Abstract:
  Optical wireless communication is an effective potential solution for
enabling high speed next generation cellular networks. In this paper, laser
sources, in particular, vertical-cavity surface-emitting (VCSEL) lasers are
used for data transmission due to their high modulation speed compared with
light emitting diode (LED) sources. To manage multi-user interference, rate
splitting (RS) is implemented where the message of a user is split into common
and private parts. However, the performance of RS is limited in high density
networks. Therefore, hierarchical rate splitting (HRS) particularly suited in
high density networks is considered. The results demonstrate the high data rate
achieved by using VCSELs. Moreover, HRS is more suitable for achieving high
performance in optical networks compared with RS.


Abstract:
  The pseudogap (PG) regime of the underdoped cuprates arguably remains one of
the most enigmatic phenomena of correlated quantum matter. Recent theoretical
ideas suggest that "fractionalized" bosonic fields can lead to the PG phase, by
opening a gap in the anti-nodal (AN) region of the Brillouin zone. Such
fractionalized boson can originate from modulated particle-particle pairs or
pair density wave (PDW), a magnetic stripe, or a modulated spin one
particle-hole pair like a spin density wave (SDW) boson, among others. The main
picture goes as follows. Electrons under strong coupling tend to form different
types of unstable bosons at high temperatures. As the temperature goes down,
the compact object gets extremely unstable, and to minimize the entropy, it
finally fractionalizes into elementary components, linked by a constraint. The
process of fractionalization involves, in this way, an emergent gauge field
directly linked to the constraint. This, in turn, couples to the Fermi surface
of electronic carriers and opens a gap in the AN region, which is partly
responsible for the PG phase. Alternative theoretical approaches invoke a
simple coexistence between the multiple quasi-degenerate orders like charge
density wave (CDW), superconductivity (SC), and magnetic orders at low
temperatures. This scenario attributes the PG formation as a "vestigial" order
showing up at $T^{*}$, which acts as a precursor to the zero temperature
orders. This intricate situation calls for a key experimental test, enabling us
to discriminate between the various theoretical scenarios. In this paper, we
focus on the case where the PDW boson has fractionalized into a CDW and SC
order below $T^{*}$, and we compare this to the situation where the two orders
simply coexist.


Abstract:
  Scene understanding based on LiDAR point cloud is an essential task for
autonomous cars to drive safely, which often employs spherical projection to
map 3D point cloud into multi-channel 2D images for semantic segmentation. Most
existing methods simply stack different point attributes/modalities (e.g.
coordinates, intensity, depth, etc.) as image channels to increase information
capacity, but ignore distinct characteristics of point attributes in different
image channels. We design FPS-Net, a convolutional fusion network that exploits
the uniqueness and discrepancy among the projected image channels for optimal
point cloud segmentation. FPS-Net adopts an encoder-decoder structure. Instead
of simply stacking multiple channel images as a single input, we group them
into different modalities to first learn modality-specific features separately
and then map the learned features into a common high-dimensional feature space
for pixel-level fusion and learning. Specifically, we design a residual dense
block with multiple receptive fields as a building block in the encoder which
preserves detailed information in each modality and learns hierarchical
modality-specific and fused features effectively. In the FPS-Net decoder, we
use a recurrent convolution block likewise to hierarchically decode fused
features into output space for pixel-level classification. Extensive
experiments conducted on two widely adopted point cloud datasets show that
FPS-Net achieves superior semantic segmentation as compared with
state-of-the-art projection-based methods. In addition, the proposed modality
fusion idea is compatible with typical projection-based methods and can be
incorporated into them with consistent performance improvements.


Abstract:
  Deep learning-based fluence map prediction(DL-FMP) method has been reported
in the literature, which generated fluence maps for desired dose by deep neural
network(DNN)-based inverse mapping. We hypothesized that DL-FMP is similar to
general fluence map optimization(FMO) because it's theoretically based on a
general inverse mapping. We designed four experiments to validate the
generalizability of DL-FMP to other types of plans apart from the training
data, which contained only clinical head and neck(HN) full-arc VMAT plans. The
first three experiments quantified the generalizability of DL-FMP to multiple
anatomical sites, different delivery modalities, and various degree of
modulation(DOM), respectively. The fourth experiment explored the
generalizability and stability to infeasible dose inputs. Results of the first
experiment manifested that DL-FMP can generalize to lung, liver, esophagus and
prostate, with gamma passing rates (GPR) higher than 95%(2%/2mm). The second
experiment showed that DL-FMP can generalize to partial-arc plans and predict
partial-arc fluences. GPR(3mm/3%) ranged from 96% to 99%. DL-FMP cannot
generate fluence maps in discrete beam angles for IMRT input. But the predicted
dose still agreed with ground truth dose with 93% GPR(5%/5mm). The third
experiment demonstrated that DL-FMP can generalize to various DOMs, with
GPRs(3%/3mm) ranged in 94%-98%. Moreover, the DOM of predicted fluence maps
correlated to the optimality of the input dose accordingly. The fourth
experiment exemplified that DL-FMP can make stable predictions for infeasible
dose input. In conclusion, we validated that DL-FMP can generalize to plans for
multiple anatomical sites, plans of different delivery modalities and plans
with various DOM. It can also make stable prediction for infeasible input.


Abstract:
  This survey provides an overview of the evolution of visually grounded models
of spoken language over the last 20 years. Such models are inspired by the
observation that when children pick up a language, they rely on a wide range of
indirect and noisy clues, crucially including signals from the visual modality
co-occurring with spoken utterances. Several fields have made important
contributions to this approach to modeling or mimicking the process of learning
language: Machine Learning, Natural Language and Speech Processing, Computer
Vision and Cognitive Science. The current paper brings together these
contributions in order to provide a useful introduction and overview for
practitioners in all these areas. We discuss the central research questions
addressed, the timeline of developments, and the datasets which enabled much of
this work. We then summarize the main modeling architectures and offer an
exhaustive overview of the evaluation metrics and analysis techniques.


Abstract:
  We present a comprehensive mechanism for the emergence of rotational
horseshoes and strange attractors in a class of two-parameter families of
periodically-perturbed differential equations defining a flow on a
three-dimensional manifold. When both parameters are zero, its flow exhibits an
attracting heteroclinic network associated to two periodic solutions. After
slightly increasing both parameters, while keeping a two-dimensional connection
unaltered, we focus our attention in the case where the two-dimensional
invariant manifolds of the periodic solutions do not intersect. We prove a wide
range of dynamical behaviour, ranging from an attracting quasi-periodic torus
to rotational horseshoes and H\'enon-like strange attractors. We illustrate our
results with an explicit example.


Abstract:
  We investigate the temperature-dependent behavior emerging in the vicinity of
the superfluid (SF) to Mott insulator (MI) transition of interacting bosons in
a two-dimensional optical lattice, described by the Bose-Hubbard model. The
equilibrium phase diagram at finite temperatures is computed by means of the
cluster mean-field theory (CMF) where the effect of non-local correlations is
analyzed systematically by finite-size scaling of the cluster size. The phase
diagram exhibits a rich structure including a transition and a crossover of the
SF and MI phases respectively to a normal fluid (NF) state at finite
temperature. In order to characterize these phases, and the NF transition and
crossover scales, we calculate, in addition to the condensate amplitude, the
superfluid fraction, sound velocity and compressibility. The phase boundaries
obtained by CMF with finite-size scaling agree quantitatively with quantum
Monte Carlo (QMC) results as well as with experiments. The von Neumann
entanglement entropy of a cluster exhibits critical enhancement near the SF-MI
quantum critical point (QCP). We also discuss the behavior of the transition
lines near this QCP at the particle-hole symmetric point located at the tip of
a Mott lobe as well as away from particle-hole symmetry.


Abstract:
  In this paper, we propose a maximum entropy method for predicting disease
risks. It is based on a patient's medical history with diseases coded in ICD-10
which can be used in various cases. The complete algorithm with strict
mathematical derivation is given. We also present experimental results on a
medical dataset, demonstrating that our method performs well in predicting
future disease risks and achieves an accuracy rate twice that of the
traditional method. We also perform a comorbidity analysis to reveal the
intrinsic relation of diseases.


Abstract:
  High-Level Synthesis (HLS) frameworks allow to easily specify a large number
of variants of the same hardware design by only acting on optimization
directives. Nonetheless, the hardware synthesis of implementations for all
possible combinations of directive values is impractical even for simple
designs. Addressing this shortcoming, many HLS Design Space Exploration (DSE)
strategies have been proposed to devise directive settings leading to
high-quality implementations while limiting the number of synthesis runs. All
these works require considerable efforts to validate the proposed strategies
and/or to build the knowledge base employed to tune abstract models, as both
tasks mandate the syntheses of large collections of implementations. Currently,
such data gathering is performed ad-hoc, a) leading to a lack of
standardization, hampering comparisons between DSE alternatives, and b) posing
a very high burden to researchers willing to develop novel DSE strategies.
Against this backdrop, we here introduce DB4HLS, a database of exhaustive HLS
explorations comprising more than 100000 design points collected over 4 years
of synthesis time. The open structure of DB4HLS allows the incremental
integration of new DSEs, which can be easily defined with a dedicated
domain-specific language. We think that of our database, available at
https://www.db4hls.inf.usi.ch/, will be a valuable tool for the research
community investigating automated strategies for the optimization of HLS-based
hardware designs.


Abstract:
  Path planning for a robot is one of the major problems in the area of
robotics. When a robot is given a task in the form of a Linear Temporal Logic
(LTL) specification such that the task needs to be carried out repetitively, we
want the robot to follow the shortest cyclic path so that the number of times
the robot completes the mission within a given duration gets maximized. In this
paper, we address the LTL path planning problem in a dynamic environment where
the newly arrived dynamic obstacles may invalidate some of the available paths
at any arbitrary point in time. We present DT*, an SMT-based receding horizon
planning strategy that solves an optimization problem repetitively based on the
current status of the workspace to lead the robot to follow the best available
path in the current situation. We implement our algorithm using the Z3 SMT
solver and evaluate it extensively on an LTL specification capturing a
pick-and-drop application in a warehouse environment. We compare our SMT-based
algorithm with two carefully crafted greedy algorithms. Our experimental
results show that the proposed algorithm can deal with the dynamism in the
workspace in LTL path planning effectively.


Abstract:
  We provide a simple forest model to encode the genealogical structure of a
multitype Galton-Watson process with immigration. We provide two encodings of
these forests by stochastic processes. We show, under appropriate conditions,
the depth-first encodings of each particular type converge to a solution to a
system of stochastic integral equations involving height processes perturbed by
functionals of their local times. The forest picture allows us to extend the
Ray-Knight theorem and show that local time of the solution to the system of
equations form a multitype continuous state branching process with immigration.
These assumptions underlying our weak convergence arguments are easily seen to
be met in the Brownian setting, and more generally an $\alpha$-stable setting
for any $\alpha\in(1,2]$.


Abstract:
  We construct a (1+1)$d$ topological field theory (TFT) whose topological
defect lines (TDLs) realize the transparent Haagerup $\mathcal{H}_3$ fusion
category. This TFT has six vacua, and each of the three non-invertible simple
TDLs hosts three defect operators, giving rise to a total of 15 point-like
operators. The TFT data, including the three-point coefficients and lasso
diagrams, are determined by solving all the sphere four-point crossing
equations and torus one-point modular invariance equations. We further verify
that the Cardy states furnish a non-negative integer matrix representation
under TDL fusion. While many of the constraints we derive are not limited to
the this particular TFT with six vacua, we leave open the construction of TFTs
with two or four vacua. Finally, TFTs realizing the Haagerup $\mathcal{H}_1$
and $\mathcal{H}_2$ fusion categories can be obtained by gauging algebra
objects. This note makes a modest offering in our pursuit of exotica and the
quest for their eventual conformity.


Abstract:
  Human-Computer Interfaces have always played a fundamental role in usability
and commands' interpretability of the modern software systems. With the
explosion of the Artificial Intelligence concept, such interfaces have begun to
fill the gap between the user and the system itself, further evolving in
Adaptive User Interfaces (AUI). Meta Interfaces are a further step towards the
user, and they aim at supporting the human activities in an ambient interactive
space; in such a way, the user can control the surrounding space and interact
with it. This work aims at proposing a meta user interface that exploits the
Put That There paradigm to enable the user to fast interaction by employing
natural language and gestures. The application scenario is a video surveillance
control room, in which the speed of actions and reactions is fundamental for
urban safety and driver and pedestrian security. The interaction is oriented
towards three environments: the first is the control room itself, in which the
operator can organize the views of the monitors related to the cameras on site
by vocal commands and gestures, as well as conveying the audio on the headset
or in the speakers of the room. The second one is related to the control of the
video, in order to go back and forth to a particular scene showing specific
events, or zoom in/out a particular camera; the third allows the operator to
send rescue vehicle in a particular street, in case of need. The gestures data
are acquired through a Microsoft Kinect 2 which captures pointing and gestures
allowing the user to interact multimodally thus increasing the naturalness of
the interaction; the related module maps the movement information to a
particular instruction, also supported by vocal commands which enable its
execution. (cont...)


Abstract:
  In this paper, as the first part of the third step of our study on developing
data analysis procedures for using 3-dimensional information offered by
directional direct Dark Matter detection experiments in the future, we present
our double-Monte Carlo "scattering-by-scattering" simulation of the
3-dimensional elastic WIMP-nucleus scattering process, which can provide 3-D
velocity information (the magnitude, the direction, and the incoming/scattering
time) of each incident halo WIMP as well as the recoil direction and the recoil
energy of the scattered target nucleus in different celestial coordinate
systems. For readers' reference, (animated) simulation plots with different
WIMP masses and several frequently used target nuclei for all functionable
underground laboratories can be found and downloaded on our online
(interactive) demonstration webpage (http://www.tir.tw/phys/hep/dm/amidas-2d/).


Abstract:
  We examine the basic conservation laws for diffeomorphism symmetry in the
context of spontaneous diffeomorphism and local Lorentz-symmetry breaking. The
conservation laws are used as constraints on a generic series of terms in an
expansion around a flat background. We find all such terms for a two-tensor
coupling to cubic order in the metric and tensor field fluctuations. The
results are presented in a form that can be used for phenomenological
calculations. One key result is that if we preserve the underlying
diffeomorphism symmetry in a spontaneous-symmetry breaking scenario, one cannot
decouple the two-tensor fluctuations from the metric fluctuations at the level
of the action, except in special cases of the quadratic actions.


Abstract:
  The assumption that wave function collapse is induced by the interactions
that generate decoherence leads to a stochastic collapse equation that does not
require the introduction of any new physical constants and that is consistent
with conservation laws. The collapse operator is based on the interaction
energy, with a variable timing parameter related to the rate at which
individual interactions generate the branching process. The approximate
localization of physical systems follows from the distance-dependent nature of
the interactions. The equation is consistent with strict conservation of
momentum and orbital angular momentum, and it is also consistent with energy
conservation within the accuracy allowed by the limited forms of energy that
can be described within nonrelativistic theory. A relativistic extension of the
proposal is outlined.


Abstract:
  In response to the soaring needs of human mobility data, especially during
disaster events such as the COVID-19 pandemic, and the associated big data
challenges, we develop a scalable online platform for extracting, analyzing,
and sharing multi-source multi-scale human mobility flows. Within the platform,
an origin-destination-time (ODT) data model is proposed to work with scalable
query engines to handle heterogenous mobility data in large volumes with
extensive spatial coverage, which allows for efficient extraction, query, and
aggregation of billion-level origin-destination (OD) flows in parallel at the
server-side. An interactive spatial web portal, ODT Flow Explorer, is developed
to allow users to explore multi-source mobility datasets with user-defined
spatiotemporal scales. To promote reproducibility and replicability, we further
develop ODT Flow REST APIs that provide researchers with the flexibility to
access the data programmatically via workflows, codes, and programs.
Demonstrations are provided to illustrate the potential of the APIs integrating
with scientific workflows and with the Jupyter Notebook environment. We believe
the platform coupled with the derived multi-scale mobility data can assist
human mobility monitoring and analysis during disaster events such as the
ongoing COVID-19 pandemic and benefit both scientific communities and the
general public in understanding human mobility dynamics.


Abstract:
  This document presents the OpenSHMEM extension for the Special Karlsruhe MPI
benchmark and the measurement algorithms used to measure the routines.


Abstract:
  The location of the obscuring "torus" in an active galactic nucleus (AGN) is
still an unresolved issue. The line widths of X-ray fluorescence lines
originated from the torus, particularly Fe K$\alpha$, carry key information on
the radii of line emitting regions. Utilizing XCLUMPY (Tanimoto et al. 2019),
an X-ray clumpy torus model, we develop a realistic model of emission line
profiles from an AGN torus where we take into account line broadening due to
the Keplerian motion around the black hole. Then, we apply the updated model to
the best available broadband spectra (3-100 keV) of the Circinus galaxy
observed with Suzaku, XMM-Newton, Nuclear Spectroscopic Telescope Array
(NuSTAR), and Chandra, including 0.62 Ms Chandra/HETG data. We confirm that the
torus is Compton-thick (hydrogen column-density along the equatorial plane is
$N_\mathrm{H}^\mathrm{Equ}=2.16^{+0.24}_{-0.16}\times 10^{25}\
\mathrm{cm}^{-2}$), geometrically thin (torus angular width
$\sigma=10.3^{+0.7}_{-0.3}\ \mathrm{degrees}$), viewed edge-on (inclination
$i=78.3^{+0.4}_{-0.9}\ \mathrm{degrees}$), and has super-solar abundance
($1.52^{+0.04}_{-0.06}$ times solar). Simultaneously analyzing the Chandra/HETG
first, second, and third order spectra with consideration of the spatial extent
of the Fe K$\alpha$ line emitting region, we constrain the inner radius of the
torus to be $1.9^{+3.1}_{-0.8}\times 10^5$ times the gravitational radius, or
$1.6^{+1.5}_{-0.9}\times 10^{-2}\ \mathrm{pc}$ for a black hole mass of
$(1.7\pm 0.3)\times 10^6\ M_{\odot}$. This is about 3 times smaller than that
estimated from the dust sublimation radius, suggesting that the inner side of
the dusty region of the torus is composed of dust-free gas.


Abstract:
  Let $\Gamma=(\mathcal{V},\mathcal{E})$ be a graph, whose vertices $v\in
\mathcal{V}$ are colored black and white and labeled with invertible elements
$\lambda_v$ from a commutative and associative ring $R$ containing $\pm 1$.
Then we consider the associative algebra $\mathfrak{C}(\Gamma)$ with identity
element $\mathbf{1}$ generated by the elements of $\mathcal{V}$ such that for
all $v,w\in \mathcal{V}$ we have
  \[\begin{array}{lll}v^2 &=\lambda_v\mathbf{1}&\textrm{if } v \textrm{ is
white},
  v^2 &=-\lambda_v\mathbf{1}&\textrm{if } v \textrm{ is black},
  vw+wv&=0&\textrm{if } \{v,w\}\in \mathcal{E},
  vw-wv&=0&\textrm{if } \{v,w\}\not\in \mathcal{E}.\\ \end{array}\] If $\Gamma$
is the complete graph, $\mathfrak{C}(\Gamma)$ is a Clifford algebra, otherwise
it is a so-called quasi-Clifford algebra. We describe this algebra as a twisted
group algebra with the help of a quadratic space $(V,Q)$ over the field
$\mathbb{F}_2$. Using this description, we determine the isomorphism type of
$\mathfrak{C}(\Gamma)$ in several interesting examples. As the algebra
$\mathfrak{C}(\Gamma)$ is associative, we can also consider the corresponding
Lie algebra and some of its subalgebras. In case $\lambda_v=1$ for all $v\in
\mathcal{V}$, and all vertices are black, we find that the elements $v,w\in
\mathcal{V}$ satisfy the following relations $$\begin{array}{lll}
  [v,w]&=0&\textrm{if } \{v,w\}\not\in \mathcal{E},
  {[v,[v,w]]}&=-w&\textrm{if } \{v,w\}\in \mathcal{E}.\\ \end{array}$$ In case
$R$ is a field of characteristic $0$, we identify these algebras as quotients
of the compact subalgebras of Kac-Moody Lie algebras and prove that they admit
a so-called generalized spin representation.


Abstract:
  Franson interferometry is a well-known quantum measurement technique for
probing photon-pair frequency correlations that is often used to certify
time-energy entanglement. We demonstrate the complementary technique in the
time basis, called conjugate-Franson interferometry, that measures photon-pair
arrival-time correlations, thus providing a valuable addition to the quantum
toolbox. We obtain a conjugate-Franson interference visibility of $96\pm 1$%
without background subtraction for entangled photon pairs generated by
spontaneous parametric down-conversion. Our measured result surpasses the
quantum-classical threshold by 25 standard deviations and validates the
conjugate-Franson interferometer (CFI) as an alternative method for certifying
time-energy entanglement. Moreover, the CFI visibility is a function of the
biphoton's joint temporal intensity and is therefore sensitive to that state's
spectral phase variation, something which is not the case for Franson
interferometry or Hong-Ou-Mandel interferometry. We highlight the CFI's utility
by measuring its visibilities for two different biphoton states, one without
and the other with spectral phase variation, and observing a 21% reduction in
the CFI visibility for the latter. The CFI is potentially useful for
applications in areas of photonic entanglement, quantum communications, and
quantum networking.


Abstract:
  The recent development of high-altitude platforms (HAPs) has attracted
increasing attention since they can serve as a promising communication method
to assist satellite-terrestrial networks. In this paper, we consider an
integrated three-layer satellite-HAP-terrestrial network where the HAP support
dual-band connectivity. Specifically, the HAP can not only communicate with
terrestrial users over C-band directly, but also provide backhaul services to
terrestrial user terminals over Ka-band. We formulate a sum-rate maximization
problem and then propose a fractional programming based algorithm to solve the
problem by optimizing the bandwidth and power allocation iteratively. The
closed-form optimal solutions for bandwidth allocation and power allocation in
each iteration are also derived. Simulation results show the capacity
enhancement brought by the dual-band connectivity of the HAP. The influence of
the power of the HAP and the power of the satellite is also discussed.


Abstract:
  Radio astronomy and Space Infrastructures in the Azores have a great
scientific and industrial interest because they benefit from a unique
geographical location in the middle of the North Atlantic allowing a vast
improvement in the sky coverage. This fact obviously has a very high added
value for: i) the establishment of space tracking and communications networks
for the emergent global small satellite fleets ii) it is invaluable to connect
the radio astronomy infrastructure networks in Africa, Europe and America
continents using Very Large Baseline Interferometry (VLBI) techniques, iii) it
allows excellent potential for monitoring space debris and Near Earth Objects
(NEOs). There is in S. Miguel island a 32-metre SATCOM antenna that could be
integrated in advanced VLBI networks and be capable of additional Deep Space
Network ground support. This paper explores the space science opportunities
offered by the upgrade of the S. Miguel 32-metre SATCOM antenna into a
world-class infrastructure for radio astronomy and space exploration: it would
enable a Deep Space Network mode and would constitute a key space facility for
data production, promoting local digital infrastructure investments and the
testing of cutting-edge information technologies. Its Atlantic location also
enables improvements in angular resolution, provides many baseline in East-West
and North-South directions connecting the emergent VLBI stations in America to
Europe and Africa VLBI arrays therefore contributing for greater array imaging
capabilities especially for sources or well studied fields close to or below
the celestial equator, where ESO facilities, ALMA, SKA and its precursors do or
will operate and observe in the coming decades.


Abstract:
  In cohort studies, non-random medication use can pose barriers to estimation
of the natural history trend in a mean biomarker value (namely, the association
between a predictor of interest and a biomarker outcome that would be observed
in the absence of biomarker-specific treatment). Common causes of treatment and
outcomes are often unmeasured, obscuring our ability to easily account for
medication use with commonly invoked assumptions such as ignorability. Further,
absent some variable satisfying the exclusion restriction, use of instrumental
variable approaches may be difficult to justify. Heckman's hybrid model with
structural shift (sometimes referred to less specifically as the treatment
effects model) can be used to correct endogeneity bias via a homogeneity
assumption (i.e., that average treatment effects do not vary across covariates)
and parametric specification of a joint model for the outcome and treatment. In
recent work, we relaxed the homogeneity assumption by allowing observed
covariates to serve as treatment effect modifiers. While this method has been
shown to be reasonably robust in settings of cross-sectional data, application
of this methodology to settings of longitudinal data remains unexplored. We
demonstrate how the assumptions of the treatment effects model can be extended
to accommodate clustered data arising from longitudinal studies. Our proposed
approach is semi-parametric in nature in that valid inference can be obtained
without the need to specify the longitudinal correlation structure. As an
illustrative example, we use data from the Multi-Ethnic Study of
Atherosclerosis to evaluate trends in low-density lipoprotein by age and
gender. We confirm that our generalization of the treatment effects model can
serve as a useful tool to uncover natural history trends in longitudinal data
that are obscured by endogenous treatment.


Abstract:
  We study the weighted Fock spaces in one and several complex variables. We
evaluate the dimension of these spaces in terms of the weight function
extending and completing earlier results by Rozenblum-Shirokov and Shigekawa.


Abstract:
  Let $\alpha$ and $\beta$ be positive real numbers. Let $F(x) \in
K[[x^\Gamma]]$ be a Hahn series. We prove that if $F(x)$ is both
$\alpha$-Mahler and $\beta$-Mahler then it must be a rational function, $F(x)
\in K(x)$, assuming some non-degeneracy conditions on $\alpha$ and $\beta$.


Abstract:
  Graph Neural Networks (GNNs), a generalization of deep neural networks on
graph data have been widely used in various domains, ranging from drug
discovery to recommender systems. However, GNNs on such applications are
limited when there are few available samples. Meta-learning has been an
important framework to address the lack of samples in machine learning, and in
recent years, researchers have started to apply meta-learning to GNNs. In this
work, we provide a comprehensive survey of different meta-learning approaches
involving GNNs on various graph problems showing the power of using these two
approaches together. We categorize the literature based on proposed
architectures, shared representations, and applications. Finally, we discuss
several exciting future research directions and open problems.


Abstract:
  We numerically investigate the topological phase transition induced purely by
disorder in a spring-mass chain. We employ two types of disorders - chiral and
random types - to explore the interplay between topology and disorder. By
tracking the evolution of real space topological invariants, we obtain the
topological phase diagrams and demonstrate the bilateral capacity of disorder
to drive topological transitions, from topologically nontrivial to trivial and
vice versa. The corresponding transition is accompanied by the realization of a
mechanical Topological Anderson Insulator. The findings from this study hint
that the combination of disorder and topology can serve as an efficient control
knob to manipulate the transfer of mechanical energy.


Abstract:
  Jupiter co-orbital comets have orbits that are not long-term stable. They may
experience flybys with Jupiter close enough to trigger tidal disruptions like
the one suffered by comet Shoemaker-Levy 9. Our aim was to study the activity
and dynamical evolution of the Jupiter co-orbital comet P/2019 LD2 (ATLAS). We
present results of an observational study carried out with the 10.4m Gran
Telescopio Canarias (GTC) that includes image analyses using a MC dust tail
fitting code to characterize its activity, and spectroscopic studies to search
for gas emission. We also present N-body simulations to explore its orbital
evolution. Images of LD2 obtained on 2020 May 16 show a conspicuous coma and
tail. The spectrum does not exhibit any evidence of CN, C2, or C3 emission. The
comet brightness in a 2.6 arcsec aperture is r'=19.34+/-0.02 mag, with colors
(g'-r')=0.78+/-0.03, (r'-i')=0.31+/-0.03, and (i'-z')=0.26+/-0.03. The temporal
dependence of the dust loss rate can be parameterized by a Gaussian having a
FWHM of 350 days and a maximum of 60 kg/s reached on 2019 August 15. The total
dust loss rate is 1.9e09 kg. LD2 is now following what looks like a short arc
of a quasi-satellite cycle that started in 2017 and will end in 2028. On 2063
January 23, it will experience a very close encounter with Jupiter at 0.016 au.
Its probability of escaping the solar system during the next 0.5 Myr is
0.53+/-0.03. LD2 is a kilometer-sized object, in the size range of the
Jupiter-family comets, with a typical comet-like activity likely linked to
sublimation of crystalline water ice and clathrates. Its origin is still an
open question. We report a probability of LD2 having been captured from
interstellar space during the last 0.5 Myr of 0.49+/-0.02, 0.67+/-0.06 during
the last 1 Myr, 0.83+/-0.06 over 3 Myr, and 0.91+/-0.09 during the last 5 Myr.


Abstract:
  Motivated by upcoming photometric and spectroscopic surveys (Vera C. Rubin
Observatory Legacy Survey of Space and Time (LSST), Manuakea Spectroscopic
Explorer), we design the statistical proxies to measure the cadence effects on
active galactic nuclei (AGN) variability-observables (time-lags, periodicity,
and structure-function (SF)). We constructed a multiple-regression model to
statistically identify the cadence-formal error pattern knowing AGN time-lags
and periodicity from different surveys. We defined the simple metric for the
SF's properties, accounting for the 'observed' SF's deviation relative to those
obtained from the homogenously-sampled light curves. We tested the regression
models on different observing strategies: the optical dataset of long
light-curves of eight AGN with peculiarities and the artificial datasets based
on several idealized and LSST-like cadences. The SFs metric is assessed on
synthetic datasets. The regression models (for both data types) predict similar
cadences for time-lags and oscillation detection, whereas for light curves with
low variability ($\sim 10\%$), cadences for oscillation detection differ. For
higher variability ($\sim20\%$), predicted cadences are larger than for
$F_{var}\sim 10\%$. The predicted cadences are decreasing with redshift. SFs
with dense and homogenous cadences are more likely to behave similarly. SFs
with oscillatory signals are sensitive to the cadences, possibly impacting
LSST-like operation strategy.
  The proposed proxies can help to select spectroscopic and photometric-surveys
cadence strategies, and they will be tested further in larger samples of
objects.


Abstract:
  Accurate prediction of future person location and movement trajectory from an
egocentric wearable camera can benefit a wide range of applications, such as
assisting visually impaired people in navigation, and the development of
mobility assistance for people with disability. In this work, a new egocentric
dataset was constructed using a wearable camera, with 8,250 short clips of a
targeted person either walking 1) toward, 2) away, or 3) across the camera
wearer in indoor environments, or 4) staying still in the scene, and 13,817
person bounding boxes were manually labelled. Apart from the bounding boxes,
the dataset also contains the estimated pose of the targeted person as well as
the IMU signal of the wearable camera at each time point. An LSTM-based
encoder-decoder framework was designed to predict the future location and
movement trajectory of the targeted person in this egocentric setting.
Extensive experiments have been conducted on the new dataset, and have shown
that the proposed method is able to reliably and better predict future person
location and trajectory in egocentric videos captured by the wearable camera
compared to three baselines.


Abstract:
  Solving inverse problems by dynamical variant of the BC-method is basically
reduced to inverting the connecting operator $C^T$ of the dynamical system, for
which the problem is stated. Realizing the method numerically, one needs to
invert the Gram matrix $\hat C^T=\{(C^Tf_i,f_j)\}_{i,j=1}^N$ for a
representative set of controls $f_i$. To raise the accuracy of determination of
the solution, one has to increase the size $N$, which, especially in the
multidimensional case, leads to a rapid increase in the amount of computations.
However, there is a way to reduce it by the proper choice of $f_j$, due to
which the matrix $\hat C^T$ gets a specific block-Toeplitz structure. In the
paper, we explain, where this property comes from, and outline a way to use it
in numerical implementation of the BC-algorithms.


Abstract:
  The size of Transformer models is growing at an unprecedented pace. It has
only taken less than one year to reach trillion-level parameters after the
release of GPT-3 (175B). Training such models requires both substantial
engineering efforts and enormous computing resources, which are luxuries most
research teams cannot afford. In this paper, we propose PipeTransformer, which
leverages automated and elastic pipelining and data parallelism for efficient
distributed training of Transformer models. PipeTransformer automatically
adjusts the pipelining and data parallelism by identifying and freezing some
layers during the training, and instead allocates resources for training of the
remaining active layers. More specifically, PipeTransformer dynamically
excludes converged layers from the pipeline, packs active layers into fewer
GPUs, and forks more replicas to increase data-parallel width. We evaluate
PipeTransformer using Vision Transformer (ViT) on ImageNet and BERT on GLUE and
SQuAD datasets. Our results show that PipeTransformer attains a 2.4 fold
speedup compared to the state-of-the-art baseline. We also provide various
performance analyses for a more comprehensive understanding of our algorithmic
and system-wise design. We also develop open-sourced flexible APIs for
PipeTransformer, which offer a clean separation among the freeze algorithm,
model definitions, and training accelerations, hence allowing it to be applied
to other algorithms that require similar freezing strategies.


Abstract:
  Conventional wisdom dictates that learning rate should be in the stable
regime so that gradient-based algorithms don't blow up. This letter introduces
a simple scenario where an unstably large learning rate scheme leads to a super
fast convergence, with the convergence rate depending only logarithmically on
the condition number of the problem. Our scheme uses a Cyclical Learning Rate
(CLR) where we periodically take one large unstable step and several small
stable steps to compensate for the instability. These findings also help
explain the empirical observations of [Smith and Topin, 2019] where they show
that CLR with a large maximum learning rate can dramatically accelerate
learning and lead to so-called "super-convergence". We prove that our scheme
excels in the problems where Hessian exhibits a bimodal spectrum and the
eigenvalues can be grouped into two clusters (small and large). The unstably
large step is the key to enabling fast convergence over the small
eigen-spectrum.


Abstract:
  Deep neural networks can be trained in reciprocal space, by acting on the
eigenvalues and eigenvectors of suitable transfer operators in direct space.
Adjusting the eigenvalues, while freezing the eigenvectors, yields a
substantial compression of the parameter space. This latter scales by
definition with the number of computing neurons. The classification scores, as
measured by the displayed accuracy, are however inferior to those attained when
the learning is carried in direct space, for an identical architecture and by
employing the full set of trainable parameters (with a quadratic dependence on
the size of neighbor layers). In this Letter, we propose a variant of the
spectral learning method as appeared in Giambagli et al {Nat. Comm.} 2021,
which leverages on two sets of eigenvalues, for each mapping between adjacent
layers. The eigenvalues act as veritable knobs which can be freely tuned so as
to (i) enhance, or alternatively silence, the contribution of the input nodes,
(ii) modulate the excitability of the receiving nodes with a mechanism which we
interpret as the artificial analogue of the homeostatic plasticity. The number
of trainable parameters is still a linear function of the network size, but the
performances of the trained device gets much closer to those obtained via
conventional algorithms, these latter requiring however a considerably heavier
computational cost. The residual gap between conventional and spectral
trainings can be eventually filled by employing a suitable decomposition for
the non trivial block of the eigenvectors matrix. Each spectral parameter
reflects back on the whole set of inter-nodes weights, an attribute which we
shall effectively exploit to yield sparse networks with stunning classification
abilities, as compared to their homologues trained with conventional means.


Abstract:
  Let $P$ be a geodesic plane in a convex cocompact, acylindrical hyperbolic
3-manifold $M$. Assume that $P^*=M^*\cap P$ is nonempty, where $M^*$ is the
interior of the convex core of $M$. Does this condition imply that $P$ is
either closed or dense in $M$? A positive answer would furnish an analogue of
Ratner's theorem in the infinite volume setting. In arXiv:1802.03853 it is
shown that $P^*$ is either closed or dense in $M^*$. Moreover, there are at
most countably many planes with $P^*$ closed, and in all previously known
examples, $P$ was also closed in $M$. In this note we show more exotic behavior
can occur: namely, we give an explicit example of a pair $(M,P)$ such that
$P^*$ is closed in $M^*$ but $P$ is not closed in $M$. In particular, the
answer to the question above is no. Thus Ratner's theorem fails to generalize
to planes in acylindrical 3-manifolds, without additional restrictions.


Abstract:
  Retinal Vessel Segmentation is important for the diagnosis of various
diseases. The research on retinal vessel segmentation focuses mainly on the
improvement of the segmentation model which is usually based on U-Net
architecture. In our study, we use the U-Net architecture and we rely on heavy
data augmentation in order to achieve better performance. The success of the
data augmentation relies on successfully addressing the problem of input
images. By analyzing input images and performing the augmentation accordingly
we show that the performance of the U-Net model can be increased dramatically.
Results are reported using the most widely used retina dataset, DRIVE.


Abstract:
  We give a formula for the geometric fixed-points spectrum of the real
topological cyclic homology of a bounded below ring spectrum, as an equaliser
of two maps between tensor products of modules over the norm. We then use this
formula to carry out computations in the fundamental examples of spherical
group-rings, perfect $\mathbb{F}_p$-algebras, and $2$-torsion free rings with
perfect modulo $2$ reduction. Our calculations agree with the normal L-theory
spectrum in the cases where the latter is known, as conjectured by Nikolaus.


Abstract:
  We present a Monte Carlo based analysis of the combined world data on
polarized lepton-nucleon deep-inelastic scattering at small Bjorken $x$ within
the polarized quark dipole formalism. We show for the first time that
double-spin asymmetries at $x<0.1$ can be successfully described using only
small-$x$ evolution derived from first-principles QCD, allowing predictions to
be made for the $g_1$ structure function at much smaller $x$. Anticipating
future data from the Electron-Ion Collider, we assess the impact of
electromagnetic and parity-violating polarization asymmetries on $g_1$ and
demonstrate an extraction of the individual flavor helicity PDFs at small $x$.


Abstract:
  Most speech separation methods, trying to separate all channel sources
simultaneously, are still far from having enough general- ization capabilities
for real scenarios where the number of input sounds is usually uncertain and
even dynamic. In this work, we employ ideas from auditory attention with two
ears and propose a speaker and direction inferred speech separation network
(dubbed SDNet) to solve the cocktail party problem. Specifically, our SDNet
first parses out the respective perceptual representations with their speaker
and direction characteristics from the mixture of the scene in a sequential
manner. Then, the perceptual representations are utilized to attend to each
corresponding speech. Our model gener- ates more precise perceptual
representations with the help of spatial features and successfully deals with
the problem of the unknown number of sources and the selection of outputs. The
experiments on standard fully-overlapped speech separation benchmarks, WSJ0-
2mix, WSJ0-3mix, and WSJ0-2&3mix, show the effectiveness, and our method
achieves SDR improvements of 25.31 dB, 17.26 dB, and 21.56 dB under anechoic
settings. Our codes will be released at https://github.com/aispeech-lab/SDNet.


Abstract:
  Non-adiabatic photo-ionization is difficult to control as it relies on the
derivatives of the envelope and not on phase-details of the short ionizing
pulse. Here, we introduce a catalyzing state, whose presence render
non-adiabatic ionization sensitive to phase-details of tailored pulses. Since a
catalyzing state is in general easy to create, this opens a perspective for
coherent control of ultra-fast ionization.


Abstract:
  We propose a high-performance atomic clock based on the 1.81 PHz transition
between the ground and first-excited state of doubly ionized lead. Utilizing an
even isotope of lead, both clock states have $I=J=F=0$, where $I$, $J$, and $F$
are the conventional quantum numbers specifying nuclear, electronic, and total
angular momentum, respectively. The clock states are nondegenerate and
completely immune to nonscalar perturbations, including first order Zeeman and
electric quadrupole shifts. Additionally, the proposed clock is relatively
insusceptible to other frequency shifts (blackbody radiation, second order
Zeeman, Doppler), accommodates "magic" rf trapping, and is robust against
decoherence mechanisms that can otherwise limit clock stability. By driving the
transition as a two-photon $E1$+$M1$ process, the accompanying probe Stark
shift is appreciable yet manageable for practical Rabi frequencies.


Abstract:
  In high-throughput genetics studies, an important aim is to identify
gene-environment interactions associated with the clinical outcomes. Recently,
multiple marginal penalization methods have been developed and shown to be
effective in G$\times$E studies. However, within the Bayesian framework,
marginal variable selection has not received much attention. In this study, we
propose a novel marginal Bayesian variable selection method for G$\times$E
studies. In particular, our marginal Bayesian method is robust to data
contamination and outliers in the outcome variables. With the incorporation of
spike-and-slab priors, we have implemented the Gibbs sampler based on MCMC. The
proposed method outperforms a number of alternatives in extensive simulation
studies. The utility of the marginal robust Bayesian variable selection method
has been further demonstrated in the case studies using data from the Nurse
Health Study (NHS). Some of the identified main and interaction effects from
the real data analysis have important biological implications.


Abstract:
  We show that the recipe for computing the expansions $\theta_\ell$ and
$\theta_n$ of outgoing and ingoing null geodesics normal to a surface admits a
covariance group with nonconstant scalar $\kappa(x)$, corresponding to the
mapping $\theta_\ell \to \kappa \theta_\ell$, $\theta_n \to \kappa^{-1}
\theta_n$. Under this mapping, the product $\theta_\ell \theta_n$ is invariant,
and thus the marginal surface computed from the vanishing of $\theta_\ell$,
which is used to define the apparent horizon, is invariant. This covariance
group naturally appears in comparing the expansions computed with different
choices of coordinate system.


Abstract:
  Brazil is home to over 200M people, the majority of which have access to the
Internet. Over 11M Brazilians live in favelas, or informal settlements with no
outside government regulation, often ruled by narcos or militias. Victims of
intimate partner violence (IPV) in these communities are made extra vulnerable
not only by lack of access to resources, but by the added layer of violence
caused by criminal activity and police confrontations. In this paper, we use an
unintended harms framework to analyze the unique online privacy needs of favela
women and present research questions that we urge tech abuse researchers to
consider.


Abstract:
  Hypergraphs, a generalization of graphs, naturally represent groupwise
relationships among multiple individuals or objects, which are common in many
application areas, including web, bioinformatics, and social networks. The
flexibility in the number of nodes in each hyperedge, which provides the
expressiveness of hypergraphs, brings about structural differences between
graphs and hypergraphs. Especially, the overlaps of hyperedges lead to complex
high-order relations beyond pairwise relations, raising new questions that have
not been considered in graphs: How do hyperedges overlap in real-world
hypergraphs? Are there any pervasive characteristics? What underlying process
can cause such patterns? In this work, we closely investigate thirteen
real-world hypergraphs from various domains and share interesting observations
of overlaps of hyperedges. To this end, we define principled measures and
statistically compare the overlaps of hyperedges in real-world hypergraphs and
those in null models. Additionally, based on the observations, we propose
HyperLap, a realistic hypergraph generative model. HyperLap is (a) Realistic:
it accurately reproduces overlapping patterns of real-world hypergraphs, (b)
Automatically Fittable: its parameters can be tuned automatically using
HyperLap+ to generate hypergraphs particularly similar to a given target
hypergraph, (c) Scalable: it generates and fits a hypergraph with 0.7 billion
hyperedges within a few hours.


Abstract:
  The vector $U$-bosons, or so called 'dark photons', are one of the possible
candidates for the dark matter mediators. They are supposed to interact with
the standard matter via a 'vector portal' due to the $U(1)-U(1)^\prime$
symmetry group mixing which might make them visible in particle and heavy-ion
experiments. While there is no confirmed observation of dark photons, the
detailed analysis of different experimental data allows to estimate the upper
limit for the kinetic mixing parameter $\epsilon^2$ depending on the mass $M_U$
of $U$-bosons which is also unknown. In this study we present theoretical
constraints on the upper limit of $\epsilon^2(M_U)$ in the mass range $M_U \le
0.6$ GeV from the comparison of the calculated dilepton spectra with the
experimental data from the HADES Collaboration at SIS18 energies where the dark
photons are not observed. Our analysis is based on the microscopic
Parton-Hadron-String Dynamics (PHSD) transport approach which reproduces well
the measured dilepton spectra in $p+p$, $p+A$ and $A+A$ collisions.
Additionally to the different dilepton channels originating from interactions
and decays of ordinary matter particles (mesons and baryons), we incorporate
the decay of hypothetical $U$-bosons to dileptons, $U\to e^+e^-$, where the
$U$-bosons themselves are produced by the Dalitz decay of pions $\pi^0\to
\gamma U$, $\eta$-mesons $\eta \to \gamma U$ and Delta resonances $\Delta \to N
U$. Our analysis can help to estimate the requested accuracy for future
experimental searches of 'light' dark photons by dilepton experiments.


Abstract:
  First investigations regarding dosimetric properties of the hybrid,
pixelated, photon-counting Dosepix detector in a pulsed photon field (RQR8) for
the personal dose equivalent $H\mathrm{_p(10)}$ are presented. The influence
quantities such as pulse duration and dose rate were varied, and their
responses were compared to the legal limits provided in PTB-A 23.2. The
variation of pulse duration at a nearly constant dose rate of 3.7$\,$Sv/h shows
a flat response around 1.0 from 3.6$\,$s down to 2$\,$ms. A response close to
1.0 is achieved for dose rates from 0.07$\,$mSv/h to 35$\,$Sv/h for both pixel
sizes. Above this dose rate, the large pixels (220$\,\mathrm{\mu}$m edge
length) are below the lower limit. The small pixels (55$\,\mathrm{\mu}$m edge
length) stay within limits up to 704$\,$Sv/h. The count rate linearity is
compared to previous results, confirming the saturating count rate for high
dose rates.


Abstract:
  This work develops problem statements related to encoders and autoencoders
with the goal of elucidating variational formulations and establishing clear
connections to information-theoretic concepts. Specifically, four problems with
varying levels of input are considered : a) The data, likelihood and prior
distributions are given, b) The data and likelihood are given; c) The data and
prior are given; d) the data and the dimensionality of the parameters is
specified. The first two problems seek encoders (or the posterior) and the
latter two seek autoencoders (i.e. the posterior and the likelihood). A
variational Bayesian setting is pursued, and detailed derivations are provided
for the resulting optimization problem. Following this, a linear Gaussian
setting is adopted, and closed form solutions are derived. Numerical
experiments are also performed to verify expected behavior and assess
convergence properties. Explicit connections are made to rate-distortion
theory, information bottleneck theory, and the related concept of sufficiency
of statistics is also explored. One of the motivations of this work is to
present the theory and learning dynamics associated with variational inference
and autoencoders, and to expose information theoretic concepts from a
computational science perspective.


Abstract:
  Tree edit distance is a well-studied measure of dissimilarity between rooted
trees with node labels. It can be computed in $O(n^3)$ time [Demaine, Mozes,
Rossman, and Weimann, ICALP 2007], and fine-grained hardness results suggest
that the weighted version of this problem cannot be solved in truly subcubic
time unless the APSP conjecture is false [Bringmann, Gawrychowski, Mozes, and
Weimann, SODA 2018].
  We consider the unweighted version of tree edit distance, where every
insertion, deletion, or relabeling operation has unit cost. Given a parameter
$k$ as an upper bound on the distance, the previous fastest algorithm for this
problem runs in $O(nk^3)$ time [Touzet, CPM 2005], which improves upon the
cubic-time algorithm for $k\ll n^{2/3}$. In this paper, we give a faster
algorithm taking $O(nk^2 \log n)$ time, improving both of the previous results
for almost the full range of $\log n \ll k\ll n/\sqrt{\log n}$.


Abstract:
  Natural and anthropogenic disasters frequently affect both the supply and
demand side of an economy. A striking recent example is the Covid-19 pandemic
which has created severe disruptions to economic output in most countries.
These direct shocks to supply and demand will propagate downstream and upstream
through production networks. Given the exogenous shocks, we derive a lower
bound on total shock propagation. We find that even in this best case scenario
network effects substantially amplify the initial shocks. To obtain more
realistic model predictions, we study the propagation of shocks bottom-up by
imposing different rationing rules on industries if they are not able to
satisfy incoming demand. Our results show that economic impacts depend strongly
on the emergence of input bottlenecks, making the rationing assumption a key
variable in predicting adverse economic impacts. We further establish that the
magnitude of initial shocks and network density heavily influence model
predictions.


Abstract:
  Recent progress on muon $g-2$ measurement prompts one to take it even more
seriously. In the general two Higgs doublet model that allows extra Yukawa
couplings, we take a simplified approach of single enhanced coupling. We fix
the charged lepton flavor violating coupling, $\rho_{\tau\mu} =
\rho_{\mu\tau}$, via the one-loop mechanism, for illustrative masses of the
heavy scalar $H$ and pseudoscalar $A$, where we assume $m_A = m_{H^+}$. Since
extra top Yukawa couplings are plausibly the largest, we turn on $\rho_{tt}$
and find that LHC search for $gg \to H,\,A \to \tau\mu$ gives more stringent
bound than from $\tau\to \mu\gamma$ with two-loop mechanism. Turning on a
second extra top Yukawa coupling, $\rho_{tc}$, can loosen the bound on
$\rho_{tt}$, but LHC constraints can again be more stringent than from $B \to
D\mu\nu$ vs $De\nu$ universality. This means that evidence for $H,\,A \to
\tau\mu$ may yet emerge with full LHC Run 2 data, while direct search for
$\tau^\pm\mu^\mp bW^+$ or $t\bar cbW^+$ (plus conjugate) may also bear fruit.


Abstract:
  Monocular depth estimation (MDE) is a fundamental task in many applications
such as scene understanding and reconstruction. However, most of the existing
methods rely on accurately labeled datasets. A weakly-supervised framework
based on attention nested U-net (ANU) named as ANUW is introduced in this paper
for cases with wrong labels. The ANUW is trained end-to-end to convert an input
single RGB image into a depth image. It consists of a dense residual network
structure, an adaptive weight channel attention (AWCA) module, a patch second
non-local (PSNL) module and a soft label generation method. The dense residual
network is the main body of the network to encode and decode the input. The
AWCA module can adaptively adjust the channel weights to extract important
features. The PSNL module implements the spatial attention mechanism through a
second-order non-local method. The proposed soft label generation method uses
the prior knowledge of the dataset to produce soft labels to replace false
ones. The proposed ANUW is trained on a defective monocular depth dataset and
the trained model is tested on three public datasets, and the results
demonstrate the superiority of ANUW in comparison with the state-of-the-art MDE
methods.


Abstract:
  We have implemented a GPU version of the Jefimenko's equations -- JefiGPU.
Given the proper distributions of the source terms $\rho$ (charge density) and
$\mathbf{J}$ (current density) in the source volume, the algorithm gives the
electromagnetic fields in the observational region (not necessarily overlaps
the vicinity of the sources). To verify the accuracy of the GPU implementation,
we have compared the obtained results with that of the theoretical ones. Our
results show that the deviations of the GPU results from the theoretical ones
are around 5\%. Meanwhile, we have also compared the performance of the GPU
implementation with a CPU version. The simulation results indicate that the GPU
code is significantly faster than the CPU version. Finally, we have studied the
parameter dependence of the execution time and memory consumption on one NVIDIA
Tesla V100 card. Our code can be consistently coupled to RBG (Relativistic
Boltzmann equations on GPUs) and many other GPU-based algorithms in physics.


Abstract:
  The standard approach to analyzing brain electrical activity is to examine
the spectral density function (SDF) and identify predefined frequency bands
that have the most substantial relative contributions to the overall variance
of the signal. However, a limitation of this approach is that the precise
frequency and bandwidth of oscillations vary with cognitive demands. Thus they
should not be arbitrarily defined a priori in an experiment. In this paper, we
develop a data-driven approach that identifies (i) the number of prominent
peaks, (ii) the frequency peak locations, and (iii) their corresponding
bandwidths (or spread of power around the peaks). We propose a Bayesian mixture
auto-regressive decomposition method (BMARD), which represents the standardized
SDFas a Dirichlet process mixture based on a kernel derived from second-order
auto-regressive processes which completely characterize the location (peak)and
scale (bandwidth) parameters. We present a Metropolis-Hastings within Gibbs
algorithm to sample from the posterior distribution of the mixture parameters.
Simulation studies demonstrate the robustness and performance of the BMARD
method. Finally, we use the proposed BMARD method to analyze local field
potential (LFP) activity from the hippocampus of laboratory rats across
different conditions in a non-spatial sequence memory experiment to identify
the most interesting frequency bands and examine the link between specific
patterns of activity and trial-specific cognitive demands.


Abstract:
  In this work, we propose a fully coupled multiscale strategy for components
made from short fiber reinforced composites, where each Gauss point of the
macroscopic finite element model is equipped with a deep material network (DMN)
which covers the different fiber orientation states varying within the
component. These DMNs need to be identified by linear elastic precomputations
on representative volume elements, and serve as high-fidelity surrogates for
full-field simulations on microstructures with inelastic constituents. We
discuss how to extend direct DMNs to account for varying fiber orientation, and
propose a simplified sampling strategy which significantly speeds up the
training process. To enable concurrent multiscale simulations, evaluating the
DMNs efficiently is crucial. We discuss dedicated techniques for exploiting
sparsity and high-performance linear algebra modules, and demonstrate the power
of the proposed approach on an industrial-scale three-dimensional component.
Indeed, the DMN is capable of accelerating two-scale simulations significantly,
providing possible speed-ups of several magnitudes.


Abstract:
  The Automatic Quasi-clique Merger algorithm is a new algorithm adapted from
early work published under the name QCM (quasi-clique merger) [Ou2006, Ou2007,
Zhao2011, Qi2014]. The AQCM algorithm performs hierarchical clustering in any
data set for which there is an associated similarity measure quantifying the
similarity of any data i and data j. Importantly, the method exhibits two
valuable performance properties: 1) the ability to automatically return either
a larger or smaller number of clusters depending on the inherent properties of
the data rather than on a parameter 2) the ability to return a very large
number of relatively small clusters automatically when such clusters are
reasonably well defined in a data set. In this work we present the general idea
of a quasi-clique agglomerative approach, provide the full details of the
mathematical steps of the AQCM algorithm, and explain some of the motivation
behind the new methodology. The main achievement of the new methodology is that
the agglomerative process now unfolds adaptively according to the inherent
structure unique to a given data set, and this happens without the time-costly
parameter adjustment that drove the previous QCM algorithm. For this reason we
call the new algorithm \emph{automatic}. We provide a demonstration of the
algorithm's performance at the task of community detection in a social media
network of 22,900 nodes.


Abstract:
  The paper discusses design techniques for a seamless architecture of
information systems (IS). A seamless architecture is understood as such an
architectural de-scription of an IS, that defines explicit connections between
elements of architec-tural models of various architectural representations.
Design techniques are based on the adaptive clustering method developed by the
author, which allows one to bridge technological gaps between architectural
abstracts of different levels and to link architectural models in such a way as
to ensure the design of a more detailed model based on a model of a higher
level of abstraction.


Abstract:
  Utilizing a data set corresponding to an integrated luminosity of 6.32~$\rm
fb^{-1}$, recorded by the BESIII detector at center-of-mass energies between
4.178 and 4.226~GeV, we perform an amplitude analysis of the decay $D_{s}^{+}
\to K_{S}^{0}\pi^{+}\pi^{0}$ and determine the relative fractions and phase
differences of different intermediate processes, which include
$K_{S}^{0}\rho(770)^{+}$, $K_{S}^{0}\rho(1450)^{+}$, $K^{*}(892)^{0}\pi^{+}$,
$K^{*}(892)^{+}\pi^{0}$, and $K^{*}(1410)^{0}\pi^{+}$. Using a double-tag
technique, and making an efficiency correction that relies on our knowledge of
the phase-space distribution of the decays coming from the amplitude analysis,
  the absolute branching fraction is measured to be $\mathcal{B}(D_{s}^{+} \to
K_{S}^{0}\pi^{+}\pi^{0})=(5.43\pm0.30_{\text{stat}}\pm
0.15_{\text{syst}})\times 10^{-3}$.


Abstract:
  We study the Solid Isotropic Material Penalisation (SIMP) method with a
density field generated by a fully-connected neural network, taking the
coordinates as inputs. In the large width limit, we show that the use of DNNs
leads to a filtering effect similar to traditional filtering techniques for
SIMP, with a filter described by the Neural Tangent Kernel (NTK). This filter
is however not invariant under translation, leading to visual artifacts and
non-optimal shapes. We propose two embeddings of the input coordinates, which
lead to (approximate) spatial invariance of the NTK and of the filter. We
empirically confirm our theoretical observations and study how the filter size
is affected by the architecture of the network. Our solution can easily be
applied to any other coordinates-based generation method.


Abstract:
  Convolutional neural networks have been successful lately enabling companies
to develop neural-based products, which demand an expensive process, involving
data acquisition and annotation; and model generation, usually requiring
experts. With all these costs, companies are concerned about the security of
their models against copies and deliver them as black-boxes accessed by APIs.
Nonetheless, we argue that even black-box models still have some
vulnerabilities. In a preliminary work, we presented a simple, yet powerful,
method to copy black-box models by querying them with natural random images. In
this work, we consolidate and extend the copycat method: (i) some constraints
are waived; (ii) an extensive evaluation with several problems is performed;
(iii) models are copied between different architectures; and, (iv) a deeper
analysis is performed by looking at the copycat behavior. Results show that
natural random images are effective to generate copycats for several problems.


Abstract:
  The goal of this article is to exhibit two new families of finitely generated
simple groups of homeomorphisms of $\mathbf{R}$. These families are strikingly
different from existing families owing to the nature of their actions on
$\mathbf{R}$, and exhibit surprising algebraic and dynamical features. In
particular, one construction provides the first examples of finitely generated
simple groups of homeomorphisms of the real line which also admit a minimal
action by homeomorphisms on the circle. This provides new examples of finitely
generated simple groups with infinite commutator width, and the first such left
orderable examples. Another construction provides the first examples of
finitely generated simple left orderable groups that admit minimal actions by
homeomorphisms on the torus.


Abstract:
  We present a machine learning approach that allows to characterize the
disorder potential of a two-dimensional electronic system from its quantum
transport properties. Numerically simulated transport data for a large number
of disorder configurations is used for the training of artificial neural
networks. We show that the trained networks are able to recognize details of
the disorder potential of an unknown sample from its transport properties, and
that they can even reconstruct the complete potential landscape seen by the
electrons.


Abstract:
  In this paper, we fill the research gap by adopting state-of-the-art computer
vision techniques for the data extraction stage in a data mining system. As
shown in Fig.1, this stage contains two subtasks, namely, plot element
detection and data conversion. For building a robust box detector, we
comprehensively compare different deep learning-based methods and find a
suitable method to detect box with high precision. For building a robust point
detector, a fully convolutional network with feature fusion module is adopted,
which can distinguish close points compared to traditional methods. The
proposed system can effectively handle various chart data without making
heuristic assumptions. For data conversion, we translate the detected element
into data with semantic value. A network is proposed to measure feature
similarities between legends and detected elements in the legend matching
phase. Furthermore, we provide a baseline on the competition of Harvesting raw
tables from Infographics. Some key factors have been found to improve the
performance of each stage. Experimental results demonstrate the effectiveness
of the proposed system.


Abstract:
  We extract verified algorithms for exact real number computation from
constructive proofs. To this end we use a coinductive representation of reals
as streams of binary signed digits. The main objective of this paper is the
formalisation of a constructive proof that real numbers are closed with respect
to limits. All the proofs of the main theorem and the first application are
implemented in the Minlog proof system and the extracted terms are further
translated into Haskell. We compare two approaches. The first approach is a
direct proof. In the second approach we make use of the representation of reals
by a Cauchy-sequence of rationals. Utilizing translations between the two
represenation and using the completeness of the Cauchy-reals, the proof is very
short. In both cases we use Minlog's program extraction mechanism to
automatically extract a formally verified program that transforms a converging
sequence of reals, i.e. a sequence of streams of binary signed digits together
with a modulus of convergence, into the binary signed digit representation of
its limit. The correctness of the extracted terms follows directly from the
soundness theorem of program extraction. As a first application we use the
extracted algorithms together with Heron's method to construct an algorithm
that computes square roots with respect to the binary signed digit
representation. In a second application we use the convergence theorem to show
that the signed digit representation of real numbers is closed under
multiplication.


Abstract:
  A discontinuous model of computation called one-way jumping finite automata
was defined by H. Chigahara et. al. This model was a restricted version of the
model jumping finite automata. One-way jumping finite automata change their
states after deleting a letter of an input and jump only in one direction.
Allowing a state to delete a subword instead of a letter, we define a new model
generalized circular one-way jumping finite automata. These automata work on an
input in a circular manner. Similar to one-way jumping finite automata,
generalized circular one-way jumping finite automata also jump only in one
direction. We show that this newly defined model is powerful than one-way
jumping finite automata. We define new variants(right and left) of the model
generalized circular one-way jumping finite automata and compare them. We also
compare the newly defined model with Chomsky hierarchy. Finally, we explore
closure properties of the model.


Abstract:
  We present a kinematic study of ionised extraplanar gas in two
low-inclination late-type galaxies (NGC 3982 and NGC 4152) using integral field
spectroscopy data from the DiskMass H$\alpha$ sample. We first isolate the
extraplanar gas emission by masking the H$\alpha$ flux from the regularly
rotating disc. The extraplanar gas emission is then modelled in the
three-dimensional position-velocity domain using a parametric model described
by three structural and four kinematic parameters. Best-fit values for the
model are determined via a Bayesian MCMC approach. The reliability and accuracy
of our modelling method are carefully determined via tests using mock data. We
detect ionised extraplanar gas in both galaxies, with scale heights
$0.83^{+0.27}_{-0.40}\,\mathrm{kpc}$ (NGC 3982) and
$1.87^{+0.43}_{-0.56}\,\mathrm{kpc}$ (NGC 4152) and flux fraction between the
extraplanar gas and the regularly rotating gas within the disc of 27% and 15%
respectively, consistent with previous determinations in other systems. We find
lagging rotation of the ionized extraplanar gas in both galaxies, with vertical
rotational gradients $-22.24^{+6.60}_{-13.13} \,\mathrm{km\,s^{-1}\,kpc^{-1}}$
and $-11.18^{+3.49}_{-4.06}\,\mathrm{km\,s^{-1}\,kpc^{-1}}$, respectively, and
weak evidence for vertical and radial inflow in both galaxies. The above
results are similar to the kinematics of the neutral extraplanar gas found in
several galaxies, though this is the first time that 3D kinematic modelling of
ionised extraplanar gas has been carried out. Our results are broadly
consistent with a galactic fountain origin combined with gas accretion.
However, a dynamical model is required to better understand the formation of
ionised extraplanar gas.


Abstract:
  The increased demand for machine learning applications made companies offer
Machine-Learning-as-a-Service (MLaaS). In MLaaS (a market estimated 8000M USD
by 2025), users pay for well-performing ML models without dealing with the
complicated training procedure. Among MLaaS, text-based applications are the
most popular ones (e.g., language translators). Given this popularity, MLaaS
must provide resiliency to adversarial manipulations. For example, a wrong
translation might lead to a misunderstanding between two parties. In the text
domain, state-of-the-art attacks mainly focus on strategies that leverage ML
models' weaknesses. Unfortunately, not much attention has been given to the
other pipeline' stages, such as the indexing stage (i.e., when a sentence is
converted from a textual to a numerical representation) that, if manipulated,
can significantly affect the final performance of the application.
  In this paper, we propose a novel text evasion technique called
"\textit{Zero-Width} attack" (ZeW) that leverages the injection of human
non-readable characters, affecting indexing stage mechanisms. We demonstrate
that our simple yet effective attack deceives MLaaS of "giants" such as Amazon,
Google, IBM, and Microsoft. Our case study, based on the manipulation of
hateful tweets, shows that out of 12 analyzed services, only one is resistant
to our injection strategy. We finally introduce and test a simple \textit{input
validation} defense that can prevent our proposed attack.


Abstract:
  To assess the effectiveness of any medical intervention, researchers must
conduct a time-intensive and highly manual literature review. NLP systems can
help to automate or assist in parts of this expensive process. In support of
this goal, we release MS^2 (Multi-Document Summarization of Medical Studies), a
dataset of over 470k documents and 20k summaries derived from the scientific
literature. This dataset facilitates the development of systems that can assess
and aggregate contradictory evidence across multiple studies, and is the first
large-scale, publicly available multi-document summarization dataset in the
biomedical domain. We experiment with a summarization system based on BART,
with promising early results. We formulate our summarization inputs and targets
in both free text and structured forms and modify a recently proposed metric to
assess the quality of our system's generated summaries. Data and models are
available at https://github.com/allenai/ms2


Abstract:
  We describe via simulation novel optimization algorithms for a Hopfield
neural network constructed using manufacturable three-terminal
Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) synaptic devices. We first present
a computationally-light, memristor-based, highly accurate compact model for the
SONOS. Using the compact model, we describe techniques of simulated annealing
in Hopfield networks by exploiting imperfect problem definitions, current
leakage, and the continuous tunability of the SONOS to enable transient chaotic
group dynamics. We project improvements in energy consumption and latency for
optimization relative to the best CPUs and GPUs by at least 4 orders of
magnitude, and also exceeding the best projected memristor-based hardware;
along with a 100-fold increase in error-resilient hardware size (i.e., problem
size).


Abstract:
  Video analytics systems critically rely on video cameras, which capture
high-quality video frames, to achieve high analytics accuracy. Although modern
video cameras often expose tens of configurable parameter settings that can be
set by end-users, deployment of surveillance cameras today often uses a fixed
set of parameter settings because the end-users lack the skill or understanding
to reconfigure these parameters.
  In this paper, we first show that in a typical surveillance camera
deployment, environmental condition changes can significantly affect the
accuracy of analytics units such as person detection, face detection and face
recognition, and how such adverse impact can be mitigated by dynamically
adjusting camera settings. We then propose CAMTUNER, a framework that can be
easily applied to an existing video analytics pipeline (VAP) to enable
automatic and dynamic adaptation of complex camera settings to changing
environmental conditions, and autonomously optimize the accuracy of analytics
units (AUs) in the VAP. CAMTUNER is based on SARSA reinforcement learning (RL)
and it incorporates two novel components: a light-weight analytics quality
estimator and a virtual camera. CAMTUNER is implemented in a system with AXIS
surveillance cameras and several VAPs (with various AUs) that processed
day-long customer videos captured at airport entrances. Our evaluations show
that CAMTUNER can adapt quickly to changing environments. We compared CAMTUNER
with two alternative approaches where either static camera settings were used,
or a strawman approach where camera settings were manually changed every hour
(based on human perception of quality). We observed that for the face detection
and person detection AUs, CAMTUNER is able to achieve up to 13.8% and 9.2%
higher accuracy, respectively, compared to the best of the two approaches
(average improvement of 8% for both AUs).


Abstract:
  The properties of semiconductors can be crucially impacted by midgap states
induced by dopants, which can be native or intentionally incorporated in the
crystal lattice. For Bernal-stacked bilayer graphene (BLG), which has a tunable
bandgap, the existence of midgap states induced by dopants has been
conjectured, but never confirmed experimentally. Here, we report scanning
tunneling microscopy and spectroscopy results, supported by tight-binding
calculations, that demonstrate the existence of midgap states in BLG. We show
that the midgap state in BLG -- for which we demonstrate gate-tunability --
appears when the dopant is hosted on the non-dimer sublattice sites. We further
evidence the presence of narrow resonances at the onset of the high energy
bands (valence or conduction, depending on the dopant type) when the dopants
lie on the dimer sublattice sites. These results suggest that dopants/defects
can play an important role in the transport and optical properties of
multilayer graphene samples, especially at energies close to the band extrema.


Abstract:
  We analyze the new redefinitions of heat Q and work W recently presented in
[arXiv: 1912.01939; arXiv:1912.01983v5] in the quantum thermodynamics domain.
According to these redefinitions, heat must be associated with the variation of
entropy, while work must be associated with variation of state vectors.
Analyzing the behavior of two specific examples, we show some peculiarities of
these new redefinitions which, based on the counterexample presented, seems to
point to a possible inadequacy of these redefinitions.


Abstract:
  The impact parameter-dependent dipole model is an ideal tool for
investigating the spatial structure of the proton. We investigate the
incoherent $ep$ crosssection in exclusive $J/\psi$ photoproduction as measured
by HERA, and find that as $|t|$ increases, the model needs several levels of
the substructure of gluon density fluctuations in order to describe the
measured data well. This substructure is modelled as hotspots within hotspots.
We find that three levels of the substructure are adequate for a good
description of all available data, up to $|t|=30~$GeV$^2$. We note that the
gluonic density fluctuation structure follows a scaling behaviour, such that
the logarithms of the number of hotspots and their size fall on a line. We
further conclude that a more advanced model containing gluon-gluon correlations
needs also will need increased complexity as $|t|$ increases. Our findings in
this paper systematically constrain which features such a model must have.


Abstract:
  A wide variety of deep reinforcement learning (DRL) models have recently been
proposed to learn profitable investment strategies. The rules learned by these
models outperform the previous strategies specially in high frequency trading
environments. However, it is shown that the quality of the extracted features
from a long-term sequence of raw prices of the instruments greatly affects the
performance of the trading rules learned by these models. Employing a neural
encoder-decoder structure to extract informative features from complex input
time-series has proved very effective in other popular tasks like neural
machine translation and video captioning in which the models face a similar
problem. The encoder-decoder framework extracts highly informative features
from a long sequence of prices along with learning how to generate outputs
based on the extracted features. In this paper, a novel end-to-end model based
on the neural encoder-decoder framework combined with DRL is proposed to learn
single instrument trading strategies from a long sequence of raw prices of the
instrument. The proposed model consists of an encoder which is a neural
structure responsible for learning informative features from the input
sequence, and a decoder which is a DRL model responsible for learning
profitable strategies based on the features extracted by the encoder. The
parameters of the encoder and the decoder structures are learned jointly, which
enables the encoder to extract features fitted to the task of the decoder DRL.
In addition, the effects of different structures for the encoder and various
forms of the input sequences on the performance of the learned strategies are
investigated. Experimental results showed that the proposed model outperforms
other state-of-the-art models in highly dynamic environments.


Abstract:
  Does the ability to protect an asset from unsecured creditors affect its
price? This paper identifies the impact of bankruptcy protection on house
prices using 139 changes in homestead exemptions. Large increases in the
homestead exemption raised house prices 3% before 2005. Smaller exemption
increases, to adjust for inflation, did not affect house prices. The effect
disappeared after BAPCPA, a 2005 federal law designed to prevent bankruptcy
abuse. The effect was bigger in inelastic locations.


Abstract:
  The overlapping of multiple beams is common in inertial confinement fusion
(ICF), making the collective stimulated Brillouin scattering (SBS) with shared
ion acoustic wave (IAW) potentially important because of the effectively larger
laser intensities to drive the instability. In this work, based on a linear
kinetic model, an exact analytic solution for the convective amplification of
SBS with the shared IAW modes stimulated by two overlapped beams is presented.
From this solution, effects of the wavelength difference, crossing angle,
polarization states, and finite beam overlapping volume of the two laser beams
on the shared IAW modes are studied. It is found that a wavelength difference
of several nanometers between the laser beams has negligible effects, except
for a very small crossing angle about one degree. However, the crossing angle,
beam polarization states, and finite beam overlapping volume can have
significant influences on the shared IAW modes. Furthermore, the out-of-plane
modes, in which the wavevectors of daughter waves lie in the different planes
from the two overlapped beams, are found to be important for certain
polarization states and crossing angles of the laser beams with the finite beam
overlapping volume. This work is helpful to comprehend and estimate the
collective SBS with shared IAW in ICF experiments.


Abstract:
  Neurite orientation dispersion and density imaging (NODDI) enables the
assessment of intracellular, extracellular and free water signals from
multi-shell diffusion MRI data. It is an insightful approach to characterize
brain tissue microstructure. Single-shell reconstruction for NODDI parameters
has been discouraged in previous studies caused by failure when fitting,
especially for the neurite density index (NDI). Here, we investigated the
possibility of creating robust NODDI parameter maps with single-shell data,
using the isotropic volume fraction (fISO) as prior. Prior estimation was made
independent of the NODDI model constraint using a dictionary learning approach.
First, we used a stochastic sparse dictionary-based network (DictNet) in
predicting fISO which is trained with data obtained from in vivo and simulated
diffusion MRI data. In single-shell cases, the mean diffusivity (MD) and raw T2
signal with no diffusion weighting (S0) was incorporated in the dictionary for
the fISO estimation. Then, the NODDI framework was used with the known fISO to
estimate the NDI and orientation dispersion index (ODI). The fISO estimated by
our model was compared with other fISO estimators in the simulation. Further,
using both synthetic data simulation and human data collected on a 3T scanner,
we compared the performance of our dictionary-based learning prior NODDI (DLpN)
with the original NODDI for both single-shell and multi-shell data. Our results
suggest that DLpN derived NDI and ODI parameters for single-shell protocols are
comparable with original multi-shell NODDI, and protocol with b=2000 s/mm2
performs the best (error ~5% in white and grey matter). This may allow NODDI
evaluation of studies on single-shell data by multi-shell scanning of two
subjects for DictNet fISO training.


Abstract:
  In this paper, we investigate the optimal design of a wireless-powered covert
communication (WP-CC) system, in which a probabilistic accumulate-then-transmit
(ATT) protocol is proposed to maximize the communication covertness subject to
a quality-of-service (QoS) requirement on communication. Specifically, in the
considered WP-CC system, a full-duplex (FD) receiver transmits artificial noise
(AN) to simultaneously charge an energy-constrained transmitter and to confuse
a warden's detection on the transmitter's communication activity. With the
probabilistic ATT protocol, the transmitter sends its information with a prior
probability, i.e., $p$, conditioned on the available energy being sufficient.
Our analysis shows that the probabilistic ATT protocol can achieve higher
covertness than the traditional ATT protocol with $p=1$. In order to facilitate
the optimal design of the WP-CC system, we also derive the warden's minimum
detection error probability and characterize the effective covert rate from the
transmitter to the receiver to quantify the communication covertness and
quality, respectively. The derived analytical results facilitate the joint
optimization of the probability $p$ and the information transmit power. We
further present the optimal design of a cable-powered covert communication
(CP-CC) system as a benchmark for comparison. Our simulation shows that the
proposed probabilistic ATT protocol (with a varying $p$) can achieve the
covertness upper bound determined by the CP-CC system, while the traditional
ATT protocol (with $p=1$) cannot, which again confirms the benefits brought by
the proposed probabilistic ATT in covert communications.


Abstract:
  Entangled systems in experiments may be lost or offline in distributed
quantum information processing. This inspires a general problem to characterize
quantum operations which result in breaking of entanglement or not. Our goal in
this work is to solve this problem both in single entanglement and network
scenarios. We firstly propose a local model for characterizing all entangled
states that are breaking for losing particles. This implies a simple criterion
for witnessing single entanglement such as generalized GHZ states and Dicke
states. It further provides an efficient witness for entangled quantum networks
depending on its connectivity such as $k$-independent quantum networks,
completely connected quantum networks, and $k$-connected quantum networks.
These networks are universal resources for measurement-based quantum
computations. The strong nonlocality can be finally verified by using nonlinear
inequalities. These results show distinctive features of both single entangled
systems and entangled quantum networks.


Abstract:
  Deep learning has been used to image compressive sensing (CS) for enhanced
reconstruction performance. However, most existing deep learning methods train
different models for different subsampling ratios, which brings additional
hardware burden. In this paper, we develop a general framework named scalable
deep compressive sensing (SDCS) for the scalable sampling and reconstruction
(SSR) of all existing end-to-end-trained models. In the proposed way, images
are measured and initialized linearly. Two sampling masks are introduced to
flexibly control the subsampling ratios used in sampling and reconstruction,
respectively. To make the reconstruction model adapt to any subsampling ratio,
a training strategy dubbed scalable training is developed. In scalable
training, the model is trained with the sampling matrix and the initialization
matrix at various subsampling ratios by integrating different sampling matrix
masks. Experimental results show that models with SDCS can achieve SSR without
changing their structure while maintaining good performance, and SDCS
outperforms other SSR methods.


Abstract:
  We consider the setting in which a strong binary instrument is available for
a binary treatment. The traditional LATE approach assumes the monotonicity
condition stating that there are no defiers (or compliers). Since this
condition is not always obvious, we investigate the sensitivity and testability
of this condition. In particular, we focus on the question: does a slight
violation of monotonicity lead to a small problem or a big problem? We find a
phase transition for the monotonicity condition. On one of the boundary of the
phase transition, it is easy to learn the sign of LATE and on the other side of
the boundary, it is impossible to learn the sign of LATE. Unfortunately, the
impossible side of the phase transition includes data-generating processes
under which the proportion of defiers tends to zero. This boundary of phase
transition is explicitly characterized in the case of binary outcomes. Outside
a special case, it is impossible to test whether the data-generating process is
on the nice side of the boundary. However, in the special case that the
non-compliance is almost one-sided, such a test is possible. We also provide
simple alternatives to monotonicity.


Abstract:
  Situation awareness (SA) is critical to improving takeover performance during
the transition period from automated driving to manual driving. Although many
studies measured SA during or after the driving task, few studies have
attempted to predict SA in real time in automated driving. In this work, we
propose to predict SA during the takeover transition period in conditionally
automated driving using eye-tracking and self-reported data. First, a tree
ensemble machine learning model, named LightGBM (Light Gradient Boosting
Machine), was used to predict SA. Second, in order to understand what factors
influenced SA and how, SHAP (SHapley Additive exPlanations) values of
individual predictor variables in the LightGBM model were calculated. These
SHAP values explained the prediction model by identifying the most important
factors and their effects on SA, which further improved the model performance
of LightGBM through feature selection. We standardized SA between 0 and 1 by
aggregating three performance measures (i.e., placement, distance, and speed
estimation of vehicles with regard to the ego-vehicle) of SA in recreating
simulated driving scenarios, after 33 participants viewed 32 videos with six
lengths between 1 and 20 s. Using only eye-tracking data, our proposed model
outperformed other selected machine learning models, having a root-mean-squared
error (RMSE) of 0.121, a mean absolute error (MAE) of 0.096, and a 0.719
correlation coefficient between the predicted SA and the ground truth. The code
is available at https://github.com/refengchou/Situation-awareness-prediction.
Our proposed model provided important implications on how to monitor and
predict SA in real time in automated driving using eye-tracking data.


Abstract:
  Slang is a common type of informal language, but its flexible nature and
paucity of data resources present challenges for existing natural language
systems. We take an initial step toward machine generation of slang by
developing a framework that models the speaker's word choice in slang context.
Our framework encodes novel slang meaning by relating the conventional and
slang senses of a word while incorporating syntactic and contextual knowledge
in slang usage. We construct the framework using a combination of probabilistic
inference and neural contrastive learning. We perform rigorous evaluations on
three slang dictionaries and show that our approach not only outperforms
state-of-the-art language models, but also better predicts the historical
emergence of slang word usages from 1960s to 2000s. We interpret the proposed
models and find that the contrastively learned semantic space is sensitive to
the similarities between slang and conventional senses of words. Our work
creates opportunities for the automated generation and interpretation of
informal language.


Abstract:
  We present three alternative derivations of the method of characteristics
(MOC) for a second order nonlinear hyperbolic partial differential equation.
The MOC gives rise to two mutually coupled systems of ordinary differential
equations. As a special case we consider the Monge-Amp\`ere equation, for which
we solve the system of ODE's using explicit one-step methods (Euler,
Runge-Kutta) and spline interpolation. Numerical examples demonstrate the
performance of the methods.


Abstract:
  Large-volume liquid scintillator detectors with ultra-low background levels
have been widely used to study neutrino physics and search for dark matter.
Event vertex and event time are not only useful for event selection but also
essential for the reconstruction of event energy. In this study, four event
vertex and event time reconstruction algorithms using charge and time
information collected by photomultiplier tubes were analyzed comprehensively.
The effects of photomultiplier tube properties were also investigated. The
results indicate that the transit time spread is the main effect degrading the
vertex reconstruction, while the effect of dark noise is limited. In addition,
when the event is close to the detector boundary, the charge information
provides better performance for vertex reconstruction than the time
information.


Abstract:
  Our contribution concerns interactive decision support systems for group
decision support. Through this study, we apply to implement a decisional
process aiming to represent the multiplicity of actors, their diversity, their
behaviors and their interactions. In this context, we contribute to the design
and development of a group decision support system. The system is modeled by a
multi agents system while exploiting a negotiation protocol based on mediation
and concession. This protocol allows decision-makers to express their
preferences using multicriteria analysis methods, mainly the method by total
aggregation AHP (Hierarchical Process Analysis) and the method by partial
aggregation PROMETHEE II .


Abstract:
  Decomposable graphical models, also known as perfect DAG models, play a
fundamental role in standard approaches to probabilistic inference via graph
representations in modern machine learning and statistics. However, such models
are limited by the assumption that the data-generating distribution does not
entail strictly context-specific conditional independence relations. The family
of staged tree models generalizes DAG models so as to accommodate
context-specific knowledge. We provide a new characterization of perfect
discrete DAG models in terms of their staged tree representations. This
characterization identifies the family of balanced staged trees as the natural
generalization of discrete decomposable models to the context-specific setting.