Abstract:
  We consider the problem to learn a concept or a query in the presence of an
ontology formulated in the description logic ELr, in Angluin's framework of
active learning that allows the learning algorithm to interactively query an
oracle (such as a domain expert). We show that the following can be learned in
polynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3)
conjunctive queries (CQs) that are chordal, symmetry-free, and of bounded
arity. In all cases, the learner can pose to the oracle membership queries
based on ABoxes and equivalence queries that ask whether a given concept/query
from the considered class is equivalent to the target. The restriction to
bounded arity in (3) can be removed when we admit unrestricted CQs in
equivalence queries. We also show that EL-concepts are not polynomial query
learnable in the presence of ELI-ontologies.


Abstract:
  We study the complexity of answer counting for ontology-mediated queries and
for querying under constraints, considering conjunctive queries and unions
thereof (UCQs) as the query language and guarded TGDs as the ontology and
constraint language, respectively. Our main result is a classification
according to whether answer counting is fixed-parameter tractable (FPT),
W[1]-equivalent, #W[1]-equivalent, #W[2]-hard, or #A[2]-equivalent, lifting a
recent classification for UCQs without ontologies and constraints due to Dell
et al. The classification pertains to various structural measures, namely
treewidth, contract treewidth, starsize, and linked matching number.
  Our results rest on the assumption that the arity of relation symbols is
bounded by a constant and, in the case of ontology-mediated querying, that all
symbols from the ontology and query can occur in the data (so-called full data
schema).
  We also study the meta-problems for the mentioned structural measures, that
is, to decide whether a given ontology-mediated query or constraint-query
specification is equivalent to one for which the structural measure is bounded.


Abstract:
  In this paper, We present our approach for IEEEBigMM 2020, Grand Challenge
(BMGC), Identifying senti-ments from tweets related to the MeToo movement. The
modelis based on an ensemble of Convolutional Neural Network,Bidirectional LSTM
and a DNN for final classification. Thispaper is aimed at providing a detailed
analysis of the modeland the results obtained. We have ranked 5th out of 10
teamswith a score of 0.51491


Abstract:
  We study the sequential resource allocation problem where a decision maker
repeatedly allocates budgets between resources. Motivating examples include
allocating limited computing time or wireless spectrum bands to multiple users
(i.e., resources). At each timestep, the decision maker should distribute its
available budgets among different resources to maximize the expected reward, or
equivalently to minimize the cumulative regret. In doing so, the decision maker
should learn the value of the resources allocated for each user from feedback
on each user's received reward. For example, users may send messages of
different urgency over wireless spectrum bands; the reward generated by
allocating spectrum to a user then depends on the message's urgency. We assume
each user's reward follows a random process that is initially unknown. We
design combinatorial multi-armed bandit algorithms to solve this problem with
discrete or continuous budgets. We prove the proposed algorithms achieve
logarithmic regrets under semi-bandit feedback.


Abstract:
  New optical sensors with a segmented photosensitive area are being developed
for the next generation of neutrino telescopes at the South Pole. In addition
to increasing sensitivity to high-energy astrophysical neutrinos, we show that
this will also lead to a significant improvement in sensitivity to MeV
neutrinos, such as those produced in core-collapse supernovae (CCSN). These
low-energy neutrinos can provide a detailed picture of the events after stellar
core collapse, testing our understanding of these violent explosions. We
present studies on the event-based detection of MeV neutrinos with a segmented
sensor and, for the first time, the potential of a corresponding detector in
the deep ice at the South Pole for the detection of extra-galactic CCSN. We
find that exploiting temporal coincidences between signals in different
photocathode segments, a $27\ \mathrm{M}_{\odot}$ progenitor mass CCSN can be
detected up to a distance of 341 kpc with a false detection rate of $0.01$
year$^{-1}$ with a detector consisting of 10000 sensors. Increasing the number
of sensors to 20000 and reducing the optical background by a factor of 70
expands the range such that a CCSN detection rate of $0.1$ per year is
achieved, while keeping the false detection rate at $0.01$ year$^{-1}$.


Abstract:
  We address the problem of decoding video file fragments when the necessary
encoding parameters are missing. With this objective, we propose a method that
automatically generates H.264 video headers containing these parameters and
extracts coded pictures in the partially available compressed video data. To
accomplish this, we examined a very large corpus of videos to learn patterns of
encoding settings commonly used by encoders and created a parameter dictionary.
Further, to facilitate a more efficient search our method identifies
characteristics of a coded bitstream to discriminate the entropy coding mode.
It also utilizes the application logs created by the decoder to identify
correct parameter values. Evaluation of the effectiveness of the proposed
method on more than 55K videos with diverse provenance shows that it can
generate valid headers on average in 11.3 decoding trials per video. This
result represents an improvement by more than a factor of 10 over the
conventional approach of video header stitching to recover video file
fragments.


Abstract:
  During the recent Coronavirus disease 2019 (COVID-19) outbreak, the
microblogging service Twitter has been widely used to share opinions and
reactions to events. Italy was one of the first European countries to be
severely affected by the outbreak and to establish lockdown and stay-at-home
orders, potentially leading to country reputation damage. We resort to
sentiment analysis to investigate changes in opinions about Italy reported on
Twitter before and after the COVID-19 outbreak. Using different lexicons-based
methods, we find a breakpoint corresponding to the date of the first
established case of COVID-19 in Italy that causes a relevant change in
sentiment scores used as proxy of the country reputation. Next, we demonstrate
that sentiment scores about Italy are strongly associated with the levels of
the FTSE-MIB index, the Italian Stock Exchange main index, as they serve as
early detection signals of changes in the values of FTSE-MIB. Finally, we make
a content-based classification of tweets into positive and negative and use two
machine learning classifiers to validate the assigned polarity of tweets posted
before and after the outbreak.


Abstract:
  We extend on ideas from standard thermodynamics to show that temperature can
be assigned to a general nonequilibrium quantum system. By choosing a
physically motivated complete set of observables and expanding the system state
thereupon, one can read a set of relevant, independent thermodynamic variables
which include internal energy. This expansion allows us to read a
nonequilibrium temperature as the partial derivative of the von Neumann entropy
with respect to internal energy. We show that this definition of temperature is
one of a set of thermodynamics parameters unambiguously describing the system
state. It has appealing features such as positivity for passive states and
consistency with the standard temperature for thermal states. By attributing
temperature to correlations in a bipartite system, we obtain a universal
relation which connects the temperatures of subsystems, total system as a
whole, and correlation. All these temperatures can be different even when the
composite system is in a well-defined Gibbsian thermal state.


Abstract:
  Development of the methods to exploit nuclear hyperpolarization and search
for molecules whose nuclear spins can be efficiently hyperpolarized is an
active area in nuclear magnetic resonance. Of particular interest are those
molecules that have long nuclear relaxation times, making them to be suitable
candidates as contrast agents in magnetic resonance imaging. In this work, we
present a detailed study of SABRE SHEATH (Signal Amplification By Reversible
Exchange in Shield Enabled Alignment Transfer to Heteronuclei) experiments of
15N,15N' azobenzene. In SABRE SHEATH experiments nuclear spins of the target
are hyperpolarized by transfer of spin polarization from parahydrogen at
ultralow fields during a reversible chemical process. The studied system is
complicated, and we are concerned only about a subset of the data, presenting
details for the molecules that experience fast chemical exchange at the
catalytic complex and thus are involved in polarizing the free azobenzene.
Azobenzene exists in two isomers trans- and cis-. We show that all nuclear
spins in cis-azobenzene can be efficiently hyperpolarized by SABRE at suitable
magnetic fields. Enhancement factors (relative to 9.4 T) reach several
thousands of times for 15N spins and a few tens of times for the 1H spins.
There are two approaches to observe either hyperpolarized magnetization of
15N/1H spins or hyperpolarized singlet order of the 15N spin pair. We compare
these approaches and present the field dependencies of SABRE experiments for
them. No hyperpolarization of trans 15N,15N' azobenzene was observed. The
results presented here will be useful for further experiments where
hyperpolarized cis-15N,15N' azobenzene is switched by light to trans 15N,15N'
azobenzene for storing the produced hyperpolarization in the long-lived spin
state of the 15N pair of trans-15N,15N' azobenzene.


Abstract:
  The use of deep learning-based techniques for approximating secure encoding
functions has attracted considerable interest in wireless communications due to
impressive results obtained for general coding and decoding tasks for wireless
communication systems. Of particular importance is the development of
model-free techniques that work without knowledge about the underlying channel.
Such techniques utilize for example generative adversarial networks to estimate
and model the conditional channel distribution, mutual information estimation
as a reward function, or reinforcement learning. In this paper, the approach of
reinforcement learning is studied and, in particular, the policy gradient
method for a model-free approach of neural network-based secure encoding is
investigated. Previously developed techniques for enforcing a certain co-set
structure on the encoding process can be combined with recent reinforcement
learning approaches. This new approach is evaluated by extensive simulations,
and it is demonstrated that the resulting decoding performance of an
eavesdropper is capped at a certain error level.


Abstract:
  We give an overview about some elementary properties of Hoggatt matrices,
which are generalizations of Pascal triangle, and study q-analogs and Fibonacci
analogs and derive a common generalization.


Abstract:
  Deep learning approaches show unprecedented results for speckle reduction in
SAR amplitude images. The wide availability of multi-temporal stacks of SAR
images can improve even further the quality of denoising. In this paper, we
propose a flexible yet efficient way to integrate temporal information into a
deep neural network for speckle suppression. Archives provide access to long
time-series of SAR images, from which multi-temporal averages can be computed
with virtually no remaining speckle fluctuations. The proposed method combines
this multi-temporal average and the image at a given date in the form of a
ratio image and uses a state-of-the-art neural network to remove the speckle in
this ratio image. This simple strategy is shown to offer a noticeable
improvement compared to filtering the original image without knowledge of the
multi-temporal average.


Abstract:
  We study the neural network (NN) compression problem, viewing the tension
between the compression ratio and NN performance through the lens of
rate-distortion theory. We choose a distortion metric that reflects the effect
of NN compression on the model output and then derive the tradeoff between rate
(compression ratio) and distortion. In addition to characterizing theoretical
limits of NN compression, this formulation shows that \emph{pruning},
implicitly or explicitly, must be a part of a good compression algorithm. This
observation bridges a gap between parts of the literature pertaining to NN and
data compression, respectively, providing insight into the empirical success of
pruning for NN compression. Finally, we propose a novel pruning strategy
derived from our information-theoretic formulation and show that it outperforms
the relevant baselines on CIFAR-10 and ImageNet datasets.


Abstract:
  If dark matter (DM) interacts with the Standard Model (SM) via the kinetic
mixing (KM) portal, it necessitates the existence of portal matter (PM)
particles which carry both dark and SM quantum numbers that will appear in
vacuum polarization-like loop graphs. In addition to the familiar $\sim
e\epsilon Q$ strength, QED-like interaction for the dark photon (DP), in some
setups different loop graphs of these PM states can also induce other coupling
structures for the SM fermions that may come to dominate in at least some
regions of parameter space regions and which can take the form of `dark'
moments, \eg, magnetic dipole-type interactions in the IR, associated with a
large mass scale, $\Lambda$. In this paper, motivated by a simple toy model, we
perform a phenomenological investigation of a possible loop-induced dark
magnetic dipole moment for SM fermions, in particular, for the electron. We
show that at the phenomenological level such a scenario can not only be made
compatible with existing experimental constraints for a significant range of
correlated values for $\Lambda$ and the dark $U(1)_D$ gauge coupling, $g_D$,
but can also lead to quantitatively different signatures once the DP is
discovered. In this setup, assuming complex scalar DM to satisfy CMB
constraints, parameter space regions where the DP decays invisibly are found to
be somewhat preferred if PM mass limits from direct searches at the LHC and our
toy model setup are all taken seriously. High precision searches for, or
measurements of, the $e^+e^- \to \gamma +{\rm DP}$ process at Belle II are
shown to provide some of the strongest future constraints on this scenario.


Abstract:
  This work considers new entropy-based proofs of some known, or otherwise
refined, combinatorial bounds for bipartite graphs. These include upper bounds
on the number of the independent sets, lower bounds on the minimal number of
colors in constrained edge coloring, and lower bounds on the number of walks of
a given length in bipartite graphs. The proofs of these combinatorial results
rely on basic properties of the Shannon entropy.


Abstract:
  To enable personalized cancer treatment, machine learning models have been
developed to predict drug response as a function of tumor and drug features.
However, most algorithm development efforts have relied on cross validation
within a single study to assess model accuracy. While an essential first step,
cross validation within a biological data set typically provides an overly
optimistic estimate of the prediction performance on independent test sets. To
provide a more rigorous assessment of model generalizability between different
studies, we use machine learning to analyze five publicly available cell
line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI. Based on observed
experimental variability across studies, we explore estimates of prediction
upper bounds. We report performance results of a variety of machine learning
models, with a multitasking deep neural network achieving the best cross-study
generalizability. By multiple measures, models trained on CTRP yield the most
accurate predictions on the remaining testing data, and gCSI is the most
predictable among the cell line data sets included in this study. With these
experiments and further simulations on partial data, two lessons emerge: (1)
differences in viability assays can limit model generalizability across
studies, and (2) drug diversity, more than tumor diversity, is crucial for
raising model generalizability in preclinical screening.


Abstract:
  Fine-grained indoor localization has attracted attention recently because of
the rapidly growing demand for indoor location-based services (ILBS).
Specifically, massive (large-scale) multiple-input and multiple-output (MIMO)
systems have received increasing attention due to high angular resolution. This
paper presents an indoor localization testbed based on a massive MIMO
orthogonal frequency-division multiplexing (OFDM) system, which supports
physical-layer channel measurements. Instead of exploiting channel state
information (CSI) directly for localization, we focus on positioning from the
perspective of multipath components (MPCs), which are extracted from the CSI
through the space-alternating generalized expectation-maximization (SAGE)
algorithm. On top of the available MPCs, we propose a generalized
fingerprinting system based on different single-metric and hybrid-metric
schemes. We evaluate the impact of the varying antenna topologies, the size of
the training set, the number of antennas, and the effective signal-to-noise
ratio (SNR). The experimental results show that the proposed fingerprinting
method can achieve centimeter-level positioning accuracy with a relatively
small training set. Specifically, the distributed uniform linear array obtains
the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from
the high spatial resolution.


Abstract:
  Fake News Detection is an essential problem in the field of Natural Language
Processing. The benefits of an effective solution in this area are manifold for
the goodwill of society. On a surface level, it broadly matches with the
general problem of text classification. Researchers have proposed various
approaches to tackle fake news using simple as well as some complex techniques.
In this paper, we try to make a comparison between the present Deep Learning
techniques by representing the news instances in some vector space using a
combination of common mathematical operations with available vector space
representations. We do a number of experiments using various combinations and
permutations. Finally, we conclude with a sound analysis of the results and
evaluate the reasons for such results.


Abstract:
  6G wireless networks will require the flexibility to accommodate an extremely
diverse set of service types. This necessitates the use of mixed numerologies
to accommodate different quality of service (QoS) requirements. Non-orthogonal
multiple access (NOMA) techniques can potentially be used to accommodate users
with different numerologies while also gaining the performance benefits
associated with NOMA. To achieve the full performance benefits of a mixed
numerology NOMA (MN-NOMA) system, resource allocation among the users is
paramount. However, the coexistence of mixed numerologies changes the nature of
the interference that each user experiences. This means that techniques used in
single-numerology NOMA (SN-NOMA) are no longer sufficient. In light of this, we
approach the problem of optimizing subcarrier and power allocation for
maximizing the spectral efficiency of MN-NOMA while considering a minimum rate
constraint for each user. In this letter, we propose a two-stage sub-optimal
approach to solve the problem. We present numerical results which show the
superiority of our proposed method over existing benchmark schemes in both
spectral efficiency and fairness.


Abstract:
  We address the issue of safety in reinforcement learning. We pose the problem
in an episodic framework of a constrained Markov decision process. Existing
results have shown that it is possible to achieve a reward regret of
$\tilde{\mathcal{O}}(\sqrt{K})$ while allowing an
$\tilde{\mathcal{O}}(\sqrt{K})$ constraint violation in $K$ episodes. A
critical question that arises is whether it is possible to keep the constraint
violation even smaller. We show that when a strictly safe policy is known, then
one can confine the system to zero constraint violation with arbitrarily high
probability while keeping the reward regret of order
$\tilde{\mathcal{O}}(\sqrt{K})$. The algorithm which does so employs the
principle of optimistic pessimism in the face of uncertainty to achieve safe
exploration. When no strictly safe policy is known, though one is known to
exist, then it is possible to restrict the system to bounded constraint
violation with arbitrarily high probability. This is shown to be realized by a
primal-dual algorithm with an optimistic primal estimate and a pessimistic dual
update.


Abstract:
  In this early draft, we provide an overview on similarities and differences
in the implementation of a paper card-based vaccine credential system and an
app-based vaccine credential system. A vaccine credential's primary goal is to
regulate entry and ensure safety of individuals within densely packed public
locations and workspaces. This is critical for containing the rapid spread of
Covid-19 in densely packed public locations since a single individual can
infect a large majority of people in a crowd. A vaccine credential can also
provide information such as an individual's Covid-19 vaccination history and
adverse symptom reaction history to judge their potential impact on the overall
health of individuals within densely packed public locations and workspaces.
After completing the comparisons, we believe a card-based implementation will
benefit regions with less socioeconomic mobility, limited resources, and
stagnant administrations. An app-based implementation on the other hand will
benefit regions with equitable internet access and lower technological divide.
We also believe an interoperable system of both credential systems will work
best for regions with enormous working-class populations and dense housing
clusters.


Abstract:
  We study discontinuous interval maps generated by the action of erasing block
substitutions on the binary expansion. After establishing some general
properties of these maps, we categorize erasing block substitutions in a
hierarchy of classes displaying progressively stronger erasing character. We
investigate how this affects the dynamics of the corresponding interval maps,
showing that the richest dynamical behavior (Devaney and Li-Yorke chaos,
infinite topological entropy) is achieved at a precise step in this hierarchy,
which we name completely erasing substitutions. KEYWORDS: Topological dynamics,
Erasing substitutions; Devaney chaos; Li-Yorke chaos; Topological entropy.


Abstract:
  We use differentiable programming and gradient descent to find unitary
matrices that can be used in the period finding algorithm to extract period
information from the state of a quantum computer post application of the
oracle. The standard procedure is to use the inverse quantum Fourier transform.
Our findings suggest that that this is not the only unitary matrix appropriate
for the period finding algorithm, There exist several unitary matrices that can
affect out the same transformation and they are significantly different from
each other as well. These unitary matrices can be learned by an algorithm.
Neural networks can be applied to differentiate such unitary matrices from
randomly generated ones indicating that these unitaries do have characteristic
features that cannot otherwise be discerned easily.


Abstract:
  In this paper, we consider the problem of privacy preservation in the average
consensus problem when communication among nodes is quantized. More
specifically, we consider a setting where some nodes in the network are curious
but not malicious and they try to identify the initial states of other nodes
based on the data they receive during their operation (without interfering in
the computation in any other way), while some nodes in the network want to
ensure that their initial states cannot be inferred exactly by the curious
nodes. We propose two privacy-preserving event-triggered quantized average
consensus algorithms that can be followed by any node wishing to maintain its
privacy and not reveal the initial state it contributes to the average
computation. Every node in the network (including the curious nodes) is allowed
to execute a privacy-preserving algorithm or its underlying average consensus
algorithm. Under certain topological conditions, both algorithms allow the
nodes who adopt privacypreserving protocols to preserve the privacy of their
initial quantized states and at the same time to obtain, after a finite number
of steps, the exact average of the initial states.


Abstract:
  The price of solar panels is decreasing rapidly and governments around the
world are investing in solar power plants to increase the portion of solar
energy in the smart grid. The price of electricity produced by solar power
plants is much cheaper than conventional thermal power plants and innovation in
solar cells is further increasing the divide. Consequently, setting up solar
power plants will decrease electricity spot prices on average. However, the
topology of the power grid must be considered while setting up solar power
plants so that full benefits of cheap solar power can be gained by all
consumers. In this paper we will calculate day-ahead spot prices using
conventional generators. Then one of the generators will be replaced by a
photovoltaic generator and effects on day-ahead market will be analyzed. Total
cost of electricity generated at each hour in a day using conventional power
plants and solar power plants will be compared. The effects of line congestion
on the benefits of solar power to end customers will be analyzed by comparing
nodal prices. The optimization problems related to generator scheduling and
day-ahead markets will be solved using Dynamic Programming, Mixed Integer
Linear Programming (MILP) and Karush-Kuhn-Tucker (KKT) method for convex
optimization.


Abstract:
  Cross correlation analyses of high resolution spectroscopic data have
recently shown great success in directly detecting planetary signals and
enabling the characterization of their atmospheres. One such technique aims to
observe a system at multiple epochs and combine the measured planetary radial
velocities from each epoch into a measurement of the planetary Keplerian
orbital velocity $K_p$, constituting a direct detection of the planetary
signal. Recent work has shown that in few-epoch ($\sim$5) data sets, unintended
structure can arise at a high level, obscuring the planetary detection. In this
work, we look to simulations to examine whether there are ways to reduce this
structured noise in few-epoch data sets by careful planning of observations.
The choice of observation date allows observers to select the primary (stellar)
velocity - through a set systemic velocity and chosen barycentric velocity -
and the planetary orbital phase, and so we focus on the effects of these two
parameters. We find that epochs taken when the primary velocity is near zero,
and the stellar lines remain relatively fixed to the telluric rest-frame,
greatly reduce the level of structured noise and allow for much stronger
planetary detections, on average more than twice the significance of detections
made with epochs using randomly selected primary velocities. Following these
results, we recommend that observers looking to build up high-resolution
multi-epoch data sets target nights when their system has a near-zero primary
velocity.


Abstract:
  We present CREST, an approach for causal reasoning in simulation to learn the
relevant state space for a robot manipulation policy. Our approach conducts
interventions using internal models, which are simulations with approximate
dynamics and simplified assumptions. These interventions elicit the structure
between the state and action spaces, enabling construction of neural network
policies with only relevant states as input. These policies are pretrained
using the internal model with domain randomization over the relevant states.
The policy network weights are then transferred to the target domain (e.g., the
real world) for fine tuning. We perform extensive policy transfer experiments
in simulation for two representative manipulation tasks: block stacking and
crate opening. Our policies are shown to be more robust to domain shifts, more
sample efficient to learn, and scale to more complex settings with larger state
spaces. We also show improved zero-shot sim-to-real transfer of our policies
for the block stacking task.


Abstract:
  Muttalib-Borodin determinants are generalizations of Hankel determinants and
depend on a parameter $\theta>0$. In this paper, we obtain large $n$
asymptotics for $n \times n$ Muttalib-Borodin determinants whose weight
possesses an arbitrary number of Fisher-Hartwig singularities. As a corollary,
we obtain asymptotics for the expectation and variance of the real and
imaginary parts of the logarithm of the characteristic polynomial, several
central limit theorems, and some global bulk rigidity upper bounds. Our results
are valid for all $\theta > 0$.


Abstract:
  It is known that a trained Restricted Boltzmann Machine (RBM) on the binary
Monte Carlo Ising spin configurations, generates a series of iterative
reconstructed spin configurations which spontaneously flow and stabilize to the
critical point of physical system. Here we construct a variety of Neural
Network (NN) flows using the RBM and (variational) autoencoders, to study the
q-state Potts and clock models on the square lattice for q = 2, 3, 4. The NN
are trained on Monte Carlo spin configurations at various temperatures. We find
that the trained NN flow does develop a stable point that coincides with
critical point of the q-state spin models. The behavior of the NN flow is
nontrivial and generative, since the training is unsupervised and without any
prior knowledge about the critical point and the Hamiltonian of the underlying
spin model. Moreover, we find that the convergence of the flow is independent
of the types of NNs and spin models, hinting a universal behavior. Our results
strengthen the potential applicability of the notion of the NN flow in studying
various states of matter and offer additional evidence on the connection with
the Renormalization Group flow.


Abstract:
  An Euler tour in a hypergraph is a closed walk that traverses each edge of
the hypergraph exactly once, while an Euler family is a family of closed walks
that jointly traverse each edge exactly once and cannot be concatenated. In
this paper, we show how the problem of existence of an Euler tour (family) in a
hypergraph $H$ can be reduced to the analogous problem in some smaller
hypergraphs that are derived from $H$ using an edge cut of $H$. In the process,
new techniques of edge cut assignments and collapsed hypergraphs are
introduced. Moreover, we describe algorithms based on these characterizations
that determine whether or not a hypergraph admits an Euler tour (family), and
can also construct an Euler tour (family) if it exists.


Abstract:
  Permissioned blockchains employ Byzantine fault-tolerant (BFT) state machine
replication (SMR) to reach agreement on an ever-growing, linearly ordered log
of transactions. A new paradigm, combined with decades of research in BFT SMR
and blockchain (namely chained-BFT, or cBFT), has emerged for directly
constructing blockchain protocols. Chained-BFT protocols have a unifying
propose-vote scheme instead of multiple different voting phases with a set of
voting and commit rules to guarantee safety and liveness. However, distinct
voting and commit rules impose varying impacts on performance under different
workloads, network conditions, and Byzantine attacks. Therefore, a fair
comparison of the proposed protocols poses a challenge that has not yet been
addressed by existing work.
  We fill this gap by studying a family of cBFT protocols with a two-pronged
systematic approach. First, we present an evaluation framework, Bamboo, for
quick prototyping of cBFT protocols and that includes helpful benchmarking
facilities. To validate Bamboo, we introduce an analytic model using queuing
theory which also offers a back-of-the-envelope guide for dissecting these
protocols. We build multiple cBFT protocols using Bamboo and we are the first
to fairly compare three representatives (i.e., HotStuff, two-chain HotStuff,
and Streamlet). We evaluated these protocols under various parameters and
scenarios, including two Byzantine attacks that have not been widely discussed
in the literature. Our findings reveal interesting trade-offs (e.g.,
responsiveness vs. forking-resilience) between different cBFT protocols and
their design choices, which provide developers and researchers with insights
into the design and implementation of this protocol family.


Abstract:
  By a ring groupoid we mean an animated ring whose i-th homotopy groups are
zero for all i>1.
  In this expository note we give an elementary treatment of the (2,1)-category
of ring groupoids (i.e., without referring to general animated rings and
without using n-categories for n>2).
  The note is motivated by the fact that ring stacks play a central role in the
Bhatt-Lurie approach to prismatic cohomology.


Abstract:
  Cortical pyramidal neurons have a complex dendritic anatomy, whose function
is an active research field. In particular, the segregation between its soma
and the apical dendritic tree is believed to play an active role in processing
feed-forward sensory information and top-down or feedback signals. In this
work, we use a simple two-compartment model accounting for the nonlinear
interactions between basal and apical input streams and show that standard
unsupervised Hebbian learning rules in the basal compartment allow the neuron
to align the feed-forward basal input with the top-down target signal received
by the apical compartment. We show that this learning process, termed
coincidence detection, is robust against strong distractions in the basal input
space and demonstrate its effectiveness in a linear classification task.


Abstract:
  Despite intense interest and considerable effort, the current generation of
neural networks suffers a significant loss of accuracy under most practically
relevant privacy training regimes. One particularly challenging class of neural
networks are the wide ones, such as those deployed for NLP typeahead prediction
or recommender systems. Observing that these models share something in
common--an embedding layer that reduces the dimensionality of the input--we
focus on developing a general approach towards training these models that takes
advantage of the sparsity of the gradients. More abstractly, we address the
problem of differentially private empirical risk minimization (ERM) for models
that admit sparse gradients. We demonstrate that for non-convex ERM problems,
the loss is logarithmically dependent on the number of parameters, in contrast
with polynomial dependence for the general case. Following the same intuition,
we propose a novel algorithm for privately training neural networks. Finally,
we provide an empirical study of a DP wide neural network on a real-world
dataset, which has been rarely explored in the previous work.


Abstract:
  The continuous convergence of machine learning algorithms, 5G and beyond
(5G+) wireless communications, and artificial intelligence (AI) hardware
implementation hastens the birth of federated learning (FL) over 5G+ mobile
devices, which pushes AI functions to mobile devices and initiates a new era of
on-device AI applications. Despite the remarkable progress made in FL, huge
energy consumption is one of the most significant obstacles restricting the
development of FL over battery-constrained 5G+ mobile devices. To address this
issue, in this paper, we investigate how to develop energy efficient FL over
5G+ mobile devices by making a trade-off between energy consumption for
"working" (i.e., local computing) and that for "talking" (i.e., wireless
communications) in order to boost the overall energy efficiency. Specifically,
we first examine energy consumption models for graphics processing unit (GPU)
computation and wireless transmissions. Then, we overview the state of the art
of integrating FL procedure with energy-efficient learning techniques (e.g.,
gradient sparsification, weight quantization, pruning, etc.). Finally, we
present several potential future research directions for FL over 5G+ mobile
devices from the perspective of energy efficiency.


Abstract:
  In networks of nonlinear oscillators, symmetries place hard constraints on
the system that can be exploited to predict universal dynamical features and
steady-states, providing a rare generic organizing principle for
far-from-equilibrium systems. However, the robustness of this class of theories
to symmetry-disrupting imperfections is untested. Here, we develop a model
experimental reaction-diffusion network of chemical oscillators to test
applications of this theory in the context of self-organizing systems relevant
to biology and soft robotics. The network is a ring of 4 identical
microreactors containing the oscillatory Belousov-Zhabotinsky reaction coupled
to nearest neighbors via diffusion. Assuming perfect symmetry, theory predicts
4 categories of stable spatiotemporal phase-locked periodic states and 4
categories of invariant manifolds that guide and structure transitions between
phase-locked states. In our experiments, we observed the predicted
symmetry-derived synchronous clustered transients that occur when the dynamical
trajectories coincide with invariant manifolds. However, we observe only 3 of
the 4 phase-locked states that are predicted for the idealized homogeneous
system. Quantitative agreement between experiment and numerical simulations is
found by accounting for the small amount of experimentally determined
heterogeneity. This work demonstrates that a surprising degree of the network's
dynamics are constrained by symmetry in spite of the breakdown of the
assumption of homogeneity and raises the question of why heterogeneity
destabilizes some symmetry predicted states, but not others.


Abstract:
  Open set recognition (OSR), aiming to simultaneously classify the seen
classes and identify the unseen classes as 'unknown', is essential for reliable
machine learning.The key challenge of OSR is how to reduce the empirical
classification risk on the labeled known data and the open space risk on the
potential unknown data simultaneously. To handle the challenge, we formulate
the open space risk problem from the perspective of multi-class integration,
and model the unexploited extra-class space with a novel concept Reciprocal
Point. Follow this, a novel learning framework, termed Adversarial Reciprocal
Point Learning (ARPL), is proposed to minimize the overlap of known
distribution and unknown distributions without loss of known classification
accuracy. Specifically, each reciprocal point is learned by the extra-class
space with the corresponding known category, and the confrontation among
multiple known categories are employed to reduce the empirical classification
risk. Then, an adversarial margin constraint is proposed to reduce the open
space risk by limiting the latent open space constructed by reciprocal points.
To further estimate the unknown distribution from open space, an instantiated
adversarial enhancement method is designed to generate diverse and confusing
training samples, based on the adversarial mechanism between the reciprocal
points and known classes. This can effectively enhance the model
distinguishability to the unknown classes. Extensive experimental results on
various benchmark datasets indicate that the proposed method is significantly
superior to other existing approaches and achieves state-of-the-art
performance.


Abstract:
  Norms represent behavioural aspects that are encouraged by a social group of
agents or the majority of agents in a system. Normative systems enable
coordinating synthesised norms of heterogeneous agents in complex multi-agent
systems autonomously. In real applications, agents have multiple objectives
that may contradict each other or contradict the synthesised norms. Therefore,
agents need a mechanism to understand the impact of a suggested norm on their
objectives and decide whether or not to adopt it. To address these challenges,
a utility based norm synthesis (UNS) model is proposed which allows the agents
to coordinate their behaviour while achieving their conflicting objectives. UNS
proposes a utility-based case-based reasoning technique, using case-based
reasoning for run-time norm synthesising in a centralised approach, and a
utility function derived from the objectives of the system and its operating
agents to decide whether or not to adopt a norm. The model is evaluated using a
two intersecting roads scenario and the results show its efficacy to optimise
multiple objectives while adopting synthesised norms.


Abstract:
  We use spectral method to prove a joint equidistribution of primitive
rational points and the same along expanding horocycle orbits in the products
of the circle and the unit cotangent bundle of the modular surface. This result
explicates the error bound in a recent work of Einsiedler, Luethi, and Shah
\cite[Theorem $1.1$]{ELS}. The error is sharp upon the best known progress
towards the Ramanujan conjecture at the finite places for the modular surface.


Abstract:
  This thesis focuses on modifications on Einstein's theory of General
Relativity, which could explain the current problems in gravitation and
cosmology. More specifically, modifications of the affine structure of the
spacetime, which is the structure that tells us how to perform derivatives, are
studied. These theories appear when imposing that the system must be invariant
under the action of the local Poincar\'e group. Within these theories, their
stability, the conditions for the appearance of singularities, the movement of
fermions, and if it is possible to find different Black Hole solutions than in
Einstein's theory, is explored. Moreover, a non-local extension of the
mentioned modification is proposed, which can make the theory free of
instabilities and singularities at the linear limit.


Abstract:
  In this work, dynamics of chiral domain walls in long and narrow magnetic
heterostructures based on non-centrosymmetric chiral magnets with bulk
Dzyaloshinskii-Moriya interactions (DMI) and perpendicular magnetic anisotropy
is systematically investigated. The driving forces can be out-of-plane magnetic
fields and in-plane currents, correspondingly both steady and precessional
flows are considered. Their dividing points (the Walker critical field and
current density) are obtained as functions of bulk DMI strength
($D_{\mathrm{b}}$) and the ratio ($\kappa$) of total (crystalline plus shape)
anisotropy in the hard axis over that in the easy one. When far beyond Walker
breakdown, the dependence curve of wall velocity on external in-plane bias
field takes parabolic shape around the compensation point where the total
in-plane field disappears. The center shift is determined by $D_{\mathrm{b}}$,
$\kappa$, and the wall's topological charge, thus can be used to measure the
bulk DMI strength in chiral magnets.


Abstract:
  Given a graph $G = (V,E)$, a subgraph $H$ is an \emph{additive $+\beta$
spanner} if $\dist_H(u,v) \le \dist_G(u,v) + \beta$ for all $u, v \in V$. A
\emph{pairwise spanner} is a spanner for which the above inequality only must
hold for specific pairs $P \subseteq V \times V$ given on input, and when the
pairs have the structure $P = S \times S$ for some subset $S \subseteq V$, it
is specifically called a \emph{subsetwise spanner}. Spanners in unweighted
graphs have been studied extensively in the literature, but have only recently
been generalized to weighted graphs.
  In this paper, we consider a multi-level version of the subsetwise spanner in
weighted graphs, where the vertices in $S$ possess varying level, priority, or
quality of service (QoS) requirements, and the goal is to compute a nested
sequence of spanners with the minimum number of total edges. We first
generalize the $+2$ subsetwise spanner of [Pettie 2008, Cygan et al., 2013] to
the weighted setting. We experimentally measure the performance of this and
several other algorithms for weighted additive spanners, both in terms of
runtime and sparsity of output spanner, when applied at each level of the
multi-level problem. Spanner sparsity is compared to the sparsest possible
spanner satisfying the given error budget, obtained using an integer
programming formulation of the problem. We run our experiments with respect to
input graphs generated by several different random graph generators:
Erd\H{o}s--R\'{e}nyi, Watts--Strogatz, Barab\'{a}si--Albert, and random
geometric models. By analyzing our experimental results we developed a new
technique of changing an initialization parameter value that provides better
performance in practice.


Abstract:
  Many of these challenges are won by neural network models created by
full-time artificial intelligence scientists. Due to this origin, they have a
black-box character that makes their use and application less clear to learning
scientists. We describe our experience with competition from the perspective of
educational data mining, a field founded in the learning sciences and connected
with roots in psychology and statistics. We describe our efforts from the
perspectives of learning scientists and the challenges to our methods, some
real and some imagined. We also discuss some basic results in the Kaggle system
and our thoughts on how those results may have been improved. Finally, we
describe how learner model predictions are used to make pedagogical decisions
for students. Their practical use entails a) model predictions and b) a
decision rule (based on the predictions). We point out how increased model
accuracy can be of limited practical utility, especially when paired with
simple decision rules and argue instead for the need to further investigate
optimal decision rules.


Abstract:
  Several examples are given illustrating the (presumably rather general) fact
that bosonic Hamiltonians that are supersymmetrizable automatically possess
Lax-pairs, and square-roots.


Abstract:
  The learning rate (LR) schedule is one of the most important hyper-parameters
needing careful tuning in training DNNs. However, it is also one of the least
automated parts of machine learning systems and usually costs significant
manual effort and computing. Though there are pre-defined LR schedules and
optimizers with adaptive LR, they introduce new hyperparameters that need to be
tuned separately for different tasks/datasets. In this paper, we consider the
question: Can we automatically tune the LR over the course of training without
human involvement? We propose an efficient method, AutoLRS, which automatically
optimizes the LR for each training stage by modeling training dynamics. AutoLRS
aims to find an LR applied to every $\tau$ steps that minimizes the resulted
validation loss. We solve this black-box optimization on the fly by Bayesian
optimization (BO). However, collecting training instances for BO requires a
system to evaluate each LR queried by BO's acquisition function for $\tau$
steps, which is prohibitively expensive in practice. Instead, we apply each
candidate LR for only $\tau'\ll\tau$ steps and train an exponential model to
predict the validation loss after $\tau$ steps. This mutual-training process
between BO and the loss-prediction model allows us to limit the training steps
invested in the BO search. We demonstrate the advantages and the generality of
AutoLRS through extensive experiments of training DNNs for tasks from diverse
domains using different optimizers. The LR schedules auto-generated by AutoLRS
lead to a speedup of $1.22\times$, $1.43\times$, and $1.5\times$ when training
ResNet-50, Transformer, and BERT, respectively, compared to the LR schedules in
their original papers, and an average speedup of $1.31\times$ over
state-of-the-art heavily-tuned LR schedules.


Abstract:
  A classical paradox in high-mass star formation is that powerful radiation
pressure can halt accretion, preventing further growth of a central star. Disk
accretion has been proposed to solve this problem, but the disks and the
accretion process in high-mass star formation are poorly understood. We
executed high-resolution ($R$=35,000-70,000) iSHELL spectroscopy in $K$-band
for eleven high-mass protostars. Br-$\gamma$ emission was observed toward eight
sources, and the line profiles for most of these sources are similar to those
of low-mass PMS stars. Using an empirical relationship between the Br-$\gamma$
and accretion luminosities, we tentatively estimate disk accretion rates
ranging from $\lesssim$10$^{-8}$ and $\sim$10$^{-4}$ $M_\odot$ yr$^{-1}$. These
low-mass-accretion rates suggest that high-mass protostars gain more mass via
episodic accretion as proposed for low-mass protostars. Given the detection
limits, CO overtone emission ($v$=2-0 and 3-1), likely associated with the
inner disk region ($r \ll 100$ au), was found towards two sources. This
low-detection rate compared with Br-$\gamma$ emission is consistent with
previous observations. Ten out of the eleven sources show absorption at the
$v$=0-2 ${\rm R(7)-R(14)}$ CO R-branch. Most of them are either blueshifted or
redshifted, indicating that the absorption is associated with an outflow or an
inflow with a velocity of up to $\sim50$ km s$^{-1}$. Our analysis indicates
that the absorption layer is well thermalized (and therefore $n_{\mathrm H_2}
\gtrsim 10^6$ cm$^{-3}$) at a single temperature of typically 100-200 K, and
located within 200-600 au of the star.


Abstract:
  Evaluating predictive models is a crucial task in predictive analytics. This
process is especially challenging with time series data where the observations
show temporal dependencies. Several studies have analysed how different
performance estimation methods compare with each other for approximating the
true loss incurred by a given forecasting model. However, these studies do not
address how the estimators behave for model selection: the ability to select
the best solution among a set of alternatives. We address this issue and
compare a set of estimation methods for model selection in time series
forecasting tasks. We attempt to answer two main questions: (i) how often is
the best possible model selected by the estimators; and (ii) what is the
performance loss when it does not. We empirically found that the accuracy of
the estimators for selecting the best solution is low, and the overall
forecasting performance loss associated with the model selection process ranges
from 1.2% to 2.3%. We also discovered that some factors, such as the sample
size, are important in the relative performance of the estimators.


Abstract:
  This paper considers the problem of multi-agent distributed linear regression
in the presence of system noises. In this problem, the system comprises
multiple agents wherein each agent locally observes a set of data points, and
the agents' goal is to compute a linear model that best fits the collective
data points observed by all the agents. We consider a server-based distributed
architecture where the agents interact with a common server to solve the
problem; however, the server cannot access the agents' data points. We consider
a practical scenario wherein the system either has observation noise, i.e., the
data points observed by the agents are corrupted, or has process noise, i.e.,
the computations performed by the server and the agents are corrupted. In
noise-free systems, the recently proposed distributed linear regression
algorithm, named the Iteratively Pre-conditioned Gradient-descent (IPG) method,
has been claimed to converge faster than related methods. In this paper, we
study the robustness of the IPG method, against both the observation noise and
the process noise. We empirically show that the robustness of the IPG method
compares favorably to the state-of-the-art algorithms.


Abstract:
  In network analysis, many community detection algorithms have been developed,
however, their implementation leaves unaddressed the question of the
statistical validation of the results. Here we present robin(ROBustness In
Network), an R package to assess the robustness of the community structure of a
network found by one or more methods to give indications about their
reliability. The procedure initially detects if the community structure found
by a set of algorithms is statistically significant and then compares two
selected detection algorithms on the same graph to choose the one that better
fits the network of interest. We demonstrate the use of our package on the
American College Football benchmark dataset.


Abstract:
  Record linkage is aimed at the accurate and efficient identification of
records that represent the same entity within or across disparate databases. It
is a fundamental task in data integration and increasingly required for
accurate decision making in application domains ranging from health analytics
to national security. Traditional record linkage techniques calculate string
similarities between quasi-identifying (QID) values, such as the names and
addresses of people. Errors, variations, and missing QID values can however
lead to low linkage quality because the similarities between records cannot be
calculated accurately. To overcome this challenge, we propose a novel technique
that can accurately link records even when QID values contain errors or
variations, or are missing. We first generate attribute signatures
(concatenated QID values) using an Apriori based selection of suitable QID
attributes, and then relational signatures that encapsulate relationship
information between records. Combined, these signatures can uniquely identify
individual records and facilitate fast and high quality linking of very large
databases through accurate similarity calculations between records. We evaluate
the linkage quality and scalability of our approach using large real-world
databases, showing that it can achieve high linkage quality even when the
databases being linked contain substantial amounts of missing values and
errors.


Abstract:
  MUSE-based emission-line maps of the spiral galaxy NGC 4030 reveal the
existence of unresolved sources with forbidden line emission enhanced with
respect to those seen in its own HII regions. This study reports our efforts to
detect and isolate these objects and identify their nature. Candidates are
first detected as unresolved sources on an image of the second principal
component of the Hb, [OIII]5007, Ha, [NII]6584, [SII]6716, 6731 emission-line
data cube, where they stand out clearly against both the dominant HII region
population and the widespread diffuse emission. The intrinsic emission is then
extracted accounting for the highly inhomogeneous emission-line "background"
throughout the field of view. Collisional to recombination line ratios like
[SII]/Ha, [NII]/Ha, and [OI]/Ha tend to increase when the background emission
is corrected for. We find that many (but not all) sources detected with the
principal component analysis have properties compatible with supernova remnants
(SNRs). Applying a combined [SII]/Ha and [NII]/Ha classification criterion
leads to a list of 59 sources with SNR-like emission lines. Many of them
exhibit conspicuous spectral signatures of SNRs around 7300 Angs, and a
stacking analysis shows that these features are also present, except weaker, in
other cases. At nearly 30 Mpc, these are the most distant SNRs detected by
optical means to date. We further report the serendipitous discovery of a
luminous (M_V ~ -12.5), blue, and variable source, possibly associated with a
supernova impostor.


Abstract:
  We consider multiverses as time-amalgamated multiply warped products of
Lorentzian (Einstein) manifolds. We define the Local Multiverse as
timely-connected component of our physical (3+1)-spacetime. It is a collection
of ``parallel universes" with (mutually) synchronized timelines. Metaphysical
considerations suggest that the Local Multiverse could be an extremely complex
agglomeration with, at least, several hundred parallel universes in the Solar
neighbourhood (and many thousands in galaxy bulks). In this paper we study a
simplified time-almagamated globally hyperbolic model. Our picture implies the
multiversality of elementary particles which are, actually, transcosmic
(super)strings with multiple endpoints on parallel universes considered as
D-branes.


Abstract:
  We investigate how light polarization affects the motion of photo-responsive
algae, \textit{Euglena gracilis}. In a uniformly polarized field, cells swim
approximately perpendicular to the polarization direction and form a nematic
state with zero mean velocity. When light polarization varies spatially, cell
motion is modulated by local polarization. In such light fields, cells exhibit
complex spatial distribution and motion patterns which are controlled by
topological properties of the underlying fields; we further show that ordered
cell swimming can generate directed transporting fluid flow. Experimental
results are quantitatively reproduced by an active Brownian particle model in
which particle motion direction is nematically coupled to local light
polarization.


Abstract:
  Recently few-shot object detection is widely adopted to deal with
data-limited situations. While most previous works merely focus on the
performance on few-shot categories, we claim that detecting all classes is
crucial as test samples may contain any instances in realistic applications,
which requires the few-shot detector to learn new concepts without forgetting.
Through analysis on transfer learning based methods, some neglected but
beneficial properties are utilized to design a simple yet effective few-shot
detector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the
pretrained RPN and Re-detector to find few-shot class objects without
forgetting previous knowledge. Extensive experiments on few-shot detection
benchmarks show that Retentive R-CNN significantly outperforms state-of-the-art
methods on overall performance among all settings as it can achieve competitive
results on few-shot classes and does not degrade the base class performance at
all. Our approach has demonstrated that the long desired never-forgetting
learner is available in object detection.


Abstract:
  With the advent of advancements in deep learning approaches, such as deep
convolution neural network, residual neural network, adversarial network; U-Net
architectures are most widely utilized in biomedical image segmentation to
address the automation in identification and detection of the target regions or
sub-regions. In recent studies, U-Net based approaches have illustrated
state-of-the-art performance in different applications for the development of
computer-aided diagnosis systems for early diagnosis and treatment of diseases
such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various
modalities. This article contributes in presenting the success of these
approaches by describing the U-Net framework, followed by the comprehensive
analysis of the U-Net variants by performing 1) inter-modality, and 2)
intra-modality categorization to establish better insights into the associated
challenges and solutions. Besides, this article also highlights the
contribution of U-Net based frameworks in the ongoing pandemic, severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.
Finally, the strengths and similarities of these U-Net variants are analysed
along with the challenges involved in biomedical image segmentation to uncover
promising future research directions in this area.


Abstract:
  Excitons in a semiconductor monolayer form a collective resonance that can
reflect resonant light with extraordinarily high efficiency. Here, we
investigate the nonlinear optical properties of such atomistically thin mirrors
and show that finite-range interactions between excitons can lead to the
generation of highly non-classical light. We describe two scenarios, in which
optical nonlinearities arise either from direct photon coupling to excitons in
excited Rydberg states or from resonant two-photon excitation of Rydberg
excitons with finite-range interactions. The latter case yields conditions of
electromagnetically induced transparency and thereby provides an efficient
mechanism for single-photon switching between high transmission and reflectance
of the monolayer, with a tunable dynamical timescale of the emerging
photon-photon interactions. Remarkably, it turns out that the resulting high
degree of photon correlations remains virtually unaffected by Rydberg-state
decoherence, in excess of non-radiative decoherence observed for ground-state
excitons in two-dimensional semiconductors. This robustness to imperfections
suggests a promising new approach to quantum photonics at the level of
individual photons.


Abstract:
  This is the first of a two part paper investigating the geometry of the
integrator reach sets, and the applications thereof. In this Part I, we
establish that this compact convex set is semialgebraic, translated zonoid, and
not a spectrahedron. We derive the parametric as well as the implicit
representation of the boundary of this reach set. We also deduce the closed
form formula for the volume and diameter of this set, and discuss their scaling
with state dimension and time. We point out that these results may be utilized
in benchmarking the performance of the reach set over-approximation algorithms.


Abstract:
  Storm water detention ponds are used to manage the discharge of rainfall
runoff from urban areas to nearby streams. Their purpose is to reduce the
hydraulic impact and sediment loads of the receiving waters. Detention ponds
are currently designed based on static controls: the output flow of a pond is
capped at a fixed value. This is not optimal with respect to the current
infrastructure capacity and for some detention ponds it might even violate
current regulations set by the European Water Framework Directive. We apply
formal methods to synthesize (i.e., derive automatically) a safe and optimal
active controller. We model the storm water detention pond, including the urban
catchment area and the rain forecasts, as a hybrid Markov decision process.
Subsequently, we use the tool Uppaal Stratego to synthesize a control strategy
minimizing the cost related to pollution (optimality) while guaranteeing no
emergency overflow of the detention pond (safety). Simulation results for an
existing pond show that Uppaal Stratego can learn optimal strategies that
prevent emergency overflows, where the current static control is not always
able to prevent it. At the same time, our approach can improve sedimentation
during low rain periods.


Abstract:
  It has been proposed that paramagnetic materials exhibit a unique
thermoelectric effect near the ferromagnetic transition point due to spin
fluctuations. This phenomenon is often referred to as paramagnon drag. We
calculate the contribution of this paramagnon drag to the Seebeck coefficient
microscopically on the basis of the linear response theory. This leed to a
general formula for the contribution to the Seebeck coefficient due to the
paramagnon drag, and we clarify the conditions in which the Seebeck coefficient
enhances near the ferromagnetic transition point for a single-band and
isotropic system. Moreover, we calculate the Seebeck coefficients for a band
$\varepsilon \propto k^n$ and a mixture of free-electron-like and flat bands.


Abstract:
  While there have been lots of work studying frequent subgraph mining, very
rare publications have discussed frequent subnet mining from more complicated
data structures such as Petri nets. This paper studies frequent subnets mining
from a single large Petri net. We follow the idea of transforming a Petri net
in net graph form and to mine frequent sub-net graphs to avoid high complexity.
Technically, we take a minimal traversal approach to produce a canonical label
of the big net graph. We adapted the maximal independent embedding set approach
to the net graph representation and proposed an incremental pattern growth
(independent embedding set reduction) way for discovering frequent sub-net
graphs from the single large net graph, which are finally transformed back to
frequent subnets. Extensive performance studies made on a single large Petri
net, which contains 10K events, 40K conditions and 30 K arcs, showed that our
approach is correct and the complexity is reasonable.


Abstract:
  This article presents the data used to evaluate the performance of
evolutionary clustering algorithm star (ECA*) compared to five traditional and
modern clustering algorithms. Two experimental methods are employed to examine
the performance of ECA* against genetic algorithm for clustering++
(GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation
(EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32
heterogenous and multi-featured datasets to determine which one performs well
on the three tests. For one, ther paper examines the efficiency of ECA* in
contradiction of its corresponding algorithms using clustering evaluation
measures. These validation criteria are objective function and cluster quality
measures. For another, it suggests a performance rating framework to measurethe
the performance sensitivity of these algorithms on varos dataset features
(cluster dimensionality, number of clusters, cluster overlap, cluster shape and
cluster structure). The contributions of these experiments are two-folds: (i)
ECA* exceeds its counterpart aloriths in ability to find out the right cluster
number; (ii) ECA* is less sensitive towards dataset features compared to its
competitive techniques. Nonetheless, the results of the experiments performed
demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based
on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA*
on several real applications has not been achieved yet.


Abstract:
  Government employees in Brazil are granted tenure after three years on the
job. Firing a tenured government employee is all but impossible, so tenure is a
big employee benefit. But exactly how big is it? In other words: how much money
is tenure worth to a government employee in Brazil? No one has ever attempted
to answer that question. I do that in this paper. I use a modified version of
the Sharpe ratio to estimate what the risk-adjusted salaries of government
workers should be. The difference between actual salary and risk-adjusted
salary gives us an estimate of how much tenure is worth to each employee. I
find that in the 2005-2019 period the monthly value of tenure was 3980 reais to
the median federal government employee, 1971 reais to the median state
government employee, and 500 reais to the median municipal government employee.


Abstract:
  In this work, we study the partial sums of independent and identically
distributed random variables with the number of terms following a fractional
Poisson (FP) distribution. The FP sum contains the Poisson and geometric
summations as particular cases. We show that the weak limit of the FP
summation, when properly normalized, is a mixture between the normal and
Mittag-Leffler distributions, which we call by Normal-Mittag-Leffler (NML) law.
A parameter estimation procedure for the NML distribution is developed and the
associated asymptotic distribution is derived. Simulations are performed to
check the performance of the proposed estimators under finite samples. An
empirical illustration on the daily log-returns of the Brazilian stock exchange
index (IBOVESPA) shows that the NML distribution captures better the tails than
some of its competitors. Related problems such as a mixed Poisson
representation for the FP law and the weak convergence for the
Conway-Maxwell-Poisson random sum are also addressed.


Abstract:
  Second-order nonlinear optical processes are used to convert light from one
wavelength to another and to generate quantum entanglement. Creating chip-scale
devices to more efficiently realize and control these interactions greatly
increases the reach of photonics. Optical crystals and guided wave devices made
from lithium niobate and potassium titanyl phosphate are typically used to
realize second-order processes but face significant drawbacks in scalability,
power, and tailorability when compared to emerging integrated photonic systems.
Silicon or silicon nitride integrated photonic circuits enhance and control the
third-order optical nonlinearity by confining light in dispersion-engineered
waveguides and resonators. An analogous platform for second-order nonlinear
optics remains an outstanding challenge in photonics. It would enable stronger
interactions at lower power and reduce the number of competing nonlinear
processes that emerge. Here we demonstrate efficient frequency doubling and
parametric oscillation in a thin-film lithium niobate photonic circuit. Our
device combines recent progress on periodically poled thin-film lithium niobate
waveguidesand low-loss microresonators. Here we realize efficient >10%
second-harmonic generation and parametric oscillation with microwatts of
optical power using a periodically-poled thin-film lithium niobate
microresonator. The operating regimes of this system are controlled using the
relative detuning of the intracavity resonances. During nondegenerate
oscillation, the emission wavelength is tuned over terahertz by varying the
pump frequency by 100's of megahertz. We observe highly-enhanced effective
third-order nonlinearities caused by cascaded second-order processes resulting
in parametric oscillation. These resonant second-order nonlinear circuits will
form a crucial part of the emerging nonlinear and quantum photonics platforms.


Abstract:
  The advent of wide-field sky surveys has led to the growth of transient and
variable source discoveries. The data deluge produced by these surveys has
necessitated the use of machine learning (ML) and deep learning (DL) algorithms
to sift through the vast incoming data stream. A problem that arises in
real-world applications of learning algorithms for classification is imbalanced
data, where a class of objects within the data is underrepresented, leading to
a bias for over-represented classes in the ML and DL classifiers. We present a
recurrent neural network (RNN) classifier that takes in photometric time-series
data and additional contextual information (such as distance to nearby galaxies
and on-sky position) to produce real-time classification of objects observed by
the Gravitational-wave Optical Transient Observer (GOTO), and use an
algorithm-level approach for handling imbalance with a focal loss function. The
classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when
using all available photometric observations to classify variable stars,
supernovae, and active galactic nuclei. The RNN architecture allows us to
classify incomplete light curves, and measure how performance improves as more
observations are included. We also investigate the role that contextual
information plays in producing reliable object classification.


Abstract:
  We demonstrated broadband highly efficient optical nonlinear processes in
on-chip integrated lithium niobate (LN) microdisk resonators. The Q factors of
the micro-resonators fabricated by femtosecond laser writing and
chemo-mechanical polishing are reliably above 10^8, approaching the intrinsic
material absorption limit of LN. Broadband nonlinear processes, including
optical parametric oscillation (OPO), second harmonic generation (SHG), third
harmonic generation, and fourth harmonic generation, were observed with
ultrahigh efficiencies in the same LN microdisk without introducing domain
inversion, thanks to the natural quasi phase-matching and the dense spectral
modes of the X-cut LN microdisk with millimeter diameter. The threshold of OPO
and the absolute conversion efficiency of SHG are 19.6 microwatt and 66%, both
surpass the state-of-the-art values among on-chip LN micro-resonators
demonstrated so far. The broadband and highly efficient nonlinear frequency
conversions achieved with the ultrahigh-Q LN microdisk resonators promise
high-density integration of nonlinear photonic devices such as frequency
convertors and entangled photon sources.


Abstract:
  Under disturbances, superconductors may experience sudden, most undesirable
phase transitions (quench) from superconducting to normal conducting state.
Quench may lead to damage or even to catastrophic conductor failure. A
superconductor is stable if it does not quench. Exact determination of
superconductor transient, resistive states (flux flow, Ohmic) thus is mandatory
to safely avoid quench. This request sharp comparison of local, transient
conductor temperature and current transport density with local values of
critical superconductor temperature, TCrit, and critical current density,
JCrit, and the latter is a strong function of temperature. Numerical, Finite
Element simulations reported previously and in this paper have provided the
requested, transient temperature distribution; under disturbances, this
distribution may strongly be non-uniform. But what happens if the other
variable, TCrit, might not uniquely be defined? A multi-physics model
(fractional parentage, Pauli selection rule, time of flight-concept with a
mediating Boson, the Yukawa model and the uncertainty principle) provides
relaxation time at which TCrit, as the thermodynamic, equilibrium state,
finally would be obtained. But relaxation time diverges the closer the electron
system approaches the phase transition, which questions existence of uniquely
defined TCrit within finite process time. In this paper, focus is on multiple
internal heat transfer (solid conduction and, in thin films, radiation), a
suggested operator method to solve the incompleteness problem of radiative
transfer, and time dependence of the order parameter obtained from the
quantum-mechanical model. Traditional stability models cannot provide this
information.


Abstract:
  In this paper, an original reduction algorithm for solving simultaneous
multivariate polynomial equations is presented. The algorithm is exponential in
complexity, but the well-known algorithms, such as the extended Euclidean
algorithm and Buchberger's algorithm, are superexponential. The
superexponential complexity of the well-known algorithms is due to their not
being "minimal" in a certain sense. Buchberger's algorithm produces a Groebner
basis. The proposed original reduction algorithm achieves the required task,
via computation of determinants of parametric Sylvester matrices, and produces
a Rabin basis, which is shown to be minimal, when two multivariate polynomials
are reduced at a time. The minimality of Rabin basis allows us to prove
exponential lower bounds for the space complexity of an algebraic proof of
certification, for a specific computational problem in the computational
complexity class PSPACE, showing that the complexity classes PSPACE and P
cannot be the same. By the same reasoning, it follows that Co-NP is not the
same as either of the complexity classes NP and P, and that the polynomial time
hierarchy does not collapse. It is also shown that the class BPP of languages
decidable by bounded error probabilistic algorithms with (probabilistic)
polynomial time proofs for the membership of input words is not the same as any
one of the complexity classes P, NP and Co-NP. It follows again, from the
discussions, that the complexity classes NP and P are not the same, by
relativization of BPP, with respect to P and NP.


Abstract:
  The generating functions for the gauge theory observables are often
represented in terms of the unitary matrix integrals. In this work, the
perturbative and non-perturbative aspects of the generic multi-critical unitary
matrix models are studied by adopting the integrable operator formalism, and
the multi-critical generalization of the Tracy--Widom distribution in the
context of random partitions. We obtain the universal results for the
multi-critical model in the weak and strong coupling phases. The free energy of
the instanton sector in the weak coupling regime, and the genus expansion of
the free energy in the strong coupling regime are explicitly computed and the
universal multi-critical phase structure of the model is explored. Finally, we
apply our results in concrete examples of supersymmetric indices of gauge
theories in the large $N$ limit.


Abstract:
  Non-equilibrium phase separating systems with reactions can break
time-reversal symmetry (TRS) in two distinct ways. Firstly, the conservative
and non-conservative sectors of the dynamics can be governed by incompatible
free energies; when both sectors are present, this is the leading-order TRS
violation, captured in its simplest form by 'Model AB'. Second, the diffusive
dynamics can break TRS in its own right. This happens only at higher order in
the gradient expansion (but is the leading behaviour without reactions present)
and is captured by 'Active Model B+' (AMB+). Each of the two mechanisms can
lead to microphase separation, by quite different routes. Here we introduce
Model AB+, for which both mechanisms are simultaneously present, and show that
for slow reaction rates the system can undergo a new type of hierarchical
microphase separation, whereby a continuous phase of fluid 1 contains large
droplets of fluid 2 within which small droplets of fluid 1 are continuously
created and then absorbed into the surrounding fluid-1 phase. In this state of
'bubbly microphase separation' the small-scale 1-in-2 droplets arise by the
conservative diffusive dynamics with the larger scale 2-in-1 structure governed
by the nonconservative reactions.


Abstract:
  Halo assembly bias is the secondary dependence of the clustering of
dark-matter haloes on their assembly histories at fixed halo mass. This
established dependence is expected to manifest itself on the clustering of the
galaxy population, a potential effect commonly known as galaxy assembly bias.
Using the IllustrisTNG300 magnetohydrodynamical simulation, we analyse the
dependence of the properties and clustering of galaxies on the shape of the
specific mass accretion history of their hosting haloes (sMAH). We first show
that several halo and galaxy properties strongly correlate with the slope of
the sMAH ($\beta$) at fixed halo mass. Namely, haloes with increasingly steeper
$\beta$ increment their halo masses faster at early times, and their hosted
galaxies present larger stellar-to-halo mass ratios, lose their gas faster,
reach the peak of their star formation histories at higher redshift, and become
quenched earlier. We also demonstrate that $\beta$ is more directly connected
to these key galaxy formation properties than other broadly employed halo
proxies, such as formation time. Finally, we measure the secondary dependence
of galaxy clustering on $\beta$ at fixed halo mass as a function of redshift.
By tracing back the evolution of individual haloes, we show that the amplitude
of the galaxy assembly bias signal for the progenitors of $z=0$ galaxies
increases with redshift, reaching a factor of 2 at $z = 1$ for haloes of
$M_\mathrm{halo}=10^{11.5}-10^{12}$ $h^{-1}\mathrm{M}_\odot$. The measurement
of the evolution of assembly bias along the merger tree provides a new
theoretical perspective to the study of secondary bias. Our findings, which
show a tight relationship between halo accretion and both the clustering and
the observational properties of the galaxy population, have also important
implications for the generation of mock catalogues for upcoming cosmological
surveys.


Abstract:
  Visual place recognition (VPR) is the problem of recognising a previously
visited location using visual information. Many attempts to improve the
performance of VPR methods have been made in the literature. One approach that
has received attention recently is the multi-process fusion where different VPR
methods run in parallel and their outputs are combined in an effort to achieve
better performance. The multi-process fusion, however, does not have a
well-defined criterion for selecting and combining different VPR methods from a
wide range of available options. To the best of our knowledge, this paper
investigates the complementarity of state-of-the-art VPR methods systematically
for the first time and identifies those combinations which can result in better
performance. The paper presents a well-defined framework which acts as a sanity
check to find the complementarity between two techniques by utilising a
McNemar's test-like approach. The framework allows estimation of upper and
lower complementarity bounds for the VPR techniques to be combined, along with
an estimate of maximum VPR performance that may be achieved. Based on this
framework, results are presented for eight state-of-the-art VPR methods on ten
widely-used VPR datasets showing the potential of different combinations of
techniques for achieving better performance.


Abstract:
  In this work, we consider the Yukawa-type and power-type non-Newtonian
corrections, which induce amplification of gravitational interaction on
submillimeter scales, and analytically calculate deviations produced by the
atomic gravitational field on the energy levels of hydrogen-like ions.
Analyzing ionic transitions between Rydberg states, we derive prospective
constraints for non-Newtonian corrections. It is shown that the results also
provide stronger constraints, due to the high accuracy for Rydberg transition
measures into optical spectrum frequency range, than the current empirical
bounds following from Casimir force measurements.


Abstract:
  Online political advertising is a central aspect of modern election
campaigning for influencing public opinion. Computational analysis of political
ads is of utmost importance in political science to understand the
characteristics of digital campaigning. It is also important in computational
linguistics to study features of political discourse and communication on a
large scale. In this work, we present the first computational study on online
political ads with the aim to (1) infer the political ideology of an ad
sponsor; and (2) identify whether the sponsor is an official political party or
a third-party organization. We develop two new large datasets for the two tasks
consisting of ads from the U.S.. Evaluation results show that our approach that
combines textual and visual information from pre-trained neural models
outperforms a state-of-the-art method for generic commercial ad classification.
Finally, we provide an in-depth analysis of the limitations of our
best-performing models and linguistic analysis to study the characteristics of
political ads discourse.


Abstract:
  Investigating the grain boundary energies of pure fcc metals and their
surface energies obtained from ab initio modeling, we introduce a robust method
to estimate the grain boundary energies for complex multicomponent alloys. The
input parameter is the surface energy of the alloy, which can easily be
accessed by modern ab initio calculations based on density functional theory.
The method is demonstrated in the case of paramagnetic Fe-Cr-Ni alloys for
which reliable grain boundary data is available.


Abstract:
  A space-time framework is applied to simulate dense granular flow. Two
different numerical experiments are performed: a column collapse and a dam
break on an inclined plane. The experiments are modeled as two-phase flows. The
dense granular material is represented by a constitutive model, the
$\mu$($I$)-rheology, that is based on the Coulomb's friction law, such that the
normal stress applied by the pressure is related to the tangential stress. The
model represents a complex shear thinning viscoplastic material behavior. The
interface between the dense granular material and the surrounding light fluid
is captured with a level set function. Due to discontinuities close to the the
interface, the mesh requires a sufficient resolution. The space-time approach
allows unstructured meshes in time and, therefore a well refined mesh in the
temporal direction around the interface. In this study, results and performance
of a flat and a simplex space time discretization are verified and analyzed.


Abstract:
  Positron emission tomography, like many other tomographic imaging modalities,
relies on an image reconstruction step to produce cross-sectional images from
projection data. Detection and localization of the back-to-back annihilation
photons produced by positron-electron annihilation defines the trajectories of
these photons, which when combined with tomographic reconstruction algorithms,
permits recovery of the distribution of positron-emitting radionuclides. Here
we produce cross-sectional images directly from the detected coincident
annihilation photons, without using a reconstruction algorithm. Ultra-fast
radiation detectors with a resolving time averaging 32 picoseconds measured the
difference in arrival time of pairs of annihilation photons, localizing the
annihilation site to 4.8 mm. This is sufficient to directly generate an image
without reconstruction and without the geometric and sampling constraints that
normally present for tomographic imaging systems.


Abstract:
  Neural abstractive summarization has been studied in many pieces of
literature and achieves great success with the aid of large corpora. However,
when encountering novel tasks, one may not always benefit from transfer
learning due to the domain shifting problem, and overfitting could happen
without adequate labeled examples. Furthermore, the annotations of abstractive
summarization are costly, which often demand domain knowledge to ensure the
ground-truth quality. Thus, there are growing appeals for Low-Resource
Abstractive Summarization, which aims to leverage past experience to improve
the performance with limited labeled examples of target corpus. In this paper,
we propose to utilize two knowledge-rich sources to tackle this problem, which
are large pre-trained models and diverse existing corpora. The former can
provide the primary ability to tackle summarization tasks; the latter can help
discover common syntactic or semantic information to improve the generalization
ability. We conduct extensive experiments on various summarization corpora with
different writing styles and forms. The results demonstrate that our approach
achieves the state-of-the-art on 6 corpora in low-resource scenarios, with only
0.7% of trainable parameters compared to previous work.


Abstract:
  Recent work in iterative voting has defined the difference in social welfare
between the truthful winner and worst-case equilibrium winner, due to repeated
strategic manipulations, known as the additive dynamic price of anarchy
(ADPoA). While all iterative plurality winners have been shown to differ from
truth by at most one initial vote, it is less understood how agents' welfare
changes in equilibrium. To this end, we differentiate agents' utility from
their iteration mechanism and determine iterative plurality's ADPoA in the
worst- and average-case. We first negatively demonstrate that the worst-case
ADPoA is linear in the number of agents. In expectation, rather, equilibrium
winners have a constant order welfare advantage over the truthful winner. Our
positive results illustrate the prospect for social welfare to increase due to
strategic manipulation.


Abstract:
  Bid optimization for online advertising from single advertiser's perspective
has been thoroughly investigated in both academic research and industrial
practice. However, existing work typically assume competitors do not change
their bids, i.e., the wining price is fixed, leading to poor performance of the
derived solution. Although a few studies use multi-agent reinforcement learning
to set up a cooperative game, they still suffer the following drawbacks: (1)
They fail to avoid collusion solutions where all the advertisers involved in an
auction collude to bid an extremely low price on purpose. (2) Previous works
cannot well handle the underlying complex bidding environment, leading to poor
model convergence. This problem could be amplified when handling multiple
objectives of advertisers which are practical demands but not considered by
previous work. In this paper, we propose a novel multi-objective cooperative
bid optimization formulation called Multi-Agent Cooperative bidding Games
(MACG). MACG sets up a carefully designed multi-objective optimization
framework where different objectives of advertisers are incorporated. A global
objective to maximize the overall profit of all advertisements is added in
order to encourage better cooperation and also to protect self-bidding
advertisers. To avoid collusion, we also introduce an extra platform revenue
constraint. We analyze the optimal functional form of the bidding formula
theoretically and design a policy network accordingly to generate auction-level
bids. Then we design an efficient multi-agent evolutionary strategy for model
optimization. Offline experiments and online A/B tests conducted on the Taobao
platform indicate both single advertiser's objective and global profit have
been significantly improved compared to state-of-art methods.


Abstract:
  We consider a composite Higgs model based on the $SU(6)/Sp(6)$ coset, where
an $U(1)$ subgroup of $Sp(6)$ is identified as the flavor symmetry. A complex
scalar field $s$, which is a pseudo-Nambu-Goldstone boson of the broken
symmetry, carries a flavor charge and plays the role of a flavon field. The
$U(1)_F$ flavor symmetry is then broken by a VEV of the flavon field, which
leads to a small parameter and generates the mass hierarchy between the top and
bottom quarks. A light flavon below the TeV scale can be naturally introduced,
which provides a fully testable model for the origin of flavor hierarchy. A
light flavon also leads to substantial flavor changing neutral currents, which
are strongly constrained by the flavor precision tests. The direct search of
additional scalar bosons can also be conducted in HL-LHC and future hadron
colliders.


Abstract:
  We study a dynamic ordered logit model for panel data with fixed effects. We
establish the validity of a set of moment conditions that are free of the fixed
effects and that can be computed using four or more periods of data. We
establish sufficient conditions for these moment conditions to identify the
regression coefficients, the autoregressive parameters, and the threshold
parameters. The parameters can be estimated using the generalized method of
moments. We document the performance of this estimator using Monte Carlo
simulations and an empirical illustration to self-reported health status using
the British Household Panel Survey.


Abstract:
  We present a new conceptual radiation detector, the Architected Multimaterial
Scintillator System, that utilizes a scintillator composed of multiple
materials arranged in architected structures to enable new capabilities. By
structuring differently-dyed materials, the wavelength of the scintillation
light encodes additional information in radiation measurements. These
structures can be realized through additive manufacture (3D-printing). Two
classes of this concept are described and modelled using Monte Carlo
simulations to evaluate their performance. The first detector design uses dye
microstructures to encode particle tracking information, allowing for
directional neutron detection and gamma/neutron discrimination. The second
design uses a dye gradient to indicate the position of radiation along the
gradient. The simulation results indicate this new concept in radiation
detection can achieve strong performance in a variety of capabilities including
particle identification, directionality and spectroscopy measurements, and
particle position reconstruction.


Abstract:
  Accurate prediction of laminar-turbulent transition is a critical element of
computational fluid dynamics simulations for aerodynamic design across multiple
flow regimes. Traditional methods of transition prediction cannot be easily
extended to flow configurations where the transition process depends on a large
set of parameters. In comparison, neural network methods allow higher
dimensional input features to be considered without compromising the efficiency
and accuracy of the traditional data driven models. Neural network methods
proposed earlier follow a cumbersome methodology of predicting instability
growth rates over a broad range of frequencies, which are then processed to
obtain the N-factor envelope, and then, the transition location based on the
correlating N-factor. This paper presents an end-to-end transition model based
on a recurrent neural network, which sequentially processes the mean
boundary-layer profiles along the surface of the aerodynamic body to directly
predict the N-factor envelope and the transition locations over a
two-dimensional airfoil. The proposed transition model has been developed and
assessed using a large database of 53 airfoils over a wide range of chord
Reynolds numbers and angles of attack. The sequence-to-sequence transduction
model proposed herein provides a more direct approach for accurate predictions
of the transition location than the earlier neural network methods, which
predict the local amplification rate of a single instability mode at a fixed
location along the airfoil. The large universe of airfoils encountered in
various applications causes additional difficulties. As such, we provide
further insights on selecting training datasets from large amounts of available
data.


Abstract:
  Acoustic waves at microwave frequencies have been widely used in signal
processing applications. They are also emerging as a promising approach for
communication and manipulation of quantum information. However, dynamic control
of acoustic waves in a low-loss and scalable manner remains an outstanding
challenge, which hinders the development of phononic integrated circuits. Here
we demonstrate electrical control of traveling acoustic waves on an integrated
lithium niobate platform at both room and cryogenic temperatures. We
electrically tune the material elasticity to modulate the phase and amplitude
of the acoustic waves and demonstrate an acoustic frequency shifter by
serrodyne phase modulation. Furthermore, we show reconfigurable nonreciprocal
modulation by tailoring the phase matching between acoustic and quasi-traveling
electric fields. Our scalable electro-acoustic platform comprises the
fundamental elements for arbitrary acoustic signal processing and manipulation
of phononic quantum information.


Abstract:
  The observation of gravitational waves from LIGO and Virgo detectors inferred
the mergers rates to be $23.9^{+14.9}_{-8.6}$ Gpc$^{-3}$ yr$^{-1}$ for binary
black holes and $320^{+490}_{-240}$ Gpc$^{-3}$ yr$^{-1}$ for binary neutron
stars. These rates suggest that there is a significant chance that two or more
of these signals will overlap with each other during their lifetime in the
sensitivity-band of future gravitational-wave detectors such as the Cosmic
Explorer and Einstein Telescope. The detection pipelines provide the
coalescence time of each signal with an accuracy $\mathcal{O}(10\,\rm ms)$. We
show that using the information of the coalescence time, it is possible to
correctly infer the properties of these "overlapping signals" with the current
data-analysis infrastructure. Studying different configurations of the signals,
we conclude that the inference is robust provided that the two signals are not
coalescing within less than $\sim 1-2\,\mathrm{s}$. Signals whose coalescence
epochs lie within $\sim 0.5\,\rm s$ of each other suffer from significant
biases in parameter inference, and new strategies and algorithms are required
to overcome such biases.


Abstract:
  We present a faster symbolic algorithm for the following central problem in
probabilistic verification: Compute the maximal end-component (MEC)
decomposition of Markov decision processes (MDPs). This problem generalizes the
SCC decomposition problem of graphs and closed recurrent sets of Markov chains.
The model of symbolic algorithms is widely used in formal verification and
model-checking, where access to the input model is restricted to only symbolic
operations (e.g., basic set operations and computation of one-step
neighborhood). For an input MDP with $n$ vertices and $m$ edges, the classical
symbolic algorithm from the 1990s for the MEC decomposition requires $O(n^2)$
symbolic operations and $O(1)$ symbolic space. The only other symbolic
algorithm for the MEC decomposition requires $O(n \sqrt{m})$ symbolic
operations and $O(\sqrt{m})$ symbolic space. A main open question is whether
the worst-case $O(n^2)$ bound for symbolic operations can be beaten. We present
a symbolic algorithm that requires $\widetilde{O}(n^{1.5})$ symbolic operations
and $\widetilde{O}(\sqrt{n})$ symbolic space. Moreover, the parametrization of
our algorithm provides a trade-off between symbolic operations and symbolic
space: for all $0<\epsilon \leq 1/2$ the symbolic algorithm requires
$\widetilde{O}(n^{2-\epsilon})$ symbolic operations and
$\widetilde{O}(n^{\epsilon})$ symbolic space ($\widetilde{O}$ hides
poly-logarithmic factors).
  Using our techniques we present faster algorithms for computing the
almost-sure winning regions of $\omega$-regular objectives for MDPs. We
consider the canonical parity objectives for $\omega$-regular objectives, and
for parity objectives with $d$-priorities we present an algorithm that computes
the almost-sure winning region with $\widetilde{O}(n^{2-\epsilon})$ symbolic
operations and $\widetilde{O}(n^{\epsilon})$ symbolic space, for all $0 <
\epsilon \leq 1/2$.