Abstract:
  Getting the distance to objects is crucial for autonomous vehicles. In
instances where depth sensors cannot be used, this distance has to be estimated
from RGB cameras. As opposed to cars, the task of estimating depth from
on-board mounted cameras is made complex on drones because of the lack of
constrains on motion during flights. In this paper, we present a method to
estimate the distance of objects seen by an on-board mounted camera by using
its RGB video stream and drone motion information. Our method is built upon a
pyramidal convolutional neural network architecture and uses time recurrence in
pair with geometric constraints imposed by motion to produce pixel-wise depth
maps. In our architecture, each level of the pyramid is designed to produce its
own depth estimate based on past observations and information provided by the
previous level in the pyramid. We introduce a spatial reprojection layer to
maintain the spatio-temporal consistency of the data between the levels. We
analyse the performance of our approach on Mid-Air, a public drone dataset
featuring synthetic drone trajectories recorded in a wide variety of
unstructured outdoor environments. Our experiments show that our network
outperforms state-of-the-art depth estimation methods and that the use of
motion information is the main contributing factor for this improvement. The
code of our method is publicly available on GitHub; see
https://github.com/michael-fonder/M4Depth


Abstract:
  Fixed length summarization aims at generating summaries with a preset number
of words or characters. Most recent researches incorporate length information
with word embeddings as the input to the recurrent decoding unit, causing a
compromise between length controllability and summary quality. In this work, we
present an effective length controlling unit Length Attention (LenAtten) to
break this trade-off. Experimental results show that LenAtten not only brings
improvements in length controllability and ROGUE scores but also has great
generalization ability. In the task of generating a summary with the target
length, our model is 732 times better than the best-performing length
controllable summarizer in length controllability on the CNN/Daily Mail
dataset.


Abstract:
  The graphical structure of Probabilistic Graphical Models (PGMs) encodes the
conditional independence (CI) relations that hold in the modeled distribution.
Graph algorithms, such as d-separation, use this structure to infer additional
conditional independencies, and to query whether a specific CI holds in the
distribution. The premise of all current systems-of-inference for deriving CIs
in PGMs, is that the set of CIs used for the construction of the PGM hold
exactly. In practice, algorithms for extracting the structure of PGMs from
data, discover approximate CIs that do not hold exactly in the distribution. In
this paper, we ask how the error in this set propagates to the inferred CIs
read off the graphical structure. More precisely, what guarantee can we provide
on the inferred CI when the set of CIs that entailed it hold only
approximately? It has recently been shown that in the general case, no such
guarantee can be provided. We prove that such a guarantee exists for the set of
CIs inferred in directed graphical models, making the d-separation algorithm a
sound and complete system for inferring approximate CIs. We also prove an
approximation guarantee for independence relations derived from marginal CIs.


Abstract:
  Variable and function names are extremely important for program
comprehension. It is therefore also important to study how developers select
names. But controlled experiments on naming are hindered by the need to
describe to experimental subjects what it is they need to name. Words appearing
in these descriptions may then find their way into the names, leading to a bias
in the results. We suggest that this problem can be alleviated by using emojis
or other small graphics in lieu of key words in the descriptions. A replication
of previous work on naming, this time including such emojis and graphics,
indeed led to a more diverse and less biased choice of words in the names than
when using English descriptions.


Abstract:
  In this work, we consider the uplink of a scalable cell-free massive MIMO
system where the users are served only by a subset of access points (APs) in
the network. The APs are physically grouped into predetermined "cell-centric
clusters", which are connected to different cooperative central processing
units (CPUs). Given the cooperative nature of the considered communications
network, we assume that each user is associated with a "virtual cluster", that,
in general, involves some APs belonging to different cell-centric clusters.
Assuming the maximum-ratio-combining at the APs, we propose a user-association
procedure aimed at the maximization of the sum-rate of the users in the system.
The proposed procedure is based on the Hungarian Algorithm and exploits only
the knowledge of the position of the APs in the network. Numerical results
reveal that the performance of the proposed approach is not always better than
the alternatives but it offers a considerably lower backhaul load with a
negligible performance loss compared to full-cell free approaches.


Abstract:
  The neutron skin thickness $\Delta r_{\rm{np}}$ of heavy nuclei is
essentially determined by the symmetry energy density slope $L({\rho })$ at
$\rho_c = 0.11/0.16\rho_0$ ($\rho_0$ is nuclear saturation density), roughly
corresponding to the average density of finite nuclei. The PREX collaboration
recently reported a model-independent extraction of $\Delta r^{208}_{\rm{np}} =
0.29 \pm 0.07$ fm for the $\Delta r_{\rm{np}}$ of $^{208}$Pb, which suggests a
rather stiff symmetry energy $E_{\rm{sym}}({\rho })$ with $L({\rho_c }) \ge 55$
MeV. We demonstrate that the $E_{\rm{sym}}({\rho })$ cannot be too stiff and
$L({\rho_c }) \le 73$ MeV is necessary to be compatible with (1) the
ground-state properties and giant monopole resonances of finite nuclei, (2) the
constraints on the equation of state of symmetric nuclear matter at
suprasaturation densities from flow data in heavy-ion collisions, (3) the
largest neutron star (NS) mass reported so far for PSR J0740+6620, (4) the NS
tidal deformability extracted from gravitational wave signal GW170817 and (5)
the mass-radius of PSR J0030+045 measured simultaneously by NICER. This allow
us to obtain $55 \le L({\rho_c }) \le 73$ MeV and $0.22 \le \Delta
r^{208}_{\rm{np}} \le 0.27$ fm, and further $E_{\rm{sym}}({\rho_0 }) = 34.5 \pm
1.5$ MeV, $L({\rho_0 }) = 85.5 \pm 22.2$ MeV, and $E_{\rm{sym}}({2\rho_0 }) =
63.9 \pm 14.8$ MeV. A number of critical implications on nuclear physics and
astrophysics are discussed.


Abstract:
  It is shown that the possibility of using Maxwell demon to cheating in
quantum non-locality tests is prohibited by the Landauer's erasure principle.


Abstract:
  Pseudo-labeling is the most adopted method for pre-training automatic speech
recognition (ASR) models. However, its performance suffers from the supervised
teacher model's degrading quality in low-resource setups and under domain
transfer. Inspired by the successes of contrastive representation learning for
computer vision and speech applications, and more recently for supervised
learning of visual objects, we propose Contrastive Semi-supervised Learning
(CSL). CSL eschews directly predicting teacher-generated pseudo-labels in favor
of utilizing them to select positive and negative examples. In the challenging
task of transcribing public social media videos, using CSL reduces the WER by
8% compared to the standard Cross-Entropy pseudo-labeling (CE-PL) when 10hr of
supervised data is used to annotate 75,000hr of videos. The WER reduction jumps
to 19% under the ultra low-resource condition of using 1hr labels for teacher
supervision. CSL generalizes much better in out-of-domain conditions, showing
up to 17% WER reduction compared to the best CE-PL pre-trained model.


Abstract:
  The planar topological Hall effect (PTHE), appeared when the magnetic field
tended to be along the current, is believed to result from the real-space Berry
curvature of the spin spiral structure and has been experimentally observed in
skyrmion-hosting materials. In this paper, we report an experimental
observation of the PTHE in a hexagonal ferromagnetic Fe5Sn3 single crystal.
With a current along the c axis of Fe5Sn3, the transverse resistivity curves
exhibited obvious peaks near the saturation field as the magnetic field rotated
to the current and appeared more obvious with increasing temperature, which was
related to the noncoplanar spin structure in Fe5Sn3. This spin structure
induced nonzero scalar spin chirality, which acted as fictitious magnetic
fields to conduction electrons and contributed the additional transverse
signal. These findings deepen the understanding of the interaction between
conduction electrons and complex magnetic structures and are instructive for
the design of next-generation spintronic devices.


Abstract:
  The Zr-based MAX phases have attracted considerable attention for their
outstanding irradiation behavior and high neutron transparency relevant to
nuclear power generation technologies. In spite of increased understanding of
physical behavior crystalline MAX phases, the high-temperature oxidation
behavior and reaction mechanism of disordered MAX phases both from theory and
experiments are not well understood due to increased system complexity. Here,
we present a detailed comparative assessment of high-temperature
thermodynamic-stability and oxidation behavior (reaction-products and chemical
activity) of ordered Ti2AlC and disordered (Ti0.5Zr0.5)2AlC. We believe that
the new insights will enhance our understanding of oxidation process in
disordered MAX phases.


Abstract:
  This is the second of two papers that study the asymptotic structure of
space-times with a non-negative cosmological constant $\Lambda$. This paper
deals with the case $\Lambda >0$. Our approach is founded on the `tidal
energies' built with the Weyl curvature and, specifically, we use the
asymptotic super-Poynting vector computed from the rescaled Bel-Robinson tensor
at infinity to provide a covariant, gauge-invariant, criterion for the
existence, or absence, of gravitational radiation at infinity. The fundamental
idea we put forward is that the physical asymptotic properties are encoded in
$(\scri,h_{ab},D_{ab})$, where the first element of the triplet is a
3-dimensional manifold, the second is a representative of a conformal class of
Riemannian metrics on $\scri$, and the third element is a traceless symmetric
tensor field on $\scri$. We similarly propose a no-incoming radiation criterion
based also on the triplet $(\scri,h_{ab},D_{ab})$ and on radiant supermomenta
deduced from the rescaled Bel-Robinson tensor too. We search for news tensors
and argue that any news-like object must be associated to, and depends on,
2-dimensional cross-sections of $\scri$. We identify one component of news for
every such cross-section and present a general strategy to find the second
component. We also introduce the concept of equipped $\scri$, consider the
limit $\Lambda\rightarrow 0$ and apply all our results to selected exact
solutions of Einstein Field Equations. The full-length abstract is available in
the paper.


Abstract:
  We propose an end-to-end network that takes a single perspective RGB image of
a complex road scene as input, to produce occlusion-reasoned layouts in
perspective space as well as a top-view parametric space. In contrast to prior
works that require dense supervision such as semantic labels in perspective
view, the only human annotations required by our method are for parametric
attributes that are cheaper and less ambiguous to obtain. To solve this
challenging task, our design is comprised of modules that incorporate inductive
biases to learn occlusion-reasoning, geometric transformation and semantic
abstraction, where each module may be supervised by appropriately transforming
the parametric annotations. We demonstrate how our design choices and proposed
deep supervision help achieve accurate predictions and meaningful
representations. We validate our approach on two public datasets, KITTI and
NuScenes, to achieve state-of-the-art results with considerably lower human
supervision.


Abstract:
  The Belle II experiment at the Super B factory SuperKEKB, an asymmetric
$e^+e^-$ collider located in Tsukuba, Japan, is tailored to perform precision B
physics measurements. The centre of mass energy of the collisions is equal to
the rest mass of the $\Upsilon(4S)$ resonance of $m_{\Upsilon(4S)} = 10.58\,\rm
GeV$. A high vertex resolution is essential for measuring the decay vertices of
B mesons. Typical momenta of the decay products are ranging from a few tens of
MeV to a few GeV and multiple scattering has a significant impact on the vertex
resolution. The VerteX Detector (VXD) for Belle II is therefore designed to
have as little material as possible inside the acceptance region. Especially
the innermost two layers, populated by the PiXel Detector (PXD), have to be
ultra-thin. The PXD is based on DEpleted P-channel Field Effect Transistors
(DEPFETs) with a thickness of only $75\,\rm\mu m$. Spatial resolution and hit
efficiency of production detector modules were studied in beam tests performed
at the DESY test beam facility. The spatial resolution was investigated as a
function of the incidence angle and improvements due to charge sharing are
demonstrated. The measured module performance is compatible with the
requirements for Belle II.


Abstract:
  In this paper, we investigate the relationship between the weak min-max
property and the diameter uniformity of domains in Banach spaces with
dimensions at least $2$. As an application, we show that diameter uniform
domains are invariant under relatively quasim\"obius mappings.


Abstract:
  The field of face recognition (FR) has witnessed great progress with the
surge of deep learning. Existing methods mainly focus on extracting
discriminative features, and directly compute the cosine or L2 distance by the
point-to-point way without considering the context information. In this study,
we make a key observation that the local con-text represented by the
similarities between the instance and its inter-class neighbors1plays an
important role forFR. Specifically, we attempt to incorporate the local
in-formation in the feature space into the metric, and pro-pose a unified
framework calledInter-class DiscrepancyAlignment(IDA), with two dedicated
modules, Discrepancy Alignment Operator(IDA-DAO) andSupport Set
Estimation(IDA-SSE). IDA-DAO is used to align the similarity scores considering
the discrepancy between the images and its neighbors, which is defined by
adaptive support sets on the hypersphere. For practical inference, it is
difficult to acquire support set during online inference. IDA-SSE can provide
convincing inter-class neighbors by introducing virtual candidate images
generated with GAN. Further-more, we propose the learnable IDA-SSE, which can
implicitly give estimation without the need of any other images in the
evaluation process. The proposed IDA can be incorporated into existing FR
systems seamlessly and efficiently. Extensive experiments demonstrate that this
frame-work can 1) significantly improve the accuracy, and 2) make the model
robust to the face images of various distributions.Without bells and whistles,
our method achieves state-of-the-art performance on multiple standard FR
benchmarks.


Abstract:
  The phenomena of intermittent and complete synchronization between two out of
three identical, magnetically coupled SQUIDs (Superconducting QUantum
Interference Devices) are investigated numerically. SQUIDs are highly nonlinear
superconducting oscillators/devices that exhibit strong resonant and tunable
response to applied magnetic field(s). Single SQUIDs and SQUID arrays are
technologically important solid state devices, and they also serve as a testbed
for exploring numerous complex dynamical phenomena. In SQUID oligomers, the
dynamic complexity increases considerably with the number of SQUIDs. The SQUID
trimer, considered here in a linear geometrical configuration using a realistic
model with accesible control parameters, exhibits chaotic and hyperchaotic
behavior in wide parameter regions. Complete chaos synchronization as well as
intermittent chaos synchronization between two SQUIDs of the trimer is
identified and characterized using the complete Lyapunov spectrum of the system
and appropriate measures. The passage from complete to intermittent
synchronization seems to be related to chaos-hyperchaos transitions as has been
conjectured in the early days of chaos synchronization.


Abstract:
  This paper contains two contributions in the study of optimal transport on
metric graphs. Firstly, we prove a Benamou-Brenier formula for the Wasserstein
distance, which establishes the equivalence of static and dynamical optimal
transport. Secondly, in the spirit of Jordan-Kinderlehrer-Otto, we show that
McKean-Vlasov equations can be formulated as gradient flow of the free energy
in the Wasserstein space of probability measures. The proofs of these results
are based on careful regularisation arguments to circumvent some of the
difficulties arising in metric graphs, namely, branching of geodesics and the
failure of semi-convexity of entropy functionals in the Wasserstein space.


Abstract:
  In this paper, we will study the existence of finite time singularity to
harmonic heat flow and their formation patterns. After works of
Coron-Ghidaglia, Ding and Chen-Ding, one knows blow-up solutions under
smallness of initial energy for m>=3. soon later, 2 dimensional blowup
solutions were found by Chang-Ding-Ye. The first part of this paper is devoted
to construction of new examples of finite time blow-up solutions without
smallness conditions for 3<=m<7. In fact, when considering rotational symmetric
harmonic heat flow from B_1\subset R^m to S^m\subset R^{m+1}, we will prove
that the maximal solution blows up in finite time if b>\vartheta_m, and exists
for all time if 0<b<\pi/2. This result can be regarded as a generalization of
results of Chang-Ding-Ye nad Chang-Ding to higher dimensional case, which
relies on a completely different argument. The second part of the paper study
the rate of blow-up solutions. When M is a bounded domain in R^2 and consider
Dirichlet boundary condition on \partial M, Hamilton has obtained that the
blowup rate must be faster than (T-t)^{-1}. Under a similar setting, it was
later improved a litttle by Topping to (T-t)^{-1}|log(T-t)|. In this paper, we
will extend the results to all Riemmanian surfaces M and improve the rate of
Topping to (T-t)^{-1}a(|log(T-t)|) for any positive nondecreasing function
a(\tau) satisfying $\int^\infty_1\frac{d\tau}{a(\tau)}=+\infty$, which is
comparable to a recent result of Raphael-Schweyer for rotational symmetric
solutions. Turning to the higher dimensional case 3<=m<7, we will demonstrate a
completely different phenomenon by showing that all rotational symmetric
blow-up solutions can not be type II, which is different to the case m>=7 by
Bizon-Wasserman. Finally, we also present result of finite time type I blowup
for heat flow from S^m to S^m\subset R^{m+1}, when 3<=m<7 and degree is no less
than 2.


Abstract:
  In topological data analysis and visualization, topological descriptors such
as persistence diagrams, merge trees, contour trees, Reeb graphs, and
Morse-Smale complexes play an essential role in capturing the shape of scalar
field data. We present a state-of-the-art report on scalar field comparison
using topological descriptors. We provide a taxonomy of existing approaches
based on visualization tasks associated with three categories of data: single
fields, time-varying fields, and ensembles. These tasks include symmetry
detection, periodicity detection, key event/feature detection, feature
tracking, clustering, and structure statistics. Our main contributions include
the formulation of a set of desirable mathematical and computational properties
of comparative measures, and the classification of visualization tasks and
applications that are enabled by these measures.


Abstract:
  In their papers, Lu et al. analysed experimental data from in-situ
transmission electron microscopy (TEM) and concluded that local stress
concentration at grain boundary-twin boundary (GB-TB) intersections in
nanotwinned (nt-) metals increases as the TB thicknesses is reduced (below
${\lambda}$ $\leq$ 18 nm). However, here, by analysing the raw experimental
data in Lu et al. together with finite element simulations and dimensional
analysis, we show that the measurement of local stress concentration in a
nt-material is highly sensitive to the adopted averaging box size due to the
high stress gradient near GB-TB intersections as well as to the TB location in
the nt-grain. By careful analysis of the raw data, a completely reverse trend,
which is consistent with well-established laws of continuum mechanics, can be
captured.


Abstract:
  The mobile robotic testbed is an essential and critical support to verify the
effectiveness of mobile robotics research. This paper introduces a novel
multi-robot testbed, named Robopheus, which exploits the ideas of
virtual-physical modeling in digital-twin. Unlike most existing testbeds, the
developed Robopheus constructs a bridge that connects the traditional physical
hardware and virtual simulation testbeds, providing scalable, interactive, and
high-fidelity simulations-tests on both sides. Another salient feature of the
Robopheus is that it enables a new form to learn the actual models from the
physical environment dynamically and is compatible with heterogeneous robot
chassis and controllers. In turn, the virtual world's learned models are
further leveraged to approximate the robot dynamics online on the physical
side. Extensive experiments demonstrate the extraordinary performance of the
Robopheus. Significantly, the physical-virtual interaction design increases the
trajectory accuracy of a real robot by 300%, compared with that of not using
the interaction.


Abstract:
  Distance-based regression model, as a nonparametric multivariate method, has
been widely used to detect the association between variations in a distance or
dissimilarity matrix for outcomes and predictor variables of interest. Based on
it, a pseudo-$F$ statistic which partitions the variation in distance matrices
is often constructed to achieve the aim. To the best of our knowledge, the
statistical properties of the pseudo-$F$ statistic has not yet been well
established in the literature. To fill this gap, we study the asymptotic null
distribution of the pseudo-$F$ statistic and show that it is asymptotically
equivalent to a mixture of chi-squared random variables. Given that the
pseudo-$F$ test statistic has unsatisfactory power when the correlations of the
response variables are large, we propose a square-root $F$-type test statistic
which replaces the similarity matric with its square root. The asymptotic null
distribution of the new test statistic and power of both tests are also
investigated.
  Simulation studies are conducted to validate the asymptotic distributions of
the tests and demonstrate that the proposed test has more robust power than the
pseudo-$F$ test. Both test statistics are exemplified with a gene expression
dataset for a prostate cancer pathway.
  Keywords: Asymptotic distribution, Chi-squared-type mixture, Nonparametric
test, Pseudo-$F$ test, Similarity matrix.


Abstract:
  In general, the laws of physics are not invariant under a change of scale. To
find out whether the 'scale-invariance hypothesis' corresponds to nature or
not, a careful examination to its implications is required. As a consequence,
the scale-invariance cosmological models need to be carefully checked with many
tests in order to confirm or disconfirm them. In this paper, three different
toy models have been introduced in the framework of a scale-invariance
cosmology to examine dark energy and cosmic transit. Although cosmic transit
exists in the three models, the pressure stays always negative during cosmic
evolution. In addition, there is always a singularity in the evolution of the
equation of state parameter which is not suitable for a complete investigation
of dark energy evolution. The undesirable features of the parameters have been
discussed, and a comparison with other cosmological contexts has been done.


Abstract:
  Particle accelerators are essential tools in science, hospitals and industry.
Yet, their costs and large footprint, ranging in length from meters to several
kilometres, limit their use. The recently demonstrated nanophotonics-based
acceleration of charged particles can reduce the cost and size of these
accelerators by orders of magnitude. In this approach, a carefully designed
nano-structure transfers energy from laser light to the particles in a
phase-synchronous manner, thereby accelerating them. However, so far,
confinement of the particle beam in the structure over extended distance has
been elusive; it requires complex control of the electron beam phase space and
is mandatory to accelerate particles to the MeV range and beyond with minimal
particle loss. Here, we demonstrate complex electron phase space control at
optical frequencies in the 225 nanometre narrow channel of a record-long
photonic nanostructure. In particular, we experimentally show alternating phase
focusing, a particle propagation scheme for, in principle, arbitrarily long
minimal-loss transport. We expect this work to enable MeV electron beam
generation on a photonic chip, with direct ramification for new forms of
radiotherapy and compact light sources, and other forms of electron phase space
control resulting in narrow energy or zeptosecond-bunched beams, for instance.


Abstract:
  For a finite group $G$, we consider the problem of counting simultaneous
conjugacy classes of $n$-tuples and simultaneous conjugacy classes of commuting
$n$-tuples in $G$. Let $\alpha_{G,n}$ denote the number of simultaneous
conjugacy classes of $n$-tuples, and $\beta_{G,n}$ the number of simultaneous
conjugacy classes of commuting $n$-tuples in $G$. The generating functions
$A_G(t) = \sum_{n\geq 0} \alpha_{G,n}t^n,$ and $B_G(t) = \sum_{n\geq 0}
\beta_{G,n}t^n$ are rational functions of $t$. This paper concern studied of
normalized functions $A_G(t/|G|)$ and $B_G(t/|G|)$ for finite $p$-groups of
rank at most $5$.


Abstract:
  Maximum composite likelihood estimation is a useful alternative to maximum
likelihood estimation when data arise from data generating processes (DGPs)
that do not admit tractable joint specification. We demonstrate that generic
composite likelihoods consisting of marginal and conditional specifications
permit the simple construction of composite likelihood ratio-like statistics
from which finite-sample valid confidence sets and hypothesis tests can be
constructed. These statistics are universal in the sense that they can be
constructed from any estimator for the parameter of the underlying DGP. We
demonstrate our methodology via a simulation study using a pair of
conditionally specified bivariate models.


Abstract:
  Johnson--Lindenstrauss Transforms are powerful tools for reducing the
dimensionality of data while preserving key characteristics of that data, and
they have found use in many fields from machine learning to differential
privacy and more. This note explains what they are; it gives an overview of
their use and their development since they were introduced in the 1980s; and it
provides many references should the reader wish to explore these topics more
deeply.


Abstract:
  Let $K$ be a quartic CM field, that is, a totally imaginary quadratic
extension of a real quadratic number field. In a 1962 article titled On the
classfields obtained by complex multiplication of abelian varieties, Shimura
considered a particular family $\{F_K(m) : m \in \mathbb{Z} >0 \}$ of abelian
extensions of $K$, and showed that the Hilbert class field $H_K$ of $K$ is
contained in $F_K(m)$ for some positive integer m. We make this m explicit. We
then give an algorithm that computes a set of defining polynomials for the
Hilbert class field using the field $F_K(m)$. Our proof-of-concept
implementation of this algorithm computes a set of defining polynomials much
faster than current implementations of the generic Kummer algorithm for certain
examples of quartic CM fields.


Abstract:
  Minimization of a stochastic cost function is commonly used for approximate
sampling in high-dimensional Bayesian inverse problems with Gaussian prior
distributions and multimodal posterior distributions. The density of the
samples generated by minimization is not the desired target density, unless the
observation operator is linear, but the distribution of samples is useful as a
proposal density for importance sampling or for Markov chain Monte Carlo
methods. In this paper, we focus on applications to sampling from multimodal
posterior distributions in high dimensions. We first show that sampling from
multimodal distributions is improved by computing all critical points instead
of only minimizers of the objective function. For applications to
high-dimensional geoscience problems, we demonstrate an efficient approximate
weighting that uses a low-rank Gauss-Newton approximation of the determinant of
the Jacobian. The method is applied to two toy problems with known posterior
distributions and a Darcy flow problem with multiple modes in the posterior.


Abstract:
  In the domain of Autonomic and Organic Computing, the entities of a
distributed system are variable as well as the efficiency and the intention of
their work. Therefore, a scalable mechanism to incentivise/sanction entities
which contribute towards/against the system goal is needed. Trust is a suited
metric to find benevolent entities. In this paper, we focus for one on the
SimTrust model which introduces trust on entities when they share interest and
opinions using tagging information. The second model is the Weighted Simple
Exponential Smoothing Trust metric (WSES) which functions on explicitly rated
items. WSES follows two basic rules which ensure a logic rating mechanism. When
putting these two models in context, SimTrust has advantages on items that have
not been rated yet or can not easily be rated. WSES is a trust metric which
returns good results on explicit rank values. We propose concepts on combining
both approaches and state in which cases they are incompatible.


Abstract:
  The simple structure of doubly torqued vectors allows for a natural
characterization of doubly twisted down to warped spacetimes, as well as Kundt
spacetimes down to PP waves. For the first ones the vectors are timelike, for
the others they are null. We also discuss some properties, and their connection
to hypersurface orthogonal conformal Killing vectors, and null Killing vectors.


Abstract:
  Current computer graphics research practices contain racial biases that have
resulted in investigations into "skin" and "hair" that focus on the hegemonic
visual features of Europeans and East Asians. To broaden our research horizons
to encompass all of humanity, we propose a variety of improvements to
quantitative measures and qualitative practices, and pose novel, open research
problems.


Abstract:
  Active cell-junction remodeling is important for tissue morphogenesis, yet
its underlying physics is not understood. We study a mechanical model that
describes junctions as dynamic active force dipoles. Their instability can
trigger cell intercalations by a critical collapse. Nonlinearities in tissue's
elastic response can stabilize the collapse either by a limit cycle or
condensation of junction lengths at cusps of the energy landscape. Furthermore,
active junction networks undergo collective instability to drive active
in-plane ordering or develop a limit cycle of collective oscillations, which
extends over regions of the energy landscape corresponding to distinct network
topologies.


Abstract:
  We determine the possible scaling limits in the quantum central limit theorem
with respect to the Gibbs state, for a growing distance-regular graph that has
so-called classical parameters with base unequal to one. We also describe
explicitly the corresponding weak limits of the normalized spectral
distribution of the adjacency matrix. We demonstrate our results with the known
infinite families of distance-regular graphs having classical parameters and
with unbounded diameter.


Abstract:
  Let $A$ be a rational function of one complex variable, and $z_0$ its
repelling fixed point with the multiplier $\lambda.$ A Poincar\'e function
associated with $z_0$ is a function $\mathcal{P}_{A,z_0,\lambda}$ meromorphic
on $\mathbb C$ such that $\mathcal{P}_{A,z_0,\lambda}(0)=z_0$,
$\mathcal{P}_{A,z_0,\lambda}'(0)\neq 0,$ and
$\mathcal{P}_{A,z_0,\lambda}(\lambda z)=A\circ \mathcal{P}_{A,z_0,\lambda}(z).$
In this paper, we investigate the following problem: given Poincar\'e functions
$\mathcal{P}_{A_1,z_1,\lambda_1}$ and $\mathcal{P}_{A_2,z_2,\lambda_2}$, find
out if there is an algebraic relation
$f(\mathcal{P}_{A_1,z_1,\lambda_1},\mathcal{P}_{A_2,z_2,\lambda_2})=0$ between
them and, if such a relation exists, describe the corresponding algebraic
curve. We provide a solution, which can be viewed as a refinement of the
classical Ritt theorem about commuting rational functions. We also complement
previous results concerning algebraic dependencies between B\"ottcher
functions.


Abstract:
  In this paper, we develop a PDE approach to consider the optimal strategy of
mean field controlled stochastic system. Firstly, we discuss mean field SDEs
and associated Fokker-Plank eqautions. Secondly, we consider a fully-coupled
system of forward-backward PDEs. The backward one is the
Hamilton-Jacobi-Bellman equation while the forward one is the Fokker-Planck
equation. Our main result is to show the existence of classical solutions of
the forward-backward PDEs in the class
$H^{1+\frac{1}{4},2+\frac{1}{2}}([0,T]\times\mathbb{R}^n)$ by use of the
Schauder fixed point theorem. Then, we use the solution to give the optimal
strategy of the mean field stochastic control problem. Finally, we give an
example to illustrate the role of our main result.


Abstract:
  The equilibrium problem defined by the Nikaid\^o-Isoda-Fan inequality
contains a number of problems such as optimization, variational inequality,
Kakutani fixed point, Nash equilibria, and others as special cases. This paper
presents a picture for the relationship between the fixed points of the Moreau
proximal mapping and the solutions of the equilibrium problem that satisfies
some kinds of monotonicity and Lipschitz-type condition.


Abstract:
  We report the low-temperature properties of SrNd$_2$O$_4$, a geometrically
frustrated magnet. Magnetisation and heat capacity measurements performed on
polycrystalline samples indicate the appearance of a magnetically ordered state
at $T_{\rm N}=2.28(4)$~K. Powder neutron diffraction measurements reveal that
an \afm\ state with the propagation vector \QV\ is stabilised below this
temperature. The magnetic order is incomplete, as only one of the two Nd$^{3+}$
sites carries a significant magnetic moment while the other site remains
largely disordered. The presence of a disordered magnetic component below
$T_{\rm N}$ is confirmed with polarised neutron diffraction measurements. In an
applied magnetic field, the bulk properties measurements indicate a phase
transition at about 30~kOe. We construct a tentative $H$-$T$ phase diagram of
\sno\ from these measurements.


Abstract:
  Certifiable local robustness, which rigorously precludes small-norm
adversarial examples, has received significant attention as a means of
addressing security concerns in deep learning. However, for some classification
problems, local robustness is not a natural objective, even in the presence of
adversaries; for example, if an image contains two classes of subjects, the
correct label for the image may be considered arbitrary between the two, and
thus enforcing strict separation between them is unnecessary. In this work, we
introduce two relaxed safety properties for classifiers that address this
observation: (1) relaxed top-k robustness, which serves as the analogue of
top-k accuracy; and (2) affinity robustness, which specifies which sets of
labels must be separated by a robustness margin, and which can be
$\epsilon$-close in $\ell_p$ space. We show how to construct models that can be
efficiently certified against each relaxed robustness property, and trained
with very little overhead relative to standard gradient descent. Finally, we
demonstrate experimentally that these relaxed variants of robustness are
well-suited to several significant classification problems, leading to lower
rejection rates and higher certified accuracies than can be obtained when
certifying "standard" local robustness.


Abstract:
  On the basis of a compartment model, the epidemic curve is investigated when
the net rate $\lambda$ of change of the number of infected individuals $I$ is
given by an ellipse in the $\lambda$-$I$ plane which is supported in
$[I_{\ell}, I_h]$. With $a \equiv (I_h - I_{\ell})/(I_h + I_{\ell})$, it is
shown that (1) when $a < 1$ or $I_{\ell} >0$, oscillation of the infection
curve is self-organized and the period of the oscillation is in proportion to
the ratio of the difference $ (I_h - I_{\ell})$ and the geometric mean
$\sqrt{I_h I_{\ell}}$ of $I_h$ and $I_{\ell}$, (2) when $a = 1$, the infection
curve shows a critical behavior where it decays obeying a power law function
with exponent $-2$ in the long time limit after a peak, and (3) when $a > 1$,
the infection curve decays exponentially in the long time limit after a peak.
The present result indicates that the pandemic can be controlled by a measure
which makes $I_{\ell} < 0$.


Abstract:
  Logistic regression (LR) is a widely used classification method for modeling
binary outcomes in many medical data classification tasks. Research that
collects and combines datasets from various data custodians and jurisdictions
can excessively benefit from the increased statistical power to support their
analyzing goals. However, combining data from these various sources creates
significant privacy concerns that need to be addressed. In this paper, we
proposed secret sharing-based privacy-preserving logistic regression protocols
using the Newton-Raphson method. Our proposed approaches are based on secure
Multi-Party Computation (MPC) with different security settings to analyze data
owned by several data holders. We conducted experiments on both synthetic data
and real-world datasets and compared the efficiency and accuracy of them with
those of an ordinary logistic regression model. Experimental results
demonstrate that the proposed protocols are highly efficient and accurate. This
study introduces iterative algorithms to simplify the federated training a
logistic regression model in a privacy-preserving manner. Our implementation
results show that our improved method can handle large datasets used in
securely training a logistic regression from multiple sources.


Abstract:
  We determine the relationship between the turnaround radius, $R_{\rm t}$, and
mass, $M_{\rm t}$, in $\Lambda$CDM, and in dark energy scenarios, using an
extended spherical collapse model taking into account the effects of shear and
vorticity. We find a more general formula than that usually described in
literature, showing a dependence of $R_{\rm t}$ from shear, and vorticity. The
$R_{\rm t}-M_{\rm t}$ relation differs from that obtained not taking into
account shear, and rotation, especially at galactic scales, differing $\simeq
30\%$ from the result given in literature. This has effects on the constraint
of the $w$ parameter of the equation of state. We compare the $R_{\rm t}-M_{\rm
t}$ relationship obtained for the $\Lambda$CDM, and different dark energy
models to that obtained in the $f(R)$ modified gravity (MG) scenario. The
$R_{\rm t}-M_{\rm t}$ relationship in $\Lambda$CDM, and dark energy scenarios
are tantamount to the prediction of the $f(R)$ theories. Then, the $R_{\rm
t}-M_{\rm t}$ relationship is not a good probe to test gravity theories beyond
Einstein's general relativity.


Abstract:
  We introduce a new variational principle for the study of eigenvalues and
eigenfunctions of the Laplacians with Neumann and Dirichlet boundary conditions
on planar domains and apply it to the famous hot spots conjecture, which we
prove for a large class of simply connected, not necessarily convex domains. We
show that each eigenfunction of the Neumann Laplacian corresponding to the
lowest positive eigenvalue is strictly monotonous along certain directions if
the domain faces two opposite quadrants. In particular, the maximum and minimum
of such an eigenfunction may only be located on the boundary.


Abstract:
  An Objective Structured Practical Examination (OSPE) is an effective and
robust, but resource-intensive, means of evaluating anatomical knowledge. Since
most OSPEs employ short answer or fill-in-the-blank style questions, the format
requires many people familiar with the content to mark the exams. However, the
increasing prevalence of online delivery for anatomy and physiology courses
could result in students losing the OSPE practice that they would receive in
face-to-face learning sessions. The purpose of this study was to test the
accuracy of Decision Trees (DTs) in marking OSPE questions as a potential first
step to creating an intelligent, online OSPE tutoring system. The study used
the results of the winter 2020 semester final OSPE from McMaster University's
anatomy and physiology course in the Faculty of Health Sciences (HTHSCI
2FF3/2LL3/1D06) as the data set. Ninety percent of the data set was used in a
10-fold validation algorithm to train a DT for each of the 54 questions. Each
DT was comprised of unique words that appeared in correct, student-written
answers. The remaining 10% of the data set was marked by the generated DTs.
When the answers marked by the DT were compared to the answers marked by staff
and faculty, the DT achieved an average accuracy of 94.49% across all 54
questions. This suggests that machine learning algorithms such as DTs are a
highly effective option for OSPE grading and are suitable for the development
of an intelligent, online OSPE tutoring system.


Abstract:
  The choice of activation functions is crucial for modern deep neural
networks. Popular hand-designed activation functions like Rectified Linear
Unit(ReLU) and its variants show promising performance in various tasks and
models. Swish, the automatically discovered activation function, has been
proposed and outperforms ReLU on many challenging datasets. However, it has two
main drawbacks. First, the tree-based search space is highly discrete and
restricted, which is difficult for searching. Second, the sample-based
searching method is inefficient, making it infeasible to find specialized
activation functions for each dataset or neural architecture. To tackle these
drawbacks, we propose a new activation function called Piecewise Linear
Unit(PWLU), which incorporates a carefully designed formulation and learning
method. It can learn specialized activation functions and achieves SOTA
performance on large-scale datasets like ImageNet and COCO. For example, on
ImageNet classification dataset, PWLU improves 0.9%/0.53%/1.0%/1.7%/1.0% top-1
accuracy over Swish for
ResNet-18/ResNet-50/MobileNet-V2/MobileNet-V3/EfficientNet-B0. PWLU is also
easy to implement and efficient at inference, which can be widely applied in
real-world applications.


Abstract:
  Neuroradiologists and neurosurgeons increasingly opt to use functional
magnetic resonance imaging (fMRI) to map functionally relevant brain regions
for noninvasive presurgical planning and intraoperative neuronavigation. This
application requires a high degree of spatial accuracy, but the fMRI
signal-to-noise ratio (SNR) decreases as spatial resolution increases. In
practice, fMRI scans can be collected at multiple spatial resolutions, and it
is of interest to make more accurate inference on brain activity by combining
data with different resolutions. To this end, we develop a new Bayesian model
to leverage both better anatomical precision in high resolution fMRI and higher
SNR in standard resolution fMRI. We assign a Gaussian process prior to the mean
intensity function and develop an efficient, scalable posterior computation
algorithm to integrate both sources of data. We draw posterior samples using an
algorithm analogous to Riemann manifold Hamiltonian Monte Carlo in an expanded
parameter space. We illustrate our method in analysis of presurgical fMRI data,
and show in simulation that it infers the mean intensity more accurately than
alternatives that use either the high or standard resolution fMRI data alone.


Abstract:
  We consider the homogeneous and inhomogeneous Landau equation for very soft
and Coulomb potentials and show that approximate Type I self-similar blow-up
solutions do not exist under mild decay assumptions on the profile. We extend
our analysis to the Vlasov-Poisson-Landau system and to the Boltzmann equation
without angular cut-off.


Abstract:
  Recent experiments demonstrating storage of optical pulses in acoustic
phonons based on stimulated Brillouin scattering raise a number of questions
about the spectral and temporal capacities of such protocols and the
limitations of the theoretical frameworks routinely used to describe them. In
this work, we consider the dynamics of photon-phonon scattering induced by
optical pulses with temporal widths comparable to the period of acoustic
oscillations. We revisit the widely adopted classical formalism of coupled
modes and demonstrate its breakdown. We propose a simple extension to
generalise the formulation and find potentially measurable consequences in the
dynamics of Brillouin experiments involving ultra-short pulses.


Abstract:
  In this paper we study the geometric structure of affine Deligne-Lusztig
varieties for $GL_3$ and $b$ basic. We completely determine the irreducible
components of the affine Deligne-Lusztig variety. In particular, we classify
the cases where all of the irreducible components are classical Deligne-Lusztig
varieties times finite-dimensional affine spaces. If this is the case, then the
irreducible components are pairwise disjoint.


Abstract:
  The amplification of evanescent waves by flat superlens requires a
near-resonance coupling which has been linked to resonant surface plasmons. A
subtle interplay has been proposed to exist between the excitation of
well-defined resonant surface plasmons and the focusing capability of a
superlens. To gain insights into these resonant modes and their contributions
to the amplification and recovery of evanescent waves, we performed simple but
robust full-wave FDTD simulations on causal negative index Lorentz models. We
found that well-defined pair of resonant surface plasmons is excited whenever a
coupling of a diverging transmitted beam and a converging refracted beam occurs
at the second interface. The resonant coupling of these modes at the interface
led to the excitation of a single interface resonance predicted by the theory.
The physical consequence of this resonance is that incident wave energy is
pumped into the negative-index material medium and beyond it. These phenomena
contribute substantially to the amplification and recovery of the near-fields
in the image plane. It is noteworthy that, for evanescent wave refocusing to be
achieved in a flat superlens, its thickness, and the source-to-superlens
distance should be optimized. This optimization is critical in that an optimal
thickness alongside optimal source-to-superlens distance will allow evanescent
wave refocusing to dominate material lost. The FDTD simulated result showed an
image of the point source inside the superlens and beyond it when its thickness
was optimized. The resolution of the image beyond the superlens was
$\sim.58\lambda$ and this superlens behaves like a near-perfect lens. Overall,
these numerically simulated results could serve as useful approximations to the
degree of resonant amplification and refocusing of near-fields that can be
achieved by flat superlens in near-fields experimental imaging setups.


Abstract:
  Ontology Alignment is an important research problem applied to various fields
such as data integration, data transfer, data preparation, etc.
State-of-the-art (SOTA) Ontology Alignment systems typically use naive
domain-dependent approaches with handcrafted rules or domain-specific
architectures, making them unscalable and inefficient. In this work, we propose
VeeAlign, a Deep Learning based model that uses a novel dual-attention
mechanism to compute the contextualized representation of a concept which, in
turn, is used to discover alignments. By doing this, not only is our approach
able to exploit both syntactic and semantic information encoded in ontologies,
it is also, by design, flexible and scalable to different domains with minimal
effort. We evaluate our model on four different datasets from different domains
and languages, and establish its superiority through these results as well as
detailed ablation studies. The code and datasets used are available at
https://github.com/Remorax/VeeAlign.


Abstract:
  In this paper, we prove two rigidity results for non-positively curved
homogeneous Finsler metrics. Our first main result yields an extension of
Hu-Deng's well-known result proven for the Randers metrics. Indeed, we prove
that every connected homogeneous Finsler space with non-positive flag curvature
and isotropic S-curvature is Riemannian or locally Minkowskian. We extend the
Szab\'{o}'s rigidity theorem for Berwald surfaces and show that homogeneous
isotropic Berwald metrics with non-positive flag curvature are Riemannian or
locally Minkowskian. We prove that a homogeneous $(\alpha, \beta)$-metrics has
isotropic mean Berwald curvature if and only if it has vanishing mean Berwald
curvature generalizing result previously only known in the case of Randers
metrics. Our second main result is to show that every homogeneous
$(\alpha,\beta)$-metric with non-positive flag curvature and almost isotropic
S-curvature is Riemannian or locally Minkowskian.


Abstract:
  This paper examines the short- and long-run effects of U.S. federal personal
income and corporate income tax cuts on a wide array of economic policy
variables in a data-rich environment. Using a panel of U.S. macroeconomic data
set, made up of 132 quarterly macroeconomic series for 1959-2018, the study
estimates factor-augmented vector autoregression (FAVARs) models where an
extended narrative tax changes dataset combined with unobserved factors. The
narrative approach classifies if tax changes are exogenous or endogenous. This
paper identifies narrative tax shocks in the vector autoregression model using
the sign restrictions with Uhlig's (2005) penalty function. Empirical findings
show a significant expansionary effect of tax cuts on the macroeconomic
variables. Cuts in personal and corporate income taxes cause a rise in output,
investment, employment, and consumption; however, cuts in personal taxes appear
to be a more effective fiscal policy tool than the cut in corporate income
taxes. Real GDP, employment, investment, and industrial production increase
significantly and reach their maximum response values two years after personal
income tax cuts. The effects of corporate tax cuts have relatively smaller
effects on output and consumption but show immediate and higher effects on
fixed investment and price levels.


Abstract:
  The classical Kubo-Martin-Schwinger (KMS) condition is a fundamental property
of statistical mechanics characterizing the equilibrium of infinite classical
mechanical systems. It was introduced in the seventies by G. Gallavotti and E.
Verboven as an alternative to the Dobrushin-Lanford-Ruelle (DLR) equation. In
this article, we consider this concept in the framework of nonlinear
Hamiltonian PDEs and discuss its relevance. In particular, we prove that Gibbs
measures are the unique KMS equilibrium states for such systems. Our proof is
based on Malliavin calculus and Gross-Sobolev spaces. The main feature of our
work is the applicability of our results to the general context of white noise,
abstract Wiener spaces and Gaussian probability spaces, as well as to
fundamental examples of PDEs like the nonlinear Schrodinger, Hartree, and wave
(Klein-Gordon) equations.


Abstract:
  Stochastic simulations such as large-scale, spatiotemporal, age-structured
epidemic models are computationally expensive at fine-grained resolution. We
propose Interactive Neural Process (INP), a Bayesian active learning framework
to proactively learn a deep learning surrogate model and accelerate simulation.
Our framework is based on the novel integration of neural process, deep
sequence model and active learning. In particular, we develop a novel
spatiotemporal neural process model to mimic the simulator dynamics. Our model
automatically infers the latent process which describes the intrinsic
uncertainty of the simulator. This also gives rise to a new acquisition
function based on the latent information gain. We design Bayesian active
learning algorithms to iteratively query the simulator, gather more data, and
continuously improve the model. We perform theoretical analysis and demonstrate
that our approach reduces sample complexity compared with random sampling in
high dimension. Empirically, we demonstrate our framework can faithfully
imitate the behavior of a complex infectious disease simulator with a small
number of examples, enabling rapid simulation and scenario exploration.


Abstract:
  When compared to fault-tolerant quantum computational strategies, variational
quantum algorithms stand as one of the candidates with the potential of
achieving quantum advantage for real-world applications in the near term.
However, the optimization of the circuit parameters remains arduous and is
impeded by many obstacles such as the presence of barren plateaus, many local
minima in the optimization landscape, and limited quantum resources. A
non-random initialization of the parameters seems to be key to the success of
the parametrized quantum circuits (PQC) training. Drawing and extending ideas
from the field of meta-learning, we address this parameter initialization task
with the help of machine learning and propose FLIP: a FLexible Initializer for
arbitrarily-sized Parametrized quantum circuits. FLIP can be applied to any
family of PQCs, and instead of relying on a generic set of initial parameters,
it is tailored to learn the structure of successful parameters from a family of
related problems which are used as the training set. The flexibility advocated
to FLIP hinges in the possibility of predicting the initialization of
parameters in quantum circuits with a larger number of parameters from those
used in the training phase. This is a critical feature lacking in other
meta-learning parameter initializing strategies proposed to date. We illustrate
the advantage of using FLIP in three scenarios: a family of problems with
proven barren plateaus, PQC training to solve max-cut problem instances, and
PQC training for finding the ground state energies of 1D Fermi-Hubbard models.


Abstract:
  Micron-scale randomness during manufacturing can ensure anti-counterfeiting
labels are unclonable. However, this security typically comes at the expense of
complex hardware being needed for authentication (e.g., microscopy systems). We
demonstrate unclonable labels that can be authenticated using a standard
light-emitting diode and smartphone camera. The labels consist of a microlens
array laminated to a polymer film that is doped with luminescent
microparticles. The micron-scale random overlap of focal volumes and
microparticles leads to a pattern of bright points of visible light emission
that can be easily imaged by a smartphone camera. 10 000 comparisons of images
demonstrate that the labels can be robustly authenticated, and that the
probability of a false authentication is on the order of $10^{-15}$. The
ability for microlens arrays to simplify the hardware needed for authentication
of unclonable labels is generalizable, and attractive for the implementation of
unclonable labels in anti-counterfeiting systems.


Abstract:
  A virtual flow meter (VFM) enables continuous prediction of flow rates in
petroleum production systems. The predicted flow rates may aid the daily
control and optimization of a petroleum asset. Gray-box modeling is an approach
that combines mechanistic and data-driven modeling. The objective is to create
a computationally feasible VFM for use in real-time applications, with high
prediction accuracy and scientifically consistent behavior. This article
investigates five different gray-box model types in an industrial case study
using real, historical production data from 10 petroleum wells, spanning at
most four years of production. The results are diverse with an oil flow rate
prediction error in the range of 1.8%-40.6%. Further, the study casts light
upon the nontrivial task of balancing learning from both physics and data.
Consequently, providing general recommendations towards the suitability of
different hybrid models is challenging. Nevertheless, the results are promising
and indicate that gray-box VFMs may reduce the prediction error of a
mechanistic VFM while remaining scientifically consistent. The findings
motivate further experimentation with gray-box VFM models and suggest several
future research directions to improve upon the performance and scientific
consistency.


Abstract:
  Person re-identification (re-ID) tackles the problem of matching person
images with the same identity from different cameras. In practical
applications, due to the differences in camera performance and distance between
cameras and persons of interest, captured person images usually have various
resolutions. We name this problem as Cross-Resolution Person Re-identification
which brings a great challenge for matching correctly. In this paper, we
propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the
above problem. Specifically, in order to restore the resolution of
low-resolution images and make reasonable use of different channel information
of feature maps, we introduce and innovate VDSR module with channel attention
(CA) mechanism, named as VDSR-CA. Then we reform the HRNet by designing a novel
representation head to extract discriminating features, named as HRNet-ReID. In
addition, a pseudo-siamese framework is constructed to reduce the difference of
feature distributions between low-resolution images and high-resolution images.
The experimental results on five cross-resolution person datasets verify the
effectiveness of our proposed approach. Compared with the state-of-the-art
methods, our proposed PS-HRNet improves 3.4\%, 6.2\%, 2.5\%,1.1\% and 4.2\% at
Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR
datasets, respectively. Our code is available at
\url{https://github.com/zhguoqing}.


Abstract:
  We investigate the spectral properties of one-dimensional lattices with
position-dependent hopping amplitudes and on-site potentials that are smooth
bounded functions of position. We find an exact integral form for the density
of states (DOS) in the limit of an infinite number of sites, which we derive
using a mixed Bloch-Wannier basis consisting of piecewise Wannier functions.
Next, we provide an exact solution for the inverse problem of constructing the
position-dependence of hopping in a lattice model yielding a given DOS. We
confirm analytic results by comparing them to numerics obtained by exact
diagonalization for various incarnations of position-dependent hoppings and
on-site potentials. Finally, we generalize the DOS integral form to
multi-orbital tight-binding models with longer-range hoppings and in higher
dimensions.


Abstract:
  The semidirect product of the real Heisenberg group ${\rm H}_1(\mathbb{R})$
with ${\rm SL}(2,\mathbb{R})$, called the real Jacobi group
$G^J_1(\mathbb{R})$, admits a four-parameter invariant metric expressed in the
S-coordinates. We determine the geodesic equations on the extended
Siegel--Jacobi upper half-plane $\tilde{\mathcal{X}}^J_1
=\frac{G^J_1(\R)}{\rm{SO}(2)}\approx\mathcal{X}^J_1\times\mathbb{R}\approx
\mathcal{X}_1 \times\mathbb{R}^3$, where $\mathcal{X}^J_1$ ($\mathcal{X}_1)$
denotes the Siegel-Jacobi upper half-plane (respectively Siegel upper
half-plane). Equating successively with zero the values of the three parameters
in the geodesic equations on $\tilde{\mathcal{X}}^J_1$, we get the geodesic
equations on $\mathcal{X}^J_1$, $\mathcal{X}_1$ and ${\rm H}_1(\mathbb{R})$.


Abstract:
  We propose a method for amplifying a train of sub-femtosecond pulses of
circularly or elliptically polarized extreme ultraviolet (XUV) radiation
constituted by high-order harmonics of an infrared (IR) laser field, in a
neon-like active medium of a plasma-based X-ray laser, additionally irradiated
with a replica of a fundamental frequency IR field. It is shown that the
ellipticity of the pulses can be maintained or increased during the
amplification process. The experimental implementation is suggested in an
active medium of an X-ray laser based on neon-like Ti^{12+} ions irradiated by
an IR laser field with a wavelength of 3.9 microns.


Abstract:
  In this article, we investigate the problem of engineering synchronization in
non-Markovian quantum systems. First, a time-convoluted linear quantum
stochastic differential equation is derived which describes the Heisenberg
evolution of a localized quantum system driven by multiple colored noise
inputs. Then, we define quantum expectation synchronization in an augmented
system consisting of two subsystems. We prove that, for two homogenous
subsystems, synchronization can always be synthesized without designing direct
Hamiltonian coupling given that the degree of non-Markovianity is below a
certain threshold. System parameters are explicitly designed to achieve quantum
synchronization. Also, a numerical example is presented to illustrate our
results.


Abstract:
  We present DeepIA, a deep neural network (DNN) framework for enabling fast
and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave)
networks. DeepIA reduces the beam sweep time compared to a conventional
exhaustive search-based IA process by utilizing only a subset of the available
beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of
beams to the beam that is best oriented to the receiver. In both line of sight
(LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and
outperforms the conventional IA's beam prediction accuracy. We show that the
beam prediction accuracy of DeepIA saturates with the number of beams used for
IA and depends on the particular selection of the beams. In LoS conditions, the
selection of the beams is consequential and improves the accuracy by up to 70%.
In NLoS situations, it improves accuracy by up to 35%. We find that, averaging
multiple RSS snapshots further reduces the number of beams needed and achieves
more than 95% accuracy in both LoS and NLoS conditions. Finally, we evaluate
the beam prediction time of DeepIA through embedded hardware implementation and
show the improvement over the conventional beam sweeping.


Abstract:
  The study of hard-particle packings is of fundamental importance in physics,
chemistry, cell biology, and discrete geometry. Much of the previous work on
hard-particle packings concerns their densest possible arrangements. By
contrast, we examine kinetic effects inevitably present in both numerical and
experimental packing protocols. Specifically, we determine how changing the
compression/shear rate of a two-dimensional packing of noncircular particles
causes it to deviate from its densest possible configuration, which is always
periodic. The adaptive shrinking cell (ASC) optimization scheme maximizes the
packing fraction of a hard-particle packing by first applying random
translations and rotations to the particles and then isotropically compressing
and shearing the simulation box repeatedly until a possibly jammed state is
reached. We use a stochastic implementation of the ASC optimization scheme to
mimic different effective time scales by varying the number of particle moves
between compressions/shears. We generate dense, effectively jammed,
monodisperse, two-dimensional packings of obtuse scalene triangle, rhombus,
curved triangle, lens, and "ice cream cone" (a semicircle grafted onto an
isosceles triangle) shaped particles, with a wide range of packing fractions
and degrees of order. To quantify these kinetic effects, we introduce the
kinetic frustration index $K$, which measures the deviation of a packing from
its maximum possible packing fraction. To investigate how kinetics affect
short- and long-range ordering in these packings, we compute their spectral
densities and characterize their contact networks. We find that kinetic effects
are most significant when the particles have greater asphericity, less
curvature, and less rotational symmetry. This work may be relevant to the
design of laboratory packing protocols.


Abstract:
  As a projection-free algorithm, Frank-Wolfe (FW) method, also known as
conditional gradient, has recently received considerable attention in the
machine learning community. In this dissertation, we study several topics on
the FW variants for scalable projection-free optimization.
  We first propose 1-SFW, the first projection-free method that requires only
one sample per iteration to update the optimization variable and yet achieves
the best known complexity bounds for convex, non-convex, and monotone
DR-submodular settings. Then we move forward to the distributed setting, and
develop Quantized Frank-Wolfe (QFW), a general communication-efficient
distributed FW framework for both convex and non-convex objective functions. We
study the performance of QFW in two widely recognized settings: 1) stochastic
optimization and 2) finite-sum optimization. Finally, we propose Black-Box
Continuous Greedy, a derivative-free and projection-free algorithm, that
maximizes a monotone continuous DR-submodular function over a bounded convex
body in Euclidean space.


Abstract:
  We propose Fit4CAD, a benchmark for the evaluation and comparison of methods
for fitting simple geometric primitives in point clouds representing CAD
objects. This benchmark is meant to help both method developers and those who
want to identify the best performing tools. The Fit4CAD dataset is composed by
225 high quality point clouds, each of which has been obtained by sampling a
CAD object. The way these elements were created by using existing platforms and
datasets makes the benchmark easily expandable. The dataset is already split
into a training set and a test set. To assess performance and accuracy of the
different primitive fitting methods, various measures are defined. To
demonstrate the effective use of Fit4CAD, we have tested it on two methods
belonging to two different categories of approaches to the primitive fitting
problem: a clustering method based on a primitive growing framework and a
parametric method based on the Hough transform.


Abstract:
  We investigate the effects of non-Hermiticity on interacting fermionic
systems. We do this by including non-Hermitian bilinear terms into the 3+1
dimensional Nambu--Jona-Lasinio (NJL) model. Two possible bilinear
modifications give rise to $\mathcal{PT}$ symmetric theories; this happens when
the standard NJL model is extended either by a pseudovector background field
$ig \bar\psi\gamma_5 B_\mu \gamma^\mu \psi$ or by an antisymmetric-tensor
background field $g \bar\psi F_{\mu\nu}\gamma^\mu \gamma^\nu \psi$. The three
remaining bilinears are {\it anti}-$\mathcal{PT}$-symmetric in nature, $ig
\bar\psi B_\mu \gamma^\mu \psi, ig\bar\psi \gamma_5 \psi$ and $ig\bar\psi
{1}\psi$, so that the Hamiltonian then has no overall symmetry. The
pseudovector $ig \bar\psi\gamma_5 B_\mu \gamma^\mu \psi$ and the vector $ig
\bar\psi B_\mu \gamma^\mu \psi$ combinations, are, in addition, chirally
symmetric. Thus, within this framework we are able to examine the effects that
the various combinations of non-Hermiticity, $\mathcal{PT}$ symmetry, chiral
symmetry and the two-body interactions of the NJL model have on the existence
and dynamical generation of a real effective fermion mass (a feature which is
absent in the corresponding modified massless free Dirac models) as well as on
the masses of the composite particles, the pseudoscalar and scalar mesonic
modes ($\pi$ and $\sigma$ mesons). Our findings demonstrate that $\mathcal{PT}$
symmetry is not necessary for real fermion mass solutions to exist, rather the
two-body interactions of the NJL model supersede the non-Hermitian bilinear
effects. The effects of chiral symmetry are evident most clearly in the meson
modes, the pseudoscalar of which will always be Goldstone in nature if the
system is chirally symmetric. Second solutions of the mesonic equations are
also discussed.


Abstract:
  Bayesian optimization (BO) is a popular paradigm for global optimization of
expensive black-box functions, but there are many domains where the function is
not completely black-box. The data may have some known structure, e.g.
symmetries, and the data generation process can yield useful intermediate or
auxiliary information in addition to the value of the optimization objective.
However, surrogate models traditionally employed in BO, such as Gaussian
Processes (GPs), scale poorly with dataset size and struggle to incorporate
known structure or auxiliary information. Instead, we propose performing BO on
complex, structured problems by using Bayesian Neural Networks (BNNs), a class
of scalable surrogate models that have the representation power and flexibility
to handle structured data and exploit auxiliary information. We demonstrate BO
on a number of realistic problems in physics and chemistry, including topology
optimization of photonic crystal materials using convolutional neural networks,
and chemical property optimization of molecules using graph neural networks. On
these complex tasks, we show that BNNs often outperform GPs as surrogate models
for BO in terms of both sampling efficiency and computational cost.


Abstract:
  The use of quantitative broadband echosounders for biological studies and
surveys offers considerable advantages over narrowband echosounders. These
include improved spectral-based target identification and significantly
increased ability to resolve individual targets. Biological studies and surveys
typically require accurate measures of backscatter strength and we present here
a systematic and comprehensive explanation of how to derive quantitative
estimates of target strength and volume backscattering, as a function of
frequency from broadband echosounder signals.


Abstract:
  It is a common practice to evaluate probability density function or matter
spatial density function from statistical samples. Kernel density estimation is
a frequently used method, but to select an optimal bandwidth of kernel
estimation, which is completely based on data samples, is a long-term issue
that has not been well settled so far. There exist analytic formulae of optimal
kernel bandwidth, but they cannot be applied directly to data samples, since
they depend on the unknown underlying density functions from which the samples
are drawn. In this work, we devise an approach to pick out the totally
data-based optimal bandwidth. First, we derive correction formulae for the
analytic formulae of optimal bandwidth to compute the roughness of the sample's
density function. Then substitute the correction formulae into the analytic
formulae for optimal bandwidth, and through iteration, we obtain the sample's
optimal bandwidth. Compared with analytic formulae, our approach gives very
good results, with relative differences from the analytic formulae being only
2%-3% for a sample size larger than 10^4. This approach can also be generalized
easily to cases of variable kernel estimations.


Abstract:
  We review spectral theory of soliton gases in integrable dispersive
hydrodynamic systems. We first present a phenomenological approach based on the
consideration of phase shifts in pairwise soliton collisions and leading to the
kinetic equation for a non-equilibrium soliton gas. Then a more detailed theory
is presented in which soliton gas dynamics are modelled by a thermodynamic type
limit of modulated finite-gap spectral solutions of the Korteweg-de Vries and
the focusing nonlinear Schr\"odinger equations. For the focusing nonlinear
Schr\"odinger equation the notions of soliton condensate and breather gas are
introduced that are related to the phenomena of spontaneous modulational
instability and the rogue wave formation. Integrability properties of the
kinetic equation for soliton gas are discussed and some physically relevant
solutions are presented and compared with direct numerical simulations of
dispersive hydrodynamic systems.


Abstract:
  The Fibonacci cube of dimension n, denoted as $\Gamma\_n$, is the subgraph of
the hypercube induced by vertices with no consecutive 1's. The irregularity of
a graph G is the sum of |d(x)-d(y)| over all edges {x,y} of G. In two recent
paper based on the recursive structure of $\Gamma\_n$ it is proved that the
irregularity of $\Gamma\_n$ and $\Lambda\_n$ are two times the number of edges
of $\Gamma\_{n-1}$ and $2n$ times the number of vertices of $\Gamma\_{n-4}$,
respectively. Using an interpretation of the irregularity in terms of couples
of incident edges of a special kind (Figure 2) we give a bijective proof of
both results. For these two graphs we deduce also a constant time algorithm for
computing the imbalance of an edge. In the last section using the same approach
we determine the number of edges and the sequence of degrees of the cube
complement of $\Gamma\_n$.


Abstract:
  Normalizing Flows (NFs) are universal density estimators based on Neuronal
Networks. However, this universality is limited: the density's support needs to
be diffeomorphic to a Euclidean space. In this paper, we propose a novel method
to overcome this limitation without sacrificing universality. The proposed
method inflates the data manifold by adding noise in the normal space, trains
an NF on this inflated manifold, and, finally, deflates the learned density.
Our main result provides sufficient conditions on the manifold and the specific
choice of noise under which the corresponding estimator is exact. Our method
has the same computational complexity as NFs and does not require computing an
inverse flow. We also show that, if the embedding dimension is much larger than
the manifold dimension, noise in the normal space can be well approximated by
Gaussian noise. This allows to use our method for approximating arbitrary
densities on non-flat manifolds provided that the manifold dimension is known.


Abstract:
  In Grammatical Error Correction (GEC), sequence labeling models enjoy fast
inference compared to sequence-to-sequence models; however, inference in
sequence labeling GEC models is an iterative process, as sentences are passed
to the model for multiple rounds of correction, which exposes the model to
sentences with progressively fewer errors at each round. Traditional GEC models
learn from sentences with fixed error rates. Coupling this with the iterative
correction process causes a mismatch between training and inference that
affects final performance. In order to address this mismatch, we propose a
GAN-like sequence labeling model, which consists of a grammatical error
detector as a discriminator and a grammatical error labeler with Gumbel-Softmax
sampling as a generator. By sampling from real error distributions, our errors
are more genuine compared to traditional synthesized GEC errors, thus
alleviating the aforementioned mismatch and allowing for better training. Our
results on several evaluation benchmarks demonstrate that our proposed approach
is effective and improves the previous state-of-the-art baseline.


Abstract:
  The Su-Schrieffer-Heeger (SSH) model on a two-dimensional square lattice has
been considered as a significant platform for studying topological multipole
insulators. However, due to the highly-degenerate bulk energy bands protected
by $ C_{4v} $ and chiral symmetry, the discussion of the zero-energy
topological corner states and the corresponding physical realization have been
rarely presented. In this work, by tuning the hopping terms to break $ C_{4v} $
symmetry down to $ C_{2v} $ symmetry but with the topological phase invariant,
we show that the degeneracies can be removed and a complete band gap can be
opened, which provides robust protection for the spectrally isolated
zero-energy corner states. Meanwhile, we propose a rigorous acoustic
crystalline insulator and therefore these states can be observed directly. Our
work reveals the topological properties of the robust zero-energy states, and
provides a new way to explore novel topological phenomena.


Abstract:
  We provide two closed-form geodesic formulas for a family of metrics on
Stiefel manifold, parameterized by two positive numbers, having both the
embedded and canonical metrics as special cases. The closed-form formulas allow
us to compute geodesics by matrix exponential in reduced dimension for low-rank
manifolds. Combining with the use of Fr{\'e}chet derivatives to compute the
gradient of the square Frobenius distance between a geodesic ending point to a
given point on the manifold, we show the logarithm map and geodesic distance
between two endpoints on the manifold could be computed by {\it minimizing}
this square distance by a {\it trust-region} solver. This leads to a new
framework to compute the geodesic distance for manifolds with known geodesic
formula but no closed-form logarithm map. We show the approach works well for
Stiefel as well as flag manifolds. The logarithm map could be used to compute
the Riemannian center of mass for these manifolds equipped with the above
metrics. We also deduce simple trigonometric formulas for the Riemannian
exponential and logarithm maps on the Grassmann manifold.


Abstract:
  In this article, we obtain an effectiveness result of strong openness
property in $L^p$ with some applications.


Abstract:
  In this paper, we propose a trust-centric privacy-preserving blockchain for
dynamic spectrum access in IoT networks. To be specific, we propose a trust
evaluation mechanism to evaluate the trustworthiness of sensing nodes and
design a Proof-of-Trust (PoT) consensus mechanism to build a scalable
blockchain with high transaction-per-second (TPS). Moreover, a privacy
protection scheme is proposed to protect sensors' real-time geolocatioin
information when they upload sensing data to the blockchain. Two smart
contracts are designed to make the whole procedure (spectrum sensing, spectrum
auction, and spectrum allocation) run automatically. Simulation results
demonstrate the expected computation cost of the PoT consensus algorithm for
reliable sensing nodes is low, and the cooperative sensing performance is
improved with the help of trust value evaluation mechanism.In addition,
incentivization and security are also analyzed, which show that our design not
only can encourage nodes' participation, but also resist to many kinds of
attacks which are frequently encountered in trust-based blockchain systems.


Abstract:
  LiDAR-based 3D detection in point cloud is essential in the perception system
of autonomous driving. In this paper, we present LiDAR R-CNN, a second stage
detector that can generally improve any existing 3D detector. To fulfill the
real-time and high precision requirement in practice, we resort to point-based
approach other than the popular voxel-based approach. However, we find an
overlooked issue in previous work: Naively applying point-based methods like
PointNet could make the learned features ignore the size of proposals. To this
end, we analyze this problem in detail and propose several methods to remedy
it, which bring significant performance improvement. Comprehensive experimental
results on real-world datasets like Waymo Open Dataset (WOD) and KITTI dataset
with various popular detectors demonstrate the universality and superiority of
our LiDAR R-CNN. In particular, based on one variant of PointPillars, our
method could achieve new state-of-the-art results with minor cost. Codes will
be released at https://github.com/tusimple/LiDAR_RCNN .


Abstract:
  In an attempt to prove the Graceful Tree Conjecture, we present two
propagation of graphs. The first is to propagate graceful graphs, and the
second is to propagate trees from a gracefully labeled tree. The motivation in
propagating such graphs is to see how graphs behave in the lens of their
adjacency matrices. Thus, we provide the necessary algorithms for the said
objectives.


Abstract:
  The next generations of ground-based cosmic microwave background experiments
will require polarisation sensitive, multichroic pixels of large focal planes
comprising several thousand detectors operating at the photon noise limit. One
approach to achieve this goal is to couple light from the telescope to a
polarisation sensitive antenna structure connected to a superconducting
diplexer network where the desired frequency bands are filtered before being
fed to individual ultra-sensitive detectors such as Transition Edge Sensors.
Traditionally, arrays constituted of horn antennas, planar phased antennas or
anti-reflection coated micro-lenses have been placed in front of planar antenna
structures to achieve the gain required to couple efficiently to the telescope
optics. In this paper are presented the design concept and a preliminary
analysis of the measured performances of a phase-engineered metamaterial
flat-lenslet. The flat lens design is inherently matched to free space,
avoiding the necessity of an anti-reflection coating layer. It can be
fabricated lithographically, making scaling to large format arrays relatively
simple. Furthermore, this technology is compatible with the fabrication process
required for the production of large-format lumped element kinetic inductance
detector arrays which have already demonstrated the required sensitivity along
with multiplexing ratios of order 1000 detectors/channel.


Abstract:
  Stellar and supernova nucleosynthesis in the first few billion years of the
cosmic history have set the scene for early structure formation in the
Universe, while little is known about their nature. Making use of stellar
physical parameters measured by GALAH Data Release 3 with accurate astrometry
from the Gaia EDR3, we have selected $\sim 100$ old main-sequence turn-off
stars (ages $\gtrsim 12$ Gyrs) with kinematics compatible with the Milky Way
stellar halo population in the Solar neighborhood. Detailed homogeneous
elemental abundance estimates by GALAH DR3 are compared with supernova yield
models of Pop~III (zero-metal) core-collapse supernovae (CCSNe), normal
(non-zero-metal) CCSNe, and Type Ia supernovae (SN Ia) to examine which of the
individual yields or their combinations best reproduce the observed elemental
abundance patterns for each of the old halo stars ("OHS"). We find that the
observed abundances in the OHS with [Fe/H]$>-1.5$ are best explained by
contributions from both CCSNe and SN~Ia, where the fraction of SN~Ia among all
the metal-enriching SNe is up to 10-20 % for stars with high [Mg/Fe] ratios and
up to 20-27 % for stars with low [Mg/Fe] ratios, depending on the assumption
about the relative fraction of near-Chandrasekhar-mass SNe Ia progenitors. The
results suggest that, in the progenitor systems of the OHS with [Fe/H]$>-1.5$,
$\sim$ 50-60% of Fe mass originated from normal CCSNe at the earliest phases of
the Milky Way formation. These results provide an insight into the birth
environments of the oldest stars in the Galactic halo.


Abstract:
  The growing availability of open source projects has facilitated developers
to reuse existing software artifacts and leverage them to develop new software.
However, it is hard to understand the notion of similarity as it varies from
developer to developer. Some developers might search for repositories with
similar source code, while some might be in search of repositories with similar
requirements or issues. Existing approaches tend to find similar projects by
comparing similar artifacts such as source-code to source-code, API usage to
API usage, documentation to documentation, and so on. Even though there is a
dissimilarity between two similar artifacts, there could be a similarity
between two dissimilar artifacts. Hence, in this paper, we aim to answer the
question - Can we find similarity of software repositories through dissimilar
artifacts?. To this end, we conduct an experiment to find similarities between
three repositories, two similar and one different project comparing similar and
dissimilar artifacts (documentation, commits, and source-code). We observed
similarities between dissimilar artifacts such as Commits, Source Code, and
Readme Files in the context of both similar and different repositories.


Abstract:
  Phonon-mediated particle detectors based on single crystals and operated at
millikelvin temperatures are used in rare-event experiments for neutrino
physics and dark-matter searches. In general, these devices are not sensitive
to the particle impact point, especially if the detection is mediated by
thermal phonons. In this Letter, we demonstrate that excellent discrimination
between interior and surface $\beta$ and $\alpha$ events can be achieved by
coating a crystal face with a thin metallic film, either continuous or in the
form of a grid. The coating affects the phonon energy down-conversion cascade
that follows the particle interaction, leading to a modified signal shape for
close-to-film events. An efficient identification of surface events was
demonstrated with detectors based on a rectangular $20 \times 20 \times 10$
mm$^3$ Li$_2$MoO$_4$ crystal coated with a Pd normal-metal film (10~nm thick)
and with Al-Pd superconductive bi-layers (100~nm-10~nm thick) on a $20 \times
20$ mm$^2$ face. Discrimination capabilities were tested with $^{238}$U sources
emitting both $\alpha$ and $\beta$ particles. Surface events are identified for
energy depositions down to millimeter-scale depths from the coated surface.
With this technology, a substantial improvement of the background figure can be
achieved in experiments searching for neutrinoless double-beta decay.


Abstract:
  Spiral acquisitions are preferred in real-time MRI because of their
efficiency, which has made it possible to capture vocal tract dynamics during
natural speech. A fundamental limitation of spirals is blurring and signal loss
due to off-resonance, which degrades image quality at air-tissue boundaries.
Here, we present a new CNN-based off-resonance correction method that
incorporates an attention-gate mechanism. This leverages spatial and channel
relationships of filtered outputs and improves the expressiveness of the
networks. We demonstrate improved performance with the attention-gate, on 1.5
Tesla spiral speech RT-MRI, compared to existing off-resonance correction
methods.


Abstract:
  Low-precision training has become a popular approach to reduce compute
requirements, memory footprint, and energy consumption in supervised learning.
In contrast, this promising approach has not yet enjoyed similarly widespread
adoption within the reinforcement learning (RL) community, partly because RL
agents can be notoriously hard to train even in full precision. In this paper
we consider continuous control with the state-of-the-art SAC agent and
demonstrate that a na\"ive adaptation of low-precision methods from supervised
learning fails. We propose a set of six modifications, all straightforward to
implement, that leaves the underlying agent and its hyperparameters unchanged
but improves the numerical stability dramatically. The resulting modified SAC
agent has lower memory and compute requirements while matching full-precision
rewards, demonstrating that low-precision training can substantially accelerate
state-of-the-art RL without parameter tuning.


Abstract:
  The operation of the future Cherenkov Telescope Array (CTA), the next
generation of Imaging Atmospheric Cherenkov Telescopes (IACTs), as an Open
Observatory requires data products and analysis tools to be accessible and
usable by the scientific community. This requirement has impelled gamma-ray
astronomers to develop open-source science tools and standardised data formats.
The objective of this presentation is to provide a perspective on the current
effort to define a prototypical format for high-level gamma-ray astronomical
data. The initiative, started by the IACT community in 2016, has gained in the
years 2018 and 2019 full recognition thanks to the first public data releases
in such format by current-generation IACTs, followed by a series of papers
employing it either to test the capabilities of open-source gamma-ray analysis
tools or to showcase examples of reproducible, multi-instrument analysis.


Abstract:
  In this paper, we focus on constructing numerical schemes preserving the
averaged energy evolution law for nonlinear stochastic wave equations driven by
multiplicative noise. We first apply the compact finite difference method and
the interior penalty discontinuous Galerkin finite element method to discretize
space variable and present two semi-discrete schemes, respectively. Then we
make use of the discrete gradient method and the Pad\'e approximation to
propose efficient fully-discrete schemes. These semi-discrete and
fully-discrete schemes are proved to preserve the discrete averaged energy
evolution law. In particular, we also prove that the proposed fully-discrete
schemes exactly inherit the averaged energy evolution law almost surely if the
considered model is driven by additive noise. Numerical experiments are given
to confirm theoretical findings.


Abstract:
  We consider the problem of scheduling in constrained queueing networks with a
view to minimizing packet delay. Modern communication systems are becoming
increasingly complex, and are required to handle multiple types of traffic with
widely varying characteristics such as arrival rates and service times. This,
coupled with the need for rapid network deployment, render a bottom up approach
of first characterizing the traffic and then devising an appropriate scheduling
protocol infeasible.
  In contrast, we formulate a top down approach to scheduling where, given an
unknown network and a set of scheduling policies, we use a policy gradient
based reinforcement learning algorithm that produces a scheduler that performs
better than the available atomic policies. We derive convergence results and
analyze finite time performance of the algorithm. Simulation results show that
the algorithm performs well even when the arrival rates are nonstationary and
can stabilize the system even when the constituent policies are unstable.


Abstract:
  We present a necessary and sufficient condition for Alt's system to be
represented by a continuous utility function. Moreover, we present a necessary
and sufficient condition for this utility function to be concave. The latter
condition can be seen as an extension of Gossen's first law, and thus has an
economic interpretation. Together with the above results, we provide a
necessary and sufficient condition for Alt's utility to be continuously
differentiable.


Abstract:
  We propose UniT, a Unified Transformer model to simultaneously learn the most
prominent tasks across different domains, ranging from object detection to
natural language understanding and multimodal reasoning. Based on the
transformer encoder-decoder architecture, our UniT model encodes each input
modality with an encoder and makes predictions on each task with a shared
decoder over the encoded input representations, followed by task-specific
output heads. The entire model is jointly trained end-to-end with losses from
each task. Compared to previous efforts on multi-task learning with
transformers, we share the same model parameters across all tasks instead of
separately fine-tuning task-specific models and handle a much higher variety of
tasks across different domains. In our experiments, we learn 7 tasks jointly
over 8 datasets, achieving strong performance on each task with significantly
fewer parameters. Our code is available in MMF at https://mmf.sh.


Abstract:
  While studying systems driven out of equilibrium, one usually employs a drive
that is not directly coupled to the degrees of freedom of the system. In
contrast to such a case, we here unveil a hitherto unexplored situation of
state-dependent driving, whereby a direct coupling exists between the two. We
demonstrate the ubiquity of such a driving, and establish that it leads to a
nontrivial steady-state that is qualitatively opposite to what is observed in
other driven systems. Further, we show how state-dependent driving in a
many-body system can be effectively captured in terms of a single-particle
model. The origin of this description may ultimately be traced to the fact that
state-dependent driving results in a force that undergoes repeated resetting in
time.


Abstract:
  As we rapidly approach the frontiers of ultra large computing resources,
software optimization is becoming of paramount interest to scientific
application developers interested in efficiently leveraging all available
on-Node computing capabilities and thereby improving a requisite science per
watt metric. The scientific application of interest here is the Basic Math
Library (BML) that provides a singular interface for linear algebra operation
frequently used in the Quantum Molecular Dynamics (QMD) community. The
provisioning of a singular interface indicates the presence of an abstraction
layer which in-turn suggests commonalities in the code-base and therefore any
optimization or tuning introduced in the core of code-base has the ability to
positively affect the performance of the aforementioned library as a whole.
With that in mind, we proceed with this investigation by performing a survey of
the entirety of the BML code-base, and extract, in form of micro-kernels,
common snippets of code. We introduce several optimization strategies into
these micro-kernels including 1.) Strength Reduction 2.) Memory Alignment for
large arrays 3.) Non Uniform Memory Access (NUMA) aware allocations to enforce
data locality and 4.) appropriate thread affinity and bindings to enhance the
overall multi-threaded performance. After introducing these optimizations, we
benchmark the micro-kernels and compare the run-time before and after
optimization for several target architectures. Finally we use the results as a
guide to propagating the optimization strategies into the BML code-base. As a
demonstration, herein, we test the efficacy of these optimization strategies by
comparing the benchmark and optimized versions of the code.


Abstract:
  We present the joint analysis of Neutral Hydrogen (HI) Intensity Mapping
observations with three galaxy samples: the Luminous Red Galaxy (LRG) and
Emission Line Galaxy (ELG) samples from the eBOSS survey, and the WiggleZ Dark
Energy Survey sample. The HI intensity maps are Green Bank Telescope
observations of the redshifted 21cm emission on 100deg2 covering the redshift
range $0.6<z<1.0$. We process the data by separating and removing the
foregrounds with FastICA, and construct a transfer function to correct for the
effects of foreground removal on the HI signal. We cross-correlate the cleaned
HI data with the galaxy samples and study the overall amplitude as well as the
scale-dependence of the power spectrum. We also qualitatively compare our
findings with the predictions by a semi-analytic galaxy evolution simulation.
The cross-correlations constrain the quantity $\Omega_{{HI}} b_{{HI}}
r_{{HI},{opt}}$ at an effective scale $k_{eff}$, where $\Omega_{HI}$ is the HI
density fraction, $b_{HI}$ is the HI bias, and $r_{{HI},{opt}}$ the
galaxy-hydrogen correlation coefficient, which is dependent on the HI content
of the optical galaxy sample. At $k_{eff}=0.31 \, h/{Mpc}$ we find
$\Omega_{{HI}} b_{{HI}} r_{{HI},{Wig}} = [0.58 \pm 0.09 \, {(stat) \pm 0.05 \,
{(sys)}}] \times 10^{-3}$ for GBT-WiggleZ, $\Omega_{{HI}} b_{{HI}}
r_{{HI,{ELG}}} = [0.40 \pm 0.09 \, {(stat) \pm 0.04 \, {(sys)}}] \times
10^{-3}$ for GBT-ELG, and $\Omega_{{HI}} b_{{HI}} r_{{HI},{LRG}} = [0.35 \pm
0.08 \, {(stat) \pm 0.03 \, {(sys)}}] \times 10^{-3}$ for GBT-LRG, at $z\simeq
0.8$. We also report results at $k_{eff}=0.24 \, h/{Mpc}$ and $k_{eff}=0.48 \,
h/{Mpc}$. With little information on HI parameters beyond our local Universe,
these are amongst the most precise constraints on neutral hydrogen density
fluctuations in an underexplored redshift range.


Abstract:
  We gave an extensive study for the quasi-periodic perturbations on the time
profiles of the line of sight (LOS) magnetic field in 10x10 sub-areas in a
solar plage region (corresponds to a facula on the photosphere). The
perturbations are found to be associated with enhancement of He I 10830 A
absorption in a moss region, which is connected to loops with million-degree
plasma. FFT analysis to the perturbations gives a kind of spectrum similar to
that of Doppler velocity: a number of discrete periods around 5 minutes. The
amplitudes of the magnetic perturbations are found to be proportional to
magnetic field strength over these sub-areas. In addition, magnetic
perturbations lag behind a quarter of cycle in phase with respect to the p-mode
Doppler velocity. We show that the relationships can be well explained with an
MHD solution for the magneto-acoustic oscillations in high-\b{eta} plasma.
Observational analysis also shows that, for the two regions with the stronger
and weaker magnetic field, the perturbations are always anti-phased. All
findings show that the magnetic perturbations are actually magneto-acoustic
oscillations on the solar surface, the photosphere, powered by p-mode
oscillations. The findings may provide a new diagnostic tool for exploring the
relationship between magneto-acoustic oscillations and the heating of solar
upper atmosphere, as well as their role in helioseismology.