pwc-archive/papers-with-abstracts huggingface dataset:
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks
Authors: Hyung-Ju Ahn, Dae-Hyeok Lee, Ji-Hoon Jeong, Seong-Whan Lee
Date: 2021-06-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Motor Imagery

Human-robot collaboration has the potential to maximize the efficiency of the operation of autonomous robots. Brain-machine interface (BMI) would be a desirable technology to collaborate with robots since the intention or state of users can be translated from the neural activities. However, the electroencephalogram (EEG), which is one of the most popularly used non-invasive BMI modalities, has low accuracy and a limited degree of freedom (DoF) due to a low signal-to-noise ratio. Thus, improving the performance of multi-class EEG classification is crucial to develop more flexible BMI-based human-robot collaboration. In this study, we investigated the possibility for inter-paradigm classification of multiple endogenous BMI paradigms, such as motor imagery (MI), visual imagery (VI), and speech imagery (SI), to enhance the limited DoF while maintaining robust accuracy. We conducted the statistical and neurophysiological analyses on MI, VI, and SI and classified three paradigms using the proposed temporal information-based neural network (TINN). We confirmed that statistically significant features could be extracted on different brain regions when classifying three endogenous paradigms. Moreover, our proposed TINN showed the highest accuracy of 0.93 compared to the previous methods for classifying three different types of mental imagery tasks (MI, VI, and SI).
Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments
Authors: Lê-Nguyên Hoang, Louis Faucon, Aidan Jungo, Sergei Volodin, Dalia Papuc, Orfeas Liossatos, Ben Crulis, Mariame Tighanimine, Isabela Constantin, Anastasiia Kucherenko, Alexandre Maurer, Felix Grimberg, Vlad Nitu, Chris Vossen, Sébastien Rouault, El-Mahdi El-Mhamdi
Date: 2021-05-29 00:00:00

Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the de-facto regulators of our societies' information diet, from shaping opinions on public health to organizing groups for social movements. This creates serious concerns, but also great opportunities to promote quality information. Addressing the concerns and seizing the opportunities is a challenging, enormous and fabulous endeavor, as intuitively appealing ideas often come with unwanted {\it side effects}, and as it requires us to think about what we deeply prefer. Understanding how today's large-scale algorithms are built is critical to determine what interventions will be most effective. Given that these algorithms rely heavily on {\it machine learning}, we make the following key observation: \emph{any algorithm trained on uncontrolled data must not be trusted}. Indeed, a malicious entity could take control over the data, poison it with dangerously manipulative fabricated inputs, and thereby make the trained algorithm extremely unsafe. We thus argue that the first step towards safe and ethical large-scale algorithms must be the collection of a large, secure and trustworthy dataset of reliable human judgments. To achieve this, we introduce \emph{Tournesol}, an open source platform available at \url{https://tournesol.app}. Tournesol aims to collect a large database of human judgments on what algorithms ought to widely recommend (and what they ought to stop widely recommending). We outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project. Most importantly, we argue that, if successful, Tournesol may then serve as the essential foundation for any safe and ethical large-scale algorithm.
Frequency-packed Faster-than-Nyquist Signaling via Symbol-level Precoding for Multi-user MISO Redundant Transmissions
Authors: Wallace A. Martins, Symeon Chatzinotas, Björn Ottersten
Date: 2021-07-14 00:00:00

This work addresses the issue of interference generated by co-channel users in downlink multi-antenna multicarrier systems with frequency-packed faster-than-Nyquist (FTN) signaling. The resulting interference stems from an aggressive strategy for enhancing the throughput via frequency reuse across different users and the squeezing of signals in the time-frequency plane beyond the Nyquist limit. The error-free spectral efficiency is proved to be increasing with the frequency packing and FTN acceleration factors. The lower bound for the FTN sampling period that guarantees information losslesness is derived as a function of the transmitting-filter roll-off factor, the frequency-packing factor, and the number of subcarriers. Space-time-frequency symbol-level precoders (SLPs) that trade off constructive and destructive interblock interference (IBI) at the single-antenna user terminals are proposed. Redundant elements are added as guard interval to cope with vestigial destructive IBI effects. The proposals can handle channels with delay spread longer than the multicarrier-symbol duration. The receiver architecture is simple, for it does not require digital multicarrier demodulation. Simulations indicate that the proposed SLP outperforms zero-forcing precoding and achieves a target balance between spectral and energy efficiencies by controlling the amount of added redundancy from zero (full IBI) to half (destructive IBI-free) the group delay of the equivalent channel.
Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication
Authors: Sueda Taner, Christoph Studer
Date: 2021-07-14 00:00:00

Beamspace processing is an emerging technique to reduce baseband complexity in massive multiuser (MU) multiple-input multiple-output (MIMO) communication systems operating at millimeter-wave (mmWave) and terahertz frequencies. The high directionality of wave propagation at such high frequencies ensures that only a small number of transmission paths exist between user equipments and basestation (BS). In order to resolve the sparse nature of wave propagation, beamspace processing traditionally computes a spatial discrete Fourier transform (DFT) across a uniform linear antenna array at the BS where each DFT output is associated with a specific beam. In this paper, we study optimality conditions of the DFT for sparsity-based beamspace processing with idealistic mmWave channel models and realistic channels. To this end, we propose two algorithms that learn unitary beamspace transforms using an $\ell^4$-norm-based sparsity measure, and we investigate their optimality theoretically and via simulations.
PHiLIP on the HiL: Automated Multi-platform OS Testing with External Reference Devices
Authors: Kevin Weiss, Michel Rottleuthner, Thomas C. Schmidt, Matthias Wählisch
Date: 2021-07-15 00:00:00

Developing an operating system (OS) for low-end embedded devices requires continuous adaptation to new hardware architectures and components, while serviceability of features needs to be assured for each individual platform under tight resource constraints. It is challenging to design a versatile and accurate heterogeneous test environment that is agile enough to cover a continuous evolution of the code base and platforms. This mission is even morehallenging when organized in an agile open-source community process with many contributors such as for the RIOT OS. Hardware in the Loop (HiL) testing and Continuous Integration (CI) are automatable approaches to verify functionality, prevent regressions, and improve the overall quality at development speed in large community projects. In this paper, we present PHiLIP (Primitive Hardware in the Loop Integration Product), an open-source external reference device together with tools that validate the system software while it controls hardware and interprets physical signals. Instead of focusing on a specific test setting, PHiLIP takes the approach of a tool-assisted agile HiL test process, designed for continuous evolution and deployment cycles. We explain its design, describe how it supports HiL tests, evaluate performance metrics, and report on practical experiences of employing PHiLIP in an automated CI test infrastructure. Our initial deployment comprises 22 unique platforms, each of which executes 98 peripheral tests every night. PHiLIP allows for easy extension of low-cost, adaptive testing infrastructures but serves testing techniques and tools to a much wider range of applications.
Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech
Authors: Christian J. Steinmetz, Vamsi Krishna Ithapu, Paul Calamia
Date: 2021-07-15 00:00:00
Tasks: Decoder, Room Impulse Response (RIR)

Deep learning approaches have emerged that aim to transform an audio signal so that it sounds as if it was recorded in the same room as a reference recording, with applications both in audio post-production and augmented reality. In this work, we propose FiNS, a Filtered Noise Shaping network that directly estimates the time domain room impulse response (RIR) from reverberant speech. Our domain-inspired architecture features a time domain encoder and a filtered noise shaping decoder that models the RIR as a summation of decaying filtered noise signals, along with direct sound and early reflection components. Previous methods for acoustic matching utilize either large models to transform audio to match the target room or predict parameters for algorithmic reverberators. Instead, blind estimation of the RIR enables efficient and realistic transformation with a single convolution. An evaluation demonstrates our model not only synthesizes RIRs that match parameters of the target room, such as the $T_{60}$ and DRR, but also more accurately reproduces perceptual characteristics of the target room, as shown in a listening test when compared to deep learning baselines.
DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search
Authors: Tahereh Zohdinasab, Vincenzo Riccio, Alessio Gambi, Paolo Tonella
Date: 2021-07-05 00:00:00

Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones. Several DL testing approaches have been recently proposed in the literature but none of them aims to assess how different interpretable features of the generated inputs affect the system's behaviour. In this paper, we resort to Illumination Search to find the highest-performing test cases (i.e., misbehaving and closest to misbehaving), spread across the cells of a map representing the feature space of the system. We introduce a methodology that guides the users of our approach in the tasks of identifying and quantifying the dimensions of the feature space for a given domain. We developed DeepHyperion, a search-based tool for DL systems that illuminates, i.e., explores at large, the feature space, by providing developers with an interpretable feature map where automatically generated inputs are placed along with information about the exposed behaviours.
SilGAN: Generating driving maneuvers for scenario-based software-in-the-loop testing
Authors: Dhasarathy Parthasarathy, Anton Johansson
Date: 2021-07-05 00:00:00
Tasks: software testing

Automotive software testing continues to rely largely upon expensive field tests to ensure quality because alternatives like simulation-based testing are relatively immature. As a step towards lowering reliance on field tests, we present SilGAN, a deep generative model that eases specification, stimulus generation, and automation of automotive software-in-the-loop testing. The model is trained using data recorded from vehicles in the field. Upon training, the model uses a concise specification for a driving scenario to generate realistic vehicle state transitions that can occur during such a scenario. Such authentic emulation of internal vehicle behavior can be used for rapid, systematic and inexpensive testing of vehicle control software. In addition, by presenting a targeted method for searching through the information learned by the model, we show how a test objective like code coverage can be automated. The data driven end-to-end testing pipeline that we present vastly expands the scope and credibility of automotive simulation-based testing. This reduces time to market while helping maintain required standards of quality.
Passivity-based Decentralized Control for Discrete-time Large-scale Systems
Authors: Ahmed Aboudonia, Andrea Martinelli, John Lygeros
Date: 2021-07-15 00:00:00

Passivity theory has recently contributed to developing decentralized control schemes for large-scale systems. Many decentralized passivity-based control schemes are designed in continuous-time. It is well-known, however, that the passivity properties of continuous-time systems may be lost under discretization. In this work, we present a novel stabilizing decentralized control scheme by ensuring passivity for discrete-time systems directly and thus avoiding the issue of passivity preservation. The controller is synthesized by locally solving a semidefinite program offline for each subsystem in a decentralized fashion. This program comprises local conditions ensuring that the corresponding subsystem is locally passive. Passivity is ensured with respect to a local virtual output which is different from the local actual output. The program also comprises local conditions ensuring that the local passivity of all subsystems implies the asymptotic stability of the whole system. The performance of the proposed controller is evaluated on a case study in DC microgrids.
Variable-Horizon Guidance for Autonomous Rendezvous and Docking to a Tumbling Target
Authors: Mirko Leomanni, Renato Quartullo, Gianni Bianchini, Andrea Garulli, Antonio Giannitrapani
Date: 2021-07-15 00:00:00
Tasks: Trajectory Planning

In this paper, the trajectory planning problem for autonomous rendezvous and docking between a controlled spacecraft and a tumbling target is addressed. The use of a variable planning horizon is proposed in order to construct an appropriate maneuver plan, within an optimization-based framework. The involved optimization problem is nonconvex and features nonlinear constraints. The main contribution is to show that such problem can be tackled effectively by solving a finite number of linear programs. To this aim, a specifically conceived horizon search algorithm is employed in combination with a polytopic constraint approximation technique. The resulting guidance scheme provides the ability to identify favourable docking configurations, by exploiting the time-varying nature of the optimization problem endpoint. Simulation results involving the capture of the nonoperational EnviSat spacecraft indicate that the method is able to generate optimal trajectories at a fraction of the computational cost incurred by a state-of-the-art nonlinear solver.
Direct-drive ocean wave-powered batch reverse osmosis
Authors: Katie M. Brodersen, Emily A. Bywater, Alec M. Lanter, Hayden H. Schennum, Kumansh N. Furia, Maulee K. Sheth, Nathaniel S. Kiefer, Brittany K. Cafferty, Akshay K. Rao, Jose M. Garcia, David M. Warsinger
Date: 2021-07-15 00:00:00

Ocean waves provide a consistent, reliable source of clean energy making them a viable energy source for desalination. Ocean wave energy is useful to coastal communities, especially island nations. However, large capital costs render current wave-powered desalination technologies economically infeasible. This work presents a high efficiency configuration for ocean wave energy powering batch reverse osmosis. The proposed system uses seawater as the working fluid in a hydro-mechanical wave energy converter and replaces the reverse osmosis high-pressure pump with a hydraulic converter for direct-drive coupling. This allows for minimal intermediary power conversions, fewer components, and higher efficiencies. The concept was analyzed with MATLAB to model the transient energy dynamics of the wave energy converter, power take-off system, and desalination load. The fully hydro-mechanical coupling, incorporating energy recovery, could achieve an SEC and LCOW as low as 2.30 kWh/m3 and $1.96, respectively, for different sea states. The results were validated at the sub-system level against existing literature on wave energy models and previous work completed on batch reverse osmosis models, as this system was the first to combine these two technologies. SEC and LCOW values were validated by comparing to known and predicted values for various types of RO systems.
Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference
Authors: Chuchu Fan, Xin Qin, Yuan Xia, Aditya Zutshi, Jyotirmoy Deshmukh
Date: 2020-04-01 00:00:00
Tasks: Autonomous Vehicles, Prediction Intervals

In this paper, we propose conformal inference based approach for statistical verification of CPS models. Cyber-physical systems (CPS) such as autonomous vehicles, avionic systems, and medical devices operate in highly uncertain environments. This uncertainty is typically modeled using a finite number of parameters or input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general. {\em Statistical model checking} (SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learn {\em surrogate models}, and uses {\em conformal inference} to provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models.
Physics-informed generative neural network: an application to troposphere temperature prediction
Authors: Zhihao Chen, Jie Gao, Weikai Wang, Zheng Yan
Date: 2021-07-08 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

The troposphere is one of the atmospheric layers where most weather phenomena occur. Temperature variations in the troposphere, especially at 500 hPa, a typical level of the middle troposphere, are significant indicators of future weather changes. Numerical weather prediction is effective for temperature prediction, but its computational complexity hinders a timely response. This paper proposes a novel temperature prediction approach in framework ofphysics-informed deep learning. The new model, called PGnet, builds upon a generative neural network with a mask matrix. The mask is designed to distinguish the low-quality predicted regions generated by the first physical stage. The generative neural network takes the mask as prior for the second-stage refined predictions. A mask-loss and a jump pattern strategy are developed to train the generative neural network without accumulating errors during making time-series predictions. Experiments on ERA5 demonstrate that PGnet can generate more refined temperature predictions than the state-of-the-art.
A topological look into the evolution of developmental programs
Authors: Somya Mani, Tsvi Tlusty
Date: 2021-07-15 00:00:00

Rapid advance of experimental techniques provides an unprecedented in-depth view into complex developmental processes. Still, little is known on how the complexity of multicellular organisms evolved by elaborating developmental programs and inventing new cell types. A hurdle to understanding developmental evolution is the difficulty of even describing the intertwined network of spatiotemporal processes underlying the development of complex multicellular organisms. Nonetheless, an overview of developmental trajectories can be obtained from cell type lineage maps. Here, we propose that these lineage maps can also reveal how developmental programs evolve: the modes of evolving new cell types in an organism should be visible in its developmental trajectories, and therefore in the geometry of its cell type lineage map. This idea is demonstrated using a parsimonious generative model of developmental programs, which allows us to reliably survey the universe of all possible programs and examine their topological features. We find that, contrary to belief, tree-like lineage maps are rare and lineage maps of complex multicellular organisms are likely to be directed acyclic graphs where multiple developmental routes can converge on the same cell type. While cell type evolution prescribes what developmental programs come into existence, natural selection prunes those programs which produce low-functioning organisms. Our model indicates that additionally, lineage map topologies are correlated with such a functional property: the ability of organisms to regenerate.
Generalized Covariance Estimator
Authors: Christian Gourieroux, Joann Jasiak
Date: 2021-07-14 00:00:00

We consider a class of semi-parametric dynamic models with strong white noise errors. This class of processes includes the standard Vector Autoregressive (VAR) model, the nonfundamental structural VAR, the mixed causal-noncausal models, as well as nonlinear dynamic models such as the (multivariate) ARCH-M model. For estimation of processes in this class, we propose the Generalized Covariance (GCov) estimator, which is obtained by minimizing a residual-based multivariate portmanteau statistic as an alternative to the Generalized Method of Moments. We derive the asymptotic properties of the GCov estimator and of the associated residual-based portmanteau statistic. Moreover, we show that the GCov estimators are semi-parametrically efficient and the residual-based portmanteau statistics are asymptotically chi-square distributed. The finite sample performance of the GCov estimator is illustrated in a simulation study. The estimator is also applied to a dynamic model of cryptocurrency prices.
A Theory of Ex Post Rationalization
Authors: Erik Eyster, Shengwu Li, Sarah Ridout
Date: 2021-07-15 00:00:00

People rationalize their past choices, even those that were mistakes in hindsight. We propose a formal theory of this behavior. The theory predicts that sunk costs affect later choices. Its model primitives are identified by choice behavior and it yields tractable comparative statics.
Exact inference from finite market data
Authors: Felix Kübler, Raghav Malhotra, Herakles Polemarchakis
Date: 2021-07-15 00:00:00

We develop conditions under which individual choices and Walrasian equilibrium prices and allocations can be exactly inferred from finite market data. First, we consider market data that consist of individual demands as prices and incomes change. Second, we show that finitely many observations of individual endowments and associated Walrasian equilibrium prices, and only prices, suffice to identify individual demands and, as a consequence, equilibrium comparative statics.