hold uniformly in the data generating process, (ii) allow
for the uniform kernel, and (iii) cover estimation of derivatives of the
regression function. The terms of the higher-order expansions, and their
associated rates as a function of the sample size and bandwidth sequence,
depend on the smoothness of the population regression function, the smoothness
exploited by the inference procedure, and on whether the evaluation point is in
the interior or on the boundary of the support. We prove that robust bias
corrected confidence intervals have the fastest coverage error decay rates in
all cases, and we use our results to deliver novel, inference-optimal bandwidth
selectors. The main methodological results are implemented in companion
\textsf{R} and \textsf{Stata} software packages.


  The energy spectrum of graphene sheet with a single barrier structure having
a time periodic oscillating height and subjected to magnetic field is analyzed.
The corresponding transmission is studied as function of the obtained energy
and the potential parameters. Quantum interference within the oscillating
barrier has an important effect on quasiparticles tunneling. In particular the
time-periodic electromagnetic field generates additional sidebands at energies
\epsilon + l\hbar \omega (l=0,\pm 1, \cdots) in the transmission probability
originating from the photon absorption or emission within the oscillating
barrier. Due to numerical difficulties in truncating the resulting coupled
channel equations we limited ourselves to low quantum channels, i.e. l=0,\pm 1.


  The static properties and semileptonic decays of ground-state doubly heavy
baryons are studied working in the framework of a non-relativistic quark model.
Using a phenomenological potential model, we calculate the ground-state masses
and magnetic moments of doubly heavy $ \Omega $ and $ \Xi $ baryons. In the
heavy quark limit, we introduce a simple form of the universal Isgur-Wise
function used as the transition form factor and then investigate the exclusive
$ b \rightarrow c $ semileptonic decay widths and branching ratios for $
\frac{1}{2}\rightarrow \frac{1}{2} $ baryon transitions. Our obtained results
are in agreement with other theoretical predictions.


  Cluster lensing has become an important tool in the search for high redshift
galaxies through its ability to magnify sources. In order to determine the
intrinsic properties of these galaxies, lensing mass models must be constructed
to determine the magnification of the images. These models are traditionally
two-dimensional, focusing on the mass within the cluster and either ignoring or
approximating any contribution from line-of-sight galaxies. In this paper, we
present the first full set of three-dimensional mass models of the six Hubble
Frontier Fields and use them to test for systematic biases in magnifications
due to using the traditional 2D approach. We find that omitting foreground or
background galaxies causes image position offsets between 0.1-0.4", a
non-negligible fraction of the typical 0.3-0.7" residuals of current
state-of-the-art models. We also find that median image magnifications can
shift by up to 6%, though it is dependent on the field. This can be alleviated
in some cases by approximating the mass in the lensing plane, but a 5%
magnification bias still exists in other cases; image position offsets are also
improved, but are still present at 0.10".


  The non-trivial ultraviolet fixed point in quantum gravity is calculated by
means of the exact renormalization group equation in d-dimensions $(2\simeq
d\leq4)$. It is shown that the ultraviolet non-Gaussian fixed point which is
expected from the perturbatively $\epsilon$-expanded calculations in
$2+\epsilon$ gravity theory remains in d=4. Hence it is possible that quantum
gravity is an asymptotically safe theory and renormalizable in 2<d.


  The coding problem for wiretap channels with causal channel state information
available at the encoder and/or the decoder is studied under the strong secrecy
criterion. This problem consists of two aspects: one is due to naive wiretap
channel coding and the other is due to one-time pad cipher based on the secret
key agreement between Alice and Bob using the channel state information. These
two aspects are closely related to each other and investigated in details from
the viewpoint of achievable rates with strong secrecy. The general wiretap
channel with various directions of channel state informations among Alice, Bob
and Eve (called the multi-way channel state information) is also studied, which
includes the above systems as special cases. We provide a unified view about
wiretap channel coding with causal multi-way channel state information.


  We investigate the constraints on the flavour violating parameters from the
decay B --> X_s gamma, taking into account the interplay of the various sources
of flavour violation in the unconstrained MSSM. We present a systematic leading
logarithmic QCD analysis of these model-independent constraints, including
contributions from gluinos, neutralinos, charginos, charged Higgs bosons and
interferences between them. We show that two simple combinations of elements of
the down squark mass matrix are stringently bounded over large parts of the
parameter space where only weak assumptions on the hierarchical structure of
the squark mass matrices are made. We also briefly analyse up to which values
SUSY contributions, compatible with B --> X_s gamma, can enhance the Wilson
coefficient C_8(m_W), which plays an important role in the phenomenology of
charmless hadronic B decays.


  Determination of nuclear moments for many nuclei relies on the computation of
hyperfine constants, with theoretical uncertainties directly affecting the
resulting uncertainties of the nuclear moments. In this work we improve the
precision of such method by including for the first time an iterative solution
of equations for the core triple cluster amplitudes into the relativistic
coupled-cluster method, with large-scale complete basis sets. We carried out
calculations of the energies and magnetic dipole and electric quadrupole
hyperfine structure constants for the low-lying states of 229Th^(3+) in the
framework of such relativistic coupled-cluster single double triple (CCSDT)
method. We present a detailed study of various corrections to all calculated
properties. Using the theory results and experimental data we found the nuclear
magnetic dipole and electric quadrupole moments to be mu = 0.366(6)*mu_N and Q
= 3.11(2) eb, and reducing the uncertainty of the quadrupole moment by a factor
of three. The Bohr-Weisskopf effect of the finite nuclear magnetization is
investigated, with bounds placed on the deviation of the magnetization
distribution from the uniform one.


  Nanodroplets on chemically structured substrates move under the action of
disjoining pressure induced forces. A detailed analysis of them shows that even
in the absence of long-ranged lateral variations of the effective interface
potential, already the fact, that due their small size nano-droplets do not
sample the disjoining pressure at all distances from the substrate, can lead to
droplet motion towards the less wettable part of the substrate, i.e., in the
direction opposite to the one expected on the basis of macroscopic wettability
considerations.


  The effect of an Aharonov-Bohm (AB) pseudo magnetic field on a two
dimensional electron gas in graphene is investigated. We consider it modeled as
in the usual AB effect but since such pseudo field is supposed to be induced by
elastic deformations, the quantization of the field flux is abandoned. For
certain constraints on the orbital angular momentum eigenvalues allowed for the
system, we can observe the zero Landau level failing to develop, due to the
degeneracy related to the Dirac valleys $K$ and $K^\prime$ which is broken. For
integer values of the pseudo AB flux, the actual quantum Hall effect is
preserved. Obtaining the Hall conductivity by summing over all orbital angular
momentum eigenvalues, the zero Landau levels is recovered. Since our problem is
closed related to the case where topological defects on a graphene sheet are
present, the questions posed here are helpful if one is interested to probe the
effects of a singular curvature in these systems.


  Here we study the wave propagation and stability of general relativistic
non-resistive dissipative second-order magnetohydrodynamic equations in curved
space-time. We solve the Boltzmann equation for a system of particles and
antiparticles using the relaxation time approximation and the
Chapman-Enskog-like gradient expansion for the off-equilibrium distribution
function, truncating beyond second-order in curved space-time in
electromagnetic fields. Unlike holographic calculation~\cite{Baier:2007ix}, we
show that the viscous evolution equations do not explicitly depend on the
curvature of space-time. Also, we have tested the causality and stability of
the second-order theory in curved space-time in the presence of linearised
metric perturbation and derived dispersion relations for various modes.
Interestingly, we found the coupling of gravitational modes with the usual
magneto-sonic modes in the small wave-number limit. Also, we show additional
non-hydrodynamical modes arise due to gravity for a bulk-viscous fluid.


  Space offers exciting opportunities to test the foundations of quantum
physics using macroscopic quantum superpositions. It has been proposed to
perform such tests in a dedicated space mission (MAQRO) using matter-wave
interferometry with massive test particles or monitoring how the wave function
of a test particle expands over time. Such experiments could, test quantum
physics with sufficiently high precision to resolve potential deviations from
its unitary evolution due to gravitational decoherence. For example, such
deviations have been predicted by the Di\'{o}si-Penrose (DP) model and the
K\'{a}rolyh\'{a}zy (K) model. The former predicts the collapse of massive or
large superpositions due to a non-linear modification of quantum evolution. The
latter predicts decoherence because of an underlying uncertainty of space time.
Potential advantages of a space environment are (1) long free-fall times, (2)
low noise, and (3) taking a high number of data points over several years in a
dedicated space mission. In contrast to interferometric tests, monitoring wave
function expansion is less complex, but it does face some practical
limitations. Here, we will discuss limitations of such non-interferometric
experiments due to the limited number of data points achievable during a
mission lifetime. Our results show that it will require an interferometric
approach to conclusively test for gravitational decoherence as predicted by the
DP or K models. In honor of the novel prize of Sir Roger Penrose, we will focus
our discussion on the Di\'{o}si-Penrose model.


  We study renormalization group flows between six-dimensional superconformal
field theories (SCFTs) using their geometric realizations as singular limits of
F-theory compactified on elliptically fibered Calabi-Yau threefolds. There are
two general types of flows: One corresponds to giving expectation values to
scalars in the tensor multiplets (tensor branch flow) realized as resolving the
base of the geometry. The other corresponds to giving expectation values to
hypermultiplets (Higgs branch flow) realized as complex structure deformations
of the geometry. To corroborate this physical picture we calculate the change
in the anomaly polynomial for these theories, finding strong evidence for a
flow from a UV fixed point to an IR fixed point. Moreover, we find evidence
against non-trivial dualities for 6D SCFTs. In addition we find non-trivial RG
flows for theories realizing small E8 instantons on ALE spaces.


  We investigated the properties of cholesteric liquid crystals (CLCs) being in
external static magnetic field directed along the helix axis. We have shown
that in the case of the wavelength dependence of magneto-optic activity
parameter, and in the presence of absorption new features appear in the optics
of CLCs. We have shown that in this case new photonic band gaps (PBGs) appear.
This new PBG is sensitive to the polarization of the incident light. But if the
chirality sign of the polarization of the incident diffracting light for the
basic PBG (which exist also at the absence of external magnetic field) is
determined only by the chirality sign of the CLC helix, then for the second one
it is determined by the external magnetic field direction (i.e., on whether the
directions of the external magnetic field and the incident light are parallel,
or they are antiparallel). We have shown that in this case besides Dirac points
there appear also Dirac lines as well as exceptional points and exceptional
lines. And moreover, at some of these points and lines there appear the lines
or wide bands of magnetically induced transparency, on others a wide coherent
perfect absorption band appears that is insensitive to incident light
polarization. And finally on some points the same reflection, transmission and
absorption takes place for any polarization of incident light. This system can
be applied as tunable narrow-band or broad-band filters and mirrors, a highly
tunable broad/narrow-band coherent perfect absorber, transmitter, ideal optical
diode, and other devices.


  Predictive coding (PC) networks are a biologically interesting class of
neural networks. Their layered hierarchy mimics the reciprocal connectivity
pattern observed in the mammalian cortex, and they can be trained using local
learning rules that approximate backpropagation [Bogacz, 2017]. However,
despite having feedback connections that enable information to flow down the
network hierarchy, discriminative PC networks are not generative. Clamping the
output class and running the network to equilibrium yields an input sample that
typically does not resemble the training input. This paper studies this
phenomenon, and proposes a simple solution that promotes the generation of
input samples that resemble the training inputs. Simple decay, a technique
already in wide use in neural networks, pushes the PC network toward a unique
minimum 2-norm solution, and that unique solution provably (for linear
networks) matches the training inputs. The method also vastly improves the
samples generated for nonlinear networks, as we demonstrate on MNIST.


  We consider the second weighted Bartholdi zeta function of a graph $G$, and
present weighted versions for the result of Li and Hou's on the partial
derivatives of the determinant part in the determinant expression of the
Bartholdi zeta function of $G$. Furthermore, we give a formula for the weighted
Kirchhoff index of a regular covering of $G$ in terms of that of $G$.


  This is a comment on the recent paper by Soumitra Nandi1 and Ulrich Nierste
``Resolving the sign ambiguity in $\Delta \Gamma_s$ with $B_s \to D_s K$'',
arXiv:0801.0143 [hep-ph].


  We present a novel architecture for a multi-pass amplifier based on a
succession of optical Fourier transforms and short propagations that shows a
superior stability for variations of the thermal lens compared to
state-of-the-art 4f-based amplifiers. We found that the proposed multi-pass
amplifier is robust to variations of the active medium dioptric power. The
superiority of the proposed architecture is demonstrated by analyzing the
variations of the size and divergence of the output beam in form of a Taylor
expansion around the design value for variations of the thermal lens in the
active medium. The dependence of the output beam divergence and size is
investigated also for variations of the number of passes, for aperture effects
in the active medium and as a function of the size of the beam on the active
medium. This architecture makes efficient use of the transverse beam filtering
inherent in the active medium to deliver a beam with excellent quality (TEM00)
without additional losses.


  In situ lossy compression allowing user-controlled data loss can
significantly reduce the I/O burden. For large-scale N-body simulations where
only one snapshot can be compressed at a time, the lossy compression ratio is
very limited because of the fairly low spatial coherence of the particle data.
In this work, we assess the state-of-the-art single-snapshot lossy compression
techniques of two common N-body simulation models: cosmology and molecular
dynamics. We design a series of novel optimization techniques based on the two
representative real-world N-body simulation codes. For molecular dynamics
simulation, we propose three compression modes (i.e., best speed, best
tradeoff, best compression mode) that can refine the tradeoff between the
compression rate (a.k.a., speed/throughput) and ratio. For cosmology
simulation, we identify that our improved SZ is the best lossy compressor with
respect to both compression ratio and rate. Its compression ratio is higher
than the second-best compressor by 11% with comparable compression rate.
Experiments with up to 1024 cores on the Blues supercomputer at Argonne show
that our proposed lossy compression method can reduce I/O time by 80% compared
with writing data directly to a parallel file system and outperforms the
second-best solution by 60%. Moreover, our proposed lossy compression methods
have the best rate-distortion with reasonable compression errors on the tested
N-body simulation data compared with state-of-the-art compressors.


  Exciton-polaritons (polaritons herein) in transition-metal dichalcogenide
monolayers have attracted significant attention due to their potential for
polariton-based optoelectronics. Many of the proposed applications rely on the
ability to trap polaritons and to reach macroscopic occupation of their ground
energy state. Here, we engineer a trap for room-temperature polaritons in an
all-dielectric optical microcavity by locally increasing the interactions
between the WS$_2$ excitons and cavity photons. The resulting confinement
enhances the population and the first-order coherence of the polaritons in the
ground state, with the latter effect related to dramatic suppression of
disorder-induced inhomogeneous dephasing. We also demonstrate efficient
population transfer into the trap when optically injecting free polaritons
outside of its periphery.


  In this paper, we investigate resource allocation strategies for a
point-to-point wireless communications system with hybrid energy sources
consisting of an energy harvester and a conventional energy source. In
particular, as an incentive to promote the use of renewable energy, we assume
that the renewable energy has a lower cost than the conventional energy. Then,
by assuming that the non-causal information of the energy arrivals and the
channel power gains are available, we minimize the total energy cost of such a
system over $N$ fading slots under a proposed outage constraint together with
the energy harvesting constraints. The outage constraint requires a minimum
fixed number of slots to be reliably decoded, and thus leads to a mixed-integer
programming formulation for the optimization problem. This constraint is
useful, for example, if an outer code is used to recover all the data bits.
Optimal linear time algorithms are obtained for two extreme cases, i.e., the
number of outage slot is $1$ or $N-1$. For the general case, a lower bound
based on the linear programming relaxation, and two suboptimal algorithms are
proposed. It is shown that the proposed suboptimal algorithms exhibit only a
small gap from the lower bound. We then extend the proposed algorithms to the
multi-cycle scenario in which the outage constraint is imposed for each cycle
separately. Finally, we investigate the resource allocation strategies when
only causal information on the energy arrivals and only channel statistics is
available. It is shown that the greedy energy allocation is optimal for this
scenario.


  This paper introduces a novel Transformed Primal-Dual with
variable-metric/preconditioner (TPDv) algorithm, designed to efficiently solve
affine constrained optimization problems common in nonlinear partial
differential equations (PDEs). Diverging from traditional methods, TPDv
iteratively updates time-evolving preconditioning operators, enhancing
adaptability. The algorithm is derived and analyzed, demonstrating global
linear convergence rates under mild assumptions. Numerical experiments on
challenging nonlinear PDEs, including the Darcy-Forchheimer model and a
nonlinear electromagnetic problem, showcase the algorithm's superiority over
existing methods in terms of iteration numbers and computational efficiency.
The paper concludes with a comprehensive convergence analysis.


  Simultaneous, precise measurements of the mass $M$ and radius $R$ of neutron
stars can yield uniquely valuable information about the still uncertain
properties of cold matter at several times the density of nuclear matter. One
method that could be used to measure $M$ and $R$ is to analyze the
energy-dependent waveforms of the X-ray flux oscillations seen during some
thermonuclear bursts from some neutron stars. These oscillations are thought to
be produced by X-ray emission from hotter regions on the surface of the star
that are rotating at or near the spin frequency of the star. Here we explore
how well $M$ and $R$ could be determined by generating, and analyzing using
Bayesian techniques, synthetic energy-resolved X-ray data that we produce
assuming a future space mission having 2--30 keV energy coverage and an
effective area of 10 m$^2$, such as the proposed \textit{LOFT} or
\textit{AXTAR} missions.
  We find that if the hot spot is within 10$^\circ$ of the rotation equator,
both $M$ and $R$ can usually be determined with an uncertainty of about 10% if
there are $10^6$ total counts from the spot, whereas waveforms from spots
within 20$^\circ$ of the rotation pole provide no useful constraints. These
constraints can usually be achieved even if the burst oscillations vary with
time and data from multiple bursts must be used to obtain 10$^6$ counts from
the hot spot. This is therefore a promising method to constrain $M$ and $R$
tightly enough to discriminate strongly between competing models of cold,
high-density matter.


  Hypothetical existence of other universes gives an opportunity not only to
extend the scope of physics, but the scope of biology, SETI, and METI as well.
Some steps of the development of alien life concept shall be briefly
summarized, then the multiverse proposal shall be used as a framework of
interpretation to introduce an extended taxonomy of possible or at least
imaginable types of life and intelligence based on either different
biochemistry or physics. Some consequences shall be presented about SETI and
METI in connection with both multiverse hypothesis and anthropic principle.


  In two-dimensional space there are possibilities for quantum statistics
continuously interpolating between the bosonic and the fermionic one.
Quasi-particles obeying such statistics can be described as ordinary bosons and
fermions with magnetic interactions. We study a limit situation where the
statistics/magnetic interaction is seen as a "perturbation from the fermionic
end". We vindicate a mean-field approximation, proving that the ground state of
a gas of anyons is described to leading order by a semi-classical, Vlasov-like,
energy functional. The ground state of the latter displays anyonic behavior in
its momentum distribution. Our proof is based on coherent states, Husimi
functions, the Diaconis-Freedman theorem and a quantitative version of a
semi-classical Pauli pinciple.


  The bounded gaps property of the prime numbers, as proven by Yitang Zhang, is
considered for sequences of lengths of closed geodesics, which by the theory of
Selberg zeta functions are the geometric analogue of the prime numbers. It
turns out that the property holds for congruence subgroups and is false for a
dense set in Teichm\"uller space.


  As the LHC continues to search for new weakly interacting particles, it is
important to remember that the search is strongly motivated by the existence of
dark matter. In view of a possible positive signal, it is essential to ask
whether the newly discovered weakly interacting particle can be be assigned the
label "dark matter". Within a given set of simplified models and modest working
assumptions, we reinterpret the relic abundance bound as a relic abundance
range, and compare the parameter space yielding the correct relic abundance
with projections of the Run II exclusion regions. Assuming that dark matter is
within the reach of the LHC, we also make the comparison with the potential
5$\sigma$ discovery regions. Reversing the logic, relic density calculations
can be used to optimize dark matter searches by motivating choices of
parameters where the LHC can probe most deeply into the dark matter parameter
space. In the event that DM is seen outside of the region giving the correct
relic abundance, we will learn that either thermal relic DM is ruled out in
that model, or the DM-quark coupling is suppressed relative to the DM coupling
strength to other SM particles.


  The recent surge of interest in brain-inspired computing and power-efficient
electronics has dramatically bolstered development of computation and
communication using neuron-like spiking signals. Devices that can produce rapid
and energy-efficient spiking could significantly advance these applications.
Here we demonstrate DC-current or voltage-driven periodic spiking with sub-20
ns pulse widths from a single device composed of a thin VO2 film with a
metallic carbon nanotube as a nanoscale heater. Compared with VO2-only devices,
adding the nanotube heater dramatically decreases the transient duration and
pulse energy, and increases the spiking frequency, by up to three orders of
magnitude. This is caused by heating and cooling of the VO2 across its
insulator-metal transition being localized to a nanoscale conduction channel in
an otherwise bulk medium. This result provides an important component of
energy-efficient neuromorphic computing systems, and a lithography-free
technique for power-scaling of electronic devices that operate via bulk
mechanisms.


  Transformer architectures have been widely adopted in foundation models. Due
to their high inference costs, there is renewed interest in exploring the
potential of efficient recurrent architectures (RNNs). In this paper, we
analyze the differences in the representational capabilities of Transformers
and RNNs across several tasks of practical relevance, including index lookup,
nearest neighbor, recognizing bounded Dyck languages, and string equality. For
the tasks considered, our results show separations based on the size of the
model required for different architectures. For example, we show that a
one-layer Transformer of logarithmic width can perform index lookup, whereas an
RNN requires a hidden state of linear size. Conversely, while constant-size
RNNs can recognize bounded Dyck languages, we show that one-layer Transformers
require a linear size for this task. Furthermore, we show that two-layer
Transformers of logarithmic size can perform decision tasks such as string
equality or disjointness, whereas both one-layer Transformers and recurrent
models require linear size for these tasks. We also show that a log-size
two-layer Transformer can implement the nearest neighbor algorithm in its
forward pass; on the other hand recurrent models require linear size. Our
constructions are based on the existence of $N$ nearly orthogonal vectors in
$O(\log N)$ dimensional space and our lower bounds are based on reductions from
communication complexity problems. We supplement our theoretical results with
experiments that highlight the differences in the performance of these
architectures on practical-size sequences.


  We develop a variational multiscale proper orthogonal decomposition
reduced-order model for turbulent incompressible Navier-Stokes equations. The
error analysis of the full discretization of the model is presented. All error
contributions are considered: the spatial discretization error (due to the
finite element discretization), the temporal discretization error (due to the
backward Euler method), and the proper orthogonal decomposition truncation
error. Numerical tests for a three-dimensional turbulent flow past a cylinder
at Reynolds number Re=1000 show the improved physical accuracy of the new model
over the standard Galerkin and mixing-length proper orthogonal decomposition
reduced-order models. The high computational efficiency of the new model is
also showcased. Finally, the theoretical error estimates are confirmed by
numerical simulations of a two-dimensional Navier-Stokes problem.


  This paper deals with the simulateneous optimization of a subset
$\mathcal{O}_0$ of some domain $\Omega$ and its complement $\mathcal{O}_1 =
\Omega \setminus \overline{\mathcal{O}}_0$ both considered as separate elastic
objects subject to a set of loading scenarios. If one asks for a configuration
which minimizes the maximal elastic cost functional both phases compete for
space since elastic shapes usually get mechanically more stable when being
enlarged. Such a problem arises in biomechanics where a bioresorbable polymer
scaffold is implanted in place of lost bone tissue and in a regeneration phase
new bone tissue grows in the scaffold complement via osteogenesis. In fact, the
polymer scaffold should be mechanically stable to bear loading in the early
stage regeneration phase and at the same time the new bone tissue grown in the
complement of this scaffold should as well bear the loading. Here, this optimal
subdomain splitting problem with appropriate elastic cost functionals is
introduced and existence of optimal two phase configurations is established for
a regularized formulation. Furthermore, based on a phase field approximation a
finite element discretization is derived. Numerical experiments are presented
for the design of optimal periodic scaffold microstructure.


  Recent advances in quantum sensors, including atomic clocks, enable searches
for a broad range of dark matter candidates. The question of the dark matter
distribution in the Solar system critically affects the reach of dark matter
direct detection experiments. Partly motivated by the NASA Deep Space Atomic
Clock (DSAC), we show that space quantum sensors present new opportunities for
ultralight dark matter searches, especially for dark matter states bound to the
Sun. We show that space quantum sensors can probe unexplored parameter space of
ultralight dark matter, covering theoretical relaxion targets motivated by
naturalness and Higgs mixing. If an atomic clock were able to make measurements
on the interior of the solar system, it could probe this highly sensitive
region directly and set very strong constraints on the existence of such a
bound-state halo in our solar system. We present sensitivity projections for
space-based probes of ultralight dark matter which couples to electron, photon,
and gluon fields, based on current and future atomic, molecular, and nuclear
clocks.


  Weight sharing is a popular approach to reduce the cost of neural
architecture search (NAS) by reusing the weights of shared operators from
previously trained child models. However, the rank correlation between the
estimated accuracy and ground truth accuracy of those child models is low due
to the interference among different child models caused by weight sharing. In
this paper, we investigate the interference issue by sampling different child
models and calculating the gradient similarity of shared operators, and
observe: 1) the interference on a shared operator between two child models is
positively correlated with the number of different operators; 2) the
interference is smaller when the inputs and outputs of the shared operator are
more similar. Inspired by these two observations, we propose two approaches to
mitigate the interference: 1) MAGIC-T: rather than randomly sampling child
models for optimization, we propose a gradual modification scheme by modifying
one operator between adjacent optimization steps to minimize the interference
on the shared operators; 2) MAGIC-A: forcing the inputs and outputs of the
operator across all child models to be similar to reduce the interference.
Experiments on a BERT search space verify that mitigating interference via each
of our proposed methods improves the rank correlation of super-pet and
combining both methods can achieve better results. Our discovered architecture
outperforms RoBERTa$_{\rm base}$ by 1.1 and 0.6 points and ELECTRA$_{\rm base}$
by 1.6 and 1.1 points on the dev and test set of GLUE benchmark. Extensive
results on the BERT compression, reading comprehension and ImageNet task
demonstrate the effectiveness and generality of our proposed methods.


  Quenched lattice-QCD data on the dressed-quark Schwinger function can be
correlated with dressed-gluon data via a rainbow gap equation so long as that
equation's kernel possesses enhancement at infrared momenta above that
exhibited by the gluon alone. The required enhancement can be ascribed to a
dressing of the quark-gluon vertex. The solutions of the rainbow gap equation
exhibit dynamical chiral symmetry breaking and are consistent with confinement.
The gap equation and related, symmetry-preserving ladder Bethe-Salpeter
equation yield estimates for chiral and physical pion observables that suggest
these quantities are materially underestimated in the quenched theory: |<bar-q
q>| by a factor of two and f_pi by 30%.


  The aim of this workshop is to foster the exchange of insights on past and
ongoing research towards effective and long-lasting collaborations between
humans and robots. This workshop will provide a forum for representatives from
academia and industry communities to analyse the different aspects of HRI that
impact on its success. We particularly focus on AI techniques required to
implement autonomous and proactive interactions, on the factors that enhance,
undermine, or recover humans' acceptance and trust in robots, and on the
potential ethical and legal concerns related to the deployment of such robots
in human-centred environments.
  Website: https://sites.google.com/view/traits-hri-2022


  We study the topology associated with physical vector and scalar fields. A
mathematical object, e.g., a ball, can be continuously deformed, without
tearing or gluing, to make other topologically equivalent objects, e.g., a cube
or a solid disk. If tearing or gluing get involved, i.e., the deformation is
not continuous anymore, the initial topology will consequently change giving
rise to a topologically distinct object, e.g., a torus. This simple concept in
general topology may be employed in the study of physical systems described by
fields. Instead of continuously deforming objects, we can take a continuously
evolving field, with an appropriately defined topology, such that the topology
remains unchanged in time unless the system undergoes an important physical
change, e.g., a transition to a different energy state. For instance, a sudden
change in the magnetic topology in an energetically relaxing plasma, a process
called reconnection, strongly affects the dynamics, e.g., it is involved in
launching solar flares and generating large scale magnetic fields in
astrophysical objects. In this topological formalism, the magnetic topology in
a plasma can spontaneously change due to the presence of dissipative terms in
the induction equation which break its time symmetry. We define a topology for
the vector field $\bf F$ in the phase space $(\bf x, F)$. As for scalar fields
represented by a perfect fluid, e.g., the inhomogeneous inflaton or Higgs
fields, the fluid velocity $\bf u$ defines the corresponding topology. The
vector field topology in its corresponding phase space $(\bf x, F)$ will be
preserved in time if certain conditions including time reversal invariance are
satisfied by the field and its governing differential equation.


  ZnO microspheres fabricated via laser ablation in superfluid helium were
found to have bubble-like voids. Even a microsphere demonstrating clear
whispering gallery mode resonances in the luminescence had voids. Our analysis
confirmed that the voids are located away from the surface and have negligible
or little effect on the whispering gallery mode resonances since the
electromagnetic energy localizes near the surface of these microspheres. The
existence of the voids indicates that helium gas or any evaporated target
material was present within the molten microparticles during the microsphere
formation.


  The effects of graphite surface geometrical deformation on the dynamics of
conducting electrons are investigated theoretically. The analysis is performed
within the framework of a deformation-induced gauge field and corresponding
deformation-induced magnetic field. It is shown that the latter gives a local
energy gap along the axis of a deformed nanotube. We compare our energy gap
results with experimental data on energy gaps in nanotubes and peapods. We also
discuss the mixing of two Fermi points and construct a general model of low
energy dynamics, including a short-range deformation of the graphite sheet.
This model is equivalent to the Weyl equation in {\it U}(1) Abelian and {\it
SU}(2) non-Abelian deformation-induced gauge fields.


  We study entanglement entropy for a class of states in quantum field theory
that are entangled superpositions of coherent states with well-separated
supports, analogous to Einstein-Podolsky-Rosen or Bell states. We calculate the
contributions beyond the area law in a simple model. In the case of strongly
coupled conformal field theories, we argue that these states are
holographically dual to superpositions of bulk geometries. We note that for
these states one can use the Ryu-Takayanagi holographic entanglement entropy
formula to calculate some terms in the entanglement entropy, but that there can
be additional O(N^2) contributions. We argue that this class of states includes
those generated by local quenches and thus that these cannot be described by a
classical dual geometry. These considerations may be important for more fine
grained treatments of holographic thermalization.


  The morphologies of vessel-like structures, such as blood vessels and nerve
fibres, play significant roles in disease diagnosis, e.g., Parkinson's disease.
Deep network-based refinement segmentation methods have recently achieved
promising vessel-like structure segmentation results. There are still two
challenges: (1) existing methods have limitations in rehabilitating subsection
ruptures in segmented vessel-like structures; (2) they are often overconfident
in predicted segmentation results. To tackle these two challenges, this paper
attempts to leverage the potential of spatial interconnection relationships
among subsection ruptures from the structure rehabilitation perspective. Based
on this, we propose a novel Vessel-like Structure Rehabilitation Network
(VSR-Net) to rehabilitate subsection ruptures and improve the model calibration
based on coarse vessel-like structure segmentation results. VSR-Net first
constructs subsection rupture clusters with Curvilinear Clustering Module
(CCM). Then, the well-designed Curvilinear Merging Module (CMM) is applied to
rehabilitate the subsection ruptures to obtain the refined vessel-like
structures. Extensive experiments on five 2D/3D medical image datasets show
that VSR-Net significantly outperforms state-of-the-art (SOTA) refinement
segmentation methods with lower calibration error. Additionally, we provide
quantitative analysis to explain the morphological difference between the
rehabilitation results of VSR-Net and ground truth (GT), which is smaller than
SOTA methods and GT, demonstrating that our method better rehabilitates
vessel-like structures by restoring subsection ruptures.


  This paper introduces a multi-period inspector scheduling problem (MPISP),
which is a new variant of the multi-trip vehicle routing problem with time
windows (VRPTW). In the MPISP, each inspector is scheduled to perform a route
in a given multi-period planning horizon. At the end of each period, each
inspector is not required to return to the depot but has to stay at one of the
vertices for recuperation. If the remaining time of the current period is
insufficient for an inspector to travel from his/her current vertex $A$ to a
certain vertex B, he/she can choose either waiting at vertex A until the start
of the next period or traveling to a vertex C that is closer to vertex B.
Therefore, the shortest transit time between any vertex pair is affected by the
length of the period and the departure time. We first describe an approach of
computing the shortest transit time between any pair of vertices with an
arbitrary departure time. To solve the MPISP, we then propose several local
search operators adapted from classical operators for the VRPTW and integrate
them into a tabu search framework. In addition, we present a constrained
knapsack model that is able to produce an upper bound for the problem. Finally,
we evaluate the effectiveness of our algorithm with extensive experiments based
on a set of test instances. Our computational results indicate that our
approach generates high-quality solutions.


  We present an unbiased mean-field analysis of magnetic and charge orders in
the two-dimensional Hubbard model on a square lattice, both at zero and finite
temperatures. Unrestricted Hartree-Fock calculations on large finite lattices
are complemented by solutions restricted to N\'eel and circular spiral order in
the thermodynamic limit. The magnetic states are classified by a systematic
scheme based on the dominant Fourier components of the spin texture. On finite
lattices a whole zoo of ordering patterns appears. We show that many of these
states are finite size artifacts related to the limited choice of ordering wave
vectors on a finite lattice. In the thermodynamic limit only three classes of
states with a relatively simple structure survive: N\'eel, circular spiral, and
stripe states. Stripes involve also charge order and can be unidirectional or
bidirectional, with horizontal and/or vertical orientation. We present complete
phase diagrams in the plane spanned by electron density and temperature, for a
moderate Hubbard interaction and various choices of the next-nearest neighbor
hopping amplitude.


  We study the effect of starlight from the first stars on the ability of other
minihaloes in their neighborhood to form additional stars. The question of what
the dynamical consequences were for these target minihaloes, of their exposure
to the ionizing and dissociating starlight from the Pop III star requires a
full radiation-hydrodynamics calculation. Towards this end, we have performed a
series of detailed, 1D, radiation-hydrodynamical simulations. We have varied
the distance to the source (and, hence, the flux) and the mass and evolutionary
stage of the target haloes to quantify this effect. We find: (1) trapping of
the I-front and its transformation from R-type to D-type, preceded by a shock
front; (2) photoevaporation of the ionized gas (i.e. all gas originally located
outside the trapping radius); (3) formation of an H_2 precursor shell which
leads the I-front, stimulated by partial photoionization; and (4) the
shock-induced formation of H_2 in the minihalo neutral core when the shock
speeds up and partially ionizes the gas. The fate of the neutral core is mostly
determined by the response of the core to this shock front, which leads to
molecular cooling and collapse that, when compared to the same halo without
external radiation, is either: (a) expedited, (b) delayed, (c) unaltered, or
(d) reversed or prevented, depending upon the flux (i.e. distance to the
source) and the halo mass and evolutionary stage. Roughly speaking, most haloes
that were destined to cool, collapse, and form stars in the absence of external
radiation are found to do so even when exposed to the first Pop III star in
their neighborhood, while those that would not have done so are still not able
to. (abridged)


  We introduce a new jet-finding algorithm for a hadron collider based on
maximizing a J_{E_T} function for all possible combinations of particles in an
event. This function prefers a larger value of the jet transverse energy and a
smaller value of the jet mass. The jet shape is proved to be a circular cone in
Cartesian coordinates with the geometric center shifted from the jet momentum
toward the central region. The jet cone size shrinks for a more forward jet. We
have implemented our J_{E_T} algorithm with a reasonable running time scaling
as N n^3, where "N" is the total number of particles and "n" (much less than N)
is the number of particles in a fiducial region. Many features of our J_{E_T}
jets are similar to anti-k_t jets, including the reconstructed jet momentum and
the "back-reaction" from soft contamination. Nevertheless, when the jet
parameters in the two algorithms are matched using QCD jets, we find that the
J_{E_T} algorithm has a larger efficiency than anti-k_t for identifying objects
with hard splittings such as a W-jet.


  Text to speech (TTS) has made rapid progress in both academia and industry in
recent years. Some questions naturally arise that whether a TTS system can
achieve human-level quality, how to define/judge that quality and how to
achieve it. In this paper, we answer these questions by first defining the
human-level quality based on the statistical significance of subjective measure
and introducing appropriate guidelines to judge it, and then developing a TTS
system called NaturalSpeech that achieves human-level quality on a benchmark
dataset. Specifically, we leverage a variational autoencoder (VAE) for
end-to-end text to waveform generation, with several key modules to enhance the
capacity of the prior from text and reduce the complexity of the posterior from
speech, including phoneme pre-training, differentiable duration modeling,
bidirectional prior/posterior modeling, and a memory mechanism in VAE.
Experiment evaluations on popular LJSpeech dataset show that our proposed
NaturalSpeech achieves -0.01 CMOS (comparative mean opinion score) to human
recordings at the sentence level, with Wilcoxon signed rank test at p-level p
>> 0.05, which demonstrates no statistically significant difference from human
recordings for the first time on this dataset.


  Quantum steering is considered as one of the most well-known nonlocal
phenomena in quantum mechanics. Unlike entanglement and Bell non-locality, the
asymmetry of quantum steering makes it vital for one-sided device-independent
quantum information processing. Although there has been much progress on
steering detection for bipartite systems, the criterion for EPR steering in
tripartite systems remains challenging and inadequate. In this paper, we
firstly derive a novel and promising steering criterion for any three-qubit
states via correlation matrix. Furthermore, we propose the monogamy relation
between the tripartite steering of system and the bipartite steering of
subsystems based on the derived criterion. Finally, as illustrations, we
demonstrate the performance of the steering criterion and the monogamy relation
by means of several representative examples. We believe that the results and
methods presented in this work could be beneficial to capture genuine
multipartite steering in the near future.


  The nature of binary black hole coalescence is the final, uncharted frontier
of the relativistic Kepler problem. In the United States, binary black hole
coalescence has been identified as a computational ``Grand Challenge'' whose
solution is the object of a coordinated effort, just reaching its half-way
point, by more than two-score researchers at nearly a dozen institutions. In
this report I highlight what I see as the most serious problems standing
between us and a general computational solution to the problem of binary black
hole coalescence:
  * the computational burden associated of the problem based on reasonable
extrapolations of present-day computing algorithms and near-term hardware
developments;
  * some of the computational issues associated with those estimates, and how,
through the use of different or more sophisticated computational algorithms we
might reduce the expected burden; and
  * some of the physical problems associated with the development of a
numerical solution of the field equations for a binary black hole system, with
particular attention to work going on in, or in association with, the Grand
Challenge.


  A common experimental strategy for demonstrating non-classical correlations
is to show violation of a Bell inequality by measuring a continuously emitted
stream of entangled photon pairs. The measurements involve the detection of
photons by two spatially separated parties. The detection times are recorded
and compared to quantify the violation. The violation critically depends on
determining which detections are coincident. Because the recorded detection
times have "jitter", coincidences cannot be inferred perfectly. In the presence
of settings-dependent timing errors, this can allow a local-realistic system to
show apparent violation--the so-called "coincidence loophole". Here we
introduce a family of Bell inequalities based on signed, directed distances
between the parties' sequences of recorded timetags. Given that the timetags
are recorded for synchronized, fixed observation periods and that the settings
choices are random and independent of the source, violation of these
inequalities unambiguously shows non-classical correlations violating local
realism. Distance-based Bell inequalities are generally useful for two-party
configurations where the effective size of the measurement outcome space is
large or infinite. We show how to systematically modify the underlying Bell
functions to improve the signal to noise ratio and to quantify the significance
of the violation.


  RRab stars are large amplitude pulsating stars in which the pulsation is
associated with strong shock wake propagating in the atmosphere. The objective
of this study is to provide a general overview of the dynamical structure of
the atmosphere occurring over a typical pulsation cycle. We report new
high-resolution observations with high time resolution of H$\alpha$ and sodium
lines in the brightest RR Lyrae star of the sky: RR Lyr (HD 182989). A detailed
analysis of line profile variations over the whole pulsation cycle is performed
to understand the dynamical structure of the atmosphere. The main shock wave
appears when it exits from the photosphere at $\varphi\simeq0.89$, i.e., when
the main H$\alpha$ emission is observed. Whereas the acceleration phase of the
shock is not observed, a significant deceleration of the shock front velocity
is clearly present. The radiative stage of the shock wave is short: $4\%$ of
the pulsation period ($0.892<\varphi<0.929$). A Mach number $M>10$ is required
to get such a radiative shock. The sodium layer reaches its maximum expansion
well before that of H$\alpha$ ($\Delta\varphi=0.135$). Thus, a rarefaction wave
is induced between the H$\alpha$ and sodium layers. A strong atmospheric
compression occurring around $\varphi=0.36$, which produces the third H$\alpha$
emission, takes place in the highest part of the atmosphere. The region located
lower in the atmosphere where the sodium line is formed is not involved. The
amplification of gas turbulence seems mainly due to strong shock waves
propagating in the atmosphere rather than to the global compression of the
atmosphere caused by the pulsation. It has not yet been clearly established
whether the microturbulence velocity increases or decreases with height in the
atmosphere. Furthermore, it seems very probable that an interstellar component
is visible within the sodium profile.


  The mathematical interpretation of L0, L1 and L2 is needed to understand how
we should use these norms for optimization problems. The L0 norm is
combinatorics which is counting certain properties of an object or an operator.
This is the least amplitude dependent norm since it is counted regardless of
the magnitude. The L1 norm could be interpreted as minimal geometric
description. It is somewhat sensitive to amplitude information. In geophysics,
it has been used to edit outliers like spikes in seismic data. This is a good
application of L1 norm. The L2 norm could be interpreted as the numerically
simplest solution to fitting data with a differential equation. It is very
sensitive to amplitude information. Previous application includes least square
migration. In this paper, we will show how to combine the usage of L0 and L1
and L2. We will not be optimizing the 3 norms simultaneously but will go from
one norm to the next norm to optimize the data before the final migration.


  We define the notion of spectral network on manifolds of dimension $\le 3$.
For a manifold $X$ equipped with a spectral network, we construct equivalences
between Chern-Simons invariants of flat ${\mathrm {SL}}(2,{\mathbb C})$-bundles
over $X$ and Chern-Simons invariants of flat ${\mathbb C}^\times$-bundles over
ramified double covers $\widetilde X$. Applications include a new viewpoint on
dilogarithmic formulas for Chern-Simons invariants of flat ${\mathrm
{SL}}(2,{\mathbb C})$-bundles over triangulated 3-manifolds, and an explicit
description of Chern-Simons lines of flat ${\mathrm {SL}}(2,{\mathbb
C})$-bundles over triangulated surfaces. Our constructions heavily exploit the
locality of Chern-Simons invariants, expressed in the language of extended
(invertible) topological field theory.


  We study the decoherence properties of a certain class of Markovian quantum
open systems from both the Decohering Histories and Environment Induced
Superselection paradigms. The class studied includes many familiar quantum
optical cases. For this class, we show that there always exists a basis which
leads to {\em exactly} consistent histories for any coarse graining {\em
irrespective} of the initial conditions. The magnitude of the off--diagonal
elements of the reduced density matrix $\rho$ in this basis however, depends on
the initial conditions. Necessary requirements for classicality as advanced by
the two paradigms are thus in direct conflict in these systems.


  Large Language Models (LLMs) have transformed the landscape of artificial
intelligence, while their enormous size presents significant challenges in
terms of computational costs. We introduce LoRAShear, a novel efficient
approach to structurally prune LLMs and recover knowledge. Given general LLMs,
LoRAShear at first creates the dependency graphs over LoRA modules to discover
minimally removal structures and analyze the knowledge distribution. It then
proceeds progressive structured pruning on LoRA adaptors and enables inherent
knowledge transfer to better preserve the information in the redundant
structures. To recover the lost knowledge during pruning, LoRAShear
meticulously studies and proposes a dynamic fine-tuning schemes with dynamic
data adaptors to effectively narrow down the performance gap to the full
models. Numerical results demonstrate that by only using one GPU within a
couple of GPU days, LoRAShear effectively reduced footprint of LLMs by 20% with
only 1.0% performance degradation and significantly outperforms
state-of-the-arts. The source code will be available at
https://github.com/microsoft/lorashear.


  Personalized conversational information retrieval (CIR) combines
conversational and personalizable elements to satisfy various users' complex
information needs through multi-turn interaction based on their backgrounds.
The key promise is that the personal textual knowledge base (PTKB) can improve
the CIR effectiveness because the retrieval results can be more related to the
user's background. However, PTKB is noisy: not every piece of knowledge in PTKB
is relevant to the specific query at hand. In this paper, we explore and test
several ways to select knowledge from PTKB and use it for query reformulation
by using a large language model (LLM). The experimental results show the PTKB
might not always improve the search results when used alone, but LLM can help
generate a more appropriate personalized query when high-quality guidance is
provided.


  In this paper, we consider a version of the bias conjecture for second
moments in the setting of elliptic curves over finite fields whose trace of
Frobenius lies in an arbitrary fixed arithmetic progression. Contrary to the
classical setting of reductions of one-parameter families over the rationals,
where it is conjectured by Steven J. Miller that the bias is always negative,
we prove that in our setting the bias is positive for a positive density of
arithmetic progressions and negative for a positive density of arithmetic
progressions. Along the way, we obtain explicit formulas for moments of traces
of Frobenius of elliptic curves over finite fields in arithmetic progressions
and related moments of Hurwitz class numbers in arithmetic progressions, the
distribution of which are of independent interest.


  In recent years the spread of the ectoparasitic mite Varroa destructor has
become the most serious threat to worldwide apiculture. In the model presented
here we extend the bee population dynamics with mite viral epidemiology
examined in an earlier paper by allowing a bee-dependent mite population size.
The results of the analysis match field observations well and give a clear
explanation of how Varroa affects the epidemiology of certain naturally
occurring bee viruses, causing considerable damages to colonies. The model
allows only four possible stable equilibria, using known field parameters. The
first one contains only the thriving healthy bees. Here the disease is
eradicated and also the mites are wiped out. Alternatively, we find the
equilibrium still with no mite population, but with endemic disease among the
thriving bee population. Thirdly, infected bees coexist with the mites in the
Varroa invasion scenario; in this situation the disease invades the hive,
driving the healthy bees to extinction and therefore affecting all the bees.
Coexistence is also possible, with both populations of bees and mites thriving
and with the disease endemically affecting both species. The analysis is in
line with field observations in natural honey bee colonies. Namely, these
diseases are endemic and if the mite population is present, necessarily the
viral infection occurs. Further, in agreement with the fact that the presence
of Varroa increments the viral transmission, the whole bee population may
become infected when the disease vector is present in the beehive. Also, a low
horizontal transmission rate of the virus among the honey bees will help in
protecting the bee colonies from Varroa infestation and viral epidemics.


  Watkins (1969) first introduced the generalized Petersen graphs (GPGs) by
modifying Petersen graph. Zhou and Feng (2012) modified GPGs and introduced the
double generalized Petersen graphs (DGPGs). Kutnar and Petecki (2016) proved
that DGPGs are Hamiltonian in special cases and conjectured that all DGPGs are
Hamiltonian. In this paper, we construct Hamilton cycles in all DGPGs.


  The Coulomb Branch Formula conjecturally expresses the refined Witten index
for $N=4$ Quiver Quantum Mechanics as a sum over multi-centered collinear black
hole solutions, weighted by so-called `single-centered' or `pure-Higgs'
indices, and suitably modified when the quiver has oriented cycles. On the
other hand, localization expresses the same index as an integral over the
complexified Cartan torus and auxiliary fields, which by Stokes' theorem leads
to the famous Jeffrey-Kirwan residue formula. Here, by evaluating the same
integral using steepest descent methods, we show the index is in fact given by
a sum over deformed multi-centered collinear solutions, which encompasses both
regular and scaling collinear solutions. As a result, we confirm the Coulomb
Branch Formula for Abelian quivers in the presence of oriented cycles, and
identify the origin of the pure-Higgs and minimal modification terms as coming
from collinear scaling solutions. For cyclic Abelian quivers, we observe that
part of the scaling contributions reproduce the stacky invariants for trivial
stability, a mathematically well-defined notion whose physics significance had
remained obscure.