Abstract:
  Tumour spheroids have the potential to be used as preclinical
chemosensitivity assays. However, the production of three dimensional (3D)
tumour spheroids remains challenging as not all tumour cell lines form
spheroids with regular morphologies and spheroid transfer often induces
disaggregation. In the field of pancreatic cancer, the MiaPaCa-2 cell line is
an interesting model for research but it is known for its difficulty to form
stable spheroids; also, when formed, spheroids from this cell line are weak and
arduous to manage and to harvest for further analyses such as multiple staining
and imaging. In this work, we compared different methods (i.e. hanging drop,
round bottom wells and Matrigel embedding, each of them with or without
methylcellulose in the media) to evaluate which one allowed to better overpass
these limitations. Morphometric analysis indicated that hanging drop in
presence of methylcellulose leaded to well-organized spheroids; interestingly,
quantitative PCR (qPCR) analysis reflected the morphometric characterization,
indicating that same spheroids expressed the highest values of CD44, VIMENTIN,
TGF beta1 and Ki67. In addition, we investigated the generation of MiaPaCa-2
spheroids when cultured on substrates of different hydrophobicity, in order to
minimize the area in contact with the culture media and to further improve
spheroid formation.


Abstract:
  We propose a method to probe chameleon particles predicted in the $F(R)$
gravity as a model of the modified gravity based on the concept of a stimulated
pulsed-radar collider. We analyze the chameleon mechanism induced by an ambient
environment consisting of focused photon beams and a dilute gas surrounding a
place where stimulated photon-photon scatterings occur. We then discuss how to
extract the characteristic feature of the chameleon signal. We find that a
chameleon with the varying mass around $(0.1-1)\,\mu$eV in a viable model of
the $F(R)$ gravity is testable by searching for the steep pressure dependence
of the 10th-power for the signal yield.


Abstract:
  We propose a robust and efficient way to store and transport quantum
information via one-dimensional discrete time quantum walks. We show how to
attain an effective dispersionless wave packet evolution using only two types
of local unitary operators (quantum coins or gates), properly engineered to act
at predetermined times and at specific lattice sites during the system's time
evolution. In particular, we show that a qubit initially localized about a
Gaussian distribution can be almost perfectly confined during long times or
sent hundreds lattice sites away from its original location and later almost
perfectly reconstructed using only Hadamard and $\sigma_x$ gates.


Abstract:
  Nested stochastic modeling has been on the rise in many fields of the
financial industry. Such modeling arises whenever certain components of a
stochastic model are stochastically determined by other models. There are at
least two main areas of applications, including (1) portfolio risk management
in the banking sector and (2) principle-based reserving and capital
requirements in the insurance sector. As financial instrument values often
change with economic fundamentals, the risk management of a portfolio (outer
loop) often requires the assessment of financial positions subject to changes
in risk factors in the immediate future. The valuation of financial position
(inner loop) is based on projections of cashflows and risk factors into the
distant future. The nesting of such stochastic modeling can be computationally
challenging.
  Most of existing techniques to speed up nested simulations are based on curve
fitting. The main idea is to establish a functional relationship between inner
loop estimator and risk factors by running a limited set of economic scenarios,
and, instead of running inner loop simulations, inner loop estimations are made
by feeding other scenarios into the fitted curve. This paper presents a
non-conventional approach based on the concept of sample recycling. Its essence
is to run inner loop estimation for a small set of outer loop scenarios and to
find inner loop estimates under other outer loop scenarios by recycling those
known inner loop paths. This new approach can be much more efficient when
traditional techniques are difficult to implement in practice.


Abstract:
  Reconstruction of object or scene surfaces has tremendous applications in
computer vision, computer graphics, and robotics. In this paper, we study a
fundamental problem in this context about recovering a surface mesh from an
implicit field function whose zero-level set captures the underlying surface.
To achieve the goal, existing methods rely on traditional meshing algorithms;
while promising, they suffer from loss of precision learned in the implicit
surface networks, due to the use of discrete space sampling in marching cubes.
Given that an MLP with activations of Rectified Linear Unit (ReLU) partitions
its input space into a number of linear regions, we are motivated to connect
this local linearity with a same property owned by the desired result of
polygon mesh. More specifically, we identify from the linear regions,
partitioned by an MLP based implicit function, the analytic cells and analytic
faces that are associated with the function's zero-level isosurface. We prove
that under mild conditions, the identified analytic faces are guaranteed to
connect and form a closed, piecewise planar surface. Based on the theorem, we
propose an algorithm of analytic marching, which marches among analytic cells
to exactly recover the mesh captured by an implicit surface network. We also
show that our theory and algorithm are equally applicable to advanced MLPs with
shortcut connections and max pooling. Given the parallel nature of analytic
marching, we contribute AnalyticMesh, a software package that supports
efficient meshing of implicit surface networks via CUDA parallel computing, and
mesh simplification for efficient downstream processing. We apply our method to
different settings of generative shape modeling using implicit surface
networks. Extensive experiments demonstrate our advantages over existing
methods in terms of both meshing accuracy and efficiency.


Abstract:
  Faced with a considerable lack of resources in African languages to carry out
work in Natural Language Processing (NLP), Natural Language Understanding (NLU)
and artificial intelligence, the research teams of NTeALan association has set
itself the objective of building open-source platforms for the collaborative
construction of lexicographic data in African languages. In this article, we
present our first reports after 2 years of collaborative construction of
lexicographic resources useful for African NLP tools.


Abstract:
  A significant number of those killed in traffic accidents annually refers to
pedestrians. most of these accidents occur when a pedestrian is going to pass
the street. One of the most hazardous areas for pedestrians crossing the street
is midblock crosswalks. These areas are often uncontrolled and there is no
separated phase for pedestrian crossing. As a result, using created gaps among
vehicles is often the only opportunity for pedestrians to cross the street.
This research aims to investigate effective factors on pedestrian gap
acceptance at uncontrolled mid-block crosswalks. In this regard, several
variables such as individual, environmental, and traffic variables were
considered and their related data were extracted by filming the location. from
video images, they were analyzed using logit regression model and the behavior
After extracting the data of pedestrian while he is crossing the street was
modeled as the probabilistic function of pedestrian gap acceptance. The results
illustrate that conflict flow rate, the temporal gap among vehicles, and driver
yielding in the face of vehicles are the most important effective factors on
pedestrian behavior. The other one is that the speed and type of vehicles do
not affect the pedestrian's decision about rejecting or accepting the gap. The
results also show that individual characteristics such as age and gender of
pedestrians do not make a significant relationship with pedestrian gap
acceptance.


Abstract:
  We construct for the first time conditionally decomposable $d$-polytopes for
$d \ge 4$. These examples have any number of vertices from $4d-4$ upwards. A
polytope is said to be conditionally decomposable if one polytope
combinatorially equivalent to it is decomposable (with respect to the Minkowski
sum) and another one combinatorially equivalent to it is indecomposable. In our
examples, one has $4d-2$ vertices and is the sum of a line segment and a
bi-pyramid over a prism. The other ones have $4d-4$ vertices, and one of them
is the sum of a line segment and a $2$-fold pyramid over a prism. We show that
the latter examples have the minimum number of vertices among all conditionally
decomposable $d$-polytopes that have a line segment for a summand.


Abstract:
  We consider various asymptotic scaling limits $N\to\infty$ for the $2N$
complex eigenvalues of non-Hermitian random matrices in the symmetry class of
the symplectic Ginibre ensemble. These are known to be integrable, forming
Pfaffian point processes, and we obtain limiting expressions for the
corresponding kernel for different potentials. The first part is devoted to the
symplectic Ginibre ensemble with a Gaussian potential. We obtain the asymptotic
at the edge of the spectrum in the vicinity of the real line. The unifying form
of the kernel allows us to make contact with the bulk scaling along the real
line and with the edge scaling away from the real line, where we recover the
known determinantal process of the complex Ginibre ensemble. Part two covers
ensembles of Mittag-Leffler type with a singularity at the origin. For
potentials $Q(\zeta)=|\zeta|^{2\lambda}-(2c/N)\log|\zeta|$, with $\lambda>0$
and $c>-1$, the limiting kernel obeys a linear differential equation of
fractional order $1/\lambda$ at the origin. For integer $m=1/\lambda$ it can be
solved in terms of Mittag-Leffler functions. In the last part, we derive the
Ward's equation for a general class of potentials as a tool to investigate
universality. This allows us to determine the functional form of kernels that
are translation invariant up to its integration domain.


Abstract:
  We report novel observational evidence on the evolutionary status of
lithium-rich giant stars by combining asteroseismic and lithium abundance data.
Comparing observations and models of the asteroseismic gravity-mode period
spacing $\Delta\Pi_{1}$, we find that super-Li-rich giants (SLR, A(Li)~$>
3.2$~dex) are almost exclusively young red-clump (RC) stars. Depending on the
exact phase of evolution, which requires more data to refine, SLR stars are
either (i) less than $\sim 2$~Myr or (ii) less than $\sim40$~Myr past the main
core helium flash (CHeF). Our observations set a strong upper limit for the
time of the inferred Li-enrichment phase of $< 40$~Myr post-CHeF, lending
support to the idea that lithium is produced around the time of the CHeF. In
contrast, the more evolved RC stars ($> 40$~Myr post-CHeF) generally have low
lithium abundances (A(Li)~$<1.0$~dex). Between the young, super-Li-rich phase,
and the mostly old, Li-poor RC phase, there is an average reduction of lithium
by about 3 orders of magnitude. This Li-destruction may occur rapidly. We find
the situation to be less clear with stars having Li abundances between the two
extremes of super-Li-rich and Li-poor. This group, the `Li-rich' stars ($3.2
>$~A(Li)~$> 1.0$~dex), shows a wide range of evolutionary states.


Abstract:
  The Coulomb Branch Formula conjecturally expresses the refined Witten index
for $N=4$ Quiver Quantum Mechanics as a sum over multi-centered collinear black
hole solutions, weighted by so-called `single-centered' or `pure-Higgs'
indices, and suitably modified when the quiver has oriented cycles. On the
other hand, localization expresses the same index as an integral over the
complexified Cartan torus and auxiliary fields, which by Stokes' theorem leads
to the famous Jeffrey-Kirwan residue formula. Here, by evaluating the same
integral using steepest descent methods, we show the index is in fact given by
a sum over deformed multi-centered collinear solutions, which encompasses both
regular and scaling collinear solutions. As a result, we confirm the Coulomb
Branch Formula for Abelian quivers in the presence of oriented cycles, and
identify the origin of the pure-Higgs and minimal modification terms as coming
from collinear scaling solutions. For cyclic Abelian quivers, we observe that
part of the scaling contributions reproduce the stacky invariants for trivial
stability, a mathematically well-defined notion whose physics significance had
remained obscure.


Abstract:
  We present an S$_4$ flavour symmetric model within a minimal seesaw framework
resulting in mass matrices that leads to TM$_1$ mixing. Minimal seesaw is
realized by adding two right-handed neutrinos to the Standard Model. The model
predicts Normal Hierarchy (NH) for neutrino masses. Using the constrained
six-dimensional parameter space, we have evaluated the effective Majorana
neutrino mass, which is the parameter of interest in neutrinoless double beta
decay experiments. The possibility of explaining baryogenesis via resonant
leptogenesis is also examined within the model. A non-zero, resonantly enhanced
CP asymmetry generated from the decay of right-handed neutrinos at the TeV
scale is studied, considering flavour effects. The evolution of lepton
asymmetry is discussed by solving the set of Boltzmann equations numerically
and obtain the value of baryon asymmetry to be $\lvert \eta_B \rvert = 6.3
\times 10^{-10}$.


Abstract:
  Automatic speech recognition (ASR) is widely used in consumer electronics.
ASR greatly improves the utility and accessibility of technology, but usually
the output is only word sequences without punctuation. This can result in
ambiguity in inferring user-intent. We first present a transformer-based
approach for punctuation prediction that achieves 8% improvement on the IWSLT
2012 TED Task, beating the previous state of the art [1]. We next describe our
multimodal model that learns from both text and audio, which achieves 8%
improvement over the text-only algorithm on an internal dataset for which we
have both the audio and transcriptions. Finally, we present an approach to
learning a model using contextual dropout that allows us to handle variable
amounts of future context at test time.


Abstract:
  Semiconductor quantum dots, where electrons or holes are isolated via
electrostatic potentials generated by surface gates, are promising building
blocks for semiconductor-based quantum technology. Here, we investigate double
quantum dot (DQD) charge qubits in GaAs, capacitively coupled to high-impedance
SQUID array and Josephson junction array resonators. We tune the strength of
the electric dipole interaction between the qubit and the resonator in-situ
using surface gates. We characterize the qubit-resonator coupling strength,
qubit decoherence, and detuning noise affecting the charge qubit for different
electrostatic DQD configurations. We find that all quantities can be tuned
systematically over more than one order of magnitude, resulting in reproducible
decoherence rates $\Gamma_2/2\pi<~5$ MHz in the limit of high interdot
capacitance. Conversely, by reducing the interdot capacitance, we can increase
the DQD electric dipole strength, and therefore its coupling to the resonator.
By employing a Josephson junction array resonator with an impedance of $\sim4$
k$\Omega$ and a resonance frequency of $\omega_r/2\pi \sim 5.6$ GHz, we observe
a coupling strength of $g/2\pi \sim 630$ MHz, demonstrating the possibility to
achieve the ultrastrong coupling regime (USC) for electrons hosted in a
semiconductor DQD. These results are essential for further increasing the
coherence of quantum dot based qubits and investigating USC physics in
semiconducting QDs.


Abstract:
  The process of the excitation of an electromagnetic field by a relativistic
electron bunch at the input of a semi-infinite plasma waveguide is
investigated. The shape and intensity of the short transition electromagnetic
pulse are determined and its evolution during propagating in the plasma
waveguide is studied. Besides, the influence of the plasma boundary on the
spatial structure of plasma wake oscillations in plasma waveguide is
considered.


Abstract:
  We revisit the classic Susceptible-Infected-Recovered (SIR) epidemic model
and one of its nonlocal variations recently developed in \cite{Guan}. We
introduce several new approaches to derive exact analytical solutions in the
classical situation and analyze the corresponding effective approximations in
the nonlocal setting. An interesting new feature of the nonlocal models,
compared with the classic SIR model, is the appearance of multiple peak
solutions for the infected population. We provide several rigorous results on
the existence and non-existence of peak solutions with sharp asymptotics.


Abstract:
  Brunel harmonics appear in the optical response of an atom in process of
laser-induced ionization, when the electron leaves the atom and is accelerated
in the strong optical field. In contrast to recollision-based harmonics, the
Brunel mechanism does not require the electron returning to the core. Here we
show that in the presence of a strong ionizing terahertz (THz) field, even a
weak driving field at the optical frequencies allow for generating Brunel
harmonics effectively. The strong ionizing THz pump suppresses recollisions,
making Brunel dominant in a wide spectral range. High-order Brunel harmonics
may form a coherent carrier-envelope-phase insensitive supercontinuum,
compressible into an isolated pulse with the duration down to 100 attoseconds.


Abstract:
  Privacy Policies are the legal documents that describe the practices that an
organization or company has adopted in the handling of the personal data of its
users. But as policies are a legal document, they are often written in
extensive legal jargon that is difficult to understand. Though work has been
done on privacy policies but none that caters to the problem of verifying if a
given privacy policy adheres to the data protection laws of a given country or
state. We aim to bridge that gap by providing a framework that analyzes privacy
policies in light of various data protection laws, such as the General Data
Protection Regulation (GDPR). To achieve that, firstly we labeled both the
privacy policies and laws. Then a correlation scheme is developed to map the
contents of a privacy policy to the appropriate segments of law that a policy
must conform to. Then we check the compliance of privacy policy's text with the
corresponding text of the law using NLP techniques. By using such a tool, users
would be better equipped to understand how their personal data is managed. For
now, we have provided a mapping for the GDPR and PDPA, but other laws can
easily be incorporated in the already built pipeline.


Abstract:
  We present a scalar-tensor theory of gravity on a torsion-free and metric
compatible Lyra manifold. This is obtained by generalizing the concept of
physical reference frame by considering a scale function defined over the
manifold. The choice of a specific frame induces a local base, naturally
non-holonomic, whose structure constants give rise to extra terms in the
expression of the connection coefficients and in the expression for the
covariant derivative. In the Lyra manifold, transformations between reference
frames involving both coordinates and scale change the transformation law of
tensor fields, when compared to those of the Riemann manifold. From a direct
generalization of the Einstein-Hilbert minimal action coupled with a matter
term, it was possible to build a Lyra invariant action, which gives rise to the
associated Lyra Scalar-Tensor theory of gravity (LyST), with field equations
for $g_{\mu\nu}$ and $\phi$. These equations have a well-defined Newtonian
limit, from which it can be seen that both metric and scale play a role in the
description gravitational interaction. We present a spherically symmetric
solution for the LyST gravity field equations. It dependent on two parameters
$m$ and $r_{L}$, whose physical meaning is carefully investigated. We highlight
the properties of LyST spherically symmetric line element and compare it to
Schwarzchild solution.


Abstract:
  Tubercles are modifications to the leading edge of an airfoil in the form of
blunt wave-like serrations. Several studies on the effect of tubercles on
isolated airfoils have shown a beneficial effect in the post-stall regime, as
reduced drag and increased lift, leading to a delay of stall. The prospect of
delaying stall is particularly attractive to designers of axial compressors in
gas turbines, as this leads to designs with higher loading and therefore higher
pressure rise with fewer number of stages. In the present study, experiments
were performed on a cascade of airfoils with NACA 65209 profile with different
tubercle geometries. The measurements were made over an exit plane using a
five-hole probe to compare the cascade performance parameters. Additionally,
hot-wire measurements were taken near the blade surface to understand the
nature of the flow in the region close to the tubercles. Oil-flow visualization
on the cascade end wall reveal the flow through the passage of blades with and
without tubercles. For the cascade considered, the estimated stall angle for
the best performing set of blades is found to increase up to 8.6{\deg} from
that of the unmodified blade of 6.0{\deg}. Application of such structures in
axial compressor blades may well lead to suppression of stall in axial
compressors and extend the operating range.


Abstract:
  Hexagonal rare-earth ferrite RFeO$_3$ family represents a unique class of
multiferroics exhibiting weak ferromagnetism, and a strong coupling between
magnetism and structural trimerization is predicted. However, the hexagonal
structure for RFeO$_3$ remains metastable in conventional condition. We have
succeeded in stabilizing the hexagonal structure of polycrystalline YbFeO$_3$
by partial Sc substitution of Yb. Using bulk magnetometry and neutron
diffraction, we find that Yb$_{0.42}$Sc$_{0.58}$FeO$_3$ orders into a canted
antiferromagnetic state with the Neel temperature $T_N$ ~ 165 K, below which
the $Fe^{3+}$ moments form the triangular configuration in the $ab$-plane and
their in-plane projections are parallel to the [100] axis, consistent with
magnetic space group $P$6$_{3}$$c'm'$. It is determined that the spin-canting
is aligned along the $c$-axis, giving rise to the weak ferromagnetism.
Furthermore, the $Fe^{3+}$ moments reorient toward a new direction below
reorientation temperature $T_R$ ~ 40 K, satisfying magnetic subgroup
$P$6$_{3}$, while the $Yb^{3+}$ moments order independently and
ferrimagnetically along the $c$-axis at the characteristic temperature $T_{Yb}$
~ 15 K. Interestingly, reproducible modulation of electric polarization induced
by magnetic field at low temperature is achieved, suggesting that the delicate
structural distortion associated with two-up/one-down buckling of the
Yb/Sc-planes and tilting of the FeO$_5$ bipyramids may mediate the coupling
between ferroelectric and magnetic orders under magnetic field. The present
work represents a substantial progress to search for high-temperature
multiferroics in hexagonal ferrites and related materials.


Abstract:
  High quality facial image editing is a challenging problem in the movie
post-production industry, requiring a high degree of control and identity
preservation. Previous works that attempt to tackle this problem may suffer
from the entanglement of facial attributes and the loss of the person's
identity. Furthermore, many algorithms are limited to a certain task. To tackle
these limitations, we propose to edit facial attributes via the latent space of
a StyleGAN generator, by training a dedicated latent transformation network and
incorporating explicit disentanglement and identity preservation terms in the
loss function. We further introduce a pipeline to generalize our face editing
to videos. Our model achieves a disentangled, controllable, and
identity-preserving facial attribute editing, even in the challenging case of
real (i.e., non-synthetic) images and videos. We conduct extensive experiments
on image and video datasets and show that our model outperforms other
state-of-the-art methods in visual quality and quantitative evaluation. Source
codes are available at https://github.com/InterDigitalInc/latent-transformer.


Abstract:
  Not all topics are equally "flammable" in terms of toxicity: a calm
discussion of turtles or fishing less often fuels inappropriate toxic dialogues
than a discussion of politics or sexual minorities. We define a set of
sensitive topics that can yield inappropriate and toxic messages and describe
the methodology of collecting and labeling a dataset for appropriateness. While
toxicity in user-generated data is well-studied, we aim at defining a more
fine-grained notion of inappropriateness. The core of inappropriateness is that
it can harm the reputation of a speaker. This is different from toxicity in two
respects: (i) inappropriateness is topic-related, and (ii) inappropriate
message is not toxic but still unacceptable. We collect and release two
datasets for Russian: a topic-labeled dataset and an appropriateness-labeled
dataset. We also release pre-trained classification models trained on this
data.


Abstract:
  Evolution equations for leading twist operators in high orders of
perturbation theory can be restored from the spectrum of anomalous dimensions
and the calculation of the special conformal anomaly at one order less using
conformal symmetry of QCD at the Wilson-Fisher critical point at non-integer
$d=4-2\epsilon$ space-time dimensions. In this work we generalize this
technique to axial-vector operators. We calculate the corresponding three-loop
evolution kernels in Larin's scheme and derive explicit expressions for the
finite renormalization kernel that describes the difference to the vector case
to restore the conventional ${\overline{\mathrm{MS}}}$-scheme. The results are
directly applicable to deeply-virtual Compton scattering and the transition
form factor $\gamma^*\gamma\to\pi$.


Abstract:
  In one complex variable, the cross ratio is a well-known quantity associated
with four given points in the complex plane that remains invariant under linear
fractional maps. In particular, if one knows where three points in the complex
plane are mapped under a linear fractional map, one can use this invariance to
explicitly determine the map and to show that linear fractional maps are
$3$-transitive. In this paper, we define a generalized cross ratio and
determine some of its basic properties. In particular, we determine which
hypotheses must be made to guarantee that our generalized cross ratio is well
defined. We thus obtain a class of maps that obey similar transitivity
properties as in one complex dimension, under some more restrictive conditions.


Abstract:
  Automated story generation remains a difficult area of research because it
lacks strong objective measures. Generated stories may be linguistically sound,
but in many cases suffer poor narrative coherence required for a compelling,
logically-sound story. To address this, we present Fabula Entropy Indexing
(FEI), an evaluation method to assess story coherence by measuring the degree
to which human participants agree with each other when answering true/false
questions about stories. We devise two theoretically grounded measures of
reader question-answering entropy, the entropy of world coherence (EWC), and
the entropy of transitional coherence (ETC), focusing on global and local
coherence, respectively. We evaluate these metrics by testing them on
human-written stories and comparing against the same stories that have been
corrupted to introduce incoherencies. We show that in these controlled studies,
our entropy indices provide a reliable objective measure of story coherence.


Abstract:
  Learning individualized treatment rules (ITRs) is an important topic in
precision medicine. Current literature mainly focuses on deriving ITRs from a
single source population. We consider the observational data setting when the
source population differs from a target population of interest. We assume
subject covariates are available from both populations, but treatment and
outcome data are only available from the source population. Although adjusting
for differences between source and target populations can potentially lead to
an improved ITR for the target population, it can substantially increase the
variability in ITR estimation. To address this dilemma, we develop a weighting
framework that aims to tailor an ITR for a given target population and protect
against high variability due to superfluous covariate shift adjustments. Our
method seeks covariate balance over a nonparametric function class
characterized by a reproducing kernel Hilbert space and can improve many ITR
learning methods that rely on weights. We show that the proposed method
encompasses importance weights and the so-called overlap weights as two extreme
cases, allowing for a better bias-variance trade-off in between. Numerical
examples demonstrate that the use of our weighting method can greatly improve
ITR estimation for the target population compared with other weighting methods.


Abstract:
  We calculate the mass spectrum and the structure of the positronium system at
a strong coupling in a basis light-front approach. We start from the
light-front QED Hamiltonian and retain one dynamical photon in our basis. We
perform the fermion mass renormalization associated with the nonperturbative
fermion self-energy correction. We present the resulting mass spectrum and wave
functions for the selected low-lying states. Next, we apply this approach to
QCD and calculate the heavy meson system with one dynamical gluon retained. We
illustrate the obtained mass spectrum and wave functions for the selected
low-lying states.


Abstract:
  We study compressible types in the context of (local and global) NIP. By
extending a result in machine learning theory (the existence of a bound on the
recursive teaching dimension), we prove density of compressible types. Using
this, we obtain explicit uniform honest definitions for NIP formulas (answering
a question of Eshel and the second author), and build compressible models in
countable NIP theories.


Abstract:
  The paper describes the construction of entropy-stable discontinuous Galerkin
difference (DGD) discretizations for hyperbolic conservation laws on
unstructured grids. The construction takes advantage of existing theory for
entropy-stable summation-by-parts (SBP) discretizations. In particular, the
paper shows how DGD discretizations -- both linear and nonlinear -- can be
constructed by defining the SBP trial and test functions in terms of
interpolated DGD degrees of freedom. In the case of entropy-stable
discretizations, the entropy variables rather than the conservative variables
must be interpolated to the SBP nodes. A fully-discrete entropy-stable scheme
is obtained by adopting the relaxation Runge-Kutta version of the midpoint
method. In addition, DGD matrix operators for the first derivative are shown to
be dense-norm SBP operators. Numerical results are presented to verify the
accuracy and entropy-stability of the DGD discretization in the context of the
Euler equations. The results suggest that DGD and SBP solution errors are
similar for the same number of degrees of freedom. Finally, an investigation of
the DGD spectra shows that spectral radius is relatively insensitive to
discretization order; however, the high-order methods do suffer from the linear
instability reported for other entropy-stable discretizations.


Abstract:
  Segmenting histology images into diagnostically relevant regions is
imperative to support timely and reliable decisions by pathologists. To this
end, computer-aided techniques have been proposed to delineate relevant regions
in scanned histology slides. However, the techniques necessitate task-specific
large datasets of annotated pixels, which is tedious, time-consuming,
expensive, and infeasible to acquire for many histology tasks. Thus,
weakly-supervised semantic segmentation techniques are proposed to utilize weak
supervision that is cheaper and quicker to acquire. In this paper, we propose
SegGini, a weakly supervised segmentation method using graphs, that can utilize
weak multiplex annotations, i.e. inexact and incomplete annotations, to segment
arbitrary and large images, scaling from tissue microarray (TMA) to whole slide
image (WSI). Formally, SegGini constructs a tissue-graph representation for an
input histology image, where the graph nodes depict tissue regions. Then, it
performs weakly-supervised segmentation via node classification by using
inexact image-level labels, incomplete scribbles, or both. We evaluated SegGini
on two public prostate cancer datasets containing TMAs and WSIs. Our method
achieved state-of-the-art segmentation performance on both datasets for various
annotation settings while being comparable to a pathologist baseline.


Abstract:
  Over the past decade, HCI researchers, design researchers, and practitioners
have increasingly addressed ethics-focused issues through a range of
theoretical, methodological and pragmatic contributions to the field. While
many forms of design knowledge have been proposed and described, we focus
explicitly on knowledge that has been codified as "methods," which we define as
any supports for everyday work practices of designers. In this paper, we
identify, analyze, and map a collection of 63 existing ethics-focused methods
intentionally designed for ethical impact. We present a content analysis,
providing a descriptive record of how they operationalize ethics, their
intended audience or context of use, their "core" or "script," and the means by
which these methods are formulated, articulated, and languaged. Building on
these results, we provide an initial definition of ethics-focused methods,
identifying potential opportunities for the development of future methods to
support design practice and research.


Abstract:
  We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by
identifying a new connection between neural tangent kernel (NTK) and MMD. This
connection enables us to develop a computationally efficient and
memory-efficient approach to compute the MMD statistic and perform NTK based
two-sample tests towards addressing the long-standing challenge of memory and
computational complexity of the MMD statistic, which is essential for online
implementation to assimilating new samples. Theoretically, such a connection
allows us to understand the NTK test statistic properties, such as the Type-I
error and testing power for performing the two-sample test, by adapting
existing theories for kernel MMD. Numerical experiments on synthetic and
real-world datasets validate the theory and demonstrate the effectiveness of
the proposed NTK-MMD statistic.


Abstract:
  This paper contributes a closed-form linear IV estimator to a class of
estimators that minimises the mean dependence of an error term on a set of
instruments. Subject to a weak uncorrelatedness exclusion restriction, root-n
consistency and asymptotic normality are achieved under a significantly weak
relevance condition in at least two respects: (1) consistent estimation without
excluded instruments is possible provided endogenous covariates are
non-linearly mean-dependent on exogenous covariates, and (2) the endogenous
covariates may be uncorrelated with but mean-dependent on instruments. In
addition, this paper proposes a test of the weak relevance condition in the
case of a single endogenous with exogenous covariates. Monte Carlo simulations
illustrate low bias relative to conventional IV methods when instruments are
very weak. An empirical example illustrates the practical usefulness of the
estimator where, for instance, reasonable estimates are still achieved when no
excluded instrument is used.


Abstract:
  We propose an algorithm based on Hilbert space-filling curves to reorder mesh
elements in memory for use with the Spectral Element Method, aiming to attain
fewer cache misses, better locality of data reference and faster execution. We
present a technique to numerically simulate acoustic wave propagation in 2D
domains using the Spectral Element Method, and discuss computational
performance aspects of this procedure. We reorder mesh-related data via Hilbert
curves to achieve sizable reductions in execution time under several mesh
configurations in shared-memory systems. Our experiments show that the Hilbert
curve approach works well with meshes of several granularities and also with
small and large variations in element sizes, achieving reductions between 9%
and 25% in execution time when compared to three other ordering schemes.


Abstract:
  We propose a novel approach to lifelong learning, introducing a compact
encapsulated support structure which endows a network with the capability to
expand its capacity as needed to learn new tasks while preventing the loss of
learned tasks. This is achieved by splitting neurons with high semantic drift
and constructing an adjacent network to encode the new tasks at hand. We call
this the Plastic Support Structure (PSS), it is a compact structure to learn
new tasks that cannot be efficiently encoded in the existing structure of the
network. We validate the PSS on public datasets against existing lifelong
learning architectures, showing it performs similarly to them but without prior
knowledge of the task and in some cases with fewer parameters and in a more
understandable fashion where the PSS is an encapsulated container for specific
features related to specific tasks, thus making it an ideal "add-on" solution
for endowing a network to learn more tasks.


Abstract:
  Entropy production characterizes irreversibility. This viewpoint allows us to
consider the thermodynamic uncertainty relation, which states that a higher
precision can be achieved at the cost of higher entropy production, as a
relation between precision and irreversibility. Considering the original and
perturbed dynamics, we show that the precision of an arbitrary counting
observable in continuous measurement of quantum Markov processes is bounded
from below by Loschmidt echo between the two dynamics, representing the
irreversibility of quantum dynamics. When considering particular perturbed
dynamics, our relation leads to several thermodynamic uncertainty relations,
indicating that our relation provides a unified perspective on classical and
quantum thermodynamic uncertainty relations.


Abstract:
  We study the repair problem for hyperproperties specified in the temporal
logic HyperLTL. Hyperproperties are system properties that relate multiple
computation traces. This class of properties includes information flow policies
like noninterference and observational determinism. The repair problem is to
find, for a given Kripke structure, a substructure that satisfies a given
specification. We show that the repair problem is decidable for HyperLTL
specifications and finite-state Kripke structures. We provide a detailed
complexity analysis for different fragments of HyperLTL and different system
types: tree-shaped, acyclic, and general Kripke structures.


Abstract:
  New integrability properties of a family of sequences of ordinary
differential equations, which contains the Riccati and Abel chains as the most
simple sequences, are studied. The determination of n generalized symmetries of
the nth-order equation in each chain provides, without any kind of integration,
n-1 functionally independent first integrals of the equation. A remaining first
integral arises by a quadrature by using a Jacobi last multiplier that is
expressed in terms of the preceding equation in the corresponding sequence. The
complete set of n first integrals is used to obtain the exact general solution
of the nth-order equation of each sequence. The results are applied to derive
directly the exact general solution of any equation in the Riccati and Abel
chains.


Abstract:
  In a previous paper we presented the results of applying machine learning to
classify whether an HI 21-cm absorption spectrum arises in a source intervening
the sight-line to a more distant radio source or within the host of the radio
source itself. This is usually determined from an optical spectrum giving the
source redshift. However, not only will this be impractical for the large
number of sources expected to be detected with the Square Kilometre Array, but
bright optical sources are the most ultra-violet luminous at high redshift and
so bias against the detection of cool, neutral gas. Adding another 44, mostly
newly detected absorbers, to the previous sample of 92, we test four different
machine learning algorithms, again using the line properties (width, depth and
number of Gaussian fits) as features. Of these algorithms, three gave a some
improvement over the previous sample, with a logistic regression model giving
the best results. This suggests that the inclusion of further training data, as
new absorbers are detected, will further increase the prediction accuracy above
the current 80%. We use the logistic regression model to classify the z = 0.42
absorption towards PKS 1657-298 and find this to be associated, which is
consistent with a previous study which determined a similar redshift from the
K-band magnitude-redshift relation.


Abstract:
  We study combinatorial problems with real world applications such as machine
scheduling, routing, and assignment. We propose a method that combines
Reinforcement Learning (RL) and planning. This method can equally be applied to
both the offline, as well as online, variants of the combinatorial problem, in
which the problem components (e.g., jobs in scheduling problems) are not known
in advance, but rather arrive during the decision-making process. Our solution
is quite generic, scalable, and leverages distributional knowledge of the
problem parameters. We frame the solution process as an MDP, and take a Deep
Q-Learning approach wherein states are represented as graphs, thereby allowing
our trained policies to deal with arbitrary changes in a principled manner.
Though learned policies work well in expectation, small deviations can have
substantial negative effects in combinatorial settings. We mitigate these
drawbacks by employing our graph-convolutional policies as non-optimal
heuristics in a compatible search algorithm, Monte Carlo Tree Search, to
significantly improve overall performance. We demonstrate our method on two
problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our
method outperforms custom-tailored mathematical solvers, state of the art
learning-based algorithms, and common heuristics, both in computation time and
performance.


Abstract:
  As weak lensing surveys become deeper, they reveal more non-Gaussian aspects
of the convergence field which can only be extracted using statistics beyond
the power spectrum. In Cheng et al. (2020) we showed that the scattering
transform, a novel statistic borrowing mathematical concepts from convolutional
neural networks, is a powerful tool for cosmological parameter estimation in
the non-Gaussian regime. Here, we extend that analysis to explore its
sensitivity to dark energy and neutrino mass parameters with weak lensing
surveys. We first use image synthesis to show visually that, compared to the
power spectrum and bispectrum, the scattering transform provides a better
statistical vocabulary to characterize the perceptual properties of lensing
mass maps. We then show that it is also better suited for parameter inference:
(i) it provides higher sensitivity in the noiseless regime, and (ii) at the
noise level of Rubin-like surveys, though the constraints are not significantly
tighter than those of the bispectrum, the scattering coefficients have a more
Gaussian sampling distribution, which is an important property for likelihood
parametrization and accurate cosmological inference. We argue that the
scattering coefficients are preferred statistics considering both constraining
power and likelihood properties.


Abstract:
  This study applies a new approach, the Theory of Functional Connections
(TFC), to solve the two-point boundary-value problem (TPBVP) in non-Keplerian
orbit transfer. The perturbations considered are drag, solar radiation
pressure, higher-order gravitational potential harmonic terms, and multiple
bodies. The proposed approach is applied to Earth-to-Moon transfers, and
obtains exact boundary condition satisfaction and with very fast convergence.
Thanks to this highly efficient approach, perturbed pork-chop plots of
Earth-to-Moon transfers are generated, and individual analyses on the
transfers' parameters are easily done at low computational costs. The minimum
fuel analysis is provided in terms of the time of flight, thrust application
points, and relative geometry of the Moon and Sun. The transfer costs obtained
are in agreement with the literature's best solutions, and in some cases are
even slightly better.


Abstract:
  We propose a fast and flexible method to scale multivariate return volatility
predictions up to high-dimensions using a dynamic risk factor model. Our
approach increases parsimony via time-varying sparsity on factor loadings and
is able to sequentially learn the use of constant or time-varying parameters
and volatilities. We show in a dynamic portfolio allocation problem with 452
stocks from the S&P 500 index that our dynamic risk factor model is able to
produce more stable and sparse predictions, achieving not just considerable
portfolio performance improvements but also higher utility gains for the
mean-variance investor compared to the traditional Wishart benchmark and the
passive investment on the market index.


Abstract:
  With the increased interest in machine learning and big data problems, the
need for large amounts of labelled data has also grown. However, it is often
infeasible to get experts to label all of this data, which leads many
practitioners to crowdsourcing solutions. In this paper, we present new
techniques to improve the quality of the labels while attempting to reduce the
cost. The naive approach to assigning labels is to adopt a majority vote
method, however, in the context of data labelling, this is not always ideal as
data labellers are not equally reliable. One might, instead, give higher
priority to certain labellers through some kind of weighted vote based on past
performance. This paper investigates the use of more sophisticated methods,
such as Bayesian inference, to measure the performance of the labellers as well
as the confidence of each label. The methods we propose follow an iterative
improvement algorithm which attempts to use the least amount of workers
necessary to achieve the desired confidence in the inferred label. This paper
explores simulated binary classification problems with simulated workers and
questions to test the proposed methods. Our methods outperform the standard
voting methods in both cost and accuracy while maintaining higher reliability
when there is disagreement within the crowd.


Abstract:
  Coded-caching is a promising technique to reduce the peak rate requirement of
backhaul links during high traffic periods. In this letter, we study the effect
of adaptive transmission on the performance of coded-caching based networks.
Particularly, concentrating on the reduction of backhaul peak load during the
high traffic periods, we develop adaptive rate and power allocation schemes
maximizing the network successful transmission probability, which is defined as
the probability of the event with all cache nodes decoding their intended
signals correctly. Moreover, we study the effect of different message decoding
and buffering schemes on the system performance. As we show, the performance of
coded-caching networks is considerably affected by rate/power allocation as
well as the message decoding/buffering schemes.


Abstract:
  Time series classification(TSC) has always been an important and challenging
research task. With the wide application of deep learning, more and more
researchers use deep learning models to solve TSC problems. Since time series
always contains a lot of noise, which has a negative impact on network
training, people usually filter the original data before training the network.
The existing schemes are to treat the filtering and training as two stages, and
the design of the filter requires expert experience, which increases the design
difficulty of the algorithm and is not universal. We note that the essence of
filtering is to filter out the insignificant frequency components and highlight
the important ones, which is similar to the attention mechanism. In this paper,
we propose an attention mechanism that acts on spectrum (SAM). The network can
assign appropriate weights to each frequency component to achieve adaptive
filtering. We use L1 regularization to further enhance the frequency screening
capability of SAM. We also propose a segmented-SAM (SSAM) to avoid the loss of
time domain information caused by using the spectrum of the whole sequence. In
which, a tumbling window is introduced to segment the original data. Then SAM
is applied to each segment to generate new features. We propose a heuristic
strategy to search for the appropriate number of segments. Experimental results
show that SSAM can produce better feature representations, make the network
converge faster, and improve the robustness and classification accuracy.


Abstract:
  A high-order Flux reconstruction implementation of the hyperbolic formulation
for the incompressible Navier-Stokes equation is presented. The governing
equations employ Chorin's classical artificial compressibility (AC) formulation
cast in hyperbolic form. Instead of splitting the second-order conservation law
into two equations, one for the solution and another for the gradient, the
Navier-Stokes equation is cast into a first-order hyperbolic system of
equations. Including the gradients in the AC iterative process results in a
significant improvement in accuracy for the pressure, velocity, and its
gradients. Furthermore, this treatment allows for taking larger time-steps
since the hyperbolic formulation eliminates the restriction due to diffusion.
Tests using the method of manufactured solutions show that solving the
conventional form of the Navier-Stokes equation lowers the order of accuracy
for gradients, while the hyperbolic method is shown to provide equal orders of
accuracy for both the velocity and its gradients which may be beneficial in
several applications. Two- and three-dimensional benchmark tests demonstrate
the superior accuracy and computational efficiency of the developed solver in
comparison to the conventional method and other published works. This study
shows that the developed high-order hyperbolic solver for incompressible flows
is attractive due to its accuracy, stability and efficiency in solving
diffusion dominated problems.


Abstract:
  In this investigation, force field-based molecular dynamics (MD) simulations
have been employed to generate detailed structural representations for a range
of amorphous quaternary CaO-MgO-Al2O3-SiO2 (CMAS) and ternary CaO-Al2O3-SiO2
(CAS) glasses. Comparison of the simulation results with select experimental
X-ray and neutron total scattering and literature data reveals that the
MD-generated structures have captured the key structural features of these CMAS
and CAS glasses. Based on the MD-generated structural representations, we have
developed two structural descriptors, specifically (i) average metal oxide
dissociation energy (AMODE) and (ii) average self-diffusion coefficient (ASDC)
of all the atoms at melting. Both structural descriptors are seen to more
accurately predict the relative glass reactivity than the commonly used degree
of depolymerization parameter, especially for the eight synthetic CAS glasses
that span a wide compositional range. Hence these descriptors hold great
promise for predicting CMAS and CAS glass reactivity in alkaline environments
from compositional information.


Abstract:
  We make use of ALMA continuum observations of $15$ luminous Lyman-break
galaxies at $z$$\sim$$7$$-$$8$ to probe their dust-obscured star-formation.
These observations are sensitive enough to probe to obscured SFRs of $20$
$M_{\odot}$$/$$yr$ ($3\sigma$). Six of the targeted galaxies show significant
($\geq$$3$$\sigma$) dust continuum detections, more than doubling the number of
known dust-detected galaxies at $z$$>$$6.5$. Their IR luminosities range from
$2.7$$\times$$10^{11}$ $L_{\odot}$ to $1.1$$\times$$10^{12}$ $L_{\odot}$,
equivalent to obscured SFRs of $20$ to $105$ $M_{\odot}$$/$$yr$. We use our
results to quantify the correlation of the infrared excess IRX on the
UV-continuum slope $\beta_{UV}$ and stellar mass. Our results are most
consistent with an SMC attenuation curve for intrinsic $UV$-slopes
$\beta_{UV,intr}$ of $-2.63$ and most consistent with an attenuation curve
in-between SMC and Calzetti for $\beta_{UV,intr}$ slopes of $-2.23$, assuming a
dust temperature $T_d$ of $50$ K. Our fiducial IRX-stellar mass results at
$z$$\sim$$7$$-$$8$ are consistent with marginal evolution from $z$$\sim$$0$. We
then show how both results depend on $T_d$. For our six dust-detected sources,
we estimate their dust masses and find that they are consistent with dust
production from SNe if the dust destruction is low ($<$$90$%). Finally we
determine the contribution of dust-obscured star formation to the star
formation rate density for $UV$ luminous ($<$$-$$21.5$ mag:
$\gtrsim$$1.7$$L_{UV} ^*$) $z$$\sim$$7$$-$$8$ galaxies, finding that the total
SFR density at $z$$\sim$$7$ and $z$$\sim$$8$ from bright galaxies is
$0.18_{-0.10}^{+0.08}$ dex and $0.20_{-0.09}^{+0.05}$ dex higher, respectively,
i.e. $\sim$$\frac{1}{3}$ of the star formation in $\gtrsim$$1.7$$L_{UV} ^*$
galaxies at $z$$\sim$$7$$-$$8$ is obscured by dust.


Abstract:
  Cycle polytopes of matroids have been introduced in combinatorial
optimization as a generalization of important classes of polyhedral objects
like cut polytopes and Eulerian subgraph polytopes associated to graphs. Here
we start an algebraic and geometric investigation of these polytopes by
studying their toric algebras, called cycle algebras, and their defining
ideals. Several matroid operations are considered which determine faces of
cycle polytopes that belong again to this class of polyhedral objects. As a key
technique used in this paper, we study certain minors of given matroids which
yield algebra retracts on the level of cycle algebras. In particular, that
allows us to use a powerful algebraic machinery. As an application, we study
highest possible degrees in minimal homogeneous systems of generators of
defining ideals of cycle algebras as well as interesting cases of cut polytopes
and Eulerian subgraph polytopes.


Abstract:
  We construct tame differential calculi coming from toral actions on a class
of C*-algebras. Relying on the existence of a unique Levi-Civita connection on
such a calculi, we prove a version of the Bianchi identity. A Gauss-Bonnet
theorem for the canonical rank 2-calculus is studied.


Abstract:
  H-principle (or homotopy principle) is the property that some solutions to a
partial differential equation/inequality can be obtained as a deformed of a
formal solution by a homotopy. Gromov defines the sheaf theoritic h-principle
in his book and shows the existence of h-principle from a very abstract
setting. In this paper, we clarify a categorical structure behind Gromov
h-principle. The main result is that a flexible sheaf can be understood as a
fibrant object in some categories.


Abstract:
  We propose a route choice model in which traveler behavior is represented as
a utility maximizing assignment of flow across an entire network under a flow
conservation constraint}. Substitution between routes depends on how much they
overlap. {\tr The model is estimated considering the full set of route
alternatives, and no choice set generation is required. Nevertheless,
estimation requires only linear regression and is very fast. Predictions from
the model can be computed using convex optimization, and computation is
straightforward even for large networks. We estimate and validate the model
using a large dataset comprising 1,337,096 GPS traces of trips in the Greater
Copenhagen road network.


Abstract:
  Context. To investigate the source of a type III radio burst storm during
encounter 2 of NASA's Parker Solar Probe (PSP) mission.
  Aims. It was observed that in encounter 2 of NASA's Parker Solar Probe
mission there was a large amount of radio activity, and in particular a noise
storm of frequent, small type III bursts from 31st March to 6th April 2019. Our
aim is to investigate the source of these small and frequent bursts.
  Methods. In order to do this, we analysed data from the Hinode EUV Imaging
Spectrometer (EIS), PSP FIELDS, and the Solar Dynamics Observatory (SDO)
Atmospheric Imaging Assembly (AIA). We studied the behaviour of active region
12737, whose emergence and evolution coincides with the timing of the radio
noise storm and determined the possible origins of the electron beams within
the active region. To do this, we probe the dynamics, Doppler velocity,
non-thermal velocity, FIP bias, densities, and carry out magnetic modelling.
  Results. We demonstrate that although the active region on the disk produces
no significant flares, its evolution indicates it is a source of the electron
beams causing the radio storm. They most likely originate from the area at the
edge of the active region that shows strong blue-shifted plasma. We demonstrate
that as the active region grows and expands, the area of the blue-shifted
region at the edge increases, which is also consistent with the increasing area
where large-scale or expanding magnetic field lines from our modelling are
anchored. This expansion is most significant between 1 and 4 April 2019,
coinciding with the onset of the type III storm and the decrease of the
individual burst's peak frequency, indicating the height at which the peak
radiation is emitted increases as the active region evolves.


Abstract:
  We provide a simple proof that the partial sums $\sum_{n\leq x}f(n)$ of a
Rademacher random multiplicative function $f$ change sign for an infinite
number of $x>0$, almost surely.


Abstract:
  We study random digraphs on sequences of expanders with bounded average
degree and weak local limit. The threshold for the existence of a giant
strongly connected component, as well as the asymptotic fraction of nodes with
giant fan-in or giant fan-out are local, in the sense that they are the same
for two sequences with the same weak local limit. The digraph has a bow-tie
structure, with all but a vanishing fraction of nodes lying either in the
unique strongly connected giant and its fan-in and fan-out, or in sets with
small fan-in and small fan-out. All local quantities are expressed in terms of
percolation on the limiting rooted graph, without any structural assumptions on
the limit, allowing, in particular, for non tree-like limits.
  In the course of proving these results, we prove that for unoriented
percolation, there is a unique giant above criticality, whose size and critical
threshold are again local. An application of our methods shows that the
critical threshold for bond percolation and random digraphs on preferential
attachment graphs is $p_c=0$, with an infinite order phase transition at $p_c$.


Abstract:
  In this paper we propose a novel point cloud generator that is able to
reconstruct and generate 3D point clouds composed of semantic parts. Given a
latent representation of the target 3D model, the generation starts from a
single point and gets expanded recursively to produce the high-resolution point
cloud via a sequence of point expansion stages. During the recursive procedure
of generation, we not only obtain the coarse-to-fine point clouds for the
target 3D model from every expansion stage, but also unsupervisedly discover
the semantic segmentation of the target model according to the
hierarchical/parent-child relation between the points across expansion stages.
Moreover, the expansion modules and other elements used in our recursive
generator are mostly sharing weights thus making the overall framework light
and efficient. Extensive experiments are conducted to demonstrate that our
proposed point cloud generator has comparable or even superior performance on
both generation and reconstruction tasks in comparison to various baselines, as
well as provides the consistent co-segmentation among 3D instances of the same
object class.


Abstract:
  Given (small amounts of) time-series' data from a high-dimensional,
fine-grained, multiscale dynamical system, we propose a generative framework
for learning an effective, lower-dimensional, coarse-grained dynamical model
that is predictive of the fine-grained system's long-term evolution but also of
its behavior under different initial conditions. We target fine-grained models
as they arise in physical applications (e.g. molecular dynamics, agent-based
models), the dynamics of which are strongly non-stationary but their transition
to equilibrium is governed by unknown slow processes which are largely
inaccessible by brute-force simulations. Approaches based on domain knowledge
heavily rely on physical insight in identifying temporally slow features and
fail to enforce the long-term stability of the learned dynamics. On the other
hand, purely statistical frameworks lack interpretability and rely on large
amounts of expensive simulation data (long and multiple trajectories) as they
cannot infuse domain knowledge. The generative framework proposed achieves the
aforementioned desiderata by employing a flexible prior on the complex plane
for the latent, slow processes, and an intermediate layer of physics-motivated
latent variables that reduces reliance on data and imbues inductive bias. In
contrast to existing schemes, it does not require the a priori definition of
projection operators from the fine-grained description and addresses
simultaneously the tasks of dimensionality reduction and model estimation. We
demonstrate its efficacy and accuracy in multiscale physical systems of
particle dynamics where probabilistic, long-term predictions of phenomena not
contained in the training data are produced.


Abstract:
  In the context of large-angle cone-beam tomography (CBCT), we present a
practical iterative reconstruction (IR) scheme designed for rapid convergence
as required for large datasets. The robustness of the reconstruction is
provided by the "space-filling" source trajectory along which the experimental
data is collected. The speed of convergence is achieved by leveraging the
highly isotropic nature of this trajectory to design an approximate
deconvolution filter that serves as a pre-conditioner in a multi-grid scheme.
We demonstrate this IR scheme for CBCT and compare convergence to that of more
traditional techniques.


Abstract:
  Boundary controlled irreversible port-Hamiltonian systems (BC-IPHS) on
1-dimensional spatial domains are defined by extending the formulation of
reversible BC-PHS to irreversible thermodynamic systems controlled at the
boundaries of their spatial domains. The structure of BC-IPHS has clear
physical interpretation, characterizing the coupling between energy storing and
energy dissipating elements. By extending the definition of boundary port
variables of BC-PHS to deal with the dissipative terms, a set of boundary port
variables are defined such that BC-IPHS are passive with respect to a given set
of conjugated inputs and outputs. As for finite dimensional IPHS, the first and
second principle are satisfied as a structural property. Several examples are
given to illustrate the proposed approach.


Abstract:
  In this paper some new proposals for method comparison are presented. On the
one hand, two new robust regressions, the M-Deming and the MM-Deming, have been
developed by modifying Linnet's method of the weighted Deming regression. The
M-Deming regression shows superior qualities to the Passing-Bablok regression;
it does not suffer from bias when the data to be validated have a reduced
precision, and therefore turns out to be much more reliable. On the other hand,
a graphical method (box and ellipses) for validations has been developed which
is also equipped with a unified statistical test. In this test the intercept
and slope pairs obtained from a bootstrap process are combined into a
multinomial distribution by robust determination of the covariance matrix. The
Mahalanobis distance from the point representing the null hypothesis is
evaluated using the $\chi^{2}$ distribution. It is emphasized that the
interpretation of the graph is more important than the probability obtained
from the test. The unified test has been evaluated through Monte Carlo
simulations, comparing the theoretical $\alpha$ levels with the empirical rate
of rejections (type-I errors). In addition, a power comparison of the various
(new and old) methods was conducted using the same techniques. This unified
method, regardless of the regression chosen, shows much higher power and allows
a significant reduction in the sample size required for validations.


Abstract:
  In this paper, we introduce a streaming keyphrase detection system that can
be easily customized to accurately detect any phrase composed of words from a
large vocabulary. The system is implemented with an end-to-end trained
automatic speech recognition (ASR) model and a text-independent speaker
verification model. To address the challenge of detecting these keyphrases
under various noisy conditions, a speaker separation model is added to the
feature frontend of the speaker verification model, and an adaptive noise
cancellation (ANC) algorithm is included to exploit cross-microphone noise
coherence. Our experiments show that the text-independent speaker verification
model largely reduces the false triggering rate of the keyphrase detection,
while the speaker separation model and adaptive noise cancellation largely
reduce false rejections.


Abstract:
  There are concerns that the ability of language models (LMs) to generate high
quality synthetic text can be misused to launch spam, disinformation, or
propaganda. Therefore, the research community is actively working on developing
approaches to detect whether a given text is organic or synthetic. While this
is a useful first step, it is important to be able to further fingerprint the
author LM to attribute its origin. Prior work on fingerprinting LMs is limited
to attributing synthetic text generated by a handful (usually < 10) of
pre-trained LMs. However, LMs such as GPT2 are commonly fine-tuned in a myriad
of ways (e.g., on a domain-specific text corpus) before being used to generate
synthetic text. It is challenging to fingerprinting fine-tuned LMs because the
universe of fine-tuned LMs is much larger in realistic scenarios. To address
this challenge, we study the problem of large-scale fingerprinting of
fine-tuned LMs in the wild. Using a real-world dataset of synthetic text
generated by 108 different fine-tuned LMs, we conduct comprehensive experiments
to demonstrate the limitations of existing fingerprinting approaches. Our
results show that fine-tuning itself is the most effective in attributing the
synthetic text generated by fine-tuned LMs.


Abstract:
  We report on the analysis of a deep Chandra observation of the high-magnetic
field pulsar (PSR) J1119-6127 and its compact pulsar wind nebula (PWN) taken in
October 2019, three years after the source went into outburst. The 0.5-7 keV
post-outburst (2019) spectrum of the pulsar is best described by a
two-component blackbody plus powerlaw model with a temperature of 0.2\pm0.1
keV, photon index of 1.8\pm0.4 and X-ray luminosity of ~1.9e33 erg s^{-1},
consistent with its pre-burst quiescent phase. We find that the pulsar has gone
back to quiescence. The compact nebula shows a jet-like morphology elongated in
the north-south direction, similar to the pre-burst phase. The post-outburst
PWN spectrum is best fit by an absorbed powerlaw with a photon index of
2.3\pm0.5 and flux of ~3.2e-14 erg cm^{-2} s^{-1} (0.5-7 keV). The PWN spectrum
shows evidence of spectral softening in the post-outburst phase, with the
pre-burst photon index of 1.2\pm0.4 changing to 2.3\pm0.5, and pre-burst
luminosity of ~1.5e32 erg s^{-1} changing to 2.7e32 erg s^{-1} in the 0.5-7 keV
band, suggesting magnetar outbursts can impact PWNe. The observed timescale for
returning to quiescence, of just a few years, implies a rather fast cooling
process and favors a scenario where J1119 is temporarily powered by magnetic
energy following the magnetar outburst, in addition to its spin-down energy.


Abstract:
  Inspired by biological molecular machines we explore the idea of an active
quantum robot whose purpose is delaying decoherence. A conceptual model capable
of partially protecting arbitrary logical qubit states against single physical
qubit errors is presented. Implementation of an instance of that model - the
entanglement qubot - is proposed using laser-dressed Rydberg atoms. Dynamics of
the system is studied using stochastic wavefunction methods.


Abstract:
  Experiments dedicated to the measurement of the electric dipole moment of the
neutron require outstanding control of the magnetic field uniformity. The
neutron electric dipole moment (nEDM) experiment at the Paul Scherrer Institute
uses a 199Hg co-magnetometer to precisely monitor magnetic field variations.
This co-magnetometer, in the presence of field non-uniformity, is responsible
for the largest systematic effect of this measurement. To evaluate and correct
that effect, offline measurements of the field non-uniformity were performed
during mapping campaigns in 2013, 2014 and 2017. We present the results of
these campaigns, and the improvement the correction of this effect brings to
the neutron electric dipole moment measurement.


Abstract:
  Given a nonparametric Hidden Markov Model (HMM) with two states, the question
of constructing efficient multiple testing procedures is considered, treating
one of the states as an unknown null hypothesis. A procedure is introduced,
based on nonparametric empirical Bayes ideas, that controls the False Discovery
Rate (FDR) at a user--specified level. Guarantees on power are also provided,
in the form of a control of the true positive rate. One of the key steps in the
construction requires supremum--norm convergence of preliminary estimators of
the emission densities of the HMM. We provide the existence of such estimators,
with convergence at the optimal minimax rate, for the case of a HMM with $J\ge
2$ states, which is of independent interest.


Abstract:
  In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL)
architecture for safe control in Radio Access Network (RAN) applications. In
our automated tool, a user can select a high-level safety specifications
expressed in Linear Temporal Logic (LTL) to shield an RL agent running in a
given cellular network with aim of optimizing network performance, as measured
through certain Key Performance Indicators (KPIs). In the proposed
architecture, network safety shielding is ensured through model-checking
techniques over combined discrete system models (automata) that are abstracted
through reinforcement learning. We demonstrate the user interface (UI) helping
the user set intent specifications to the architecture and inspect the
difference in allowed and blocked actions.


Abstract:
  One of the distinct features of this century has been the population of older
adults which has been on a constant rise. Elderly people have several needs and
requirements due to physical disabilities, cognitive issues, weakened memory
and disorganized behavior, that they face with increasing age. The extent of
these limitations also differs according to the varying diversities in elderly,
which include age, gender, background, experience, skills, knowledge and so on.
These varying needs and challenges with increasing age, limits abilities of
older adults to perform Activities of Daily Living (ADLs) in an independent
manner. To add to it, the shortage of caregivers creates a looming need for
technology-based services for elderly people, to assist them in performing
their daily routine tasks to sustain their independent living and active aging.
To address these needs, this work consists of making three major contributions
in this field. First, it provides a rather comprehensive review of assisted
living technologies aimed at helping elderly people to perform ADLs. Second,
the work discusses the challenges identified through this review, that
currently exist in the context of implementation of assisted living services
for elderly care in Smart Homes and Smart Cities. Finally, the work also
outlines an approach for implementation, extension and integration of the
existing works in this field for development of a much-needed framework that
can provide personalized assistance and user-centered behavior interventions to
elderly as per their varying and ever-changing needs.


Abstract:
  In solving optimization problems, objective functions generally need to be
minimized or maximized. However, objective functions cannot always be
formulated explicitly in a mathematical form for complicated problem settings.
Although several regression techniques infer the approximate forms of objective
functions, they are at times expensive to evaluate. Optimal points of
"black-box" objective functions are computed in such scenarios, while
effectively using a small number of clues. Recently, an efficient method by use
of inference by sparse prior for a black-box objective function with binary
variables has been proposed. In this method, a surrogate model was proposed in
the form of a quadratic unconstrained binary optimization (QUBO) problem, and
was iteratively solved to obtain the optimal solution of the black-box
objective function. In the present study, we employ the D-Wave 2000Q quantum
annealer, which can solve QUBO by driving the binary variables by quantum
fluctuations. The D-Wave 2000Q quantum annealer does not necessarily output the
ground state at the end of the protocol due to freezing effect during the
process. We investigate effects from the output of the D-Wave quantum annealer
in performing black-box optimization. We demonstrate a benchmark test by
employing the sparse Sherrington-Kirkpatrick (SK) model as the black-box
objective function, by introducing a parameter controlling the sparseness of
the interaction coefficients. Comparing the results of the D-Wave quantum
annealer to those of the simulated annealing (SA) and semidefinite programming
(SDP), our results by the D-Wave quantum annealer and SA exhibit superiority in
black-box optimization with SDP. On the other hand, we did not find any
advantage of the D-Wave quantum annealer over the simulated annealing. As far
as in our case, any effects by quantum fluctuation are not found.


Abstract:
  We describe a procedure to introduce general dependence structures on a set
of random variables. These include order-$q$ moving average-type structures, as
well as seasonal, periodic, spatial and spatio-temporal dependences. The
invariant marginal distribution can be in any family that is conjugate to an
exponential family with quadratic variance function. Dependence is induced via
a set of suitable latent variables whose conditional distribution mirrors the
sampling distribution in a Bayesian conjugate analysis of such exponential
families. We obtain strict stationarity as a special case.


Abstract:
  Consider a geodesic triangle on a surface of constant curvature and subdivide
it recursively into 4 triangles by joining the midpoints of its edges. We show
the existence of a uniform $\delta>0$ such that, at any step of the
subdivision, all the triangle angles lie in the interval $(\delta, \pi
-\delta)$. Additionally, we exhibit stabilising behaviours for both angles and
lengths as this subdivision progresses.


Abstract:
  (Abridged) We present a systematic investigation of physical conditions and
elemental abundances in four optically thick Lyman-limit systems (LLSs) at
$z=0.36-0.6$ discovered within the Cosmic Ultraviolet Baryon Survey (CUBS).
CUBS LLSs exhibit multi-component kinematic structure and a complex mix of
multiphase gas, with associated metal transitions from multiple ionization
states that span several hundred km/s in line-of-sight velocity. Specifically,
higher column density components (log N(HI)>16) in all four absorbers comprise
dynamically cool gas with $\langle T \rangle =(2\pm1) \times10^4\,$K and modest
non-thermal broadening of $5\pm3\,$ km/s. The high quality of the QSO
absorption spectra allows us to infer the physical conditions of the gas, using
a detailed ionization modeling that takes into account the resolved component
structures of HI and metal transitions. The range of inferred gas densities
indicates that these absorbers consist of spatially compact clouds with a
median line-of-sight thickness of $160^{+140}_{-50}$ pc. While obtaining robust
metallicity constraints for the low-density, highly ionized phase remains
challenging due to the uncertain N(HI), we demonstrate that the cool-phase gas
in LLSs has a median metallicity of
$\mathrm{[\alpha/H]_{1/2}}=-0.7^{+0.1}_{-0.2}$, with a 16-84 percentile range
of $\mathrm{[\alpha/H]}=(-1.3,-0.1)$. Furthermore, the wide range of inferred
elemental abundance ratios ($\mathrm{[C/\alpha]}$, $\mathrm{[N/\alpha]}$, and
$\mathrm{[Fe/\alpha]}$) indicate a diversity of chemical enrichment histories.
Combining the absorption data with deep galaxy survey data characterizing the
galaxy environment of these absorbers, we discuss the physical connection
between star-forming regions in galaxies and diffuse gas associated with
optically thick absorption systems in the $z<1$ circumgalactic medium.


Abstract:
  Offline reinforcement learning (RL) aims at learning a good policy from a
batch of collected data, without extra interactions with the environment during
training. However, current offline RL benchmarks commonly have a large reality
gap, because they involve large datasets collected by highly exploratory
policies, and the trained policy is directly evaluated in the environment. In
real-world situations, running a highly exploratory policy is prohibited to
ensure system safety, the data is commonly very limited, and a trained policy
should be well validated before deployment. In this paper, we present a near
real-world offline RL benchmark, named NeoRL, which contains datasets from
various domains with controlled sizes, and extra test datasets for policy
validation. We evaluate existing offline RL algorithms on NeoRL and argue that
the performance of a policy should also be compared with the deterministic
version of the behavior policy, instead of the dataset reward. The empirical
results demonstrate that the tested offline RL algorithms become less
competitive to the deterministic policy on many datasets, and the offline
policy evaluation hardly helps. The NeoRL suit can be found at
http://polixir.ai/research/neorl. We hope this work will shed some light on
future research and draw more attention when deploying RL in real-world
systems.


Abstract:
  In this paper we establish uniqueness in the inverse boundary value problem
for the two coefficients in the inhomogeneous porous medium equation
$\epsilon\partial_tu-\nabla\cdot(\gamma\nabla u^m)=0$, with $m>1$, in dimension
3 or higher, which is a degenerate parabolic type quasilinear PDE. Our approach
relies on using a Laplace transform to turn the original equation into a
coupled family of nonlinear elliptic equations, indexed by the frequency
parameter ($1/h$ in our definition) of the transform. A careful analysis of the
asymptotic expansion in powers of $h$, as $h\to\infty$, of the solutions to the
transformed equation, with special boundary data, allows us to obtain
sufficient information to deduce the uniqueness result.


Abstract:
  Accurate estimation of cratering asymmetry on the Moon is crucial for
understanding Moon evolution history. Early studies of cratering asymmetry have
omitted the contributions of high lunar obliquity and inclination. Here, we
include lunar obliquity and inclination as new controlling variables to derive
the cratering rate spatial variation as a function of longitude and latitude.
With examining the influence of lunar obliquity and inclination on the
asteroids population encountered by the Moon, we then have derived general
formulas of the cratering rate spatial variation based on the crater scaling
law. Our formulas with addition of lunar obliquity and inclination can
reproduce the lunar cratering rate asymmetry at the current Earth-Moon distance
and predict the apex/ant-apex ratio and the pole/equator ratio of this lunar
cratering rate to be 1.36 and 0.87, respectively. The apex/ant-apex ratio is
decreasing as the obliquity and inclination increasing. Combining with the
evolution of lunar obliquity and inclination, our model shows that the
apex/ant-apex ratio does not monotonically decrease with Earth-Moon distance
and hence the influences of obliquity and inclination are not negligible on
evolution of apex/ant-apex ratio. This model is generalizable to other planets
and moons, especially for different spin-orbit resonances.


Abstract:
  We present the N-body simulation techniques in EXP. EXP uses
empirically-chosen basis functions to expand the potential field of an ensemble
of particles. Unlike other basis function expansions, the derived basis
functions are adapted to an input mass distribution, enabling accurate
expansion of highly non-spherical objects, such as galactic discs. We measure
the force accuracy in three models, one based on a spherical or aspherical
halo, one based on an exponential disc, and one based on a bar-based disc
model. We find that EXP is as accurate as a direct-summation or tree-based
calculation, and in some ways is better, while being considerably less
computationally intensive. We discuss optimising the computation of the basis
function representation. We also detail numerical improvements for performing
orbit integrations, including timesteps.


Abstract:
  An analysis of 1,955 physics graduate students from 19 PhD programs shows
that undergraduate grade point average predicts graduate grades and PhD
completion more effectively than GRE scores. Students' undergraduate GPA (UGPA)
and GRE Physics (GRE-P) scores are small but statistically significant
predictors of graduate course grades, while GRE quantitative and GRE verbal
scores are not. We also find that males and females score equally well in their
graduate coursework despite a statistically significant 18 percentile point gap
in median GRE-P scores between genders. A counterfactual mediation analysis
demonstrates that among admission metrics tested only UGPA is a significant
predictor of overall PhD completion, and that UGPA predicts PhD completion
indirectly through graduate grades. Thus UGPA measures traits linked to
graduate course grades, which in turn predict graduate completion. Although
GRE-P scores are not significantly associated with PhD completion, our results
suggest that any predictive effect they may have are also linked indirectly
through graduate GPA. Overall our results indicate that among commonly used
quantitative admissions metrics, UGPA offers the most insight into two
important measures of graduate school success, while posing fewer concerns for
equitable admissions practices.


Abstract:
  We propose a general method for optimizing periodic input waveforms for
global entrainment of weakly forced limit-cycle oscillators based on phase
reduction and nonlinear programming. We derive averaged phase dynamics from the
mathematical model of a limit-cycle oscillator driven by a weak periodic input
and optimize the Fourier coefficients of the input waveform to maximize
prescribed objective functions. In contrast to the optimization methods that
rely on the calculus of variations, the proposed method can be applied to a
wider class of optimization problems including global entrainment objectives.
As an illustration, we consider two optimization problems, one for achieving
fast global convergence of the oscillator to the entrained state and the other
for realizing prescribed global phase distributions in a population of
identical uncoupled noisy oscillators. We show that the proposed method can
successfully yield optimal input waveforms to realize the desired states in
both cases.


Abstract:
  Using a generalized Madelung transformation, we derive the hydrodynamic
representation of the Dirac equation in arbitrary curved space-times coupled to
an electromagnetic field. We obtain Dirac-Euler equations for fermions
involving a continuity equation and a first integral of the Bernoulli equation.
Using the comparison of the Dirac and Klein-Gordon equations we obtain the
balance equation for fermion particles. We also use the correspondence between
fermions and bosons to derive the hydrodynamic representation of the Weyl
equation which is a chiral form of the Dirac equation.


Abstract:
  Hate speech and profanity detection suffer from data sparsity, especially for
languages other than English, due to the subjective nature of the tasks and the
resulting annotation incompatibility of existing corpora. In this study, we
identify profane subspaces in word and sentence representations and explore
their generalization capability on a variety of similar and distant target
tasks in a zero-shot setting. This is done monolingually (German) and
cross-lingually to closely-related (English), distantly-related (French) and
non-related (Arabic) tasks. We observe that, on both similar and distant target
tasks and across all languages, the subspace-based representations transfer
more effectively than standard BERT representations in the zero-shot setting,
with improvements between F1 +10.9 and F1 +42.9 over the baselines across all
tested monolingual and cross-lingual scenarios.


Abstract:
  The debate on the role of school closures as a mitigation strategy against
the spread of Covid-19 is gaining relevance due to emerging variants in Europe.
According to WHO, decisions on schools "should be guided by a risk-based
approach". However, risk evaluation requires sound methods, transparent data
and careful consideration of the context at the local level. We review a recent
study by Gandini et al., on the role of school opening as a driver of the
second COVID-19 wave in Italy, which concluded that there was no connection
between school openings/closures and SARS-CoV-2 incidence. infections. This
analysis has been widely commented in Italian media as conclusive proof that
"schools are safe". However the study presents severe oversights and careless
interpretation of data.


Abstract:
  As autonomous driving and augmented reality evolve, a practical concern is
data privacy. In particular, these applications rely on localization based on
user images. The widely adopted technology uses local feature descriptors,
which are derived from the images and it was long thought that they could not
be reverted back. However, recent work has demonstrated that under certain
conditions reverse engineering attacks are possible and allow an adversary to
reconstruct RGB images. This poses a potential risk to user privacy. We take
this a step further and model potential adversaries using a privacy threat
model. Subsequently, we show under controlled conditions a reverse engineering
attack on sparse feature maps and analyze the vulnerability of popular
descriptors including FREAK, SIFT and SOSNet. Finally, we evaluate potential
mitigation techniques that select a subset of descriptors to carefully balance
privacy reconstruction risk while preserving image matching accuracy; our
results show that similar accuracy can be obtained when revealing less
information.


Abstract:
  The electric power grid is a critical societal resource connecting multiple
infrastructural domains such as agriculture, transportation, and manufacturing.
The electrical grid as an infrastructure is shaped by human activity and public
policy in terms of demand and supply requirements. Further, the grid is subject
to changes and stresses due to solar weather, climate, hydrology, and ecology.
The emerging interconnected and complex network dependencies make such
interactions increasingly dynamic causing potentially large swings, thus
presenting new challenges to manage the coupled human-natural system. This
paper provides a survey of models and methods that seek to explore the
significant interconnected impact of the electric power grid and interdependent
domains. We also provide relevant critical risk indicators (CRIs) across
diverse domains that may influence electric power grid risks, including
climate, ecology, hydrology, finance, space weather, and agriculture. We
discuss the convergence of indicators from individual domains to explore
possible systemic risk, i.e., holistic risk arising from cross-domains
interconnections. Our study provides an important first step towards
data-driven analysis and predictive modeling of risks in the coupled
interconnected systems. Further, we propose a compositional approach to risk
assessment that incorporates diverse domain expertise and information, data
science, and computer science to identify domain-specific CRIs and their union
in systemic risk indicators.


Abstract:
  One or more scalar leptoquarks with masses around a few TeV may provide a
solution to some of the flavor anomalies that have been observed. We discuss
the impact of such new degrees on baryon number violation when the theory is
embedded in a Pati-Salam model. The Pati-Salam embedding can suppress
renormalizable and dimension-five baryon number violation in some cases. Our
work extends the results of Assad, Grinstein, and Fornal who considered the
same issue for vector leptoquarks.


Abstract:
  The inclusion of domain (point) sources into a three dimensional boundary
element method while solving the Helmholtz equation is described. The method is
fully desingularized which allows for the use of higher order quadratic
elements on the surfaces of the problem with ease. The effect of the monopole
sources ends up on the right hand side of the resulting matrix system. Several
carefully chosen examples are shown, such as sources near and within a
concentric spherical core-shell scatterer as a verification case, a curved
focusing surface and a multi-scale acoustic lens.


Abstract:
  Single-crystal inorganic halide perovskites are attracting interest for
quantum device applications. Here we present low-temperature quantum
magnetotransport measurements on thin film devices of epitaxial single-crystal
CsSnBr$_{3}$, which exhibit two-dimensional Mott variable range hopping (VRH)
and giant negative magnetoresistance. These findings are described by a model
for quantum interference between different directed hopping paths and we
extract the temperature-dependent hopping length of charge carriers, their
localization length, and a lower bound for their phase coherence length of ~100
nm at low temperatures. These observations demonstrate that epitaxial halide
perovskite devices are emerging as a material class for low-dimensional quantum
coherent transport devices.


Abstract:
  We present observations of quantum depletion in expanding condensates
released from a harmonic trap. We confirm experimental observations of
slowly-decaying tails in the far-field beyond the thermal component, consistent
with the survival of the quantum depletion. Our measurements support the
hypothesis that the depletion survives the expansion, and even appears stronger
in the far-field than expected before release based on the Bogoliubov theory.
This result is in conflict with the hydrodynamic theory which predicts that the
in-situ depletion does not survive when atoms are released from a trap.
Simulations of our experiment show that the depletion should indeed survive
into the far field and become stronger. However, while in qualitative
agreement, the final depletion observed in the experiment is much larger than
in the simulation. In light of the predicted power-law decay of the momentum
density, we discuss general issues inherent in characterizing power laws.


Abstract:
  Precise theoretical predictions are a key ingredient for an accurate
determination of the structure of the Langrangian of particle physics,
including its free parameters, which summarizes our understanding of the
fundamental interactions among particles. Furthermore, due to the absence of
clear new-physics signals, precise theoretical calculations are required in
order to pin down possible subtle deviations from the Standard Model
predictions. The error associated with such calculations must be scrutinized,
as non-perturbative power corrections, dubbed infrared renormalons, can limit
the ultimate precision of truncated perturbative expansions in quantum
chromodynamics. In this review we focus on linear power corrections that can
arise in certain kinematic distributions relevant for collider phenomenology
where an operator product expansion is missing, e.g. those obtained from the
top-quark decay products, shape observables and the transverse momentum of
massive gauge bosons. Only the last one is found to be free from such
corrections, while the mass of the system comprising the top decay products has
a larger power correction if the perturbative expansion is expressed in terms
of a short-distance mass instead of the pole mass. A proper modelization of
non-perturbative corrections is crucial in the context of shape observables to
obtain reliable strong coupling constant extractions.


Abstract:
  Mobile Crowdsourcing (MC) is an effective way of engaging large groups of
smart devices to perform tasks remotely while exploiting their built-in
features. It has drawn great attention in the areas of smart cities and urban
computing communities to provide decentralized, fast, and flexible ubiquitous
technological services. The vast majority of previous studies focused on
non-cooperative MC schemes in Internet of Things (IoT) systems. Advanced
collaboration strategies are expected to leverage the capability of MC services
and enable the execution of more complicated crowdsourcing tasks. In this
context, Collaborative Mobile Crowdsourcing (CMC) enables task requesters to
hire groups of IoT devices' users that must communicate with each other and
coordinate their operational activities in order to accomplish complex tasks.
In this paper, we present and discuss the novel CMC paradigm in IoT. Then, we
provide a detailed taxonomy to classify the different components forming CMC
systems. Afterwards, we investigate the challenges in designing CMC tasks and
discuss different team formation strategies involving the crowdsourcing
platform and selected team leaders. We also analyze and compare the
performances of certain proposed CMC recruitment algorithms. Finally, we shed
the light on open research directions to leverage CMC service design.


Abstract:
  A special place in climatology is taken by the so-called conceptual climate
models. These, relatively simple, sets of differential equations can
successfully describe single mechanisms of the climate. We focus on one family
of such models based on the global energy balance. This gives rise to a
degenerate nonlocal parabolic nonlinear partial differential equation for the
zonally averaged temperature. We construct a fully discrete numerical method
that has an optimal spectral accuracy in space and second order in time. Our
scheme is based on Galerkin formulation of the Legendre basis expansion which
is particularly convenient for this setting. By using extrapolation the
numerical scheme is linear even though the original equation is strongly
nonlinear. We also test our theoretical result during various numerical
simulations that confirm the aforementioned accuracy of the scheme. All
implementations are coded in Julia programming language with the use of
parallelization (multi-threading).


Abstract:
  Effectively parsing the facade is essential to 3D building reconstruction,
which is an important computer vision problem with a large amount of
applications in high precision map for navigation, computer aided design, and
city generation for digital entertainments. To this end, the key is how to
obtain the shape grammars from 2D images accurately and efficiently. Although
enjoying the merits of promising results on the semantic parsing, deep learning
methods cannot directly make use of the architectural rules, which play an
important role for man-made structures. In this paper, we present a novel
translational symmetry-based approach to improving the deep neural networks.
Our method employs deep learning models as the base parser, and a module taking
advantage of translational symmetry is used to refine the initial parsing
results. In contrast to conventional semantic segmentation or bounding box
prediction, we propose a novel scheme to fuse segmentation with anchor-free
detection in a single stage network, which enables the efficient training and
better convergence. After parsing the facades into shape grammars, we employ an
off-the-shelf rendering engine like Blender to reconstruct the realistic
high-quality 3D models using procedural modeling. We conduct experiments on
three public datasets, where our proposed approach outperforms the
state-of-the-art methods. In addition, we have illustrated the 3D building
models built from 2D facade images.


Abstract:
  The reflexive completion of a category consists of the Set-valued functors on
it that are canonically isomorphic to their double conjugate. After reviewing
both this construction and Isbell conjugacy itself, we give new examples and
revisit Isbell's main results from 1960 in a modern categorical context. We
establish the sense in which reflexive completion is functorial, and find
conditions under which two categories have equivalent reflexive completions. We
describe the relationship between the reflexive and Cauchy completions,
determine exactly which limits and colimits exist in an arbitrary reflexive
completion, and make precise the sense in which the reflexive completion of a
category is the intersection of the categories of covariant and contravariant
functors on it.