Abstract:
  Ensembles of machine learning models yield improved system performance as
well as robust and interpretable uncertainty estimates; however, their
inference costs may often be prohibitively high. \emph{Ensemble Distribution
Distillation} is an approach that allows a single model to efficiently capture
both the predictive performance and uncertainty estimates of an ensemble. For
classification, this is achieved by training a Dirichlet distribution over the
ensemble members' output distributions via the maximum likelihood criterion.
Although theoretically principled, this criterion exhibits poor convergence
when applied to large-scale tasks where the number of classes is very high. In
our work, we analyze this effect and show that the Dirichlet log-likelihood
criterion classes with low probability induce larger gradients than
high-probability classes. This forces the model to focus on the distribution of
the ensemble tail-class probabilities. We propose a new training objective that
minimizes the reverse KL-divergence to a \emph{Proxy-Dirichlet} target derived
from the ensemble. This loss resolves the gradient issues of Ensemble
Distribution Distillation, as we demonstrate both theoretically and empirically
on the ImageNet and WMT17 En-De datasets containing 1000 and 40,000 classes,
respectively.


Abstract:
  In this paper, dyadic Green's function for a graphene-dielectric stack is
formulated based on the scattering superposition method. To this end,
scattering Green's function in each layer is expanded in terms of cylindrical
vector wave functions with unknown coefficients. Using the Kronecker delta
function in the field expansion, it is considered that the field and source
points lie in the arbitrary layers. Afterward, recurrence relations for
calculating the unknown expansion coefficients are derived by applying the
impedance boundary conditions at the interface of a graphene sheet surrounded
by two adjacent dielectric layers. The verification of the calculated
coefficients is conducted by utilizing them in the analysis of graphene-based
structures with different numbers of layers, including 1) free-standing
frequency-selective surfaces (FSSs) and 2) parallel plates (PPs) with graphene
walls. A potential application of our proposed structure is investigating the
interaction of donor-acceptor pairs resided in the arbitrary layers of the
graphene-dielectric stack with a desired number of layers.


Abstract:
  Portfolio management aims at maximizing the return on investment while
minimizing risk by continuously reallocating the assets forming the portfolio.
These assets are not independent but correlated during a short time period. A
graph convolutional reinforcement learning framework called DeepPocket is
proposed whose objective is to exploit the time-varying interrelations between
financial instruments. These interrelations are represented by a graph whose
nodes correspond to the financial instruments while the edges correspond to a
pair-wise correlation function in between assets. DeepPocket consists of a
restricted, stacked autoencoder for feature extraction, a convolutional network
to collect underlying local information shared among financial instruments, and
an actor-critic reinforcement learning agent. The actor-critic structure
contains two convolutional networks in which the actor learns and enforces an
investment policy which is, in turn, evaluated by the critic in order to
determine the best course of action by constantly reallocating the various
portfolio assets to optimize the expected return on investment. The agent is
initially trained offline with online stochastic batching on historical data.
As new data become available, it is trained online with a passive concept drift
approach to handle unexpected changes in their distributions. DeepPocket is
evaluated against five real-life datasets over three distinct investment
periods, including during the Covid-19 crisis, and clearly outperformed market
indexes.


Abstract:
  Lagrangian cobordisms between Legendrian knots arise in Symplectic Field
Theory and impose an interesting and not well-understood relation on Legendrian
knots. There are some known "elementary" building blocks for Lagrangian
cobordisms that are smoothly the attachment of $0$- and $1$-handles. An
important question is whether every pair of non-empty Legendrians that are
related by a connected Lagrangian cobordism can be related by a ribbon
Lagrangian cobordism, in particular one that is "decomposable" into a
composition of these elementary building blocks. We will describe these and
other combinatorial building blocks as well as some geometric methods,
involving the theory of satellites, to construct Lagrangian cobordisms. We will
then survey some known results, derived through Heegaard Floer Homology and
contact surgery, that may provide a pathway to proving the existence of
nondecomposable (nonribbon) Lagrangian cobordisms.


Abstract:
  We study the dynamics of a generic endomorphism $f$ of an Oka-Stein manifold
$X$. Such manifolds include all connected linear algebraic groups and, more
generally, all Stein homogeneous spaces of complex Lie groups. We give several
descriptions of the Fatou set and the Julia set of $f$. In particular, we show
that the Julia set is the derived set of the set of attracting periodic points
of $f$ and that it is also the closure of the set of repelling periodic points
of $f$. Among other results, we prove that $f$ is chaotic on the Julia set and
that every periodic point of $f$ is hyperbolic. We also give an explicit
description of the "Conley decomposition" of $X$ induced by $f$ into
chain-recurrence classes and basins of attractors. For $X=\mathbb{C}$, we prove
that every Fatou component is a disc and that every point in the Fatou set is
attracted to an attracting cycle or lies in a dynamically bounded wandering
domain (whether such domains exist is an open question).


Abstract:
  Integrated access and backhaul (IAB) networks have the potential to provide
high data rate in both access and backhaul networks by sharing the same
spectrum. Due to the dense deployment of small base stations (SBSs), IAB
networks connect SBSs to the core network in a wireless manner without the
deployment of high-cost optical fiber. As large spectrum is available in mmWave
bands and high data rate is achieved by using directional beamforming, the
access and backhaul links can be integrated in the same frequency band while
satisfying quality-of-service constraints. In this work, we optimize the
scheduling of access and backhaul links such that the minimum throughput of the
access links is maximized based on the revised simplex method. By considering a
probability based line-ofsight (LOS) and non-line-of-sight (NLOS) path loss
model and the antenna array gains, we compare the achievable minimum access
throughput of the IAB network with the network with only macro base stations,
and study the effect of the network topology and antenna parameters on the
achievable minimum throughput. Simulation results show that, for a broad range
of parameter settings, the implementation of IABs improves the access minimum
achievable throughput.


Abstract:
  What fits the human brain does not necessarily mean whatever is invented is
meant for human brain. This is exactly what is implied by the definition of
algorithm: \textit{Reducing and Simplifying}. Our cognitive apparatus, although
endowed with certain parameters, ceases to comprehend chains of mathematical
and logical steps used for algorithmic or mathematical purposes. Such a
cognitive limit allows us to pay attention to the symbolic regularities which
are meaningful. The more meaningless these symbols become, the fuzzier is our
mental representation for it. Needless to say, that we actually tend to
interact with activities that are meaningful, and discard those that are
meaningless. By activities we mean steps, steps that form an algorithm. Could
it be just an assumption? Are we really aversing from algorithms in modern day
life?
  --
  Ziel dieser Arbeit war eine kritische Auseinandersetzung mit dem
biokybernetischen Denkansatz anhand des Sensitivit\"atsmodells von Frederic
Vester, das in die sozioinformatische Analyse eines sozio-technischen Systems
integriert und hinsichtlich der Fragestellung gepr\"uft wurde, inwieweit sich
dieses Konzept zur Beurteilung von Planungsvorhaben bei sozio-technischen
Systemen eignet. Dazu wurden F. Vesters Biokybernetischenn Grundregeln zur
Feststellung der \"Uberlebensf\"ahigkeit eines Systems, auf ihre Anwendbarkeit
in Bezug auf sozio-technische Systeme untersucht. Die Funktionalit\"at der
Analysemethode wurde auf das nach der Definition von Andrea Kienle und Gabriele
Kunau als sozio-technisches System charakterisierte Unternehmen Uber
angewendet. Im Gegensatz zu unsystemischen Analysen besteht der Vorteil des
Sensitivit\"atsmodells in der Vermittlung von Erkenntnissen \"uber innere
Systemzusammenh\"ange. Abschlie{\ss}end wurde der vorgestellte Analyseansatz
als transdisziplin\"are Weiterentwicklung des Sensitivit\"atsmodells
charakterisiert.


Abstract:
  A prototype of a single-gap glass Resistive Plate Chamber (RPC) is
constructed by the authors. To find the considerations required for better
operation of the detector's gas system, we have simulated the flow of the Ar
gas through the detector by using computational fluid dynamic methods.
Simulations show that the pressure inside the chamber linearly depends on the
gas flow rate and the chamber's output hose length. The simulation results were
compatible with experiments. We have found that the pressure-driven speed of
the gas molecules is two orders of magnitude larger in the inlet and outlet
regions than the blocked corners of a 14x14 cm2 chamber, and most likely seems
to be higher for larger detectors and different geometries.


Abstract:
  Daisy (Data Analysis Integrated Software System) has been designed for the
analysis and visualization of the X-ray experiments. To address an extensive
range of Chinese radiation facilities community's requirements from purely
algorithmic problems to scientific computing infrastructure, Daisy sets up a
cloud-native platform to support on-site data analysis services with fast
feedback and interaction. The plugs-in based application is convenient to
process the expected high throughput data flow in parallel at next-generation
facilities such as the High Energy Photon Source (HEPS). The objectives,
functionality and architecture of Daisy are described in this article.


Abstract:
  A recent work [Phys. Rev. Lett. 125, 110602] showed that among a pair of
\textit{thermodynamically} equidistant quenches from a colder and a hotter
initial state at a fixed ambient temperature, the relaxation from the colder
initial state (\textit{uphill} relaxation) is always faster, for dynamics close
to stable minima. Here we show that this is not generically the case for open
quantum systems with two or three energy levels. We find that both faster
uphill and faster downhill relaxation and symmetric thermal relaxation can be
observed in equidistant quenches, depending on the transition rates and the
choice of the distance measure used. Furthermore, we obtain a phase diagram in
the parameter space for the three-level system corresponding to different
thermalization behaviours.


Abstract:
  The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in
various types of optimization problems in real-life. Recently, some
Multifactorial Evolutionary Algorithm (MFEA) have been introduced to deal with
the CluSPT, however these researches still have some shortcomings such as
evolution operators only perform on complete graphs, huge resource consumption
for finding the solution on large search spaces. To overcome these limitations,
this paper describes a MFEA-based approach to solve the CluSPT. The proposed
algorithm utilizes Dijkstra's algorithm to construct the spanning trees in
clusters while using evolutionary operators for building the spanning tree
connecting clusters. This approach takes advantage of both exact and
approximate algorithms so it enables the algorithm to function efficiently on
complete and sparse graphs alike. Furthermore, evolutionary operators such as
individual encoding and decoding methods are also designed with great
consideration regarding performance and memory usage. We have included a proof
on the repairing method's efficacy in ensuring all solutions are valid. We have
conducted tests on various types of Euclidean instances to assess the
effectiveness of the proposed algorithm and methods. Experiment results point
out the effectiveness of the proposed algorithm existing heuristic algorithms
in most of the test cases. The impact of the proposed MFEA was analyzed and a
possible influential factor that may be useful for further study was also
pointed out.


Abstract:
  Successful adoption of deep learning (DL) in the wild requires models to be:
(1) compact, (2) accurate, and (3) robust to distributional shifts.
Unfortunately, efforts towards simultaneously meeting these requirements have
mostly been unsuccessful. This raises an important question: Is the inability
to create Compact, Accurate, and Robust Deep neural networks (CARDs)
fundamental? To answer this question, we perform a large-scale analysis of
popular model compression techniques which uncovers several intriguing
patterns. Notably, in contrast to traditional pruning approaches (e.g., fine
tuning and gradual magnitude pruning), we find that "lottery ticket-style"
approaches can surprisingly be used to produce CARDs, including binary-weight
CARDs. Specifically, we are able to create extremely compact CARDs that,
compared to their larger counterparts, have similar test accuracy and matching
(or better) robustness -- simply by pruning and (optionally) quantizing.
Leveraging the compactness of CARDs, we develop a simple domain-adaptive
test-time ensembling approach (CARD-Decks) that uses a gating module to
dynamically select appropriate CARDs from the CARD-Deck based on their
spectral-similarity with test samples. The proposed approach builds a "winning
hand'' of CARDs that establishes a new state-of-the-art (on RobustBench) on
CIFAR-10-C accuracies (i.e., 96.8% standard and 92.75% robust) and CIFAR-100-C
accuracies (80.6% standard and 71.3% robust) with better memory usage than
non-compressed baselines (pretrained CARDs and CARD-Decks available at
https://github.com/RobustBench/robustbench). Finally, we provide theoretical
support for our empirical findings.


Abstract:
  We use sub-arcsecond resolution ($\sim$0.4$''$) observations with NOEMA at
1.37 mm to study the dust emission and molecular gas of 18 high-mass
star-forming regions. We combine the derived physical and chemical properties
of individual cores in these regions to estimate their ages. The temperature
structure of these regions are determined by fitting H2CO and CH3CN line
emission. The density profiles are inferred from the 1.37 mm continuum
visibilities. The column densities of 11 different species are determined by
fitting the emission lines with XCLASS. Within the 18 observed regions, we
identify 22 individual cores with associated 1.37 mm continuum emission and
with a radially decreasing temperature profile. We find an average temperature
power-law index of q = 0.4$\pm$0.1 and an average density power-law index of p
= 2.0$\pm$0.2 on scales on the order of several 1 000 au. Comparing these
results with values of p derived in the literature suggest that the density
profiles remain unchanged from clump to core scales. The column densities
relative to N(C18O) between pairs of dense gas tracers show tight correlations.
We apply the physical-chemical model MUSCLE to the derived column densities of
each core and find a mean chemical age of $\sim$60 000 yrs and an age spread of
20 000-100 000 yrs. With this paper we release all data products of the CORE
project available at https://www.mpia.de/core. The CORE sample reveals well
constrained density and temperature power-law distributions. Furthermore, we
characterize a large variety in molecular richness that can be explained by an
age spread confirmed by our physical-chemical modeling. The hot molecular cores
show the most emission lines, but we also find evolved cores at an evolutionary
stage, in which most molecules are destroyed and thus the spectra appear
line-poor again.


Abstract:
  In this paper, we propose the beginnings of a formal framework for modeling
narrative \textit{qua} narrative. Our framework affords the ability to discuss
key qualities of stories and their communication, including the flow of
information from a Narrator to a Reader, the evolution of a Reader's story
model over time, and Reader uncertainty. We demonstrate its applicability to
computational narratology by giving explicit algorithms for measuring the
accuracy with which information was conveyed to the Reader and two novel
measurements of story coherence.


Abstract:
  Design and construction of the instruments for ESO's Extremely Large
Telescope (ELT) began in 2015. We present here a brief overview of the status
of the ELT Instrumentation Plan. Dedicated articles on each instrument are
presented elsewhere this volume.


Abstract:
  Time-dependent density functional theory has emerged as a method of choice
for calculations of spectra and response properties in physics, chemistry, and
biology, with its system-size scaling enabling computations on systems much
larger than possible otherwise. While increasingly complex and interesting
systems have been successfully tackled with relatively simple functional
approximations, there has also been increasing awareness that these functionals
tend to fail for certain classes of approximations. I review the fundamental
challenges the approximate functionals have in describing double-excitations
and charge-transfer excitations, which are two of the most common impediments
for the theory to be applied in a black box way. At the same time, I describe
the progress made in recent decades in developing functional approximations
that give useful predictions for these excitations.


Abstract:
  A two-step concept for 3D segmentation on 5 abdominal organs inside
volumetric CT images is presented. First each relevant organ's volume of
interest is extracted as bounding box. The extracted volume acts as input for a
second stage, wherein two compared U-Nets with different architectural
dimensions re-construct an organ segmentation as label mask. In this work, we
focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial results
indicate Dice improvements of about 6\% at maximum. In this study to our
surprise, liver and kidneys for instance were tackled significantly better
using the faster and GPU-memory saving 2D U-Nets. For other abdominal key
organs, there were no significant differences, but we observe highly
significant advantages for the 2D U-Net in terms of GPU computational efforts
for all organs under study.


Abstract:
  Electronic transport in Weyl semimetals is quite extraordinary due to the
topological property of the chiral anomaly generating the charge pumping
between two distant Weyl nodes with opposite chiralities under parallel
electric and magnetic fields. Here, we develop a full nonequilibrium quantum
transport theory of the chiral anomaly, based on the fact that the chiral
charge pumping is essentially nothing but the Bloch oscillation. Specifically,
by using the Keldysh nonequilibrium Green function method, it is shown that
there is a rich structure in the chiral anomaly transport, including the
negative magnetoresistance, the non-Ohmic behavior, the Esaki-Tsu peak, and
finally the resonant oscillation of the DC electric current as a function of
electric field, called the electric quantum oscillation. We argue that, going
beyond the usual behavior of linear response, the non-Ohmic behavior observed
in BiSb alloys can be regarded as a precursor to the occurrence of electric
quantum oscillation, which is both topologically and energetically protected in
Weyl semimetals.


Abstract:
  Let $F$ be a distribution function on the integer lattice $\mathbb{Z}$ and
$S=(S_n)$ the random walk with step distribution $F$. Suppose $S$ is
oscillatory and denote by $U_{\rm a}(x)$ and $u_{\rm a}(x)$ the renewal
function and sequence, respectively, of the strictly ascending ladder height
process associated with $S$. Putting $A(x) =\int_0^x [1-F(t)-F(-t)] dt$,
$H(x)=1-F(x)+F(-x)$ we suppose
  $$A(x)/\big(xH(x)\big) \to -\infty \quad (x\to\infty).$$ Under some
additional regularity condition on the positive tail of $F$, we show that
$$u_{\rm a}(x) \sim U_{\rm a}(x)[1-F(x)]/|A(x)|$$ as $x\to\infty$ and uniformly
for $0\leq x\leq R\in \mathbb{Z}$, as $R\to\infty$ $$P [ S\; \mbox{leaves
$[0,R]$ on its upper side}\, |\, S_0=x] \, \sim\, c^{-1}A(x)u_{\rm a}(x), $$
where $c= \sum_{n=1}^\infty P[S_{n}>S_0;\, S_k < S_0$ for $0<k<n]$ and the
regularity condition is satisfied at least if $S$ is recurrent, $\limsup
[1-F(x)]/F(-x)<1$, and $x[1-F(x)]/L(x) $ ($x\geq 1$) is bounded away from zero
and infinity for some slowly varying function $L$. We also give some asymptotic
estimates of the probability that $S$ visits $R$ before entering the negative
half-line for asymptotically stable walks and obtain asymptotic behaviour of
the probability that $R$ is ever hit by $S$ conditioned to avoid the negative
half-line forever.


Abstract:
  Minimizing the number of necessary two-qubit gates is an important task in
quantum information processing. By introducing non-computational quantum states
in auxiliary spaces, we construct effective circuits for the controlled-NOT
(CNOT) gate and the n-control-qubit Toffoli gate with (2n-1) qubit-qudit gates
and (2n-2) single-qudit gates. We propose the polarization CNOT and Toffoli
gates based on the designed quantum circuits in linear optics by operating on
the spatial-mode degree of freedom of photons. Our optical schemes can be
achieved with a higher success probability and no extra auxiliary photons are
needed.


Abstract:
  This paper introduces a new class of right-angled Coxeter groups with totally
disconnected Morse boundaries. We construct this class recursively by examining
how the Morse boundary of a right-angled Coxeter group changes if we glue a
graph to its defining graph. More generally, we present a method to construct
amalgamated free products of CAT(0) groups with totally disconnected Morse
boundaries that act geometrically on CAT(0) spaces that have a treelike block
decomposition.


Abstract:
  We apply the Noether Symmetry Approach to point-like teleparallel Lagrangians
in view to derive minisuperspaces suitable for Quantum Cosmology. Adopting the
Arnowitt-Deser-Misner formalism, we find out related Wave Functions of the
Universe. Specifically, by means of appropriate changes of variables suggested
by the existence of Noether symmetries, it is possible to obtain the
cosmological Hamiltonians whose solutions are classical trajectories
interpretable as observable universes.


Abstract:
  Imitation learning (IL) enables robots to acquire skills quickly by
transferring expert knowledge, which is widely adopted in reinforcement
learning (RL) to initialize exploration. However, in long-horizon motion
planning tasks, a challenging problem in deploying IL and RL methods is how to
generate and collect massive, broadly distributed data such that these methods
can generalize effectively. In this work, we solve this problem using our
proposed approach called {self-imitation learning by planning (SILP)}, where
demonstration data are collected automatically by planning on the visited
states from the current policy. SILP is inspired by the observation that
successfully visited states in the early reinforcement learning stage are
collision-free nodes in the graph-search based motion planner, so we can plan
and relabel robot's own trials as demonstrations for policy learning. Due to
these self-generated demonstrations, we relieve the human operator from the
laborious data preparation process required by IL and RL methods in solving
complex motion planning tasks. The evaluation results show that our SILP method
achieves higher success rates and enhances sample efficiency compared to
selected baselines, and the policy learned in simulation performs well in a
real-world placement task with changing goals and obstacles.


Abstract:
  Jet reconstruction is critical for the precision measurement of Higgs boson
properties and the electroweak observables at the CEPC. We analyze the jet
energy and angular responses of benchmark 2- and 4-jet processes with fully
simulated samples with the CEPC baseline detector geometry. We observe a
relative resolution of 3.5$\%$ and 1$\%$ on the jet energy and angular
measurement for jets in the detector barrel region ($|cos{\theta}| < 0.6$) with
energy greater than 60 GeV. Meanwhile, the jet energy/angular scale can be
controlled within 0.5/0.01$\%$. The differential dependences of the jet
response on the jet direction and energy are extracted. We also analyze the
impact on the jet responses induced by different jet clustering algorithms and
matching criteria, which yields a relative difference of up to 8$\%$.


Abstract:
  This paper considers two types of boundary control problems for linear
transport equations. The first one shows that transport solutions on a
subdomain of a domain X can be controlled exactly from incoming boundary
conditions for X under appropriate convexity assumptions. This is in contrast
with the only approximate control one typically obtains for elliptic equations
by an application of a unique continuation property, a property which we prove
does not hold for transport equations. We also consider the control of an
outgoing solution from incoming conditions, a transport notion similar to the
Dirichlet-to-Neumann map for elliptic equations. We show that for well-chosen
coefficients in the transport equation, this control may not be possible. In
such situations and by (Fredholm) duality, we obtain the existence of
non-trivial incoming conditions that are compatible with vanishing outgoing
conditions.


Abstract:
  The field explainable artificial intelligence (XAI) has brought about an
arsenal of methods to render Machine Learning (ML) predictions more
interpretable. But how useful explanations provided by transparent ML methods
are for humans remains difficult to assess. Here we investigate the quality of
interpretable computer vision algorithms using techniques from psychophysics.
In crowdsourced annotation tasks we study the impact of different
interpretability approaches on annotation accuracy and task time. We compare
these quality metrics with classical XAI, automated quality metrics. Our
results demonstrate that psychophysical experiments allow for robust quality
assessment of transparency in machine learning. Interestingly the quality
metrics computed without humans in the loop did not provide a consistent
ranking of interpretability methods nor were they representative for how useful
an explanation was for humans. These findings highlight the potential of
methods from classical psychophysics for modern machine learning applications.
We hope that our results provide convincing arguments for evaluating
interpretability in its natural habitat, human-ML interaction, if the goal is
to obtain an authentic assessment of interpretability.


Abstract:
  Inspired by real-world applications such as the assignment of pupils to
schools or the allocation of social housing, the one-sided matching problem
studies how a set of agents can be assigned to a set of objects when the agents
have preferences over the objects, but not vice versa. For fairness reasons,
most mechanisms use randomness, and therefore result in a probabilistic
assignment. We study the problem of decomposing these probabilistic assignments
into a weighted sum of ex-post (Pareto-)efficient matchings, while maximizing
the worst-case number of assigned agents. This decomposition preserves all the
assignments' desirable properties, most notably strategy-proofness. For a
specific class of probabilistic assignments, including the assignment by the
Probabilistic Serial mechanism, we propose a polynomial-time algorithm for this
problem that obtains a decomposition in which all matchings assign at least the
expected number of assigned agents by the probabilistic assignment, rounded
down, thus achieving the theoretically best possible guarantee. For general
probabilistic assignments, the problem becomes NP-hard. For the Random Serial
Dictatorship mechanism, we show that the worst-case number of assigned agents
is at least half of the optimal, and that this bound is asymptotically tight.
Lastly, we propose a column generation framework for the introduced problem,
which we evaluate both on randomly generated data, and on real-world school
choice data from the Belgian cities Antwerp and Ghent.


Abstract:
  Sampling-based algorithms solve the path planning problem by generating
random samples in the search-space and incrementally growing a connectivity
graph or a tree. Conventionally, the sampling strategy used in these algorithms
is biased towards exploration to acquire information about the search-space. In
contrast, this work proposes an optimization-based procedure that generates new
samples to improve the cost-to-come value of vertices in a neighborhood. The
application of proposed algorithm adds an exploitative-bias to sampling and
results in a faster convergence to the optimal solution compared to other
state-of-the-art sampling techniques. This is demonstrated using benchmarking
experiments performed fora variety of higher dimensional robotic planning
tasks.


Abstract:
  Face image manipulation via three-dimensional guidance has been widely
applied in various interactive scenarios due to its semantically-meaningful
understanding and user-friendly controllability. However, existing
3D-morphable-model-based manipulation methods are not directly applicable to
out-of-domain faces, such as non-photorealistic paintings, cartoon portraits,
or even animals, mainly due to the formidable difficulties in building the
model for each specific face domain. To overcome this challenge, we propose, as
far as we know, the first method to manipulate faces in arbitrary domains using
human 3DMM. This is achieved through two major steps: 1) disentangled mapping
from 3DMM parameters to the latent space embedding of a pre-trained StyleGAN2
that guarantees disentangled and precise controls for each semantic attribute;
and 2) cross-domain adaptation that bridges domain discrepancies and makes
human 3DMM applicable to out-of-domain faces by enforcing a consistent latent
space embedding. Experiments and comparisons demonstrate the superiority of our
high-quality semantic manipulation method on a variety of face domains with all
major 3D facial attributes controllable-pose, expression, shape, albedo, and
illumination. Moreover, we develop an intuitive editing interface to support
user-friendly control and instant feedback. Our project page is
https://cassiepython.github.io/cddfm3d/index.html


Abstract:
  It is well-known that a BKP tau-function is the square root of a certain KP
tau-function, provided one puts the even KP times equal to zero. In this paper
we compute for all polynomial BKP tau-function its corresponding KP "square".
We also give, in the polynomial case, a representation theoretical proof of a
recent result by Alexandov, viz. that a KdV tau-function becomes a BKP
tau-function when one divides all KdV times by 2.


Abstract:
  In shared spaces, motorized and non-motorized road users share the same space
with equal priority. Their movements are not regulated by traffic rules, hence
they interact more frequently to negotiate priority over the shared space. To
estimate the safeness and efficiency of shared spaces, reproducing the traffic
behavior in such traffic places is important. In this paper, we consider and
combine different levels of interaction between pedestrians and cars in shared
space environments. Our proposed model consists of three layers: a layer to
plan trajectories of road users; a force-based modeling layer to reproduce free
flow movement and simple interactions; and a game-theoretic decision layer to
handle complex situations where road users need to make a decision over
different alternatives. We validate our model by simulating scenarios involving
various interactions between pedestrians and cars and also car-to-car
interaction. The results indicate that simulated behaviors match observed
behaviors well.


Abstract:
  We construct infinite new classes of $AdS_4\times S^1\times S^5$ solutions of
type IIB string theory which have non-trivial $SL(2,\mathbb{Z})$ monodromy
along the $S^1$ direction. The solutions are supersymmetric and holographically
dual, generically, to $\mathcal{N}=1$ SCFTs in $d=3$. The solutions are first
constructed as $AdS_4\times \mathbb{R}$ solutions in $D=5$ $SO(6)$ gauged
supergravity and then uplifted to $D=10$. Unlike the known $AdS_4\times
\mathbb{R}$ S-fold solutions, there is no continuous symmetry associated with
the $\mathbb{R}$ direction. The solutions all arise as limiting cases of Janus
solutions of $d=4$, $\mathcal{N}=4$ SYM theory which are supported both by a
different value of the coupling constant on either side of the interface, as
well as by fermion and boson mass deformations. As special cases, the
construction recovers three known S-fold constructions, preserving
$\mathcal{N}=1,2$ and 4 supersymmetry, as well as a recently constructed
$\mathcal{N}=1$ $AdS_4\times S^1\times S^5$ solution (not S-folded). We also
present some novel "one-sided Janus" solutions that are non-singular.


Abstract:
  In this paper, we develop a novel analytic method to prove the prime number
theorem in de la Vall\'ee Poussin's form: $$
\pi(x)=\operatorname{li}(x)+\mathcal O(xe^{-c\sqrt{\log x}}) $$ Instead of
performing asymptotic expansion on Chebyshev functions as in conventional
analytic methods, this new approach uses contour-integration method to analyze
Riemann's prime counting function $J(x)$, which only differs from $\pi(x)$ by
$\mathcal O(\sqrt x/\log x)$.


Abstract:
  The viscous flow of two immiscible fluids in a porous medium on the Darcy
scale is governed by a system of nonlinear parabolic equations. If infinite
mobility of one phase can be assumed (e.g. in soil layers in contact with the
atmosphere) the system can be substituted by the scalar Richards model. Thus,
the domain of the porous medium may be partitioned into disjoint subdomains
with either the full two-phase or the simplified Richards model dynamics.
Extending the one-model approach from [1, 2] we suggest coupling conditions for
this hybrid model approach. Based on an Euler implicit discretisation, a linear
iterative (-type) domain decomposition scheme is proposed, and proven to be
convergent. The theoretical findings are verified by a comparative numerical
study that in particular confirms the efficiency of the hybrid ansatz as
compared to full two-phase model computations.


Abstract:
  IIoT services focused on industry-oriented services often require objects run
more than one task. IIoT objects poses the challenge of distributing and
managing task allocation among them. The fairness of task allocation brings
flexible network reconfiguration and maximizes the tasks to be performed.
Although existing approaches optimize and manage the dynamics of objects, not
all them consider both co-relationship between tasks and object capabilities
and the distributed allocation over the cluster service. This paper introduces
the ACADIA mechanism for task allocation in IIoT networks in order to
distribute task among objects. It relies on relational consensus strategies to
allocate tasks and similarity capabilities to determine which objects can play
in accomplishing those tasks. Evaluation on NS-3 showed that ACADIA achieved
98% of allocated tasks in an IIoT-Health considering all scenarios, average
more than 95% of clusters apt to performed tasks in a low response time, and
achieved 50% more effectiveness in task allocation compared to the literature
solution CONTASKI.


Abstract:
  By the method of intense terahertz laser spectroscopy, we provide strong
evidence that if an integer quantum Hall (IQH) system has asymmetric confining
potential and the external quantizing magnetic field has a nonzero in-plane
component, then a quantum phase may arise with spatially-ordered
quasi-one-dimensional orbit-like states similar to the current-carrying edging
states in conventional IQH system. The emergence of the phase may be
interpreted in terms of a quantum phase transition from the IQH system, which
is accompanied by the breaking of translational symmetry in the direction of
the in-plane magnetic field. As a result, we get a fully-correlated macroscopic
quantum object that gives one a unique opportunity to test the foundations of
quantum mechanics (QM) at a macroscopic level. In particular, by means of this
object, we show experimentally that the concept of quantum spatial dynamics of
electrons without a definable trajectory remains relevant in the macro-world in
accordance with the foundations of standard QM while an alternative description
of such dynamics in terms of so-called Bohmian trajectories appears untenable.


Abstract:
  The role of gravity in human motor control is at the same time obvious and
difficult to isolate. It can be assessed by performing experiments in variable
gravity. We propose that adiabatic invariant theory may be used to reveal
nearly-conserved quantities in human voluntary rhythmic motion, an individual
being seen as a complex time-dependent dynamical system with bounded motion in
phase-space. We study an explicit realization of our proposal: An experiment in
which we asked participants to perform $\infty-$ shaped motion of their right
arm during a parabolic flight, either at self-selected pace or at a metronome's
given pace. Gravity varied between $0$ and $1.8$ $g$ during a parabola. We
compute the adiabatic invariants in participant's frontal plane assuming a
separable dynamics. It appears that the adiabatic invariant in vertical
direction increases linearly with $g$, in agreement with our model. Differences
between the free and metronome-driven conditions show that participants'
adaptation to variable gravity is maximal without constraint. Furthermore,
motion in the participant's transverse plane induces trajectories that may be
linked to higher-derivative dynamics. Our results show that adiabatic
invariants are relevant quantities to show the changes in motor strategy in
time-dependent environments.


Abstract:
  The unprecedented sky coverage and observing cadence of the All-Sky Automated
Survey for SuperNovae (ASAS-SN) has resulted in the discovery and continued
monitoring of a large sample of Galactic transients. The vast majority of these
are accretion-powered dwarf nova outbursts in cataclysmic variable systems, but
a small subset are thermonuclear-powered classical novae. Despite improved
monitoring of the Galaxy for novae from ASAS-SN and other surveys, the observed
Galactic nova rate is still lower than predictions. One way classical novae
could be missed is if they are confused with the much larger population of
dwarf novae. Here, we examine the properties of 1617 dwarf nova outbursts
detected by ASAS-SN and compare them to classical novae. We find that the mean
classical nova brightens by ~11 magnitudes during outburst, while the mean
dwarf nova brightens by only ~5 magnitudes, with the outburst amplitude
distributions overlapping by roughly 15%. For the first time, we show that the
amplitude of an outburst and the time it takes to decline by two magnitudes
from maximum are positively correlated for dwarf nova outbursts. For classical
novae, we find that these quantities are negatively correlated, but only
weakly, compared to the strong anti-correlation of these quantities found in
some previous work. We show that, even if located at large distances, only a
small number of putative dwarf novae could be mis-classified classical novae
suggesting that there is minimal confusion between these populations. Future
spectroscopic follow-up of these candidates can show whether any are indeed
classical novae.


Abstract:
  We consider Hamiltonian simulation using the first order Lie-Trotter product
formula under the assumption that the initial state has a high overlap with an
energy eigenstate, or a collection of eigenstates in a narrow energy band. This
assumption is motivated by quantum phase estimation (QPE) and digital adiabatic
simulation (DAS). Treating the effective Hamiltonian that generates the
Trotterized time evolution using rigorous perturbative methods, we show that
the Trotter step size needed to estimate an energy eigenvalue within precision
$\epsilon$ using QPE can be improved in scaling from $\epsilon$ to
$\epsilon^{1/2}$ for a large class of systems (including any Hamiltonian which
can be decomposed as a sum of local terms or commuting layers that each have
real-valued matrix elements). For DAS we improve the asymptotic scaling of the
Trotter error with the total number of gates $M$ from $\mathcal{O}(M^{-1})$ to
$\mathcal{O}(M^{-2})$, and for any fixed circuit depth we calculate an
approximately optimal step size that balances the error contributions from
Trotterization and the adiabatic approximation. These results partially
generalize to diabatic processes, which remain in a narrow energy band
separated from the rest of the spectrum by a gap, thereby contributing to the
explanation of the observed similarities between the quantum approximate
optimization algorithm and diabatic quantum annealing at small system sizes.
Our analysis depends on the perturbation of eigenvectors as well as
eigenvalues, and on quantifying the error using state fidelity (instead of the
matrix norm of the difference of unitaries which is sensitive to an overall
global phase).


Abstract:
  Accurate force fields are necessary for predictive molecular simulations.
However, developing force fields that accurately reproduce experimental
properties is challenging. Here, we present a machine learning directed,
multiobjective optimization workflow for force field parameterization that
evaluates millions of prospective force field parameter sets while requiring
only a small fraction of them to be tested with molecular simulations. We
demonstrate the generality of the approach and identify multiple low-error
parameter sets for two distinct test cases: simulations of hydrofluorocarbon
(HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal
phase. We discuss the challenges and implications of our force field
optimization workflow.


Abstract:
  Minimally invasive image guided treatment procedures often employ advanced
image processing algorithms. The recent developments of artificial intelligence
algorithms harbor potential to further enhance this domain. In this article we
explore several application areas within the minimally invasive treatment space
and discuss the deployment of artificial intelligence within these areas.


Abstract:
  Text analytics directly on compression (TADOC) has proven to be a promising
technology for big data analytics. GPUs are extremely popular accelerators for
data analytics systems. Unfortunately, no work so far shows how to utilize GPUs
to accelerate TADOC. We describe G-TADOC, the first framework that provides
GPU-based text analytics directly on compression, effectively enabling
efficient text analytics on GPUs without decompressing the input data. G-TADOC
solves three major challenges. First, TADOC involves a large amount of
dependencies, which makes it difficult to exploit massive parallelism on a GPU.
We develop a novel fine-grained thread-level workload scheduling strategy for
GPU threads, which partitions heavily-dependent loads adaptively in a
fine-grained manner. Second, in developing G-TADOC, thousands of GPU threads
writing to the same result buffer leads to inconsistency while directly using
locks and atomic operations lead to large synchronization overheads. We develop
a memory pool with thread-safe data structures on GPUs to handle such
difficulties. Third, maintaining the sequence information among words is
essential for lossless compression. We design a sequence-support strategy,
which maintains high GPU parallelism while ensuring sequence information. Our
experimental evaluations show that G-TADOC provides 31.1x average speedup
compared to state-of-the-art TADOC.


Abstract:
  We design a virtual element method for the numerical treatment of the
two-dimensional parabolic variational inequality problem on unstructured
polygonal meshes. Due to the expected low regularity of the exact solution, the
virtual element method is based on the lowest-order virtual element space that
contains the subspace of the linear polynomials defined on each element. The
connection between the nonnegativity of the virtual element functions and the
nonnegativity of the degrees of freedom, i.e., the values at the mesh vertices,
is established by applying the Maximum and Minimum Principle Theorem. The mass
matrix is computed through an approximate L 2 polynomial projection, whose
properties are carefully investigated in the paper. We prove the well-posedness
of the resulting scheme in two different ways that reveal the contractive
nature of the VEM and its connection with the minimization of quadratic
functionals. The convergence analysis requires the existence of a nonnegative
quasi-interpolation operator, whose construction is also discussed in the
paper. The variational crime introduced by the virtual element setting produces
five error terms that we control by estimating a suitable upper bound.
Numerical experiments confirm the theoretical convergence rate for the
refinement in space and time on three different mesh families including
distorted squares, nonconvex elements, and Voronoi tesselations.


Abstract:
  A finite set of integers $A$ tiles the integers by translations if
$\mathbb{Z}$ can be covered by pairwise disjoint translated copies of $A$.
Restricting attention to one tiling period, we have $A\oplus B=\mathbb{Z}_M$
for some $M\in\mathbb{N}$ and $B\subset\mathbb{Z}$. This can also be stated in
terms of cyclotomic divisibility of the mask polynomials $A(X)$ and $B(X)$
associated with $A$ and $B$.
  In this article, we introduce a new approach to a systematic study of such
tilings. Our main new tools are the box product, multiscale cuboids, and
saturating spaces, developed through a combination of harmonic-analytic and
combinatorial methods. We provide new criteria for tiling and cyclotomic
divisibility in terms of these concepts. As an application, we can determine
whether a set $A$ containing certain configuration can tile a cyclic group
$\mathbb{Z}_M$, or recover a tiling set based on partial information about it.
We also develop tiling reductions where a given tiling can be replaced by one
or more tilings with a simpler structure. The tools introduced here are crucial
in our proof in a follow-up paper that all tilings of period $(pqr)^2$, where
$p,q,r$ are distinct odd primes, satisfy a tiling condition proposed by Coven
and Meyerowitz.


Abstract:
  Modifying Besicovitch's construction of a set $\mathcal{B}$ of positive
integers whose set of multiples $\mathcal{M}_{\mathcal{B}}$ has no asymptotic
density, we provide examples of such sets $\mathcal{B}$ for which
$\eta:=1_{\mathbb{Z}\setminus\mathcal{M}_{\mathcal{B}}}\in\{0,1\}^{\mathbb{Z}}$
is a Toeplitz sequence. Moreover our construction produces examples, for which
$\eta$ is not only quasi-generic for the Mirsky measure (which has discrete
dynamical spectrum), but also for some measure of positive entropy. On the
other hand, modifying slightly an example from Kasjan, Keller, and Lema\'nczyk,
we construct a set $\mathcal{B}$ for which $\eta$ is an irregular Toeplitz
sequence but for which the orbit closure of $\eta$ in $\{0,1\}^{\mathbb{Z}}$ is
uniquely ergodic.


Abstract:
  We define a generalised model for three-stranded DNA consisting of two chains
of one type and a third chain of a different type. The DNA strands are modelled
by random walks on the three-dimensional cubic lattice with different
interactions between two chains of the same type and two chains of different
types. This model may be thought of as a classical analogue of the quantum
three-body problem. In the quantum situation it is known that three identical
quantum particles will form a triplet with an infinite tower of bound states at
the point where any pair of particles would have zero binding energy. The phase
diagram is mapped out, and the different phase transitions examined using
finite-size scaling. We look particularly at the scaling of the DNA model at
the equivalent Efimov point for chains up to 10000 steps in length. We find
clear evidence of several bound states in the finite-size scaling. We compare
these states with the expected Efimov behaviour.


Abstract:
  The scalar field theory of cosmological inflation constitutes nowadays one of
the preferred scenarios for the physics of the early universe. In this paper we
aim at studying the inflationary universe making use of a numerical lattice
simulation. Various lattice codes have been written in the last decades and
have been extensively used for understating the reheating phase of the
universe, but they have never been used to study the inflationary phase itself
far from the end of inflation (i.e. about 50 e-folds before the end of
inflation). In this paper we use a lattice simulation to reproduce the
well-known results of some simple models of single-field inflation,
particularly for the scalar field perturbation. The main model that we consider
is the standard slow-roll inflation with an harmonic potential for the inflaton
field. We explore the technical aspects that need to be accounted for in order
to reproduce with precision the nearly scale invariant power spectrum of
inflaton perturbations. We also consider the case of a step potential, and show
that the simulation is able to correctly reproduce the oscillatory features in
the power spectrum of this model. Even if a lattice simulation is not needed in
these cases, that are well within the regime of validity of linear perturbation
theory, this sets the basis to future work on using lattice simulations to
study more complicated models of inflation.


Abstract:
  Multisite trials, in which treatment is randomized separately in multiple
sites, offer a unique opportunity to disentangle treatment effect variation due
to "compositional" differences in the distributions of unit-level features from
variation due to "contextual" differences in site-level features. In
particular, if we can re-weight (or "transport") each site to have a common
distribution of unit-level covariates, the remaining effect variation captures
contextual differences across sites. In this paper, we develop a framework for
transporting effects in multisite trials using approximate balancing weights,
where the weights are chosen to directly optimize unit-level covariate balance
between each site and the target distribution. We first develop our approach
for the general setting of transporting the effect of a single-site trial. We
then extend our method to multisite trials, assess its performance via
simulation, and use it to analyze a series of multisite trials of
welfare-to-work programs. Our method is available in the balancer R package.


Abstract:
  This study analyzes patterns of physical, mental, lifestyle, and personality
factors in college students in different periods over the course of a semester
and models their relationships with students' academic performance. The data
analyzed was collected through smartphones and Fitbit. The use of machine
learning models derived from the gathered data was employed to observe the
extent of students' behavior associated with their GPA, lifestyle, physical
health, mental health, and personality attributes. A mutual agreement method
was used in which rather than looking at the accuracy of results, the model
parameters and weights of features were used to find common behavioral trends.
From the results of the model creation, it was determined that the most
significant indicator of academic success defined as a higher GPA, was the
places a student spent their time. Lifestyle and personality factors were
deemed more significant than mental and physical factors. This study will
provide insight into the impact of different factors and the timing of those
factors on students' academic performance.


Abstract:
  The current computational study investigates the changes in solvation
dynamics of water when introducing hydrophobic side chains to the molecular
probe N-methyl-6-oxyquinolinium betaine. High precision transient fluorescence
and absorption measurements published in the companion paper revealed an
influence of hydrophobic sidechain alterations on the observed solvation
dynamics of a chromophore in water. As the influence of shape, size and
structure of chromophores on the time-dependent Stokes shift was so far thought
to play a role only in slowly rotating solvents compared to the solute or if
the hydrogen bonding ability of the solute changes, this finding is quite
unexpected. Analysis of the time-dependent Stokes shift obtained from
nonequilibrium simulations corroborates experimental retardation factors and
activation energies, and indicates that solute motion, namely vibration, is
mainly responsible for the observed retardation of solvation dynamics. The
faster dynamics around the smaller chromophore is in fact achieved by some
normal modes located at the pyridinium part of the chromophore. Rotation also
contributes to a very small extent to hydration dynamics, but for small and
large derivatives alike. Local residence times furthermore reveal slight
retardations in the first solvent shell around the chromophores. The current
picture of the solute acting as a passive molecular probe therefore needs to be
revised even for solvents like water.


Abstract:
  We study bifurcation phenomena in natural families of rational,
(transcendental) entire or meromorphic functions of finite type $\{f_\lambda :=
\varphi_\lambda \circ f_{\lambda_0} \circ \psi^{-1}_\lambda\}_{\lambda\in M}$,
where $M$ is a complex connected manifold, $\lambda_0\in M$, $f_{\lambda_0}$ is
a meromorphic map and $\varphi_\lambda$ and $\psi_\lambda$ are families of
quasiconformal homeomorphisms depending holomorphically on $\lambda$ and with
$\psi_\lambda(\infty)=\infty$. There are fundamental differences compared to
the rational or entire setting due to the presence of poles and therefore of
parameters for which singular values are eventually mapped to infinity
(singular parameters). Under mild geometric conditions we show that singular
(asymptotic) parameters are the endpoint of a curve of parameters for which an
attracting cycle progressively exits de domain, while its multiplier tends to
zero. This proves the main conjecture by Fagella and Keen (asymptotic
parameters are virtual centers) in a very general setting. Other results in the
paper show the connections between cycles exiting the domain, singular
parameters, activity of singular orbits and $J$-unstability, converging to a
theorem in the spirit of the celebrated result by Ma\~{n}\'e-Sad-Sullivan and
Lyubich.


Abstract:
  The air source heat pump (ASHP) systems assisted by solar energy have drawn
great attentions, owing to their great feasibility in buildings for space
heating/cooling and hot water purposes. However, there are a variety of
configurations, parameters and performance criteria of solar assisted ASHP
systems, leading to a major inconsistency that increase the degree of
complexity to compare and implement different systems. A comparative literature
review is lacking, with the aim to evaluate the performance of various ASHP
systems from three main solar sources, such as solar thermal (ST), photovoltaic
(PV) and hybrid photovoltaic/thermal (PV/T). This paper thus conducts a
systematic review of the prevailing solar assisted ASHP systems, including
their boundary conditions, system configurations, performance indicators,
research methodologies and system performance. The comparison result indicates
that PV-ASHP system has the best techno-economic performance, which performs
best in average with coefficient of performance (COP) of around 3.75, but with
moderate cost and payback time. While ST-ASHP and PV/T-ASHP systems have lower
performance with mean COP of 2.90 and 3.03, respectively. Moreover, PV/T-ASHP
system has the highest cost and longest payback time, while ST-ASHP has the
lowest ones. Future research are discussed from aspects of methodologies,
system optimization and standard evaluation.


Abstract:
  We consider an entanglement harvesting protocol between two Unruh-deWitt
detectors in quantum superpositions of static trajectories in the static de
Sitter and thermal Minkowski spacetimes. We demonstrate for the first time that
the spatial superposition of each detector's path allows entanglement to be
harvested from the quantum field in regimes where it would be otherwise
impossible for detectors on classical trajectories. Surprisingly, for detectors
on sufficiently delocalised trajectories in a thermal bath, the amount of
harvested entanglement grows with the temperature of the field, violating a
no-go theorem derived by Simidzija et. al. (Phys. Rev. D 98, 085007). We also
discover interesting effects for mutual information harvesting, which depends
sensitively on the nonlocal correlations between the superposed trajectories
between the paths of the respective detectors.


Abstract:
  While multitask representation learning has become a popular approach in
reinforcement learning (RL), theoretical understanding of why and when it works
remains limited. This paper presents analyses for the statistical benefit of
multitask representation learning in linear Markov Decision Process (MDP) under
a generative model. In this paper, we consider an agent to learn a
representation function $\phi$ out of a function class $\Phi$ from $T$ source
tasks with $N$ data per task, and then use the learned $\hat{\phi}$ to reduce
the required number of sample for a new task. We first discover a
\emph{Least-Activated-Feature-Abundance} (LAFA) criterion, denoted as $\kappa$,
with which we prove that a straightforward least-square algorithm learns a
policy which is $\tilde{O}(H^2\sqrt{\frac{\mathcal{C}(\Phi)^2 \kappa
d}{NT}+\frac{\kappa d}{n}})$ sub-optimal. Here $H$ is the planning horizon,
$\mathcal{C}(\Phi)$ is $\Phi$'s complexity measure, $d$ is the dimension of the
representation (usually $d\ll \mathcal{C}(\Phi)$) and $n$ is the number of
samples for the new task. Thus the required $n$ is $O(\kappa d H^4)$ for the
sub-optimality to be close to zero, which is much smaller than
$O(\mathcal{C}(\Phi)^2\kappa d H^4)$ in the setting without multitask
representation learning, whose sub-optimality gap is
$\tilde{O}(H^2\sqrt{\frac{\kappa \mathcal{C}(\Phi)^2d}{n}})$. This
theoretically explains the power of multitask representation learning in
reducing sample complexity. Further, we note that to ensure high sample
efficiency, the LAFA criterion $\kappa$ should be small. In fact, $\kappa$
varies widely in magnitude depending on the different sampling distribution for
new task. This indicates adaptive sampling technique is important to make
$\kappa$ solely depend on $d$. Finally, we provide empirical results of a noisy
grid-world environment to corroborate our theoretical findings.


Abstract:
  For gene expression data measured in a crossover trial, a multivariate
mixed-effects model seems to be most appropriate. Standard statistical
inference fails to provide reliable results when some responses are missing.
Particularly for crossover studies, missingness is a serious concern as the
trial requires a small number of participants. A Monte Carlo EM (MCEM) based
technique has been adopted to deal with this situation. Along with estimation,
a MCEM likelihood ratio test (LRTs) is developed for testing the fixed effects
in such a multivariate crossover model with missing data. Intensive simulation
studies have been carried out prior to the analysis of the gene expression
data.


Abstract:
  Black-box optimization (BBO) has a broad range of applications, including
automatic machine learning, engineering, physics, and experimental design.
However, it remains a challenge for users to apply BBO methods to their
problems at hand with existing software packages, in terms of applicability,
performance, and efficiency. In this paper, we build OpenBox, an open-source
and general-purpose BBO service with improved usability. The modular design
behind OpenBox also facilitates flexible abstraction and optimization of basic
BBO components that are common in other existing systems. OpenBox is
distributed, fault-tolerant, and scalable. To improve efficiency, OpenBox
further utilizes "algorithm agnostic" parallelization and transfer learning.
Our experimental results demonstrate the effectiveness and efficiency of
OpenBox compared to existing systems.


Abstract:
  Descent methods for deep networks are notoriously capricious: they require
careful tuning of step size, momentum and weight decay, and which method will
work best on a new benchmark is a priori unclear. To address this problem, this
paper conducts a combined study of neural architecture and optimisation,
leading to a new optimiser called Nero: the neuronal rotator. Nero trains
reliably without momentum or weight decay, works in situations where Adam and
SGD fail, and requires little to no learning rate tuning. Also, Nero's memory
footprint is ~ square root that of Adam or LAMB. Nero combines two ideas: (1)
projected gradient descent over the space of balanced networks; (2)
neuron-specific updates, where the step size sets the angle through which each
neuron's hyperplane turns. The paper concludes by discussing how this geometric
connection between architecture and optimisation may impact theories of
generalisation in deep learning.


Abstract:
  The end-to-end Human Mesh Recovery (HMR) approach has been successfully used
for 3D body reconstruction. However, most HMR-based frameworks reconstruct
human body by directly learning mesh parameters from images or videos, while
lacking explicit guidance of 3D human pose in visual data. As a result, the
generated mesh often exhibits incorrect pose for complex activities. To tackle
this problem, we propose to exploit 3D pose to calibrate human mesh.
Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial
PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in
a serial or parallel manner, these two frameworks can effectively correct human
mesh with guidance of a concise pose calibration module. Furthermore, since the
calibration module is designed via non-rigid pose transformation, our PC-HMR
frameworks can flexibly tackle bone length variations to alleviate misplacement
in the calibrated mesh. Finally, our frameworks are based on generic and
complementary integration of data-driven learning and geometrical modeling. Via
plug-and-play modules, they can be efficiently adapted for both
image/video-based human mesh recovery. Additionally, they have no requirement
of extra 3D pose annotations in the testing phase, which releases inference
difficulties in practice. We perform extensive experiments on the popular
bench-marks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks
achieve the SOTA results.


Abstract:
  Diffusion-weighted (DW) magnetic resonance imaging is essential for the
diagnosis and treatment of ischemic stroke. DW images (DWIs) are usually
acquired in multi-slice settings where lesion areas in two consecutive 2D
slices are highly discontinuous due to large slice thickness and sometimes even
slice gaps. Therefore, although DWIs contain rich 3D information, they cannot
be treated as regular 3D or 2D images. Instead, DWIs are somewhere in-between
(or 2.5D) due to the volumetric nature but inter-slice discontinuities. Thus,
it is not ideal to apply most existing segmentation methods as they are
designed for either 2D or 3D images. To tackle this problem, we propose a new
neural network architecture tailored for segmenting highly-discontinuous 2.5D
data such as DWIs. Our network, termed LambdaUNet, extends UNet by replacing
convolutional layers with our proposed Lambda+ layers. In particular, Lambda+
layers transform both intra-slice and inter-slice context around a pixel into
linear functions, called lambdas, which are then applied to the pixel to
produce informative 2.5D features. LambdaUNet is simple yet effective in
combining sparse inter-slice information from adjacent slices while also
capturing dense contextual features within a single slice. Experiments on a
unique clinical dataset demonstrate that LambdaUNet outperforms existing 3D/2D
image segmentation methods including recent variants of UNet. Code for
LambdaUNet will be released with the publication to facilitate future research.


Abstract:
  In this paper, we prove various results for circle actions on compact unitary
manifolds with discrete fixed point sets, generalizing results for almost
complex manifolds. For a circle action on a compact unitary manifold with a
discrete fixed point set, we prove relationships between the weights at the
fixed points. As a consequence, we show that there is a multigraph that encodes
the fixed point data (a collection of multisets of weights at the fixed points)
of the manifold; this can be used to study unitary $S^1$-manifolds in terms of
multigraphs. We derive results regarding the first equivariant Chern class,
obtaining a lower bound on the number of fixed points under an assumption on a
manifold. We determine the Hirzebruch $\chi_y$-genus of a compact unitary
manifold admitting a semi-free $S^1$-action, and obtain a lower bound on the
number of fixed points.


Abstract:
  We continue the study of fuzzy geometries inside Connes' spectral formalism
and their relation to multimatrix models. In this companion paper to [arXiv
2007:10914, Ann. Henri Poincar\'e] we propose a gauge theory setting based on
noncommutative geometry, which -- just as the traditional formulation in terms
of almost-commutative manifolds -- has the ability to also accommodate a Higgs
field. However, in contrast to "almost-commutative manifolds", the present
framework employs only finite dimensional algebras which we call gauge matrix
spectral triples. In a path-integral quantization approach to the Spectral
Action, this allows to state Yang-Mills--Higgs theory (on four-dimensional
Euclidean fuzzy space) as an explicit random multimatrix model obtained here,
whose matrix fields mirror those of the Yang-Mills--Higgs theory on a smooth
manifold.


Abstract:
  Twist defects in surface codes can be used to encode more logical qubits,
improve the code rate, and implement logical gates. In this work we provide a
rigorous formalism for constructing surface codes with twists generalizing the
well-defined homological formalism introduced by Kitaev for describing CSS
surface codes. In particular, we associate a surface code to \emph{any} graph
$G$ embedded on \emph{any} 2D-manifold, in such a way that (1) qubits are
associated to the vertices of the graph, (2) stabilizers are associated to
faces, (3) twist defects are associated to odd-degree vertices. In this way, we
are able to reproduce the variety of surface codes, with and without twists, in
the literature and produce some new examples. We also calculate and bound
various code properties such as the rate and distance in terms of topological
graph properties such as genus, systole, and face-width.


Abstract:
  The objective of this study is to predict the near-future flooding status of
road segments based on their own and adjacent road segments current status
through the use of deep learning framework on fine-grained traffic data.
Predictive flood monitoring for situational awareness of road network status
plays a critical role to support crisis response activities such as evaluation
of the loss of access to hospitals and shelters. Existing studies related to
near-future prediction of road network flooding status at road segment level
are missing. Using fine-grained traffic speed data related to road sections,
this study designed and implemented three spatio-temporal graph convolutional
network (STGCN) models to predict road network status during flood events at
the road segment level in the context of the 2017 Hurricane Harvey in Harris
County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering
the adjacency and distance between road segments, while Model 2 contains an
additional elevation block to account for elevation difference between road
segments. Model 3 includes three blocks for considering the adjacency and the
product of distance and elevation difference between road segments. The
analysis tested the STGCN models and evaluated their prediction performance.
Our results indicated that Model 1 and Model 2 have reliable and accurate
performance for predicting road network flooding status in near future (e.g.,
2-4 hours) with model precision and recall values larger than 98% and 96%,
respectively. With reliable road network status predictions in floods, the
proposed model can benefit affected communities to avoid flooded roads and the
emergency management agencies to implement evacuation and relief resource
delivery plans.


Abstract:
  We derive a Riemann--Hilbert representation for the solution of an integrable
nonlinear evolution equation with a $3 \times 3$ Lax pair. We use the derived
representation to obtain formulas for the long-time asymptotics.


Abstract:
  Medical image segmentation models are typically supervised by expert
annotations at the pixel-level, which can be expensive to acquire. In this
work, we propose a method that combines the high quality of pixel-level expert
annotations with the scale of coarse DNN-generated saliency maps for training
multi-label semantic segmentation models. We demonstrate the application of our
semi-supervised method, which we call CheXseg, on multi-label chest X-ray
interpretation. We find that CheXseg improves upon the performance (mIoU) of
fully-supervised methods that use only pixel-level expert annotations by 9.7%
and weakly-supervised methods that use only DNN-generated saliency maps by
73.1%. Our best method is able to match radiologist agreement on three out of
ten pathologies and reduces the overall performance gap by 57.2% as compared to
weakly-supervised methods.


Abstract:
  The Collatz problem with $3x+k$ is revisited. Positive and negative limit
cycles are given up to k=9997 starting with $x_0=-2\cdot10^7...+2\cdot10^7$. A
simple relation between the probability distribution for the Syracuse iterates
for various k (not divisible by 2 and 3) is obtained. From this it follows that
the oscillation considered by Tao 2019 ( arXiv:1909.03562v2 ) does not depend
on k. Thus this piece of the proof of his theorem 1.3 "Almost all Collatz
orbits attain almost bounded values" holds for all k not divisible by 2 and 3.


Abstract:
  The topological order of a (2+1)D topological phase of matter is
characterized by its chiral central charge and a unitary modular tensor
category that describes the universal fusion and braiding properties of its
anyonic quasiparticles. I discuss the topologically invariant quantities
associated with these and identify ones that are useful for determining the
topological order. I propose a variety of physical experiments that probe these
quantities and detail the relation of the measured data to the topological
invariants.


Abstract:
  We calculate complete basis set (CBS) limit extrapolated ionization
potentials (IP) and electron affinities (EA) with Slater Type Basis sets for
the molecules in the GW100 database. To this end, we present two new Slater
Type orbital (STO) basis sets of triple- (TZ) and quadruple-$\zeta$ (QZ)
quality whose polarization is adequate for correlated-electron methods and
which contain extra diffuse functions to be able to correctly calculate
electron affinities of molecules with a positive Lowest Unoccupied Molecular
Orbital (LUMO). We demonstrate, that going from TZ to QZ quality consistently
reduces the basis set error of our computed IPs and EAs and we conclude that a
good estimate of these quantities at the CBS limit can be obtained by
extrapolation. With MADs from 70 to 85 meV, our CBS limit extrapolated
ionization potentials are in good agreement with results from FHI-AIMS,
TURBOMOLE, VASP and WEST while they differ by more than 130 meV on average from
nanoGW. With a MAD of 160 meV, our electron affinities are also in good
agreement with the WEST code. Especially for systems with positive LUMOs, the
agreement is excellent. With respect to other codes, the STO type basis sets
generally underestimate EAs of small molecules with strongly bound LUMOs. With
62 meV for IPs and 93 meV for EAs, we find much better agreement to CBS limit
extrapolated results from FHI-AIMS for a set of 250 medium to large organic
molecules.


Abstract:
  A few years ago, flow equations were introduced as a technique for
calculating the ground-state energies of cold Bose gases with and without
impurities. In this paper, we extend this approach to compute observables other
than the energy. As an example, we calculate the densities, and phase
fluctuations of one-dimensional Bose gases with one and two impurities. For a
single mobile impurity, we use flow equations to validate the mean-field
results obtained upon the Lee-Low-Pines transformation. We show that the
mean-field approximation is accurate for all values of the boson-impurity
interaction strength as long as the phase coherence length is much larger than
the healing length of the condensate. For two static impurities, we calculate
impurity-impurity interactions induced by the Bose gas. We find that leading
order perturbation theory fails when boson-impurity interactions are stronger
than boson-boson interactions. The mean-field approximation reproduces the flow
equation results for all values of the boson-impurity interaction strength as
long as boson-boson interactions are weak.


Abstract:
  The black string solution is a model of black holes in (3+1) dimensions that
can be related to the BTZ (2+1) dimensions black hole solution. This object can
be seen as a cylindrical black hole. In this work it is considered the black
string Hawking temperature obtained by the Hamilton-Jacobi method and a
description of a system composed by a black string and a thermal reservoir,
considering the thermal equilibrium state.


Abstract:
  We present a reformulation of the functional renormalization group (fRG) for
many-electron systems, which relies on the recently introduced single boson
exchange (SBE) representation of the parquet equations [Phys. Rev. B 100,
155149 (2019)]. The latter exploits a diagrammatic decomposition, which
classifies the contributions to the full scattering amplitude in terms of their
reducibility with respect to cutting one interaction line, naturally
distinguishing the processes mediated by the exchange of a single boson in the
different channels. We apply this idea to the fRG by splitting the one-loop fRG
flow equations for the vertex function into SBE contributions and a residual
four-point fermionic vertex. Similarly as in the case of parquet solvers,
recasting the fRG algorithm in the SBE representation offers both computational
and interpretative advantages: the SBE decomposition not only significantly
reduces the numerical effort of treating the high-frequency asymptotics of the
flowing vertices, but it also allows for a clear physical identification of the
collective degrees of freedom at play. We illustrate the advantages of an SBE
formulation of fRG-based schemes, by computing through the merger of dynamical
mean-field theory and fRG the susceptibilities and the Yukawa couplings of the
two-dimensional Hubbard model from weak to strong coupling, for which we also
present an intuitive physical explanation of the results. The SBE formulation
of the one-loop flow equations paves a promising route for future multiboson
and multiloop extensions of fRG-based algorithms.


Abstract:
  An ultrafast fiber chirped-pulse amplifier comprising 8 coherently combined
amplifier channels is presented. The laser delivers 1 kW average power at 1 mJ
pulse energy and 260 fs pulse duration. Excellent beam quality and low noise
performance are confirmed. The laser has proven suitable for demanding
scientific applications. Further power scaling is possible right away using
even more amplifier channels


Abstract:
  Terao's factorization theorem shows that if an arrangement is free, then its
characteristic polynomial factors into the product of linear polynomials over
the integer ring. This is not a necessary condition, but there are not so many
non-free arrangements whose characteristic polynomial factors over the integer
ring. On the other hand, the localization of a free arrangement is free, and
its restriction is in many cases free, thus its characteristic polynomial
factors. In this paper, we consider how their integer, or real roots behave.


Abstract:
  In critical situations involving discrimination, gender inequality, economic
damage, and even the possibility of casualties, machine learning models must be
able to provide clear interpretations for their decisions. Otherwise, their
obscure decision-making processes can lead to socioethical issues as they
interfere with people's lives. In the aforementioned sectors, random forest
algorithms strive, thus their ability to explain themselves is an obvious
requirement. In this paper, we present LionForests, which relies on a
preliminary work of ours. LionForests is a random forest-specific
interpretation technique, which provides rules as explanations. It is
applicable from binary classification tasks to multi-class classification and
regression tasks, and it is supported by a stable theoretical background.
Experimentation, including sensitivity analysis and comparison with
state-of-the-art techniques, is also performed to demonstrate the efficacy of
our contribution. Finally, we highlight a unique property of LionForests,
called conclusiveness, that provides interpretation validity and distinguishes
it from previous techniques.


Abstract:
  Specific heat measurements of hydrogenated amorphous silicon prepared by
hot-wire chemical vapor deposition show a large density of two-level systems at
low temperature. Annealing at 200 {\deg}C, well below the growth temperature,
does not significantly affect the already-low internal friction or the sound
velocity, but irreversibly reduces the non-Debye specific heat by an order of
magnitude at 2 K, indicating a large reduction of the density of two-level
systems. Comparison of the specific heat to the internal friction suggests that
the two-level systems are uncharacteristically decoupled from acoustic waves,
both before and after annealing. Analysis yields an anomalously low value of
the coupling constant, which increases upon annealing but still remains
anomalously low. The results suggest that the coupling constant value is
lowered by the presence of hydrogen.


Abstract:
  We study the problem of learning predictors that are robust to adversarial
examples with respect to an unknown perturbation set, relying instead on
interaction with an adversarial attacker or access to attack oracles, examining
different models for such interactions. We obtain upper bounds on the sample
complexity and upper and lower bounds on the number of required interactions,
or number of successful attacks, in different interaction models, in terms of
the VC and Littlestone dimensions of the hypothesis class of predictors, and
without any assumptions on the perturbation set.


Abstract:
  With the rise of deep learning models in the field of computer vision, new
possibilities for their application in industrial processes proves to return
great benefits. Nevertheless, the actual fit of machine learning for highly
standardised industrial processes is still under debate. This paper addresses
the challenges on the industrial realization of the AI tools, considering the
use case of Laser Beam Welding quality control as an example. We use object
detection algorithms from the TensorFlow object detection API and adapt them to
our use case using transfer learning. The baseline models we develop are used
as benchmarks and evaluated and compared to models that undergo dataset scaling
and hyperparameter tuning. We find that moderate scaling of the dataset via
image augmentation leads to improvements in intersection over union (IoU) and
recall, whereas high levels of augmentation and scaling may lead to
deterioration of results. Finally, we put our results into perspective of the
underlying use case and evaluate their fit.


Abstract:
  Convolutional neural networks (CNNs) often have poor generalization
performance under domain shift. One way to improve domain generalization is to
collect diverse source data from multiple relevant domains so that a CNN model
is allowed to learn more domain-invariant, and hence generalizable
representations. In this work, we address domain generalization with MixStyle,
a plug-and-play, parameter-free module that is simply inserted to shallow CNN
layers and requires no modification to training objectives. Specifically,
MixStyle probabilistically mixes feature statistics between instances. This
idea is inspired by the observation that visual domains can often be
characterized by image styles which are in turn encapsulated within
instance-level feature statistics in shallow CNN layers. Therefore, inserting
MixStyle modules in effect synthesizes novel domains albeit in an implicit way.
MixStyle is not only simple and flexible, but also versatile -- it can be used
for problems whereby unlabeled images are available, such as semi-supervised
domain generalization and unsupervised domain adaptation, with a simple
extension to mix feature statistics between labeled and pseudo-labeled
instances. We demonstrate through extensive experiments that MixStyle can
significantly boost the out-of-distribution generalization performance across a
wide range of tasks including object recognition, instance retrieval, and
reinforcement learning.


Abstract:
  We proposed the method that translates the 2-D CSP for minimizing the number
of cuts to the Ising model. After that, we conducted computer experiments of
the proposed model using the benchmark problem. From the above, the following
results are obtained. (1) The proposed Ising model adequately represents the
target problem. (2) Acceptance rates were low as 0.2% to 9.8% and from 21.8% to
49.4%. (3) Error rates from optimal solution were broad as 0% to 25.9%. As the
future work, (1) Improve the Hamiltonian for Constraints. (2) Improve the
proposed model to adjust more complex 2-D CSP and reduce the number of spins
when it deals with large materials and components. (3) Conduct experiments
using a quantum annealer.


Abstract:
  The zeta-function of a manifold is closely related to, and sometimes can be
calculated completely, in terms of its periods. We report here on a practical
and computationally rapid implementation of this procedure for families of
Calabi-Yau manifolds with one complex structure parameter phi. Although partly
conjectural, it turns out to be possible to compute the matrix of the Frobenius
map on the third cohomology group of X(phi) directly from the Picard-Fuchs
differential operator of the family. To illustrate our method, we compute
tables of the quartic numerators of the zeta-functions for six manifolds of
increasing complexity as the parameter phi varies in Fp. For four of these
manifolds, we do this for the 500 primes p=5,7,...,3583, while for two
manifolds we extend the calculation to 1000 primes. The tables for 5 <= p <= 97
are part of this article while the remaining tables are attached in electronic
form. Interest attaches to the cases for which the numerators factorise. Some
of these factorisations can be associated with parameter values for which the
underlying manifold becomes singular. For the cases we consider here, the
singularities are all of conifold or hyperconifold type. In these cases the
numerator degenerates to a cubic and this factorises into the product of a
linear and a quadratic factor. The quadratic term contains a coefficient that
is the p'th coefficient of a modular form. Some of our examples have
singularities when the parameter satisfies a polynomial equation that does not
factorise over Q. When this happens, the corresponding forms are modular forms
with neben type or Hilbert modular forms. The numerator can also factorise into
two quadrics. This happens when the Hodge structure of the manifold splits,
sometimes this happens for algebraic values of the parameter and we identify,
in this way, attractor points of rank two of the parameter space.


Abstract:
  Building on the recent theoretical work of Wray, Duffy and Wilson [J. Fluid
Mech. 884, A45 (2020)] concerning the competitive diffusion-limited evaporation
of multiple thin sessile droplets in proximity to each other, we obtain
theoretical predictions for the spatially non-uniform densities of the
contact-line deposits (often referred to as "coffee stains" or "ring stains")
left on the substrate after such droplets containing suspended solid particles
have completely evaporated. Neighbouring droplets interact via their vapour
fields, which results in a spatially non-uniform "shielding" effect. We give
predictions for the deposits from a pair of identical droplets, which show that
the deposit is reduced the most where the droplets are closest together, and
demonstrate excellent quantitative agreement with experimental results of
Pradhan and Panigrahi [Coll. Surf. A 482, 562-567 (2015)]. We also give
corresponding predictions for a triplet of identical droplets arranged in an
equilateral triangle, which show that the effect of shielding on the deposit is
more subtle in this case.


Abstract:
  A myriad of explainability methods have been proposed in recent years, but
there is little consensus on how to evaluate them. While automatic metrics
allow for quick benchmarking, it isn't clear how such metrics reflect human
interaction with explanations. Human evaluation is of paramount importance, but
previous protocols fail to account for belief biases affecting human
performance, which may lead to misleading conclusions. We provide an overview
of belief bias, its role in human evaluation, and ideas for NLP practitioners
on how to account for it. For two experimental paradigms, we present a case
study of gradient-based explainability introducing simple ways to account for
humans' prior beliefs: models of varying quality and adversarial examples. We
show that conclusions about the highest performing methods change when
introducing such controls, pointing to the importance of accounting for belief
bias in evaluation.


Abstract:
  We report the results of optical spectroscopy of the Small Magellanic Cloud
supernova remnant (SNR) MCSNR J0127-7332 and the mass donor Be star, 2dFS 3831,
in its associated high-mass X-ray binary SXP 1062 carried out with the Southern
African Large Telescope (SALT). Using high-resolution long-slit spectra, we
measured the expansion velocity of the SNR shell of \approx 140 km/s,
indicating that MCSNR J0127-7332 is in the radiative phase. We found that the
observed line ratios in the SNR spectrum can be understood if the local
interstellar medium is ionized by 2dFS 3831 and/or OB stars around the SNR. We
propose that MCSNR J0127-7332 is the result of supernova explosion within a
bubble produced by the stellar wind of the supernova progenitor and that the
bubble was surrounded by a massive shell at the moment of supernova explosion.
We estimated the age of MCSNR J0127-7332 to be \la 10 000 yr. We found that the
spectrum of 2dFS 3831 changes with orbital phase. Namely, the equivalent width
of the Halpha emission line decreased by \approx 40 per cent in \approx 130 d
after periastron passage of the neutron star and then almost returned to its
original value in the next \approx 100 d. Also, the spectrum of 2dFS 3831
obtained closest to the periastron epoch (about three weeks after the
periastron) shows a noticeable emission line of He II \lambda 4686, which
disappeared in the next about two weeks. We interpret these changes as a result
of the temporary perturbation and heating of the disk as the neutron star
passes through it.


Abstract:
  Extensive infrared spectra of the weakly-bound CO$_2$-CO dimer are observed
in the carbon dioxide $\nu_3$ asymmetric stretch region (~2350 cm-1) using a
tunable infrared OPO laser source to probe a pulsed slit jet supersonic
expansion. Both C-bonded and O-bonded isomers are analyzed for the normal
isotopologue as well as for 13CO$_2$-CO and $^{16}$O$^{13}$C$^{18}$O-CO, the
latter being the first observation of an asymmetrically substituted form for
which all values of Ka are allowed. Combination bands involving the lowest
in-plane intermolecular bending modes are also studied for both isomers. Weak
bands near 2337 cm-1 are assigned to CO$_2$ hot band transitions (v1, v2l2, v3)
= (0111) <-- (0110), yielding the splitting of the degenerate CO$_2$ $\nu_2$
bend into in-plane and out-of-plane components due to the presence of the CO.
This splitting has rather different values for the C- and O- bonded isomers,
4.56 and 1.59 cm-1, respectively, with the out-of-plane mode higher in energy
than the in-plane for both cases.


Abstract:
  For the interaction energy with repulsive-attractive potentials, we give
generic conditions which guarantee the radial symmetry of the local minimizers
in the infinite Wasserstein distance. As a consequence, we obtain the
uniqueness of local minimizers in this topology for a class of interaction
potentials. We introduce a novel notion of concavity of the interaction
potential allowing us to show certain fractal-like behavior of the local
minimizers. We provide a family of interaction potentials such that the support
of the associated local minimizers has no isolated points and any superlevel
set has no interior points.


Abstract:
  In Alexandrov's work \cite{al2, al3} it has been shown that the extended
partition function $\exp(F^{o,ext}+F^c)$ introduced by Buryak in \cite{bu, bu2}
is a tau-function of the KP hierarchy. In this work, we compute the affine
coordinates of this tau-function on the Sato Grassmannian, and rewrite the
Virasoro constraints as recursions for the affine coordinates in the fermionic
picture. As applications we derive some formulas for the extended partition
function and the connected $n$-point functions using methods developed by Zhou
in \cite{zhou1} based on the boson-fermion correspondence.


Abstract:
  This paper describes Resh, a new, statically typed, interpreted programming
language and associated runtime for orchestrating multirobot systems. The main
features of Resh are: (1) It offloads much of the tedious work of programming
such systems away from the programmer and into the language runtime; (2) It is
based on a small set of temporal and locational operators; and (3) It is not
restricted to specific robot types or tasks. The Resh runtime consists of three
engines that collaborate to run a Resh program using the available robots in
their current environment. This paper describes both Resh and its runtime and
gives examples of its use.


Abstract:
  We revisit the stability issue of determining the conductivity at the
boundary from the corresponding Dirichlet-to-Neumann map. We discuss both the
method based on singular solutions and the one built on the localized
oscillating solutions. Our primary objective is not establishing new results on
this subject even if the present work contains some new ones. We mainly clarify
some points in the existing proofs and make some comments. We also derive some
consequences of the stability inequality of the determination of the
conductivity at the boundary from the Dirichlet-to-Neumann map.


Abstract:
  While AI has benefited humans, it may also harm humans if not appropriately
developed. The focus of HCI work is transiting from conventional human
interaction with non-AI computing systems to interaction with AI systems. We
conducted a high-level literature review and a holistic analysis of current
work in developing AI systems from an HCI perspective. Our review and analysis
highlight the new changes introduced by AI technology and the new challenges
that HCI professionals face when applying the human-centered AI (HCAI) approach
in the development of AI systems. We also identified seven main issues in human
interaction with AI systems, which HCI professionals did not encounter when
developing non-AI computing systems. To further enable the implementation of
the HCAI approach, we identified new HCI opportunities tied to specific
HCAI-driven design goals to guide HCI professionals in addressing these new
issues. Finally, our assessment of current HCI methods shows the limitations of
these methods in support of developing AI systems. We propose alternative
methods that can help overcome these limitations and effectively help HCI
professionals apply the HCAI approach to the development of AI systems. We also
offer strategic recommendations for HCI professionals to effectively influence
the development of AI systems with the HCAI approach, eventually developing
HCAI systems.


Abstract:
  Recently, it was found that clipping can significantly improve the section
error rate (SER) performance of sparse regression (SR) codes if an optimal
clipping threshold is chosen. In this paper, we propose irregularly clipped SR
codes, where multiple clipping thresholds are applied to symbols according to a
distribution, to further improve the SER performance of SR codes. Orthogonal
approximate message passing (OAMP) algorithm is used for decoding. Using state
evolution, the distribution of irregular clipping thresholds is optimized to
minimize the SER of OAMP decoding. As a result, optimized irregularly clipped
SR codes achieve a better tradeoff between clipping distortion and noise
distortion than regularly clipped SR codes. Numerical results demonstrate that
irregularly clipped SR codes achieve 0.4 dB gain in signal-to-noise-ratio (SNR)
over regularly clipped SR codes at code length$\,\approx2.5\!\times\! 10^4$ and
SER$\,\approx10^{-5}$. We further show that irregularly clipped SR codes are
robust over a wide range of code rates.


Abstract:
  We propose a "small-uniform" statistic for the inference of the functional
PCA estimator in a functional linear regression model. The literature has shown
two extreme behaviors: on the one hand, the FPCA estimator does not converge in
distribution in its norm topology; but on the other hand, the FPCA estimator
does have a pointwise asymptotic normal distribution. Our statistic takes a
middle ground between these two extremes: after a suitable rate normalization,
our small-uniform statistic is constructed as the maximizer of a fractional
programming problem of the FPCA estimator over a finite-dimensional subspace,
and whose dimensions will grow with sample size. We show the rate for which our
scalar statistic converges in probability to the supremum of a Gaussian
process. The small-uniform statistic has applications in hypothesis testing.
Simulations show our statistic has comparable to slightly better power
properties for hypothesis testing than the two statistics of Cardot, Ferraty,
Mas and Sarda (2003).


Abstract:
  We check the impact of the gluon distribution due to the number of active
flavor employed in the calculation of heavy-quark deep-inelastic scattering
(DIS) electro-production on the reduced cross section and structure functions
determined in the leading order (LO) and next-to-leading order (NLO) quantum
chromodynamics (QCD) analysis using the Hadron Electron Ring Accelerator (HERA)
combined data. The charm and beauty structure functions are determined and
compared with the HERA combined data. The results are in good agreement with
respect to the experimental data. We also compare the obtained charm and bottom
structure functions with the results from CTEQ6.6 [Phys.Rev.D{\bf78},
013004(2008)] and NNPDF3.1[Eur.Phys.J.C{\bf77}, 663(2017)] parameterization
models. The effect of gluon density on these calculations depends on the number
of active flavor at asymptotical values of the momentum transfer $Q^{2}$. Also,
in the HERA kinematic range, the ratio of $F_{2}^{c\overline{c}}/F_{2}$ and
$F_{2}^{b\overline{b}}/F_{2}$ are obtained. These results and comparison with
the HERA combined data demonstrate that the suggested method can be applied in
analysis of new colliders.


Abstract:
  It has been shown that global scene understanding tasks like layout
estimation can benefit from wider field of views, and specifically spherical
panoramas. While much progress has been made recently, all previous approaches
rely on intermediate representations and postprocessing to produce
Manhattan-aligned estimates. In this work we show how to estimate full room
layouts in a single-shot, eliminating the need for postprocessing. Our work is
the first to directly infer Manhattan-aligned outputs. To achieve this, our
data-driven model exploits direct coordinate regression and is supervised
end-to-end. As a result, we can explicitly add quasi-Manhattan constraints,
which set the necessary conditions for a homography-based Manhattan alignment
module. Finally, we introduce the geodesic heatmaps and loss and a
boundary-aware center of mass calculation that facilitate higher quality
keypoint estimation in the spherical domain. Our models and code are publicly
available at https://vcl3d.github.io/SingleShotCuboids/.