Abstract:
  Large area van der Waals (vdW) thin films are assembled materials consisting
of a network of randomly stacked nanosheets. The multi-scale structure and the
two-dimensional nature of the building block mean that interfaces naturally
play a crucial role in the charge transport of such thin films. While single or
few stacked nanosheets (i.e. vdW heterostructures) have been the subject of
intensive works, little is known about how charges travel through multilayered,
more disordered networks. Here we report a comprehensive study of a
prototypical system given by networks of randomly stacked reduced graphene
oxide 2D nanosheets, whose chemical and geometrical properties can be
controlled independently, permitting to explore percolated networks ranging
from a single nanosheet to some billions with room temperature resistivity
spanning from 10-5 to 10-1 ohm m. We systematically observe a clear transition
between two different regimes at a critical temperature T*: Efros-Shklovskii
variable range hopping (ESVRH) below T* and power law (PL) behavior above.
Firstly, we demonstrate that the two regimes are strongly correlated with each
other, both depending on the charge localization length xi, calculated by
ES-VRH model, which corresponds to the characteristic size of overlapping sp2
domains belonging to different nanosheets. Thus, we propose a microscopic model
describing the charge transport as a geometrical phase transition, given by the
metal-insulator transition associated with the percolation of quasi-1D
nanofillers with length xi, showing that the charge transport behavior of the
networks is valid for all geometries and defects of the nanosheets, ultimately
suggesting a generalized description on vdW and disordered thin films.


Abstract:
  We provide a construction for categorical representation learning and
introduce the foundations of "$\textit{categorifier}$". The central theme in
representation learning is the idea of $\textbf{everything to vector}$. Every
object in a dataset $\mathcal{S}$ can be represented as a vector in
$\mathbb{R}^n$ by an $\textit{encoding map}$ $E:
\mathcal{O}bj(\mathcal{S})\to\mathbb{R}^n$. More importantly, every morphism
can be represented as a matrix $E:
\mathcal{H}om(\mathcal{S})\to\mathbb{R}^{n}_{n}$. The encoding map $E$ is
generally modeled by a $\textit{deep neural network}$. The goal of
representation learning is to design appropriate tasks on the dataset to train
the encoding map (assuming that an encoding is optimal if it universally
optimizes the performance on various tasks). However, the latter is still a
$\textit{set-theoretic}$ approach. The goal of the current article is to
promote the representation learning to a new level via a
$\textit{category-theoretic}$ approach. As a proof of concept, we provide an
example of a text translator equipped with our technology, showing that our
categorical learning model outperforms the current deep learning models by 17
times. The content of the current article is part of the recent US patent
proposal (patent application number: 63110906).


Abstract:
  We study the number of points in the family of plane curves defined by a
trinomial \[
  \mathcal{C}(\alpha,\beta)=
  \{(x,y)\in\mathbb{F}_q^2\,:\,\alpha x^{a_{11}}y^{a_{12}}+\beta
x^{a_{21}}y^{a_{22}}=x^{a_{31}}y^{a_{32}}\} \] with fixed exponents (not
collinear) and varying coefficients over finite fields. We prove that each of
these curves has an almost predictable number of points, given by a closed
formula that depends on the coefficients, exponents, and the field, with a
small error term $N(\alpha,\beta)$ that is bounded in absolute value by
$2\tilde{g}q^{1/2}$, where $\tilde{g}$ is a constant that depends only on the
exponents and the field. A formula for $\tilde{g}$ is provided, as well as a
comparison of $\tilde{g}$ with the genus $g$ of the projective closure of the
curve over $\overline{\mathbb{F}_q}$. We also give several linear and quadratic
identities for the numbers $N(\alpha,\beta)$ that are strong enough to prove
the estimate above, and in some cases, to characterize them completely.


Abstract:
  Because of the increasing demand for computation in DNN, researchers develope
both hardware and software mechanisms to reduce the compute and memory burden.
A widely adopted approach is to use mixed precision data types. However, it is
hard to leverage mixed precision without hardware support because of the
overhead of data casting. Hardware vendors offer tensorized instructions for
mixed-precision tensor operations, like Intel VNNI, Tensor Core, and ARM-DOT.
These instructions involve a computing idiom that reduces multiple low
precision elements into one high precision element. The lack of compilation
techniques for this makes it hard to utilize these instructions: Using
vendor-provided libraries for computationally-intensive kernels is inflexible
and prevents further optimizations, and manually writing hardware intrinsics is
error-prone and difficult for programmers. Some prior works address this
problem by creating compilers for each instruction. This requires excessive
effort when it comes to many tensorized instructions. In this work, we develop
a compiler framework to unify the compilation for these instructions -- a
unified semantics abstraction eases the integration of new instructions, and
reuses the analysis and transformations. Tensorized instructions from different
platforms can be compiled via UNIT with moderate effort for favorable
performance. Given a tensorized instruction and a tensor operation, UNIT
automatically detects the applicability, transforms the loop organization of
the operation,and rewrites the loop body to leverage the tensorized
instruction. According to our evaluation, UNIT can target various mainstream
hardware platforms. The generated end-to-end inference model achieves 1.3x
speedup over Intel oneDNN on an x86 CPU, 1.75x speedup over Nvidia cuDNN on an
NvidiaGPU, and 1.13x speedup over a carefully tuned TVM solution for ARM DOT on
an ARM CPU.


Abstract:
  Federated Learning (FL) is a collaborative machine learning technique to
train a global model without obtaining clients' private data. The main
challenges in FL are statistical diversity among clients, limited computing
capability among clients' equipments, and the excessive communication overhead
between servers and clients. To address these challenges, we propose a novel
sparse personalized federated learning scheme via maximizing correlation
FedMac. By incorporating an approximated L1-norm and the correlation between
client models and global model into standard FL loss function, the performance
on statistical diversity data is improved and the communicational and
computational loads required in the network are reduced compared with
non-sparse FL. Convergence analysis shows that the sparse constraints in FedMac
do not affect the convergence rate of the global model, and theoretical results
show that FedMac can achieve good sparse personalization, which is better than
the personalized methods based on L2-norm. Experimentally, we demonstrate the
benefits of this sparse personalization architecture compared with the
state-of-the-art personalization methods (e.g. FedMac respectively achieves
98.95%, 99.37%, 90.90% and 89.06% accuracy on the MNIST, FMNIST, CIFAR-100 and
Synthetic datasets under non-i.i.d. variants).


Abstract:
  We present the analytical framework for converting projected light
distributions with a S\'ersic profile into three-dimensional light
distributions for stellar systems of arbitrary triaxial shape. The main
practical result is the definition of a simple yet robust measure of intrinsic
galaxy size: the median radius $r_\mathrm{med}$, defined as the radius of a
sphere that contains 50% of the total luminosity or mass, that is, the median
distance of a star to the galaxy center. We examine how $r_\mathrm{med}$
depends on projected size measurements as a function of S\'ersic index and
intrinsic axis ratios, and demonstrate its relative independence of these
parameters. As an application we show that the projected semi-major axis length
of the ellipse enclosing 50% of the light is an unbiased proxy for
$r_\mathrm{med}$, with small galaxy-to-galaxy scatter of $\sim$10% (1$\sigma$),
under the condition that the variation in triaxiality within the population is
small. For galaxy populations with unknown or a large range in triaxiality an
unbiased proxy for $r_\mathrm{med}$ is $1.3\times R_{e}$, where $R_{e}$ is the
circularized half-light radius, with galaxy-to-galaxy scatter of 20-30%
(1$\sigma$). We also describe how inclinations can be estimated for individual
galaxies based on the measured projected shape and prior knowledge of the
intrinsic shape distribution of the corresponding galaxy population. We make
the numerical implementation of our calculations available.


Abstract:
  We investigate the onset of chaos in a periodically kicked Dicke model (KDM),
using the out-of-time-order correlator (OTOC) as a diagnostic tool, in both the
oscillator and the spin subspaces. In the large spin limit, the classical
Hamiltonian map is constructed, which allows us to investigate the
corresponding phase space dynamics and to compute the Lyapunov exponent. We
show that the growth rate of the OTOC for the canonically conjugate coordinates
of the oscillator is able to capture the Lyapunov exponent in the chaotic
regime. The onset of chaos is further investigated using the saturation value
of the OTOC, that can serve as an alternate indicator of chaos in a generic
interacting quantum system. This is also supported by a system independent
effective random matrix model. We further identify the quantum scars in KDM and
detect their dynamical signature by using the OTOC dynamics. The relevance of
the present study in the context of ongoing cold atom experiments is also
discussed.


Abstract:
  Identification of parameters in ordinary differential equations (ODEs) is an
important and challenging task when modeling dynamic systems in biomedical
research and other scientific areas, especially with the presence of
time-varying parameters. This article proposes a fast and accurate method,
TVMAGI (Time-Varying MAnifold-constrained Gaussian process Inference), to
estimate both time-constant and time-varying parameters in the ODE using noisy
and sparse observation data. TVMAGI imposes a Gaussian process model over the
time series of system components as well as time-varying parameters, and
restricts the derivative process to satisfy ODE conditions. Consequently,
TVMAGI completely bypasses numerical integration and achieves substantial
savings in computation time. By incorporating the ODE structures through
manifold constraints, TVMAGI enjoys a principled statistical construction under
the Bayesian paradigm, which further enables it to handle systems with missing
data or unobserved components. The Gaussian process prior also alleviates the
identifiability issue often associated with the time-varying parameters in ODE.
Unlike existing approaches, TVMAGI assumes no specific linearity of the ODE
structure, and can be applied to general nonlinear systems. We demonstrate the
robustness and efficiency of our method through three simulation examples,
including an infectious disease compartmental model.


Abstract:
  We compute topological Hochschild homology of sufficiently structured forms
of truncated Brown--Peterson spectra with coefficients. In particular, we
compute $\operatorname{THH}_*(\operatorname{taf}^D;M)$ for $M\in \{
H\mathbb{Z}_{(3)},k(1),k(2)\}$ where $\operatorname{taf}^D$ is the $E_{\infty}$
form of $BP\langle 2\rangle$ constructed by Hill--Lawson. We compute
$\operatorname{THH}_*(\operatorname{tmf}_1(3);M)$ when $M\in \{
H\mathbb{Z}_{(2)},k(2)\}$ where $\operatorname{tmf}_1(3)$ is the $E_{\infty}$
form of $BP\langle 2\rangle$ constructed by Lawson--Naumann. We also compute
$\operatorname{THH}_*(B\langle n\rangle;M)$ for $M=H\mathbb{Z}_{(p)}$ and
certain $E_3$ forms $B\langle n\rangle$ of $BP\langle n\rangle$. For example at
$p=2$, this result applies to the $E_3$ forms of $BP\langle n\rangle$
constructed by Hahn--Wilson.


Abstract:
  This paper proposes an approach that generates multiple 3D human meshes from
text. The human shapes are represented by 3D meshes based on the SMPL model.
The model's performance is evaluated on the COCO dataset, which contains
challenging human shapes and intricate interactions between individuals. The
model is able to capture the dynamics of the scene and the interactions between
individuals based on text. We further show how using such a shape as input to
image synthesis frameworks helps to constrain the network to synthesize humans
with realistic human shapes.


Abstract:
  Here, a physical formalism is proposed for an unconditional microwave quantum
teleportation of Gaussian states via two-mode squeezed states in lossy
environments. The proposed formalism is controllable to be used in both the
fridge and free space in case of entanglement between two parties survives.
Some possible experimental parameters are estimated for the teleportation of
microwave signals with a frequency of 5GHz based on the proposed physical
framework. This would be helpful for superconducting inter- and intra-fridge
quantum communication as well as open-air quantum microwave communication,
which can be applied to quantum local area networks (QLANs) and distributed
quantum computing protocols.


Abstract:
  We study quotients of the Toeplitz C*-algebra of a random walk, similar to
those studied by the author and Markiewicz for finite stochastic matrices. We
introduce a new Cuntz-type quotient C*-algebra for random walks that have
convergent ratios of transition probabilities. These C*-algebras give rise to
new notions of ratio limit space and boundary for such random walks, which are
computed by appealing to a companion paper by Woess. Our combined results are
leveraged to identify a unique smallest symmetry-equivariant quotient
C*-algebra for any symmetric random walk on a hyperbolic group, shedding light
on a question of Viselter on C*-algebras of subproduct systems.


Abstract:
  We build the bosonic $\eta$-deformed $AdS_4\times\mathbb{CP}^3$ background
generated by an $r$-matrix that satisfies the modified classical Yang-Baxter
equation. In a special limit we find that it is the gravity dual of the
noncommutative ABJM theory.


Abstract:
  We study pure exploration in bandits, where the dimension of the feature
representation can be much larger than the number of arms. To overcome the
curse of dimensionality, we propose to adaptively embed the feature
representation of each arm into a lower-dimensional space and carefully deal
with the induced model misspecifications. Our approach is conceptually very
different from existing works that can either only handle low-dimensional
linear bandits or passively deal with model misspecifications. We showcase the
application of our approach to two pure exploration settings that were
previously under-studied: (1) the reward function belongs to a possibly
infinite-dimensional Reproducing Kernel Hilbert Space, and (2) the reward
function is nonlinear and can be approximated by neural networks. Our main
results provide sample complexity guarantees that only depend on the effective
dimension of the feature spaces in the kernel or neural representations.
Extensive experiments conducted on both synthetic and real-world datasets
demonstrate the efficacy of our methods.


Abstract:
  Oumuamua, the first known object of extrasolar origin seen to enter our Solar
System, has multiple unusual characteristics that, taken together, are very
difficult to explain with conventional astronomical entities like asteroids and
comets. Consequently, it has been hypothesized that Oumuamua is an interstellar
probe that was constructed by an alien civilization. We demonstrate that the
accomplishments that can be achieved with large space
telescopes/interferometers in the alien's planetary system will completely
quench any motivation for construction and launch of an Oumuamua-like probe.
The absence of any such motivation proves that Oumuamua is not an alien
creation.


Abstract:
  Despite remarkable progress achieved, most neural architecture search (NAS)
methods focus on searching for one single accurate and robust architecture. To
further build models with better generalization capability and performance,
model ensemble is usually adopted and performs better than stand-alone models.
Inspired by the merits of model ensemble, we propose to search for multiple
diverse models simultaneously as an alternative way to find powerful models.
Searching for ensembles is non-trivial and has two key challenges: enlarged
search space and potentially more complexity for the searched model. In this
paper, we propose a one-shot neural ensemble architecture search (NEAS)
solution that addresses the two challenges. For the first challenge, we
introduce a novel diversity-based metric to guide search space shrinking,
considering both the potentiality and diversity of candidate operators. For the
second challenge, we enable a new search dimension to learn layer sharing among
different models for efficiency purposes. The experiments on ImageNet clearly
demonstrate that our solution can improve the supernet's capacity of ranking
ensemble architectures, and further lead to better search results. The
discovered architectures achieve superior performance compared with
state-of-the-arts such as MobileNetV3 and EfficientNet families under aligned
settings. Moreover, we evaluate the generalization ability and robustness of
our searched architecture on the COCO detection benchmark and achieve a 3.1%
improvement on AP compared with MobileNetV3. Codes and models are available at
https://github.com/researchmm/NEAS.


Abstract:
  Optical wave-based computing has enabled the realization of real-time
information processing in both space and time domains. In the past few years,
analog computing has experienced rapid development but mostly for a single
function. Motivated by parallel space-time computing and miniaturization, we
show that reconfigurable graphene-based metasurfaces offer a promising path
towards spatiotemporal computing with integrated functionalities by properly
engineering both spatial- and temporal-frequency responses. This paper employs
a tunable graphene-based metasurface to enable analog signal and image
processing in both space and time by tuning the electrostatic bias. In the
first part of the paper, we propose a switchable analog computing paradigm in
which the proposed metasurface can switch among defined performances by
selecting a proper external voltage for graphene monolayers. Spatial isotropic
differentiation and edge detection in the spatial channel and first-order
temporal differentiation and metasurface-based phaser with linear group-delay
response in the temporal channel are demonstrated. In the second section of the
paper, simultaneous and parallel spatiotemporal analog computing is
demonstrated. The proposed metasurface processor has almost no static power
consumption due to its floating-gate configuration. The spatial- and
temporal-frequency transfer functions (TFs) are engineered by using a
transmission line (TL) model, and the obtained results are validated with
full-wave simulations. Our proposal will enable real-time parallel
spatiotemporal analog signal and image processing.


Abstract:
  In this paper, we propose a novel SpatioTemporal convolutional Dense Network
(STDNet) to address the video-based crowd counting problem, which contains the
decomposition of 3D convolution and the 3D spatiotemporal dilated dense
convolution to alleviate the rapid growth of the model size caused by the
Conv3D layer. Moreover, since the dilated convolution extracts the multiscale
features, we combine the dilated convolution with the channel attention block
to enhance the feature representations. Due to the error that occurs from the
difficulty of labeling crowds, especially for videos, imprecise or
standard-inconsistent labels may lead to poor convergence for the model. To
address this issue, we further propose a new patch-wise regression loss (PRL)
to improve the original pixel-wise loss. Experimental results on three
video-based benchmarks, i.e., the UCSD, Mall and WorldExpo'10 datasets, show
that STDNet outperforms both image- and video-based state-of-the-art methods.
The source codes are released at \url{https://github.com/STDNet/STDNet}.


Abstract:
  Multi-level exciton-polariton systems offer an attractive platform for
studies of non-linear optical phenomena. However, studies of such consequential
non-linear phenomena as polariton condensation and lasing in planar
microcavities have so far been limited to two-level systems, where the
condensation takes place in the lowest attainable state. Here, we report
non-equilibrium Bose-Einstein condensation of exciton-polaritons and low
threshold, dual-wavelength polariton lasing in vertically coupled, double
planar microcavities. Moreover, we find that the presence of the non-resonantly
driven condensate triggers interbranch exciton-polariton transfer in the form
of energy-degenerate parametric scattering. Such an effect has so far been
observed only under excitation that is strictly resonant in terms of the energy
and incidence angle. We describe theoretically our time-integrated and
time-resolved photoluminescence investigations by a set of rate equations
involving an open-dissipative Gross-Pitaevskii equation. Our platform's
inherent tunability is promising for construction of planar lattices, enabling
three-dimensional polariton hopping and realization of photonic devices, such
as two-qubit polariton-based logic gates.


Abstract:
  The HAWC Collaboration has observed gamma rays at energies above 56 TeV from
a collection of nine sources. It has been suggested that this emission could be
hadronic in nature, requiring that these systems accelerate cosmic-ray protons
or nuclei up to PeV-scale energies. In this paper, we instead show that the
spectra of these objects favor a leptonic (inverse Compton) origin for their
emission. More specifically, the gamma-ray emission from these objects can be
straightforwardly accommodated within a model in which $\sim \mathcal{O}(10\%)$
of the host pulsar's spindown power is transferred into the acceleration of
electrons and positrons with a power-law spectrum that extends to several
hundred TeV or higher. The spectral break that is observed among these sources
is naturally explained within the context of this simple model, and occurs at
the energy where the timescale for energy losses matches the age of the pulsar.
In contrast, this spectral feature cannot be straightforwardly accommodated in
hadronic scenarios. Furthermore, hadronic models predict that these sources
should produce more emission at GeV-scale energies than is observed. In light
of these considerations, we conclude that HAWC's highest energy sources should
be interpreted as TeV halos or pulsar wind nebulae, which produce their
emission through inverse Compton scattering, and are powered by the rotational
kinetic energy of their host pulsar.


Abstract:
  Regional facial image synthesis conditioned on semantic mask has achieved
great success using generative adversarial networks. However, the appearance of
different regions may be inconsistent with each other when conducting regional
image editing. In this paper, we focus on the problem of harmonized regional
style transfer and manipulation for facial images. The proposed approach
supports regional style transfer and manipulation at the same time. A
multi-scale encoder and style mapping networks are proposed in our work. The
encoder is responsible for extracting regional styles of real faces. Style
mapping networks generate styles from random samples for all facial regions. As
the key part of our work, we propose a multi-region style attention module to
adapt the multiple regional style embeddings from a reference image to a target
image for generating harmonious and plausible results. Furthermore, we propose
a new metric "harmony score" and conduct experiments in a challenging setting:
three widely used face datasets are involved and we test the model by
transferring the regional facial appearance between datasets. Images in
different datasets are usually quite different, which makes the inconsistency
between target and reference regions more obvious. Results show that our model
can generate reliable style transfer and multi-modal manipulation results
compared with SOTAs. Furthermore, we show two face editing applications using
the proposed approach.


Abstract:
  We present MoonLight, a tool for monitoring temporal and spatio-temporal
properties of mobile and spatially distributed cyber-physical systems (CPS). In
the proposed framework, space is represented as a weighted graph, describing
the topological configurations in which the single CPS entities (nodes of the
graph) are arranged. Both nodes and edges have attributes modelling physical
and logical quantities that can change in time. MoonLight is implemented in
Java and supports the monitoring of Spatio-Temporal Reach and Escape Logic
(STREL). MoonLight can be used as a standalone command line tool, as a Java
API, or via Matlab interface. We provide here some examples using the Matlab
interface and we evaluate the tool performance also by comparing with other
tools specialized in monitoring only temporal properties.


Abstract:
  Numerous improvements for feedback mechanisms have contributed to the great
progress in object detection. In this paper, we first present an
evaluation-feedback module, which is proposed to consist of evaluation system
and feedback mechanism. Then we analyze and summarize the disadvantages and
improvements of traditional evaluation-feedback module. Finally, we focus on
both the evaluation system and the feedback mechanism, and propose Control
Distance IoU and Control Distance IoU loss function (or CDIoU and CDIoU loss
for short) without increasing parameters or FLOPs in models, which show
different significant enhancements on several classical and emerging models.
Some experiments and comparative tests show that coordinated
evaluation-feedback module can effectively improve model performance. CDIoU and
CDIoU loss have different excellent performances in several models such as
Faster R-CNN, YOLOv4, RetinaNet and ATSS. There is a maximum AP improvement of
1.9% and an average AP of 0.8% improvement on MS COCO dataset, compared to
traditional evaluation-feedback modules.


Abstract:
  Following a tidal disruption event (TDE), the accretion rate can evolve from
quiescent to near-Eddington levels and back over months - years timescales.
This provides a unique opportunity to study the formation and evolution of the
accretion flow around supermassive black holes (SMBHs). We present two years of
multi-wavelength monitoring observations of the TDE AT2018fyk at X-ray, UV,
optical and radio wavelengths. We identify three distinct accretion states and
two state transitions between them. These appear remarkably similar to the
behaviour of stellar-mass black holes in outburst. The X-ray spectral
properties show a transition from a soft (thermal-dominated) to a hard
(power-law dominated) spectral state around L$_{\rm bol} \sim $few $ \times
10^{-2}$ L$_{\rm Edd}$, and the strengthening of the corona over time
$\sim$100--200 days after the UV/optical peak. Contemporaneously, the spectral
energy distribution (in particular, the UV-to-X-ray spectral slope
$\alpha_{ox}$) shows a pronounced softening as the outburst progresses. The
X-ray timing properties also show a marked change, initially dominated by
variability at long ($>$day) timescales while a high frequency ($\sim$10$^{-3}$
Hz) component emerges after the transition into the hard state. At late times
($\sim$500 days after peak), a second accretion state transition occurs, from
the hard into the quiescent state, as identified by the sudden collapse of the
bolometric (X-ray+UV) emission to levels below 10$^{-3.4}$ L$_{\rm Edd}$. Our
findings illustrate that TDEs can be used to study the scale (in)variance of
accretion processes in individual SMBHs. Consequently, they provide a new
avenue to study accretion states over seven orders of magnitude in black hole
mass, removing limitations inherent to commonly used ensemble studies.


Abstract:
  In this short note we classify the Cartan subalgebras in all von Neumann
algebras associated with graph product groups and their free ergodic measure
preserving actions on probability spaces.


Abstract:
  In this paper, we consider the problem of reducing the semitotal domination
number of a given graph by contracting $k$ edges, for some fixed $k \geq 1$. We
show that this can always be done with at most 3 edge contractions and further
characterise those graphs requiring 1, 2 or 3 edge contractions, respectively,
to decrease their semitotal domination number. We then study the complexity of
the problem for $k=1$ and obtain in particular a complete complexity dichotomy
for monogenic classes.


Abstract:
  Bayesian optimization has emerged as a powerful strategy to accelerate
scientific discovery by means of autonomous experimentation. However, expensive
measurements are required to accurately estimate materials properties, and can
quickly become a hindrance to exhaustive materials discovery campaigns. Here,
we introduce Gemini: a data-driven model capable of using inexpensive
measurements as proxies for expensive measurements by correcting systematic
biases between property evaluation methods. We recommend using Gemini for
regression tasks with sparse data and in an autonomous workflow setting where
its predictions of expensive to evaluate objectives can be used to construct a
more informative acquisition function, thus reducing the number of expensive
evaluations an optimizer needs to achieve desired target values. In a
regression setting, we showcase the ability of our method to make accurate
predictions of DFT calculated bandgaps of hybrid organic-inorganic perovskite
materials. We further demonstrate the benefits that Gemini provides to
autonomous workflows by augmenting the Bayesian optimizer Phoenics to yeild a
scalable optimization framework leveraging multiple sources of measurement.
Finally, we simulate an autonomous materials discovery platform for optimizing
the activity of electrocatalysts for the oxygen evolution reaction. Realizing
autonomous workflows with Gemini, we show that the number of measurements of a
composition space comprising expensive and rare metals needed to achieve a
target overpotential is significantly reduced when measurements from a proxy
composition system with less expensive metals are available.


Abstract:
  Virtual clusters are widely used computing platforms than can be deployed in
multiple cloud platforms. The ability to dynamically grow and shrink the number
of nodes has paved the way for customised elastic computing both for High
Performance Computing and High Throughput Computing workloads. However,
elasticity is typically restricted to a single cloud site, thus hindering the
ability to provision computational resources from multiple geographically
distributed cloud sites. To this aim, this paper introduces an architecture of
open-source components that coherently deploy a virtual elastic cluster across
multiple cloud sites to perform large-scale computing. These hybrid virtual
elastic clusters are automatically deployed and configured using an
Infrastructure as Code (IaC) approach on a distributed hybrid testbed that
spans different organizations, including on-premises and public clouds,
supporting automated tunneling of communications across the cluster nodes with
advanced VPN topologies. The results indicate that cluster-based computing of
embarrassingly parallel jobs can benefit from hybrid virtual clusters that
aggregate computing resources from multiple cloud back-ends and bring them
together into a dedicated, albeit virtual network.
  The work presented in this article has been partially funded by the European
Union's (EU) Horizon 2020 research project DEEP Hybrid-DataCloud (grant
agreement No 777435).


Abstract:
  As an integral part of our culture and way of life, language is intricately
related to migrations of people. To understand whether and how migration shapes
language formation processes we examine the dynamics of the naming game with
migrating agents. (i) When all agents may migrate, the dynamics generates an
effective surface tension, which drives the coarsening. Such a behaviour is
very robust and appears for a wide range of densities of agents and their
migration rates. (ii) However, when only multilingual agents are allowed to
migrate, monolingual islands are typically formed. In such a case, when the
migration rate is sufficiently large, the majority of agents acquire a common
language, which spontaneously emerges with no indication of the surface-tension
driven coarsening. A relatively slow coarsening that takes place in a dense
static population is very fragile, and most likely, an arbitrarily small
migration rate can divert the system toward quick formation of monolingual
islands. Our work shows that migration influences language formation processes
but additional details like density, or mobility of agents are needed to
specify more precisely this influence.


Abstract:
  Continued fractions are used to give an alternate proof of $e^{x/y}$ is
irrational.


Abstract:
  We investigate a general formulation for clustering and transductive few-shot
learning, which integrates prototype-based objectives, Laplacian regularization
and supervision constraints from a few labeled data points. We propose a
concave-convex relaxation of the problem, and derive a computationally
efficient block-coordinate bound optimizer, with convergence guarantee. At each
iteration,our optimizer computes independent (parallel) updates for each
point-to-cluster assignment. Therefore, it could be trivially distributed for
large-scale clustering and few-shot tasks. Furthermore, we provides a thorough
convergence analysis based on point-to-set maps. Were port comprehensive
clustering and few-shot learning experiments over various data sets, showing
that our method yields competitive performances, in term of accuracy and
optimization quality, while scaling up to large problems. Using standard
training on the base classes, without resorting to complex meta-learning and
episodic-training strategies, our approach outperforms state-of-the-art
few-shot methods by significant margins, across various models, settings and
data sets. Surprisingly, we found that even standard clustering procedures
(e.g., K-means), which correspond to particular, non-regularized cases of our
general model, already achieve competitive performances in comparison to the
state-of-the-art in few-shot learning. These surprising results point to the
limitations of the current few-shot benchmarks, and question the viability of a
large body of convoluted few-shot learning techniques in the recent literature.


Abstract:
  This paper deals with Hensel minimal, non-trivially valued fields $K$ of
equicharacteristic zero, whose axiomatic theory was introduced in a recent
article by Cluckers-Halupczok-Rideau. We additionally require that the standard
algebraic language be induced (up to interdefinability) for the imaginary sort
$RV$. This condition is satisfied by the majority of classical tame structures
on Henselian fields, including Henselian fields with analytic structure. The
main purpose is to carry over many results of our previous papers to the above
general axiomatic settings including, among others, the theorem on existence of
the limit, curve selection, the closedness theorem, several non-Archimedean
versions of the Lojasiewicz inequalities as well as the theorems on extending
continuous definable functions and on existence of definable retractions. We
establish an embedding theorem for regular definable spaces and the definable
ultranormality of definable Hausdorff LC-spaces. Also given are examples that
curve selection and the closedness theorem, key results for numerous
applications, may be no longer true after expanding the language for the
leading term structure $RV$. In the case of Henselian fields with analytic
structure, a more precise version of the theorem on existence of the limit (a
version of Puiseux's theorem) is provided. Further, we establish definable
versions of resolution of singularities (hypersurface case) and transformation
to normal crossings by blowing up, on arbitrary strong analytic manifolds in
Hensel minimal expansions of analytic structures. Also introduced are
meromorphous functions, i.e. continuous quotients of strong analytic functions
on strong analytic manifolds. Finally, we prove a finitary meromorphous version
of the Nullstellensatz.


Abstract:
  In this paper, we prove that the Fechner and Stevens laws are equivalent
(coincide up to isomorphism). Therefore, the problem does not exist.


Abstract:
  Deep learning has achieved promising segmentation performance on 3D left
atrium MR images. However, annotations for segmentation tasks are expensive,
costly and difficult to obtain. In this paper, we introduce a novel
hierarchical consistency regularized mean teacher framework for 3D left atrium
segmentation. In each iteration, the student model is optimized by multi-scale
deep supervision and hierarchical consistency regularization, concurrently.
Extensive experiments have shown that our method achieves competitive
performance as compared with full annotation, outperforming other
state-of-the-art semi-supervised segmentation methods.


Abstract:
  We present a collection recommender system that can automatically create and
recommend collections of items at a user level. Unlike regular recommender
systems, which output top-N relevant items, a collection recommender system
outputs collections of items such that the items in the collections are
relevant to a user, and the items within a collection follow a specific theme.
Our system builds on top of the user-item representations learnt by item
recommender systems. We employ dimensionality reduction and clustering
techniques along with intuitive heuristics to create collections with their
ratings and titles.
  We test these ideas in a real-world setting of music recommendation, within a
popular music streaming service. We find that there is a 2.3x increase in
recommendation-driven consumption when recommending collections over items.
Further, it results in effective utilization of real estate and leads to
recommending a more and diverse set of items. To our knowledge, these are first
of its kind experiments at such a large scale.


Abstract:
  The number and importance of AI-based systems in all domains is growing. With
the pervasive use and the dependence on AI-based systems, the quality of these
systems becomes essential for their practical usage. However, quality assurance
for AI-based systems is an emerging area that has not been well explored and
requires collaboration between the SE and AI research communities. This paper
discusses terminology and challenges on quality assurance for AI-based systems
to set a baseline for that purpose. Therefore, we define basic concepts and
characterize AI-based systems along the three dimensions of artifact type,
process, and quality characteristics. Furthermore, we elaborate on the key
challenges of (1) understandability and interpretability of AI models, (2) lack
of specifications and defined requirements, (3) need for validation data and
test input generation, (4) defining expected outcomes as test oracles, (5)
accuracy and correctness measures, (6) non-functional properties of AI-based
systems, (7) self-adaptive and self-learning characteristics, and (8) dynamic
and frequently changing environments.


Abstract:
  An action functional is developed for nonlinear dislocation dynamics. This
serves as a first step towards the application of effective field theory in
physics to evaluate its potential in obtaining a macroscopic description of
dislocation dynamics describing the plasticity of crystalline solids.
Connections arise between the continuum mechanics and material science of
defects in solids, effective field theory techniques in physics, and fracton
tensor gauge theories.


Abstract:
  Cyber Physical Systems (CPS) are characterized by their ability to integrate
the physical and information or cyber worlds. Their deployment in critical
infrastructure have demonstrated a potential to transform the world. However,
harnessing this potential is limited by their critical nature and the far
reaching effects of cyber attacks on human, infrastructure and the environment.
An attraction for cyber concerns in CPS rises from the process of sending
information from sensors to actuators over the wireless communication medium,
thereby widening the attack surface. Traditionally, CPS security has been
investigated from the perspective of preventing intruders from gaining access
to the system using cryptography and other access control techniques. Most
research work have therefore focused on the detection of attacks in CPS.
However, in a world of increasing adversaries, it is becoming more difficult to
totally prevent CPS from adversarial attacks, hence the need to focus on making
CPS resilient. Resilient CPS are designed to withstand disruptions and remain
functional despite the operation of adversaries. One of the dominant
methodologies explored for building resilient CPS is dependent on machine
learning (ML) algorithms. However, rising from recent research in adversarial
ML, we posit that ML algorithms for securing CPS must themselves be resilient.
This paper is therefore aimed at comprehensively surveying the interactions
between resilient CPS using ML and resilient ML when applied in CPS. The paper
concludes with a number of research trends and promising future research
directions. Furthermore, with this paper, readers can have a thorough
understanding of recent advances on ML-based security and securing ML for CPS
and countermeasures, as well as research trends in this active research area.


Abstract:
  I point out fatal mathematical errors in the paper "Quantum correlations are
weaved by the spinors of the Euclidean primitives" by Joy Christian, published
(2019) in the journal Royal Society Open Science.


Abstract:
  This article presents an algorithm for reducing measurement uncertainty of
one physical quantity when given oversampled measurements of two physical
quantities with correlated noise. The algorithm assumes that the aleatoric
measurement uncertainty in both physical quantities follows a Gaussian
distribution and relies on sampling faster than it is possible for the
measurand (the true value of the physical quantity that we are trying to
measure) to change (due to the system thermal time constant) to calculate the
parameters of the noise distribution. In contrast to the Kalman and particle
filters, which respectively require state update equations and a map of one
physical quality, our algorithm requires only the oversampled sensor
measurements. When applied to temperature-compensated humidity sensors, it
provides reduced uncertainty in humidity estimates from correlated temperature
and humidity measurements. In an experimental evaluation, the algorithm
achieves average uncertainty reduction of 10.3 %. The algorithm incurs an
execution time overhead of 5.3 % when compared to the minimum algorithm
required to measure and calculate the uncertainty. Detailed instruction-level
emulation of a C-language implementation compiled to the RISC-V architecture
shows that the uncertainty reduction program required 0.05 % more instructions
per iteration than the minimum operations required to calculate the
uncertainty.


Abstract:
  MOA-2006-BLG-074 was selected as one of the most promising planetary
candidates in a retrospective analysis of the MOA collaboration: its asymmetric
high-magnification peak can be perfectly explained by a source passing across a
central caustic deformed by a small planet. However, after a detailed analysis
of the residuals, we have realized that a single lens and a source orbiting
with a faint companion provides a more satisfactory explanation for all the
observed deviations from a Paczynski curve and the only physically acceptable
interpretation. Indeed the orbital motion of the source is constrained enough
to allow a very good characterization of the binary source from the
microlensing light curve. The case of MOA-2006-BLG-074 suggests that the
so-called xallarap effect must be taken seriously in any attempts to obtain
accurate planetary demographics from microlensing surveys.


Abstract:
  Iteratively reweighted least square (IRLS) is a popular approach to solve
sparsity-enforcing regression problems in machine learning. State of the art
approaches are more efficient but typically rely on specific coordinate pruning
schemes. In this work, we show how a surprisingly simple reparametrization of
IRLS, coupled with a bilevel resolution (instead of an alternating scheme) is
able to achieve top performances on a wide range of sparsity (such as Lasso,
group Lasso and trace norm regularizations), regularization strength (including
hard constraints), and design matrices (ranging from correlated designs to
differential operators). Similarly to IRLS, our method only involves linear
systems resolutions, but in sharp contrast, corresponds to the minimization of
a smooth function. Despite being non-convex, we show that there is no spurious
minima and that saddle points are "ridable", so that there always exists a
descent direction. We thus advocate for the use of a BFGS quasi-Newton solver,
which makes our approach simple, robust and efficient. We perform a numerical
benchmark of the convergence speed of our algorithm against state of the art
solvers for Lasso, group Lasso, trace norm and linearly constrained problems.
These results highlight the versatility of our approach, removing the need to
use different solvers depending on the specificity of the ML problem under
study.


Abstract:
  Self-interacting dark matter (SIDM) models offer one way to reconcile
inconsistencies between observations and predictions from collisionless cold
dark matter (CDM) models on dwarf-galaxy scales. In order to incorporate the
effects of both baryonic and SIDM interactions, we study a suite of
cosmological-baryonic simulations of Milky-Way (MW)-mass galaxies from the
Feedback in Realistic Environments (FIRE-2) project where we vary the SIDM
self-interaction cross-section $\sigma/m$. We compare the shape of the main
dark matter (DM) halo at redshift $z=0$ predicted by SIDM simulations (at
$\sigma/m=0.1$, $1$, and $10$ cm$^2$ g$^{-1}$) with CDM simulations using the
same initial conditions. In the presence of baryonic feedback effects, we find
that SIDM models do not produce the large differences in the inner structure of
MW-mass galaxies predicted by SIDM-only models. However, we do find that the
radius where the shape of the total mass distribution begins to differ from
that of the stellar mass distribution is dependent on $\sigma/m$. This
transition could potentially be used to set limits on the SIDM cross-section in
the MW.


Abstract:
  We reanalyze the experimental NMC data on the nonsinglet structure function
$F_2^p-F_2^n$ and E866 data on the nucleon sea asymmetry $\bar{d}/\bar{u}$
using the truncated moments approach elaborated in our previous papers. With
help of the special truncated sum one can overcome the problem of the
unavoidable experimental restrictions on the Bjorken $x$ and effectively study
the fundamental sum rules for the parton distributions and structure functions.
Using only the data from the measured region of $x$, we obtain the Gottfried
sum $\int_0^1 F_2^{ns}/x\, dx$ and the integrated nucleon sea asymmetry
$\int_0^1 (\bar{d}-\bar{u})\, dx$. We compare our results with the reported
experimental values and with the predictions obtained for different global
parametrizations for the parton distributions. We also discuss the discrepancy
between the NMC and E866 results on $\int_0^1 (\bar{d}-\bar{u})\, dx$. We
demonstrate that this discrepancy can be resolved by taking into account the
higher-twist effects.


Abstract:
  The emission properties of tin plasmas, produced by the irradiation of
preformed liquid tin targets by several-ns-long 2-$\mu$m-wavelength laser
pulses, are studied in the extreme ultraviolet (EUV) regime. In a two-pulse
scheme, a pre-pulse laser is first used to deform tin microdroplets into thin,
extended disks before the main (2$\mu$m) pulse creates the EUV-emitting plasma.
Irradiating 30- to 300-$\mu$m-diameter targets with 2-$\mu$m laser pulses, we
find that the efficiency in creating EUV light around 13.5nm follows the
fraction of laser light that overlaps with the target. Next, the effects of a
change in 2-$\mu$m drive laser intensity (0.6-1.8$\times 10^{11}$W/cm$^2$) and
pulse duration (3.7-7.4ns) are studied. It is found that the angular dependence
of the emission of light within a 2\% bandwidth around 13.5nm and within the
backward 2$\pi$ hemisphere around the incoming laser beam is almost independent
of intensity and duration of the 2-$\mu$m drive laser. With increasing target
diameter, the emission in this 2\% bandwidth becomes increasingly anisotropic,
with a greater fraction of light being emitted into the hemisphere of the
incoming laser beam. For direct comparison, a similar set of experiments is
performed with a 1-$\mu$m-wavelength drive laser. Emission spectra, recorded in
a 5.5-25.5nm wavelength range, show significant self-absorption of light around
13.5nm in the 1-$\mu$m case, while in the 2-$\mu$m case only an opacity-related
broadening of the spectral feature at 13.5nm is observed. This work
demonstrates the enhanced capabilities and performance of 2-$\mu$m-driven
plasmas produced from disk targets when compared to 1-$\mu$m-driven plasmas,
providing strong motivation for the use of 2-$\mu$m lasers as drive lasers in
future high-power sources of EUV light.


Abstract:
  Advancements in the digital technologies have enabled researchers to develop
a variety of Computational Music applications. Such applications are required
to capture, process, and generate data related to music. Therefore, it is
important to digitally represent music in a music theoretic and concise manner.
Existing approaches for representing music are ineffective in terms of
utilizing music theory. In this paper, we address the disjoint of music theory
and computational music by developing an opensource representation tool based
on music theory. Through the wide range of use cases, we run an analysis on the
classical music pieces to show the usefulness of the developed music embedding.


Abstract:
  Network function virtualization (NFV) and content caching are two promising
technologies that hold great potential for network operators and designers.
This paper optimizes the deployment of NFV and content caching in 5G networks
and focuses on the associated power consumption savings. In addition, it
introduces an approach to combine content caching with NFV in one integrated
architecture for energy aware 5G networks. A mixed integer linear programming
(MILP) model has been developed to minimize the total power consumption by
jointly optimizing the cache size, virtual machine (VM) workload, and the
locations of both cache nodes and VMs. The results were investigated under the
impact of core network virtual machines (CNVMs) inter-traffic. The result show
that the optical line terminal (OLT) access network nodes are the optimum
location for content caching and for hosting VMs during busy times of the day
whilst IP over WDM core network nodes are the optimum locations for caching and
VM placement during off-peak time. Furthermore, the results reveal that a
virtualization-only approach is better than a caching-only approach for video
streaming services where the virtualization-only approach compared to
caching-only approach, achieves a maximum power saving of 7% (average 5%) when
no CNVMs inter-traffic is considered and 6% (average 4%) with CNVMs
inter-traffic at 10% of the total backhaul traffic. On the other hand, the
integrated approach has a maximum power saving of 15% (average 9%) with and
without CNVMs inter-traffic compared to the virtualization-only approach, and
it achieves a maximum power saving of 21% (average 13%) without CNVMs
inter-traffic and 20% (average 12%) when CNVMs inter-traffic is considered
compared with the caching-only approach. In order to validate the MILP models
and achieve real-time operation in our approaches, a heuristic was developed.


Abstract:
  A symbolic method for solving linear recurrences of combinatorial and
statistical interest is introduced. This method essentially relies on a
representation of polynomial sequences as moments of a symbol that looks as the
framework of a random variable with no reference to any probability space. We
give several examples of applications and state an explicit form for the class
of linear recurrences involving Sheffer sequences satisfying a special initial
condition. The results here presented can be easily implemented in a symbolic
software.


Abstract:
  By Hacon-McKernan-Xu, there is a positive lower bound in each dimension for
the volume of all klt varieties with ample canonical class. We show that these
bounds must go to zero extremely fast as the dimension increases, by
constructing a klt $n$-fold with ample canonical class whose volume is less
than $1/2^{2^n}$. These examples should be close to optimal.
  We also construct a klt Fano variety of each dimension $n$ such that
$H^0(X,-mK_X)=0$ for all $1\leq m < b$ with $b$ roughly $2^{2^n}$. Here again
there is some bound in each dimension, by Birkar's theorem on boundedness of
complements, and we are showing that the bound must increase extremely fast
with the dimension.


Abstract:
  The effective low-energy late-time description of many body systems near
thermal equilibrium provided by classical hydrodynamics in terms of dissipative
transport phenomena receives important corrections once the effects of
stochastic fluctuations are taken into account. One such physical effect is the
occurrence of long-time power law tails in correlation functions of conserved
currents. In the hydrodynamic regime $\vec{k} \rightarrow 0$ this amounts to
non-analytic dependence of the correlation functions on the frequency $\omega$.
In this article, we consider a relativistic fluid with a conserved global
$U(1)$ charge in the presence of a strong background magnetic field, and
compute the long-time tails in correlation functions of the stress tensor. The
presence of the magnetic field renders the system anisotropic. In the absence
of the magnetic field, there are three out-of-equilibrium transport parameters
that arise at the first order in the hydrodynamic derivative expansion, all of
which are dissipative. In the presence of a background magnetic field, there
are ten independent out-of-equilibrium transport parameters at the first order,
three of which are non-dissipative and the rest are dissipative. We provide the
most general linearized equations about a given state of thermal equilibrium
involving the various transport parameters in the presence of a magnetic field,
and use them to compute the long-time tails for the fluid.


Abstract:
  Contending hate speech in social media is one of the most challenging social
problems of our time. There are various types of anti-social behavior in social
media. Foremost of them is aggressive behavior, which is causing many social
issues such as affecting the social lives and mental health of social media
users. In this paper, we propose an end-to-end ensemble-based architecture to
automatically identify and classify aggressive tweets. Tweets are classified
into three categories - Covertly Aggressive, Overtly Aggressive, and
Non-Aggressive. The proposed architecture is an ensemble of smaller subnetworks
that are able to characterize the feature embeddings effectively. We
demonstrate qualitatively that each of the smaller subnetworks is able to learn
unique features. Our best model is an ensemble of Capsule Networks and results
in a 65.2% F1 score on the Facebook test set, which results in a performance
gain of 0.95% over the TRAC-2018 winners. The code and the model weights are
publicly available at
https://github.com/parthpatwa/Hater-O-Genius-Aggression-Classification-using-Capsule-Networks.


Abstract:
  We study the transport properties for a family of geometrically frustrated
models on the triangular lattice with an interaction scale far exceeding the
single-particle bandwidth. Starting from the interaction-only limit, which can
be solved exactly, we analyze the transport and thermodynamic behavior as a
function of filling and temperature at the leading non-trivial order in the
single-particle hopping. Over a broad range of intermediate temperatures, we
find evidence of a dc resistivity scaling linearly with temperature and with
typical values far exceeding the quantum of resistance, $h/e^2$. At a sequence
of commensurate fillings, the bad-metallic regime eventually crosses over into
interaction induced insulating phases in the limit of low temperatures. We
discuss the relevance of our results to experiments in cold-atom and moir\'e
heterostructure based platforms.


Abstract:
  The aim of this note is to completely determine the second homology group of
the special queer Lie superalgebra $\mathfrak{sq}_n(R)$ coordinatized by a
unital associative superalgebra $R$, which will be achieved via an isomorphism
between the special linear Lie superalgebra $\mathfrak{sl}_{n}(R\otimes Q_1)$
and the special queer Lie superalgebra $\mathfrak{sq}_n(R)$.


Abstract:
  In a multiple linear regression model, the algebraic formula of the
decomposition theorem explains the relationship between the univariate
regression coefficient and partial regression coefficient using geometry. It
was found that univariate regression coefficients are decomposed into their
respective partial regression coefficients according to the parallelogram rule.
Multicollinearity is analyzed with the help of the decomposition theorem. It
was also shown that it is a sample phenomenon that the partial regression
coefficients of important explanatory variables are not significant, but the
sign expectation deviation cause may be the population structure between the
explained variables and explanatory variables or may be the result of sample
selection. At present, some methods of diagnostic multicollinearity only
consider the correlation of explanatory variables, so these methods are
basically unreliable, and handling multicollinearity is blind before the causes
are not distinguished. The increase in the sample size can help identify the
causes of multicollinearity, and the difference method can play an auxiliary
role.


Abstract:
  As the earliest stage of planet formation, massive, optically thick, and gas
rich protoplanetary disks provide key insights into the physics of star and
planet formation. When viewed edge-on, high resolution images offer a unique
opportunity to study both the radial and vertical structures of these disks and
relate this to vertical settling, radial drift, grain growth, and changes in
the midplane temperatures. In this work, we present multi-epoch HST and Keck
scattered light images, and an ALMA 1.3 mm continuum map for the remarkably
flat edge-on protoplanetary disk SSTC2DJ163131.2-242627, a young solar-type
star in $\rho$ Ophiuchus. We model the 0.8 $\mu$m and 1.3 mm images in separate
MCMC runs to investigate the geometry and dust properties of the disk using the
MCFOST radiative transfer code. In scattered light, we are sensitive to the
smaller dust grains in the surface layers of the disk, while the sub-millimeter
dust continuum observations probe larger grains closer to the disk midplane. An
MCMC run combining both datasets using a covariance-based log-likelihood
estimation was marginally successful, implying insufficient complexity in our
disk model. The disk is well characterized by a flared disk model with an
exponentially tapered outer edge viewed nearly edge-on, though some degree of
dust settling is required to reproduce the vertically thin profile and lack of
apparent flaring. A colder than expected disk midplane, evidence for dust
settling, and residual radial substructures all point to a more complex radial
density profile to be probed with future, higher resolution observations.


Abstract:
  Silicon ferroelectric field-effect transistors (FeFETs) with low-k
interfacial layer (IL) between ferroelectric gate stack and silicon channel
suffers from high write voltage, limited write endurance and large
read-after-write latency due to early IL breakdown and charge trapping and
detrapping at the interface. We demonstrate low voltage, high speed memory
operation with high write endurance using an IL-free back-end-of-line (BEOL)
compatible FeFET. We fabricate IL-free FeFETs with 28nm channel length and
126nm width under a thermal budget <400C by integrating 5nm thick Hf0.5Zr0.5O2
gate stack with amorphous Indium Tungsten Oxide (IWO) semiconductor channel. We
report 1.2V memory window and read current window of 10^5 for program and
erase, write latency of 20ns with +/-2V write pulses, read-after-write latency
<200ns, write endurance cycles exceeding 5x10^10 and 2-bit/cell programming
capability. Array-level analysis establishes IL-free BEOL FeFET as a promising
candidate for logic-compatible high-performance on-chip buffer memory and
multi-bit weight cell for compute-in-memory accelerators.


Abstract:
  We prove the asymptotic functional Poisson laws in the total variation norm
and obtain estimates of the corresponding convergence rates for a large class
of hyperbolic dynamical systems. These results generalize the ones obtained
before in this area. Applications to intermittent solenoids, Axiom A systems,
H\'enon attractors and to billiards, are also considered.


Abstract:
  Software engineering educators are continually challenged by rapidly evolving
concepts, technologies, and industry demands. Due to the omnipresence of
software in a digitalized society, higher education institutions (HEIs) have to
educate the students such that they learn how to learn, and that they are
equipped with a profound basic knowledge and with latest knowledge about modern
software and system development. Since industry demands change constantly, HEIs
are challenged in meeting such current and future demands in a timely manner.
This paper analyzes the current state of practice in software engineering
education. Specifically, we want to compare contemporary education with
industrial practice to understand if frameworks, methods and practices for
software and system development taught at HEIs reflect industrial practice. For
this, we conducted an online survey and collected information about 67 software
engineering courses. Our findings show that development approaches taught at
HEIs quite closely reflect industrial practice. We also found that the choice
of what process to teach is sometimes driven by the wish to make a course
successful. Especially when this happens for project courses, it could be
beneficial to put more emphasis on building learning sequences with other
courses.


Abstract:
  This study explores the potential of modern implicit solvers for stochastic
partial differential equations in the simulation of real-time complex Langevin
dynamics. Not only do these methods offer asymptotic stability, rendering the
issue of runaway solution moot, but they also allow us to simulate at
comparatively largeLangevin time steps, leading to lower computational cost. We
compare different ways of regularizing the underlying path integral and
estimate the errors introduced due to the finite Langevin time. Based on that
insight, we implement benchmark (non-)thermal simulations of the quantum
anharmonic oscillator on the canonical Schwinger-Keldysh contour of short
real-time extent.


Abstract:
  In this article, we consider a class of finite rank perturbations of Toeplitz
operators that have simple eigenvalues on the unit circle. Under a suitable
assumption on the behavior of the essential spectrum, we show that such
operators are power bounded. The problem originates in the approximation of
hyperbolic partial differential equations with boundary conditions by means of
finite difference schemes. Our result gives a positive answer to a conjecture
by Trefethen, Kreiss and Wu that only a weak form of the so-called Uniform
Kreiss-Lopatinskii Condition is sufficient to imply power boundedness.


Abstract:
  We address the problem of exposure correction of dark, blurry and noisy
images captured in low-light conditions in the wild. Classical image-denoising
filters work well in the frequency space but are constrained by several factors
such as the correct choice of thresholds, frequency estimates etc. On the other
hand, traditional deep networks are trained end-to-end in the RGB space by
formulating this task as an image-translation problem. However, that is done
without any explicit constraints on the inherent noise of the dark images and
thus produce noisy and blurry outputs. To this end we propose a DCT/FFT based
multi-scale loss function, which when combined with traditional losses, trains
a network to translate the important features for visually pleasing output. Our
loss function is end-to-end differentiable, scale-agnostic, and generic; i.e.,
it can be applied to both RAW and JPEG images in most existing frameworks
without additional overhead. Using this loss function, we report significant
improvements over the state-of-the-art using quantitative metrics and
subjective tests.


Abstract:
  The \textit{node reliability} of a graph $G$ is the probability that at least
one node is operational and that the operational nodes can all communicate in
the subgraph that they induce, given that the edges are perfectly reliable but
each node operates independently with probability $p\in[0,1]$. We show that
unlike many other notions of graph reliability, the number of maximal intervals
of decrease in $[0,1]$ is unbounded, and that there can be arbitrarily many
inflection points in the interval as well.


Abstract:
  For the Minkowski question mark function $?(x)$ we consider derivative of the
function $f_n(x) = \underbrace{?(?(...?}_\text{n times}(x)))$. Apart from
obvious cases (rational numbers for example) it is non-trivial to find explicit
examples of numbers $x$ for which $f'_n(x)=0$. In this paper we present a set
of irrational numbers, such that for every element $x_0$ of this set and for
any $n\in\mathbb{Z}_+$ one has $f'_n(x_0)=0$.


Abstract:
  Since their inception, learning techniques under the Reservoir Computing
paradigm have shown a great modeling capability for recurrent systems without
the computing overheads required for other approaches. Among them, different
flavors of echo state networks have attracted many stares through time, mainly
due to the simplicity and computational efficiency of their learning algorithm.
However, these advantages do not compensate for the fact that echo state
networks remain as black-box models whose decisions cannot be easily explained
to the general audience. This work addresses this issue by conducting an
explainability study of Echo State Networks when applied to learning tasks with
time series, image and video data. Specifically, the study proposes three
different techniques capable of eliciting understandable information about the
knowledge grasped by these recurrent models, namely, potential memory, temporal
patterns and pixel absence effect. Potential memory addresses questions related
to the effect of the reservoir size in the capability of the model to store
temporal information, whereas temporal patterns unveils the recurrent
relationships captured by the model over time. Finally, pixel absence effect
attempts at evaluating the effect of the absence of a given pixel when the echo
state network model is used for image and video classification. We showcase the
benefits of our proposed suite of techniques over three different domains of
applicability: time series modeling, image and, for the first time in the
related literature, video classification. Our results reveal that the proposed
techniques not only allow for a informed understanding of the way these models
work, but also serve as diagnostic tools capable of detecting issues inherited
from data (e.g. presence of hidden bias).


Abstract:
  We point out qualitatively different possibilities on the role of
CP-conserving processes in generating cosmological particle-antiparticle
asymmetries, with illustrative examples from models in leptogenesis and
asymmetric dark matter production. In particular, we consider scenarios in
which the CP-violating and CP-conserving processes are either both decays or
both scatterings, thereby being naturally of comparable rates. This is in
contrast to the previously considered CP-conserving processes in models of
leptogenesis in different see-saw mechanisms, in which the CP-conserving
scatterings typically have lower rates compared to the CP-violating decays, due
to a Boltzmann suppression. We further point out that the CP-conserving
processes can play a dual role if the asymmetry is generated in the mother
sector itself, in contrast to the conventional scenarios in which it is
generated in the daughter sector. This is because, the CP-conserving processes
initially suppress the asymmetry generation by controlling the
out-of-equilibrium number densities of the bath particles, but subsequently
modify the ratio of particle anti-particle yields at the present epoch by
eliminating the symmetric component of the bath particles through
pair-annihilations, leading to a competing effect stemming from the same
process at different epochs. We find that the asymmetric yields for relevant
particle-antiparticle systems can vary by orders of magnitude depending upon
the relative size of the CP-conserving and violating reaction rates.


Abstract:
  Magnetic field-line reconnection is a universal plasma process responsible
for the conversion of magnetic field energy to the plasma heating and charged
particle acceleration. Solar flares and Earth's magnetospheric substorms are
two most investigated dynamical systems where magnetic reconnection is believed
to be responsible for global magnetic field reconfiguration and energization of
plasma populations. Such a reconfiguration includes formation of a long-living
current systems connecting the primary energy release region and cold dense
conductive plasma of photosphere/ionosphere. In both flares and substorms the
evolution of this current system correlates with formation and dynamics of
energetic particle fluxes. Our study is focused on this similarity between
flares and substorms. Using a wide range of datasets available for flare and
substorm investigations, we compare qualitatively dynamics of currents and
energetic particle fluxes for one flare and one substorm. We showed that there
is a clear correlation between energetic particle bursts (associated with
energy release due to magnetic reconnection) and magnetic field
reconfiguration/formation of current system. We then discuss how datasets of
in-situ measurements in the magnetospheric substorm can help in interpretation
of datasets gathered for the solar flare.


Abstract:
  The design of provably correct controllers for continuous-state stochastic
systems crucially depends on approximate finite-state abstractions and their
accuracy quantification. For this quantification, one generally uses
approximate stochastic simulation relations, whose constant precision limits
the achievable guarantees on the control design. This limitation especially
affects higher dimensional stochastic systems and complex formal
specifications. This work allows for variable precision by defining a
simulation relation that contains multiple precision layers. For bi-layered
simulation relations, we develop a robust dynamic programming approach yielding
a lower bound on the satisfaction probability of temporal logic specifications.
We illustrate the benefit of bi-layered simulation relations for linear
stochastic systems in an example.


Abstract:
  We are concerned with interior and global gradient estimates for solutions to
a class of singular quasilinear elliptic equations with measure data, whose
prototype is given by the $p$-Laplace equation $-\Delta_p u=\mu$ with $p\in
(1,2)$. The cases when $p\in \big(2-\frac 1 n,2\big)$ and $p\in
\big(\frac{3n-2}{2n-1},2-\frac{1}{n}\big]$ were studied in [9] and [22],
respectively. In this paper, we improve the results in [22] and address the
open case when $p\in \big(1,\frac{3n-2}{2n-1}\big]$. Interior and global
modulus of continuity estimates of the gradients of solutions are also
established.


Abstract:
  A system of interacting classical oscillators is discussed, similar to a
quantum mechanical system of a discrete energy level, interacting with the
energy quasi-continuum of states considered Fano. The limit of a continuous
spectrum is analyzed together with the possible connection of the problem under
study with the generation of coherent phonons.


Abstract:
  The sequence of deformation bursts during plastic deformation exhibits
scale-free features. In addition to the burst or avalanche sizes and the rate
of avalanches the process is characterized by correlations in the series which
become manifest in the resulting shape of the stress-strain curve. We analyze
such features of plastic deformation with 2D and 3D simulations of discrete
dislocation dynamics models and we show, that only with severe plastic
deformation the ensuing memory effects become negligible. The role of past
deformation history and dislocation pinning by disorder are studied. In
general, the correlations have the effect of reducing the scatter of the
individual stress-strain curves around the mean one.


Abstract:
  We introduce the concept of impedance matching to axion dark matter by posing
the question of why axion detection is difficult, even though there is enough
power in each square meter of incident dark-matter flux to energize a LED light
bulb. By quantifying backreaction on the axion field, we show that a small
axion-photon coupling does not by itself prevent an order-unity fraction of the
dark matter from being absorbed through optimal impedance match. We further
show, in contrast, that the electromagnetic charges and the self-impedance of
their coupling to photons provide the principal constraint on power absorption
integrated across a search band. Using the equations of axion electrodynamics,
we demonstrate stringent limitations on absorbed power in linear,
time-invariant, passive receivers. Our results yield fundamental constraints,
arising from the photon-electron interaction, on improving integrated power
absorption beyond the cavity haloscope technique. The analysis also has
significant practical implications, showing apparent tension with the
sensitivity projections for a number of planned axion searches. We additionally
provide a basis for more accurate signal power calculations and calibration
models, especially for receivers using multi-wavelength open configurations
such as dish antennas and dielectric haloscopes.


Abstract:
  Given a simple connected compact Lie group $K$ and a maximal torus $T$ of
$K$, the Weyl group $W=N_K(T)/T$ naturally acts on $T$. First, we use the
combinatorics of the (extended) affine Weyl group to provide an explicit
$W$-equivariant triangulation of $T$. We describe the associated cellular
homology chain complex and give a formula for the cup product on its dual
cochain complex, making it a $\mathbb{Z}[W]$-dg-algebra. Next, remarking that
the combinatorics of this dg-algebra is still valid for Coxeter groups, we
associate a closed compact manifold $\mathbf{T}(W)$ to any finite irreducible
Coxeter group $W$, which coincides with a torus if $W$ is a Weyl group and is
hyperbolic in other cases. Of course, we focus our study on
non-crystallographic groups, which are $I_2(m)$ with $m=5$ or $m\ge 7$, $H_3$
and $H_4$. The manifold $\mathbf{T}(W)$ comes with a $W$-action and an
equivariant triangulation, whose related $\mathbb{Z}[W]$-dg-algebra is the one
mentioned above. We finish by computing the homology of $\mathbf{T}(W)$, as a
representation of $W$.


Abstract:
  We present an approach for compressing volumetric scalar fields using
implicit neural representations. Our approach represents a scalar field as a
learned function, wherein a neural network maps a point in the domain to an
output scalar value. By setting the number of weights of the neural network to
be smaller than the input size, we achieve compressed representations of scalar
fields, thus framing compression as a type of function approximation. Combined
with carefully quantizing network weights, we show that this approach yields
highly compact representations that outperform state-of-the-art volume
compression approaches. The conceptual simplicity of our approach enables a
number of benefits, such as support for time-varying scalar fields, optimizing
to preserve spatial gradients, and random-access field evaluation. We study the
impact of network design choices on compression performance, highlighting how
simple network architectures are effective for a broad range of volumes.


Abstract:
  The proliferation of resourceful mobile devices that store rich,
multidimensional and privacy-sensitive user data motivate the design of
federated learning (FL), a machine-learning (ML) paradigm that enables mobile
devices to produce an ML model without sharing their data. However, the
majority of the existing FL frameworks rely on centralized entities. In this
work, we introduce IPLS, a fully decentralized federated learning framework
that is partially based on the interplanetary file system (IPFS). By using IPLS
and connecting into the corresponding private IPFS network, any party can
initiate the training process of an ML model or join an ongoing training
process that has already been started by another party. IPLS scales with the
number of participants, is robust against intermittent connectivity and dynamic
participant departures/arrivals, requires minimal resources, and guarantees
that the accuracy of the trained model quickly converges to that of a
centralized FL framework with an accuracy drop of less than one per thousand.


Abstract:
  In this paper, we introduce a new concept: the Lions tree. These objects
arise in Taylor expansions involving the Lions derivative and prove invaluable
in classifying the dynamics of mean-field stochastic differential equations.
  We discuss Lions trees, derive an Algebra spanned by Lions trees and explore
how couplings between Lions trees lead to a coupled Hopf algebra. Using this
framework, we construct a new way to characterise rough signals driving
mean-field equations: the probabilistic rough path. A comprehensive
generalisation of the ideas first introduced in \cite{2019arXiv180205882.2B},
these ideas promise powerful insights into how interactions with a collective
determine the dynamics of an individual within this collective.


Abstract:
  We consider a Bayesian framework based on "probability of decision" for
dose-finding trial designs. The proposed PoD-BIN design evaluates the posterior
predictive probabilities of up-and-down decisions. In PoD-BIN, multiple grades
of toxicity, categorized as the mild toxicity (MT) and dose-limiting toxicity
(DLT), are modeled simultaneously, and the primary outcome of interests is
time-to-toxicity for both MT and DLT. This allows the possibility of enrolling
new patients when previously enrolled patients are still being followed for
toxicity, thus potentially shortening trial length. The Bayesian decision rules
in PoD-BIN utilize the probability of decisions to balance the need to speed up
the trial and the risk of exposing patients to overly toxic doses. We
demonstrate via numerical examples the resulting balance of speed and safety of
PoD-BIN and compare to existing designs.


Abstract:
  Recently, Blockchain technology adoption has expanded to many application
areas due to the evolution of smart contracts. However, developing smart
contracts is non-trivial and challenging due to the lack of tools and expertise
in this field. A promising solution to overcome this issue is to use
Model-Driven Engineering (MDE), however, using models still involves a learning
curve and might not be suitable for non-technical users. To tackle this
challenge, chatbot or conversational interfaces can be used to assess the
non-technical users to specify a smart contract in gradual and interactive
manner.
  In this paper, we propose iContractBot, a chatbot for modeling and developing
smart contracts. Moreover, we investigate how to integrate iContractBot with
iContractML, a domain-specific modeling language for developing smart
contracts, and instantiate intention models from the chatbot. The iContractBot
framework provides a domain-specific language (DSL) based on the user intention
and performs model-to-text transformation to generate the smart contract code.
A smart contract use case is presented to demonstrate how iContractBot can be
utilized for creating models and generating the deployment artifacts for smart
contracts based on a simple conversation.


Abstract:
  The physical mechanism on meridians (acupuncture lines) is studied and a
theoretical model is proposed. The meridians are explained as an alternating
system responsible for the integration and the regulation of life in addition
to the neuro-humoral regulation. We proposed that the meridian conduction is a
kind of mechanical waves (soliton) of low frequency along the slits of muscles.
The anatomical-physiological and experimental evidences are reviewed. It is
demonstrated that the stabilization of the soliton is guaranteed by the
coupling between muscle vibration and cell activation. Therefore the
propagation of mechanical wave dominates the excitation of cell groups along
the meridian. The meridian wave equations and its solution are deduced and how
these results can be used in studying human healthy is briefly discussed .


Abstract:
  We investigate the dynamics brought on by an impulse perturbation in two
infinite-range quantum Ising models coupled to each other and to a dissipative
bath. We show that, if dissipation is faster the higher the excitation energy,
the pulse perturbation cools down the low-energy sector of the system, at the
expense of the high-energy one, eventually stabilising a transient
symmetry-broken state at temperatures higher than the equilibrium critical one.
Such non-thermal quasi-steady state may survive for quite a long time after the
pulse, if the latter is properly tailored.


Abstract:
  Consistent alpha generation, i.e., maintaining an edge over the market,
underpins the ability of asset traders to reliably generate profits. Technical
indicators and trading strategies are commonly used tools to determine when to
buy/hold/sell assets, yet these are limited by the fact that they operate on
known values. Over the past decades, multiple studies have investigated the
potential of artificial intelligence in stock trading in conventional markets,
with some success. In this paper, we present RCURRENCY, an RNN-based trading
engine to predict data in the highly volatile digital asset market which is
able to successfully manage an asset portfolio in a live environment. By
combining asset value prediction and conventional trading tools, RCURRENCY
determines whether to buy, hold or sell digital currencies at a given point in
time. Experimental results show that, given the data of an interval $t$, a
prediction with an error of less than 0.5\% of the data at the subsequent
interval $t+1$ can be obtained. Evaluation of the system through backtesting
shows that RCURRENCY can be used to successfully not only maintain a stable
portfolio of digital assets in a simulated live environment using real
historical trading data but even increase the portfolio value over time.


Abstract:
  The formation of Uranus' regular moons has been suggested to be linked to the
origin of its enormous spin axial tilt (~98^o). A giant impact between
proto-Uranus and a 2-3 M_Earth impactor could lead to a large tilt and to the
formation of an impact generated disc, where prograde and circular satellites
are accreted. The most intriguing features of the current regular Uranian
satellite system is that it possesses a positive trend in the mass-distance
distribution and likely also in the bulk density, implying that viscous
spreading of the disc after the giant impact plays a crucial role in shaping
the architecture of the final system. In this paper, we investigate the
formation of Uranus' satellites by combining results of SPH simulations for the
giant impact, a 1D semi-analytic disc model for viscous spreading of the
post-impact disc, and N-body simulations for the assembly of satellites from a
disc of moonlets. Assuming the condensed rock (i.e., silicate) remains small
and available to stick onto the relatively rapid growing condensed water-ice,
we find that the best case in reproducing the observed mass and bulk
composition of Uranus' satellite system is a pure-rocky impactor with 3 M_Earth
colliding with the young Uranus with an impact parameter b = 0.75. Such an
oblique collision could also naturally explain Uranus' large tilt and possibly,
its low internal heat flux. The giant impact scenario can naturally explain the
key features of Uranus and its regular moons. We therefore suggest that the
Uranian satellite system formed as a result of an impact rather than from a
circumplanetary disc.


Abstract:
  We investigate a set of techniques for RNN Transducers (RNN-Ts) that were
instrumental in lowering the word error rate on three different tasks
(Switchboard 300 hours, conversational Spanish 780 hours and conversational
Italian 900 hours). The techniques pertain to architectural changes, speaker
adaptation, language model fusion, model combination and general training
recipe. First, we introduce a novel multiplicative integration of the encoder
and prediction network vectors in the joint network (as opposed to additive).
Second, we discuss the applicability of i-vector speaker adaptation to RNN-Ts
in conjunction with data perturbation. Third, we explore the effectiveness of
the recently proposed density ratio language model fusion for these tasks. Last
but not least, we describe the other components of our training recipe and
their effect on recognition performance. We report a 5.9% and 12.5% word error
rate on the Switchboard and CallHome test sets of the NIST Hub5 2000 evaluation
and a 12.7% WER on the Mozilla CommonVoice Italian test set.


Abstract:
  The autoregressive (AR) models, such as attention-based encoder-decoder
models and RNN-Transducer, have achieved great success in speech recognition.
They predict the output sequence conditioned on the previous tokens and
acoustic encoded states, which is inefficient on GPUs. The non-autoregressive
(NAR) models can get rid of the temporal dependency between the output tokens
and predict the entire output tokens in at least one step. However, the NAR
model still faces two major problems. On the one hand, there is still a great
gap in performance between the NAR models and the advanced AR models. On the
other hand, it's difficult for most of the NAR models to train and converge. To
address these two problems, we propose a new model named the two-step
non-autoregressive transformer(TSNAT), which improves the performance and
accelerating the convergence of the NAR model by learning prior knowledge from
a parameters-sharing AR model. Furthermore, we introduce the two-stage method
into the inference process, which improves the model performance greatly. All
the experiments are conducted on a public Chinese mandarin dataset ASIEHLL-1.
The results show that the TSNAT can achieve a competitive performance with the
AR model and outperform many complicated NAR models.


Abstract:
  Mathematical models are formal and simplified representations of the
knowledge related to a phenomenon. In classical epidemic models, a neglected
aspect is the heterogeneity of disease transmission and progression linked to
the viral load of each infectious individual. Here, we attempt to investigate
the interplay between the evolution of individuals' viral load and the epidemic
dynamics from a theoretical point of view. In the framework of multi-agent
systems, we propose a particle stochastic model describing the infection
transmission through interactions among agents and the individual physiological
course of the disease. Agents have a double microscopic state: a discrete
label, that denotes the epidemiological compartment to which they belong and
switches in consequence of a Markovian process, and a microscopic trait,
representing a normalized measure of their viral load, that changes in
consequence of binary interactions or interactions with a background.
Specifically, we consider Susceptible--Infected--Removed--like dynamics where
infectious individuals may be isolated from the general population and the
isolation rate may depend on the viral load sensitivity and frequency of tests.
We derive kinetic evolution equations for the distribution functions of the
viral load of the individuals in each compartment, whence, via suitable
upscaling procedures, we obtain a macroscopic model for the densities and viral
load momentum. We perform then a qualitative analysis of the ensuing
macroscopic model, and we present numerical tests in the case of both constant
and viral load-dependent isolation control. Also, the matching between the
aggregate trends obtained from the macroscopic descriptions and the original
particle dynamics simulated by a Monte Carlo approach is investigated.


Abstract:
  This is a summation of research done in the author's second and third year of
undergraduate mathematics at The University of Toronto. As the previous details
were largely scattered and disorganized; the author decided to rewrite the
cumulative research. The goal of this paper is to construct a family of
analytic functions $\alpha \uparrow^n z : (1,e^{1/e}) \times \mathbb{C}_{\Re(z)
> 0} \to \mathbb{C}_{\Re(z) > 0}$ using methods from fractional calculus. This
family satisfies the hyper-operator chain, $\alpha \uparrow^{n-1} \alpha
\uparrow^n z = \alpha \uparrow^n (z+1)$; with the initial condition $\alpha
\uparrow^0 z = \alpha \cdot z$.


Abstract:
  Fiber-reinforced ceramic-matrix composites are advanced materials resistant
to high temperatures, with application to aerospace engineering. Their analysis
depends on the detection of embedded fibers, with semi-supervised techniques
usually employed to separate fibers within the fiber beds. Here we present an
open computational pipeline to detect fibers in ex-situ X-ray computed
tomography fiber beds. To separate the fibers in these samples, we tested four
different architectures of fully convolutional neural networks. When comparing
our neural network approach to a semi-supervised one, we obtained Dice and
Matthews coefficients greater than $92.28 \pm 9.65\%$, reaching up to $98.42
\pm 0.03 \%$, showing that the network results are close to the
human-supervised ones in these fiber beds, in some cases separating fibers that
human-curated algorithms could not find. The software we generated in this
project is open source, released under a permissive license, and can be freely
adapted and re-used in other domains. All data and instructions on how to
download and use it are also available.


Abstract:
  We have designed a two-stage, 10-step process to give organisations a method
to analyse small local energy systems (SLES) projects based on their Cyber
Physical System components in order to develop future-proof energy systems.
  SLES are often developed for a specific range of use cases and functions, and
these match the specific requirements and needs of the community, location or
site under consideration. During the design and commissioning, new and specific
cyber physical architectures are developed. These are the control and data
systems that are needed to bridge the gap between the physical assets, the
captured data and the control signals. Often, the cyber physical architecture
and infrastructure is focused on functionality and the delivery of the specific
applications.
  But we find that technologies and approaches have arisen from other fields
that, if used within SLES, could support the flexibility, scalability and
reusability vital to their success. As these can improve the operational data
systems then they can also be used to enhance predictive functions If used and
deployed effectively, these new approaches can offer longer term improvements
in the use and effectiveness of SLES, while allowing the concepts and designs
to be capitalised upon through wider roll-out and the offering of commercial
services or products.


Abstract:
  Mukai varieties are Fano varieties of Picard number one and coindex three. In
genus seven to ten they are linear sections of some special homogeneous
varieties. We describe the generic automorphism groups of these varieties. When
they are expected to be trivial for dimensional reasons, we show they are
indeed trivial, up to three interesting and unexpected exceptions in genera 7,
8, 9, and codimension 4, 3, 2 respectively. We conclude in particular that a
generic prime Fano threefold of genus g has no automorphisms for 7 $\le$ g
$\le$ 10. In the Appendix by Y. Prokhorov, the latter statement is extended to
g = 12.


Abstract:
  Over a decade ago De Loera, Haws and K\"oppe conjectured that Ehrhart
polynomials of matroid polytopes have only positive coefficients and that the
coefficients of the corresponding $h^*$-polynomials form a unimodal sequence.
The first of these intensively studied conjectures has recently been disproved
by the first author who gave counterexamples in all ranks greater or equal to
three. In this article we complete the picture by showing that Ehrhart
polynomials of matroids of lower rank have indeed only positive coefficients.
Moreover, we show that they are coefficient-wise bounded by the Ehrhart
polynomials of minimal and uniform matroids. We furthermore address the second
conjecture by proving that $h^*$-polynomials of matroid polytopes of sparse
paving matroids of rank two are real-rooted and therefore have log-concave and
unimodal coefficients.


Abstract:
  We present GrammarTagger, an open-source grammar profiler which, given an
input text, identifies grammatical features useful for language education. The
model architecture enables it to learn from a small amount of texts annotated
with spans and their labels, which 1) enables easier and more intuitive
annotation, 2) supports overlapping spans, and 3) is less prone to error
propagation, compared to complex hand-crafted rules defined on
constituency/dependency parses. We show that we can bootstrap a grammar
profiler model with $F_1 \approx 0.6$ from only a couple hundred sentences both
in English and Chinese, which can be further boosted via learning a
multilingual model. With GrammarTagger, we also build Octanove Learn, a search
engine of language learning materials indexed by their reading difficulty and
grammatical features. The code and pretrained models are publicly available at
\url{https://github.com/octanove/grammartagger}.


Abstract:
  Recently, research on mental health conditions using public online data,
including Reddit, has surged in NLP and health research but has not reported
user characteristics, which are important to judge generalisability of
findings. This paper shows how existing NLP methods can yield information on
clinical, demographic, and identity characteristics of almost 20K Reddit users
who self-report a bipolar disorder diagnosis. This population consists of
slightly more feminine- than masculine-gendered mainly young or middle-aged
US-based adults who often report additional mental health diagnoses, which is
compared with general Reddit statistics and epidemiological studies.
Additionally, this paper carefully evaluates all methods and discusses ethical
issues.


Abstract:
  In the present article, we study the Hawking effect and the bounds on
greybody factor in a spacetime with radial deformation. This deformation is
expected to carry the imprint of a non-Einsteinian theory of gravity, but
shares some of the important characteristics of general relativity (GR). In
particular, this radial deformation will restore the asymptotic behavior, and
also allows for the separation of the scalar field equation in terms of the
angular and radial coordinates -- making it suitable to study the Hawking
effect and greybody factors. However, the radial deformation would introduce a
change in the locations of the horizon, and therefore, the temperature of the
Hawking effect naturally alters. In fact, we observe that the deformation
parameter has an enhancing effect on both temperature and bounds on the
greybody factor, which introduces a useful distinction with the Kerr spacetime.
We discuss these effects elaborately, and broadly study the thermal behavior of
a radially deformed spacetime.


Abstract:
  This paper demonstrates how spectrum up to 1 THz will support mobile
communications beyond 5G in the coming decades. Results of rooftop surrogate
satellite/tower base station measurements at 140 GHz show the natural isolation
between terrestrial networks and surrogate satellite systems, as well as
between terrestrial mobile users and co-channel fixed backhaul links. These
first-of-their-kind measurements and accompanying analysis show that by keeping
the energy radiated by terrestrial emitters on the horizon (e.g., elevation
angles $\leq$15\textdegree), there will not likely be interference in the same
or adjacent bands between passive satellite sensors and terrestrial terminals,
or between mobile links and terrestrial backhaul links at frequencies above 100
GHz.


Abstract:
  In this paper we discuss applications of the theory developed in [21] and
[22] in studying certain Galois groups and splitting fields of rational
functions in $\mathbb Q\left(X_0(N)\right)$ using Hilbert's irreducibility
theorem and modular forms. We also consider computational aspect of the problem
using MAGMA and SAGE.


Abstract:
  We provide the first construction of stationary measures for the open KPZ
equation on the spatial interval $[0,1]$ with general inhomogeneous Neumann
boundary conditions at $0$ and $1$ depending on real parameters $u$ and $v$,
respectively. When $u+v\geq 0$ we uniquely characterize the constructed
stationary measures through their multipoint Laplace transform which we prove
is given in terms of a stochastic process that we call the continuous dual Hahn
process.