  Starting from the Random Phase Approximation (RPA), we generalize the
schematic model of separable interaction defning subspaces of ph excitations
with different coupling constants between them. This ansatz simplifies the RPA
eigenvalue problem to a finite, small dimensional system of equations which
reduces the numerical effort. Associated dispersion relation and the
normalization condition are derived for the new defined unknowns of the system.
In contrast with the standard separable approach, the present formalism is able
to describe more than one collective excitation even in the degenerate limit.
The theoretical framework is applied to neutral and singly charged spherical
sodium clusters and C60 fullerene with results in good agreement with full RPA
calculations and experimental data.


  We determine the total enclosed mass profile from 0.7 to 35 kpc in the
elliptical galaxy NGC 4636 based on the hot interstellar medium temperature
profile measured using the Chandra X-ray Observatory, and other X-ray and
optical data. The total mass increases as radius to the power 1.2 to a good
approximation over this range in radii, attaining a total of 1.5 trillion solar
masses (corresponding to a mass-to-light ratio of 40 solar masses per solar
visual luminosity) at 35 kpc. We find that at least half, and as much as 80%,
of the mass within the optical half-light radius is non-luminous, implying that
NGC 4636 has an exceptionally low baryon fraction. The large inferred dark
matter concentration and central dark matter density, consistent with the upper
end of the range expected for standard cold dark matter halos, imply that
mechanisms proposed to explain low dark matter densities in less massive
galaxies (e.g., self-interacting dark matter, warm dark matter, explosive
feedback) are not effective in elliptical galaxies (and presumably, by
extension, in galaxy clusters). The composite (black hole, stars, and dark
matter) mass distribution has a generally steep slope with no core, consistent
with gravitational lensing studies.


  We present two main theorems along the lines of Lyapunov's second method that
guarantee asymptotic state consensus in multi-agent systems of agents in R^m
with switching interconnection topologies. The two theorems complement each
other in the sense that the first one is formulated in terms of the states of
the agents in the multi-agent system, whereas the second one is formulated in
terms of the pairwise states for each pair of agents in the multi-agent system.
In the first theorem, under the assumption that the interconnection topology is
uniformly strongly connected and the agents are contained in a compact set, a
strong form of attractiveness of the consensus set is assured. In the second
theorem, under the weaker assumption that the interconnection topology is
uniformly quasi strongly connected, the consensus set is guaranteed to be
uniformly asymptotically stable.


  We observed a large HEMD effect in the bilayer manganite
(La$_{0.4}$Pr$_{0.6}$)$_{1.2}$Sr$_{1.8}$Mn$_2$O$_7$, a direct consequence of
field driven spin-glass insulator to ferromagnetic metal transition. The
remnants of the transition can be used to achieve dielectric contrast at room
temperature. This discovery suggests that electronic mechanisms such as the
metal-insulator transition, charge ordering, and orbital ordering can be
exploited to give substantial dielectric contrast in other materials.


  These lectures are devoted to the low energy limit of \N2 SUSY gauge
theories, which is described in terms of integrable systems. A special emphasis
is on a duality that naturally acts on these integrable systems. The duality
turns out to be an effective tool in constructing the double elliptic
integrable system which describes the six-dimensional Seiberg-Witten theory. At
the same time, it implies a series of relations between other Seiberg-Witten
systems.


  We derive analytical expressions for the conductivity of bilayer graphene
(BLG) using the Boltzmann approach within the the Born approximation for a
model of Gaussian disorders describing both short- and long-range impurity
scattering. The range of validity of the Born approximation is established by
comparing the analytical results to exact tight-binding numerical calculations.
A comparison of the obtained density dependencies of the conductivity with
experimental data shows that the BLG samples investigated experimentally so far
are in the quantum scattering regime where the Fermi wavelength exceeds the
effective impurity range. In this regime both short- and long-range scattering
lead to the same linear density dependence of the conductivity. Our
calculations imply that bilayer and single layer graphene have the same
scattering mechanisms. We also provide an upper limit for the effective,
density dependent spatial extension of the scatterers present in the
experiments.


  We analyze the rank-frequency distributions of words in selected English and
Polish texts. We show that for the lemmatized (basic) word forms the
scale-invariant regime breaks after about two decades, while it might be
consistent for the whole range of ranks for the inflected word forms. We also
find that for a corpus consisting of texts written by different authors the
basic scale-invariant regime is broken more strongly than in the case of
comparable corpus consisting of texts written by the same author. Similarly,
for a corpus consisting of texts translated into Polish from other languages
the scale-invariant regime is broken more strongly than for a comparable corpus
of native Polish texts. Moreover, we find that if the words are tagged with
their proper part of speech, only verbs show rank-frequency distribution that
is almost scale-invariant.


  Two new types of electro-optic effect that are linear in the applied electric
field strength are theoretically predicted to exist in transparent dielectric
crystals due to high order spatial dispersion. The first effect, which is
quadratic in the wave vector of light, is possible in materials belonging to
all noncentrosymmetric crystal classes. The second, which is cubic in the wave
vector, is possible in all crystals. In the ${\bm O}(432)$ and $\bm{O_h}(m\bar
3m)$ crystal classes, for which the primary and secondary linear electro-optic
effects and linear electrogyration are simultaneously absent, these effects
lead, respectively, to qualitatively new behavior and constitute the dominant
bulk electro-optic effect in the limit of small fields. Thus, bulk linear
electro-optic effects are predicted to exist in a wide range of
materials---including many of considerable technological importance, such as
silicon---where they were previously considered impossible.


  In the Italian education system, secondary students (ages 14-19) are
confronted with the foundations of quantum physics during the final term of
scientific high school (pre-university year). The Italian Ministry of
Education, University and Research (acronym MIUR) has remarked its importance
in the syllabus to address the high school exit examination (30 percent of the
5th year physics course) but, due to limited learning time and intrinsic
difficulty, this branch of physics is neither assimilated nor appreciated as it
should. We wish to illustrate six didactic suggestions focused on learning
motivation, emerged during a 17-year long teaching experience, which could help
to tackle the main problems found. The key references are two peer-reviewed
talks given, respectively, in 2018 at the "6th Annual International Conference
on Physics" by the Athens Institute for Education and Research and in 2013 at
the 2nd Rome workshop "Science Perception" by the Roma Tre University together
with a concise and evocative poster outlining the history of quanta. Other
useful resources are a 2015 conceptual diagram and four invited lectures held
in the years 2010-2017.


  The exact ground state of the many-body Schr\"odinger equation for $N$ bosons
on a one-dimensional ring interacting via pairwise $\delta$-function
interaction is presented for up to fifty particles. The solutions are obtained
by solving Lieb and Liniger's system of coupled transcendental equations for
finite $N$. The ground state energies for repulsive and attractive interaction
are shown to be smoothly connected at the point of zero interaction strength,
implying that the \emph{Bethe-ansatz} can be used also for attractive
interaction for all cases studied. For repulsive interaction the exact energies
are compared to (i) Lieb and Liniger's thermodynamic limit solution and (ii)
the Tonks-Girardeau gas limit. It is found that the energy of the thermodynamic
limit solution can differ substantially from that of the exact solution for
finite $N$ when the interaction is weak or when $N$ is small. A simple relation
between the Tonks-Girardeau gas limit and the solution for finite interaction
strength is revealed. For attractive interaction we find that the true ground
state energy is given to a good approximation by the energy of the system of
$N$ attractive bosons on an infinite line, provided the interaction is stronger
than the critical interaction strength of mean-field theory.


  The Maunakea Spectroscopic Explorer (MSE) is a planned 11.25-m aperture
facility with a 1.5 square degree field of view that will be fully dedicated to
multi-object spectroscopy. A rebirth of the 3.6m Canada-France-Hawaii Telescope
on Maunakea, MSE will use 4332 fibers operating at three different resolving
powers (R ~ 2500, 6000, 40000) across a wavelength range of 0.36-1.8mum, with
dynamical fiber positioning that allows fibers to match the exposure times of
individual objects. MSE will enable spectroscopic surveys with unprecedented
scale and sensitivity by collecting millions of spectra per year down to
limiting magnitudes of g ~ 20-24 mag, with a nominal velocity precision of ~100
m/s in high-resolution mode. This white paper describes science cases for
stellar astrophysics and exoplanet science using MSE, including the discovery
and atmospheric characterization of exoplanets and substellar objects, stellar
physics with star clusters, asteroseismology of solar-like oscillators and
opacity-driven pulsators, studies of stellar rotation, activity, and
multiplicity, as well as the chemical characterization of AGB and extremely
metal-poor stars.


  As an "input" state of a linear (Mach-Zehnder or Ramsey) interferometer, the
phase-squeezed state proposed by Berry and Wiseman exhibits the best
sensitivity approaching to the Heisenberg limit [Phys. Rev. Lett. 85, 5098
(2000)]. In this paper, we find that it can be generated dynamically with
atomic Bose-Einstein condensates confined in a symmetric double well. Similar
with the Berry and Wiseman's state, the prepared states show the squeezing
along spin operator S_y and the anti-squeezing along S_z, leading to a
sub-shot-noise of the phase sensitivity.


  We construct the AdS$_5$ holographic duals to all non-Lagrangian 4d
$\mathcal{N}=2$ superconformal field theories of Argyres--Douglas type, namely,
$\mathcal{D}_p^{\,b}(G)$, arising from class $\mathcal{S}$ of classical type
involving irregular punctures of regular semi-simple type. The 11d supergravity
duals contain an internal space of the form of a fibered product of a disc with
a squashed and fibered four-sphere and includes orbifold projections which
depend on the type of twist lines/outer-automorphism twists in the class
$\mathcal{S}$ theory. We verify the holographic duality by determining and
matching the anomalies (including the central charges $a$ and $c$ and the
flavor central charges) at leading and subleading orders. The Higgs branch of
the conformal field theory is described via Higgsing by a nilpotent orbit of a
classical Lie algebra; we find the exact closed form formulae for the central
charges for every Higgsing. We prove that in the supergravity duals,
constraints on the type of partitions associated to allowable Higgsings are
enforced by the consistency condition known as the t-rule.


  Contrastive learning, a dominant self-supervised technique, emphasizes
similarity in representations between augmentations of the same input and
dissimilarity for different ones. Although low contrastive loss often
correlates with high classification accuracy, recent studies challenge this
direct relationship, spotlighting the crucial role of inductive biases. We
delve into these biases from a clustering viewpoint, noting that contrastive
learning creates locally dense clusters, contrasting the globally dense
clusters from supervised learning. To capture this discrepancy, we introduce
the "RLD (Relative Local Density)" metric. While this cluster property can
hinder linear classification accuracy, leveraging a Graph Convolutional Network
(GCN) based classifier mitigates this, boosting accuracy and reducing parameter
requirements. The code is available
\href{https://github.com/xsgxlz/How-does-Contrastive-Learning-Organize-Images/tree/main}{here}.


  Collaborative machine learning techniques such as federated learning (FL)
enable the training of models on effectively larger datasets without data
transfer. Recent initiatives have demonstrated that segmentation models trained
with FL can achieve performance similar to locally trained models. However, FL
is not a fully privacy-preserving technique and privacy-centred attacks can
disclose confidential patient data. Thus, supplementing FL with
privacy-enhancing technologies (PTs) such as differential privacy (DP) is a
requirement for clinical applications in a multi-institutional setting. The
application of PTs to FL in medical imaging and the trade-offs between privacy
guarantees and model utility, the ramifications on training performance and the
susceptibility of the final models to attacks have not yet been conclusively
investigated. Here we demonstrate the first application of differentially
private gradient descent-based FL on the task of semantic segmentation in
computed tomography. We find that high segmentation performance is possible
under strong privacy guarantees with an acceptable training time penalty. We
furthermore demonstrate the first successful gradient-based model inversion
attack on a semantic segmentation model and show that the application of DP
prevents it from divulging sensitive image features.


  Decentralized Internet is booming. People are fascinated by its promise that
users can truly own their data. However, in a decentralized Internet,
completing a task usually involves multiple nodes with mutual distrust. Such
distrust might eventually become a major obstacle for the growth of the
decentralized Internet. In this paper, we analyze the distrust using a simple
model and highlight the properties required to faithfully accomplish one task
in a decentralized Internet. We also introduce our draft solution -- PIXIU, a
framework to mitigate the distrust among different nodes. In PIXIU, we design
and utilize trust-{\lambda} and decentralized executor to achieve the
above-needed properties.


  We introduce a generalization of the notion of operad that we call a
contractad, whose set of operations is indexed by connected graphs and whose
composition rules are numbered by contractions of connected subgraphs. We show
that many classical operads, such as the operad of commutative algebras, Lie
algebras, associative algebras, pre-Lie algebras, the little disks operad, and
the operad of moduli spaces of stable curves $\overline{\mathcal{M}}_{0,n+1}$
admit generalizations to contractads. We explain that standard tools like
Koszul duality and the machinery of Gr\"obner bases can be easily generalized
to contractads. We verify the Koszul property of the commutative, Lie,
associative, and Gerstenhaber contractads.


  We calculate the magnetic field dependence of Rabi rates for two-photon
optical Raman processes in alkali atoms. Due to a decoupling of the nuclear and
electronic spins, these rates fall with increasing field. At the typical
magnetic fields of alkali atom Feshbach resonances (B\sim 200G-1200G), the
Raman rates have the same order of magnitude as their zero field values,
suggesting one can combine Raman-induced gauge fields/spin-orbital coupling
with strong Feshbach-induced interactions. The exception is $^6$Li, where there
is a factor of 7 suppression in the Raman coupling, compared to its already
small zero-field value.


  The discrete unitary (reversible) analogues of the continuous (irreversible)
tent maps are numerically investigated, in particular, the lengths probability
distribution of their periodic orbits. It is found that its density can be well
approximated by the inverse proportional law.


  Robot manipulators are often tasked with working in environments with
vibrations and are subject to load uncertainty. Providing an accurate tracking
control design with implementable torque input for these robots is a complex
topic. This paper presents two approaches to solve this problem. The approaches
consider joint space tracking control design in the presence of nonlinear
uncertain torques caused by external vibration and payload variation. The
properties of the uncertain torques are used in both approaches. The first
approach is based on the boundedness property, while the second approach
considers the differentiability and boundedness together. The controllers
derived from each approach differ from the perspectives of accuracy, control
effort, and disturbance properties. A Lyapunov-based analysis is utilized to
guarantee the stability of the control design in each case. Simulation results
validate the approaches and demonstrate the performance of the controllers. The
derived controllers show stable results at the cost of the mentioned
properties.


  The method of smoothed particle hydrodynamics (SPH) is developped
appropriately for the study of relativistic heavy ion collision processes. In
order to describe the flow of a high energy but low baryon number density
fluid, the entropy is taken as the SPH base. We formulate the method in terms
of the variational principle. Several examples show that the method is very
promising for the study of hadronic flow in RHIC physics.


  We investigate existence and multiplicity of weak solutions for fourth-order
problems involving the Leray-Lions type operators in variable exponent spaces
and improve a result of Bonanno and Chinn\`{i} (2011). We use variational
methods and apply a multiplicity theorem of Bonanno and Marano (2010).


  For the purpose of automatically evaluating speakers' humor usage, we build a
presentation corpus containing humorous utterances based on TED talks. Compared
to previous data resources supporting humor recognition research, ours has
several advantages, including (a) both positive and negative instances coming
from a homogeneous data set, (b) containing a large number of speakers, and (c)
being open. Focusing on using lexical cues for humor recognition, we
systematically compare a newly emerging text classification method based on
Convolutional Neural Networks (CNNs) with a well-established conventional
method using linguistic knowledge. The advantages of the CNN method are both
getting higher detection accuracies and being able to learn essential features
automatically.


  In order to separate a mixture of hydrogen ($H_2$) and carbon dioxide
($CO_2$) gases, we have proposed a new approach employing the graphdiyne-like
membrane (GDY-H) using density functional theory (DFT) calculations and
molecular dynamics (MD) simulations. GDY-H is constructed by removing one-third
diacetylenic ($-$C$\equiv$C$-$C$\equiv$C$-$) bonds linkages and replacing with
hydrogen atoms in graphdiyne structure. Our DFT calculations exhibit poor
selectivity and good permeances for $H_2$/$CO_2$ gases passing through this
membrane. To improve the performance of the GDY-H membrane for $H_2$/$CO_2$
separation, we have placed two layers of GDY-H adjacent to each other which the
distance between them is 2 nm. Then, we have inserted 1,3,5-triaminobenzene
between two layers. In this approach, the selectivity of $H_2$/$CO_2$ is
increased from 5.65 to completely purified $H_2$ gas. Furthermore, GDY-H
membrane represents excellent permeance, about $10^8$ gas permeation unit
(GPU), for $H_2$ molecule at temperatures above 20 K. The $H_2$ permeance is
much higher than the value of the usual industrial limits. Moreover, our
proposed approach shows a good balance between the selectivity and permeance
parameters for the gas separation which is an essential factor for $H_2$
purification and $CO_2$ capture processes in the industry.


  Molecular materials enable a vast variety of functionalities for novel
electronic and spintronic devices. The unique possibility to alter or
substitute organic molecules or metallic substrates offers the opportunity to
modify and optimize interfacial properties for almost any desired field of
application. For this reason, we extend the successful approach to control
molecular interfaces by surface alloying. We present a comprehensive
characterization of the structural and electronic properties of the interface
formed between the prototypical molecule PTCDA and a Sn-Ag surface alloy grown
on an Ag(111) single crystal surface. We monitor the changes of adsorption
height of the surface alloy atoms and electronic valence band structure upon
adsorption of one layer of PTCDA using the normal incidence x-ray standing wave
technique in combination with momentum-resolved photoelectron spectroscopy. We
find that the vertical buckling and the surface band structure of the SnAg$_2$
surface alloy is not altered by the adsorption of one layer of PTCDA, in
contrast to our recent study of PTCDA on a PbAg$_2$ surface alloy [Phys. Rev.
Lett. 117, 096805 (2016)] . In addition, the vertical adsorption geometry of
PTCDA and the interfacial energy level alignment indicate the absence of any
chemical interaction between the molecule and the surface alloy. We attribute
the different interactions at these PTCDA/surface alloy interfaces to the
presence or absence of local $\sigma$-bonds between the PTCDA oxygen atoms and
the surface atoms. Combining our findings with results from literature, we are
able to propose an empiric rule for engineering the surface band structure of
alloys by adsorption of organic molecules.


  Predictive models that generalize well under distributional shift are often
desirable and sometimes crucial to building robust and reliable machine
learning applications. We focus on distributional shift that arises in causal
inference from observational data and in unsupervised domain adaptation. We
pose both of these problems as prediction under a shift in design. Popular
methods for overcoming distributional shift make unrealistic assumptions such
as having a well-specified model or knowing the policy that gave rise to the
observed data. Other methods are hindered by their need for a pre-specified
metric for comparing observations, or by poor asymptotic properties. We devise
a bound on the generalization error under design shift, incorporating both
representation learning and sample re-weighting. Based on the bound, we propose
an algorithmic framework that does not require any of the above assumptions and
which is asymptotically consistent. We empirically study the new framework
using two synthetic datasets, and demonstrate its effectiveness compared to
previous methods.


  This article investigates the mobility management of an ultra dense cellular
network (UDN) from an energy-efficiency (EE) point of view. Many dormant base
stations (BSs) in a UDN do not transmit signals, and thus a received power
based handover (HO) approach as in traditional cellular networks is hardly
applicable. In addition, the limited front/backhaul capacity compared to a huge
number of BSs makes it difficult to implement a centralized HO and power
control. For these reasons, a novel user-centric association rule is proposed,
which jointly optimizes HO and power control for maximizing EE. The proposed
mobility management is able to cope not only with the spatial randomness of
user movement but also with temporally correlated wireless channels. The
proposed approach is implemented over a HO time window and tractable power
control policy by exploiting mean-field game (MFG) and stochastic geometry
(SG). Compared to a baseline with a fixed HO interval and transmit power, the
proposed approach achieves the 1.2 times higher long-term average EE at a
typical active BS.


  This paper provides novel analytic expressions for the incomplete Toronto
function, $T_{B}(m,n,r)$, and the incomplete Lipschitz-Hankel Integrals of the
modified Bessel function of the first kind, $Ie_{m,n}(a,z)$. These expressions
are expressed in closed-form and are valid for the case that $n$ is an odd
multiple of $1/2$, i.e. $n \pm 0.5\in\mathbb{N}$. Capitalizing on these, tight
upper and lower bounds are subsequently proposed for both $T_{B}(m,n,r)$
function and $Ie_{m,n}(a,z)$ integrals. Importantly, all new representations
are expressed in closed-form whilst the proposed bounds are shown to be rather
tight. To this effect, they can be effectively exploited in various analytical
studies related to wireless communication theory. Indicative applications
include, among others, the performance evaluation of digital communications
over fading channels and the information-theoretic analysis of multiple-input
multiple-output systems.


  A graph H is a square root of a graph G if G can be obtained from H by adding
an edge between any two vertices in H that are of distance 2. The Square Root
problem is that of deciding whether a given graph admits a square root. This
problem is only known to be NP-complete for chordal graphs and polynomial-time
solvable for non-trivial minor-closed graph classes and a very limited number
of other graph classes. We prove that Square Root is O(n)-time solvable for
graphs of maximum degree 5 and O(n^4)-time solvable for graphs of maximum
degree at most 6.


  We calculate the lowest order charm and beauty parton distribution functions
in and fragmentation functions into D and B mesons using the operator
definitions of factorized perturbative QCD. In the vacuum, we find the leading
corrections that arise from the structure of the final-state hadrons.
Quark-antiquark potentials extracted from the lattice are employed to
demonstrate the existence of open heavy flavor bound-state solutions in the QGP
in the vicinity of the critical temperature. We provide first results for the
in-medium modification of the heavy quark distribution and decay probabilities
in a co-moving plasma. In an improved perturbative QCD description of heavy
flavor dynamics in the thermal medium, we combine D and B meson formation and
dissociation with parton-level charm and beauty quark quenching to obtain
predictions for the heavy meson and non-photonic electron suppression in Cu+Cu
and Pb+Pb collisions at RHIC and the LHC, respectively.


  For a compact Lie group $G$ with maximal torus $T$, Pittie and Smith showed
that the flag variety $G/T$ is always a stably framed boundary. We generalize
this to the category of $p$-compact groups, where the geometric argument is
replaced by a homotopy theoretic argument showing that the class in the stable
homotopy groups of spheres represented by $G/T$ is trivial, even
$G$-equivariantly. As an application, we consider an unstable construction of a
$G$-space mimicking the adjoint representation sphere of $G$ inspired by work
of the second author and Kitchloo. This construction stably and
$G$-equivariantly splits off its top cell, which is then shown to be a
dualizing spectrum for $G$.


  We characterize the incidence and intensity of low-level super-massive black
hole activity within the Fornax cluster, through X-ray observations of the
nuclei of 29 quiescent early-type galaxies. Using the \textit{Chandra X-ray
Telescope}, we target 17 galaxies from the HST Fornax Cluster Survey, down to a
uniform (3$\sigma$) limiting X-ray luminosity threshold of
$5\cdot10^{38}$ergs$^{-1}$, which we combine with deeper, archival observations
for an additional 12 galaxies. A nuclear X-ray point-source is detected in 11
out of 29 targets. After accounting for the low mass X-ray binary contamination
to the nuclear X-ray signal, the X-ray active fraction is measured at $26.6\%
\pm 9.6\%$. The results from this analysis are compared to similar
investigations targeting quiescent early types in the Virgo cluster, as well as
the field. After correcting for the different mass distributions, the measured
Fornax active fraction is less than the field fraction, at more than 3$\sigma$,
confirming that the funneling of gas to the nuclear regions of cluster members
is inhibited compared to those galaxies in the field. At the same time, we find
no statistically significant difference between Fornax and Virgo galaxies, with
only marginal evidence for a lower active fraction in Fornax (1 $\sigma$); if
real, owing to Fornax's higher galaxy number density, this could indicate that
galaxy-galaxy interactions are more effective at gas removal than galaxy-gas
effects.


  On a smooth, bounded pseudoconvex domain $\Omega$ in $\mathbb{C}^n$, to
verify that Catlin's Property ($P$) holds for $b\Omega$, it suffices to check
that it holds on the set of D'Angelo infinite type boundary points. In this
note, we consider the support of the Levi core,
$S_{\mathfrak{C}(\mathcal{N})}$, a subset of the infinite type points, and show
that Property ($P$) holds for $b\Omega$ if and only if it holds for
$S_{\mathfrak{C}(\mathcal{N})}$. Consequently, if Property ($P$) holds on
$S_{\mathfrak{C}(\mathcal{N})}$, then the $\overline{\partial}$-Neumann
operator $N_1$ is compact on $\Omega$.


  The recent discovery of non-saturating giant positive magnetoresistance in
Td-WTe2 has aroused great interest in this material. We have studied the
structural, electronic and vibrational properties of bulk and few-layer Td-WTe2
experimentally and theoretically. Spin-orbit coupling is found to govern the
semi-metallic character of Td-WTe2. Its structural link with the metallic 1T
form provides an understanding of its structural stability. We observe a metal
to insulator transition and a change in the sign of the Seebeck coefficient
around 373 K. Lattice vibrations in Td-WTe2 have been analyzed by first
principle calculations. Out of the 33 possible zone-center Raman active modes,
five distinct Raman bands are observed around 112, 118, 134, 165 and 212 cm-1
in bulk Td-WTe2. Based on symmetry analysis and the calculated Raman tensors,
we assign the intense bands at 165 cm-1 and 212 cm-1 to the A_1^' and A_1^"
modes respectively. We have examined the effect of temperature and the number
of layers on the Raman spectrum. Most of the bands of Td-WTe2 stiffen, and the
ratio of the integrated intensities of the A_1^" to A_1^' bands decreases in
the few-layer sample, while all the bands soften in both bulk and few-layer
samples with increasing temperature.


  Protoplanetary disks are dust- and gas-rich structures surrounding
protostars. Depending on the distance from the protostar, this dust is
thermally processed to different degrees and accreted to form bodies of varying
chemical compositions. The primordial accretion processes occurring in the
early protoplanetary disk such as chondrule formation and metal segregation are
not well understood. One way to constrain them is to study the morphology and
composition of forsteritic grains from the matrix of carbonaceous chondrites.
Here, we present high-resolution ptychographic X-ray nanotomography and
multimodal chemical micro-tomography (X-ray diffraction and X-ray fluorescence)
to reveal the early history of forsteritic grains extracted from the matrix of
the Murchison CM2.5 chondrite. The 3D electron density maps revealed, at
unprecedented resolution (64~nm), spherical inclusions containing Fe-Ni, very
little silica-rich glass and void caps (i.e., volumes where the electron
density is consistent with conditions close to vacuum) trapped in forsterite.
The presence of the voids along with the overall composition, petrological
textures, and shrinkage calculations is consistent with the grains experiencing
one or more heating events with peak temperatures close to the melting point of
forsterite ($\sim$2100~K) and subsequently cooled and contracted, in agreement
with chondrule-forming conditions.


  The coronavirus (COVID-19) has appeared as the greatest challenge due to its
continuous structural evolution as well as the absence of proper antidotes for
this particular virus. The virus mainly spreads and replicates itself among
mass people through close contact which unfortunately can happen in many
unpredictable ways. Therefore, to slow down the spread of this novel virus, the
only relevant initiatives are to maintain social distance, perform contact
tracing, use proper safety gears, and impose quarantine measures. But despite
being theoretically possible, these approaches are very difficult to uphold in
densely populated countries and areas. Therefore, to control the virus spread,
researchers and authorities are considering the use of smartphone based mobile
applications (apps) to identify the likely infected persons as well as the
highly risky zones to maintain isolation and lockdown measures. However, these
methods heavily depend on advanced technological features and expose
significant privacy loopholes. In this paper, we propose a new method for
COVID-19 contact tracing based on mobile phone users' geolocation data. The
proposed method will help the authorities to identify the number of probable
infected persons without using smartphone based mobile applications. In
addition, the proposed method can help people take the vital decision of when
to seek medical assistance by letting them know whether they are already in the
list of exposed persons. Numerical examples demonstrate that the proposed
method can significantly outperform the smartphone app-based solutions.


  We study large-scale outflows in a sample of 96 star-forming galaxies at
1<z<2, using near-UV spectroscopy of FeII and MgII absorption and emission. The
average blueshift of the FeII interstellar absorption lines with respect to the
systemic velocity is -85+/-10 km/s at z~1.5, with standard deviation 87 km/s;
this is a decrease of a factor of two from the average blueshift measured for
far-UV interstellar absorption lines in similarly selected galaxies at z~2. The
profiles of the MgII 2796, 2803 lines show much more variety than the FeII
profiles, which are always seen in absorption; MgII ranges from strong emission
to pure absorption, with emission more common in galaxies with blue UV slopes
and at lower stellar masses. Outflow velocities, as traced by the centroids and
maximum extent of the absorption lines, increase with increasing stellar mass
with 2-3sigma significance, in agreement with previous results. We study fine
structure emission from FeII*, finding several lines of evidence in support of
the model in which this emission is generated by the re-emission of continuum
photons absorbed in the FeII resonance transitions in outflowing gas. In
contrast, photoionization models indicate that MgII emission arises from the
resonant scattering of photons produced in HII regions, accounting for the
differing profiles of the MgII and FeII lines. A comparison of the strengths of
the FeII absorption and FeII* emission lines indicates that massive galaxies
have more extended outflows and/or greater extinction, while two-dimensional
composite spectra indicate that emission from the outflow is stronger at a
radius of ~10 kpc in high mass galaxies than in low mass galaxies.


  We develop a linearly-scaling variant of the Force Coupling Method [K. Yeo
and M. R. Maxey, J. Fluid Mech. 649, 205-231 (2010)] for computing hydrodynamic
interactions among particles confined to a doubly-periodic geometry with either
a single bottom wall or two walls (slit channel) in the aperiodic direction.
Our spectrally-accurate Stokes solver uses the Fast Fourier Transform (FFT) in
the periodic $xy$ plane and Chebyshev polynomials in the aperiodic $z$
direction normal to the wall(s). We decompose the problem into two problems.
The first is a doubly-periodic subproblem in the presence of particles (source
terms) with free-space boundary conditions in the $z$ direction, which we solve
by borrowing ideas from a recent method for rapid evaluation of electrostatic
interactions in doubly-periodic geometries [O. Maxian, R. P. Pel\'aez, L.
Greengard and A. Donev, J. Chem. Phys. 154, 204107 (2021)]. The second is a
correction subproblem to impose the boundary conditions on the wall(s). Instead
of the traditional Gaussian kernel, we use the exponential of a semicircle
kernel to model the source terms (body force) due to the presence of particles,
and provide optimum values for the kernel parameters that ensure a given
hydrodynamic radius with at least two digits of accuracy and rotational and
translational invariance. The computation time of our solver, which is
implemented in graphical processing units, scales linearly with the number of
particles, and allows computations with about a million particles in less than
a second for a sedimented layer of colloidal microrollers. We find that in a
slit channel, a driven dense suspension of microrollers maintains the same
two-layer structure as above a single wall, but moves at a substantially lower
collective speed due to increased confinement.


  We here present the results from a detailed analysis of nebular abundances of
commonly observed ions in the collisional ring galaxy Cartwheel using the Very
Large Telescope (VLT) Multi-Unit Spectroscopic Explorer (MUSE) dataset. The
analysis includes 221 HII regions in the star-forming ring, in addition to 40
relatively fainter H$\alpha$ emitting regions in the spokes, disk and the inner
ring. The ionic abundances of He, N, O and Fe are obtained using the direct
method (DM) for 9, 20, 20, and 17 ring HII regions, respectively, where the
S$^{++}$ temperature-sensitive line is detected. For the rest of the regions,
including all the nebulae between the inner and the outer ring, we obtained O
abundances using the strong-line method (SLM). The ring regions have a median
$12+\log\rm{\frac{O}{H}}$=8.19$\pm$0.15, $\log\rm{\frac{N}{O}}=-$1.57$\pm$0.09
and $\log\rm{\frac{Fe}{O}}=-$2.24$\pm$0.09 using the DM. Within the range of O
abundances seen in the Cartwheel, the N/O and Fe/O values decrease
proportionately with increasing O, suggesting local enrichment of O without
corresponding enrichment of primary N and Fe. The O abundances of the disk HII
regions obtained using the SLM show a well-defined radial gradient. The mean O
abundance of the ring HII regions is lower by $\sim$0.1 dex as compared to the
extrapolation of the radial gradient. The observed trends suggest the
preservation of the pre-collisional abundance gradient, displacement of most of
the processed elements to the ring, as predicted by the recent simulation by
Renaud et al. (2018), and post-collisional infall of metal-poor gas in the
ring.


  Biological systems need to react to stimuli over a broad spectrum of
timescales. If and how this ability can emerge without external fine-tuning is
a puzzle. We consider here this problem in discrete Markovian systems, where we
can leverage results from random matrix theory. Indeed, generic large
transition matrices are governed by universal results, which predict the
absence of long timescales unless fine-tuned. We consider an ensemble of
transition matrices and motivate a temperature-like variable that controls the
dynamic range of matrix elements, which we show plays a crucial role in the
applicability of the large matrix limit: as the dynamic range increases, a
phase transition occurs whereby the random matrix theory result is avoided, and
long relaxation times ensue, in the entire `ordered' phase. We furthermore show
that this phase transition is accompanied by a drop in the entropy rate and a
peak in complexity, as measured by predictive information (Bialek, Nemenman,
Tishby Neural Computation 13(21) 2001). Extending the Markov model to a Hidden
Markov model (HMM), we show that observable sequences inherit properties of the
hidden sequences, allowing HMMs to be understood in terms of more accessible
Markov models. We then apply our findings to fMRI data from 820 human subjects
scanned at wakeful rest. We show that the data can be quantitatively understood
in terms of the random model, and that brain activity lies close to the phase
transition when engaged in unconstrained, task-free cognition -- supporting the
brain criticality hypothesis in this context.


  We obtain general analytical solutions of third-order viscous hydrodynamic
equations for Bjorken and Gubser flows in systems with vanishing bulk viscosity
and chemical potential, and having a constant shear relaxation time. We also
analytically characterize and determine the hydrodynamic attractors for such
systems by studying the universal behavior of these solutions at both early and
late times. Finally we study the properties of these hydrodynamic attractors
for transport coefficients obtained from relativistic kinetic theory in the
relaxation-time approximation.


  We use the fitted Pareto law to construct an accompanying approximation of
the excess distribution function. A selection rule of the location of the
excess distribution function is proposed based on a stagewise lack-of-fit
testing procedure. Our main result is an oracle type inequality for the
Kullback--Leibler loss.


  Two-dimensional electron gas in the integer quantum Hall regime is
investigated numerically by studying the dynamics of an electron hopping on a
square lattice subject to a perpendicular magnetic field and random on-site
energy with white noise distribution. Focusing on the lowest Landau band we
establish an anti-levitation scenario of the extended states: As either the
disorder strength $W$ increases or the magnetic field strength $B$ decreases,
the energies of the extended states move below the Landau energies pertaining
to a clean system. Moreover, for strong enough disorder, there is a disorder
dependent critical magnetic field $B_c(W)$ below which there are no extended
states at all. A general phase diagram in the $W-1/B$ plane is suggested with a
line separating domains of localized and delocalized states.


  We consider the problem of finding the global optimum of a real-valued
complex polynomial on a compact set defined by real-valued complex polynomial
inequalities. It reduces to solving a sequence of complex semidefinite
programming relaxations that grow tighter and tighter thanks to D'Angelo's and
Putinar's Positivstellenstatz discovered in 2008. In other words, the Lasserre
hierarchy may be transposed to complex numbers. We propose a method for
exploiting sparsity and apply the complex hierarchy to problems with several
thousand complex variables. These problems consist of computing optimal power
flows in the European high-voltage transmission network.


  We address an old (1977) conjecture of a subset of the authors (a variant of
Ryser's conjecture): in every r-coloring of the edges of a biclique [A,B]
(complete bipartite graph), the vertex set can be covered by the vertices of at
most 2r-2 monochromatic connected components. We reduce this conjecture to
design-like conjectures, where the monochromatic components of the color
classes are bicliques [X,Y] with nonempty blocks X and Y. We prove this
conjecture for r<6.
  We show that the width (the number of bicliques) in every color class of any
spanning r-coloring is at most 2^{r-1} (and this is best possible).


  With the discovery of a Higgs boson at LHC, all particles of the Standard
Model seem to have been observed experimentally, yet many questions are left
unanswered. The discovery has intensified the planning for future high-energy
colliders, which aim to probe the Standard Model and the mechanism of
electroweak symmetry breaking with higher precision and to extend and
complement the search for new particles currently under way at the LHC. The
most mature option for such a future facility is the International Linear
Collider ILC, an electron-positron collider with a centre-of-mass energy of 500
GeV, and the potential for upgrades into the TeV region. The ILC will fully
explore the Higgs sector, including model-independent coupling and width
measurements, direct measurements of the coupling to the top quark and the
Higgs self-coupling, enable precision measurements of top quark properties and
couplings as well as other electroweak precision measurements and provide
extensive discovery potential for new physics complementary to the capabilities
of hadron colliders. This paper will give an overview of the physics case of
the ILC, put in context of the running scenario covering different
centre-of-mass energies, and discuss the current status and perspectives of
this global facility.


  We consider a well-known exactly solvable model of an open quantum system
with pure decoherence. The aim of this paper is twofold. Firstly, decoherence
is a property of open quantum systems important for both quantum technologies
and the fundamental question of quantum-classical transition. It is worthwhile
to study how the long-term rate of decoherence depends on the spectral density
characterizing the system-bath interaction in this exactly solvable model.
Secondly, we address a more general problem of the Markovian embedding of a
non-Markovian open system dynamics. It is often assumed that a non-Markovian
open quantum system can be embedded into a larger Markovian system. However, we
show that such embedding is possible only for the Ohmic spectral densities (for
the case of a positive bath temperature) and is impossible for both the sub-
and super-Ohmic spectral densities. From the other side, for the Ohmic spectral
densities, an asymptotic large-time Markovianity (in terms of the quantum
regression formula) takes place.


  Making a "which-way" measurement (WWM) to identify which slit a particle goes
through in a double-slit apparatus will reduce the visibility of interference
fringes. There has been a long-standing controversy over whether this can be
attributed to an uncontrollable momentum transfer. To date, no experiment has
characterised the momentum change in a way that relates quantitatively to the
loss of visibility. Here, by reconstructing the Bohmian trajectories of single
photons, we experimentally obtain the distribution of momentum change, which is
observed to be not a momentum kick that occurs at the point of the WWM, but
nonclassically accumulates during the propagation of the photons. We further
confirm a quantitative relation between the loss of visibility consequent on a
WWM and the total (late-time) momentum disturbance. Our results emphasize the
role of the Bohmian momentum in giving an intuitive picture of wave-particle
duality and complementarity.


  The classical stability margin analysis based on the linearized model is
widely used in practice even in nonlinear systems. Although linear analysis
techniques are relatively standard and have simple implementation structures,
they are prone to misbehavior and failure when the system is performing an
off-nominal operation. To avoid the drawbacks and exploit the advantages of
linear analysis methods and frequency-domain stability margin analysis while
tackling system nonlinearity, a state-compensation-linearization-based
stability margin analysis method is studied in the paper. Based on the
state-compensation-linearization-based stabilizing control, the definition and
measurement of the stability margin are given. The l2 gain margin and l2
time-delay margin for the closed-loop nonlinear system with
state-compensation-linearization-based stabilizing control are defined and
derived approximatively by the small-gain theorem in theory. The stability
margin measurement can be carried out by the frequency-sweep method in
practice. The proposed method is a data-driven method for obtaining the
stability margin of nonlinear systems, which is practical and can be applied to
practical systems directly. Finally, three numerical examples are given to
illustrate the effectiveness of the proposed method.


  We present a computational study of the pump-probe response of a single atom
to assess any microscopic nonlinear optical anisotropy due to freed electrons
for non-resonant optical excitation. Using simulations of the Schr\"odinger
equation for an atom exposed to a strong, short-duration linearly polarized
pump, we calculate the induced dipole moment along probe directions parallel
and perpendicular to the pump polarization. Our simulations show birefringence
ratios of approximately 0.78-0.9 on the timescale of tens of femtoseconds
following the excitation pulse.


  Transferable adversarial attack has drawn increasing attention due to their
practical threaten to real-world applications. In particular, the feature-level
adversarial attack is one recent branch that can enhance the transferability
via disturbing the intermediate features. The existing methods usually create a
guidance map for features, where the value indicates the importance of the
corresponding feature element and then employs an iterative algorithm to
disrupt the features accordingly. However, the guidance map is fixed in
existing methods, which can not consistently reflect the behavior of networks
as the image is changed during iteration. In this paper, we describe a new
method called Feature-Momentum Adversarial Attack (FMAA) to further improve
transferability. The key idea of our method is that we estimate a guidance map
dynamically at each iteration using momentum to effectively disturb the
category-relevant features. Extensive experiments demonstrate that our method
significantly outperforms other state-of-the-art methods by a large margin on
different target models.


  In an effort to develop computational tools for predicting radiation hazards
from solar energetic particles (SEPs), we have created a data-driven
physics-based particle transport model to calculate the injection, acceleration
and propagation of SEPs from coronal mass ejection (CME) shocks traversing
through the solar corona and interplanetary magnetic fields. The model runs on
an input of corona and heliospheric plasma and magnetic field configuration
from an MHD model driven by solar photospheric magnetic field measurements
superposed with observed CME shocks determined from coronagraph images. Using
several advanced computation techniques involving stochastic simulation and
integration, it rigorously solves the time-dependent 5-dimensional focus
transport equation in the phase space that includes pitch-angle scattering,
diffusion across magnetic field line, and particle acceleration by CME shocks.
We apply the model to the 2011 November 3 CME event. The calculation results
reproduce multi-spacecraft SEP observations reasonably well without
normalization of particle flux. This circumsolar SEP event seen by spacecraft
at Earth, STEREO-A and STEREO-B at widely separated longitudes can be explained
by diffusive shock acceleration by a single CME shock with a moderate speed.


  Smart lighting is an underlying concept that links three main aspects:
solid-state lighting (SSL) technologies, advanced control and universal
communication interfaces following global standards. However, this
conceptualization is constantly evolving to comply with the guidelines of the
next generation of devices that work in the Internet of Things (IoT) ecosystem.
Modern smart lighting systems are based on light emitting diode (LED)
technology and involve advanced drivers that have features such as dynamic
spectral light reproduction and advanced sensing capabilities. The ultimate
feature is of additional advanced services serving as the hub for optical
communications that allows coexistence with traditional Wi-Fi gateways in
indoor environments. In this context, lighting systems are evolving to support
different wireless communications interfaces compatible with the IoT ecosystem.
Market tendencies of SSL systems predict the accelerated expansion of connected
IoT lighting control systems in different markets from smart homes and
industrial environments. These systems offer advanced features never seen
before such as advanced spectral control of the light source and also, the
inclusion of several communication interfaces. These are mainly wired,
radiofrequencies (RF) and optical wireless communications (OWC) interfaces for
advanced services such as sensing and visible light communications (VLC). In
this paper we present how to design and realize IoT-based smart lighting
systems for different applications using different IoT-centric lighting
architectures. Finally, different standards and aspects related to
interoperability and web services are explained taking into account commercial
smart lighting platforms.


  We investigate the dynamics of the kinks that emerge in a one-dimensional
scalar field theory with an octic potential containing a quartic minimum and
two quadratic minima. We show analytically that kink-antikink and kink-kink
pairs interact with a force that scales with the fourth power of the inter-kink
distance, and calculate its strength. This is done using two different
techniques. The first employs a collective coordinate method to approximately
solve the equation of motion for the profile of an accelerating kink. The
second is based on modifying the potential to one that is able to support
static solutions containing multiple kinks. We show that the two methods give
consistent results. All calculations are supported by numerical work that
confirms the validity of our results.


  We develop a method to efficiently construct phase diagrams using machine
learning. Uncertainty sampling (US) in active learning is utilized to
intensively sample around phase boundaries. Here, we demonstrate constructions
of three known experimental phase diagrams by the US approach. Compared with
random sampling, the US approach decreases the number of sampling points to
about 20%. In particular, the reduction rate is pronounced in more complicated
phase diagrams. Furthermore, we show that using the US approach, undetected new
phase can be rapidly found, and smaller number of initial sampling points are
sufficient. Thus, we conclude that the US approach is useful to construct
complicated phase diagrams from scratch and will be an essential tool in
materials science.


  When nonconforming discontinuous Galerkin methods are implemented for
hyperbolic equations using quadrature, exponential energy growth can result
even when the underlying scheme with exact integration does not support such
growth. Using linear elasticity as a model problem, we propose a skew-symmetric
formulation that has the same energy stability properties for both exact and
inexact quadrature-based integration. These stability properties are maintained
even when the material properties are variable and discontinuous, and the
elements are non-affine (e.g., curved). Additionally, we show how the
nonconforming scheme can be made conservative and constant preserving with
variable material properties and curved elements. The analytic stability,
conservation, and constant preserving results are confirmed through numerical
experiments demonstrating the stability as well as the accuracy of the method.


  We show that, by simple modifications of the usual three-level $\Lambda$-type
scheme used for obtaining electromagnetically induced transparency (EIT), phase
dependence in the response of the atomic medium to a weak probe field can be
introduced. This gives rise to phase dependent susceptibility. By properly
controlling phase and amplitudes of the drive fields we obtain variety of
interesting effects. On one hand we obtain phase control of the group velocity
of a probe field passing through medium to the extent that continuous tuning of
the group velocity from subluminal to superluminal and back is possible. While
on the other hand, by choosing one of the drive fields to be a standing wave
field inside a cavity, we obtain sub-wavelength localization of moving atoms
passing through the cavity field.


  The World Health Organization (WHO) guidelines for monitoring the
effectiveness of HIV treatment in resource-limited settings (RLS) are mostly
based on clinical and immunological markers (e.g., CD4 cell counts). Recent
research indicates that the guidelines are inadequate and can result in high
error rates. Viral load (VL) is considered the "gold standard", yet its
widespread use is limited by cost and infrastructure. In this paper, we propose
a diagnostic algorithm that uses information from routinely-collected clinical
and immunological markers to guide a selective use of VL testing for diagnosing
HIV treatment failure, under the assumption that VL testing is available only
at a certain portion of patient visits. Our algorithm identifies the patient
subpopulation, such that the use of limited VL testing on them minimizes a
pre-defined risk (e.g., misdiagnosis error rate). Diagnostic properties of our
proposal algorithm are assessed by simulations. For illustration, data from the
Miriam Hospital Immunology Clinic (RI, USA) are analyzed.


  As a realization of the quantum Zeno effect, we consider electron tunneling
between two quantum dots with one of the dots coupled to a quantum point
contact detector. The coupling leads to decoherence and to the suppression of
tunneling. When the detector is driven with an ac voltage, a parametric
resonance occurs which strongly counteracts decoherence. We propose a novel
experiment with which it is possible to observe both the quantum Zeno effect
and the parametric resonance in electric transport.


  This work explores the conditions under which global contraction manifests in
the leaky continuous time reservoirs, thus guaranteeing Generalized
Synchronization. Results on continuous time reservoirs make use of the
logarithmic norm of the connectivity matrix. Further analysis yields some
simple guidelines on how to better construct the connectivity matrix in these
systems. Additionally, we outline how the Universal Approximation Property of
discrete time reservoirs is readily satisfied by virtue of the activation
function being contracting, and how continuous time reservoirs may inherit a
limited form of universal approximation by virtue of them overlapping with
Neural Ordinary Differential Equations. The ability of the Reservoir Computing
framework to universally approximate topological conjugates, along with their
fast training, make them a compelling data-driven, black-box surrogate of
dynamical systems, and a potential mechanism for developing digital twins.


  In considering evolution of transcribed regions, regulatory modules, and
other genomic loci of interest, we are often faced with a situation in which
the number of allelic states greatly exceeds the population size. In this
limit, the population eventually adopts a steady state characterized by
mutation-selection-drift balance. Although new alleles continue to be explored
through mutation, the statistics of the population, and in particular the
probabilities of seeing specific allelic configurations in samples taken from a
population, do not change with time. In the absence of selection, probabilities
of allelic configurations are given by the Ewens sampling formula, widely used
in population genetics to detect deviations from neutrality. Here we develop an
extension of this formula to arbitrary, possibly epistatic, fitness landscapes.
Although our approach is general, we focus on the class of landscapes in which
alleles are grouped into two, three, or several fitness states. This class of
landscapes yields sampling probabilities that are computationally more
tractable, and can form a basis for the inference of selection signatures from
sequence data. We demonstrate that, for a sizeable range of mutation rates and
selection coefficients, the steady-state allelic diversity is not neutral.
Therefore, it may be used to infer selection coefficients, as well as other key
evolutionary parameters, using high-throughput sequencing of evolving
populations to collect data on locus polymorphisms. We also find that our
theory remains sufficiently accurate even if the assumptions such as the
infinite allele limit and the "full connectivity" assumption in which each
allele can mutate into any other allele are relaxed. Thus, our framework
establishes a theoretical foundation for inferring selection signatures from
samples of sequences produced by evolution on epistatic fitness landscapes.


  We report efficient terahertz (THz) generation in epitaxial
$\alpha$-W/Co$_6$0Fe$_4$0 spintronic emitters. Two types of emitters have been
investigated; epitaxial $\alpha$-W(110)/Co$_6$0Fe$_4$0(110) and
$\alpha$-W(001)/Co$_6$0Fe$_4$0(001) deposited on single crystalline
Al$_2$O$_3$(11-20) and MgO(001) substrates, respectively. The generated THz
radiation is about 10% larger for $\alpha$-W(110)/Co$_6$0Fe$_4$0(110) grown on
single crystalline Al$_2$O$_3$(11-20), which is explained by the fact that the
$\alpha$-W(110)/Co$_6$0Fe$_4$0(110) interface for this emitter is more
transparent to the spin current due to the presence of Angstrom-scale interface
intermixing at the W/CoFe interface. Our results also reveal that the
generation of THz radiation is larger when pumping with the laser light from
the substrate side, which is explained by a larger part of the laser light due
to interference effects in the film stack being absorbed in the ferromagnetic
Co$_6$0Fe$_4$0 layer in this measurement configuration.


  A clean measurement of the evolution of the galaxy cluster mass function can
significantly improve our understanding of cosmology from the rapid growth of
cluster masses below z < 0.5. Here we examine the consistency of cluster
catalogues selected from the SDSS by applying two independent gravity-based
methods using all available spectroscopic redshifts from the DR10 release.
First, we detect a gravitational redshift related signal for 20,119 and 13,128
clusters with spectroscopic redshifts contained in the GMBCG and redMaPPer
catalogues, respectively, at a level of $\sim - 10$ km s$^{-1}$. This we show
is consistent with the magnitude expected using the richness-mass relations
provided by the literature and after applying recently clarified relativistic
and flux bias corrections. This signal is also consistent with the richest
clusters in the larger catalogue of Wen et al. (2012), corresponding to
$M_{200m} \gtrsim 2 \times 10^{14}\,\mathrm{M}_\odot\,h^{-1}$, however we find
no significant detection of gravitational redshift signal for less riched
clusters, which may be related to bulk motions from substructure and spurious
cluster detections. Second, we find all three catalogues generate
mass-dependent levels of lensing magnification bias, which enhances the mean
redshift of flux-selected background galaxies from the BOSS survey. The
magnitude of this lensing effect is generally consistent with the corresponding
richness-mass relations advocated for the surveys. We conclude that all
catalogues comprise a high proportion of reliable clusters, and that the GMBCG
and redMaPPer cluster finder algorithms favor more relaxed clusters with a
meaningful gravitational redshift signal, as anticipated by the red-sequence
colour selection of the GMBCG and redMaPPer samples.


  We investigate experimentally a Josephson vortex ratchet -- a fluxon in an
asymmetric periodic potential driven by a deterministic force with zero time
average. The highly asymmetric periodic potential is created in an underdamped
annular long Josephson junction by means of a current injector providing
efficiency of the device up to 91%. We measured the ratchet effect for driving
forces with different spectral content. For monochromatic high-frequency drive
the rectified voltage becomes quantized. At high driving frequencies we also
observe chaos, sub-harmonic dynamics and voltage reversal due to the inertial
mass of a fluxon.


  Both animal and plant tissue exhibit a nonlinear rheological phenomenon known
as compression stiffening, or an increase in moduli with increasing uniaxial
compressive strain. Does such a phenomenon exist in single cells, which are the
building blocks of tissues? One expects an individual cell to compression
soften since the semiflexible biopolymer-based cytoskeletal network maintains
the mechanical integrity of the cell and in vitro semiflexible biopolymer
networks typically compression soften. To the contrary, we find that mouse
embryonic fibroblasts (mEFs) compression stiffen under uniaxial compression via
atomic force microscopy (AFM) studies. To understand this finding, we uncover
several potential mechanisms for compression stiffening. First, we study a
single semiflexible polymer loop modeling the actomyosin cortex enclosing a
viscous medium modeled as an incompressible fluid. Second, we study a
two-dimensional semiflexible polymer/fiber network interspersed with
area-conserving loops, which are a proxy for vesicles and fluid-based
organelles. Third, we study two-dimensional fiber networks with
angular-constraining crosslinks, i.e. semiflexible loops on the mesh scale. In
the latter two cases, the loops act as geometric constraints on the fiber
network to help stiffen it via increased angular interactions. We find that the
single semiflexible polymer loop model agrees well with our AFM experiments
until approximately 35% compressive strain. We also find for the fiber network
with area-conserving loops model that the stress-strain curves are sensitive to
the packing fraction and size distribution of the area-conserving loops,
thereby creating a mechanical fingerprint across different cell types. Finally,
we make comparisons between this model and experiments on fibrin networks
interlaced with beads as well as discuss the tissue-scale implications of
cellular compression stiffening.


  The STM induced rotation and dissociation dynamics of a single oxygen
molecule on the Pt(111) surface have been finally determined by first
principles calculations together with a newly developed statistical model for
inelastic electron tunneling. Several long-standing puzzles associated with
these dynamic processes in this classic system have been fully resolved. It is
found that the unexpected low energy barrier of the O2 rotation is originated
from an ingenious pathway, while the prior occupation of the metastable
hcp-hollow site after the O2 dissociation can be attributed to a dynamic
process of surface accommodation. The experimentally observed non-integer
power-law dependence of the rotation rate as a function of the current can be
perfectly explained by taking into account the randomness of multi-electron
inelastic tunneling processes.


  Effects of finite temperature correction to the dilepton emission rate in
ultrarelativistic heavy ion collisions have been looked into. It has been seen
that although the $\rho$-peak in the dilepton spectra from the hadronic sector
is suppressed at very high temperatures, almost no effect can be observed in
the space time integrated count rate.


  Venus's past climate evolution is uncertain. General circulation model
simulations permit a habitable climate as late as ~0.7 Ga, and there is
suggestive-albeit inconclusive-evidence for previous liquid water from surface
geomorphology and mineralogy. However, it is unclear whether a habitable past
can be reconciled with Venus's inferred atmospheric evolution. In particular,
the lack of leftover atmospheric oxygen argues against recent water loss. Here,
we apply a fully coupled model of Venus's atmospheric-interior-climate
evolution from post-accretion magma ocean to present. The model
self-consistently tracks C-, H-, and O-bearing volatiles and surface climate
through the entirety of Venus's history. Atmospheric escape, mantle convection,
melt production, outgassing, deep water cycling, and carbon cycling are
explicitly coupled to climate and redox evolution. Plate tectonic and stagnant
lid histories are considered. Using this coupled model, we conclude that both a
habitable Venusian past and one where Venus never possessed liquid surface
water can be reconciled with known constraints. Specifically, either scenario
can reproduce bulk atmospheric composition, inferred surface heat flow, and
observed $^{40}$Ar and $^{4}$He. Moreover, the model suggests that Venus could
have been habitable with a ~100 m global ocean as late as 1 Ga, without
violating any known constraints. In fact, if diffusion-limited water loss is
throttled by a cool, CO$_2$-dominated upper atmosphere, then a habitable past
is tentatively favored by our model. This escape throttling makes it difficult
to simultaneously recover negligible water vapor and ~90 bar CO$_2$ in the
modern atmosphere without temporarily sequestering carbon in the interior via
silicate weathering to enhance H escape.


  We present a new model for the chemical evolution of elliptical galaxies
taking into account SN feedback, detailed nucleosynthesis and galactic winds.
We discuss the effect of galactic winds on the chemical enrichment of the ICM
and compute the energy per particle injected by the galaxies into the ICM.


  Conversion rate (CVR) prediction plays an important role in advertising
systems. Recently, supervised deep neural network-based models have shown
promising performance in CVR prediction. However, they are data hungry and
require an enormous amount of training data. In online advertising systems,
although there are millions to billions of ads, users tend to click only a
small set of them and to convert on an even smaller set. This data sparsity
issue restricts the power of these deep models. In this paper, we propose the
Contrastive Learning for CVR prediction (CL4CVR) framework. It associates the
supervised CVR prediction task with a contrastive learning task, which can
learn better data representations exploiting abundant unlabeled data and
improve the CVR prediction performance. To tailor the contrastive learning task
to the CVR prediction problem, we propose embedding masking (EM), rather than
feature masking, to create two views of augmented samples. We also propose a
false negative elimination (FNE) component to eliminate samples with the same
feature as the anchor sample, to account for the natural property in user
behavior data. We further propose a supervised positive inclusion (SPI)
component to include additional positive samples for each anchor sample, in
order to make full use of sparse but precious user conversion events.
Experimental results on two real-world conversion datasets demonstrate the
superior performance of CL4CVR. The source code is available at
https://github.com/DongRuiHust/CL4CVR.


  The data on differential cross sections and photon-beam asymmetries for the
$\gamma p \to K^+\Lambda(1520)$ reaction have been analyzed within a tree-level
effective Lagrangian approach. In addition to the $t$-channel $K$ and $K^\ast$
exchanges, the $u$-channel $\Lambda$ exchange, the $s$-channel nucleon
exchange, and the interaction current, a minimal number of nucleon resonances
in the $s$ channel are introduced in constructing the reaction amplitudes to
describe the data. The results show that the experimental data can be well
reproduced by including either the $N(2060)5/2^-$ or the $N(2120)3/2^-$
resonance. In both cases, the contact term and the $K$ exchange are found to
make significant contributions, while the contributions from the $K^\ast$ and
$\Lambda$ exchanges are negligible in the former case and considerable in the
latter case. Measurements of the data on target asymmetries are called on to
further pin down the resonance contents and to clarify the roles of the
$K^\ast$ and $\Lambda$ exchanges in this reaction.


  This article provides a central limit theorem for a consistent estimator of
population eigenvalues with large multiplicities based on sample covariance
matrices. The focus is on limited sample size situations, whereby the number of
available observations is known and comparable in magnitude to the observation
dimension. An exact expression as well as an empirical, asymptotically
accurate, approximation of the limiting variance is derived. Simulations are
performed that corroborate the theoretical claims. A specific application to
wireless sensor networks is developed.


  Word Embeddings have recently imposed themselves as a standard for
representing word meaning in NLP. Semantic similarity between word pairs has
become the most common evaluation benchmark for these representations, with
vector cosine being typically used as the only similarity metric. In this
paper, we report experiments with a rank-based metric for WE, which performs
comparably to vector cosine in similarity estimation and outperforms it in the
recently-introduced and challenging task of outlier detection, thus suggesting
that rank-based measures can improve clustering quality.


  Communication is a critical factor for the big multi-agent world to stay
organized and productive. Typically, most previous multi-agent
"learning-to-communicate" studies try to predefine the communication protocols
or use technologies such as tabular reinforcement learning and evolutionary
algorithm, which can not generalize to changing environment or large collection
of agents.
  In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework
for solving "learning-to-communicate" problem. The ACCNet naturally combines
the powerful actor-critic reinforcement learning technology with deep learning
technology. It can efficiently learn the communication protocols even from
scratch under partially observable environment. We demonstrate that the ACCNet
can achieve better results than several baselines under both continuous and
discrete action space environments. We also analyse the learned protocols and
discuss some design considerations.


  Brownian motion is a central scientific paradigm. Recently, due to increasing
efforts and interests towards miniaturization and small-scale physics or
biology, the effects of confinement on such a motion have become a key topic of
investigation. Essentially, when confined near a wall, a particle moves much
slower than in the bulk due to friction at the boundaries. The mobility is
therefore locally hindered and space-dependent, which in turn leads to the
apparition of so-called multiplicative noises, and associated non-Gaussianities
which remain difficult to resolve at all times. Here, we exploit simple,
optimized and efficient numerical simulations to address Brownian motion in
confinement in a broadrange and quantitative way. To do so, we integrate the
overdamped Langevin equation governing the thermal dynamics of a
negatively-buoyant single spherical colloid within a viscous fluid confined by
two rigid walls, including surface charges. From the produced large set of long
random trajectories, we perform a complete statistical analysis and extract all
the key quantities, such as the probability distributions in displacements and
their main moments. In particular, we propose a novel method to compute
high-order cumulants by reducing convergence problems, and employ it to
efficiently characterize the inherent non-Gaussianity of the confined process.


  Luscher has shown that in single channel problem (elastic region below first
inelastic threshold) there exists a direct link between the discrete value of
the energy in a finite QCD volume and the scattering phase shift at the same
energy. However, when the extension of the theorem is made to the baryon
resonance sector (multi-channel situation in the inelastic region above first
inelastic threshold), eigenphases (diagonal multi-channel quantities) replace
phase shifts (single channel quantities). It is necessary to stress that the
renowned pi/2 resonance criterion is formulated for eigenphases and not for
phase shifts, so the resonance extracting procedure has to be applied with
utmost care. The potential instability of extracting eigenphases from
experimental data which occurs if insufficient number of channels is used can
be reduced if a trace function which explicitly takes multi-channel aspect of
the problem into account is used instead of single-channel phase shifts.


  We use the p-adic local Langlands correspondence for GL_2(Q_p) to find the
reduction modulo p of certain two-dimensional crystalline Galois
representations. In particular, we resolve a conjecture of Breuil, Buzzard, and
Emerton in the case when the slope is strictly between one and three, and prove
partial results towards this conjecture for arbitrary slopes. Moreover, we
partially classify the reduction modulo p of these representations when the
slope is equal to one.


  We prove that the second moment of the number of critical points of any
sufficiently regular random field, for example with almost surely $ C^3 $
sample paths, defined over a compact Whitney stratified manifold is finite. Our
results hold without the assumption of stationarity - which has traditionally
been assumed in other work. Under stationarity we demonstrate that our imposed
conditions imply the generalized Geman condition of Estrade 2016.


  In recent years, machine learning algorithms have become ubiquitous in a
multitude of high-stakes decision-making applications. The unparalleled ability
of machine learning algorithms to learn patterns from data also enables them to
incorporate biases embedded within. A biased model can then make decisions that
disproportionately harm certain groups in society -- limiting their access to
financial services, for example. The awareness of this problem has given rise
to the field of Fair ML, which focuses on studying, measuring, and mitigating
unfairness in algorithmic prediction, with respect to a set of protected groups
(e.g., race or gender). However, the underlying causes for algorithmic
unfairness still remain elusive, with researchers divided between blaming
either the ML algorithms or the data they are trained on. In this work, we
maintain that algorithmic unfairness stems from interactions between models and
biases in the data, rather than from isolated contributions of either of them.
To this end, we propose a taxonomy to characterize data bias and we study a set
of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML
algorithms exhibit under different data bias settings. On our real-world
account-opening fraud use case, we find that each setting entails specific
trade-offs, affecting fairness in expected value and variance -- the latter
often going unnoticed. Moreover, we show how algorithms compare differently in
terms of accuracy and fairness, depending on the biases affecting the data.
Finally, we note that under specific data bias conditions, simple
pre-processing interventions can successfully balance group-wise error rates,
while the same techniques fail in more complex settings.


  The condition of the Glomeruli, or filter sacks, in renal Direct
Immunofluorescence (DIF) specimens is a critical indicator for diagnosing
kidney diseases. A digital pathology system which digitizes a glass histology
slide into a Whole Slide Image (WSI) and then automatically detects and zooms
in on the glomeruli with a higher magnification objective will be extremely
helpful for pathologists. In this paper, using glomerulus detection as the
study case, we provide analysis and observations on several important issues to
help with the development of Computer Aided Diagnostic (CAD) systems to process
WSIs. Large image resolution, large file size, and data scarcity are always
challenging to deal with. To this end, we first examine image downsampling
rates in terms of their effect on detection accuracy. Second, we examine the
impact of image compression. Third, we examine the relationship between the
size of the training set and detection accuracy. To understand the above
issues, experiments are performed on the state-of-the-art detectors: Faster
R-CNN, R-FCN, Mask R-CNN and SSD. Critical findings are observed: (1) The best
balance between detection accuracy, detection speed and file size is achieved
at 8 times downsampling captured with a $40\times$ objective; (2) compression
which reduces the file size dramatically, does not necessarily have an adverse
effect on overall accuracy; (3) reducing the amount of training data to some
extents causes a drop in precision but has a negligible impact on the recall;
(4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus
detection task. We show that the image file size of $40\times$ WSI images can
be reduced by a factor of over 6000 with negligible loss of glomerulus
detection accuracy.


  New hypotheses are proposed that there are s-particles and v-particles which
are symmetric and mutually repulsive, there are S-space and V-space whose
essential difference is only that their expectation values of the Higgs fields
are different. In S-space the S-SU(5) symmetry is broken into S-SU(3)XU(1), and
V-SU(5) symmetry still holds. As a consequence, s-particles get their masses
determined by the SU(5) GUT and form the S-world, and v-particles are all
massless and form SU(5) colour-single states which are identified with dark
energy. The following results are obtained. There is no spacetime singularty,
and there is the highest temperature in the universe. The creating process of
one world is just the annihilating process of the other world in the highest
temperature. A formula which well describes the luminous distance and redshift
is obtained. The results of the Guth's inflationary scenario are obtained.
Decelerated expanding early stage and accelerated expanding now stage of the
universe are explained. New predictions are follows. Some huge cavities in
V-space are not empty, in which there is s-matter with larger density, and are
equivalent to huge concave lenses. The given tharacters of some huge cavities
are well explained. The gravitation between two galaxies distant enough will
lesser than that predicted by the conventional theory. A possible explanation
for the big redshift of quasi-stellar objects is presented. Huge redshifts of
quasars are mass redshifts. The universe is composed of infinite universal
islands. It is possible that there is a new annihilating mode of black holes
with their very huge masses, and there are very huge white holes which are
different from that predicted by conventional theory.


  We establish a surprisingly close relationship between universal Horn classes
of directed graphs and varieties generated by so-called adjacency semigroups
which are Rees matrix semigroups over the trivial group with the unary
operation of reversion. In particular, the lattice of subvarieties of the
variety generated by adjacency semigroups that are regular unary semigroups is
essentially the same as the lattice of universal Horn classes of reflexive
directed graphs. A number of examples follow, including a limit variety of
regular unary semigroups and finite unary semigroups with NP-hard variety
membership problems.


  In this paper, the performance of signaling strategies with high
peak-to-average power ratio is analyzed in both coherent and noncoherent fading
channels. Two recently proposed modulation schemes, namely on-off binary
phase-shift keying and on-off quaternary phase-shift keying, are considered.
For these modulation formats, the optimal decision rules used at the detector
are identified and analytical expressions for the error probabilities are
obtained. Numerical techniques are employed to compute the error probabilities.
It is concluded that increasing the peakedness of the signals results in
reduced error rates for a given power level and hence improve the energy
efficiency.


  We consider an isothermal self-gravitating system surrounding a central body.
This model can represent a galaxy or a globular cluster harboring a central
black hole. It can also represent a gaseous atmosphere surrounding a
protoplanet. In three dimensions, the Boltzmann-Poisson equation must be solved
numerically in order to obtain the density profile of the gas [Chavanis {\it et
al.}, Phys. Rev. E {\bf 109}, 014118 (2024)]. In one and two dimensions, we
show that the Boltzmann-Poisson equation can be solved analytically. We obtain
explicit analytical expressions of the density profile around a central body
which generalize the analytical solutions found by Camm (1950) and Ostriker
(1964) in the absence of a central body. Our results also have applications for
self-gravitating Brownian particles (Smoluchowski-Poisson system), for the
chemotaxis of bacterial populations (Keller-Segel model), and for
two-dimensional point vortices (Onsager's model). In the case of bacterial
populations, the central body could represent a supply of ``food'' that
attracts the bacteria (chemoattractant). In the case of two-dimensional
vortices, the central body could be a central vortex


  Parkinson's disease patients develop different speech impairments that affect
their communication capabilities. The automatic assessment of the speech of the
patients allows the development of computer aided tools to support the
diagnosis and the evaluation of the disease severity. This paper introduces a
methodology to classify Parkinson's disease from speech in three different
languages: Spanish, German, and Czech. The proposed approach considers
convolutional neural networks trained with time frequency representations and a
transfer learning strategy among the three languages. The transfer learning
scheme aims to improve the accuracy of the models when the weights of the
neural network are initialized with utterances from a different language than
the used for the test set. The results suggest that the proposed strategy
improves the accuracy of the models in up to 8\% when the base model used to
initialize the weights of the classifier is robust enough. In addition, the
results obtained after the transfer learning are in most cases more balanced in
terms of specificity-sensitivity than those trained without the transfer
learning strategy.


  The relativistic quantum Toda chain model is studied with the generalized
algebraic Bethe Ansatz method. By employing a set of local gauge
transformations, proper local vacuum states can be obtained for this model. The
exact spectrum and eigenstates of the model are thus constructed
simultaneously.


  We prove matrix and scalar differential Harnack inequalities for linear
parabolic equations on Riemannian and K\"ahler manifolds.


  Islanded microgrids powered by renewable energy require costly energy storage
systems due to the uncontrollable generators. Energy storage needs are
amplified when load and generation are misaligned on hourly, monthly, or
seasonal timescales. Diversification of both loads and generation can smooth
out such mismatches. The ideal type of battery to smooth out remaining
generation deficits will depend on the duration(s) that energy is stored. This
study presents a controls co-design approach to design an islanded microgrid,
showing the benefit of hybridizing tidal and solar generation and hybridizing
lithium-ion and flow battery energy storage. The optimization of the
microgrid's levelized cost of energy is initially studied in grid-search slices
to understand convexity and smoothness, then a particle swarm optimization is
proposed and used to study the sensitivity of the hybrid system configuration
to variations in component costs. The study highlights the benefits of controls
co-design, the need to model premature battery failure, and the importance of
using battery cost models that are applicable across orders of magnitude
variations in energy storage durations. The results indicate that such a hybrid
microgrid would currently produce energy at five times the cost of diesel
generation, but flow battery innovations could bring this closer to only twice
the cost while using 100% renewable energy.


  The lines-of-sight to highly reddened SNe Ia show peculiar continuum
polarization curves, growing toward blue wavelengths and peaking at
$\lambda_{max} \lesssim 0.4 \mu m$, like no other sight line to any normal
Galactic star. We examined continuum polarization measurements of a sample of
asymptotic giant branch (AGB) and post-AGB stars from the literature, finding
that some PPNe have polarization curves similar to those observed along SNe Ia
sight lines. Those polarization curves are produced by scattering on
circumstellar dust. We discuss the similarity and the possibility that at least
some SNe Ia might explode during the post-AGB phase of their binary companion.
Furthermore, we speculate that the peculiar SNe Ia polarization curves might
provide observational support to the core-degenerate progenitor model.


  We prove various results on the cohomology of arithmetic lattices arising
from quaternion algebras over a number field with at least one complex place,
including a strong restriction on the allowable weights of cuspidal
cohomological automorphic forms and a conditional power saving for their
multiplicities when the associated locally symmetric space is a hyperbolic
3-manifold.


  It is shown that the nuclear shadowing of charmonium due to the modification
of the nuclear parton distribution is similar in the factorization approach
based on non relativistic QCD and in the color evaporation model. In the first
model, a separate study of the color octet and color singlet contributions to
the yields of the various charmonium states as well as the contributions of
these states to the total $J/\Psi$ production is performed. It is found a clear
$x_F$ dependence of these contributions which can reproduce experimental data
for moderate $x_F$.


  Recent advances in archaeogenomics have granted access to previously
unavailable biological information with the potential to further our
understanding of past social dynamics at a range of scales. However, to
properly integrate these data within archaeological narratives, new
methodological and theoretical tools are required. Effort must be put into
finding new methods for weaving together different datasets where material
culture and archaeogenomic data are both constitutive elements. This is true on
a small scale, when we study relationships at the individual level, and at a
larger scale when we deal with social and population dynamics. Specifically, in
the study of kinship systems it is essential to contextualize and make sense of
biological relatedness through social relations, which, in archaeology, is
achieved by using material culture as a proxy. In this paper we propose a
Network Science framework to integrate archaeogenomic data and material culture
at an intrasite scale to study biological relatedness and social organization
at the Neolithic site of \c{C}atalh\"oy\"uk. Methodologically, we propose the
use of network variance to investigate the concentration of biological
relatedness and material culture within networks of houses. This approach
allowed us to observe how material culture similarity between buildings gives
valuable information on potential biological relationships between individuals
and how biogenetic ties concentrate at specific localities on site.


  We present a solution to obtain a high-resolution image of a wide field with
the central source removed by destructive interference. The wide-field image is
created by aperture synthesis with a rotating sparse array of telescopes in
space. Nulling of the central source is achieved using a phase-mask
coronagraph. The full (u,v) plane coverage delivered by the 60m, six 3-meter
telescope array is particularly well-suited for the detection and
characterization of exoplanets in the infrared (DARWIN and Terrestrial Planet
Finder (TPF) missions) as well as for other generic science observations.
Detection (S/N=10) of an Earth-like planet is achieved in less than 10 hours
with a 1 micron bandwidth at 10 micron.


  We give a generalisation of the character formula of Deligne--Lusztig
representations from the finite field case to the truncated formal power series
case. Motivated by this generalisation, we give a definition of Green functions
for these local rings, and we prove some basic properties along the lines of
the finite field case, like a summation formula. Among the applications we show
that the higher Deligne--Lusztig characters and G\'erardin's characters agree
at regular semisimple elements. We also derive a generalisation of Braverman
and Kazhdan's result on gamma functions for Deligne--Lusztig characters, with a
more elementary argument.


  We study the evolution of nonlinear surface gravity water-wave packets
developing from modulational instability over an uneven bottom. A nonlinear
Schr\"odinger equation (NLSE) with coefficients varying in space along
propagation is used as a reference model. Based on a low-dimensional
approximation obtained by considering only three complex harmonic modes, we
discuss how to stabilize a one-dimensional pattern in the form of train of
large peaks sitting on a background and propagating over a significant
distance. Our approach is based on a gradual depth variation, while its
conceptual framework is the theory of autoresonance in nonlinear systems and
leads to a quasi-frozen state. Three main stages are identified: amplification
from small sideband amplitudes, separatrix crossing, and adiabatic conversion
to orbits oscillating around an elliptic fixed point. Analytical estimates on
the three stages are obtained from the low-dimensional approximation and
validated by NLSE simulations. Our result will contribute to understand
dynamical stabilization of nonlinear wave packets and the persistence of large
undulatory events in hydrodynamics and other nonlinear dispersive media.


  We consider some bases in the Hecke algebra and exhibit certain dualities
between them.


  A famous result in renewal theory is the Central Limit Theorem for renewal
processes. As in applications usually only observations from a finite time
interval are available, a bound on the Kolmogorov distance to the normal
distribution is desirable. Here we provide an explicit non-uniform bound for
the Renewal Central Limit Theorem based on Stein's method and track the
explicit values of the constants. For this bound the inter-arrival time
distribution is required to have only a second moment. As an intermediate
result of independent interest we obtain explicit bounds in a non-central
Berry-Ess\'{e}n theorem under second moment conditions.


  Federated Learning is a distributed machine learning approach which enables
model training on a large corpus of decentralized data. We have built a
scalable production system for Federated Learning in the domain of mobile
devices, based on TensorFlow. In this paper, we describe the resulting
high-level design, sketch some of the challenges and their solutions, and touch
upon the open problems and future directions.


  This paper proposes to cast end-to-end span-based SRL as a word-based graph
parsing task. The major challenge is how to represent spans at the word level.
Borrowing ideas from research on Chinese word segmentation and named entity
recognition, we propose and compare four different schemata of graph
representation, i.e., BES, BE, BIES, and BII, among which we find that the BES
schema performs the best. We further gain interesting insights through detailed
analysis. Moreover, we propose a simple constrained Viterbi procedure to ensure
the legality of the output graph according to the constraints of the SRL
structure. We conduct experiments on two widely used benchmark datasets, i.e.,
CoNLL05 and CoNLL12. Results show that our word-based graph parsing approach
achieves consistently better performance than previous results, under all
settings of end-to-end and predicate-given, without and with pre-trained
language models (PLMs). More importantly, our model can parse 669/252 sentences
per second, without and with PLMs respectively.


  We construct the potentials that describe the spectrum and decay of
electromagnetic bound states of hadrons, and are consistent with ChPT. These
potentials satisfy the matching condition which enables one to express the
parameters of the potential through the threshold scattering amplitudes
calculated in ChPT. We further analyze the ambiguity in the choice of the
short-range hadronic potentials, which satisfy this matching condition.


  Neural NLP models tend to rely on spurious correlations between labels and
input features to perform their tasks. Minority examples, i.e., examples that
contradict the spurious correlations present in the majority of data points,
have been shown to increase the out-of-distribution generalization of
pre-trained language models. In this paper, we first propose using example
forgetting to find minority examples without prior knowledge of the spurious
correlations present in the dataset. Forgettable examples are instances either
learned and then forgotten during training or never learned. We empirically
show how these examples are related to minorities in our training sets. Then,
we introduce a new approach to robustify models by fine-tuning our models
twice, first on the full training data and second on the minorities only. We
obtain substantial improvements in out-of-distribution generalization when
applying our approach to the MNLI, QQP, and FEVER datasets.


  This paper considers physical systems described by hidden states and
indirectly observed through repeated measurements corrupted by unmodeled
nuisance parameters. A network-based representation learns to disentangle the
coherent information (relative to the state) from the incoherent nuisance
information (relative to the sensing). Instead of physical models, the
representation uses symmetry and stochastic regularization to inform an
autoencoder architecture called SymAE. It enables redatuming, i.e., creating
virtual data instances where the nuisances are uniformized across measurements.


  Of the vast variety of animal gaits, one of the most striking is the
non-planar undulating motion of a sidewinder. But non-planar gaits are not
limited to sidewinders. Here we report a new non-planar mode used as an escape
strategy in juvenile anacondas (Eunectes notaeus). In the S-start, named for
its eponymous shape, transient locomotion arises when the snake writhes and
bends out of the plane while rolling forward about its midsection without
slippage. To quantify our observations, we present a mathematical model for an
active non-planar filament that interacts anisotropically with a frictional
substrate and show that locomotion is due to a propagating localized pulse of a
topological quantity, the link density. A two-dimensional phase space
characterized by scaled body weight and muscular torque shows that relatively
light juveniles are capable of S-starts but heavy adults are not, consistent
with our experiments. Finally, we show that a periodic sequence of S-starts
naturally leads to a sidewinding gait. All together, our characterization of a
novel escape strategy in snakes highlights the role of topology in locomotion,
provides a phase diagram for mode feasibility as a function of body size, and
suggests a role for the S-start in the evolution of sidewinding.