  We investigate the localisation properties of quasiperiodic tight-binding
chains with hopping terms modulated by the interpolating
Aubry-Andr\'e-Fibonacci (IAAF) function. This off-diagonal IAAF model allows
for a smooth and controllable interpolation between two paradigmatic
quasiperiodic models: the Aubry-Andr\'e and the Fibonacci model. Our analysis
shows that the spectrum of this model can be divided into three principal
bands, namely, two molecular bands at the edge of the spectrum and one atomic
band in the middle, for all values of the interpolating parameter. We reveal
that the states in the molecular bands undergo multiple re-entrant localisation
transitions, a behaviour previously reported in the diagonal IAAF model. We
link the emergence of these reentrant phenomena to symmetry points of the
quasiperiodic modulation and, with that, explain the main ground state
properties of the system. The atomic states in the middle band show no traces
of localised phases and remain either extended or critical for any value of the
interpolating parameter. Using a renormalisation group approach, adapted from
the Fibonacci model, we explain the extended nature of the middle band. These
findings expand our knowledge of phase transitions within quasiperiodic systems
and highlight the interplay between extended, critical, and localised states.


  We establish the Schur-Weyl type duality between double affine Hecke algebras
and quantum toroidal superalgebras, generalizing the well known result of
Vasserot-Varagnolo [VV96] to the super case.


  We present millimeter wavelength detections of three faint sources that are
most likely high-redshift starburst galaxies. For one of the sources, which was
previously discovered with SCUBA at 850 mu m, we present a detection with the
IRAM interferometer at 240 GHz (1.25 mm) that shows the object unresolved at an
angular resolution of 2.''5, and coincident within 1'' with a radio source and
a galaxy detected in the near-infrared. The two other sources were discovered
in a deep 250 GHz (1.2 mm) survey with the Max-Planck Millimeter Bolometer
(MAMBO) array at the IRAM 30m telescope. Both have fluxes of ~4 mJy and radio
counterparts with a 1.4 GHz flux density of ~75 muJy. Their radio-to-mm flux
ratios suggest redshifts larger than 2. Both sources are faint in the optical
and near-infrared, one showing a 20.5 mag K-band counterpart. From our data and
that available in the literature, we estimate the redshift distribution of
twenty-two faint mm and sub-mm sources and conclude that the majority of them
are likely to be at z>2.


  We address the problem of predicting whether sufficient memory and CPU
resources have been requested for jobs at submission time. For this purpose, we
examine the task of training a supervised machine learning system to predict
the outcome - whether the job will fail specifically due to insufficient
resources - as a classification task. Sufficiently high accuracy, precision,
and recall at this task facilitates more anticipatory decision support
applications in the domain of HPC resource allocation. Our preliminary results
using a new test bed show that the probability of failed jobs is associated
with information freely available at job submission time and may thus be usable
by a learning system for user modeling that gives personalized feedback to
users.


  Agent-based simulators (ABS) are a popular epidemiological modelling tool to
study the impact of various non-pharmaceutical interventions in managing an
epidemic in a city (or a region). They provide the flexibility to accurately
model a heterogeneous population with time and location varying,
person-specific interactions as well as detailed governmental mobility
restrictions. Typically, for accuracy, each person is modelled separately. This
however may make computational time prohibitive when the city population and
the simulated time is large. In this paper, we dig deeper into the underlying
probabilistic structure of a generic, locally detailed ABS for epidemiology to
arrive at modifications that allow smaller models (models with less number of
agents) to give accurate statistics for larger ones, thus substantially
speeding up the simulation. We observe that simply considering a smaller
aggregate model and scaling up the output leads to inaccuracies. We exploit the
observation that in the initial disease spread phase, the starting infections
create a family tree of infected individuals more-or-less independent of the
other trees and are modelled well as a multi-type super-critical branching
process. Further, although this branching process grows exponentially, the
relative proportions amongst the population types stabilise quickly. Once
enough people have been infected, the future evolution of the epidemic is
closely approximated by its mean field limit with a random starting state. We
build upon these insights to develop a shifted, scaled and restart-based
algorithm that accurately evaluates the ABS's performance using a much smaller
model while carefully reducing the bias that may otherwise arise. We apply our
algorithm for Covid-19 epidemic in a city and theoretically support the
proposed algorithm through an asymptotic analysis where the population size
increases to infinity.


  We tackle the problem of large scale visual place recognition, where the task
is to quickly and accurately recognize the location of a given query
photograph. We present the following three principal contributions. First, we
develop a convolutional neural network (CNN) architecture that is trainable in
an end-to-end manner directly for the place recognition task. The main
component of this architecture, NetVLAD, is a new generalized VLAD layer,
inspired by the "Vector of Locally Aggregated Descriptors" image representation
commonly used in image retrieval. The layer is readily pluggable into any CNN
architecture and amenable to training via backpropagation. Second, we develop a
training procedure, based on a new weakly supervised ranking loss, to learn
parameters of the architecture in an end-to-end manner from images depicting
the same places over time downloaded from Google Street View Time Machine.
Finally, we show that the proposed architecture significantly outperforms
non-learnt image representations and off-the-shelf CNN descriptors on two
challenging place recognition benchmarks, and improves over current
state-of-the-art compact image representations on standard image retrieval
benchmarks.


  In this paper, we prove several formulas related to Hodge theory, and using
them to prove the deformations of a compact $H$-twisted generalized Calabi-Yau
manifold are unobstructed and $L^2$ convergence in a neighborhood in another
power series . And if we assume that the deformation is smooth in a fixed
neighborhood, and assume the existence of a global canonical family of
deformation, we also construct the global canonical family of the deformations
of generalized K\"ahler manifolds.


  We analyze the superconducting state and the c-axis charge dynamics of
cuprates using a charged ordered bilayer superlattice model in which pairing is
supported by inter-layer Coulomb energy gain. The superlattice nature of high
temperature superconductivity is experimentally suggested by the smallness of
the coherent length \xi = 10 to 30 A which is comparable with a width of a 4X4
to 8X8 square supercell lattice layer. The temperature induced 2D-3D quantum
phase transition of the hole-content is also studied. Pair condensation leads
to the sharp decrease of the normal state c-axis anisotropy of the hole-content
and reduces inter-layer dielectric screening. The decrease of the c-axis
dielectric screening can be the primary source of the condensation energy. The
2D pair condensate can be characterized by a charge ordered state with a
"checkboard" pattern seen by scanning tunneling microscopy.


  We investigate origin of three features that are often assumed in analysis of
two-Higgs doublet models: (i) softly broken Z2 symmetry, (ii) CP invariant
Higgs potential, and (iii) degenerated mass spectra. We extend electroweak
gauge symmetry, introducing extra gauge symmetry and extra scalars, and we show
that our models effectively derive two-Higgs dou- blet models at low energy
which naturally hold the three features. We also find that the models can solve
the strong CP problem.


  A laser frequency combs is a broad spectrum composed of equidistant narrow
lines. Initially invented for frequency metrology, such combs enable new
approaches to spectroscopy over broad spectral bandwidths, of particular
relevance to molecules. With optical frequency combs, the performance of
existing spectrometers, such as Michelson-based Fourier transform
interferometers or crossed dispersers, involving e.g. virtual imaging phase
array (VIPA) \'etalons, is dramatically enhanced. Novel types of instruments,
such as dual-comb spectrometers, lead to a new class of devices without moving
parts for accurate measurements over broad spectral ranges. The direct
self-calibration of the frequency scale of the spectra within the accuracy of
an atomic clock and the negligible contribution of the instrumental line-shape
will enable determinations of all spectral parameters with high accuracy for
stringent comparisons with theories in atomic and molecular physics. Chip-scale
frequency-comb spectrometers promise integrated devices for real-time sensing
in analytical chemistry and biomedicine. This review article gives a summary of
advances in the emerging and rapidly advancing field of atomic and molecular
broadband spectroscopy with frequency combs.


  The genome-scale metabolic model with protein constraint (PC-model) has been
increasingly popular for microbial metabolic simulations. We present PROSO
Toolbox, a unified and simple-to-use PC-model toolbox that takes any
high-quality genome-scale metabolic reconstruction as the input. The toolbox
can construct a PC-model automatically, apply various algorithms for
computational strain design and simulation, and help unveil metabolism from
gene expression data through a state-of-the-art OVERLAY workflow. It also has
detailed tutorials and documentation for maximum accessibility to researchers
from diverse backgrounds. PROSO Toolbox, tutorials, and documentation are
freely available online: https://github.com/QCSB/PROSO-Toolbox.


  In a self-organizing particle system, an abstraction of programmable matter,
simple computational elements called particles with limited memory and
communication self-organize to solve system-wide problems of movement,
coordination, and configuration. In this paper, we consider a stochastic,
distributed, local, asynchronous algorithm for "shortcut bridging", in which
particles self-assemble bridges over gaps that simultaneously balance
minimizing the length and cost of the bridge. Army ants of the genus Eciton
have been observed exhibiting a similar behavior in their foraging trails,
dynamically adjusting their bridges to satisfy an efficiency trade-off using
local interactions. Using techniques from Markov chain analysis, we rigorously
analyze our algorithm, show it achieves a near-optimal balance between the
competing factors of path length and bridge cost, and prove that it exhibits a
dependence on the angle of the gap being "shortcut" similar to that of the ant
bridges. We also present simulation results that qualitatively compare our
algorithm with the army ant bridging behavior. Our work gives a plausible
explanation of how convergence to globally optimal configurations can be
achieved via local interactions by simple organisms (e.g., ants) with some
limited computational power and access to random bits. The proposed algorithm
also demonstrates the robustness of the stochastic approach to algorithms for
programmable matter, as it is a surprisingly simple extension of our previous
stochastic algorithm for compression.


  We extend the quandle cocycle invariant to the context of stuck links. More
precisely, we define an invariant of stuck links by assigning Boltzmann weights
at both classical and stuck crossings. As an application, we define a
single-variable and a two-variable polynomial invariant of stuck links.
Furthermore, we define a single-variable and two-variable polynomial invariant
of arc diagrams of RNA foldings. We provide explicit computations of the new
invariants.


  We present a quantitative geometric rigidity estimate in dimensions $d=2,3$
generalizing the celebrated result by Friesecke, James, and M\"uller to the
setting of variable domains. Loosely speaking, we show that for each $y \in
H^1(U;\mathbb{R}^d)$ and for each connected component of a smooth open, bounded
set $U \subset \mathbb{R}^d$, the $L^2$-distance of $\nabla y$ from a single
rotation can be controlled up to a constant by its $L^2$-distance from the
group $SO(d)$, with the constant not depending on the precise shape of $U$, but
only on an integral curvature functional related to $\partial U$. We further
show that for linear strains the estimate can be refined, leading to a uniform
control independent of the set $U$. The estimate can be used to establish
compactness in the space of generalized special functions of bounded
deformation ($GSBD^2$) for sequences of displacements related to deformations
with uniformly bounded elastic energy. As an application, we rigorously derive
linearized models for nonlinearly elastic materials with free surfaces by means
of $\Gamma$-convergence. In particular, we study energies related to
epitaxially strained crystalline films and to the formation of material voids
inside elastically stressed solids.


  The International System is a self-organized system that shows emergent
behavior. During the timeframe (1495-1945) covered in this study, a finite-time
singularity and four accompanying accelerating log-periodic cycles shaped the
dynamics of the International System. Each cycle began and ended with a
systemic war. During their life span, these cycles show remarkable regularities
in their dynamics. The accelerated growth of the connectivity of the regulatory
network of the International System, in combination with its anarchistic
structure, produce and shape the war dynamics of the system. The accelerated
growth of the connectivity of the International System is fed by population
growth and the need for social systems to fulfill basic requirements. The
finite-time singularity and accompanying log-periodic oscillations were
instrumental in the periodic reorganization of the regulatory network of the
International System and contributed to a long-term process of social expansion
and integration in Europe. The singularity dynamic produced a series of
organizational innovations. At the critical time of the singularity (1939), the
connectivity of the system reached a critical threshold and resulted in a
critical transition that led to a fundamental reorganization of the
International System. Europe transformed from an anarchistic system to a
cooperative security community. During the life span of the cycles, the war
dynamics showed chaotic characteristics. These findings have implications for
the social sciences and historical research.


  We investigate a model of transportation networks with nonlinear elements
which may represent local shortage of resources. Frustrations arise from
competition for resources. When the initial resources are uniform, different
regimes with discrete fractions of satisfied nodes are observed, resembling the
Devil's staircase. We demonstrate how functional recursions are converted to
simple recursions of probabilities. Behavior similar to those in the vertex
cover or close packing problems are found. When the initial resources are
bimodally distributed, increases in the fraction of rich nodes induce a glassy
transition, entering an algorithmically hard regime.


  We prove existence, uniqueness and regularity results for mixed boundary
value problems associated with fully nonlinear, possibly singular or degenerate
elliptic equations. Our main result is a global H\"older estimate for
solutions, obtained by means of the comparison principle and the construction
of ad hoc barriers. The global H\"older estimate immediately yields a
compactness result in the space of solutions, which could be applied in the
study of principal eigenvalues and principal eigenfunctions of mixed boundary
value problems.


  We study the massless minimally coupled scalar field on a two--dimensional de
Sitter space-time in the setting of axiomatic quantum field theory. We
construct the invariant Wightman distribution obtained as the renormalized
zero--mass limit of the massive one. Insisting on gauge invariance of the model
we construct a vacuum state and a Hilbert space of physical states which are
invariant under the action of the whole de Sitter group. We also present the
integral expression of the conserved charge which generates the gauge
invariance and propose a definition of dual field.


  Differential MAP-Elites is a novel algorithm that combines the illumination
capacity of CVT-MAP-Elites with the continuous-space optimization capacity of
Differential Evolution. The algorithm is motivated by observations that
illumination algorithms, and quality-diversity algorithms in general, offer
qualitatively new capabilities and applications for evolutionary computation
yet are in their original versions relatively unsophisticated optimizers. The
basic Differential MAP-Elites algorithm, introduced for the first time here, is
relatively simple in that it simply combines the operators from Differential
Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25
numerical optimization problems suggest that Differential MAP-Elites clearly
outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.


  Many microorganisms and artificial microswimmers use helical appendages in
order to generate locomotion. Though often rotated so as to produce thrust,
some species of bacteria such Spiroplasma, Rhodobacter sphaeroides and
Spirochetes induce movement by deforming a helical-shaped body. Recently,
artificial devices have been created which also generate motion by deforming
their helical body in a non-reciprocal way (Mourran et al., Adv. Mater., 29,
1604825, 2017). Inspired by these systems, we investigate the transport of a
deforming helix within a viscous fluid. Specifically, we consider a swimmer
that maintains a helical centreline and a single handedness while changing its
helix radius, pitch and wavelength uniformly across the body. We first discuss
how a deforming helix can create a non-reciprocal translational and rotational
swimming stroke and identify its principle direction of motion. We then
determine the leading-order physics for helices with small helix radius before
considering the general behaviour for different configuration parameters and
how these swimmers can be optimised. Finally, we explore how the presence of
walls, gravity, and defects in the centreline allow the helical device to break
symmetries, increase its speed, and generate transport in directions not
available to helices in bulk fluids.


  We prove a Hardy-Sobolev-Maz'ya inequality for arbitrary domains
\Omega\subset\R^N with a constant depending only on the dimension N\geq 3. In
particular, for convex domains this settles a conjecture by Filippas, Maz'ya
and Tertikas. As an application we derive Hardy-Lieb-Thirring inequalities for
eigenvalues of Schr\"odinger operators on domains.


  After the success of affine quantization in proving through Monte Carlo
analysis that the covariant euclidean scalar field theory, $\varphi^r_n$, where
$r$ denotes the power of the interaction term and $n = s + 1$ with $s$ the
spatial dimension and $1$ adds imaginary time, such that $r \geq 2n/(n-2)$ can
be acceptably quantized and the resulting theory is nontrivial, unlike what
happens using canonical quantization, we show here that the same has to be
expected for $r>2$ and any $n$ even for the ultralocal field theory. In
particular we consider the ultralocal $\varphi^4_2$ model and study its
renormalized properties for both the scaled canonical quantization version and
the scaled affine quantization version through path integral Monte Carlo.


  We have resolved the classical nova V1663 Aql using long-baseline near-IR
interferometry covering the period from 5--18 days after peak brightness. We
directly measure the shape and size of the fireball, which we find to be
asymmetric. In addition we measure an apparent expansion rate of 0.21 +/- 0.03
mas/day. Assuming a linear expansion model we infer a time of initial outburst
approximately 4 days prior to peak brightness. When combined with published
spectroscopic expansion velocities our angular expansion rate implies a
distance of 8.9 +/- 3.6 kpc. This distance measurement is independent of, but
consistent with, determinations made using widely available photometric
relations for novae.


  We report the design of a new electromagnetic device with a new mapping
function to have simultaneous electromagnetic concentration and rotation using
a singular radial mapping. We implement such a device only by using alternating
structure of zero index metamaterials and perfect electric conductors.
Numerical simulations are performed to verify its functionality.


  Goal-directed manipulation of representations is a key element of human
flexible behaviour, while consciousness is often related to several aspects of
higher-order cognition and human flexibility. Currently these two phenomena are
only partially integrated (e.g., see Neurorepresentationalism) and this (a)
limits our understanding of neuro-computational processes that lead conscious
states to produce flexible goal-directed behaviours, (b) prevents a
computational formalisation of conscious goal-directed manipulations of
representations occurring in the brain, and (c) inhibits the exploitation of
this knowledge for modelling and technological purposes. Addressing these
issues, here we extend our `three-component theory of flexible cognition' by
proposing the `Goal-Aligning Representations Internal Manipulation' (GARIM)
theory of conscious and flexible goal-directed cognition. The central idea of
the theory is that conscious states support the active manipulation of
goal-relevant internal representations (e.g., of world states, objects, and
action sequences) to make them more aligned with the pursued goals. This leads
to the generation of the knowledge which is necessary to face novel
situations/goals, thus increasing the flexibility of goal-directed behaviours.
The GARIM theory integrates key aspects of the main theories of consciousness
into the functional neuro-computational framework of goal-directed behaviour.
Moreover, it takes into account the subjective sensation of agency that
accompanies conscious goal-directed processes (`GARIM agency'). The proposal
has also implications for experimental studies on consciousness and clinical
aspects of conscious goal-directed behaviour. Finally, the GARIM theory benefit
technological fields such as autonomous robotics and machine learning (e.g.,
the manipulation process may describe the operations performed by systems based
on transformers).


  Hot-carrier solar cells are envisioned to utilize energy filtering to extract
power from photogenerated electron-hole pairs before they thermalize with the
lattice, and thus potentially offer higher power conversion efficiency compared
to conventional, single absorber solar cells. The efficiency of hot-carrier
solar cells can be expected to strongly depend on the details of the energy
filtering process, a relationship which to date has not been satisfactorily
explored. Here, we establish the conditions under which electron-hole
separation in hot-carrier solar cells can occur reversibly, that is, at maximum
energy conversion efficiency. We thus focus our analysis on the internal
operation of the hot-carrier solar cell itself, and in this work do not
consider the photon-mediated coupling to the sun. After deriving an expression
for the voltage of a hot-carrier solar cell valid under conditions of both
reversible and irreversible electrical operation, we identify separate
contributions to the voltage from the thermoelectric effect and the
photovoltaic effect. We find that, under specific conditions, the energy
conversion efficiency of a hot-carrier solar cell can exceed the Carnot limit
set by the intra-device temperature gradient alone, due to the additional
contribution of the quasi-Fermi level splitting in the absorber. We also
establish that the open-circuit voltage of a hot-carrier solar cell is not
limited by the band gap of the absorber, due to the additional thermoelectric
contribution to the voltage. Additionally, we find that a hot-carrier solar
cell can be operated in reverse as a thermally driven solid-state light
emitter. Our results help explore the fundamental limitations of hot-carrier
solar cells, and provide a first step towards providing experimentalists with a
guide to the optimal configuration of devices.


  A sublimation process governs the innermost region of the dusty torus of
active galactic nuclei. However, the observed inner radius of the torus is
systematically smaller than the expected radius by a factor of ~ 1/3. We show
that the anisotropy of the emission from accretion disks resolves this conflict
naturally and quantitatively. An accretion disk emits lesser radiation in the
direction closer to its equatorial plane (i.e., to the torus). We find that the
anisotropy makes the torus inner region closer to the central black hole and
concave. Moreover, the innermost edge of the torus may connect with the
outermost edge of the disk continuously. Considering the anisotropic emission
of each clump in the torus, we calculate the near-infrared flux variation in
response to a UV flash. For an observer at the polar angle theta_obs = 25 deg,
the centroid of the time delay is found to be 37% of the delay expected in the
case of isotropic illumination, which explains the observed systematic
deviation.


  The satellite endofunctors are used to extend the definition of linkage of
ideals to the linkage of totally finitely presented functors. The new notion
for linkage works over a larger class of rings and is consistent with the
functorial approach of encoding information about modules into the category of
finitely presented functors. In the process of extending linkage, we recover
the Auslander-Gruson-Jensen duality using injective resolutions of finitely
presented functors. Using the satellite endofunctors we give general
definitions of derived functors which do not require the existence of
projective or injective objects. A general formula for calculating the defect
of a totally finitely presented functor is given.


  We propose a polynomial time construction of an $(n,d)$-universal set over
alphabet $\Sigma=\{0,1\}$, of size $d\cdot 2^{d+o(d)}\cdot\log n$. This is an
improvement over the size, $d^{5}2^{2.66d}\log n$, of an $(n,d)$-universal set
constructed by Bshouty, \cite{BshoutyTesters}, over alphabet $\Sigma=\{0,1\}$.


  We have done a series of two-dimensional hydrodynamic simulations of the
rotational collapse of a supernova core in axisymmetry. We have employed a
realistic equation of state (EOS) and taken into account electron captures and
neutrino transport by the so-called leakage scheme. It is an important progress
to apply the realistic EOS coupled with the microphysics to 2-D simulations for
computing gravitational radiation in rotational core collapse.
  We have used the quadrupole formula to calculate the amplitudes and the
waveforms of gravitational wave assuming the Newtonian gravity. From these
computations, we have extended the conventional category of the gravitational
waveforms. Our results have shown that the peak amplitudes of gravitational
wave are mostly within the sensitivity range of the laser interferometers such
as TAMA and first
  LIGO for a source at a distance of 10 kpc. Furthermore we have found that the
amplitudes of the second peaks are within the detection limit of first LIGO for
the source and first pointed out the importance of the detections, since they
will give us the information as to the angular momentum distribution of evolved
massive stars.


  The most useful measure of a bipartite entanglement is the von Neumann
entropy of either of the reduced density matrices. For a particular class of
continuous-variable states, the Gaussian states, the entropy of entanglement
can be expressed rather elegantly in terms of the symplectic eigenvalues,
elements that characterize a Gaussian state and depend on the correlations of
the canonical variables. We give a pedagogical step-by-step derivation of this
result and provide some insights that can be useful in practical calculations.


  We have developed a unified scheme for studying Non-Commutative algebras
based on Generalized Uncertainty Principle (GUP) and Snyder form in a
relativistically covariant point particle Lagrangian (or symplectic) framework.
Even though the GUP based algebra and Snyder algebra are very distinct, the
more involved latter algebra emerges from an approximation of the Lagrangian
model of the former algebra. Deformed Poincare generators for the systems that
keep space-time symmetries of the relativistic particle models have been
studied thoroughly.
  From a purely constrained dynamical analysis perspective the models studied
here are very rich and provide insights on how to consistently construct
approximate models from the exact ones when non-linear constraints are present
in the system.
  We also study dynamics of the GUP particle in presence of external
electromagnetic field.


  Beyond implementation correctness of a distributed system, it is equally
important to understand exactly what users should expect to see from that
system. Even if the system itself works as designed, insufficient understanding
of its user-visible semantics can cause bugs in its dependencies. By focusing a
formal specification effort on precisely defining the expected user-facing
behaviors of the Azure Cosmos DB service at Microsoft, we were able to write a
formal specification of the database that was significantly smaller and
conceptually simpler than any other specification of Cosmos DB, while
representing a wider range of valid user-observable behaviors than existing
more detailed specifications. Many of the additional behaviors we documented
were previously poorly understood outside of the Cosmos DB development team,
even informally, leading to data consistency errors in Microsoft products that
depend on it. Using this model, we were able to raise two key issues in Cosmos
DB's public-facing documentation, which have since been addressed. We were also
able to offer a fundamental solution to a previous high-impact outage within
another Azure service that depends on Cosmos DB.


  We consider the fundamental derandomization problem of deterministically
finding a satisfying assignment to a CNF formula that has many satisfying
assignments. We give a deterministic algorithm which, given an $n$-variable
$\mathrm{poly}(n)$-clause CNF formula $F$ that has at least $\varepsilon 2^n$
satisfying assignments, runs in time \[ n^{\tilde{O}(\log\log n)^2} \] for
$\varepsilon \ge 1/\mathrm{polylog}(n)$ and outputs a satisfying assignment of
$F$. Prior to our work the fastest known algorithm for this problem was simply
to enumerate over all seeds of a pseudorandom generator for CNFs; using the
best known PRGs for CNFs [DETT10], this takes time $n^{\tilde{\Omega}(\log n)}$
even for constant $\varepsilon$. Our approach is based on a new general
framework relating deterministic search and deterministic approximate counting,
which we believe may find further applications.


  In the present paper, progression of non-alcoholic fatty liver disease
(NAFLD) process is modeled by Continuous time Markov chains (CTMC) with 4
states .The transition intensities among the states are estimated using maximum
likelihood estimation (MLE) method. The transition probabilities are also
calculated. The mean sojourn time and its variance are estimated as well as the
state probability distribution and its asymptotic covariance matrix. The life
expectancy of the patient, one of the important statistical indices, is also
obtained. The paper illustrates the new approach of using MLE to compensate for
missing values in the follow up periods of patients in the longitudinal
studies. This new approach also yields that the estimated rates among states
are approximately equals to the observed rates.


  We consider hundreds of thousands of individual economic transactions to ask:
how predictable are consumers in their merchant visitation patterns? Our
results suggest that, in the long-run, much of our seemingly elective activity
is actually highly predictable. Notwithstanding a wide range of individual
preferences, shoppers share regularities in how they visit merchant locations
over time. Yet while aggregate behavior is largely predictable, the
interleaving of shopping events introduces important stochastic elements at
short time scales. These short- and long-scale patterns suggest a theoretical
upper bound on predictability, and describe the accuracy of a Markov model in
predicting a person's next location. We incorporate population-level transition
probabilities in the predictive models, and find that in many cases these
improve accuracy. While our results point to the elusiveness of precise
predictions about where a person will go next, they suggest the existence, at
large time-scales, of regularities across the population.


  Finding a reasonably good upper bound for the clique number of Paley graphs
is an open problem in additive combinatorics. A recent breakthrough by Hanson
and Petridis using Stepanov's method gives an improved upper bound on Paley
graphs defined on a prime field $\mathbb{F}_p$, where $p \equiv 1 \pmod 4$. We
extend their idea to the finite field $\mathbb{F}_q$, where $q=p^{2s+1}$ for a
prime $p\equiv 1 \pmod 4$ and a non-negative integer $s$. We show the clique
number of the Paley graph over $\mathbb{F}_{p^{2s+1}}$ is at most $\min
\bigg(p^s \bigg\lceil \sqrt{\frac{p}{2}} \bigg\rceil,
\sqrt{\frac{q}{2}}+\frac{p^s+1}{4}+\frac{\sqrt{2p}}{32}p^{s-1}\bigg)$.


  In applications such as object tracking, time-series data inevitably carry
missing observations. Following the success of deep learning-based models for
various sequence learning tasks, these models increasingly replace classic
approaches in object tracking applications for inferring the objects' motion
states. While traditional tracking approaches can deal with missing
observations, most of their deep counterparts are, by default, not suited for
this.
  Towards this end, this paper introduces a transformer-based approach for
handling missing observations in variable input length trajectory data. The
model is formed indirectly by successively increasing the complexity of the
demanded inference tasks. Starting from reproducing noise-free trajectories,
the model then learns to infer trajectories from noisy inputs. By providing
missing tokens, binary-encoded missing events, the model learns to in-attend to
missing data and infers a complete trajectory conditioned on the remaining
inputs. In the case of a sequence of successive missing events, the model then
acts as a pure prediction model. The abilities of the approach are demonstrated
on synthetic data and real-world data reflecting prototypical object tracking
scenarios.


  A class of complete potential-density basis sets in cylindrical (R,phi,z)
coordinates is presented. This class is suitable for stability studies of
galactic disks in three dimensions and includes basis sets tailored for disks
with vertical density profiles that are exponential (exp(-|z|/\zn)), Gaussian
(exp(-(z/\zn)^2) or locally isothermal (sech^2(z/\zn)). The basis sets are
non-discrete and non-biorthogonal; however, the extra numerical computations
required (compared with discrete biorthogonal sets) are explained and
constitute a small overhead. The method of construction (and proof of
completeness) is simple and can be used to construct basis sets for other
density distributions that are best described in circular or elliptic
cylindrical coordinates. When combined with a basis set designed for spheroidal
systems, the basis sets presented here can be used to study the stability of
realistic disks embedded in massive halos.


  We consider a network of randomly coupled rate-based neurons influenced by
external and internal noise. We derive a second-order stochastic mean-field
model for the network dynamics and use it to analyze the stability and
bifurcations in the thermodynamic limit, as well as to study the fluctuations
due to the finite-size effect. It is demonstrated that the two types of noise
have substantially different impact on the network dynamics. While both sources
of noise give rise to stochastic fluctuations in the case of the finite-size
network, only the external noise affects the stationary activity levels of the
network in the thermodynamic limit.We compare the theoretical predictions with
the direct simulation results and show that they agree for large enough network
sizes and for parameter domains sufficiently away from bifurcations.


  In this work, we derive first order continuum traffic flow models from a
microscopic delayed follow-the-leader model. Those are applicable in the
context of vehicular traffic flow as well as pedestrian traffic flow. The
microscopic model is based on an optimal velocity function and a reaction time
parameter. The corresponding macroscopic formulations in Eulerian or Lagrangian
coordinates result in first order convection-diffusion equations. More
precisely, the convection is described by the optimal velocity while the
diffusion term depends on the reaction time. A linear stability analysis for
homogeneous solutions of both continuous and discrete models are provided. The
conditions match the ones of the car-following model for specific values of the
space discretization. The behavior of the novel model is illustrated thanks to
numerical simulations. Transitions to collision-free self-sustained stop-and-go
dynamics are obtained if the reaction time is sufficiently large. The results
show that the dynamics of the microscopic model can be well captured by the
macroscopic equations. For non--zero reaction times we observe a scattered
fundamental diagram. The scattering width is compared to real pedestrian and
road traffic data.


  This report is a self-contained and comprehensive review of the physics of
propagating pulses of high-intensity high-energy particle beams in pre-existing
or self-generated plasmas. Consideration is given to beams of electrons,
protons, muons, and their antiparticles, as well as to neutral-hydrogen,
positronium, and electron-positron-plasmoid beams. The first part is a
systematic overview of the theory pertaining to propagation, plasma
self-generation, energy/current-losses, and stability of such pulses. The
second part reviews the major full-scale propagation experiments which have
been carried out, in atmospheric and outer-space plasmas, to assess the
validity of theoretical models. It is found that the data available on these
experiments demonstrate that range and stability are in agreement with theory.
In particular, stable self-pinched propagation of high-current charged-particle
beams in the atmosphere is possible over distances equal to several Nordsieck
lengths. In order not to be deflected by Earth's magnetic field, electron-beam
pulses need to be guided by a pre-formed channel, while proton-beam pulses may
under suitable conditions propagate undeflected through both the low- and
high-atmosphere. In ionospheric or outer-space plasmas, very-long-range
propagation across Earth's magnetic field requires GeV to TeV electrons or
positron beams in order for the transverse deflection to be acceptable, while
undeflected propagation is possible for plasmoid beams consisting of co-moving
high-energy particle pairs such as electrons and positrons.


  We show that for a finite-type Lie algebra $\mathfrak{g}$, the representation
theory of quiver Hecke algebras provides a natural framework for the
construction of Newton-Okounkov bodies associated to the quantum coordinate
rings $\Aqnw$. When $\mathfrak{g}$ is simply-laced, we use
Kang-Kashiwara-Kim-Oh's monoidal categorification to investigate the cluster
theory of these bodies. In particular, our construction yields a simplex $\ds$
for every seed $\s$ of $\Aqnw$.
  We exhibit various properties of these simplices by characterizing their
rational points, normal fans, and volumes. As an application, we prove an
equality of rational functions relating Nakada's hook formula with the root
partitions associated to cluster variables, suggesting further connections
between cluster theory and the combinatorics of fully-commutative elements of
Weyl groups.


  This paper suggests that aging is influenced synthetically by pro-aging
factors such as ROS and anti-aging factors such as protective responses. The
anti-aging effect may be side effects of retrograde responses motivated against
adverse circumstances. ROS may be more closely correlated with metabolism
rather than aging.


This paper has been withdrawn by the authors due to an error.


  We present new estimates of the luminosity function (LF) and star formation
rate density (SFRD) for an H$\alpha$ selected sample at $z\sim0.62$ from the
Deep And Wide Narrow-band (DAWN) survey. Our results are based on a new
H$\alpha$ sample in the extended COSMOS region (compared to Coughlin et al.
2018) with the inclusion of flanking fields, resulting in a total area coverage
of $\sim$1.5 deg$^2$. A total of 241 H$\alpha$ emitters were selected based on
robust selection criteria using spectro-photometric redshifts and broadband
color-color classification. We explore the effect of different dust correction
prescriptions by calculating the LF and SFRD using a constant dust extinction
correction, A{$_{\textrm{H}\alpha}=1$} mag, a luminosity-dependent correction,
and a stellar-mass dependent correction. The resulting H$\alpha$ LFs are well
fitted using Schechter functions with best-fit parameters: L$^*=10^{42.24}$ erg
s$^{-1}$, $\phi^*=10^{-2.85}$ Mpc$^{-3}$, $\alpha = -1.62$ for constant dust
correction, L$^*=10^{42.31}$ erg s$^{-1}$, $\phi^*=10^{-2.8}$ Mpc$^{-3}$,
$\alpha=-1.39$ for luminosity-dependent dust correction, and L$^*=10^{42.36}$
erg s$^{-1}$, $\phi^*=10^{-2.91}$ Mpc$^{-3}$, $\alpha = -1.48$, for stellar
mass-dependent dust correction. The deep and wide nature of the DAWN survey
effectively samples H$\alpha$ emitters over a wide range of luminosities,
thereby providing better constraints on both the faint and bright end of the
LF. Also, the SFRD estimates $\rho_{\textrm{SFR}}=10^{-1.39}$
M$_{\odot}$yr$^{-1}$Mpc$^{-3}$ (constant dust correction),
$\rho_{\textrm{SFR}}=10^{-1.47}$ M$_{\odot}$yr$^{-1}$Mpc$^{-3}$
(luminosity-dependent dust correction), and $\rho_{\textrm{SFR}}=10^{-1.49}$
M$_{\odot}$yr$^{-1}$Mpc$^{-3}$ (stellar mass-dependent dust correction) are in
good agreement with the evolution of SFRD across redshifts ($0 < z < 2$) seen
from previous H$\alpha$ surveys.


  We consider a stochastic variant of the game of Bulgarian solitaire [M.
Gardner (1983), Sci. Amer. 249, 12-21]. For the stationary measure of the
random Bulgarian solitaire, we prove that most of its mass is concentrated on
(roughly) triangular configurations of certain type.


  Exploration is critical for deep reinforcement learning in complex
environments with high-dimensional observations and sparse rewards. To address
this problem, recent approaches proposed to leverage intrinsic rewards to
improve exploration, such as novelty-based exploration and prediction-based
exploration. However, many intrinsic reward modules require sophisticated
structures and representation learning, resulting in prohibitive computational
complexity and unstable performance. In this paper, we propose Rewarding
Episodic Visitation Discrepancy (REVD), a computation-efficient and quantified
exploration method. More specifically, REVD provides intrinsic rewards by
evaluating the R\'enyi divergence-based visitation discrepancy between
episodes. To make efficient divergence estimation, a k-nearest neighbor
estimator is utilized with a randomly-initialized state encoder. Finally, the
REVD is tested on Atari games and PyBullet Robotics Environments. Extensive
experiments demonstrate that REVD can significantly improves the sample
efficiency of reinforcement learning algorithms and outperforms the
benchmarking methods.


  The rest-frame intrinsic UV luminosity is often used as an indicator of the
instantaneous star formation rate (SFR) in a galaxy. While it is in general a
robust indicator of the ongoing star formation activity, the precise value of
the calibration relating the UV luminosity to the SFR ($B_{\nu}$), is sensitive
to various physical properties, such as the recent star formation and metal
enrichment histories, along with the choice of stellar initial mass function.
The distribution of these properties for the star-forming galaxy population
then suggests that the adoption of a single calibration is not appropriate
unless properly qualified with the uncertainties on the calibration. We
investigate, with the aid of the {\sc galform} semi-analytic model of galaxy
formation, the distribution of UV-SFR calibrations obtained using realistic
star formation and metal enrichment histories. At $z=0$, we find that when the
initial mass function is fixed (to the Kennicutt IMF), the median calibration
is $B_{\rm fuv}=0.9$ where ${\rm SFR}/[{\rm M_{\odot}\,yr^{-1}}]=B_{\nu}\times
10^{-28}\times L_{\nu}/[{\rm ergs\,s^{-1}\,Hz^{-1}}]$. However, the width of
the distribution $B_{\rm fuv}$ suggests that for a single object there is
around a 20% {\em intrinsic} uncertainty (at $z=0$, rising to $\simeq 30%$ at
$z=6$) on the star formation rate inferred from the FUV luminosity without
additional constraints on the star formation history or metallicity. We also
find that the median value of the calibration $B_{\rm fuv}$ is correlated with
the star formation rate and redshift (at $z>3$) raising implications for the
correct determination of the star formation rate from the UV.


  Deep learning has recently been successfully applied in automatic modulation
classification by extracting and classifying radio features in an end-to-end
way. However, deep learning-based radio modulation classifiers are lack of
interpretability, and there is little explanation or visibility into what kinds
of radio features are extracted and chosen for classification. In this paper,
we visualize different deep learning-based radio modulation classifiers by
introducing a class activation vector. Specifically, both convolutional neural
networks (CNN) based classifier and long short-term memory (LSTM) based
classifier are separately studied, and their extracted radio features are
visualized. Extensive numerical results show both the CNN-based classifier and
LSTM-based classifier extract similar radio features relating to modulation
reference points. In particular, for the LSTM-based classifier, its obtained
radio features are similar to the knowledge of human experts. Our numerical
results indicate the radio features extracted by deep learning-based
classifiers greatly depend on the contents carried by radio signals, and a
short radio sample may lead to misclassification.


  Introduction: The amount of data generated by original research is growing
exponentially. Publicly releasing them is recommended to comply with the Open
Science principles. However, data collected from human participants cannot be
released as-is without raising privacy concerns. Fully synthetic data represent
a promising answer to this challenge. This approach is explored by the French
Centre de Recherche en {\'E}pid{\'e}miologie et Sant{\'e} des Populations in
the form of a synthetic data generation framework based on Classification and
Regression Trees and an original distance-based filtering. The goal of this
work was to develop a refined version of this framework and to assess its
risk-utility profile with empirical and formal tools, including novel ones
developed for the purpose of this evaluation.Materials and Methods: Our
synthesis framework consists of four successive steps, each of which is
designed to prevent specific risks of disclosure. We assessed its performance
by applying two or more of these steps to a rich epidemiological dataset.
Privacy and utility metrics were computed for each of the resulting synthetic
datasets, which were further assessed using machine learning
approaches.Results: Computed metrics showed a satisfactory level of protection
against attribute disclosure attacks for each synthetic dataset, especially
when the full framework was used. Membership disclosure attacks were formally
prevented without significantly altering the data. Machine learning approaches
showed a low risk of success for simulated singling out and linkability
attacks. Distributional and inferential similarity with the original data were
high with all datasets.Discussion: This work showed the technical feasibility
of generating publicly releasable synthetic data using a multi-step framework.
Formal and empirical tools specifically developed for this demonstration are a
valuable contribution to this field. Further research should focus on the
extension and validation of these tools, in an effort to specify the intrinsic
qualities of alternative data synthesis methods.Conclusion: By successfully
assessing the quality of data produced using a novel multi-step synthetic data
generation framework, we showed the technical and conceptual soundness of the
Open-CESP initiative, which seems ripe for full-scale implementation.


  The fourth edition of the "VIPriors: Visual Inductive Priors for
Data-Efficient Deep Learning" workshop features two data-impaired challenges.
These challenges address the problem of training deep learning models for
computer vision tasks with limited data. Participants are limited to training
models from scratch using a low number of training samples and are not allowed
to use any form of transfer learning. We aim to stimulate the development of
novel approaches that incorporate inductive biases to improve the data
efficiency of deep learning models. Significant advancements are made compared
to the provided baselines, where winning solutions surpass the baselines by a
considerable margin in both tasks. As in previous editions, these achievements
are primarily attributed to heavy use of data augmentation policies and large
model ensembles, though novel prior-based methods seem to contribute more to
successful solutions compared to last year. This report highlights the key
aspects of the challenges and their outcomes.


  In this paper we show the existence and uniqueness of strong solutions for a
large class of backward SPDE where the coefficients satisfy a specific type
Lyapunov condition instead of the classical coercivity condition. Moreover,
based on the generalized variational framework, we also use the local
monotonicity condition to replace the standard monotonicity condition, which is
applicable to various quasilinear and semilinear BSPDE models.


  AMoRE is an international project to search for the neutrinoless double beta
decay of $^{100}$Mo using a detection technology consisting of magnetic
microcalorimeters (MMCs) and molybdenum-based scintillating crystals. Data
collection has begun for the current AMORE-I phase of the project, an upgrade
from the previous pilot phase. AMoRE-I employs thirteen
$^\mathrm{48depl.}$Ca$^{100}$MoO$_4$ crystals and five Li$_2$$^{100}$MoO$_4$
crystals for a total crystal mass of 6.2 kg. Each detector module contains a
scintillating crystal with two MMC channels for heat and light detection. We
report the present status of the experiment and the performance of the detector
modules.


  This report describes our speaker verification systems for the tasks of the
CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022). This challenge
includes two tasks, namely speaker verification(SV) and speaker retrieval(SR).
The SV task involves two tracks: fixed track and open track. In the fixed
track, we only used CN-Celeb.T as the training set. For the open track of the
SV task and SR task, we added our open-source audio data. The ResNet-based,
RepVGG-based, and TDNN-based architectures were developed for this challenge.
Global statistic pooling structure and MQMHA pooling structure were used to
aggregate the frame-level features across time to obtain utterance-level
representation. We adopted AM-Softmax and AAM-Softmax combined with the
Sub-Center method to classify the resulting embeddings. We also used the
Large-Margin Fine-Tuning strategy to further improve the model performance. In
the backend, Sub-Mean and AS-Norm were used. In the SV task fixed track, our
system was a fusion of five models, and two models were fused in the SV task
open track. And we used a single system in the SR task. Our approach leads to
superior performance and comes the 1st place in the open track of the SV task,
the 2nd place in the fixed track of the SV task, and the 3rd place in the SR
task.


  We present a new method for calculating internal forces in DNA structures
using coarse-grained models and demonstrate its utility with the oxDNA model.
The instantaneous forces on individual nucleotides are explored and related to
model potentials, and using our framework, internal forces are calculated for
two simple DNA systems and for a recently-published nanoscopic force clamp. Our
results highlight some pitfalls associated with conventional methods for
estimating internal forces, which are based on elastic polymer models, and
emphasise the importance of carefully considering secondary structure and ionic
conditions when modelling the elastic behaviour of single-stranded DNA. Beyond
its relevance to the DNA nanotechnological community, we expect our approach to
be broadly applicable to calculations of internal force in a variety of
structures -- from DNA to protein -- and across other coarse-grained simulation
models.


  The recent observation of replica bands in single-layer FeSe/SrTiO$_3$ by
angle-resolved photoemission spectroscopy (ARPES) has triggered intense
discussions concerning the potential influence of the FeSe electrons coupling
with substrate phonons on the superconducting transition temperature. Here we
provide strong evidence that the replica bands observed in the single-layer
FeSe/SrTiO$_3$ system and several other cases are largely due to the energy
loss processes of the escaping photoelectron, resulted from the well-known
strong coupling of external propagating electrons to Fuchs-Kliewer (F-K)
surface phonons in ionic materials in general. The photoelectron energy loss in
ARPES on single-layer FeSe/SrTiO$_3$ is calculated using the demonstrated
successful semi-classical dielectric theory in describing low energy electron
energy loss spectroscopy of ionic insulators. Our result shows that the
observed replica bands are mostly a result of extrinsic photoelectron energy
loss and not a result of the electron phonon interaction of the Fe d electrons
with the substrate phonons. The strong enhancement of the superconducting
transition temperature in these monolayers remains an open question.


  Le but de cet article est d'obtenir la cyclicite de certaines classes de
fonctions pour des operateurs de Toeplitz anti-analytique associes a un produit
fini de Blaschke dans les espaces $H^p$ ou $1<p<\infty$. Il s'agit aussi de
decrire les sous-espaces invariants par ce type d'operateur et engendres par
des decompositions lacunaires de fonctions.


  Long distance communication protocols cannot ignore the existence of the
Earth's gravitational field and its effects on quantum states. In this work, we
show a very general method to consider the effects of the Earth's gravitational
field on continuous-variable quantum key distribution protocols. Our results
show that the Earth's gravitational field erodes the ability of the two parties
to perform QKD in all the protocols. However, our findings also exhibit some
interesting features, i.e., the key rates initially increase for a specific
range of height parameter $h\simeq r_A/2$ and then gradually decrease with the
increasing of the orbits of satellite $h$. A possible explanation is also
provided in our analysis, considering the fact that gravitational frequency
shift and special relativistic effects play different roles in the key rates.
In addition, our findings show that the change in key rate effected by
gravitational frequency shift can be determined at a level of $<1.0\%$ within
the satellite height at geostationary Earth orbits. Our work could provide some
interesting possibilities to reduce the loss key rate through the control of
the orbital height of satellites.


  Compute-and-forward (CPF) strategy is one category of network coding in which
a relay will compute and forward a linear combination of source messages
according to the observed channel coefficients, based on the algebraic
structure of lattice codes. Recently, based on the idea of CPF, integer forcing
(IF) linear receiver architecture for MIMO system has been proposed to recover
different integer combinations of lattice codewords for further original
message detection. In this paper, we consider the problem of IF linear receiver
design with respect to the channel conditions. Instead of exhaustive search, we
present practical and efficient suboptimal algorithms to design the IF
coefficient matrix with full rank such that the total achievable rate is
maximized, based on the slowest descent method. Numerical results demonstrate
the effectiveness of our proposed algorithms.


  Marine debris is a problem both for the health of marine environments and for
the human health since tiny pieces of plastic called "microplastics" resulting
from the debris decomposition over the time are entering the food chain at any
levels. For marine debris detection and removal, autonomous underwater vehicles
(AUVs) are a potential solution. In this letter, we focus on the efficiency of
AUV vision for real-time and low-light object detection. First, we improved the
efficiency of a class of state-of-the-art object detectors, namely
EfficientDets, by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3
without increasing the GPU latency. Subsequently, we created and made publicly
available a dataset for the detection of in-water plastic bags and bottles and
trained our improved EfficientDets on this and another dataset for marine
debris detection. Finally, we investigated how the detector performance is
affected by low-light conditions and compared two low-light underwater image
enhancement strategies both in terms of accuracy and latency. Source code and
dataset are publicly available.


  Protein allostery requires dynamical structural correlations. Physical origin
of which, however, remain elusive despite intensive studies during last two
decades. Based on analysis of molecular dynamics (MD) simulation trajectories
for ten proteins with different sizes and folds, we found that nonlinear
backbone torsional pair (BTP) correlations, which are spatially more
long-ranged and are mainly executed by loop residues, exist extensively in most
analyzed proteins. Examination of torsional motion for correlated BTPs
suggested that aharmonic torsional state transitions are essential for such
non-linear correlations, which correspondingly occur on widely different and
relatively longer time scales. In contrast, BTP correlations between backbone
torsions in stable $\alpha$ helices and $\beta$ strands are mainly linear and
spatially more short-ranged, and are more likely to associate with intra-well
torsional dynamics. Further analysis revealed that the direct cause of
non-linear contributions are heterogeneous, and in extreme cases canceling,
linear correlations associated with different torsional states of participating
torsions. Therefore, torsional state transitions of participating torsions for
a correlated BTP are only necessary but not sufficient condition for
significant non-linear contributions. These findings implicate a general search
strategy for novel allosteric modulation of protein activities. Meanwhile, it
was suggested that ensemble averaged correlation calculation and static contact
network analysis, while insightful, are not sufficient to elucidate mechanisms
underlying allosteric signal transmission in general, dynamical and time scale
resolved analysis are essential.


  A level-set method is developed for numerically capturing the equilibrium
solute-solvent interface that is defined by the recently proposed variational
implicit solvent model (Dzubiella, Swanson, and McCammon, Phys. Rev. Lett. {\bf
104}, 527 (2006) and J. Chem.\Phys. {\bf 124}, 084905 (2006)). In the level-set
method, a possible solute-solvent interface is represented by the zero
level-set (i.e., the zero level surface) of a level-set function and is
eventually evolved into the equilibrium solute-solvent interface. The evolution
law is determined by minimization of a solvation free energy {\it functional}
that couples both the interfacial energy and the van der Waals type
solute-solvent interaction energy. The surface evolution is thus an energy
minimizing process, and the equilibrium solute-solvent interface is an output
of this process. The method is implemented and applied to the solvation of
nonpolar molecules such as two xenon atoms, two parallel paraffin plates,
helical alkane chains, and a single fullerene $C_{60}$. The level-set solutions
show good agreement for the solvation energies when compared to available
molecular dynamics simulations. In particular, the method captures solvent
dewetting (nanobubble formation) and quantitatively describes the interaction
in the strongly hydrophobic plate system.


  Holographic Predictive Search (HPS) is a novel approach to search-based
hologram generation that uses a mathematical understanding of the optical
transforms to make informed optimisation decisions. Existing search techniques
such as Direct Search (DS) and Simulated Annealing (SA) rely on trialling
modifications to a test hologram and observing the results. A formula is used
to decide whether the change should be accepted. HPS operates presciently,
using knowledge of the underlying mathematical relationship to make exact
changes to the test hologram that guarantee the 'best' outcome for that change.
In this work, we extend the scope of the original research to cover both phase
and amplitude modulating Spatial Light Modulators (SLMs), both phase sensitive
and phase insensitive systems and both Fresnel and Fraunhofer diffraction. In
the cases discussed, improvements of up to 10x are observed in final error and
the approach also offers significant performance benefits in generation time.
This comes at the expense of increased complexity and loss of generality.


  Metasurfaces are artificial electromagnetic boundaries or interfaces usually
implemented as two-dimensional periodic structures with subwavelength
periodicity and engineered properties of constituent unit cells. The
electromagnetic bandgap (EBG) effect in metasurfaces prevents all surface modes
from propagating in a certain frequency band. While metasurfaces provide a
number of important applications in microwave antennas and antenna arrays,
their features are also highly suitable for MRI applications. In this work we
manufacture and experimentally study finite samples based on mushroom-type EBG
metasurfaces and employ them for suppression of inter-element coupling in
dipole transmit coil arrays for body imaging at 7T. We show experimentally that
employment of the samples EBG leads to reduction of coupling between adjacent
closely-spaced dipole antenna elements of a 7T transmit/receive body array,
which reduces scattering losses in neighboring channels and thereby improves
the B1+ efficiency. The setup consists of two fractionated dipole antennas
previously designed by the authors for body imaging at 7 Tesla. These are
placed on top of a body-mimicking phantom and equipped with the manufactured
finite-size sample of the metasurface tuned to have EBG properties at the
Larmor frequency of 298 MHz. To improve the detection range of the B1+ field
distribution of the top elements, four additional elements were positioned
along the bottom side of the phantom. Scattering matrix measurements show that
coupling between the two top elements is indeed reduced while the measurements
performed on a 7T MRI machine confirm the array's B1+ efficiency improvement
due to reduced scattering losses. This study provides a tool for the decoupling
of dipole antennas in ultrahigh field transmit arrays, possibly resulting in
denser element placement and/or larger subject-element spacing.


  For any group $G$ and any set $A$, consider the shift action of $G$ on the
full shift $A^G$. A configuration $x \in A^G$ has \emph{least period} $H \leq
G$ if the stabiliser of $x$ is precisely $H$. Among other things, the number of
such configurations is interesting as it provides an upper bound for the size
of the corresponding $\text{Aut}(A^G)$-orbit. In this paper we show that if $G$
is finitely generated and $H$ is of finite index, then the number of
configurations in $A^G$ with least period $H$ may be computed using the
M\"obius function of the lattice of subgroups of finite index in $G$. Moreover,
when $H$ is a normal subgroup, we classify all situations such that the number
of $G$-orbits with least period $H$ is at most $10$.


  Explainable artificial intelligence provides tools to better understand
predictive models and their decisions, but many such methods are limited to
producing insights with respect to a single class. When generating explanations
for several classes, reasoning over them to obtain a complete view may be
difficult since they can present competing or contradictory evidence. To
address this challenge we introduce the novel paradigm of multi-class
explanations. We outline the theory behind such techniques and propose a local
surrogate model based on multi-output regression trees -- called LIMEtree --
that offers faithful and consistent explanations of multiple classes for
individual predictions while being post-hoc, model-agnostic and data-universal.
On top of strong fidelity guarantees, our implementation delivers a range of
diverse explanation types, including counterfactual statements favoured in the
literature. We evaluate our algorithm with respect to explainability
desiderata, through quantitative experiments and via a pilot user study, on
image and tabular data classification tasks, comparing it to LIME, which is a
state-of-the-art surrogate explainer. Our contributions demonstrate the
benefits of multi-class explanations and wide-ranging advantages of our method
across a diverse set of scenarios.


  We reconsider the variational integration of optimal control problems for
mechanical systems based on a direct discretization of the Lagrange-d'Alembert
principle. This approach yields discrete dynamical constraints which by
construction preserve important structural properties of the system, like the
evolution of the momentum maps or the energy behavior. Here, we employ higher
order quadrature rules based on polynomial collocation. The resulting
variational time discretization decreases the overall computational effort.


  We study the prisoner's dilemma model with a noisy imitation evolutionary
dynamics on directed out-homogeneous and uncorrelated directed random networks.
An heterogeneous pair mean-field approximation is presented showing good
agreement with Monte Carlo simulations in the limit of weak selection (high
noise) where we obtain analytical predictions for the critical temptations. We
discuss the phase diagram as a function of temptation, intensity of noise and
coordination number of the networks and we consider both the model with and
without self-interaction. We compare our results with available results for
non-directed lattices and networks.


  We demonstrate high-efficiency superconducting nanowire single-photon
detectors (SNSPDs) fabricated from MoSi thin-films. We measure a maximum system
detection efficiency (SDE) of 87 +- 0.5 % at 1542 nm at a temperature of 0.7 K,
with a jitter of 76 ps, maximum count rate approaching 10 MHz, and polarization
dependence as low as 3.4 +- 0.7 % The SDE curves show saturation of the
internal efficiency similar to WSi-based SNSPDs at temperatures as high as 2.3
K. We show that at similar cryogenic temperatures, MoSi SNSPDs achieve
efficiencies comparable to WSi-based SNSPDs with nearly a factor of two
reduction in jitter.


  Fine resolution of the discrete eigenvalues at the spectral edge of an
$N\times N$ random matrix is required in many applications. Starting from a
finite-size scaling ansatz for the Stieltjes transform of the maximum
likelihood spectrum, we show that the scaling function satisfies a first-order
ODE of the Riccati type. Further transformation yields a linear second-order
ODE for the characteristic function, whose nodes yield the exact locations of
leading eigenvalues. With this new technique, we examine in detail the spectral
crossover of the annealed Sherrington-Kirkpatrick (SK) spin glass model where a
gap develops below a certain critical temperature. Our analysis yields analytic
predictions for the finite-size scaling of the spin condensation phenomenon in
both quenched and annealed SK models, which are then compared and validated in
Monte Carlo simulations of the annealed model. More generally, rescaling of the
spectral axis, adjusted to the distance of neighboring eigenvalues, offers a
powerful strategy to deal with singularities that arise in the infinite size
limit.


  We show that there exists an atomic representable polyadic equality algebra
of finite dimension n\geq 3, such that the cylindric reduct of its completion
is not in SNr_n\CA_{n+4}, hence the result in the title. This solves an open
problem in algebraic logic, though the values for k=n+1, n+2, n+3, is still, to
the best of our knowlege unknown.


  A method is proposed for constructing an exact ground-state wave function of
a two-dimensional model with spin 1/2. The basis of the method is to represent
the wave function by a product of fourth-rank spinors associated with the sites
of a lattice and the metric spinors corresponding to bonds between nearest
neighbor sites. The function so constructed is an exact wave function of a
14-parameter model. The special case of this model depending on one parameter
is analyzed in detail. The ground state is always a nondegenerate singlet, and
the spin correlation functions decay exponentially with distance. The method
can be generalized for models with spin 1/2 to other types of lattices.


  Specific heat and resistivity measurements were performed on polycrystalline
samples of the solid-solution Y_xLu_(1-x)Ni2B2C in order to determine
thermodynamic properties such as the specific-heat difference, the
thermodynamic critical field H_c(T), as well as the upper critical field
H_{c2}(T). These properties were analyzed within the Eliashberg theory
including anisotropy effects, yielding electron-phonon coupling anisotropy
parameters <a_k^2> ranging beween 0.02 and 0.03 for the whole series, and Fermi
velocity anisotropy parameters of <b_k^2> = 0.245-0.3. Excellent agreement
between theory and experiment was achieved for these parameters, the Sommerfeld
constant and model phonon spectra determined from specific heat measurements.
An analysis of the previously investigated boronitride La3Ni2B2N3 for
comparison revealed the electron-phonon anisotropy to be of great significance
in describing its thermodynamic properties and the calculations yielded <a_k^2>
\~ 0.08 and <b_k^2> ~ 0.245. The T_c-behavior within the series
Y_xLu_(1-x)Ni2B2C is discussed in terms of coupling and impurity effects, and
the density of states at the Fermi level N(0).


  This paper demonstrates how Automated Machine Learning (AutoML) methods can
be used as effective surrogate models in engineering design problems. To do so,
we consider the challenging problem of structurally-performant bicycle frame
design and demonstrate across-the-board dominance by AutoML in regression and
classification surrogate modeling tasks. We also introduce FRAMED -- a
parametric dataset of 4500 bicycle frames based on bicycles designed by
practitioners and enthusiasts worldwide. Accompanying these frame designs, we
provide ten structural performance values such as weight, displacements under
load, and safety factors computed using finite element simulations for all the
bicycle frame designs. We formulate two challenging test problems: a
performance-prediction regression problem and a feasibility-prediction
classification problem. We then systematically search for optimal surrogate
models using Bayesian hyperparameter tuning and neural architecture search.
Finally, we show how a state-of-the-art AutoML method can be effective for both
regression and classification problems. We demonstrate that the proposed AutoML
models outperform the strongest gradient boosting and neural network surrogates
identified through Bayesian optimization by an improved F1 score of 24\% for
classification and reduced mean absolute error by 12.5\% for regression. Our
work introduces a dataset for bicycle design practitioners, provides two
benchmark problems for surrogate modeling researchers, and demonstrates the
advantages of AutoML in machine learning tasks. The dataset and code are
provided at \url{http://decode.mit.edu/projects/framed/}.


  Diverse sociocultural influences in rapidly growing dense urban areas may
induce strain on civil services and reduce the resilience of those areas to
exogenous and endogenous shocks. We present a novel approach with foundations
in computer and social sciences, to estimate the resilience of dense urban
areas at finer spatiotemporal scales compared to the state-of-the-art. We fuse
multi-modal data sources to estimate resilience indicators from social science
theory and leverage a structured ontology for factor combinations to enhance
explainability. Estimates of destabilizing areas can improve the
decision-making capabilities of civil governments by identifying critical areas
needing increased social services.


  We present an accurate transfer matrix renormalization group calculation of
the thermodynamics in a quantum spin-1 planar ferromagnetic chain. We also
calculate the field dependence of the magnon gap and confirm the accuracy of
the magnon dispersion derived earlier through an 1/n expansion. We are thus
able to examine the validity of a number of previous calculations and further
analyze a wide range of experiments on CsNiF_3 concerning the magnon
dispersion, magnetization, susceptibility, and specific heat. Although it is
not possible to account for all data with a single set of parameters, the
overall qualitative agreement is good and the remaining discrepancies may
reflect departure from ideal quasi-one-dimensional model behavior. Finally, we
present some indirect evidence to the effect that the popular interpretation of
the excess specific heat in terms of sine-Gordon solitons may not be
appropriate.


  Unmanned aerial vehicle (UAV) as an aerial base station is a promising
technology to rapidly provide wireless connectivity to ground users. Given
UAV's agility and mobility, a key question is how to adapt UAV deployment to
best cater to the instantaneous wireless traffic in a territory. In this paper,
we propose an adaptive deployment scheme for a UAV-aided communication network,
where the UAV adapts its displacement direction and distance to serve randomly
moving users' instantaneous traffic in the target cell. In our adaptive scheme,
the UAV does not need to learn users' exact locations in real time, but chooses
its displacement direction based on a simple majority rule by flying to the
spatial sector with the greatest number of users in the cell. To balance the
service qualities of the users in different sectors, we further optimize the
UAV's displacement distance in the chosen sector to maximize the average
throughput and the successful transmission probability, respectively. We prove
that the optimal displacement distance for average throughput maximization
decreases with the user density: the UAV moves to the center of the chosen
sector when the user density is small and the UAV displacement becomes mild
when the user density is large. In contrast, the optimal displacement distance
for success probability maximization does not necessarily decrease with the
user density and further depends on the target signal-to-noise ratio (SNR)
threshold. Extensive simulations show that the proposed adaptive deployment
scheme outperforms the traditional non-adaptive scheme, especially when the
user density is not large.


  In 1980, V.I. Arnold studied the classification problem for convex lattice
polygons of given area. Since then this problem and its analogues have been
studied by B'ar'any, Pach, Vershik, Liu, Zong and others. Upper bounds for the
numbers of non-equivalent ddimensional convex lattice polytopes of given volume
or cardinality have been achieved. In this paper, by introducing and studying
the unimodular groups acting on convex lattice polytopes, we obtain lower
bounds for the number of non-equivalent d-dimensional convex lattice polytopes
of bounded volume or given cardinality, which are essentially tight.


  Recently, the syndrome loss has been proposed to achieve "unsupervised
learning" for neural network-based BCH/LDPC decoders. However, the design
approach cannot be applied to polar codes directly and has not been evaluated
under varying channels. In this work, we propose two modified syndrome losses
to facilitate unsupervised learning in the receiver. Then, we first apply it to
a neural network-based belief propagation (BP) polar decoder. With the aid of
CRC-enabled syndrome loss, the BP decoder can even outperform conventional
supervised learning methods in terms of block error rate. Secondly, we propose
a jointly optimized syndrome-enabled blind equalizer, which can avoid the
transmission of training sequences and achieve global optimum with 1.3 dB gain
over non-blind minimum mean square error (MMSE) equalizer.


  We consider the discrete Laplacian $\Delta$ on the cubic lattice $\mathbb
Z^d$, and deduce estimates on the group $e^{it\Delta}$ and the resolvent
$(\Delta-z)^{-1}$, weighted by $\ell^q(\mathbb Z^d)$-weights for suitable
$q\geq 2$. We apply the obtained results to discrete Schr\"odinger operators in
dimension $d\geq 3$ with potentials from $\ell^p(\mathbb Z^d)$ with suitable
$p\geq 1$.


  We explore extensions to $\operatorname{SL}(n,\mathbb{C})$-Chern-Simons
theory of some results obtained for $\operatorname{SU}(n)$-Chern-Simons theory
via the asymptotic properties of the Hitchin connection and its relation to
Toeplitz operators developed previously by the first named author. We define a
formal Hitchin-Witten connection for the imaginary part $s$ of the quantum
parameter $t = k+is$ and investigate the existence of a formal trivialisation.
After reducing the problem to a recursive system of differential equations, we
identify a cohomological obstruction to the existence of a solution. We
explicitly find one for the first step, in the specific case of an operator of
order $0$, and show in general the vanishing of a weakened version of the
obstruction. We also find a solution of the whole recursion in the case of a
surface of genus $1$.


  The renormalization group transformation for the hierarchical O(N) spin model
in four dimensions is studied by means of characteristic functions of
single-site measures, and convergence of the critical trajectory to the
Gaussian fixed point is shown for a sufficiently large N. In the strong
coupling regime, the trajectory is controlled by the help of the exactly solved
O(\infty) trajectory, while, in the weak coupling regime, convergence to the
Gaussian fixed point is shown by power decay of the effective coupling
constant.


  We investigate dark matter (DM)-electron scattering in a minimal $U(1)_X$
extension of the Standard Model (SM), where the DM can appear as a Majorana
fermion, a complex singlet scalar or a Dirac fermion. To study bounds on the
$U(1)_X$ gauge coupling $(g_X)$ and new gauge boson mass $(M_{Z^\prime})$, from
DM-electron scattering, we consider several direct search experiments like
CDMS, DAMIC, SENSEI, PandaX-II, DarkSide-50 and XENON1T-S2 for different
$U(1)_X$ charges. In this set-up we consider DM production via freeze-in both
in radiation dominated and modified cosmological background to project
sensitivities on $g_X-M_{Z^\prime}$ plane satisfying observed relic abundance.
DM-electron scattering could provide comparable, or even stronger bounds than
those obtained from the electron/ muon $(g-2)$, low energy scattering and
intensity frontier experiments within 0.01 GeV $\lesssim M_{Z^\prime} \lesssim$
0.1 GeV. Constrains from freeze-in could provide stronger sensitivities for
$M_{Z^\prime}\gtrsim \mathcal{O}(1)$ GeV, however, these limits are comparable
to those obtained from LHCb, LEP experiments for $\mathcal{O}(10)$ GeV
$\lesssim M_{Z^\prime} \lesssim 150$ GeV. In future, electron-muon scattering
(MUonE), proton (FASER, DUNE) and electron/positron (ILC) beam dump experiments
could probe these parameters.


  Dlab and Ringel showed that algebras being quasi-hereditary in all total
orders for indices of primitive idempotents becomes hereditary. So, we are
interested in for which orders a given quasi-hereditary algebra is again
quasi-hereditary. As a matter of fact, we consider permutations of indices
instead of total orders. If the standard modules defined by two permutations
coincide, we say that the permutations are equivalent. Moreover if the algebra
with permuted indices is quasi-hereditary, then this equivalence class of the
permutation is called a quasi-hereditary structure. In this article, we give a
method of counting the number of quasi-hereditary structures for certain
algebras.


  Using two volume-limited Main galaxy samples of the Sloan Digital Sky Survey
Data Release 7 (SDSS DR7), we explore influences of galaxy interactions on AGN
activity. It is found that in the faint volume-limited sample, paired galaxies
have a slightly higher AGN fraction than isolated galaxies, whereas in the
luminous volume-limited sample, an opposite trend can be observed. The
significance is <1 sigma. Thus, we do not observe strong evidence that
interactions or mergers likely trigger the AGN activity.


  In situ and remotely sensed observations have potential to facilitate
data-driven predictive models for oceanography. A suite of machine learning
models, including regression, decision tree and deep learning approaches were
developed to estimate sea surface temperatures (SST). Training data consisted
of satellite-derived SST and atmospheric data from The Weather Company. Models
were evaluated in terms of accuracy and computational complexity. Predictive
skill were assessed against observations and a state-of-the-art, physics-based
model from the European Centre for Medium Weather Forecasting. Results
demonstrated that by combining automated feature engineering with
machine-learning approaches, accuracy comparable to existing state-of-the-art
can be achieved. Models captured seasonal patterns in the data and
qualitatively reproduce short-term variations driven by atmospheric forcing.
Further, it demonstrated that machine-learning-based approaches can be used as
transportable prediction tools for ocean variables -- the data-driven nature of
the approach naturally integrates with automatic deployment frameworks, where
model deployments are guided by data rather than user-parametrisation and
expertise. The low computational cost of inference makes the approach
particularly attractive for edge-based computing where predictive models could
be deployed on low-power devices in the marine environment.


  Many predictions of Doppler cooling theory of two-level atoms have never been
verified in a three-dimensional geometry, including the celebrated minimum
achievable temperature $\hbar \Gamma/2 k_B$, where $\Gamma$ is the transition
linewidth. Here, we show that, despite their degenerate level structure, we can
use Helium-4 atoms to achieve a situation in which these predictions can be
verified. We make measurements of atomic temperatures, magneto-optical trap
sizes, and the sensitivity of optical molasses to a power imbalance in the
laser beams, finding excellent agreement with the Doppler theory. We show that
the special properties of Helium, particularly its small mass and narrow
transition linewidth, prevent effective sub-Doppler cooling with red-detuned
optical molasses.


  Decomposing a network flow into weighted paths has numerous applications.
Some applications require any decomposition that is optimal w.r.t. some
property such as number of paths, robustness, or length. Many bioinformatic
applications require a specific decomposition where the paths correspond to
some underlying data that generated the flow. For real inputs, no optimization
criteria guarantees to uniquely identify the correct decomposition. Therefore,
we propose to report safe paths, i.e., subpaths of at least one path in every
flow decomposition.
  Ma, Zheng, and Kingsford [WABI 2020] addressed the existence of multiple
optimal solutions in a probabilistic framework, i.e., non-identifiability.
Later [RECOMB 2021], they gave a quadratic-time algorithm based on a global
criterion for solving a problem called AND-Quant, which generalizes the problem
of reporting whether a given path is safe.
  We give the first local characterization of safe paths for flow
decompositions in directed acyclic graphs (DAGs), leading to a practical
algorithm for finding the complete set of safe paths. We evaluated our
algorithms against the trivial safe algorithms (unitigs, extended unitigs) and
the popularly used heuristic (greedy-width) for flow decomposition on RNA
transcripts datasets. Despite maintaining perfect precision our algorithm
reports significantly higher coverage ($\approx 50\%$ more) than trivial safe
algorithms. The greedy-width algorithm though reporting a better coverage, has
significantly lower precision on complex graphs. Overall, our algorithm
outperforms (by $\approx 20\%$) greedy-width on a unified metric (F-Score) when
the dataset has significant number of complex graphs. Moreover, it has superior
time ($3-5\times$) and space efficiency ($1.2-2.2\times$), resulting in a
better and more practical approach for bioinformatics applications of flow
decomposition.


  This note is purely expository. The statement of the Gauss theorem on the
constructibility of regular polygons by means of compass and ruler is simple
and well-known. However, its proofs given in most textbooks rely upon much
unmotivated material and are far from being economic. In this note a short
elementary proof of the Gauss theorem is presented. The note is accessible for
students familiar with polynomials and complex numbers, and could be an
interesting easy reading for professional mathematicians.


  Stock market comovements are examined using cointegration, Granger causality
tests and nonlinear approaches in context of mutual information and
correlations. Underlying data sets are affected by non-stationarities and
trends, we also apply AMF-DFA and AMF-DXA. We find only 170 pair of Stock
markets cointegrated, and according to the Granger causality and mutual
information, we realize that the strongest relations lies between emerging
markets, and between emerging and frontier markets. According to scaling
exponent given by AMF-DFA, $h(q=2)>1$, we find that all underlying data sets
belong to non-stationary process. According to EMH, only 8 markets are
classified in uncorrelated processes at $2\sigma$ confidence interval. 6 Stock
markets belong to anti-correlated class and dominant part of markets has memory
in corresponding daily index prices during January 1995 to February 2014.
New-Zealand with $H=0.457\pm0.004$ and Jordan with $H=0.602\pm 0.006$ are far
from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost
multifractal for all pair of Stock markets. The empirical relation, $H_{xy}\le
[H_{xx}+H_{yy}]/2$, is confirmed. Mentioned relation for $q>0$ is also
satisfied while for $q<0$ there is a deviation from this relation confirming
behavior of markets for small fluctuations is affected by contribution of major
pair. For larger fluctuations, the cross-correlation contains information from
both local and global conditions. Width of singularity spectrum for
auto-correlation and cross-correlation are $\Delta \alpha_{xx}\in
[0.304,0.905]$ and $\Delta \alpha_{xy}\in [0.246,1.178]$, respectively. The
wide range of singularity spectrum for cross-correlation confirms that the
bilateral relation between Stock markets is more complex. The value of
$\sigma_{DCCA}$ indicates that all pairs of stock market studied in this time
interval belong to cross-correlated processes.


  We present here the Temporal Clustering Algorithm (TCA), an incremental
learning algorithm applicable to problems of anticipatory computing in the
context of the Internet of Things. This algorithm was tested in a specific
prediction scenario of consumption of an electric water dispenser typically
used in tropical countries, in which the ambient temperature is around
30-degree Celsius. In this context, the user typically wants to drinking iced
water therefore uses the cooler function of the dispenser. Real and synthetic
water consumption data was used to test a forecasting capacity on how much
energy can be saved by predicting the pattern of use of the equipment. In
addition to using a small constant amount of memory, which allows the algorithm
to be implemented at the lowest cost, while using microcontrollers with a small
amount of memory (less than 1Kbyte) available on the market. The algorithm can
also be configured according to user preference, prioritizing comfort, keeping
the water at the desired temperature longer, or prioritizing energy savings.
The main result is that the TCA achieved energy savings of up to 40% compared
to the conventional mode of operation of the dispenser with an average success
rate higher than 90% in its times of use.


  Cognitive control, the ability of a system to adapt to the demands of a task,
is an integral part of cognition. A widely accepted fact about cognitive
control is that it is context-sensitive: Adults and children alike infer
information about a task's demands from contextual cues and use these
inferences to learn from ambiguous cues. However, the precise way in which
people use contextual cues to guide adaptation to a new task remains poorly
understood. This work connects the context-sensitive nature of cognitive
control to a method for meta-learning with context-conditioned adaptation. We
begin by identifying an essential difference between human learning and current
approaches to meta-learning: In contrast to humans, existing meta-learning
algorithms do not make use of task-specific contextual cues but instead rely
exclusively on online feedback in the form of task-specific labels or rewards.
To remedy this, we introduce a framework for using contextual information about
a task to guide the initialization of task-specific models before adaptation to
online feedback. We show how context-conditioned meta-learning can capture
human behavior in a cognitive task and how it can be scaled to improve the
speed of learning in various settings, including few-shot classification and
low-sample reinforcement learning. Our work demonstrates that guiding
meta-learning with task information can capture complex, human-like behavior,
thereby deepening our understanding of cognitive control.


  We study the distance dependence of the rate of electronic excitation energy
transfer from a dye molecule to a metal nanoparticle. Using the spherical
jellium model, we evaluate the rates corresponding to the excitation of l = 1,
2, and 3 modes of the nanoparticle. Our calculation takes into account both the
electron-hole pair and the plasmon excitations of the nanoparticle. The rate
follows conventional R^-6 dependence at large distances while small deviations
from this behavior are observed at shorter distances. Within the framework of
the jellium model, it is not possible to attribute the experimentally observed
d^-4 dependence of the rate to energy transfer to plasmons or e-h pair
excitations.


  We introduce our novel Bayesian parton density determination code,
PartonDensity.jl. The motivation for this new code, the framework and its
validation are described. As we show, PartonDensity.jl provides both a flexible
environment for the determination of parton densities and a wealth of
information concerning the knowledge update provided by the analyzed data set.


  We theoretically study the low-energy scattering of ultracold atoms by a
dielectric nanosphere of silica glass levitated in a vacuum. The atom and
dielectric surface interact via dispersion force of which strength sensitively
depends on the polarizability, dielectric function, and geometry. For cesium
and rubidium atoms, respectively, we compute the atom-surface interaction
strength, and characterize the stationary scattering states by taking
adsorption of the atoms onto the surface into account. As the energy of the
incoming atoms is lowered, we find that differences between quantum and
classical scatterings emerge in two steps. First, the quantum-mechanical
differential cross section of the elastic scattering starts to deviate from the
classical one at an energy scale comparable to a few microkelvin in units of
temperature due to the de Broglie matter-wave diffraction. Second, the
differences are found in the cross sections in a regime lower than a few
nanokelvin, where the classically forbidden reflection occurs associated with
the $s$-wave scattering, and the discrete nature of angular momentum. We also
study the dependencies of quantum and classical scattering properties on the
radius of the nanosphere. This paper paves the way to identify the quantum
regime and to understand the physical origin of quantum effects in the
collisions between a nanoparticle and environmental gas over various
temperatures.


  We report on the observation of the eta_{c}^{prime}(2^{1}S_{0}), the radial
excitation of eta_{c}(1^{1}S_{0}) ground state of charmonium, in the two-photon
fusion reaction gamma gamma rightarrow eta_{c}^{prime} rightarrow
K_{S}^{0}K^{pm}pi^{mp} in 13.6 fb^{-1} of CLEO II/II.V data and 13.1 fb^{-1} of
CLEO III data. We obtain M(eta_{c}^{prime})=3642.9 pm 3.1(stat) pm1.5(syst)
MeV, and M(eta_{c})=2981.8 pm1.3(stat) pm 1.5(syst) MeV. The corresponding
values of hyperfine splittings between ^{1}S_{0} and ^{3}S_{1} states are
\Delta M_{hf}(1S)=115.1 pm 2.0 MeV, Delta M_{hf}(2S)=43.1 pm3.4 MeV. Assuming
that the eta_{c} and eta_{c}^{prime} have equal branching fractions to K_{S}K
pi, we obtain Gamma_{gamma gamma}(eta_{c}^{prime})=1.3 pm 0.6 keV.


  We derive a closed form for the generalized Bernoulli polynomial of order $n$
in terms of Bell polynomials and Stirling numbers of the second kind using the
Fa\`a di Bruno's formula.


  We provide an extension to lattice systems of the reptation quantum Monte
Carlo algorithm, originally devised for continuous Hamiltonians. For systems
affected by the sign problem, a method to systematically improve upon the
so-called fixed-node approximation is also proposed. The generality of the
method, which also takes advantage of a canonical worm algorithm scheme to
measure off-diagonal observables, makes it applicable to a vast variety of
quantum systems and eases the study of their ground-state and excited-states
properties. As a case study, we investigate the quantum dynamics of the
one-dimensional Heisenberg model and we provide accurate estimates of the
ground-state energy of the two-dimensional fermionic Hubbard model.


  Detecting 3D objects keypoints is of great interest to the areas of both
graphics and computer vision. There have been several 2D and 3D keypoint
datasets aiming to address this problem in a data-driven way. These datasets,
however, either lack scalability or bring ambiguity to the definition of
keypoints. Therefore, we present KeypointNet: the first large-scale and diverse
3D keypoint dataset that contains 103,450 keypoints and 8,234 3D models from 16
object categories, by leveraging numerous human annotations. To handle the
inconsistency between annotations from different people, we propose a novel
method to aggregate these keypoints automatically, through minimization of a
fidelity loss. Finally, ten state-of-the-art methods are benchmarked on our
proposed dataset. Our code and data are available on
https://github.com/qq456cvb/KeypointNet.


  Supersolids are characterized by the counter-intuitive coexistence of
superfluid and crystalline order. Here we study a supersolid phase emerging in
the steady state of a driven-dissipative system. We consider a transversely
pumped Bose-Einstein condensate trapped along the axis of a ring cavity and
coherently coupled to a pair of degenerate counter-propagating cavity modes.
Above a threshold pump strength the interference of photons scattered into the
two cavity modes results in an emergent superradiant lattice, which
spontaneously breaks the continuous translational symmetry towards a periodic
atomic pattern. The crystalline steady state inherits the superfluidity of the
Bose-Einstein condensate, thus exhibiting genuine properties of a supersolid. A
gapless collective Goldstone mode correspondingly appears in the superradiant
phase, which can be non-destructively monitored via the relative phase of the
two cavity modes on the cavity output. Despite cavity-photon losses the
Goldstone mode remains undamped, indicating the robustness of the supersolid
phase.


  This paper investigates the mean stability of a class of discrete-time
stochastic switched linear systems using the $L^p$-norm joint spectral radius
of the probability distributions governing the switched systems. First we prove
a converse Lyapunov theorem that shows the equivalence between the mean
stability and the existence of a homogeneous Lyapunov function. Then we show
that, when $p$ goes to $\infty$, the stability of the $p$th mean becomes
equivalent to the absolute asymptotic stability of an associated deterministic
switched system. Finally we study the mean stability of Markovian switched
systems. Numerical examples are presented to illustrate the results.