Abstract:
  We consider two variants of private stochastic online learning. The first
variant is differentially private stochastic bandits. Previously, Sajed and
Sheffet (2019) devised the DP Successive Elimination (DP-SE) algorithm that
achieves the optimal $ O \biggl(\sum\limits_{1\le j \le K: \Delta_j >0} \frac{
\log T}{ \Delta_j} + \frac{ K\log T}{\epsilon} \biggr)$ problem-dependent
regret bound, where $K$ is the number of arms, $\Delta_j$ is the mean reward
gap of arm $j$, $T$ is the time horizon, and $\epsilon$ is the required privacy
parameter. However, like other elimination style algorithms, it is not an
anytime algorithm. Until now, it was not known whether UCB-based algorithms
could achieve this optimal regret bound. We present an anytime, UCB-based
algorithm that achieves optimality. Our experiments show that the UCB-based
algorithm is competitive with DP-SE. The second variant is the full information
version of private stochastic online learning. Specifically, for the problems
of decision-theoretic online learning with stochastic rewards, we present the
first algorithm that achieves an $ O \left( \frac{ \log K}{ \Delta_{\min}} +
\frac{ \log K}{\epsilon} \right)$ regret bound, where $\Delta_{\min}$ is the
minimum mean reward gap. The key idea behind our good theoretical guarantees in
both settings is the forgetfulness, i.e., decisions are made based on a certain
amount of newly obtained observations instead of all the observations obtained
from the very beginning.


Abstract:
  We consider moderately interacting particle systems with singular interaction
kernel and environmental noise. It is shown that the mollified empirical
measures converge in strong norms to the unique (local) solutions of nonlinear
Fokker-Planck equations. The approach works for the Biot-Savart and repulsive
Poisson kernels.


Abstract:
  The variational method is a powerful approach to solve many-body quantum
problems non perturbatively. However, in the context of relativistic quantum
field theory (QFT), it needs to meet 3 seemingly incompatible requirements
outlined by Feynman: extensivity, computability, and lack of UV sensitivity. In
practice, variational methods break one of the 3, which translates into the
need to have an IR or UV cutoff. In this letter, I introduce a relativistic
modification of continuous matrix product states that satisfies the 3
requirements jointly in 1+1 dimensions. I apply it to the self-interacting
scalar field, without UV cutoff and directly in the thermodynamic limit.
Numerical evidence suggests the error decreases faster than any power law in
the number of parameters, while the cost remains only polynomial.


Abstract:
  Results of Koebe (1936), Schramm (1992), and Springborn (2005) yield
realizations of $3$-polytopes with edges tangent to the unit sphere. Here we
study the algebraic degrees of such realizations. This initiates the research
on constrained realization spaces of polytopes.


Abstract:
  The superconducting gap structure of a topological crystalline insulator
(TCI) candidate ZrRuAs ($T^{\rm on}_{\rm c}$ = 7.9(1) K) with a
noncentrosymmetric crystal structure has been investigated using muon spin
rotation/relaxation ($\mu$SR) measurements in transverse-field (TF) and
zero-field (ZF) geometries. We also present the results of magnetization,
electrical resistivity and heat capacity measurements on ZrRuAs, which reveal
bulk superconductivity below 7.9~K. The temperature dependence of the effective
penetration depth obtained from the analysis of the TF-$\mu$SR spectra below
$T_{\rm c}$ is well described by an isotropic $s$-wave gap model as also
inferred from an analysis of the heat capacity in the superconducting state. ZF
$\mu$SR data do not show any significant change in the muon spin relaxation
rate above and below the superconducting transition temperature indicating that
time-reversal symmetry is preserved in the superconducting state of this
material.


Abstract:
  Despite their success in massive engineering applications, deep neural
networks are vulnerable to various perturbations due to their black-box nature.
Recent study has shown that a deep neural network can misclassify the data even
if the input data is perturbed by an imperceptible amount. In this paper, we
address the robustness issue of neural networks by a novel close-loop control
method from the perspective of dynamic systems. Instead of modifying the
parameters in a fixed neural network architecture, a close-loop control process
is added to generate control signals adaptively for the perturbed or corrupted
data. We connect the robustness of neural networks with optimal control using
the geometrical information of underlying data to design the control objective.
The detailed analysis shows how the embedding manifolds of state trajectory
affect error estimation of the proposed method. Our approach can simultaneously
maintain the performance on clean data and improve the robustness against many
types of data perturbations. It can also further improve the performance of
robustly trained neural networks against different perturbations. To the best
of our knowledge, this is the first work that improves the robustness of neural
networks with close-loop control.


Abstract:
  Despite commercial airlines mandating masks, there have been multiple
documented events of COVID-19 superspreading on flights. We used available data
from three flights, including cabin layout and seat locations of infected and
uninfected passengers, to suggest interventions to mitigate COVID-19
superspreading events during air travel. Specifically, we studied: 1) London to
Hanoi with 201 passengers, including 13 secondary infections among passengers;
2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary
infections; 3) a non-superspreading event on a private jet in Japan with 9
passengers and no secondary infections. We show that inclusion of passenger
movement better explains the infection spread patterns than conventional models
do. We also found that FFP2/N95 mask usage would have reduced infection by
95-100%, while cloth masks would have reduced it by only 40-80%. This suggests
the need for more stringent guidelines to reduce aviation-related
superspreading events of COVID-19.


Abstract:
  We study the structure of Gromov-Hausdorff limits of sequences of Riemannian
manifolds $\{(M_\alpha^n,g_\alpha)\}_{\alpha \in A}$ whose Ricci curvature
satisfies a uniform Kato bound. We first obtain Mosco convergence of the
Dirichlet energies to the Cheeger energy and show that tangent cones of such
limits satisfy the $\mathrm{RCD}(0,n)$ condition. When assuming a
non-collapsing assumption, we introduce a new family of monotone quantities,
which allows us to prove that tangent cones are also metric cones. We then show
the existence of a well-defined stratification in terms of splittings of
tangent cones. We finally prove volume convergence to the Hausdorff
$n$-measure.


Abstract:
  Many modern nanofabrication and imaging techniques require an ultra-quiet
environment to reach optimal resolution. Isolation from ambient vibrations is
often achieved by placing the sensitive instrument atop a massive block that
floats on air springs and is surrounded by acoustic barriers. Because typical
building noise drops off above 120 Hz, it is advantageous to raise the flexural
resonance frequencies of the inertia block and instrument far above 120 Hz.
However, it can be challenging to obtain a high fundamental frequency of the
floating block using a simple rectangular design. Here we design, construct,
and characterize a vibration isolation system with a cylindrical inertia block,
whose lowest resonance frequency of 249 Hz shows good agreement between finite
element analysis simulation and directly measured modes. Our simulations show
that a cylindrical design can achieve higher fundamental resonance frequency
than a rectangular design of the same mass.


Abstract:
  We consider the problem of predicting the covariance of a zero mean Gaussian
vector, based on another feature vector. We describe a covariance predictor
that has the form of a generalized linear model, i.e., an affine function of
the features followed by an inverse link function that maps vectors to
symmetric positive definite matrices. The log-likelihood is a concave function
of the predictor parameters, so fitting the predictor involves convex
optimization. Such predictors can be combined with others, or recursively
applied to improve performance.


Abstract:
  In this paper, we propose a Riemannian smoothing steepest descent method to
minimize a nonconvex and non-Lipschitz function on submanifolds. The
generalized subdifferentials on Riemannian manifold and the Riemannian gradient
sub-consistency are defined and discussed. We prove that any accumulation point
of the sequence generated by the Riemannian smoothing steepest descent method
is a stationary point associated with the smoothing function employed in the
method, which is necessary for the local optimality of the original
non-Lipschitz problem. Under the Riemannian gradient sub-consistency condition,
we also prove that any accumulation point is a Riemannian limiting stationary
point of the original non-Lipschitz problem. Numerical experiments are
conducted to demonstrate the efficiency of the proposed method.


Abstract:
  The transport layer is ossified. With most of the research and deployment
efforts in the past decade focussing on the Transmission Control Protocol (TCP)
and its extensions, the QUIC standardization by the Internet Engineering Task
Force (IETF) is to be finalized in early 2021. In addition to addressing the
most urgent issues of TCP, QUIC ensures its future extendibility and is
destined to drastically change the transport protocol landscape. In this work,
we present a first look at emerging protocols and their IETF standardization
efforts beyond QUIC v1. While multiple proposed extensions improve on QUIC
itself, Multiplexed Application Substrate over QUIC Encryption (MASQUE) as well
as WebTransport present different approaches to address long-standing problems,
and their interplay extends on QUIC's take to address transport layer
ossification challenges.


Abstract:
  The formation and evolution of galaxies is known to be sensitive to tidal
processes leading to intrinsic correlations between their shapes and
orientations. Such correlations can be measured to high significance today,
suggesting that cosmological information can be extracted from them. Among the
most pressing questions in particle physics and cosmology is the nature of dark
matter. If dark matter is self-interacting, it can leave an imprint on galaxy
shapes. In this work, we investigate whether self-interactions can produce a
long-lasting imprint on intrinsic galaxy shape correlations. We investigate
this observable at low redshift ($z<0.4$) using a state-of-the-art suite of
cosmological hydro-dynamical simulations where the dark matter model is varied.
We find that dark matter self-interactions induce a mass dependent suppression
in the intrinsic alignment signal by up to 50\% out to ten's of mega-parsecs,
showing that self-interactions can impact structure outside the very core of
clusters. We find evidence that self-interactions have a scale-dependent impact
on the intrinsic alignment signal that is sufficiently different from
signatures introduced by differing baryonic physics prescriptions, suggesting
that it is detectable with up-coming all-sky surveys.


Abstract:
  The problem of answering questions using knowledge from pre-trained language
models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA
context (question and answer choice), methods need to (i) identify relevant
knowledge from large KGs, and (ii) perform joint reasoning over the QA context
and KG. In this work, we propose a new model, QA-GNN, which addresses the above
challenges through two key innovations: (i) relevance scoring, where we use LMs
to estimate the importance of KG nodes relative to the given QA context, and
(ii) joint reasoning, where we connect the QA context and KG to form a joint
graph, and mutually update their representations through graph neural networks.
We evaluate our model on QA benchmarks in the commonsense (CommonsenseQA,
OpenBookQA) and biomedical (MedQA-USMLE) domains. QA-GNN outperforms existing
LM and LM+KG models, and exhibits capabilities to perform interpretable and
structured reasoning, e.g., correctly handling negation in questions.


Abstract:
  Sleep disorders can be a negative factor both for learning as for the mental
and physical development of adolescents. It has been shown that, in many
populations, adolescents tend to have a poor sleep quality, and a very late
chronotype. Furthermore, these features peak at adolescence, in the sense that
adults tend to sleep better and have an earlier chronotype. But what happens
when we consider adolescents in a population where already adults have poor
sleep quality and a very late chronotype? We have conducted two non-clinical
studies in the city of Bariloche, Argentina aimed at measuring sleep quality,
chronotype, and social jet lag, using the Pittsburgh and Munich questionnaires.
These were administered individually to groups of high school students, as well
as to smaller samples of adults and preadolescents, in order to study
differences between adolescents and these groups. The results show that in this
population sleep quality is much poorer than in most other healthy populations
recorded elsewhere. Furthermore, sleep quality is consistently worse for
adolescents than for the other groups. The difference with adults seems to be
due mainly to increased daytime sleepiness and sleep latency, whereas the
difference with preadolescents seems to be due mainly to shorter sleep
duration. We also found that the chronotypes of all the groups are very late,
with a peak at an age between 18 and 24 ys. Social jet lag and sleep onset
latency are also large, and they peak at adolescence, which suggests that they
might be closely related to the large prevalence of poor sleep quality that we
find in adolescents.


Abstract:
  Accurate 7Li(d,n)24He thermonuclear reaction rates are crucial for precise
prediction of the primordial abundances of Lithium and Beryllium and to probe
the mysteries beyond fundamental physics and the standard cosmological model.
However, uncertainties still exist in current reaction rates of 7Li(d,n)24He
widely used in Big Bang Nucleosynthesis (BBN) simulations. In this work, we
reevaluate the 7Li(d,n)24He reaction rate using the latest data on the three
near-threshold 9Be excited states from experimental measurements. We present
for the first time uncertainties that are directly constrained by experiments.
Additionally, we take into account for the first time the contribution from the
subthreshold resonance at 16.671 MeV of 9Be. We obtain a 7Li(d,n)24He rate that
is overall smaller than the previous estimation by about a factor of 60 at the
typical temperature of the onset of primordial nucleosynthesis. We implemented
our new rate in BBN nucleosynthesis calculations, and we show that the new
rates have a very limited impact on the final light element abundances in
uniform density models. Typical abundance variations are in the order of
0.002%. For nonuniform density BBN models, the predicted 7Li production can be
increased by 10% and the primordial production of light nuclides with mass
number A>7 can be increased by about 40%. Our results confirm that the
cosmological lithium problem remains a long-standing unresolved puzzle from the
standpoint of nuclear physics.


Abstract:
  We investigate the performance of wall-modeled LES for external aerodynamics
in the NASA Juncture Flow. We characterize the errors in the prediction of mean
velocity profiles and pressure coefficient for three different locations over
the aircraft: the upstream region of the fuselage, the wing-body juncture, and
the wing-body juncture close to the trailing-edge. The last two locations are
characterized by strong mean-flow three-dimensionality and separation. The
message conveyed by our error analysis is that WMLES performs as expected in
regions where the flow resembles a zero-pressure-gradient flat plate boundary
layer. However, there is a clear decline of the current models in the presence
of wing-body junctions and, more acutely, in separated zones. The slow
convergence to the solution in these regions renders the brute-force
grid-refinement approach to improve the accuracy of the solution unfeasible.
The results reported above pertain to the mean velocity profile predicted using
the typical grid resolution for external aerodynamics applications, i.e., 5--20
points per boundary-layer thickness. The prediction of the pressure coefficient
is below 5\% error for all grid sizes considered, even when boundary layers
were marginally resolved. The latter accuracy can be attributed to the
outer-layer nature of the mean pressure, which becomes less sensitive to flow
details within the turbulent boundary layer. Finally, we show that
boundary-layer-conforming grids (i.e., grids maintaining a constant number of
points per boundary-layer thickness) allow for a more efficient distribution of
grid points and smaller errors. Our results suggest that novel modeling venues
encompassing physical insights, together with numerical and gridding
advancements, must be exploited to attain predictions within the tolerance
required for Certification by Analysis.


Abstract:
  We show that there is an operator space notion of Lipschitz embeddability
between operator spaces which is strictly weaker than its linear counterpart
but which is still strong enough to impose linear restrictions on operator
space structures. This shows that there is a nontrivial theory of nonlinear
geometry for operator spaces and it answers a question in [BCD21]. For that, we
introduce the operator space version of Lipschitz-free Banach spaces and prove
several properties of it. In particular, we show that separable operator spaces
satisfy a sort of isometric Lipschitz-lifting property in the sense of G.
Godefroy and N. Kalton. Gateaux differentiability of Lipschitz maps in the
operator space category is also studied.


Abstract:
  The intracluster medium (ICM) is expected to experience on average about
three passages of weak shocks with low sonic Mach numbers, $M\lesssim 3$,
during the formation of galaxy clusters. Both protons and electrons could be
accelerated to become high energy cosmic rays (CRs) at such ICM shocks via
diffusive shock acceleration (DSA). We examine the effects of DSA by multiple
shocks on the spectrum of accelerated CRs by including {\it in situ}
injection/acceleration at each shock, followed by repeated re-acceleration at
successive shocks in the test-particle regime. For simplicity, the accelerated
particles are assumed to undergo adiabatic decompression without energy loss
and escape from the system, before they encounter subsequent shocks. We show
that in general the CR spectrum is flattened by multiple shock passages,
compared to a single episode of DSA, and that the acceleration efficiency
increases with successive shock passages. However, the decompression due to the
expansion of shocks into the cluster outskirts may reduce the amplification and
flattening of the CR spectrum by multiple shock passages. The final CR spectrum
behind the last shock is determined by the accumulated effects of repeated
re-acceleration by all previous shocks, but it is relatively insensitive to the
ordering of the shock Mach numbers. Thus multiple passages of shocks may cause
the slope of the CR spectrum to deviate from the canonical DSA power-law slope
of the current shock.


Abstract:
  The thermal properties of materials are critically important to various
technologies and are increasingly the target of materials design efforts.
However, it is only relatively recent advances in first-principles
computational techniques that have enabled researchers to explore the
microscopic mechanisms of thermal properties, such as thermal expansion. We use
the Gr\"{u}neisen theory of thermal expansion in combination with density
functional calculations and the quasiharmonic approximation to uncover
mechanisms of thermal expansion in PbTiO$_3$ thin-films in terms of elastic and
vibrational contributions to the free energy. Surprisingly, we find that
although the structural parameters of PbTiO$_3$ thin-films evolve with
temperature as if they are dominated by linear elasticity, PbTiO$_3$ thin-films
are strongly anharmonic, with large changes in the elastic constants and
Gr\"{u}neisen parameters with both misfit strain and temperature. We show that
a fortuitous near-cancellation between different types of anharmonicity gives
rise to this behavior. Our results illustrate the importance of high-order
phonon-strain anharmonicity in determining the temperature-dependent structural
parameters of PbTiO$_3$ thin-films, and highlight the complex manner in which
thermal expansion, misfit strain and elastic and vibrational properties are
intertwined.


Abstract:
  Deep learning has become the dominant approach in coping with various tasks
in Natural LanguageProcessing (NLP). Although text inputs are typically
represented as a sequence of tokens, there isa rich variety of NLP problems
that can be best expressed with a graph structure. As a result, thereis a surge
of interests in developing new deep learning techniques on graphs for a large
numberof NLP tasks. In this survey, we present a comprehensive overview onGraph
Neural Networks(GNNs) for Natural Language Processing. We propose a new
taxonomy of GNNs for NLP, whichsystematically organizes existing research of
GNNs for NLP along three axes: graph construction,graph representation
learning, and graph based encoder-decoder models. We further introducea large
number of NLP applications that are exploiting the power of GNNs and summarize
thecorresponding benchmark datasets, evaluation metrics, and open-source codes.
Finally, we discussvarious outstanding challenges for making the full use of
GNNs for NLP as well as future researchdirections. To the best of our
knowledge, this is the first comprehensive overview of Graph NeuralNetworks for
Natural Language Processing.


Abstract:
  Two-dimensional angular correlation of annihilation radiation (2D-ACAR) and
Compton scattering are both powerful techniques to investigate the bulk
electronic structure of crystalline solids through the momentum density of the
electrons. Here we apply both methods to a single crystal of Pd to study the
electron momentum density and the occupancy in the first Brillouin zone, and to
point out the complementary nature of the two techniques. To retrieve the 2D
spectra from 1D Compton profiles, a new direct inversion method (DIM) is
implemented and benchmarked against the well-established Cormack's method. The
comparison of experimental spectra with first principles density functional
theory calculations of the electron momentum density and the two photon
momentum density clearly reveals the importance of positron probing effects on
the determination of the electronic structure. While the calculations are in
good agreement with the experimental data, our results highlight some
significant discrepancies.


Abstract:
  The prospects of using a reconfigurable intelligent surface (RIS) to aid
wireless communication systems have recently received much attention. Among the
different use cases, the most popular one is where each element of the RIS
scatters the incoming signal with a controllable phase-shift, without
increasing its power. In prior literature, this setup has been analyzed by
neglecting the electromagnetic interference, consisting of the inevitable
incoming waves from external sources. In this letter, we provide a physically
meaningful model for the electromagnetic interference that can be used as a
baseline when evaluating RIS-aided communications. The model is used to show
that electromagnetic interference has a non-negligible impact on communication
performance, especially when the size of the RIS grows large. When the direct
link is present (though with a relatively weak gain), the RIS can even reduce
the communication performance. Importantly, it turns out that the SNR grows
quadratically with the number of RIS elements only when the spatial correlation
matrix of the electromagnetic interference is asymptotically orthogonal to that
of the effective channel (including RIS phase-shifts) towards the intended
receiver. Otherwise, the SNR only increases linearly.


Abstract:
  A dynamical symmetry is employed to determine the structure of the quantum
non-Markovian time-local master equation. Such a structure is composed from two
components: scalar kinetic coefficients and the standard quantum Markovian
operator form. The kinetic coefficients are generally time-dependent and
incorporate information on the kinematics and memory effects, while the
operators manifest the dynamical symmetry. Specifically, we focus on
time-translation symmetric dynamics, where the Lindblad jump operators
constitute the eigenoperators of the free dynamics. This symmetry is motivated
by thermodynamic microscopic considerations, where strict energy conservation
between system and environment imposes the time-translation symmetry. The
construction is generalized to other symmetries, and to driven quantum systems.
The formalism is illustrated by three exactly solvable non-Markovian models,
where the exact reduced description exhibits a dynamical symmetric structure.
The formal structure of the master equation leads to a first principle
calculation of the exact kinetic coefficients. This opens the possibility to
simulate in a modular fashion non-Markovian dynamics.


Abstract:
  We prove three theorems about the exact solutions of a generalized or
interacting Black-Scholes equation that explicitly includes arbitrage bubbles.
These arbitrage bubbles can be characterized by an arbitrage number $A_N(T)$.
The first theorem states that if $A_N(T) = 0$, then the solution at the
maturity of the interacting equation is identical to the solution of the free
Black-Scholes equation with the same initial interest rate $r$. The second
theorem states that if $A_N(T) \ne 0$, the solution can be expressed in terms
of all higher derivatives of solutions to the free Black-Scholes equation with
the initial interest rate $r$. The third theorem states that whatever the
arbitrage number is, the solution is a solution to the free Black-Scholes
equation with a variable interest rate $r(\tau) = r + (1/\tau) A_N(\tau)$.
Also, we show, by using the Feynman-Kac theorem, that for the special case of a
Call contract, the exact solution for a Call with strike price $K$ is
equivalent to the usual Call solution to the Black-Scholes equation with strike
price $\tilde{K} = K e^{-A_N(T)}$.


Abstract:
  In this paper, we study the bandits with knapsacks (BwK) problem and develop
a primal-dual based algorithm that achieves a problem-dependent logarithmic
regret bound. The BwK problem extends the multi-arm bandit (MAB) problem to
model the resource consumption associated with playing each arm, and the
existing BwK literature has been mainly focused on deriving asymptotically
optimal distribution-free regret bounds. We first study the primal and dual
linear programs underlying the BwK problem. From this primal-dual perspective,
we discover symmetry between arms and knapsacks, and then propose a new notion
of sub-optimality measure for the BwK problem. The sub-optimality measure
highlights the important role of knapsacks in determining algorithm regret and
inspires the design of our two-phase algorithm. In the first phase, the
algorithm identifies the optimal arms and the binding knapsacks, and in the
second phase, it exhausts the binding knapsacks via playing the optimal arms
through an adaptive procedure. Our regret upper bound involves the proposed
sub-optimality measure and it has a logarithmic dependence on length of horizon
$T$ and a polynomial dependence on $m$ (the numbers of arms) and $d$ (the
number of knapsacks). To the best of our knowledge, this is the first
problem-dependent logarithmic regret bound for solving the general BwK problem.


Abstract:
  Transferability estimation has been an essential tool in selecting a
pre-trained model and the layers of it to transfer, so as to maximize the
performance on a target task and prevent negative transfer. Existing estimation
algorithms either require intensive training on target tasks or have
difficulties in evaluating the transferability between layers. We propose a
simple, efficient, and effective transferability measure named TransRate. With
single pass through the target data, TransRate measures the transferability as
the mutual information between the features of target examples extracted by a
pre-trained model and labels of them. We overcome the challenge of efficient
mutual information estimation by resorting to coding rate that serves as an
effective alternative to entropy. TransRate is theoretically analyzed to be
closely related to the performance after transfer learning. Despite its
extraordinary simplicity in 10 lines of codes, TransRate performs remarkably
well in extensive evaluations on 22 pre-trained models and 16 downstream tasks.


Abstract:
  Leveraging the advances of natural language processing, most recent scene
text recognizers adopt an encoder-decoder architecture where text images are
first converted to representative features and then a sequence of characters
via `direct decoding'. However, scene text images suffer from rich noises of
different sources such as complex background and geometric distortions which
often confuse the decoder and lead to incorrect alignment of visual features at
noisy decoding time steps. This paper presents I2C2W, a novel scene text
recognizer that is accurate and tolerant to various noises in scenes. I2C2W
consists of an image-to-character module (I2C) and a character-to-word module
(C2W) which are complementary and can be trained end-to-end. I2C detects
characters and predicts their relative positions in a word. It strives to
detect all possible characters including incorrect and redundant ones based on
different alignments of visual features without the restriction of time steps.
Taking the detected characters as input, C2W learns from character semantics
and their positions to filter out incorrect and redundant detection and produce
the final word recognition. Extensive experiments over seven public datasets
show that I2C2W achieves superior recognition performances and outperforms the
state-of-the-art by large margins on challenging irregular scene text datasets.


Abstract:
  Even though superconductivity has been studied intensively for more than a
century, the vast majority of superconductivity research today is carried out
in nearly the same manner as decades ago. That is, each study tends to focus on
only a single material or small subset of materials, and discoveries are made
more or less serendipitously. Recent increases in computing power, novel
machine learning algorithms, and improved experimental capabilities offer new
opportunities to revolutionize superconductor discovery. These will enable the
rapid prediction of structures and properties of novel materials in an
automated, high-throughput fashion and the efficient experimental testing of
these predictions. Here, we review efforts to use machine learning to attain
this goal.


Abstract:
  A numerical study of laminar flow through symmetric and slightly asymmetric
sudden expansion, of expansion ratio 1:3, in channels with increasing cross
section, is carried out using two different approaches - Conventional CFD and
Lattice Boltzmann Method. The effect of divergence of walls of the channel,
after a sudden expansion, on the symmetry of flow and recirculation is studied
for various Reynolds numbers. It is seen that the angles of the walls play an
active role in disrupting the symmetry of flow. For non-parallel walls, the
symmetry breaking bifurcation phenomenon no longer exists and the loss of
symmetry is a gradual process. The effect of asymmetry of geometry on flow is
also studied by considering two types of asymmetry - First type is at the plane
of expansion where the steps on either side are of unequal heights, while the
second one deals the walls of the channel are at a different inclination with
the direction of inflow. The present study reveals that small asymmetry impedes
the sharp transition and thus smoothens the pitchfork bifurcation. Increased
asymmetry finally leads to the unique solution of the governing Equations,
indicating complete loss of bifurcation pattern.


Abstract:
  We study $\bar{Q}Q\bar{q}q$ and $\bar{Q}qQ\bar{q}$ states as mixed states in
QCD sum rules. By calculating the two-point correlation functions of pure
states of their corresponding currents, we review the mass and coupling
constant predictions of $J^{PC}=1^{++}$, $1^{--}$, $1^{-+}$ states. By
calculating the two-point mixed correlation functions of $\bar{Q}Q\bar{q}q$ and
$\bar{Q}qQ\bar{q}$ currents, and we estimate the mass and coupling constants of
the corresponding ``physical state" that couples to both $\bar{Q}Q\bar{q}q$ and
$\bar{Q}qQ\bar{q}$ currents. Our results suggest that $1^{++}$ states are more
likely mixing from $\bar{Q}Q\bar{q}q$ and $\bar{Q}qQ\bar{q}$ components, while
for $1^{--}$ and $1^{-+}$ states, there is less mixing between
$\bar{Q}Q\bar{q}q$ and $\bar{Q}qQ\bar{q}$. Our results suggest the $Y$ series
of states have more complicated components.


Abstract:
  The possibility of detection of 5.5 MeV and 14.4 keV solar axions by
observing axion-induced nuclear and atomic transitions is investigated. The
presence of nuclear transitions between spin-orbit partners can be manifested
by the subsequent de-excitation via gamma ray emissions. The transition rates
can also be studied in the context of radiative axion absorption by a nucleus.
The elementary interaction is obtained in the context of the axion-quark
couplings predicted by existing axion models. Then these couplings will be
transformed to the nucleon level utilizing reasonable existing models, which
lead to effective transition operators. Using these operators we calculate the
needed nuclear matrix elements employing wave functions obtained in the context
of the nuclear shell model. With these ingredients, we discuss possibilities of
experimental observation of the axion-induced nuclear gamma-rays. In the second
part, we will examine the axion-induced production of X-rays (axion-photon
conversion) or ionization from deeply bound electron orbits. In this case, the
axion electron coupling is predicted by existing axion models, no
renormalization is needed. The experimental signal is the observation of
directly produced electrons and/or the emission of hard X-rays and Auger
electrons, following the de-excitation of the final atom. Critical discussion
is made on the experimental feasibility of detecting the solar axions by using
multi-ton scale NaI detectors.


Abstract:
  Earth's magnetosphere is vital for today's technologically dependent society.
To date, numerous design studies have been conducted and over a dozen science
missions have own to study the magnetosphere. However, a majority of these
solutions relied on large monolithic satellites, which limited the spatial
resolution of these investigations, as did the technological limitations of the
past. To counter these limitations, we propose the use of a satellite swarm
carrying numerous and distributed payloads for magnetospheric measurements. Our
mission is named APIS (Applications and Potentials of Intelligent Swarms),
which aims to characterize fundamental plasma processes in the Earth's
magnetosphere and measure the effect of the solar wind on our magnetosphere. We
propose a swarm of 40 CubeSats in two highly-elliptical orbits around the
Earth, which perform radio tomography in the magnetotail at 8-12 Earth Radii
(RE) downstream, and the subsolar magnetosphere at 8-12RE upstream. In
addition, in-situ measurements of the magnetic and electric fields, plasma
density will be performed by on-board instruments.
  In this article, we present an outline of previous missions and designs for
magnetospheric studies, along with the science drivers and motivation for the
APIS mission. Furthermore, preliminary design results are included to show the
feasibility of such a mission. The science requirements drive the APIS mission
design, the mission operation and the system requirements. In addition to the
various science payloads, critical subsystems of the satellites are
investigated e.g., navigation, communication, processing and power systems. We
summarize our findings, along with the potential next steps to strengthen our
design study.


Abstract:
  Decline in gait features is common in older adults and an indicator of
disability and mortality. Cortical control of gait, specifically in the
pre-frontal cortex as measured by functional near infrared spectroscopy
(fNIRS), during dual task walking has shown to be moderated by age, gender,
cognitive status, and various age-related disease conditions. In this study, we
develop classification models using machine learning methods to classify active
walking tasks in older adults based on fNIRS signals into either
Single-Task-Walk (STW) or Dual-Task-Walk (DTW) conditions. In this study, we
develop classification models using machine learning methods to classify active
walking tasks in older adults based on fNIRS signals into either single-task
walking (STW) or dual-task walking (DTW). The fNIRS measurements included
oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) signals obtained from prefrontal
cortex (PFC) of the subject performing on the ground active walking tasks with
or without a secondary cognitive task. We extract the fNIRS-related features by
calculating the minimum, maximum, mean, skewness and kurtosis values of Hb and
Hbo2 signals. We then use feature encoding to map the values into binary space.
Using these features, we apply and evaluate various machine learning methods
including logistic regression (LR), decision tree (DT), support vector machine
(SVM), k-nearest neighbors (kNN), multilayer perceptron (MLP), and Random
Forest (RF). Results showed that the machine learning models can achieve around
97\% classification accuracy.


Abstract:
  As data-driven methods are deployed in real-world settings, the processes
that generate the observed data will often react to the decisions of the
learner. For example, a data source may have some incentive for the algorithm
to provide a particular label (e.g. approve a bank loan), and manipulate their
features accordingly. Work in strategic classification and decision-dependent
distributions seeks to characterize the closed-loop behavior of deploying
learning algorithms by explicitly considering the effect of the classifier on
the underlying data distribution. More recently, works in performative
prediction seek to classify the closed-loop behavior by considering general
properties of the mapping from classifier to data distribution, rather than an
explicit form. Building on this notion, we analyze repeated risk minimization
as the perturbed trajectories of the gradient flows of performative risk
minimization. We consider the case where there may be multiple local minimizers
of performative risk, motivated by situations where the initial conditions may
have significant impact on the long-term behavior of the system. We provide
sufficient conditions to characterize the region of attraction for the various
equilibria in this settings. Additionally, we introduce the notion of
performative alignment, which provides a geometric condition on the convergence
of repeated risk minimization to performative risk minimizers.


Abstract:
  For $1<p\leq q$ we show that the Poisson semigroup $e^{-t\sqrt{-\Delta}}$ on
the $n$-sphere is hypercontractive from $L^{p}$ to $L^{q}$ in dimensions $n
\leq 3$ if and only if $e^{-t\sqrt{n}} \leq \sqrt{\frac{p-1}{q-1}}$. We also
show that the equivalence fails in large dimensions.


Abstract:
  Binary millisecond pulsars (MSPs) are believed to have descended from
low-mass X-ray binaries (LMXBs), which have experienced substantial mass
transfer and tidal circularization. Therefore, they should have very circular
orbits. However, the discovery of several eccentric binary MSPs (with
eccentricity $e\sim 0.01-0.1$) challenges this standard picture. Three models
have been proposed thus far based on accretion-induced collapse of massive
white dwarfs (WDs), neutron star-strange star transition, and formation of
circumbinary disks. All of them are subject to various uncertainties, and are
not entirely consistent with observations. Here we propose an alternative model
taking into account the influence of thermonuclear flashes on proto-WDs. We
assume that the flashes lead to asymmetrical mass ejection, which imparts a
mild kick on the proto-WDs. By simulating orbital changes of binary MSPs with
multiple shell flashes, we show that it is possible to reproduce the observed
eccentricities, provided that the kick velocities are around a few kms$^{-1}$.


Abstract:
  Message passing neural networks have become a method of choice for learning
on graphs, in particular the prediction of chemical properties and the
acceleration of molecular dynamics studies. While they readily scale to large
training data sets, previous approaches have proven to be less data efficient
than kernel methods. We identify limitations of invariant representations as a
major reason and extend the message passing formulation to rotationally
equivariant representations. On this basis, we propose the polarizable atom
interaction neural network (PaiNN) and improve on common molecule benchmarks
over previous networks, while reducing model size and inference time. We
leverage the equivariant atomwise representations obtained by PaiNN for the
prediction of tensorial properties. Finally, we apply this to the simulation of
molecular spectra, achieving speedups of 4-5 orders of magnitude compared to
the electronic structure reference.


Abstract:
  This work consists in the theorical development on the analysis of the
Thermodynamic Laws and thermodynamic systems in relative motion, according to
the laws of Classical Mechanics. The difference of this work for many of the
literature is the methodology used. We follow a strictly mathematical line,
instead of a phenomenological line. We proved that the First and Second Laws of
Thermodynamics in non-inertial frames remain invariant under any spatial
translation between reference frames in motion. From the results obtained, we
show the transformation of the main thermodynamic quantities as temperature,
pressure, chemical potential, internal energy, heat, state equation for an
ideal gas and efficiency of Carnot's machine are also invariant under any
transformation between non-inertial reference frames.


Abstract:
  We introduce a simple and concrete way of visualizing in three dimensions a
"flat" Klein bottle -- one whose local intrinsic geometry is the same as that
of a flat plane -- which preserves most its topological and geometric
structure. Concretely, the flatness property means that a small patch of the
surface around any point can be flattened to a patch of the plane without
stretching or compressing. Thus we can use the medium of curved-crease origami
with inelastic film to make a model which, except for its self-intersections,
necessarily has the flatness property, even along its folded edges. As such,
the sculpture presented here illustrates both the flatness, and, through its
coloring, the non-orientability of a Klein bottle.


Abstract:
  In this work we investigate the short-term variations in air quality
emissions, attributed to the prevention measures, applied in different cities,
to mitigate the COVID-19 spread. In particular, we emphasize on the
concentration effects regarding specific pollutant gases, such as carbon
monoxide (CO), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2).
The assessment of the impact of lockdown on air quality focused on four
European Cities (Athens, Gladsaxe, Lodz and Rome). Available data on pollutant
factors were obtained using global satellite observations. The level of the
employed prevention measures is employed using the Oxford COVID-19 Government
Response Tracker. The second part of the analysis employed a variety of machine
learning tools, utilized for estimating the concentration of each pollutant,
two days ahead. The results showed that a weak to moderate correlation exists
between the corresponding measures and the pollutant factors and that it is
possible to create models which can predict the behaviour of the pollutant
gases under daily human activities.


Abstract:
  In pulsar astronomy, detecting effective pulsar signals among numerous pulsar
candidates is an important research topic. Starting from space X-ray pulsar
signals, the two-dimensional autocorrelation profile map (2D-APM) feature
modelling method, which utilizes epoch folding of the autocorrelation function
of X-ray signals and expands the time-domain information of the periodic axis,
is proposed. A uniform setting criterion regarding the time resolution of the
periodic axis addresses pulsar signals without any prior information. Compared
with the traditional profile, the model has a strong anti-noise ability, a
greater abundance of information and consistent characteristics. The new
feature is simulated with double Gaussian components, and the characteristic
distribution of the model is revealed to be closely related to the distance
between the double peaks of the profile. Next, a deep convolutional neural
network (DCNN) is built, named Inception-ResNet. According to the order of the
peak separation and number of arriving photons, 30 data sets based on the
Poisson process are simulated to construct the training set, and the
observation data of PSRs B0531+21, B0540-69 and B1509-58 from the Rossi X-ray
Timing Explorer (RXTE) are selected to generate the test set. The number of
training sets and the number of test sets are 30,000 and 5,400, respectively.
After achieving convergence stability, more than 99$\%$ of the pulsar signals
are recognized, and more than 99$\%$ of the interference is successfully
rejected, which verifies the high degree of agreement between the network and
the feature model and the high potential of the proposed method in searching
for pulsars.


Abstract:
  Due to the continuously changing environment of service-based applications
(SBAs), the ability to adapt to environmental and contextual changes has become
a crucial characteristic of such applications. Providing SBAs with this ability
is a complex task, usually prepared in an unsystematic way and interwoven with
application logic. As a result, developing and maintaining adaptive SBAs has
become a costly and hardly repeatable process. The objective of this paper is
to present a model-based approach to developing adaptive SBAs, which separates
development of adaptation concerns from development of SBAs behaviors. This
approach aims to facilitate and automate development of adaptive behaviors. In
this paper, the process of developing an adaptive SBA is defined as specifying
adaptive SBA models based on a metamodel and reusable adaptation tactics. These
models are then transformed into runtime model artifacts and running system
units performing runtime adaptive behaviors. The approach introduces a
systematic method to derive adaptation behaviors from adaptation models, which
facilitates the development of adaptive behaviors. The results show that using
our approach can enhance the development process of adaptive behaviors in terms
of development time and code quality, particularly for scenarios that are more
complex in terms of adaptation rather than business logic.


Abstract:
  In an earlier study, we reported that the ram gauge of the COmet Pressure
Sensor (COPS), one of the three instruments of the Rosetta Orbiter Spectrometer
for Ion and Neutral Analysis (ROSINA), could be used to obtain information
about the sublimating content of icy particles, made up of volatiles and
conceivably refractories coming from comet 67P/Churyumov-Gerasimenko.
  In this work, we extend the investigation to the second COPS gauge, the nude
gauge. In particular, we analyse the volume of the volatile content of coma
particles, along with a search for possible dependencies between the nude gauge
detection rate (i.e. the rate at which icy particles are detected by the nude
gauge) and the position of the Rosetta spacecraft. We also investigate the
correlations of the nude gauge detection rate with the quantities associated
with cometary activity.
  Although it was not originally designed for such a purpose, the COPS nude
gauge has been able to detect $\sim$67000 features generated by the sublimation
of the volatile content of icy particles. The nude gauge detection rate follows
a trend that is inversely proportional to the heliocentric distance. This
result is interpreted as a confirmation of a possible relation between the nude
gauge detection rate and cometary activity. Thus, we compared the former with
parameters related to cometary activity and obtained significant correlations,
indicating that the frequency of icy particle detection is driven by cometary
activity. Furthermore, by representing the volatile part of the icy particles
as equivalent spheres with a density of 1 g cm$^{-3}$, we obtained a range of
diameters between 60 and 793 nanometres, with the smaller ones
($<390\,\mathrm{nm}$ in diameter) having a size distribution power index of
$-4.79\pm 0.26$.


Abstract:
  Given a social network of users with selection cost, the \textsc{Budgeted
Influence Maximization Problem} (\emph{BIM Problem} in short) asks for
selecting a subset of the nodes (known as \emph{seed nodes}) within an
allocated budget for initial activation to maximize the influence in the
network. In this paper, we study this problem under the
\emph{co\mbox{-}operative game theoretic} framework. We model this problem as a
co\mbox{-}operative game where the users of the network are the players and for
a group of users, the expected influence by them under the \emph{Maximum
Influence Arborences} diffusion model is its utility. We call this game as
\emph{BIM Game} and show this is `non-convex' and `sub-additive'. Based on the
proposed game\mbox{-}theoretic model and using the solution concept called
`Shapley Value', we propose an iterative algorithm for finding seed nodes. The
proposed methodology is divided into mainly two broad steps: the first one is
computing the approximate marginal gain in \emph{Shapley Value} for all the
nodes of the network, and the second one is selecting seed nodes from the
sorted list until the budget is exhausted. We also show that the proposed
methodology can even be more effective when the community structure of the
network is exploited. The proposed methodologies have been implemented, and an
extensive set of experiments have been conducted with three publicly available
social network datasets. From the experiments, we observe that the seed set
selected by the proposed methodologies lead to more number of influence nodes
compared to many standard and baseline methods from the literature with a
reasonable computational overhead. In particular, if the community structure of
the network is exploited then there is an increase upto $2 \%$ in number of
influenced nodes.


Abstract:
  We develop a product functional quantization of rough volatility. Since the
quantizers can be computed offline, this new technique, built on the insightful
works by Luschgy and Pages, becomes a strong competitor in the new arena of
numerical tools for rough volatility. We concentrate our numerical analysis to
pricing VIX Futures in the rough Bergomi model and compare our results to other
recently suggested benchmarks.


Abstract:
  Time series forecasting is a crucial task in machine learning, as it has a
wide range of applications including but not limited to forecasting electricity
consumption, traffic, and air quality. Traditional forecasting models rely on
rolling averages, vector auto-regression and auto-regressive integrated moving
averages. On the other hand, deep learning and matrix factorization models have
been recently proposed to tackle the same problem with more competitive
performance. However, one major drawback of such models is that they tend to be
overly complex in comparison to traditional techniques. In this paper, we
report the results of prominent deep learning models with respect to a
well-known machine learning baseline, a Gradient Boosting Regression Tree
(GBRT) model. Similar to the deep neural network (DNN) models, we transform the
time series forecasting task into a window-based regression problem.
Furthermore, we feature-engineered the input and output structure of the GBRT
model, such that, for each training window, the target values are concatenated
with external features, and then flattened to form one input instance for a
multi-output GBRT model. We conducted a comparative study on nine datasets for
eight state-of-the-art deep-learning models that were presented at top-level
conferences in the last years. The results demonstrate that the window-based
input transformation boosts the performance of a simple GBRT model to levels
that outperform all state-of-the-art DNN models evaluated in this paper.


Abstract:
  This document shows how Z specifications can be translated into $\{log\}$
and, later, on how $\{log\}$ can be used to run simulations and automated
proofs. This can help users of other specification languages such as B and VDM
to use $\{log\}$ along the same lines. The presentation is rather informal and
user-oriented. More technical and formal presentations can be found in the
papers published by the authors. We also assume the reader has at least a basic
knowledge of the Z notation.


Abstract:
  In this paper we develop the theory of presentations for globular operads and
construct presentations for the globular operads corresponding to several key
theories of $n$-category for $n \leqslant 4$.


Abstract:
  This paper presents a stereo object matching method that exploits both 2D
contextual information from images as well as 3D object-level information.
Unlike existing stereo matching methods that exclusively focus on the
pixel-level correspondence between stereo images within a volumetric space
(i.e., cost volume), we exploit this volumetric structure in a different
manner. The cost volume explicitly encompasses 3D information along its
disparity axis, therefore it is a privileged structure that can encapsulate the
3D contextual information from objects. However, it is not straightforward
since the disparity values map the 3D metric space in a non-linear fashion.
Thus, we present two novel strategies to handle 3D objectness in the cost
volume space: selective sampling (RoISelect) and 2D-3D fusion
(fusion-by-occupancy), which allow us to seamlessly incorporate 3D object-level
information and achieve accurate depth performance near the object boundary
regions. Our depth estimation achieves competitive performance in the KITTI
dataset and the Virtual-KITTI 2.0 dataset.


Abstract:
  Generative adversarial networks (GANs) are among the most successful models
for learning high-complexity, real-world distributions. However, in theory, due
to the highly non-convex, non-concave landscape of the minmax training
objective, GAN remains one of the least understood deep learning models. In
this work, we formally study how GANs can efficiently learn certain
hierarchically generated distributions that are close to the distribution of
images in practice. We prove that when a distribution has a structure that we
refer to as Forward Super-Resolution, then simply training generative
adversarial networks using gradient descent ascent (GDA) can indeed learn this
distribution efficiently, both in terms of sample and time complexities. We
also provide concrete empirical evidence that not only our assumption "forward
super-resolution" is very natural in practice, but also the underlying learning
mechanisms that we study in this paper (to allow us efficiently train GAN via
GDA in theory) simulates the actual learning process of GANs in practice on
real-world problems.


Abstract:
  Recently, object detection in aerial images has gained much attention in
computer vision. Different from objects in natural images, aerial objects are
often distributed with arbitrary orientation. Therefore, the detector requires
more parameters to encode the orientation information, which are often highly
redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model
the orientation variation, large amounts of rotation augmented data is needed
to train an accurate object detector. In this paper, we propose a
Rotation-equivariant Detector (ReDet) to address these issues, which explicitly
encodes rotation equivariance and rotation invariance. More precisely, we
incorporate rotation-equivariant networks into the detector to extract
rotation-equivariant features, which can accurately predict the orientation and
lead to a huge reduction of model size. Based on the rotation-equivariant
features, we also present Rotation-invariant RoI Align (RiRoI Align), which
adaptively extracts rotation-invariant features from equivariant features
according to the orientation of RoI. Extensive experiments on several
challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016, show that
our method can achieve state-of-the-art performance on the task of aerial
object detection. Compared with previous best results, our ReDet gains 1.2, 3.5
and 2.6 mAP on DOTA-v1.0, DOTA-v1.5 and HRSC2016 respectively while reducing
the number of parameters by 60\% (313 Mb vs. 121 Mb). The code is available at:
\url{https://github.com/csuhan/ReDet}.


Abstract:
  We describe conformal defects of $p$ dimensions in a free scalar theory on a
$d$-dimensional flat space as boundary conditions on the conformally flat space
$\mathbb{H}^{p+1}\times \mathbb{S}^{d-p-1}$. We classify two types of boundary
conditions, Dirichlet type and Neumann type, on the boundary of the subspace
$\mathbb{H}^{p+1}$ which correspond to the types of conformal defects in the
free scalar theory. We find Dirichlet boundary conditions always exist while
Neumann boundary conditions are allowed only for defects of lower codimensions.
Our results match with a recent classification of the non-monodromy defects,
showing Neumann boundary conditions are associated with non-trivial defects. We
check this observation by calculating the difference of the free energies on
$\mathbb{H}^{p+1}\times \mathbb{S}^{d-p-1}$ between Dirichlet and Neumann
boundary conditions. We also examine the defect RG flows from Neumann to
Dirichlet boundary conditions and provide more support for a conjectured
$C$-theorem in defect CFTs.


Abstract:
  Self-supervised learning (SSL) of energy based models has an intuitive
relation to equilibrium thermodynamics because the softmax layer, mapping
energies to probabilities, is a Gibbs distribution. However, in what way SSL is
a thermodynamic process? We show that some SSL paradigms behave as a
thermodynamic composite system formed by representations and self-labels in
contact with a nonequilibrium reservoir. Moreover, this system is subjected to
usual thermodynamic cycles, such as adiabatic expansion and isochoric heating,
resulting in a generalized Gibbs ensemble (GGE). In this picture, we show that
learning is seen as a demon that operates in cycles using feedback measurements
to extract negative work from the system. As applications, we examine some SSL
algorithms using this idea.


Abstract:
  Differentially private algorithms protect individuals in data analysis
scenarios by ensuring that there is only a weak correlation between the
existence of the user in the data and the result of the analysis. Dynamic graph
algorithms maintain the solution to a problem (e.g., a matching) on an evolving
input, i.e., a graph where nodes or edges are inserted or deleted over time.
They output the value of the solution after each update operation, i.e.,
continuously. We study (event-level and user-level) differentially private
algorithms for graph problems under continual observation, i.e., differentially
private dynamic graph algorithms. We present event-level private algorithms for
partially dynamic counting-based problems such as triangle count that improve
the additive error by a polynomial factor (in the length $T$ of the update
sequence) on the state of the art, resulting in the first algorithms with
additive error polylogarithmic in $T$.
  We also give $\varepsilon$-differentially private and partially dynamic
algorithms for minimum spanning tree, minimum cut, densest subgraph, and
maximum matching. The additive error of our improved MST algorithm is $O(W
\log^{3/2}T / \varepsilon)$, where $W$ is the maximum weight of any edge,
which, as we show, is tight up to a $(\sqrt{\log T} / \varepsilon)$-factor. For
the other problems, we present a partially-dynamic algorithm with
multiplicative error $(1+\beta)$ for any constant $\beta > 0$ and additive
error $O(W \log(nW) \log(T) / (\varepsilon \beta) )$. Finally, we show that the
additive error for a broad class of dynamic graph algorithms with user-level
privacy must be linear in the value of the output solution's range.


Abstract:
  Modeling nuclear reaction networks for nuclear science applications and for
simulations of astrophysical environments relies on cross section data for a
vast number of reactions, many of which have never been measured.
  In this paper we examine the validity of the Weisskopf-Ewing approximation
for determining unknown (n,n') and (n, 2n) cross sections from surrogate data.
Using statistical reaction calculations with realistic parameterizations, we
investigate first whether the assumptions underlying the Weisskopf-Ewing
approximation are valid for (n,n') and (n, 2n) reactions on representative
target nuclei. We then produce simulated surrogate reaction data and assess the
impact of applying the Weisskopf-Ewing approximation when extracting (n,n') and
(n, 2n) cross sections in situations where the approximation is not strictly
justified.
  We find that peak cross sections can be estimated using the Weisskopf-Ewing
approximation, but the shape of the (n,n') and (n,2n) cross sections,
especially for low neutron energies, cannot be reliably determined without
accounting for the angular-momentum differences between the neutron-induced and
the surrogate reaction.


Abstract:
  Despite having promising results, style transfer, which requires preparing
style images in advance, may result in lack of creativity and accessibility.
Following human instruction, on the other hand, is the most natural way to
perform artistic style transfer that can significantly improve controllability
for visual effect applications. We introduce a new task -- language-driven
image style transfer (\texttt{LDIST}) -- to manipulate the style of a content
image, guided by a text. We propose contrastive language visual artist (CLVA)
that learns to extract visual semantics from style instructions and accomplish
\texttt{LDIST} by the patch-wise style discriminator. The discriminator
considers the correlation between language and patches of style images or
transferred results to jointly embed style instructions. CLVA further compares
contrastive pairs of content image and style instruction to improve the mutual
relativeness between transfer results. The transferred results from the same
content image can preserve consistent content structures. Besides, they should
present analogous style patterns from style instructions that contain similar
visual semantics. The experiments show that our CLVA is effective and achieves
superb transferred results on \texttt{LDIST}.


Abstract:
  This work focuses on the analysis that whether 3D face models can be learned
from only the speech inputs of speakers. Previous works for cross-modal face
synthesis study image generation from voices. However, image synthesis includes
variations such as hairstyles, backgrounds, and facial textures, that are
arguably irrelevant to voice or without direct studies to show correlations. We
instead investigate the ability to reconstruct 3D faces to concentrate on only
geometry, which is more physiologically grounded. We propose both the
supervised learning and unsupervised learning frameworks. Especially we
demonstrate how unsupervised learning is possible in the absence of a direct
voice-to-3D-face dataset under limited availability of 3D face scans when the
model is equipped with knowledge distillation. To evaluate the performance, we
also propose several metrics to measure the geometric fitness of two 3D faces
based on points, lines, and regions. We find that 3D face shapes can be
reconstructed from voices. Experimental results suggest that 3D faces can be
reconstructed from voices, and our method can improve the performance over the
baseline. The best performance gains (15% - 20%) on ear-to-ear distance ratio
metric (ER) coincides with the intuition that one can roughly envision whether
a speaker's face is overall wider or thinner only from a person's voice. See
our project page for codes and data.


Abstract:
  We argue that in quantum gravity there is no Born rule. The quantum-gravity
regime, described by a non-normalisable Wheeler-DeWitt wave functional $\Psi$,
must be in quantum nonequilibrium with a probability distribution
$P\neq\left\vert \Psi\right\vert ^{2}$ (initially and always). A Born rule can
emerge only in the semiclassical regime of quantum systems on a classical
spacetime background, with normalisable Schr\"{o}dinger wave functions $\psi$.
Conditioning on the underlying quantum-gravitational ensemble yields a
nonequilibrium distribution $\rho\neq\left\vert \psi\right\vert ^{2}$ at the
beginning of the semiclassical regime, with quantum relaxation
$\rho\rightarrow\left\vert \psi\right\vert ^{2}$ taking place only afterwards.
Quantum gravity naturally creates an early nonequilibrium universe. We also
show how small corrections to the Schr\"{o}dinger equation yield an
intermediate regime in which the Born rule is unstable: an initial distribution
$\rho=\left\vert \psi\right\vert ^{2}$ can evolve to a final distribution
$\rho\neq\left\vert \psi\right\vert ^{2}$. These results arise naturally in the
de Broglie-Bohm pilot-wave formulation of quantum gravity. We show that quantum
instability during inflation generates a large-scale deficit $\sim1/k^{3}$ in
the primordial power spectrum at wavenumber $k$, though the effect is too small
to observe. Similarly we find an unobservably large timescale for quantum
instability in a radiation-dominated universe. Quantum instability may be
important in black-hole evaporation, with a final burst of Hawking radiation
that violates the Born rule. Deviations from the Born rule can also be
generated for atomic systems in the gravitational field of the earth, though
the effects are unlikely to be observable. The most promising scenario for the
detection of Born-rule violations appears to be in radiation from exploding
primordial black holes.


Abstract:
  We present a model predictive controller (MPC) that automatically discovers
collision-free locomotion while simultaneously taking into account the system
dynamics, friction constraints, and kinematic limitations. A relaxed barrier
function is added to the optimization's cost function, leading to collision
avoidance behavior without increasing the problem's computational complexity.
Our holistic approach does not require any heuristics and enables legged robots
to find whole-body motions in the presence of static and dynamic obstacles. We
use a dynamically generated euclidean signed distance field for static
collision checking. Collision checking for dynamic obstacles is modeled with
moving cylinders, increasing the responsiveness to fast-moving agents.
Furthermore, we include a Kalman filter motion prediction for moving obstacles
into our receding horizon planning, enabling the robot to anticipate possible
future collisions. Our experiments demonstrate collision-free motions on a
quadrupedal robot in challenging indoor environments. The robot handles complex
scenes like overhanging obstacles and dynamic agents by exploring motions at
the robot's dynamic and kinematic limits.


Abstract:
  This is the second article of a sequence of research on deformations of
Q-curvature. In the previous one, we studied local stability and rigidity
phenomena of Q-curvature. In this article, we mainly investigate the volume
comparison with respect to Q-curvature. In particular, we show that volume
comparison theorem holds for metrics close to strictly stable positive Einstein
metrics. This result shows that Q-curvature can still control the volume of
manifolds under certain conditions, which provides a fundamental geometric
characterization of Q-curvature. Applying the same technique, we derive the
local rigidity of strictly stable Ricci-flat manifolds with respect to
Q-curvature, which shows the non-existence of metrics with positive Q-curvature
near the reference metric.


Abstract:
  We report on atom probe tomography analyses of Pd and Pd@Au nanoparticles
embedded in a Ni matrix and the effects of local evaporation field variations
on the atom probe data. In order to assess the integrity of the reconstructed
atom maps, we performed numerical simulations of the field evaporation
processes and compared the simulated datasets with experimentally acquired
data. The distortions seen in the atom maps for both Pd and Pd@Au nanoparticles
could be mostly ascribed to local variations in chemical composition and
elemental evaporation fields. The evaporation field values for Pd and Ni, taken
from the image hump model and assumed in the simulations, yielded a good
agreement between experimental and simulation results. In contrast, the
evaporation field for Au, as predicted from the image hump model, appeared to
be substantially overestimated and resulted in a large discrepancy between
experiments and simulations.


Abstract:
  The multi-reference alignment (MRA) problem entails estimating an image from
multiple noisy and rotated copies of itself. If the noise level is low, one can
reconstruct the image by estimating the missing rotations, aligning the images,
and averaging out the noise. While accurate rotation estimation is impossible
if the noise level is high, the rotations can still be approximated, and thus
can provide indispensable information. In particular, learning the
approximation error can be harnessed for efficient image estimation. In this
paper, we propose a new computational framework, called Synch-EM, that consists
of angular synchronization followed by expectation-maximization (EM). The
synchronization step results in a concentrated distribution of rotations; this
distribution is learned and then incorporated into the EM as a Bayesian prior.
The learned distribution also dramatically reduces the search space, and thus
the computational load, of the EM iterations. We show by extensive numerical
experiments that the proposed framework can significantly accelerate EM for MRA
in high noise levels, occasionally by a few orders of magnitude, without
degrading the reconstruction quality.


Abstract:
  We propose automatic speech recognition (ASR) models inspired by echo state
network (ESN), in which a subset of recurrent neural networks (RNN) layers in
the models are randomly initialized and untrained. Our study focuses on RNN-T
and Conformer models, and we show that model quality does not drop even when
the decoder is fully randomized. Furthermore, such models can be trained more
efficiently as the decoders do not require to be updated. By contrast,
randomizing encoders hurts model quality, indicating that optimizing encoders
and learn proper representations for acoustic inputs are more vital for speech
recognition. Overall, we challenge the common practice of training ASR models
for all components, and demonstrate that ESN-based models can perform equally
well but enable more efficient training and storage than fully-trainable
counterparts.


Abstract:
  This report describes our winning submission to the Real Robot Challenge
(https://real-robot-challenge.com/). The Real Robot Challenge is a three-phase
dexterous manipulation competition that involves manipulating various
rectangular objects with the TriFinger Platform. Our approach combines motion
planning with several motion primitives to manipulate the object. For Phases 1
and 2, we additionally learn a residual policy in simulation that applies
corrective actions on top of our controller. Our approach won first place in
Phase 2 and Phase 3 of the competition. We were anonymously known as
`ardentstork' on the competition leaderboard
(https://real-robot-challenge.com/leader-board). Videos and our code can be
found at https://github.com/ripl-ttic/real-robot-challenge.


Abstract:
  Highly reliable Monte-Carlo event generators and detector simulation programs
are important for the precision measurement in the high energy physics. Huge
amounts of computing resources are required to produce a sufficient number of
simulated events. Moreover, simulation parameters have to be fine-tuned to
reproduce situations in the high energy particle interactions which is not
trivial in some phase spaces in physics interests. In this paper, we suggest a
new method based on the Wasserstein Generative Adversarial Network (WGAN) that
can learn the probability distribution of the real data. Our method is capable
of event generation at a very short computing time compared to the traditional
MC generators. The trained WGAN is able to reproduce the shape of the real data
with high fidelity.


Abstract:
  Chiral spin-triplet superconductivity is a topologically nontrivial pairing
state with broken time-reversal symmetry, which can host Majorana
quasiparticles. The recently discovered heavy-fermion superconductor UTe$_2$
exhibits peculiar properties of spin-triplet pairing, and the possible chiral
state has been actively discussed. However, the symmetry and nodal structure of
its order parameter in the bulk, which determine the Majorana surface states,
remains controversial. Here we focus on the number and positions of
superconducting gap nodes in the ground state of UTe$_2$. Our magnetic
penetration depth measurements for three field orientations in the Meissner
state reveal the power-law temperature dependence with exponents less than 2,
which excludes single-component spin-triplet states. The anisotropy of
low-energy quasiparticle excitations indicates multiple point nodes near the
$k_y$- and $k_z$-axes, evidencing that the order parameter has multiple
components in a chiral complex form. We find that most consistent is a chiral
$B_{3u}+iA_u$ non-unitary state, which provides fundamentals of the topological
properties in UTe$_2$.


Abstract:
  We study the distribution of a general class of asymptoticallylinear
statistics which are symmetric functions of $N$ independent observations. The
distribution functions of these statistics are approximated by an Edgeworth
expansion with a remainder of order $o(N^{-1})$. The Edgeworth expansion is
based on Hoeffding's decomposition which provides a stochastic expansion into a
linear part, a quadratic part as well as smaller higher order parts. The
validity of this Edgeworth expansion is proved under Cram\'er's condition on
the linear part, moment assumptions for all parts of the statistic and an
optimal dimensionality requirement for the non linear part.


Abstract:
  In a hybrid speech model, both voiced and unvoiced components can coexist in
a segment. Often, the voiced speech is regarded as the deterministic component,
and the unvoiced speech and additive noise are the stochastic components.
Typically, the speech signal is considered stationary within fixed segments of
20-40 ms, but the degree of stationarity varies over time. For decomposing
noisy speech into its voiced and unvoiced components, a fixed segmentation may
be too crude, and we here propose to adapt the segment length according to the
signal local characteristics. The segmentation relies on parameter estimates of
a hybrid speech model and the maximum a posteriori (MAP) and log-likelihood
criteria as rules for model selection among the possible segment lengths, for
voiced and unvoiced speech, respectively. Given the optimal segmentation
markers and the estimated statistics, both components are estimated using
linear filtering. A codebook-based approach differentiates between unvoiced
speech and noise. A better extraction of the components is possible by taking
into account the adaptive segmentation, compared to a fixed one. Also, a lower
distortion for voiced speech and higher segSNR for both components is possible,
as compared to other decomposition methods.


Abstract:
  Prediction of a molecule's 3D conformer ensemble from the molecular graph
holds a key role in areas of cheminformatics and drug discovery. Existing
generative models have several drawbacks including lack of modeling important
molecular geometry elements (e.g. torsion angles), separate optimization stages
prone to error accumulation, and the need for structure fine-tuning based on
approximate classical force-fields or computationally expensive methods such as
metadynamics with approximate quantum mechanics calculations at each geometry.
We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant
machine learning approach to generate distributions of low-energy molecular 3D
conformers. Leveraging the power of message passing neural networks (MPNNs) to
capture local and global graph information, we predict local atomic 3D
structures and torsion angles, avoiding unnecessary over-parameterization of
the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such
local predictions suffice both for the training loss computation, as well as
for the full deterministic conformer assembly (at test time). We devise a
non-adversarial optimal transport based loss function to promote diverse
conformer generation. GeoMol predominantly outperforms popular open-source,
commercial, or state-of-the-art machine learning (ML) models, while achieving
significant speed-ups. We expect such differentiable 3D structure generators to
significantly impact molecular modeling and related applications.


Abstract:
  Spike-timing-dependent plasticity(STDP) is a biological process in which the
precise order and timing of neuronal spikes affect the degree of synaptic
modification. While there have been numerous research focusing on the role of
STDP in neural coding, the functional implications of STDP at the macroscopic
level in the brain have not been fully explored yet. In this work, we propose a
neurodynamical model based on STDP that renders storage and retrieval of a
group of associative memories. We showed that the function of STDP at the
macroscopic level is to form a "memory plane" in the neural state space which
dynamically encodes high dimensional data. We derived the analytic relation
between the input, the memory plane, and the induced macroscopic neural
oscillations around the memory plane. Such plane produces a limit cycle in
reaction to a similar memory cue, which can be used for retrieval of the
original input.


Abstract:
  With the advent of 21st century and increasing advancements in the field of
technology and connectivity, inter-networking in real-time has achieved great
importance. Distributed control and multi-agent paradigm has groped rapidly
with history of big time failures of centralized systems in the past. The
concepts of synchronization and network control systems have been used
extensively in the near past to map, analyze and solve defined set of
objectives. In this thesis, a diverse set of applications from power flow point
of view are taken into consideration and modelled/analyzed using
synchronization as the central theme. These systems are proposed (or assumed)
to be network connected and its control has been devised accordingly. It has
been shown how some examples from nature can help recreate similar dynamics
synthetically and help achieve system objectives. Few of the applications of
the smart world have been ascribed in the thesis and distributed control of
these seen from a multi-agent perspective have been devised in order to better
design and operate such systems in real-time. Synchronization happens to be the
heart of all the networks, as all the agents work in tandem in order to provide
to a common objective as well abide by the defined constraints. As an
inflection to the work, I set up a platform for development of new distributed
and fast acting control strategies for conventional as well as futuristic
control systems existing/non-existing in the literature. As a major
contribution of this dissertation, by combining the ideas from physics and
power systems, I open up various interesting phenomena already existing in
either fields to be explored for one another.


Abstract:
  Solar wind stream interaction regions (SIRs) are often characterized by
energetic ion enhancements. The mechanisms accelerating these particles, as
well as the locations where the acceleration occurs, remain debated. Here, we
report the findings of a simulation of a SIR event observed by Parker Solar
Probe at ~0.56 au and the Solar Terrestrial Relations Observatory-Ahead at
~0.95 au in 2019 September when both spacecraft were approximately radially
aligned with the Sun. The simulation reproduces the solar wind configuration
and the energetic particle enhancements observed by both spacecraft. Our
results show that the energetic particles are produced at the compression waves
associated with the SIR and that the suprathermal tail of the solar wind is a
good candidate to provide the seed population for particle acceleration. The
simulation confirms that the acceleration process does not require shock waves
and can already commence within Earth's orbit, with an energy dependence on the
precise location where particles are accelerated. The three-dimensional
configuration of the solar wind streams strongly modulates the energetic
particle distributions, illustrating the necessity of advanced models to
understand these particle events.


Abstract:
  The existence of a fundamental scale is expected to be a key feature of
quantum gravity. Many approaches take this property as a starting assumption.
Here, instead, we take a less conventional viewpoint based on a critical
inspection of both fundamental principles and kinematic laws. We point out that
rigorous arguments suggest a more urgent need to revise known theories to
incorporate a fundamental acceleration scale already in flat space. The
reciprocity principle can naturally do so. In addition to noticing links with
string theory, we argue that the reciprocity principle implies an
infinite-derivative generalization of the Einstein-Hilbert action that makes
the gravitational interaction fundamentally nonlocal, thus providing a guiding
principle that could lead us towards the formulation of a consistent theory of
quantum gravity.


Abstract:
  As an alternative to the popular parametrisations of the dark energy equation
of state, we construct a quintessence model where the scalar field has a linear
dependence on the number of e-folds. Constraints on more complex models are
typically limited by the degeneracies that increase with the number of
parameters. The proposed parametrisation conveniently constrains the evolution
of the dark energy equation of state as it allows for a wide variety of time
evolutions. We also consider a non-minimal coupling to cold dark matter. We fit
the model with Planck and KiDS observational data. The CMB favours a
non-vanishing coupling with energy transfer from dark energy to dark matter.
Conversely, gravitational weak lensing measurements slightly favour energy
transfer from dark matter to dark energy, with a substantial departure of the
dark energy equation of state from -1.


Abstract:
  We propose a novel and theoretical model, blocked and hierarchical
variational autoencoder (BHiVAE), to get better-disentangled representation. It
is well known that information theory has an excellent explanatory meaning for
the network, so we start to solve the disentanglement problem from the
perspective of information theory. BHiVAE mainly comes from the information
bottleneck theory and information maximization principle. Our main idea is that
(1) Neurons block not only one neuron node is used to represent attribute,
which can contain enough information; (2) Create a hierarchical structure with
different attributes on different layers, so that we can segment the
information within each layer to ensure that the final representation is
disentangled. Furthermore, we present supervised and unsupervised BHiVAE,
respectively, where the difference is mainly reflected in the separation of
information between different blocks. In supervised BHiVAE, we utilize the
label information as the standard to separate blocks. In unsupervised BHiVAE,
without extra information, we use the Total Correlation (TC) measure to achieve
independence, and we design a new prior distribution of the latent space to
guide the representation learning. It also exhibits excellent disentanglement
results in experiments and superior classification accuracy in representation
learning.


Abstract:
  In this paper we study a dynamical optimal transport problem on a network
that allows for transport of mass between different edges if a penalty $\kappa$
is paid. We show existence of minimisers using duality and discuss the
relationships of the distance-functional to other metrics such as the
Fisher-Rao and the classical Wasserstein metric and analyse the resulting
distance functional in the limiting case $\kappa \rightarrow \infty$.


Abstract:
  In this paper we continue the discussion about relations between exponential
polynomials and generalized moment generating functions on a commutative
hypergroup. We are interested in the following problem: is it true that every
finite dimensional variety is spanned by moment functions? Let $m$ be an
exponential on $X$. In our former paper we have proved that if the linear space
of all $m$-sine functions in the variety of an $m$-exponential monomial is (at
most) one dimensional, then this variety is spanned by moment functions
generated by $m$. In this paper we show that this may happen also in cases
where the $m$-sine functions span a more than one dimensional subspace in the
variety. We recall the notion of a polynomial hypergroup in $d$ variables,
describe exponentials on it and give the characterization of the so called
$m$-sine functions. Next we show that the Fourier algebra of a polynomial
hypergroup in $d$ variables is the polynomial ring in $d$ variables. Finally,
using Ehrenpreis--Palamodov Theorem we show that every exponential polynomial
on the polynomial hypergroup in $d$ variables is a linear combination of moment
functions contained in its variety.


Abstract:
  Among the multitude of software development processes available, hardly any
is used by the book. Regardless of company size or industry sector, a majority
of project teams and companies use customized processes that combine different
development methods -- so-called hybrid development methods. Even though such
hybrid development methods are highly individualized, a common understanding of
how to systematically construct synergetic practices is missing. In this paper,
we make a first step towards devising such guidelines. Grounded in 1,467 data
points from a large-scale online survey among practitioners, we study the
current state of practice in process use to answer the question: What are
hybrid development methods made of? Our findings reveal that only eight methods
and few practices build the core of modern software development. This small set
allows for statistically constructing hybrid development methods. Using an 85%
agreement level in the participants' selections, we provide two examples
illustrating how hybrid development methods are characterized by the practices
they are made of. Our evidence-based analysis approach lays the foundation for
devising hybrid development methods.


Abstract:
  Mixed-precision quantization is a powerful tool to enable memory and compute
savings of neural network workloads by deploying different sets of bit-width
precisions on separate compute operations. Recent research has shown
significant progress in applying mixed-precision quantization techniques to
reduce the memory footprint of various workloads, while also preserving task
performance. Prior work, however, has often ignored additional objectives, such
as bit-operations, that are important for deployment of workloads on hardware.
Here we present a flexible and scalable framework for automated mixed-precision
quantization that optimizes multiple objectives. Our framework relies on
Neuroevolution-Enhanced Multi-Objective Optimization (NEMO), a novel search
method, to find Pareto optimal mixed-precision configurations for memory and
bit-operations objectives. Within NEMO, a population is divided into
structurally distinct sub-populations (species) which jointly form the Pareto
frontier of solutions for the multi-objective problem. At each generation,
species are re-sized in proportion to the goodness of their contribution to the
Pareto frontier. This allows NEMO to leverage established search techniques and
neuroevolution methods to continually improve the goodness of the Pareto
frontier. In our experiments we apply a graph-based representation to describe
the underlying workload, enabling us to deploy graph neural networks trained by
NEMO to find Pareto optimal configurations for various workloads trained on
ImageNet. Compared to the state-of-the-art, we achieve competitive results on
memory compression and superior results for compute compression for
MobileNet-V2, ResNet50 and ResNeXt-101-32x8d. A deeper analysis of the results
obtained by NEMO also shows that both the graph representation and the
species-based approach are critical in finding effective configurations for all
workloads.


Abstract:
  In this paper we argue that data science is a coherent and novel approach to
empirical problems that, in its most general form, does not build understanding
about phenomena. Within the new type of mathematization at work in data
science, mathematical methods are not selected because of any relevance for a
problem at hand; mathematical methods are applied to a specific problem only by
'forcing', i.e. on the basis of their ability to reorganize the data for
further analysis and the intrinsic richness of their mathematical structure. In
particular, we argue that deep learning neural networks are best understood
within the context of forcing optimization methods. We finally explore the
broader question of the appropriateness of data science methods in solving
problems. We argue that this question should not be interpreted as a search for
a correspondence between phenomena and specific solutions found by data science
methods; rather, it is the internal structure of data science methods that is
open to precise forms of understanding.


Abstract:
  This paper provides a new solution approach to a multi-player perimeter
defense game, in which the intruders' team tries to enter the territory, and a
team of defenders protects the territory by capturing intruders on the
perimeter of the territory. The objective of the defenders is to detect and
capture the intruders before the intruders enter the territory. Each defender
independently senses the intruder and computes his trajectory to capture the
assigned intruders in a cooperative fashion. The intruder is estimated to reach
a specific location on the perimeter at a specific time. Each intruder is
viewed as a spatio-temporal task, and the defenders are assigned to execute
these spatio-temporal tasks. At any given time, the perimeter defense problem
is converted into a Decentralized Multi-UAV Spatio-Temporal Multi-Task
Allocation (DMUST-MTA) problem. The cost of executing a task for a trajectory
is defined by a composite cost function of both the spatial and temporal
components. In this paper, a decentralized consensus-based bundle algorithm has
been modified to solve the spatio-temporal multi-task allocation problem, and
the performance evaluation of the proposed approach is carried out based on
Monte-Carlo simulations. The simulation results show the effectiveness of the
proposed approach to solve the perimeter defense game under different
scenarios. Performance comparison with a state-of-the-art centralized approach
with full observability, clearly indicates that DMUST-MTA achieves similar
performance in a decentralized way with partial observability conditions with a
lesser computational time and easy scaling up.


Abstract:
  Bose-Einstein condensation, observed in either strongly interacting liquid
helium or weakly interacting atomic Bose gases, is widely known to be a
second-order phase transition. Here, we predict a first-order Bose-Einstein
condensation in a cloud of harmonically trapped bosons interacting with both
attractive two-body interaction and repulsive three-body interaction,
characterized respectively by an $s$-wave scattering length $a<0$ and a
three-body scattering hypervolume $D>0$. It happens when the harmonic trapping
potential is weak, so with increasing temperature the system changes from a
low-temperature liquid-like quantum droplet to a normal gas, and therefore
experiences a first-order liquid-to-gas transition. At large trapping
potential, however, the quantum droplet can first turn into a superfluid gas,
rendering the condensation transition occurred later from a superfluid gas to a
normal gas smooth. We determine a rich phase diagram and show the existence of
a tri-critical point, where the three phases - quantum droplet, superfluid gas
and normal gas - meet together. We argue that an ensemble of spin-polarized
tritium atoms could be a promising candidate to observe the predicted
first-order Bose-Einstein condensation, across which the condensate fraction or
central condensate density jumps to zero and the surface-mode frequencies
diverge.


Abstract:
  A central goal of neuroscience is to understand the representations formed by
brain activity patterns and their connection to behavior. The classical
approach is to investigate how individual neurons encode the stimuli and how
their tuning determines the fidelity of the neural representation. Tuning
analyses often use the Fisher information to characterize the sensitivity of
neural responses to small changes of the stimulus. In recent decades,
measurements of large populations of neurons have motivated a complementary
approach, which focuses on the information available to linear decoders. The
decodable information is captured by the geometry of the representational
patterns in the multivariate response space. Here we review neural tuning and
representational geometry with the goal of clarifying the relationship between
them. The tuning induces the geometry, but different sets of tuned neurons can
induce the same geometry. The geometry determines the Fisher information, the
mutual information, and the behavioral performance of an ideal observer in a
range of psychophysical tasks. We argue that future studies can benefit from
considering both tuning and geometry to understand neural codes and reveal the
connections between stimulus, brain activity, and behavior.


Abstract:
  Wireless indoor coverage and data capacity are important aspects of cellular
networks. With the ever-increasing data traffic, demand for more data capacity
indoors is also growing. The lower frequencies of the legacy frequency bands of
macro outdoor cells manage to provide coverage inside buildings, however, new
frequencies foreseen for the 5th generation (5G) of mobile communications in
the millimeter wave (mmWave) spectrum penetrate very poorly into buildings.
Therefore, a massive densification of the network would require to deploy a
large number of indoor small cells, which would lead to high deployment costs
to install the necessary wired/optical backhaul. Hence, other methods are
needed that allow an increase of the data capacity indoors, bearing a lower
cost than a fiber deployment. We propose a cost-efficient out-of-band repeater
architecture that provides more data capacity indoors than an outdoor
macro/micro network can provide to indoor, without adversely affecting a legacy
network, and which readily works with the established cellular infrastructure
as well as standard handsets/smartphones. This proposal is compared to
conventional in- and out-of-band repeaters and relay nodes in order to
highlight the advantages of our solution. While the data capacity for a single
link is similar to that of repeaters and relays, a macro cell can be
effectively offloaded. Cell capacities corresponding to at least 3--4 times
that of a repeater or relay solution can be provided, depending on the number
of parallel installed links and the bandwidth in the mmWave spectrum.


Abstract:
  We are developing tools for working with arbitrary left-exact localizations
of $\infty$-topoi. We introduce a notion of higher sheaf with respect to an
arbitrary set of maps $\Sigma$ in an $\infty$-topos $\mathscr{E}$. We show that
the full subcategory of higher sheaves $\mathrm{Sh}(\mathscr{E},\Sigma)$ is an
$\infty$-topos, and that the sheaf reflection $\mathscr{E}\to
\mathrm{Sh}(\mathscr{E},\Sigma)$ is the left-exact localization generated by
$\Sigma$. The proof depends on the notion of congruence, which is a substitute
for the notion of Grothendieck topology in 1-topos theory.