Abstract:
  Access to large and diverse computer-aided design (CAD) drawings is critical
for developing symbol spotting algorithms. In this paper, we present
FloorPlanCAD, a large-scale real-world CAD drawing dataset containing over
10,000 floor plans, ranging from residential to commercial buildings. CAD
drawings in the dataset are all represented as vector graphics, which enable us
to provide line-grained annotations of 30 object categories. Equipped by such
annotations, we introduce the task of panoptic symbol spotting, which requires
to spot not only instances of countable things, but also the semantic of
uncountable stuff. Aiming to solve this task, we propose a novel method by
combining Graph Convolutional Networks (GCNs) with Convolutional Neural
Networks (CNNs), which captures both non-Euclidean and Euclidean features and
can be trained end-to-end. The proposed CNN-GCN method achieved
state-of-the-art (SOTA) performance on the task of semantic symbol spotting,
and help us build a baseline network for the panoptic symbol spotting task. Our
contributions are three-fold: 1) to the best of our knowledge, the presented
CAD drawing dataset is the first of its kind; 2) the panoptic symbol spotting
task considers the spotting of both thing instances and stuff semantic as one
recognition problem; and 3) we presented a baseline solution to the panoptic
symbol spotting task based on a novel CNN-GCN method, which achieved SOTA
performance on semantic symbol spotting. We believe that these contributions
will boost research in related areas.


Abstract:
  We devise an efficient scheme to determine vibrational properties from Path
Integral Molecular Dynamics (PIMD) simulations. The method is based on
zero-time Kubo-transformed correlation functions and captures the anharmonicity
of the potential due to both temperature and quantum effects. Using analytical
derivations and numerical calculations on toy-model potentials, we show that
two different estimators built upon PIMD correlation functions fully
characterize the phonon spectra and the anharmonicity strength. The first
estimator is associated with force-force quantum correlators and gives access
to the fundamental frequencies and thermodynamic properties of the quantum
system. The second one is instead connected to displacement-displacement
correlators and probes the lowest-energy phonon excitations with high accuracy.
We also prove that the use of generalized eigenvalue equations, in place of the
standard normal mode equations, leads to a significant speed-up in the PIMD
phonon calculations, both in terms of faster convergence rate and smaller
time-step bias. Within this framework, using ab initio PIMD simulations, we
compute phonon dispersions of diamond and of the high-pressure I41/amd phase of
atomic hydrogen. We find that, in the latter case, the anharmonicity is
stronger than previously estimated and yields a sizeable red-shift in the
vibrational spectrum of atomic hydrogen.


Abstract:
  This three-part paper comprises: (i) a critique by Halvorson of Bell's (1973)
paper "Subject and Object"; (ii) a comment by Butterfield; (iii) a reply by
Halvorson. An Appendix gives the passage from Bell that is the focus of
Halvorson's critique.


Abstract:
  We study the order of convergence of Galerkin variational integrators for
ordinary differential equations. Galerkin variational integrators approximate a
variational (Lagrangian) problem by restricting the space of curves to the set
of polynomials of degree at most $s$ and approximating the action integral
using a quadrature rule. We show that, if the quadrature rule is sufficiently
accurate, the order of the integrators thus obtained is $2s$.


Abstract:
  We report herein, the potential of raw pinecone powder (PCP) and pinecone
biochar (PCBC) as alternatives to activated carbon used in Permeable Adsorptive
Barriers (PABs) for the in situ remediation of polluted groundwater. A
constructed lab-scale unconfined aquifer ($38\times30\times17$ $cm$) fitted
with PCP and PCBC PABs ($21\times3\times20$ $cm$), was evaluated for the
removal of $Pb^{2+}$ ions in a continuous flow setup. Results indicate that
after $3600$ minutes, PCP was able to reduce $Pb^{2+}$ ions from a Co=$50$
$mg/L$ to $7.94$ $mg/L$ for the first run and $19.4$ $mg/L$ for a second run,
respectively. Comparatively, PCBC reached $6.5$ $mg/L$ for the first run and
$8.94$ for the second run. It was confirmed that adsorption was best described
by the first-order kinetic model with $R^2$ values above $0.95$. Maximum
adsorption capacity values were found to be $1.00$, $0.63$, $1.08$, and $0.85$
mg/g for each scenario respectively. In addition, nonlinear regression models
of exponential and Gaussian Processes are fit to explain remediation by time
for $Pb^{2+}$ and Methylene Blue. Gaussian Processes are able to better explain
the variation of pollution removal compared to simpler exponential models. When
regressed against true removal percentages all models are able to provide
$R^2>0.99$.


Abstract:
  Vision-language Navigation (VLN) tasks require an agent to navigate
step-by-step while perceiving the visual observations and comprehending a
natural language instruction. Large data bias, which is caused by the disparity
ratio between the small data scale and large navigation space, makes the VLN
task challenging. Previous works have proposed various data augmentation
methods to reduce data bias. However, these works do not explicitly reduce the
data bias across different house scenes. Therefore, the agent would overfit to
the seen scenes and achieve poor navigation performance in the unseen scenes.
To tackle this problem, we propose the Random Environmental Mixup (REM) method,
which generates cross-connected house scenes as augmented data via mixuping
environment. Specifically, we first select key viewpoints according to the room
connection graph for each scene. Then, we cross-connect the key views of
different scenes to construct augmented scenes. Finally, we generate augmented
instruction-path pairs in the cross-connected scenes. The experimental results
on benchmark datasets demonstrate that our augmentation data via REM help the
agent reduce its performance gap between the seen and unseen environment and
improve the overall performance, making our model the best existing approach on
the standard VLN benchmark. The code have released:
https://github.com/LCFractal/VLNREM.


Abstract:
  End-to-end multilingual speech recognition involves using a single model
training on a compositional speech corpus including many languages, resulting
in a single neural network to handle transcribing different languages. Due to
the fact that each language in the training data has different characteristics,
the shared network may struggle to optimize for all various languages
simultaneously. In this paper we propose a novel multilingual architecture that
targets the core operation in neural networks: linear transformation functions.
The key idea of the method is to assign fast weight matrices for each language
by decomposing each weight matrix into a shared component and a language
dependent component. The latter is then factorized into vectors using rank-1
assumptions to reduce the number of parameters per language. This efficient
factorization scheme is proved to be effective in two multilingual settings
with $7$ and $27$ languages, reducing the word error rates by $26\%$ and $27\%$
rel. for two popular architectures LSTM and Transformer, respectively.


Abstract:
  People assume different and important roles within social networks. Some
roles have received extensive study: that of influencers who are
well-connected, and that of brokers who bridge unconnected parts of the
network. However, very little work has explored another potentially important
role, that of creating opportunities for people to interact and facilitating
conversation between them. These individuals bring people together and act as
social catalysts. In this paper, we test for the presence of social catalysts
on the online social network Facebook. We first identify posts that have
spurred conversations between the poster's friends and summarize the
characteristics of such posts. We then aggregate the number of catalyzed
comments at the poster level, as a measure of the individual's "catalystness."
The top 1% of such individuals account for 31% of catalyzed interactions,
although their network characteristics do not differ markedly from others who
post as frequently and have a similar number of friends. By collecting survey
data, we also validate the behavioral measure of catalystness: a person is more
likely to be nominated as a social catalyst by their friends if their posts
prompt discussions between other people more frequently. The measure, along
with other conversation-related features, is one of the most predictive of a
person being nominated as a catalyst. Although influencers and brokers may have
gotten more attention for their network positions, our findings provide
converging evidence that another important role exists and is recognized in
online social networks.


Abstract:
  This paper presents a new derivation method of converse bounds on the
non-asymptotic achievable rate of memoryless discrete channels. It is based on
the finite blocklength statistics of the channel, where with the use of an
auxiliary channel the converse bound is produced. This methodology is general
and initially presented for an arbitrary channel. Afterwards, the main result
is specialized for the $q$-ary erasure (QEC), binary symmetric (BSC), and Z
channels.


Abstract:
  Modeling equity in the allocation of scarce resources is a fast-growing
concern in the humanitarian logistics field. The Gini coefficient is one of the
most widely recognized measures of inequity and it was originally characterized
by means of the Lorenz curve, which is a mathematical function that links the
cumulative share of income to rank-ordered groups in a population. So far,
humanitarian logistics models that have approached equity using the Gini
coefficient do not actually optimize its original formulation, but use
alternative definitions that do not necessarily replicate that original Gini
measure. In this paper, we derive the original Gini coefficient via the Lorenz
curve to optimize the effectiveness-equity trade-off in a humanitarian
location-allocation problem. We also propose new valid inequalities based on an
upper-bounding Lorenz curve to tighten the linear relaxation of our model and
develop a clustering-based construction of the Lorenz curve that requires fewer
additional constraints and variables than the original one. The computational
study, based on the floods and landslides in Rio de Janeiro state, Brazil,
reveals that while alternative Gini definitions have interesting properties,
they can generate vastly different decisions compared to the original Gini
coefficient. In addition, viewed from the perspective of the original Gini
coefficient, these decisions can be significantly less equitable.


Abstract:
  In topological equivalence, a bounded linear operator between Banach spaces -
we focus on the case of Hilbert spaces - is viewed as only acting linearly and
continuously between them qua different spaces with the structure of linear
topological space. For instance, invertible operators in Banach spaces (that
is, isomorphisms among them) will make up one equivalence class for each class
of isomorphic spaces. On the other hand, compact and non-compact operators, or
operators with or without a kernel, clearly will not. We make some crucial
steps towards describing invariants that will characterize these topological
equivalence classes.


Abstract:
  We introduce a general framework for large-scale model-based derivative-free
optimization based on iterative minimization within random subspaces. We
present a probabilistic worst-case complexity analysis for our method, where in
particular we prove high-probability bounds on the number of iterations before
a given optimality is achieved. This framework is specialized to nonlinear
least-squares problems, with a model-based framework based on the Gauss-Newton
method. This method achieves scalability by constructing local linear
interpolation models to approximate the Jacobian, and computes new steps at
each iteration in a subspace with user-determined dimension. We then describe a
practical implementation of this framework, which we call DFBGN. We outline
efficient techniques for selecting the interpolation points and search
subspace, yielding an implementation that has a low per-iteration linear
algebra cost (linear in the problem dimension) while also achieving fast
objective decrease as measured by evaluations. Extensive numerical results
demonstrate that DFBGN has improved scalability, yielding strong performance on
large-scale nonlinear least-squares problems.


Abstract:
  In this study, we explore the evolution of lithium in giant stars based on
data assembled from the literature on asteroseismology and Li abundances for
giants. Our final sample of 187 giants consists of 44 red giant branch (RGB),
140 core He-burning (CHeB) and three giants with an unclassified evolutionary
phase. For all 187 stars, the seismic parameters $\nu\rm_{max}$ (frequency of
maximum oscillation power) and $\Delta \nu$ (large frequency spacing) are
available, while $\Delta \Pi\rm_{1}$ (the asymptotic gravity-mode period
spacing) is available for a subset of 64. For some of the CHeB giants, mass
estimates from the asteroseismic scaling relations are found to be
underestimated when compared with mass estimates from isochrones based on
seismic data. Whilst most of the Li-rich giants in the sample have masses less
than 1.5 $M_\odot$, they are also present up to and beyond the maximum mass
expected to have suffered a core He-flash, i.e. $M$ $\leq$ 2.25 $M_\odot$: this
suggests contributions from other processes towards Li enrichment. To
understand the evolution of giants in the $\Delta \Pi\rm_{1}$ $-$ $\Delta \nu$
plane, we use the {\it Modules for Experiments in Stellar Astrophysics} models
which show the presence of mini He-flashes following the initial strong core
He-flash. From the distribution of A(Li) as a function of $\Delta \nu$, which
is similar to the distribution of A(Li) as a function of luminosity, we find no
indication of Li enrichment near the luminosity bump. Also, A(Li) trends to
$\sim$ -1.5 dex near the RGB tip. The data also suggest a decrease in A(Li)
with an increase in $\Delta \Pi\rm_{1}$ for CHeB giants.


Abstract:
  We propose the novel use of a generative adversarial network (GAN) (i) to
make predictions in time (PredGAN) and (ii) to assimilate measurements
(DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like
properties of generative models and the ability to simulate forwards and
backwards in time. GANs have received much attention recently, after achieving
excellent results for their generation of realistic-looking images. We wish to
explore how this property translates to new applications in computational
modelling and to exploit the adjoint-like properties for efficient data
assimilation. To predict the spread of COVID-19 in an idealised town, we apply
these methods to a compartmental model in epidemiology that is able to model
space and time variations. To do this, the GAN is set within a reduced-order
model (ROM), which uses a low-dimensional space for the spatial distribution of
the simulation states. Then the GAN learns the evolution of the low-dimensional
states over time. The results show that the proposed methods can accurately
predict the evolution of the high-fidelity numerical simulation, and can
efficiently assimilate observed data and determine the corresponding model
parameters.


Abstract:
  A high-throughput correlative study of the local mechanical properties,
chemical composition and crystallographic orientation has been carried out in
selected areas of cast Inconel 718 specimens subjected to three different
tempers. The specimens showed a strong Nb segregation at the scale of the
dendrite arms, with local Nb contents that varied between 2 wt.% in the core of
the dendrite arms to 8 wt.% in the interdendritic regions and 25 wt.% within
the second phase particles (MC carbides, Laves phases and {\delta} phase
needles). The nanohardness was found to correlate strongly with the local Nb
content and the temper condition. On the contrary, the indentation elastic
moduli were not influenced by the local chemical composition or temper
condition, but directly correlated with the crystallographic grain orientation,
due to the high elastic anisotropy of nickel alloys.


Abstract:
  Cosmological models of the early or late universe exhibit (quasi) de Sitter
space-times with different stability properties. Considering models derived
from string theory, the swampland program does not provide for now a definite
characterisation of this stability. In this work we focus on de Sitter
solutions of 10d type II supergravities, candidates for classical de Sitter
string backgrounds: surprisingly, all known examples are unstable with $\eta_V
< -1$. We aim at proving the existence of such a systematic tachyon, and
getting formally a bound on the value of $\eta_V$. To that end, we develop
three methods, giving us various sufficient conditions for having a tachyon
upon assumptions, in analogy with de Sitter no-go theorems. Our analysis
eventually indicates the existence of variety of different tachyons, and
related bounds on $\eta_V$. We use this knowledge to find 10 new de Sitter
solutions of type IIB supergravity, that have tachyons of a different kind,
higher $\eta_V$ values and new 6d geometries. One solution even appears to be
stable, with however non-compact extra dimensions.


Abstract:
  Hyperspectral images (HSIs) have been widely used in a variety of
applications thanks to the rich spectral information they are able to provide.
Among all HSI processing tasks, HSI denoising is a crucial step. Recently, deep
learning-based image denoising methods have made great progress and achieved
great performance. However, existing methods tend to ignore the correlations
between adjacent spectral bands, leading to problems such as spectral
distortion and blurred edges in denoised results. In this study, we propose a
novel HSI denoising network, termed SSCAN, that combines group convolutions and
attention modules. Specifically, we use a group convolution with a spatial
attention module to facilitate feature extraction by directing models'
attention to band-wise important features. We propose a spectral-spatial
attention block (SSAB) to exploit the spatial and spectral information in
hyperspectral images in an effective manner. In addition, we adopt residual
learning operations with skip connections to ensure training stability. The
experimental results indicate that the proposed SSCAN outperforms several
state-of-the-art HSI denoising algorithms.


Abstract:
  We identify an unusual mechanism for quantum oscillations in nodal
semimetals, driven by a single pair of Landau levels periodically closing their
gap at the Fermi energy as a magnetic field is varied. These `zero Landau
level' quantum oscillations (ZQOs) appear in the nodal limit where the
zero-field Fermi volume vanishes, and have distinctive periodicity and
temperature dependence. We link the Landau spectrum of a two-dimensional (2D)
nodal semimetal to the Rabi model, and show by exact solution that across the
entire Landau fan, pairs of opposite-parity Landau levels are intertwined in a
`serpentine' manner. We propose 2D surfaces of topological crystalline
insulators as natural settings for ZQOs, and comment on implications for
anomaly physics in 3D nodal semimetals.


Abstract:
  This paper builds Wasserstein ambiguity sets for the unknown probability
distribution of dynamic random variables leveraging noisy partial-state
observations. The constructed ambiguity sets contain the true distribution of
the data with quantifiable probability and can be exploited to formulate robust
stochastic optimization problems with out-of-sample guarantees. We assume the
random variable evolves in discrete time under uncertain initial conditions and
dynamics, and that noisy partial measurements are available. All random
elements have unknown probability distributions and we make inferences about
the distribution of the state vector using several output samples from multiple
realizations of the process. To this end, we leverage an observer to estimate
the state of each independent realization and exploit the outcome to construct
the ambiguity sets. We illustrate our results in an economic dispatch problem
involving distributed energy resources over which the scheduler has no direct
control.


Abstract:
  Time-resolved photon-counting plays an indispensable role in precision
metrology in both classical and quantum regimes. In particular, time-correlated
single-photon counting (TCSPC) has been the key enabling technology for
applications such as low-light fluorescence lifetime spectroscopy and photon
counting time-of-flight (ToF) 3D imaging. However, state-of-the-art TCSPC
single-photon timing resolution (SPTR) is limited in the range of 10-100 ps by
the available single-photon detector technology. In this paper, we
experimentally demonstrate a time-magnified TCSPC (TM-TCSPC) that achieves an
unprecedentedly short SPTR of 550 fs for the first time with an off-the-shelf
single-photon detector. The TM-TCSPC can resolve ultrashort pulses with a
130-fs pulsewidth difference at a 22-fs accuracy. When applied to photon
counting ToF 3D imaging, the TM-TCSPC greatly suppresses the range walk error
that limits all photon counting ToF 3D imaging systems by 99.2 % (130 times)
and thus provides unprecedentedly high depth measurement accuracy and precision
of 26 {\mu}m and 3 {\mu}m, respectively.


Abstract:
  Disruptions in tokamak plasmas may lead to the generation of runaway
electrons that have the potential to damage plasma-facing components. Improved
understanding of the runaway generation process requires interpretative
modelling of experiments. In this work we simulate eight discharges in the
ASDEX Upgrade and JET tokamaks, where argon gas was injected to trigger the
disruption. We use a fluid modelling framework with the capability to model the
generation of runaway electrons through the hot-tail, Dreicer and avalanche
mechanisms, as well as runaway electron losses. Using experimentally based
initial values of plasma current and electron temperature and density, we can
reproduce the plasma current evolution using realistic assumptions about
temperature evolution and assimilation of the injected argon in the plasma. The
assumptions and results are similar for the modelled discharges in ASDEX
Upgrade and JET, indicating that the implemented models are applicable to
machines of varying size, which is important for the modelling of future,
larger machines. For the modelled discharges in ASDEX Upgrade, where the
initial temperature was comparatively high, we had to assume that a large
fraction of the hot-tail runaway electrons were lost in order to reproduce the
measured current evolution.


Abstract:
  Metamaterials are artificial structures with unusual and superior properties
that come from their carefully designed building blocks -- also called
meta-atoms. Metamaterials have permeated large swatches of science, including
electromagnetics and mechanics. Although metamaterials hold the promise for
realizing technological advances, their potential to enhance interactions
between humans and materials has remained unexplored. Here, we devise
meta-atoms with tailored fracture properties to control mouthfeel sensory
experience. Using chocolate as a model material, we first use meta-atoms to
control the fracture anisotropy and the number of cracks and demonstrate that
these properties are captured in mouthfeel experience. We further use topology
optimization to rationally design edible meta-atoms with maximally anisotropic
fracture strength. Our work opens avenues for the use of meta-atoms and
metamaterials to control fracture and to enhance human-matter interactions.


Abstract:
  We define $\Delta$-Baire spaces. If a paratopological group $G$ is
$\Delta$-Baire space, then $G$ is a topological group. Locally pseudocompact
spaces, Baire $p$-spaces, Baire $\Sigma$-spaces, products of \v{C}ech-complete
spaces are $\Delta$-Baire spaces.


Abstract:
  The $3x+1$ map $T$ is defined on the $2$-adic integers $\mathbb{Z}_2$ by
$T(x)=x/2$ for even $x$ and $T(x)=(3x+1)/2$ for odd $x$. It is still unproved
that under iteration of $T$ the trajectory of any rational $2$-adic integer is
eventually cyclic. A $2$-adic integer is rational if and only if its
representation with $1$'s and $0$'s is eventually periodic. We prove that the
$3x+1$ conjugacy $\Phi$ maps aperiodic $v\in\mathbb{Z}_2$ onto aperiodic
$2$-adic integers provided that
$\underline{\lim}\;(\frac{h}{\ell})_{\ell=1}^{\infty} > \frac{\ln(2)}{\ln(3)}$
where $h$ is the number of $1$'s in the first $\ell$ digits of $v$ with the
following constraint: if there is a rational $2$-adic integer with a non-cyclic
trajectory, then necessarily
$\underline{\lim}\;(\frac{h}{\ell})_{\ell=1}^{\infty}=\frac{\ln(2)}{\ln(3)}$.
We study $\Phi$ as an infinite series in $\mathbb{R}$ and obtain negative
irrational numbers for which we compute their aperiodic $2$-adic expansion. We
find prominent behaviors of the orbit of $x$ taking Sturmian words as parity
vector. We also found amazing results of the terms of $\Phi$ in $\mathbb{R}$.
We define the $\ell$'th iterate of $T$ for $\ell\rightarrow \infty$ in the ring
of $3$-adic integers and obtain positive irrational numbers for which we
compute their aperiodic $3$-adic expansion.


Abstract:
  Transverse thermoelectric devices consist of only one thermoelectric
material, unlike conventional longitudinal thermoelectric devices that require
two types of thermoelectric materials with p- and n-type polarities. However,
scalable synthesis of materials that demonstrate axis-dependent carrier
polarity, which is a prospective component to demonstrate the transverse
thermoelectric device, is challenging. This paper reports that polycrystalline
NaSn$_2$As$_2$, which was prepared by using uniaxial hot pressing, displayed
axis-dependent carrier polarity. The preferred orientation of the sample was
confirmed through X-ray diffraction measurements. Seebeck coefficient
measurements indicate that carrier polarity depends on the measurement
direction, which is consistent with recently reported results on single
crystals of NaSn$_2$As$_2$. Given that our sample preparation procedure is
readily scalable, the present work shows the possibility for preparing
transverse thermoelectric devices using polycrystalline NaSn$_2$As$_2$ with a
preferred orientation.


Abstract:
  Let $G=SL(2,5)$ be the special linear group of $2 \times 2$-matrices with
coefficients in the field with $5$ elements. We show that the principal block
over a splitting field $K$ of characteristic two of the group algebra $KG$ has
a $3$-cluster tilting module. This gives the first example of a
representation-infinite block of a group algebra having a cluster tilting
module and answers a question by Erdmann and Holm.


Abstract:
  ML workloads are becoming increasingly popular in the cloud. Good cloud
training performance is contingent on efficient parameter exchange among VMs.
We find that Collectives, the widely used distributed communication algorithms,
cannot perform optimally out of the box due to the hierarchical topology of
datacenter networks and multi-tenancy nature of the cloudenvironment.In this
paper, we present Cloud Collectives , a prototype that accelerates collectives
by reordering theranks of participating VMs such that the communication pattern
dictated by the selected collectives operation best exploits the locality in
the network.Collectives is non-intrusive, requires no code changes nor rebuild
of an existing application, and runs without support from cloud providers. Our
preliminary application of Cloud Collectives on allreduce operations in public
clouds results in a speedup of up to 3.7x in multiple microbenchmarks and 1.3x
in real-world workloads of distributed training of deep neural networks and
gradient boosted decision trees using state-of-the-art frameworks.


Abstract:
  In rate-distortion (RD) problems one seeks reduced representations of a
source that meet a target distortion constraint. Such optimal representations
undergo topological transitions at some critical rate values, when their
cardinality or dimensionality change. We study the convergence time of the
Arimoto-Blahut alternating projection algorithms, used to solve such problems,
near those critical points, both for the rate-distortion and information
bottleneck settings. We argue that they suffer from critical slowing down -- a
diverging number of iterations for convergence -- near the critical points.
This phenomenon can have theoretical and practical implications for both
machine learning and data compression problems.


Abstract:
  Besides conventional geostationary (GSO) satellite broadband communication
services, non-geostationary (NGSO) satellites are envisioned to support various
new communication use cases from countless industries. These new scenarios
bring many unprecedented challenges that will be discussed in this paper
alongside with several potential future research opportunities. NGSO systems
are known for various advantages, including their important features of low
cost, lower propagation delay, smaller size, and lower losses in comparison to
GSO satellites. However, there are still many deployment challenges to be
tackled to ensure seamless integration not only with GSO systems but also with
terrestrial networks. In this paper, we discuss several key challenges
including satellite constellation and architecture designs, coexistence with
GSO systems in terms of spectrum access and regulatory issues, resource
management algorithms, and NGSO networking requirements. Additionally, the
latest progress in provisioning secure communication via NGSO systems is
discussed. Finally, this paper identifies multiple important open issues and
research directions to inspire further studies towards the next generation of
satellite networks.


Abstract:
  Noisy Intermediate-Scale Quantum (NISQ) hardware has unavoidable noises, and
crosstalk error is a significant error source. When multiple quantum operations
are executed simultaneously, the quantum state can be corrupted due to the
crosstalk between gates during simultaneous operations, decreasing the circuit
fidelity. In this work, we first report on several protocols for characterizing
crosstalk. Then, we discuss different crosstalk mitigation methods from the
hardware and software perspectives. Finally, we perform crosstalk injection
experiments on the IBM quantum device and demonstrate the fidelity improvement
with the crosstalk mitigation method.


Abstract:
  Spin canting and complex spin textures in antiferromagnetic materials can
often be described in terms of Dzyaloshinskii-Moriya interactions (DMI). Values
for DMI parameters are not easily measurable directly, and often inferred from
other quantities. In this work, we examine how domain wall dynamics in an
antiferromagnetic optomagnonic system can display unique features directly
related to the existence of DMI. Our results indicate that the presence of DMI
enables spin interactions with cavity photons in a geometry which otherwise
allows no magneto-optical coupling, and on the other hand modulates frequencies
in a geometry where coupling is already realized in the absence of DMI. This
result may be used to measure the DMI constant in optomagnonic experiments by
comparing resonances obtained with different polarisations of the exciting
field.


Abstract:
  To raise the superconducting-transition temperature (Tc) has been the driving
force for the long, sustained effort in superconductivity research. Recent
progress in hydrides with Tcs up to 287 K under 267 GPa has heralded a new era
of room-temperature superconductivity (RTS) with immense technological promise.
Indeed, RTS has lifted the temperature barrier for the ubiquitous application
of superconductivity. Unfortunately, formidable pressure is required to attain
such high Tcs. The most effective relief to this impasse is to remove the
pressure needed while retaining the pressure-induced Tc without pressure. Here
we show such a possibility in the pure and doped high-temperature
superconductor (HTS) FeSe by retaining, at ambient via pressure-quenching (PQ),
its Tc up to 37 K (quadrupling that of a pristine FeSe) and other
pressure-induced phases. We have also observed that some phases remain stable
without pressure at up to 300 K and for at least 7 days. The observations are
in qualitative agreement with our ab initio simulations using the solid-state
nudged elastic band (SSNEB) method. We strongly believe that the PQ technique
developed here can be adapted to the RTS hydrides and other materials of value
with minimal effort.


Abstract:
  We prove that every elliptic curve defined over a totally real number field
of degree 4 not containing $\sqrt{5}$ is modular. To this end, we study the
quartic points on four modular curves.


Abstract:
  This paper is the first of a three part series in which we explore geometric
and analytic properties of the Kohn Laplacian and its inverse on general
quadric submanifolds of $\mathbb{C}^n\times\mathbb{C}^m$. In this paper, we
present a streamlined calculation for a general integral formula for the
complex Green operator $N$ and the projection onto the nullspace of $\Box_b$.
The main application of our formulas is the critical case of codimension two
quadrics in $\mathbb{C}^4$ where we discuss the known solvability and
hypoellipticity criteria of Peloso and Ricci \cite{PeRi03}. We also provide
examples to show that our formulas yield explicit calculations in some
well-known cases: the Heisenberg group and a Cartesian product of Heisenberg
groups.


Abstract:
  Spoken intent detection has become a popular approach to interface with
various smart devices with ease. However, such systems are limited to the
preset list of intents-terms or commands, which restricts the quick
customization of personal devices to new intents. This paper presents a
few-shot spoken intent classification approach with task-agnostic
representations via meta-learning paradigm. Specifically, we leverage the
popular representation-based meta-learning learning to build a task-agnostic
representation of utterances, that then use a linear classifier for prediction.
We evaluate three such approaches on our novel experimental protocol developed
on two popular spoken intent classification datasets: Google Commands and the
Fluent Speech Commands dataset. For a 5-shot (1-shot) classification of novel
classes, the proposed framework provides an average classification accuracy of
88.6% (76.3%) on the Google Commands dataset, and 78.5% (64.2%) on the Fluent
Speech Commands dataset. The performance is comparable to traditionally
supervised classification models with abundant training samples.


Abstract:
  Nitrogen-vacancy (NV) color centers in diamond have been demonstrated as
useful magnetic sensors, in particular for measuring spin fluctuations,
achieving high sensitivity and spatial resolution. These abilities can be used
to explore various biological and chemical processes, catalyzed by Reactive
Oxygen Species (ROS). Here we demonstrate a novel approach to measure and
quantify Hydroxyl radicals with high spatial resolution, using the fluorescence
difference between NV charged states. According to the results, the achieved NV
sensitivity is $11 \pm 4 \frac{nM}{\sqrt Hz}$, realized in-situ without spin
labels and localized to a volume of $\sim 10$ picoliter.


Abstract:
  Suppose a researcher observes individuals within a county within a state.
Given concerns about correlation across individuals, at which level should they
cluster their observations for inference? This paper proposes a modified
randomization test as a robustness check for their chosen specification in a
linear regression setting. Existing tests require either the number of states
or number of counties to be large. Our method is designed for settings with few
states and few counties. While the method is conservative, it has competitive
power in settings that may be relevant to empirical work.


Abstract:
  Machine learning classification models are vulnerable to adversarial examples
-- effective input-specific perturbations that can manipulate the model's
output. Universal Adversarial Perturbations (UAPs), which identify noisy
patterns that generalize across the input space, allow the attacker to greatly
scale up the generation of these adversarial examples. Although UAPs have been
explored in application domains beyond computer vision, little is known about
their properties and implications in the specific context of realizable
attacks, such as malware, where attackers must reason about satisfying
challenging problem-space constraints.
  In this paper, we explore the challenges and strengths of UAPs in the context
of malware classification. We generate sequences of problem-space
transformations that induce UAPs in the corresponding feature-space embedding
and evaluate their effectiveness across threat models that consider a varying
degree of realistic attacker knowledge. Additionally, we propose adversarial
training-based mitigations using knowledge derived from the problem-space
transformations, and compare against alternative feature-space defenses. Our
experiments limit the effectiveness of a white box Android evasion attack to
~20 % at the cost of 3 % TPR at 1 % FPR. We additionally show how our method
can be adapted to more restrictive application domains such as Windows malware.
  We observe that while adversarial training in the feature space must deal
with large and often unconstrained regions, UAPs in the problem space identify
specific vulnerabilities that allow us to harden a classifier more effectively,
shifting the challenges and associated cost of identifying new universal
adversarial transformations back to the attacker.


Abstract:
  The quantum to classical transition of fluctuations in the early universe is
still not completely understood. Some headway has been made incorporating the
effects of decoherence and the squeezing of states, though the methods and
procedures continue to be challenged. But new developments in the analysis of
the most recent Planck data suggest that the primordial power spectrum has a
cutoff associated with the very first quantum fluctuation to have emerged into
the semi-classical universe from the Planck domain at about the Planck time. In
this paper, we examine the implications of this result on the question of
classicalization, and demonstrate that the birth of quantum fluctuations at the
Planck scale would have been a `process' supplanting the need for a
`measurement' in quantum mechanics. Emerging with a single wavenumber, these
fluctuations would have avoided the interference between different degrees of
freedom in a superposed state. Moreover, the implied scalar-field potential had
an equation-of-state consistent with the zero active mass condition in general
relativity, allowing the quantum fluctuations to emerge in their ground state
with a time-independent frequency. They were therefore effectively quantum
harmonic oscillators with classical correlations in phase space from the very
beginning.


Abstract:
  Digital sensing provides an unprecedented opportunity to assess and
understand mobility. However, incompleteness, missing information, possible
inaccuracies, and temporal heterogeneity in the geolocation data can undermine
its applicability. As mobility patterns are often repeated, we propose a method
to use similar trajectory patterns from the local vicinity and
probabilistically ensemble them to robustly reconstruct missing or unreliable
observations. We evaluate the proposed approach in comparison with traditional
functional trajectory interpolation using a case of sea vessel trajectory data
provided by The Automatic Identification System (AIS). By effectively
leveraging the similarities in real-world trajectories, our pattern ensembling
method helps to reconstruct missing trajectory segments of extended length and
complex geometry. It can be used for locating mobile objects when temporary
unobserved as well as for creating an evenly sampled trajectory interpolation
useful for further trajectory mining.


Abstract:
  We consider the existence and spectral stability of static multi-kink
structures in the discrete sine-Gordon equation, as a representative example of
the family of discrete Klein-Gordon models. The multi-kinks are constructed
using Lin's method from an alternating sequence of well-separated kink and
antikink solutions. We then locate the point spectrum associated with these
multi-kink solutions by reducing the spectral problem to a matrix equation. For
an $m$-structure multi-kink, there will be $m$ eigenvalues in the point
spectrum near each eigenvalue of the primary kink, and, as long as the spectrum
of the primary kink is imaginary, the spectrum of the multi-kink will be as
well. We obtain analytic expressions for the eigenvalues of a multi-kink in
terms of the eigenvalues and corresponding eigenfunctions of the primary kink,
and these are in very good agreement with numerical results. We also perform
numerical time-stepping experiments on perturbations of multi-kinks, and the
outcomes of these simulations are interpreted using the spectral results.


Abstract:
  Slow or even stop electromagnetic (EM) waves attract researchers' attentions
for its potential applications in energy storage, optical buffer and
nonlinearity enhancement. However, in most cases of the EM waves trapping, the
EM waves are not truly trapped due to the existence of reflection. In this
paper, a novel metal-semiconductor-semiconductor-metal (MSSM) structure, and a
novel truly rainbow trapping in a tapered MSSM model at terahertz frequencies
are demonstrated by theoretical analysis and numerical simulations. More
importantly, functional devices such as optical buffer, optical switch and
optical filter are achieved in our designed MSSM structure based on truly
rainbow trapping theory. Owing to the property of one-way propagation, these
new types of optical devices can be high-performance and are expected to be
used in integrated optical circuits.


Abstract:
  In this work we consider particle creation by the expansion of the universe,
using two Bianchi type I anisotropic models. The particles studied are of spin
0 and 1/2. The cosmological models have rotational symmetry, which allows us to
solve exactly the equations of motion. The number density of the created
particles is calculated with the method of Bogolubov transformations.


Abstract:
  Aerial scene recognition is a fundamental research problem in interpreting
high-resolution aerial imagery. Over the past few years, most studies focus on
classifying an image into one scene category, while in real-world scenarios, it
is more often that a single image contains multiple scenes. Therefore, in this
paper, we investigate a more practical yet underexplored task -- multi-scene
recognition in single images. To this end, we create a large-scale dataset,
called MultiScene, composed of 100,000 unconstrained high-resolution aerial
images. Considering that manually labeling such images is extremely arduous, we
resort to low-cost annotations from crowdsourcing platforms, e.g.,
OpenStreetMap (OSM). However, OSM data might suffer from incompleteness and
incorrectness, which introduce noise into image labels. To address this issue,
we visually inspect 14,000 images and correct their scene labels, yielding a
subset of cleanly-annotated images, named MultiScene-Clean. With it, we can
develop and evaluate deep networks for multi-scene recognition using clean
data. Moreover, we provide crowdsourced annotations of all images for the
purpose of studying network learning with noisy labels. We conduct experiments
with extensive baseline models on both MultiScene-Clean and MultiScene to offer
benchmarks for multi-scene recognition in single images and learning from noisy
labels for this task, respectively. To facilitate progress, we make our dataset
and trained models available on
https://gitlab.lrz.de/ai4eo/reasoning/multiscene.


Abstract:
  In this paper, we ask whether vocal source features (pitch, shimmer, jitter,
etc) can improve the performance of automatic sung speech recognition, arguing
that conclusions previously drawn from spoken speech studies may not be valid
in the sung speech domain. We first use a parallel singing/speaking corpus
(NUS-48E) to illustrate differences in sung vs spoken voicing characteristics
including pitch range, syllables duration, vibrato, jitter and shimmer. We then
use this analysis to inform speech recognition experiments on the sung speech
DSing corpus, using a state of the art acoustic model and augmenting
conventional features with various voice source parameters. Experiments are run
with three standard (increasingly large) training sets, DSing1 (15.1 hours),
DSing3 (44.7 hours) and DSing30 (149.1 hours). Pitch combined with degree of
voicing produces a significant decrease in WER from 38.1% to 36.7% when
training with DSing1 however smaller decreases in WER observed when training
with the larger more varied DSing3 and DSing30 sets were not seen to be
statistically significant. Voicing quality characteristics did not improve
recognition performance although analysis suggests that they do contribute to
an improved discrimination between voiced/unvoiced phoneme pairs.


Abstract:
  Co$_3$Sn$_2$S$_2$ is a magnetic Weyl semimetal, in which ferromagnetic
ordering at 177K is predicted to stabilize Weyl points. We perform temperature
and spatial dependent angle--resolved photoemission spectroscopy measurements
through the Curie temperature ($T_c$), which show large band shifts and
renormalization concomitant with the onset of magnetism. We argue that
Co$_3$Sn$_2$S$_2$ evolves from a Mott ferromagnet below $T_c$ to a correlated
metallic state above $T_c$. To understand the magnetism, we derive a
tight-binding model of Co-$3d_{x^2-y^2}$ orbitals on the kagome lattice. At the
filling obtained by first-principles calculations, this model reproduces the
ferromagnetic ground state, and results in the reduction of Coulomb
interactions due to cluster effects. Using a disordered local moment
simulation, we show how this reduced Hubbard-$U$ leads to a collapse of the
bands across the magnetic transition, resulting in a correlated state which
carries associated characteristic photoemission signatures that are distinct
from those of a simple lifting of exchange splitting. The behavior of topology
across $T_c$ is discussed in the context of this description of the magnetism.


Abstract:
  The Green Bank North Celestial Cap (GBNCC) survey is a 350-MHz all-sky survey
for pulsars and fast radio transients using the Robert C. Byrd Green Bank
Telescope. To date, the survey has discovered over 190 pulsars, including 33
millisecond pulsars (MSPs) and 24 rotating radio transients(RRATs). Several
exotic pulsars have been discovered in the survey, including PSR J1759+5036, a
binary pulsar with a 176-ms spin period in an orbit with a period of 2.04 days,
an eccentricity of 0.3,and a projected semi-major axis of 6.8 light seconds.
Using seven years of timing data, we are able to measure one post-Keplerian
parameter, advance of periastron, which has allowed us to constrain the total
system mass to 2.62(3) solar masses. This constraint, along with the spin
period and orbital parameters, suggests that this is a double neutron star
system, although we cannot entirely rule out a pulsar-white dwarf binary. This
pulsar is only detectable in roughly 45% of observations, most likely due to
scintillation. However, additional observations are required to determine
whether there may be other contributing effects.


Abstract:
  The class of assignment problems is a fundamental and well-studied class in
the intersection of Social Choice, Computational Economics and Discrete
Allocation. In a general assignment problem, a group of agents expresses
preferences over a set of items, and the task is to allocate items to agents in
an "optimal" way. A verification variant of this problem includes an allocation
as part of the input, and the question becomes whether this allocation is
"optimal". In this paper, we generalize the verification variant to the setting
where each agent is equipped with multiple incomplete preference lists: Each
list (called a layer) is a ranking of items in a possibly different way
according to a different criterion.
  In particular, we introduce three multi-layer verification problems, each
corresponds to an optimality notion that weakens the notion of global
optimality (that is, pareto optimality in multiple layers) in a different way.
Informally, the first notion requires that, for each group of agents whose size
is exactly some input parameter k, the agents in the group will not be able to
trade their assigned items among themselves and benefit in at least alpha
layers; the second notion is similar, but it concerns all groups of size at
most k rather than exactly k; the third notion strengthens these notions by
requiring that groups of k agents will not be part of possibly larger groups
that benefit in at least alpha layers. We study the three problems from the
perspective of parameterized complexity under several natural parameterizations
such as the number of layers, the number of agents, the number of items, the
number of allocated items, the maximum length of a preference list, and more.
We present an almost comprehensive picture of the parameterized complexity of
the problems with respect to these parameters.


Abstract:
  Starting with an entropy that includes volumetric, area and length terms as
well as logarithmic contributions, we derive the corresponding modified
Newtonian gravity and derive the expression for planetary orbits. We calculate
the shift of the perihelion of Mercury to find bounds to the parameters
associated to the modified Newtonian gravity. We compare the parameter
associated to the volumetric contribution in the entropy-area relationship with
the value derived for galactic rotation curves and the value obtained from the
cosmological constant.


Abstract:
  End-to-end models reach state-of-the-art performance for speech recognition,
but global soft attention is not monotonic, which might lead to convergence
problems, to instability, to bad generalisation, cannot be used for online
streaming, and is also inefficient in calculation. Monotonicity can potentially
fix all of this. There are several ad-hoc solutions or heuristics to introduce
monotonicity, but a principled introduction is rarely found in literature so
far. In this paper, we present a mathematically clean solution to introduce
monotonicity, by introducing a new latent variable which represents the audio
position or segment boundaries. We compare several monotonic latent models to
our global soft attention baseline such as a hard attention model, a local
windowed soft attention model, and a segmental soft attention model. We can
show that our monotonic models perform as good as the global soft attention
model. We perform our experiments on Switchboard 300h. We carefully outline the
details of our training and release our code and configs.


Abstract:
  Simulated quantum annealing based on the path-integral Monte Carlo is one of
the most common tools to simulate quantum annealing on classical hardware.
Nevertheless, it is in principle highly non-trivial whether or not this
classical algorithm can correctly reproduce the quantum dynamics of quantum
annealing, particularly in the diabatic regime. We study this problem
numerically through the generalized Kibble-Zurek mechanism of defect
distribution in the simplest ferromagnetic one-dimensional transverse-field
Ising model with and without coupling to the environment. We find that,in the
absence of coupling to the environment, simulated quantum annealing correctly
describes the annealing-time dependence of the average number of defects, but a
detailed analysis of the defect distribution shows clear deviations from the
theoretical prediction. When the system is open (coupled to the environment),
the average number of defects does not follow the theoretical prediction but is
qualitatively compatible with the numerical result by the infinite
time-evolving block decimation combined with the quasi-adiabatic propagator
path integral, which is valid in a very short time region. The distribution of
defects in the open system turns out to be not far from the theoretical
prediction. It is surprising that the classical stochastic dynamics of
simulated quantum annealing ostensibly reproduce some aspects of the quantum
dynamics. However, a serious problem is that it is hard to predict for which
physical quantities in which system it is reliable. Those results suggest the
necessity to exert a good amount of caution in using simulated quantum
annealing to study the detailed quantitative aspects of the dynamics of quantum
annealing.


Abstract:
  We investigate certain properties of tilted (oblique) domains, associated
with the Janowski function $(1+Az)/(1+Bz),$ where $A,B\in\mathbb{C}$ with
$A\neq B$ and $|B|\leq 1$. We find several bounds for these oblique domains.
Further, we extend the various known argument, radius, and subordination
results involving Janowski functions with complex parameters.


Abstract:
  We study a model of collective search by teams. Discoveries beget discoveries
and correlated search results are governed by a Brownian path. Search results'
variation at any point -- the search scope -- is jointly controlled. Agents
individually choose when to cease search and implement their best discovery. We
characterize equilibrium and optimal policies. Search scope is constant and
independent of search outcomes as long as no member leaves. It declines after
departures. A simple drawdown stopping boundary governs each agent's search
termination. We show the emergence of endogenous exit waves, whereby possibly
heterogeneous agents cease search all at once.


Abstract:
  A new approach to normal operators in real Hilbert spaces is discussed, and
spectral decompositions are obtained, via unique associated real spectral
measures on complex subsets, having a more classical flavor. The results are
then applied to quaternionic normal operators, which are regarded as special
classes of real normal operators.


Abstract:
  Motivated from the central role of the mean-square displacement and its
second time-derivative -- that is the velocity autocorrelation function
$\left\langle v(0)v(t)\right\rangle=\frac{1}{2} \frac{\mathrm{d}^{2}
\left\langle \Delta r^{2} (t)\right\rangle}{\mathrm{d}t^{2}} $ in the
description of Brownian motion, we revisit the physical meaning of the first
time-derivative of the mean-square displacement of Brownian particles. By
employing a rheological analogue for Brownian motion, we show that the
time-derivative of the mean-square displacement $\frac{\mathrm{d}\left\langle
\Delta r^{2} (t) \right\rangle}{\mathrm{d}t}$ of Brownian microspheres with
mass $m$ and radius $R$ immersed in any linear, isotropic viscoelastic material
is identical to $\frac{N K_B T}{3 \pi R}h(t)$, where $h(t)$ is the impulse
response function of a rheological network that is a parallel connection of the
linear viscoelastic material with an inerter with distributed inertance
$m_R=\frac{m}{6 \pi R}$. The impulse response function $h(t)=\frac{3\pi R}{N
K_B T}\frac{\mathrm{d}\left\langle \Delta r^{2} (t)
\right\rangle}{\mathrm{d}t}$ of the viscoelastic material-inerter parallel
connection derived in this paper at the stress-strain level of the rheological
analogue is essentially the response function $\chi(t)=\frac{h(t)}{6\pi R}$ of
the Brownian particles expressed at the force-displacement level by Nishi
\textit{et al.} (2018). By employing the viscoelastic material-inerter
rheological analogue we derive the mean-square displacement and its
time-derivatives of Brownian particles immersed in a viscoelastic material
described with a Maxwell element connected in parallel with a dashpot which
captures the high-frequency viscous behavior and we show that for Brownian
motion in such fluid-like soft matter the impulse response function, $h(t)$
maintains a finite constant value in the long term.


Abstract:
  Many preceding works have shown in $^{11}$Li the presence of the halo
structure comprised of the weakly bound two neutrons around $^9$Li, and it is
intriguing to see how this halo structure changes when another $^9$Li
approaches. In this study, we introduce a four-body model for $^{20}$C with two
$^9$Li clusters and two valence neutrons. The recent development of the
antisymmetrized quasi cluster model (AQCM) makes it possible to generate
$jj$-coupling shell-model wave functions from $\alpha$ cluster models. Here,
$jj$-coupling shell model wave function of $^9$Li is regarded as a cluster,
which corresponds to the subclosure configuration of $p_{3/2}$ for the
neutrons, and we discuss how the two neutrons connect two $^9$Li clusters.
Until now, most of the clusters in the conventional models have been limited to
the closures of the three-dimensional harmonic oscillators, such as $^4$He,
$^{16}$O, and $^{40}$Ca; however, owing to AQCM, it is feasible to utilize the
$jj$-coupling shell model wave functions as plural subsystems quite easily. The
appearance of a rotational band structure with a cluster structure around the
four-body threshold energy is discussed.


Abstract:
  In this paper we introduce SMPLicit, a novel generative model to jointly
represent body pose, shape and clothing geometry. In contrast to existing
learning-based approaches that require training specific models for each type
of garment, SMPLicit can represent in a unified manner different garment
topologies (e.g. from sleeveless tops to hoodies and to open jackets), while
controlling other properties like the garment size or tightness/looseness. We
show our model to be applicable to a large variety of garments including
T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The
representation flexibility of SMPLicit builds upon an implicit model
conditioned with the SMPL human body parameters and a learnable latent space
which is semantically interpretable and aligned with the clothing attributes.
The proposed model is fully differentiable, allowing for its use into larger
end-to-end trainable systems. In the experimental section, we demonstrate
SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in
images of dressed people. In both cases we are able to go beyond state of the
art, by retrieving complex garment geometries, handling situations with
multiple clothing layers and providing a tool for easy outfit editing. To
stimulate further research in this direction, we will make our code and model
publicly available at http://www.iri.upc.edu/people/ecorona/smplicit/.


Abstract:
  In a companion paper [ABS1] we introduced the stable $\infty$-category of
noncommutative CW-spectra, which we denoted $\mathtt{NSp}$. Let $\mathcal{M}$
denote the full spectrally enriched subcategory of $\mathtt{NSp}$ whose objects
are the non-commutative suspension spectra of matrix algebras. In [ABS1] we
proved that $\mathtt{NSp}$ is equivalent to the $\infty$-category of spectral
presheaves on $\mathcal{M}$. In this paper we investigate the structure of
$\mathcal{M}$, and derive some consequences regarding the structure of
$\mathtt{NSp}$.
  To begin with, we introduce a rank filtration of $\mathcal{M}$. We show that
the mapping spectra of $\mathcal{M}$ map naturally to the connective $K$-theory
spectrum $ku$, and that the rank filtration of $\mathcal{M}$ is a lift of the
classical rank filtration of $ku$. We describe the subquotients of the rank
filtration in terms of complexes of direct-sum decompositions which also arose
in the study of $K$-theory and of Weiss's orthogonal calculus. We prove that
the rank filtration stabilizes rationally after the first stage. Using this we
give an explicit model of the rationalization of $\mathtt{NSp}$ as presheaves
of rational spectra on the category of finite-dimensional Hilbert spaces and
unitary transformations up to scaling. Our results also have consequences for
the $p$-localization and the chromatic localization of $\mathcal{M}$.


Abstract:
  Neyman and Pearson's theory of testing hypotheses does not warrant minimal
epistemic reliability: the feature of driving to true conclusions more often
than to false ones. The theory does not protect from the possible negative
effects of the pragmatic value-laden unequal setting of error probabilities on
the theory's epistemic reliability. Most importantly, in the case of a negative
impact, no methodological adjustment is available to neutralize it, so in such
cases, the discussed pragmatic-value-ladenness of the theory inevitably
compromises the goal of attaining truth.


Abstract:
  In this article a new family of preconditioners is introduced for symmetric
positive definite linear systems. The new preconditioners, called the AWG
preconditioners (for Algebraic-Woodbury-GenEO) are constructed algebraically.
By this, we mean that only the knowledge of the matrix A for which the linear
system is being solved is required. Thanks to the GenEO spectral coarse space
technique, the condition number of the preconditioned operator is bounded
theoretically from above. This upper bound can be made smaller by enriching the
coarse space with more spectral modes.The novelty is that, unlike in previous
work on the GenEO coarse spaces, no knowledge of a partially non-assembled form
of A is required. Indeed, the spectral coarse space technique is not applied
directly to A but to a low-rank modification of A of which a suitable
non-assembled form is known by construction. The extra cost is a second (and to
this day rather expensive) coarse solve in the preconditioner. One of the AWG
preconditioners has already been presented in the short preprint [38]. This
article is the first full presentation of the larger family of AWG
preconditioners. It includes proofs of the spectral bounds as well as numerical
illustrations.


Abstract:
  We review the derivation of the pulsations equations for spherically
symmetric boson stars and then make a thorough study of the radial oscillation
frequencies for the fundamental and first excited modes. We do this for
self-interacting boson stars and consider a range of values for the
self-coupling constant. We also numerically evolve boson stars and Fourier
transform the dynamic solutions. The Fourier transform gives an independent
computation of the radial oscillation frequencies and allows us to verify our
results obtained from the pulsation equations. We find excellent agreement
between the two methods.


Abstract:
  Subjective classification of spiral galaxies is not sufficient for studying
the effect of bars on their physical characteristics. In reality the problem is
to comprehend the complex correlations in a multivariate parametric space.
Multivariate tools are the best ones for understanding this complex
correlation. In this work an objective classification of a large set (26,089)
of spiral galaxies was compiled as a value added galaxy catalogue from sdss DR
15 virtual data archive.
  Initially for dimensionality reduction, Independent Component Analysis is
performed to determine a set of Independent Components that are linear
combinations of 48 observed features (namely ionised lines, Lick indices,
photometric and morphological properties). Subsequently a K-means cluster
analysis is carried out on the basis of the 14 best chosen Independent
Components to obtain 12 distinct homogeneous groups of spiral galaxies. Amongst
these, 3 groups are the oldest ones (1.6 Gyr - 5.9 Gyr), while 5 groups fall in
the medium aged category (1.4 Gyr - 1.6 Gyr), 2 groups consist of only unbarred
spirals, 1 group is the youngest one and the remaining one is an outlier. In
many groups there are clear indication of recurrent bar formation phenomena
which is consistent with few previous simulation works. In order to study the
robustness a second method of clustering by Gaussian Mixture Modeling Method
(GMMBC) is applied.


Abstract:
  This article provides the domain of existence $\Omega$ of the Laplace
transform of infinitely divisible negative multinomial distributions. We define
a negative multinomial distribution on $\mathbb{N}^{n},$ where $\mathbb{N}$ is
the set of nonnegative integers, by its probability generating function which
will be of the form $\left( A\left( a_{1}z_{1} ,\ldots,a_{n}z_{n}\right)
/A\left( a_{1},\ldots,a_{n}\right) \right) ^{-\lambda}$ where $A\left(
\mathbf{z}\right) = {\displaystyle\sum\limits_{T\subset\left\{
1,2,\ldots,n\right\} }} a_{T}\prod\limits_{i\in T}z_{i},$ where
$a_{\emptyset}\neq0,$ and where $\lambda$ is a positive number. Finding couples
$\left( A,\lambda\right) $ for which we obtain a probability generating
function is a difficult problem. Necessary and sufficient conditions on the
coefficients $a_{T}$ of $A$ for which we obtain a probability generating
function for any positive number $\lambda$ are know by (Bernardoff, 2003). Thus
we obtain necessary and sufficient conditions on $\mathbf{a}=\left(
a_{1},\ldots,a_{n}\right) $ so that $\mathbf{a}=\left(
\mathbf{e}^{t_{1}},\ldots,\mathbf{e}^{t_{n}}\right) $ with $\mathbf{t}=\left(
t_{1},\ldots,t_{n}\right) $ belonging to $\Omega$. This makes it possible to
construct all the infinitely divisible multinomial distributions on
$\mathbb{N}^{n}$. We give examples of construction in dimensions 2 and 3.


Abstract:
  There exists a small family of analytic SO(4)-invariant but time-dependent
SU(2) Yang-Mills solutions in any conformally flat four-dimensional spacetime.
These might play a role in early-universe cosmology for stabilizing the
symmetric Higgs vacuum. We analyze the linear stability of these "cosmic gauge
fields" against general gauge-field perturbations while keeping the metric
frozen, by diagonalizing the (time-dependent) Yang-Mills fluctuation operator
around them and applying Floquet theory to its eigenfrequencies and normal
modes. Except for the exactly solvable SO(4) singlet perturbation, which is
found to be marginally stable linearly but bounded nonlinearly, generic normal
modes often grow exponentially due to resonance effects. Even at very high
energies, all cosmic Yang-Mills backgrounds are rendered linearly unstable.


Abstract:
  Computing the closed convex envelope or biconjugate is the core operation
that bridges the domain of nonconvex with convex analysis. We focus here on
computing the conjugate of a bivariate piecewise quadratic function defined
over a polytope. First, we compute the convex envelope of each piece, which is
characterized by a polyhedral subdivision such that over each member of the
subdivision, it has a rational form (square of a linear function over a linear
function). Then we compute the conjugate of all such rational functions. It is
observed that the conjugate has a parabolic subdivision such that over each
member of its subdivision, it has a fractional form (linear function over
square root of a linear function). This computation of the conjugate is
performed with a worst-case linear time complexity algorithm. Our results are
an important step toward computing the conjugate of a piecewise quadratic
function, and further in obtaining explicit formulas for the convex envelope of
piecewise rational functions.


Abstract:
  The 4D 4-point scattering amplitude of massless scalars via a massive
exchange is expressed in a basis of conformal primary particle wavefunctions.
This celestial amplitude is expanded in a basis of 2D conformal partial waves
on the unitary principal series, and then rewritten as a sum over 2D conformal
blocks via contour deformation. The conformal blocks include intermediate
exchanges of spinning light-ray states, as well as scalar states with positive
integer conformal weights. The conformal block prefactors are found as expected
to be quadratic in the celestial OPE coefficients.


Abstract:
  Opportunistic relay selection (ORS) has been recognized as a simple but
efficient method for mobile nodes to achieve cooperative diversity in slow
fading channels. However, the wrong selection of the best relay arising from
outdated channel state information (CSI) in fast time-varying channels
substantially degrades its performance. With the proliferation of high-mobility
applications and the adoption of higher frequency bands in 5G and beyond
systems, the problem of outdated CSI will become more serious. Therefore, the
design of a novel cooperative method that is applicable to not only slow fading
but also fast fading is increasingly of importance. To this end, we develop and
analyze a deep-learning-aided cooperative method coined predictive relay
selection (PRS) in this article. It can remarkably improve the quality of CSI
through fading channel prediction while retaining the simplicity of ORS by
selecting a single opportunistic relay so as to avoid the complexity of
multi-relay coordination and synchronization. Information-theoretic analysis
and numerical results in terms of outage probability and channel capacity
reveal that PRS achieves full diversity gain in slow fading wireless
environments and substantially outperforms the existing schemes in fast fading
channels.


Abstract:
  Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs)
are used for a wide variety of missions related to exploration and scientific
research. Successful navigation by these systems requires a good localization
system. Kalman filter based localization techniques have been prevalent since
the early 1960s and extensive research has been carried out using them, both in
development and in design. It has been found that the use of a dynamic model
(instead of a kinematic model) in the Kalman filter can lead to more accurate
predictions, as the dynamic model takes the forces acting on the AUV into
account. Presented in this paper is a motion-predictive extended Kalman filter
(EKF) for AUVs using a simplified dynamic model. The dynamic model is derived
first and then it was simplified for a RexROV, a type of submarine vehicle used
in simple underwater exploration, inspection of subsea structures, pipelines
and shipwrecks. The filter was implemented with a simulated vehicle in an
open-source marine vehicle simulator called UUV Simulator and the results were
compared with the ground truth. The results show good prediction accuracy for
the dynamic filter, though improvements are needed before the EKF can be used
on real-time. Some perspective and discussion on practical implementation is
presented to show the next steps needed for this concept.


Abstract:
  Profiling of whole transcriptomes has become a cornerstone of molecular
biology and an invaluable tool for the characterization of clinical phenotypes
and the identification of disease subtypes. Analyses of these data are becoming
ever more sophisticated as we move beyond simple comparisons to consider
networks of higher-order interactions and associations. Gene regulatory
networks model the regulatory relationships of transcription factors and genes
and have allowed the identification of differentially regulated processes in
disease systems. In this perspective we discuss gene targeting scores, which
measure changes in inferred regulatory network interactions, and their use in
identifying disease-relevant processes. In addition, we present an example
analysis or pancreatic ductal adenocarcinoma demonstrating the power of gene
targeting scores to identify differential processes between complex phenotypes;
processes which would have been missed by only performing differential
expression analysis. This example demonstrates that gene targeting scores are
an invaluable addition to gene expression analysis in the characterization of
diseases and other complex phenotypes.


Abstract:
  High-resolution satellite imagery has proven useful for a broad range of
tasks, including measurement of global human population, local economic
livelihoods, and biodiversity, among many others. Unfortunately,
high-resolution imagery is both infrequently collected and expensive to
purchase, making it hard to efficiently and effectively scale these downstream
tasks over both time and space. We propose a new conditional pixel synthesis
model that uses abundant, low-cost, low-resolution imagery to generate accurate
high-resolution imagery at locations and times in which it is unavailable. We
show that our model attains photo-realistic sample quality and outperforms
competing baselines on a key downstream task -- object counting -- particularly
in geographic locations where conditions on the ground are changing rapidly.


Abstract:
  Quantum circuit simulators have a long tradition of exploiting massive
hardware parallelism. Most of the times, parallelism has been supported by
special purpose libraries tailored specifically for the quantum circuits.
Quantum circuit simulators are integral part of quantum software stacks, which
are mostly written in Python. Our focus has been on ease of use, implementation
and maintainability within the Python ecosystem. We report the performance
gains we obtained by using CuPy, a general purpose library (linear algebra)
developed specifically for CUDA-based GPUs, to simulate quantum circuits. For
supremacy circuits the speedup is around 2x, and for quantum multipliers almost
22x compared to state-of-the-art C++-based simulators.


Abstract:
  We present sub-arcsecond resolution ALMA imaging of the CO(3-2) emission in
two $z\sim2.5$ heavily reddened quasars (HRQs) - ULASJ1234+0907 and
ULASJ2315+0143 - and their companion galaxies. Dynamical modeling of the
resolved velocity fields enables us to constrain the molecular gas morphologies
and host galaxy masses. Combining the new data with extensive multi-wavelength
observations, we are able to study the relative kinematics of different
molecular emission lines, the molecular gas fractions and the locations of the
quasars on the M$_{\rm{BH}}$-M$_{\rm{gal}}$ relation. Despite having similar
black-hole properties, the two HRQs display markedly different host galaxy
properties and local environments. J1234 has a very massive host, M$_{\rm{dyn}}
\sim 5 \times 10^{11}$M$_\odot$ and two companion galaxies that are similarly
massive located within 200 kpc of the quasar. The molecular gas fraction is low
($\sim$6%). The significant ongoing star formation in the host galaxy is
entirely obscured at rest-frame UV and optical wavelengths. J2315 is resolved
into a close-separation major-merger ($\Delta$r=15 kpc; $\Delta$v=170 km/s)
with a $\sim$1:2 mass ratio. The total dynamical mass is estimated to be
$\lesssim$10$^{11}$M$_\odot$ and the molecular gas fraction is high ($>$45%). A
new HSC image of the galaxy shows unobscured UV-luminous star-forming regions
co-incident with the extended reservoir of cold molecular gas in the merger. We
use the outputs from the Illustris simulations to track the growth of such
massive black holes from $z\sim6$ to the present day. While J1234 is consistent
with the simulated $z\sim2$ relation, J2315 has a black hole that is
over-massive relative to its host galaxy.


Abstract:
  In this paper, we consider the Cauchy problem for the one-dimensional
compressible isentropic magnetohydrodynamic (MHD) equations with no vacuum at
infinity, but the initial vacuum can be permitted inside the region. By
deriving a priori $\nu $ (resistivity coefficient)-independent estimates, we
establish the non-resistive limit of the global strong solutions with large
initial data. Moreover, as a by-product, the global well-posedness of strong
solutions for both the compressible resistive MHD equations and non-resistive
MHD equations are also established, respectively.


Abstract:
  In this paper, we develop a new neural network family based on power series
expansion, which is proved to achieve a better approximation accuracy in
comparison with existing neural networks. This new set of neural networks
embeds the power series expansion (PSE) into the neural network structure. Then
it can improve the representation ability while preserving comparable
computational cost by increasing the degree of PSE instead of increasing the
depth or width. Both theoretical approximation and numerical results show the
advantages of this new neural network.


Abstract:
  We show that the set of all measures on any measurable space is a complete
lattice, i.e. every collection of measures has both a greatest lower bound and
a least upper bound.


Abstract:
  The ability to automatically detect stuttering events in speech could help
speech pathologists track an individual's fluency over time or help improve
speech recognition systems for people with atypical speech patterns. Despite
increasing interest in this area, existing public datasets are too small to
build generalizable dysfluency detection systems and lack sufficient
annotations. In this work, we introduce Stuttering Events in Podcasts
(SEP-28k), a dataset containing over 28k clips labeled with five event types
including blocks, prolongations, sound repetitions, word repetitions, and
interjections. Audio comes from public podcasts largely consisting of people
who stutter interviewing other people who stutter. We benchmark a set of
acoustic models on SEP-28k and the public FluencyBank dataset and highlight how
simply increasing the amount of training data improves relative detection
performance by 28\% and 24\% F1 on each. Annotations from over 32k clips across
both datasets will be publicly released.


Abstract:
  A simple method to increase the flow power extraction efficiency of a
circular cylinder, undergoing vortex-induced vibration (VIV), by confining it
between two parallel plates is proposed. A two-dimensional numerical study was
performed on VIV of a circular cylinder inside a parallel plate channel of
height H at Reynolds number 150 to quantify the improvement. The cylinder is
elastically mounted with a spring such that it is only free to vibrate in the
direction transverse to the channel flow and has a fixed mass ratio (m*) of 10.
The energy extraction process is modelled as a damper, with spatially constant
damping ration ((), attached to the cylinder. The simulations are performed by
varying the reduced velocity for a set of fixed mass-damping ({\alpha} = m*()
values ranging between 0 to 1. The blockage ratio (b = D/H) is varied from 0.25
to 0.5 by changing the channel height. The quasi-periodic initial branch found
for the unconfined cylinder shrinks with the increasing blockage. The extracted
power is found to increase rapidly with the blockage. For maximum blockage (b =
0.2), the maximum flow power extracted by the cylinder is an order of magnitude
larger as compared to what it would extract in an open domain with free stream
velocity equal to the channel mean velocity. The optimal mass-damping
({\alpha}c ) for extracting maximum power is found to lie between 0.2 to 0.3.
An expression is derived to predict the maximum extracted power from the
undamped response of a confined/unconfined cylinder. With the assumption
{\alpha}c = 0.25, the derived expression can predict the maximum power
extraction within +-20% of the actual values obtained from present and previous
numerical and experimental studies.


Abstract:
  We report on the formation of the dilute $Pd_{1-x}Fe_x$ compositions with
tunable magnetic properties under an ion-beam implantation of epitaxial Pd thin
films. Binary $Pd_{1-x}Fe_x$ alloys with a mean iron content $x$ of $0.025$,
$0.035$ or $0.075$ were obtained by the implantation of $40 keV$ $Fe^+$ ions
into the palladium films on MgO (001) substrate to the doses of
$0.5\cdot10^{16}, 1.0\cdot10^{16}$ and $3.0\cdot10^{16}$ $ions/cm^2$,
respectively. Structural and magnetic studies have shown that iron atoms occupy
regular fcc-lattice Pd-sites without the formation of any secondary
crystallographic phase. All the iron implanted Pd films reveal ferromagnetism
at low temperatures (below $200 K$) with both the Curie temperature and
saturation magnetization determined by the implanted iron dose. In contrast to
the magnetic properties of the molecular beam epitaxy grown $Pd_{1-x}Fe_x$
alloy films with the similar iron contents, the Fe-implanted Pd films possess
weaker in-plane magnetocrystalline anisotropy, and, accordingly, a lower
coercivity. The observed multiple ferromagnetic resonances in the implanted
$Pd_{1-x}Fe_x$ films indicate a formation of a magnetically inhomogeneous state
due to spinodal decomposition into regions, presumably layers, with identical
crystal symmetry but different iron contents. The multiphase magnetic structure
is robust with respect to the vacuum annealing at $770 K$, though develops
towards well-defined local $Pd-Fe$ compositions.


Abstract:
  The field of galaxy evolution will make a great leap forward in the next
decade as a consequence of the huge effort by the scientific community in
multi-object spectroscopic facilities. To maximise the impact of such incoming
data, the analysis methods must also step up, extracting reliable information
from the available spectra. In this paper, we aim to investigate the limits and
the reliability of different spectral synthesis methods in the estimation of
the mean stellar age and metallicity. The main question this work aims to
address is which signal-to-noise ratios (S/N) are needed to reliably determine
the mean stellar age and metallicity from a galaxy spectrum and how this
depends on the tool used to model the spectra. To address this question we
built a set of realistic simulated spectra containing stellar and nebular
emission, reproducing the evolution of a galaxy in two limiting cases: a
constant star formation rate and an exponentially declining star formation. We
degraded the synthetic spectra built from these two star formation histories
(SFHs) to different S/N and analysed them with three widely used spectral
synthesis codes, namely FADO, STECKMAP, and STARLIGHT. For S/N < 5 all three
tools show a large diversity in the results. The FADO and STARLIGHT tools find
median differences in the light-weighted mean stellar age of ~0.1 dex, while
STECKMAP shows a higher value of ~0.2 dex. Detailed investigations of the
best-fit spectrum for galaxies with overestimated mass-weighted quantities
point towards the inability of purely stellar models to fit the observed
spectra around the Balmer jump. Our results imply that when a galaxy enters a
phase of high specific star formation rate the neglect of the nebular continuum
emission in the fitting process has a strong impact on the estimation of its
SFH when purely stellar fitting codes are used, even in presence of high S/N
spectra.


Abstract:
  In this paper we focus on the dynamic state estimation which harnesses a vast
amount of sensing data harvested by multiple parties and recognize that in many
applications, to improve collaborations between parties, the estimation
procedure must be designed with the awareness of protecting participants' data
and model privacy, where the latter refers to the privacy of key parameters of
observation models. We develop a state estimation paradigm for the scenario
where multiple parties with data and model privacy concerns are involved.
Multiple parties monitor a physical dynamic process by deploying their own
sensor networks and update the state estimate according to the average state
estimate of all the parties calculated by a cloud server and security module.
The paradigm taps additively homomorphic encryption which enables the cloud
server and security module to jointly fuse parties' data while preserving the
data privacy. Meanwhile, all the parties collaboratively develop a stable (or
optimal) fusion rule without divulging sensitive model information. For the
proposed filtering paradigm, we analyze the stabilization and the optimality.
First, to stabilize the multi-party state estimator while preserving
observation model privacy, two stabilization design methods are proposed. For
special scenarios, the parties directly design their estimator gains by the
matrix norm relaxation. For general scenarios, after transforming the original
design problem into a convex semi-definite programming problem, the parties
collaboratively derive suitable estimator gains based on the ADMM. Second, an
optimal collaborative gain design method with model privacy guarantees is
provided, which results in the asymptotic MMSE state estimation. Finally,
numerical examples are presented to illustrate our design and theoretical
findings.


Abstract:
  We study the economic interactions among sellers and buyers in online
markets. In such markets, buyers have limited information about the product
quality, but can observe the sellers' reputations which depend on their past
transaction histories and ratings from past buyers. Sellers compete in the same
market through pricing, while considering the impact of their heterogeneous
reputations. We consider sellers with limited as well as unlimited capacities,
which correspond to different practical market scenarios. In the unlimited
seller capacity scenario, buyers prefer the seller with the highest
reputation-price ratio. If the gap between the highest and second highest
seller reputation levels is large enough, then the highest reputation seller
dominates the market as a monopoly. If sellers' reputation levels are
relatively close to each other, then those sellers with relatively high
reputations will survive at the equilibrium, while the remaining relatively low
reputation sellers will get zero market share. In the limited seller capacity
scenario, we further consider two different cases. If each seller can only
serve one buyer, then it is possible for sellers to set their monopoly prices
at the equilibrium while all sellers gain positive market shares; if each
seller can serve multiple buyers, then it is possible for sellers to set
maximum prices at the equilibrium. Simulation results show that the dynamics of
reputations and prices in the longer-term interactions will converge to stable
states, and the initial buyer ratings of the sellers play the critical role in
determining sellers' reputations and prices at the stable state.


Abstract:
  Illuminated gapped-gold-nanorod dimers hold surface plasmon polaritons (SPPs)
that can be engineered, by an appropriate choice of geometrical parameters, to
enhance the electromagnetic field at the gap, allowing applications in
molecular detection via surface-enhanced Raman spectroscopy (SERS). Envisioning
hybrid devices in which the SERS spectroscopy of molecules in the gap is
complemented by electrical measurements, it arises the question of designing
efficient geometries to contact the nanorods without decreasing the enhancement
factor (EF) of the nanoantenna, i.e., the figure of merit for SERS
spectroscopy. Within this framework we theoretically study the feasibility to
fabricate designs based on covering with gold the far-from-the-gap areas of the
dimer. We show that by tuning the geometrical parameters of the designs these
systems can reach enhancement factors larger than the best achieved in the
uncovered dimer: this supremacy survives even in the presence of dimer
asymmetries and vacancies at the interfaces between the nanorods and the
covering layers. Our results show that geometrical modifications away from the
gap can improve the optical response at the gap, thus enabling the use of these
devices both for hybrid and optical applications.


Abstract:
  Non-Independent and Identically Distributed (non- IID) data distribution
among clients is considered as the key factor that degrades the performance of
federated learning (FL). Several approaches to handle non-IID data such as
personalized FL and federated multi-task learning (FMTL) are of great interest
to research communities. In this work, first, we formulate the FMTL problem
using Laplacian regularization to explicitly leverage the relationships among
the models of clients for multi-task learning. Then, we introduce a new view of
the FMTL problem, which in the first time shows that the formulated FMTL
problem can be used for conventional FL and personalized FL. We also propose
two algorithms FedU and dFedU to solve the formulated FMTL problem in
communication-centralized and decentralized schemes, respectively.
Theoretically, we prove that the convergence rates of both algorithms achieve
linear speedup for strongly convex and sublinear speedup of order 1/2 for
nonconvex objectives. Experimentally, we show that our algorithms outperform
the conventional algorithm FedAvg in FL settings, MOCHA in FMTL settings, as
well as pFedMe and Per-FedAvg in personalized FL settings.


Abstract:
  State-of-the-art human-in-the-loop robot grasping is hugely suffered by
Electromyography (EMG) inference robustness issues. As a workaround,
researchers have been looking into integrating EMG with other signals, often in
an ad hoc manner. In this paper, we are presenting a method for end-to-end
training of a policy for human-in-the-loop robot grasping on real reaching
trajectories. For this purpose we use Reinforcement Learning (RL) and Imitation
Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation
environment with real human trajectories that are augmented and selected using
a Monte Carlo (MC) simulation method. We also offer a success model which once
trained on the expert policy data and the RL policy roll-out transitions, can
provide transparency to how the deep policy works and when it is probably going
to fail.


Abstract:
  Equations of motion for excited states of weakly self-interacting bosons
forming fuzzy dark matter are solved using the WKB approximation. The
contribution of self-interactions are neglected in the equations of motion.
Wave functions of excited states are expressed in terms of a yet undetermined
gravitational potential. At equilibrium, the contributions of states to the
density distribution are summed using Bose-Einstein statistics. Combined with
the Poisson equation, a differential equation is obtained for the gravitational
potential, which has physically acceptable solutions only if the energy
spectrum of excited states has a finite gap, corresponding to a finite virial
radius. Such a gap could be created by decay processes, in first order
perturbation of the self-interaction potential. The obtained density profile is
found to be similar to the Burkert profile.


Abstract:
  We re-create the essential results of a 1989 unpublished article by Mazzola
and Muzzulini that contains musicological aspects of a first-species
counterpoint model. We include a summary of the mathematical counterpoint
theory and several variations of the model that offer different perspectives on
Mazzola's original principles.


Abstract:
  In this conceptual work, we present Deep Convolutional Gaussian Mixture
Models (DCGMMs): a new formulation of deep hierarchical Gaussian Mixture Models
(GMMs) that is particularly suitable for describing and generating images.
Vanilla (i.e., flat) GMMs require a very large number of components to describe
images well, leading to long training times and memory issues. DCGMMs avoid
this by a stacked architecture of multiple GMM layers, linked by convolution
and pooling operations. This allows to exploit the compositionality of images
in a similar way as deep CNNs do. DCGMMs can be trained end-to-end by
Stochastic Gradient Descent. This sets them apart from vanilla GMMs which are
trained by Expectation-Maximization, requiring a prior k-means initialization
which is infeasible in a layered structure. For generating sharp images with
DCGMMs, we introduce a new gradient-based technique for sampling through
non-invertible operations like convolution and pooling. Based on the MNIST and
FashionMNIST datasets, we validate the DCGMMs model by demonstrating its
superiority over flat GMMs for clustering, sampling and outlier detection.


Abstract:
  Active fluids, such as cytoskeletal filaments, bacterial colonies and
epithelial cell layers, exhibit distinctive orientational coherence, often
characterized by nematic order and topological defects. By contrast, little is
known about positional coherence -- i.e., how a hidden dynamic skeleton
organizes the underlying chaotic motion -- despite this being one of their most
prominent and experimentally accessible features. Using a combination of
dynamical systems theory, experiments on two-dimensional mixtures of
microtubules and kinesin and hydrodynamic simulations, we characterize
positional coherence in active nematics. These coherent structures can be
identified in the framework of Lagrangian dynamics as moving attractors and
repellers, which orchestrate complex motion. To understand the interaction of
positional and orientational coherence on the dynamics of defects, we then
analysed observations and simulations and see that +1/2 defects move and deform
the attractors, thus functioning as control centers for collective motion.
Additionally, we find that regions around isolated +1/2 defects undergo high
bending and low stretching/shearing deformations, consistent with the local
stress distribution. The stress is minimum at the defect, while high
differential stress along the defect orientation induces folding. Our work
offers a new perspective to describe self-organization in active fluids, with
potential applications to multicellular systems.


Abstract:
  Consider a finite collection $\{T_1, \ldots, T_J\}$ of differential operators
with constant coefficients on $\mathbb{T}^n$ ($n\geq 2$) and the space of
smooth functions generated by this collection, namely, the space of functions
$f$ such that $T_j f \in C(\mathbb{T}^n)$, $1\leq j\leq J$. We prove that if
there are at least two linearly independent operators among their senior parts
(relative to some mixed pattern of homogeneity), then this space does not have
local unconditional structure. This fact generalizes the previously known
result that such spaces are not isomorphic to a complemented subspace of $C(S)$


Abstract:
  This study aimed to compare and investigate the efficacy of the real-world
experiences, immersive virtual reality (IVR) experiences, and video walkthrough
representations on layout-learning in a complex building. A quasi-experimental,
intervention, and delayed post-test research design was used among three
groups: real-world, IVR, and video walkthrough representation. A total of 41
first-year design students from architecture, and game design departments were
attended the study. Design students were selected as they already know how to
communicate graphically the layout of a building on paper. IVR and video
walkthrough groups experienced the representations of the building by
themselves, but real-world group experienced the building within a group as
imitating a design education field trip. After 10 days, a total of 26
participants out of 41 tested for spatial recall performance. Recall
performances were measured as an indicator of layout learning by analysing the
plan sketches drawn by the participants. Results showed IVR group recalled
significantly more spatial memories compared with the real-world and video
walkthrough representation groups. Real-world group performed the worst among
three groups. This result was interpreted to three conclusions. First, the
layout learning tends to be overly sensitive to distractions and lower levels
of distraction may lead to better levels of layout learning. Second, for the
domain of layout knowledge learning, a remarkably simple IVR model is enough.
Third, although real-world experiences give direct and richer sensory
information about the spaces, layout knowledge acquisition from the surrounding
environment requires psychologically active wayfinding decisions.


Abstract:
  The current concept of Smart Cities influences urban planners and researchers
to provide modern, secured and sustainable infrastructure and give a decent
quality of life to its residents. To fulfill this need video surveillance
cameras have been deployed to enhance the safety and well-being of the
citizens. Despite technical developments in modern science, abnormal event
detection in surveillance video systems is challenging and requires exhaustive
human efforts. In this paper, we surveyed various methodologies developed to
detect anomalies in intelligent video surveillance. Firstly, we revisit the
surveys on anomaly detection in the last decade. We then present a systematic
categorization of methodologies developed for ease of understanding.
Considering the notion of anomaly depends on context, we identify different
objects-of-interest and publicly available datasets in anomaly detection. Since
anomaly detection is considered a time-critical application of computer vision,
our emphasis is on anomaly detection using edge devices and approaches
explicitly designed for them. Further, we discuss the challenges and
opportunities involved in anomaly detection at the edge.


Abstract:
  In some memory-constrained settings like IoT devices and over-the-network
data pipelines, it can be advantageous to have smaller contextual embeddings.
We investigate the efficacy of projecting contextual embedding data (BERT) onto
a manifold, and using nonlinear dimensionality reduction techniques to compress
these embeddings. In particular, we propose a novel post-processing approach,
applying a combination of Isomap and PCA. We find that the geodesic distance
estimations, estimates of the shortest path on a Riemannian manifold, from
Isomap's k-Nearest Neighbors graph bolstered the performance of the compressed
embeddings to be comparable to the original BERT embeddings. On one dataset, we
find that despite a 12-fold dimensionality reduction, the compressed embeddings
performed within 0.1% of the original BERT embeddings on a downstream
classification task. In addition, we find that this approach works particularly
well on tasks reliant on syntactic data, when compared with linear
dimensionality reduction. These results show promise for a novel geometric
approach to achieve lower dimensional text embeddings from existing
transformers and pave the way for data-specific and application-specific
embedding compressions.