Abstract:
  We report on the design, construction, and commissioning of a prototype
aperture masking technology implemented at the Keck OSIRIS Imager: the
holographic aperture mask. Holographic aperture masking (HAM) aims at (i)
increasing the throughput of sparse aperture masking (SAM) by selectively
combining all subapertures across a telescope pupil in multiple interferograms
using a phase mask, and (ii) adding low-resolution spectroscopic capabilities.
Using liquid-crystal geometric phase patterns, we manufacture a HAM mask that
uses an 11-hole SAM design as the central component and a holographic component
comprising 19 different subapertures. Thanks to a multilayer liquid-crystal
implementation, the mask has a diffraction efficiency higher than 96% from 1.1
to 2.5 micron. We create a pipeline that extracts monochromatic closure phases
from the central component as well as multiwavelength closure phases from the
holographic component. We test the performance of the HAM mask in the
laboratory and on-sky. The holographic component yields 26 closure phases with
spectral resolutions between R$\sim$6.5 and R$\sim$15. On April 19, 2019, we
observed the binary star HDS 1507 in the Hbb filter ($\lambda_0 = 1638$ nm and
$\Delta \lambda = 330$ nm) and retrieved a constant separation of 120.9 $\pm
0.5$ mas for the independent wavelength bins, which is in excellent agreement
with literature values. For both the laboratory measurements and the
observations of unresolved reference stars, we recorded nonzero closure phases
-- a potential source of systematic error that we traced to polarization
leakage of the HAM optic. We propose a future upgrade that improves the
performance, reducing this effect to an acceptable level. Holographic aperture
masking is a simple upgrade of SAM with increased throughput and a new
capability of simultaneous low-resolution spectroscopy that provides new
differential observables.


Abstract:
  The numerous compact sources associated with neutron stars and white dwarfs
discovered in recent decades are analyzed in terms of the Gravimagnetic Rotator
model (GMR paradigm - Lipunov, 1987a; Lipunov, 1992). We offer the instrument
for understanding of various observed features and evolutionary relationships
of neutron stars and white dwarfs. We depict in a single diagram all objects
from radio pulsars and dwarf novae to ultra luminous X-ray sources and a radio
pulsating white dwarf. This diagram directly demonstrates the genetic link
between different types of compact sources thereby making it possible to
confirm and illustrate clearly the established evolutionary connections - such
as that between bulge X-ray sources and millisecond pulsars. This approach
allows us to understand the evolutionary status of Ultra Luminous X-ray
sources. In addition, we propose an additional evolutionary branch of the
formation of Magnetars. When our work was completed, an article by Kirsten et
al.2021, was published, which reports the localization of FRB 20200120 in one
of the globular clusters of the galaxy M81. This shows that the
accretion-induced collapse scenario of the white dwarf (Lipunov and Postnov,
1985), considered in detail in this work, is a possible genealogical branch of
Magnetar production.


Abstract:
  Automatic segmentation of brain abnormalities is challenging, as they vary
considerably from one pathology to another. Current methods are supervised and
require numerous annotated images for each pathology, a strenuous task. To
tackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are
proposed, detecting anomalies as outliers of a healthy model learned using a
Variational Autoencoder (VAE). Previous work on UAD adopted a 2D approach,
meaning that MRIs are processed as a collection of independent slices. Yet, it
does not fully exploit the spatial information contained in MRI. Here, we
propose to perform UAD in a 3D fashion and compare 2D and 3D VAEs. As a side
contribution, we present a new loss function guarantying a robust training.
Learning is performed using a multicentric dataset of healthy brain MRIs, and
segmentation performances are estimated on White-Matter Hyperintensities and
tumors lesions. Experiments demonstrate the interest of 3D methods which
outperform their 2D counterparts.


Abstract:
  The widespread adoption of Free/Libre and Open Source Software (FLOSS) means
that the ongoing maintenance of many widely used software components relies on
the collaborative effort of volunteers who set their own priorities and choose
their own tasks. We argue that this has created a new form of risk that we call
'underproduction' which occurs when the supply of software engineering labor
becomes out of alignment with the demand of people who rely on the software
produced. We present a conceptual framework for identifying relative
underproduction in software as well as a statistical method for applying our
framework to a comprehensive dataset from the Debian GNU/Linux distribution
that includes 21,902 source packages and the full history of 461,656 bugs. We
draw on this application to present two experiments: (1) a demonstration of how
our technique can be used to identify at-risk software packages in a large
FLOSS repository and (2) a validation of these results using an alternate
indicator of package risk. Our analysis demonstrates both the utility of our
approach and reveals the existence of widespread underproduction in a range of
widely-installed software components in Debian.


Abstract:
  We present Low-Frequency Array (LOFAR) 143.5-MHz radio observations of
flaring activity during 2019 May from the X-ray binary Cygnus X-3. Similar to
radio observations of previous outbursts from Cygnus X-3, we find that this
source was significantly variable at low frequencies, reaching a maximum flux
density of about 5.8 Jy. We compare our LOFAR light curve with contemporaneous
observations taken at 1.25 and 2.3 GHz with the RATAN-600 telescope, and at 15
GHz with the Arcminute Microkelvin Imager (AMI) Large Array. The initial
143.5-MHz flux density level, $\sim$2 Jy, is suggested to be the delayed and
possibly blended emission from at least some of the flaring activity that had
been detected at higher frequencies before our LOFAR observations had begun.
There is also evidence of a delay of more than four days between a bright flare
that initially peaked on May 6 at 2.3 and 15 GHz, and the corresponding peak
($\gtrsim$ 5.8 Jy) at 143.5 MHz. From the multi-frequency light curves, we
estimate the minimum energy and magnetic field required to produce this flare
to be roughly 10$^{44}$ erg and 40 mG, respectively, corresponding to a minimum
mean power of $\sim$10$^{38}$ erg s$^{-1}$. Additionally, we show that the
broadband radio spectrum evolved over the course of our observing campaign; in
particular, the two-point spectral index between 143.5 MHz and 1.25 GHz
transitioned from being optically thick to optically thin as the flare
simultaneously brightened at 143.5 MHz and faded at GHz frequencies.


Abstract:
  Previous works indicate that the glyph of Chinese characters contains rich
semantic information and has the potential to enhance the representation of
Chinese characters. The typical method to utilize the glyph features is by
incorporating them into the character embedding space. Inspired by previous
methods, we innovatively propose a Chinese pre-trained representation model
named as GlyphCRM, which abandons the ID-based character embedding method yet
solely based on sequential character images. We render each character into a
binary grayscale image and design two-channel position feature maps for it.
Formally, we first design a two-layer residual convolutional neural network,
namely HanGlyph to generate the initial glyph representation of Chinese
characters, and subsequently adopt multiple bidirectional encoder Transformer
blocks as the superstructure to capture the context-sensitive information.
Meanwhile, we feed the glyph features extracted from each layer of the HanGlyph
module into the underlying Transformer blocks by skip-connection method to
fully exploit the glyph features of Chinese characters. As the HanGlyph module
can obtain a sufficient glyph representation of any Chinese character, the
long-standing out-of-vocabulary problem could be effectively solved. Extensive
experimental results indicate that GlyphCRM substantially outperforms the
previous BERT-based state-of-the-art model on 9 fine-tuning tasks, and it has
strong transferability and generalization on specialized fields and
low-resource tasks. We hope this work could spark further research beyond the
realms of well-established representation of Chinese texts.


Abstract:
  As the length-scales of materials decrease, heterogeneities associated with
interfaces approach the importance of the surrounding materials. Emergent
electronic and magnetic interface properties in superlattices have been studied
extensively by both experiments and theory. $^{1-6}$ However, the presence of
interfacial vibrations that impact phonon-mediated responses, like thermal
conductivity $^{7,8}$, has only been inferred in experiments indirectly. While
it is accepted that intrinsic phonons change near boundaries $^{9,10}$, the
physical mechanisms and length-scales through which interfacial effects
influence materials remain unclear. Herein, we demonstrate the localized
vibrational response associated with the interfaces in SrTiO$_3$-CaTiO$_3$
superlattices by combining advanced scanning transmission electron microscopy
imaging and spectroscopy and density-functional-theory calculations. Symmetries
atypical of either constituent material are observed within a few atomic planes
near the interface. The local symmetries create local phonon modes that
determine the global response of the superlattice once the spacing of the
interfaces approaches the phonon spatial extent. The results provide direct
visualization and quantification, illustrating the progression of the local
symmetries and interface vibrations as they come to determine the vibrational
response of an entire superlattice; stated differently, the progression from a
material with interfaces, to a material dominated by interfaces, to a material
of interfaces as the period decreases. Direct observation of such local atomic
and vibrational phenomena demonstrates that their spatial extent needs to be
quantified to understand macroscopic behavior. Tailoring interfaces, and
knowing their local vibrational response, provides a means of pursuing designer
solids having emergent infrared and thermal responses.


Abstract:
  Metastability is a quintessential feature of first order quantum phase
transitions, which is lost either by dynamical instability or by nucleating
bubbles of a true vacuum through quantum tunneling. By considering a drive
across the first order quantum phase transition in the quantum Ising chain in
the presence of both transverse and longitudinal fields, we reveal multiple
regions in the parameter space where the initial metastable state loses its
metastability in successive stages. The mechanism responsible is found to be
semi-degenerate resonant tunnelings to states with specific bubble sizes. We
show that such dynamics of quantized bubble nucleations can be understood in
terms of Landau-Zener transitions, which provide quantitative predictions of
nucleation probabilities for different bubble sizes.


Abstract:
  Whether tidal disruption events (TDEs) circularise or accrete directly as a
highly eccentric disc is the subject of current research and appears to depend
sensitively on the disc thermodynamics. In a previous paper we applied the
theory of eccentric discs to TDE discs using an $\alpha-$prescription for the
disc stress, which leads to solutions that exhibit extreme, potentially
unphysical, behaviour. In this paper we further explore the dynamical vertical
structure of highly eccentric discs using alternative stress models that are
better motivated by the behaviour of magnetic fields in eccentric discs. We
find that the presence of a coherent magnetic field has a stabilising effect on
the dynamics and can significantly alter the behaviour of highly eccentric
radiation dominated discs. We conclude that magnetic fields are important for
the evolution of TDE discs.


Abstract:
  Awareness of cybersecurity topics facilitates software developers to produce
secure code. This awareness is especially important in industrial environments
for the products and services in critical infrastructures. In this work, we
address how to raise awareness of software developers on the topic of secure
coding. We propose the "CyberSecurity Challenges", a serious game designed to
be used in an industrial environment and address software developers' needs.
Our work distils the experience gained in conducting these CyberSecurity
Challenges in an industrial setting. The main contributions are the design of
the CyberSecurity Challenges events, the analysis of the perceived benefits,
and practical advice for practitioners who wish to design or refine these
games.


Abstract:
  Motion completion is a challenging and long-discussed problem, which is of
great significance in film and game applications. For different motion
completion scenarios (in-betweening, in-filling, and blending), most previous
methods deal with the completion problems with case-by-case designs. In this
work, we propose a simple but effective method to solve multiple motion
completion problems under a unified framework and achieves a new state of the
art accuracy under multiple evaluation settings. Inspired by the recent great
success of attention-based models, we consider the completion as a sequence to
sequence prediction problem. Our method consists of two modules - a standard
transformer encoder with self-attention that learns long-range dependencies of
input motions, and a trainable mixture embedding module that models temporal
information and discriminates key-frames. Our method can run in a
non-autoregressive manner and predict multiple missing frames within a single
forward propagation in real time. We finally show the effectiveness of our
method in music-dance applications.


Abstract:
  The magnetic nature of the formation of solar active regions lies at the
heart of understanding solar activity and, in particular, solar eruptions. A
widespread model, used in many theoretical studies, simulations and the
interpretation of observations is that the basic structure of an active region
is created by the emergence of a large tube of pre-twisted magnetic field.
Despite plausible reasons and the availability of various proxies suggesting
the veracity of this model, there has not yet been any direct observational
evidence of the emergence of large twisted magnetic flux tubes. Thus, the
fundamental question, "are active regions formed by large twisted flux tubes?"
has remained open. In this work, we answer this question in the affirmative and
provide direct evidence to support this. We do this by investigating a robust
topological quantity, called magnetic winding, in solar observations. This
quantity, combined with other signatures that are currently available, provides
the first direct evidence that large twisted flux tubes do emerge to create
active regions.


Abstract:
  Shubnikov-de Haas (SdH) oscillations, the most well-known
magneto-oscillations caused by the quantization of electron energy levels in
the presence of magnetic fields in two-dimensional (2D) electron systems, can
be used to determine Fermi-surface properties and directly measure the Berry
phase of the 2D systems. It is usually thought that transport measurements are
required to measure the SdH oscillations. Contradicting this belief, we
demonstrate that the SdH oscillations can be measured in graphene systems by
carrying out scanning tunneling spectroscopy (STS) measurements. The
energy-momentum dispersions and Berry phases of monolayer, Bernal-stacked
bilayer, and ABC-stacked trilayer graphene are obtained according to the
measured SdH oscillations in the STS spectra. It is possible to obtain the SdH
oscillations when the size of the 2D systems is larger than the magnetic length
and, importantly, no gate electrode is required in the STS measurement,
therefore, the reported method in this work is applicable to a wide range of
materials.


Abstract:
  To maximize safety and driving comfort, autonomous driving systems can
benefit from implementing foresighted action choices that take different
potential scenario developments into account. While artificial scene prediction
methods are making fast progress, an attentive human driver may still be able
to identify relevant contextual features which are not adequately considered by
the system or for which the human driver may have a lack of trust into the
system's capabilities to treat them appropriately. We implement an approach
that lets a human driver quickly and intuitively supplement scene predictions
to an autonomous driving system by gaze. We illustrate the feasibility of this
approach in an existing autonomous driving system running a variety of
scenarios in a simulator. Furthermore, a Graphical User Interface (GUI) was
designed and integrated to enhance the trust and explainability of the system.
The utilization of such cooperatively augmented scenario predictions has the
potential to improve a system's foresighted driving abilities and make
autonomous driving more trustable, comfortable and personalized.


Abstract:
  The hydrodynamics of droplets is significant in wide-ranging applications
involving immiscible fluids and emulsions in food and pharmaceutical. The
control and manipulation of droplets are primarily a function of flow governing
and geometrical parameters. The finite element and level set approaches are
used in this work to explore the influences of capillary number (Ca) and flow
rate ratio (Qr) of dispersed and continuous phases on hydrodynamics of droplet
generation in two-phase flow through T-junction cross-flow microfluidic device.
A mathematical model based on a mass continuity, Navier-Stokes, and level set
equations are solved computationally using the Eulerian framework for Ca = 1e-4
- 1 and Qr =0.1 - 10. Both immiscible phases, having equal density and unequal
viscosity, flow (Re=0.1) through equal-sized channels. In particular,
instantaneous phase flow field, droplet size, droplet detachment time and
generation frequency are presented and discussed as a function of governing
parameters (Ca and Qr). Considered parametric space is characterized as
squeezing, first transition, dripping, second transition, parallel, and jet
flow regimes. In contrast to threshold Ca ~ 0.01 in earlier studies, squeezing
regime exists for all Ca and Qr = 2- 10. Flow regimes are also mapped into
droplets and non-droplet zones. Threshold interfacial Ca, defining the boundary
between droplet and non-droplet zones, scales quadratically with Qr. Droplet
dynamics shows a complex dependence on Ca and Qr. Droplet length varies
linearly with Qr in squeezing regime whereas power-law variation with Ca and Qr
in dripping regime. Droplet frequency shows a power-law function of Ca and Qr
in droplet zone. Present results compare excellently with earlier limited
experimental and numerical studies. Finally, present results and predictive
correlations can guide engineering and design of droplet microfluidics devices.


Abstract:
  This paper describes the design and development of a prototype technique for
artificial intelligence based on the fusion of genetic algorithm, neural
network and fuzzy logic. It starts by establishing a relationship between the
neural network and fuzzy logic. Then, it combines the genetic algorithm with
them. Information fusions are at the confidence level, where matching scores
can be reported and discussed. The technique is called the Genetic Neuro-Fuzzy
(GNF). It can be used for high accuracy real-time environments.


Abstract:
  Understanding of the interaction of antiferromagnetic solitons including
domain walls and skyrmions with boundaries of chiral antiferromagnetic slabs is
important for the design of prospective antiferromagnetic spintronic devices.
Here, we derive the transition from spin lattice to micromagnetic nonlinear
$\sigma$-model with the corresponding boundary conditions for a chiral cubic
G-type antiferromagnet and analyze the impact of the slab boundaries and
antisymmetric exchange (Dzyaloshinskii--Moriya interaction) on the vector order
parameter. We apply this model to evaluate modifications of antiferromagnetic
domain walls and skyrmions upon interaction with boundaries for different
strengths of the antisymmetric exchange. Due to the presence of the
antisymmetric exchange, both types of antiferromagnetic solitons become broader
when approaching the boundary and transform to a mixed Bloch--N\'{e}el
structure. Both textures feel the boundary at the distance of about 5 magnetic
lengths. In this respect, our model provides design rules for antiferromagnetic
racetracks, which can support bulk-like properties of solitons.


Abstract:
  Mesoporous bioactive glasses, MBG, are gaining increasing interest in the
design of new biomaterials for bone defects treatment. An important research
trend to enhance their biological behavior is the inclusion of moderate amounts
of oxides with therapeutical action such as CuO. In this paper, MBG with
composition were synthesized, investigating the influence of the CuO content
and some synthesis parameters in their properties. Two batch were developed;
first one using HCl as catalyst and chlorides as CaO and CuO precursors, second
one, using HNO3 and nitrates. MBG of chlorides batch exhibited calcium/copper
phosphate nanoparticles, between 10 and 20 nm. Nevertheless, CuO- containing
MBG of nitrates batch showed nuclei of metallic copper nanoparticles larger
than 50 nm and quicker in vitro bioactive responses. Thus, they were coated by
an apatite-like layer after 24 h soaked in simulated body fluid, a remarkably
short period for MBG containing up to 5 % of CuO. A model, focused in the
copper location in the glass network, was proposed to relate nanostructure and
in vitro behaviour. Moreover, after 24 h in MEM or THB culture media, all the
MBG released therapeutic amounts of Ca2+ and Cu2+ ions. Because the quick
bioactive response in SBF, the capacity to host biomolecules in their pores and
to release therapeutic concentrations of Ca2+ and Cu2+ ions, MBG of the nitrate
batch are considered as excellent biomaterials for bone regeneration.


Abstract:
  The radio $k$-coloring is an assignment $l$ of non-negative integers to the
vertices of a graph in such a way that for two vertices $u,v$ which are $d$
distance apart, we must have $|l(u)-l(v)| \geq k+1 - d$. The difference between
the greatest and the least image of $l$ is its span and our objective is to
minimize the span. That is, the radio $k$-chromatic number $rc_k(G)$ is the
minimum $\lambda$ such that it is a span of some radio $k$-coloring of $G$. In
this article, we find exact values of $rc_k(P_n^{m})$ when $k > diam(P_n^{m})+
\epsilon$, where $P_n^{m}$ denotes the $m^{th}$ power of a path on $(n+1)$
vertices, and $diam(P_n^{m})$ denotes its diameter.


Abstract:
  Laser-induced-continuum-structure(LICS) allows for coherent control
techniques to be applied in a Raman type system with intermediate continuum
state. To this end the standard LICS problem treats two bound states coupled to
one or more continua states. In this paper we discuss the simplest non-trivial
multilevel generalization of LICS coupling two bound levels, each composed of
two degenerate states through a common continuum state. We reduce the
complexity of the effective system of bound states by switching to a rotated
basis in which different sub-systems of lower dimension evolve independently.
We derive a trapping condition and explore the dynamics of the sub-systems
under different initial conditions.


Abstract:
  Most 3D neural networks are trained from scratch owing to the lack of
large-scale labeled 3D datasets. In this paper, we present a novel 3D
pretraining method by leveraging 2D networks learned from rich 2D datasets. We
propose the contrastive pixel-to-point knowledge transfer to effectively
utilize the 2D information by mapping the pixel-level and point-level features
into the same embedding space. Due to the heterogeneous nature between 2D and
3D networks, we introduce the back-projection function to align the features
between 2D and 3D to make the transfer possible. Additionally, we devise an
upsampling feature projection layer to increase the spatial resolution of
high-level 2D feature maps, which enables learning fine-grained 3D
representations. With a pretrained 2D network, the proposed pretraining process
requires no additional 2D or 3D labeled data, further alleviating the expensive
3D data annotation cost. To the best of our knowledge, we are the first to
exploit existing 2D trained weights to pretrain 3D deep neural networks. Our
intensive experiments show that the 3D models pretrained with 2D knowledge
boost the performances of 3D networks across various real-world 3D downstream
tasks.


Abstract:
  We demonstrate direct transport between two opposing sets of Yu-Shiba-Rusinov
(YSR) subgap states realized in a double quantum dot. This sub-gap transport
relies on intrinsic quasiparticle relaxation, but the tunability of the device
allows us to explore also an additional relaxation mechanism based on charge
transferring Andreev reflections. The transition between these two relaxation
regimes is identified in the experiment as a marked gate-induced stepwise
change in conductance. We present a transport calculation, including YSR bound
states and multiple Andreev reflections alongside with quasiparticle
relaxation, due to a weak tunnel coupling to a nearby normal metal, and obtain
excellent agreement with the data.


Abstract:
  The exploration of interstellar space will require autonomous navigation
systems that do not rely on tracking from the Earth. Here I develop a method to
determine the 3D position and 3D velocity of a spacecraft in deep space using a
star catalogue. As a spacecraft moves away from the Sun, the observed positions
and velocities of the stars will change relative to those in a Earth-based
catalogue due to parallax and aberration. By measuring just the angular
distances between pairs of stars, and comparing these to the catalogue, we can
infer the coordinates of the spacecraft via an iterative forward-modelling
process. I perform simulations with existing star catalogues to demonstrate the
method and to compute its performance. Using the 20 nearest stars and a modest
angular distance measurement accuracy of 1", the position and velocity of a
spacecraft light years from the Sun moving at relativistic speeds can be
determined to within 3 au and 2 km/s respectively. These accuracies improve
linearly with the measurement accuracy, e.g. with angles measured to 1 mas the
navigation accuracy is 1000 times better. Performance can also be improved
using more stars, or by including onboard measurements of the stars' radial
velocities, as these too are affected by the spacecraft's position and motion.


Abstract:
  The well-known Kullback-Leibler divergence is known to be superadditive,
enabling its use in many information-theoretic applications. However, such a
proof is still missing for Tsallis divergence to the best of our knowledge. In
this work, we first prove that Tsallis divergence is $\alpha$-superadditive for
$\alpha\in\mathbb{R}$. Then, we show that this fact entails ordinary
superadditivity in the same aforementioned interval. This opens up a novel
route to many future applications of Tsallis divergence in information theory.


Abstract:
  Many differentiated products have key attributes that are unstructured and
thus high-dimensional (e.g., design, text). Instead of treating unstructured
attributes as unobservables in economic models, quantifying them can be
important to answer interesting economic questions. To propose an analytical
framework for this type of products, this paper considers one of the simplest
design products -- fonts -- and investigates merger and product differentiation
using an original dataset from the world's largest online marketplace for
fonts. We quantify font shapes by constructing embeddings from a deep
convolutional neural network. Each embedding maps a font's shape onto a
low-dimensional vector. In the resulting product space, designers are assumed
to engage in Hotelling-type spatial competition. From the image embeddings, we
construct two alternative measures that capture the degree of design
differentiation. We then study the causal effects of a merger on the merging
firm's creative decisions using the constructed measures in a synthetic control
method. We find that the merger causes the merging firm to increase the visual
variety of font design. Notably, such effects are not captured when using
traditional measures for product offerings (e.g., specifications and the number
of products) constructed from structured data.


Abstract:
  Accurate modeling of chemically reactive systems has traditionally relied on
either expensive ab initio approaches or flexible bond-order force fields such
as ReaxFF that require considerable time, effort, and expertise to
parameterize. Here, we introduce FLARE++, a Bayesian active learning method for
training reactive many-body force fields on the fly during molecular dynamics
(MD) simulations. During the automated training loop, the predictive
uncertainties of a sparse Gaussian process (SGP) force field are evaluated at
each timestep of an MD simulation to determine whether additional ab initio
data are needed. Once trained, the SGP is mapped onto an equivalent and much
faster model that is polynomial in the local environment descriptors and whose
prediction cost is independent of the training set size. We apply our method to
a canonical reactive system in the field of heterogeneous catalysis, hydrogen
splitting and recombination on a platinum (111) surface, obtaining a trained
model within three days of wall time that is twice as fast as a recent Pt/H
ReaxFF force field and considerably more accurate. Our method is fully open
source and is expected to reduce the time and effort required to train fast and
accurate reactive force fields for complex systems.


Abstract:
  We have found stable chaotic solutions for optomechanical systems coupled
with a Two-Level System or qubit. In this system methods have been found which
can be used to Tune in and out of Chaos as well as various n-period motions.
This includes achieving chaos by changing the detuning, coupling parameters,
and Power of the driving laser. This allows us to manipulate chaos using either
the qubit or the optical cavity. Chaotic motion was also observed in both the
qubit and cavity by only changing the relative phase between of driving fields
of the two. This gives us the prospect of creating and exploring chaotic motion
in quantum mechanical systems with further ease


Abstract:
  During the last decades, quantum dots within the Coulomb blockade regime of
transport have been proposed as essential building blocks for a wide variety of
nanomachines. This includes thermoelectric devices, quantum shuttles, quantum
pumps, and even quantum motors. However, in this regime, the role of quantum
mechanics is commonly limited to provide energy quantization while the working
principle of the devices is ultimately the same as their classic counterparts.
Here, we study quantum-dot-based nanomachines in the Coulomb blockade regime,
but in a configuration where the coherent superpositions of the dots' states
plays a crucial role. We show that the studied system can be used as the basis
for different forms of "true" quantum machines that should only work in the
presence of these coherent superpositions. We analyze the efficiency of these
machines against different nonequilibrium sources (bias voltage, temperature
gradient, and external driving) and the factors that limit it, including
decoherence and the role of the different orders appearing in the adiabatic
expansion of the charge/heat currents.


Abstract:
  We reconsider the all-optical homodyne-measurement based experimental schemes
that aim to reveal Bell nonclassicality of a single photon, often termed
`nonlocality'. We focus on the schemes put forward by Tan, Walls and Collett
(TWC, 1991) and Hardy (1994). In the light of our previous work the Tan, Walls
and Collett setup can be described by a precise local hidden variable model,
hence the claimed nonclassicality of this proposal is apparent, whereas the
nonclassicality proof proposed by Hardy is impeccable. In this work we resolve
the following problem: which feature of the Hardy's approach is crucial for its
successful confirmation of nonclassicality. The scheme of Hardy differs from
the Tan, Walls and Collett setup in two aspects. (i) It introduces a
superposition of a single photon excitation with vacuum as the initial state of
one of the input modes of a 50-50 beamsplitter, which creates the superposition
state of two separable (exit) modes under investigation. (ii) In the final
measurements Hardy's proposal utilises a varying strengths of the local
oscillator fields, whereas in the TWC case they are constant. In fact the local
oscillators in Hardy's scheme are either on or off (the local setting is
specified by the presence or absence of the local auxiliary field). We show
that it is the varying strength of the local oscillators, from setting to
setting, which is the crucial feature enabling violation of local realism in
the Hardy setup, whereas it is not necessary to use initial superposition of a
single photon excitation with vacuum as the initial state of the input mode.
Neither one needs to operate in the fully on/off detection scheme. Despite the
failure of the Tan, Walls and Collett scheme in proving Bell nonclassicality,
we show that their scheme can serve as an entanglement indicator.


Abstract:
  We give a short analysis of the \emph{transversal achievement game} on a
square grid due to M. Erickson (2010).


Abstract:
  Women's football is gaining supporters and practitioners worldwide, raising
questions about what the differences are with men's football. While the two
sports are often compared based on the players' physical attributes, we analyze
the spatio-temporal events during matches in the last World Cups to compare
male and female teams based on their technical performance. We train an
artificial intelligence model to recognize if a team is male or female based on
variables that describe a match's playing intensity, accuracy, and performance
quality. Our model accurately distinguishes between men's and women's football,
revealing crucial technical differences, which we investigate through the
extraction of explanations from the classifier's decisions. The differences
between men's and women's football are rooted in play accuracy, the recovery
time of ball possession, and the players' performance quality. Our methodology
may help journalists and fans understand what makes women's football a distinct
sport and coaches design tactics tailored to female teams.


Abstract:
  In this paper, we review several results from singularly perturbed
differential equations with multiple small parameters. In addition, we develop
a general conceptual framework to compare and contrast the different results by
proposing a three-step process. First, one specifies the setting and
restrictions of the differential equation problem to be studied and identifies
the relevant small parameters. Second, one defines a notion of equivalence via
a property/observable for partitioning the parameter space into suitable
regions near the singular limit. Third, one studies the possible asymptotic
singular limit problems as well as perturbation results to complete the
diagrammatic subdivision process. We illustrate this approach for two simple
problems from algebra and analysis. Then we proceed to the review of several
modern double-limit problems including multiple time scales, stochastic
dynamics, spatial patterns, and network coupling. For each example, we
illustrate the previously mentioned three-step process and show that already
double-limit parametric diagrams provide an excellent unifying theme. After
this review, we compare and contrast the common features among the different
examples. We conclude with a brief outlook, how our methodology can help to
systematize the field better, and how it can be transferred to a wide variety
of other classes of differential equations.


Abstract:
  We describe the architecture of Sammen Om Demens (SOD), an application for
portable devices aiming at helping persons with dementia when wandering and
getting lost through the involvement of caregivers, family members, and
ordinary citizens who volunteer.
  To enable the real-time detection of a person with dementia that has lost
orientation, we transfer location data at high frequency from a frontend on the
smartphone of a person with dementia to a backend system. The backend system
must be able to cope with the high throughput data and carry out possibly heavy
computations for the detection of anomalous behavior via artificial
intelligence techniques. This sets certain performance and architectural
requirements on the design of the backend.
  In the paper, we discuss our design and implementation choices for the
backend of SOD that involve microservices and serverless services to achieve
efficiency and scalability. We give evidence of the achieved goals by deploying
the SOD backend on a public cloud and measuring the performance on simulated
load tests.


Abstract:
  The still-maturing noisy intermediate-scale quantum (NISQ) technology faces
strict limitations on the algorithms that can be implemented efficiently. In
quantum chemistry, the variational quantum eigensolver (VQE) algorithm has
become ubiquitous, using the functional form of the ansatz as a degree of
freedom, whose parameters are found variationally in a feedback loop between
the quantum processor and its conventional counterpart. Alternatively, a
promising new avenue has been unraveled by the quantum variants of techniques
grounded on expansions of the moments of the Hamiltonian, among which two stand
out: the connected moments expansion (CMX) [Phys. Rev. Lett. 58, 53 (1987)] and
the Peeters-Devreese-Soldatov (PDS) functional [J. Phys. A 17, 625 (1984); Int.
J. Mod. Phys. B 9, 2899], the latter based on the standard moments <$H^k$>.
Contrasting with VQE-based methods and provided the quantum circuit prepares a
state with non-vanishing overlap with the true ground state, CMX often
converges to the ground state energy, while PDS is guaranteed to converge by
virtue of being variational. However, for a finite CMX/PDS order, the circuit
may significantly impact the energy accuracy. Here we use the ADAPT-VQE
algorithm to test shallow circuit construction strategies that are not expected
to impede their implementation in the present quantum hardware while granting
sizable accuracy improvement in the computed ground state energies. We also
show that we can take advantage of the fact that the terms in the connected
moments are highly recurring in different powers, incurring a sizable reduction
in the number of necessary measurements. By coupling this measurement caching
with a threshold that determines whether a given term is to be measured based
on its associated scalar coefficient, we observe a further reduction in the
number of circuit implementations while allowing for tunable accuracy.


Abstract:
  The rapid spread of COVID-19 infections on a global level has highlighted the
need for accurate, transparent and timely information regarding collective
mobility patterns to inform de-escalation strategies as well as to provide
forecasting capacity for re-escalation policies aiming at addressing further
waves of the virus. Such information can be extracted using aggregate
anonymised data from innovative sources such as mobile positioning data. This
paper presents lessons learnt and results of a unique Business-to-Government
(B2G) initiative between several Mobile Network Operators in Europe and the
European Commission. Mobile positioning data have supported policy makers and
practitioners with evidence and data-driven knowledge to understand and predict
the spread of the disease, the effectiveness of the containment measures, their
socio-economic impacts while feeding scenarios at EU scale and in a comparable
way across countries. The challenges of this data sharing initiative are not
limited to data quality, harmonisation, and comparability across countries,
however important they are. Equally essential aspects that need to be addressed
from the onset are related to data privacy, security, fundamental rights and
commercial sensitivity.


Abstract:
  This paper presents a proof of concept for the usefulness of second-order
texture features for the qualitative analysis and classification of chromogenic
in-situ hybridization whole slide images in high-throughput imaging
experiments. The challenge is that currently, the gold standard for gene
expression grading in such images is expert assessment. The idea of the
research team is to use different approaches in the analysis of these images
that will be used for structural segmentation and functional analysis in gene
expression. The article presents such perspective idea to select a number of
textural features that are going to be used for classification. In our
experiment, natural grouping of image samples (tiles) depending on their local
texture properties was explored in an unsupervised classification procedure.
The features are reduced to two dimensions with fuzzy c-means clustering. The
overall conclusion of this experiment is that Haralick features are a viable
choice for classification and analysis of chromogenic in-situ hybridization
image data. The principal component analysis approach produced slightly more
"understandable" from an annotator's point of view classes.


Abstract:
  Although machine learning (ML) is widely used in practice, little is known
about practitioners' actual understanding of potential security challenges. In
this work, we close this substantial gap in the literature and contribute a
qualitative study focusing on developers' mental models of the ML pipeline and
potentially vulnerable components. Studying mental models has helped in other
security fields to discover root causes or improve risk communication. Our
study reveals four characteristic ranges in mental models of industrial
practitioners. The first range concerns the intertwined relationship of
adversarial machine learning (AML) and classical security. The second range
describes structural and functional components. The third range expresses
individual variations of mental models, which are neither explained by the
application nor by the educational background of the corresponding subjects.
The fourth range corresponds to the varying levels of technical depth, which
are however not determined by our subjects' level of knowledge. Our
characteristic ranges have implications for the integration of AML into
corporate workflows, security enhancing tools for practitioners, and creating
appropriate regulatory frameworks for AML.


Abstract:
  New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced
to provide a better opportunity for the developers to make software development
robust and easy. At the early stage, a programming language is likely to have
resource constraints that encourage the developers to seek help frequently from
experienced peers active in QA sites such as Stack Overflow (SO). In this
study, we have formally studied the discussions on three popular new languages
introduced after the inception of SO (2008) and match those with the relevant
activities in GitHub whenever appropriate. For that purpose, we have mined
4,17,82,536 questions and answers from SO and 7,846 issue information along
with 6,60,965 repository information from GitHub. Initially, the development of
new languages is relatively slow compared to mature languages (e.g., C, C++,
Java). The expected outcome of this study is to reveal the difficulties and
challenges faced by the developers working with these languages so that
appropriate measures can be taken to expedite the generation of relevant
resources. We have used the LDA method on SO's questions and answers to
identify different topics of new languages. We have extracted several features
of the answer pattern of the new languages from SO to study their
characteristics. These attributes were used to identify difficult topics. We
explored the background of developers who are contributing to these languages.
We have created a model by combining Stack Overflow data and issues,
repository, user data of GitHub. Finally, we have used that model to identify
factors that affect language evolution. We believe that the outcome of our
study is likely to help the owner/sponsor of these languages to design better
features and documentation. It will also help the software developers or
students to prepare themselves to work on these languages in an informed way.


Abstract:
  Model checking has been developed for verifying the behaviour of systems with
stochastic and non-deterministic behavior. It is used to provide guarantees
about such systems. While most model checking methods focus on propositional
models, various probabilistic planning and reinforcement frameworks deal with
relational domains, for instance, STRIPS planning and relational Markov
Decision Processes. Using propositional model checking in relational settings
requires one to ground the model, which leads to the well known state explosion
problem and intractability. We present pCTL-REBEL, a lifted model checking
approach for verifying pCTL properties on relational MDPs. It extends REBEL,
the relational Bellman update operator, which is a lifted value iteration
approach for model-based relational reinforcement learning, toward relational
model-checking. PCTL-REBEL is lifted, which means that rather than grounding,
the model exploits symmetries and reasons at an abstract relational level.
Theoretically, we show that the pCTL model checking approach is decidable for
relational MDPs even for possibly infinite domains provided that the states
have a bounded size. Practically, we contribute algorithms and an
implementation of lifted relational model checking, and we show that the lifted
approach improves the scalability of the model checking approach.


Abstract:
  We study $p$-adic $L$-functions $L_p(s,\chi)$ for Dirichlet characters
$\chi$. We show that $L_p(s,\chi)$ has a Dirichlet series expansion for each
regularization parameter $c$ that is prime to $p$ and the conductor of $\chi$.
The expansion is proved by transforming a known formula for $p$-adic
$L$-functions and by controlling the limiting behavior. A finite number of
Euler factors can be factored off in a natural manner from the $p$-adic
Dirichlet series. We also provide an alternative proof of the expansion using
$p$-adic measures and give an explicit formula for the values of the
regularized Bernoulli distribution. The result is particularly simple for
$c=2$, where we obtain a Dirichlet series expansion that is similar to the
complex case.


Abstract:
  Accretion disks and coronae around massive black holes have been studied
extensively, and they are known to be coupled. Over a period of 30 years,
however, the X-ray (coronal) flux of Mrk 817 increased by a factor of 40 while
its UV (disk) flux remained relatively steady. Recent high-cadence monitoring
finds that the X-ray and UV continua in Mrk 817 are also decoupled on time
scales of weeks and months. These findings could require mechanical beaming of
the innermost accretion flow, and/or an absorber that shields the disk and/or
broad line region (BLR) from the X-ray corona. Herein, we report on a 135 ks
observation of Mrk 817 obtained with NuSTAR, complemented by simultaneous X-ray
coverage via the Neil Gehrels Swift Observatory. The X-ray data strongly prefer
a standard relativistic disk reflection model over plausible alternatives.
Comparable fits with related models constrain the spin to lie in the range 0.5
< a < 1, and the viewing angle to lie between 10 deg. < theta < 22 deg.
(including 1-sigma statistical errors and small systematic errors related to
differences between the models). The spectra also reveal strong evidence of
moderately ionized absorption, similar to but likely less extreme than
obscuring events in NGC 5548 and NGC 3783. Archival Swift data suggest that the
absorption may be variable. Particularly if the column density of this absorber
is higher along the plane of the disk, it may intermittently mask or prevent
coupling between the central engine, disk, and BLR in Mrk 817.


Abstract:
  Artificial neural networks perform state-of-the-art in an ever-growing number
of tasks, and nowadays they are used to solve an incredibly large variety of
tasks. There are problems, like the presence of biases in the training data,
which question the generalization capability of these models. In this work we
propose EnD, a regularization strategy whose aim is to prevent deep models from
learning unwanted biases. In particular, we insert an "information bottleneck"
at a certain point of the deep neural network, where we disentangle the
information about the bias, still letting the useful information for the
training task forward-propagating in the rest of the model. One big advantage
of EnD is that we do not require additional training complexity (like decoders
or extra layers in the model), since it is a regularizer directly applied on
the trained model. Our experiments show that EnD effectively improves the
generalization on unbiased test sets, and it can be effectively applied on
real-case scenarios, like removing hidden biases in the COVID-19 detection from
radiographic images.


Abstract:
  Computations on the dendritic trees of neurons have important constraints.
Voltage dependent conductances in dendrites are not similar to arbitrary
direct-current generation, they are the basis for dendritic nonlinearities and
they do not allow converting positive currents into negative currents. While it
has been speculated that the dendritic tree of a neuron can be seen as a
multi-layer neural network and it has been shown that such an architecture
could be computationally strong, we do not know if that computational strength
is preserved under these biological constraints. Here we simulate models of
dendritic computation with and without these constraints. We find that
dendritic model performance on interesting machine learning tasks is not hurt
by these constraints but may benefit from them. Our results suggest that single
real dendritic trees may be able to learn a surprisingly broad range of tasks.


Abstract:
  We propose DeRenderNet, a deep neural network to decompose the albedo and
latent lighting, and render shape-(in)dependent shadings, given a single image
of an outdoor urban scene, trained in a self-supervised manner. To achieve this
goal, we propose to use the albedo maps extracted from scenes in videogames as
direct supervision and pre-compute the normal and shadow prior maps based on
the depth maps provided as indirect supervision. Compared with state-of-the-art
intrinsic image decomposition methods, DeRenderNet produces shadow-free albedo
maps with clean details and an accurate prediction of shadows in the
shape-independent shading, which is shown to be effective in re-rendering and
improving the accuracy of high-level vision tasks for urban scenes.


Abstract:
  The experimental thermal neutron cross sections of the twenty proteinogenic
amino acids have been measured over the incident-neutron energy range spanning
from 1 meV to 10 keV and data have been interpreted using the multi-phonon
expansion based on first-principles calculations. The scattering cross section,
dominated by the incoherent inelastic contribution from the hydrogen atoms, can
be rationalised in terms of the average contributions of different functional
groups, thus neglecting their correlation. These results can be used for
modelling the total neutron cross sections of complex organic systems like
proteins, muscles, or human tissues from a limited number of starting input
functions. This simplification is of crucial importance for fine-tuning of
transport simulations used in medical applications, including boron neutron
capture therapy as well as secondary neutrons-emission induced during proton
therapy. Moreover, the parametrized neutron cross sections allow a better
treatment of neutron scattering experiments, providing detailed sample
self-attenuation corrections for a variety of biological and soft-matter
systems.


Abstract:
  We study solutions of the stellar structure equations for spherically
symmetric objects in Palatini $f(R)=R+\alpha R^2$ and $f(R,Q)=R+\alpha
R^2+\beta Q$ in the mass-radius region associated to neutron stars. We
illustrate the potential impact of the $R^2$ and $Q$ terms by studying a range
of viable values of $\alpha$ and $\beta$. Similarly, we use different equations
of state (SLy, FPS, HS(DD2) and HS(TMA)) as a simple way to account for the
equation of state uncertainty. Our results show that for certain combinations
of the $\alpha$ and $\beta$ parameters and equation of state, the effect of
modifications of general relativity on the properties of stars is sizeable.
Therefore, with increasing accuracy in the determination of the equation of
state for neutron stars, astrophysical observations may serve as discriminators
of modifications of General Relativity.


Abstract:
  Success in Open Source Software (OSS) is often perceived as an exclusively
code-centric endeavor. This perception can exclude a variety of individuals
with a diverse set of skills and backgrounds, in turn helping create the
current diversity & inclusion imbalance in OSS. Because people's perspectives
of success affect their personal, professional, and life choices, to be able to
support a diverse class of individuals, we must first understand what OSS
contributors consider successful. Thus far, research has used a
uni-dimensional, code-centric lens to define success. In this paper, we
challenge this status-quo and reveal the multi-faceted definition of success
among OSS contributors. We do so through interviews with 27 OSS contributors
who are recognized as successful in their communities, and a follow-up open
survey with 193 OSS contributors. Our study provides nuanced definitions of
success perceptions in OSS, which might help devise strategies to attract and
retain a diverse set of contributors, helping them attain their "pots of gold
at the end of the rainbow".


Abstract:
  In a class of neutrino mass models with modular flavor symmetries, it has
been observed that CP symmetry is preserved at the fixed point (or stabilizer)
of the modulus parameter $\tau = {\rm i}$, whereas significant CP violation
emerges within the neighbourhood of this stabilizer. In this paper, we first
construct a viable model with the modular $A^\prime_5$ symmetry, and explore
the phenomenological implications for lepton masses and flavor mixing. Then, we
introduce explicit perturbations to the stabilizer at $\tau = {\rm i}$, and
present both numerical and analytical results to understand why a small
deviation from the stabilizer leads to large CP violation. As low-energy
observables are very sensitive to the perturbations to model parameters, we
further demonstrate that the renormalization-group running effects play an
important role in confronting theoretical predictions at the high-energy scale
with experimental measurements at the low-energy scale.


Abstract:
  It is generally thought that FRII Radio Galaxies host thin optically thick
disks, while FRIs are powered by Advected Dominated Accretion Flows. The
sources with an efficient engine are optically classified as High Excitation
Radio Galaxies (HERGs) and those with an inefficient motor as Low Excitation
Radio Galaxies (LERGs). Recently, the study of Radio Galaxies down to mJy
fluxes has cast serious doubts on the LERG-FRI and HERG-FRII correspondence,
revealing that many LERGs show FRII radio morphologies. The FR catalogs
recently compiled by Capetti et al. (2017a,b) and Baldi et al. (2018) have
allowed us to explore this issue in the local ($z\le 0.15$) mJy Universe. Our
statistical study shows that the majority of nearby mJy objects are in a late
stage of their life. FRII-LERGs appear more similar to the old FRI-LERGs than
to the young FRII-HERGs. FRII-LERGs may be aged HERGs that, exhausted the cold
fuel, have changed their accretion regime or a separate LERG class particularly
efficient in launching jets. Exploiting the empirical relations which convert
L$_{\rm [OIII]}$ and L$_{\rm 1.4~GHz}$ into accretion power and jet kinetic
power, respectively, we observed that LERGs with similar masses and accretion
rates seem to expel jets of different power. We speculate that intrinsic
differences related to the black hole properties (spin and magnetic field at
its horizon) can determine the observed spread in jet luminosity. In this view,
FRII-LERGs should have the fastest spinning black holes and/or the most intense
magnetic fluxes. On the contrary, compact LERGs (i.e. FR0s) should host
extremely slow black holes and/or weak magnetic fields.


Abstract:
  In this paper we perform systematic investigation of all possible exponential
solutions in Einstein-Gauss-Bonnet gravity with the spatial section being a
product of two subspaces. We describe a scheme which always allow to find
solution for a given $\{p, q\} > 2$ (number of dimensions of two subspaces) and
$\zeta$ (ratio of the expansion rates of these two subspaces). Depending on the
parameters, for given $\{\alpha, \Lambda\}$ (Gauss-Bonnet coupling and
cosmological constant) there could be up to four distinct solutions (with
different $\zeta$'s). Stability requirement introduces relation between
$\zeta$, $\{p, q\}$ and sign of the expansion rate. Nevertheless, for any $\{p,
q\} > 2$ we can always choose sign for expansion rates so that the resulting
solution would be stable. The scheme for finding solutions is described and the
bounds on the parameters are drawn. Specific cases with $\{p, q\} = \{1, 2\}$
are also considered. Finally, we separately described physically sensible case
with one of the subspaces being three-dimensional and expanding (resembling our
Universe) while another to be contracting (resembling extra dimensions),
describing successful compactification; for this case we also drawn bounds on
the parameters where such regime occurs.


Abstract:
  In the problem of active sequential hypothesis testing (ASHT), a learner
seeks to identify the true hypothesis from among a known set of hypotheses. The
learner is given a set of actions and knows the random distribution of the
outcome of any action under any true hypothesis. Given a target error
$\delta>0$, the goal is to sequentially select the fewest number of actions so
as to identify the true hypothesis with probability at least $1 - \delta$.
Motivated by applications in which the number of hypotheses or actions is
massive (e.g., genomics-based cancer detection), we propose efficient (greedy,
in fact) algorithms and provide the first approximation guarantees for ASHT,
under two types of adaptivity. Both of our guarantees are independent of the
number of actions and logarithmic in the number of hypotheses. We numerically
evaluate the performance of our algorithms using both synthetic and real-world
DNA mutation data, demonstrating that our algorithms outperform previously
proposed heuristic policies by large margins.


Abstract:
  Deep neural networks (DNNs) have recently found emerging use in accelerated
MRI reconstruction. DNNs typically learn data-driven priors from large datasets
constituting pairs of undersampled and fully-sampled acquisitions. Acquiring
such large datasets, however, might be impractical. To mitigate this
limitation, we propose a few-shot learning approach for accelerated MRI that
merges subject-driven priors obtained via physical signal models with
data-driven priors obtained from a few training samples. Demonstrations on
brain MR images from the NYU fastMRI dataset indicate that the proposed
approach requires just a few samples to outperform traditional parallel imaging
and DNN algorithms.


Abstract:
  In recent years, much work have studied the use of convolutional neural
networks for gravitational-wave detection. However little work pay attention to
whether the transient noise can trigger the CNN model or not. In this paper, we
study the responses of the sine-Gaussian glitches, the Gaussian glitches and
the ring-down glitches in the trained convolutional neural network classifier.
We find that the network is robust to the sine-Gaussian and Gaussian glitches,
whose false alarm probabilities are close to that of the LIGO-like noises, in
contrast to the case of the ring-down glitches, in which the false alarm
probability is far larger than that of the LIGO-like noises. We also
investigate the responses of the glitches with different frequency. We find
that when the frequency of the glitches falls in that of the trained GW
signals, the false alarm probability of the glitches will be much larger than
that of the LIGO-like noises, and the probability of the glitches being
misjudged as the GW signals may even exceed 30%.


Abstract:
  Huayi Chen introduces the notion of an approximable graded algebra, which he
uses to prove a Fujita-type theorem in the arithmetic setting, and asked if any
such algebra is the graded ring of a big line bundle on a projective variety.
This was proved to be false in a previous paper of the author's, who
subsequently proved that any such algebra is associated to an infinite Weil
divisor. In this paper, we show that over the complex numbers, this infinite
Weil divisor necessarily has finite cohomology class.


Abstract:
  Self-supervision has demonstrated to be an effective learning strategy when
training target tasks on small annotated data-sets. While current research
focuses on creating novel pretext tasks to learn meaningful and reusable
representations for the target task, these efforts obtain marginal performance
gains compared to fully-supervised learning. Meanwhile, little attention has
been given to study the robustness of networks trained in a self-supervised
manner. In this work, we demonstrate that networks trained via self-supervised
learning have superior robustness and generalizability compared to
fully-supervised learning in the context of medical imaging. Our experiments on
pneumonia detection in X-rays and multi-organ segmentation in CT yield
consistent results exposing the hidden benefits of self-supervision for
learning robust feature representations.


Abstract:
  We investigate the harmonic response of neon atoms to mid-IR laser fields
(2000-3000 nm) using a single-active electron (SAE) model and the fully ab
initio all-electron R-Matrix with Time-dependence (RMT) method. The laser peak
intensity and wavelength are varied to find optimal parameters for
high-harmonic imaging in the water window. Comparison of the SAE and RMT
results shows qualitative agreement between them as well as parameters such as
the cutoff frequency predicted by the classical three-step model. However,
there are significant differences in the details, thus indicating the
importance of multi-electron effects.


Abstract:
  We show that for every integer $m > 0$, there is an ordinary abelian variety
over ${\mathbb F}_2$ that has exactly $m$ rational points.


Abstract:
  Autonomous landing of Unmanned Aerial Vehicles (UAVs) in crowded scenarios is
crucial for successful deployment of UAVs in populated areas, particularly in
emergency landing situations where the highest priority is to avoid hurting
people. In this work, a new visual-based algorithm for identifying Safe Landing
Zones (SLZ) in crowded scenarios is proposed, considering a camera mounted on
an UAV, where the people in the scene move with unknown dynamics. To do so, a
density map is generated for each image frame using a Deep Neural Network, from
where a binary occupancy map is obtained aiming to overestimate the people's
location for security reasons. Then, the occupancy map is projected to the
head's plane, and the SLZ candidates are obtained as circular regions in the
head's plane with a minimum security radius. Finally, to keep track of the SLZ
candidates, a multiple instance tracking algorithm is implemented using Kalman
Filters along with the Hungarian algorithm for data association. Several
scenarios were studied to prove the validity of the proposed strategy,
including public datasets and real uncontrolled scenarios with people moving in
public squares, taken from an UAV in flight. The study showed promising results
in the search of preventing the UAV from hurting people during emergency
landing.


Abstract:
  It has recently been shown that the inner region of protoplanetary disks
(PPDs) is governed by wind-driven accretion, and the resulting accretion flow
showing complex vertical profiles. Such complex flow structures are further
enhanced due to the Hall effect, especially when the background magnetic field
is aligned with disk rotation. We investigate how such flow structures impact
global dust transport via Monte-Carlo simulations, focusing on two scenarios.
In the first scenario, the toroidal magnetic field is maximized in the miplane,
leading to accretion and decretion flows above and below. In the second
scenario, the toroidal field changes sign across the midplane, leading to an
accretion flow at the disk midplane, with decretion flows above and below. We
find that in both cases, the contribution from additional gas flows can still
be accurately incorporated into the advection-diffusion framework for
vertically-integrated dust transport, with enhanced dust radial diffusion up to
an effective $\alpha^{\rm eff}\sim10^{-2}$ for strongly coupled dust, even when
background turbulence is weak $\alpha<10^{-4}$. Dust radial drift is also
modestly enhanced in the second scenario. We provide a general analytical
theory that accurately reproduces our simulation results, thus establishing a
framework to model global dust transport that realistically incorporates
vertical gas flow structures. We also note that the theory is equally
applicable to the transport of chemical species.


Abstract:
  This survey reviews several approaches of data mining (DM) in healthindustry
from many research groups world wide. The focus is on modern multi-core
processors built into today's commodity computers, which are typically found at
university institutes both as small server and workstation computers. So they
are deliberately not high-performance computers. Modern multi-core processors
consist of several (2 to over 100) computer cores, which work independently of
each other according to the principle of "multiple instruction multiple data"
(MIMD). They have a common main memory (shared memory). Each of these computer
cores has several (2-16) arithmetic-logic units, which can simultaneously carry
out the same arithmetic operation on several data in a vector-like manner
(single instruction multiple data, SIMD). DM algorithms must use both types of
parallelism (SIMD and MIMD), with access to the main memory (centralized
component) being the main barrier to increased efficiency. This is important
for DM in healthindustry applications like ECG, EEG, CT, SPECT, fMRI, DTI,
ultrasound, microscopy, dermascopy, etc.


Abstract:
  We tackle the crucial challenge of fusing different modalities of features
for multimodal sentiment analysis. Mainly based on neural networks, existing
approaches largely model multimodal interactions in an implicit and
hard-to-understand manner. We address this limitation with inspirations from
quantum theory, which contains principled methods for modeling complicated
interactions and correlations. In our quantum-inspired framework, the word
interaction within a single modality and the interaction across modalities are
formulated with superposition and entanglement respectively at different
stages. The complex-valued neural network implementation of the framework
achieves comparable results to state-of-the-art systems on two benchmarking
video sentiment analysis datasets. In the meantime, we produce the unimodal and
bimodal sentiment directly from the model to interpret the entangled decision.


Abstract:
  It is well-known that the embedding of the Sobolev space of weakly
differentiable functions into H\"{o}lder spaces holds if the integrability
exponent is higher than the space dimension of the domain. In this paper, the
embedding of the Sobolev functions into the H\"{o}lder spaces is expressed in
terms of the minimal weak differentiability requirement independent of the
integrability exponent. The proof is based on the generalized Newton-Leibniz
formula in the rectangular prism and inductive application of the Sobolev trace
embedding results. The particular motivation for the new embedding theorem
appears in proving compactness of the family of multilinear interpolations of
discrete grid functions arising as solutions of the discretized PDE problems.


Abstract:
  State-of-the-art convolutional neural networks excel in machine learning
tasks such as face recognition, and object classification but suffer
significantly when adversarial attacks are present. It is crucial that machine
critical systems, where machine learning models are deployed, utilize robust
models to handle a wide range of variability in the real world and malicious
actors that may use adversarial attacks. In this study, we investigate eye
closedness detection to prevent vehicle accidents related to driver
disengagements and driver drowsiness. Specifically, we focus on adversarial
attacks in this application domain, but emphasize that the methodology can be
applied to many other domains. We develop two models to detect eye closedness:
first model on eye images and a second model on face images. We adversarially
attack the models with Projected Gradient Descent, Fast Gradient Sign and
DeepFool methods and report adversarial success rate. We also study the effect
of training data augmentation. Finally, we adversarially train the same models
on perturbed images and report the success rate for the defense against these
attacks. We hope our study sets up the work to prevent potential vehicle
accidents by capturing drivers' face images and alerting them in case driver's
eyes are closed due to drowsiness.


Abstract:
  The generalization ability of most meta-reinforcement learning (meta-RL)
methods is largely limited to test tasks that are sampled from the same
distribution used to sample training tasks. To overcome the limitation, we
propose Latent Dynamics Mixture (LDM) that trains a reinforcement learning
agent with imaginary tasks generated from mixtures of learned latent dynamics.
By training a policy on mixture tasks along with original training tasks, LDM
allows the agent to prepare for unseen test tasks during training and prevents
the agent from overfitting the training tasks. LDM significantly outperforms
standard meta-RL methods in test returns on the gridworld navigation and MuJoCo
tasks where we strictly separate the training task distribution and the test
task distribution.


Abstract:
  The coarse Ricci curvature of graphs introduced by Ollivier as well as its
modification by Lin-Lu-Yau have been studied from various aspects. In this
paper, we propose a new transport distance appropriate for hypergraphs and
study a generalization of Lin-Lu-Yau type curvature of hypergraphs. As an
application, we derive a Bonnet-Myers type estimate for hypergraphs under a
lower Ricci curvature bound associated with our transport distance. We remark
that our transport distance is new even for graphs and worthy of further study.


Abstract:
  Animal-vehicle collisions (AVCs) are common around the world and result in
considerable loss of animal and human life, as well as significant property
damage and regular insurance claims. Understanding their occurrence in relation
to various contributing factors and being able to identify locations of high
risk are valuable to AVC prevention, yielding economic, social and
environmental cost savings. However, many challenges exist in the study of AVC
datasets. These include seasonality of animal activity, unknown exposure (i.e.,
the number of animal crossings), very low AVC counts across most sections of
extensive roadway networks, and computational burdens that come with discrete
response analysis using large datasets. To overcome these challenges, a
Bayesian hierarchical model is proposed where the exposure is modeled with
nonparametric Dirichlet process, and the number of segment-level AVCs is
assumed to follow a Binomial distribution. A P\'olya-Gamma augmented Gibbs
sampler is derived to estimate the proposed model. By using the AVC data of
multiple years across about 100,000 segments of state-controlled highways in
Texas, U.S., it is demonstrated that the model is scalable to large datasets,
with a preponderance of zeros and clear monthly seasonality in counts, while
identifying high-risk locations (for application of design treatments, like
separated animal crossings with fencing) and key explanatory factors based on
segment-specific factors (such as changes in speed limit) can be done within
the modelling framework, which provide useful information for policy-making
purposes.


Abstract:
  Quantum computing technologies pose a significant threat to the currently
employed public-key cryptography protocols. In this paper, we discuss the
impact of the quantum threat on public key infrastructures (PKIs), which are
used as a part of security systems for protecting production environments. We
analyze security issues of existing models with a focus on requirements for a
fast transition to post-quantum solutions. Although our primary focus is on the
attacks with quantum computing, we also discuss some security issues that are
not directly related to the used cryptographic algorithms but are essential for
the overall security of the PKI. We attempt to provide a set of security
recommendations regarding the PKI from the viewpoints of attacks with quantum
computers.


Abstract:
  This paper investigates the initial boundary value problem for a
non-stationary one-dimensional heat equation that simulates the temperature
distribution in freshwater ice near the Earth's poles. The mathematical model
has been constructed taking into account solid-liquid phase transitions. Data
from meteorological stations were used to determine the model parameters, with
the help of which the necessary physical and thermophysical characteristics of
the computational domain were obtained. For the numerical solution of the
problem, the finite volume method (FVM) was used. For the obtained periodic
regime, the temperature versus depth dependences for each month were plotted,
and the depth of the active layer, as well as the depth of zero annual
amplitudes were found for each meteorological station. A forecast of the ice
temperature regime for 2100 was modeled for three Representative Concentration
Pathway (RCP) scenarios of global warming: moderate RCP2.6, corresponding to
the current emissions of RCP7 and adopted at the Paris Agreement in 2015
RCP1.9. By analyzing the impact of an additional 0,5 degrees Celsius of warming
on other areas, a reduction in the full range of risks to humanity and the
planet as a whole becomes evident with the proper efforts of the global
community. Thus, the conducted modeling has confirmed the need to reduce the
rate of global warming.


Abstract:
  Let $\cX$ be a family of subsets of a finite set $E$. A matroid on $E$ is
called an $\cX$-matroid if each set in $\cX$ is a circuit. We consider the
problem of determining when there exists a unique maximal $\cX$-matroid in the
weak order poset of all $\cX$-matroids on $E$, and characterizing its rank
function when it exists.


Abstract:
  Hydrogen deuteride (HD) is prevalent in a wide variety of astrophysical
environments, and measuring its large-scale distribution at different epochs
can in principle provide information about the properties of these
environments. In this paper, we explore the prospects for accessing this
distribution using line intensity mapping of emission from the lowest
rotational transition in HD, focusing on observations of the epoch of
reionization ($z\sim6-10$) and earlier. We find the signal from the epoch of
reionization to be strongest most promising, through cross-correlations within
existing [CII] intensity mapping surveys. While the signal we predict is out of
reach for current-generation projects, planned future improvements should be
able to detect reionization-era HD without any additional observations, and
would help to constrain the properties of the star-forming galaxies thought to
play a key role in reionization. We also investigate several avenues for
measuring HD during "cosmic dawn" ($z\sim10-30$), a period in which HD could
provide one of the only complementary observables to 21$\,$cm intensity maps.
We conclude that existing and planned facilities are poorly matched to the
specifications desirable for a significant detection, though such a measurement
may be achievable with sustained future effort. Finally, we explain why HD
intensity mapping of the intergalactic medium during the cosmic dark ages
($z\gtrsim 30$) appears to be out of reach of any conceivable experiment.


Abstract:
  Human is able to conduct 3D recognition by a limited number of haptic
contacts between the target object and his/her fingers without seeing the
object. This capability is defined as `haptic glance' in cognitive
neuroscience. Most of the existing 3D recognition models were developed based
on dense 3D data. Nonetheless, in many real-life use cases, where robots are
used to collect 3D data by haptic exploration, only a limited number of 3D
points could be collected. In this study, we thus focus on solving the
intractable problem of how to obtain cognitively representative 3D key-points
of a target object with limited interactions between the robot and the object.
A novel reinforcement learning based framework is proposed, where the haptic
exploration procedure (the agent iteratively predicts the next position for the
robot to explore) is optimized simultaneously with the objective 3D recognition
with actively collected 3D points. As the model is rewarded only when the 3D
object is accurately recognized, it is driven to find the sparse yet efficient
haptic-perceptual 3D representation of the object. Experimental results show
that our proposed model outperforms the state of the art models.


Abstract:
  This paper introduces QAConv, a new question answering (QA) dataset that uses
conversations as a knowledge source. We focus on informative conversations
including business emails, panel discussions, and work channels. Unlike
open-domain and task-oriented dialogues, these conversations are usually long,
complex, asynchronous, and involve strong domain knowledge. In total, we
collect 34,204 QA pairs, including span-based, free-form, and unanswerable
questions, from 10,259 selected conversations with both human-written and
machine-generated questions. We segment long conversations into chunks, and use
a question generator and dialogue summarizer as auxiliary tools to collect
multi-hop questions. The dataset has two testing scenarios, chunk mode and full
mode, depending on whether the grounded chunk is provided or retrieved from a
large conversational pool. Experimental results show that state-of-the-art QA
systems trained on existing QA datasets have limited zero-shot ability and tend
to predict our questions as unanswerable. Fine-tuning such systems on our
corpus can achieve significant improvement up to 23.6% and 13.6% in both chunk
mode and full mode, respectively.


Abstract:
  Based on current trends in computer architectures, faster compute speeds must
come from increased parallelism rather than increased clock speeds, which are
currently stagnate. This situation has created the well-known bottleneck for
sequential time-integration, where each individual time-value (i.e., time-step)
is computed sequentially. One approach to alleviate this and achieve
parallelism in time is with multigrid. In this work, we consider
multigrid-reduction-in-time (MGRIT), a multilevel method applied to the time
dimension that computes multiple time-steps in parallel. Like all multigrid
methods, MGRIT relies on the complementary relationship between relaxation on a
fine-grid and a correction from the coarse grid to solve the problem. All
current MGRIT implementations are based on unweighted-Jacobi relaxation; here
we introduce the concept of weighted relaxation to MGRIT. We derive new
convergence bounds for weighted relaxation, and use this analysis to guide the
selection of relaxation weights. Numerical results then demonstrate that
non-unitary relaxation weights consistently yield faster convergence rates and
lower iteration counts for MGRIT when compared with unweighted relaxation. In
most cases, weighted relaxation yields a 10%-20% saving in iterations. For
A-stable integration schemes, results also illustrate that under-relaxation can
restore convergence in some cases where unweighted relaxation is not
convergent.


Abstract:
  In computer vision and natural language processing, innovations in model
architecture that increase model capacity have reliably translated into gains
in performance. In stark contrast with this trend, state-of-the-art
reinforcement learning (RL) algorithms often use small MLPs, and gains in
performance typically originate from algorithmic innovations. It is natural to
hypothesize that small datasets in RL necessitate simple models to avoid
overfitting; however, this hypothesis is untested. In this paper we investigate
how RL agents are affected by exchanging the small MLPs with larger modern
networks with skip connections and normalization, focusing specifically on
actor-critic algorithms. We empirically verify that naively adopting such
architectures leads to instabilities and poor performance, likely contributing
to the popularity of simple models in practice. However, we show that dataset
size is not the limiting factor, and instead argue that instability from taking
gradients through the critic is the culprit. We demonstrate that spectral
normalization (SN) can mitigate this issue and enable stable training with
large modern architectures. After smoothing with SN, larger models yield
significant performance improvements -- suggesting that more "easy" gains may
be had by focusing on model architectures in addition to algorithmic
innovations.


Abstract:
  We show that deciding the equality of two Dedekind sums $S(c,b)$, $S(d,b)$ is
equivalent to deciding whether a Dedekind sum defined by $b, c, d$ takes a
certain value. By means of this result we construct infinite sequences of
pairwise equal Dedekind sums. Moreover, we prove a result that says how many
Dedekind sums $S(d,b)$, $1\le d\le b-1$, may be equal to a given $S(c,b)$ if
$b$ is a square-free number.


Abstract:
  Global transformations form a categorical framework adapting graph
transformations to describe fully synchronous rule systems on a given data
structure.In this work we focus on data structures that can be captured as
presheaves and study the computational aspects of such synchronous rule
systems.To obtain an online algorithm, a complete study of the sub-steps within
each synchronous step is done at the semantic level.This leads to the
definition of accretive rule systems and a local criterion to characterize
these systems.Finally an online computation algorithm for theses systems is
given.


Abstract:
  We clarify universal critical properties of delocalization-localization
transitions in three-dimensional (3D) unitary and orthogonal classes with
particle-hole and/or chiral symmetries (classes AIII, BDI, D, C and CI). We
first introduce tight-binding models on cubic lattice that belong to these five
nonstandard symmetry classes respectively. Unlike the Bogoliubov-de Gennes
Hamiltonian for superconductors, all the five models have finite areas of Fermi
surfaces in the momentum space in the clean limit. Thereby, the scaling theory
of the Anderson transition guarantees the presence of the
delocalization-localization transitions at finite disorder strength in these
models. Based on this expectation, we carry out extensive transfer matrix
calculations of the Lyapunov exponents for zero-energy eigenstates of the
disordered tight-binding models with quasi-one-dimensional geometry. Near the
Anderson transition point, the correlation length diverges with a universal
critical exponent $\nu$. Using finite-size scaling analysis of the localization
length, we determine the critical exponent of the transitions and scaling
function of the (normalized) localization length in the three non-standard
unitary symmetry classes: $\nu_{\text{AIII}}=1.06\pm0.02$,
$\nu_{\text{D}}=0.87\pm0.03$, and $\nu_{\text{C}}=0.996\pm0.012$. Our result of
the class C is consistent with a previous study of classical network model [M.
Ortu\~no et al, Phys. Rev. Lett. 102, 070603 (2009)]. The critical exponents of
the two non-standard orthogonal classes are estimated as $\nu_{\text{CI}}=1.17
\pm 0.02$ and $\nu_{\text{BDI}}=1.12 \pm 0.06$. Our result of the class CI is
consistent with another previous study of a lattice model, while the exponent
of the class BDI is at variance with the previous evaluation of nodal Dirac
ring model [X. L. Luo et al, Phys. Rev. B. 101, 020202(R) (2020)].


Abstract:
  The growing popularity of e-scooters and their rapid expansion across urban
streets has attracted widespread attention. A major policy question is whether
e-scooters substitute existing mobility options or fill the service gaps left
by them. This study addresses this question by analyzing the spatiotemporal
patterns of e-scooter service availability and use in Washington DC, focusing
on their spatial relationships with public transit and bikesharing. Results
from an analysis of three open big datasets suggest that e-scooters have both
competing and complementary effects on transit and bikesharing services. The
supply of e-scooters significantly overlaps with the service areas of transit
and bikesharing, and we classify a majority of e-scooter trips as substitutes
to transit and bikesharing uses. A travel-time-based analysis further reveals
that when choosing e-scooters over transit, travelers pay a price premium and
save some travel time. The price premium is greater during the COVID-19
pandemic but the associated travel-time savings are smaller. This implies that
public health considerations rather than time-cost tradeoffs are the main
driver for many to choose e-scooters over transit during COVID. In addition, we
find that e-scooters complement bikesharing and transit by providing services
to underserved neighborhoods. A sizeable proportion (about 10 percent) of
e-scooter trips are taken to connect with the rail services. Future research
may combine the big-data-based analysis presented here with traditional methods
to further shed light on the interactions between e-scooter services,
bikesharing, and public transit.


Abstract:
  We study the behavior of linear discriminant functions for binary
classification in the infinite-imbalance limit, where the sample size of one
class grows without bound while the sample size of the other remains fixed. The
coefficients of the classifier minimize an expected loss specified through a
weight function. We show that for a broad class of weight functions, the
intercept diverges but the rest of the coefficient vector has a finite limit
under infinite imbalance, extending prior work on logistic regression. The
limit depends on the left tail of the weight function, for which we distinguish
three cases: bounded, asymptotically polynomial, and asymptotically
exponential. The limiting coefficient vectors reflect robustness or
conservatism properties in the sense that they optimize against certain
worst-case alternatives. In the bounded and polynomial cases, the limit is
equivalent to an implicit choice of upsampling distribution for the minority
class. We apply these ideas in a credit risk setting, with particular emphasis
on performance in the high-sensitivity and high-specificity regions.


Abstract:
  We propose a new way to share licensed spectrum bandwidth capacity in mobile
networks between operators, service providers and consumers using
blockchain-based smart contracts. We discuss the foundational building blocks
in the contract as well as various extensions to support more advanced features
such as bulk purchases, future reservations, and various auction mechanisms.
Furthermore, we demonstrate how the system can be implemented with an
open-source, permissioned Enterprise blockchain, Hyperledger Sawtooth. We show
that our smart contract implementation can improve blockchain transaction
performance, by approximately four orders of magnitude compared to serial
transactions and one order of magnitude compared to parallell transactions,
using PKI-driven bulk purchases of mobile access grants, paving the way for
fully automated, efficient, and fine-grained roaming agreements.


Abstract:
  Redundancy mechanisms consist in sending several copies of a same job to a
subset of servers. It constitutes one of the most promising ways to exploit
diversity in multiservers applications. However, its pros and cons are still
not sufficiently understood in the context of realistic models with generic
statistical properties of service-times distributions and correlation
structures of copies. We aim at giving a survey of recent results concerning
the stability-arguably the first benchmark of performance-of systems with
cancel-oncompletion redundancy. We also point out open questions and
conjectures.


Abstract:
  We present an explicit matrix algebra quantization of the algebra of
volume-preserving diffeomorphisms of the $n$-torus. That is, we approximate the
corresponding classical Nambu brackets using
$\mathfrak{sl}(N^{\lceil\frac{n}{2}\rceil},\mathbb{C})$-matrices equipped with
the finite bracket given by the completely anti-symmetrized matrix product,
such that the classical brackets are retrieved in the $N\rightarrow \infty$
limit. We then apply this approximation to the super $4$-brane in $9$
dimensions and give a regularized action in analogy with the matrix
quantization of the supermembrane. This action exhibits a reduced gauge
symmetry that we discuss from the viewpoint of $L_\infty$-algebras in a slight
generalization to the construction of Lie $2$-algebras from Bagger--Lambert
$3$-algebras.


Abstract:
  Given an undirected graph, a conflict-free coloring (CFON*) is an assignment
of colors to a subset of the vertices of the graph such that for every vertex
there exists a color that is assigned to exactly one vertex in its open
neighborhood. The minimum number of colors required for such a coloring is
called the conflict-free chromatic number. The decision version of the CFON*
problem is NP-complete even on planar graphs. In this paper, we show the
following results.
  The CFON* problem is fixed-parameter tractable with respect to the combined
parameters clique width and the solution size. We study the problem on block
graphs and cographs, which have bounded clique width. For both graph classes,
we give tight bounds of three and two respectively for the CFON* chromatic
number. We study the problem on the following intersection graphs: interval
graphs, unit square graphs and unit disk graphs. We give tight bounds of two
and three for the CFON* chromatic number for proper interval graphs and
interval graphs. Moreover, we give upper bounds for the CFON* chromatic number
on unit square and unit disk graphs. We also study the problem on split graphs
and Kneser graphs. For split graphs, we show that the problem is NP-complete.
For Kneser graphs $K(n,k)$, when $n\geq k(k+1)^2 + 1$, we show that the CFON*
chromatic number is $k+1$. We also study the closed neighborhood variant of the
problem denoted by CFCN*, and obtain analogous results in some of the above
cases.


Abstract:
  Let $D\subset \mathbb C^n$ be a bounded domain. A pair of distinct boundary
points $\{p,q\}$ of $D$ has the visibility property provided there exist a
compact subset $K_{p,q}\subset D$ and open neighborhoods $U_p$ of $p$ and $U_q$
of $q$, such that the real geodesics for the Kobayashi metric of $D$ which join
points in $U_p$ and $U_q$ intersect $K_{p,q}$. Every Gromov hyperbolic convex
domain enjoys the visibility property for any couple of boundary points. The
Goldilocks domains introduced by Bharali and Zimmer and the log-type domains of
Liu and Wang also enjoy the visibility property.
  In this paper we relate the growth of the Kobayashi distance near the
boundary with visibility and provide new families of convex domains where that
property holds. We use the same methods to provide refinements of localization
results for the Kobayashi distance, and give a localized sufficient condition
for visibility. We also exploit visibility to study the boundary behavior of
biholomorphic maps.


Abstract:
  The activations of language transformers like GPT-2 have been shown to
linearly map onto brain activity during speech comprehension. However, the
nature of these activations remains largely unknown and presumably conflate
distinct linguistic classes. Here, we propose a taxonomy to factorize the
high-dimensional activations of language models into four combinatorial
classes: lexical, compositional, syntactic, and semantic representations. We
then introduce a statistical method to decompose, through the lens of GPT-2's
activations, the brain activity of 345 subjects recorded with functional
magnetic resonance imaging (fMRI) during the listening of ~4.6 hours of
narrated text. The results highlight two findings. First, compositional
representations recruit a more widespread cortical network than lexical ones,
and encompass the bilateral temporal, parietal and prefrontal cortices. Second,
contrary to previous claims, syntax and semantics are not associated with
separated modules, but, instead, appear to share a common and distributed
neural substrate. Overall, this study introduces a versatile framework to
isolate, in the brain activity, the distributed representations of linguistic
constructs.


Abstract:
  The paper is concerned with the spatially homogeneous isotropic Boltzmann
equation for Bose-Einstein particles with quantum collision kernel where the
interaction potential $\phi({\bf x})$ can be approximately written as the delta
function plus a certain attractive potential such that the Fourier transform
$\widehat{\phi}$ of $\phi$ behaves like $0 \le \widehat{\phi}(\xi) \le {\rm
const.} |\xi|^{\eta}$ for $|\xi|<<1$ for some constant $\eta\ge 1$. We prove
that in this case, there is no condensation in finite time for all temperatures
and all solutions, and thus it is completely different from the case
$\widehat{\phi}(\xi) \ge {\rm const.}|\xi|^{\eta}$ for $|\xi|<<1$ with $0\le
\eta<1/4$ as considered in \cite{Cai-Lu}. For a class of initial data that have
some nice integrability near the origin, we also get some regularity, stability
and $L^{\infty}$ estimate.


Abstract:
  Charge exchange between highly charged ions and neutral atoms and molecules
has been considered as one of the important mechanisms controlling soft X ray
emissions in many astrophysical objects and environments. However, for modeling
charge exchange soft X ray emission, the data of n and l resolved state
selective capture cross sections are often obtained by empirical and
semiclassical theory calculations. With a newly built cold target recoil ion
momentum spectroscopy (COLTRIMS) apparatus, we perform a series of measurements
of the charge exchange of Ne(8,9)+ ions with He and H2 for collision energy
ranging from 1 to 24.75 keV/u. n resolved state selective capture
cross-sections are reported. By comparing the measured state selective capture
cross sections to those calculated by the multichannel Landau Zener method
(MCLZ), it is found that MCLZ calculations are in good agreement with the
measurement for the dominant n capture for He target. Furthermore, by using nl
resolved cross sections calculated by MCLZ and applying l distributions
commonly used in the astrophysical literature to experimentally derived n
resolved cross sections, we calculate the soft X ray emissions in the charge
exchange between 4 keV/u Ne8+ and He by considering the radiative cascade from
the excited Ne7+ ions. Reasonable agreement is found in comparison to the
measurement for even and separable models, and MCLZ calculations give results
in a better agreement.


Abstract:
  The brain performs unsupervised learning and (perhaps) simultaneous
supervised learning. This raises the question as to whether a hybrid of
supervised and unsupervised methods will produce better learning. Inspired by
the rich space of Hebbian learning rules, we set out to directly learn the
unsupervised learning rule on local information that best augments a supervised
signal. We present the Hebbian-augmented training algorithm (HAT) for combining
gradient-based learning with an unsupervised rule on pre-synpatic activity,
post-synaptic activities, and current weights. We test HAT's effect on a simple
problem (Fashion-MNIST) and find consistently higher performance than
supervised learning alone. This finding provides empirical evidence that
unsupervised learning on synaptic activities provides a strong signal that can
be used to augment gradient-based methods.
  We further find that the meta-learned update rule is a time-varying function;
thus, it is difficult to pinpoint an interpretable Hebbian update rule that
aids in training. We do find that the meta-learner eventually degenerates into
a non-Hebbian rule that preserves important weights so as not to disturb the
learner's convergence.