pwc-archive/papers-with-abstracts huggingface dataset:
Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller
Date: 2019-10-24 00:00:00
Tasks: Autonomous Racing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

This paper describes a verification case study on an autonomous racing car
with a neural network (NN) controller. Although several verification approaches
have been proposed over the last year, they have only been evaluated on
low-dimensional systems or systems with constrained environments. To explore
the limits of existing approaches, we present a challenging benchmark in which
the NN takes raw LiDAR measurements as input and outputs steering for the car.
We train a dozen NNs using two reinforcement learning algorithms and show that
the state of the art in verification can handle systems with around 40 LiDAR
rays, well short of a typical LiDAR scan with 1081 rays. Furthermore, we
perform real experiments to investigate the benefits and limitations of
verification with respect to the sim2real gap, i.e., the difference between a
system's modeled and real performance. We identify cases, similar to the
modeled environment, in which verification is strongly correlated with safe
behavior. Finally, we illustrate LiDAR fault patterns that can be used to
develop robust and safe reinforcement learning algorithms.
Analysis of a reduced model of epithelial-mesenchymal fate determination in cancer metastasis as a singularly-perturbed monotone system
Authors: M. Ali Al-Radhawi, Eduardo D. Sontag
Date: 2019-10-24 00:00:00

Tumor metastasis is one of the main factors responsible for the high fatality rate of cancer. Metastasis can occur after malignant cells transition from the epithelial phenotype to the mesenchymal phenotype. This transformation allows cells to migrate via the circulatory system and subsequently settle in distant organs after undergoing the reverse transition from the mesenchymal to the epithelial phenotypes. The core gene regulatory network controlling these transitions consists of a system made up of coupled SNAIL/miRNA-34 and ZEB1/miRNA-200 subsystems. In this work, we formulate a mathematical model of the core regulatory motif and analyze its long-term behavior. We start by developing a detailed reaction network with 24 state variables. Assuming fast promoter and mRNA kinetics, we then show how to reduce our model to a monotone four-dimensional system. For the reduced system, monotone dynamical systems theory can be used to prove generic convergence to the set of equilibria for all bounded trajectories. The theory does not apply to the full model, which is not monotone, but we briefly discuss results for singularly-perturbed monotone systems that provide a tool to extend convergence results from reduced to full systems, under appropriate time separation assumptions.
Coalition-structured governance improves cooperation to provide public goods
Date: 2019-10-24 00:00:00

While the benefits of common and public goods are shared, they tend to be
scarce when contributions are provided voluntarily. Failure to cooperate in the
provision or preservation of these goods is fundamental to sustainability
challenges, ranging from local fisheries to global climate change. In the real
world, such cooperative dilemmas occur in multiple interactions with complex
strategic interests and frequently without full information. We argue that
voluntary cooperation enabled across multiple coalitions (akin to
polycentricity) not only facilitates greater generation of non-excludable
public goods, but may also allow evolution toward a more cooperative, stable,
and inclusive approach to governance. Contrary to any previous study, we show
that these merits of multi-coalition governance are far more general than the
singular examples occurring in the literature, and are robust under diverse
conditions of excludability, congestability of the non-excludable public good,
and arbitrary shapes of the return-to-contribution function. We first confirm
the intuition that a single coalition without enforcement and with players
pursuing their self-interest without knowledge of returns to contribution is
prone to cooperative failure. Next, we demonstrate that the same pessimistic
model but with a multi-coalition structure of governance experiences relatively
higher cooperation by enabling recognition of marginal gains of cooperation in
the game at stake. In the absence of enforcement, public-goods regimes that
evolve through a proliferation of voluntary cooperative forums can maintain and
increase cooperation more successfully than singular, inclusive regimes.
3D model-based restoration with positivity constraint using a reduced number of 3D-SIM images
Date: 2019-10-24 00:00:00

We extend our previous three-dimensional (3D) model-based (MB) approach for
3D structured illumination microscopy (SIM) by introducing a positivity
constraint (PC) through the reconstruction of an auxiliary function using a
conjugate-gradient method. The performance of the new 3D-MBPC method is
investigated with noisy simulation and compared to our previous 3D-MB approach
and to the 3D Generalized Wiener filter approach used traditionally for 3D
processing of 3D-SIM data. Results show more accurate 3D restoration is
possible with the 3D-MBPC method over the other two methods. Moreover,
information redundancy in 3D-SIM data is exploited and results obtained with
the 3D-MBPC method when the number of raw SIM images is reduced from 15 down to
7 and 5 are promising.
Underwater Cooperative MIMO Communications using Hybrid Acoustic and Magnetic Induction Technique
Date: 2019-10-24 00:00:00

Future smart ocean applications require long distance and reliable
communications to connect underwater sensors/robots with remote surface base
stations. It is challenging to achieve such goal due to the harsh and dynamic
underwater acoustic channel. While Multiple-Input and Multiple-Output (MIMO)
technique can enhance reliability and transmission range, it is difficult to
place multiple acoustic transducers on one single underwater device due to the
large wavelength. Although the cooperative MIMO technique that let multiple
underwater devices form a virtual MIMO system could solve the issue, it was
impossible to synchronize the distributed underwater devices due to the
extremely large and dynamic propagation delay of acoustic waves. To this end,
this paper proposes an underwater cooperative MIMO communication mechanism,
which is based on a hybrid acoustic and Magnetic Induction (MI) technique. The
inter-node synchronization problem can be perfectly solved by using the MI
technique so that the distributed acoustic transducers can cooperatively form
narrow beams for long distance underwater communications. The synchronization
time and errors are significantly reduced since MI has negligible signal
propagation delays. To quantitatively analyze the improvement, the closed-form
expressions of the synchronization error, signal-to-noise ratio (SNR),
effective communication time, and throughput of the proposed system are
rigorously derived. The proposed hybrid system is implemented in a
software-defined testbed under the beamforming and space-time coding scheme.
Through both numerical analysis and real-world experiments, the paper shows
that the proposed hybrid cooperative MIMO mechanism achieves much lower bit
error rate and synchronization error than the conventional acoustic systems.
Information Based Data-Driven Characterization of Stability and Influence in Power Systems
Authors: Subhrajit Sinha, Pranav Sharma, Venkataramana Ajjarapu, Umesh Vaidya
Date: 2019-10-24 00:00:00

Stability analysis of a power network and its characterization (voltage or angle) is an important problem in the power system community. However, these problems are mostly studied using linearized models and participation factor analysis. In this paper, we provide a purely data-driven technique for small-signal stability classification (voltage or angle stability) and influence characterization for a power network. In particular, we use Koopman operator framework for data-driven discovery of the underlying power system dynamics and then leverage the newly developed concept of information transfer for discovering the causal structure. We further use it to not only identify the influential states (subspaces) in a power network, but also to clearly characterize and classify angle and voltage instabilities. We demonstrate the efficacy of the proposed framework on two different systems, namely the 3-bus system, where we reproduce the already known results regarding the types of instabilities, and the IEEE 9-bus system where we identify the influential generators and also the generator (and its states) which contribute to the system instability, thus identifying the type of instability.
The Persuasion Duality
Authors: Piotr Dworczak, Anton Kolotilin
Date: 2019-10-24 00:00:00

We present a unified duality approach to Bayesian persuasion. The optimal dual variable, interpreted as a price function on the state space, is shown to be a supergradient of the concave closure of the objective function at the prior belief. Strong duality holds when the objective function is Lipschitz continuous. When the objective depends on the posterior belief through a set of moments, the price function induces prices for posterior moments that solve the corresponding dual problem. Thus, our general approach unifies known results for one-dimensional moment persuasion, while yielding new results for the multi-dimensional case. In particular, we provide a necessary and sufficient condition for the optimality of convex-partitional signals, derive structural properties of solutions, and characterize the optimal persuasion scheme in the case when the state is two-dimensional and the objective is quadratic.
Speaker diarization using latent space clustering in generative adversarial network
Date: 2019-10-24 00:00:00
Tasks: Clustering, Diagnostic, Generative Adversarial Network, speaker-diarization, Speaker Diarization

In this work, we propose deep latent space clustering for speaker diarization
using generative adversarial network (GAN) backprojection with the help of an
encoder network. The proposed diarization system is trained jointly with GAN
loss, latent variable recovery loss, and a clustering-specific loss. It uses
x-vector speaker embeddings at the input, while the latent variables are
sampled from a combination of continuous random variables and discrete one-hot
encoded variables using the original speaker labels. We benchmark our proposed
system on the AMI meeting corpus, and two child-clinician interaction corpora
(ADOS and BOSCC) from the autism diagnosis domain. ADOS and BOSCC contain
diagnostic and treatment outcome sessions respectively obtained in clinical
settings for verbal children and adolescents with autism. Experimental results
show that our proposed system significantly outperform the state-of-the-art
x-vector based diarization system on these databases. Further, we perform
embedding fusion with x-vectors to achieve a relative DER improvement of 31%,
36% and 49% on AMI eval, ADOS and BOSCC corpora respectively, when compared to
the x-vector baseline using oracle speech segmentation.
Meta-learning for robust child-adult classification from speech
Date: 2019-10-28 00:00:00
Tasks: Classification, Meta-Learning, speaker-diarization, Speaker Diarization, Weakly Supervised Classification

Computational modeling of naturalistic conversations in clinical applications
has seen growing interest in the past decade. An important use-case involves
child-adult interactions within the autism diagnosis and intervention domain.
In this paper, we address a specific sub-problem of speaker diarization, namely
child-adult speaker classification in such dyadic conversations with specified
roles. Training a speaker classification system robust to speaker and channel
conditions is challenging due to inherent variability in the speech within
children and the adult interlocutors. In this work, we propose the use of
meta-learning, in particular, prototypical networks which optimize a metric
space across multiple tasks. By modeling every child-adult pair in the training
set as a separate task during meta-training, we learn a representation with
improved generalizability compared to conventional supervised learning. We
demonstrate improvements over state-of-the-art speaker embeddings (x-vectors)
under two evaluation settings: weakly supervised classification (up to 14.53%
relative improvement in F1-scores) and clustering (up to relative 9.66%
improvement in cluster purity). Our results show that protonets can potentially
extract robust speaker embeddings for child-adult classification from speech.
Deep learning-based beam alignment in mmWave vehicular networks
Date: 2019-11-12 00:00:00
Tasks: compressed sensing, Deep Learning

Millimeter wave channels exhibit structure that allows beam alignment with
fewer channel measurements than exhaustive beam search. From a compressed
sensing (CS) perspective, the received channel measurements are usually
obtained by multiplying a CS matrix with a sparse representation of the channel
matrix. Due to the constraints imposed by analog processing, designing CS
matrices that efficiently exploit the channel structure is, however,
challenging. In this paper, we propose an end-to-end deep learning technique to
design a structured CS matrix that is well suited to the underlying channel
distribution, leveraging both sparsity and the particular spatial structure
that appears in vehicular channels. The channel measurements acquired with the
designed CS matrix are then used to predict the best beam for link
configuration. Simulation results for vehicular communication channels indicate
that our deep learning-based approach achieves better beam alignment than
standard CS techniques that use the random phase shift-based design.
The Politics of Personalized News Aggregation
Authors: Lin Hu, Anqi Li, Ilya Segal
Date: 2019-10-24 00:00:00

We study how personalized news aggregation for rational inattentive voters (NARI) affects policy polarization and public opinion. In a two-candidate electoral competition game, an attention-maximizing infomediary aggregates information about candidates' valence into news. Voters decide whether to consume news, trading off the expected gain from improved expressive voting against the attention cost. NARI generates policy polarization even if candidates are office-motivated. Personalized news serves extreme voters with skewed signals and makes them the disciplining entities of policy polarization. Analysis of disciplining voters' identities and policy latitudes yields insights into the political effects of recent regulatory proposals to tame tech giants.
Possible traces of resonance signaling in the genome
Authors: Ivan Savelev, Max Myakishev-Rempel
Date: 2019-10-25 00:00:00

Although theories regarding the role of sequence-specific DNA resonance in biology have abounded for over 40 years, the published evidence for it is lacking. Here, the authors reasoned that for sustained resonance signaling, the number of oscillating DNA sequences per genome should be exceptionally high and that, therefore, genomic repeats of various sizes are good candidates for serving as resonators. Moreover, it was suggested that for the two DNA sequences to resonate, they do not necessarily have to be identical. Therefore, the existence of sequences differing in the primary sequence but having similar resonating sub-structures was proposed. It was hypothesized that such sequences, named HIDERs, would be enriched in the genomes of multicellular species. Specifically, it was hypothesized that delocalized electron clouds of purine-pyrimidine sequences could serve as the basis of HIDERs. The consequent genomic analysis confirmed the enrichment of purine-pyrimidine HIDERs in a few selected genomes of mammals, an insect, and a plant, compared to randomized sequence controls. Similarly, it was suggested that hypothetical delocalized proton clouds of the hydrogen bonds of multiple stacked bases could serve as sequence-dependent hydrogen-bond-based HIDERs. Similarly, the enrichment of such HIDERs was observed. It is suggested that these enrichments are the first evidence in support of sequence-specific resonance signaling in the genome.
Does car sharing reduce greenhouse gas emissions? Life cycle assessment of the modal shift and lifetime shift rebound effects
Date: 2019-10-25 00:00:00

Car-sharing platforms provide access to a shared rather than a private fleet
of automobiles distributed in the region. Participation in such services
induces changes in mobility behaviour as well as vehicle ownership patterns
that could have positive environmental impacts. This study contributes to the
understanding of the total mobility-related greenhouse gas emissions reduction
related to business-to-consumer car-sharing participation. A comprehensive
model which takes into account distances travelled annually by the major urban
transport modes as well as their life-cycle emissions factors is proposed, and
the before-and-after analysis is conducted for an average car-sharing member in
three geographical cases (Netherlands, San Francisco, Calgary). In addition to
non-operational emissions for all the transport modes involved, this approach
considers the rebound effects associated with the modal shift effect
(substituting driving distances with alternative modes) and the lifetime shift
effect for the shared automobiles, phenomena which have been barely analysed in
the previous studies. As a result, in contrast to the previous impact
assessments in the field, a significantly more modest reduction of the annual
total mobility-related life-cycle greenhouse gas emissions caused by
car-sharing participation has been estimated, 3-18% for three geographical case
studies investigated (versus up to 67% estimated previously). This suggests the
significance of the newly considered effects and provides with the practical
implications for improved assessments in the future.
Autonomous docking using direct optimal control
Date: 2019-10-25 00:00:00
Tasks: Collision Avoidance

We propose a method for performing autonomous docking of marine vessels using
numerical optimal control. The task is framed as a dynamic positioning problem,
with the addition of spatial constraints that ensure collision avoidance. The
proposed method is an all-encompassing procedure for performing both docking,
maneuvering, dynamic positioning and control allocation. In addition, we show
that the method can be implemented as a real-time MPC-based algorithm on
simulation results of a supply vessel.
Polymorphism in rapidly-changing cyclic environment
Date: 2019-10-25 00:00:00
Tasks: Diversity

Selection in a time-periodic environment is modeled via the continuous-time
two-player replicator dynamics, which for symmetric pay-offs reduces to the
Fisher equation of mathematical genetics. For a sufficiently rapid and cyclic
[fine-grained] environment, the time-averaged population frequencies are shown
to obey a replicator dynamics with a non-linear fitness that is induced by
environmental changes. The non-linear terms in the fitness emerge due to
populations tracking their time-dependent environment. These terms can induce a
stable polymorphism, though they do not spoil the polymorphism that exists
already without them. In this sense polymorphic populations are more robust
with respect to their time-dependent environments. The overall fitness of the
problem is still given by its time-averaged value, but the emergence of
polymorphism during genetic selection can be accompanied by decreasing mean
fitness of the population. The impact of the uncovered polymorphism scenario on
the models of diversity is examplified via the rock-paper-scissors dynamics,
and also via the prisoner's dilemma in a time-periodic environment.
Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection
Date: 2019-10-25 00:00:00

We address the problem of effectively handling overlapping speech in a
diarization system. First, we detail a neural Long Short-Term Memory-based
architecture for overlap detection. Secondly, detected overlap regions are
exploited in conjunction with a frame-level speaker posterior matrix to make
two-speaker assignments for overlapped frames in the resegmentation step. The
overlap detection module achieves state-of-the-art performance on the AMI,
DIHARD, and ETAPE corpora. We apply overlap-aware resegmentation on AMI,
resulting in a 20% relative DER reduction over the baseline system. While this
approach is by no means an end-all solution to overlap-aware diarization, it
reveals promising directions for handling overlap.
Momentum-based Accelerated Q-learning
Date: 2019-10-23 00:00:00
Tasks: Atari Games, Q-Learning

This paper studies accelerated algorithms for Q-learning. We propose an
acceleration scheme by incorporating the historical iterates of the Q-function.
The idea is conceptually inspired by the momentum-based acceleration methods in
the optimization theory. Under finite state-action space settings, the proposed
accelerated Q-learning algorithm provably converges to the global optimum with
a rate of $\mathcal{O}(1/\sqrt{T})$. While sharing a comparable theoretic
convergence rate with the existing Speedy Q-learning (SpeedyQ) algorithm, we
numerically show that the proposed algorithm outperforms SpeedyQ via playing
the FrozenLake grid world game. Furthermore, we generalize the acceleration
scheme to the continuous state-action space case where function approximation
of the Q-function is necessary. In this case, the algorithms are validated
using commonly adopted testing problems in reinforcement learning, including
two discrete-time linear quadratic regulation (LQR) problems from the Deepmind
Control Suite, and the Atari 2600 games. Simulation results show that the
proposed accelerated algorithms can improve the convergence performance
compared with the vanilla Q-learning algorithm.
Building social networks under consent: A survey
Date: 2020-04-11 00:00:00
Tasks: Survey

This survey explores the literature on game-theoretic models of network
formation under the hypothesis of mutual consent in link formation. The
introduction of consent in link formation imposes a coordination problem in the
network formation process. This survey explores the conclusions from this
theory and the various methodologies to avoid the main pitfalls. The main
insight originates from Myerson's work on mutual consent in link formation and
his main conclusion that the empty network (the network without any links)
always emerges as a strong Nash equilibrium in any game-theoretic model of
network formation under mutual consent and positive link formation costs.
Jackson and Wolinsky introduced a cooperative framework to avoid this main
pitfall. They devised the notion of a pairwise stable network to arrive at
equilibrium networks that are mainly non-trivial. Unfortunately, this notion of
pairwise stability requires coordinated action by pairs of decision makers in
link formation. I survey the possible solutions in a purely non-cooperative
framework of network formation under mutual consent by exploring potential
refinements of the standard Nash equilibrium concept to explain the emergence
of non-trivial networks. This includes the notions of unilateral and monadic
stability. The first one is founded on advanced rational reasoning of
individuals about how others would respond to one's efforts to modify the
network. The latter incorporates trusting, boundedly rational behaviour into
the network formation process. The survey is concluded with an initial
exploration of external correlation devices as an alternative framework to
address mutual consent in network formation.