  Hamiltonian Boundary Value Methods are a new class of energy preserving one
step methods for the solution of polynomial Hamiltonian dynamical systems. They
can be thought of as a generalization of collocation methods in that they may
be defined by imposing a suitable set of extended collocation conditions. In
particular, in the way they are described in this note, they are related to
Gauss collocation methods with the difference that they are able to precisely
conserve the Hamiltonian function in the case where this is a polynomial of any
high degree in the momenta and in the generalized coordinates. A description of
these new formulas is followed by a few test problems showing how, in many
relevant situations, the precise conservation of the Hamiltonian is crucial to
simulate on a computer the correct behavior of the theoretical solutions.


  We show that the spectral energy distribution of relic gravitons mildly
increases for frequencies smaller than the $\mu$Hz and then flattens out
whenever the refractive index of the tensor modes is dynamical during a
quasi-de Sitter stage of expansion. For a conventional thermal history the
high-frequency plateau ranges between the mHz and the audio band but it is
supplemented by a spike in the GHz region if a stiff post-inflationary phase
precedes the standard radiation-dominated epoch. Even though the slope is blue
at intermediate frequencies, it may become violet in the MHz window. For a
variety of post-inflationary histories, including the conventional one, a
dynamical index of refraction leads to a potentially detectable spectral energy
density in the kHz and in the mHz regions while all the relevant
phenomenological constraints are concurrently satisfied.


  In this paper, the repeat-groundtrack orbit maintenance problem is addressed
for spacecraft driven by electric propulsion. An adaptive solution is proposed,
which combines an hysteresis controller and a recursive least squares filter.
The controller provides a pulse-width modulated command to the thruster, in
compliance with the peculiarities of the electric propulsion technology. The
filter takes care of estimating a set of environmental disturbance parameters,
from inertial position and velocity measurements. The resulting control scheme
is able to compensate for the groundtrack drift due to atmospheric drag, in a
fully autonomous manner. A numerical study of a low Earth orbit mission
confirms the effectiveness of the proposed method.


  This paper considers a cell-free massive multipleinput multiple-output (MIMO)
integrated sensing and communication (ISAC) system, where distributed MIMO
access points (APs) are used to jointly serve the communication users and
detect the presence of a single target. We investigate the problem of AP
operation mode selection, wherein some APs are dedicated for downlink
communication, while the remaining APs are used for sensing purposes.
Closed-form expressions for the individual spectral efficiency (SE) and
mainlobe-to-average-sidelobe ratio (MASR) are derived, which are respectively
utilized to assess the communication and sensing performances. Accordingly, a
maxmin fairness problem is formulated and solved, where the minimum SE of the
users is maximized, subject to the per-AP power constraints as well as sensing
MASR constraint. Our numerical results show that the proposed AP operation mode
selection with power control can significantly improve the communication
performance for given sensing requirements.


  We trap absorbing micro-particles in air by photophoretic forces generated
using a single loosely focused Gaussian trapping beam. We measure a component
of the radial Brownian motion of a trapped particle cluster and determine the
power spectral density, mean squared displacement, and normalized position and
velocity autocorrelation functions in order to characterize the photophoretic
body force in a quantitative fashion for the first time. The trapped particles
also undergo spontaneous rotation due to the action of this force. This is
evident from the spectral density that displays clear peaks at the rotation and
the particles' inertial resonance frequencies. We fit the spectral density to
the well-known analytical function derived from the Langevin equation, measure
the resonance and rotation frequencies and determine values for particle mass
that we verify at different trapping laser powers with reasonable accuracy.


  The use of social media as a means of communication has significantly
increased over recent years. There is a plethora of information flow over the
different topics of discussion, which is widespread across different domains.
The ease of information sharing has increased noisy data being induced along
with the relevant data stream. Finding such relevant data is important,
especially when we are dealing with a time-critical domain like disasters. It
is also more important to filter the relevant data in a real-time setting to
timely process and leverage the information for decision support.
  However, the short text and sometimes ungrammatical nature of social media
data challenge the extraction of contextual information cues, which could help
differentiate relevant vs. non-relevant information. This paper presents a
novel method to classify relevant social media posts during disaster events by
ensembling the features of both domain-specific word embeddings as well as more
generic domain-agnostic word embeddings. Therefore, we develop and evaluate a
hybrid feature engineering framework for integrating diverse semantic
representations using a combination of word embeddings to efficiently classify
a relevant social media post. The application of the proposed classification
framework could help in filtering public posts at large scale, given the
growing usage of social media posts in recent years.


  A multi-physics model for shale gas production in fractured system, that
couples real gas property, nano-flow mechanism and geomechanics


  We determine the fermionic corrections to the nucleation rate of bubbles at
the electroweak phase transition. The fermion determinant is evaluated exactly
and by using the gradient expansion. The gradient expansion is found to be a
reliable approximation and is used to extrapolate to the large values of $\nu_n
= (2n+1)\pi T$ needed in the Matsubara sum. The contribution to effective
action is found to be negative and to be given, essentially, by the gradient
terms, the finite part of the wave function renormalization. Only the top quark
contribution is evaluated, it is of the same order as the Higgs- and W-boson
contributions found previously, but of opposite sign.


  The classification of topological Floquet systems with time-periodic
Hamiltonians transcends that of static systems. For example, spinless fermions
in periodically driven two-dimensional lattices are not completely
characterized by the Chern numbers of the quasienergy bands, but rather by a
set of winding numbers associated with the quasienergy gaps. We propose a
scheme for measuring these winding numbers in a system of fermionic cold atoms
in a periodically driven optical lattice efficiently and directly. It is based
on the construction of a one-parameter family of experimentally feasible
drives, continuously connecting the Floquet system of interest to a trivial
reference system. The winding numbers are then determined by the identification
and the tomography of the band-touching singularities occurring on the way. As
a byproduct, we also propose a method for probing spectral properties of time
evolution operators via a time analog of crystallography.


  The goal of this paper is to study the lookdown model with selection in the
case of a population containing two types of individuals, with a reproduction
model which is dual to the $\Lambda$-coalescent. In particular we formulate the
infinite population "$\Lambda$-lookdown model with selection". When the measure
$\Lambda$ gives no mass to 0, we show that the proportion of one of the two
types converges, as the population size $N$ tends to infinity, towards the
solution of a stochastic differential equation driven by a Poisson point
process. We show that one of the two types fixates in finite time if and only
if the $\Lambda$-coalescent comes down from infinity. We give precise
asymptotic results in the case of the Bolthausen-Sznitman coalescent. We also
consider the general case of a combination of the Kingman and the
$\Lambda$-lookdown model.


  Improvements in the thermoelectric properties of Half-Heusler materials have
been achieved by means of a micrometer-scale phase separation that increases
the phonon scattering and reduces the lattice thermal conductivity. A detailed
study of the p-type Half-Heusler compounds Ti(1-x)Hf(x)CoSb0.85Sn0.15 using
high-resolution synchrotron powder X-ray diffraction and element mapping
electron microscopy evidences the outstanding thermoelectric properties of this
system. A combination of intrinsic phase separation and adjustment of the
carrier concentration via Sn substitution is used to realize a record
thermoelectric figure of merit for p-type Half-Heusler compounds of ZT around
1.15 at 710C in Ti0.25Hf0.75CoSb0.85Sn0.15. The phase separation approach can
form a significant alternative to nanostructuring processing time, energy
consumption and increasing the thermoelectric efficiency.


  In [Q. Xu et al., The solutions to some operator equations, Linear Algebra
Appl.(2008), doi:10.1016/j.laa.2008.05.034], Xu et al. provided the necessary
and sufficient conditions for the existence of a solution to the equation
$AXB^*-BX^*A^*=C$ in the general setting of the adjointable operators between
Hilbert $C^*$-modules. Based on the generalized inverses, they also obtained
the general expression of the solution in the solvable case. In this paper, we
generalize their work in the more general setting of ring $R$ with involution *
and reobtain results for rectangular matrices and operators between Hilbert
$C^*$-modules by embedding the "rectangles" into rings of square matrices or
rings of operators acting on the same space.


  We construct many nonpolytopal nonsimplicial Gorenstein* meet semi-lattices
with nonnegative toric g-vector, supporting a conjecture of Stanley. These are
formed as Bier spheres over the face posets of multiplexes, polytopes
constructed by Bisztriczky as generalizations of simplices.


  The adoption of renewable energy resources, such as solar power, is on the
rise. However, the excessive installation and lack of recycling facilities pose
environmental risks. This paper suggests a circular economy approach to address
the issue. By implementing blockchain technology, the end-of-life (EOL) of
solar panels can be tracked, and responsibilities can be assigned to relevant
stakeholders. The degradation of panels can be monetized by tracking users'
energy-related activities, and these funds can be used for future recycling. A
new coin, the recycling coin (RC-Coin), incentivizes solar panel recycling and
utilizes decentralized finance to stabilize the coin price and supply issue.


  The thermopower, S, of CexR1-xB6 (R=La, Pr, Nd) was investigated. S with a
positive sign shows a typical behavior observed in the Ce Kondo system, an
increase with decreasing temperature at high temperatures and a maximum at low
temperatures. The S values of all the systems at high temperatures are roughly
linearly dependent on the Ce concentration, indicating the conservation of the
single-impurity character of the Kondo effect in a wide x range. However, the
maximum value of S, S_max, and the temperature, T_max, at which S_max is
observed exhibit different x dependences between CexLa1-xB6 and CexR1-xB6
(R=Pr, Nd). In CexLa1-xB6, T_max, which is ~8 K in CeB6, decreases with
decreasing x and converges to ~1 K in a very dilute alloy and S_max shows an
increase below x ~ 0.1 after decreasing with decreasing x. In CexR1-xB6 (R=Pr,
Nd), T_max shows a weak x dependence but S_max shows a roughly linear decrease
in x. These results are discussed from the standpoint of the chemical pressure
effect and the Ce-Ce interaction. S in the long-range ordered phase shows very
different behavior between CexPr1-xB6 and CexNd1-xB6.


  We have provided a numerical study of the influence of the resonant
active-sterile neutrino oscillations between electron and sterile neutrinos, on
the primordial production of helium-4. The evolution of the neutrino ensembles
was followed selfconsistently with the evolution of the nucleons, using exact
kinetic equations for the neutrino density matrix and the nucleon number
densities in momentum space, from the time of neutrino decoupling till the
freeze-out of nucleons at 0.3 MeV.
  The exact kinetic approach enabled us to study precisely the neutrino
depletion, spectrum distortion and neutrino mixing generated asymmetry due to
oscillations at each momentum mode, and to prove that their effect on
nucleosynthesis is considerable.
  We have calculated the dependence of the primordially produced helium-4 Y_p
on the oscillation parameters for the full range of mixing parameters of the
model of oscillations with small mass differences less than 10^{-7} eV^2. We
have obtained iso-helium contours on the oscillation parameters plane.
Cosmological constraints on oscillation parameters, more precise than the
existing ones, were extracted, due to the exact kinetic approach and the proper
account for the neutrino spectrum distortion and the oscillations generated
asymmetry.


  We develop a tropical intersection formalism of forms and currents that
extends classical tropical intersection theory in two ways. First, it allows to
work with arbitrary polytopes, also non-rational ones. Second, it allows for
smooth differential forms as coefficients. The intersection product in our
formalism can be defined through the diagonal intersection method of
Allermann--Rau or the fan displacement rule. We prove with a limiting argument
that both definitions agree.


  Coupled quantum dots are potential candidates for qubit systems in quantum
computing. We use a non-invasive voltage probe to study the evolution of a
coupled dot system from a situation where the dots are coupled to the leads to
a situation where they are isolated from the leads. Our measurements allow us
to identify the movement of electrons between the dots and we can also identify
the presence of a charge trap in our system by detecting the movement of
electrons between the dots and the charge trap. The data also reveals evidence
of electrons moving between the dots via excited states of either the single
dots or the double dot molecule.


  Reversible logic circuits have been historically motivated by theoretical
research in low-power electronics as well as practical improvement of
bit-manipulation transforms in cryptography and computer graphics. Recently,
reversible circuits have attracted interest as components of quantum
algorithms, as well as in photonic and nano-computing technologies where some
switching devices offer no signal gain. Research in generating reversible logic
distinguishes between circuit synthesis, post-synthesis optimization, and
technology mapping. In this survey, we review algorithmic paradigms ---
search-based, cycle-based, transformation-based, and BDD-based --- as well as
specific algorithms for reversible synthesis, both exact and heuristic. We
conclude the survey by outlining key open challenges in synthesis of reversible
and quantum logic, as well as most common misconceptions.


  Quantum mechanics requires that identical particles are treated as
indistinguishable. This requirement leads to correlations in the fluctuating
properties of a system. Theoretical predictions are made for an experiment on a
multi-lead chaotic quantum dot which can identify exchange effects in
electronic current-current correlations. Interestingly, we find that the
ensemble averaged exchange effects are of the order of the channel number, and
are insensitive to dephasing.


  Local non-Hermitian (NH) quantum systems generically exhibit breakdown of
Lieb-Robinson (LR) bounds, motivating study of whether new locality measures
might shed light not seen by existing measures. In this paper we discuss
extensions of the connected correlation function (CC) as measures of locality
and information spreading in both Hermitian and NH systems. We find that in
Hermitian systems, $\delta\rho = \rho-\rho_A\otimes\rho_B$ can be written as a
linear combination of CCs, allowing placement of a LR bound on
$\Vert\delta\rho\Vert_2$, which we show generically extends to a LR bound on
mutual information. Additionally, we extend the CC to NH systems in a form that
recovers locality, and use the metric formalism to derive a modified CC which
recovers not just locality but even LR bounds. We find that even with these
CCs, the bound on $\Vert\delta\rho\Vert_2$ breaks down in certain NH cases,
which can be used to place a necessary condition on which NH Hamiltonians are
capable of nonlocal entanglement generation. Numerical simulations are provided
by means of exact diagonalization for the NH Transverse-Field Ising Model,
demonstrating both breakdown and recovery of LR bounds.


  The ability to accurately identify authorship is crucial for verifying
content authenticity and mitigating misinformation. Large Language Models
(LLMs) have demonstrated exceptional capacity for reasoning and
problem-solving. However, their potential in authorship analysis, encompassing
authorship verification and attribution, remains underexplored. This paper
conducts a comprehensive evaluation of LLMs in these critical tasks.
Traditional studies have depended on hand-crafted stylistic features, whereas
state-of-the-art approaches leverage text embeddings from pre-trained language
models. These methods, which typically require fine-tuning on labeled data,
often suffer from performance degradation in cross-domain applications and
provide limited explainability. This work seeks to address three research
questions: (1) Can LLMs perform zero-shot, end-to-end authorship verification
effectively? (2) Are LLMs capable of accurately attributing authorship among
multiple candidates authors (e.g., 10 and 20)? (3) How can LLMs provide
explainability in authorship analysis, particularly through the role of
linguistic features? Moreover, we investigate the integration of explicit
linguistic features to guide LLMs in their reasoning processes. Our extensive
assessment demonstrates LLMs' proficiency in both tasks without the need for
domain-specific fine-tuning, providing insights into their decision-making via
a detailed analysis of linguistic features. This establishes a new benchmark
for future research on LLM-based authorship analysis. The code and data are
available at https://github.com/baixianghuang/authorship-llm.


  In population biology, the Allee dynamics refer to negative growth rates
below a critical population density. In this Letter, we study a
reaction-diffusion (RD) model of population growth and dispersion in one
dimension, which incorporates the Allee effect in both the growth and mortality
rates. In the absence of diffusion, the bifurcation diagram displays regions of
both finite population density and zero population density, i.e., extinction.
The early signatures of the transition to extinction at a bifurcation point are
computed in the presence of additive noise. For the full RD model, the
existence of travelling wave solutions of the population density is
demonstrated. The parameter regimes in which the travelling wave advances
(range expansion) and retreats are identified. In the weak Allee regime, the
transition from the pushed to the pulled wave is shown as a function of the
mortality rate constant. The results obtained are in agreement with the recent
experimental observations on budding yeast populations.


  Building on large-scale quantum Monte Carlo simulations, we investigate the
zero-temperature phase diagram of hard-core bosons in a random potential on
site-centered Cayley trees with branching number $K=2$. In order to follow how
the Bose-Einstein condensate (BEC) is affected by the disorder, we focus on
both the zero-momentum density, probing the quantum coherence, and the one-body
density matrix (1BDM) whose largest eigenvalue monitors the off-diagonal
long-range order. We further study its associated eigenstate which brings
useful information about the real-space properties of this leading eigenmode.
Upon increasing randomness, we find that the system undergoes a quantum phase
transition at finite disorder strength between a long-range ordered BEC state,
fully ergodic at large scale, and a new disordered Bose glass regime showing
conventional localization for the coherence fraction while the 1BDM displays a
non-trivial algebraic vanishing BEC density together with a non-ergodic
occupation in real-space. These peculiar properties can be analytically
captured by a simple phenomenological description on the Cayley tree which
provides a physical picture of the Bose glass regime.


  Anchoring our understanding of low-energy nuclear and hadronic physics to the
fundamental theory of strong interactions, QCD, remains and outstanding
challenge for physicists. Lattice QCD and chiral perturbation theory are the
most powerful theoretical tools we have at our disposal to make this
connection. These tools share a symbiotic relationship as chiral perturbation
theory is used to understand the light quark mass dependence of observables,
while lattice QCD is used to determine the values of the unknown operator
coefficients appearing in the chiral Lagrangian. In this talk, I review our
present understanding of select single baryon properties from lattice QCD and
chiral perturbation theory, highlighting some of the challenges and discussing
some unresolved puzzles.


  Dialog Structure Induction (DSI) is the task of inferring the latent dialog
structure (i.e., a set of dialog states and their temporal transitions) of a
given goal-oriented dialog. It is a critical component for modern dialog system
design and discourse analysis. Existing DSI approaches are often purely
data-driven, deploy models that infer latent states without access to domain
knowledge, underperform when the training corpus is limited/noisy, or have
difficulty when test dialogs exhibit distributional shifts from the training
domain. This work explores a neural-symbolic approach as a potential solution
to these problems. We introduce Neural Probabilistic Soft Logic Dialogue
Structure Induction (NEUPSL DSI), a principled approach that injects symbolic
knowledge into the latent space of a generative neural model. We conduct a
thorough empirical investigation on the effect of NEUPSL DSI learning on hidden
representation quality, few-shot learning, and out-of-domain generalization
performance. Over three dialog structure induction datasets and across
unsupervised and semi-supervised settings for standard and cross-domain
generalization, the injection of symbolic knowledge using NEUPSL DSI provides a
consistent boost in performance over the canonical baselines.


  This paper describes a grapheme-to-phoneme conversion method using phoneme
connectivity and CCV conversion rules. The method consists of mainly four
modules including morpheme normalization, phrase-break detection, morpheme to
phoneme conversion and phoneme connectivity check.
  The morpheme normalization is to replace non-Korean symbols into standard
Korean graphemes. The phrase-break detector assigns phrase breaks using
part-of-speech (POS) information. In the morpheme-to-phoneme conversion module,
each morpheme in the phrase is converted into phonetic patterns by looking up
the morpheme phonetic pattern dictionary which contains candidate phonological
changes in boundaries of the morphemes. Graphemes within a morpheme are grouped
into CCV patterns and converted into phonemes by the CCV conversion rules. The
phoneme connectivity table supports grammaticality checking of the adjacent two
phonetic morphemes.
  In the experiments with a corpus of 4,973 sentences, we achieved 99.9% of the
grapheme-to-phoneme conversion performance and 97.5% of the sentence conversion
performance. The full Korean TTS system is now being implemented using this
conversion method.


  We compare Higgs inflation in the metric and Palatini formulations of general
relativity, with loop corrections are treated in a simple approximation. We
consider Higgs inflation on the plateau, at a critical point, at a hilltop and
in a false vacuum. In the last case there are only minor differences. Otherwise
we find that in the Palatini formulation the tensor-to-scalar ratio is
consistently suppressed, spanning the range $1\times10^{-13}<r<7\times10^{-5}$,
compared to the metric case result $2\times10^{-5}<r<0.2$. Even when the values
of $n_s$ and $r$ overlap, the running and running of the running are different
in the two formulations. Therefore, if Higgs is the inflaton, inflationary
observables can be used to distinguish between different gravitational degrees
of freedom, in this case to determine whether the connection is an independent
variable. Non-detection of $r$ in foreseeable future observations would not
rule out Higgs inflation, only its metric variant. We conclude that in order to
fix the theory of Higgs inflation, not only the particle physics UV completion
but also the gravitational degrees of freedom have to be explicated.


  In this paper we treat certain elliptic and hyper-elliptic integrals in a
unified way. We introduce a new basis of these integrals coming from certain
basis ${\phi}_n(x)$ of polynomials and show that the transition matrix between
this basis and the traditional monomial basis is certain upper triangular band
matrix. This allows us to obtain explicit formulas for the considered
integrals. Our approach, specified to elliptic case, is more effective than
known recursive procedures for elliptic integrals. We also show that basic
integrals enjoy symmetry coming from the action of the dihedral group $D_{M}$
on a real projective line. This action is closely connected with the properties
of homographic transformation of a real projective line. This explains
similarities occurring in some formulas in popular tables of elliptic
integrals. As a consequence one can reduce the number of necessary formulas in
a significant way.
  We believe that our results will simplify programming and computing the
hyper-elliptic integrals in various problems of mathematical physics and
engineering.


  Given a finite nonempty sequence of integers S, by grouping adjacent terms it
is always possible to write it, possibly in many ways, as S = X Y^k, where X
and Y are sequences and Y is nonempty. Choose the version which maximizes the
value of k: this k is the curling number of S. The Curling Number Conjecture is
that if one starts with any initial sequence S, and extends it by repeatedly
appending the curling number of the current sequence, the sequence will
eventually reach 1. The conjecture remains open, but we will report on some
numerical results and conjectures in the case when S consists of only 2's and
3's.


  Human Activity Recognition (HAR) using on-body devices identifies specific
human actions in unconstrained environments. HAR is challenging due to the
inter and intra-variance of human movements; moreover, annotated datasets from
on-body devices are scarce. This problem is mainly due to the difficulty of
data creation, i.e., recording, expensive annotation, and lack of standard
definitions of human activities. Previous works demonstrated that transfer
learning is a good strategy for addressing scenarios with scarce data. However,
the scarcity of annotated on-body device datasets remains. This paper proposes
using datasets intended for human-pose estimation as a source for transfer
learning; specifically, it deploys sequences of annotated pixel coordinates of
human joints from video datasets for HAR and human pose estimation. We
pre-train a deep architecture on four benchmark video-based source datasets.
Finally, an evaluation is carried out on three on-body device datasets
improving HAR performance.


  Practical video analytics systems that are deployed in bandwidth constrained
environments like autonomous vehicles perform computer vision tasks such as
face detection and recognition. In an end-to-end face analytics system, inputs
are first compressed using popular video codecs like HEVC and then passed onto
modules that perform face detection, alignment, and recognition sequentially.
Typically, the modules of these systems are evaluated independently using
task-specific imbalanced datasets that can misconstrue performance estimates.
In this paper, we perform a thorough end-to-end evaluation of a face analytics
system using a driving-specific dataset, which enables meaningful
interpretations. We demonstrate how independent task evaluations, dataset
imbalances, and inconsistent annotations can lead to incorrect system
performance estimates. We propose strategies to create balanced evaluation
subsets of our dataset and to make its annotations consistent across multiple
analytics tasks and scenarios. We then evaluate the end-to-end system
performance sequentially to account for task interdependencies. Our experiments
show that our approach provides consistent, accurate, and interpretable
estimates of the system's performance which is critical for real-world
applications.


  Stackelberg equilibria have become increasingly important as a solution
concept in computational game theory, largely inspired by practical problems
such as security settings. In practice, however, there is typically uncertainty
regarding the model about the opponent. This paper is, to our knowledge, the
first to investigate Stackelberg equilibria under uncertainty in extensive-form
games, one of the broadest classes of game. We introduce robust Stackelberg
equilibria, where the uncertainty is about the opponent's payoffs, as well as
ones where the opponent has limited lookahead and the uncertainty is about the
opponent's node evaluation function. We develop a new mixed-integer program for
the deterministic limited-lookahead setting. We then extend the program to the
robust setting for Stackelberg equilibrium under unlimited and under limited
lookahead by the opponent. We show that for the specific case of interval
uncertainty about the opponent's payoffs (or about the opponent's node
evaluations in the case of limited lookahead), robust Stackelberg equilibria
can be computed with a mixed-integer program that is of the same asymptotic
size as that for the deterministic setting.


  Testing for causation, defined as the preceding impact of the past values of
one variable on the current value of another one when all other pertinent
information is accounted for, is increasingly utilized in empirical research of
the time-series data in different scientific disciplines. A relatively recent
extension of this approach has been allowing for potential asymmetric impacts
since it is harmonious with the way reality operates in many cases according to
Hatemi-J (2012). The current paper maintains that it is also important to
account for the potential change in the parameters when asymmetric causation
tests are conducted, as there exists a number of reasons for changing the
potential causal connection between variables across time. The current paper
extends therefore the static asymmetric causality tests by making them dynamic
via the usage of subsamples. An application is also provided consistent with
measurable definitions of economic or financial bad as well as good news and
their potential interaction across time.


  While funding agencies have provided substantial support for the developers
and vendors of services that facilitate the unfettered flow of information
through the Internet, little consolidated knowledge exists on the basic
communications network infrastructure of the Islamic Republic of Iran. In the
absence open access and public data, rumors and fear have reigned supreme.
During provisional research on the country's censorship regime, we found
initial indicators that telecommunications entities in Iran allowed private
addresses to route domestically, whether intentionally or unintentionally,
creating a hidden network only reachable within the country. Moreover, records
such as DNS entries lend evidence of a 'dual stack' approach, wherein servers
are assigned a domestic IP addresses, in addition to a global one. Despite the
clear political implications of the claim we put forward, particularly in light
of rampant speculation regarding the mandate of Article 46 of the 'Fifth Five
Year Development Plan' to establish a "national information network," we
refrain from hypothesizing the purpose of this structure. In order to solicit
critical feedback for future research, we outline our initial findings and
attempt to demonstrate that the matter under contention is a nation-wide
phenomenom that warrants broader attention.


  We implement the inverse scattering method in the case of the $A_n$ affine
Toda field theories, by studying the space-time evolution of simple poles in
the underlying loop group. We find the known single soliton solutions, as well
as additional solutions with non-linear modes of oscillation around the
standard solution, by studying the particularly simple case where the residue
at the pole is a rank one projection. We show that these solutions with extra
modes have the same mass and topological charges as the standard solutions, so
we do not shed any light on the missing topological charge problem in these
models. We also show that the integrated energy-momentum density can be
calculated from the central extension of the loop group.


  In this paper, we introduce differential exponential maps in Cartesian
differential categories, which generalizes the exponential function $e^x$ from
classical differential calculus. A differential exponential map is an
endomorphism which is compatible with the differential combinator in such a way
that generalizations of $e^0 = 1$, $e^{x+y} = e^x e^y$, and $\frac{\partial
e^x}{\partial x} = e^x$ all hold. Every differential exponential map induces a
commutative rig, which we call a differential exponential rig, and conversely,
every differential exponential rig induces a differential exponential map. In
particular, differential exponential maps can be defined without the need of
limits, converging power series, or unique solutions of certain differential
equations -- which most Cartesian differential categories do not necessarily
have. That said, we do explain how every differential exponential map does
provide solutions to certain differential equations, and conversely how in the
presence of unique solutions, one can derivative a differential exponential
map. Examples of differential exponential maps in the Cartesian differential
category of real smooth functions include the exponential function, the complex
exponential function, the split complex exponential function, and the dual
numbers exponential function. As another source of interesting examples, we
also study differential exponential maps in the coKleisli category of a
differential category.


  We construct quantum Separation of Variables (SoV) bases for both the
fundamental inhomogeneous $gl_{\mathcal{M}|\mathcal{N}}$ supersymmetric
integrable models and for the inhomogeneous Hubbard model both defined with
quasi-periodic twisted boundary conditions given by twist matrices having
simple spectrum. The SoV bases are obtained by using the integrable structure
of these quantum models,i.e. the associated commuting transfer matrices,
following the general scheme introduced in [1]; namely, they are given by set
of states generated by the multiple action of the transfer matrices on a
generic co-vector. The existence of such SoV bases implies that the
corresponding transfer matrices have non degenerate spectrum and that they are
diagonalizable with simple spectrum if the twist matrices defining the
quasi-periodic boundary conditions have that property. Moreover, in these SoV
bases the resolution of the transfer matrix eigenvalue problem leads to the
resolution of the full spectral problem, i.e. both eigenvalues and
eigenvectors. Indeed, to any eigenvalue is associated the unique (up to a
trivial overall normalization) eigenvector whose wave-function in the SoV bases
is factorized into products of the corresponding transfer matrix eigenvalue
computed on the spectrum of the separate variables. As an application, we
characterize completely the transfer matrix spectrum in our SoV framework for
the fundamental $gl_{1|2}$ supersymmetric integrable model associated to a
special class of twist matrices. From these results we also prove the
completeness of the Bethe Ansatz for that case. The complete solution of the
spectral problem for fundamental inhomogeneous $gl_{\mathcal{M}|\mathcal{N}}$
supersymmetric integrable models and for the inhomogeneous Hubbard model under
the general twisted boundary conditions will be addressed in a future
publication.


  Multi-objective integer or mixed-integer programming problems typically have
disconnected feasible domains, making the task of constructing an approximation
of the Pareto front challenging. The present paper shows that certain
algorithms which were originally devised for continuous problems can be
successfully adapted to approximate the Pareto front for integer, and
mixed-integer, multi-objective optimization problems. Relationships amongst
various scalarization techniques are established to motivate the choice of a
particular scalarization in these algorithms. The proposed algorithms are
tested by means of two-, three- and four-objective integer and mixed-integer
problems, and comparisons are made. In particular, a new four-objective
algorithm is used to solve a rocket injector design problem with a discrete
variable, which is a challenging mixed-integer programming problem.


  In this paper we prove the universality of covariance matrices of the form
$H_{N\times N}={X}^{\dagger}X$ where $X$ is an ${M\times N}$ rectangular matrix
with independent real valued entries $x_{ij}$ satisfying $\mathbb{E}x_{ij}=0$
and $\mathbb{E}x^2_{ij}={\frac{1}{M}}$, $N$, $M\to \infty$. Furthermore it is
assumed that these entries have sub-exponential tails or sufficiently high
number of moments. We will study the asymptotics in the regime
$N/M=d_N\in(0,\infty),\lim_{N\to\infty}d_N\neq0,\infty$. Our main result is the
edge universality of the sample covariance matrix at both edges of the
spectrum. In the case $\lim_{N\to\infty}d_N=1$, we only focus on the largest
eigenvalue. Our proof is based on a novel version of the Green function
comparison theorem for data matrices with dependent entries. En route to
proving edge universality, we establish that the Stieltjes transform of the
empirical eigenvalue distribution of $H$ is given by the Marcenko-Pastur law
uniformly up to the edges of the spectrum with an error of order $(N\eta)^{-1}$
where $\eta$ is the imaginary part of the spectral parameter in the Stieltjes
transform. Combining these results with existing techniques we also show bulk
universality of covariance matrices. All our results hold for both real and
complex valued entries.


  This article shows the interfacial relation in electrodynamics shall be
corrected in discrete grid form which can be seen as certain numerical
dispersion beyond the usual bulk type. Furthermore we construct a lossy
conductor model to illustrate how to simulate more general materials other than
traditional PEC or simple dielectrics, by a new conformal FDTD method which
main considers the effects of penetrative depth and the distribution of free
bulk electric charge and current.


  We report an experimental demonstration of a coupled-mode heat engine in a
two-membrane-in-the-middle cavity optomechanical system. The normal mode of the
cavity-mediated strongly coupled nanoresonators is utilized as the working
medium and an Otto cycle is realized by extracting work between two phononic
thermal reservoirs. The heat engine performance is characterized in both normal
mode and bare mode pictures, which reveals that the correlation of two
membranes plays a significant role during the thermodynamic cycle. Moreover, a
straight-twin nanomechanical engine is implemented by engineering the normal
modes and operating two cylinders out-of-phase. Our results demonstrate an
essential class of heat engine for the first time in cavity optomechanical
systems and provide a novel platform for investigating heat engines of
interacting subsystems in small scales with controllability and scalability.


  Multilayer Laue lenses were used for the first time to focus x-rays from an
X-ray Free Electron Laser (XFEL). In an experiment, which was performed at the
European XFEL, we demonstrated focusing to a spot size of a few tens of
nanometers. A series of runs in which the number of pulses per train was
increased from 1 to 2, 3, 4, 5, 6, 7, 10, 20 and 30 pulses per train, all with
a pulse separation of 3.55 us, was done using the same set of lenses. The
increase in the number of pulses per train was accompanied with an increase of
x-ray intensity (transmission) from 9% to 92% at 5 pulses per train, and then
the transmission was reduced to 23.5 % when the pulses were increased further.
The final working condition was 30 pulses per train and 23.5% transmission.
Only at this condition we saw that the diffraction efficiency of the MLLs
changed over the course of a pulse train, and this variation was reproducible
from train to train. We present the procedure to align and characterize these
lenses and discuss challenges working with the pulse trains from this unique
x-ray source.


  In single-molecule magnets, the exchange between a localized spin moment and
the electronic background provides a suitable laboratory for studies of
dynamical aspects of both local spin and transport properties. Here we address
the time evolution of a localized spin moment coupled to an electronic level in
a molecular quantum dot embedded in a tunnel junction between metallic leads.
The interactions between the localized spin moment and the electronic level
generate an effective interaction between the spin moment at different
instances in time. Therefore, we show that, despite being a single-spin system,
there are effective contributions of isotropic Heisenberg, and anisotropic
Ising and Dzyaloshinski-Moriya character acting on the spin moment. The
interactions can be controlled by gate voltage, voltage bias, the spin
polarization in the leads, in addition to external magnetic fields. Signatures
of the spin dynamics are found in the transport properties of the tunneling
system, and we demonstrate that measurements of the spin current may be used
for read-out of the local spin moment orientation.


  To characterize the novel quantum phase transition for a hybrid system
consisting of an array of coupled cavities and two-level atoms doped in each
cavity, we study the atomic entanglement and photonic visibility in comparison
with the quantum fluctuation of total excitations. Analytical and numerical
simulation results show the happen of quantum critical phenomenon similar to
the Mott insulator to superfluid transition. Here, the contour lines
respectively representing the atomic entanglement, photonic visibility and
excitation variance in the phase diagram are consistent in the vicinity of the
non-analytic locus of atomic concurrences.


  Richard Guy asked for the largest set of points which can be placed in the
plane so that their pairwise distances are rational numbers. In this article,
we consider such a set of rational points restricted to a given hyperbola.
  To be precise, for rational numbers $a$, $b$, $c$, and $d$ such that the
quantity $D = \bigl(a \, d - b \, c \bigr) / \bigl(2 \, a^2 \bigr)$ is defined
and nonzero, we consider rational distance sets on the conic section $a \, x \,
y + b \, x + c \, y + d = 0$. We show that, if the elliptic curve $Y^2 = X^3 -
D^2 \, X$ has infinitely many rational points, then there are infinitely many
sets consisting of four rational points on the hyperbola such that their
pairwise distances are rational numbers. We also show that any rational
distance set of three such points can always be extended to a rational distance
set of four such points.


  Coravel observations of 103 F5-K0 stars in the Praesepe cluster yielded 24
spectroscopic binaries (3 are non-members), and 20 orbits were determined, with
periods from 4 to 7400 days. Based on a complete sample in the colour range
0.40 < B-V < 0.80 (80 stars, including KW 244 = TX Cnc), the companion star
fraction CSF = 0.45. The percentage of spectroscopic binaries with P < 1000d is
20% (16/80). The combined photometric and spectroscopic analysis showed that 12
among 18 single-lined spectroscopic binaries are located within the "single"
star sequence in the (V,B-V) diagram and cannot be detected by the photometric
analysis in the UBV system. In addition, seven photometrically analysed
binaries were not detected with the radial velocity observations, but are
confirmed members. The number of single:binary:triple stars is 47:30:3.


  Program refinement involves correctness-preserving transformations from
formal high-level specification statements into executable programs.
Traditional verification tool support for program refinement is highly
interactive and lacks automation. On the other hand, the emergence of large
language models (LLMs) enables automatic code generations from informal natural
language specifications. However, code generated by LLMs is often unreliable.
Moreover, the opaque procedure from specification to code provided by LLM is an
uncontrolled black box. We propose LLM4PR, a tool that combines formal program
refinement techniques with informal LLM-based methods to (1) transform the
specification to preconditions and postconditions, (2) automatically build
prompts based on refinement calculus, (3) interact with LLM to generate code,
and finally, (4) verify that the generated code satisfies the conditions of
refinement calculus, thus guaranteeing the correctness of the code. We have
implemented our tool using GPT4, Coq, and Coqhammer, and evaluated it on the
HumanEval and EvalPlus datasets.


  A relativistic particle in an attractive Coulomb field as well as a static
and spherically symmetric gravitational field is studied. The gravitational
field is treated perturbatively and the energy levels are obtained for both
spin 0 (Klein-Gordon) and spin 1/2 (Dirac) particles. The results are shown to
coincide with each other as well as the result of the nonrelativistic
(Schrodinger) equation in the nonrelativistic limit.


  This study investigates how the insertion of multimaterial circular regions
embracing the tip of a finite V-notch can be used to reduce the Notch Stress
Intensity Factors (NSIFs) in structures subjected to antiplane shear or
torsion. Towards this goal, this work presents a novel theoretical framework to
calculate stress distributions and NSIFs in closed-form. Thanks to the new
solution, it is shown that by tuning multimaterial region radii and elastic
properties it is possible to significantly reduce the NSIFs and stress
concentrations at the material interfaces.
  To investigate whether the proposed multimaterial system translates into
increased structural capacity even in the presence of significant nonlinear
deformations, computational simulations were conducted using nonlinear
hyperelastic-damage and elasto-plastic-damage models. The preliminary results
show increases of structural capacity up to 46% and of nominal strain at
failure of up to 86% at the expenses of only a 8% reduction in structural
stiffness.
  It is expected that a similar approach can be extended to other loading
conditions (e.g. mode I and mode II, and fatigue) and that even larger gains
can be obtained by performing thorough optimization studies.


  The analysis of samples of random objects that do not lie in a vector space
is gaining increasing attention in statistics. An important class of such
object data is univariate probability measures defined on the real line.
Adopting the Wasserstein metric, we develop a class of regression models for
such data, where random distributions serve as predictors and the responses are
either also distributions or scalars. To define this regression model, we
utilize the geometry of tangent bundles of the space of random measures endowed
with the Wasserstein metric for mapping distributions to tangent spaces. The
proposed distribution-to-distribution regression model provides an extension of
multivariate linear regression for Euclidean data and function-to-function
regression for Hilbert space valued data in functional data analysis. In
simulations, it performs better than an alternative transformation approach
where one maps distributions to a Hilbert space through the log quantile
density transformation and then applies traditional functional regression. We
derive asymptotic rates of convergence for the estimator of the regression
operator and for predicted distributions and also study an extension to
autoregressive models for distribution-valued time series. The proposed methods
are illustrated with data on human mortality and distributional time series of
house prices.


  We discuss the possibility of self-hybridisation in high-index dielectric
nanoparticles, where Mie modes of electric or magnetic type can couple to the
interband transitions of the material, leading to spectral anticrossings.
Starting with an idealised system described by moderately high constant
permittivity with a narrow Lorentzian, in which self-hybridisation is visible
for both plane-wave and electron-beam excitation, we embark on a quest for
realistic systems where this effect should be visible. We explore a variety of
spherical particles made of traditional semiconductors such as Si, GaAs, and
GaP. With the effect hardly discernible, we identify two major causes hindering
observation of self-hybridisation: the very broad spectral fingerprints of
interband transitions in most candidate materials, and the significant overlap
between electric and magnetic Mie modes in nanospheres. We thus depart from the
spherical shape, and show that interband--Mie hybridisation is indeed feasible
in the example of GaAs cylinders, even with a simple plane-wave source. This
so-far unreported kind of polariton has to be considered when interpreting
experimental spectra of Mie-resonant nanoparticles and assigning modal
characters to specific features. On the other hand, it has the potential to be
useful for the characterisation of the optical properties of dielectric
materials, through control of the hybridisation strength via nanoparticle size
and shape.


  Let f(x) be a differentiable function on the real line R, and let P be a
point not on the graph of f(x). Define the illumination index of P to be the
number of distinct tangents to the graph of f which pass thru P. We prove that
if f '' is continuous and nonnegative on R, f '' > m >0 outside a closed
interval of R, and f '' has finitely many zeroes on R, then every point below
the graph of f has illumination index 2. This result fails in general if f ''
is not bounded away from 0 on R. Also, if f '' has finitely many zeroes and f
'' is not nonnnegative on R, then some point below the graph has illumination
index not equal to 2. Finally, we generalize our results to illumination by odd
order Taylor polynomials.


  We have found conic programs for getting different types of quantum channels
as outputs of interpolation problems. Afterwards, we have generalized our
results for getting channels that belong to a convex set as outputs of the
interpolation problem. We show the existence of an Entanglement breaking
channel for orthogonal sets of input and output matrices.


  Current autonomic computing systems are ad hoc solutions that are designed
and implemented from the scratch. When designing software, in most cases two or
more patterns are to be composed to solve a bigger problem. A composite design
patterns shows a synergy that makes the composition more than just the sum of
its parts which leads to ready-made software architectures. As far as we know,
there are no studies on composition of design patterns for autonomic computing
domain. In this paper we propose pattern-oriented software architecture for
self-optimization in autonomic computing system using design patterns
composition and multi objective evolutionary algorithms that software designers
and/or programmers can exploit to drive their work. Main objective of the
system is to reduce the load in the server by distributing the population to
clients. We used Case Based Reasoning, Database Access, and Master Slave design
patterns. We evaluate the effectiveness of our architecture with and without
design patterns compositions. The use of composite design patterns in the
architecture and quantitative measurements are presented. A simple UML class
diagram is used to describe the architecture.


  This paper describes AaltoASR's speech recognition system for the INTERSPEECH
2020 shared task on Automatic Speech Recognition (ASR) for non-native
children's speech. The task is to recognize non-native speech from children of
various age groups given a limited amount of speech. Moreover, the speech being
spontaneous has false starts transcribed as partial words, which in the test
transcriptions leads to unseen partial words. To cope with these two
challenges, we investigate a data augmentation-based approach. Firstly, we
apply the prosody-based data augmentation to supplement the audio data.
Secondly, we simulate false starts by introducing partial-word noise in the
language modeling corpora creating new words. Acoustic models trained on
prosody-based augmented data outperform the models using the baseline recipe or
the SpecAugment-based augmentation. The partial-word noise also helps to
improve the baseline language model. Our ASR system, a combination of these
schemes, is placed third in the evaluation period and achieves the word error
rate of 18.71%. Post-evaluation period, we observe that increasing the amounts
of prosody-based augmented data leads to better performance. Furthermore,
removing low-confidence-score words from hypotheses can lead to further gains.
These two improvements lower the ASR error rate to 17.99%.


  There is rising interest in differentiable rendering, which allows explicitly
modeling geometric priors and constraints in optimization pipelines using
first-order methods such as backpropagation. Incorporating such domain
knowledge can lead to deep neural networks that are trained more robustly and
with limited data, as well as the capability to solve ill-posed inverse
problems. Existing efforts in differentiable rendering have focused on imagery
from electro-optical sensors, particularly conventional RGB-imagery. In this
work, we propose an approach for differentiable rendering of Synthetic Aperture
Radar (SAR) imagery, which combines methods from 3D computer graphics with
neural rendering. We demonstrate the approach on the inverse graphics problem
of 3D Object Reconstruction from limited SAR imagery using high-fidelity
simulated SAR data.


  We combine the design of two \emph{random number generators}, \emph{Mersenne
Twister} and \emph{Xorgens}, to obtain a new class of generators with
heavy-weight characteristic polynomials (exceeded only by the {\sc well}
generators) and high speed (comparable with the originals). Tables with
parameter combinations are included for state sizes ranging from 521 to 44497
bits and each of the word lengths 32, 64, 128. These generators passed all
tests of the \emph{TestU01}-package for each 32-bit integer part and each
64-bit derived real part of the output. We determine \emph{dimension gaps} for
32-bit words, neglecting the non-linear tempering, and compare with an
alternative experimental linear tempering.


  The reactive synthesis problem consists of automatically producing
correct-by-construction operational models of systems from high-level formal
specifications of their behaviours. However, specifications are often
unrealisable, meaning that no system can be synthesised from the specification.
To deal with this problem, we present AuRUS, a search-based approach to repair
unrealisable Linear-Time Temporal Logic (LTL) specifications. AuRUS aims at
generating solutions that are similar to the original specifications by using
the notions of syntactic and semantic similarities. Intuitively, the syntactic
similarity measures the text similarity between the specifications, while the
semantic similarity measures the number of behaviours preserved/removed by the
candidate repair. We propose a new heuristic based on model counting to
approximate semantic similarity. We empirically assess AuRUS on many
unrealisable specifications taken from different benchmarks and show that it
can successfully repair all of them. Also, compared to related techniques,
AuRUS can produce many unique solutions while showing more scalability.


  This paper deals with strong structural controllability of linear systems. In
contrast to existing work, the structured systems studied in this paper have a
so-called zero/nonzero/arbitrary structure, which means that some of the
entries are equal to zero, some of the entries are arbitrary but nonzero, and
the remaining entries are arbitrary (zero or nonzero). We formalize this in
terms of pattern matrices whose entries are either fixed zero, arbitrary
nonzero, or arbitrary. We establish necessary and sufficient algebraic
conditions for strong structural controllability in terms of full rank tests of
certain pattern matrices. We also give a necessary and sufficient graph
theoretic condition for the full rank property of a given pattern matrix. This
graph theoretic condition makes use of a new color change rule that is
introduced in this paper. Based on these two results, we then establish a
necessary and sufficient graph theoretic condition for strong structural
controllability. Moreover, we relate our results to those that exists in the
literature, and explain how our results generalize previous work.


  We give an exposition of recent results on regularity and Fredholm properties
for first-order one-dimensional hyperbolic PDEs. We show that large classes of
boundary operators cause an effect that smoothness increases with time. This
property is the key in finding regularizers (parametrices) for hyperbolic
problems. We construct regularizers for periodic problems for dissipative
first-order linear hyperbolic PDEs and show that these problems are modeled by
Fredholm operators of index zero.


  Motivated by the practical challenge in monitoring the performance of a large
number of algorithmic trading orders, this paper provides a methodology that
leads to automatic discovery of the causes that lie behind a poor trading
performance. It also gives theoretical foundations to a generic framework for
real-time trading analysis. Academic literature provides different ways to
formalize these algorithms and show how optimal they can be from a
mean-variance, a stochastic control, an impulse control or a statistical
learning viewpoint. This paper is agnostic about the way the algorithm has been
built and provides a theoretical formalism to identify in real-time the market
conditions that influenced its efficiency or inefficiency. For a given set of
characteristics describing the market context, selected by a practitioner, we
first show how a set of additional derived explanatory factors, called anomaly
detectors, can be created for each market order. We then will present an online
methodology to quantify how this extended set of factors, at any given time,
predicts which of the orders are underperforming while calculating the
predictive power of this explanatory factor set. Armed with this information,
which we call influence analysis, we intend to empower the order monitoring
user to take appropriate action on any affected orders by re-calibrating the
trading algorithms working the order through new parameters, pausing their
execution or taking over more direct trading control. Also we intend that use
of this method in the post trade analysis of algorithms can be taken advantage
of to automatically adjust their trading action.


  We show that the topology of the Fermi sea of a two-dimensional electron gas
(2DEG) is reflected in the ballistic Landauer transport along a long and narrow
Josephson $\pi$-junction that proximitizes the 2DEG. The low-energy Andreev
states bound to the junction are shown to exhibit a dispersion that is
sensitive to the Euler characteristic of the Fermi sea ($\chi_F$). We highlight
two important relations: one connects the electron/hole nature of Andreev
states to the convex/concave nature of Fermi surface critical points, and one
relates these critical points to $\chi_F$. We then argue that the transport of
Andreev states leads to a quantized conductance that probes $\chi_F$. An
experiment is proposed to measure this effect, from which we predict an $I$-$V$
characteristic that not only captures the topology of Fermi sea in metals, but
also resembles the rectification effect in diodes. Finally, we evaluate the
feasibility of measuring this quantized response in graphene, InAs and HgTe
2DEGs.


  Enhancing accurate molecular property prediction relies on effective and
proficient representation learning. It is crucial to incorporate diverse
molecular relationships characterized by multi-similarity (self-similarity and
relative similarities) between molecules. However, current molecular
representation learning methods fall short in exploring multi-similarity and
often underestimate the complexity of relationships between molecules.
Additionally, previous multi-similarity approaches require the specification of
positive and negative pairs to attribute distinct predefined weights to
different relative similarities, which can introduce potential bias. In this
work, we introduce Graph Multi-Similarity Learning for Molecular Property
Prediction (GraphMSL) framework, along with a novel approach to formulate a
generalized multi-similarity metric without the need to define positive and
negative pairs. In each of the chemical modality spaces (e.g.,molecular
depiction image, fingerprint, NMR, and SMILES) under consideration, we first
define a self-similarity metric (i.e., similarity between an anchor molecule
and another molecule), and then transform it into a generalized
multi-similarity metric for the anchor through a pair weighting function.
GraphMSL validates the efficacy of the multi-similarity metric across
MoleculeNet datasets. Furthermore, these metrics of all modalities are
integrated into a multimodal multi-similarity metric, which showcases the
potential to improve the performance. Moreover, the focus of the model can be
redirected or customized by altering the fusion function. Last but not least,
GraphMSL proves effective in drug discovery evaluations through post-hoc
analyses of the learnt representations.


  We have developed a method to calculate the fundamental parameters of the
vertical structure of the Galaxy in the solar neighborhood from trigonometric
parallaxes alone. The method takes into account Lutz-Kelker-type biases in a
self-consistent way and has been applied to a sample of O-B5 stars obtained
from the Hipparcos catalog. We find that the Sun is located 24.2 +/- 1.7
(random) +/- 0.4 (systematic) pc above the galactic plane and that the disk
O-B5 stellar population is distributed with a scale height of 34.2 +/- 0.8
(random) +/- 2.5 (systematic) pc and an integrated surface density of (1.62 +/-
0.04 (random) +/- 0.14 (systematic)) 10^{-3} stars pc^{-2}. A halo component is
also detected in the distribution and constitutes at least ~5% of the total
O-B5 population. The O-B5 stellar population within ~100 pc of the Sun has an
anomalous spatial distribution, with a less-than-average number density. This
local disturbance is probably associated with the expansion of Gould's belt.


  A variety $X/k$ is said to have the Hilbert Property if $X(k)$ is not thin.
We shall describe some examples of varieties, for which the Hilbert Property is
a new result. We give a criterion for determining when the Hilbert Property for
a variety $X$ implies the Hilbert Property for quotients $X/G$ of the variety
by an action of a finite group. In the case of linear actions of the group $G$,
this gives examples of (non-rational) unirational varieties with the Hilbert
Property, providing positive examples to a conjecture by Colliot-Th\'el\`ene
and Sansuc. We focus then on the study of the Hilbert Property for K3 surfaces
that have two elliptic fibrations, in particular on diagonal quartic surfaces,
i.e. varieties of the form $ax^4+by^4+cz^4+dw^4=0$. We then show, through an
explicit application, how one may use the criterion above to provide other
examples of K3 surfaces with the Hilbert Property. Since the Hilbert Property
is related to an abudance of rational points, K3 surfaces should
(conjecturally) represent a limiting case in dimension 2.


  In this work, we critique two papers, "A Polynomial-Time Solution to the
Clique Problem" by Tamta, Pande, and Dhami, and "A Polynomial-Time Algorithm
For Solving Clique Problems" by LaPlante. We summarize and analyze both papers,
noting that the algorithms presented in both papers are flawed. We conclude
that neither author has successfully established that P = NP.


  We study the zero temperature properties of a trapped polarized Fermi gas at
unitarity by assuming phase separation between an unpolarized superfluid and a
polarized normal phase. The effects of the interaction are accounted using the
formalism of quasi-particles to build up the equation of state of the normal
phase with the Monte Carlo results for the relevant parameters. Our predictions
for the Chandrasekhar-Clogston limit of critical polarization and for the
density profiles, including the density jump at the interface, are confirmed
with excellent accuracy by the recent experimental results at MIT. The role of
interaction on the radial width of the minority component, on the gap of the RF
transition and on the spin oscillations in the normal phase is also discussed.
Our analysis points out the Fermi liquid nature of these strongly interacting
spin polarized configurations.


  A large number of theoretically predicted waveforms are required by
matched-filtering searches for the gravitational-wave signals produced by
compact binary coalescence. In order to substantially alleviate the
computational burden in gravitational-wave searches and parameter estimation
without degrading the signal detectability, we propose a novel
reduced-order-model (ROM) approach with applications to adiabatic 3PN-accurate
inspiral waveforms of sources that evolve on either highly or slightly
eccentric orbits. We provide a singular-value decomposition-based reduced-basis
method in the frequency domain to generate reduced-order approximations of any
gravitational waves with acceptable accuracy and precision within the parameter
range of the model. We construct efficient reduced bases comprised of a
relatively small number of the most relevant waveforms over 3-dimensional
parameter-space covered by the template bank (total mass $2.15M_{\odot} \leq M
\leq 215M_{\odot}$, mass ratio $0.01 \leq q \leq 1$, and initial orbital
eccentricity $0 \leq e_{0} \leq 0.95$). The ROM is designed to predict signals
in the frequency band from 10 Hz to 2 kHz for aLIGO and aVirgo design
sensitivity. Beside moderating the data reduction, finer sampling of fiducial
templates improves the accuracy of surrogates. Considerable increase in the
speedup from several hundreds to thousands can be achieved by evaluating
surrogates for low-mass systems especially when combined with
high-eccentricity.


  We demonstrate how the quantum paraelectric ground state of SrTiO$_3$ can be
accessed via a microscopic $ab~initio$ approach based on density functional
theory. At low temperature the quantum fluctuations are strong enough to
stabilize the paraelectric phase even though a classical description would
predict a ferroelectric phase. We find that accounting for quantum fluctuations
of the lattice and for the strong coupling between the ferroelectric soft mode
and lattice elongation is necessary to achieve quantitative agreement with
experimental frequency of the ferroelectric soft mode. The temperature
dependent properties in SrTiO$_3$ are also well captured by the present
microscopic framework.


  Deep learning has contributed greatly to many successes in artificial
intelligence in recent years. Today, it is possible to train models that have
thousands of layers and hundreds of billions of parameters. Large-scale deep
models have achieved great success, but the enormous computational complexity
and gigantic storage requirements make it extremely difficult to implement them
in real-time applications. On the other hand, the size of the dataset is still
a real problem in many domains. Data are often missing, too expensive, or
impossible to obtain for other reasons. Ensemble learning is partially a
solution to the problem of small datasets and overfitting. However, ensemble
learning in its basic version is associated with a linear increase in
computational complexity. We analyzed the impact of the ensemble
decision-fusion mechanism and checked various methods of sharing the decisions
including voting algorithms. We used the modified knowledge distillation
framework as a decision-fusion mechanism which allows in addition compressing
of the entire ensemble model into a weight space of a single model. We showed
that knowledge distillation can aggregate knowledge from multiple teachers in
only one student model and, with the same computational complexity, obtain a
better-performing model compared to a model trained in the standard manner. We
have developed our own method for mimicking the responses of all teachers at
the same time, simultaneously. We tested these solutions on several benchmark
datasets. In the end, we presented a wide application use of the efficient
multi-teacher knowledge distillation framework. In the first example, we used
knowledge distillation to develop models that could automate corrosion
detection on aircraft fuselage. The second example describes detection of smoke
on observation cameras in order to counteract wildfires in forests.


  In light of the significant progress of the LHC to determine the properties
of the Higgs boson, we investigate the capability of the Manohar-Wise model in
explaining the Higgs data. This model extends the SM by one family of
color-octet and isospin-doublet scalars, and it can sizably alter the coupling
strengths of the Higgs boson with gluons and photons. We first examine the
current constraints on the model, which are from unitarity, the LHC searches
for the scalars and the electroweak precision data (EWPD).In implementing the
unitarity constraint, we use the properties of the SU(3) group to simplify the
calculation. Then in the allowed parameter space we perform a fit of the model,
using the latest ATLAS and CMS data, respectively. We find that the
Manohar-Wise model is able to explain the data with \chi^2 significantly
smaller than the SM value. We also find that the current Higgs data, especially
the ATLAS data, are very powerful in further constraining the parameter space
of the model. In particular, in order to explain the diphoton enhancement
reported by the ATLAS collaboration, the sign of the hgg coupling is usually
opposite to that in the SM.


  Aiming at exploiting the rich information in user behaviour sequences,
sequential recommendation has been widely adopted in real-world recommender
systems. However, current methods suffer from the following issues: 1) sparsity
of user-item interactions, 2) uncertainty of sequential records, 3) long-tail
items. In this paper, we propose to incorporate contrastive learning into the
framework of Variational AutoEncoders to address these challenges
simultaneously. Firstly, we introduce ContrastELBO, a novel training objective
that extends the conventional single-view ELBO to two-view case and
theoretically builds a connection between VAE and contrastive learning from a
two-view perspective. Then we propose Contrastive Variational AutoEncoder
(ContrastVAE in short), a two-branched VAE model with contrastive
regularization as an embodiment of ContrastELBO for sequential recommendation.
We further introduce two simple yet effective augmentation strategies named
model augmentation and variational augmentation to create a second view of a
sequence and thus making contrastive learning possible. Experiments on four
benchmark datasets demonstrate the effectiveness of ContrastVAE and the
proposed augmentation methods. Codes are available at
https://github.com/YuWang-1024/ContrastVAE


  Accessibility of websites for visually impaired persons is mishandled by
screen readers which are not always adapted to interactivity needed by actual
web/multimedia technologies. This paper analyses the difficulties to access to
information with the use of different technologies and the existing
recommendations for the conception of accessible websites for all. Preliminary
results with visually impaired persons on our ACCESSPACE project website are
presented.


  In this note we obtain estimates on the relative growth of normal subgroups
of non-elementary hyperbolic groups, particularly those with free abelian
quotient. As a corollary, we deduce that the associated relative growth series
fail to be rational.


  We introduce an exactly solvable SU(2) symmetric Kitaev model with exotic
spin excitations. With time reversal symmetry (TRS), the ground state is a spin
liquid with gapless or gapped spin-1 but fermionic excitations. When TRS is
broken, the resulting spin liquid exhibits deconfined vortex excitations which
carry spin-1/2 quantum number and obey non-Abelian statistics. We show that
this SU(2) invariant non-Abelian spin liquid exhibits spin quantum Hall effect
with quantized spin Hall conductivity \sigma^s_{xy}=\hbar/2\pi, and that the
spin response is effectively described the SO(3) level-1 Chern-Simons theory at
low energy. We further propose that a SU(2) level-2 Chern-Simons theory is the
effective field theory describing the topological structure of the non-Abelian
SU(2) invariant spin liquid.


  Due to the relativistic motion of gamma-ray burst remnant and its
deceleration in the circumburst medium, the equal arrival time surfaces at any
moment are not spherical, but should be distorted ellipsoids. This will leave
some imprints in the afterglows. In this article, we study the effect of equal
arrival time surfaces numerically under various conditions, i.e., for isotropic
fireballs, collimated jets, density jump conditions, and energy injection
events. For each condition, direct comparison between the two instances when
the effect is and is not included, is presented. For isotropic fireballs and
jets viewed on axis, the effect slightly hardens the spectra and postpones the
peak time of afterglows, but does not change the shapes of the spectra and
light curves significantly. In the cases when a density jump or an energy
injection is involved, the effect smears the variability of the afterglows
markedly.


  I discuss LHC physics in the historical perspective of the progress in
particle physics. After a recap of the Standard Model of particle physics, I
discuss the high energy colliders leading up to LHC and their role in the
discovery of these SM particles. Then I discuss the two main physics issues of
LHC, i.e. Higgs mechanism and Supersymmetry. I briefly touch upon Higgs and
SUSY searches at LHC along with their cosmological implications.


  We study the problem of distributed training of neural networks (NNs) on
devices with heterogeneous, limited, and time-varying availability of
computational resources. We present an adaptive, resource-aware, on-device
learning mechanism, DISTREAL, which is able to fully and efficiently utilize
the available resources on devices in a distributed manner, increasing the
convergence speed. This is achieved with a dropout mechanism that dynamically
adjusts the computational complexity of training an NN by randomly dropping
filters of convolutional layers of the model. Our main contribution is the
introduction of a design space exploration (DSE) technique, which finds
Pareto-optimal per-layer dropout vectors with respect to resource requirements
and convergence speed of the training. Applying this technique, each device is
able to dynamically select the dropout vector that fits its available resource
without requiring any assistance from the server. We implement our solution in
a federated learning (FL) system, where the availability of computational
resources varies both between devices and over time, and show through extensive
evaluation that we are able to significantly increase the convergence speed
over the state of the art without compromising on the final accuracy.


  The time dependent entropy (or Leipnik's entropy) of harmonic and damped
harmonic oscillators is extensively investigated by using time dependent wave
function obtained by the Feynman path integral method. Our results for simple
harmonic oscillator are in agrement with the literature. However, the joint
entropy of damped harmonic oscillator shows remarkable discontinuity with time
for certain values of damping factor. According to the results, the envelop of
the joint entropy curve increases with time monotonically. This results is the
general properties of the envelop of the joint entropy curve for quantum
systems.


  This paper reviews the basic technical characteristics of the ground-based
photometric searches for transiting planets, and discusses a possible
observational selection effect. I suggest that additional photometric
observations of the already observed fields might discover new transiting
planets with periods around 4-6 days. The set of known transiting planets
support the intriguing correlation between the planetary mass and the orbital
period suggested already in 2005.


  We present a novel trajectory prediction algorithm for pedestrians based on a
personality-aware probabilistic feature map. This map is computed using a
spatial query structure and each value represents the probability of the
predicted pedestrian passing through various positions in the crowd space. We
update this map dynamically based on the agents in the environment and prior
trajectory of a pedestrian. Furthermore, we estimate the personality
characteristics of each pedestrian and use them to improve the prediction by
estimating the shortest path in this map. Our approach is general and works
well on crowd videos with low and high pedestrian density. We evaluate our
model on standard human-trajectory datasets. In practice, our prediction
algorithm improves the accuracy by 5-9% over prior algorithms.


  Numerical simulations of thin sheets undergoing large deformations are
computationally challenging. Depending on the scenario, they may spontaneously
buckle, wrinkle, fold, or crumple. Nature's thin tissues often experience
significant anisotropic growth, which can act as the driving force for such
instabilities. We use a recently developed finite element model to simulate the
rich variety of nonlinear responses of Kirchhoff-Love sheets. The model uses
subdivision surface shape functions in order to guarantee convergence of the
method, and to allow a finite element description of anisotropically growing
sheets in the classical Rayleigh-Ritz formalism. We illustrate the great
potential in this approach by simulating the inflation of airbags, the buckling
of a stretched cylinder, as well as the formation and scaling of wrinkles at
free boundaries of growing sheets. Finally, we compare the folding of spatially
confined sheets subject to growth and shrinking confinement to find that the
two processes are equivalent.


  String Unified Models based on the $k=1$ level of the Kac-Moody Algebra,
predict the existence of ``exotic'' new states which carry fractional electric
charges. We analyse the possibility of considering these ``exotics'' as preonic
matter which can be used to form the families and the gauge group breaking
higgs scalars. It is proposed that such a formation may occur provided that
these states transform non-trivially under a non-Abelian gauge group with a
relatively large rank in order to confine them at a sufficiently large scale.
Such a situation is natural in string derived unified models, since the role of
the confining group can be played by (part of) the Hidden symmetry. As an
example, we present a string derived toy model based on the $SU(4)\times
SU(2)_L \times SU(2)_R$ Pati-Salam gauge group.


  Through additional training, we explore embedding specialized scientific
knowledge into the Llama 2 Large Language Model (LLM). Key findings reveal that
effective knowledge integration requires reading texts from multiple
perspectives, especially in instructional formats. We utilize text augmentation
to tackle the scarcity of specialized texts, including style conversions and
translations. Hyperparameter optimization proves crucial, with different size
models (7b, 13b, and 70b) reasonably undergoing additional training. Validating
our methods, we construct a dataset of 65,000 scientific papers. Although we
have succeeded in partially embedding knowledge, the study highlights the
complexities and limitations of incorporating specialized information into
LLMs, suggesting areas for further improvement.


  The performance of financial market prediction systems depends heavily on the
quality of features it is using. While researchers have used various techniques
for enhancing the stock specific features, less attention has been paid to
extracting features that represent general mechanism of financial markets. In
this paper, we investigate the importance of extracting such general features
in stock market prediction domain and show how it can improve the performance
of financial market prediction. We present a framework called U-CNNpred, that
uses a CNN-based structure. A base model is trained in a specially designed
layer-wise training procedure over a pool of historical data from many
financial markets, in order to extract the common patterns from different
markets. Our experiments, in which we have used hundreds of stocks in S\&P 500
as well as 14 famous indices around the world, show that this model can
outperform baseline algorithms when predicting the directional movement of the
markets for which it has been trained for. We also show that the base model can
be fine-tuned for predicting new markets and achieve a better performance
compared to the state of the art baseline algorithms that focus on constructing
market-specific models from scratch.


  We prove $L^p$-bounds on the Fourier transform of measures $\mu$ supported on
two dimensional surfaces. Our method allows to consider surfaces whose Gauss
curvature vanishes on a one-dimensional submanifold. Under a certain
non-degeneracy condition, we prove that $\wh\mu\in L^{4+\beta}$, $\beta>0$, and
we give a logarithmically divergent bound on the $L^4$-norm. We use this latter
bound to estimate almost singular integrals involving the dispersion relation,
$e(p)= \sum_1^3 [1-\cos p_j]$, of the discrete Laplace operator on the cubic
lattice. We briefly explain our motivation for this bound originating in the
theory of random Schr\"odinger operators.


  Establishing a correlation (or lack thereof) between the bimodal colour
distribution of galaxies and their structural parameters is crucial to
understand the origin of bimodality. To achieve that, we have performed 2D
mass-based structural decomposition (bulge+disc) of all disc galaxies
(total$=$1263) in the Herschel imaging area of the Stripe 82 region using $K_s$
band images from the VICS82 survey. The scaling relations thus derived are
found to reflect the internal kinematics and are employed in combination to
select an indubitable set of classical and pseudo bulge hosting disc galaxies.
The rest of the galaxies ($<20\%$) are marked as discs with "ambiguous" bulges.
Pseudo and classical bulge disc galaxies exhibit clear bimodality in terms of
all stellar parameters ($M_*$, sSFR, $r-K_s$). All pseudo bulge disc galaxies
are blue and star-forming and all classical bulge disc galaxies are red and
quiescent with less than $5\%$ digressions. Ambiguous bulge disc galaxies are
intermittent to pseudo and classical bulge disc galaxies in the distribution of
all structural and stellar parameters. $\Delta$$\langle\mu_{eb}\rangle$ - based
on the placement of bulges on the Kormendy relation - is found to be the most
efficient single structural indicator of both bulge type and stellar activity.
The placement of ambiguous bulge disc galaxies on scaling relations and
fundamental plane, in addition to their peculiar stellar properties suggest
that they are dominantly a part of the green valley.


  NASA's Transiting Exoplanet Survey Satellite (TESS) mission promises to
improve our understanding of hot Jupiters by providing an all-sky,
magnitude-limited sample of transiting hot Jupiters suitable for population
studies. Assembling such a sample requires confirming hundreds of planet
candidates with additional follow-up observations. Here, we present twenty hot
Jupiters that were detected using TESS data and confirmed to be planets through
photometric, spectroscopic, and imaging observations coordinated by the TESS
Follow-up Observing Program (TFOP). These twenty planets have orbital periods
shorter than 7 days and orbit relatively bright FGK stars ($10.9 < G < 13.0$).
Most of the planets are comparable in mass to Jupiter, although there are four
planets with masses less than that of Saturn. TOI-3976 b, the longest period
planet in our sample ($P = 6.6$ days), may be on a moderately eccentric orbit
($e = 0.18\pm0.06$), while observations of the other targets are consistent
with them being on circular orbits. We measured the projected stellar obliquity
of TOI-1937A b, a hot Jupiter on a 22.4 hour orbit with the Rossiter-McLaughlin
effect, finding the planet's orbit to be well-aligned with the stellar spin
axis ($|\lambda| = 4.0\pm3.5^\circ$). We also investigated the possibility that
TOI-1937 is a member of the NGC 2516 open cluster, but ultimately found the
evidence for cluster membership to be ambiguous. These objects are part of a
larger effort to build a complete sample of hot Jupiters to be used for future
demographic and detailed characterization work.


  We present a detailed study of the phase diagram of the Ising model in random
graphs with arbitrary degree distribution. By using the replica method we
compute exactly the value of the critical temperature and the associated
critical exponents as a function of the minimum and maximum degree, and the
degree distribution characterizing the graph. As expected, there is a
ferromagnetic transition provided <k> <= <k^2> < \infty. However, if the fourth
moment of the degree distribution is not finite then non-trivial scaling
exponents are obtained. These results are analyzed for the particular case of
power-law distributed random graphs.


  In this work, we propose a motion embedding strategy known as motion codes,
which is a vectorized representation of motions based on a manipulation's
salient mechanical attributes. These motion codes provide a robust motion
representation, and they are obtained using a hierarchy of features called the
motion taxonomy. We developed and trained a deep neural network model that
combines visual and semantic features to identify the features found in our
motion taxonomy to embed or annotate videos with motion codes. To demonstrate
the potential of motion codes as features for machine learning tasks, we
integrated the extracted features from the motion embedding model into the
current state-of-the-art action recognition model. The obtained model achieved
higher accuracy than the baseline model for the verb classification task on
egocentric videos from the EPIC-KITCHENS dataset.


  We develop a matricial version of Rieffel's Gromov-Hausdorff distance for
compact quantum metric spaces within the setting of operator systems and unital
C*-algebras. Our approach yields a metric space of ``isometric'' unital
complete order isomorphism classes of metrized operator systems which in many
cases exhibits the same convergence properties as those in the quantum metric
setting, as for example in Rieffel's approximation of the sphere by matrix
algebras using Berezin quantization. Within the metric subspace of metrized
unital C*-algebras we establish the convergence of sequences which are Cauchy
with respect to a larger Leibniz distance, and we also prove an analogue of the
precompactness theorems of Gromov and Rieffel.


  We show that the rather unexpected pressure dependence of superfluid density
observed near the superfluid-supersolid transition by Kim {\em et.al.}[M.H.W.
Chan, {\em private communication}], can be understood if the transition from
superfluid to supersolid state is a second order or weakly first order
transition from the superfluid state to a super-CDW state with non-uniform
Bose-condensation amplitude. The suppression of superfluid density is a direct
consequence of softening of phonon mode at finite wave-vector $|\vec{Q}|\sim
Q_0$ around the quantum phase transition.


  We aim to investigate the relationship between radio jet activity on
parsec-scales and the characteristics of both the bright active galactic nuclei
(AGN) and their broad line regions (BLR). For this purpose, we combine 2cm Very
Long Baseline Array observations of AGN with their optical spectral
observations. This would enable us to investigate the optical spectra of a set
of 172 relativistically beamed, flat-spectrum AGN with the nuclear disk
oriented near to the plane of sky. Here, we present first results from optical
spectroscopic observations of the brightest AGN from the 2 cm VLBA survey, and
show a diversity of their spectral morphologies.


  We use entropy statistics in this paper to measure the synergies of knowledge
exploration, knowledge exploitation, and organizational control in the
Hungarian innovation system. Our data consists of high- and medium-tech firms
and knowledge-intensive services categorized by sub-regions (proxy for
geography), industrial sectors (proxy for technology) and firm size (proxy for
organization). Configurational information along these three dimensions is used
as an indicator of reduction of uncertainty or, in other words, the synergy
across the knowledge functions. Our results indicate that three regimes have
been created during the Hungarian transition with very different dynamics: (1)
Budapest and its agglomeration emerge as a knowledge-based innovation system on
every indicator; (2) the north-western part of the country, where foreign-owned
companies have induced a shift in knowledge-organization; while (3) the system
in the eastern and southern part of the country seems to be organized as a
response to government expenditure. The national level no longer adds to the
synergy across these regional innovation systems.


  This paper presents a method for the resolution of lexical ambiguity and its
automatic evaluation over the Brown Corpus. The method relies on the use of the
wide-coverage noun taxonomy of WordNet and the notion of conceptual distance
among concepts, captured by a Conceptual Density formula developed for this
purpose. This fully automatic method requires no hand coding of lexical
entries, hand tagging of text nor any kind of training process. The results of
the experiment have been automatically evaluated against SemCor, the
sense-tagged version of the Brown Corpus.


  The Mindlin plate equations with the consideration of thickness-shear
deformation as an independent variable have been used for the analysis of
vibrations of quartz crystal resonators of both rectangular and circular types.
The Mindlin or Lee plate theories that treat thickness-shear deformation as an
independent higher-order vibration mode in a coupled system of two-dimensional
variables are the choice of theory for analysis. For circular plates, we
derived the Mindlin plate equations in a systematic manner as demonstrated by
Mindlin and others and obtained the truncated two-dimensional equations of
closely coupled modes in polar coordinates. We simplified the equations for
vibration modes in the vicinity of fundamental thickness-shear frequency and
validated the equations and method. To explore newer structures of quartz
crystal resonators, we utilized the Mindlin plate equations for the analysis of
annular plates with fixed inner and free outer edges for frequency spectra. The
detailed analysis of vibrations of circular plates for the normalized frequency
versus dimensional parameters provide references for optimal selection of
parameters based on the principle of strong thickness-shear mode and minimal
presence of other modes to enhance energy trapping through maintaining the
strong and pure thickness-shear vibrations insensitive to some complication
factors such as thermal and initial stresses.


  We investigate the long-term dynamics of spin textures prepared by cooling
unmagnetized spinor gases of F=1 $^{87}$Rb to quantum degeneracy, observing
domain coarsening and a strong dependence of the equilibration dynamics on the
quadratic Zeeman shift $q$. For small values of $|q|$, the textures arrive at a
configuration independent of the initial spin-state composition, characterized
by large length-scale spin domains, and the establishment of easy-axis
(negative $q$) or easy-plane (positive $q$) magnetic anisotropy. For larger
$|q|$, equilibration is delayed as the spin-state composition of the degenerate
spinor gas remains close to its initial value. These observations support the
mean-field equilibrium phase diagram predicted for a ferromagnetic spinor
Bose-Einstein condensate, but also illustrate that equilibration is achieved
under a narrow range of experimental settings, making the F=1 $^{87}$Rb gas
more suitable for studies of nonequilibrium quantum dynamics.


  A Banach space E is said to have Property (w) if every (bounded linear)
operator from E into E' is weakly compact. We give some interesting examples of
James type Banach spaces with Property (w). We also consider the passing of
Property (w) from E to C(K,E).


  Non-Volatile Memory (NVM) can deliver higher density and lower cost per bit
when compared with DRAM. Its main drawback is that it is slower than DRAM. On
the other hand, DRAM has scalability problems due to its cost and energy
consumption. NVM will likely coexist with DRAM in computer systems and the
biggest challenge is to know which data to allocate on each type of memory. A
state-of-the-art approach is AutoNUMA, in the Linux kernel. Prior work is
limited to measuring AutoNUMA solely in terms of the application execution
time, without understanding AutoNUMA's behavior. In this work we provide a more
in-depth characterization of AutoNUMA, for instance, identifying where exactly
a set of pages are allocated, while keeping track of promotion and demotion
decisions performed by AutoNUMA. Our analysis shows that AutoNUMA's benefits
can be modest when running graph processing applications, or graph analytics,
because most pages have only one access over the entire execution time and
other pages accesses have no temporal locality. We make a case for exploring
application characteristics using object-level mappings between DRAM and NVM.
Our preliminary experiments show that an object-level memory tiering can better
capture the application behavior and reduce the execution time of graph
analytics by 21% (avg) and 51% (max), when compared to AutoNUMA, while
significantly reducing the number of memory accesses in NVM.


  We consider bandit optimization of a smooth reward function, where the goal
is cumulative regret minimization. This problem has been studied for
$\alpha$-H\"older continuous (including Lipschitz) functions with $0<\alpha\leq
1$. Our main result is in generalization of the reward function to H\"older
space with exponent $\alpha>1$ to bridge the gap between Lipschitz bandits and
infinitely-differentiable models such as linear bandits. For H\"older
continuous functions, approaches based on random sampling in bins of a
discretized domain suffices as optimal. In contrast, we propose a class of
two-layer algorithms that deploy misspecified linear/polynomial bandit
algorithms in bins. We demonstrate that the proposed algorithm can exploit
higher-order smoothness of the function by deriving a regret upper bound of
$\tilde{O}(T^\frac{d+\alpha}{d+2\alpha})$ for when $\alpha>1$, which matches
existing lower bound. We also study adaptation to unknown function smoothness
over a continuous scale of H\"older spaces indexed by $\alpha$, with a bandit
model selection approach applied with our proposed two-layer algorithms. We
show that it achieves regret rate that matches the existing lower bound for
adaptation within the $\alpha\leq 1$ subset.


  Given a collection of strings, each with an associated probability of
occurrence, the guesswork of each of them is their position in a list ordered
from most likely to least likely, breaking ties arbitrarily. Guesswork is
central to several applications in information theory: Average guesswork
provides a lower bound on the expected computational cost of a sequential
decoder to decode successfully the transmitted message; the complementary
cumulative distribution function of guesswork gives the error probability in
list decoding; the logarithm of guesswork is the number of bits needed in
optimal lossless one-to-one source coding; and guesswork is the number of
trials required of an adversary to breach a password protected system in a
brute-force attack. In this paper, we consider memoryless string-sources that
generate strings consisting of i.i.d. characters drawn from a finite alphabet,
and characterize their corresponding guesswork. Our main tool is the tilt
operation. We show that the tilt operation on a memoryless string-source
parametrizes an exponential family of memoryless string-sources, which we refer
to as the tilted family. We provide an operational meaning to the tilted
families by proving that two memoryless string-sources result in the same
guesswork on all strings of all lengths if and only if their respective
categorical distributions belong to the same tilted family. Establishing some
general properties of the tilt operation, we generalize the notions of weakly
typical set and asymptotic equipartition property to tilted weakly typical sets
of different orders. We use this new definition to characterize the large
deviations for all atypical strings and characterize the volume of weakly
typical sets of different orders. We subsequently build on this
characterization to prove large deviation bounds on guesswork and provide an
accurate approximation of its PMF.