Abstract:
  Generative models are now capable of producing highly realistic images that
look nearly indistinguishable from the data on which they are trained. This
raises the question: if we have good enough generative models, do we still need
datasets? We investigate this question in the setting of learning
general-purpose visual representations from a black-box generative model rather
than directly from data. Given an off-the-shelf image generator without any
access to its training data, we train representations from the samples output
by this generator. We compare several representation learning methods that can
be applied to this setting, using the latent space of the generator to generate
multiple "views" of the same semantic content. We show that for contrastive
methods, this multiview data can naturally be used to identify positive pairs
(nearby in latent space) and negative pairs (far apart in latent space). We
find that the resulting representations rival those learned directly from real
data, but that good performance requires care in the sampling strategy applied
and the training method. Generative models can be viewed as a compressed and
organized copy of a dataset, and we envision a future where more and more
"model zoos" proliferate while datasets become increasingly unwieldy, missing,
or private. This paper suggests several techniques for dealing with visual
representation learning in such a future. Code is released on our project page:
https://ali-design.github.io/GenRep/


Abstract:
  Generative Adversarial Networks (GANs) currently achieve the state-of-the-art
sound synthesis quality for pitched musical instruments using a 2-channel
spectrogram representation consisting of log magnitude and instantaneous
frequency (the "IFSpectrogram"). Many other synthesis systems use
representations derived from the magnitude spectra, and then depend on a
backend component to invert the output magnitude spectrograms that generally
result in audible artefacts associated with the inversion process. However, for
signals that have closely-spaced frequency components such as non-pitched and
other noisy sounds, training the GAN on the 2-channel IFSpectrogram
representation offers no advantage over the magnitude spectra based
representations. In this paper, we propose that training GANs on single-channel
magnitude spectra, and using the Phase Gradient Heap Integration (PGHI)
inversion algorithm is a better comprehensive approach for audio synthesis
modeling of diverse signals that include pitched, non-pitched, and dynamically
complex sounds. We show that this method produces higher-quality output for
wideband and noisy sounds, such as pops and chirps, compared to using the
IFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable
to using the IFSpectrogram, even while using a simpler representation with half
the memory requirements.


Abstract:
  While the anomalous Hall effect can manifest even without an external
magnetic field, time reversal symmetry is nonetheless still broken by the
internal magnetization of the sample. Recently, it has been shown that certain
materials without an inversion center allow for a nonlinear type of anomalous
Hall effect whilst retaining time reversal symmetry. The effect may arise from
either Berry curvature or through various asymmetric scattering mechanisms.
Here, we report the observation of an extremely large $c$-axis nonlinear
anomalous Hall effect in the non-centrosymmetric T$_d$ phase of MoTe$_2$ and
WTe$_2$ without intrinsic magnetic order. We find that the effect is dominated
by skew-scattering at higher temperatures combined with another scattering
process active at low temperatures. Application of higher bias yields an
extremely large Hall ratio of $E_\perp /E_\parallel$=2.47 and corresponding
anomalous Hall conductivity of order 8x10$^7$S/m.


Abstract:
  Rationalizing which parts of a molecule drive the predictions of a molecular
graph convolutional neural network (GCNN) can be difficult. To help, we propose
two simple regularization techniques to apply during the training of GCNNs:
Batch Representation Orthonormalization (BRO) and Gini regularization. BRO,
inspired by molecular orbital theory, encourages graph convolution operations
to generate orthonormal node embeddings. Gini regularization is applied to the
weights of the output layer and constrains the number of dimensions the model
can use to make predictions. We show that Gini and BRO regularization can
improve the accuracy of state-of-the-art GCNN attribution methods on artificial
benchmark datasets. In a real-world setting, we demonstrate that medicinal
chemists significantly prefer explanations extracted from regularized models.
While we only study these regularizers in the context of GCNNs, both can be
applied to other types of neural networks


Abstract:
  Most online multi-object trackers perform object detection stand-alone in a
neural net without any input from tracking. In this paper, we present a new
online joint detection and tracking model, TraDeS (TRAck to DEtect and
Segment), exploiting tracking clues to assist detection end-to-end. TraDeS
infers object tracking offset by a cost volume, which is used to propagate
previous object features for improving current object detection and
segmentation. Effectiveness and superiority of TraDeS are shown on 4 datasets,
including MOT (2D tracking), nuScenes (3D tracking), MOTS and Youtube-VIS
(instance segmentation tracking). Project page:
https://jialianwu.com/projects/TraDeS.html.


Abstract:
  For partial, nondeterministic, finite state machines, a new conformance
relation called strong reduction is presented. It complements other existing
conformance relations in the sense that the new relation is well-suited for
model-based testing of systems whose inputs are enabled or disabled, depending
on the actual system state. Examples of such systems are graphical user
interfaces and systems with interfaces that can be enabled or disabled in a
mechanical way. We present a new test generation algorithm producing complete
test suites for strong reduction. The suites are executed according to the
grey-box testing paradigm: it is assumed that the state-dependent sets of
enabled inputs can be identified during test execution, while the
implementation states remain hidden, as in black-box testing. It is shown that
this grey-box information is exploited by the generation algorithm in such a
way that the resulting best-case test suite size is only linear in the state
space size of the reference model. Moreover, examples show that this may lead
to significant reductions of test suite size in comparison to true black-box
testing for strong reduction.


Abstract:
  Residual coherence is a graphical tool for selecting potential second-order
interaction terms as functions of a single time series and its lags. This paper
extends the notion of residual coherence to account for interaction terms of
multiple time series. Moreover, an alternative criterion, integrated spectrum,
is proposed to facilitate this graphical selection.
  A financial market application shows that new insights can be gained
regarding implied market volatility.


Abstract:
  V838 Mon erupted in 2002 quickly becoming the prototype of a new type of
stellar eruptions known today as (luminous) red novae. The red nova outbursts
are thought to be caused by stellar mergers. The merger in V838 Mon took place
in a triple or higher system involving two B-type stars. We mapped the merger
site with ALMA at a resolution of 25 mas in continuum dust emission and in
rotational lines of simple molecules, including CO, SiO, SO, SO$_2$, AlOH, and
H$_2$S. We use radiative transfer calculations to reproduce the remnant's
architecture at the epoch of the ALMA observations. For the first time, we
identify the position of the B-type companion relative to the outbursting
component of V838 Mon. The stellar remnant is surrounded by a clumpy wind with
characteristics similar to winds of red supergiants. The merger product is also
associated with an elongated structure, $17.6 \times 7.6$ mas, seen in
continuum emission, and which we interpret as a disk seen at a moderate
inclination. Maps of continuum and molecular emission show also a complex
region of interaction between the B-type star (its gravity, radiation, and
wind) and the flow of matter ejected in 2002. The remnant's molecular mass is
about 0.1 M$_{\odot}$ and the dust mass is 8.3$\cdot$10$^{-3}$ M$_{\odot}$. The
mass of the atomic component remains unconstrained. The most interesting region
for understanding the merger of V838 Mon remains unresolved but appears
elongated. To study it further in more detail will require even higher angular
resolutions. ALMA maps show us an extreme form of interaction between the
merger ejecta with a distant (250 au) companion. This interaction is similar to
that known from the Antares AB system but at a much higher mass loss rate. The
B-type star not only deflects the merger ejecta but also changes its chemical
composition with an involvement of circumstellar shocks.


Abstract:
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of
wireless networks. In this paper, we aim to find collision-free paths for
multiple cellular-connected UAVs, while satisfying requirements of connectivity
with ground base stations (GBSs) in the presence of a dynamic jammer. We first
formulate the problem as a sequential decision making problem in discrete
domain, with connectivity, collision avoidance, and kinematic constraints. We,
then, propose an offline temporal difference (TD) learning algorithm with
online signal-to-interference-plus-noise ratio (SINR) mapping to solve the
problem. More specifically, a value network is constructed and trained offline
by TD method to encode the interactions among the UAVs and between the UAVs and
the environment; and an online SINR mapping deep neural network (DNN) is
designed and trained by supervised learning, to encode the influence and
changes due to the jammer. Numerical results show that, without any information
on the jammer, the proposed algorithm can achieve performance levels close to
that of the ideal scenario with the perfect SINR-map. Real-time navigation for
multi-UAVs can be efficiently performed with high success rates, and collisions
are avoided.


Abstract:
  A multi-objective optimization problem is $C^r$ weakly simplicial if there
exists a $C^r$ surjection from a simplex onto the Pareto set/front such that
the image of each subsimplex is the Pareto set/front of a subproblem, where
$0\leq r\leq \infty$. This property is helpful to compute a parametric-surface
approximation of the entire Pareto set and Pareto front. It is known that all
unconstrained strongly convex $C^r$ problems are $C^{r-1}$ weakly simplicial
for $1\leq r \leq \infty$. In this paper, we show that all unconstrained
strongly convex problems are $C^0$ weakly simplicial. The usefulness of this
theorem is demonstrated in a sparse modeling application: we reformulate the
elastic net as a non-differentiable multi-objective strongly convex problem and
approximate its Pareto set (the set of all trained models with different
hyper-parameters) and Pareto front (the set of performance metrics of the
trained models) by using a B\'ezier simplex fitting method, which accelerates
hyper-parameter search.


Abstract:
  Three $q$-versions of Lommel polynomials are studied. Included are explicit
representations, recurrences, continued fractions, and connections to
associated Askey--Wilson polynomials. Combinatorial results are emphasized,
including a general theorem when $R_I$ moments coincide with orthogonal
polynomial moments. The combinatorial results use weighted Motzkin paths,
Schr\"oder paths, and parallelogram polyominoes.


Abstract:
  Unsupervised time series clustering is a challenging problem with diverse
industrial applications such as anomaly detection, bio-wearables, etc. These
applications typically involve small, low-power devices on the edge that
collect and process real-time sensory signals. State-of-the-art time-series
clustering methods perform some form of loss minimization that is extremely
computationally intensive from the perspective of edge devices. In this work,
we propose a neuromorphic approach to unsupervised time series clustering based
on Temporal Neural Networks that is capable of ultra low-power, continuous
online learning. We demonstrate its clustering performance on a subset of UCR
Time Series Archive datasets. Our results show that the proposed approach
either outperforms or performs similarly to most of the existing algorithms
while being far more amenable for efficient hardware implementation. Our
hardware assessment analysis shows that in 7 nm CMOS the proposed architecture,
on average, consumes only about 0.005 mm^2 die area and 22 uW power and can
process each signal with about 5 ns latency.


Abstract:
  Various methods for solving the inverse reinforcement learning (IRL) problem
have been developed independently in machine learning and economics. In
particular, the method of Maximum Causal Entropy IRL is based on the
perspective of entropy maximization, while related advances in the field of
economics instead assume the existence of unobserved action shocks to explain
expert behavior (Nested Fixed Point Algorithm, Conditional Choice Probability
method, Nested Pseudo-Likelihood Algorithm). In this work, we make previously
unknown connections between these related methods from both fields. We achieve
this by showing that they all belong to a class of optimization problems,
characterized by a common form of the objective, the associated policy and the
objective gradient. We demonstrate key computational and algorithmic
differences which arise between the methods due to an approximation of the
optimal soft value function, and describe how this leads to more efficient
algorithms. Using insights which emerge from our study of this class of
optimization problems, we identify various problem scenarios and investigate
each method's suitability for these problems.


Abstract:
  The thermodynamic uncertainty relation originally proven for systems driven
into a non-equilibrium steady state (NESS) allows one to infer the total
entropy production rate by observing any current in the system. This kind of
inference scheme is especially useful when the system contains hidden degrees
of freedom or hidden discrete states, which are not accessible to the
experimentalist. A recent generalization of the thermodynamic uncertainty
relation to arbitrary time-dependent driving allows one to infer entropy
production not only by measuring current-observables but also by observing
state variables. A crucial question then is to understand which observable
yields the best estimate for the total entropy production. In this paper we
address this question by analyzing the quality of the thermodynamic uncertainty
relation for various types of observables for the generic limiting cases of
fast driving and slow driving. We show that in both cases observables can be
found that yield an estimate of order one for the total entropy production. We
further show that the uncertainty relation can even be saturated in the limit
of fast driving.


Abstract:
  The KOALA experiment measures the differential cross section of
(anti)proton-proton elastic scattering over a wide range of four-momentum
transfer squared 0.0008 < |t| < 0.1 (GeV/c)$^2$ . The forward scattering
parameters and the absolute luminosity can be deduced by analyzing the
characteristic shape of the differential cross-section spectrum. The experiment
is based on fixed target kinematics and uses an internal hydrogen cluster jet
target. The wide range of |t| is achieved by measuring the total kinetic energy
of the recoil protons near 90{\deg} with a recoil detector, which consists of
silicon and germanium single-sided strip sensors. The energy resolution of the
recoil detector is better than 30 keV (FWHM). A forward detector consisting of
two layers of plastic scintillators measures the elastically scattered beam
particles in the forward direction close to the beam axis. It helps suppress
the large background at small recoil angles and improves the identification of
elastic scattering events in the low |t| range. The KOALA setup has been
installed and commissioned at COSY in order to validate the detector by
measuring the proton-proton elastic scattering. The results from this
commissioning are presented here.


Abstract:
  Nowadays High Energy Physics experiments can accumulate unprecedented
statistics of heavy flavour decays that allows to apply new methods, based on
the study of very rare phenomena, which used to be just desperate. In this
paper we propose a new method to measure composition of $K^0$-$\overline{K}^0$,
produced in a decay of heavy hadrons. This composition contains important
information, in particular about weak and strong phases between amplitudes of
the produced $K^0$ and $\overline{K}^0$. We consider possibility to measure
these parameters with time-dependent $K^0 \to \pi^+ \pi^-$ analysis. Due to
$CP$-violation in kaon mixing time-dependent decay rates of $K^0$ and
$\overline{K}^0$ differ, and the initial amplitudes revealed in the
$CP$-violating decay pattern. In particular we consider cases of charmed
hadrons decays: $D^+ \to K^0 \pi^+$, $D_s^+ \to K^0 K^+$, $\Lambda_c \to p K^0$
and with some assumptions $D^0 \to K^0 \pi^0$. This can be used to test the sum
rule for charmed mesons and to obtain input for the full constraint of the two
body amplitudes of $D$-mesons.


Abstract:
  The levitation of a volatile droplet on a highly superheated surface is known
as the Leidenfrost effect. Wetting state during transition from full wetting of
a surface by a droplet at room temperature to Leidenfrost bouncing, i.e.,
zero-wetting at high superheating, is not fully understood. Here,
visualizations of droplet thermal and wetting footprint in the Leidenfrost
transition state are presented using two optical techniques: mid-infrared
thermography and wetting sensitive total internal reflection imaging under
carefully selected experimental conditions, impact Weber number < 10 and
droplet diameter < capillary length, using an indium-tin-oxide coated sapphire
heater. The experimental regime was designed to create relatively stable
droplet dynamics, where the effects of oscillatory and capillary instabilities
were minimized. The thermography for ethanol droplet in Leidenfrost transition
state (superheat range of 82K-97K) revealed thermal footprint with a central
hot zone surrounded by a cooler periphery, indicative of a partial wetting
state during Leidenfrost transition. High-speed total internal reflection
imaging also confirmed the partial wetting footprint such that there are
wetting areas around a central non-wetting zone. Result presented here using
ethanol as a test fluid shed light on the geometry and dynamics of a volatile
droplet footprint in Leidenfrost transition state.


Abstract:
  We study the null set $N(\mathcal{P})$ of the Fourier-Laplace transform of a
polytope $\mathcal{P} \subset \mathbb{R}^d$, and we find that $N(\mathcal{P})$
does not contain (almost all) circles in $\mathbb{R}^d$. As a consequence, the
null set does not contain the algebraic varieties $\{z \in \mathbb{C}^d \mid
z_1^2 + \dots + z_d^2 = \alpha^2\}$ for each fixed $\alpha \in \mathbb{C}$, and
hence we get an explicit proof that the Pompeiu property is true for all
polytopes. Our proof uses the Brion-Barvinok theorem, which gives a concrete
formulation for the Fourier-Laplace transform of a polytope, and it also uses
properties of Bessel functions. The original proof that polytopes (as well as
other bodies) possess the Pompeiu property was given by Brown, Schreiber, and
Taylor (1973) for dimension 2. Williams (1976) later observed that the same
proof also works for $d>2$ and, using eigenvalues of the Laplacian, gave
another proof valid for $d \geq 2$ that polytopes have the Pompeiu property.


Abstract:
  In today's world data is being generated at a high rate due to which it has
become inevitable to analyze and quickly get results from this data. Most of
the relational databases primarily support SQL querying with a limited support
for complex data analysis. Due to this reason, data scientists have no other
option, but to use a different system for complex data analysis. Due to this,
data science frameworks are in huge demand. But to use such a framework, all
the data needs to be loaded into it. This requires significant data movement
across multiple systems, which can be expensive.
  We believe that it has become the need of the hour to come up with a single
system which can perform both data analysis tasks and SQL querying. This will
save the data scientists from the expensive data transfer operation across
systems. In our work, we present DaskDB, a system built over the Python's Dask
framework, which is a scalable data science system having support for both data
analytics and in situ SQL query processing over heterogeneous data sources.
DaskDB supports invoking any Python APIs as User-Defined Functions (UDF) over
SQL queries. So, it can be easily integrated with most existing Python data
science applications, without modifying the existing code. Since joining two
relations is a very vital but expensive operation, so a novel distributed
learned index is also introduced to improve the join performance. Our
experimental evaluation demonstrates that DaskDB significantly outperforms
existing systems.


Abstract:
  We give sufficient conditions on the exponent $p: \mathbb R^d\rightarrow
[1,\infty)$ for the boundedness of the non-centered Gaussian maximal function
on variable Lebesgue spaces $L^{p(\cdot)}(\mathbb R^d, \gamma_d)$, as well as
of the new higher order Riesz transforms associated with the Ornstein-Uhlenbeck
semigroup, which are the natural extensions of the supplementary first order
Gaussian Riesz transforms defined by A. Nowak and K. Stempak in
\cite{nowakstempak}.


Abstract:
  We derive the explicit form of the martingale representation for
square-integrable processes that are martingales with respect to the natural
filtration of the super-Brownian motion. This is done by using a weak extension
of the Dupire derivative for functionals of superprocesses.


Abstract:
  The polymer model framework is a classical tool from statistical mechanics
that has recently been used to obtain approximation algorithms for spin systems
on classes of bounded-degree graphs; examples include the ferromagnetic Potts
model on expanders and on the grid. One of the key ingredients in the analysis
of polymer models is controlling the growth rate of the number of polymers,
which has been typically achieved so far by invoking the bounded-degree
assumption. Nevertheless, this assumption is often restrictive and obstructs
the applicability of the method to more general graphs. For example, sparse
random graphs typically have bounded average degree and good expansion
properties, but they include vertices with unbounded degree, and therefore are
excluded from the current polymer-model framework.
  We develop a less restrictive framework for polymer models that relaxes the
standard bounded-degree assumption, by reworking the relevant polymer models
from the edge perspective. The edge perspective allows us to bound the growth
rate of the number of polymers in terms of the total degree of polymers, which
in turn can be related more easily to the expansion properties of the
underlying graph. To apply our methods, we consider random graphs with
unbounded degrees from a fixed degree sequence (with minimum degree at least 3)
and obtain approximation algorithms for the ferromagnetic Potts model, which is
a standard benchmark for polymer models. Our techniques also extend to more
general spin systems.


Abstract:
  In this note, we extend the renormalization horseshoe we have recently
constructed with N. Goncharuk for analytic diffeomorphisms of the circle to
their small two-dimensional perturbations. As one consequence, Herman rings
with rotation numbers of bounded type survive on a codimension one set of
parameters under small two-dimensional perturbations.


Abstract:
  Type Ia supernovae (SNe Ia) span a range of luminosities and timescales, from
rapidly evolving subluminous to slowly evolving overluminous subtypes. Previous
theoretical work has, for the most part, been unable to match the entire
breadth of observed SNe Ia with one progenitor scenario. Here, for the first
time, we apply non-local thermodynamic equilibrium radiative transfer
calculations to a range of accurate explosion models of sub-Chandrasekhar-mass
white dwarf detonations. The resulting photometry and spectra are in excellent
agreement with the range of observed non-peculiar SNe Ia through 15 d after the
time of B-band maximum, yielding one of the first examples of a quantitative
match to the entire Phillips (1993) relation. The intermediate-mass element
velocities inferred from theoretical spectra at maximum light for the more
massive white dwarf explosions are higher than those of bright observed SNe Ia,
but these and other discrepancies likely stem from the one-dimensional nature
of our explosion models and will be improved upon by future non-local
thermodynamic equilibrium radiation transport calculations of multi-dimensional
sub-Chandrasekhar-mass white dwarf detonations.


Abstract:
  This article explores the territorial differences in the onset and spread of
COVID-19 and the excess mortality associated with the pandemic, across the
European NUTS3 regions and US counties. Both in Europe and in the US, the
pandemic arrived earlier and recorded higher Rt values in urban regions than in
intermediate and rural ones. A similar gap is also found in the data on excess
mortality. In the weeks during the first phase of the pandemic, urban regions
in EU countries experienced excess mortality of up to 68pp more than rural
ones. We show that, during the initial days of the pandemic, territorial
differences in Rt by the degree of urbanisation can be largely explained by the
level of internal, inbound and outbound mobility. The differences in the spread
of COVID-19 by rural-urban typology and the role of mobility are less clear
during the second wave. This could be linked to the fact that the infection is
widespread across territories, to changes in mobility patterns during the
summer period as well as to the different containment measures which reverse
the causality between mobility and Rt.


Abstract:
  Neural information retrieval systems typically use a cascading pipeline, in
which a first-stage model retrieves a candidate set of documents and one or
more subsequent stages re-rank this set using contextualized language models
such as BERT. In this paper, we propose DeepImpact, a new document
term-weighting scheme suitable for efficient retrieval using a standard
inverted index. Compared to existing methods, DeepImpact improves impact-score
modeling and tackles the vocabulary-mismatch problem. In particular, DeepImpact
leverages DocT5Query to enrich the document collection and, using a
contextualized language model, directly estimates the semantic importance of
tokens in a document, producing a single-value representation for each token in
each document. Our experiments show that DeepImpact significantly outperforms
prior first-stage retrieval approaches by up to 17% on effectiveness metrics
w.r.t. DocT5Query, and, when deployed in a re-ranking scenario, can reach the
same effectiveness of state-of-the-art approaches with up to 5.1x speedup in
efficiency.


Abstract:
  Performance metrics are a core component of the evaluation of any machine
learning model and used to compare models and estimate their usefulness. Recent
work started to question the validity of many performance metrics for this
purpose in the context of software defect prediction. Within this study, we
explore the relationship between performance metrics and the cost saving
potential of defect prediction models. We study whether performance metrics are
suitable proxies to evaluate the cost saving capabilities and derive a theory
for the relationship between performance metrics and cost saving potential.


Abstract:
  Many real-life applications involve simultaneously forecasting multiple time
series that are hierarchically related via aggregation or disaggregation
operations. For instance, commercial organizations often want to forecast
inventories simultaneously at store, city, and state levels for resource
planning purposes. In such applications, it is important that the forecasts, in
addition to being reasonably accurate, are also consistent w.r.t one another.
Although forecasting such hierarchical time series has been pursued by
economists and data scientists, the current state-of-the-art models use strong
assumptions, e.g., all forecasts being unbiased estimates, noise distribution
being Gaussian. Besides, state-of-the-art models have not harnessed the power
of modern nonlinear models, especially ones based on deep learning. In this
paper, we propose using a flexible nonlinear model that optimizes quantile
regression loss coupled with suitable regularization terms to maintain the
consistency of forecasts across hierarchies. The theoretical framework
introduced herein can be applied to any forecasting model with an underlying
differentiable loss function. A proof of optimality of our proposed method is
also provided. Simulation studies over a range of datasets highlight the
efficacy of our approach.


Abstract:
  The consequences of the attractive, short-range nucleon-nucleon (NN)
interaction on the wave functions of the Elliott SU(3) and the proxy-SU(3)
symmetry are discussed. The NN interaction favors the most symmetric spatial
SU(3) irreducible representation, which corresponds to the maximal spatial
overlap among the fermions. The percentage of the symmetric components out of
the total in an SU(3) wave function is introduced, through which it is found,
that no SU(3) irrep is more symmetric than the highest weight irrep for a
certain number of valence particles in a three dimensional, isotropic, harmonic
oscillator shell. The consideration of the highest weight irreps in nuclei and
in alkali metal clusters, leads to the prediction of a prolate to oblate shape
transition beyond the mid-shell region.


Abstract:
  While deep learning-based 3D face generation has made a progress recently,
the problem of dynamic 3D (4D) facial expression synthesis is less
investigated. In this paper, we propose a novel solution to the following
question: given one input 3D neutral face, can we generate dynamic 3D (4D)
facial expressions from it? To tackle this problem, we first propose a mesh
encoder-decoder architecture (Expr-ED) that exploits a set of 3D landmarks to
generate an expressive 3D face from its neutral counterpart. Then, we extend it
to 4D by modeling the temporal dynamics of facial expressions using a
manifold-valued GAN capable of generating a sequence of 3D landmarks from an
expression label (Motion3DGAN). The generated landmarks are fed into the mesh
encoder-decoder, ultimately producing a sequence of 3D expressive faces. By
decoupling the two steps, we separately address the non-linearity induced by
the mesh deformation and motion dynamics. The experimental results on the CoMA
dataset show that our mesh encoder-decoder guided by landmarks brings a
significant improvement with respect to other landmark-based 3D fitting
approaches, and that we can generate high quality dynamic facial expressions.
This framework further enables the 3D expression intensity to be continuously
adapted from low to high intensity. Finally, we show our framework can be
applied to other tasks, such as 2D-3D facial expression transfer.


Abstract:
  We propose a method to exploit high finesse optical resonators for light
assisted coherent manipulation of atomic ensembles, overcoming the limit
imposed by the finite response time of the cavity. The key element of our
scheme is to rapidly switch the interaction between the atoms and the cavity
field with an auxiliary control process as, for example, the light shift
induced by an optical beam. The scheme is applicable to many different atomic
species, both in trapped and free fall configurations, and can be adopted to
control the internal and/or external atomic degrees of freedom. Our method will
open new possibilities in cavity-aided atom interferometry and in the
preparation of highly non-classical atomic states.


Abstract:
  We investigate the quantum transport through Kondo impurity assuming both a
large number of orbital channels $\mathcal K$$\gg $$1$ for the itinerant
electrons and a semi-classical spin ${\cal S}$ $\gg $ $1$ for the impurity. The
non-Fermi liquid regime of the Kondo problem is achieved in the overscreened
sector $\mathcal K>2\mathcal{S}$. We show that there exist two distinct
semiclassical regimes for the quantum transport through impurity: i) $\mathcal
K$ $\gg$ $\mathcal S$ $\gg$ $1$, differential conductance vanishes and ii)
$\mathcal S$$/$$\mathcal K{=}\mathcal C$ with $ 0$$<$$\mathcal C$$<$$1/2$,
differential conductance reaches some non-vanishing fraction of its unitary
value. Using conformal field theory approach we analyze behavior of the quantum
transport observables and residual entropy in both semiclassical regimes. We
show that the semiclassical limit ii) preserves the key features of resonance
scattering and the most essential fingerprints of the non-Fermi liquid
behavior. We discuss possible realization of two semiclassical regimes in
semiconductor quantum transport experiments.


Abstract:
  The Multi-voltage Threshold (MVT) method, which samples the signal by certain
reference voltages, has been well developed as being adopted in pre-clinical
and clinical digital positron emission tomography(PET) system. To improve its
energy measurement performance, we propose a Peak Picking MVT(PP-MVT) Digitizer
in this paper. Firstly, a sampled Peak Point(the highest point in pulse
signal), which carries the values of amplitude feature voltage and amplitude
arriving time, is added to traditional MVT with a simple peak sampling circuit.
Secondly, an amplitude deviation statistical analysis, which compares the
energy deviation of various reconstruction models, is used to select adaptive
reconstruction models for signal pulses with different amplitudes. After
processing 30,000 randomly-chosen pulses sampled by the oscilloscope with a
22Na point source, our method achieves an energy resolution of 17.50% within a
450-650 KeV energy window, which is 2.44% better than the result of traditional
MVT with same thresholds; and we get a count number at 15225 in the same energy
window while the result of MVT is at 14678. When the PP-MVT involves less
thresholds than traditional MVT, the advantages of better energy resolution and
larger count number can still be maintained, which shows the robustness and the
flexibility of PP-MVT Digitizer. This improved method indicates that adding
feature peak information could improve the performance on signal sampling and
reconstruction, which canbe proved by the better performance in energy
determination in radiation measurement.


Abstract:
  We present a Python-based renderer built on NVIDIA's OptiX ray tracing engine
and the OptiX AI denoiser, designed to generate high-quality synthetic images
for research in computer vision and deep learning. Our tool enables the
description and manipulation of complex dynamic 3D scenes containing object
meshes, materials, textures, lighting, volumetric data (e.g., smoke), and
backgrounds. Metadata, such as 2D/3D bounding boxes, segmentation masks, depth
maps, normal maps, material properties, and optical flow vectors, can also be
generated. In this work, we discuss design goals, architecture, and
performance. We demonstrate the use of data generated by path tracing for
training an object detector and pose estimator, showing improved performance in
sim-to-real transfer in situations that are difficult for traditional
raster-based renderers. We offer this tool as an easy-to-use, performant,
high-quality renderer for advancing research in synthetic data generation and
deep learning.


Abstract:
  In this paper, we consider visualization of displacement fields via optical
flow methods in elastographic experiments consisting of a static compression of
a sample. We propose an elastographic optical flow method (EOFM) which takes
into account experimental constraints, such as appropriate boundary conditions,
the use of speckle information, as well as the inclusion of structural
information derived from knowledge of the background material. We present
numerical results based on both simulated and experimental data from an
elastography experiment in order to demonstrate the relevance of our proposed
approach.


Abstract:
  Population growth in the last decades has resulted in the production of about
2.01 billion tons of municipal waste per year. The current waste management
systems are not capable of providing adequate solutions for the disposal and
use of these wastes. Recycling and reuse have proven to be a solution to the
problem, but large-scale waste segregation is a tedious task and on a small
scale it depends on public awareness. This research used convolutional neural
networks and computer vision to develop a tool for the automation of solid
waste sorting. The Fotini10k dataset was constructed, which has more than
10,000 images divided into the categories of 'plastic bottles', 'aluminum cans'
and 'paper and cardboard'. ResNet50, MobileNetV1 and MobileNetV2 were retrained
with ImageNet weights on the Fotini10k dataset. As a result, top-1 accuracy of
99% was obtained in the test dataset with all three networks. To explore the
possible use of these networks in mobile applications, the three nets were
quantized in float16 weights. By doing so, it was possible to obtain inference
times twice as low for Raspberry Pi and three times as low for computer
processing units. It was also possible to reduce the size of the networks by
half. When quantizing the top-1 accuracy of 99% was maintained with all three
networks. When quantizing MobileNetV2 to int-8, it obtained a top-1 accuracy of
97%.


Abstract:
  We consider speeding up stochastic gradient descent (SGD) by parallelizing it
across multiple workers. We assume the same data set is shared among $N$
workers, who can take SGD steps and coordinate with a central server. While it
is possible to obtain a linear reduction in the variance by averaging all the
stochastic gradients at every step, this requires a lot of communication
between the workers and the server, which can dramatically reduce the gains
from parallelism. The Local SGD method, proposed and analyzed in the earlier
literature, suggests machines should make many local steps between such
communications. While the initial analysis of Local SGD showed it needs $\Omega
( \sqrt{T} )$ communications for $T$ local gradient steps in order for the
error to scale proportionately to $1/(NT)$, this has been successively improved
in a string of papers, with the state of the art requiring $\Omega \left( N
\left( \mbox{ poly} (\log T) \right) \right)$ communications. In this paper, we
suggest a Local SGD scheme that communicates less overall by communicating less
frequently as the number of iterations grows. Our analysis shows that this can
achieve an error that scales as $1/(NT)$ with a number of communications that
is completely independent of $T$. In particular, we show that $\Omega(N)$
communications are sufficient. Empirical evidence suggests this bound is close
to tight as we further show that $\sqrt{N}$ or $N^{3/4}$ communications fail to
achieve linear speed-up in simulations. Moreover, we show that under mild
assumptions, the main of which is twice differentiability on any neighborhood
of the optimal solution, one-shot averaging which only uses a single round of
communication can also achieve the optimal convergence rate asymptotically.


Abstract:
  Intent classification and slot filling are two critical tasks for natural
language understanding. Traditionally the two tasks have been deemed to proceed
independently. However, more recently, joint models for intent classification
and slot filling have achieved state-of-the-art performance, and have proved
that there exists a strong relationship between the two tasks. This article is
a compilation of past work in natural language understanding, especially joint
intent classification and slot filling. We observe three milestones in this
research so far: Intent detection to identify the speaker's intention, slot
filling to label each word token in the speech/text, and finally, joint intent
classification and slot filling tasks. In this article, we describe trends,
approaches, issues, data sets, evaluation metrics in intent classification and
slot filling. We also discuss representative performance values, describe
shared tasks, and provide pointers to future work, as given in prior works. To
interpret the state-of-the-art trends, we provide multiple tables that describe
and summarise past research along different dimensions, including the types of
features, base approaches, and dataset domain used.


Abstract:
  This is the user manual for CosmoLattice, a modern package for lattice
simulations of the dynamics of interacting scalar and gauge fields in an
expanding universe. CosmoLattice incorporates a series of features that makes
it very versatile and powerful: $i)$ it is written in C++ fully exploiting the
object oriented programming paradigm, with a modular structure and a clear
separation between the physics and the technical details, $ii)$ it is MPI-based
and uses a discrete Fourier transform parallelized in multiple spatial
dimensions, which makes it specially appropriate for probing scenarios with
well-separated scales, running very high resolution simulations, or simply very
long ones, $iii)$ it introduces its own symbolic language, defining field
variables and operations over them, so that one can introduce differential
equations and operators in a manner as close as possible to the continuum,
$iv)$ it includes a library of numerical algorithms, ranging from $O(\delta
t^2)$ to $O(\delta t^{10})$ methods, suitable for simulating global and gauge
theories in an expanding grid, including the case of `self-consistent'
expansion sourced by the fields themselves. Relevant observables are provided
for each algorithm (e.g.~energy densities, field spectra, lattice snapshots)
and we note that remarkably all our algorithms for gauge theories always
respect the Gauss constraint to machine precision. In this manual we explain
how to obtain and run CosmoLattice in a computer (let it be your laptop,
desktop or a cluster). We introduce the general structure of the code and
describe in detail the basic files that any user needs to handle. We explain
how to implement any model characterized by a scalar potential and a set of
scalar fields, either singlets or interacting with $U(1)$ and/or $SU(2)$ gauge
fields. CosmoLattice is publicly available at www.cosmolattice.net.


Abstract:
  A spin-1/2 Heisenberg model on honeycomb lattice is investigated by doing
triplon analysis and quantum Monte Carlo calculations. This model, inspired by
Cu$_2$(pymca)$_3$(ClO$_4$), has three different antiferromagnetic exchange
interactions ($J_A$, $J_B$, $J_C$) on three different sets of nearest-neighbour
bonds which form a kagome superlattice. While the model is bipartite and
unfrustrated, its quantum phase diagram is found to be dominated by a quantum
paramagnetic phase that is best described as a spin-gapped hexagonal-singlet
state. The N\'eel antiferromagnetic order survives only in a small region
around $J_A=J_B=J_C$. The magnetization produced by external magnetic field is
found to exhibit plateaus at 1/3 and 2/3 of the saturation value, or at 1/3
alone, or no plateaus. Notably, the plateaus exist only inside a bounded region
within the hexagonal-singlet phase. This study provides a clear understanding
of the spin-gapped behaviour and magnetization plateaus observed in
Cu$_2$(pymca)$_3$(ClO$_4$), and also predicts the possible disappearance of 2/3
plateau under pressure.


Abstract:
  Atomic carbon (CI) has been proposed to be a global tracer of the molecular
gas as a substitute for CO, however, its utility remains unproven. To evaluate
the suitability of CI as the tracer, we performed [CI]$(^3P_1-^3P_0)$
(hereinafter [CI](1-0)) mapping observations of the northern part of the nearby
spiral galaxy M83 with the ASTE telescope and compared the distributions of
[CI](1-0) with CO lines (CO(1-0), CO(3-2), and $^{13}$CO(1-0)), HI, and
infrared (IR) emission (70, 160, and 250$ \mu$m). The [CI](1-0) distribution in
the central region is similar to that of the CO lines, whereas [CI](1-0) in the
arm region is distributed outside the CO. We examined the dust temperature,
$T_{\rm dust}$, and dust mass surface density, $\Sigma_{\rm dust}$, by fitting
the IR continuum-spectrum distribution with a single-temperature modified
blackbody. The distribution of $\Sigma_{\rm dust}$ shows a much better
consistency with the integrated intensity of CO(1-0) than with that of
[CI](1-0), indicating that CO(1-0) is a good tracer of the cold molecular gas.
The spatial distribution of the [CI] excitation temperature, $T_{\rm ex}$, was
examined using the intensity ratio of the two [CI] transitions. An appropriate
$T_{\rm ex}$ at the central, bar, arm, and inter-arm regions yields a constant
[C]/[H$_2$] abundance ratio of $\sim7 \times 10^{-5}$ within a range of 0.1 dex
in all regions. We successfully detected weak [CI](1-0) emission, even in the
inter-arm region, in addition to the central, arm, and bar regions, using
spectral stacking analysis. The stacked intensity of [CI](1-0) is found to be
strongly correlated with $T_{\rm dust}$. Our results indicate that the atomic
carbon is a photodissociation product of CO, and consequently, compared to
CO(1-0), [CI](1-0) is less reliable in tracing the bulk of "cold" molecular gas
in the galactic disk.


Abstract:
  Using Galois theory of functional equations, we give a new proof of the main
result of the paper "Transcendental transcendency of certain functions of
Poincar\'e" by J.F. Ritt, on the differential transcendence of the solutions of
the functional equation R(y(t))=y(qt), where R is a rational function with
complex coefficients which verifies R(0)=0, R'(0)=q, where q is a complex
number with |q|>1. We also give a partial result in the case of an algebraic
function R.


Abstract:
  We construct an injection from the set of permutations of length $n$ that
contain exactly one copy of the decreasing pattern of length $k$ to the set of
permutations of length $n+2$ that avoid that pattern. We then prove that the
generating function counting the former is not rational, and in the case when
$k$ is even and $k\geq 4$, it is not even algebraic. We extend our injection
and our nonrationality result to a larger class of patterns.


Abstract:
  This paper proposes a non-autoregressive extension of our previously proposed
sequence-to-sequence (S2S) model-based voice conversion (VC) methods. S2S
model-based VC methods have attracted particular attention in recent years for
their flexibility in converting not only the voice identity but also the pitch
contour and local duration of input speech, thanks to the ability of the
encoder-decoder architecture with the attention mechanism. However, one of the
obstacles to making these methods work in real-time is the autoregressive (AR)
structure. To overcome this obstacle, we develop a method to obtain a model
that is free from an AR structure and behaves similarly to the original S2S
models, based on a teacher-student learning framework. In our method, called
"FastS2S-VC", the student model consists of encoder, decoder, and attention
predictor. The attention predictor learns to predict attention distributions
solely from source speech along with a target class index with the guidance of
those predicted by the teacher model from both source and target speech. Thanks
to this structure, the model is freed from an AR structure and allows for
parallelization. Furthermore, we show that FastS2S-VC is suitable for real-time
implementation based on a sliding-window approach, and describe how to make it
run in real-time. Through speaker-identity and emotional-expression conversion
experiments, we confirmed that FastS2S-VC was able to speed up the conversion
process by 70 to 100 times compared to the original AR-type S2S-VC methods,
without significantly degrading the audio quality and similarity to target
speech. We also confirmed that the real-time version of FastS2S-VC can be run
with a latency of 32 ms when run on a GPU.


Abstract:
  A nested coordinate system is a reassigning of independent variables to take
advantage of geometric or symmetry properties of a particular application.
Polar, cylindrical and spherical coordinate systems are primary examples of
such a regrouping that have proved their importance in the separation of
variables method for solving partial differential equations. Geometric algebra
offers powerful complimentary algebraic tools that are unavailable in other
treatments.


Abstract:
  The study of the mapping class group of the plane minus a Cantor set uses a
graph of loops, which is an analogous of the curve graph in the study of
mapping class groups of compact surfaces. The Gromov boundary of this loop
graph can be described in terms of "cliques of high-filling rays": high-filling
rays are simple geodesics of the surface which are complicated enough to be
infinitely far away from any loop in the graph. Moreover, these rays are
arranged in cliques: any two high-filling rays which are both disjoint from a
third one are necessarily mutually disjoint. Every such clique is a point of
the Gromov-boundary of the loop graph. Some examples of cliques with any finite
number of high-filling rays are already known.
  In this paper, we construct an infinite clique of high-filling rays.


Abstract:
  We consider the asymptotic expansion of the functional series
\[S_{\mu,\gamma}(a;\lambda)=\sum_{n=1}^\infty \frac{n^\gamma e^{-\lambda
n^2/a^2}}{(n^2+a^2)^\mu}\] for real values of the parameters $\gamma$,
$\lambda>0$ and $\mu\geq0$ as $|a|\to \infty$ in the sector $|\arg\,a|<\pi/4$.
For general values of $\gamma$ the expansion is of algebraic type with terms
involving the Riemann zeta function and a terminating confluent hypergeometric
function. Of principal interest in this study is the case corresponding to even
integer values of $\gamma$, where the algebraic-type expansion consists of a
finite number of terms together with a contribution comprising an infinite
sequence of increasingly subdominant exponentially small expansions. This
situation is analogous to the well-known Poisson-Jacobi formula corresponding
to the case $\mu=\gamma=0$. Numerical examples are provided to illustrate the
accuracy of these expansions.


Abstract:
  We present a novel method for predicting accurate depths from monocular
images with high efficiency. This optimal efficiency is achieved by exploiting
wavelet decomposition, which is integrated in a fully differentiable
encoder-decoder architecture. We demonstrate that we can reconstruct
high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast
with previous works, we show that wavelet coefficients can be learned without
direct supervision on coefficients. Instead we supervise only the final depth
image that is reconstructed through the inverse wavelet transform. We
additionally show that wavelet coefficients can be learned in fully
self-supervised scenarios, without access to ground-truth depth. Finally, we
apply our method to different state-of-the-art monocular depth estimation
models, in each case giving similar or better results compared to the original
model, while requiring less than half the multiply-adds in the decoder network.
Code at https://github.com/nianticlabs/wavelet-monodepth


Abstract:
  This work presents a novel target-free extrinsic calibration algorithm for a
3D Lidar and an IMU pair using an Extended Kalman Filter (EKF) which exploits
the \textit{motion based calibration constraint} for state update. The steps
include, data collection by motion excitation of the Lidar Inertial Sensor
suite along all degrees of freedom, determination of the inter sensor rotation
by using rotational component of the aforementioned \textit{motion based
calibration constraint} in a least squares optimization framework, and finally,
the determination of inter sensor translation using the \textit{motion based
calibration constraint} for state update in an Extended Kalman Filter (EKF)
framework. We experimentally validate our method using data collected in our
lab and open-source (https://github.com/unmannedlab/imu_lidar_calibration) our
contribution for the robotics research community.


Abstract:
  The field of quantum simulations in ultra-cold atomic gases has been
remarkably successful. In principle it allows for an exact treatment of a
variety of highly relevant lattice models and their emergent phases of matter.
But so far there is a lack in the theoretical literature concerning the
systematic study of the effects of the trap potential as well as the finite
size of the systems, as numerical studies of such non periodic, correlated
fermionic lattices models are numerically demanding beyond one dimension. We
use the recently introduced real-space truncated unity functional
renormalization group to study these boundary and trap effects with a focus on
their impact on the superconducting phase of the $2$D Hubbard model. We find
that in the experiments not only lower temperatures need to be reached compared
to current capabilities, but also system size and trap potential shape play a
crucial role to simulate emergent phases of matter.


Abstract:
  The vast majority of semantic segmentation approaches rely on pixel-level
annotations that are tedious and time consuming to obtain and suffer from
significant inter and intra-expert variability. To address these issues, recent
approaches have leveraged categorical annotations at the slide-level, that in
general suffer from robustness and generalization. In this paper, we propose a
novel weakly supervised multi-instance learning approach that deciphers
quantitative slide-level annotations which are fast to obtain and regularly
present in clinical routine. The extreme potentials of the proposed approach
are demonstrated for tumor segmentation of solid cancer subtypes. The proposed
approach achieves superior performance in out-of-distribution, out-of-location,
and out-of-domain testing sets.


Abstract:
  The purpose of this report is to look at the measures of importance of
components in systems in terms of reliability. In the first work of Birnbaum
(1968) on this subject, many interesting studies were created and important
indicators were constructed that allowed to organize the components of complex
systems. They are helpful in analyzing the reliability of the designed systems,
establishing the principles of operation and maintenance. The significance
measures presented here are collected and discussed regarding the motivation
behind their creation. They concern an approach in which both elements and
systems are binary, and the possibility of generalization to multistate systems
is only mentioned. Among the discussed is one new proposal using the methods of
game theory, combining the sensitivity to the structure of the system and the
operational effects on the system performance. The presented severity measures
use a knowledge of the system structure as well as reliability and wear and
tear, and whether the components can be repaired and maintained.


Abstract:
  Driven by increased complexity of dynamical systems, the solution of system
of differential equations through numerical simulation in optimization problems
has become computationally expensive. This paper provides a smart data driven
mechanism to construct low dimensional surrogate models. These surrogate models
reduce the computational time for solution of the complex optimization problems
by using training instances derived from the evaluations of the true objective
functions. The surrogate models are constructed using combination of proper
orthogonal decomposition and radial basis functions and provides system
responses by simple matrix multiplication. Using relative maximum absolute
error as the measure of accuracy of approximation, it is shown surrogate models
with latin hypercube sampling and spline radial basis functions dominate
variable order methods in computational time of optimization, while preserving
the accuracy. These surrogate models also show robustness in presence of model
non-linearities. Therefore, these computational efficient predictive surrogate
models are applicable in various fields, specifically to solve inverse problems
and optimal control problems, some examples of which are demonstrated in this
paper.


Abstract:
  First-order nonadiabatic coupling matrix elements (fo-NACMEs) are the basic
quantities in theoretical descriptions of electronically nonadiabatic processes
that are ubiquitous in molecular physics and chemistry. Given the large size of
systems of chemical interests, time-dependent density functional theory (TDDFT)
is usually the first choice. However, the lack of wave functions in TDDFT
renders the formulation of NAC-TDDFT for fo-NACMEs conceptually difficult. The
present account aims to analyze the available variants of NAC-TDDFT in a
critical but concise manner and meanwhile point out the proper ways for
implementation. It can be concluded, from both theoretical and numerical points
of view, that the equation of motion-based variant of NAC-TDDFT is the right
choice. Possible future developments of this variant are also highlighted.


Abstract:
  Immense field enhancement and nanoscale confinement of light are possible
within nanoparticle-on-mirror (NPoM) plasmonic resonators, which enable novel
optically-activated physical and chemical phenomena, and render these
nanocavities greatly sensitive to minute structural changes, down to the atomic
scale. Although a few of these structural parameters, primarily linked to the
nanoparticle and the mirror morphology, have been identified, the impact of
molecular assembly and organization of the spacer layer between them has often
been left uncharacterized. Here, we experimentally investigate how the complex
and reconfigurable nature of a thiol-based self-assembled monolayer (SAM)
adsorbed on the mirror surface impacts the optical properties of the NPoMs. We
fabricate NPoMs with distinct molecular organizations by controlling the
incubation time of the mirror in the thiol solution. Afterwards, we investigate
the structural changes that occur under laser irradiation by tracking the
bonding dipole plasmon mode, while also monitoring Stokes and anti-Stokes Raman
scattering from the molecules as a probe of their integrity. First, we find an
effective decrease in the SAM height as the laser power increases, compatible
with an irreversible change of molecule orientation caused by heating. Second,
we observe that the nanocavities prepared with a densely packed and more
ordered monolayer of molecules are more prone to changes in their resonance
compared to samples with sparser and more disordered SAMs. Our measurements
indicate that molecular orientation and packing on the mirror surface play a
key role in determining the stability of NPoM structures and hence highlight
the under-recognized significance of SAM characterization in the development of
NPoM-based applications.


Abstract:
  Recent literature has demonstrated that the use of per-channel energy
normalization (PCEN), has significant performance improvements over traditional
log-scaled mel-frequency spectrograms in acoustic sound event detection (SED)
in a multi-class setting with overlapping events. However, the configuration of
PCEN's parameters is sensitive to the recording environment, the
characteristics of the class of events of interest, and the presence of
multiple overlapping events. This leads to improvements on a class-by-class
basis, but poor cross-class performance. In this article, we experiment using
PCEN spectrograms as an alternative method for SED in urban audio using the
UrbanSED dataset, demonstrating per-class improvements based on parameter
configuration. Furthermore, we address cross-class performance with PCEN using
a novel method, Multi-Rate PCEN (MRPCEN). We demonstrate cross-class SED
performance with MRPCEN, demonstrating improvements to cross-class performance
compared to traditional single-rate PCEN.


Abstract:
  We study the Bose polaron problem in a nonequilibrium setting, by considering
an impurity embedded in a quantum fluid of light realized by exciton-polaritons
in a microcavity, subject to a coherent drive and dissipation on account of
pump and cavity losses. We obtain the polaron effective mass, the drag force
acting on the impurity, and determine polaron trajectories at a semiclassical
level. We find different dynamical regimes, originating from the unique
features of the excitation spectrum of driven-dissipative polariton fluids, in
particular a non-trivial regime of acceleration against the flow. Our work
promotes the study of impurity dynamics as an alternative testbed for probing
superfluidity in quantum fluids of light.


Abstract:
  In this paper, we focus on the performance of vehicle-to-vehicle (V2V)
communication adopting the Dedicated Short Range Communication (DSRC)
application in periodic broadcast mode. An analytical model is studied and a
fixed point method is used to analyze the packet delivery ratio (PDR) and mean
delay based on the IEEE 802.11p standard in a fully connected network under the
assumption of perfect PHY performance. With the characteristics of V2V
communication, we develop the Semi-persistent Contention Density Control
(SpCDC) scheme to improve the DSRC performance. We use Monte Carlo simulation
to verify the results obtained by the analytical model. The simulation results
show that the packet delivery ratio in SpCDC scheme increases more than 10%
compared with IEEE 802.11p in heavy vehicle load scenarios. Meanwhile, the mean
reception delay decreases more than 50%, which provides more reliable road
safety.


Abstract:
  Spectral factorization is a prominent tool with several important
applications in various areas of applied science. Wiener and Masani proved the
existence of matrix spectral factorization. Their theorem has been extended to
the multivariable case by Helson and Lowdenslager. Solving the problem
numerically is challenging in both situations, and also important due to its
practical applications. Therefore, several authors have developed algorithms
for factorization. The Janashia-Lagvilava algorithm is a relatively new method
for matrix spectral factorization which has proved to be useful in several
applications. In this paper, we extend this method to the multivariable case.
Consequently, a new numerical algorithm for multivariable matrix spectral
factorization is constructed.


Abstract:
  We investigate the torque field and skyrmion movement at an interface between
a ferromagnet hosting a skyrmion and a material with strong spin-orbit
interaction. We analyze both semiconductor materials and topological insulators
using a Hamiltonian model that includes a linear term. The spin torque inducing
current is considered to flow in the single band limit therefore a quantum
model of current is used. Skyrmion movement due spin transfer torque proves to
be more difficult in presence of spin orbit interaction in the case where only
interface in-plane currents are present. However, edge effects in narrow
nanowires can be used to drive the skyrmion movement and to exert a limited
control on its movement direction. We also show the differences and
similarities between torque fields due to electric current in the many and in
the single band limits.


Abstract:
  For any second-order scalar PDE $\mathcal{E}$ in one unknown function, that
we interpret as a hypersurface of a second-order jet space $J^2$, we construct,
by means of the characteristics of $\mathcal{E}$, a sub-bundle of the contact
distribution of the underlying contact manifold $J^1$, consisting of conic
varieties. We call it the contact cone structure associated with $\mathcal{E}$.
We then focus on symplectic Monge-Amp\`ere equations in 3 independent
variables, that are naturally parametrized by a 13-dimensional real projective
space. If we pass to the field of complex numbers $\mathbb{C}$, this projective
space turns out to be the projectivization of the 14-dimensional irreducible
representation of the simple Lie group $\mathsf{Sp}(6,\mathbb{C})$: the
associated moment map allows to define a rational map $\varpi$ from the space
of symplectic 3D Monge-Amp\`ere equations to the projectivization of the space
of quadratic forms on a $6$-dimensional symplectic vector space. We study in
details the relationship between the zero locus of the image of $\varpi$,
herewith called the cocharacteristic variety, and the contact cone structure of
a 3D Monge-Amp\`ere equation $\mathcal{E}$: under the hypothesis of
non-degenerate symbol, we prove that these two constructions coincide. A key
tool in achieving such a result will be a complete list of mutually
non-equivalent quadratic forms on a $6$-dimensional symplectic space, which has
an interest on its own.


Abstract:
  What do word vector representations reveal about the emotions associated with
words? In this study, we consider the task of estimating word-level emotion
intensity scores for specific emotions, exploring unsupervised, supervised, and
finally a self-supervised method of extracting emotional associations from word
vector representations. Overall, we find that word vectors carry substantial
potential for inducing fine-grained emotion intensity scores, showing a far
higher correlation with human ground truth ratings than achieved by
state-of-the-art emotion lexicons.


Abstract:
  Landau suggested that the low-temperature properties of metals can be
understood in terms of long-lived quasiparticles with all complex interactions
included in Fermi-liquid parameters, such as the effective mass $m^{\star}$.
Despite its wide applicability, electronic transport in bad or strange metals
and unconventional superconductors is controversially discussed towards a
possible collapse of the quasiparticle concept. Here we explore the
electrodynamic response of correlated metals at half filling for varying
correlation strength upon approaching a Mott insulator. We reveal persistent
Fermi-liquid behavior with pronounced quadratic dependences of the optical
scattering rate on temperature and frequency, along with a puzzling elastic
contribution to relaxation. The strong increase of the resistivity beyond the
Ioffe-Regel-Mott limit is accompanied by a `displaced Drude peak' in the
optical conductivity. Our results, supported by a theoretical model for the
optical response, demonstrate the emergence of a bad metal from resilient
quasiparticles that are subject to dynamical localization and dissolve near the
Mott transition.


Abstract:
  In his 1935 Gedankenexperiment, Erwin Schr\"{o}dinger imagined a poisonous
substance which has a 50% probability of being released, based on the decay of
a radioactive atom. As such, the life of the cat and the state of the poison
become entangled, and the fate of the cat is determined upon opening the box.
We present an experimental technique that keeps the cat alive on any account.
This method relies on the time-resolved Hong-Ou-Mandel effect: two long,
identical photons impinging on a beam splitter always bunch in either of the
outputs. Interpreting the first photon detection as the state of the poison,
the second photon is identified as the state of the cat. Even after the
collapse of the first photon's state, we show their fates are intertwined
through quantum interference. We demonstrate this by a sudden phase change
between the inputs, administered conditionally on the outcome of the first
detection, which steers the second photon to a pre-defined output and ensures
that the cat is always observed alive.


Abstract:
  The exsitance of three-dimensional Hall effect (3DQHE) due to spontaneous
Fermi surface instabilities in strong magnetic field was proposed decades ago,
and has stimulated recent progress in experiments. The reports in recent
experiments show that the Hall plateaus and vanishing transverse
magneto-resistivities (TMRs) (which are two main signatures of 3DQHE) are not
easy to be observed in natural materials. And two main different explanations
of the slow varying slope like Hall plateaus and non-vanishing
  TMRs (which can be called as quasi-quantized Hall effect (QQHE)) have been
proposed. By studying the magneto-transport with a simple effective periodic 3D
system, we show how 3DQHE can be achieved in certain parameter regimes at
first. We find two new mechanisms that may give rise to QQHE. One mechanism is
the "low" Fermi energy effect, and the other is the "strong" impurity effect.
Our studies also proved that the artificial superlattice is an ideal platform
for realizing 3DQHE with high layer barrier periodic potential.


Abstract:
  Presence often is considered the most important quale describing the
subjective feeling of being in a computer-generated (virtual) or
computer-mediated environment. The identification and separation of two
orthogonal presence components, i.e., the place illusion and the plausibility
illusion, has been an accepted theoretical model describing Virtual Reality
(VR) experiences for some time. In this model, immersion is a proposed
contributing factor to the place illusion. Lately, copresence and social
presence illusions have extended this model, and coherence was proposed as a
contributing factor to the plausibility illusion. Such factors strive to
identify (objectively) measurable characteristics of an experience, e.g.,
systems properties that allow controlled manipulations of VR experiences. This
perspective article challenges this presence-oriented VR theory. First, we
argue that a place illusion cannot be the major construct to describe the much
wider scope of Virtual, Augmented, and Mixed Reality (VR, AR, MR: or XR for
short). Second, we argue that there is no plausibility illusion but merely
plausibility, and we derive the place illusion as a consequence of a plausible
generation of spatial cues, and similarly for all of the current model's
so-defined illusions. Finally, we propose coherence and plausibility to become
the central essential conditions in a novel theoretical model describing XR
experiences and effects.


Abstract:
  Many teleoperation tasks require three or more tools working together, which
need the cooperation of multiple operators. The effectiveness of such schemes
may be limited by communication. Trimanipulation by a single operator using an
artificial third arm controlled together with their natural arms is a promising
solution to this issue. Foot-controlled interfaces have previously shown the
capability to be used for the continuous control of robot arms. However, the
use of such interfaces for controlling a supernumerary robotic limb (SRLs) in
coordination with the natural limbs, is not well understood. In this paper, a
teleoperation task imitating physically coupled hands in a virtual reality
scene was conducted with 14 subjects to evaluate human performance during
tri-manipulation. The participants were required to move three limbs together
in a coordinated way mimicking three arms holding a shared physical object. It
was found that after a short practice session, the three-hand tri-manipulation
using a single subject's hands and foot was still slower than dyad operation,
however, they displayed similar performance in success rate and higher motion
efficiency than two person's cooperation.


Abstract:
  The linear frequency modulated (LFM) frequency agile radar (FAR) can
synthesize a wide signal bandwidth through coherent processing while keeping
the bandwidth of each pulse narrow. In this way, high range resolution profiles
(HRRP) can be obtained without increasing the hardware system cost.
Furthermore, the agility provides improved both robustness to jamming and
spectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit
(NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed
as NOMP-FAR is designed to process each coarse range bin to extract the HRRP
and velocities of multiple targets, including the guide for determining the
oversampling factor and the stopping criterion. In addition, it is shown that
the target will cause false alarm in the nearby coarse range bins, a
postprocessing algorithm is then proposed to suppress the ghost targets.
Numerical simulations are conducted to demonstrate the effectiveness of
NOMP-FAR.


Abstract:
  We present the task description and discussion on the results of the DCASE
2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound
detection (ASD) task, identifying whether a given sound was normal or anomalous
without anomalous training data. In 2021, we organized an advanced unsupervised
ASD task under domain-shift conditions, which focuses on the inevitable problem
of the practical use of ASD systems. The main challenge of this task is to
detect unknown anomalous sounds where the acoustic characteristics of the
training and testing samples are different, i.e., domain-shifted. This problem
frequently occurs due to changes in seasons, manufactured products, and/or
environmental noise. We received 75 submissions from 26 teams, and several
novel approaches have been developed in this challenge. On the basis of the
analysis of the evaluation results, we found that there are two types of
remarkable approaches that TOP-5 winning teams adopted: 1) ensemble approaches
of ``outlier exposure'' (OE)-based detectors and ``inlier modeling'' (IM)-based
detectors and 2) approaches based on IM-based detection for features learned in
a machine-identification task.


Abstract:
  Quantitative information on tumor heterogeneity and cell load could assist in
designing effective and refined personalized treatment strategies. It was
recently shown by us that such information can be inferred from the diffusion
parameter D derived from the diffusion-weighted MRI (DWI) if a relation between
D and cell density can be established. However, such relation cannot a priori
be assumed to be constant for all patients and tumor types. Hence to assist in
clinical decisions in palliative settings, the relation needs to be established
without tumor resection. It is here demonstrated that biopsies may contain
sufficient information for this purpose if the localization of biopsies is
chosen as systematically elaborated in this paper. A superpixel-based method
for automated optimal localization of biopsies from the DWI D-map is proposed.
The performance of the DWI-guided procedure is evaluated by extensive
simulations of biopsies. Needle biopsies yield sufficient histological
information to establish a quantitative relationship between D-value and cell
density, provided they are taken from regions with high, intermediate, and low
D-value in DWI. The automated localization of the biopsy regions is
demonstrated from a NSCLC patient tumor. In this case, even two or three
biopsies give a reasonable estimate. Simulations of needle biopsies under
different conditions indicate that the DWI-guidance highly improves the
estimation results. Tumor cellularity and heterogeneity in solid tumors may be
reliably investigated from DWI and a few needle biopsies that are sampled in
regions of well-separated D-values, excluding adipose tissue. This procedure
could provide a way of embedding in the clinical workflow assistance in cancer
diagnosis and treatment based on personalized information.


Abstract:
  In this paper we give a classification of the asymptotic expansion of the
$q$-expansion of reciprocals of Eisenstein series $E_k$ of weight $k$ for the
modular group $\func{SL}_2(\mathbb{Z})$. For $k \geq 12$ even, this extends
results of Hardy and Ramanujan, and Berndt, Bialek and Yee, utilizing the
Circle Method on the one hand, and results of Petersson, and Bringmann and
Kane, developing a theory of meromorphic Poincar{\'e} series on the other. We
follow a uniform approach, based on the zeros of the Eisenstein series with the
largest imaginary part. These special zeros provide information on the
singularities of the Fourier expansion of $1/E_k(z)$ with respect to $q = e^{2
\pi i z}$.


Abstract:
  Interactive single-image segmentation is ubiquitous in the scientific and
commercial imaging software. In this work, we focus on the single-image
segmentation problem only with some seeds such as scribbles. Inspired by the
dynamic receptive field in the human being's visual system, we propose the
Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the
contextual information for neural networks. The core idea is randomly selecting
the spatial sampling area according to the Gaussian distribution offsets. Our
GDC can be easily used as a module to build lightweight or complex segmentation
networks. We adopt the proposed GDC to address the typical single-image
segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid
Pooling to show its potential and generality in common semantic segmentation.
Experiments demonstrate that the GDC outperforms other existing convolutions on
three benchmark segmentation datasets including Pascal-Context, Pascal-VOC
2012, and Cityscapes. Additional experiments are also conducted to illustrate
that the GDC can produce richer and more vivid features compared with other
convolutions. In general, our GDC is conducive to the convolutional neural
networks to form an overall impression of the image.


Abstract:
  For any positive integer $r$, we construct a smooth complex projective
rational surface which has at least $r$ real forms not isomorphic over
$\mathbb{R}$.


Abstract:
  We present a very simple form of the supercharges and the Hamiltonian of
${\cal N} {=}\,2$ supersymmetric extension of $n$-particle
Ruijsenaars--Schneider models for three cases of the interaction:
$1/(x_i-x_j)$, $1/tan(x_i-x_j)$, $1/tanh(x_i-x_j)$. The long "fermionic tails"
of the supercharges and Hamiltonian rolled up in the simple rational functions
depending on fermionic bilinears.


Abstract:
  We present a study of the structure and differential capacitance of electric
double layers of aqueous electrolytes. We consider Electric Double Layer
Capacitors (EDLC) composed of spherical cations and anions in a dielectric
continuum confined between a planar cathode and anode. The model system
includes steric as well as Coulombic ion-ion and ion-electrode interactions. We
compare results of computationally expensive, but "exact", Brownian Dynamics
(BD) simulations with approximate, but cheap, calculations based on classical
Density Functional Theory (DFT). Excellent overall agreement is found for a
large set of system parameters $-$ including variations in concentrations,
ionic size- and valency-asymmetries, applied voltages, and electrode separation
$-$ provided the differences between the canonical ensemble of the BD
simulations and the grand-canonical ensemble of DFT are properly taken into
account. In particular a careful distinction is made between the differential
capacitance $C_N$ at fixed number of ions and $C_\mu$ at fixed ionic chemical
potential. Furthermore, we derive and exploit their thermodynamic relations. In
the future these relations are also useful for comparing and contrasting.


Abstract:
  We analyse an extremal question on the degrees of the link graphs of a finite
regular graph, that is, the subgraphs induced by non-trivial spheres. We show
that if $G$ is $d$-regular and connected but not complete then some link graph
of $G$ has minimum degree at most $\lfloor 2d/3\rfloor-1$, and if $G$ is
sufficiently large in terms of $d$ then some link graph has minimum degree at
most $\lfloor d/2\rfloor-1$; both bounds are best possible. We also give the
corresponding best-possible result for the corresponding problem where
subgraphs induced by balls, rather than spheres, are considered.
  We motivate these questions by posing a conjecture concerning expansion of
link graphs in large bounded-degree graphs, together with a heuristic
justification thereof.


Abstract:
  #P-hardness of computing matrix immanants are proved for each member of a
broad class of shapes and restricted sets of matrices. The class is
characterized in the following way. If a shape of size $n$ in it is in form
$(w,\mathbf{1}+\lambda)$ or its conjugate is in that form, where $\mathbf{1}$
is the all-$1$ vector, then $|\lambda|$ is $n^{\varepsilon}$ for some
$0<\varepsilon$, $\lambda$ can be tiled with $1\times 2$ dominos and
$(3w+3h(\lambda)+1)|\lambda| \le n$, where $h(\lambda)$ is the height of
$\lambda$. The problem remains \#P-hard if the immanants are evaluated on
$0$-$1$ matrices. We also give hardness proofs of some immanants whose shape
$\lambda = (\mathbf{1}+\lambda_d)$ has size $n$ such that $|\lambda_d| =
n^{\varepsilon}$ for some $0<\varepsilon<\frac{1}{2}$, and for some $w$, the
shape $\lambda_d/(w)$ is tilable with $1\times 2$ dominos. The \#P-hardness
result holds when these immanants are evaluated on adjacency matrices of
planar, directed graphs, however, in these cases the edges have small positive
integer weights.


Abstract:
  The chromaticity diagram associated with the CIE 1931 color matching
functions is shown to be slightly non-convex. While having no impact on
practical colorimetric computations, the non-convexity does have a significant
impact on the shape of some optimal object color reflectance distributions
associated with the outer surface of the object color solid. Instead of the
usual two-transition Schrodinger form, many optimal colors exhibit higher
transition counts. A linear programming formulation is developed and is used to
locate where these higher-transition optimal object colors reside on the object
color solid surface. The regions of higher transition count appear to have a
point-symmetric complementary structure. The final peer-reviewed version (to
appear) contains additional material concerning convexification of the
color-matching functions and and additional analysis of modern
"physiologically-relevant" CMFs transformed from cone fundamentals.


Abstract:
  Raman spectroscopy is an advantageous method for studying the local structure
of materials, but the interpretation of measured spectra is complicated by the
presence of oblique phonons in polycrystals of polar materials. Whilst group
theory considerations and standard ab initio calculations are helpful, they are
often valid only for single crystals. In this paper, we introduce a method for
computing Raman spectra of polycrystalline materials from first principles. We
start from the standard approach based on the (Placzek) rotation invariants of
the Raman tensors and extend it to include the effect of the coupling between
the lattice vibrations and the induced electric field, and the electro-optic
contribution, relevant for polar materials like ferroelectrics. As exemplified
by applying the method to rhombohedral BaTiO3, AlN, and LiNbO3, such an
extension brings the simulated Raman spectrum to a much better correspondence
with the experimental one. Additional advantages of the method are that it is
general, permits automation, and thus can be used in high-throughput fashion.


Abstract:
  This paper is a continuation of our article (European J. Math.,
https://doi.org/10.1007/s40879-020-00419-8). The notion of a poor complex
compact manifold was introduced there and the group $Aut(X)$ for a $P^1$-bundle
over such a manifold was proven to be very Jordan. We call a group $G$ very
Jordan if it contains a normal abelian subgroup $G_0$ such that the orders of
finite subgroups of the quotient $G/G_0$ are bounded by a constant depending on
$G$ only.
  In this paper we provide explicit examples of infinite families of poor
manifolds of any complex dimension, namely simple tori of algebraic dimension
zero. Then we consider a non-trivial holomorphic $P^1$-bundle $(X,p,Y)$ over a
non-uniruled complex compact Kaehler manifold $Y$. We prove that $Aut(X)$ is
very Jordan provided some additional conditions on the set of sections of $p$
are met. Applications to $P^1$-bundles over non-algebraic complex tori are
given.


Abstract:
  In this work we obtain a geometric characterization of the measures $\mu$ in
$\mathbb{R}^{n+1}$ with polynomial upper growth of degree $n$ such that the
$n$-dimensional Riesz transform $\mathcal{R}\mu (x) = \int
\frac{x-y}{|x-y|^{n+1}}\,d\mu(y)$ belongs to $L^2(\mu)$, under the assumption
that $\mu$ satisfies the following Wolff energy estimate, for any ball
$B\subset\mathbb{R}^{n+1}$: $$\int_B \int_0^\infty
\left(\frac{\mu(B(x,r))}{r^{n-\frac38}}\right)^2\,\frac{dr}r\,d\mu(x)\leq
M\,\bigg(\frac{\mu(2B)}{r(B)^{n-\frac38}}\bigg)^2\,\mu(2B).$$ More precisely,
we show that $\mu$ satisfies the following estimate:
$$\|\mathcal{R}\mu\|_{L^2(\mu)}^2 + \|\mu\|\approx \int\!\!\int_0^\infty
\beta_{\mu,2}(x,r)^2\,\frac{\mu(B(x,r))}{r^n}\,\frac{dr}r\,d\mu(x) + \|\mu\|,$$
where $\beta_{\mu,2}(x,r)^2 = \inf_L \frac1{r^n}\int_{B(x,r)}
\left(\frac{\mathrm{dist}(y,L)}r\right)^2\,d\mu(y),$ with the infimum taken
over all affine $n$-planes $L\subset\mathbb{R}^{n+1}$. In a companion paper
which relies on the results obtained in this work it is shown that the same
result holds without the above assumption regarding the Wolff energy of $\mu$.
This result has important consequences for the Painlev\'e problem for Lipschitz
harmonic functions.


Abstract:
  We propose a new approach for trading VIX futures. We assume that the term
structure of VIX futures follows a Markov model. Our trading strategy selects a
position in VIX futures by maximizing the expected utility for a day-ahead
horizon given the current shape and level of the term structure.
Computationally, we model the functional dependence between the VIX futures
curve, the VIX futures positions, and the expected utility as a deep neural
network with five hidden layers. Out-of-sample backtests of the VIX futures
trading strategy suggest that this approach gives rise to reasonable portfolio
performance, and to positions in which the investor will be either long or
short VIX futures contracts depending on the market environment.


Abstract:
  In this paper we study the connectivity of Fatou components for maps in a
large family of singular perturbations. We prove that, for some parameters
inside the family, the dynamical planes for the corresponding maps present
Fatou components of arbitrarily large connectivity and we determine precisely
these connectivities. In particular, these results extend the ones obtained in
[Can17, Can18].


Abstract:
  Cross features play an important role in click-through rate (CTR) prediction.
Most of the existing methods adopt a DNN-based model to capture the cross
features in an implicit manner. These implicit methods may lead to a
sub-optimized performance due to the limitation in explicit semantic modeling.
Although traditional statistical explicit semantic cross features can address
the problem in these implicit methods, it still suffers from some challenges,
including lack of generalization and expensive memory cost. Few works focus on
tackling these challenges. In this paper, we take the first step in learning
the explicit semantic cross features and propose Pre-trained Cross Feature
learning Graph Neural Networks (PCF-GNN), a GNN based pre-trained model aiming
at generating cross features in an explicit fashion. Extensive experiments are
conducted on both public and industrial datasets, where PCF-GNN shows
competence in both performance and memory-efficiency in various tasks.


Abstract:
  The number of photographs taken worldwide is growing rapidly and steadily.
While a small subset of these images is annotated and shared by users through
social media platforms, due to the sheer number of images in personal photo
repositories (shared or not shared), finding specific images remains
challenging. This survey explores existing image retrieval techniques as well
as photo-organizer applications to highlight their relative strengths in
addressing this challenge.


Abstract:
  Partitioning graphs into blocks of roughly equal size is widely used when
processing large graphs. Currently there is a gap in the space of available
partitioning algorithms. On the one hand, there are streaming algorithms that
have been adopted to partition massive graph data on small machines. In the
streaming model, vertices arrive one at a time including their neighborhood and
then have to be assigned directly to a block. These algorithms can partition
huge graphs quickly with little memory, but they produce partitions with low
solution quality. On the other hand, there are offline (shared-memory)
multilevel algorithms that produce partitions with high quality but also need a
machine with enough memory. We make a first step to close this gap by
presenting an algorithm that computes significantly improved partitions of huge
graphs using a single machine with little memory in streaming setting. First,
we adopt the buffered streaming model which is a more reasonable approach in
practice. In this model, a processing element can store a buffer, or batch, of
nodes before making assignment decisions. When our algorithm receives a batch
of nodes, we build a model graph that represents the nodes of the batch and the
already present partition structure. This model enables us to apply multilevel
algorithms and in turn compute much higher quality solutions of huge graphs on
cheap machines than previously possible. To partition the model, we develop a
multilevel algorithm that optimizes an objective function that has previously
shown to be effective for the streaming setting. This also removes the
dependency on the number of blocks k from the running time compared to the
previous state-of-the-art. Overall, our algorithm computes, on average, 75.9%
better solutions than Fennel using a very small buffer size. In addition, for
large values of k our algorithm becomes faster than Fennel.


Abstract:
  Let $X$ be a nonempty set and let $T(X)$ be the full transformation semigroup
on $X$. The main objective of this paper is to study the subsemigroup
$\overline{\Omega}(X, Y)$ of $T(X)$ defined by \[\overline{\Omega}(X, Y) =
\{f\in T(X)\colon Yf = Y\},\] where $Y$ is a fixed nonempty subset of $X$. We
describe regular elements in $\overline{\Omega}(X, Y)$ and show that
$\overline{\Omega}(X, Y)$ is regular if and only if $Y$ is finite. We
characterize unit-regular elements in $\overline{\Omega}(X, Y)$ and prove that
$\overline{\Omega}(X, Y)$ is unit-regular if and only if $X$ is finite. We
characterize Green's relations on $\overline{\Omega}(X, Y)$ and prove that
$\mathcal{D} =\mathcal{J}$ on $\overline{\Omega}(X, Y)$ if and only if $Y$ is
finite. We also determine ideals of $\overline{\Omega}(X, Y)$ and investigate
its kernel. This paper extends several results appeared in the literature.


Abstract:
  We elaborate on the correspondence between the canonical partition function
in asymptotically AdS universes and the no-boundary proposal for positive
vacuum energy. For the case of a pure cosmological constant, the analytic
continuation of the AdS partition function is seen to define the no-boundary
wave function (in dS) uniquely in the simplest minisuperspace model. A
consideration of the AdS gravitational path integral implies that on the dS
side, saddle points with Hawking-Moss/Coleman-De Luccia-type tunnelling
geometries are irrelevant. This implies that simple topology changing
geometries do not contribute to the nucleation of the universe. The analytic
AdS/dS equivalence holds up once tensor fluctuations are added. It also works,
at the level of the saddle point approximation, when a scalar field with a mass
term is included, though in the latter case, it is the mass that must be
analytically continued. Our results illustrate the emergence of time from space
by means of a Stokes phenomenon, in the case of positive vacuum energy.
Furthermore, we arrive at a new characterisation of the no-boundary condition,
namely that there should be no momentum flux at the nucleation of the universe.


Abstract:
  The ultimate detection limit of optical biosensors is often limited by
various noise sources, including those introduced by the optical measurement
setup. While sophisticated modifications to instrumentation may reduce noise, a
simpler approach that can benefit all sensor platforms is the application of
signal processing to minimize the deleterious effects of noise. In this work,
we show that applying complex Morlet wavelet convolution to Fabry-P\'erot
interference fringes characteristic of thin film reflectometric biosensors
effectively filters out white noise and low frequency reflectance variations.
Subsequent calculation of an average difference in phase between the filtered
analyte and reference signals enables a significant reduction in the limit of
detection (LOD) enabling closer competition with current state-of-the-art
techniques. This method is applied on experimental data sets of thin film
porous silicon sensors (PSi) in buffered solution and complex media obtained
from two different laboratories. The demonstrated improvement in LOD achieved
using wavelet convolution and average phase difference paves the way for PSi
optical biosensors to operate with clinically relevant detection limits for
medical diagnostics, environmental monitoring, and food safety.


Abstract:
  In addition to the well-known gas phase mass-metallicity relation (MZR),
recent spatially-resolved observations have shown that local galaxies also obey
a mass-metallicity gradient relation (MZGR) whereby metallicity gradients can
vary systematically with galaxy mass. In this work, we use our
recently-developed analytic model for metallicity distributions in galactic
discs, which includes a wide range of physical processes -- radial advection,
metal diffusion, cosmological accretion, and metal-enriched outflows -- to
simultaneously analyse the MZR and MZGR. We show that the same physical
principles govern the shape of both: centrally-peaked metal production favours
steeper gradients, and this steepening is diluted by the addition of metal-poor
gas, which is supplied by inward advection for low-mass galaxies and by
cosmological accretion for massive galaxies. The MZR and the MZGR both bend at
galaxy stellar mass $\sim 10^{10} - 10^{10.5}\,\rm{M_{\odot}}$, and we show
that this feature corresponds to the transition of galaxies from the
advection-dominated to the accretion-dominated regime. We also find that both
the MZR and MZGR strongly suggest that low-mass galaxies preferentially lose
metals entrained in their galactic winds. While this metal-enrichment of the
galactic outflows is crucial for reproducing both the MZR and the MZGR at the
low-mass end, we show that the flattening of gradients in massive galaxies is
expected regardless of the nature of their winds.


Abstract:
  Direct Volume Rendering (DVR) using Volumetric Path Tracing (VPT) is a
scientific visualization technique that simulates light transport with objects'
matter using physically-based lighting models. Monte Carlo (MC) path tracing is
often used with surface models, yet its application for volumetric models is
difficult due to the complexity of integrating MC light-paths in volumetric
media with none or smooth material boundaries. Moreover, auxiliary
geometry-buffers (G-buffers) produced for volumes are typically very noisy,
failing to guide image denoisers relying on that information to preserve image
details. This makes existing real-time denoisers, which take noise-free
G-buffers as their input, less effective when denoising VPT images. We propose
the necessary modifications to an image-based denoiser previously used when
rendering surface models, and demonstrate effective denoising of VPT images. In
particular, our denoising exploits temporal coherence between frames, without
relying on noise-free G-buffers, which has been a common assumption of existing
denoisers for surface-models. Our technique preserves high-frequency details
through a weighted recursive least squares that handles heterogeneous noise for
volumetric models. We show for various real data sets that our method improves
the visual fidelity and temporal stability of VPT during classic DVR operations
such as camera movements, modifications of the light sources, and editions to
the volume transfer function.


Abstract:
  In the last decade, substantial progress has been made towards standardizing
the syntax of graph query languages, and towards understanding their semantics
and complexity of evaluation. In this paper, we consider temporal property
graphs (TPGs) and propose temporal regular path queries (TRPQs) that
incorporate time into TPG navigation. Starting with design principles, we
propose a natural syntactic extension of the MATCH clause of popular graph
query languages. We then formally present the semantics of TRPQs, and study the
complexity of their evaluation. We show that TRPQs can be evaluated in
polynomial time if TPGs are time-stamped with time points, and identify
fragments of the TRPQ language that admit efficient evaluation over a more
succinct interval-annotated representation. Finally, we implement a fragment of
the language in a state-of-the-art dataflow framework, and experimentally
demonstrate that TRPQ can be evaluated efficiently.


Abstract:
  Let $G$ be a simple undirected graph with vertex set $V(G)=\{v_1, v_2,
\ldots, v_n\}$ and edge set $E(G)$. The Sombor matrix $\mathcal{S}(G)$ of a
graph $G$ is defined so that its $(i,j)$-entry is equal to $\sqrt{d_i^2+d_j^2}$
if the vertices $v_i$ and $v_j$ are adjacent, and zero otherwise, where $d_i$
denotes the degree of vertex $v_i$ in $G$. In this paper, lower and upper
bounds on the spectral radius, energy and Estrada index of the Sombor matrix of
graphs are obtained, and the respective extremal graphs are characterized.