Abstract:
  Recent observations revealed a coherence between the spin vector of a galaxy
and the orbital motion of its neighbors. We refer to the phenomenon as "the
spin$-$orbit alignment (SOA)" and explore its physical origin via the
IllustrisTNG simulation. This is the first study to utilize a cosmological
hydrodynamic simulation to investigate the SOA of galaxy pairs. In particular,
we identify paired galaxies at $z = 0$ having the nearest neighbor with mass
ratios from 1/10 to 10 and calculate the spin$-$orbit angle for each pair. Our
results are as follows. (a) There exists a clear preference for prograde
orientations (i.e., SOA) for galaxy pairs, qualitatively consistent with
observations. (b) The SOA is significant for both baryonic and dark matter
spins, being the strongest for gas and the weakest for dark matter. (c) The SOA
is stronger for less massive targets and for targets having closer neighbors.
(d) The SOA strengthens for galaxies in low-density regions, and the signal is
dominated by central$-$satellite pairs in low-mass halos. (e) There is an
explicit dependence of the SOA on the duration of interaction with its current
neighbor. Taken together, we propose that the SOA witnessed at $z = 0$ has been
developed mainly by interactions with a neighbor for an extended period of
time, rather than tidal torque from the ambient large-scale structure.


Abstract:
  It is shown that the physical damage resulting from severe weather phenomenon
such as hurricanes and tornadoes encapsulated in various scales such as
Saffir-Simpson, TORRO and the Enhanced Fujita Scale, and measured by the Power
Dissipation Index can be predicted using the concept of the Peak Virtual Power.
This corresponds to the maximum rate of Entropy production, which is considered
to be the basic physical principle underlying the damage.


Abstract:
  We provide some basic and sensible definitions of different types of digital
twins and recommendations on when and how to use them. Following up on our
recent publication of the Learning Causal Digital Twin, this article reports on
a stochastic formulation and solution of the problem. Structural Vector
Autoregressive Model (SVAR) for Causal estimation is recast as a state-space
model. Kalman filter (and smoother) is then employed to estimate causal factors
in a system of connected machine bearings. The previous neural network
algorithm and Kalman Smoother produced very similar results; however, Kalman
Filter/Smoother may show better performance for noisy data from industrial IoT
sources.


Abstract:
  We propose a design pattern for tackling text ranking problems, dubbed
"Expando-Mono-Duo", that has been empirically validated for a number of ad hoc
retrieval tasks in different domains. At the core, our design relies on
pretrained sequence-to-sequence models within a standard multi-stage ranking
architecture. "Expando" refers to the use of document expansion techniques to
enrich keyword representations of texts prior to inverted indexing. "Mono" and
"Duo" refer to components in a reranking pipeline based on a pointwise model
and a pairwise model that rerank initial candidates retrieved using keyword
search. We present experimental results from the MS MARCO passage and document
ranking tasks, the TREC 2020 Deep Learning Track, and the TREC-COVID challenge
that validate our design. In all these tasks, we achieve effectiveness that is
at or near the state of the art, in some cases using a zero-shot approach that
does not exploit any training data from the target task. To support
replicability, implementations of our design pattern are open-sourced in the
Pyserini IR toolkit and PyGaggle neural reranking library.


Abstract:
  We investigate the cyclotron dynamics of Bose-Einstein condensate (BEC) in a
quadruple-well potential with synthetic gauge fields. We use laser-assisted
tunneling to generate large tunable effective magnetic fields for BEC. The mean
position of BEC follows an orbit that simulated the cyclotron orbits of charged
particles in a magnetic field. In the absence of atomic interaction, atom
dynamics may exhibit periodic or quasi-periodic cyclotron orbits. In the
presence of atomic interaction, the system may exhibit self-trapping, which
depends on synthetic gauge fields and atomic interaction strength. In
particular, the competition between synthetic gauge fields and atomic
interaction leads to the generation of several discontinuous parameter windows
for the transition to self-trapping, which is obviously different from that
without synthetic gauge fields.


Abstract:
  Training machine learning (ML) models typically involves expensive iterative
optimization. Once the model's final parameters are released, there is
currently no mechanism for the entity which trained the model to prove that
these parameters were indeed the result of this optimization procedure. Such a
mechanism would support security of ML applications in several ways. For
instance, it would simplify ownership resolution when multiple parties contest
ownership of a specific model. It would also facilitate the distributed
training across untrusted workers where Byzantine workers might otherwise mount
a denial-of-service by returning incorrect model updates.
  In this paper, we remediate this problem by introducing the concept of
proof-of-learning in ML. Inspired by research on both proof-of-work and
verified computations, we observe how a seminal training algorithm, stochastic
gradient descent, accumulates secret information due to its stochasticity. This
produces a natural construction for a proof-of-learning which demonstrates that
a party has expended the compute require to obtain a set of model parameters
correctly. In particular, our analyses and experiments show that an adversary
seeking to illegitimately manufacture a proof-of-learning needs to perform *at
least* as much work than is needed for gradient descent itself.
  We also instantiate a concrete proof-of-learning mechanism in both of the
scenarios described above. In model ownership resolution, it protects the
intellectual property of models released publicly. In distributed training, it
preserves availability of the training procedure. Our empirical evaluation
validates that our proof-of-learning mechanism is robust to variance induced by
the hardware (ML accelerators) and software stacks.


Abstract:
  Modern data analytics take advantage of ensemble learning and transfer
learning approaches to tackle some of the most relevant issues in data
analysis, such as lack of labeled data to use to train the analysis models,
sparsity of the information, and unbalanced distributions of the records.
Nonetheless, when applied to multimodal datasets (i.e., datasets acquired by
means of multiple sensing techniques or strategies), the state-of-theart
methods for ensemble learning and transfer learning might show some
limitations. In fact, in multimodal data analysis, not all observations would
show the same level of reliability or information quality, nor an homogeneous
distribution of errors and uncertainties. This condition might undermine the
classic assumptions ensemble learning and transfer learning methods rely on. In
this work, we propose an adaptive approach for dimensionality reduction to
overcome this issue. By means of a graph theory-based approach, the most
relevant features across variable size subsets of the considered datasets are
identified. This information is then used to set-up ensemble learning and
transfer learning architectures. We test our approach on multimodal datasets
acquired in diverse research fields (remote sensing, brain-computer interfaces,
photovoltaic energy). Experimental results show the validity and the robustness
of our approach, able to outperform state-of-the-art techniques.


Abstract:
  Sonorant sounds are characterized by regions with prominent formant
structure, high energy and high degree of periodicity. In this work, the
vocal-tract system, excitation source and suprasegmental features derived from
the speech signal are analyzed to measure the sonority information present in
each of them. Vocal-tract system information is extracted from the Hilbert
envelope of numerator of group delay function. It is derived from zero time
windowed speech signal that provides better resolution of the formants. A
five-dimensional feature set is computed from the estimated formants to measure
the prominence of the spectral peaks. A feature representing strength of
excitation is derived from the Hilbert envelope of linear prediction residual,
which represents the source information. Correlation of speech over ten
consecutive pitch periods is used as the suprasegmental feature representing
periodicity information. The combination of evidences from the three different
aspects of speech provides better discrimination among different sonorant
classes, compared to the baseline MFCC features. The usefulness of the proposed
sonority feature is demonstrated in the tasks of phoneme recognition and
sonorant classification.


Abstract:
  We introduce an algorithm for a search of extremal fractal curves in large
curve classes. It heavily uses SAT-solvers~ -- heuristic algorithms that find
models for CNF boolean formulas. Our algorithm was implemented and applied to
the search of fractal surjective curves $\gamma\colon[0,1]\to[0,1]^d$ with
minimal dilation $$
\sup_{t_1<t_2}\frac{\|\gamma(t_2)-\gamma(t_1)\|^d}{t_2-t_1}. $$
  We report new results of that search in the case of Euclidean norm. We have
found a new curve that we call "YE", a self-similar (monofractal) plane curve
of genus $5\times 5$ with dilation $5\frac{43}{73}=5.5890\ldots$. In dimension
$3$ we have found facet-gated bifractals (that we call "Spring") of genus
$2\times2\times 2$ with dilation $<17$. In dimension $4$ we obtained that there
is a curve with dilation $<62$.
  Some lower bounds on the dilation for wider classes of cubically decomposable
curves are proved.


Abstract:
  Graph neural networks (GNNs) have been proposed for a wide range of
graph-related learning tasks. In particular, in recent years, an increasing
number of GNN systems were applied to predict molecular properties. However, a
direct impediment is to select appropriate hyperparameters to achieve
satisfactory performance with lower computational cost. Meanwhile, many
molecular datasets are far smaller than many other datasets in typical deep
learning applications. Most hyperparameter optimization (HPO) methods have not
been explored in terms of their efficiencies on such small datasets in the
molecular domain. In this paper, we conducted a theoretical analysis of common
and specific features for two state-of-the-art and popular algorithms for HPO:
TPE and CMA-ES, and we compared them with random search (RS), which is used as
a baseline. Experimental studies are carried out on several benchmarks in
MoleculeNet, from different perspectives to investigate the impact of RS, TPE,
and CMA-ES on HPO of GNNs for molecular property prediction. In our
experiments, we concluded that RS, TPE, and CMA-ES have their individual
advantages in tackling different specific molecular problems. Finally, we
believe our work will motivate further research on GNN as applied to molecular
machine learning problems in chemistry and materials sciences.


Abstract:
  We study the moments $M_k(T;\alpha) = \int_T^{2T} |\zeta(s,\alpha)|^{2k}\,dt$
of the Hurwitz zeta function $\zeta(s,\alpha)$ on the critical line, $s = 1/2 +
it$ with a rational shift $\alpha \in \mathbb Q$. We conjecture, in analogy
with the Riemann zeta function, that $M_k(T;\alpha) \sim c_k(\alpha) T (\log
T)^{k^2}$ . Using heuristics from analytic number theory and random matrix
theory, we conjecturally compute $c_k(\alpha)$. In the process, we investigate
moments of products of Dirichlet $L$-functions on the critical line. We prove
our conjectures for the cases $k = 1,2$.


Abstract:
  We theoretically investigate the phase and voltage correlation dynamics,
which includes both the deterministic contribution and stochastic fluctuations,
under a current noise generated by a resistor including thermal and quantum
fluctuations in a resistively and capacitively shunted Josephson junction. An
external current is found to shift and intensify the deterministic
contributions in phase and voltage. In addition to effects of external current,
we observe the relaxation of autocorrelation functions of phase and voltage,
which includes the variances due to the current noise, to finite values in the
long-time limit. In particular, we find that the asymptotic correlations depend
on the resistance as a consequence of quantum effects. We also find an earlier
decay of coherence at a higher temperature in which thermal fluctuations
dominate over quantum ones. These theoretical predictions can be tested in the
next future experiments.


Abstract:
  In this paper, we consider downlink low Earth orbit (LEO) satellite
communication systems where multiple LEO satellites are uniformly distributed
over a sphere at a certain altitude according to a homogeneous binomial point
process (BPP). Based on the characteristics of the BPP, we analyze the distance
distributions and the distribution cases for the serving satellite. We
analytically derive the exact outage probability, and the approximated
expression is obtained using the Poisson limit theorem. With these derived
expressions, the system throughput maximization problem is formulated under the
satellite-visibility and outage constraints. To solve this problem, we
reformulate it with bounded feasible sets and propose an iterative algorithm to
obtain near-optimal solutions. Simulation results perfectly match the derived
exact expressions for the outage probability and system throughput. The
analytical results of the approximated expressions are fairly close to those of
the exact ones. It is also shown that the proposed algorithm for the throughput
maximization is very close to the optimal performance obtained by a
two-dimensional exhaustive search.


Abstract:
  We study the problem of performing automated experiment design for drug
screening through Bayesian inference and optimisation. In particular, we
compare and contrast the behaviour of linear-Gaussian models and Gaussian
processes, when used in conjunction with upper confidence bound algorithms,
Thompson sampling, or bounded horizon tree search. We show that non-myopic
sophisticated exploration techniques using sparse tree search have a distinct
advantage over methods such as Thompson sampling or upper confidence bounds in
this setting. We demonstrate the significant superiority of the approach over
existing and synthetic datasets of drug toxicity.


Abstract:
  Neural network architectures are achieving superhuman performance on an
expanding range of tasks. To effectively and safely deploy these systems, their
decision-making must be understandable to a wide range of stakeholders. Methods
to explain AI have been proposed to answer this challenge, but a lack of theory
impedes the development of systematic abstractions which are necessary for
cumulative knowledge gains. We propose Bayesian Teaching as a framework for
unifying explainable AI (XAI) by integrating machine learning and human
learning. Bayesian Teaching formalizes explanation as a communication act of an
explainer to shift the beliefs of an explainee. This formalization decomposes
any XAI method into four components: (1) the inference to be explained, (2) the
explanatory medium, (3) the explainee model, and (4) the explainer model. The
abstraction afforded by Bayesian Teaching to decompose any XAI method
elucidates the invariances among them. The decomposition of XAI systems enables
modular validation, as each of the first three components listed can be tested
semi-independently. This decomposition also promotes generalization through
recombination of components from different XAI systems, which facilitates the
generation of novel variants. These new variants need not be evaluated one by
one provided that each component has been validated, leading to an exponential
decrease in development time. Finally, by making the goal of explanation
explicit, Bayesian Teaching helps developers to assess how suitable an XAI
system is for its intended real-world use case. Thus, Bayesian Teaching
provides a theoretical framework that encourages systematic, scientific
investigation of XAI.


Abstract:
  The superconformal index of the $\mathcal{N}=4$ $SU(N)$ supersymmetric
Yang-Mills theory counts the 1/16-BPS states in this theory, and has been used
via the AdS/CFT correspondence to count black hole microstates of 1/16-BPS
black holes. On one hand, this index may be related to the Euclidean partition
function of the theory on $S^3\times S^1$ with complex chemical potentials,
which maps by the AdS/CFT correspondence to a sum over Euclidean gravity
solutions. On the other hand, the index may be expressed as a sum over
solutions to Bethe Ansatz Equations (BAEs). We show that the known solutions to
the BAEs that have a good large $N$ limit, for the case of equal chemical
potentials for the two angular momenta, have a one-to-one mapping to (complex)
Euclidean black hole solutions on the gravity side. This mapping captures both
the leading contribution from the classical gravity action (of order $N^2$), as
well as non-perturbative corrections in $1/N$, which on the gravity side are
related to wrapped D3-branes. Some of the BA solutions map to orbifolds of the
standard Euclidean black hole solutions (that obey exactly the same boundary
conditions as the other solutions). A priori there are many more gravitational
solutions than Bethe Ansatz solutions, but we show that by considering the
non-perturbative effects, the extra solutions are ruled out, leading to a
precise match between the solutions on both sides.


Abstract:
  Crowdsourced testing is increasingly dominant in mobile application (app)
testing, but it is a great burden for app developers to inspect the incredible
number of test reports. Many researches have been proposed to deal with test
reports based only on texts or additionally simple image features. However, in
mobile app testing, texts contained in test reports are condensed and the
information is inadequate. Many screenshots are included as complements that
contain much richer information beyond texts. This trend motivates us to
prioritize crowdsourced test reports based on a deep screenshot understanding.
  In this paper, we present a novel crowdsourced test report prioritization
approach, namely DeepPrior. We first represent the crowdsourced test reports
with a novelly introduced feature, namely DeepFeature, that includes all the
widgets along with their texts, coordinates, types, and even intents based on
the deep analysis of the app screenshots, and the textual descriptions in the
crowdsourced test reports. DeepFeature includes the Bug Feature, which directly
describes the bugs, and the Context Feature, which depicts the thorough context
of the bug. The similarity of the DeepFeature is used to represent the test
reports' similarity and prioritize the crowdsourced test reports. We formally
define the similarity as DeepSimilarity. We also conduct an empirical
experiment to evaluate the effectiveness of the proposed technique with a large
dataset group. The results show that DeepPrior is promising, and it outperforms
the state-of-the-art approach with less than half the overhead.


Abstract:
  We introduce a notion of the \emph{crux} of a graph $G$, measuring the order
of a smallest dense subgraph in $G$. This simple-looking notion leads to some
generalisations of known results about cycles, offering an interesting paradigm
of `replacing average degree by crux'. In particular, we prove that
\emph{every} graph contains a cycle of length linear in its crux.
  Long proved that every subgraph of a hypercube $Q^m$ (resp. discrete torus
$C_3^m$) with average degree $d$ contains a path of length $2^{d/2}$ (resp.
$2^{d/4}$), and conjectured that there should be a path of length $2^{d}-1$
(resp. $3^{d/2}-1$). As a corollary of our result, together with isoperimetric
inequalities, we close these exponential gaps giving asymptotically optimal
bounds on long paths in hypercubes, discrete tori, and more generally Hamming
graphs.
  We also consider random subgraphs of $C_4$-free graphs and hypercubes,
proving near optimal bounds on lengths of long cycles.


Abstract:
  We perform a detailed computational study of the recently introduced Sombor
indices on random graphs. Specifically, we apply Sombor indices on three models
of random graphs: Erd\"os-R\'enyi graphs, random geometric graphs, and
bipartite random graphs. Within a statistical random matrix theory approach, we
show that the average values of Sombor indices, normalized to the order of the
graph, scale with the graph average degree. Moreover, we discuss the
application of average Sombor indices as complexity measures of random graphs
and, as a consequence, we show that selected normalized Sombor indices are
highly correlated with the Shannon entropy of the eigenvectors of the graph
adjacency matrix.


Abstract:
  It has been known that the Hosoya index of caterpillar graph can be
calculated as the numerator of the simple continued fraction. Recently, the
author \cite{Komatsu2020} introduces a more general graph called
caterpillar-bond graph and shows that its Hosoya index can be calculated as the
numerator of the general continued fraction.
  In this paper, we show how the Hosoya index of the graph with non-uniform
ring structure can be calculated from the negative continued fraction. We also
give the relation between some radial graphs and multidimensional continued
fractions in the sense of the Hosoya index.


Abstract:
  Bilingual English speakers speak English as one of their languages. Their
English is of a non-native kind, and their conversations are of a code-mixed
fashion. The intelligibility of a bilingual text-to-speech (TTS) system for
such non-native English speakers depends on a lexicon that captures the phoneme
sequence used by non-native speakers. However, due to the lack of non-native
English lexicon, existing bilingual TTS systems employ native English lexicons
that are widely available, in addition to their native language lexicon. Due to
the inconsistency between the non-native English pronunciation in the audio and
native English lexicon in the text, the intelligibility of synthesized speech
in such TTS systems is significantly reduced.
  This paper is motivated by the knowledge that the native language of the
speaker highly influences non-native English pronunciation. We propose a
generic approach to obtain rules based on letter to phoneme alignment to map
native English lexicon to their non-native version. The effectiveness of such
mapping is studied by comparing bilingual (Indian English and Hindi) TTS
systems trained with and without the proposed rules. The subjective evaluation
shows that the bilingual TTS system trained with the proposed non-native
English lexicon rules obtains a 6% absolute improvement in preference.


Abstract:
  We solve the equations of two-dimensional hydrodynamics describing a
circumbinary disk accreting onto an eccentric, equal-mass binary. We compute
the time rate of change of the binary semimajor axis $a$ and eccentricity $e$
over a continuous range of eccentricities spanning $e=0$ to $e=0.9$. We find
that binaries with initial eccentricities $e_0 \lesssim 0.1$ tend to $e=0$,
where the binary semimajor axis expands. All others are attracted to $e \approx
0.4$, where the binary semimajor axis decays. The $e \approx 0.4$ attractor is
caused by a rapid change in the disk response from a nearly origin-symmetric
state to a precessing asymmetric state. The state change causes the time rates
of change $\dot{a}$ and $\dot{e}$ to steeply change sign at the same critical
eccentricity resulting in an attracting solution where $\dot{a} = \dot{e} = 0$.
This does not, however, result in a stalled, eccentric binary. The
finite-transition time between disk states causes the binary eccentricity to
evolve beyond the attracting eccentricity in both directions resulting in
oscillating orbital parameters and a drift of the semimajor axis. For the
chosen disk parameters, binaries with $e_0 \gtrsim 0.1$ evolve toward and then
oscillate around $e \approx 0.4$ where they shrink in semimajor axis. Because
unequal mass binaries grow toward equal mass through preferential accretion,
our results are applicable to a wide range of initial binary mass ratios.
Hence, these findings merit further investigations of this disk transition;
understanding its dependence on disk parameters is vital for determining the
fate of binaries undergoing orbital evolution with a circumbinary disk.


Abstract:
  This paper is, in a first stage, devoted to establish a
topological--algebraic characterization of the principal component,
$\mathcal{U}^0 (M)$, of the set of unitary elements, $\mathcal{U} (M)$, in a
unital JB$^*$-algebra $M$. We arrive to the conclusion that, as in the case of
unital C$^*$-algebras, $$\begin{aligned}\mathcal{U}^0(M) &=
M^{-1}_{\textbf{1}}\cap\mathcal{U} (M) =\left\lbrace U_{e^{i h_n}}\cdots
U_{e^{i h_1}}(\textbf{1}) \colon \begin{array}{c}
  n\in \mathbb{N}, \ h_j\in M_{sa}
  \forall\ 1\leq j \leq n
  \end{array}
  \right\rbrace \end{aligned}$$ is analytically arcwise connected. Our second
goal is to provide a complete description of the surjective isometries between
the principal components of two unital JB$^*$-algebras $M$ and $N$. Contrary to
the case of unital C$^*$-algebras, we shall deduce the existence of connected
components in $\mathcal{U} (M)$ which are not isometric as metric spaces. We
shall also establish necessary and sufficient conditions to guarantee that a
surjective isometry $\Delta: \mathcal{U}(M)\to \mathcal{U} (N)$ admits an
extension to a surjective linear isometry between $M$ and $N$, a conclusion
which is not always true. Among the consequences it is proved that $M$ and $N$
are Jordan $^*$-isomorphic if, and only if, their principal components are
isometric as metric spaces if, and only if, there exists a surjective isometry
$\Delta: \mathcal{U}(M)\to \mathcal{U}(N)$ mapping the unit of $M$ to an
element in $\mathcal{U}^0(N)$. These results provide an extension to the
setting of unital JB$^*$-algebras of the results obtained by O. Hatori for
unital C$^*$-algebras.


Abstract:
  A flavour-tagged time-dependent angular analysis of $B^0_s\to J/\psi\phi$
decays is presented where the $J/\psi$ meson is reconstructed through its decay
to an $e^+e^-$ pair. The analysis uses a sample of $pp$ collision data recorded
with the LHCb experiment at centre-of-mass energies of 7 and 8 TeV,
corresponding to an integrated luminosity of 3 fb$^{-1}$. The $CP$-violating
phase and lifetime parameters of the $B^0_s$ system are measured to be
$\phi_s=0.00\pm0.28\pm0.07$ rad, $\Delta\Gamma_s=0.115\pm0.045\pm0.011$
ps$^{-1}$ and $\Gamma_s=0.608\pm0.018\pm0.012$ ps$^{-1}$ where the first
uncertainty is statistical and the second systematic. This is the first time
that $CP$-violating parameters are measured in the $B^0_s\to J/\psi\phi$ decay
with an $e^+e^-$ pair in the final state. The results are consistent with
previous measurements in other channels and with the Standard Model
predictions.


Abstract:
  We find a new contribution in wave-packet scatterings, which has been
overlooked in the standard formulation of S-matrix. As a concrete example, we
consider a two-to-two scattering of light scalars $\phi$ by another
intermediate heavy scalar $\Phi$, in the Gaussian wave-packet formalism:
$\phi\phi\to\Phi\to\phi\phi$. This contribution can be interpreted as an
"in-time-boundary effect" of $\Phi$ for the corresponding $\Phi\to\phi\phi$
decay, proposed by Ishikawa et al., with a newly found modification that would
cure the previously observed ultraviolet divergence. We show that such an
effect can be understood as a Stokes phenomenon in an integral over complex
energy plane: The number of relevant saddle points and Lefschetz thimbles
(steepest descent paths) discretely changes depending on the configurations of
initial and final states in the scattering.


Abstract:
  In this paper we generalize a weak sequential result of \cite{fan20182} to a
non-scattering solutions in dimension $d \geq 2$. No symmetry assumptions are
required for the initial data. We build on a previous result of
\cite{dodson20202} for one dimension.


Abstract:
  In this paper, we present novel secret key distribution protocols using
low-cost, hardware chipsets containing millions of synchronized self-powered
timers. The secret keys are derived based on the timers' physical dynamic
responses which provide security against any potential side-channel attacks,
malicious tampering, or snooping. Using the behavioral model of the
self-powered timers, we first show that the key-strings derived from the timers
can pass the randomness test as defined by the National Institute of Standards
and Technology (NIST) suite. The key-strings are then used in two key exchange
protocols which exploit elapsed time as one-way functions. The protocols
proposed in this paper facilitate secure communication between (a) a user and a
remote Server; and (b) two users. Using Monte-Carlo simulations, we investigate
the scalability and the security of these protocols against different
adversarial attacks. We also investigate the robustness of these protocols in
the presence of real-world operating conditions and we investigate the use of
error-correcting codes to mitigate noise-related artifacts.


Abstract:
  Simulation environments are good for learning different driving tasks like
lane changing, parking or handling intersections etc. in an abstract manner.
However, these simulation environments often restrict themselves to operate
under conservative interaction behavior amongst different vehicles. But, as we
know, real driving tasks often involve very high risk scenarios where other
drivers often don't behave in the expected sense. There can be many reasons for
this behavior like being tired or inexperienced. The simulation environment
doesn't take this information into account while training the navigation agent.
Therefore, in this study we especially focus on systematically creating these
risk prone scenarios with heavy traffic and unexpected random behavior for
creating better model-free learning agents. We generate multiple autonomous
driving scenarios by creating new custom Markov Decision Process (MDP)
environment iterations in the highway-env simulation package. The behavior
policy is learnt by agents trained with the help from deep reinforcement
learning models. Our behavior policy is deliberated to handle collisions and
risky randomized driver behavior. We train model free learning agents with
supplement information of risk prone driving scenarios and compare their
performance with baseline agents. Finally, we casually measure the impact of
adding these perturbations in the training process to precisely account for the
performance improvement obtained from utilizing the learnings from these
scenarios.


Abstract:
  Recently, adversarial attack methods have been developed to challenge the
robustness of machine learning models. However, mainstream evaluation criteria
experience limitations, even yielding discrepancies among results under
different settings. By examining various attack algorithms, including
gradient-based and query-based attacks, we notice the lack of a consensus on a
uniform standard for unbiased performance evaluation. Accordingly, we propose a
Piece-wise Sampling Curving (PSC) toolkit to effectively address the
aforementioned discrepancy, by generating a comprehensive comparison among
adversaries in a given range. In addition, the PSC toolkit offers options for
balancing the computational cost and evaluation effectiveness. Experimental
results demonstrate our PSC toolkit presents comprehensive comparisons of
attack algorithms, significantly reducing discrepancies in practice.


Abstract:
  In this paper, we propose a Bayesian switching dynamical model for
segmentation of 3D pose data over time that uncovers interpretable patterns in
the data and is generative. Our model decomposes highly correlated skeleton
data into a set of few spatial basis of switching temporal processes in a
low-dimensional latent framework. We parameterize these temporal processes with
regard to a switching deep vector autoregressive prior in order to accommodate
both multimodal and higher-order nonlinear inter-dependencies. This results in
a dynamical deep generative latent model that parses the meaningful intrinsic
states in the dynamics of 3D pose data using approximate variational inference,
and enables a realistic low-level dynamical generation and segmentation of
complex skeleton movements. Our experiments on four biological motion data
containing bat flight, salsa dance, walking, and golf datasets substantiate
superior performance of our model in comparison with the state-of-the-art
methods.


Abstract:
  In this paper, we continue the investigation of topological properties of
unbounded norm (un-)topology in normed lattices. We characterize separability
and second countability of un-topology in terms of properties of the underlying
normed lattice. We apply our results to prove that an order continuous Banach
function space $X$ over a semi-finite measure space is separable if and only if
it has a $\sigma$-finite carrier and is separable with respect to the topology
of local convergence in measure. We also address the question when a normed
lattice is a normal space with respect to the un-topology.


Abstract:
  Reconfigurable intelligent surfaces (RISs) are considered as potential
technologies for the upcoming sixth-generation (6G) wireless communication
system. Various benefits brought by deploying one or multiple RISs include
increased spectrum and energy efficiency, enhanced connectivity, extended
communication coverage, reduced complexity at transceivers, and even improved
localization accuracy. However, to unleash their full potential, fundamentals
related to RISs, ranging from physical-layer (PHY) modelling to RIS phase
control, need to be addressed thoroughly. In this paper, we provide an overview
of some timely research problems related to the RIS technology, i.e., PHY
modelling (including also physics), channel estimation, potential RIS
architectures, and RIS phase control (via both model-based and data-driven
approaches), along with recent numerical results. We envision that more efforts
will be devoted towards intelligent wireless environments, enabled by RISs.


Abstract:
  Deep packet inspection (DPI) has been extensively investigated in
software-defined networking (SDN) as complicated attacks may intractably inject
malicious payloads in the packets. Existing proprietary pattern-based or
port-based third-party DPI tools can suffer from limitations in efficiently
processing a large volume of data traffic. In this paper, a novel
OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on
the SDN paradigm to provide adaptive and efficient packet inspection. First,
OFDPI prescribes an early detection at the flow-level granularity by checking
the IP addresses of each new flow via OpenFlow protocols. Then, OFDPI allows
for deep packet inspection at the packet-level granularity: (i) for unencrypted
packets, OFDPI extracts the features of accessible payloads, including tri-gram
frequency based on Term Frequency and Inverted Document Frequency (TF-IDF) and
linguistic features. These features are concatenated into a sparse matrix
representation and are then applied to train a binary classifier with logistic
regression rather than matching with specific pattern combinations. In order to
balance the detection accuracy and performance bottleneck of the SDN
controller, OFDPI introduces an adaptive packet sampling window based on the
linear prediction; and (ii) for encrypted packets, OFDPI extracts notable
features of packets and then trains a binary classifier with a decision tree,
instead of decrypting the encrypted traffic to weaken user privacy. A prototype
of OFDPI is implemented on the Ryu SDN controller and the Mininet platform. The
performance and the overhead of the proposed sulotion are assessed using the
real-world datasets through experiments. The numerical results indicate that
OFDPI can provide a significant improvement in detection accuracy with
acceptable overheads.


Abstract:
  Visual context provides grounding information for multimodal machine
translation (MMT). However, previous MMT models and probing studies on visual
features suggest that visual information is less explored in MMT as it is often
redundant to textual information. In this paper, we propose an object-level
visual context modeling framework (OVC) to efficiently capture and explore
visual information for multimodal machine translation. With detected objects,
the proposed OVC encourages MMT to ground translation on desirable visual
objects by masking irrelevant objects in the visual modality. We equip the
proposed with an additional object-masking loss to achieve this goal. The
object-masking loss is estimated according to the similarity between masked
objects and the source texts so as to encourage masking source-irrelevant
objects. Additionally, in order to generate vision-consistent target words, we
further propose a vision-weighted translation loss for OVC. Experiments on MMT
datasets demonstrate that the proposed OVC model outperforms state-of-the-art
MMT models and analyses show that masking irrelevant objects helps grounding in
MMT.


Abstract:
  Abstractive summarization is the task of compressing a long document into a
coherent short document while retaining salient information. Modern abstractive
summarization methods are based on deep neural networks which often require
large training datasets. Since collecting summarization datasets is an
expensive and time-consuming task, practical industrial settings are usually
low-resource. In this paper, we study a challenging low-resource setting of
summarizing long legal briefs with an average source document length of 4268
words and only 120 available (document, summary) pairs. To account for data
scarcity, we used a modern pretrained abstractive summarizer BART (Lewis et
al., 2020), which only achieves 17.9 ROUGE-L as it struggles with long
documents. We thus attempt to compress these long documents by identifying
salient sentences in the source which best ground the summary, using a novel
algorithm based on GPT-2 (Radford et al., 2019) language model perplexity
scores, that operates within the low resource regime. On feeding the compressed
documents to BART, we observe a 6.0 ROUGE-L improvement. Our method also beats
several competitive salience detection baselines. Furthermore, the identified
salient sentences tend to agree with an independent human labeling by domain
experts.


Abstract:
  The face-centered cubic (fcc) antiferromagnet is a typical example of a
geometrically frustrated system in three dimensions: the classical
nearest-neighbor fcc Heisenberg model exhibits a ground state manifold with
large accidental degeneracy. Here, we study the most general nearest- and
next-nearest-neighbor exchange model allowed by the space-group symmetry of the
noncentrosymmetric half-Heusler compounds, which includes a variety of
anisotropic coupling terms. We show that, while the so-called Kitaev and
symmetric off-diagonal ($\Gamma$) interactions do not fully lift the accidental
ground state degeneracy of the isotropic $J_1$-$J_2$ model,
Dzyaloshinskii-Moriya interactions which are allowed in the half-Heuslers
unambiguously select a noncollinear ground state. We then discuss the role of
other effects in the ground state selection, such as quantum fluctuations and
the coupling to a magnetic field, and show that these effects can stabilize
noncoplanar (triple-q) orders. These results suggest that some half-Heusler
antiferromagnets might host rare noncollinear/noncoplanar orders, which may in
turn affect the transport properties of these semimetals.


Abstract:
  We investigate the effect of 2-beam coupling in different imaging geometries
in generating intensity-difference squeezing from four-wave mixing (4WM) in Rb
atomic vapors. A recently-introduced dual-seeding technique can cancel out the
classical noise in a seeded four-wave mixing process. This dual-seeding
technique, however, can introduce new complications that involve 2-beam
coupling between different seeded spatial modes in the atomic vapor and can
ruin squeezing at frequencies on the order of the atomic linewidth and below.
This complicates some forms of quantum imaging using these systems. Here we
show that seeding the 4WM process with skew rays can eliminate the excess noise
caused by 2-beam coupling. To avoid 2-beam coupling in bright, seeded images,
it is important to re-image the object in the gain medium, instead of focussing
through it.


Abstract:
  Given their location on the Hertzsprung-Russell (HR) diagram, thoroughly
characterized subgiant stars can place stringent constraints on a wide range of
astrophysical problems. Accordingly, they are prime asteroseismic targets for
the Transiting Exoplanet Survey Satellite (TESS) mission. In this work, we
infer stellar properties for a sample of 347 subgiants located in the TESS
Continuous Viewing Zones (CVZs), which we select based on their likelihood of
showing asteroseismic oscillations. We investigate how well they can be
characterized using classical constraints (photometry, astrometry), and
validate our results using spectroscopic values. We derive luminosities,
effective temperatures, and radii with mean 1$\sigma$ random (systematic)
uncertainties of 4.5% (2%), 33 K (60 K), and 2.2% (2%), as well as more
model-dependent quantities such as surface gravities, masses, and ages. We use
our sample to demonstrate that subgiants are ideal targets for mass and age
determination based on HR diagram location alone, discuss the advantages of
stellar parameters derived from a detailed characterization over widely
available catalogs, show that the generally used 3D extinction maps tend to
overestimate the extinction for nearby stars (distance $\lesssim$ 500 pc), and
find a correlation that supports the rotation-activity connection in post main
sequence stars. The complementary roles played by classical and asteroseismic
data sets will open a window to unprecedented astrophysical studies using
subgiant stars.


Abstract:
  We generalize Nakajima-Yoshioka's blowup formula to calculate the partition
functions counting the spectrum of bound states to half-BPS Wilson loop
operators in 5d (and 6d) supersymmetric field theories. The partition function
in the presence of a Wilson loop operator on the $\Omega$-background is
factorized when put on the blowup $\hat{\mathbb{C}}^2$ into two Wilson loop
partition functions under the localization. This structure provides a set of
blowup equations for Wilson loop operators. We explain how to formulate the
blowup equations and solve them to compute the partition functions of Wilson
loop operators. We test this idea by explicitly calculating the Wilson loop
partition functions in various 5d/6d field theories and comparing them against
known results and expected dualities.


Abstract:
  An effective field theory of composite Dirac fermions was proposed by Son
[Phys. Rev. X 5, 031027 (2015)] as a theory of the half-filled Landau level
with explicit particle-hole symmetry. We compute the electromagnetic response
of this Son-Dirac theory on the level of the random phase approximation (RPA),
where we pay particular attention to the effect of an additional
composite-fermion dipole term that is needed to restore Galilean invariance. We
find that once this dipole correction is taken into account, spurious interband
transitions and collective modes that are present in the response of the
unmodified theory either cancel or are strongly suppressed. We demonstrate that
this gives rise to a consistent theory of the half-filled Landau level valid at
all frequencies, at least to leading order in the momentum. In addition, the
dipole contribution modifies the Fermi-liquid response at small frequency and
momentum, which is a prediction of the Son-Dirac theory within the RPA that
distinguishes it from a separate description of the half-filled Landau level by
Halperin, Lee, and Read within the RPA.


Abstract:
  Score-based generative models (SGMs) have recently demonstrated impressive
results in terms of both sample quality and distribution coverage. However,
they are usually applied directly in data space and often require thousands of
network evaluations for sampling. Here, we propose the Latent Score-based
Generative Model (LSGM), a novel approach that trains SGMs in a latent space,
relying on the variational autoencoder framework. Moving from data to latent
space allows us to train more expressive generative models, apply SGMs to
non-continuous data, and learn smoother SGMs in a smaller space, resulting in
fewer network evaluations and faster sampling. To enable training LSGMs
end-to-end in a scalable and stable manner, we (i) introduce a new
score-matching objective suitable to the LSGM setting, (ii) propose a novel
parameterization of the score function that allows SGM to focus on the mismatch
of the target distribution with respect to a simple Normal one, and (iii)
analytically derive multiple techniques for variance reduction of the training
objective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10,
outperforming all existing generative results on this dataset. On
CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while
outperforming them in sampling time by two orders of magnitude. In modeling
binary images, LSGM achieves state-of-the-art likelihood on the binarized
OMNIGLOT dataset. Our project page and code can be found at
https://nvlabs.github.io/LSGM .


Abstract:
  This paper proposes an efficient and robust algorithm to estimate target
trajectories with unknown target detection profiles and clutter rates using
measurements from multiple sensors. In particular, we propose to combine the
multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate
target trajectories and robust Cardinalized Probability Hypothesis Density
(CPHD) filters to estimate the clutter rates. The target detection probability
is augmented to the filtering state space for joint estimation. Experimental
results show that the proposed robust filter exhibits near-optimal performance
in the sense that it is comparable to the optimal MS-GLMB operating with true
clutter rate and detection probability. More importantly, it outperforms other
studied filters when the detection profile and clutter rate are unknown and
time-variant. This is attributed to the ability of the robust filter to learn
the background parameters on-the-fly.


Abstract:
  Viscous vortex layers subject to a more general uniform strain are
considered. They include Townsend's steady solution for plane strain
(corresponding to a parameter $a = 1$) in which all the strain in the plane of
the layer goes toward vorticity stretching, as well as Migdal's recent steady
asymmetric solution for axisymmetric strain ($a = 1/2$) in which half of the
strain goes into vorticity stretching. In addition to considering asymmetric,
symmetric and antisymmetric steady solutions $\forall a \ge 0$, it is shown
that for $a < 1$, i.e., anything less than the Townsend case, the vorticity
inherently decays in time: only boundary conditions that maintain a supply of
vorticity at one or both ends lead to a non-zero steady state. For the
super-Townsend case $a > 1$, steady states have a sheath of opposite sign
vorticity. Comparison is made with homogeneous-isotropic turbulence in which
case the average vorticity in the strain eigenframe is layer-like, has wings of
opposite vorticity, and the strain configuration is found to be super-Townsend.
Only zero-integral perturbations of the $a > 1$ steady solutions are stable;
otherwise, the solution grows. Finally, the appendix shows that the average
flow in the strain eigenframe is (apart from an extra term) the
Reynolds-averaged Navier-Stokes equation.


Abstract:
  It is known that, for any positive non-square integer multiplier $k$, there
is an infinity of multiples of triangular numbers which are triangular numbers.
We analyze the congruence properties of the indices $\xi$ of triangular numbers
that are multiples of other triangular numbers. We show that the remainders in
the congruence relations of $\xi$ modulo k come always in pairs whose sum
always equal $\left(k-1\right)$, always include 0 and $\left(k-1\right)$, and
only 0 and $\left(k-1\right)$ if $k$ is prime, or an odd power of a prime, or
an even square plus one or an odd square minus one or minus two. If the
multiplier $k$ is twice the triangular number of $n$, the set of remainders
includes also $n$ and $\left(n^{2}-1\right)$ and if $k$ has integer factors,
the set of remainders include multiples of a factor following certain rules.
Finally, algebraic expressions are found for remainders in function of $k$ and
its factors. Several exceptions are noticed and superseding rules exist between
various rules and expressions of remainders. This approach allows to eliminate
in numerical searches those $\left(k-\upsilon\right)$ values of $\xi_{i}$ that
are known not to provide solutions, where $\upsilon$ is the even number of
remainders. The gain is typically in the order of $k/\upsilon$, with
$\upsilon\ll k$ for large values of $k$.


Abstract:
  We present a deterministic $(1+\varepsilon)$-approximate maximum matching
algorithm in $\mathsf{poly} 1/\varepsilon$ passes in the semi-streaming model,
solving the long-standing open problem of breaking the exponential barrier in
the dependence on $1/\varepsilon$. Our algorithm exponentially improves on the
well-known randomized $(1/\varepsilon)^{O(1/\varepsilon)}$-pass algorithm from
the seminal work by McGregor~[APPROX05], the recent deterministic algorithm by
Tirodkar with the same pass complexity~[FSTTCS18]. Up to polynomial factors in
$1/\varepsilon$, our work matches the state-of-the-art deterministic $(\log n /
\log \log n) \cdot (1/\varepsilon)$-pass algorithm by Ahn and Guha~[TOPC18],
that is allowed a dependence on the number of nodes $n$. Our result also makes
progress on the Open Problem 60 at sublinear.info.
  Moreover, we design a general framework that simulates our approach for the
streaming setting in other models of computation. This framework requires
access to an algorithm computing a maximal matching and an algorithm for
processing disjoint $(\mathsf{poly} 1 / \varepsilon)$-size connected
components. Instantiating our framework in $\mathsf{CONGEST}$ yields a
$\mathsf{poly}(\log{n}, 1/\varepsilon)$ round algorithm for computing
$(1+\varepsilon$)-approximate maximum matching. In terms of the dependence on
$1/\varepsilon$, this result improves exponentially state-of-the-art result by
Lotker, Patt-Shamir, and Pettie~[LPSP15]. Our framework leads to the same
quality of improvement in the context of the Massively Parallel Computation
model as well.


Abstract:
  We develop a new algorithm for non-convex stochastic optimization that finds
an $\epsilon$-critical point in the optimal $O(\epsilon^{-3})$ stochastic
gradient and Hessian-vector product computations. Our algorithm uses
Hessian-vector products to "correct" a bias term in the momentum of SGD with
momentum. This leads to better gradient estimates in a manner analogous to
variance reduction methods. In contrast to prior work, we do not require
excessively large batch sizes, and are able to provide an adaptive algorithm
whose convergence rate automatically improves with decreasing variance in the
gradient estimates. We validate our results on a variety of large-scale deep
learning architectures and benchmarks tasks.


Abstract:
  Three-dimensional shell-like structures can be obtained spontaneously at the
microscale from the self-folding of 2D templates of rigid panels. At least for
simple structures, the motion of each panel is consistent with a Brownian
process and folding occurs through a sequence of binding events, where pairs of
panels meet at a specific closing angle. Here, we propose a lattice model to
describe the dynamics of self-folding. As an example, we study the folding of a
pyramid of N lateral faces. We combine analytical and numerical Monte Carlo
simulations to find how the folding time depends on the number of faces,
closing angle, and initial configuration. Implications for the study of more
complex structures are discussed.


Abstract:
  This study presents a new risk-averse multi-stage stochastic
epidemics-ventilator-logistics compartmental model to address the resource
allocation challenges of mitigating COVID-19. This epidemiological logistics
model involves the uncertainty of untested asymptomatic infections and
incorporates short-term human migration. Disease transmission is also
forecasted through a new formulation of transmission rates that evolve over
space and time with respect to various non-pharmaceutical interventions, such
as wearing masks, social distancing, and lockdown. The proposed multi-stage
stochastic model overviews different scenarios on the number of asymptomatic
individuals while optimizing the distribution of resources, such as
ventilators, to minimize the total expected number of newly infected and
deceased people. The Conditional Value at Risk (CVaR) is also incorporated into
the multi-stage mean-risk model to allow for a trade-off between the weighted
expected loss due to the outbreak and the expected risks associated with
experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk
epidemics-ventilator-logistics model to the case of controlling the COVID-19 in
highly-impacted counties of New York and New Jersey. We calibrate, validate,
and test our model using actual infection, population, and migration data. The
results indicate that short-term migration influences the transmission of the
disease significantly. The optimal number of ventilators allocated to each
region depends on various factors, including the number of initial infections,
disease transmission rates, initial ICU capacity, the population of a
geographical location, and the availability of ventilator supply. Our
data-driven modeling framework can be adapted to study the disease transmission
dynamics and logistics of other similar epidemics and pandemics.


Abstract:
  In this paper we study the non-relativistic dynamic of a charged particle in
the electromagnetic field induced by a periodically time dependent current J
along an infinitely long and infinitely thin straight wire. The motions are
described by the Lorentz-Newton equation, in which the electromagnetic field is
obtained by solving the Maxwell's equations with the current distribution J as
data. We prove that many features of the integrable time independent case are
preserved. More precisely, introducing cylindrical coordinates, we prove the
existence of (non-resonant) radially periodic motions that are also of twist
type. In particular, these solutions are Lyapunov stable and accumulated by
subharmonic and quasiperiodic motions.


Abstract:
  Operator inference learns low-dimensional dynamical-system models with
polynomial nonlinear terms from trajectories of high-dimensional physical
systems (non-intrusive model reduction). This work focuses on the large class
of physical systems that can be well described by models with quadratic
nonlinear terms and proposes a regularizer for operator inference that induces
a stability bias onto quadratic models. The proposed regularizer is physics
informed in the sense that it penalizes quadratic terms with large norms and so
explicitly leverages the quadratic model form that is given by the underlying
physics. This means that the proposed approach judiciously learns from data and
physical insights combined, rather than from either data or physics alone.
Additionally, a formulation of operator inference is proposed that enforces
model constraints for preserving structure such as symmetry and definiteness in
the linear terms. Numerical results demonstrate that models learned with
operator inference and the proposed regularizer and structure preservation are
accurate and stable even in cases where using no regularization or Tikhonov
regularization leads to models that are unstable.


Abstract:
  The coronavirus disease (COVID-19) pandemic affected our lives deeply, just
like everyone else, the children also suffered from the restrictions due to
COVID-19 affecting their education and social interactions with others, being
restricted from play areas and schools for a long time. Although social robots
provide a promising solution to support children in their education, healthcare
and social interaction with others, the precautions due to COVID-19 also
introduced new constraints in the social robotics research. In this paper, we
will discuss the benefits and challenges encountered in child-robot interaction
due to COVID-19 based on two user studies. The first study involves children
with hearing disabilities, and Pepper humanoid robot to support their
audiometry tests. The second study includes the child-sized humanoid robot
Kaspar and interaction games with children with autism spectrum disorder (ASD).


Abstract:
  Standard optical potentials use off-resonant laser standing wave induced
AC-Stark shift. In a recent development [Phys. Rev. Lett. {\bf 117}, 233001
(2016)] a three-level scheme in $\Lambda$ configuration coupled coherently by
resonant laser fields was introduced leading to an effective lattice with
subwavelength potential peaks. Here as an extension of that work to a four
level atomic setup in the tripod configuration is used to create spin
$1/2$-like two-dimensional dark-space with 1D motion and the presence of
external gauge fields. Most interestingly for a possible application, the
lifetime for a dark subspace motion is up to two orders of magnitude larger
than for a similar $\Lambda$ system. The model is quite flexible leading to
lattices with significant nearest, next-nearest, or next-next-nearest hopping
rates, $J_1,J_2,J_3$ opening up new intriguing possibilities to study, e.g.
frustrated systems. The characteristic Wannier functions lead also to new type
of inter-site interactions not realizable in typical optical lattices.


Abstract:
  In this paper we consider well-posedness properties of vector optimization
problems with objective function $f: X \to Y$ where $X$ and $Y$ are Banach
spaces and $Y$ is partially ordered by a closed convex pointed cone with
nonempty interior. The vector well-posedness notion considered in this paper is
the one due to Dentcheva and Helbig, which is a natural extension of Tykhonov
well-posedness for scalar optimization problems. When a scalar optimization
problem is considered it is possible to prove that under some assumptions the
set of functions for which the related optimzation problem is well-posed is
dense or even more in "big" i.e. contains a dense $G_{\delta}$ set (these
results are called genericity results). The aim of this paper is to extend
these genericity results to vector optimization problems.


Abstract:
  Self-duality is a very important concept in the study and applications of
topological solitons in many areas of Physics. The rich mathematical structures
underlying it lead, in many cases, to the development of exact and
non-perturbative methods. We present a generalization of the Yang-Mills-Higgs
system by the introduction of scalar fields assembled in a symmetric and
invertible matrix h of the same dimension as the gauge group. The coupling of
such new fields to the gauge and Higgs fields is made by replacing the Killing
form, in the contraction of the group indices, by the matrix h in the kinetic
term for the gauge fields, and by its inverse in the Higgs field kinetic term.
The theory is conformally invariant in the three dimensional space R^3. An
important aspect of the model is that for practically all configurations of the
gauge and Higgs fields the new scalar fields adjust themselves to solve the
modified self-duality equations. We construct solutions using a spherically
symmetric ans\"atz and show that the 't Hooft-Polyakov monopole becomes a
self-dual solution of such modified Yang-Mills-Higgs system. We use an ans\"atz
based on the conformal symmetry to construct vacuum solutions presenting
non-trivial toroidal magnetic fields.


Abstract:
  Based on the resource theory for quantifying the coherence of quantum
channels, we introduce a new coherence quantifier for quantum channels via
maximum relative entropy. We prove that the maximum relative entropy for
coherence of quantum channels is directly related to the maximally coherent
channels under a particular class of superoperations, which results in an
operational interpretation of the maximum relative entropy for coherence of
quantum channels. We also introduce the conception of sub-superchannels and
sub-superchannel discrimination. For any quantum channels, we show that the
advantage of quantum channels in sub-superchannel discrimination can be exactly
characterized by the maximum relative entropy of coherence for quantum
channels. Similar to the maximum relative entropy of coherence for channels,
the robustness of coherence for quantum channels has also been investigated. We
show that the maximum relative entropy of coherence for channels provides new
operational interpretations of robustness of coherence for quantum channels and
illustrates the equivalence of the dephasing-covariant superchannels,
incoherent superchannels, and strictly incoherent superchannels in these two
operational tasks.


Abstract:
  In this paper, we consider the problem of joint beam selection and link
activation across a set of communication pairs to effectively control the
interference between communication pairs via inactivating part communication
pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The
resulting optimization problem is formulated as an integer programming problem
that is nonconvex and NP-hard. Consequently, the global optimal solution, even
the local optimal solution, cannot be generally obtained. To overcome this
challenge, this paper resorts to design a deep learning architecture based on
graph neural network to finish the joint beam selection and link activation,
with taking the network topology information into account. Meanwhile, we
present an unsupervised Lagrangian dual learning framework to train the
parameters of the GBLinks model. Numerical results show that the proposed
GBLinks model can converges to a stable point with the number of iterations
increases, in terms of the weighted sum rate. Furthermore, the GBLinks model
can reach near-optimal solution through comparing with the exhaustive search
scheme in small-scale ultra-dense D2D mmWave communication networks and
outperforms GreedyNoSched and the SCA-based method. It also shows that the
GBLinks model can generalize to varying densities and coverage regions of
ultra-dense D2D mmWave communication networks.


Abstract:
  Retinal degenerative diseases cause profound visual impairment in more than
10 million people worldwide, and retinal prostheses are being developed to
restore vision to these individuals. Analogous to cochlear implants, these
devices electrically stimulate surviving retinal cells to evoke visual percepts
(phosphenes). However, the quality of current prosthetic vision is still
rudimentary. Rather than aiming to restore "natural" vision, there is potential
merit in borrowing state-of-the-art computer vision algorithms as image
processing techniques to maximize the usefulness of prosthetic vision. Here we
combine deep learning--based scene simplification strategies with a
psychophysically validated computational model of the retina to generate
realistic predictions of simulated prosthetic vision, and measure their ability
to support scene understanding of sighted subjects (virtual patients) in a
variety of outdoor scenarios. We show that object segmentation may better
support scene understanding than models based on visual saliency and monocular
depth estimation. In addition, we highlight the importance of basing
theoretical predictions on biologically realistic models of phosphene shape.
Overall, this work has the potential to drastically improve the utility of
prosthetic vision for people blinded from retinal degenerative diseases.


Abstract:
  This paper considers the problem of unsupervised person re-identification
(re-ID), which aims to learn discriminative models with unlabeled data. One
popular method is to obtain pseudo-label by clustering and use them to optimize
the model. Although this kind of approach has shown promising accuracy, it is
hampered by 1) noisy labels produced by clustering and 2) feature variations
caused by camera shift. The former will lead to incorrect optimization and thus
hinders the model accuracy. The latter will result in assigning the intra-class
samples of different cameras to different pseudo-label, making the model
sensitive to camera variations. In this paper, we propose a unified framework
to solve both problems. Concretely, we propose a Dynamic and Symmetric
Cross-Entropy loss (DSCE) to deal with noisy samples and a camera-aware
meta-learning algorithm (MetaCam) to adapt camera shift. DSCE can alleviate the
negative effects of noisy samples and accommodate the change of clusters after
each clustering step. MetaCam simulates cross-camera constraint by splitting
the training data into meta-train and meta-test based on camera IDs. With the
interacted gradient from meta-train and meta-test, the model is enforced to
learn camera-invariant features. Extensive experiments on three re-ID
benchmarks show the effectiveness and the complementary of the proposed DSCE
and MetaCam. Our method outperforms the state-of-the-art methods on both fully
unsupervised re-ID and unsupervised domain adaptive re-ID.


Abstract:
  Objective: The objective of this study is to develop a deep learning pipeline
to detect signals on dietary supplement-related adverse events (DS AEs) from
Twitter. Material and Methods: We obtained 247,807 tweets ranging from 2012 to
2018 that mentioned both DS and AE. We annotated biomedical entities and
relations on 2,000 randomly selected tweets. For the concept extraction task,
we compared the performance of traditional word embeddings with SVM, CRF and
LSTM-CRF classifiers to BERT models. For the relation extraction task, we
compared GloVe vectors with CNN classifiers to BERT models. We chose the best
performing models in each task to assemble an end-to-end deep learning pipeline
to detect DS AE signals and compared the results to the known DS AEs from a DS
knowledge base (i.e., iDISK). Results: In both tasks, the BERT-based models
outperformed traditional word embeddings. The best performing concept
extraction model is the BioBERT model that can identify supplement, symptom,
and body organ entities with F1-scores of 0.8646, 0.8497, and 0.7104,
respectively. The best performing relation extraction model is the BERT model
that can identify purpose and AE relations with F1-scores of 0.8335 and 0.7538,
respectively. The end-to-end pipeline was able to extract DS indication and DS
AEs with an F1-score of 0.7459 and 0,7414, respectively. Comparing to the
iDISK, we could find both known and novel DS-AEs. Conclusion: We have
demonstrated the feasibility of detecting DS AE signals from Twitter with a
BioBERT-based deep learning pipeline.


Abstract:
  A key aspect for the forklifts is the state-of-health (SoH) assessment to
ensure the safety and the reliability of uninterrupted power source.
Forecasting the battery SoH well is imperative to enable preventive maintenance
and hence to reduce the costs. This paper demonstrates the capabilities of
gradient boosting regression for predicting the SoH timeseries under
circumstances when there is little prior information available about the
batteries. We compared the gradient boosting method with light gradient
boosting, extra trees, extreme gradient boosting, random forests, long
short-term memory networks and with combined convolutional neural network and
long short-term memory networks methods. We used multiple predictors and lagged
target signal decomposition results as additional predictors and compared the
yielded prediction results with different sets of predictors for each method.
For this work, we are in possession of a unique data set of 45 lithium-ion
battery packs with large variation in the data. The best model that we derived
was validated by a novel walk-forward algorithm that also calculates point-wise
confidence intervals for the predictions; we yielded reasonable predictions and
confidence intervals for the predictions. Furthermore, we verified this model
against five other lithium-ion battery packs; the best model generalised to
greater extent to this set of battery packs. The results about the final model
suggest that we were able to enhance the results in respect to previously
developed models. Moreover, we further validated the model for extracting cycle
counts presented in our previous work with data from new forklifts; their
battery packs completed around 3000 cycles in a 10-year service period, which
corresponds to the cycle life for commercial Nickel-Cobalt-Manganese (NMC)
cells.


Abstract:
  The tunneling of electrons and holes in quantum structures plays a crucial
role in studying the transport properties of materials and the related devices.
8-Pmmn borophene is a new two-dimensional Dirac material, which hosts tilted
Dirac cone and chiral, anisotropic massless Dirac fermions. We develop the
transfer matrix method to investigate the Klein tunneling of massless fermions
across the smooth NP junctions and NPN junctions of 8-Pmmn borophene. Like the
sharp NP junctions of 8-Pmmn borophene, the tilted Dirac cones induce the
oblique Klein tunneling. The angle of perfect transmission to the normal
incidence is 20.4 degrees, a constant determined by the Hamiltonian of 8-Pmmn
borophene. For the NPN junction, there are branches of the Klein tunneling in
the phase diagram. We find that the asymmetric Klein tunneling is induced by
the chirality and anisotropy of the carriers. Furthermore, we show the
oscillation of electrical resistance related to the Klein tunneling in the NPN
junctions. One may analyze the pattern of electrical resistance and verify the
existence of asymmetric Klein tunneling experimentally.


Abstract:
  Numerical simulations of systems at coexistence are known to yield unstable
fields in some regions of the density parameters, as well as inequivalence of
ensembles. The Van der Waals-like loops are attributed to effects of the
interface between the coexisting phases, but the question of convexity remains
unresolved. We recover the theory developed by Hill in the 1960's, and adapt it
to include the interface free energy. Our adapted theory is verified through
carefully planned simulations, and gives a thermodynamic description of the
interface behaviour inside the coexistence region which restores the proper
convexity of the thermodynamic potentials, as well as yields convergence of
grandcanonical and canonical results. As a bonus, our interpretation allows
direct calculation of surface tension in very good accordance with Onsager's
analytic prediction.


Abstract:
  We study the (characteristic) Cauchy problem for the Maxwell-Bloch equations
of light-matter interaction via asymptotics, under assumptions that prevent the
generation of solitons. Our analysis clarifies some features of the sense in
which physically-motivated initial/boundary conditions are satisfied. In
particular, we present a proper Riemann-Hilbert problem that generates the
unique causal solution to the Cauchy problem, that is, the solution vanishes
outside of the light cone. Inside the light cone, we relate the leading-order
asymptotics to self-similar solutions that satisfy a system of ordinary
differential equations related to the Painlev\'e-III (PIII) equation. We
identify these solutions and show that they are related to a family of PIII
solutions recently discovered in connection with several limiting processes
involving the focusing nonlinear Schr\"odinger equation. We fully explain a
resulting boundary layer phenomenon in which, even for smooth initial data (an
incident pulse), the solution makes a sudden transition over an infinitesimally
small propagation distance. At a formal level, this phenomenon has been
described by other authors in terms of the PIII self-similar solutions. We make
this observation precise and for the first time we relate the PIII self-similar
solutions to the Cauchy problem. Our analysis of the asymptotic behavior
satisfied by the optical field and medium density matrix reveals slow decay of
the optical field in one direction that is actually inconsistent with the
simplest version of scattering theory. Our results identify a precise generic
condition on an optical pulse incident on an initially-unstable medium
sufficient for the pulse to stimulate the decay of the medium to its stable
state.


Abstract:
  The traditional adjacency matrix of a mixed graph is not symmetric in
general, hence its eigenvalues may be not real. To overcome this obstacle,
several authors have recently defined and studied various Hermitian adjacency
matrices of digraphs or mixed graphs. The usual idea is to choose a complex
number $\alpha \in \mathbb{C}$ with $\vert \alpha\vert=1$. The traditional
adjacency matrix is modified such that the value $\alpha$ (resp.\ $\bar\alpha$)
replaces the previous value at position $(i,j)$ whenever the only edge between
the vertices $i$ and $j$ is an arc from $i$ to $j$ (resp.\ from $j$ to $i$). So
far, the choices $\alpha=i$ and $\alpha=e^{\frac{\pi}{3}i}$ have attracted
particular interest. It has even been argued that the latter choice might be
ideal, in a certain sense. The present paper extends and generalizes previous
results and, moreover, proposes another particular choice for $\alpha$ that
appears just as favourable, if not more.


Abstract:
  Microbiome studies have recently transitioned from experimental designs with
a few hundred samples to designs spanning tens of thousands of samples. Modern
studies such as the Earth Microbiome Project (EMP) afford the statistics
crucial for untangling the many factors that influence microbial community
composition. Analyzing those data used to require access to a compute cluster,
making it both expensive and inconvenient. We show that recent improvements in
both hardware and software now allow to compute key bioinformatics tasks on
EMP-sized data in minutes using a gaming-class laptop, enabling much faster and
broader microbiome science insights.


Abstract:
  The rising need for hybrid physical platforms has triggered a renewed
interest for the development of agile radio-frequency phononic circuits with
complex functionalities. The combination of travelling waves with resonant
mechanical elements appears as an appealing means of harnessing elastic
vibration. In this work, we demonstrate that this combination can be further
enriched by the occurrence of elastic non-linearities induced travelling
surface acoustic waves (SAW) interacting with a pair of otherwise linear
micron-scale mechanical resonators. Reducing the resonator gap distance and
increasing the SAW amplitude results in a frequency softening of the resonator
pair response that lies outside the usual picture of geometrical Duffing
non-linearities. The dynamics of the SAW excitation scheme allows further
control of the resonator motion, notably leading to circular polarization
states. These results paves the way towards versatile high-frequency
phononic-MEMS/NEMS circuits fitting both classical and quantum technologies.


Abstract:
  Entangled photon spectroscopy is a nascent field that has important
implications for measurement and imaging across chemical, biology, and
materials fields. Entangled photon spectroscopy potentially offers improved
spatial and temporal-frequency resolutions, increased cross sections for
multiphoton and nonlinear measurements, and new abilities in inducing or
measuring quantum correlations. A critical step in enabling entangled photon
spectroscopies is the creation of high-flux entangled sources that can use
conventional detectors, as well as provide redundancy for the losses in
realistic samples. Here, we report a periodically poled, chirped, lithium
tantalate platform that generates entangled photon pairs with a $10^{-7}$
efficiency. For a near watt level diode laser, this results in a near
$\mu$W-level flux. The single photon per mode limit that is necessary to
maintain non-classical photon behavior is still satisfied by distributing this
power over up to an octave-spanning bandwidth. The spectral-temporal photon
correlations are observed via a Michelson-type interferometer that measures the
broadband Hong-Ou-Mandel two-photon interference. A coherence time of 245 fs
for a 10 nm bandwidth in the collinear case and a 62 fs for a 125 nm bandwidth
in the non-collinear case is measured using a CW pump laser, and, essentially,
collecting the full photon cone. We outline in detail the numerical methods
used for designing and tailoring the entangled photons source, such as changing
center wavelength or bandwidth, with the ultimate aim of increasing the
availability of high-flux UV-Vis entangled photon sources in the optical
spectroscopy community.


Abstract:
  We introduce a new approach for audio-visual speech separation. Given a
video, the goal is to extract the speech associated with a face in spite of
simultaneous background sounds and/or other human speakers. Whereas existing
methods focus on learning the alignment between the speaker's lip movements and
the sounds they generate, we propose to leverage the speaker's face appearance
as an additional prior to isolate the corresponding vocal qualities they are
likely to produce. Our approach jointly learns audio-visual speech separation
and cross-modal speaker embeddings from unlabeled video. It yields
state-of-the-art results on five benchmark datasets for audio-visual speech
separation and enhancement, and generalizes well to challenging real-world
videos of diverse scenarios. Our video results and code:
http://vision.cs.utexas.edu/projects/VisualVoice/.


Abstract:
  We propose to address the issue of sample efficiency, in Deep Convolutional
Neural Networks (DCNN), with a semi-supervised training strategy that combines
Hebbian learning with gradient descent: all internal layers (both convolutional
and fully connected) are pre-trained using an unsupervised approach based on
Hebbian learning, and the last fully connected layer (the classification layer)
is trained using Stochastic Gradient Descent (SGD). In fact, as Hebbian
learning is an unsupervised learning method, its potential lies in the
possibility of training the internal layers of a DCNN without labels. Only the
final fully connected layer has to be trained with labeled examples.
  We performed experiments on various object recognition datasets, in different
regimes of sample efficiency, comparing our semi-supervised (Hebbian for
internal layers + SGD for the final fully connected layer) approach with
end-to-end supervised backprop training, and with semi-supervised learning
based on Variational Auto-Encoder (VAE). The results show that, in regimes
where the number of available labeled samples is low, our semi-supervised
approach outperforms the other approaches in almost all the cases.


Abstract:
  In this paper, we analyze 10 globular clusters in order to measure their
rotational properties by using high precision radial velocity data from the
SDSS-IV APOGEE-2 survey. Out of the 10 clusters we were able to successfully
measure the rotation speed and position angle of the rotation axis for 9
clusters (M2, M3, M5, M12, M13, M15, M53, M92, M107). The comparison between
our results and previous ones shows a really good agreement within our
uncertainties. For four of the globular clusters, M3, M13, M5 and M15, we
separated the sample into two generation of stars using their [Al/Fe]
abundances and examined the kinematic features of these generations separately
from one another. In case of M3, we found significant difference between the
rotational properties of first and second populations, confirming for the first
time the predictions of several numerical simulations from the literature. The
other three clusters (M5, M13, M15) also show smaller deviation between the two
groups of stars, but those deviations are comparable to our errors.


Abstract:
  Spin defect centers with long quantum coherence times ($T_2$) are key
solid-state platforms for a variety of quantum applications. Recently, cluster
correlation expansion (CCE) techniques have emerged as a powerful tool to
simulate the $T_2$ of defect electron spins in these solid-state systems with
good accuracy. Here, based on CCE, we uncover an algebraic expression for $T_2$
generalized for host compounds with dilute nuclear spin baths, which enables a
quantitative and comprehensive materials exploration with a near instantaneous
estimate of the coherence. We investigate more than 12,000 host compounds at
natural isotopic abundance, and find that silicon carbide (SiC), a prominent
widegap semiconductor for quantum applications, possesses the longest coherence
times among widegap non-chalcogenides. In addition, more than 700 chalcogenides
are shown to possess a longer $T_2$ than SiC. We suggest new potential host
compounds with promisingly long $T_2$ up to 47 ms, and pave the way to explore
unprecedented functional materials for quantum applications.


Abstract:
  The Transiting Exoplanet Survey Satellite (TESS) is the latest observational
effort to find exoplanets and map bright transient optical phenomena.
Supernovae (SN) are particularly interesting as cosmological standard candles
for cosmological distance measures. The limiting magnitude of TESS strongly
constrains supernova detection to the very nearby Universe ($m \sim$ 19,
$z<0.05$). We explore the possibility that more distant supernovae that are
gravitationally lensed and magnified by a foreground galaxy can be detected by
TESS, an opportunity to measure the time delay between light paths and
constrain the Hubble constant independently.
  We estimate the rate of occurrence of such systems, assuming reasonable
distributions of magnification, host dust attenuation and redshift. There are
approximately 16 type Ia and 43 core-collapse SN (SNcc) expected to be
observable with TESS each year, which translates to 18% and 43% chance of
detection per year, respectively. Monitoring the largest collections of known
strong galaxy-galaxy lenses from Petrillo et al., this translates into 0.6% and
1.3% chances of a SNIa and SNcc per year. The TESS all-sky detection rates are
lower than those of the Zwicky Transient Facility (ZTF) and Vera Rubin
Observatory. However, on the ecliptic poles, TESS performs almost as well as
its all-sky search thanks to its continuous coverage: 2 and 4% chance of an
observed SN (Ia or cc) each year. These rates argue for timely processing of
full-frame TESS imaging to facilitate follow-up and should motivate further
searches for low-redshift lensing system.


Abstract:
  In this paper, we develop topological data analysis methods for
classification tasks on univariate time series. As an application, we perform
binary and ternary classification tasks on two public datasets that consist of
physiological signals collected under stress and non-stress conditions. We
accomplish our goal by using persistent homology to engineer stable topological
features after we use a time delay embedding of the signals and perform a
subwindowing instead of using windows of fixed length. The combination of
methods we use can be applied to any univariate time series and in this
application allows us to reduce noise and use long window sizes without
incurring an extra computational cost. We then use machine learning models on
the features we algorithmically engineered to obtain higher accuracies with
fewer features.


Abstract:
  We prove the existence of semi-infinite geodesics for Brownian last-passage
percolation (BLPP). Specifically, on a single event of probability one, there
exist semi-infinite geodesics started from every space-time point and traveling
in every asymptotic direction. Properties of these geodesics include uniqueness
for a fixed initial point and direction, non-uniqueness for fixed direction but
random initial points, and coalescence of all geodesics traveling in a common,
fixed direction. Along the way, we prove that for fixed northeast and southwest
directions, there almost surely exist no bi-infinite geodesics in the given
directions. The semi-infinite geodesics are constructed from Busemann
functions. Our starting point is a result of Alberts, Rassoul-Agha and Simper
that established Busemann functions for fixed points and directions. Out of
this, we construct the global process of Busemann functions simultaneously for
all initial points and directions, and then the family of semi-infinite
Busemann geodesics. The uncountable space of the semi-discrete setting requires
extra consideration and leads to new phenomena, compared to discrete models.


Abstract:
  The need for accurate yield estimates for viticulture is becoming more
important due to increasing competition in the wine market worldwide. One of
the most promising methods to estimate the harvest is berry counting, as it can
be approached non-destructively, and its process can be automated. In this
article, we present a method that addresses the challenge of occluded berries
with leaves to obtain a more accurate estimate of the number of berries that
will enable a better estimate of the harvest. We use generative adversarial
networks, a deep learning-based approach that generates a likely scenario
behind the leaves exploiting learned patterns from images with non-occluded
berries. Our experiments show that the estimate of the number of berries after
applying our method is closer to the manually counted reference. In contrast to
applying a factor to the berry count, our approach better adapts to local
conditions by directly involving the appearance of the visible berries.
Furthermore, we show that our approach can identify which areas in the image
should be changed by adding new berries without explicitly requiring
information about hidden areas.


Abstract:
  Machine learning and deep learning algorithms can be used to classify
encrypted Internet traffic. Classification of encrypted traffic can become more
challenging in the presence of adversarial attacks that target the learning
algorithms. In this paper, we focus on investigating the effectiveness of
different evasion attacks and see how resilient machine and deep learning
algorithms are. Namely, we test C4.5 Decision Tree, K-Nearest Neighbor (KNN),
Artificial Neural Network (ANN), Convolutional Neural Networks (CNN) and
Recurrent Neural Networks (RNN). In most of our experimental results, deep
learning shows better resilience against the adversarial samples in comparison
to machine learning. Whereas, the impact of the attack varies depending on the
type of attack.


Abstract:
  A 2-structure $\sigma$ consists of a vertex set $V(\sigma)$ and of an
equivalence relation $\equiv_\sigma$ defined on $(V(\sigma)\times
V(\sigma))\setminus\{(v,v):v\in V(\sigma)\}$. Given a 2-structure $\sigma$, a
subset $M$ of $V(\sigma)$ is a module of $\sigma$ if for $x,y\in M$ and $v\in
V(\sigma)\setminus M$, $(x,v)\equiv_{\sigma}(y,v)$ and
$(v,x)\equiv_{\sigma}(v,y)$. For instance, $\emptyset$, $V(\sigma)$ and
$\{v\}$, for $v\in V(\sigma)$, are modules of $\sigma$ called trivial modules
of $\sigma$. A 2-structure $\sigma$ is prime if $v(\sigma)\geq 3$ and all the
modules of $\sigma$ are trivial. A prime 2-structure $\sigma$ is critical if
for each $v\in V(\sigma)$, $\sigma-v$ is not prime. A prime 2-structure
$\sigma$ is partially critical if there exists $X\subsetneq V(\sigma)$ such
that $\sigma[X]$ is prime, and for each $v\in V(\sigma)\setminus X$, $\sigma-v$
is not prime. We characterize finite or infinite partially critical
2-structures.


Abstract:
  Cross-chain interaction is among different blockchains. When the number of
blockchains increases, it is difficult for blockchains to form a single star
topology or a fully connected topology. Meanwhile, different from legacy
networks, the propagation method is required to keep the data validity. Thus,
the blockchain topology and associated propagation methods are two key issues,
which should be ensured during the propagation. In this paper, we first propose
the confirmation-based propagation method to keep the validity of the
cross-chain data. The confirmation method is to seal the cross-chain
transaction synchronized from other blockchains. Second, we point out that a
valid topology requires blockchains to be strongly connected. With this
requirement, we propose several topologies, which match different connection
scenarios. The simulation results show that the proposed methods can securely
propagate the cross-chain data and the connection way is flexible.


Abstract:
  This agent-based model contributes to a theory of corporate culture in which
company performance and employees' behaviour result from the interaction
between financial incentives, motivational factors and endogenous social norms.
Employees' personal values are the main drivers of behaviour. They shape
agents' decisions about how much of their working time to devote to individual
tasks, cooperative, and shirking activities. The model incorporates two aspects
of the management style, analysed both in isolation and combination: (i)
monitoring efforts affecting intrinsic motivation, i.e. the firm is either
trusting or controlling, and (ii) remuneration schemes affecting extrinsic
motivation, i.e. individual or group rewards. The simulations show that
financial incentives can (i) lead to inefficient levels of cooperation, and
(ii) reinforce value-driven behaviours, amplified by emergent social norms. The
company achieves the highest output with a flat wage and a trusting management.
Employees that value self-direction highly are pivotal, since they are strongly
(de-)motivated by the management style.


Abstract:
  Atomically thin two-dimensional (2D) materials can be vertically stacked with
van der Waals bonds, which enable interlayer coupling. In the particular case
of transition metal dichalcogenide (TMD) bilayers, the relative direction
between the two monolayers, coined as twist-angle, modifies the crystal
symmetry and creates a superlattice with exciting properties. Here, we
demonstrate an all-optical method for pixel-by-pixel mapping of the twist-angle
with resolution of 0.23 degrees, via polarization-resolved second harmonic
generation (P-SHG) microscopy and we compare it with four-dimensional scanning
transmission electron microscopy (4D-STEM). It is found that the twist-angle
imaging of WS2 bilayers, using the P-SHG technique is in excellent agreement
with that obtained using electron diffraction. The main advantages of the
optical approach are that the characterization is performed on the same
substrate that the device is created on and that it is three orders of
magnitude faster than the 4D-STEM. We envisage that the optical P-SHG imaging
could become the gold standard for the quality examination of TMD
superlattice-based devices.


Abstract:
  We leverage state-of-the-art machine learning methods and a decade's worth of
archival data from CFHT to predict observatory image quality (IQ) from
environmental conditions and observatory operating parameters. Specifically, we
develop accurate and interpretable models of the complex dependence between
data features and observed IQ for CFHT's wide-field camera, MegaCam. Our
contributions are several-fold. First, we collect, collate and reprocess
several disparate data sets gathered by CFHT scientists. Second, we predict
probability distribution functions (PDFs) of IQ and achieve a mean absolute
error of $\sim0.07''$ for the predicted medians. Third, we explore the
data-driven actuation of the 12 dome "vents" installed in 2013-14 to accelerate
the flushing of hot air from the dome. We leverage epistemic and aleatoric
uncertainties in conjunction with probabilistic generative modeling to identify
candidate vent adjustments that are in-distribution (ID); for the optimal
configuration for each ID sample, we predict the reduction in required
observing time to achieve a fixed SNR. On average, the reduction is $\sim12\%$.
Finally, we rank input features by their Shapley values to identify the most
predictive variables for each observation. Our long-term goal is to construct
reliable and real-time models that can forecast optimal observatory operating
parameters to optimize IQ. We can then feed such forecasts into scheduling
protocols and predictive maintenance routines. We anticipate that such
approaches will become standard in automating observatory operations and
maintenance by the time CFHT's successor, the Maunakea Spectroscopic Explorer,
is installed in the next decade.


Abstract:
  In this paper we characterise the pointwise size and regularity estimates for
the Dunkl Riesz transform kernel involving both the Euclidean metric and the
Dunkl metric, where the two metrics are not equivalent. We further establish a
suitable version of the pointwise lower bound via the Euclidean metric and then
characterise boundedness of commutator of the Dunkl Riesz transform via the BMO
space associated with the Euclidean metric and the Dunkl measure. This shows
that BMO space via the Euclidean metric is the suitable one associated to the
Dunkl setting but not the one via the Dunkl metric.


Abstract:
  We propose a novel deep generative model based on causal convolutions for
multi-subject motion modeling and synthesis, which is inspired by the success
of WaveNet in multi-subject speech synthesis. However, it is nontrivial to
adapt WaveNet to handle high-dimensional and physically constrained motion
data. To this end, we add an encoder and a decoder to the WaveNet to translate
the motion data into features and back to the predicted motions. We also add 1D
convolution layers to take skeleton configuration as an input to model skeleton
variations across different subjects. As a result, our network can scale up
well to large-scale motion data sets across multiple subjects and support
various applications, such as random and controllable motion synthesis, motion
denoising, and motion completion, in a unified way. Complex motions, such as
punching, kicking and, kicking while punching, are also well handled. Moreover,
our network can synthesize motions for novel skeletons not in the training
dataset. After fine-tuning the network with a few motion data of the novel
skeleton, it is able to capture the personalized style implied in the motion
and generate high-quality motions for the skeleton. Thus, it has the potential
to be used as a pre-trained network in few-shot learning for motion modeling
and synthesis. Experimental results show that our model can effectively handle
the variation of skeleton configurations, and it runs fast to synthesize
different types of motions on-line. We also perform user studies to verify that
the quality of motions generated by our network is superior to the motions of
state-of-the-art human motion synthesis methods.


Abstract:
  As Europe is experiencing a second violent CoVid-19 storm, with the PCR-based
testing system deteriorating due to the high volumes of people to be tested
daily, there is a general reconsideration of the mathematical theories at the
basis of our contact tracing and testing approaches. Drawing upon the concept
of super spreader, we propose the use of (less sensitive) rapid tests to detect
those secondary infections that do not need the use of PCRs, thus saving the
most part of PCR tests currently used. This before the system fails.


Abstract:
  We analyze the residual gauge freedom in gravity, in four dimensions, in the
light-cone gauge, in a formulation where unphysical fields are integrated out.
By checking the invariance of the light-cone Hamiltonian, we obtain a set of
residual gauge transformations, which satisfy the BMS algebra realized on the
two physical fields in the theory. Hence, the BMS algebra appears as a
consequence of residual gauge invariance in the bulk and not just at the
asymptotic boundary. We highlight the key features of the light-cone BMS
algebra and discuss its connection with the quadratic form structure of the
Hamiltonian.


Abstract:
  This paper investigates a Hamilton-Jacobi (HJ) analysis to solve
finite-horizon optimal control problems for high-dimensional systems. Although
grid-based methods, such as the level-set method [1], numerically solve a
general class of HJ partial differential equations, the computational
complexity is exponential in the dimension of the continuous state. To manage
this computational complexity, methods based on Lax-Hopf theory have been
developed for the state-unconstrained optimal control problem under certain
assumptions, such as affine dynamics and state-independent stage cost. Based on
the Lax formula [2], this paper proposes an HJ formula for the
state-constrained optimal control problem for nonlinear systems. We call this
formula \textit{the generalized Lax formula} for the optimal control problem.
The HJ formula provides both the optimal cost and an optimal control signal. We
also provide an efficient computational method for a class of problems for
which the dynamics is affine in the state, and for which the stage and terminal
cost, as well as the state constraints, are convex in the state. This class of
problems does not require affine dynamics and convex stage cost in the control.
This paper also provides three practical examples.


Abstract:
  Using the isothermal molecular dynamics (MD), coalescence/sintering of Au
nanoparticles (NPs) was simulated by employing the Nose-Hoover thermostat. The
MD simulation was realized by using the well-known open program LAMMPS, its
version for parallel calculations on GPUs. We have found that the solid NP
sintering scenario is switched to the coalescence scenario not at the NP
melting temperature Tm exactly but at a lower temperature T0=0.9Tm interpreted
as the critical temperature corresponding to a coalescence/sintering
bifurcation phenomenon: in the temperature range from T0-2K to T0+2K the
resulting (daughter) NPs of the same size can have either liquid-like or
crystalline structure after coalescence/sintering at the same fixed
temperature. The crystallize and liquid states were identify by analyzing the
degree of crystallinity and the radial distribution function. For this purpose
we employed the OVITO program.