Abstract:
  In its most traditional setting, the main concern of optimization theory is
the search for optimal solutions for instances of a given computational
problem. A recent trend of research in artificial intelligence, called solution
diversity, has focused on the development of notions of optimality that may be
more appropriate in settings where subjectivity is essential. The idea is that
instead of aiming at the development of algorithms that output a single optimal
solution, the goal is to investigate algorithms that output a small set of
sufficiently good solutions that are sufficiently diverse from one another. In
this way, the user has the opportunity to choose the solution that is most
appropriate to the context at hand. It also displays the richness of the
solution space.
  When combined with techniques from parameterized complexity theory, the
paradigm of diversity of solutions offers a powerful algorithmic framework to
address problems of practical relevance. In this work, we investigate the
impact of this combination in the field of Kemeny Rank Aggregation, a
well-studied class of problems lying in the intersection of order theory and
social choice theory and also in the field of order theory itself. In
particular, we show that the Kemeny Rank Aggregation problem is fixed-parameter
tractable with respect to natural parameters providing natural formalizations
of the notions of diversity and of the notion of a sufficiently good solution.
Our main results work both when considering the traditional setting of
aggregation over linearly ordered votes, and in the more general setting where
votes are partially ordered.


Abstract:
  In this paper we present a novel approach to emulating a universal quantum
computer with a classical system, one that uses a signal of bounded duration
and amplitude to represent an arbitrary quantum state. The signal may be of any
modality (e.g., acoustic, electromagnetic, etc), but we focus our discussion
here on electronic signals. Unitary gate operations are performed using analog
electronic circuit devices, such as four-quadrant multipliers, operational
amplifiers, and analog filters, although non-unitary operations may be
performed as well. In this manner, the Hilbert space structure of the quantum
state, as well as a universal set of gate operations, may be fully emulated
classically. The required bandwidth scales exponentially with the number of
qubits, however, thereby limiting the scalability of the approach, but the
intrinsic parallelism, ease of construction, and classical robustness to
decoherence may nevertheless lead to capabilities and efficiencies rivaling
that of current high performance computers.


Abstract:
  We present a scalable technique for upper bounding the Lipschitz constant of
generative models. We relate this quantity to the maximal norm over the set of
attainable vector-Jacobian products of a given generative model. We approximate
this set by layerwise convex approximations using zonotopes. Our approach
generalizes and improves upon prior work using zonotope transformers and we
extend to Lipschitz estimation of neural networks with large output dimension.
This provides efficient and tight bounds on small networks and can scale to
generative models on VAE and DCGAN architectures.


Abstract:
  Despite quantum networking concepts, designs, and hardware becoming
increasingly mature, there is no consensus on the optimal wavelength for
free-space systems. We present an in-depth analysis of a daytime free-space
quantum channel as a function of wavelength and atmospheric spatial coherence
(Fried coherence length). We choose decoy-state quantum key distribution bit
yield as a performance metric in order to reveal the ideal wavelength choice
for an actual qubit-based protocol under realistic atmospheric conditions. Our
analysis represents a rigorous framework to analyze requirements for spatial,
spectral, and temporal filtering. These results will help guide the development
of free-space quantum communication and networking systems. In particular, our
results suggest that shorter wavelengths in the optical band should be
considered for free-space quantum communication systems. Our results are also
interpreted in the context of atmospheric compensation by higher-order adaptive
optics.


Abstract:
  Duality in the entanglement of identical particles manifests that
entanglement in only one variable can be revealed at a time. We demonstrate
this using polarization and orbital angular momentum (OAM) variables of
indistinguishable photons generated from parametric down conversion. We show
polarization entanglement by sorting photons in even and odd OAM basis, while
sorting them in two orthogonal polarization modes reveals the OAM entanglement.
The duality assisted observation of entanglement can be used as a verification
for the preservation of quantum indistinguishability over communication
channels. Indistinguishable photons entangled in complementary variables could
also evoke interest in distributed quantum sensing protocols and remote
entanglement generation.


Abstract:
  Customer product reviews play a role in improving the quality of products and
services for business organizations or their brands. Complaining is an attitude
that expresses dissatisfaction with an event or a product not meeting customer
expectations. In this paper, we build a Open-domain Complaint Detection dataset
(UIT-ViOCD), including 5,485 human-annotated reviews on four categories about
product reviews on e-commerce sites. After the data collection phase, we
proceed to the annotation task and achieve the inter-annotator agreement Am of
87%. Then, we present an extensive methodology for the research purposes and
achieve 92.16% by F1-score for identifying complaints. With the results, in the
future, we aim to build a system for open-domain complaint detection in
E-commerce websites.


Abstract:
  We construct a new set of asymptotically flat, static vacuum solutions to the
Einstein equations in dimensions 4 and 5, which may be interpreted as a
superposition of positive and negative mass black holes. The resulting
spacetimes are axisymmetric in 4-dimensions and bi-axisymmetric in
5-dimensions, and are regular away from the negative mass singularities, for
instance conical singularities are absent along the axes. In 5-dimensions, the
topologies of signed mass black holes used in the construction may be either
spheres $S^3$ or rings $S^1 \times S^2$; in particular, the negative mass
static black ring solution is introduced. A primary observation that
facilitates the superposition is the fact that, in Weyl-Papapetrou coordinates,
negative mass singularities arise as overlapping singular support for a
particular type of Green's function. Furthermore, a careful analysis of conical
singularities along axes is performed, and formulas are obtained for their
propagation across horizons, negative mass singularities, and corners. The
methods are robust, and may be used to construct a multitude of further
examples. Lastly, we show that balancing does not occur between any two signed
mass black holes of the type studied here in 4 dimensions, while in 5
dimensions two-body balancing is possible.


Abstract:
  We study static and spherically symmetric charged stars with a nontrivial
profile of the scalar field $\phi$ in Einstein-Maxwell-scalar theories. The
scalar field is coupled to a $U(1)$ gauge field $A_{\mu}$ with the form
$-\alpha(\phi)F_{\mu \nu}F^{\mu \nu}/4$, where $F_{\mu
\nu}=\partial_{\mu}A_{\nu}-\partial_{\nu} A_{\mu}$ is the field strength
tensor. Analogous to the case of charged black holes, we show that this type of
interaction can induce spontaneous scalarization of charged stars under the
conditions $({\rm d}\alpha/{\rm d}\phi) (0)=0$ and $({\rm d}^2\alpha/{\rm
d}\phi^2) (0)>0$. For the coupling $\alpha (\phi)=\exp (-\beta \phi^2/M_{\rm
pl}^2)$, where $\beta~(<0)$ is a coupling constant and $M_{\rm pl}$ is a
reduced Planck mass, there is a branch of charged star solutions with a
nontrivial profile of $\phi$ approaching $0$ toward spatial infinity, besides a
branch of general relativistic solutions with a vanishing scalar field, i.e.,
solutions in the Einstein-Maxwell model. As the ratio $\rho_c/\rho_m$ between
charge density $\rho_c$ and matter density $\rho_m$ increases toward its
maximum value, the mass $M$ of charged stars in general relativity tends to be
enhanced due to the increase of repulsive Coulomb force against gravity. In
this regime, the appearance of nontrivial branches induced by negative $\beta$
of order $-1$ effectively reduces the Coulomb force for a wide range of central
matter densities, leading to charged stars with smaller masses and radii in
comparison to those in the general relativistic branch. Our analysis indicates
that spontaneous scalarization of stars can be induced not only by the coupling
to curvature invariants but also by the scalar-gauge coupling in Einstein
gravity.


Abstract:
  The purpose of this study is to explore the relationship between the first
affiliation and the corresponding affiliation at the different levels via the
scientometric analysis We select over 18 million papers in the core collection
database of Web of Science (WoS) published from 2000 to 2015, and measure the
percentage of match between the first and the corresponding affiliation at the
country and institution level. We find that a paper's the first affiliation and
the corresponding affiliation are highly consistent at the country level, with
over 98% of the match on average. However, the match at the institution level
is much lower, which varies significantly with time and country. Hence, for
studies at the country level, using the first and corresponding affiliations
are almost the same. But we may need to take more cautions to select
affiliation when the institution is the focus of the investigation. In the
meanwhile, we find some evidence that the recorded corresponding information in
the WoS database has undergone some changes since 2013, which sheds light on
future studies on the comparison of different databases or the affiliation
accuracy of WoS. Our finding relies on the records of WoS, which may not be
entirely accurate. Given the scale of the analysis, our findings can serve as a
useful reference for further studies when country allocation or institute
allocation is needed. Existing studies on comparisons of straight counting
methods usually cover a limited number of papers, a particular research field
or a limited range of time. More importantly, using the number counted can not
sufficiently tell if the corresponding and first affiliation are similar. This
paper uses a metric similar to Jaccard similarity to measure the percentage of
the match and performs a comprehensive analysis based on a large-scale
bibliometric database.


Abstract:
  The development of intelligent tutoring system has greatly influenced the way
students learn and practice, which increases their learning efficiency. The
intelligent tutoring system must model learners' mastery of the knowledge
before providing feedback and advices to learners, so one class of algorithm
called "knowledge tracing" is surely important. This paper proposed Deep
Self-Attentive Knowledge Tracing (DSAKT) based on the data of PTA, an online
assessment system used by students in many universities in China, to help these
students learn more efficiently. Experimentation on the data of PTA shows that
DSAKT outperforms the other models for knowledge tracing an improvement of AUC
by 2.1% on average, and this model also has a good performance on the ASSIST
dataset.


Abstract:
  The Landau-Lifshitz equation governing magnetization dynamics is written in
terms of the amplitudes of normal modes associated with the micromagnetic
system's appropriate ground state. This results in a system of nonlinear
ordinary differential equations (ODEs), the right-hand side of which can be
expressed as the sum of a linear term and nonlinear terms with increasing order
of nonlinearity (quadratic, cubic, etc.). The application of the method to
nanostructured magnetic systems demonstrates that the accurate description of
magnetization dynamics requires a limited number of normal modes, which results
in a considerable improvement in computational speed. The proposed method can
be used to obtain a reduced-order dynamical description of magnetic
nanostructures which allows to adjust the accuracy between low-dimensional
models, such as macrospin, and micromagnetic models with full spatial
discretization. This new paradigm for micromagnetic simulations is tested for
three problems relevant to the areas of spintronics and magnonics: directional
spin-wave coupling in magnonic waveguides, high power ferromagnetic resonance
in a magnetic nanodot, and injection-locking in spin-torque nano-oscillators.
The case studies considered demonstrate the validity of the proposed approach
to systematically obtain an intermediate order dynamical model based on normal
modes for the analysis of magnetic nanosystems. The time-consuming calculation
of the normal modes has to be done only one time for the system. These modes
can be used to optimize and predict the system response for all possible
time-varying external excitations (magnetic fields, spin currents). This is of
utmost importance for applications where fast and accurate system simulations
are required, such as in electronic circuits including magnetic devices.


Abstract:
  We investigate the motivation and means through which individuals expand
their skill-set by analyzing a survey of applicants from the Facebook Jobs
product. Individuals who report being influenced by their networks or local
economy are over 29% more likely to have a postsecondary degree, but peer
effects still exist among those who do not acknowledge such influences. Users
with postsecondary degrees are more likely to upskill in general, by continuing
coursework or applying to higher-skill jobs, though the latter is more common
among users across all education backgrounds. These findings indicate that
policies aimed at connecting individuals with different educational backgrounds
can encourage upskilling. Policies that encourage users to enroll in coursework
may not be as effective among individuals with a high school degree or less.
Instead, connecting such individuals to opportunities that value skills
acquired outside of a formal education, and allow for on-the-job training, may
be more effective.


Abstract:
  Single-photon emitters are essential for enabling several emerging
applications in quantum information technology, quantum sensing and quantum
communication. Scalable photonic platforms capable of hosting intrinsic or
directly embedded sources of single-photon emission are of particular interest
for the realization of integrated quantum photonic circuits. Here, we report on
the first-time observation of room-temperature single-photon emitters in
silicon nitride (SiN) films grown on silicon dioxide substrates. As SiN has
recently emerged as one of the most promising materials for integrated quantum
photonics, the proposed platform is suitable for scalable fabrication of
quantum on-chip devices. Photophysical analysis reveals bright (>$10^5$
counts/s), stable, linearly polarized, and pure quantum emitters in SiN films
with the value of the second-order autocorrelation function at zero time delay
$g^{(2)}(0)$ below 0.2 at room temperatures. The emission is suggested to
originate from a specific defect center in silicon nitride due to the narrow
wavelength distribution of the observed luminescence peak. Single-photon
emitters in silicon nitride have the potential to enable direct, scalable and
low-loss integration of quantum light sources with the well-established
photonic on-chip platform.


Abstract:
  Quantum techniques can be used to enhance the signal-to-noise ratio in
optical imaging. Leveraging the latest advances in single photon avalanche
diode array cameras and multi-photon detection techniques, here we introduce a
super-sensitive phase imager, which uses space-polarization hyper-entanglement
to operate over a large field-of-view without the need of scanning operation.
We show quantum-enhanced imaging of birefringent and non-birefringent phase
samples over large areas, with sensitivity improvements over equivalent
classical measurements carried out with equal number of photons. The practical
applicability is demonstrated by imaging a biomedical protein microarray
sample. Our quantum-enhanced phase imaging technology is inherently scalable to
high resolution images, and represents an essential step towards practical
quantum imaging.


Abstract:
  Interactive Task Learning (ITL) is an emerging research agenda that studies
the design of complex intelligent robots that can acquire new knowledge through
natural human teacher-robot learner interactions. ITL methods are particularly
useful for designing intelligent robots whose behavior can be adapted by humans
collaborating with them. Various research communities are contributing methods
for ITL and a large subset of this research is \emph{robot-centered} with a
focus on developing algorithms that can learn online, quickly. This paper
studies the ITL problem from a \emph{human-centered} perspective to provide
guidance for robot design so that human teachers can naturally teach ITL
robots. In this paper, we present 1) a qualitative bidirectional analysis of an
interactive teaching study (N=10) through which we characterize various aspects
of actions intended and executed by human teachers when teaching a robot; 2) an
in-depth discussion of the teaching approach employed by two participants to
understand the need for personal adaptation to individual teaching styles; and
3) requirements for ITL robot design based on our analyses and informed by a
computational theory of collaborative interactions, SharedPlans.


Abstract:
  The Debye sheath is shown to vanish completely in magnetised plasmas for a
sufficiently small electron gyroradius and small angle between the magnetic
field and the wall. This angle depends on the current onto the wall. When the
Debye sheath vanishes, there is still a potential drop between the wall and the
plasma across the magnetic presheath. The magnetic field angle corresponding to
sheath collapse is shown to be much smaller than previous estimates, scaling
with the electron-ion mass ratio and not with the square root of the mass
ratio. This is shown to be a consequence of the finite ion orbit width effects,
which are not captured by fluid models. The wall potential with respect to the
bulk plasma at which the Debye sheath vanishes is calculated. Above this wall
potential, it is possible that the Debye sheath will invert.


Abstract:
  Beloved Curry--Howard correspondence tells that types are intuitionistic
propositions, and in constructive math, a proof of proposition can be seen as
some kind of a construction, or witness, conveying the information of the
proposition. We demonstrate how useful this point of view is as the guiding
principle for developing dependently-typed programs.


Abstract:
  Manifold learning algorithms are valuable tools for the analysis of
high-dimensional data, many of which include a step where nearest neighbors of
all observations are found. This can present a computational bottleneck when
the number of observations is large or when the observations lie in more
general metric spaces, such as statistical manifolds, which require all
pairwise distances between observations to be computed. We resolve this problem
by using a broad range of approximate nearest neighbor algorithms within
manifold learning algorithms and evaluating their impact on embedding accuracy.
We use approximate nearest neighbors for statistical manifolds by exploiting
the connection between Hellinger/Total variation distance for discrete
distributions and the L2/L1 norm. Via a thorough empirical investigation based
on the benchmark MNIST dataset, it is shown that approximate nearest neighbors
lead to substantial improvements in computational time with little to no loss
in the accuracy of the embedding produced by a manifold learning algorithm.
This result is robust to the use of different manifold learning algorithms, to
the use of different approximate nearest neighbor algorithms, and to the use of
different measures of embedding accuracy. The proposed method is applied to
learning statistical manifolds data on distributions of electricity usage. This
application demonstrates how the proposed methods can be used to visualize and
identify anomalies and uncover underlying structure within high-dimensional
data in a way that is scalable to large datasets.


Abstract:
  With the rapid advancement of technology, different biometric user
authentication, and identification systems are emerging. Traditional biometric
systems like face, fingerprint, and iris recognition, keystroke dynamics, etc.
are prone to cyber-attacks and suffer from different disadvantages.
Electroencephalography (EEG) based authentication has shown promise in
overcoming these limitations. However, EEG-based authentication is less
accurate due to signal variability at different psychological and physiological
conditions. On the other hand, keystroke dynamics-based identification offers
high accuracy but suffers from different spoofing attacks. To overcome these
challenges, we propose a novel multimodal biometric system combining EEG and
keystroke dynamics. Firstly, a dataset was created by acquiring both keystroke
dynamics and EEG signals from 10 users with 500 trials per user at 10 different
sessions. Different statistical, time, and frequency domain features were
extracted and ranked from the EEG signals and key features were extracted from
the keystroke dynamics. Different classifiers were trained, validated, and
tested for both individual and combined modalities for two different
classification strategies - personalized and generalized. Results show that
very high accuracy can be achieved both in generalized and personalized cases
for the combination of EEG and keystroke dynamics. The identification and
authentication accuracies were found to be 99.80% and 99.68% for Extreme
Gradient Boosting (XGBoost) and Random Forest classifiers, respectively which
outperform the individual modalities with a significant margin (around 5
percent). We also developed a binary template matching-based algorithm, which
gives 93.64% accuracy 6X faster. The proposed method is secured and reliable
for any kind of biometric authentication.


Abstract:
  We report here the discovery of a hot Jupiter at an orbital period of
$3.208666\pm0.000016$ days around TOI-1789 (TYC 1962-00303-1, $TESS_{mag}$ =
9.1) based on the TESS photometry, ground-based photometry, and high-precision
radial velocity observations. The high-precision radial velocity observations
were obtained from the high-resolution spectrographs, PARAS at Physical
Research Laboratory (PRL), India, and TCES at Th\"uringer Landessternwarte
Tautenburg (TLS), Germany, and the ground-based transit observations were
obtained using the 0.43~m telescope at PRL with the Bessel-$R$ filter. The host
star is a slightly evolved ($\log{g_*}$ = $3.939^{+0.024}_{-0.046}$), late
F-type ($T_{eff}$ = $5984^{+55}_{-57}$ K), metal-rich star ([Fe/H] =
$0.370^{+0.073}_{-0.089}$ dex) with a radius of {\ensuremath{$R_{*}$}} =
$2.172^{+0.037}_{-0.035}$ \(R_\odot\) located at a distance of
$223.56^{+0.91}_{-0.90}$ pc. The simultaneous fitting of the multiple light
curves and the radial velocity data of TOI-1789 reveals that TOI-1789b has a
mass of $M_{P}$ = $0.70\pm0.16 $ $M_{J}$, a radius of $R_{P}$ =
$1.40^{+0.22}_{-0.13}$ $R_{J}$, and a bulk density of $\rho_P$ =
$0.31^{+0.15}_{-0.13}$ g cm$^{-3}$ with an orbital separation of a =
$0.04873^{+0.00065}_{-0.0016}$ AU. This puts TOI-1789b in the category of
inflated hot Jupiters. It is one of the few nearby evolved stars with a
close-in planet. The detection of such systems will contribute to our
understanding of mechanisms responsible for inflation in hot Jupiters and also
provide an opportunity to understand the evolution of planets around stars
leaving the main sequence branch.


Abstract:
  We present a method for approximating outcomes of road traffic simulations
using BERT-based models, which may find applications in, e.g., optimizing
traffic signal settings, especially with the presence of autonomous and
connected vehicles. The experiments were conducted on a dataset generated using
the Traffic Simulation Framework software runs on a realistic road network. The
BERT-based models were compared with 4 other types of machine learning models
(LightGBM, fully connected neural networks and 2 types of graph neural
networks) and gave the best results in terms of all the considered metrics.


Abstract:
  Optical antennas made of low-loss dielectrics have several advantages over
plasmonic antennas, including high radiative quantum efficiency, negligible
heating and excellent photostability. However, due to weak spatial confinement,
conventional dielectric antennas fail to offer light-matter interaction
strengths on par with those of plasmonic antennas. We propose here an
all-dielectric antenna configuration that can support strongly confined modes
($V\sim10^{-4}\lambda_{0}^3$) while maintaining unity antenna quantum
efficiency. This configuration consists of a high-index pillar structure with a
transverse gap that is filled with a low-index material, where the contrast of
indices induces a strong enhancement of the electric field perpendicular to the
gap. We provide a detailed explanation of the operation principle of such
Photonic Gap Antennas (PGAs) based on the dispersion relation of symmetric and
asymmetric horizontal slot-waveguides. To discuss the properties of PGAs, we
consider silicon pillars with air or CYTOP as the gap-material. We show by
full-wave simulations that PGAs with an emitter embedded in the gap can enhance
the spontaneous emission rate by a factor of $\sim$1000 for air gaps and
$\sim$400 for CYTOP gaps over a spectral bandwidth of $\Delta\lambda\approx300$
nm at $\lambda=1.25$ \textmu m. Furthermore, the PGAs can be designed to
provide unidirectional out-of-plane radiation across a substantial portion of
their spectral bandwidth. This is achieved by setting the position of the gap
at an optimized off-centered position of the pillar so as to properly break the
vertical symmetry of the structure. We also demonstrate that, when acting as
receivers, PGAs can lead to a near-field intensity enhancement by a factor of
$\sim$3000 for air gaps and $\sim$1200 for CYTOP gaps.


Abstract:
  We study the multiple populations of $\omega$ Cen by using the abundances of
Fe, C, N, O, Mg, Al, Si, K, Ca, and Ce from the high-resolution, high
signal-to-noise (S/N$>$70) spectra of 982 red giant stars observed by the
SDSS-IV/APOGEE-2 survey. We find that the shape of the Al-Mg and N-C
anticorrelations changes as a function of metallicity, continuous for the
metal-poor groups, but bimodal (or unimodal) at high metallicities. There are
four Fe populations, similar to what has been found in previously published
investigations, but we find seven populations based on Fe, Al, and Mg
abundances. The evolution of Al in $\omega$ Cen is compared to its evolution in
the Milky Way and in five representative globular clusters. We find that the
distribution of Al in metal-rich stars of $\omega$ Cen closely follows what is
observed in the Galaxy. Other $\alpha-$elements and C, N, O, and Ce are also
compared to the Milky Way, and significantly elevated abundances are observed
over what is found in the thick disk for almost all elements. However, we also
find some stars with high metallicity and low [Al/Fe], suggesting that $\omega$
Cen could be the remnant core of a dwarf galaxy, but the existence of these
peculiar stars needs an independent confirmation. We also confirm the increase
in the sum of CNO as a function of metallicity previously reported in the
literature and find that the [C/N] ratio appears to show opposite correlations
between Al-poor and Al-rich stars as a function of metallicity.


Abstract:
  A weak topological insulator in dimension $3$ is known to have a
topologically protected gapless mode along the screw dislocation. In this paper
we formulate and prove this fact with the language of C*-algebra K-theory. The
proof is based on the coarse index theory of the helical surface.


Abstract:
  A study of the dynamical formation of networks of friends and enemies in
social media, in this case Twitter, is presented. We characterise the single
node properties of such networks, as the clustering coefficient and the degree,
to investigate the structure of links. The results indicate that the network is
made from three kinds of nodes: one with high clustering coefficient but very
small degree, a second group has zero clustering coefficient with variable
degree, and finally, a third group in which the clustering coefficient as a
function of the degree decays as a power law. This third group represents
$\sim2\%$ of the nodes and is characteristic of dynamical networks with
feedback. This part of the lattice seemingly represents strongly interacting
friends in a real social network.


Abstract:
  We consider the problem of recovering equations of motion from multivariate
time series of oscillators interacting on sparse networks. We reconstruct the
network from an initial guess which can include expert knowledge about the
system such as main motifs and hubs. When sparsity is taken into account the
number of data points needed is drastically reduced when compared to the
least-squares recovery. We show that the sparse solution is stable under basis
extensions, that is, once the correct network topology is obtained, the result
does not change if further motifs are considered.


Abstract:
  This paper focuses on finite-time in-network computation of linear transforms
of distributed graph data. Finite-time transform computation problems are of
interest in graph-based computing and signal processing applications in which
the objective is to compute, by means of distributed iterative methods, various
(linear) transforms of the data distributed at the agents or nodes of the
graph. While finite-time computation of consensus-type or more generally
rank-one transforms have been studied, systematic approaches toward scalable
computing of general linear transforms, specifically in the case of
heterogeneous agent objectives in which each agent is interested in obtaining a
different linear combination of the network data, are relatively less explored.
In this paper, by employing ideas from algebraic geometry, we develop a
systematic characterization of linear transforms that are amenable to
distributed in-network computation in finite-time using linear iterations.
Further, we consider the general case of directed inter-agent communication
graphs. Specifically, it is shown that \emph{almost all} linear transformations
of data distributed on the nodes of a digraph containing a Hamiltonian cycle
may be computed using at most $N$ linear distributed iterations. Finally, by
studying an associated matrix factorization based reformulation of the
transform computation problem, we obtain, as a by-product, certain results and
characterizations on sparsity-constrained matrix factorization that are of
independent interest.


Abstract:
  Not only does cold climate pose a problem for outdoor plants during winter in
the northern hemisphere, but for indoor plants as well: low sunlight, low
humidity, and simultaneous cold breezes from windows and heat from radiators
all cause problems for indoor plants. People often treat their indoor plants
like mere decoration, which can often lead to health issues for the plant or
even death of the plant, especially during winter. A plant monitoring system
was developed to solve this problem, collecting information on plants' indoor
environmental conditions (light, humidity, and temperature) and providing this
information in an accessible format for the user. Preliminary functional tests
were conducted in similar settings where the system would be used. In addition,
the concept was evaluated by interviewing an expert in the field of
horticulture.
  The evaluation results indicate that this kind of system could prove useful;
however, the tests indicated that the system requires further development to
achieve more practical value and wider usage.


Abstract:
  Most work in NLP makes the assumption that it is desirable to develop
solutions in the native language in question. There is consequently a strong
trend towards building native language models even for low-resource languages.
This paper questions this development, and explores the idea of simply
translating the data into English, thereby enabling the use of pretrained, and
large-scale, English language models. We demonstrate empirically that a large
English language model coupled with modern machine translation outperforms
native language models in most Scandinavian languages. The exception to this is
Finnish, which we assume is due to inferior translation quality. Our results
suggest that machine translation is a mature technology, which raises a serious
counter-argument for training native language models for low-resource
languages. This paper therefore strives to make a provocative but important
point. As English language models are improving at an unprecedented pace, which
in turn improves machine translation, it is from an empirical and environmental
stand-point more effective to translate data from low-resource languages into
English, than to build language models for such languages.


Abstract:
  In neural machine translation, cross entropy (CE) is the standard loss
function in two training methods of auto-regressive models, i.e., teacher
forcing and scheduled sampling. In this paper, we propose mixed cross entropy
loss (mixed CE) as a substitute for CE in both training approaches. In teacher
forcing, the model trained with CE regards the translation problem as a
one-to-one mapping process, while in mixed CE this process can be relaxed to
one-to-many. In scheduled sampling, we show that mixed CE has the potential to
encourage the training and testing behaviours to be similar to each other, more
effectively mitigating the exposure bias problem. We demonstrate the
superiority of mixed CE over CE on several machine translation datasets, WMT'16
Ro-En, WMT'16 Ru-En, and WMT'14 En-De in both teacher forcing and scheduled
sampling setups. Furthermore, in WMT'14 En-De, we also find mixed CE
consistently outperforms CE on a multi-reference set as well as a challenging
paraphrased reference set. We also found the model trained with mixed CE is
able to provide a better probability distribution defined over the translation
output space. Our code is available at https://github.com/haorannlp/mix.


Abstract:
  Current storytelling systems focus more ongenerating stories with coherent
plots regard-less of the narration style, which is impor-tant for controllable
text generation. There-fore, we propose a new task, stylized story gen-eration,
namely generating stories with speci-fied style given a leading context. To
tacklethe problem, we propose a novel generationmodel that first plans the
stylized keywordsand then generates the whole story with theguidance of the
keywords. Besides, we pro-pose two automatic metrics to evaluate theconsistency
between the generated story andthe specified style. Experiments
demonstratesthat our model can controllably generateemo-tion-driven
orevent-driven stories based onthe ROCStories dataset (Mostafazadeh et
al.,2016). Our study presents insights for stylizedstory generation in further
research.


Abstract:
  In this paper, we completely solve the problem when a Cantor dynamical system
$(X,f)$ can be embedded in $\mathbb{R}$ with vanishing derivative everywhere.
For this purpose, we construct a refining sequence of marked clopen partitions
of $X$ which is adapted to a dynamical system of this kind. It turns out that
there is a huge class of such systems.


Abstract:
  Recently, distributed controller architectures have been quickly gaining
popularity in Software-Defined Networking (SDN). However, the use of
distributed controllers introduces a new and important Request Dispatching (RD)
problem with the goal for every SDN switch to properly dispatch their requests
among all controllers so as to optimize network performance. This goal can be
fulfilled by designing an RD policy to guide distribution of requests at each
switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning
(MA-DRL) approach to automatically design RD policies with high adaptability
and performance. This is achieved through a new problem formulation in the form
of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy
design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies
show that our MA-DRL technique can effectively train RD policies to
significantly outperform man-made policies, model-based policies, as well as RD
policies learned via single-agent DRL algorithms.


Abstract:
  We extend Fring-Tenney approach of constructing invariants of constant mass
time-dependent system to the case of a time-dependent mass particle. From a
coupled set of equations described in terms of guiding parameter functions, we
track down a modified Ermakov-Pinney equation involving a time-dependent mass
function. As a concrete example we focus on an exponential choice of the mass
function.


Abstract:
  We consider the problem of collective exploration of a known $n$-node
edge-weighted graph by $k$ mobile agents that have limited energy but are
capable of energy transfers. The agents are initially placed at an arbitrary
subset of nodes in the graph, and each agent has an initial, possibly
different, amount of energy. The goal of the exploration problem is for every
edge in the graph to be traversed by at least one agent. The amount of energy
used by an agent to travel distance $x$ is proportional to $x$. In our model,
the agents can {\em share} energy when co-located: when two agents meet, one
can transfer part of its energy to the other.
  For an $n$-node path, we give an $O(n+k)$ time algorithm that either finds an
exploration strategy, or reports that one does not exist. For an $n$-node tree
with $\ell $ leaves, we give an $O(n+ \ell k^2)$ algorithm that finds an
exploration strategy if one exists. Finally, for the general graph case, we
show that the problem of deciding if exploration is possible by energy-sharing
agents is NP-hard, even for 3-regular graphs. In addition, we show that it is
always possible to find an exploration strategy if the total energy of the
agents is at least twice the total weight of the edges; moreover, this is
asymptotically optimal.


Abstract:
  We study some semi-linear equations for the $(m,p)$-Laplacian operator on
locally finite weighted graphs. We prove existence of weak solutions for all
$m\in\mathbb{N}$ and $p\in(1,+\infty)$ via a variational method already known
in the literature by exploiting the continuity properties of the energy
functionals involved. When $m=1$, we also establish a uniqueness result in the
spirit of the Brezis-Strauss Theorem. We finally provide some applications of
our main results by dealing with some Yamabe-type and Kazdan-Warner-type
equations on locally finite weighted graphs.


Abstract:
  Recent work in AI safety has highlighted that in sequential decision making,
objectives are often underspecified or incomplete. This gives discretion to the
acting agent to realize the stated objective in ways that may result in
undesirable outcomes. We contend that to learn to act safely, a reinforcement
learning (RL) agent should include contemplation of the impact of its actions
on the wellbeing and agency of others in the environment, including other
acting agents and reactive processes. We endow RL agents with the ability to
contemplate such impact by augmenting their reward based on expectation of
future return by others in the environment, providing different criteria for
characterizing impact. We further endow these agents with the ability to
differentially factor this impact into their decision making, manifesting
behavior that ranges from self-centred to self-less, as demonstrated by
experiments in gridworld environments.


Abstract:
  Most modern reinforcement learning algorithms optimize a cumulative
single-step cost along a trajectory. The optimized motions are often
'unnatural', representing, for example, behaviors with sudden accelerations
that waste energy and lack predictability. In this work, we present a novel
paradigm of controlling nonlinear systems via the minimization of the Koopman
spectrum cost: a cost over the Koopman operator of the controlled dynamics.
This induces a broader class of dynamical behaviors that evolve over stable
manifolds such as nonlinear oscillators, closed loops, and smooth movements. We
demonstrate that some dynamics realizations that are not possible with a
cumulative cost are feasible in this paradigm. Moreover, we present a provably
efficient online learning algorithm for our problem that enjoys a sub-linear
regret bound under some structural assumptions.


Abstract:
  Bayesian optimization (BO) is a popular method for optimizing
expensive-to-evaluate black-box functions. BO budgets are typically given in
iterations, which implicitly assumes each evaluation has the same cost. In
fact, in many BO applications, evaluation costs vary significantly in different
regions of the search space. In hyperparameter optimization, the time spent on
neural network training increases with layer size; in clinical trials, the
monetary cost of drug compounds vary; and in optimal control, control actions
have differing complexities. Cost-constrained BO measures convergence with
alternative cost metrics such as time, money, or energy, for which the sample
efficiency of standard BO methods is ill-suited. For cost-constrained BO, cost
efficiency is far more important than sample efficiency. In this paper, we
formulate cost-constrained BO as a constrained Markov decision process (CMDP),
and develop an efficient rollout approximation to the optimal CMDP policy that
takes both the cost and future iterations into account. We validate our method
on a collection of hyperparameter optimization problems as well as a sensor set
selection application.


Abstract:
  Annotated datasets have become one of the most crucial preconditions for the
development and evaluation of machine learning-based methods designed for the
automated interpretation of remote sensing data. In this paper, we review the
historic development of such datasets, discuss their features based on a few
selected examples, and address open issues for future developments.


Abstract:
  We investigate special solutions to the Bethe Ansatz equations (BAE) for open
integrable $XXZ$ Heisenberg spin chains containing phantom (infinite) Bethe
roots. The phantom Bethe roots do not contribute to the energy of the Bethe
state, so the energy is determined exclusively by the remaining regular
excitations. We rederive the phantom Bethe roots criterion and focus on BAE
solutions for mixtures of phantom roots and regular (finite) Bethe roots. We
prove that in the presence of phantom Bethe roots, all eigenstates are split
between two invariant subspaces, spanned by chiral shock states. Bethe
eigenstates are described by two complementary sets of Bethe Ansatz equations
for regular roots, one for each invariant subspace. The respective
"semi-phantom" Bethe vectors are states of chiral nature, with chirality
properties getting less pronounced when more regular Bethe roots are added. For
the easy plane case "semi-phantom" Bethe states carry nonzero magnetic current,
and are characterized by quasi-periodic modulation of the magnetization
profile, the most prominent example being the spin helix states (SHS). We
illustrate our results investigating "semi-phantom" Bethe states generated by
one regular Bethe root (the other Bethe roots being phantom), with simple
structure of the invariant subspace, in all details. We obtain the explicit
expressions for Bethe vectors, and calculate the simplest correlation
functions, including the spin-current for all the states in the single particle
multiplet.


Abstract:
  Lunar explorations have provided us with information about its abundant
resources that can be utilized in orbiting-resource depots as lunar-derived
commodities. To reduce the energy requirements of a launcher to send these
commodities from the lunar surface to the space depots, this paper explores the
application of the electromagnetic acceleration principle and provides an
assessment of the actual technical characteristics of the launcher's
installation to ensure the acceleration of a payload with a mass of 1,500 kg to
a speed of 2,200 m/s (circumlunar orbit speed). To fulfill a lightweight (fewer
materials and less energy) support structure for the electromagnetic launcher
with strength requirements, the tensegrity structure minimum mass principle
without global buckling has been developed and applied to support the
electromagnetic acceleration device. Therefore, this paper proposes and
develops a minimal mass electromagnetic tensegrity lunar launcher. We first
demonstrate the mechanics of launcher and payload, how a payload can be
accelerated to a specific velocity, and how a payload carrier can be recycled
for another launch. Then, a detailed discussion on the lunar launch system,
procedures of propulsion, the required mass, and energy of the launch barrel
are given. The governing equations of tensegrity minimal mass tensegrity design
algorithm with gravity and without global buckling. Finally, a case study is
conducted to show a feasible structure design, the required mass, and energy.
The principles developed in this paper are also applicable to the rocket launch
system, space elevator, space train transportation, interstellar payload
package delivery, etc.


Abstract:
  We present high-fidelity numerical simulations of expiratory biosol transport
during normal breathing under indoor, stagnant air conditions with and without
a facial mask. We investigate mask efficacy to suppress the spread of saliva
particles that is underpinnings existing social distancing recommendations. The
present simulations incorporate the effect of human anatomy and consider a
spectrum of saliva particulate sizes that ranges from 0.1 micrometers to 10
micrometers while accounting also for their evaporation. The simulations
elucidate the vorticity dynamics of human breathing and show that without a
facial mask, saliva particulates could travel over 2.2 m away from the person.
However, a non-medical grade face mask can drastically reduce saliva
particulate propagation to 0.72 m away from the person. This study provides new
quantitative evidence that facial masks can successfully suppress the spreading
of saliva particulates due to normal breathing in indoor environments.


Abstract:
  Domain walls in fractional quantum Hall ferromagnets are gapless helical
one-dimensional channels formed at the boundaries of topologically distinct
quantum Hall (QH) liquids. Na\"{i}vely, these helical domain walls (hDWs)
constitute two counter-propagating chiral states with opposite spins. Coupled
to an s-wave superconductor, helical channels are expected to lead to
topological superconductivity with high order non-Abelian excitations. Here we
investigate transport properties of hDWs in the $\nu=2/3$ fractional QH regime.
Experimentally we found that current carried by hDWs is substantially smaller
than the prediction of the na\"{i}ve model. Luttinger liquid theory of the
system reveals redistribution of currents between quasiparticle charge, spin
and neutral modes, and predicts the reduction of the hDW current. Inclusion of
spin-non-conserving tunneling processes reconciles theory with experiment. The
theory confirms emergence of spin modes required for the formation of
fractional topological superconductivity.


Abstract:
  We calculate the vacuum (Casimir) energy for a scalar field with $\phi^4$
self-interaction in (1+1) dimensions non perturbatively, i.e., in all orders of
the self-interaction. We consider massive and massless fields in a finite box
with Dirichlet boundary conditions and on the whole axis as well. For strong
coupling, the vacuum energy is negative indicating some instability.


Abstract:
  Multi input multi output (MIMO) systems\' capability of using seperate
signals brings many advantages to radar signal processing and time frequency
analysis. In this paper, a variety of properties of MIMO ambiguity functions
related with representations of Heisenberg group are given. Some of the
existing results for SIMO ambiguity functions are generalized to MIMO case.
Combined effect of seperate signals is investigated.


Abstract:
  Recently, autonomous driving has made substantial progress in addressing the
most common traffic scenarios like intersection navigation and lane changing.
However, most of these successes have been limited to scenarios with
well-defined traffic rules and require minimal negotiation with other vehicles.
In this paper, we introduce a previously unconsidered, yet everyday,
high-conflict driving scenario requiring negotiations between agents of equal
rights and priorities. There exists no centralized control structure and we do
not allow communications. Therefore, it is unknown if other drivers are willing
to cooperate, and if so to what extent. We train policies to robustly negotiate
with opposing vehicles of an unobservable degree of cooperativeness using
multi-agent reinforcement learning (MARL). We propose Discrete Asymmetric Soft
Actor-Critic (DASAC), a maximum-entropy off-policy MARL algorithm allowing for
centralized training with decentralized execution. We show that using DASAC we
are able to successfully negotiate and traverse the scenario considered over
99% of the time. Our agents are robust to an unknown timing of opponent
decisions, an unobservable degree of cooperativeness of the opposing vehicle,
and previously unencountered policies. Furthermore, they learn to exhibit
human-like behaviors such as defensive driving, anticipating solution options
and interpreting the behavior of other agents.


Abstract:
  Reservoir computers (RC) are a form of recurrent neural network (RNN) used
for forecasting timeseries data. As with all RNNs, selecting the
hyperparameters presents a challenge when training onnew inputs. We present a
method based on generalized synchronization (GS) that gives direction in
designing and evaluating the architecture and hyperparameters of an RC. The
'auxiliary method' for detecting GS provides a computationally efficient
pre-training test that guides hyperparameterselection. Furthermore, we provide
a metric for RC using the reproduction of the input system's Lyapunov
exponentsthat demonstrates robustness in prediction.


Abstract:
  Structural defects and chemical impurities exist in organic semiconductors
acting as trap centers for the excited states. This work presents a novel
analytical model to calculate the trapping and detrapping rates between two
Gaussian density of states. Miller-Abrahams rate and Fermi-Dirac statistics are
employed in this model. The introduction of effective filled and empty sites
for correlated bands greatly simplifies the expression of recombination rate. A
technology computer-aided design simulator was used to simulate the donor-like
traps in an organic semiconductor DPP-DTT based thin-film transistor, showing
good agreement with the measured transfer characteristic.


Abstract:
  Recent work has shown that in a dataset of user ratings on items there exists
a group of Core Users who hold most of the information necessary for
recommendation. This set of Core Users can be as small as 20 percent of the
users. Core Users can be used to make predictions for out-of-sample users
without much additional work. Since Core Users substantially shrink a ratings
dataset without much loss of information, they can be used to improve
recommendation efficiency. We propose a method, combining latent factor models,
ensemble boosting and K-means clustering, to generate a small set of Artificial
Core Users (ACUs) from real Core User data. Our ACUs have dense rating
information, and improve the recommendation performance of real Core Users
while remaining interpretable.


Abstract:
  Asteroseismic measurements enable inferences of the underlying stellar
structure, such as the density and the speed of sound at various points within
the interior of the star. This provides an opportunity to test stellar
evolution theory by assessing whether the predicted structure of a star agrees
with the measured structure. Thus far, this kind of inverse analysis has only
been applied to the Sun and three solar-like main-sequence stars. Here we
extend the technique to stars on the subgiant branch, and apply it to one of
the best-characterized subgiants of the Kepler mission, HR 7322. The
observation of mixed oscillation modes in this star facilitates inferences of
the conditions of its inert helium core, nuclear-burning hydrogen shell, and
the deeper parts of its radiative envelope. We find that despite significant
differences in the mode frequencies, the structure near to the center of this
star does not differ significantly from the predicted structure.


Abstract:
  In many engineering systems operating with a working fluid, the best
efficiency is reached close to a condition of flow separation, which makes its
prediction very crucial in industry. Providing that wall-based quantities can
be measured, we know today how to obtain good predictions for two and
three-dimensional steady and periodic flows. In these flows, the separation is
defined on a fixed line attached to a material surface. The last case to
elucidate is the one where this line is no longer attached to the wall but on
the contrary is contained within the flow. This moving separation is probably,
however, the most common case of separation in natural flows and industrial
applications. Since this case has received less attention during the past few
years, we propose in this study to examine some properties of moving separation
in two-dimensional, unsteady flows where the separation does not leave a
signature on the wall. Since in this framework separation can be extracted by
using a Lagrangian frame where the separation profile can be viewed as a
hyperbolic unstable manifold, we propose a method to extract the separation
point defined by the Lagrangian saddle point that belongs to this unstable
manifold. In practice, the separation point and profile are initially extracted
by detecting the most attracting Lagrangian coherent structure near the wall,
and can then be advected in time for following instants. It is found that
saddle points, which initially act as separation points in the viscous wall
flow region, remarkably preserve their hyperbolicity even if they are ejected
from the wall toward the inviscid region. Two test cases are studied, the
creeping flow of a rotating and translating cylinder close to a wall, and the
unsteady separation in the boundary layer generated by a planar jet impinging
onto a plane wall.


Abstract:
  In this paper we continue a long line of work on representing the cut
structure of graphs. We classify the types minimum vertex cuts, and the
possible relationships between multiple minimum vertex cuts.
  As a consequence of these investigations, we exhibit a simple $O(\kappa
n)$-space data structure that can quickly answer pairwise
$(\kappa+1)$-connectivity queries in a $\kappa$-connected graph. We also show
how to compute the "closest" $\kappa$-cut to every vertex in near linear
$\tilde{O}(m+poly(\kappa)n)$ time.


Abstract:
  In this paper we prove the soliton resolution conjecture for all times, for
all solutions in the energy space, of the co-rotational wave map equation. To
our knowledge this is the first such result for all initial data in the energy
space for a wave-type equation. We also prove the corresponding results for
radial solutions, which remain bounded in the energy norm, of the cubic
(energy-critical) nonlinear wave equation in space dimension 4.


Abstract:
  Magnetoelectric (ME) effect refers to the coupling between electric and
magnetic fields in a medium resulting in electric polarization induced by
magnetic fields and magnetization induced by electric fields. The linear ME
effect in certain magnetoelectric materials such as multiferroics has been of
great interest due to its application in the fabrication of spintronics
devices, memories, and magnetic sensors. However, the exclusive studies on the
nonlinear ME effect are mostly centered on the investigation of second-harmonic
generation in chiral materials. Here, we report the demonstration of nonlinear
wave mixing of optical electric fields and radio-frequency (rf) magnetic fields
in thermal atomic vapor, which is the consequence of the higher-order nonlinear
ME effect in the medium. The experimental results are explained by comparing
with density matrix calculations of the system. We also experimentally verify
the expected dependence of the generated field amplitudes on the rf field
magnitude as evidence of the magnetoelectric effect. This study can open up the
possibility for precision rf-magnetometry due to its advantage in terms of
larger dynamic range and arbitrary frequency resolution.


Abstract:
  This paper studies point identification of the distribution of the
coefficients in some random coefficients models with exogenous regressors when
their support is a proper subset, possibly discrete but countable. We exhibit
trade-offs between restrictions on the distribution of the random coefficients
and the support of the regressors. We consider linear models including those
with nonlinear transforms of a baseline regressor, with an infinite number of
regressors and deconvolution, the binary choice model, and panel data models
such as single-index panel data models and an extension of the Kotlarski lemma.


Abstract:
  We report a precision measurement of the parity-violating asymmetry $A_{PV}$
in the elastic scattering of longitudinally polarized electrons from
$^{208}$Pb. We measure $A_{PV}=550\pm 16 {\rm (stat)}\pm 8\ {\rm (syst)}$ parts
per billion, leading to an extraction of the neutral weak form factor $F_W(Q^2
= 0.00616\ {\rm GeV}^2) = 0.368 \pm 0.013$. Combined with our previous
measurement, the extracted neutron skin thickness is $R_n-R_p=0.283 \pm
0.071$~fm. The result also yields the first significant direct measurement of
the interior weak density of $^{208}$Pb: $\rho^0_W = -0.0796\pm0.0036\ {\rm
(exp.)}\pm0.0013\ {\rm (theo.)}\ {\rm fm}^{-3}$ leading to the interior baryon
density $\rho^0_b = 0.1480\pm0.0036\ {\rm (exp.)}\pm0.0013\ {\rm (theo.)}\ {\rm
fm}^{-3}$. The measurement accurately constrains the density dependence of the
symmetry energy of nuclear matter near saturation density, with implications
for the size and composition of neutron stars.


Abstract:
  Connected vehicles, whether equipped with advanced driver-assistance systems
or fully autonomous, are currently constrained to visual information in their
lines-of-sight. A cooperative perception system among vehicles increases their
situational awareness by extending their perception ranges. Existing solutions
imply significant network and computation load, as well as high flow of
not-always-relevant data received by vehicles. To address such issues, and thus
account for the inherently diverse informativeness of the data, we present
Augmented Informative Cooperative Perception (AICP) as the first fast-filtering
system which optimizes the informativeness of shared data at vehicles. AICP
displays the filtered data to the drivers in augmented reality head-up display.
To this end, an informativeness maximization problem is presented for vehicles
to select a subset of data to display to their drivers. Specifically, we
propose (i) a dedicated system design with custom data structure and
light-weight routing protocol for convenient data encapsulation, fast
interpretation and transmission, and (ii) a comprehensive problem formulation
and efficient fitness-based sorting algorithm to select the most valuable data
to display at the application layer. We implement a proof-of-concept prototype
of AICP with a bandwidth-hungry, latency-constrained real-life augmented
reality application. The prototype realizes the informative-optimized
cooperative perception with only 12.6 milliseconds additional latency. Next, we
test the networking performance of AICP at scale and show that AICP effectively
filter out less relevant packets and decreases the channel busy time.


Abstract:
  In recent years, programming has witnessed a shift towards using standard
libraries as a black box. However, there has not been a synchronous development
of tools that can help demonstrate the working of such libraries in general
programs, which poses an impediment to improved learning outcomes and makes
debugging exasperating. We introduce Eye, an interactive pedagogical tool that
visualizes a program's execution as it runs. It demonstrates properties and
usage of data structures in a general environment, thereby helping in learning,
logical debugging, and code comprehension. Eye provides a comprehensive
overview at each stage during run time including the execution stack and the
state of data structures. The modular implementation allows for extension to
other languages and modification of the graphics as desired.
  Eye opens up a gateway for CS2 students to more easily understand myriads of
programs that are available on online programming websites, lowering the
barrier towards self-learning of coding. It expands the scope of visualizing
data structures from standard algorithms to general cases, benefiting both
teachers as well as programmers who face issues in debugging. Line by line
interpreting allows Eye to describe the execution and not only the current
state. We also conduct experiments to evaluate the efficacy of Eye for
debugging and comprehending a new piece of code. Our findings show that it
becomes faster and less frustrating to debug certain problems using this tool,
and also makes understanding new code a much more pleasant experience.


Abstract:
  The classic Riesz representation theorem characterizes all linear and
increasing functionals on the space $C_{c}(X)$ of continuous compactly
supported functions. A geometric version of this result, which characterizes
all linear increasing functionals on the set of convex bodies in
$\mathbb{R}^{n}$, was essentially known to Alexandrov. This was used by
Alexandrov to prove the existence of mixed area measures in convex geometry.
  In this paper we characterize linear and increasing functionals on the class
of log-concave functions on $\mathbb{R}^{n}$. Here "linear" means linear with
respect to the natural addition on log-concave functions which is the
sup-convolution. Equivalently, we characterize pointwise-linear and increasing
functionals on the class of convex functions. For some choices of the exact
class of functions we prove that there are no non-trivial such functionals. For
another choice we obtain the expected analogue of the result for convex bodies.
And most interestingly, for yet another choice we find a new unexpected family
of such functionals.
  Finally, we explain the connection between our results and recent work done
in convex geometry regarding the surface area measure of a log-concave
functions. An application of our results in this direction is also given.


Abstract:
  We have analyzed the ALMA archival data of the SO ($J_N=6_5-5_4$ and
$J_N=7_6-6_5$), CO ($J=2-1$), and CCH ($N=3-2, J=7/2-5/2, F=4-3$) lines from
the class 0 protobinary system, NGC1333 IRAS 4A. The images of SO ($J_N =
6_5-5_4$) and CO ($J=2-1$) successfully separate two northern outflow lobes
connected to each protostar, IRAS 4A1 and IRAS 4A2. The outflow from IRAS 4A2
shows an S-shaped morphology, consisting of a flattened envelope around IRAS
4A2 with two outflow lobes connected to both edges of the envelope. The
flattened envelope surrounding IRAS 4A2 has an opposite velocity gradient to
that of the circumbinary envelope. The observed features are reproduced by the
magnetohydrodynamic simulation of the collapsing core whose magnetic field
direction is misaligned to the rotational axis. Our simulation shows that the
intensity of the outflow lobes is enhanced on one side, resulting in the
formation of S-shaped morphology. The S-shaped outflow can also be explained by
the precessing outflow launched from an unresolved binary with a separation
larger than 12 au (0.04arcsec). Additionally, we discovered a previously
unknown extremely high velocity component at $\sim$45-90 km/s near IRAS 4A2
with CO. CCH ($J_{N,F}=7/2_{3,4}-5/2_{2,3}$) emission shows two pairs of blobs
attaching to the bottom of shell like feature, and the morphology is
significantly different from those of SO and CO lines. Toward IRAS 4A2, the
S-shaped outflow shown in SO is overlapped with the edges of CCH shells, while
CCH shells have the velocity gradients opposite to the flattened structure
around IRAS 4A2.


Abstract:
  We prove the existence of small amplitude time quasi-periodic solutions of
the pure gravity water waves equations with constant vorticity, for a
bidimensional fluid over a flat bottom delimited by a space periodic free
interface. Using a Nash-Moser implicit function iterative scheme we construct
traveling nonlinear waves which pass through each other slightly deforming and
retaining forever a quasiperiodic structure. These solutions exist for any
fixed value of depth and gravity and restricting the vorticity parameter to a
Borel set of asymptotically full Lebesgue measure.


Abstract:
  Quantum annealing is an emerging new platform for combinatorial optimization,
requiring an Ising model formulation for optimization problems. The formulation
can be an essential obstacle to the permeation of this innovation into broad
areas of everyday life. Our research is aimed at the proposal of a Petri net
modeling approach for an Ising model formulation. Although the proposed method
requires users to model their optimization problems with Petri nets, this
process can be carried out in a relatively straightforward manner if we know
the target problem and the simple Petri net modeling rules. With our method,
the constraints and objective functions in the target optimization problems are
represented as fundamental characteristics of Petri net models, extracted
systematically from Petri net models, and then converted into binary quadratic
nets, equivalent to Ising models. The proposed method can drastically reduce
the difficulty of the Ising model formulation.


Abstract:
  We consider the classical molecular beam epitaxy (MBE) model with logarithmic
type potential known as no-slope-selection. We employ a third order backward
differentiation (BDF3) in time with implicit treatment of the surface diffusion
term. The nonlinear term is approximated by a third order explicit
extrapolation (EP3) formula. We exhibit mild time step constraints under which
the modified energy dissipation law holds. We break the second Dahlquist
barrier and develop a new theoretical framework to prove unconditional uniform
energy boundedness with no size restrictions on the time step. This is the
first unconditional result for third order BDF methods applied to the MBE
models without introducing any stabilization terms or fictitious variables. A
novel theoretical framework is also established for the error analysis of high
order methods.


Abstract:
  In this draft paper, we introduce a novel architecture for graph networks
which is equivariant to the Euclidean group in $n$-dimensions. The model is
designed to work with graph networks in their general form and can be shown to
include particular variants as special cases. Thanks to its equivariance
properties, we expect the proposed model to be more data efficient with respect
to classical graph architectures and also intrinsically equipped with a better
inductive bias. We defer investigating this matter to future work.


Abstract:
  Let $\Sigma_{g}$ be a closed surface of genus $g\geq 2$ and $\Gamma_{g}$
denote the fundamental group of $\Sigma_{g}$. We establish a generalization of
Voiculescu's theorem on the asymptotic $*$-freeness of Haar unitary matrices
from free groups to $\Gamma_{g}$. We prove that for a random representation of
$\Gamma_{g}$ into $\mathsf{SU}(n)$, with law given by the volume form arising
from the Atiyah-Bott-Goldman symplectic form on moduli space, the expected
value of the trace of a fixed non-identity element of $\Gamma_{g}$ is bounded
as $n\to\infty$. The proof involves an interplay between Dehn's work on the
word problem in $\Gamma_{g}$ and classical invariant theory.


Abstract:
  We prove some Strichartz estimates for the massless, radial Dirac-Coulomb
equation in 3D. The main tools in our argument are the use of a "relativistic
Hankel transform" together with some precise estimates on the generalized
eigenfunctions of the Dirac-Coulomb operator.


Abstract:
  We derive $q$-versions of Green's theorem from the Leibniz rules of partial
derivatives for the $q$-deformed Euclidean space. Using these results and the
Schr\"{o}dinger equations for a $q$-deformed nonrelativistic particle, we
derive continuity equations for the probability density, the energy density,
and the momentum density of a $q$-deformed nonrelativistic particle.


Abstract:
  Given a smooth convex cone in the Euclidean $(n+1)$-space ($n\geq2$), we
consider strictly mean convex hypersurfaces with boundary which are star-shaped
with respect to the center of the cone and which meet the cone perpendicularly.
If those hypersurfaces inside the cone evolve by a class of inverse curvature
flows, then, by using the convexity of the cone in the derivation of the
gradient and H\"{o}lder estimates, we can prove that this evolution exists for
all the time and the evolving hypersurfaces converge smoothly to a piece of a
round sphere as time tends to infinity.


Abstract:
  Emitted photons stemming from the radiative recombination of electron-hole
pairs carry chemical potential in radiative energy converters. This luminescent
effect can substantially alter the local net photogeneration in near-field
thermophotovoltaic cells. Several assumptions involving the luminescent effect
are commonly made in modeling photovoltaic devices; in particular, the photon
chemical potential is assumed to be zero or a constant prescribed by the bias
voltage. The significance of photon chemical potential depends upon the emitter
temperature, the semiconductor properties, and the injection level. Hence,
these assumptions are questionable in thermophotovoltaic devices operating in
the near-field regime. In the present work, an iterative solver that combines
fluctuational electrodynamics with the drift-diffusion model is developed to
tackle the coupled photon and charge transport problem, enabling the
determination of the spatial profile of photon chemical potential beyond the
detailed balance approach. The difference between the results obtained by
allowing the photon chemical potential to vary spatially and by assuming a
constant value demonstrates the limitations of the conventional approaches.
This study is critically important for performance evaluation of near-field
thermophotovoltaic systems.


Abstract:
  We propose to use tensor diagrams and the Fomin-Pylyavskyy conjectures to
explore the connection between symbol alphabets of $n$-particle amplitudes in
planar $\mathcal{N}=4$ Yang-Mills theory and certain polytopes associated to
the Grassmannian G(4, $n$). We show how to assign a web (a planar tensor
diagram) to each facet of these polytopes. Webs with no inner loops are
associated to cluster variables (rational symbol letters). For webs with a
single inner loop we propose and explicitly evaluate an associated web series
that contains information about algebraic symbol letters. In this manner we
reproduce the results of previous analyses of $n \le 8$, and find that the
polytope $\mathcal{C}^\dagger(4,9)$ encodes all rational letters, and all
square roots of the algebraic letters, of known nine-particle amplitudes.


Abstract:
  In this study, the stability dependence of turbulent Prandtl number ($Pr_t$)
is quantified via a novel and simple analytical approach. Based on the variance
and flux budget equations, a hybrid length scale formulation is first proposed
and its functional relationships to well-known length scales are established.
Next, the ratios of these length scales are utilized to derive an explicit
relationship between $Pr_t$ and gradient Richardson number. In addition,
theoretical predictions are made for several key turbulence variables (e.g.,
dissipation rates, normalized fluxes). The results from our proposed approach
are compared against other competing formulations as well as published
datasets. Overall, the agreement between the different approaches is rather
good despite their different theoretical foundations and assumptions.


Abstract:
  The problem of estimating the angular speed of a solid body from attitude
measurements is addressed. To solve this problem, we propose an observer whose
dynamics are not constrained to evolve on any specific manifold. This
drastically simplifies the analysis of the proposed observer. Using Lyapunov
analysis, sufficient conditions for global asymptotic stability of a set
wherein the estimation error is equal to zero are established. In addition, the
proposed methodology is adapted to deal with angular speed estimation for
systems evolving on the unit circle. The approach is illustrated through
several numerical simulations.


Abstract:
  Exoplanet detection in the past decade by efforts including NASA's Kepler and
TESS missions has discovered many worlds that differ substantially from planets
in our own Solar system, including more than 400 exoplanets orbiting binary or
multi-star systems. This not only broadens our understanding of the diversity
of exoplanets, but also promotes our study of exoplanets in the complex binary
and multi-star systems and provides motivation to explore their habitability.
In this study, we analyze orbital stability of exoplanets in non-coplanar
circumbinary systems using a numerical simulation method, with which a large
number of circumbinary planet samples are generated in order to quantify the
effects of various orbital parameters on orbital stability. We also train a
machine learning model that can quickly determine the stability of the
circumbinary planetary systems. Our results indicate that larger inclinations
of the planet tend to increase the stability of its orbit, but change in the
planet's mass range between Earth and Jupiter has little effect on the
stability of the system. In addition, we find that Deep Neural Networks (DNNs)
have higher accuracy and precision than other machine learning algorithms.


Abstract:
  Let $M_n$ be a random $n\times n$ matrix with i.i.d. $\text{Bernoulli}(1/2)$
entries. We show that for fixed $k\ge 1$, \[\lim_{n\to
\infty}\frac{1}{n}\log_2\mathbb{P}[\text{corank }M_n\ge k] = -k.\]


Abstract:
  Any binary string can be associated with a unary predicate $P$ on
$\mathbb{N}$. In this paper we investigate subsets named by a predicate $P$
such that the relation $P(x+y)$ has finite VC dimension. This provides a
measure of complexity for binary strings with different properties than the
standard string complexity function (based on diversity of substrings). We
prove that strings of bounded VC dimension are meagre in the topology of the
reals, provide simple rules for bounding the VC dimension of a string, and show
that the bi-infinite strings of VC dimension $d$ are a non-sofic shift space.
Additionally we characterize the irreducible strings of low VC dimension (0,1
and 2), and provide connections to mathematical logic.


Abstract:
  The goal of this paper is extend Kottwitz's theory of $B(G)$ for global
fields. In particular, we show how to extend the definition of ``$B(G)$ with
adelic coefficients'' from tori to all connected reductive groups. As an
application, we give an explicit construction of certain transfer factors for
non-regular semisimple elements of non-quasisplit groups. This generalizes some
results of Kaletha and Taibi. These formulas are used in the stabilization of
the cohomology of Shimura and Igusa varieties.


Abstract:
  As an extension of previous ungraded work, we define a graded $p$-polar ring
to be an analog of a graded commutative ring where multiplication is only
allowed on $p$-tuples (instead of pairs) of elements of equal degree. We show
that the free affine $p$-adic group scheme functor, as well as the free formal
group functor, defined on $k$-algebras for a perfect field $k$ of
characteristic $p$, factors through $p$-polar $k$-algebras. It follows that the
same is true for any affine $p$-adic or formal group functor, in particular for
the functor of $p$-typical Witt vectors. As an application, we show that the
latter is free on the $p$-polar affine line.


Abstract:
  Bismuth ferrite is one of the most widely studied multiferroic materials
because of its large ferroelectric polarisation coexisting with magnetic order
at room temperature. Using density functional theory (DFT), we identify several
previously unknown polar and non-polar structures within the low-energy phase
space of perovskite-structure bismuth ferrite, BiFeO$_3$. Of particular
interest is a series of non-centrosymmetric structures with polarisation along
one lattice vector, combined with anti-polar distortions, reminiscent of
ferroelectric domains, along a perpendicular direction. We discuss possible
routes to stabilising the new phases using biaxial heteroepitaxial strain or
interfacial electrostatic control in heterostructures.


Abstract:
  Primordial black holes may have been produced in the early stages of the
thermal history of the Universe after cosmic inflation. If so, dark matter in
the form of elementary particles can be subsequently accreted around these
objects, in particular when it gets non-relativistic and further streams freely
in the primordial plasma. A dark matter mini-spike builds up gradually around
each black hole, with density orders of magnitude larger than the cosmological
one. We improve upon previous work by carefully inspecting the computation of
the mini-spike radial profile as a function of black hole mass, dark matter
particle mass and temperature of kinetic decoupling. We identify a phase-space
contribution that has been overlooked and that leads to changes in the final
results. We also derive complementary analytical formulae using convenient
asymptotic regimes, which allows us to bring out peculiar power-law behaviors
for which we provide tentative physical explanations.


Abstract:
  The $k$-mappability problem has two integers parameters $m$ and $k$. For
every subword of size $m$ in a text $S$, we wish to report the number of
indices in $S$ in which the word occurs with at most $k$ mismatches.
  The problem was lately tackled by Alzamel et al. For a text with constant
alphabet $\Sigma$ and $k \in O(1)$, they present an algorithm with linear space
and $O(n\log^{k+1}n)$ time. For the case in which $k = 1$ and a constant size
alphabet, a faster algorithm with linear space and $O(n\log(n)\log\log(n))$
time was presented in a 2020 paper by Alzamel et al.
  In this work, we enhance the techniques of Alzamel et al.'s 2020 paper to
obtain an algorithm with linear space and $O(n \log(n))$ time for $k = 1$. Our
algorithm removes the constraint of the alphabet being of constant size. We
also present linear algorithms for the case of $k=1$, $|\Sigma|\in O(1)$ and
$m=\Omega(\sqrt{n})$.


Abstract:
  In the past few years, the detection of gravitational waves from compact
binary coalescences with the Advanced LIGO and Advanced Virgo detectors has
become routine. Future observatories will detect even larger numbers of
gravitational-wave signals, which will also spend a longer time in the
detectors' sensitive band. This will eventually lead to overlapping signals,
especially in the case of Einstein Telescope (ET) and Cosmic Explorer (CE).
Using realistic distributions for the merger rate as a function of redshift as
well as for component masses in binary neutron star and binary black hole
coalescences, we map out how often signal overlaps of various types will occur
in an ET-CE network over the course of a year. We find that a binary neutron
star signal will typically have tens of overlapping binary black hole and
binary neutron star signals. Moreover, it will happen up to tens of thousands
of times per year that two signals will have their end times within seconds of
each other. In order to understand to what extent this would lead to
measurement biases with current parameter estimation methodology, we perform
injection studies with overlapping signals from binary black hole and/or binary
neutron star coalescences. Varying the signal-to-noise ratios, the durations of
overlap, and the kinds of overlapping signals, we find that in most scenarios
the intrinsic parameters can be recovered with negligible bias. However, biases
do occur for a short binary black hole or a quieter binary neutron star signal
overlapping with a long and louder binary neutron star event when the merger
times are sufficiently close. Hence our studies show where improvements are
required to ensure reliable estimation of source parameters for all detected
compact binary signals as we go from second-generation to third-generation
detectors.


Abstract:
  Computer vision (CV) techniques try to mimic human capabilities of visual
perception to support labor-intensive and time-consuming tasks like the
recognition and localization of critical objects. Nowadays, CV increasingly
relies on artificial intelligence (AI) to automatically extract useful
information from images that can be utilized for decision support and business
process automation. However, the focus of extant research is often exclusively
on technical aspects when designing AI-based CV systems while neglecting
socio-technical facets, such as trust, control, and autonomy. For this purpose,
we consider the design of such systems from a hybrid intelligence (HI)
perspective and aim to derive prescriptive design knowledge for CV-based HI
systems. We apply a reflective, practice-inspired design science approach and
accumulate design knowledge from six comprehensive CV projects. As a result, we
identify four design-related mechanisms (i.e., automation, signaling,
modification, and collaboration) that inform our derived meta-requirements and
design principles. This can serve as a basis for further socio-technical
research on CV-based HI systems.


Abstract:
  Plasmon-free surface-enhanced Raman scattering (SERS) substrates have
attracted tremendous attention for their abundant sources, excellent chemical
stability, superior biocompatibility, good signal uniformity, and unique
selectivity to target molecules. Recently, researchers have made great progress
in fabricating novel plasmon-free SERS substrates and exploring new enhancement
strategies to improve their sensitivity. This review summarizes the recent
developments of plasmon-free SERS substrates and specially focuses on the
enhancement mechanisms and strategies. Furthermore, the promising applications
of plasmon-free SERS substrates in biomedical diagnosis, metal ions and organic
pollutants sensing, chemical and biochemical reactions monitoring, and
photoelectric characterization are introduced. Finally, current challenges and
future research opportunities in plasmon-free SERS substrates are briefly
discussed.


Abstract:
  This paper studies bandit algorithms under data poisoning attacks in a
bounded reward setting. We consider a strong attacker model in which the
attacker can observe both the selected actions and their corresponding rewards,
and can contaminate the rewards with additive noise. We show that \emph{any}
bandit algorithm with regret $O(\log T)$ can be forced to suffer a regret
$\Omega(T)$ with an expected amount of contamination $O(\log T)$. This amount
of contamination is also necessary, as we prove that there exists an $O(\log
T)$ regret bandit algorithm, specifically the classical UCB, that requires
$\Omega(\log T)$ amount of contamination to suffer regret $\Omega(T)$. To
combat such poising attacks, our second main contribution is to propose a novel
algorithm, Secure-UCB, which uses limited \emph{verification} to access a
limited number of uncontaminated rewards. We show that with $O(\log T)$
expected number of verifications, Secure-UCB can restore the order optimal
$O(\log T)$ regret \emph{irrespective of the amount of contamination} used by
the attacker. Finally, we prove that for any bandit algorithm, this number of
verifications $O(\log T)$ is necessary to recover the order-optimal regret. We
can then conclude that Secure-UCB is order-optimal in terms of both the
expected regret and the expected number of verifications, and can save
stochastic bandits from any data poisoning attack.


Abstract:
  The close-packed AB$_2$ structures called Laves phases constitute the largest
group of intermetallic compounds. In this paper we computationally investigated
the pseudo-binary Laves phase system Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$
spanning between the YFe$_2$, YCo$_2$, GdFe$_2$, and GdCo$_2$ vertices. While
the vast majority of the Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$ phase diagram is
the ferrimagnetic phase, YCo$_2$ along with a narrow range of concentrations
around it is the paramagnetic phase. We presented results obtained by Monte
Carlo simulations of the Heisenberg model with parameters derived from
first-principles calculations. For calculations, we used the Uppsala atomistic
spin dynamics (UppASD) code together with the spin-polarized relativistic
Korringa-Kohn-Rostoker (SPR-KKR) code. From first principles we calculated the
magnetic moments and exchange integrals for the considered pseudo-binary
system, together with spin-polarized densities of states for boundary
compositions. Furthermore, we showed how the compensation point with the
effective zero total moment depends on the concentration in the considered
ferrimagnetic phases. However, the main result of our study was the
determination of the Curie temperature dependence for the system
Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$. Except for the paramagnetic region
around YCo$_2$, the predicted temperatures were in good qualitative and
quantitative agreement with experimental results, which confirmed the ability
of the method to predict magnetic transition temperatures for systems
containing up to three different magnetic elements (Fe, Co, and Gd)
simultaneously. For the Y(Fe$_{1-y}$Co$_y$)$_2$ and Gd(Fe$_{1-y}$Co$_y$)$_2$
systems our calculations matched the experimentally-confirmed
Slater-Pauling-like behavior of T$_C$ dependence on the Co concentration.


Abstract:
  Quantum steering refers to correlations that can be classified as
intermediate between entanglement and Bell nonlocality. Every state exhibiting
Bell nonlocality exhibits also quantum steering and every state exhibiting
quantum steering is also entangled. In low dimensional cases similar
hierarchical relations have been observed between the temporal counterparts of
these correlations. Here, we study the hierarchy of such temporal correlations
for a general multilevel quantum system. We demonstrate that the same hierarchy
holds for two definitions of state over time. In order to compare different
types of temporal correlations, we show that temporal counterparts of Bell
nonlocality and entanglement can be quantified with a temporal nonlocality
robustness and temporal entanglement robustness. Our numerical result reveal
that in contrast to temporal steering, for temporal nonlocality to manifest
itself we require the initial state not to be in a completely mixed state.


Abstract:
  The development of respiratory failure is common among patients in intensive
care units (ICU). Large data quantities from ICU patient monitoring systems
make timely and comprehensive analysis by clinicians difficult but are ideal
for automatic processing by machine learning algorithms. Early prediction of
respiratory system failure could alert clinicians to patients at risk of
respiratory failure and allow for early patient reassessment and treatment
adjustment. We propose an early warning system that predicts moderate/severe
respiratory failure up to 8 hours in advance. Our system was trained on
HiRID-II, a data-set containing more than 60,000 admissions to a tertiary care
ICU. An alarm is typically triggered several hours before the beginning of
respiratory failure. Our system outperforms a clinical baseline mimicking
traditional clinical decision-making based on pulse-oximetric oxygen saturation
and the fraction of inspired oxygen. To provide model introspection and
diagnostics, we developed an easy-to-use web browser-based system to explore
model input data and predictions visually.


Abstract:
  Herein we shall consider Lorentz boosts and Wigner rotations from a
(complexified) quaternionic point of view. We shall demonstrate that for a
suitably defined self-adjoint complex quaternionic 4-velocity, pure Lorentz
boosts can be phrased in terms of the quaternion square root of the relative
4-velocity connecting the two inertial frames. Straightforward computations
then lead to quite explicit and relatively simple algebraic formulae for the
composition of 4-velocities and the Wigner angle. We subsequently relate the
Wigner rotation to the generic non-associativity of the composition of three
4-velocities, and develop a necessary and sufficient condition for
associativity to hold. Finally, we relate the composition of 4-velocities to a
specific implementation of the Baker-Campbell-Hausdorff theorem. As compared to
ordinary 4x4 Lorentz transformations, the use of self-adjoint complexified
quaternions leads, from a computational view, to storage savings and more rapid
computations, and from a pedagogical view to to relatively simple and explicit
formulae.


Abstract:
  C. elegans shows chemotaxis using klinokinesis where the worm senses the
concentration based on a single concentration sensor to compute the
concentration gradient to perform foraging through gradient ascent/descent
towards the target concentration followed by contour tracking. The biomimetic
implementation requires complex neurons with multiple ion channel dynamics as
well as interneurons for control. While this is a key capability of autonomous
robots, its implementation on energy-efficient neuromorphic hardware like
Intel's Loihi requires adaptation of the network to hardware-specific
constraints, which has not been achieved. In this paper, we demonstrate the
adaptation of chemotaxis based on klinokinesis to Loihi by implementing
necessary neuronal dynamics with only LIF neurons as well as a complete
spike-based implementation of all functions e.g. Heaviside function and
subtractions. Our results show that Loihi implementation is equivalent to the
software counterpart on Python in terms of performance - both during foraging
and contour tracking. The Loihi results are also resilient in noisy
environments. Thus, we demonstrate a successful adaptation of chemotaxis on
Loihi - which can now be combined with the rich array of SNN blocks for SNN
based complex robotic control.


Abstract:
  Deep neural networks (DNNs) have shown to perform very well on large scale
object recognition problems and lead to widespread use for real-world
applications, including situations where DNN are implemented as "black boxes".
A promising approach to secure their use is to accept decisions that are likely
to be correct while discarding the others. In this work, we propose DOCTOR, a
simple method that aims to identify whether the prediction of a DNN classifier
should (or should not) be trusted so that, consequently, it would be possible
to accept it or to reject it. Two scenarios are investigated: Totally Black Box
(TBB) where only the soft-predictions are available and Partially Black Box
(PBB) where gradient-propagation to perform input pre-processing is allowed.
Empirically, we show that DOCTOR outperforms all state-of-the-art methods on
various well-known images and sentiment analysis datasets. In particular, we
observe a reduction of up to $4\%$ of the false rejection rate (FRR) in the PBB
scenario. DOCTOR can be applied to any pre-trained model, it does not require
prior information about the underlying dataset and is as simple as the simplest
available methods in the literature.


Abstract:
  A search is presented for a heavy vector resonance decaying into a Z boson
and the standard model Higgs boson, where the Z boson is identified through its
leptonic decays to electrons, muons, or neutrinos, and the Higgs boson is
identified through its hadronic decays. The search is performed in a
Lorentz-boosted regime and is based on data collected from 2016 to 2018 at the
CERN LHC, corresponding to an integrated luminosity of 137 fb$^{-1}$. Upper
limits are derived on the production of a narrow heavy resonance Z', and a mass
below 3.5 and 3.7 TeV is excluded at 95% confidence level in models where the
heavy vector boson couples exclusively to fermions and to bosons, respectively.
These are the most stringent limits placed on the Heavy Vector Triplet Z' model
to date. If the heavy vector boson couples exclusively to standard model
bosons, upper limits on the product of the cross section and branching fraction
are set between 23 and 0.3 fb for a Z' mass between 0.8 and 4.6 TeV,
respectively. This is the first limit set on a heavy vector boson coupling
exclusively to standard model bosons in its production and decay.


Abstract:
  Obtaining labeled data for machine learning tasks can be prohibitively
expensive. Active learning mitigates this issue by exploring the unlabeled data
space and prioritizing the selection of data that can best improve the model
performance. A common approach to active learning is to pick a small sample of
data for which the model is most uncertain. In this paper, we explore the
efficacy of Bayesian neural networks for active learning, which naturally
models uncertainty by learning distribution over the weights of neural
networks. By performing a comprehensive set of experiments, we show that
Bayesian neural networks are more efficient than ensemble based techniques in
capturing uncertainty. Our findings also reveal some key drawbacks of the
ensemble techniques, which was recently shown to be more effective than Monte
Carlo dropouts.


Abstract:
  The advent of automated vehicles operating at SAE levels 4 and 5 poses high
fault tolerance demands for all functions contributing to the driving task. At
the actuator level, fault-tolerant vehicle motion control, which exploits
functional redundancies among the actuators, is one means to achieve the
required degree of fault tolerance. Therefore, we give a comprehensive overview
of the state of the art in actuator fault-tolerant vehicle motion control with
a focus on drive, brake, and steering degradations, as well as tire blowouts.
This review shows that actuator fault-tolerant vehicle motion is a widely
studied field; yet, the presented approaches differ with respect to many
aspects. To provide a starting point for future research, we survey the
employed actuator topologies, the tolerated degradations, the presented control
approaches, as well as the experiments conducted for validation. Overall, and
despite the large number of different approaches, the covered literature
reveals the potential of increasing fault tolerance by fault-tolerant vehicle
motion control. Thus, besides developing novel approaches or demonstrating
real-time applicability, future research should aim at investigating
limitations and enabling comparison of fault-tolerant motion control approaches
in order to allow for a thorough safety argumentation.


Abstract:
  The odd isotopologues of ytterbium monohydroxide, $^{171,173}$YbOH, have been
identified as promising molecules in which to measure parity (P) and time
reversal (T) violating physics. Here we characterize the
$\tilde{A}^{2}\Pi_{1/2}(0,0,0)-\tilde{X}^2\Sigma^+(0,0,0)$ band near 577 nm for
these odd isotopologues. Both laser-induced fluorescence (LIF) excitation
spectra of a supersonic molecular beam sample and absorption spectra of a
cryogenic buffer-gas cooled sample were recorded. Additionally, a novel
spectroscopic technique based on laser-enhanced chemical reactions is
demonstrated and utilized in the absorption measurements. This technique is
especially powerful for disentangling congested spectra. An effective
Hamiltonian model is used to extract the fine and hyperfine parameters for the
$\tilde{A}^{2}\Pi_{1/2}(0,0,0)$ and $\tilde{X}^2\Sigma^+(0,0,0)$ states. A
comparison of the determined $\tilde{X}^2\Sigma^+(0,0,0)$ hyperfine parameters
with recently predicted values (M. Denis, et al., J. Chem. Phys. $\bf{152}$,
084303 (2020), K. Gaul and R. Berger, Phys. Rev. A $\bf{101}$, 012508 (2020),
J. Liu et al., J. Chem. Phys. $\bf{154}$, 064110 (2021)) is made. The measured
hyperfine parameters provide experimental confirmation of the computational
methods used to compute the P,T-violating coupling constants $W_d$ and $W_M$,
which correlate P,T-violating physics to P,T-violating energy shifts in the
molecule. The dependence of the fine and hyperfine parameters of the
$\tilde{A}^{2}\Pi_{1/2}(0,0,0)$ and $\tilde{X}^2\Sigma^+(0,0,0)$ states for all
isotopologues of YbOH are discussed and a comparison to isoelectronic YbF is
made.


Abstract:
  The two-dimensional Helmholtz equation separates in elliptic coordinates
based on two distinct foci, a limit case of which includes polar coordinate
systems when the two foci coalesce. This equation is invariant under the
Euclidean group of translations and orthogonal transformations; we replace the
latter by the discrete dihedral group of N discrete rotations and reflections.
The separation of variables in polar and elliptic coordinates is then used to
define discrete Bessel and Mathieu functions, as approximants to the well-known
continuous Bessel and Mathieu functions, as N-point Fourier transforms
approximate the Fourier transform over the circle, with integrals replaced by
finite sums. We find that these 'discrete' functions approximate the numerical
values of their continuous counterparts very closely and preserve some key
special function relations.