pwc-archive/papers-with-abstracts huggingface dataset:
Development and Virtual Testing of 5G Connected Adaptive Cruise Control
Authors: Andrea Leo, Giacomo Reatti, Stefano Arrigoni, Michael Khayyat, Federico Cheli
Date: 2022-01-11 00:00:00
Tasks: Friction

It is estimated that 90% of crashes occur due to human error, mainly induced by poor judgement, distraction or lack of situation awareness. The development of systems that aim to increase road safety and to improve driving comfort is the key challenge that many OEMs are currently tackling. The use of fast and reliable of innovative communication technologies such as 5G can enhance advancements and developments in the field of ADAS. The aim of the paper is the study of the potential improvements connectivity can bring to ACC systems in terms of safety and vehicle comfort. Starting from state-of-art ACC present in literature, a novel connected ACC is designed and deeply reported. The main features of the algorithms consist in integrating information about other road vehicles fed by 5G and about potential grip given by smart sensors such as Pirelli Cyber Tyre. The novel solution is then compared to the standard ACC by means of several simulations considering two real traffic situations with different road friction coefficients. Finally benefits and limitations of the proposed algorithm are deeply discussed.
Electric vehicle charge scheduling with flexible service operations
Authors: Patrick Sean Klein, Maximilian Schiffer
Date: 2022-01-11 00:00:00
Tasks: Scheduling

Operators who deploy large fleets of electric vehicles often face a challenging charge scheduling problem. Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off-duty at a central depot. Here, high investment cost and grid capacity limit available charging infrastructure such that operators need to schedule charging operations to keep the fleet operational. In this context, flexible service operations, i.e. allowing to delay or expedite vehicle departures, can potentially increase charger utilization. Beyond this, jointly scheduling charging and service operations promises operational cost savings through better utilization of Time-of-Use energy tariffs and carefully crafted charging schedules designed to minimize battery wear. Against this background, we study the resulting joint charging and service operations scheduling problem accounting for battery degradation, non-linear charging, and Time-of-Use energy tariffs. We propose an exact Branch & Price algorithm, leveraging a custom branching rule and a primal heuristic to remain efficient during the Branch & Bound phase. Moreover, we develop an exact labeling algorithm for our pricing problem, constituting a resource-constrained shortest path problem that considers variable energy prices and non-linear charging operations. We benchmark our algorithm in a comprehensive numerical study and show that it can solve problem instances of realistic size with computational times below one hour, thus enabling its application in practice. Additionally, we analyze the benefit of jointly scheduling charging and service operations. We find that our integrated approach lowers the amount of charging infrastructure required by up to 57% besides enabling operational cost savings of up to 5%.
Co-Design of Lipschitz Nonlinear Systems
Authors: Prasad Vilas Chanekar, Nikhil Chopra
Date: 2022-01-11 00:00:00

Empirical experiences have shown that simultaneous (rather than conventional sequential) plant and controller design procedure leads to an improvement in performance and saving of plant resources. Such a simultaneous synthesis procedure is called as "co-design". In this letter we study the co-design problem for a class of Lipschitz nonlinear dynamical systems having a quadratic control objective and state-feedback controller. We propose a novel time independent reformulation of the co-design optimization problem whose constraints ensure stability of the system. We also present a gradient-based co-design solution procedure which involves system coordinate transformation and whose output is provably stable solution for the original system. We show the efficacy of the solution procedure through co-design of a single-link robot.
Data-driven Meets Geometric Control: Zero Dynamics, Subspace Stabilization, and Malicious Attacks
Authors: Federico Celi, Fabio Pasqualetti
Date: 2022-01-10 00:00:00

Studying structural properties of linear dynamical systems through invariant subspaces is one of the key contributions of the geometric approach to system theory. In general, a model of the dynamics is required in order to compute the invariant subspaces of interest. In this paper we overcome this limitation by finding data-driven formulas for some of the foundational tools of geometric control. In particular, for an unknown linear system, we show how controlled and conditioned invariant subspaces can be found directly from experimental data. We use our formulas and approach to (i) find a feedback gain that confines the system state within a desired subspace, (ii) compute the invariant zeros of the unknown system, and (iii) design attacks that remain undetectable.
Robust Trajectory Tracking and Payload Delivery of a Quadrotor Under Multiple State Constraints
Authors: Sourish Ganguly
Date: 2022-01-07 00:00:00
Tasks: Position

With quadrotors becoming immensely popular in applications such as relief operations, infrastructure maintenance etc., a key control design challenge arises when the quadrotor has to manoeuvre through constrained spaces during various operational scenarios: for example, inspecting a pipeline within predefined velocity and space, dropping relief material at a precise location under tight spaces etc., under the face of parametric uncertainties and external disturbances. To tackle such scenarios, a controller needs to ensure a predefined tracking accuracy so as not to violate the constraints while simultaneously tackling uncertainties and disturbances. However, state-of-the-art controllers dealing with constrained system motion are either not applicable for an underactuated system like quadrotor, or cannot tackle system uncertainties under full state constraints. This work attempts to fill such a gap in literature by designing Barrier Lyapunov Function (BLF) based robust controllers to satisfy multiple state-constraints while simultaneously negotiating parametric uncertainties and external disturbances. The superiority of the BLF control method over a typical unconstrained controller is demonstrated, followed by a robust control design to satisfy position and orientation constraints on quadrotor dynamics. Finally, full state-constraints on a quadrotor(i.e., constraints on the position, orientation, linear velocity and angular velocity) are satisfied with robust control. For each control design, the closed-loop system stability is studied analytically and the efficacy of the design is validated extensively either via realistic simulation scenarios or via experiments performed on a real quadrotor.
Application of Common Spatial Patterns in Gravitational Waves Detection
Authors: Damodar Dahal
Date: 2022-01-11 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), regression, Time Series, Time Series Analysis

Common Spatial Patterns (CSP) is a feature extraction algorithm widely used in Brain-Computer Interface (BCI) Systems for detecting Event-Related Potentials (ERPs) in multi-channel magneto/electroencephalography (MEG/EEG) time series data. In this article, we develop and apply a CSP algorithm to the problem of identifying whether a given epoch of multi-detector Gravitational Wave (GW) strains contains coalescenses. Paired with Signal Processing techniques and a Logistic Regression classifier, we find that our pipeline is correctly able to detect 76 out of 82 confident events from Gravitational Wave Transient Catalog, using H1 and L1 strains, with a classification score of $93.72 \pm 0.04\%$ using $10 \times 5$ cross validation. The false negative events were: GW170817-v3, GW191219 163120-v1, GW200115 042309-v2, GW200210 092254-v1, GW200220 061928-v1, and GW200322 091133-v1.
SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery
Authors: J. Zachary Gazak, Ian McQuaid, Ryan Swindle, Matthew Phelps, Justin Fletcher
Date: 2022-01-10 00:00:00
Tasks: Bayesian Inference, Management, Object, Vocal Bursts Intensity Prediction

Effective space traffic management requires positive identification of artificial satellites. Current methods for extracting object identification from observed data require spatially resolved imagery which limits identification to objects in low earth orbits. Most artificial satellites, however, operate in geostationary orbits at distances which prohibit ground based observatories from resolving spatial information. This paper demonstrates an object identification solution leveraging modified residual convolutional neural networks to map distance-invariant spectroscopic data to object identity. We report classification accuracies exceeding 80% for a simulated 64-class satellite problem--even in the case of satellites undergoing constant, random re-orientation. An astronomical observing campaign driven by these results returned accuracies of 72% for a nine-class problem with an average of 100 examples per class, performing as expected from simulation. We demonstrate the application of variational Bayesian inference by dropout, stochastic weight averaging (SWA), and SWA-focused deep ensembling to measure classification uncertainties--critical components in space traffic management where routine decisions risk expensive space assets and carry geopolitical consequences.
Machine learning enabling high-throughput and remote operations at large-scale user facilities
Authors: Tatiana Konstantinova, Phillip M. Maffettone, Bruce Ravel, Stuart I. Campbell, Andi M. Barbour, Daniel Olds
Date: 2022-01-09 00:00:00
Tasks: BIG-bench Machine Learning, Management

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.
Atomistic Simulations for Reactions and Spectroscopy in the Era of Machine Learning -- Quo Vadis?
Authors: M. Meuwly
Date: 2022-01-11 00:00:00

Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas- and in the condensed phase. Together with recently developed and currently pursued efforts in integrating and combining this with machine learning techniques provides a unique opportunity to bring such dynamics simulations closer to reality. This perspective delineates the present status of the field from efforts of others in the field and some of your own work and discusses open questions and future prospects.
An analysis of reconstruction noise from undersampled 4D flow MRI
Authors: Lauren Partin, Daniele E. Schiavazzi, Carlos A. Sing Long
Date: 2022-01-11 00:00:00

Novel Magnetic Resonance (MR) imaging modalities can quantify hemodynamics but require long acquisition times, precluding its widespread use for early diagnosis of cardiovascular disease. To reduce the acquisition times, reconstruction methods from undersampled measurements are routinely used, that leverage representations designed to increase image compressibility. Reconstructed anatomical and hemodynamic images may present visual artifacts. Although some of these artifact are essentially reconstruction errors, and thus a consequence of undersampling, others may be due to measurement noise or the random choice of the sampled frequencies. Said otherwise, a reconstructed image becomes a random variable, and both its bias and its covariance can lead to visual artifacts; the latter leads to spatial correlations that may be misconstrued for visual information. Although the nature of the former has been studied in the literature, the latter has not received as much attention. In this study, we investigate the theoretical properties of the random perturbations arising from the reconstruction process, and perform a number of numerical experiments on simulated and MR aortic flow. Our results show that the correlation length remains limited to two to three pixels when a Gaussian undersampling pattern is combined with recovery algorithms based on $\ell_1$-norm minimization. However, the correlation length may increase significantly for other undersampling patterns, higher undersampling factors (i.e., 8x or 16x compression), and different reconstruction methods.
Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples
Authors: Peter Dawood, Felix Breuer, Paul R. Burd, István Homolya, Johannes Oberberger, Peter M. Jakob, Martin Blaimer
Date: 2022-01-10 00:00:00
Tasks: Data Augmentation, Image Reconstruction

Purpose: To evaluate an iterative learning approach for enhanced performance of Robust Artificial-neural-networks for K-space Interpolation (RAKI), when only a limited amount of training data (auto-calibration signals, ACS) are available for accelerated standard 2D imaging. Methods: In a first step, the RAKI model was optimized for the case of strongly limited training data amount. In the iterative learning approach (termed iterative RAKI), the optimized RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in-vivo datasets from the fastMRI neuro database with different contrast settings. Results: For limited training data (18 and 22 ACS lines for R=4 and R=5, respectively), iterative RAKI outperforms standard RAKI by reducing residual artefacts and yields strong noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. In combination with a phase constraint, further reconstruction improvements can be achieved. Additionally, iterative RAKI shows better performance than both GRAPPA and RAKI in case of pre-scan calibration with varying contrast between training- and undersampled data. Conclusion: The iterative learning approach with RAKI benefits from standard RAKIs well known noise suppression feature but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.
A Novel Home-Built Metrology to Analyze Oral Fluid Droplets and Quantify the Efficacy of Masks
Authors: Ava Tan Bhowmik
Date: 2022-01-03 00:00:00

Wearing masks is crucial to preventing the spread of potentially pathogen-containing droplets, especially amidst the COVID-19 pandemic. However, not all face coverings are equally effective and most experiments evaluating mask efficacy are very expensive and complex to operate. In this work, a novel, home-built, low-cost, and accurate metrology to visualize orally-generated fluid droplets has been developed. The project includes setup optimization, data collection, data analysis, and applications. The final materials chosen were quinine-containing tonic water, 397-402 nm wavelength UV tube lights, an iPhone and tripod, string, and a spray bottle. The experiment took place in a dark closet with a dark background. During data collection, the test subject first wets their mouth with an ingestible fluorescent liquid (tonic water) and speaks, sneezes, or coughs under UV darklight. The fluorescence from the tonic water droplets generated can be visualized, recorded by an iPhone 8+ camera in slo-mo (240 fps), and analyzed. The software VLC is used for frame separation and Fiji/ImageJ is used for image processing and analysis. The dependencies of oral fluid droplet generation and propagation on different phonics, the loudness of speech, and the type of expiratory event were studied in detail and established using the metrology developed. The efficacy of different types of masks was evaluated and correlated with fabric microstructures. All masks blocked droplets to varying extent. Masks with smaller-sized pores and thicker material were found to block the most droplets. This low-cost technique can be easily constructed at home using materials that total to a cost of less than $50. Despite the minimal cost, the method is very accurate and the data is quantifiable.
Systematic Literature Review: Quantum Machine Learning and its applications
Authors: David Peral García, Juan Cruz-Benito, Francisco José García-Peñalvo
Date: 2022-01-11 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Quantum Machine Learning, Systematic Literature Review

Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles for subsequent use in performing calculations, as well as for large-scale information processing. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, there are scientific challenges that are impossible to perform by classical computation due to computational complexity or the time the calculation would take, and quantum computation is one of the possible answers. However, current quantum devices have not yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023 to identify, analyze and classify the different algorithms used in quantum machine learning and their applications. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. Many articles try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.
Quantum activation functions for quantum neural networks
Authors: Marco Maronese, Claudio Destri, Enrico Prati
Date: 2022-01-10 00:00:00
Tasks: Quantum Machine Learning

The field of artificial neural networks is expected to strongly benefit from recent developments of quantum computers. In particular, quantum machine learning, a class of quantum algorithms which exploit qubits for creating trainable neural networks, will provide more power to solve problems such as pattern recognition, clustering and machine learning in general. The building block of feed-forward neural networks consists of one layer of neurons connected to an output neuron that is activated according to an arbitrary activation function. The corresponding learning algorithm goes under the name of Rosenblatt perceptron. Quantum perceptrons with specific activation functions are known, but a general method to realize arbitrary activation functions on a quantum computer is still lacking. Here we fill this gap with a quantum algorithm which is capable to approximate any analytic activation functions to any given order of its power series. Unlike previous proposals providing irreversible measurement--based and simplified activation functions, here we show how to approximate any analytic function to any required accuracy without the need to measure the states encoding the information. Thanks to the generality of this construction, any feed-forward neural network may acquire the universal approximation properties according to Hornik's theorem. Our results recast the science of artificial neural networks in the architecture of gate-model quantum computers.
Price Heterogeneity as a source of Heterogenous Demand
Authors: John K. -H. Quah, Gerelt Tserenjigmid
Date: 2022-01-11 00:00:00

We explore heterogenous prices as a source of heterogenous or stochastic demand. Heterogenous prices could arise either because there is actual price variation among consumers or because consumers (mis)perceive prices differently. Our main result says the following: if heterogenous prices have a distribution among consumers that is (in a sense) stable across observations, then a model where consumers have a common utility function but face heterogenous prices has precisely the same implications as a heterogenous preference/random utility model (with no price heterogeneity)
Image quality measurements and denoising using Fourier Ring Correlations
Authors: J. Kaczmar-Michalska, N. R. Hajizadeh, A. J. Rzepiela, S. F. Nørrelykke
Date: 2022-01-11 00:00:00
Tasks: Denoising, SSIM, Super-Resolution

Image quality is a nebulous concept with different meanings to different people. To quantify image quality a relative difference is typically calculated between a corrupted image and a ground truth image. But what metric should we use for measuring this difference? Ideally, the metric should perform well for both natural and scientific images. The structural similarity index (SSIM) is a good measure for how humans perceive image similarities, but is not sensitive to differences that are scientifically meaningful in microscopy. In electron and super-resolution microscopy, the Fourier Ring Correlation (FRC) is often used, but is little known outside of these fields. Here we show that the FRC can equally well be applied to natural images, e.g. the Google Open Images dataset. We then define a loss function based on the FRC, show that it is analytically differentiable, and use it to train a U-net for denoising of images. This FRC-based loss function allows the network to train faster and achieve similar or better results than when using L1- or L2- based losses. We also investigate the properties and limitations of neural network denoising with the FRC analysis.