Abstract:
  Let $\mathcal{M}$ be a smooth submanifold of $\mathbb{R}^n$ equipped with the
Euclidean (chordal) metric. This note considers the smallest dimension $m$ for
which there exists a bi-Lipschitz function $f: \mathcal{M} \mapsto
\mathbb{R}^m$ with bi-Lipschitz constants close to one. The main result bounds
the embedding dimension $m$ below in terms of the bi-Lipschitz constants of $f$
and the reach, volume, diameter, and dimension of $\mathcal{M}$. This new lower
bound is applied to show that prior upper bounds by Eftekhari and Wakin
(arXiv:1306.4748) on the minimal low-distortion embedding dimension of such
manifolds using random matrices achieve near-optimal dependence on both reach
and volume. This supports random linear maps as being nearly as efficient as
the best possible nonlinear maps at reducing the ambient dimension for manifold
data. In the process of proving our main result, we also prove similar results
concerning the impossibility of achieving better nonlinear measurement maps
with the Restricted Isometry Property (RIP) in compressive sensing
applications.


Abstract:
  A method of generating differential operators is used to solve the spectral
problem for a generalisation of the Sylvester-Kac matrix. As a by-product, we
find a linear differential operator with polynomial coefficients of the first
order that has a finite sequence of polynomial eigenfunctions generalising the
operator considered by M. Kac. In addition, we explain spectral properties of
two related tridiagonal matrices whose shape differ from our generalisation.


Abstract:
  The milestone improvements brought about by deep representation learning and
pre-training techniques have led to large performance gains across downstream
NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large
high-quality visio-linguistic datasets for learning complementary information
(across image and text modalities). In this paper, we introduce the
Wikipedia-based Image Text (WIT) Dataset
(https://github.com/google-research-datasets/wit) to better facilitate
multimodal, multilingual learning. WIT is composed of a curated set of 37.6
million entity rich image-text examples with 11.5 million unique images across
108 Wikipedia languages. Its size enables WIT to be used as a pretraining
dataset for multimodal models, as we show when applied to downstream tasks such
as image-text retrieval. WIT has four main and unique advantages. First, WIT is
the largest multimodal dataset by the number of image-text examples by 3x (at
the time of writing). Second, WIT is massively multilingual (first of its kind)
with coverage over 100+ languages (each of which has at least 12K examples) and
provides cross-lingual texts for many images. Third, WIT represents a more
diverse set of concepts and real world entities relative to what previous
datasets cover. Lastly, WIT provides a very challenging real-world test set, as
we empirically illustrate using an image-text retrieval task as an example.


Abstract:
  Graphical languages, like quantum circuits or ZX-calculus, have been
successfully designed to represent (memoryless) quantum computations acting on
a finite number of qubits. Meanwhile, delayed traces have been used as a
graphical way to represent finite-memory computations on streams, in a
classical setting (cartesian data types). We merge those two approaches and
describe a general construction that extends any graphical language, equipped
with a notion of discarding, to a graphical language of finite memory
computations. In order to handle cases like the ZX-calculus, which is complete
for post-selected quantum mechanics, we extend the delayed trace formalism
beyond the causal case, refining the notion of causality for stream
transformers. We design a stream semantics based on stateful morphism sequences
and, under some assumptions, show universality and completeness results.
Finally, we investigate the links of our framework with previous works on
cartesian data types, signal flow graphs, and quantum channels with memories.


Abstract:
  Ransomware is a growing threat that typically operates by either encrypting a
victim's files or locking a victim's computer until the victim pays a ransom.
However, it is still challenging to detect such malware timely with existing
traditional malware detection techniques. In this paper, we present a novel
ransomware detection system, called "Peeler" (Profiling kErnEl -Level Events to
detect Ransomware). Peeler deviates from signatures for individual ransomware
samples and relies on common and generic characteristics of ransomware depicted
at the kernel-level. Analyzing diverse ransomware families, we observed
ransomware's inherent behavioral characteristics such as stealth operations
performed before the attack, file I/O request patterns, process spawning, and
correlations among kernel-level events. Based on those characteristics, we
develop Peeler that continuously monitors a target system's kernel events and
detects ransomware attacks on the system. Our experimental results show that
Peeler achieves more than 99\% detection rate with 0.58\% false-positive rate
against 43 distinct ransomware families, containing samples from both crypto
and screen-locker types of ransomware. For crypto ransomware, Peeler detects
them promptly after only one file is lost (within 115 milliseconds on average).
Peeler utilizes around 4.9\% of CPU time with only 9.8 MB memory under the
normal workload condition. Our analysis demonstrates that Peeler can
efficiently detect diverse malware families by monitoring their kernel-level
events.


Abstract:
  One of the most important and well-established empirical results in astronomy
is the Kennicutt-Schmidt (KS) relation between the density of interstellar gas
and the rate at which that gas forms stars. A tight correlation between these
quantities has long been measured at galactic scales. More recently, using
surveys of YSOs, a KS relationship has been found within molecular clouds
relating the surface density of star formation to the surface density of gas;
however, the scaling of these laws varies significantly from cloud to cloud. In
this Letter, we use a recently developed, high-accuracy catalog of young
stellar objects from $\textit{Spitzer}$ combined with high-dynamic-range gas
column density maps of twelve nearby ($<$1.5 kpc) molecular clouds from
$\textit{Herschel}$ to re-examine the KS relation within individual molecular
clouds. We find a tight, linear correlation between clouds' star formation rate
per unit area and their gas surface density normalized by the gas free-fall
time. The measured intracloud KS relation, which relates star formation rate to
the volume density, extends over more than two orders of magnitude within each
cloud and is nearly identical in each of the twelve clouds, implying a constant
star formation efficiency per free-fall time $\epsilon_{\rm ff}\approx 0.026$.
The finding of a universal correlation within individual molecular clouds,
including clouds that contain no massive stars or massive stellar feedback,
favors models in which star formation is regulated by local processes such as
turbulence or stellar feedback such as protostellar outflows, and disfavors
models in which star formation is regulated only by galaxy properties or
supernova feedback on galactic scales.


Abstract:
  The paper estimates the need for additional attorney hours annually to
address the legal needs of indigent clients throughout the United States in
matters that comprise the so-called social determinants of health (SDoH). The
result will inform stakeholders such as policy makers and private donors so
they can allocate resources appropriately and design programs to close the
do-called justice gap. As a pilot study, the scope of the project covers only a
few major justice problems related to the social determinants of health
(standard housing, evictions and foreclosures, guardianships for the
incapacitated, and victims of domestic violence) because they significantly
impact health outcomes and there are data available. Based on our calculations,
we estimate that the total number of attorney hours to address only these five
legal issues is over 34 million per year. To put that in perspective, the
American Bar Association estimated that in 2018, the year from which much of
our data comes, the total number of practicing attorneys in the United States
was 1,338,678 (Weiss). Thus, to provide the needed hours, every single
practicing attorney in the United States would need to contribute about 26
hours a year. While many lawyers do in fact contribute pro bono hours, they
address the full range of legal needs that go well beyond just the five we
studied.


Abstract:
  Community structure is an important feature of many networks. One of the most
popular ways to capture community structure is using a quantitative measure,
modularity, which can serve as both a standard benchmark comparing different
community detection algorithms, and a optimization objective for detecting
communities. Previous works on modularity mainly focus on the approximation
method for modularity maximization to detect communities, or minor
modifications to the definition.
  In this paper, we study modularity from an information-theoretical
perspective and show that modularity and mutual information in networks are
essentially the same. The main contribution is that we develop a family of
generalized modularity measure, $f$-Modularity, which includes the original
modularity as a special case. At a high level, we show the significance of
community structure is equivalent to the amount of information contained in the
network. On the one hand, $f$-Modularity has an information-theoretical
interpretation and enjoys the desired properties of mutual information measure.
On the other hand, quantifying community structure also provides an approach to
estimate the mutual information between discrete random samples with a large
value space but given only limited samples. We demonstrate the algorithm for
optimizing $f$-Modularity in a relatively general case, and validate it through
experimental results on simulated networks. We also apply $f$-Modularity to
real-world market networks. Our results bridge two important fields, complex
network and information theory, and also shed light on the design of measures
on community structure in the future.


Abstract:
  Biological evidence shows that animals are capable of evading eminent
collision without using depth information, relying solely on looming stimuli.
In robotics, collision avoidance among uncooperative vehicles requires
measurement of relative distance to the obstacle. Small, low-cost mobile robots
and UAVs might be unable to carry distance measuring sensors, like LIDARS and
depth cameras. We propose a control framework suitable for a unicycle-like
vehicle moving in a 2D plane that achieves collision avoidance. The control
strategy is inspired by the reaction of invertebrates to approaching obstacles,
relying exclusively on line-of-sight (LOS) angle, LOS angle rate, and
time-to-collision as feedback. Those quantities can readily be estimated from a
monocular camera vision system onboard a mobile robot. The proposed avoidance
law commands the heading angle to circumvent a moving obstacle with unknown
position, while the velocity controller is left as a degree of freedom to
accomplish other mission objectives. Theoretical guarantees are provided to
show that minimum separation between the vehicle and the obstacle is attained
regardless of the exogenous tracking controller.


Abstract:
  Power law distributed fluctuations are known to accompany \emph{terminal}
failure in disordered brittle solids. The associated intermittent scale-free
behavior is of interest from the fundamental point of view as it emerges
universally from an intricate interplay of threshold-type nonlinearity,
quenched disorder, and long-range interactions. We use the simplest mean-field
description of such systems to show that they can be expected to undergo a
transition between brittle and quasi-brittle (ductile) responses. While the
former is characterized by a power law distribution of avalanches, in the
latter, the statistics of avalanches is predominantly Gaussian. The realization
of a particular regime depends on the variance of disorder and the effective
rigidity represented by a combination of elastic moduli. We argue that the
robust criticality, as in the cases of earthquakes and collapsing porous
materials, indicates the self-tuning of the system towards the boundary
separating brittle and ductile regimes.


Abstract:
  In this paper we discuss the possibility to generate and accelerate proton
nanobeams in fully dielectric laser-driven accelerators (p-DLAs). High gradient
on-chip optical-power dielectric laser accelerators (DLAs) could represent one
of the most promising way towards future miniaturized particle accelerator. A
primary challenge for DLAs are small beam apertures having a size of the order
of the driving laser wavelength where low charge high-repetition (or also CW)
ultralow emittance nanobeams have to be transported. For electrons beams
generation and acceleration, intense research activities are ongoing, and
several demonstrations have been already obtained by using electrons nanotip
(or flat photocathode) sources feeding dielectric microstructures. In this
article we aim at the possibility to integrate a nanosource for the generation
of a light ion or proton nano-beams suitable for the subsequent acceleration
into sub-relativistic (low-beta) p-DLA stages. Such integration includes the
idea to use a proton dielectric radiofrequency quadrupole (p-DRFQ) for bridging
the gap between the accelerator front-end and the drift-tube and high-beta
sections. The paper has been prepared as a white book including state-of-art
technologies and new solutions that now put the ambitious frontier of a fully
nanostructured proton accelerator into reach. Conceptual studies of p-DLAs here
presented could enable table-top proton nano-beams for several applications:
proton beam writing, nuclear reaction analysis at sub-micrometer scales, the
construction of miniaturized Proton-Boron Nuclear Fusion based Reactors,
biological analysis at the micrometer scale, ion beam analysis at the
sub-cellular level, mini-beams ion therapy to spare the shallow tissues, proton
irradiation of transistors, compact proton linac for neutron generation.


Abstract:
  Topological photonics, featured by stable topological edge states resistant
to perturbations, has been utilized to design robust integrated devices. Here,
we present a study exploring the intriguing topological rotated Weyl physics in
a 3D parameter space based on quaternary waveguide arrays on lithium
niobate-on-insulator (LNOI) chips. Unlike previous works that focus on the
Fermi arc surface states of a single Weyl structure, we can experimentally
construct arbitrary interfaces between two Weyl structures whose orientations
can be freely rotated in the synthetic parameter space. This intriguing system
was difficult to realize in usual 3D Weyl semimetals due to lattice mismatch.
We found whether the interface can host gapless topological interface states
(TISs) or not, is determined by the relative rotational directions of the two
Weyl structures. In the experiment, we have probed the local characteristics of
the TISs through linear optical transmission and nonlinear second harmonic
generation. Our study introduces a novel path to explore topological photonics
on LNOI chips and various applications in integrated nonlinear and quantum
optics.


Abstract:
  This paper studies two families of constraints for two-dimensional and
multidimensional arrays. The first family requires that a multidimensional
array will not contain a cube of zeros of some fixed size and the second
constraint imposes that there will not be two identical cubes of a given size
in the array. These constraints are natural extensions of their one-dimensional
counterpart that have been rigorously studied recently. For both of these
constraint we present conditions of the size of the cube for which the
asymptotic rate of the set of valid arrays approaches 1 as well as conditions
for the redundancy to be at most a single symbol. For the first family we
present an efficient encoding algorithm that uses a single symbol to encode
arbitrary information into a valid array and for the second family we present a
similar encoder for the two-dimensional case. The results in the paper are also
extended to similar constraints where the sub-array is not necessarily a cube,
but a box of arbitrary dimensions and only its volume is bounded.


Abstract:
  Supersonic turbulence in the interstellar medium (ISM) is closely linked to
the formation of stars, and hence many theories connect the stellar initial
mass function (IMF) with the turbulent properties of molecular clouds. Here we
test three turbulence-based IMF models (by Padoan & Nordlund 2002, Hennebelle &
Chabrier 2008, and Hopkins 2012), which predict the relation between the
high-mass slope ($\Gamma$) of the IMF, $\mathrm{d} N/\mathrm{d} \log M \propto
M^{\Gamma}$ and the exponent n of the velocity power spectrum of turbulence,
$E_v(k)\propto k^{-n} $, where $n\approx 2$ corresponds to typical ISM
turbulence. Using hydrodynamic simulations, we drive turbulence with an unusual
index of $n\approx 1$, measure $\Gamma$, and compare the results with $n\approx
2$. We find that reducing $n$ from 2 to 1 primarily changes the high-mass
region of the IMF (beyond the median mass), where we measure high-mass slopes
within the 95 per cent confidence interval of $-1.5<\Gamma<-1$ for $n \approx
1$ and $-3.7<\Gamma<-2.4$ for $n\approx 2$, respectively. Thus, we find that
$n=1$ results in a significantly flatter high-mass slope of the IMF, with more
massive stars formed than for $n \approx 2$. We compare these simulations with
the predictions of the three IMF theories. We find that while the Padoan &
Nordlund theory matches our simulations with fair accuracy, the other theories
either fail to reproduce the main qualitative outcome of the simulations or
require some modifications. We conclude that turbulence plays a key role in
shaping the IMF, with a shallower turbulence power spectrum producing a
shallower high-mass IMF, and hence more massive stars.


Abstract:
  We establish various analogs of the Kronecker-Weyl equidistribution theorem
that can be considered higher-dimensional versions of results established in
our earlier investigation of the discrete 2-circle problem studied in 1969 by
Veech. Whereas the Veech problem can be viewed as one of geodesic flow on a
2-dimensional flat surface, here we study geodesic flow in higher-dimensional
flat manifolds. This is more challenging, as the overwhelming majority of the
available proof techniques for non-integrable flat systems are based on
arguments in dimension 2. For higher dimensions, we need a new approach.


Abstract:
  Dynamic response of the one-dimensional viscoelastic rod of finite length,
that has one end fixed and the other subject to prescribed either displacement
or stress, is analyzed by the analytical means of Laplace transform, yielding
the displacement and stress of an arbitrary rod's point as a convolution of the
boundary forcing and solution kernel. Thermodynamically consistent Burgers
models are adopted as the constitutive equations describing mechanical
properties of the rod. Short-time asymptotics implies the finite wave
propagation speed in the case of the second class models, contrary to the case
of the first class models. Moreover, Burgers model of the first class yield
quite classical shapes of displacement and stress time profiles resulting from
the boundary forcing assumed as the Heaviside function, while model of the
second class yield responses that resemble to the sequence of excitation and
relaxation processes.


Abstract:
  Epitaxial thin films of (Sn$_{x}$Pb$_{1-x}$)$_{1-y}$In$_{y}$Te were
successfully grown by molecular-beam-epitaxy (MBE) in a broad range of
compositions (0 $\leq$ x $\leq$ 1, 0 $\leq$ y $\leq$ 0.23). We investigated
electronic phases of the films by the measurements of electrical transport and
optical second harmonic generation. In this system, one can control the
inversion of band gap, the electric polarization that breaks the inversion
symmetry, and the Fermi level position by tuning the Pb/Sn ratio and In
composition. A plethora of topological electronic phases are expected to
emerge, such as topological crystalline insulator, topological semimetal, and
superconductivity. For the samples with large Sn compositions (x > 0.5), hole
density increases with In composition (y), which results in the appearance of
superconductivity. On the other hand, for those with small Sn compositions (x <
0.5), increase in In composition reduces the hole density and changes the
carrier type from p-type to n-type. In a narrow region centered at (x, y) =
(0.16, 0.07) where the n-type carriers are slightly doped, charge transport
with high mobility exceeding 5,000 cm$^{2}$V$^{-1}$s$^{-1}$ shows up,
representing the possible semimetal states. In those samples, the optical
second harmonic generation measurement shows the breaking of inversion symmetry
along the out-of-plane [111] direction, which ensures the presence of polar
semimetal state. The thin films of (Sn$_{x}$Pb$_{1-x}$)$_{1-y}$In$_{y}$Te
materials systems with a variety of electronic states would become a promising
materials platform for the exploration of novel quantum phenomena.


Abstract:
  A fundamental theorem of P. Deligne (2002) states that a pre-Tannakian
category over an algebraically closed field of characteristic zero admits a
fiber functor to the category of supervector spaces (i.e., is the
representation category of an affine proalgebraic supergroup) if and only if it
has moderate growth (i.e., the lengths of tensor powers of an object grow at
most exponentially). In this paper we prove a characteristic p version of this
theorem. Namely we show that a pre-Tannakian category over an algebraically
closed field of characteristic p>0 admits a fiber functor into the Verlinde
category Ver_p (i.e., is the representation category of an affine group scheme
in Ver_p) if and only if it has moderate growth and is Frobenius exact. This
implies that Frobenius exact pre-Tannakian categories of moderate growth admit
a well-behaved notion of Frobenius-Perron dimension.
  It follows that any semisimple pre-Tannakian category of moderate growth has
a fiber functor to Ver_p (so in particular Deligne's theorem holds on the nose
for semisimple pre-Tannakian categories in characteristics 2,3). This settles a
conjecture of the third author from 2015.
  In particular, this result applies to semisimplifications of categories of
modular representations of finite groups (or, more generally, affine group
schemes), which gives new applications to classical modular representation
theory. For example, it allows us to characterize, for a modular representation
V, the possible growth rates of the number of indecomposable summands in
V^{\otimes n} of dimension prime to p.


Abstract:
  Smart meters are utilized to transmit the consumption information to the
metering data management system for observing and management in smart grid
advanced metering infrastructure systems. In the meantime, for efficient
utilization for spectrum, Device-to-Device (D2D) communications underlaying LTE
networks are a promising wireless communication technology for advanced
metering infrastructure which supporting a technique for reusing the same radio
resources (RRs) of LTE networks. Therefore, we examine the utilization of D2D
communication technology for advanced metering infrastructure communications
underlaying LTE networks. A novel approach is suggested for provisioning the
mandatory communication between serving data concentrator and its set of smart
meters using this technology. The suggested approach is dependent on two main
stages. The group of permissible cellular user equipment reuse candidates for
every smart meter is calculated with taking the quality of service demands for
cellular user devices and smart meters into consideration in the first stage.
The optimal RR allocation for every smart meter is determined based on
maximizing the access rate of smart meters which can be accepted and operated
in D2D reuse mode in the second stage. Simulation results prove the efficacy of
the suggested approach for efficient advanced metering infrastructure
communication underlaying LTE systems with accepting remarkable number of SMs
and accomplishing outstanding throughput gain.


Abstract:
  We describe the vote package in R, which implements the plurality (or
first-past-the-post), two-round runoff, score, approval and single transferable
vote (STV) electoral systems, as well as methods for selecting the Condorcet
winner and loser. We emphasize the STV system, which we have found to work well
in practice for multi-winner elections with small electorates, such as
committee and council elections, and the selection of multiple job candidates.
For single-winner elections, the STV is also called instant runoff voting
(IRV), ranked choice voting (RCV), or the alternative vote (AV) system. The
package also implements the STV system with equal preferences, for the first
time in a software package, to our knowledge. It also implements a new variant
of STV, in which a minimum number of candidates from a specified group are
required to be elected. We illustrate the package with several real examples.


Abstract:
  Deep learning has achieved notable performance in the denoising task of
low-quality medical images and the detection task of lesions, respectively.
However, existing low-quality medical image denoising approaches are
disconnected from the detection task of lesions. Intuitively, the quality of
denoised images will influence the lesion detection accuracy that in turn can
be used to affect the denoising performance. To this end, we propose a
play-and-plug medical image denoising framework, namely Lesion-Inspired
Denoising Network (LIDnet), to collaboratively improve both denoising
performance and detection accuracy of denoised medical images. Specifically, we
propose to insert the feedback of downstream detection task into existing
denoising framework by jointly learning a multi-loss objective. Instead of
using perceptual loss calculated on the entire feature map, a novel
region-of-interest (ROI) perceptual loss induced by the lesion detection task
is proposed to further connect these two tasks. To achieve better optimization
for overall framework, we propose a customized collaborative training strategy
for LIDnet. On consideration of clinical usability and imaging characteristics,
three low-dose CT images datasets are used to evaluate the effectiveness of the
proposed LIDnet. Experiments show that, by equipping with LIDnet, both of the
denoising and lesion detection performance of baseline methods can be
significantly improved.


Abstract:
  We address the issue of point value reconstructions from cell averages in the
context of third order finite volume schemes, focusing in particular on the
cells close to the boundaries of the domain. In fact, most techniques known in
the literature rely on the creation of ghost cells outside the boundary and on
some form of extrapolation from the inside that, taking into account the
boundary conditions, fills the ghost cells with appropriate values, so that a
standard reconstruction can be applied also in boundary cells. In (Naumann,
Kolb, Semplice, 2018), motivated by the difficulty of choosing appropriate
boundary conditions at the internal nodes of a network, a different technique
was explored that avoids the use of ghost cells, but instead employs for the
boundary cells a different stencil, biased towards the interior of the domain.
  In this paper, extending that approach, which does not make use of ghost
cells, we propose a more accurate reconstruction for the one-dimensional case
and a two-dimensional one for Cartesian grids. In several numerical tests we
compare the novel reconstruction with the standard approach using ghost cells.


Abstract:
  We present a theoretical study of the effects of heterostrain and lattice
relaxation on the optical conductivity of twisted bilayer graphene near the
magic angle, based on the band structures obtained from a continuum model. We
find that heterostrain, lattice relaxation and their combination give rise to
very distinctive spectroscopic features in the optical conductivity, which can
be used to probe and distinguish these effects. From the spectrum at various
Fermi energies, important features in the strain- and relaxation-modified band
structure such as the bandgap, bandwidth and van Hove singularities can be
directly measured. The peak associated with the transition between the flat
bands in the optical conductivity are highly sensitive to the direction of the
strain, which can provide direct information on the strain-modified flat bands.


Abstract:
  Regret has been established as a foundational concept in online learning, and
likewise has important applications in the analysis of learning dynamics in
games. Regret quantifies the difference between a learner's performance against
a baseline in hindsight. It is well-known that regret-minimizing algorithms
converge to certain classes of equilibria in games; however, traditional forms
of regret used in game theory predominantly consider baselines that permit
deviations to deterministic actions or strategies. In this paper, we revisit
our understanding of regret from the perspective of deviations over partitions
of the full \emph{mixed} strategy space (i.e., probability distributions over
pure strategies), under the lens of the previously-established $\Phi$-regret
framework, which provides a continuum of stronger regret measures. Importantly,
$\Phi$-regret enables learning agents to consider deviations from and to mixed
strategies, generalizing several existing notions of regret such as external,
internal, and swap regret, and thus broadening the insights gained from
regret-based analysis of learning algorithms. We prove here that the
well-studied evolutionary learning algorithm of replicator dynamics (RD)
seamlessly minimizes the strongest possible form of $\Phi$-regret in generic $2
\times 2$ games, without any modification of the underlying algorithm itself.
We subsequently conduct experiments validating our theoretical results in a
suite of 144 $2 \times 2$ games wherein RD exhibits a diverse set of behaviors.
We conclude by providing empirical evidence of $\Phi$-regret minimization by RD
in some larger games, hinting at further opportunity for $\Phi$-regret based
study of such algorithms from both a theoretical and empirical perspective.


Abstract:
  For a prime $\ell$, the McKay conjecture suggests a bijection between the set
of irreducible characters of a finite group with $\ell'$-degree and the
corresponding set for the normalizer of a Sylow $\ell$- subgroup. Navarro's
refinement suggests that the values of the characters on either side of this
bijection should also be related, proposing that the bijection commutes with
certain Galois automorphisms. Recently, Navarro--Spaeth--Vallejo have reduced
the McKay--Navarro conjecture to certain "inductive" conditions on finite
simple groups. We prove that these inductive McKay--Navarro (also called the
inductive Galois--McKay) conditions hold for the prime $\ell = 2$ for several
groups of Lie type, including the untwisted groups without nontrivial graph
automorphisms.


Abstract:
  Reinforcement learning (RL) is one of the most active fields of AI research.
Despite the interest demonstrated by the research community in reinforcement
learning, the development methodology still lags behind, with a severe lack of
standard APIs to foster the development of RL applications. OpenAI Gym is
probably the most used environment to develop RL applications and simulations,
but most of the abstractions proposed in such a framework are still assuming a
semi-structured methodology. This is particularly relevant for agent-based
models whose purpose is to analyse adaptive behaviour displayed by
self-learning agents in the simulation. In order to bridge this gap, we present
a workflow and tools for the decoupled development and maintenance of
multi-purpose agent-based models and derived single-purpose reinforcement
learning environments, enabling the researcher to swap out environments with
ones representing different perspectives or different reward models, all while
keeping the underlying domain model intact and separate. The Sim-Env Python
library generates OpenAI-Gym-compatible reinforcement learning environments
that use existing or purposely created domain models as their simulation
back-ends. Its design emphasizes ease-of-use, modularity and code separation.


Abstract:
  In this article, we introduce a deformation cohomology of Leibniz
superalgebras. Also, we introduce formal deformation theory of Leibniz
superalgebras. Using deformation cohomology we study the formal deformation
theory of Leibniz superalgebras.


Abstract:
  We address the problem of recovering the shape and spatially-varying
reflectance of an object from multi-view images (and their camera poses) of an
object illuminated by one unknown lighting condition. This enables the
rendering of novel views of the object under arbitrary environment lighting and
editing of the object's material properties. The key to our approach, which we
call Neural Radiance Factorization (NeRFactor), is to distill the volumetric
geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020]
representation of the object into a surface representation and then jointly
refine the geometry while solving for the spatially-varying reflectance and
environment lighting. Specifically, NeRFactor recovers 3D neural fields of
surface normals, light visibility, albedo, and Bidirectional Reflectance
Distribution Functions (BRDFs) without any supervision, using only a
re-rendering loss, simple smoothness priors, and a data-driven BRDF prior
learned from real-world BRDF measurements. By explicitly modeling light
visibility, NeRFactor is able to separate shadows from albedo and synthesize
realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor
is able to recover convincing 3D models for free-viewpoint relighting in this
challenging and underconstrained capture setup for both synthetic and real
scenes. Qualitative and quantitative experiments show that NeRFactor
outperforms classic and deep learning-based state of the art across various
tasks. Our videos, code, and data are available at
people.csail.mit.edu/xiuming/projects/nerfactor/.


Abstract:
  Software crowdsourcing platforms employ extrinsic rewards such as rating or
ranking systems to motivate workers. Such rating systems are noisy and provide
limited knowledge about workers' preferences and performance. To develop better
understanding of worker reliability and trustworthiness in software
crowdsourcing, this paper reports an empirical study conducted on more than one
year's real-world data from TopCoder, one of the leading software crowdsourcing
platforms.
  To do so, first, we create a bipartite network of active workers based on
common task registrations. Then, we use the Clauset-Newman-Moore graph
clustering algorithm to identify worker clusters in the network. Finally, we
conduct an empirical evaluation to measure and analyze workers' behavior per
identified community in the platform by workers' rating. More specifically,
workers' behavior is analyzed based on their performances in terms of
reliability, trustworthiness, and success; their preferences in terms of
efficiency and elasticity; and strategies in terms of comfort, confidence, and
deceitfulness. The main result of this study identified four communities of
active workers: mixed-ranked, high-ranked, mid-ranked, and low-ranked. This
study shows that the low-ranked community associates with the highest reliable
workers with an average reliability of 25%, while the mixed-ranked community
contains the most trustworthy workers with average trustworthiness of 16%. Such
empirical evidence is beneficial to help exploring resourcing options while
understanding the relations among unknown resources to improve task success.


Abstract:
  Particle Filtering (PF) methods are an established class of procedures for
performing inference in non-linear state-space models. Resampling is a key
ingredient of PF, necessary to obtain low variance likelihood and states
estimates. However, traditional resampling methods result in PF-based loss
functions being non-differentiable with respect to model and PF parameters. In
a variational inference context, resampling also yields high variance gradient
estimates of the PF-based evidence lower bound. By leveraging optimal transport
ideas, we introduce a principled differentiable particle filter and provide
convergence results. We demonstrate this novel method on a variety of
applications.


Abstract:
  We give a sharp lower bound for the Hilbert function in degree $d$ of
artinian quotients $\Bbbk[x_1,\ldots,x_n]/I$ failing the Strong Lefschetz
property, where $I$ is a monomial ideal generated in degree $d \geq 2$. We also
provide sharp lower bounds for other classes of ideals, and connect our result
to the classification of the Hilbert functions forcing the Strong Lefschetz
property by Zanello and Zylinski.


Abstract:
  Let $n\in\omega$. The weak choice principle $\operatorname{RC}_n$ states that
for every infinite set $x$ there is an infinite subset $y\subseteq x$ with a
choice function on $[y]^n:=\{z\subseteq y\mid \lvert z\rvert =n\}$.
$\operatorname{C}_n^-$ states that for every infinite family of $n$-element
sets, there is an infinite subfamily $\mathcal{G}\subseteq\mathcal{F}$ with a
choice function. $\operatorname{LOC}_n^-$ and $\operatorname{WOC}_n^-$ are the
same statement but we assume that the family $\mathcal{F}$ is linearly
orderable ($\operatorname{LOC}_n^-$) or well-orderable
($\operatorname{WOC}_n^-$).
  In the first part of this paper we will give a full characterization of when
the implication $\operatorname{RC}_m\Rightarrow \operatorname{WOC}_n^-$ with
$m,n\in\omega$ holds in $\operatorname{ZF}$. We will prove the independence
results by using suitable Fraenkel-Mostowski permutation models. In the second
part of we will show some generalizations. In particular we will show that
$\operatorname{RC}_5\Rightarrow \operatorname{LOC}_5^-$ and
$\operatorname{RC}_6\Rightarrow \operatorname{C}_3^-$, answering two open
questions from Halbeisen and Tachtsis. Furthermore we will show that
$\operatorname{RC}_6\Rightarrow \operatorname{C}_9^-$ and
$\operatorname{RC}_7\Rightarrow \operatorname{LOC}_7^-$.


Abstract:
  The coronavirus is a global event of historical proportions and just a few
months changed the time series properties of the data in ways that make many
pre-covid forecasting models inadequate. It also creates a new problem for
estimation of economic factors and dynamic causal effects because the
variations around the outbreak can be interpreted as outliers, as shifts to the
distribution of existing shocks, or as addition of new shocks. I take the
latter view and use covid indicators as controls to 'de-covid' the data prior
to estimation. I find that economic uncertainty remains high at the end of 2020
even though real economic activity has recovered and covid uncertainty has
receded. Dynamic responses of variables to shocks in a VAR similar in magnitude
and shape to the ones identified before 2020 can be recovered by directly or
indirectly modeling covid and treating it as exogenous. These responses to
economic shocks are distinctly different from those to a covid shock which are
much larger but shorter lived. Disentangling the two types of shocks can be
important in macroeconomic modeling post-covid.


Abstract:
  A multiphysics data analytic platform is established for imaging poroelastic
interfaces of finite permeability (e.g., hydraulic fractures) from elastic
waveforms and/or acoustic pore pressure measurements. This is accomplished
through recent advances in design of non-iterative sampling methods to inverse
scattering. The direct problem is formulated via the Biot equations in the
frequency domain where a network of discontinuities is illuminated by a set of
total body forces and fluid volumetric sources, while monitoring the induced
(acoustic and elastic) scattered waves in an arbitrary near-field
configuration. A thin-layer approximation is deployed to capture the rough and
multiphase nature of interfaces whose spatially varying hydro-mechanical
properties are a priori unknown. In this setting, the well-posedness analysis
of the forward problem yields the admissibility conditions for the contact
parameters. In light of which, the poroelastic scattering operator and its
first and second factorizations are introduced and their mathematical
properties are carefully examined. It is shown that the non-selfadjoint nature
of the Biot system leads to an intrinsically asymmetric factorization of the
scattering operator which may be symmetrized at certain limits. These results
furnish a robust framework for systematic design of regularized and convex cost
functionals whose minimizers underpin the multiphysics imaging indicators. The
proposed solution is synthetically implemented with application to
spatiotemporal reconstruction of hydraulic fracture networks via deep-well
measurements.


Abstract:
  This paper presents an architecture of a Personal Information Management
System, in which individuals can define the access to their personal data by
means of smart contracts. These smart contracts, running on the Ethereum
blockchain, implement access control lists and grant immutability, traceability
and verifiability of the references to personal data, which is stored itself in
a (possibly distributed) file system. A distributed authorization mechanism is
devised, where trust from multiple network nodes is necessary to grant the
access to the data. To this aim, two possible alternatives are described: a
Secret Sharing scheme and Threshold Proxy Re-Encryption scheme. The performance
of these alternatives is experimentally compared in terms of execution time.
Threshold Proxy Re-Encryption appears to be faster in different scenarios, in
particular when increasing message size, number of nodes and the threshold
value, i.e. number of nodes needed to grant the data disclosure.


Abstract:
  The influence maximization (IM) problem aims at finding a subset of seed
nodes in a social network that maximize the spread of influence. In this study,
we focus on a sub-class of IM problems, where whether the nodes are willing to
be the seeds when being invited is uncertain, called contingency-aware IM. Such
contingency aware IM is critical for applications for non-profit organizations
in low resource communities (e.g., spreading awareness of disease prevention).
Despite the initial success, a major practical obstacle in promoting the
solutions to more communities is the tremendous runtime of the greedy
algorithms and the lack of high performance computing (HPC) for the non-profits
in the field -- whenever there is a new social network, the non-profits usually
do not have the HPCs to recalculate the solutions. Motivated by this and
inspired by the line of works that use reinforcement learning (RL) to address
combinatorial optimization on graphs, we formalize the problem as a Markov
Decision Process (MDP), and use RL to learn an IM policy over historically seen
networks, and generalize to unseen networks with negligible runtime at test
phase. To fully exploit the properties of our targeted problem, we propose two
technical innovations that improve the existing methods, including
state-abstraction and theoretically grounded reward shaping. Empirical results
show that our method achieves influence as high as the state-of-the-art methods
for contingency-aware IM, while having negligible runtime at test phase.


Abstract:
  We study how chaos, introduced by a weak perturbation, affects the
reliability of the output of analog quantum simulation. As a toy model, we
consider the Lipkin-Meshkov-Glick (LMG) model. Inspired by the semiclassical
behavior of the order parameter in the thermodynamic limit, we propose a
protocol to measure the quantum phase transition in the ground state and the
dynamical quantum phase transition associated with quench dynamics. We show
that the presence of a small time-dependent perturbation can render the
dynamics of the system chaotic. We then show that the estimates of the critical
points of these quantum phase transitions, obtained from the quantum simulation
of its dynamics, are robust to the presence of this chaotic perturbation, while
other aspects of the system, such as the mean magnetization are fragile, and
therefore cannot be reliably extracted from this simulator. This can be
understood in terms of the simulated quantities that depend on the global
structure of phase space vs. those that depend on local trajectories.


Abstract:
  Green's general hyperplane restriction theorem gives a sharp upper bound for
the Hilbert function of a standard graded algebra over and infinite field K
modulo a general linear form. We strengthen Green's result by showing that the
linear forms that do not satisfy such estimate belong to a finite union of
proper linear spaces. As an application we give a method to derive variations
of the Eakin-Sathaye theorem on reductions. In particular, we recover and
extend results by O'Carroll on the Eakin-Sathaye theorem for complete and joint
reductions.


Abstract:
  Oikawa defined an unknotting operation on virtual knots, called a CF-move,
and gave a classification of 2-component virtual links up to CF-moves by the
virtual linking number and his $n$-invariant. In particular, it was proved that
a CF-move characterizes the information contained in the virtual linking number
for 2-component odd virtual links. In this paper, we extend this result by
classifying odd virtual links and almost odd virtual links with arbitrary
number of components up to CF-moves, using the virtual linking number.
Moreover, we extend Oikawa's $n$-invariant and introduce two invariants for
3-component even virtual links. Using these invariants together with the
virtual linking number, we classify 3-component even virtual links up to
CF-moves. As a result, a classification of 3-component virtual links up to
CF-moves is provided.


Abstract:
  We present a precise measurement of the cross section, proton and $\rm ^3He$
analyzing powers, and spin correlation coefficient $C_{y,y}$ for $p$-$\rm ^3He$
elastic scattering near 65 MeV, and a comparison with rigorous four-nucleon
scattering calculations based on realistic nuclear potentials and a model with
$\Delta$-isobar excitation. Clear discrepancies are seen in some of the
measured observables in the regime around the cross section minimum.
Theoretical predictions using scaling relations between the calculated cross
section and the $\rm ^3 He$ binding energy are not successful in reproducing
the data. Large sensitivity to the $NN$ potentials and rather small
$\Delta$-isobar effects in the calculated cross section are noticed as
different features from those in the deuteron-proton elastic scattering. The
results obtained above indicate that $p$-$\rm ^3He$ scattering at intermediate
energies is an excellent tool to explore nuclear interactions not accessible by
three-nucleon scattering.


Abstract:
  Large language models have shown promising results in zero-shot settings
(Brown et al.,2020; Radford et al., 2019). For example, they can perform
multiple choice tasks simply by conditioning on a question and selecting the
answer with the highest probability.
  However, ranking by string probability can be problematic due to surface form
competition-wherein different surface forms compete for probability mass, even
if they represent the same underlying concept, e.g. "computer" and "PC." Since
probability mass is finite, this lowers the probability of the correct answer,
due to competition from other strings that are valid answers (but not one of
the multiple choice options).
  We introduce Domain Conditional Pointwise Mutual Information, an alternative
scoring function that directly compensates for surface form competition by
simply reweighing each option according to a term that is proportional to its a
priori likelihood within the context of the specific zero-shot task. It
achieves consistent gains in zero-shot performance over both calibrated (Zhao
et al., 2021) and uncalibrated scoring functions on all GPT-2 and GPT-3 models
over a variety of multiple choice datasets.


Abstract:
  In this paper we report an experiment that verifies an atomic-ensemble
quantum memory via a measurement-device-independent scheme. A single photon
generated via Rydberg blockade in one atomic ensemble is stored in another
atomic ensemble via electromagnetically induced transparency. After storage for
a long duration, this photon is retrieved and interfered with a second photon
to perform joint Bell-state measurement (BSM). Quantum state for each photon is
chosen based on a quantum random number generator respectively in each run. By
evaluating correlations between the random states and BSM results, we certify
that our memory is genuinely entanglement-preserving.


Abstract:
  The recent discovery of superconductivity in doped infinite-layer nickelates
has stimulated intensive interest, especially for similarities and differences
compared to that in cuprate superconductors. In contrast to cuprates, although
earlier magnetization measurement reveals a Curie-Weiss-like behavior in
undoped infinite-layer nickelates, there is no magnetic ordering observed by
elastic neutron scattering down to liquid helium temperature. Until now, the
nature of the magnetic ground state in undoped infinite-layer nickelates was
still elusive. Here, we perform a nuclear magnetic resonance (NMR) experiment
through 139La nuclei to study the intrinsic spin susceptibility of
infinite-layer LaNiO2. First, the signature for magnetic ordering or freezing
is absent in the 139La NMR spectrum down to 0.24 K, which unambiguously
confirms a paramagnetic ground state in LaNiO2. Second, a pseudogap-like
behavior instead of Curie-Weiss-like behavior is observed in both the
temperature-dependent Knight shift and nuclear spin-lattice relaxation rate
(1/T1), which is widely observed in both underdoped cuprates and iron-based
superconductors. Furthermore, the scaling behavior between the Knight shift and
1/T1T has also been discussed. Finally, the present results imply a
considerable exchange interaction in infinite-layer nickelates, which sets a
strong constraint for the proposed theoretical models.


Abstract:
  Transition metal (TM) defects in silicon carbide (SiC) are a promising
platform in quantum technology, especially because some TM defects emit in the
telecom band. We develop a theory for the interaction of an active electron in
the $D$-shell of a TM defect in SiC with the TM nuclear spin and derive the
effective hyperfine tensor within the Kramers doublets formed by the spin-orbit
coupling. Based on our theory we discuss the possibility to exchange the
nuclear and electron states with potential applications for nuclear spin
manipulation and long-lived nunclear-spin based quantum memories.


Abstract:
  We consider a Pfaffian system expressing isomonodromy of an irregular system
of Okubo type, depending on complex deformation parameters u=(u_1,...,u_n),
which are eigenvalues of the leading matrix at the irregular singuilarity. At
the same time, we consider a Pfaffian system of non-normalized Schlesinger type
expressing isomonodromy of a Fuchsian system, whose poles are the deformation
parameters u_1,...,u_n. The parameters vary in a polydisc containing a
coalescence locus for the eigenvalues of the leading matrix of the irregular
system, corresponding to confluence of the Fuchsian singularities. We construct
isomonodromic selected and singular vector solutions of the Fuchsian Pfaffian
system together with their isomonodromic connection coefficients, so extending
a result of references [4] and [20] to the isomonodromic case, including
confluence of singularities. Then, we introduce an isomonodromic Laplace
transform of the selected and singular vector solutions, allowing to obtain
isomonodromic fundamental solutions for the irregular system, and their Stokes
matrices expressed in terms of connection coefficients. These facts, in
addition to extending [4] and [20] to the isomonodromic case with
coalescences/confluences, allow to prove by means of Laplace transform the main
result of reference [11], which is the analytic theory of non-generic
isomonodromic deformations of the irregular system with coalescing eigenvalues.


Abstract:
  Updating observations of a signal due to the delays in the measurement
process is a common problem in signal processing, with prominent examples in a
wide range of fields. An important example of this problem is the nowcasting of
COVID-19 mortality: given a stream of reported counts of daily deaths, can we
correct for the delays in reporting to paint an accurate picture of the
present, with uncertainty? Without this correction, raw data will often mislead
by suggesting an improving situation. We present a flexible approach using a
latent Gaussian process that is capable of describing the changing
auto-correlation structure present in the reporting time-delay surface. This
approach also yields robust estimates of uncertainty for the estimated
nowcasted numbers of deaths. We test assumptions in model specification such as
the choice of kernel or hyper priors, and evaluate model performance on a
challenging real dataset from Brazil. Our experiments show that Gaussian
process nowcasting performs favourably against both comparable methods, and
against a small sample of expert human predictions. Our approach has
substantial practical utility in disease modelling -- by applying our approach
to COVID-19 mortality data from Brazil, where reporting delays are large, we
can make informative predictions on important epidemiological quantities such
as the current effective reproduction number.


Abstract:
  This paper introduces the unsupervised learning problem of playable video
generation (PVG). In PVG, we aim at allowing a user to control the generated
video by selecting a discrete action at every time step as when playing a video
game. The difficulty of the task lies both in learning semantically consistent
actions and in generating realistic videos conditioned on the user input. We
propose a novel framework for PVG that is trained in a self-supervised manner
on a large dataset of unlabelled videos. We employ an encoder-decoder
architecture where the predicted action labels act as bottleneck. The network
is constrained to learn a rich action space using, as main driving loss, a
reconstruction loss on the generated video. We demonstrate the effectiveness of
the proposed approach on several datasets with wide environment variety.
Further details, code and examples are available on our project page
willi-menapace.github.io/playable-video-generation-website.


Abstract:
  We consider the nonlinear Schr\"odinger equation (NLS) on a torus of
arbitrary dimension. The equation is studied in presence of an external
potential field whose time-dependent amplitude is taken as control. Assuming
that the potential satisfies a saturation property, we show that the NLS
equation is approximately controllable between any pair of eigenstates in
arbitrarily small time. The proof is obtained by developing a multiplicative
version of a geometric control approach introduced by Agrachev and Sarychev. We
give an application of this result to the study of the large time behavior of
the NLS equation with random potential. More precisely, we assume that the
amplitude of the potential is a random process whose law is $1$-periodic in
time and non-degenerate. Combining the controllability with a stopping time
argument and the Markov property, we show that the trajectories of the random
equation are almost surely unbounded in regular Sobolev spaces.


Abstract:
  We present the implementation of a trust-region Newton algorithm ExaTron for
bound-constrained nonlinear programming problems, fully running on multiple
GPUs. Without data transfers between CPU and GPU, our implementation has
achieved the elimination of a major performance bottleneck under a memory-bound
situation, particularly when solving many small problems in batch. We discuss
the design principles and implementation details for our kernel function and
core operations. Different design choices are justified by numerical
experiments. By using the application of distributed control of alternating
current optimal power flow, where a large problem is decomposed into many
smaller nonlinear programs using a Lagrangian approach, we demonstrate
computational performance of ExaTron on the Summit supercomputer at Oak
RidgeNational Laboratory. Our numerical results show the linear scaling with
respect to the batch size and the number of GPUs and more than 35 times speedup
on 6 GPUs than on 40 CPUs available on a single node.


Abstract:
  In the present paper, personalized treatment means choosing the best
treatment for a patient while taking into account certain relevant personal
covariate values. We study the design of trials whose goal is to find the best
treatment for a given patient with given covariates. We assume that the
subjects in the trial represent a random sample from the population, and
consider the allocation, possibly with randomization, of these subjects to the
different treatment groups in a way that depends on their covariates.
  We derive approximately optimal allocations, aiming to minimize expected
regret, assuming that future patients will arrive from the same population as
the trial subjects. We find that, for the case of two treatments, an
approximately optimal allocation design does not depend on the value of the
covariates but only on the variances of the responses. In contrast, for the
case of three treatments the optimal allocation design does depend on the
covariates as we show for specific scenarios. Another finding is that the
optimal allocation can vary a lot as a function of the sample size, and that
randomized allocations are relevant for relatively small samples, and may not
be needed for very large studies.


Abstract:
  In present article we consider an axion F(R) gravity model and described with
the help of holographic principle the cosmological models of viscous dark fluid
coupled with axion matter in a spatially flat Friedmann-Robertson-Walker (FRW)
universe. This description based on generalized infrared-cutoff holographic
dark energy, proposed by Nojiri and Odintsov. We explored the Little Rip, the
Pseudo Rip, and the power-law bounce cosmological models in terms of the
parameters of the inhomogeneous equation of the state of viscous dark fluid and
calculated the infrared cutoffs analytically. We represented the energy
conservation equation for the dark fluid from a holographic point of view and
showed a correspondence between the cosmology of a viscous fluid and
holographic cosmology. We analyzed the autonomous dynamic system. In the
absence of interaction between fluids, solutions are obtained corresponding to
two cases. In the first case, dark energy is missing and the extension
describes the component of dark matter. The second case corresponds to
cosmological models with an extension due to dark energy. The solutions
obtained are investigated for stability. For a cosmological model with the
interaction of a special type, the stability of solutions of the dynamic system
is also investigated.


Abstract:
  Accurately matching visual and textual data in cross-modal retrieval has been
widely studied in the multimedia community. To address these challenges posited
by the heterogeneity gap and the semantic gap, we propose integrating Shannon
information theory and adversarial learning. In terms of the heterogeneity gap,
we integrate modality classification and information entropy maximization
adversarially. For this purpose, a modality classifier (as a discriminator) is
built to distinguish the text and image modalities according to their different
statistical properties. This discriminator uses its output probabilities to
compute Shannon information entropy, which measures the uncertainty of the
modality classification it performs. Moreover, feature encoders (as a
generator) project uni-modal features into a commonly shared space and attempt
to fool the discriminator by maximizing its output information entropy. Thus,
maximizing information entropy gradually reduces the distribution discrepancy
of cross-modal features, thereby achieving a domain confusion state where the
discriminator cannot classify two modalities confidently. To reduce the
semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss
are used to associate the intra- and inter-modality similarity between features
in the shared space. Furthermore, a regularization term based on KL-divergence
with temperature scaling is used to calibrate the biased label classifier
caused by the data imbalance issue. Extensive experiments with four deep models
on four benchmarks are conducted to demonstrate the effectiveness of the
proposed approach.


Abstract:
  Owing to its superior visible light absorption and high chemical stability,
chalcogenide perovskite barium zirconium sulfide has attracted significant
attention in the past few years as a potential alternative to hybrid halide
perovskites for optoelectronics. However, the high processing temperatures of
BaZrS3 thin films at above 1000 C severely limits their potential for device
applications. Herein, we report the synthesis of BaZrS3 thin films at
temperatures as low as 500 C, by changing the chemical reaction pathway. The
single phase BaZrS3 thin film was confirmed by X-ray diffraction and Raman
spectroscopies. Atomic force microscopy and scanning electron microscopy show
that crystalline size and surface roughness were consistently reduced with
decreasing annealing temperature. The lower temperatures further eliminate
sulfur vacancies and carbon contaminations associated with high temperature
processing. The ability to synthesize chalcogenide perovskite thin films at
lower temperatures removes a major hurdle for their device fabrication. The
photodetectors demonstrate fast response and an on/off ratio of 80. The
fabricated field effect transistors show an ambipolar behavior with electron
and hole mobilities of 16.8 cm2/Vs and 2.6 cm2/Vs, respectively.


Abstract:
  Switchbacks are widely acknowledged phenomena observed by the Parker Solar
Probe and appear to occur in patches. Previous studies focused on the
fluctuations at the magnetic reversals. However, the nature of the fluctuations
inside the switchbacks remains unknown. Here we utilize the magnetic field data
and plasma data measured by the Parker Solar Probe in the first four
encounters. We investigate the fluctuations in the switchback intervals of 100
s with BR>0 at every instant and compare them to the fluctuations in the
nonswitchback intervals of 100 s with theta_RB>160o at every instant. We
calculate normalized cross-helicity sigma_c, normalized residual energy
sigma_r, correlation coefficient C_vb between dvA and dv, Alfven ratio rA, and
the amplitude of magnetic and kinetic fluctuations. We find that the switchback
intervals exhibit a distribution of sigma_c similar with the nonswitchback
intervals. However, the rA of switchback intervals is around 0.35, while the
nonswitchback intervals have rA around 0.65, indicating the fluctuations in the
switchbacks are more magnetically dominated. We also find that the distribution
pattern of pixel average amplitude of both dvA and dv of switchback intervals
in the C_vb-sigma_r plane show a vertical stripe feature at C_vb>0.8,
illustrating the possible magnetically dominant magnetic-velocity alignment
structure. These results will help us to understand the nature and the
formation of the switchback turbulence.


Abstract:
  In our past few years' of commercial deployment experiences, we identify
localization as a critical task in autonomous machine applications, and a great
acceleration target. In this paper, based on the observation that the visual
frontend is a major performance and energy consumption bottleneck, we present
our design and implementation of an energy-efficient hardware architecture for
ORB (Oriented-Fast and Rotated- BRIEF) based localization system on FPGAs. To
support our multi-sensor autonomous machine localization system, we present
hardware synchronization, frame-multiplexing, and parallelization techniques,
which are integrated in our design. Compared to Nvidia TX1 and Intel i7, our
FPGA-based implementation achieves 5.6x and 3.4x speedup, as well as 3.0x and
34.6x power reduction, respectively.


Abstract:
  We consider a solution $u(\cdot,t)$ to an initial boundary value problem for
time-fractional diffusion-wave equation with the order $\alpha \in (0,2)
\setminus \{ 1\}$ where $t$ is a time variable. We first prove that a suitable
norm of $u(\cdot,t)$ is bounded by $\frac{1}{t^{\alpha}}$ for $0<\alpha<1$ and
$\frac{1}{t^{\alpha-1}}$ for $1<\alpha<2$ for all large $t>0$. Moreover we
characterize initial values in the cases where the decay rates are faster than
the above critical exponents. Differently from the classical diffusion equation
$\alpha=1$, the decay rate can give some local characterization of initial
values. The proof is based on the eigenfunction expansions of solutions and the
asymptotic expansions of the Mittag-Leffler functions for large time.


Abstract:
  We study the polarizations of gravitational waves (GWs) in two classes of
extended gravity theories. First, we formulate the polarizations in linear
massive gravity (MG) with generic mass terms of non-Fierz-Pauli type by
identifying all the independent variables that obey Klein-Gordon-type
equations. The dynamical degrees of freedom (dofs) in the generic MG consist of
spin-2 and spin-0 modes, the former breaking down into two tensor (helicity-2),
two vector (helicity-1) and one scalar (helicity-0) components, while the
latter just corresponding to a scalar. We find convenient ways of decomposing
the two scalar modes of each spin into distinct linear combinations of the
transverse and longitudinal polarizations with coefficients directly expressed
by the mass parameters, thereby serving as a useful tool in measuring the
masses of GWs. Then we analyze the linear perturbations of generic
higher-curvature gravity (HCG) whose Lagrangian is an arbitrary polynomial of
the Riemann tensor. On a flat background, the linear dynamical dofs in this
theory are identified as massless spin-2, massive spin-2, and massive spin-0
modes. As its massive part encompasses the identical structure to the generic
MG, GWs in the generic HCG provide six massive polarizations on top of the
ordinary two massless modes. In parallel to MG, we find convenient
representations for the scalar-polarization modes directly connected to the
parameters of HCG. In this analysis, we employ two distinct methods; One takes
full advantage of the partial equivalence between the generic HCG and MG at the
linear level, whereas the other relies upon a gauge-invariant formalism. We
confirm that the two results agree. We also discuss methods to determine the
theory parameters by GW-polarization measurements. Our method does not require
measuring the propagation speeds or the details of the waveforms of the GWs.
[Abridged]


Abstract:
  We study the Lie group structure of asymptotic symmetry groups in General
Relativity from the viewpoint of infinite-dimensional geometry. To this end, we
review the geometric definition of asymptotic simplicity and emptiness due to
Penrose and the coordinate-wise definition of asymptotic flatness due to Bondi
et al. Then we construct the Lie group structure of the BMS group and discuss
its Lie theoretic properties. We find that the BMS group is regular in the
sense of Milnor, but not real analytic. This motivates us to conjecture that it
is not locally exponential. Finally, we verify the Trotter property as well as
the commutator property. As an outlook, we comment on the situation of related
asymptotic symmetry groups. In particular, the much more involved situation of
the Newman--Unti group is highlighted, which will be studied in future work.


Abstract:
  In this study, we have developed a parallel version of the random time
simulation algorithm.
  Firstly, we gave a rigorous basis of the random time description of the
stochastic process of chemical reaction network time evolution.
  And then we reviewed the random time simulation algorithm and gave the
implementations for the parallel version of next reaction random time
algorithm.
  The discussion of computational complexity suggested a factor of $M$ (which
is the connection number of the network) folds time consuming reduction for
random time simulation algorithm as compared to other exact stochastic
simulation algorithms, such as the Gillespie algorithm.
  For large-scale system, such like the protein-protein interaction network,
$M$ is on order of $10^8$.
  We further demonstrate the power of random time simulation with a GPGPU
parallel implementation which achieved roughly 100 folds acceleration as
compared with CPU implementations.
  Therefore the stochastic simulation method we developed here can be of great
application value for simulating time evolution process of large-scale network.


Abstract:
  Dominating electron-electron scattering enables viscous electron flow
exhibiting hydrodynamic current density patterns such as Poiseuille profiles or
vortices. The viscous regime has recently been observed in graphene by
non-local transport experiments and mapping of the Poiseuille profile. Here, we
probe the current-induced surface potential maps of graphene field effect
transistors with moderate mobility using scanning probe microscopy at room
temperature. We discover micron-sized large areas appearing close to charge
neutrality that show current induced electric fields opposing the externally
applied field. By estimating the local scattering lengths from the gate
dependence of local in-plane electric fields, we find that electron-electron
scattering dominates in these areas as expected for viscous flow. Moreover, we
suppress the inverted fields by artificially decreasing the electron-disorder
scattering length via mild ion bombardment. These results imply that viscous
electron flow is omnipresent in graphene devices, even at moderate mobility.


Abstract:
  Some of the highest-transition-temperature superconductors across various
materials classes exhibit linear-in-temperature `strange metal' or `Planckian'
electrical resistivities in their normal state. It is thus believed by many
that this behavior holds the key to unlock the secrets of high-temperature
superconductivity. However, these materials typically display complex phase
diagrams governed by various competing energy scales, making an unambiguous
identification of the physics at play difficult. Here we use electrical
resistivity measurements into the micro-Kelvin regime to discover
superconductivity condensing out of an extreme strange metal state -- with
linear resistivity over 3.5 orders of magnitude in temperature. We propose that
the Cooper pairing is mediated by the modes associated with a recently
evidenced dynamical charge localization-delocalization transition, a mechanism
that may well be pertinent also in other strange metal superconductors.


Abstract:
  We provide the structure of the unit group of $\f_{p^k}(\SL(3,2))$, where
$p\geq 11$ is a prime and $\SL(3,2)$ denotes the $3\times 3$ invertible
matrices over $\f_2$.


Abstract:
  We provide algorithms for isotonic regression minimizing $L_0$ error (Hamming
distance). This is also known as monotonic relabeling, and is applicable when
labels have a linear ordering but not necessarily a metric. There may be
exponentially many optimal relabelings, so we look at secondary criteria to
determine which are best. For arbitrary ordinal labels the criterion is
maximizing the number of labels which are only changed to an adjacent label
(and recursively apply this). For real-valued labels we minimize the $L_p$
error. For linearly ordered sets we also give algorithms which minimize the sum
of the $L_p$ and weighted $L_0$ errors, a form of penalized (regularized)
regression. We also examine $L_0$ isotonic regression on multidimensional
coordinate-wise orderings. Previous algorithms took $\Theta(n^3)$ time, but we
reduce this to $o(n^{3/2})$.


Abstract:
  This paper presents algorithms for parallelization of inference in hidden
Markov models (HMMs). In particular, we propose parallel backward-forward type
of filtering and smoothing algorithm as well as parallel Viterbi-type
maximum-a-posteriori (MAP) algorithm. We define associative elements and
operators to pose these inference problems as parallel-prefix-sum computations
in sum-product and max-product algorithms and parallelize them using
parallel-scan algorithms. The advantage of the proposed algorithms is that they
are computationally efficient in HMM inference problems with long time
horizons. We empirically compare the performance of the proposed methods to
classical methods on a highly parallel graphical processing unit (GPU).


Abstract:
  Recommendation algorithms typically build models based on historical
user-item interactions (e.g., clicks, likes, or ratings) to provide a
personalized ranked list of items. These interactions are often distributed
unevenly over different groups of items due to varying user preferences.
However, we show that recommendation algorithms can inherit or even amplify
this imbalanced distribution, leading to unfair recommendations to item groups.
Concretely, we formalize the concepts of ranking-based statistical parity and
equal opportunity as two measures of fairness in personalized ranking
recommendation for item groups. Then, we empirically show that one of the most
widely adopted algorithms -- Bayesian Personalized Ranking -- produces unfair
recommendations, which motivates our effort to propose the novel fairness-aware
personalized ranking model. The debiased model is able to improve the two
proposed fairness metrics while preserving recommendation performance.
Experiments on three public datasets show strong fairness improvement of the
proposed model versus state-of-the-art alternatives.
  This is paper is an extended and reorganized version of our SIGIR
2020~\cite{zhu2020measuring} paper. In this paper, we re-frame the studied
problem as `item recommendation fairness' in personalized ranking
recommendation systems, and provide more details about the training process of
the proposed model and details of experiment setup.


Abstract:
  In this paper, we investigate the pointwise time analyticity of three
differential equations. They are the biharmonic heat equation, the heat
equation with potentials and some nonlinear heat equations with power
nonlinearity of order $p$. The potentials include all the nonnegative ones. For
the first two equations, we prove if $u$ satisfies some growth conditions in
$(x,t)\in \mathrm{M}\times [0,1]$, then $u$ is analytic in time $(0,1]$. Here
$\mathrm{M}$ is $R^d$ or a complete noncompact manifold with Ricci curvature
bounded from below by a constant. Then we obtain a necessary and sufficient
condition such that $u(x,t)$ is analytic in time at $t=0$. Applying this
method, we also obtain a necessary and sufficient condition for the solvability
of the backward equations, which is ill-posed in general.
  For the nonlinear heat equation with power nonlinearity of order $p$, we
prove that a solution is analytic in time $t\in (0,1]$ if it is bounded in
$\mathrm{M}\times[0,1]$ and $p$ is a positive integer. In addition, we
investigate the case when $p$ is a rational number with a stronger assumption
$0<C_3 \leq |u(x,t)| \leq C_4$. It is also shown that a solution may not be
analytic in time if it is allowed to be $0$. As a lemma, we obtain an estimate
of $\partial_t^k \Gamma(x,t;y)$ where $\Gamma(x,t;y)$ is the heat kernel on a
manifold, with an explicit estimation of the coefficients.
  An interesting point is that a solution may be analytic in time even if it is
not smooth in the space variable $x$, implying that the analyticity of space
and time can be independent. Besides, for general manifolds, space analyticity
may not hold since it requires certain bounds on curvature and its derivatives.


Abstract:
  In many operations management problems, we need to make decisions
sequentially to minimize the cost while satisfying certain constraints. One
modeling approach to study such problems is constrained Markov decision process
(CMDP). When solving the CMDP to derive good operational policies, there are
two key challenges: one is the prohibitively large state space and action
space; the other is the hard-to-compute transition kernel. In this work, we
develop a sampling-based primal-dual algorithm to solve CMDPs. Our approach
alternatively applies regularized policy iteration to improve the policy and
subgradient ascent to maintain the constraints. Under mild regularity
conditions, we show that the algorithm converges at rate $
O(\log(T)/\sqrt{T})$, where T is the number of iterations. When the CMDP has a
weakly coupled structure, our approach can substantially reduce the dimension
of the problem through an embedded decomposition. We apply the algorithm to two
important applications with weakly coupled structures: multi-product inventory
management and multi-class queue scheduling, and show that it generates
controls that outperform state-of-art heuristics.


Abstract:
  We investigate the asymptotic expansion of integrals analogous to Ball's
integral \[\int_0^\infty
\left(\frac{\Gamma(1+\nu)|J_\nu(x)|}{(x/2)^\nu}\right)^{\!n}dx\] for large $n$
in which the Bessel function $J_\nu(x)$ is replaced by the modified Bessel
functions $I_\nu(x)$ and $K_\nu(x)$ together with appropriate exponential
factors $e^{\mp x}$, respectively.
  The above integral with $J_\nu(x)$ replaced by a hyper-Bessel function of the
type recently discussed in Aktas {\it et al.} [The Ramanujan J., 2019] and
taken over a finite interval determined by the first positive zero of the
function is also considered for $n\to\infty$. We give the leading asymptotic
behaviour of the hyper-Bessel function for $x\to+\infty$ in an appendix.
Numerical examples are given to illustrate the accuracy of the various
expansions obtained.


Abstract:
  We present a molecular dynamics simulation method for the computation of the
solubility of organic crystals in solution. The solubility is calculated based
on the equilibrium free energy difference between the solvated solute and its
crystallized state at the crystal surface kink site. In order to efficiently
sample the growth and dissolution process, we have carried out well-tempered
Metadynamics simulations with a collective variable that captures the slow
degrees of freedom, namely the solute diffusion to and adsorption at the kink
site together with the desolvation of the kink site. Simulations were performed
at different solution concentrations using constant chemical potential
molecular dynamics and the solubility was identified at the concentration at
which the free energy values between the grown and dissolved kink states were
equal. The effectiveness of this method is demonstrated by its success in
reproducing the experimental trends of solubility of urea and naphthalene in a
variety of solvents.


Abstract:
  Waves dissipate energy when they propagate through real medium. Theoretical
study of waves is one of important way to understand the nature of waves in
medium with dissipation. The study points out that the theoretical solution to
the wave equation describing a disturbance propagating in a dissipative medium
is not unique, which is determined by the dissipation mechanism of the medium.
A new general solution is proposed by assuming that the attenuations of
disturbance can occur in the time and space domains. The general solution is
further used in case studies. The properties of viscoelastic waves propagating
in the Kelvin-Voigt medium and electromagnetic waves propagating in conductive
medium with the reciprocal attenuation in time and space domains are analyzed.
The result shows that the attenuation mechanism has an obvious influence on the
properties of waves in the dissipative medium when the wave equations are the
same.


Abstract:
  Processing massive application graphs on distributed memory systems requires
to map the graphs onto the system's processing elements (PEs). This task
becomes all the more important when PEs have non-uniform communication costs or
the input is highly irregular. Typically, mapping is addressed using
partitioning, in a two-step approach or an integrated one. Parallel
partitioning tools do exist; yet, corresponding mapping algorithms or their
public implementations all have major sequential parts or other severe scaling
limitations. In this paper, we propose a parallel algorithm that maps graphs
onto the PEs of a hierarchical system. Our solution integrates partitioning and
mapping; it models the system hierarchy in a concise way as an implicit labeled
tree. The vertices of the application graph are labeled as well, and these
vertex labels induce the mapping. The mapping optimization follows the basic
idea of parallel label propagation, but we tailor the gain computations of
label changes to quickly account for the induced communication costs. Our
MPI-based code is the first public implementation of a parallel graph mapping
algorithm; to this end, we extend the partitioning library ParHIP. To evaluate
our algorithm's implementation, we perform comparative experiments with complex
networks in the million- and billion-scale range. In general our mapping tool
shows good scalability on up to a few thousand PEs. Compared to other MPI-based
competitors, our algorithm achieves the best speed to quality trade-off and our
quality results are even better than non-parallel mapping tools.


Abstract:
  Non-radiative losses to the open-circuit voltage are a primary factor in
limiting the power conversion efficiency of organic photovoltaic devices. The
dominant non-radiative loss is intrinsic to the active layer and can be
determined from the quasi-Fermi level splitting (QFLS) and the radiative
thermodynamic limit of the photovoltage. Quantification of the QFLS in thin
film devices with low mobility is challenging due to the excitonic nature of
photoexcitation and additional sources of nonradiative loss associated with the
device structure. This work outlines an experimental approach based on
electro-modulated photoluminescence, which can be used to directly measure the
intrinsic non-radiative loss to the open-circuit voltage; thereby, quantifying
the QFLS. Drift-diffusion simulations are carried out to show that this method
accurately predicts the QFLS in the bulk of the device regardless of
device-related non-radiative losses. State-of-the-art PM6:Y6-based organic
solar cells are used as a model to test the experimental approach, and the QFLS
is quantified and shown to be independent of device architecture. This work
provides a method to quantify the QFLS of organic solar cells under operational
conditions, fully characterizing the different contributions to the
non-radiative losses of the open-circuit voltage. The reported method will be
useful in not only characterizing and understanding losses in organic solar
cells, but also other device platforms such as light-emitting diodes and
photodetectors.


Abstract:
  Integrating intelligent reflecting surface (IRS) and Rate-Splitting Multiple
Access (RSMA) is an effective solution to improve the spectral/energy
efficiency in next-generation (beyond 5G (B5G) and 6G) wireless networks. In
this paper, we investigate a rate-splitting (RS)-based transmission technique
for an IRS-aided communication network involving both near and cell-edge users.
In particular, we derive a new architecture called IRS-RS that leverages the
interplay between RS and IRS, with an aim to maximize the weighted sum-rate
(WSR) of users by selecting the reflecting coefficients at the IRS and
designing beamformers at the BS under the constraints of power at the base
station (BS), quality of service (QoS) at each user and finite resolution at
the IRS. To solve the non-convex WSR maximization problem, we propose an
alternating algorithm and compare its performance with baseline non-orthogonal
multiple access (NOMA) based transmission for an IRS-aided communication
network for both perfect and imperfect CSIT cases. Through numerical results,
it is shown that the proposed IRS-RS architecture yields better QoS with
respect to the cell-edge users when compared to IRS-NOMA transmission scheme.


Abstract:
  Quantitative MR imaging is increasingly favoured for its richer information
content and standardised measures. However, computing quantitative parameter
maps, such as those encoding longitudinal relaxation rate (R1), apparent
transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat),
involves inverting a highly non-linear function. Many methods for deriving
parameter maps assume perfect measurements and do not consider how noise is
propagated through the estimation procedure, resulting in needlessly noisy
maps. Instead, we propose a probabilistic generative (forward) model of the
entire dataset, which is formulated and inverted to jointly recover (log)
parameter maps with a well-defined probabilistic interpretation (e.g., maximum
likelihood or maximum a posteriori). The second order optimisation we propose
for model fitting achieves rapid and stable convergence thanks to a novel
approximate Hessian. We demonstrate the utility of our flexible framework in
the context of recovering more accurate maps from data acquired using the
popular multi-parameter mapping protocol. We also show how to incorporate a
joint total variation prior to further decrease the noise in the maps, noting
that the probabilistic formulation allows the uncertainty on the recovered
parameter maps to be estimated. Our implementation uses a PyTorch backend and
benefits from GPU acceleration. It is available at
https://github.com/balbasty/nitorch.


Abstract:
  The problem is to evaluate a polynomial in several variables and its gradient
at a power series truncated to some finite degree with multiple double
precision arithmetic. To compensate for the cost overhead of multiple double
precision and power series arithmetic, data parallel algorithms for general
purpose graphics processing units are presented. The reverse mode of
algorithmic differentiation is organized into a massively parallel computation
of many convolutions and additions of truncated power series. Experimental
results demonstrate that teraflop performance is obtained in deca double
precision with power series truncated at degree 152. The algorithms scale well
for increasing precision and increasing degrees.


Abstract:
  This paper investigates the motion planning of autonomous dynamical systems
modeled by Markov decision processes (MDP) with unknown transition
probabilities over continuous state and action spaces. Linear temporal logic
(LTL) is used to specify high-level tasks over infinite horizon, which can be
converted into a limit deterministic generalized B\"uchi automaton (LDGBA) with
several accepting sets. The novelty is to design an embedded product MDP
(EP-MDP) between the LDGBA and the MDP by incorporating a synchronous
tracking-frontier function to record unvisited accepting sets of the automaton,
and to facilitate the satisfaction of the accepting conditions. The proposed
LDGBA-based reward shaping and discounting schemes for the model-free
reinforcement learning (RL) only depend on the EP-MDP states and can overcome
the issues of sparse rewards. Rigorous analysis shows that any RL method that
optimizes the expected discounted return is guaranteed to find an optimal
policy whose traces maximize the satisfaction probability. A modular deep
deterministic policy gradient (DDPG) is then developed to generate such
policies over continuous state and action spaces. The performance of our
framework is evaluated via an array of OpenAI gym environments.


Abstract:
  Many software engineering research papers rely on time-based data (e.g.,
commit timestamps, issue report creation/update/close dates, release dates).
Like most real-world data however, time-based data is often dirty. To date,
there are no studies that quantify how frequently such data is used by the
software engineering research community, or investigate sources of and quantify
how often such data is dirty. Depending on the research task and method used,
including such dirty data could affect the research results. This paper
presents the first survey of papers that utilize time-based data, published in
the Mining Software Repositories (MSR) conference series. Out of the 690
technical track and data papers published in MSR 2004--2020, we saw at least
35% of papers utilized time-based data. We then used the Boa and Software
Heritage infrastructures to help identify and quantify several sources of dirty
commit timestamp data. Finally we provide guidelines/best practices for
researchers utilizing time-based data from Git repositories.


Abstract:
  A simple expression for the Gamow factor is obtained using a second order
curvature corrected tunneling potential. Our results show that it approximates
accurately the `exact-WKB' transmission coefficient obtained by numerically
integrating over the tunneling region to obtain the Gamow factor. The average
difference in current density using the respective transmission coefficients is
about $1.5 \%$, across a range of work-functions $\phi \in [3-5.5]$eV, Fermi
energy ${\cal{E}}_F$ in [5-10]eV, local electric fields $E_l$ in[3-9]eV and
radius of curvature $R \geq 5$nm). An easy-to-use correction factor $\lambda_P$
is also provided to approximately map the `exact-WKB' current density to the
`exact' current density in terms of ${\cal{E}}_F/\phi$. The average error on
using $\lambda_P$ is found to be around $3.5\%$. This is a vast improvement
over the average error of $15\%$ when $\lambda_P = 1$. Finally, an analytical
expression for the curvature-corrected current density is obtained using the
Gamow factor. It is found to compare well with the `exact-WKB' current density
even at small values of local electric field and radius of curvature.


Abstract:
  The Run Length Encoding (RLE) compression method is a long standing simple
lossless compression scheme which is easy to implement and achieves a good
compression on input data which contains repeating consecutive symbols. In its
pure form RLE is not applicable on natural text or other input data with short
sequences of identical symbols. We present a combination of preprocessing steps
that turn arbitrary input data in a byte-wise encoding into a bit-string which
is highly suitable for RLE compression. The main idea is to first read all most
significant bits of the input byte-string, followed by the second most
significant bit, and so on. We combine this approach by a dynamic byte
remapping as well as a Burrows-Wheeler-Scott transform on a byte level.
Finally, we apply a Huffman Encoding on the output of the bit-wise RLE encoding
to allow for more dynamic lengths of code words encoding runs of the RLE. With
our technique we can achieve a lossless average compression which is better
than the standard RLE compression by a factor of 8 on average.


Abstract:
  A key assumption of top-down human pose estimation approaches is their
expectation of having a single person/instance present in the input bounding
box. This often leads to failures in crowded scenes with occlusions. We propose
a novel solution to overcome the limitations of this fundamental assumption.
Our Multi-Instance Pose Network (MIPNet) allows for predicting multiple 2D pose
instances within a given bounding box. We introduce a Multi-Instance Modulation
Block (MIMB) that can adaptively modulate channel-wise feature responses for
each instance and is parameter efficient. We demonstrate the efficacy of our
approach by evaluating on COCO, CrowdPose, and OCHuman datasets. Specifically,
we achieve 70.0 AP on CrowdPose and 42.5 AP on OCHuman test sets, a significant
improvement of 2.4 AP and 6.5 AP over the prior art, respectively. When using
ground truth bounding boxes for inference, MIPNet achieves an improvement of
0.7 AP on COCO, 0.9 AP on CrowdPose, and 9.1 AP on OCHuman validation sets
compared to HRNet. Interestingly, when fewer, high confidence bounding boxes
are used, HRNet's performance degrades (by 5 AP) on OCHuman, whereas MIPNet
maintains a relatively stable performance (drop of 1 AP) for the same inputs.


Abstract:
  Diversity plays a crucial role in multiple contexts such as team formation,
representation of minority groups and generally when allocating resources
fairly. Given a community composed by individuals of different types, we study
the problem of partitioning this community such that the global diversity is
preserved as much as possible in each subgroup. We consider the diversity
metric introduced by Simpson in his influential work that, roughly speaking,
corresponds to the inverse probability that two individuals are from the same
type when taken uniformly at random, with replacement, from the community of
interest. We provide a novel perspective by reinterpreting this quantity in
geometric terms. We characterize the instances in which the optimal partition
exactly preserves the global diversity in each subgroup. When this is not
possible, we provide an efficient polynomial-time algorithm that outputs an
optimal partition for the problem with two types. Finally, we discuss further
challenges and open questions for the problem that considers more than two
types.


Abstract:
  We prove the Extended Delta Conjecture of Haglund, Remmel, and Wilson, a
combinatorial formula for $\Delta _{h_l}\Delta' _{e_k} e_{n}$, where $\Delta'
_{e_k}$ and $\Delta_{h_l}$ are Macdonald eigenoperators and $e_n$ is an
elementary symmetric function. We actually prove a stronger identity of
infinite series of $GL_m$ characters expressed in terms of LLT series. This is
achieved through new results in the theory of the Schiffmann algebra and its
action on the algebra of symmetric functions.


Abstract:
  Finding experts drives successful collaborations and high-quality product
development in academic and research domains. To contribute to the expert
finding research community, we have developed ExpFinder which is a novel
ensemble model for expert finding by integrating an $N$-gram vector space model
($n$VSM) and a graph-based model ($\mu$CO-HITS). This paper provides
descriptions of ExpFinder's architecture, key components, functionalities, and
illustrative examples. ExpFinder is an effective and competitive model for
expert finding, significantly outperforming a number of expert finding models.


Abstract:
  Three decades ago, Atick et al. suggested that human frequency sensitivity
may emerge from the enhancement required for a more efficient analysis of
retinal images. Here we reassess the relevance of low-level vision tasks in the
explanation of the Contrast Sensitivity Functions (CSFs) in light of (1) the
current trend of using artificial neural networks for studying vision, and (2)
the current knowledge of retinal image representations.
  As a first contribution, we show that a very popular type of convolutional
neural networks (CNNs), called autoencoders, may develop human-like CSFs in the
spatio-temporal and chromatic dimensions when trained to perform some basic
low-level vision tasks (like retinal noise and optical blur removal), but not
others (like chromatic adaptation) or pure reconstruction after simple
bottlenecks). As an illustrative example, the best CNN (in the considered set
of simple architectures for enhancement of the retinal signal) reproduces the
CSFs with an RMSE error of 11\% of the maximum sensitivity.
  As a second contribution, we provide experimental evidence of the fact that,
for some functional goals (at low abstraction level), deeper CNNs that are
better in reaching the quantitative goal are actually worse in replicating
human-like phenomena (such as the CSFs). This low-level result (for the
explored networks) is not necessarily in contradiction with other works that
report advantages of deeper nets in modeling higher-level vision goals.
However, in line with a growing body of literature, our results suggests
another word of caution about CNNs in vision science since the use of
simplified units or unrealistic architectures in goal optimization may be a
limitation for the modeling and understanding of human vision.


Abstract:
  In this work we propose a new algorithm for the computation of statistical
equilibrium quantities on a cubic lattice when both an energy and a statistical
temperature are involved. We demonstrate that the pivot algorithm used in
situations such as protein folding works well for a small range of temperatures
near the polymeric case, but it fails in other situations. The new algorithm,
using localized transformations, seems to perform well for all possible
temperature values. Having reliably approximated the values of equilibrium
energy, we also propose an efficient way to compute equilibrium entropy for all
temperature values. We apply the algorithms in the context of suction or
supercritical vortices in a tornadic flow, which are approximated by vortex
filaments on a cubic lattice. We confirm that supercritical (smooth,
"straight") vortices have the highest energy and correspond to negative
temperatures in this model. The lowest-energy configurations are folded up and
"balled up" to a great extent. The results support A. Chorin's findings that,
in the context of supercritical vortices in a tornadic flow, when such
high-energy vortices stretch, they need to fold.