  Suppose the ground field is algebraically closed and of characteristic
different from 2. In this paper, we described the intrinsic connections among
linear super-commuting maps, super-biderivations and centroids for Lie
superalgebras satisfying certain assumptions. This is a generalization of the
results of Bre$\check{s}$ar and Zhao on Lie algebras.


  We investigate the possibility of a delayed detonation in a type Ia supernova
under the assumption that the transition to detonation is triggered by
turbulence only. Our discussion is based on the Zeldovich mechanism and
suggests that typical turbulent velocities present during the explosion are not
strong enough to allow this transition to occur. Although we are able to show
that in carbon-rich matter (e.g., $X(^{12}$C$) = 0.75$) the possibility of a
deflagration to detonation transition (DDT) is enhanced, even in this case the
turbulent velocities needed are larger than the expected value of $u'(L)
\approx 10^7 {cm s}^{-1}$ on a length-scale of $L \approx 10^6$ cm. Thus we
conclude that a DDT may not be a common event during a thermonuclear explosion
of a Chandrasekhar-mass white dwarf.


  It is proved that if a smooth function $u(x)$, $x\in \mathbb{R}^3$, such that
$\inf_{s\in S}|u_N(s)|>0$, where $u_N$ is the normal derivative of $u$ on $S$,
has a closed smooth surface $S$ of zeros, then the function $u(x)+\epsilon
v(x)$ has also a closed smooth surface $S_\epsilon$ of zeros. Here $v$ is a
smooth function and $\epsilon>0$ is a sufficiently small number.


  Recent epidemiological evidences indicate that arsenic exposure increases
risk of atherosclerosis, cardiovascular diseases and microangiopathies in
addition to the serious global health concern related to its carcinogenic
effects. In experiments on animals, acute and chronic exposure to arsenic
directly correlates cardiac tachyarrhythmia, and atherogenesis in a
concentration and duration dependent manner. Moreover, the other effects of
long-term arsenic exposure include induction of non-insulin dependent diabetes
by mechanisms yet to be understood. On the other hand, there are controversial
issues, gaps in knowledge, and future research priorities of accelerated
incidences of CVD and mortalities in patients with HIV who are under long-term
anti-retroviral therapy (ART). Although, both HIV infection itself and various
components of ART initiate significant pathological alterations in the
myocardium and the vasculature, simultaneous environmental exposure to arsenic
which is more convincingly being recognized as a facilitator of HIV viral
cycling in the infected immune cells, may contribute an additional layer of
adversity in these patients. In this mini-review which have been fortified with
our own preliminary data, we will discuss some of the key current understating
of chronic arsenic exposure, and its possible impact on the accelerated HIV/ART
induced CVD. The review will conclude with notes on recent developments in
mathematical modeling in this field that probabilistically forecast incidence
prevalence as functions of aging and life style parameters, most of which vary
with time themselves; this interdisciplinary approach provides a complementary
kernel to conventional biology.


  Results are reported on the measurement of the atmospheric neutrino-induced
muon flux at a depth of 2 kilometers below the Earth's surface from 1229 days
of operation of the Sudbury Neutrino Observatory (SNO). By measuring the flux
of through-going muons as a function of zenith angle, the SNO experiment can
distinguish between the oscillated and un-oscillated portion of the neutrino
flux. A total of 514 muon-like events are measured between $-1 \le
\cos{\theta}_{\rm zenith} \le 0.4$ in a total exposure of 2.30\times 10^{14}$
cm$^{2}$ s. The measured flux normalization is $1.22 \pm 0.09$ times the Bartol
three-dimensional flux prediction. This is the first measurement of the
neutrino-induced flux where neutrino oscillations are minimized. The zenith
distribution is consistent with previously measured atmospheric neutrino
oscillation parameters. The cosmic ray muon flux at SNO with zenith angle
$\cos{\theta}_{\rm zenith} > 0.4$ is measured to be $(3.31 \pm 0.01 {\rm
(stat.)} \pm 0.09 {\rm (sys.)}) \times 10^{-10}~\mu$/s/cm$^{2}$.


  Recently we reported some interesting features of the Wolff's algorithm
behavior when applied to the site-bond-correlated Ising model.Our main results
were that a stronger correlation diminishes the autocorrelation time but it
does not change or affectthe critical dynamic exponent.In this paper, we
analyse the Wolff's cluster structure and how it varies with the spatial
correlation. The fractal dimensions are determined for several values of the
magnetic atoms concentration and spacial correlation, giving an estimative of
the critical dynamic exponent.


  In this work we developed a method for simulating polar species in the
dissipative particle dynamics (DPD) method. The main idea behind the method is
to treat each bead as a dumb-bell, i.e. two sub-beads (the sub-beads can bear
charges) kept at a fixed distance, instead of a point-like particle. It was
shown that at small enough separations the composite beads act essentially as
conventional point-like beads. Next, the relation between the bead dipole
moment and the bulk dielectric permittivity was obtained. The interaction of
single charges in polar liquid showed that the observed dielectric permittivity
is somewhat smaller than that obtained for the bulk case at large separation
between the charges; at distances comparable to the bead size the solvation
shells of the charges start to interfere and oscillations in the observed
permittivity occur. Such oriented molecules effectively have smaller
polarizability compared to the bulk liquid, so the field of one charge in the
vicinity of another charge is reduced not as strong as in the bulk. Finally, we
showed why it is necessary to treat the polar species in DPD explicitly instead
of implicitly by calculating the local polarizability based on the local
species concentrations: the latter leads to the violation of the Newton's third
law resulting in simulation artifacts. We investigated the behavior of a
charged colloidal particle at an interface of polar and non-polar liquids. We
obtained that when the polar molecules are treated explicitly, the charged
colloidal particle moved into the polar liquid since it is energetically more
favorable for the charged molecules to be immersed in a polar medium; however,
within the "implicit polarity" method the colloidal particle is found on top of
a "bump" formed by the molecules of the non-polar liquid, which increases the
interface area between the liquids instead of decreasing it.


  We study the electronic states of core multi-shell semiconductor nanowires,
including the effect of strong magnetic fields. We show that the multi-shell
overgrowth of a free-standing nanowire, together with the prismatic symmetry of
the substrate, may induce quantum confinement of carriers in a set of quasi-1D
quantum channels corresponding to the nanowire edges. Localization and
inter-channel tunnel coupling are controlled by the curvature at the edges and
the diameter of the underlying nanowire. We also show that a magnetic field may
induce either Aharonov-Bohm oscillations of the energy levels in the axial
configuration, or a dimensional transition of the quantum states from quasi-1D
to Landau levels for fields normal to the axis. Explicit predictions are given
for nanostructures based on GaAs, InAs, and InGaN with different symmetries.


  Let $f\colon X\to Y$ be a perfect map between finite-dimensional metrizable
spaces and $p\geq 1$. It is shown that the space $C^*(X,\R^p)$ of all bounded
maps from $X$ into $\R^p$ with the source limitation topology contains a dense
$G_{\delta}$-subset consisting of $f$-regularly branched maps. Here, a map
$g\colon X\to\R^p$ is $f$-regularly branched if, for every $n\geq 1$, the
dimension of the set $\{z\in Y\times\R^p: |(f\times g)^{-1}(z)|\geq n\}$ is
$\leq n\cdot\big(\dim f+\dim Y\big)-(n-1)\cdot\big(p+\dim Y\big)$. This is a
parametric version of the Hurewicz theorem on regularly branched maps.


  We consider a second order gradient flow of the p-elastic energy for a planar
theta-network of three curves with fixed lengths. We construct a weak solution
of the flow by means of an implicit variational scheme. We show long-time
existence of the evolution and convergence to a critical point of the energy.


  We apply the renormalisation-group to two-body scattering by a combination of
known long-range and unknown short-range forces. A crucial feature is that the
low-energy effective theory is regulated by applying a cut-off in the basis of
distorted waves for the long range potential. We illustrate the method by
applying it to scattering in the presence of a repulsive 1/r^2 potential. We
find a trivial fixed point, describing systems with weak short-range
interactions, and a unstable fixed point. The expansion around the latter
corresponds to a distorted-wave effective-range expansion.


  This manuscript is a preprint version of Part 1 (General Introduction and
Synopsis) of the book Applied Evaluative Informetrics, to be published by
Springer in the summer of 2017. This book presents an introduction to the field
of applied evaluative informetrics, and is written for interested scholars and
students from all domains of science and scholarship. It sketches the field's
history, recent achievements, and its potential and limits. It explains the
notion of multi-dimensional research performance, and discusses the pros and
cons of 28 citation-, patent-, reputation- and altmetrics-based indicators. In
addition, it presents quantitative research assessment as an evaluation
science, and focuses on the role of extra-informetric factors in the
development of indicators, and on the policy context of their application. It
also discusses the way forward, both for users and for developers of
informetric tools.


  We give a necessary and sufficient condition for a 2-dimensional or a
three-generator Artin group $A$ to be (virtually) cocompactly cubulated, in
terms of the defining graph of $A$.


  The design of compact deep neural networks is a crucial task to enable
widespread adoption of deep neural networks in the real-world, particularly for
edge and mobile scenarios. Due to the time-consuming and challenging nature of
manually designing compact deep neural networks, there has been significant
recent research interest into algorithms that automatically search for compact
network architectures. A particularly interesting class of compact architecture
search algorithms are those that are guided by baseline network architectures.
Such algorithms have been shown to be significantly more computationally
efficient than unguided methods. In this study, we explore the current state of
compact architecture search for deep neural networks through both theoretical
and empirical analysis of four different state-of-the-art compact architecture
search algorithms: i) group lasso regularization, ii) variational dropout, iii)
MorphNet, and iv) Generative Synthesis. We examine these methods in detail
based on a number of different factors such as efficiency, effectiveness, and
scalability. Furthermore, empirical evaluations are conducted to compare the
efficacy of these compact architecture search algorithms across three
well-known benchmark datasets. While by no means an exhaustive exploration, we
hope that this study helps provide insights into the interesting state of this
relatively new area of research in terms of diversity and real, tangible gains
already achieved in architecture design improvements. Furthermore, the hope is
that this study would help in pushing the conversation forward towards a deeper
theoretical and empirical understanding where the research community currently
stands in the landscape of compact architecture search for deep neural
networks, and the practical challenges and considerations in leveraging such
approaches for operational usage.


  For prime $p\ge 7$, by using Baker's method we obtain two explicit bounds in
terms of $p$ for the $j$-invariant of an integral point on $X_{\ns}^{+}(p)$
which is the modular curve of level $p$ corresponding to the normalizer of a
non-split Cartan subgroup of $\GL_2(\Z/p\Z)$.


  This paper presents an interdisciplinary PhD project using a humanoid robot
to encourage interactive activities for people with dementia living in two aged
care facilities. The aim of the project was to develop software and use
technologies to achieve successful robot-led engagement with older people with
dementia. This paper outlines the qualitative findings from the project's
feasibility stage. The researcher's observations, the participants' attitudes
and the feedback from carers are presented and discussed.


  Scale transformations have played an extremely successful role in studies of
cosmological large-scale structure by relating the non-linear spectrum of
cosmological density fluctuations to the linear primordial power at longer
wavelengths. Here we generalize this approach to investigate the usefulness of
scale transformations for nonlinear higher-order statistics, specifically the
bispectrum. We find that the bispectrum predicted by perturbation theory at
tree-level can be rescaled to match the results of full numerical simulations
in the weakly and intermediately nonlinear regimes, especially at high
redshifts, with an accuracy that is surprising given the simplicity of the
procedure used. This discovery not only offers a simple practical way of
calculating the matter bispectrum, but also suggests that scale transformations
may yet yield even deeper insights into the physics of hierarchical clustering.


  We study triangular clusters of three spin-1/2 Kondo or Anderson impurities
that are coupled to two conduction leads. In the case of Kondo impurities, the
model takes the form of an antiferromagnetic Heisenberg ring with Kondo-like
exchange coupling to continuum electrons. We show that this model exhibits many
types of the behavior found in various simpler one and two-impurity models,
thereby enabling the study of crossovers between a number of Fermi-liquid (FL)
and non-Fermi-liquid (NFL) fixed points. In particular, we explore a direct
crossover between the two-impurity Kondo-model NFL fixed point and the
two-channel Kondo-model NFL fixed point. We show that the concept of the
two-stage Kondo effect applies even in the case when the first-stage Kondo
state is of NFL type. In the case of Anderson impurities, we consider the
transport properties of three coupled quantum dots. This class of models
includes as limiting cases the familiar serial double quantum dot and triple
quantum dot nanostructures. By extracting the quasiparticle scattering phase
shifts, we compute the low-temperature conductance as a function of the
inter-impurity tunneling-coupling. We point out that due to the existence of
exponentially low temperature scales, there is a parameter range where the
stable "zero-temperature" fixed point is essentially never reached (not even in
numerical renormalization group calculations). The "zero-temperature"
conductance is then of no interest and it may only be meaningful to compute the
conductance at finite temperature. This illustrates the perils of studying the
conductance in the ground state and considering thermal fluctuations only as a
small correction.


  We propose a new adaptive Particle-in-Cloud (AP-Cloud) method for obtaining
optimal numerical solutions to the Vlasov-Poisson equation. Unlike the
traditional particle-in-cell (PIC) method, which is commonly used for solving
this problem, the AP-Cloud adaptively selects computational nodes or particles
to deliver higher accuracy and efficiency when the particle distribution is
highly non-uniform. Unlike other adaptive techniques for PIC, our method
balances the errors in PDE discretization and Monte Carlo integration, and
discretizes the differential operators using a generalized finite difference
(GFD) method based on a weighted least square formulation. As a result,
AP-Cloud is independent of the geometric shapes of computational domains and is
free of artificial parameters. Efficient and robust implementation is achieved
through an octree data structure with 2:1 balance. We analyze the accuracy and
convergence order of AP-Cloud theoretically, and verify the method using an
electrostatic problem of a particle beam with halo. Simulation results show
that the AP-Cloud method is substantially more accurate and faster than the
traditional PIC, and it is free of artificial forces that are typical for some
adaptive PIC techniques.


  Applying a recently suggested new form [Phys. Rev. A {\bf 94}, 042704 (2016)]
for the stopping power in terms of scattering phase shifts, here we show
analytically that an exact leading phase shift may contain that information
which is completely enough to characterize the asymptotic
charge-sign-independent Bethe term ($\propto{Z_1^2}$) and a
charge-sign-dependent Barkas term ($\propto{Z_1^3}$) in the stopping force of a
degenerate electron gas for fast projectiles with charge $Z_1$. Our analytic
implementation is based on a Hulth\'en-type potential with velocity-dependent
screening. The next term in an asymptotic expansion, the Bloch term
($\propto{Z_1^4}$), measures the difference of the exact and first-order Born
treatments with Coulomb potential. We found a reduced value for the Barkas
term, in comparison with the conventional estimation which rests on the
transport cross section.


  Context: Open clusters (OCs) are widely used as test particles to investigate
a variety of astrophysical phenomena, from stellar evolution to Galactic
evolution. Gaia and the complementary massive spectroscopic surveys are
providing an unprecedented wealth of information about these systems. Aims: The
Open Cluster Chemical Abundances from Spanish Observatories (OCCASO) survey
aims to complement all this work by determining OCs accurate radial velocities
and chemical abundances from high-resolution, R$\geq$60\,000, spectra. Methods:
Radial velocities have been obtained by cross-correlating the observed spectra
with a library of synthetic spectra which covers from early M to A spectral
types. Results: We provide radial velocities for 336 stars, including several
Gaia Benchmark Stars and objects belonging to 51 open clusters. The internal
uncertainties of the derived radial velocities go from 10 m/s to 21 m/s as a
function of the instrumental configuration used. The derived radial velocities,
together with the Gaia proper motions, have been used to investigate the
cluster membership of the observed stars. After this careful membership
analysis, we obtain average velocities for 47 open clusters. To our knowledge,
this is the first radial velocity determination for 5 of these clusters.
Finally, the radial velocities, proper motions, distances and ages have been
used to investigate the kinematics of the observed clusters and in the
integration of their orbits.


  A dual formalism for Lagrange multipliers is developed. The formalism is used
to minimize an action function $S(q_2,q_1,T)$ without any dynamical input other
than that $S$ is convex. All the key equations of analytical mechanics -- the
Hamilton-Jacobi equation, the generating functions for canonical
transformations, Hamilton's equations of motion and $S$ as the time integral of
the Lagrangian -- emerge as simple consequences. It appears that to a large
extent, analytical mechanics is simply a footnote to the most basic problem in
the calculus of variations: that the shortest distance between two points is a
straight line.


  The use of natural language processing (NLP) techniques in engineering
education can provide valuable insights into the underlying processes involved
in generating text. While accessing these insights can be labor-intensive if
done manually, recent advances in NLP and large language models have made it a
realistic option for individuals. This study explores and evaluates a
combination of clustering, summarization, and prompting techniques to analyze
over 1,000 student essays in which students discussed their career interests.
The specific assignment prompted students to define and explain their career
goals as engineers. Using text embedding representations of student responses,
we clustered the responses together to identify thematically similar statements
from students. The clustered responses were then summarized to quickly identify
career interest themes. We also used a set of a priori codes about career
satisfaction and sectors to demonstrate an alternative approach to using these
generative text models to analyze student writing. The results of this study
demonstrate the feasibility and usefulness of NLP techniques in engineering
education research. By automating the initial analysis of student essays,
researchers and educators can more efficiently and accurately identify key
themes and patterns in student writing. The methods presented in this paper
have broader applications for engineering education and research purposes
beyond analyzing student essays. By explaining these methods to the engineering
education community, readers can utilize them in their own contexts.


  The LHCb experiment is one of the four large detectors operating at the LHC
at CERN and it is mainly devoted to CP violation measurements and to the search
for new physics in rare decays of beauty and charm hadrons. The data from the
two Ring Image Cherenkov (RICH-1 and RICH-2) detectors are essential to
identify particles in a wide momentum range. From 2019 onwards 14 TeV
collisions with luminosities reaching up to $2\cdot10^{33}$ cm$^{-2}$ s$^{-1}$
with 25 ns bunch spacing are planned, with the goal of collecting 5 fb$^{-1}$
of data per year. In order to avoid degradation of the PID performance at such
high rate (40 MHz), the RICH detector has to be upgraded. New photodetectors
(Multi-anode photomultiplier tubes, MaPMTs) have been chosen and will be read
out using a 8-channels chip, named CLARO, designed to sustain a photon counting
rate up to 40 MHz, while minimizing the power consumption and the cross-talk. A
128-bit digital register allows selection of thresholds and attenuation values
and provides features useful for testing and debugging. Photosensors and
electronics are arranged in basic units, the first prototypes of which have
been tested in charged particle beams in autumn 2014. An overview of the CLARO
features and of the readout electronics is presented.


  One of the most significant discrete invariants of a quadratic form $\phi$
over a field $k$ is its (full) splitting pattern, a finite sequence of integers
which describes the possible isotropy behaviour of $\phi$ under scalar
extension to arbitrary overfields of $k$. A similarly important, but more
accessible variant of this notion is that of the Knebusch splitting pattern of
$\phi$, which captures the isotropy behaviour of $\phi$ as one passes over a
certain prescribed tower of $k$-overfields. In this paper, we determine all
possible values of this latter invariant in the case where $\phi$ is totally
singular. This includes an extension of Karpenko's theorem (formerly Hoffmann's
conjecture) on the possible values of the first Witt index to the totally
singular case. Contrary to the existing approaches to this problem (in the
nonsingular case), our results are achieved by means of a new structural result
on the higher anisotropic kernels of totally singular quadratic forms.
Moreover, the methods used here readily generalise to give analogous results
for arbitrary Fermat-type forms of degree $p$ over fields of characteristic
$p>0$. Related problems concerning the Knebusch splitting of symmetric bilinear
forms over fields of characteristic 2 and the full splitting of totally
singular quadratic forms are also considered.


  Analytical solutions describing quantum swap and Hadamard gate are given with
the use of tight-binding approximation. Decoherence effects are described
analytically for two interacting electrons confined by local potentials with
use of tight-binding simplistic model and in Schroedinger formalism with
omission of spin degree of freedom. The obtained results can be generalized for
the case of N electrostatically interacting quantum bodies confined by local
potentials (N-qubit) system representing any electrostatic quantum gate with
N1/N-N1 inputs/outputs. The mathematical structure of system evolution with
time is specified.
  Index Terms: quantum computation, entanglement, single-electron devices,
position-dependent qubit, Q-Swap Gate, quantum CMOS


  Ultra-faint dwarf galaxies are among the oldest and most metal-poor galaxies
in the cosmos, observed to contain no gas and a high dark matter mass fraction.
Understanding the chemical abundance dispersion in such extreme environments
could shed light on the very first generations of stars. We present a novel
inhomogeneous chemical evolution model, {\tt i-GEtool}, that we apply to two
ultra-faint dwarf galaxies, Carina II and Reticulum II, both satellites of the
Large Magellanic Cloud. Our model is based on the Monte Carlo sampling of the
initial mass function as star formation proceeds in different gas cells of the
galaxy volume. We account for the chemical enrichment of Supernova bubbles as
they spread in the interstellar medium, causing dispersion in the elemental
abundances. We recreate the abundance patterns of $\alpha$- and
odd-$\textit{Z}$ elements, predicting two sequences in [C/Fe] and [N/Fe] at all
metallicities. Our models underestimate [C/Fe] and [Ti/Fe] because of the large
uncertainty in the adopted stellar nucleosynthesis yields. We discuss that the
observed C and N abundances had likely been affected by internal mixing
processes, which changed the initial surface abundances in the red giants. Our
Supernova feedback scheme is responsible for driving galactic outflows, which
quench the star formation activity at early times. We predict an average
outflow mass-loading factor $\approx 10^{3}$, which extrapolates towards very
low galaxy stellar masses the trend observed at high masses. Finally, by
combining our model with the MIST isochrone database, we compare our synthetic
colour-magnitude diagrams to observations.


  Random walks are ubiquitous in the sciences, and they are interesting from
both theoretical and practical perspectives. They are one of the most
fundamental types of stochastic processes; can be used to model numerous
phenomena, including diffusion, interactions, and opinions among humans and
animals; and can be used to extract information about important entities or
dense groups of entities in a network. Random walks have been studied for many
decades on both regular lattices and (especially in the last couple of decades)
on networks with a variety of structures. In the present article, we survey the
theory and applications of random walks on networks, restricting ourselves to
simple cases of single and non-adaptive random walkers. We distinguish three
main types of random walks: discrete-time random walks, node-centric
continuous-time random walks, and edge-centric continuous-time random walks. We
first briefly survey random walks on a line, and then we consider random walks
on various types of networks. We extensively discuss applications of random
walks, including ranking of nodes (e.g., PageRank), community detection,
respondent-driven sampling, and opinion models such as voter models.


  The Calogero-Moser (or CM) particle system and its generalizations appear, in
a variety of ways, in integrable systems, nonlinear PDE, representation theory,
and string theory. Moreover, the partially completed CM systems--in which
dynamics of particles are continued through collisions--have been identified as
meromorphic Hitchin systems, giving natural ``geometric action-angle
variables'' for the CM system. Motivated by relations of the CM system to
nonlinear PDE, we introduce a new class of generalizations of the spin CM
particle systems, the framed (rational, trigonometric and elliptic) CM systems.
We give two algebro-geometric descriptions of these systems, via meromorphic
Hitchin systems with decorations (framing data) on (cuspidal, nodal and smooth)
cubic curves and via one-dimensional sheaves on corresponding ``twisted'' ruled
surfaces. We also present a simple geometric formulation of the flows of all
meromorphic GL_n Hitchin systems (with no regularity assumptions) as tweaking
flows on spectral sheaves. Using this formulation, we show that all spin and
framed CM systems are identified with hierarchies of tweaking flows on the
corresponding spectral sheaves. This generalizes the well-known description of
spinless CM systems in terms of tangential covers.


  We study the formation and final structure of the rare earth peak
($A\sim160$) of the $r$-process nucleosynthesis. The rare earth peak forms at
late times in the $r$-process after neutron exhaustion (neutron-to-seed ratio
unity or R=1) as matter decays back to stability. Since rare earth peak
formation does not occur during \nggn \ equilibrium it is sensitive to the
strong interplay between late time thermodynamic evolution and nuclear physics
input. Depending on the conditions the peak forms either because of the pattern
of the neutron capture rates or because of the pattern of the separation
energies. We analyze three mass models under different thermodynamic
conditions. We find that the subtleties of each mass model, including
separation energies and neutron capture rates, influence not only the final
shape of the peak but also when it forms. We identify the range of nuclei which
are influential in rare earth peak formation.


  In this paper, we explore an integrated sensing and communication (ISAC)
system with backscattering RFID tags. In this setup, an access point employs a
communication beam to serve a user while leveraging a sensing beam to detect an
RFID tag. Under the total transmit power constraint of the system, our
objective is to design sensing and communication beams by considering the tag
detection and communication requirements. First, we adopt zero-forcing to
design the beamforming vectors, followed by solving a convex optimization
problem to determine the power allocation between sensing and communication.
Then, we study a joint beamforming design problem with the goal of minimizing
the total transmit power while satisfying the tag detection and communication
requirements. To resolve this, we re-formulate the non-convex constraints into
convex second-order cone constraints. The simulation results demonstrate that,
under different communication SINR requirements, joint beamforming optimization
outperforms the zero-forcing-based method in terms of achievable detection
distance, offering a promising approach for the ISAC-backscattering systems.


  We present the results of a muon-spin spectroscopy investigation of
GaV$_4$S$_{8-y}$Se$_{y}$ with $y=0, 2, 4$ and 8. Zero-field measurements
suggest that GaV$_{4}$Se$_{8}$ and GaV$_{4}$S$_{8}$ have distinct magnetic
ground states, with the latter material showing an anomalous
temperature-dependence of the local magnetic field. It is not possible to
evolve the magnetic state continuously between these two systems, with the
intermediate $y=2$ and $4$ materials showing glassy magnetic behaviour at low
temperature. The skyrmion lattice (SkL) phase is evident in the $y=0$ and 8
materials through an enhanced response of the muon-spin relaxation to the
emergent dynamics that accompany the SkL. For our polycrystalline samples of
GaV$_4$Se$_{8}$, this enhanced dynamic response is confined to a smaller region
of the magnetic field-temperature phase diagram than the previous reports of
the SkL in single crystals.


  In this survey, we formulate the Gr\"{o}bner-Shirshov bases theory for
associative algebras and Lie algebras. Some new Composition-Diamond lemmas and
applications are mentioned.


  We consider Cauchy problem for a divergence form second order parabolic
operator with rapidly oscillating coefficients that are periodic in spatial
variable and random stationary ergodic in time. As was proved in [25] and [13]
in this case the homogenized operator is deterministic. We obtain the leading
terms of the asymptotic expansion of the solution , these terms being
deterministic functions, and show that a properly renormalized difference
between the solution and the said leading terms converges to a solution of some
SPDE.


  We analyze the transformation of the polarization of a photon propagating
along an arbitrary null geodesic in Kerr geometry. The motivation comes from
the problem of an observer trying to communicate quantum information to another
observer in Kerr spacetime by transmitting polarized photons. It is essential
that the observers understand the relationship between their frames of
reference and also know how the photon's polarization transforms as it travels
through Kerr spacetime. Existing methods to calculate the rotation of the
photon polarization (Faraday rotation) depend on choices of coordinate systems,
are algebraically complex and yield results only in the weak-field limit.
  We give a closed-form expression for a parallel propagated frame along an
arbitrary null geodesic using Killing-Yano theory, and thereby solve the
problem of parallel transport of the polarization vector in an intrinsic,
geometrically-motivated fashion. The symmetries of Kerr geometry are utilized
to obtain a remarkably compact expression for the geometrically induced phase
of the photon's polarization. We show that this phase vanishes on the
equatorial plane and the axis of symmetry.


  We present a thorough stability analysis of modified gravity theories in the
presence of matter fields. We use the Effective Field Theory framework for Dark
Energy and Modified Gravity to retain a general approach for the gravity sector
and a Sorkin-Schutz action for the matter one. Then, we work out the proper
viability conditions to guarantee in the scalar sector the absence of ghosts,
gradient and tachyonic instabilities. The absence of ghosts can be achieved by
demanding a positive kinetic matrix, while the lack of a gradient instability
is ensured by imposing a positive speed of propagation for all the scalar
modes. In case of tachyonic instability, the mass eigenvalues have been studied
and we work out the appropriate expressions. For the latter, an instability
occurs only when the negative mass eigenvalue is much larger, in absolute
value, than the Hubble parameter. We discuss the results for the minimally
coupled quintessence model showing for a particular set of parameters two
typical behaviours which in turn lead to a stable and an unstable
configuration. Moreover, we find that the speeds of propagation of the scalar
modes strongly depend on matter densities, for the beyond Horndeski theories.
Our findings can be directly employed when testing modified gravity theories as
they allow to identify the correct viability space.


  We have studied the transverse-momentum ($p_{\rm T}$) dependence of the
inclusive J/$\psi$ production in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$
TeV, in three center-of-mass rapidity ($y_{\rm cms}$) regions, down to zero
$p_{\rm T}$. Results in the forward and backward rapidity ranges ($2.03 <
y_{\rm cms} < 3.53$ and $-4.46 <y_{\rm cms}< -2.96$) are obtained by studying
the J/$\psi$ decay to $\mu^+\mu^-$, while the mid-rapidity region ($-1.37 <
y_{\rm cms} < 0.43$) is investigated by measuring the ${\rm e}^+{\rm e}^-$
decay channel. The $p_{\rm T}$ dependence of the J/$\psi$ production cross
section and nuclear modification factor are presented for each of the rapidity
intervals, as well as the J/$\psi$ mean $p_{\rm T}$ values. Forward and
mid-rapidity results show a suppression of the J/$\psi$ yield, with respect to
pp collisions, which decreases with increasing $p_{\rm T}$. At backward
rapidity no significant J/$\psi$ suppression is observed. Theoretical models
including a combination of cold nuclear matter effects such as shadowing and
partonic energy loss, are in fair agreement with the data, except at forward
rapidity and low transverse momentum. The implications of the p-Pb results for
the evaluation of cold nuclear matter effects on J/$\psi$ production in Pb-Pb
collisions are also discussed.


  We have developed an automated cross-correlation technique to detect 21cm
emission in sample spectra obtained from the HI Parkes All Sky Survey.
  The initial sample selection was the nearest spectra to 2435 low surface
brightness galaxies in the catalogue of Morshidi-Esslinger et al. (1999).
  The galaxies were originally selected to have properties similar to Fornax
cluster dE galaxies. As dE galaxies are generally gas poor it is not surprising
that there were only 26 secure detections. All of the detected galaxies have
very high values of $(M_{H}/L_{B})_{\odot}$. Thus the HI selection of faint
optical sources leads to the detection of predominately gas rich galaxies. The
gas rich galaxies tend to reside on the outskirts of the large scale structure
delineated by optically selected galaxies, but they do appear to be associated
with it. These objects appear to have similar relative dark matter content to
optically selected galaxies. The HI column densities are lower than the
'critical density' necessary for sustainable star formation and they appear,
relatively, rather isolated from companion galaxies. These two factors may
explain their high relative gas content. We have considered the HI mass
function by looking at the distribution of velocities of HI detections in
random spectra on the sky.
  The inferred HI mass function is steep though confirmation of this results
awaits a detailed study of the noise characteristics of the HI survey.


  We consider several classes of complete intersection numerical semigroups,
aris- ing from many different contexts like algebraic geometry, commutative
algebra, coding theory and factorization theory. In particular, we determine
all the logical implications among these classes and provide examples. Most of
these classes are shown to be well-behaved with respect to the operation of
gluing.


  We consider the problem of estimating a mean planar curve from a set of $J$
random planar curves observed on a $k$-points deterministic design. We study
the consistency of a smoothed Procrustean mean curve when the observations obey
a deformable model including some nuisance parameters such as random
translations, rotations and scaling. The main contribution of the paper is to
analyze the influence of the dimension $k$ of the data and of the number $J$ of
observed configurations on the convergence of the smoothed Procrustean
estimator to the mean curve of the model. Some numerical experiments illustrate
these results.


  Optical coherence tomography angiography (OCTA) is a new imaging modality to
visualize retinal microvasculature and has been readily adopted in clinics.
High-resolution OCT angiograms are important to qualitatively and
quantitatively identify potential biomarkers for different retinal diseases
accurately. However, one significant problem of OCTA is the inevitable decrease
in resolution when increasing the field-of-view given a fixed acquisition time.
To address this issue, we propose a novel reference-based super-resolution
(RefSR) framework to preserve the resolution of the OCT angiograms while
increasing the scanning area. Specifically, textures from the normal RefSR
pipeline are used to train a learnable texture generator (LTG), which is
designed to generate textures according to the input. The key difference
between the proposed method and traditional RefSR models is that the textures
used during inference are generated by the LTG instead of being searched from a
single reference image. Since the LTG is optimized throughout the whole
training process, the available texture space is significantly enlarged and no
longer limited to a single reference image, but extends to all textures
contained in the training samples. Moreover, our proposed LTGNet does not
require a reference image at the inference phase, thereby becoming invulnerable
to the selection of the reference image. Both experimental and visual results
show that LTGNet has superior performance and robustness over state-of-the-art
methods, indicating good reliability and promise in real-life deployment. The
source code will be made available upon acceptance.


  Rate-distortion optimization (RDO) of codecs, where distortion is quantified
by the mean-square error, has been a standard practice in image/video
compression over the years. RDO serves well for optimization of codec
performance for evaluation of the results in terms of PSNR. However, it is well
known that the PSNR does not correlate well with perceptual evaluation of
images; hence, RDO is not well suited for perceptual optimization of codecs.
Recently, rate-distortion-perception trade-off has been formalized by taking
the Kullback-Leibner (KL) divergence between the distributions of the original
and reconstructed images as a perception measure. Learned image compression
methods that simultaneously optimize rate, mean-square loss, VGG loss, and an
adversarial loss were proposed. Yet, there exists no easy approach to fix the
rate, distortion or perception at a desired level in a practical learned image
compression solution to perform an analysis of the trade-off between rate,
distortion and perception measures. In this paper, we propose a practical
approach to fix the rate to carry out perception-distortion analysis at a fixed
rate in order to perform perceptual evaluation of image compression results in
a principled manner. Experimental results provide several insights for
practical rate-distortion-perception analysis in learned image compression.


  For any two-by-two game $\G$, we define a new two-player game $\G^Q$. The
definition is motivated by a vision of players in game $\G$ communicating via
quantum technology according to a certain standard protocol originally
introduced by Eisert and Wilkins [EW].
  In the game $\G^Q$, each players' strategy set consists of the set of all
probability distributions on the 3-sphere $S^3$. Nash equilibria in this game
can be difficult to compute.
  Our main theorems classify all possible equilibria in $\G^Q$ for a
Zariski-dense set of games $\G$ that we call {\it generic games}. First, we
show that up to a suitable definition of equivalence, any strategy that arises
in equilibrium is supported on at most four points; then we show that those
four points must lie in one of a small number of geometric configurations. One
easy consequence is that for zero-sum games, the payoff to either player in a
mixed strategy quantum equilibrium must equal the average of that player's four
possible payoffs.


  We consider the poset of weighted partitions $\Pi_n^w$, introduced by
Dotsenko and Khoroshkin in their study of a certain pair of dual operads. The
maximal intervals of $\Pi_n^w$ provide a generalization of the lattice $\Pi_n$
of partitions, which we show possesses many of the well-known properties of
$\Pi_n$. In particular, we prove these intervals are EL-shellable, we show that
the M\"obius invariant of each maximal interval is given up to sign by the
number of rooted trees on on node set $\{1,2,\dots,n\}$ having a fixed number
of descents, we find combinatorial bases for homology and cohomology, and we
give an explicit sign twisted $\mathfrak{S}_n$-module isomorphism from
cohomology to the multilinear component of the free Lie algebra with two
compatible brackets. We also show that the characteristic polynomial of
$\Pi_n^w$ has a nice factorization analogous to that of $\Pi_n$.


  We propose and analyze heralded quantum gates between qubits in optical
cavities. They employ an auxiliary qubit to report if a successful gate
occurred. In this manner, the errors, which would have corrupted a
deterministic gate, are converted into a non-unity probability of success: once
successful the gate has a much higher fidelity than a similar deterministic
gate. Specifically, we describe that a heralded , near-deterministic controlled
phase gate (CZ-gate) with the conditional error arbitrarily close to zero and
the success probability that approaches unity as the cooperativity of the
system, C, becomes large. Furthermore, we describe an extension to
near-deterministic N- qubit Toffoli gate with a favorable error scaling. These
gates can be directly employed in quantum repeater networks to facilitate
near-ideal entanglement swapping, thus greatly speeding up the entanglement
distribution.


  We analyze the possibility of inequivalent boundary conditions for a scalar
field propagating in the BTZ black hole space-time. We find that for certain
ranges of the black hole parameters, the Klein-Gordon operator admits a
one-parameter family of self-adjoint extensions. For this range, the BTZ
space-time is not quantum mechanically complete. We suggest a physically
motivated method for determining the spectra of the Klein-Gordon operator.


  We use recently calculated next-to-next-to-leading order (NNLO) anaomalous
dimension coefficients for the moments of the xF_3 structure function in
neutrino- nucleon scattering, together with the corresponding three-loop Wilson
coefficients, to obtain improved QCD predictions for both odd and even moments.
To investigate the issue of renormalization scheme dependence the Complete
Renormalization Group Improvement (CORGI) approach is used, in which all
dependence on renormalization or factorization scales is avoided by a complete
resummation of RG-predictable scale logarithms. We also consider predictions
using the method of effective charges and compare with the standard ``physical
scale'' choice. The Bernstein polynomial method is used to construct
experimental moments from the xF_3 data of the CCFR collaboration insensitive
to the data in the region of x which is inaccessible experimentally. Direct
fits for \Lambda^(5)MSbar (\alpha_s(M_Z)) are performed. The CORGI fits
including target mass corrections give a value \alpha_s(M_Z)=0.119+/-0.002,
nicely consistent with the world average. The effective charge and physical
scale fits give slightly smaller values which are still consistent within the
errors.


  We revisit the model of the laser with feedback and the minimal nonlinearity
leading to chaos. Although the model has its origin in laser physics, with
peculiarities related to the CO2 laser, it belongs to the class of the three
dimensional paradigmatic nonlinear oscillator models giving chaos. The proposed
model contains three key nonlinearities, two of which are of the type xy, where
x and y are the fast and slow variables. The third one is of the type xz^2,
where z is an intermediate feedback variable. We analytically demonstrate that
it is essential for producing chaos via local or global homoclinic
bifurcations. Its electronic implementation in the range of kilo Hertz region
confirms its potential in describing phenomena evolving on different time
scales.


  Spacetime must be foliable by spacelike surfaces for the quantum mechanics of
matter fields to be formulated in terms of a unitarily evolving state vector
defined on spacelike surfaces. When a spacetime cannot be foliated by spacelike
surfaces, as in the case of spacetimes with closed timelike curves, a more
general formulation of quantum mechanics is required. In such generalizations
the transition matrix between alternatives in regions of spacetime where states
{\it can} be defined may be non-unitary. This paper describes a generalized
quantum mechanics whose probabilities consistently obey the rules of
probability theory even in the presence of such non-unitarity. The usual notion
of state on a spacelike surface is lost in this generalization and familiar
notions of causality are modified. There is no signaling outside the light
cone, no non-conservation of energy, no ``Everett phones'', and probabilities
of present events do not depend on particular alternatives of the future.
However, the generalization is acausal in the sense that the existence of
non-chronal regions of spacetime in the future can affect the probabilities of
alternatives today. The detectability of non-unitary evolution and violations
of causality in measurement situations are briefly considered. The evolution of
information in non-chronal spacetimes is described.


  We propose to realize a mass gap in QCD not imposing the transversality
condition on the full gluon self-energy, while preserving the color gauge
invariance condition for the full gluon propagator. Since due to color
confinement the gluon is not a physical state, none of physical
observables/processes in low-energy QCD will be directly affected by such a
temporary violation of color gauge invriance/symmetry. In order to make the
existence of a mass gap perfectly clear the corresponding subtraction procedure
is introduced. All this llows one to etablish the general structure of the full
gluon propagator in the presence of a mass gap. It is mainly generated by the
nonlinear interaction of massless gluon modes. The physical meaning of the mass
gap is to be responsible for the large-scale (low-energy/momentum), i.e.,
nonperturbative structure of the true QCD vacuum. The direct nonlinear
iteration solution of the transcendental equation for the full gluon propagator
in the presence of a mass gap is present. We formulate a generl method how to
restore the transversality of the full gluon propagator relevant for the
nonperturbative QCD. It is explicitly shown that such a solution confines QCD.
The exact and gauge-invariant criterion of gluon confinement is derived. The
gauge-invariant quark confinement criterion is also formulated.


  Bayesian optimization (BO) is an integral part of automated scientific
discovery -- the so-called self-driving lab -- where human inputs are ideally
minimal or at least non-blocking. However, scientists often have strong
intuition, and thus human feedback is still useful. Nevertheless, prior works
in enhancing BO with expert feedback, such as by incorporating it in an offline
or online but blocking (arrives at each BO iteration) manner, are incompatible
with the spirit of self-driving labs. In this work, we study whether a small
amount of randomly arriving expert feedback that is being incorporated in a
non-blocking manner can improve a BO campaign. To this end, we run an
additional, independent computing thread on top of the BO loop to handle the
feedback-gathering process. The gathered feedback is used to learn a Bayesian
preference model that can readily be incorporated into the BO thread, to steer
its exploration-exploitation process. Experiments on toy and chemistry datasets
suggest that even just a few intermittent, asynchronous expert feedback can be
useful for improving or constraining BO. This can especially be useful for its
implication in improving self-driving labs, e.g. making them more
data-efficient and less costly.


  The aim of this paper is to investigate the asymptotic behavior of a biphase
tumor fluid flow derived by 2-scale homogenisation techniques in recent works.
This biphase fluid flow model accounts for the capillary wall permeability, and
the interstitial avascular phase, both being mixed in the limit homogenised
problem. When the vessel walls become more permeable, we show that the biphase
fluid flow exhibits a boundary layer that makes the computation of the full
problem costly and unstable. In the limit, both capillary and interstitial
pressures coincide except in the vicinity of the boundary where different
boundary conditions are applied. Thanks to a rigorous asymptotic analysis, we
prove that the solution to the full problem can be approached at any order of
approximation by a monophasic model with appropriate boundary conditions on the
tumor boundary and appropriate correcting terms near the boundary are given.
Numerical simulations in spherical geometry illustrate the theoretical results.


  Quantum mechanics describes seemingly paradoxical relations between the
outcomes of measurements that cannot be performed jointly. In Hilbert space,
the outcomes of such incompatible measurements are represented by
non-orthogonal states. In this paper, we investigate how the relation between
outcomes represented by non-orthogonal quantum states differs from the
relations suggested by a joint assignment of measurement outcomes that do not
depend on the actual measurement context. The analysis is based on a well-known
scenario where three statements about the impossibilities of certain outcomes
would seem to make a specific fourth outcome impossible as well, yet quantum
theory allows the observation of that outcome with a non-vanishing probability.
We show that the Hilbert space formalism modifies the relation between the four
measurement outcomes by defining a lower bound of the fourth probability that
increases as the total probability of the first three outcomes drops to zero.
Quantum theory thus makes the violation of non-contextual consistency between
the measurement outcomes not only possible, but actually requires it as a
necessary consequence of the Hilbert space inner products that describe the
contextual relation between the outcomes of different measurements.


  Here, we present results from a systematic study on cleaning of oily deposits
from solid surfaces (porcelain and stainless steel) by solutions of fatty acid
sulfonated methyl esters (SME), sodium salts. The zwitterionic
dodecyldimethylamine oxide (DDAO) has been used as a cosurfactant. As
representatives of the vegetable and mineral oils, sunflower seed oil and light
mineral oil have been used. The process of oil drop detachment from the solid
substrates (roll-up mechanism) has been monitored. In the case of porcelain,
excellent cleaning of oil is achieved by mixed solutions of SME and DDAO. In
the case of stainless steel, excellent cleaning (superior than that by linear
alkylbenzene sulfonate and sodium lauryl ether sulfate) is provided by binary
and ternary mixtures of SMEs, which may contain also DDAO. For the studied
systems, the good cleaning correlates neither with the oil/water interfacial
tension, nor with the surfactant chainlength and headgroup type. The data imply
that governing factors might be the thickness and morphology of admicelle
layers formed on the solid/water interface. The results indicate that the SME
mixtures represent a promising system for formulations in house-hold
detergency, having in mind also other useful properties of SME, such as
biodegradability, skin compatibility and hard water tolerance.


  We reply to Creutz's comments on our paper " 't Hooft vertices, partial
quenching, and rooted staggered QCD." We show that his criticisms are incorrect
and result from a misunderstanding both of our work, and of the related work of
Adams.


  We examine how Canadians living in the East York section of Toronto exchange
social support. Just as we have had to deconstruct social support to understand
its component parts, we now deconstruct how different types of communication
technologies play socially supportive roles. We draw on 101 in-depth interviews
conducted in 2013-2014 to shed light on the support networks of a sample of
East York residents and discern the role of communication technologies in the
exchange of different types of social support across age groups. Our findings
show that not much has changed since the 1960s in terms of the social ties that
our sample of East Yorkers have, and the types of support mobilized via social
networks: companionship, small and large services, emotional aid, and financial
support. What has changed is how communication technologies interweave in
complex ways with different types of social ties (partners, siblings, friends,
etc.) to mobilize social support. We found that with siblings and extended kin
communication technologies could boost the frequency of interaction and help
exchange support at a distance. With friendship ties, communication
technologies provide a continuous, constant flow of interaction. We draw
implications for theories of social support and for social policy linked to
interventions aimed at helping vulnerable groups during the COVID-19 pandemic.


  The state of neural network pruning has been noticed to be unclear and even
confusing for a while, largely due to "a lack of standardized benchmarks and
metrics" [3]. To standardize benchmarks, first, we need to answer: what kind of
comparison setup is considered fair? This basic yet crucial question has barely
been clarified in the community, unfortunately. Meanwhile, we observe several
papers have used (severely) sub-optimal hyper-parameters in pruning
experiments, while the reason behind them is also elusive. These sub-optimal
hyper-parameters further exacerbate the distorted benchmarks, rendering the
state of neural network pruning even more obscure.
  Two mysteries in pruning represent such a confusing status: the
performance-boosting effect of a larger finetuning learning rate, and the
no-value argument of inheriting pretrained weights in filter pruning.
  In this work, we attempt to explain the confusing state of network pruning by
demystifying the two mysteries. Specifically, (1) we first clarify the fairness
principle in pruning experiments and summarize the widely-used comparison
setups; (2) then we unveil the two pruning mysteries and point out the central
role of network trainability, which has not been well recognized so far; (3)
finally, we conclude the paper and give some concrete suggestions regarding how
to calibrate the pruning benchmarks in the future. Code:
https://github.com/mingsun-tse/why-the-state-of-pruning-so-confusing.


  The recently reported 3rd and 4th biological transparency windows located
respectively at 1.6-1.9um and 2.1-2.3um promise deeper tissue penetration and
reduced collateral photodamage, yet they haven't been utilized in photothermal
therapy applications. Nanoparticle based plasmonic photothermal therapy poses a
nontrivial optimization problem in which the light absorption efficiency of the
nanoparticle has to be maximized subject to various constraints that are
imposed by application environment. Upscaling the typical absorber-dominant
nanoparticle designs (rod, sphere etc.) that operate in the 1st and 2nd
transparency windows is not a viable option as their size gets prohibitively
large for cell intrusion and they become scatterer-dominant. The present study
addresses this issue and suggests a versatile approach for designing both
lithography based and self-assembling absorber dominant nanostructures for the
new transparency windows, while keeping their size relatively small. The
proposed nanoparticles demonstrate up to 40% size reduction and 2-fold increase
in absorption efficiency compared to the conventional nanobar design. The
overall photothermal performance per nanoparticle in the 4th window is boosted
up by 250% compared to the 2nd window.


  This paper studies Linear Temporal Logic over Finite Traces (LTLf) where
proposition letters are replaced with first-order formulas interpreted over
arbitrary theories, in the spirit of Satisfiability Modulo Theories. The
resulting logic, called LTLf Modulo Theories (LTLfMT), is semi-decidable.
Nevertheless, its high expressiveness comes useful in a number of use cases,
such as model-checking of data-aware processes and data-aware planning. Despite
the general undecidability of these problems, being able to solve satisfiable
instances is a compromise worth studying. After motivating and describing such
use cases, we provide a sound and complete semi-decision procedure for LTLfMT
based on the SMT encoding of a one-pass tree-shaped tableau system. The
algorithm is implemented in the BLACK satisfiability checking tool, and an
experimental evaluation shows the feasibility of the approach on novel
benchmarks.


  Baryons of lower spin in partially quenched large-$N_c$ QCD are studied with
particular emphasis to interpolation between standard unquenched and fully
quenched limits. In large $N_c$ limit of partially quenched QCD, we calculate
$\Delta m$, the chiral one-loop correction to baryon masses. We find that
leading order contribution to $\Delta m$ is independent of number of ghost
quarks introduced. For finite $N_c$, chiral correction $\Delta m$ does satisfy
the Bernard--Golterman's third theorem and has no infared quenching singularity
except for fully quenched limit. At large $N_c$ limit, however, we show that
the third theorem is bypassed and nontrivial quenched chiral corrections do
arise. In unquenched limit, we also show that standard chiral perturbation
theory results are reproduced in which $\eta'$ loop contributions are
explicitly taken into account.


  Universal scaling relations are of tremendous importance in science, as they
reveal fundamental laws of nature. Several such scaling relations have recently
been proposed for superconductors; however, they are not really universal in
the sense that some important families of superconductors appear to fail the
scaling relations, or obey the scaling with different scaling pre-factors. In
particular, a large group of materials called organic (or molecular)
superconductors are a notable example. Here, we show that such apparent
violations are largely due to the fact that the required experimental
parameters were collected on different samples, with different experimental
techniques. When experimental data is taken on the same sample, using a single
experimental technique, organic superconductors, as well as all other studied
superconductors, do in fact follow universal scaling relations.


  This paper provides an overview of the NIST TREC 2020 Fair Ranking track. For
2020, we again adopted an academic search task, where we have a corpus of
academic article abstracts and queries submitted to a production academic
search engine. The central goal of the Fair Ranking track is to provide fair
exposure to different groups of authors (a group fairness framing). We
recognize that there may be multiple group definitions (e.g. based on
demographics, stature, topic) and hoped for the systems to be robust to these.
We expected participants to develop systems that optimize for fairness and
relevance for arbitrary group definitions, and did not reveal the exact group
definitions until after the evaluation runs were submitted.The track contains
two tasks,reranking and retrieval, with a shared evaluation.


  We reply to the comment made by Su et al. on "Optimal working conditions for
thermoelectric generators with realistic thermal coupling". In particular we
justify the efficiency definition used in the main paper.


  We investigate the design challenges of constructing effective and efficient
neural sequence labeling systems, by reproducing twelve neural sequence
labeling models, which include most of the state-of-the-art structures, and
conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking,
and POS tagging). Misconceptions and inconsistent conclusions in existing
literature are examined and clarified under statistical experiments. In the
comparison and analysis process, we reach several practical conclusions which
can be useful to practitioners.


  The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods
for multi-modal distributions presents a significant challenge in practical
applications such as Bayesian inference and molecular dynamics. Addressing
this, we propose Diffusive Gibbs Sampling (DiGS), an innovative family of
sampling methods designed for effective sampling from distributions
characterized by distant and disconnected modes. DiGS integrates recent
developments in diffusion models, leveraging Gaussian convolution to create an
auxiliary noisy distribution that bridges isolated modes in the original space
and applying Gibbs sampling to alternately draw samples from both spaces. A
novel Metropolis-within-Gibbs scheme is proposed to enhance mixing in the
denoising sampling step. DiGS exhibits a better mixing property for sampling
multi-modal distributions than state-of-the-art methods such as parallel
tempering, attaining substantially improved performance across various tasks,
including mixtures of Gaussians, Bayesian neural networks and molecular
dynamics.


  Let $\M_{n\times n}$ be the algebra of all $n\times n$ matrices. For $x,y\in
{R}^{n}$ it is said that $x$ is majorized by $y$ if there is a double
stochastic matrix $A\in {M}_{n\times n}$ such that $x=Ay$ (denoted by $x\prec
y$). Suppose that $\Phi$ is a linear mapping from ${R}^{n}$ into ${R}^{n}$,
which is said to be strictly isotone if $\Phi(x)\prec \Phi(y)$ whenever $x\prec
y$. We say that an element $\alpha\in {R}^{n}$ is a strictly all-isotone point
if every strictly isotone $\varphi$ at $\alpha$ (i.e.
$\Phi(\alpha)\prec\Phi(y)$ whenever $x\in {R}^{n}$ with $\alpha\prec x$, and
$\Phi(x)\prec\Phi(\alpha)$ whenever $x\in {R}^{n}$ with $x\prec \alpha$) is a
strictly isotone. In this paper we show that every
$\alpha=(\alpha_{1},\alpha_{2},...,\alpha_{n})\in {R}^{n}$ with
$\alpha_{1}>\alpha_{2}>...>\alpha_{n}$ is a strictly all-isotone point.


  Facial action unit detection has emerged as an important task within facial
expression analysis, aimed at detecting specific pre-defined, objective facial
expressions, such as lip tightening and cheek raising. This paper presents our
submission to the Affective Behavior Analysis in-the-wild (ABAW) 2023
Competition for AU detection. We propose a multi-modal method for facial action
unit detection with visual, acoustic, and lexical features extracted from the
large pre-trained models. To provide high-quality details for visual feature
extraction, we apply super-resolution and face alignment to the training data
and show potential performance gain. Our approach achieves the F1 score of
52.3% on the official validation set of the 5th ABAW Challenge.


  Since 2006, Twitter's Application Programming Interface (API) has been a
treasure trove of high-quality data for researchers studying everything from
the spread of misinformation, to social psychology and emergency management.
However, in the spring of 2023, Twitter (now called X) began changing
$42,000/month for its Enterprise access level, an essential death knell for
researcher use. Lacking sufficient funds to pay this monthly fee, academics are
now scrambling to continue their research without this important data source.
This study collects and tabulates the number of studies, number of citations,
dates, major disciplines, and major topic areas of studies that used Twitter
data between 2006 and 2023. While we cannot know for certain what will be lost
now that Twitter data is cost prohibitive, we can illustrate its research value
during the time it was available. A search of 8 databases and 3 related APIs
found that since 2006, a total of 27,453 studies have been published in 7,432
publication venues, with 1,303,142 citations, across 14 disciplines. Major
disciplines include: computational social science, engineering, data science,
social media studies, public health, and medicine. Major topics include:
information dissemination, assessing the credibility of tweets, strategies for
conducting data research, detecting and analyzing major events, and studying
human behavior. Twitter data studies have increased every year since 2006, but
following Twitter's decision to begin charging for data in the spring of 2023,
the number of studies published in 2023 decreased by 13% compared to 2022. We
assume that much of the data used for studies published in 2023 were collected
prior to Twitter's shutdown, and thus the number of new studies are likely to
decline further in subsequent years.


  We investigate negative spectra of 1--D Schr\"odinger operators with
$\delta$- and $\delta'$-interactions on a discrete set in the framework of a
new approach. Namely, using technique of boundary triplets and the
corresponding Weyl functions, we complete and generalize the results of S.
Albeverio and L. Nizhnik. For instance, we propose the algorithm for
determining the number of negative squares of the operator with
$\delta$-interactions. We also show that the number of negative squares of the
operator with $\delta'$-interactions equals the number of negative strengths.


  We determine the isomorphism classes of the first family of infinite
dimensional simple Lie algebras recently introduced by Xu. The structure space
of these algebras is given explicitly. The derivations of these algebras are
also determined.


  Prediction intervals are a valuable way of quantifying uncertainty in
regression problems. Good prediction intervals should be both correct,
containing the actual value between the lower and upper bound at least a target
percentage of the time; and tight, having a small mean width of the bounds.
Many prior techniques for generating prediction intervals make assumptions on
the distribution of error, which causes them to work poorly for problems with
asymmetric distributions.
  This paper presents Expanded Interval Minimization (EIM), a novel loss
function for generating prediction intervals using neural networks. This loss
function uses minibatch statistics to estimate the coverage and optimize the
width of the prediction intervals. It does not make the same assumptions on the
distributions of data and error as prior work. We compare to three published
techniques and show EIM produces on average 1.37x tighter prediction intervals
and in the worst case 1.06x tighter intervals across two large real-world
datasets and varying coverage levels.


  In a previous paper, we showed that a class of time travel paradoxes which
cannot be resolved using Novikov's self-consistency conjecture can be resolved
by assuming the existence of multiple histories or parallel timelines. However,
our proof was obtained using a simplistic toy model, which was formulated using
contrived laws of physics. In the present paper we define and analyze a new
model of time travel paradoxes, which is more compatible with known physics.
This model consists of a traversable Morris-Thorne wormhole time machine in 3+1
spacetime dimensions. We define the spacetime topology and geometry of the
model, calculate the geodesics of objects passing through the time machine, and
prove that this model inevitably leads to paradoxes which cannot be resolved
using Novikov's conjecture, but can be resolved using multiple histories. An
open-source simulation of our new model using Mathematica is available for
download on GitHub. We also provide additional arguments against the Novikov
self-consistency conjecture by considering two new paradoxes, the switch
paradox and the password paradox, for which assuming self-consistency
inevitably leads to counter-intuitive consequences. Our new results provide
more substantial support to our claim that if time travel is possible, then
multiple histories or parallel timelines must also be possible.


  We employ the heat perturbations correlation function to study thermal
transport in the one-dimensional (1D) Fermi-Pasta-Ulam-$\beta$ lattice with
both nearest-neighbor and next-nearest-neighbor couplings. We find that such a
system bears a peculiar phonon dispersion relation, and thus there exists a
competition between phonon dispersion and nonlinearity that can strongly affect
the heat correlation function' shape and scaling property. Specifically, for
small and large anharmoncity, the scaling laws are ballistic and superdiffusive
types, respectively, which are in good agreement with the recent theoretical
predictions; whereas in the intermediate range of the nonlinearity, we observe
an unusual multiscaling property characterized by a \emph{nonmonotonic}
delocalization process of the central peak of the heat correlation function. To
understand this multiscaling laws, we also examine the momentum perturbations
correlation function and find a transition process with the same turning point
of the anharmonicity as that shown in the heat correlation function. This
suggests coupling between the momentum transport and the heat transport, in
agreement with the theoretical arguments of mode cascade theory.


  Nakariakov et al. (1996) investigated the linear magnetosonic waves trapped
within solar wind flow tubes, where they accounted for a slab having boundaries
at $x = \pm d$ and extended up to infinity in the $y$ and $z$ directions.
Srivastava and Dwivedi (2006) claimed to extend that work by considering a
two-dimensional slab. We find that the work of Srivastava and Dwivedi (2006) is
not for a two-dimensional slab and has a number of discrepancies. Further,
their results for body waves are not reliable.


  An additive cellular automaton is a linear map on the set of infinite
multidimensional arrays of elements in a finite cyclic group
$\mathbb{Z}/m\mathbb{Z}$. In this paper, we consider simplices appearing in the
orbits generated from arithmetic arrays by additive cellular automata. We prove
that they are a source of balanced simplices, that are simplices containing all
the elements of $\mathbb{Z}/m\mathbb{Z}$ with the same multiplicity. For any
additive cellular automaton of dimension $1$ or higher, the existence of
infinitely many balanced simplices of $\mathbb{Z}/m\mathbb{Z}$ appearing in
such orbits is shown, and this, for an infinite number of values $m$. The
special case of the Pascal cellular automata, the cellular automata generating
the Pascal simplices, that are a generalization of the Pascal triangle into
arbitrary dimension, is studied in detail.


A room, a teacher and many friends.


  The ubiquity of deep neural networks (DNNs), cloud-based training, and
transfer learning is giving rise to a new cybersecurity frontier in which
unsecure DNNs have `structural malware' (i.e., compromised weights and
activation pathways). In particular, DNNs can be designed to have backdoors
that allow an adversary to easily and reliably fool an image classifier by
adding a pattern of pixels called a trigger. It is generally difficult to
detect backdoors, and existing detection methods are computationally expensive
and require extensive resources (e.g., access to the training data). Here, we
propose a rapid feature-generation technique that quantifies the robustness of
a DNN, `fingerprints' its nonlinearity, and allows us to detect backdoors (if
present). Our approach involves studying how a DNN responds to noise-infused
images with varying noise intensity, which we summarize with titration curves.
We find that DNNs with backdoors are more sensitive to input noise and respond
in a characteristic way that reveals the backdoor and where it leads (its
`target'). Our empirical results demonstrate that we can accurately detect
backdoors with high confidence orders-of-magnitude faster than existing
approaches (seconds versus hours).


  An increasing number of mobile applications rely on Machine Learning (ML)
routines for analyzing data. Executing such tasks at the user devices saves the
energy spent on transmitting and processing large data volumes at distant
cloud-deployed servers. However, due to memory and computing limitations, the
devices often cannot support the required resource-intensive routines and fail
to accurately execute the tasks. In this work, we address the problem of
edge-assisted analytics in resource-constrained systems by proposing and
evaluating a rigorous selective offloading framework. The devices execute their
tasks locally and outsource them to cloudlet servers only when they predict a
significant performance improvement. We consider the practical scenario where
the offloading gain and resource costs are time-varying; and propose an online
optimization algorithm that maximizes the service performance without requiring
to know this information. Our approach relies on an approximate dual
subgradient method combined with a primal-averaging scheme, and works under
minimal assumptions about the system stochasticity. We fully implement the
proposed algorithm in a wireless testbed and evaluate its performance using a
state-of-the-art image recognition application, finding significant performance
gains and cost savings.


  Sharing and working on sensitive data in distributed settings from healthcare
to finance is a major challenge due to security and privacy concerns. Secure
multiparty computation (SMC) is a viable panacea for this, allowing distributed
parties to make computations while the parties learn nothing about their data,
but the final result. Although SMC is instrumental in such distributed
settings, it does not provide any guarantees not to leak any information about
individuals to adversaries. Differential privacy (DP) can be utilized to
address this; however, achieving SMC with DP is not a trivial task, either. In
this paper, we propose a novel Secure Multiparty Distributed Differentially
Private (SM-DDP) protocol to achieve secure and private computations in a
multiparty environment. Specifically, with our protocol, we simultaneously
achieve SMC and DP in distributed settings focusing on linear regression on
horizontally distributed data. That is, parties do not see each others' data
and further, can not infer information about individuals from the final
constructed statistical model. Any statistical model function that allows
independent calculation of local statistics can be computed through our
protocol. The protocol implements homomorphic encryption for SMC and functional
mechanism for DP to achieve the desired security and privacy guarantees. In
this work, we first introduce the theoretical foundation for the SM-DDP
protocol and then evaluate its efficacy and performance on two different
datasets. Our results show that one can achieve individual-level privacy
through the proposed protocol with distributed DP, which is independently
applied by each party in a distributed fashion. Moreover, our results also show
that the SM-DDP protocol incurs minimal computational overhead, is scalable,
and provides security and privacy guarantees.


  In this paper, we consider an expanding construction of a distributed control
system, which is obtained by adding a new subsystem one after the other, until
all $n$ subsystems, where $n \ge 2$, are included in the distributed control
system. It is assumed that a small random perturbation enters only into the
first subsystem and is then subsequently transmitted to the other subsystems.
Moreover, for any $\ell \in \{2, ..., n\}$, the distributed control system,
compatible with the expanding construction, which is obtained from the first
$\ell$ subsystems, satisfies an appropriate H\"{o}rmander condition. As a
result of this, the diffusion process is degenerate, i.e., the backward
operator associated with it is a degenerate parabolic equation. Our main
interest here is to prevent the diffusion process (that corresponds to a
particular subsystem) from leaving a given bounded open domain. In particular,
we consider a risk-sensitive version of the mean escape time criterion with
respect to each of the subsystems. Using a variational representation, we
characterize the risk-sensitive escape control for the diffusion process as the
lower and upper values of an associated stochastic differential game. Finally,
we comment on the implication of our results, where one is also interested in
evaluating the performance of the risk-sensitive escape control, when there is
some modeling error in the distributed control system.


  We present the discovery of the second binary with a Roche lobe-filling hot
subdwarf transferring mass to a white dwarf (WD) companion. This 56 minute
binary was discovered using data from the Zwicky Transient Facility.
Spectroscopic observations reveal an He-sdOB star with an effective temperature
of $T_{\rm eff}=33,700\pm1000$ K and a surface gravity of $log(g)=5.54\pm0.11$.
The GTC+HiPERCAM light curve is dominated by the ellipsoidal deformation of the
He-sdOB star and shows an eclipse of the He-sdOB by an accretion disk as well
as a weak eclipse of the WD. We infer a He-sdOB mass of $M_{\rm
sdOB}=0.41\pm0.04$ M$_\odot$ and a WD mass of $M_{\rm WD}=0.68\pm0.05$
M$_\odot$. The weak eclipses imply a WD black-body temperature of
$63,000\pm10,000$ K and a radius $R_{\rm WD}=0.0148\pm0.0020$ M$_\odot$ as
expected for a WD of such high temperature.
  The He-sdOB star is likely undergoing hydrogen shell burning and will
continue transferring mass for $\approx1$ Myrs at a rate of $10^{-9} M_\odot
{\rm yr}^{-1}$ which is consistent with the high WD temperature. The hot
subdwarf will then turn into a WD and the system will merge in $\approx30$
Myrs. We suggest that Galactic reddening could bias discoveries towards
preferentially finding Roche lobe-filling systems during the short-lived shell
burning phase. Studies using reddening corrected samples should reveal a large
population of helium core-burning hot subdwarfs with $T_{\rm eff}\approx25,000$
K in binaries of 60-90 minutes with WDs. Though not yet in contact, these
binaries would eventually come into contact through gravitational wave emission
and explode as a sub-luminous thermonuclear supernova or evolve into a massive
single WD.


  Planets form in the circumstellar disks of young stars. We review the basic
physical processes by which solid bodies accrete each other and alter each
others' random velocities, and we provide order-of-magnitude derivations for
the rates of these processes. We discuss and exercise the two-groups
approximation, a simple yet powerful technique for solving the evolution
equations for protoplanet growth. We describe orderly, runaway, neutral, and
oligarchic growth. We also delineate the conditions under which each occurs. We
refute a popular misconception by showing that the outer planets formed quickly
by accreting small bodies. Then we address the final stages of planet
formation. Oligarchy ends when the surface density of the oligarchs becomes
comparable to that of the small bodies. Dynamical friction is no longer able to
balance viscous stirring and the oligarchs' random velocities increase. In the
inner-planet system, oligarchs collide and coalesce. In the outer-planet
system, some of the oligarchs are ejected. In both the inner- and outer-planet
systems, this stage ends once the number of big bodies has been reduced to the
point that their mutual interactions no longer produce large-scale chaos.
Subsequently, dynamical friction by the residual small bodies circularizes and
flattens their orbits. The final stage of planet formation involves the clean
up of the residual small bodies. Clean up has been poorly explored.


  We consider a localized electronic spin controlled by a circularly polarized
optical beam and an external magnetic field. When the frequency of the beam is
tuned near an optical resonance with a continuum of higher energy states,
effective magnetic fields are induced on the two-level system via the Inverse
Faraday Effect. We explore the process in which the frequency of the beam is
made linearly time-dependent so that it sweeps through the optical resonance,
starting and ending at the values far away from it. In addition to changes of
spin states, Kramers-Kronig relations guarantee that a localized electron can
also escape into a continuum of states. We argue that probabilities of
transitions between different possible electronic states after such a sweep of
the optical frequency can be found exactly regardless the shape of the
resonance. We also discuss extension of our results to multistate systems.


  We consider f(R,T) modified theory of gravity in which, in general, the
gravitational Lagrangian is given by an arbitrary function of the Ricci scalar
and the trace of the energy-momentum tensor. We indicate that in this type of
the theory, the coupling energy-momentum tensor is~not conserved. However, we
mainly focus on a particular model that matter is minimally coupled to the
geometry in the metric formalism and wherein, its coupling energy--momentum
tensor is also conserved. We obtain the corresponding Raychaudhuri dynamical
equation that presents the evolution of the kinematic quantities. Then for the
chosen model, we derive the behavior of the deceleration parameter, and show
that the coupling term can lead to an acceleration phase after the matter
dominated phase. On the other hand, the curvature of the universe corresponds
with the deviation from parallelism in the geodesic motion. Thus, we also
scrutinize the motion of the free test particles on their geodesics, and derive
the geodesic deviation equation in this modified theory to study the
accelerating universe within the spatially flat FLRW background. Actually, this
equation gives the relative accelerations of adjacent particles as a measurable
physical quantity, and provides an elegant tool to investigate the timelike and
the null structures of spacetime geometries. Then, through the null deviation
vector, we find the observer area-distance as a function of the redshift for
the chosen model, and compare the results with the corresponding results
obtained in the literature.


  We report the most sensitive direct search for pair production of
fourth-generation bottom-like chiral quarks ($b'$) each decaying promptly to
$tW$. We search for an excess of events with an electron or muon, at least five
jets (one indentified as due to a $b$ or $c$ quark) and an imbalance of
transverse momentum using data from $p\bar{p}$ collisions collected by the CDF
II detector at Fermilab with an integrated luminosity of 4.8 fb$^{-1}$. We
observe events consistent with background expectation and calculate upper
limits on the $b'$ pair production cross section ($\sigma_{b\bar{b'}}\lesssim
30$ fb for $m_{b'}>$375 GeV/$c^2$) and exclude $m_{b'}<372$ \gevcc at 95%
confidence level.


  We studied the magnetization reversal rates of thermally assisted spin
transfer torque switching in a ferromagnetically coupled synthetic free layer
theoretically. By solving the Fokker-Planck equation, we obtained the
analytical expression of the switching probability for both the weak and the
strong coupling limit. We found that the thermal stability is proportional to
Delta_{0}(1-I/I_{c})^{2}, not Delta_{0}(1-I/I_{c}) argued by Koch et al. [Phys.
Rev. Lett. 92, 088302 (2004)], where I and I_{c} are the electric current and
the critical current of spin transfer torque switching at absolute zero
temperature. The difference in the exponent of (1-I/I_{c}) leads to a
significant underestimation of the thermal stability Delta_{0}. We also found
that fast switching is achieved by choosing the appropriate direction of the
applied field.


  In this note we relate the property of a semisimple l-adic Galois
representation being "F-split" for a number field F to its having abelian
image. By F-split we mean that the characteristic polynomials of Frobenii all
split in F.


  This paper studies formality of the differential graded algebra $RHom(E,E)$,
where $E$ is a semistable sheaf on a K3 surface. The main tool is Kaledin's
theorem on formality in families. For a large class of sheaves $E$, this DG
algebra is formal, therefore we have an explicit description of the singularity
type of the moduli space of semistable sheaves at the point represented by $E$.
This paper also explains why Kaledin's theorem fails to apply in the remaining
case.


  We study Jacobi matrices on trees whose coefficients are generated by
multiple orthogonal polynomials. Hilbert space decomposition into an orthogonal
sum of cyclic subspaces is obtained. For each subspace, we find generators and
the generalized eigenfunctions written in terms of the orthogonal polynomials.
The spectrum and its spectral type are studied for large classes of
orthogonality measures.


  Many one-dimensional systems of experimental interest possess multiple bands
arising from shallow confining potentials. In this work, we study a gas of
weakly interacting fermions and show that the bulk viscosity is dramatically
altered by the occupation of more than one band. The reasons for this are
two-fold: a multichannel system is more easily displaced from equilibrium and
the associated relaxation processes lead to more rapid equilibration than in
the single channel case. We estimate the bulk viscosity in terms of the
underlying microscopic interactions. The experimental relevance of this physics
is discussed in the context of quantum wires and trapped cold atomic gases.


  Any $n$-vertex $3$-graph with minimum codegree at least $\lfloor n/3\rfloor$
must have a spanning tight component, but immediately below this threshold it
is possible for no tight component to span more than $\lceil 2n/3\rceil$
vertices. Motivated by this observation, we ask which codegree forces a tight
component of at least any given size. The corresponding function seems to have
infinitely many discontinuities, but we provide upper and lower bounds, which
asymptotically converge as the function nears the origin.


  We find a unique direction in the CMB sky around which giant rings have an
anomalous mean temperature profile. This direction is in very close alignment
with the afore measured anomalously large bulk flow direction. Using Monte
Carlo simulations, we estimate the significance of the giant rings at the
$3\sigma$ level and the alignment with the bulk flow at $2.5\sigma$. We argue
that a cosmic defect seeded by a pre-inflationary particle could explain the
giant rings, the large bulk flow and their alignment.


  Empirical networks of weighted dyadic relations often contain noisy edges
that alter the global characteristics of the network and obfuscate the most
important structures therein. Graph pruning is the process of identifying the
most significant edges according to a generative null model, and extracting the
subgraph consisting of those edges. Here, we focus on integer-weighted graphs
commonly arising when weights count the occurrences of an "event" relating the
nodes. We introduce a simple and intuitive null model related to the
configuration model of network generation, and derive two significance filters
from it: the Marginal Likelihood Filter (MLF) and the Global Likelihood Filter
(GLF). The former is a fast algorithm assigning a significance score to each
edge based on the marginal distribution of edge weights whereas the latter is
an ensemble approach which takes into account the correlations among edges. We
apply these filters to the network of air traffic volume between US airports
and recover a geographically faithful representation of the graph. Furthermore,
compared with thresholding based on edge weight, we show that our filters
extract a larger and significantly sparser giant component.


  Precise identification of multiple cell classes in high-resolution Giga-pixel
whole slide imaging (WSI) is critical for various clinical scenarios. Building
an AI model for this purpose typically requires pixel-level annotations, which
are often unscalable and must be done by skilled domain experts (e.g.,
pathologists). However, these annotations can be prone to errors, especially
when distinguishing between intricate cell types (e.g., podocytes and mesangial
cells) using only visual inspection. Interestingly, a recent study showed that
lay annotators, when using extra immunofluorescence (IF) images for reference
(referred to as molecular-empowered learning), can sometimes outperform domain
experts in labeling. Despite this, the resource-intensive task of manual
delineation remains a necessity during the annotation process. In this paper,
we explore the potential of bypassing pixel-level delineation by employing the
recent segment anything model (SAM) on weak box annotation in a zero-shot
learning approach. Specifically, we harness SAM's ability to produce
pixel-level annotations from box annotations and utilize these SAM-generated
labels to train a segmentation model. Our findings show that the proposed
SAM-assisted molecular-empowered learning (SAM-L) can diminish the labeling
efforts for lay annotators by only requiring weak box annotations. This is
achieved without compromising annotation accuracy or the performance of the
deep learning-based segmentation. This research represents a significant
advancement in democratizing the annotation process for training pathological
image segmentation, relying solely on non-expert annotators.


  We develop a hydrodynamic description of electron magnetotransport in
conductors without Galilean invariance in the presence of a weak long-range
disorder potential. We show that magnetoresistance becomes strong (of order 100
%) at relatively small fields, at which the inverse square of the magnetic
length becomes comparable to disorder-induced variations of the electron
density. The mechanism responsible for this anomalously strong
magnetoresistance can be traced to the appearance of magnetic friction force in
liquids with nonvanishing intrinsic conductivity. We derive general results for
the galvanomagnetic and thermomagnetic kinetic coefficients, and obtain their
dependence on the intrinsic dissipative properties of the electron liquid and
the correlation function of the disorder potential. We apply this theory to
graphene close to charge neutrality and cover the crossover to a high-density
regime.


  We obtain the almost sure strong consistency and the Berry-Esseen type bound
for the maximum likelihood estimator Ln of the ensemble L for determinantal
point processes (DPPs), strengthening and completing previous work initiated in
Brunel, Moitra, Rigollet, and Urschel [BMRU17]. Numerical algorithms of
estimating DPPs are developed and simulation studies are performed. Lastly, we
give explicit formula and a detailed discussion for the maximum likelihood
estimator for blocked determinantal matrix of two by two submatrices and
compare it with the frequency method.


  In this paper, we characterize all graphs with eigenvectors of the signless
Laplacian and adjacency matrices with components equal to $\{- 1, 0, 1\}.$ We
extend the graph parameter max $k$-cut to square matrices and prove a general
sharp upper bound, which implies upper bounds on the max $k$-cut of a graph
using the smallest signless Laplacian eigenvalue, the smallest adjacency
eigenvalue, and the largest Laplacian eigenvalue of the graph. In addition, we
construct infinite families of extremal graphs for the obtained upper bounds.


  Relying entirely on an attention mechanism, the Transformer introduced by
Vaswani et al. (2017) achieves state-of-the-art results for machine
translation. In contrast to recurrent and convolutional neural networks, it
does not explicitly model relative or absolute position information in its
structure. Instead, it requires adding representations of absolute positions to
its inputs. In this work we present an alternative approach, extending the
self-attention mechanism to efficiently consider representations of the
relative positions, or distances between sequence elements. On the WMT 2014
English-to-German and English-to-French translation tasks, this approach yields
improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations,
respectively. Notably, we observe that combining relative and absolute position
representations yields no further improvement in translation quality. We
describe an efficient implementation of our method and cast it as an instance
of relation-aware self-attention mechanisms that can generalize to arbitrary
graph-labeled inputs.


  A new Skyrme functional has been developed with tensor term guided by ab
initio relativistic Brueckner-Hartree-Fock (RBHF) studies on neutron-proton
drops. Instead of extracting information on the tensor force from experimental
single-particle energy splittings, the RBHF calculations do not contain beyond
mean-field effects such as particle-vibration coupling and therefore the
information on the tensor force can be obtained without ambiguities. The new
functional gives a good description of nuclear ground-state properties as well
as various giant resonances. The description for the evolution of
single-particle energy splittings is also improved by the new functional.


  Let C be a uniform clutter and let I=I(C) be its edge ideal. We prove that if
C satisfies the packing property (resp. max-flow min-cut property), then there
is a uniform Cohen-Macaulay clutter C1 satisfying the packing property (resp.
max-flow min-cut property) such that C is a minor of C1. For arbitrary edge
ideals of clutters we prove that the normality property is closed under
parallelizations. Then we show some applications to edge ideals and clutters
which are related to a conjecture of Conforti and Cornu\'ejols and to max-flow
min-cut problems.


  Recent work in synthetic data generation in the time-series domain has
focused on the use of Generative Adversarial Networks. We propose a novel
architecture for synthetically generating time-series data with the use of
Variational Auto-Encoders (VAEs). The proposed architecture has several
distinct properties: interpretability, ability to encode domain knowledge, and
reduced training times. We evaluate data generation quality by similarity and
predictability against four multivariate datasets. We experiment with varying
sizes of training data to measure the impact of data availability on generation
quality for our VAE method as well as several state-of-the-art data generation
methods. Our results on similarity tests show that the VAE approach is able to
accurately represent the temporal attributes of the original data. On next-step
prediction tasks using generated data, the proposed VAE architecture
consistently meets or exceeds performance of state-of-the-art data generation
methods. While noise reduction may cause the generated data to deviate from
original data, we demonstrate the resulting de-noised data can significantly
improve performance for next-step prediction using generated data. Finally, the
proposed architecture can incorporate domain-specific time-patterns such as
polynomial trends and seasonalities to provide interpretable outputs. Such
interpretability can be highly advantageous in applications requiring
transparency of model outputs or where users desire to inject prior knowledge
of time-series patterns into the generative model.