Abstract:
  In this paper, we introduce the NameRec* task, which aims to do highly
accurate and fine-grained person name recognition. Traditional Named Entity
Recognition models have good performance in recognising well-formed person
names from text with consistent and complete syntax, such as news articles.
However, there are rapidly growing scenarios where sentences are of incomplete
syntax and names are in various forms such as user-generated contents and
academic homepages. To address person name recognition in this context, we
propose a fine-grained annotation scheme based on anthroponymy. To take full
advantage of the fine-grained annotations, we propose a Co-guided Neural
Network (CogNN) for person name recognition. CogNN fully explores the
intra-sentence context and rich training signals of name forms. To better
utilize the inter-sentence context and implicit relations, which are extremely
essential for recognizing person names in long documents, we further propose an
Inter-sentence BERT Model (IsBERT). IsBERT has an overlapped input processor,
and an inter-sentence encoder with bidirectional overlapped contextual
embedding learning and multi-hop inference mechanisms. To derive benefit from
different documents with a diverse abundance of context, we propose an advanced
Adaptive Inter-sentence BERT Model (Ada-IsBERT) to dynamically adjust the
inter-sentence overlapping ratio to different documents. We conduct extensive
experiments to demonstrate the superiority of the proposed methods on both
academic homepages and news articles.


Abstract:
  Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary
computations on encrypted data, learning neither the inputs nor the computation
results. Hence, it provides resilience in situations where computations are
carried out by an untrusted or potentially compromised party. This powerful
concept was first conceived by Rivest et al. in the 1970s. However, it remained
unrealized until Craig Gentry presented the first feasible FHE scheme in 2009.
  The advent of the massive collection of sensitive data in cloud services,
coupled with a plague of data breaches, moved highly regulated businesses to
increasingly demand confidential and secure computing solutions. This demand,
in turn, has led to a recent surge in the development of FHE tools. To
understand the landscape of recent FHE tool developments, we conduct an
extensive survey and experimental evaluation to explore the current state of
the art and identify areas for future development.
  In this paper, we survey, evaluate, and systematize FHE tools and compilers.
We perform experiments to evaluate these tools' performance and usability
aspects on a variety of applications. We conclude with recommendations for
developers intending to develop FHE-based applications and a discussion on
future directions for FHE tools development.


Abstract:
  A search for R-parity violating supersymmetry in final states characterised
by high jet multiplicity, at least one isolated light lepton and either zero or
at least three $b$-tagged jets is presented. The search uses 139 fb$^{-1}$ of
$\sqrt{s} = 13$ TeV proton-proton collision data collected by the ATLAS
experiment during Run 2 of the Large Hadron Collider. The results are
interpreted in the context of R-parity-violating supersymmetry models that
feature gluino production, top-squark production, or electroweakino production.
The dominant sources of background are estimated using a data-driven model,
based on observables at medium jet multiplicity, to predict the $b$-tagged jet
multiplicity distribution at the higher jet multiplicities used in the search.
Machine learning techniques are used to reach sensitivity to electroweakino
production, extending the data-driven background estimation to the shape of the
machine learning discriminant. No significant excess over the Standard Model
expectation is observed and exclusion limits at the 95% confidence-level are
extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark
mass, and 320 (365) GeV in higgsino (wino) mass.


Abstract:
  Digital news outlets rely on a variety of outside contributors, from
freelance journalists, to political commentators, to executives and
politicians. These external dependencies create a network among news outlets,
traced along the contributors they share. Using connections between outlets, we
demonstrate how contributors' publishing trajectories tend to align with outlet
political leanings. We also show how polarized clustering of outlets translates
to differences in the topics of news covered and the style and tone of articles
published. In addition, we demonstrate how contributors who cross partisan
divides tend to focus on less explicitly political topics. This work addresses
an important gap in the media polarization literature, by highlighting how
structural factors on the production side of news media create an ecosystem
shaped by political leanings, independent of the priorities of any one person
or organization.


Abstract:
  Most data in cold-atom experiments comes from images, the analysis of which
is limited by our preconceptions of the patterns that could be present in the
data. We focus on the well-defined case of detecting dark solitons -- appearing
as local density depletions in a Bose-Einstein condensate (BEC) -- using a
methodology that is extensible to the general task of pattern recognition in
images of cold atoms. Studying soliton dynamics over a wide range of parameters
requires the analysis of large datasets, making the existing
human-inspection-based methodology a significant bottleneck. Here we describe
an automated classification and positioning system for identifying localized
excitations in atomic BECs utilizing deep convolutional neural networks to
eliminate the need for human image examination. Furthermore, we openly publish
our labeled dataset of dark solitons, the first of its kind, for further
machine learning research.


Abstract:
  Predefined evenly-distributed class centroids (PEDCC) can be widely used in
models and algorithms of pattern classification, such as CNN classifiers,
classification autoencoders, clustering, and semi-supervised learning, etc. Its
basic idea is to predefine the class centers, which are evenly-distributed on
the unit hypersphere in feature space, to maximize the inter-class distance.
The previous method of generating PEDCC uses an iterative algorithm based on a
charge model, that is, the initial values of various centers (charge positions)
are randomly set from the normal distribution, and the charge positions are
updated iteratively with the help of the repulsive force between charges of the
same polarity. The class centers generated by the algorithm will produce some
errors with the theoretically evenly-distributed points, and the generation
time will be longer. This paper takes advantage of regular polyhedron in
high-dimensional space and the evenly distribution of points on the n
dimensional hypersphere to generate PEDCC mathematically. Then, we discussed
the basic and extensive characteristics of the frames formed by PEDCC. Finally,
experiments show that new algorithm is not only faster than the iterative
method, but also more accurate in position. The mathematical analysis and
experimental results of this paper can provide a theoretical tool for using
PEDCC to solve the key problems in the field of pattern recognition, such as
interpretable supervised/unsupervised learning, incremental learning,
uncertainty analysis and so on.


Abstract:
  We leverage the interplay between microscopic variability and macroscopic
order to connect physical descriptions across scales directly from data,
without underlying equations. We reconstruct a state space by concatenating
measurements in time, building a maximum entropy partition of the resulting
sequences, and choosing the sequence length to maximize predictive information.
Trading non-linear trajectories for linear, ensemble evolution, we analyze
reconstructed dynamics through transfer operators. The evolution is
parameterized by a transition time $\tau$: capturing the source entropy rate at
small $\tau$ and revealing timescale separation with collective, coherent
states through the operator spectrum at larger $\tau$. Applicable to both
deterministic and stochastic systems, we illustrate our approach through the
Langevin dynamics of a particle in a double-well potential and the Lorenz
system. Applied to the behavior of the nematode worm $C. elegans$, we derive a
"run-and-pirouette" navigation strategy directly from posture dynamics. We
demonstrate how sequences simulated from the ensemble evolution capture both
fine scale posture dynamics and large scale effective diffusion in the worm's
centroid trajectories and introduce a top-down, operator-based clustering which
reveals subtle subdivisions of the "run" behavior.


Abstract:
  The concept of bounded expansion provides a robust way to capture sparse
graph classes with interesting algorithmic properties. Most notably, every
problem definable in first-order logic can be solved in linear time on bounded
expansion graph classes. First-order interpretations and transductions of
sparse graph classes lead to more general, dense graph classes that seem to
inherit many of the nice algorithmic properties of their sparse counterparts.
  In this work we introduce lacon- and shrub-decompositions and use them to
characterize transductions of bounded expansion graph classes and other graph
classes. If one can efficiently compute sparse shrub- or lacon-decompositions
of transductions of bounded expansion classes then one can solve every problem
definable in first-order logic in linear time on these classes.


Abstract:
  We study the initial value problem of fully nonlinear third-order dispersive
equations on the torus. Under some conditions on the nonlinearity and the data,
we prove that the equation behaves like a parabolic one: there exists a unique
local solution in one direction of time that is infinitely smooth and the
problem in not well-posed in the other direction. Under other conditions on the
nonlinearity and the data, we prove that the equation behaves like a dispersive
one: there exists a unique local solution in both directions of time.


Abstract:
  NEWAGE is a direction-sensitive dark matter search using a low-pressure
gaseous time projection chamber. A low alpha-ray emission rate micro pixel
chamber had been developed in order to reduce background for dark matter
search. We conducted the dark matter search at the Kamioka Observatory in 2018.
The total live time was 107.6 days corresponding to an exposure of 1.1
kg${\cdot}$days. Two events remained in the energy region of 50-60 keV which
was consistent with 2.5 events of the expected background. A directional
analysis was carried out and no significant forward-backward asymmetry derived
from the WIMP-nucleus elastic scatterings was found. Thus a 90% confidence
level upper limit on Spin-Dependent WIMP-proton cross section of 50 pb for a
WIMP mass of 100 GeV/c2 was derived. This limit is the most stringent yet
obtained from direction-sensitive dark matter search experiments.


Abstract:
  While current autonomous navigation systems allow robots to successfully
drive themselves from one point to another in specific environments, they
typically require extensive manual parameter re-tuning by human robotics
experts in order to function in new environments. Furthermore, even for just
one complex environment, a single set of fine-tuned parameters may not work
well in different regions of that environment. These problems prohibit reliable
mobile robot deployment by non-expert users. As a remedy, we propose Adaptive
Planner Parameter Learning (APPL), a machine learning framework that can
leverage non-expert human interaction via several modalities -- including
teleoperated demonstrations, corrective interventions, and evaluative feedback
-- and also unsupervised reinforcement learning to learn a parameter policy
that can dynamically adjust the parameters of classical navigation systems in
response to changes in the environment. APPL inherits safety and explainability
from classical navigation systems while also enjoying the benefits of machine
learning, i.e., the ability to adapt and improve from experience. We present a
suite of individual APPL methods and also a unifying cycle-of-learning scheme
that combines all the proposed methods in a framework that can improve
navigation performance through continual, iterative human interaction and
simulation training.


Abstract:
  For nonlinear differential-algebraic equations (DAEs), we define two kinds of
equivalences, namely, the external and internal equivalence. Roughly speaking,
the word "external" means that we consider a DAE (locally) everywhere and
"internal" means that we consider the DAE on its (locally) maximal invariant
submanifold (i.e., where its solutions exist) only. First, we revise the
geometric reduction method in DAEs solution theory and formulate an
implementable algorithm to realize that method. Then a procedure named
explicitation with driving variables is proposed to connect nonlinear DAEs with
nonlinear control systems and we show that the driving variables of an
explicitation system can be reduced under some involutivity conditions.
Finally, due to the explicitation, we will use some notions from nonlinear
control theory to derive two nonlinear generalizations of the Weierstrass form.


Abstract:
  The multiple scattering of an ultrashort laser pulse by a turbid dispersive
medium (namely a cloud of bubbles in water) is investigated by means of Monte
Carlo simulations. The theory of Gouesbet and Gr\'ehan [Part. Part. Syst.
Charact. 17 213-224 (2000)] is used to derive an energetic model of the
scattering transient. The spreading and extinction of the pulse is decoupled
from the transient of scattering to describe each phenomenon individually. The
transient of scattering is modeled with the Lorenz-Mie Theory, thus also valid
for a relative refractive index lower than one, contrary to the Debye series
expansion which does not converge close to the critical angle. To this aim, the
Scattering Impulse Response Function (SIRF) allows to detect the different
modes of scattering transient in time and direction. The present approach is
more generic and can simulate clouds of air bubbles in water. Two Monte-Carlo
approaches are proposed. The first is a pure Monte Carlo approach where the
delay due to the scattering is randomly drawn at each event, while the second
is based on the transport of the whole scattering signal. They are both
embedded in the Monte Carlo code Scatter3D [JOSA A 24, 2206-2219 (2007)]. Both
models produce equivalent trends and are validated against published numerical
results. They are applied to the multiple scattering of ultra short pulse by a
cloud of bubble in water in the forward direction. The pulse spread due to the
propagation in water is computed for a wide range of traveled distances and
pulse durations, and the optimal pulse duration is given to minimize the pulse
spread at a given distance. The main result is that the scattered photons exit
the turbid medium earlier than the ballistic photons and produce a double peak
related to the refraction in the bubble. This demonstrates the possibility to
develop new diagnostics to characterize dynamic bubbly flows.


Abstract:
  We derive two upper bounds for the probability of deviation of a
vector-valued Lipschitz function of a collection of random variables from its
expected value. The resulting upper bounds can be tighter than bounds obtained
by a direct application of a classical theorem due to Bobkov and G\"{o}tze.


Abstract:
  We formulate a general statement of the problem of defining invariant
measures with certain properties and suggest an ergodic method of perturbations
for describing such measures.


Abstract:
  We study the extended Kitaev model called the K$\Gamma$ model, using a
perturbative expansion combined with a well-controlled mean-field approximation
and a cutting-edge exact diagonalization. In the phase diagram, we discover a
nematic Kitaev spin liquid and a Kekul\'e Kitaev spin liquid. The former
potentially explains the high-field nematic state with zero Chern number
experimentally observed in $\alpha$-RuCl$_3$, even for sufficiently small
values of $\Gamma/|K|$. The latter has a Majorana zero mode in its $Z_3$ vortex
core, which can be potentially controlled by the domain wall motion. This opens
a possible application of the quantum liquid crystal phases in the K$\Gamma$
model to the topological quantum computation.


Abstract:
  Intermittent motion, called stick--slip, is a friction instability that
commonly occurs during relative sliding of two elastic solids. In adhesive
polymer contacts, where elasticity and interface adhesion are strongly coupled,
stick--slip results from the propagation of slow detachment waves at the
interface. Using \emph{in situ} imaging experiments at an adhesive contact, we
show the occurrence of two distinct detachment waves moving parallel
(Schallamach wave) and anti-parallel (separation wave) to the applied remote
sliding. Both waves cause slip in the same direction and travel at speeds much
lesser than any elastic wave speed. We use an elastodynamic framework to
describe the propagation of these slow detachment waves at an elastic-rigid
interface and obtain governing integral equations in the low wave speed limit.
These integral equations are solved in closed form when the elastic solid is
incompressible. Two solution branches emerge, corresponding to opposite moving
detachment waves, just as seen in the experiments. A numerical scheme is used
to obtain interface stresses and velocities for the incompressible case for
arbitrary Poisson ratio. Based on these results, we explicitly demonstrate a
correspondence between propagating slow detachment waves and a static
bi-material interface crack. Based on this, and coupled with a recently
proposed fracture analogy for dynamic friction, we develop a phase diagram
showing domains of possible occurrence of stick--slip via detachment waves
vis-\'a-vis steady interface sliding.


Abstract:
  The Hua-Radon and polarized Hua-Radon transform are two orthogonal
projections defined on holomorphic functions in the Lie sphere. Both
transformations can be written as integral transforms with respect to a
suitable reproducing kernel. Integrating both kernels over a Stiefel manifold
yields a linear combination of zonal spherical monogenics. Using an Almansi
type decomposition of holomorphic functions and reproducing properties of the
zonal monogenics, we obtain an inversion formula for both the Hua-Radon and the
polarized Hua-Radon transform.


Abstract:
  We estimate the effects on the decay constants of charmonium and on heavy
meson masses due to the charm quark in the sea. Our goal is to understand
whether for these quantities $N_f=2+1$ lattice QCD simulations provide results
that can be compared with experiments or whether $N_f=2+1+1$ QCD including the
charm quark in the sea needs to be simulated. We consider two theories, $N_f=0$
QCD and QCD with $N_f=2$ charm quarks in the sea. The charm sea effects (due to
two charm quarks) are estimated comparing the results obtained in these two
theories, after matching them and taking the continuum limit. The absence of
light quarks allows us to simulate the $N_f=2$ theory at lattice spacings down
to $0.023$ fm that are crucial for reliable continuum extrapolations. We find
that sea charm quark effects are below $1\%$ for the decay constants of
charmonium. Our results show that decoupling of charm works well up to energies
of about $500$ MeV. We also compute the derivatives of the decay constants and
meson masses with respect to the charm mass. For these quantities we again do
not see a significant dynamical charm quark effect, albeit with a lower
precision. For mesons made of a charm quark and a heavy antiquark, whose mass
is twice that of the charm quark, sea effects are only about $0.1\%$ in the
ratio of vector to pseudoscalar masses.


Abstract:
  In this paper, free Bosons are used to study some integrable properties of
the CKP hierarchy, from the aspects of tau functions. Firstly, the modified CKP
hierarchy is constructed by using free Bosons and the corresponding Lax
structure is given. Then the constrained CKP hierarchy is found to be related
with the modified CKP hierarchy, and the corresponding solutions are derived by
using free Bosons. Next by using the relations between the Darboux
transformations and the squared eigenfunction symmetries, we express the
Darboux transformations of the CKP hierarchy in terms of free Bosons, by which
one can better understand the essential of the CKP Darboux transformations. In
particular, the additional symmetries of the CKP hierarchy can be viewed as the
infinitesimal generator of the CKP Darboux transformations. Based upon these
results, we finally obtain the actions of the CKP additional symmetries on the
CKP tau functions constructed by free Bosons.


Abstract:
  Two-dimensional (2D) materials with robust magnetism have drawn immense
attention for their promising applications in spintronics. Recently, intrinsic
ferromagnetic vanadium triiodide (VI3) has been synthesized experimentally. To
enhance its spintronic property, we modified VI3 by interstitial doping with 3d
transition metals (TM) and used first-principles calculations to investigate
the geometric structure, formation energy, electronic property, and magnetism
of pristine VI3 and 3d TM-doped VI3 monolayer. Among eight transition metal
(Sc-, Ti-, V-, Cr-, Mn-, Fe-, Co-, and Ni-) doped VI3 materials, four of them
(Ti-, V-, Mn-, and Ni-doped VI3) show robust magnetism with full spin
polarization near the Fermi energy. Our research demonstrates that Ti-doped VI3
results in half-metallic semiconductor properties (HMS), while V-doped VI3 and
Ni-doped VI3 result in half-semiconductor properties (HSC). Surprisingly,
Mn-doped VI3 exhibits an unusual bipolar magnetic semiconductor property (BMS).
This unique combination of strong ferromagnetism and 100% spin polarization
with a half-metallic, half-semiconductor, or bipolar semiconductor property
renders 3d TM-doped VI3 as potential candidates for next generation
semiconductor spintronic applications. These spin-polarized materials will be
extremely useful for spin-current generation and other spintronic applications.


Abstract:
  This paper shows the usefulness of Perov's contraction principle, which
generalizes Banach's contraction principle to a vector-valued metric, for
studying dynamic programming problems in which the discount factor can be
stochastic. The discounting condition $\beta<1$ is replaced by $\rho(B)<1$,
where $B$ is an appropriate nonnegative matrix and $\rho$ denotes the spectral
radius. Blackwell's sufficient condition is also generalized in this setting.
Applications to asset pricing and optimal savings are discussed.


Abstract:
  This paper is concerned with the linear theory of thermoelasticity with
microtemperatures, based on the entropy balance proposed by Green and Naghdi,
which permits the transmission of heat as thermal waves of finite speed. We
analyze the behavior of Rayleigh waves in an unbounded isotropic homogeneous
strongly elliptic thermoelastic material with microtemperatures. The related
solution of the Rayleigh surface wave problem is expressed as a linear
combination of the elements of the bases of the kernels of appropriate
matrices. The secular equation is established and afterwards an explicit form
is written when some coupling constitutive coefficients vanish. Then, we solve
numerically the secular equation by mean of a grafical metod and by taking
arbitrary data for strongly elliptic thermoelastic material.


Abstract:
  Computation of the instantaneous phase and amplitude via the Hilbert
Transform is a powerful tool of data analysis. This approach finds many
applications in various science and engineering branches but is not proper for
causal estimation because it requires knowledge of the signal's past and
future. However, several problems require real-time estimation of phase and
amplitude; an illustrative example is phase-locked or amplitude-dependent
stimulation in neuroscience. In this paper, we discuss and compare three causal
algorithms that do not rely on the Hilbert Transform but exploit well-known
physical phenomena, the synchronization and the resonance. After testing the
algorithms on a synthetic data set, we illustrate their performance computing
phase and amplitude for the accelerometer tremor measurements and a
Parkinsonian patient's beta-band brain activity.


Abstract:
  We investigate the creation and control of emergent collective behavior and
quantum correlations using feedback in an emitter-waveguide system using a
minimal model. Employing homodyne detection of photons emitted from a
laser-driven emitter ensemble into the modes of a waveguide allows to generate
intricate dynamical phases. In particular, we show the emergence of a
time-crystal phase, the transition to which is controlled by the feedback
strength. Feedback enables furthermore the control of many-body quantum
correlations, which become manifest in spin squeezing in the emitter ensemble.
Developing a theory for the dynamics of fluctuation operators we discuss how
the feedback strength controls the squeezing and investigate its temporal
dynamics and dependence on system size. The largely analytical results allow to
quantify spin squeezing and fluctuations in the limit of large number of
emitters, revealing critical scaling of the squeezing close to the transition
to the time-crystal. Our study corroborates the potential of integrated
emitter-waveguide systems -- which feature highly controllable photon emission
channels -- for the exploration of collective quantum phenomena and the
generation of resources, such as squeezed states, for quantum enhanced
metrology.


Abstract:
  Optical nanosensors are promising for hydrogen sensing because they are
small, free from spark generation, and feasible for remote optical readout.
Conventional optical nanosensors require broadband excitation and
spectrometers, rendering the devices bulky and complex. An alternative is
spatial intensity-based optical sensing, which only requires an imaging system
and a smartly designed platform to report the spatial distribution of
analytical optical signals. Here, we present a spatial intensity-based hydrogen
sensing platform based on Fano-like spatial distribution of the transmission in
a Pd-Al2O3-Au metal-insulator-metal plasmonic Doppler grating (MIM-PDG). The
MIM-PDG manifests the Fano resonance as an asymmetric spatial transmission
intensity profile. The absorption of hydrogen changes the spatial Fano-like
transmission profiles, which can be analyzed with a "spatial" Fano-resonance
model and the extracted Fano resonance parameters can be used to establish
analytical calibration lines. While gratings sensitive to hydrogen absorption
are suitable for hydrogen sensing, we also found hydrogen insensitive gratings,
which provide an unperturbed reference signal and may find applications in
nanophotonic devices, that require a stable optical response under fluctuating
hydrogen atmosphere. The MIM-PDG platform is a spectrometer-free and
intensity-based optical sensor that requires only an imaging system, making it
promising for cellphone-based optical sensing applications.


Abstract:
  In this note we first consider a ternary matrix group related to the von
Neumann regular semigroups and to the Artin braid group (in an algebraic way).
The product of a special kind of ternary matrices (idempotent and of finite
order) reproduces the regular semigroups and braid groups with their binary
multiplication of components. We then generalize the construction to the higher
arity case, which allows us to obtain some higher degree versions (in our
sense) of the regular semigroups and braid groups. The latter are connected
with the generalized polyadic braid equation and $R$-matrix introduced by the
author, which differ from any version of the well-known tetrahedron equation
and higher-dimensional analogs of the Yang-Baxter equation, $n$-simplex
equations. The higher degree (in our sense) Coxeter group and symmetry groups
are then defined, and it is shown that these are connected only in the
non-higher case.


Abstract:
  Despite outstanding performance on public benchmarks, face recognition still
suffers due to domain mismatch between training (source) and testing (target)
data. Furthermore, these domains are not shared classes, which complicates
domain adaptation. Since this is also a fine-grained classification problem
which does not strictly follow the low-density separation principle,
conventional domain adaptation approaches do not resolve these problems. In
this paper, we formulate domain mismatch in face recognition as a style
mismatch problem for which we propose two methods. First, we design a domain
discriminator with human-level judgment to mine target-like images in the
training data to mitigate the domain gap. Second, we extract style
representations in low-level feature maps of the backbone model, and match the
style distributions of the two domains to find a common style representation.
Evaluations on verification and open-set and closed-set identification
protocols show that both methods yield good improvements, and that performance
is more robust if they are combined. Our approach is competitive with related
work, and its effectiveness is verified in a practical application.


Abstract:
  We report on the generation of bulk photocurrents in materials driven by
non-resonant bi-chromatic fields that are circularly polarized and co-rotating.
The nonlinear photocurrents have a fully controllable directionality and
amplitude without requiring carrier-envelope-phase stabilization or few-cycle
pulses, and are generated with photon energies much smaller than the band gap
(reducing heating in the photo-conversion process). We demonstrate with
ab-initio calculations that the photocurrent generation mechanism is universal
and arises in gaped materials (Si, diamond, MgO, hBN), in semi-metals
(graphene), and in two- and three-dimensional systems. Photocurrents are shown
to rely on sub-laser-cycle asymmetries in the nonlinear response that build-up
coherently from cycle-to-cycle as the conduction band is populated.
Importantly, the photocurrents are always transverse to the major axis of the
co-circular lasers regardless of the material's structure and orientation
(analogously to a Hall current), which we find originates from a generalized
time-reversal symmetry in the driven system. At high laser powers (~10^13
W/cm^2) this symmetry can be spontaneously broken by vast electronic
excitations, which is accompanied by an onset of carrier-envelope-phase
sensitivity and ultrafast many-body effects. Our results are directly
applicable for efficient light-driven control of electronics, and for enhancing
sub-band-gap bulk photovoltaic effects.


Abstract:
  We investigate the effects of disorder in a simple model of a hybridized
two-dimensional two-band s-wave superconductor. We take into account the
situations in which these bands are formed by electronic orbitals with angular
momentum such that the hybridization $V(\mathbf{k})$ among them is
antisymmetric, under inversion symmetry. The impurity potential is given by an
independent random variable $W$ which controls the strength of the on-site
disorder. We find that while the random disorder acts in detriment of
superconductivity, hybridization proceeds favoring superconductivity. This
shows that hybridization, which may be induced by pressure or doping, plays an
important hole in two-band models of superconductivity, making them eligible
candidates to describe real materials. We also find that in moderate and strong
disorder, the system is broken into islands, with correlated local order
parameters. These correlations persist to distances of several order lattice
spacing which corresponds to the size of the SC-Islands.


Abstract:
  A new mechanism for Topological Hall Effect (THE) was recently proposed for
the spiral magnet YMn$_{6}$Sn$_{6},$ which requires transverse conical spiral
(TCS) magnetism, induced by external magnetic field, combined with thermally
excite helical spiral magnons. In principle, this mechanism should be
applicable to other itinerant spiral magnets as well. In this paper, we show
that another metamagnetic compound, Fe$_{3}$Ga$_{4}$, where THE was observed
experimentally before, in one of its phases satisfies this condition, and the
proposed theory of thermal-fluctuation driven THE is quantitatively consistent
with the experiment. This finding suggests that this mechanism is indeed rather
universal, and the effect may have been observed in other compounds before, but
overlooked.


Abstract:
  We ascertain conditions and structures on categories and semigroups which
admit the construction of pseudo-products and trace products respectively,
making their connection as precise as possible. This topic is modelled on the
ESN Theorem and its generalization to ample semigroups. Unlike some other
variants of ESN, it is self-dual (two-sided), and the condition of commuting
projections is relaxed. The condition that projections form a band (are closed
under multiplication) is shown to be a very natural one. One-sided reducts are
considered, and compared to (generalized) D-semigroups. Finally the special
case when the category is a groupoid is examined.


Abstract:
  A protocol for fast and robust magnon transport in a one-dimensional spin
chain is devised. Employing an approximate mapping between the chain and a
single harmonically trapped particle, we exploit the known analytic control
protocols for the latter and adopt them to achieve fast, high-fidelity
transport in the chain. We compare the performance with finite time adiabatic
protocols, establishing that the designed scheme allows for significantly
faster and more stable transport. Furthermore, we show that a sharp transition
exists between regions in which the protocol is effective and when it breaks
down, giving rise to a heuristic speed limit for the process.


Abstract:
  This paper is devoted to studying an augmented Lagrangian method for solving
a class of manifold optimization problems, which have nonsmooth objective
functions and non-negative constraints. Under the constant positive linear
dependence condition on manifolds, we show that the proposed method converges
to a stationary point of the nonsmooth manifold optimization problem. Moreover,
we propose a globalized semismooth Newton method to solve the augmented
Lagrangian subproblem on manifolds efficiently. The local superlinear
convergence of the manifold semismooth Newton method is also established under
some suitable conditions. We also prove that the semismoothness on submanifolds
can be inherited from that in the ambient manifold. Finally, numerical
experiments on compressed modes and (constrained) sparse principal component
analysis illustrate the advantages of the proposed method.


Abstract:
  In the setting of secure multiparty computation (MPC), a set of mutually
distrusting parties wish to jointly compute a function, while guaranteeing the
privacy of their inputs and the correctness of the output. An MPC protocol is
called \emph{fully secure} if no adversary can prevent the honest parties from
obtaining their outputs. A protocol is called \emph{fair} if an adversary can
prematurely abort the computation, however, only before learning any new
information.
  We present highly efficient transformations from fair computations to fully
secure computations, assuming the fraction of honest parties is constant (e.g.,
$1\%$ of the parties are honest). Compared to previous transformations that
require linear invocations (in the number of parties) of the fair computation,
our transformations require super-logarithmic, and sometimes even
super-constant, such invocations. The main idea is to delegate the computation
to chosen random committees that invoke the fair computation. Apart from the
benefit of uplifting security, the reduction in the number of parties is also
useful, since only committee members are required to work, whereas the
remaining parties simply "listen" to the computation over a broadcast channel.


Abstract:
  Polarisation labelling spectroscopy technique was employed to study the
3$^{1}\Pi_{u}$ state of Cs$_2$ molecule. The main equlibrium constants are
$T_e=20684.60$cm$^{-1}$, $\omega_e=30.61$cm$^{-1}$ and $R_e=5.27$\r{A}.
Vibrational levels $v=4-35$ of the 3$^{1}\Pi_{u}$ state were found to be
subject to strong perturbations by the neighbouring electronic states. Energies
of 3094 rovibronic levels of the perturbed complex were determined.


Abstract:
  This paper presents a physics-based modeling framework for the analysis and
transient simulation of circuits containing Spin-Transfer Torque (STT) Magnetic
Tunnel Junction (MTJ) devices. The framework provides the tools to analyze the
stochastic behavior of MTJs and to generate Verilog-A compact models for their
simulation in large VLSI designs, addressing the need for an industry-ready
model accounting for real-world reliability and scalability requirements.
Device dynamics are described by the Landau-Lifshitz-Gilbert-Slonczewsky
(s-LLGS ) stochastic magnetization considering Voltage-Controlled Magnetic
Anisotropy (VCMA) and the non-negligible statistical effects caused by thermal
noise. Model behavior is validated against the OOMMF magnetic simulator and its
performance is characterized on a 1-Mb 28 nm Magnetoresistive-RAM (MRAM) memory
product.


Abstract:
  Microservice architecture advocates a number of technologies and practices
such as lightweight container, container orchestration, and DevOps, with the
promised benefits of faster delivery, improved scalability, and greater
autonomy. However, microservice systems implemented in industry vary a lot in
terms of adopted practices and achieved benefits, drastically different from
what is advocated in the literature. In this article, we conduct an empirical
study, including an online survey with 51 responses and 14 interviews for
experienced microservice experts to advance our understanding regarding to
microservice practices in industry. As a part of our findings, the empirical
study clearly revealed three levels of maturity of microservice systems (from
basic to advanced): independent development and deployment, high scalability
and availability, and service ecosystem, categorized by the fulfilled benefits
of microservices. We also identify 11 practical issues that constrain the
microservice capabilities of organizations. For each issue, we summarize the
practices that have been explored and adopted in industry, along with the
remaining challenges. Our study can help practitioners better position their
microservice systems and determine what infrastructures and capabilities are
worth investing. Our study can also help researchers better understand
industrial microservice practices and identify useful research problems.


Abstract:
  We study both the static properties of $^{11}$Be and its reaction dynamics
during electromagnetic breakup under a unified framework. A many-body approach
- the antisymmetrized molecular dynamics (AMD) is used to describe the
structure of the neutron-halo nucleus, $^{11}$Be. The same AMD wave function is
then adapted as an input to the fully quantum theory of Coulomb breakup under
the aegis of the finite range distorted wave Born approximation theory. The
calculated observables are also compared with those obtained with a
phenomenological Woods-Saxon potential model wave function. The experimental
core-valence neutron relative energy spectrum and dipole response along with
other observables are well described by our calculations.


Abstract:
  In this paper, we investigate problems which are dual to the unification
problem, namely the Fixed Point (FP) problem, Common Term (CT) problem and the
Common Equation (CE) problem for string rewriting systems. Our main motivation
is computing fixed points in systems, such as loop invariants in programming
languages. We show that the fixed point (FP) problem is reducible to the common
term problem. Our new results are: (i) the fixed point problem is undecidable
for finite convergent string rewriting systems whereas it is decidable in
polynomial time for finite, convergent and dwindling string rewriting systems,
(ii) the common term problem is undecidable for the class of dwindling string
rewriting systems, and (iii) for the class of finite, monadic and convergent
systems, the common equation problem is decidable in polynomial time but for
the class of dwindling string rewriting systems, common equation problem is
undecidable.


Abstract:
  In this paper we analyze the singular set in the Stefan problem and prove the
following results:
  - The singular set has parabolic Hausdorff dimension at most $n-1$.
  - The solution admits a $C^\infty$-expansion at all singular points, up to a
set of parabolic Hausdorff dimension at most $n-2$.
  - In $\mathbb R^3$, the free boundary is smooth for almost every time $t$,
and the set of singular times $\mathcal S\subset \mathbb R$ has Hausdorff
dimension at most $1/2$.
  These results provide us with a refined understanding of the Stefan problem's
singularities and answer some long-standing open questions in the field.


Abstract:
  Reconfigurable intelligent surface (RIS) is a promising technology for RF
wireless power transfer (WPT) as it is capable of beamforming and beam focusing
without using active and power-hungry components. In this paper, we propose a
multi-tile RIS beam scanning (MTBS) algorithm for powering up
internet-of-things (IoT) devices. Considering the hardware limitations of the
IoT devices, the proposed algorithm requires only power information to enable
the beam focusing capability of the RIS. Specifically, we first divide the RIS
into smaller RIS tiles. Then, all RIS tiles and the phased array transmitter
are iteratively scanned and optimized to maximize the receive power. We
elaborately analyze the proposed algorithm and build a simulator to verify it.
Furthermore, we have built a real-life testbed of RIS-aided WPT systems to
validate the algorithm. The experimental results show that the proposed MTBS
algorithm can properly control the transmission phase of the transmitter and
the reflection phase of the RIS to focus the power at the receiver.
Consequently, after executing the algorithm, about 20 dB improvement of the
receive power is achieved compared to the case that all unit cells of the RIS
are in OFF state. By experiments, we confirm that the RIS with the MTBS
algorithm can greatly enhance the power transfer efficiency.


Abstract:
  We show that in pool-based active classification without assumptions on the
underlying distribution, if the learner is given the power to abstain from some
predictions by paying the price marginally smaller than the average loss $1/2$
of a random guess, exponential savings in the number of label requests are
possible whenever they are possible in the corresponding realizable problem. We
extend this result to provide a necessary and sufficient condition for
exponential savings in pool-based active classification under the model
misspecification.


Abstract:
  We propose Skip-Convolutions to leverage the large amount of redundancies in
video streams and save computations. Each video is represented as a series of
changes across frames and network activations, denoted as residuals. We
reformulate standard convolution to be efficiently computed on residual frames:
each layer is coupled with a binary gate deciding whether a residual is
important to the model prediction,~\eg foreground regions, or it can be safely
skipped, e.g. background regions. These gates can either be implemented as an
efficient network trained jointly with convolution kernels, or can simply skip
the residuals based on their magnitude. Gating functions can also incorporate
block-wise sparsity structures, as required for efficient implementation on
hardware platforms. By replacing all convolutions with Skip-Convolutions in two
state-of-the-art architectures, namely EfficientDet and HRNet, we reduce their
computational cost consistently by a factor of 3~4x for two different tasks,
without any accuracy drop. Extensive comparisons with existing model
compression, as well as image and video efficiency methods demonstrate that
Skip-Convolutions set a new state-of-the-art by effectively exploiting the
temporal redundancies in videos.


Abstract:
  In this paper we investigate the \emph{approximate string matching problem}
when the allowed edit operations are \emph{non-overlapping unbalanced
translocations of adjacent factors}. Such kind of edit operations take place
when two adjacent sub-strings of the text swap, resulting in a modified string.
The two involved substrings are allowed to be of different lengths.
  Such large-scale modifications on strings have various applications. They are
among the most frequent chromosomal alterations, accounted for 30\% of all
losses of heterozygosity, a major genetic event causing inactivation of cancer
suppressor genes. In addition, among other applications, they are frequent
modifications accounted in musical or in natural language information
retrieval. However, despite of their central role in so many fields of text
processing, little attention has been devoted to the problem of matching
strings allowing for this kind of edit operation.
  In this paper we present three algorithms for solving the problem, all of
them with a $\bigO(nm^3)$ worst-case and a $\bigO(m^2)$-space complexity, where
$m$ and $n$ are the length of the pattern and of the text, respectively. % In
particular, our first algorithm is based on the dynamic-programming approach.
Our second solution improves the previous one by making use of the Directed
Acyclic Word Graph of the pattern. Finally our third algorithm is based on an
alignment procedure. We also show that under the assumptions of equiprobability
and independence of characters, our second algorithm has a
$\bigO(n\log^2_{\sigma} m)$ average time complexity, for an alphabet of size
$\sigma \geq 4$.


Abstract:
  Modern machine learning algorithms usually involve tuning multiple (from one
to thousands) hyperparameters which play a pivotal role in terms of model
generalizability. Black-box optimization and gradient-based algorithms are two
dominant approaches to hyperparameter optimization while they have totally
distinct advantages. How to design a new hyperparameter optimization technique
inheriting all benefits from both approaches is still an open problem. To
address this challenging problem, in this paper, we propose a new
hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG).
Specifically, we first exactly formulate hyperparameter optimization as an
A-based constrained optimization problem, where A is a black-box optimization
algorithm (such as deep neural network). Then, we use the average zeroth-order
hyper-gradients to update hyperparameters. We provide the feasibility analysis
of using HOZOG to achieve hyperparameter optimization. Finally, the
experimental results on three representative hyperparameter (the size is from 1
to 1250) optimization tasks demonstrate the benefits of HOZOG in terms of
simplicity, scalability, flexibility, effectiveness and efficiency compared
with the state-of-the-art hyperparameter optimization methods.


Abstract:
  Our main goal is to compute the decomposition of arbitrary Kronecker powers
of the Harmonics of $S_n$. To do this, we give a new way of decomposing the
character for the action of $S_n$ on polynomial rings with $k$ sets of $n$
variables. There are two aspects to this decomposition. The first is algebraic,
in which formulas can be given for certain restrictions from $GL_n$ to $S_n$
occurring in Schur-Weyl duality. The second is combinatorial. We give a
generalization of the $comaj$ statistic on permutations which includes the
$comaj$ statistic on standard tableaux. This statistic allows us to write a
generalized principal evaluation for Schur functions and Gessel Fundamental
quasisymmetric functions.


Abstract:
  In this paper, a proactive dynamic spectrum sharing scheme between 4G and 5G
systems is proposed. In particular, a controller decides on the resource split
between NR and LTE every subframe while accounting for future network states
such as high interference subframes and multimedia broadcast single frequency
network (MBSFN) subframes. To solve this problem, a deep reinforcement learning
(RL) algorithm based on Monte Carlo Tree Search (MCTS) is proposed. The
introduced deep RL architecture is trained offline whereby the controller
predicts a sequence of future states of the wireless access network by
simulating hypothetical bandwidth splits over time starting from the current
network state. The action sequence resulting in the best reward is then
assigned. This is realized by predicting the quantities most directly relevant
to planning, i.e., the reward, the action probabilities, and the value for each
network state. Simulation results show that the proposed scheme is able to take
actions while accounting for future states instead of being greedy in each
subframe. The results also show that the proposed framework improves
system-level performance.


Abstract:
  In this paper, we describe the birational structure of moduli spaces of rank
2 logarithmic connections with the fixed trace connection. Our approach is to
analyze the underlying parabolic bundles and apparent singularities of the
parabolic connections.


Abstract:
  In this paper we consider an anisotropic Robin problem driven by the
$p(x)$-Laplacian and a superlinear reaction. Applying variational tools along
with truncation and comparison techniques as well as critical groups, we prove
that the problem has at least five nontrivial smooth solutions to be ordered
and with sign information: two positive, two negative and the fifth nodal.


Abstract:
  Relations among fundamental invariants play an important role in algebraic
geometry. It is known that an $n$-dimensional variety of general type with nef
canonical divisor and canonical singularities, whose image $Y$ under the
canonical map is of maximal dimension, satisfies $K_X^n \ge 2 (p_g-n)$. We
investigate the very interesting extremal situation $K_X^n=2(p_g-n)$, which
appears in a number of geometric situations. Since these extremal varieties are
natural higher dimensional analogues of Horikawa surfaces, we name them
Horikawa varieties. These varieties have been previously dealt with inthe works
of Fujita and Kobayashi. We carry out further studies of Horikawa varieties,
proving new results on various geometric and topological issues concerning
them. In particular, we prove that the geometric genus of those Horikawa
varieties whose image under the canonical map is singular is bounded. We give
an analogous result for polarized hyperelliptic subcanonical varieties, in
particular, for polarized Calabi-Yau and Fano varieties. The pleasing
numerology that emerges puts Horikawa's result on surfaces in a broader
perspective. We obtain a structure theorem for Horikawa varieties and explore
their pluriregularity. We use this to prove optimal results on projective
normality of pluricanonical linear systems. We study the fundamental groups of
Horikawa varieties, showing that they are simply connected, even if $Y$ is
singular. We also prove results on deformations of Horikawa varieties, whose
implications on the moduli space make them the higher dimensional analogue of
curves of genus $2$.


Abstract:
  We study the statistical mechanics of supercooled liquids when the system
evolves at a temperature $T$ with a linear coupling of amplitude $\epsilon$ to
its overlap with a reference configuration of the same liquid sampled at a
temperature $T_0$. We use mean-field theory to fully characterize the influence
of the reference temperature $T_0$, and we mainly study the case of a fixed,
low-$T_0$ value in computer simulations. We numerically investigate the
extended phase diagram in the $(\epsilon,T)$ plane of model glass-forming
liquids in spatial dimensions $d=2$ and $d=3$, relying on umbrella sampling and
reweighting techniques. For both $2d$ and $3d$ cases, a similar phenomenology
with nontrivial thermodynamic fluctuations of the overlap is observed at low
temperatures, but a detailed finite-size scaling analysis reveals qualitatively
distinct behaviors. We establish the existence of a first-order transition line
for nonzero $\epsilon$ ending in a critical point in the universality class of
the random-field Ising model (RFIM) in $d=3$. In $d=2$ instead, no phase
transition exists in the thermodynamic limit at finite temperatures. Our
results show that glass-forming liquid samples of limited size display the
thermodynamic fluctuations expected for finite systems undergoing a random
first-order transition. They also establish the relevance of the physics of the
RFIM for supercooled liquids, which in particular explains the qualitative
difference between $2d$ and $3d$ glass-formers.


Abstract:
  We performed rheological measurements of the typical deep eutectic solvents
(DESs) glyceline, ethaline, and reline in a very broad temperature and dynamic
range, extending from the low-viscosity to the high-viscosity
supercooled-liquid regime. We find that the mechanical compliance spectra can
be well described by the random free-energy barrier hopping model, while the
dielectric spectra on the same materials involve significant contributions
arising from reorientational dynamics. The temperature-dependent viscosity and
structural relaxation time, revealing non-Arrhenius behavior typical for glassy
freezing, are compared to the ionic dc conductivity and relaxation times
determined by broadband dielectric spectroscopy. For glyceline and ethaline we
find essentially identical temperature dependences for all dynamic quantities.
These findings point to a close coupling of the ionic and molecular
translational and reorientational motions in these systems. However, for reline
the ionic charge transport appears decoupled from the structural and
reorientational dynamics, following a fractional Walden rule. Especially, at
low temperatures the ionic conductivity in this DES is enhanced by about one
decade compared to expectations based on the temperature dependence of the
viscosity. The results for all three DESs can be understood without invoking a
revolving-door mechanism previously considered as a possible charge-transport
mechanism in DESs.


Abstract:
  Gaussian processes (GPs) enable principled computation of model uncertainty,
making them attractive for safety-critical applications. Such scenarios demand
that GP decisions are not only accurate, but also robust to perturbations. In
this paper we present a framework to analyse adversarial robustness of GPs,
defined as invariance of the model's decision to bounded perturbations. Given a
compact subset of the input space $T\subseteq \mathbb{R}^d$, a point $x^*$ and
a GP, we provide provable guarantees of adversarial robustness of the GP by
computing lower and upper bounds on its prediction range in $T$. We develop a
branch-and-bound scheme to refine the bounds and show, for any $\epsilon > 0$,
that our algorithm is guaranteed to converge to values $\epsilon$-close to the
actual values in finitely many iterations. The algorithm is anytime and can
handle both regression and classification tasks, with analytical formulation
for most kernels used in practice. We evaluate our methods on a collection of
synthetic and standard benchmark datasets, including SPAM, MNIST and
FashionMNIST. We study the effect of approximate inference techniques on
robustness and demonstrate how our method can be used for interpretability. Our
empirical results suggest that the adversarial robustness of GPs increases with
accurate posterior estimation.


Abstract:
  Modern vehicles, including connected vehicles and autonomous vehicles,
nowadays involve many electronic control units connected through intra-vehicle
networks to implement various functionalities and perform actions. Modern
vehicles are also connected to external networks through vehicle-to-everything
technologies, enabling their communications with other vehicles,
infrastructures, and smart devices. However, the improving functionality and
connectivity of modern vehicles also increase their vulnerabilities to
cyber-attacks targeting both intra-vehicle and external networks due to the
large attack surfaces. To secure vehicular networks, many researchers have
focused on developing intrusion detection systems (IDSs) that capitalize on
machine learning methods to detect malicious cyber-attacks. In this paper, the
vulnerabilities of intra-vehicle and external networks are discussed, and a
multi-tiered hybrid IDS that incorporates a signature-based IDS and an
anomaly-based IDS is proposed to detect both known and unknown attacks on
vehicular networks. Experimental results illustrate that the proposed system
can detect various types of known attacks with 99.99% accuracy on the
CAN-intrusion-dataset representing the intra-vehicle network data and 99.88%
accuracy on the CICIDS2017 dataset illustrating the external vehicular network
data. For the zero-day attack detection, the proposed system achieves high
F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The
average processing time of each data packet on a vehicle-level machine is less
than 0.6 ms, which shows the feasibility of implementing the proposed system in
real-time vehicle systems. This emphasizes the effectiveness and efficiency of
the proposed IDS.


Abstract:
  Subsampling is used in convolutional neural networks (CNNs) in the form of
pooling or strided convolutions, to reduce the spatial dimensions of feature
maps and to allow the receptive fields to grow exponentially with depth.
However, it is known that such subsampling operations are not translation
equivariant, unlike convolutions that are translation equivariant. Here, we
first introduce translation equivariant subsampling/upsampling layers that can
be used to construct exact translation equivariant CNNs. We then generalise
these layers beyond translations to general groups, thus proposing group
equivariant subsampling/upsampling. We use these layers to construct group
equivariant autoencoders (GAEs) that allow us to learn low-dimensional
equivariant representations. We empirically verify on images that the
representations are indeed equivariant to input translations and rotations, and
thus generalise well to unseen positions and orientations. We further use GAEs
in models that learn object-centric representations on multi-object datasets,
and show improved data efficiency and decomposition compared to non-equivariant
baselines.


Abstract:
  We investigate the electroweak phase transition in the real-singlet extension
of the Standard Model at two-loop level, building upon existing one-loop
studies. We calculate the effective potential in the high-temperature
approximation and detail the required resummations at two-loop order. In
typical strong-transition scenarios, we find deviations of order $20\% - 50\%$
from one-loop results in transition strength and critical temperature for both
one- and two-step phase transitions. For extremely strong transitions, the
discrepancy with one-loop predictions is even larger, presumably due to sizable
scalar couplings in the tree-level potential. Along the way, we obtain a
dimensionally-reduced effective theory applicable for non-perturbative lattice
studies of the model.


Abstract:
  Micro-Doppler analysis has become increasingly popular in recent years owning
to the ability of the technique to enhance classification strategies.
Applications include recognising everyday human activities, distinguishing
drone from birds, and identifying different types of vehicles. However, noisy
time-frequency spectrograms can significantly affect the performance of the
classifier and must be tackled using appropriate denoising algorithms. In
recent years, deep learning algorithms have spawned many deep neural
network-based denoising algorithms. For these methods, noise modelling is the
most important part and is used to assist in training. In this paper, we
decompose the problem and propose a novel denoising scheme: first, a Generative
Adversarial Network (GAN) is used to learn the noise distribution and
correlation from the real-world environment; then, a simulator is used to
generate clean Micro-Doppler spectrograms; finally, the generated noise and
clean simulation data are combined as the training data to train a
Convolutional Neural Network (CNN) denoiser. In experiments, we qualitatively
and quantitatively analyzed this procedure on both simulation and measurement
data. Besides, the idea of learning from natural noise can be applied well to
other existing frameworks and demonstrate greater performance than other noise
models.


Abstract:
  This paper is devoted to the study of the higher index theory of codimension
$2$ submanifolds originated by Gromov-Lawson and Hanke-Pape-Schick. The first
main result is to construct the `codimension $2$ transfer' map from the
Higson-Roe analytic surgery exact sequence of a manifold $M$ to that of its
codimension $2$ submanifold $N$ under some assumptions on homotopy groups. This
map sends the primary and secondary higher index invariants of $M$ to those of
$N$. The second is to establish that the codimension 2 transfer map is adjoint
to the co-transfer map in cyclic cohomology, defined by the cup product with a
group cocycle. This relates the Connes-Moscovici higher index pairing and
Lott's higher $\rho$-number of $M$ with those of $N$.


Abstract:
  Circular Nim is a two-player impartial combinatorial game consisting of $n$
stacks of tokens placed in a circle. A move consists of choosing $k$
consecutive stacks and taking at least one token from one or more of the
stacks. The last player able to make a move wins. The question of interest is:
Who can win from a given position if both players play optimally? In an
impartial combinatorial game, there are only two types of positions. An
$\mathcal{N}$-position is one from which the next player to move has a winning
strategy. A $\mathcal{P}$-position is one from which the next player is bound
to lose, no matter what moves s/he makes. Therefore, the question who wins is
answered by identifying the $\mathcal{P}$-positions. We will prove results on
the structure of the $\mathcal{P}$-positions for $n = 7$ and $k = 4$, extending
known results for other games in this family. The interesting feature of the
set of $\mathcal{P}$-positions of this game is that it splits into different
subsets, unlike the structure for the known games in this family.


Abstract:
  Goal of this paper is to study positive semiclassical solutions of the
nonlinear Schr\"odinger equation $$ \varepsilon^{2s}(- \Delta)^s u+ V(x) u=
f(u), \quad x \in \mathbb{R}^N,$$ where $s \in (0,1)$, $N \geq 2$, $V \in
C(\mathbb{R}^N,\mathbb{R})$ is a positive potential and $f$ is assumed critical
and satisfying general Berestycki-Lions type conditions. We obtain existence
and multiplicity for $\varepsilon>0$ small, where the number of solutions is
related to the cup-length of a set of local minima of $V$. Furthermore, these
solutions are proved to concentrate in the potential well, exhibiting a
polynomial decay. We highlight that these results are new also in the limiting
local setting $s=1$ and $N\geq 3$, with an exponential decay of the solutions.


Abstract:
  TV serials are a popular source of entertainment. The ongoing COVID19
lockdown has a high probability of degrading the publics mental health. The
Government of India started the retelecast of yesteryears popular TV serials on
public broadcaster Doordarshan from 28th March 2020 to 31st July 2020. Tweets
corresponding to the Doordarshan hashtag were mined to create a dataset. The
experiment aims to analyze the publics response to the retelecast of TV serials
by calculating the sentiment score of the tweet dataset. Datasets mean
sentiment score of 0.65 and high share 64.58% of positive tweets signifies the
acceptance of Doordarshans retelecast decision. The sentiment analysis result
also reflects the positive state of mind of the public.


Abstract:
  We consider optimal control of an elliptic two-point boundary value problem
governed by functions of bounded variation (BV). The cost functional is
composed of a tracking term for the state and the BV-seminorm of the control.
We use the mixed formulation for the state equation together with the
variational discretization approach, where we use the classical lowest order
Raviart-Thomas finite elements for the state equation. Consequently the
variational discrete control is a piecewise constant function over the finite
element grid. We prove error estimates for the variational discretization
approach in combination with the mixed formulation of the state equation and
confirm our analytical findings with numerical experiments.


Abstract:
  When studied by small-angle neutron scattering the vortex lattice (VL) in
UPt3 undergoes a gradual disordering as a function of time due to 235U fission.
This temporarily heats regions of the sample above the critical temperature,
where, upon re-cooling, the vortices remain in a quenched vortex glass state.
The disordering rate is proportional to the magnetic field, suggesting that it
is governed by collective VL properties such as the elastic moduli. An ordered
VL can be re-formed by applying a small field oscillation, showing that the
fission does not cause significant radiation damage to the UPt3 crystals, even
after long exposure.


Abstract:
  In response to the COVID-19 pandemic, Bluetooth-based contact tracing has
been deployed in many countries with the help of the developers of smartphone
operating systems that provide APIs for privacy-preserving exposure
notification. However, it has been assumed by the design that the OS
developers, smartphone vendors, or governments will not violate people's
privacy. We propose a privacy-preserving exposure notification under situations
where none of the middle entities can be trusted. We believe that it can be
achieved with small changes to the existing mechanism: random numbers are
generated on the application side instead of the OS, and the positive test
results are reported to a public ledger (e.g. blockchain) rather than to a
government server, with endorsements from the medical institutes with blind
signatures. We also discuss how to incentivize the peer-to-peer maintenance of
the public ledger if it should be newly built. We show that the level of
verifiability is much higher with our proposed design if a consumer group were
to verify the privacy protections of the deployed systems. We believe that this
will allow for safer contact tracing, and contribute to healthier lifestyles
for citizens who may want to or have to go out under pandemic situations.


Abstract:
  We have studied systematically microscopic properties of a quantum vortex in
neutron matter at finite temperatures and densities corresponding to different
layers of the inner crust of a neutron star. To this end and in preparation of
future simulations of the vortex dynamics, we have carried out fully
self-consistent 3D Hartree-Fock-Bogoliubov calculations, using one of the
latest nuclear energy-density functionals from the Brussels-Montreal family,
which has been developed specifically for applications to neutron superfluidity
in neutron-star crusts. By analyzing the flow around the vortex, we have
determined the effective radius relevant for the vortex filament model. We have
also calculated the specific heat in the presence of the quantum vortex and
have shown that it is substantially larger than for a uniform system at low
temperatures. The low temperature limit of the specific heat has been
identified as being determined by Andreev states inside the vortex core. We
have shown that the specific heat in this limit does not scale linearly with
temperature. The typical energy scale associated with Andreev states is defined
by the minigap, which we have extracted for various neutron-matter densities.
Our results suggest that vortices may be spin-polarized in the crust of
magnetars. Finally, we have obtained a lower bound for the specific heat of a
collection of vortices with given surface density, taking into account both the
contributions from the vortex core states and from the hydrodynamic flow.


Abstract:
  Multi-modal sentiment analysis plays an important role for providing better
interactive experiences to users. Each modality in multi-modal data can provide
different viewpoints or reveal unique aspects of a user's emotional state. In
this work, we use text, audio and visual modalities from MOSI dataset and we
propose a novel fusion technique using a multi-head attention LSTM network.
Finally, we perform a classification task and evaluate its performance.


Abstract:
  Managing the data for Information Retrieval (IR) experiments can be
challenging. Dataset documentation is scattered across the Internet and once
one obtains a copy of the data, there are numerous different data formats to
work with. Even basic formats can have subtle dataset-specific nuances that
need to be considered for proper use. To help mitigate these challenges, we
introduce a new robust and lightweight tool (ir_datasets) for acquiring,
managing, and performing typical operations over datasets used in IR. We
primarily focus on textual datasets used for ad-hoc search. This tool provides
both a Python and command line interface to numerous IR datasets and
benchmarks. To our knowledge, this is the most extensive tool of its kind.
Integrations with popular IR indexing and experimentation toolkits demonstrate
the tool's utility. We also provide documentation of these datasets through the
ir_datasets catalog: https://ir-datasets.com/. The catalog acts as a hub for
information on datasets used in IR, providing core information about what data
each benchmark provides as well as links to more detailed information. We
welcome community contributions and intend to continue to maintain and grow
this tool.


Abstract:
  This paper uses data-driven operator theoretic approaches to explore the
global phase space of a dynamical system. We defined conditions for discovering
new invariant subspaces in the state space of a dynamical system starting from
an invariant subspace based on the spectral properties of the Koopman operator.
When the system evolution is known locally in several invariant subspaces in
the state space of a dynamical system, a phase space stitching result is
derived that yields the global Koopman operator. Additionally, in the case of
equivariant systems, a phase space stitching result is developed to identify
the global Koopman operator using the symmetry properties between the invariant
subspaces of the dynamical system and time-series data from any one of the
invariant subspaces. Finally, these results are extended to topologically
conjugate dynamical systems; in particular, the relation between the Koopman
tuple of topologically conjugate systems is established. The proposed results
are demonstrated on several second-order nonlinear dynamical systems including
a bistable toggle switch. Our method elucidates a strategy for designing
discovery experiments: experiment execution can be done in many steps, and
models from different invariant subspaces can be combined to approximate the
global Koopman operator.


Abstract:
  Optimization via simulation (OvS) procedures that assume the simulation
inputs are generated from the real-world distributions are subject to the risk
of selecting a suboptimal solution when the distributions are substituted with
input models estimated from finite real-world data -- known as input model
risk. Focusing on discrete OvS, this paper proposes a new Bayesian framework
for analyzing input model risk of implementing an arbitrary solution, $x$,
where uncertainty about the input models is captured by a posterior
distribution. We define the $\alpha$-level risk set of solution $x$ as the set
of solutions whose expected performance is better than $x$ by a practically
meaningful margin $(>\delta)$ given common input models with significant
probability ($>\alpha$) under the posterior distribution. The user-specified
parameters, $\delta$ and $\alpha$, control robustness of the procedure to the
desired level as well as guards against unnecessary conservatism. An empty risk
set implies that there is no practically better solution than $x$ with
significant probability even though the real-world input distributions are
unknown. For efficient estimation of the risk set, the conditional mean
performance of a solution given a set of input distributions is modeled as a
Gaussian process (GP) that takes the solution-distributions pair as an input.
In particular, our GP model allows both parametric and nonparametric input
models. We propose the sequential risk set inference procedure that estimates
the risk set and selects the next solution-distributions pair to simulate using
the posterior GP at each iteration. We show that simulating the pair expected
to change the risk set estimate the most in the next iteration is the
asymptotic one-step optimal sampling rule that minimizes the number of
incorrectly classified solutions, if the procedure runs without stopping.


Abstract:
  Many HPC applications suffer from a bottleneck in the shared caches,
instruction execution units, I/O or memory bandwidth, even though the remaining
resources may be underutilized. It is hard for developers and runtime systems
to ensure that all critical resources are fully exploited by a single
application, so an attractive technique for increasing HPC system utilization
is to colocate multiple applications on the same server. When applications
share critical resources, however, contention on shared resources may lead to
reduced application performance.
  In this paper, we show that server efficiency can be improved by first
modeling the expected performance degradation of colocated applications based
on measured hardware performance counters, and then exploiting the model to
determine an optimized mix of colocated applications. This paper presents a new
intelligent resource manager and makes the following contributions: (1) a new
machine learning model to predict the performance degradation of colocated
applications based on hardware counters and (2) an intelligent scheduling
scheme deployed on an existing resource manager to enable application
co-scheduling with minimum performance degradation. Our results show that our
approach achieves performance improvements of 7% (avg) and 12% (max) compared
to the standard policy commonly used by existing job managers.


Abstract:
  We propose a new method for discriminating sub-micron nuclear recoil tracks
from an instrumental background in fine-grain nuclear emulsions used in the
directional dark matter search. The proposed method is based on the Deep
Learning approach and uses a 3D Convolutional Neural Network architecture with
parameters optimised by Bayesian search. Unlike previous studies focused on
extracting the directional information, we focus on the signal/background
separation exploiting the polarisation dependence of the Localised Surface
Plasmon Resonance phenomenon. Comparing the proposed method with the
conventional cut-based approach shows a significant boost in the rejection
power while keeping the signal efficiency at the same level.


Abstract:
  We present the analysis of XMM-Newton European Photon Imaging Camera (EPIC)
observations of the nova shell IPHASX J210204.7$+$471015. We detect X-ray
emission from the progenitor binary star with properties that resemble those of
underluminous intermediate polars such as DQ Her: an X-ray-emitting plasma with
temperature of $T_\mathrm{X}=(6.4\pm3.1)\times10^{6}$ K, a non-thermal X-ray
component, and an estimated X-ray luminosity of $L_\mathrm{X}=10^{30}$ erg
s$^{-1}$. Time series analyses unveil the presence of two periods, the dominant
with a period of $2.9\pm0.2$ hr, which might be attributed to the spin of the
white dwarf, and a secondary of $4.5\pm0.6$ hr that is in line with the orbital
period of the binary system derived from optical observations. We do not detect
extended X-ray emission as in other nova shells probably due to its relatively
old age (130-170 yr) or to its asymmetric disrupted morphology which is
suggestive of explosion scenarios different to the symmetric ones assumed in
available numerical simulations of nova explosions.


Abstract:
  Thermally conductive nanopapers fabricated from graphene and related
materials are currently showing a great potential in thermal management
applications. However, thermal contacts between conductive plates represent the
bottleneck for thermal conductivity of nanopapers prepared in the absence of a
high temperature step for graphitization. In this work, the problem of
ineffective thermal contacts is addressed by the use of bifunctional
polyaromatic molecules designed to drive self-assembly of graphite nanoplates
(GnP) and establish thermal bridges between them. To preserve the high
conductivity associated to defect-free sp2 structure, non-covalent
functionalization with bispyrene compounds, synthesised on purpose with
variable tethering chain length, was exploited. Pyrene terminal groups granted
for a strong {\pi}-{\pi} interaction with graphene surface, as demonstrated by
UV-Vis, fluorescence and Raman spectroscopies. Bispyrene molecular junctions
between GnP were found to control GnP organization and orientation within the
nanopaper, delivering significant enhancement in both in-plane and cross-plane
thermal diffusivity. Finally, nanopapers were validated as heat spreader
devices for electronic components, evidencing comparable or better thermal
dissipation performance than conventional Cu foil, while delivering over 90%
weight reduction.


Abstract:
  Power grid parameter estimation involves the estimation of unknown
parameters, such as inertia and damping coefficients, using observed dynamics.
In this work, we present a comparison of data-driven algorithms for the power
grid parameter estimation problem. First, we propose a new algorithm to solve
the parameter estimation problem based on the Sparse Identification of
Nonlinear Dynamics (SINDy) approach, which uses linear regression to infer the
parameters that best describe the observed data. We then compare its
performance against two benchmark algorithms, namely, the unscented Kalman
filter (UKF) approach and the physics-informed neural networks (PINN) approach.
We perform extensive simulations on IEEE bus systems to examine the performance
of the aforementioned algorithms. Our results show that the SINDy algorithm
outperforms the PINN and UKF algorithms in being able to accurately estimate
the power grid parameters over a wide range of system parameters (including
high and low inertia systems). Moreover, it is extremely efficient
computationally and so takes significantly less time than the PINN algorithm,
thus making it suitable for real-time parameter estimation.


Abstract:
  As multi-robot systems (MRS) are widely used in various tasks such as natural
disaster response and social security, people enthusiastically expect an MRS to
be ubiquitous that a general user without heavy training can easily operate.
However, humans have various preferences on balancing between task performance
and safety, imposing different requirements onto MRS control. Failing to comply
with preferences makes people feel difficult in operation and decreases human
willingness of using an MRS. Therefore, to improve social acceptance as well as
performance, there is an urgent need to adjust MRS behaviors according to human
preferences before triggering human corrections, which increases cognitive
load. In this paper, a novel Meta Preference Learning (MPL) method was
developed to enable an MRS to fast adapt to user preferences. MPL based on meta
learning mechanism can quickly assess human preferences from limited
instructions; then, a neural network based preference model adjusts MRS
behaviors for preference adaption. To validate method effectiveness, a task
scenario "An MRS searches victims in an earthquake disaster site" was designed;
20 human users were involved to identify preferences as "aggressive", "medium",
"reserved"; based on user guidance and domain knowledge, about 20,000
preferences were simulated to cover different operations related to "task
quality", "task progress", "robot safety". The effectiveness of MPL in
preference adaption was validated by the reduced duration and frequency of
human interventions.


Abstract:
  In this work, we address the problem of cross-view geo-localization, which
estimates the geospatial location of a street view image by matching it with a
database of geo-tagged aerial images. The cross-view matching task is extremely
challenging due to drastic appearance and geometry differences across views.
Unlike existing methods that predominantly fall back on CNN, here we devise a
novel evolving geo-localization Transformer (EgoTR) that utilizes the
properties of self-attention in Transformer to model global dependencies, thus
significantly decreasing visual ambiguities in cross-view geo-localization. We
also exploit the positional encoding of Transformer to help the EgoTR
understand and correspond geometric configurations between ground and aerial
images. Compared to state-of-the-art methods that impose strong assumption on
geometry knowledge, the EgoTR flexibly learns the positional embeddings through
the training objective and hence becomes more practical in many real-world
scenarios. Although Transformer is well suited to our task, its vanilla
self-attention mechanism independently interacts within image patches in each
layer, which overlooks correlations between layers. Instead, this paper propose
a simple yet effective self-cross attention mechanism to improve the quality of
learned representations. The self-cross attention models global dependencies
between adjacent layers, which relates between image patches while modeling how
features evolve in the previous layer. As a result, the proposed self-cross
attention leads to more stable training, improves the generalization ability
and encourages representations to keep evolving as the network goes deeper.
Extensive experiments demonstrate that our EgoTR performs favorably against
state-of-the-art methods on standard, fine-grained and cross-dataset cross-view
geo-localization tasks.


Abstract:
  We propose and analyze a randomized zeroth-order approach based on
approximating the exact gradient byfinite differences computed in a set of
orthogonal random directions that changes with each iteration. A number
ofpreviously proposed methods are recovered as special cases including
spherical smoothing, coordinate descent, as wellas discretized gradient
descent. Our main contribution is proving convergence guarantees as well as
convergence ratesunder different parameter choices and assumptions. In
particular, we consider convex objectives, but also possiblynon-convex
objectives satisfying the Polyak-{\L}ojasiewicz (PL) condition. Theoretical
results are complemented andillustrated by numerical experiments.


Abstract:
  We demonstrate supermode-based second harmonic generation in an integrated
nonlinear interferometer made of linear and nonlinear directional couplers. We
use a fully-fibered pump shaper to demonstrate second harmonic generation
pumped by the symmetric or anti-symmetric fundamental spatial modes. The
selection of the pumping mode and thus of a specific SHG spectral profile is
achieved through the selection of the fundamental wavelength and via a robust
phase setting scheme. We use two methods: either post-selecting or actively
setting the pumping mode. Such a modal phase matching paves the way for
classical and quantum applications of coupled nonlinear photonic circuits,
where multimode excitation, encoding and detection are a route for multiplexing
and scaling up light-processing.


Abstract:
  We propose an analytical framework based on stochastic geometry (SG)
formulations to estimate a radar's detection performance under generalized
discrete clutter conditions. We model the spatial distribution of discrete
clutter scatterers as a homogeneous Poisson point process and the radar
cross-section of each extended scatterer as a random variable of the Weibull
distribution. Using this framework, we derive a metric called the radar
detection coverage probability as a function of radar parameters such as
transmitted power, system noise temperature and radar bandwidth; and clutter
parameters such as clutter density and mean clutter cross-section. We derive
the optimum radar bandwidth for maximizing this metric under noisy and
cluttered conditions. We also derive the peak transmitted power beyond which
there will be no discernible improvement in radar detection performance due to
clutter limited conditions. When both transmitted power and bandwidth are
fixed, we show how the detection threshold can be optimized for best
performance. We experimentally validate the SG results with a hybrid of Monte
Carlo and full wave electromagnetic solver based simulations using finite
difference time domain (FDTD) techniques.


Abstract:
  Survival analysis is a challenging variation of regression modeling because
of the presence of censoring, where the outcome measurement is only partially
known, due to, for example, loss to follow up. Such problems come up frequently
in medical applications, making survival analysis a key endeavor in
biostatistics and machine learning for healthcare, with Cox regression models
being amongst the most commonly employed models. We describe a new approach for
survival analysis regression models, based on learning mixtures of Cox
regressions to model individual survival distributions. We propose an
approximation to the Expectation Maximization algorithm for this model that
does hard assignments to mixture groups to make optimization efficient. In each
group assignment, we fit the hazard ratios within each group using deep neural
networks, and the baseline hazard for each mixture component
non-parametrically.
  We perform experiments on multiple real world datasets, and look at the
mortality rates of patients across ethnicity and gender. We emphasize the
importance of calibration in healthcare settings and demonstrate that our
approach outperforms classical and modern survival analysis baselines, both in
terms of discriminative performance and calibration, with large gains in
performance on the minority demographics.


Abstract:
  In traditional models only an order one fraction of energy is transferred
from the inflaton to radiation through nonperturbative resonance production in
preheating immediately after inflation, due to backreaction effects. We propose
a particle production mechanism that could improve the depletion of the
inflaton energy density by up to four orders of magnitude. The improvement
comes from the fast perturbative decays of resonantly produced daughter
particles. They act as a "spillway" to drain these daughter particles, reducing
their backreaction on the inflaton and keeping the resonant production
effective for a longer period. Thus we dub the scenario "spillway preheating".
We also show that the fraction of energy density remaining in the inflaton has
a simple inverse power-law scaling in the scenario. In general, spillway
preheating is a much more efficient energy dissipation mechanism, which may
have other applications in model building for particle physics.


Abstract:
  View synthesis aims to produce unseen views from a set of views captured by
two or more cameras at different positions. This task is non-trivial since it
is hard to conduct pixel-level matching among different views. To address this
issue, most existing methods seek to exploit the geometric information to match
pixels. However, when the distinct cameras have a large baseline (i.e., far
away from each other), severe geometry distortion issues would occur and the
geometric information may fail to provide useful guidance, resulting in very
blurry synthesized images. To address the above issues, in this paper, we
propose a novel deep generative model, called Self-Consistent Generative
Network (SCGN), which synthesizes novel views from the given input views
without explicitly exploiting the geometric information. The proposed SCGN
model consists of two main components, i.e., a View Synthesis Network (VSN) and
a View Decomposition Network (VDN), both employing an Encoder-Decoder
structure. Here, the VDN seeks to reconstruct input views from the synthesized
novel view to preserve the consistency of view synthesis. Thanks to VDN, SCGN
is able to synthesize novel views without using any geometric rectification
before encoding, making it easier for both training and applications. Finally,
adversarial loss is introduced to improve the photo-realism of novel views.
Both qualitative and quantitative comparisons against several state-of-the-art
methods on two benchmark tasks demonstrated the superiority of our approach.


Abstract:
  We show that the set $\ell^{\infty}\setminus c_0$ is dense-lineable and
dense-algebrable answering a question posed by Nestoridis and complementing a
result by Garc\'ia-Pacheco, Mart\'in and Seoane-Sep\'ulveda.


Abstract:
  We report our progress on the project for solving larger scale quadratic
assignment problems (QAPs). Our main approach to solve large scale NP-hard
combinatorial optimization problems such as QAPs is a parallel branch-and-bound
method efficiently implemented on a powerful computer system using the Ubiquity
Generator (UG) framework that can utilize more than 100,000 cores. Lower
bounding procedures incorporated in the branch-and-bound method play a crucial
role in solving the problems. For a strong lower bounding procedure, we employ
the Lagrangian doubly nonnegative (DNN) relaxation and the Newton-bracketing
method developed by the authors' group. In this report, we describe some basic
tools used in the project including the lower bounding procedure and branching
rules, and present some preliminary numerical results.
  Our next target problem is QAPs with dimension at least 50, as we have
succeeded to solve tai30a and sko42 from QAPLIB for the first time.


Abstract:
  As a natural language generation task, it is challenging to generate
informative and coherent review text. In order to enhance the informativeness
of the generated text, existing solutions typically learn to copy entities or
triples from knowledge graphs (KGs). However, they lack overall consideration
to select and arrange the incorporated knowledge, which tends to cause text
incoherence.
  To address the above issue, we focus on improving entity-centric coherence of
the generated reviews by leveraging the semantic structure of KGs. In this
paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based
on knowledge graphs (KGs) to improve both global and local coherence for review
generation. The proposed model learns a two-level text plan for generating a
document: (1) the document plan is modeled as a sequence of sentence plans in
order, and (2) the sentence plan is modeled as an entity-based subgraph from
KG. Local coherence can be naturally enforced by KG subgraphs through
intra-sentence correlations between entities. For global coherence, we design a
hierarchical self-attentive architecture with both subgraph- and node-level
attention to enhance the correlations between subgraphs. To our knowledge, we
are the first to utilize a KG-based text planning model to enhance text
coherence for review generation. Extensive experiments on three datasets
confirm the effectiveness of our model on improving the content coherence of
generated texts.


Abstract:
  We present high-resolution optical images from the Hubble Space Telescope,
X-ray images from the Chandra X-ray Observatory, and optical spectra from the
Nordic Optical Telescope for a newly-discovered galaxy cluster, CHIPS1911+4455,
at z=0.485+/-0.005. CHIPS1911+4455 was discovered in the Clusters Hiding in
Plain Sight (CHiPS) survey, which sought to discover galaxy clusters with
extreme central galaxies that were misidentified as isolated X-ray point
sources in the ROSAT All-Sky Survey. With new Chandra X-ray observations, we
find the core (r=10 kpc) entropy to be 17+2-9 keV cm^2, suggesting a strong
cool core, which are typically found at the centers of relaxed clusters.
However, the large-scale morphology of CHIPS1911+4455 is highly asymmetric,
pointing to a more dynamically active and turbulent cluster. Furthermore, the
Hubble images reveal a massive, filamentary starburst near the brightest
cluster galaxy (BCG). We measure the star formation rate for the BCG to be
140--190 Msun/yr, which is one of the highest rates measured in a central
cluster galaxy to date. One possible scenario for CHIPS1911+4455 is that the
cool core was displaced during a major merger and rapidly cooled, with cool,
star-forming gas raining back toward the core. This unique system is an
excellent case study for high-redshift clusters, where such phenomena are
proving to be more common. Further studies of such systems will drastically
improve our understanding of the relation between cluster mergers and cooling,
and how these fit in the bigger picture of active galactic nuclei (AGN)
feedback.


Abstract:
  The membrane curvature of cells and intracellular compartments continuously
adapts to enable cells to perform vital functions, from cell division to signal
trafficking. Understanding how membrane geometry affects these processes in
vivo is challenging because of the membrane complexity as well as the short
time and small length scales involved. By contrast, in vitro model membranes
with engineered curvature provide a versatile platform for this investigation
and applications to biosensing and biocomputing. However, a general route to
the fabrication of lipid membranes with prescribed curvature and high spatial
resolution is still missing. Here, we present a strategy that overcomes these
challenges and achieve lipid membranes with designed shape by combining 3D
micro-printing and replica-molding lithography to create scaffolds with
virtually any geometry and high spatial resolution. The resulting supported
lipid membranes are homogeneous, fluid, and can form chemically distinct lipid
domains. These features are essential for understanding curvature-dependent
cellular processes and developing programmable bio-interfaces for living cells
and nanostructures.


Abstract:
  In the last twenty years, there have been significant advances in the study
of the blow-up phenomenon for the critical generalized Korteweg-de Vries
equation, including the determination of sufficient conditions for blowup, the
stability of blowup in a refined topology and the classification of minimal
mass blowup. Exotic blow-up solutions with a continuum of blow-up rates and
multi-point blow-up solutions were also constructed. However, all these
results, as well as numerical simulations, involve the bubbling of a solitary
wave going at infinity at the blow-up time, which means that the blow-up
dynamics and the residue are eventually uncoupled. Even at the formal level,
there was no indication whether blowup at a finite point could occur for this
equation. In this article, we answer this question by constructing solutions
that blow up in finite time under the form of a single-bubble concentrating the
ground state at a finite point with an unforeseen blow-up rate. Finding a
blow-up rate intermediate between the self-similar rate and other rates
previously known also reopens the question of which blow-up rates are actually
possible for this equation.


Abstract:
  In this paper we study the minimal faithful permutation representations of
$SL_n(\mathbb F_q)$ and $GL_n(\mathbb F_q)$.


Abstract:
  In this paper we study the phonon's effect on the position of the 1s
excitonic resonance of the fundamental absorption transition line in
two-dimensional transition metal dichalcogenides. We apply our theory to
WS$_{2}$a two-dimensional material where the shift in absorption peak position
has been measured as a function of temperature. The theory is composed of two
ingredients only: i) the effect of longitudinal optical phonons on the
absorption peak position, which we describe with second order perturbation
theory; ii) the effect of phonons on the value of the single particle energy
gap, which we describe with the Huang Rhys model. Our results show an excellent
agreement with the experimentally measured shift of the absorption peak with
the temperature.


Abstract:
  The automatic recognition of food on images has numerous interesting
applications, including nutritional tracking in medical cohorts. The problem
has received significant research attention, but an ongoing public benchmark to
develop open and reproducible algorithms has been missing. Here, we report on
the setup of such a benchmark using publicly available food images sourced
through the mobile MyFoodRepo app. Through four rounds, the benchmark released
the MyFoodRepo-273 dataset constituting 24,119 images and a total of 39,325
segmented polygons categorized in 273 different classes. Models were evaluated
on private tests sets from the same platform with 5,000 images and 7,865
annotations in the final round. Top-performing models on the 273 food
categories reached a mean average precision of 0.568 (round 4) and a mean
average recall of 0.885 (round 3). We present experimental validation of round
4 results, and discuss implications of the benchmark setup designed to increase
the size and diversity of the dataset for future rounds.


Abstract:
  The traditional approach in FL tries to learn a single global model
collaboratively with the help of many clients under the orchestration of a
central server. However, learning a single global model might not work well for
all clients participating in the FL under data heterogeneity. Therefore, the
personalization of the global model becomes crucial in handling the challenges
that arise with statistical heterogeneity and the non-IID distribution of data.
Unlike prior works, in this work we propose a new approach for obtaining a
personalized model from a client-level objective. This further motivates all
clients to participate in federation even under statistical heterogeneity in
order to improve their performance, instead of merely being a source of data
and model training for the central server. To realize this personalization, we
leverage finding a small subnetwork for each client by applying hybrid pruning
(combination of structured and unstructured pruning), and unstructured pruning.
Through a range of experiments on different benchmarks, we observed that the
clients with similar data (labels) share similar personal parameters. By
finding a subnetwork for each client ...