pwc-archive/papers-with-abstracts huggingface dataset:
SOPHIE: SOft and flexible aerial vehicle for PHysical Interaction with the Environment
Authors: Fernando Ruiz, Begona Arrue, Anibal Ollero
Date: 2022-05-25 00:00:00

This paper presents the first design of a soft, 3D-printed in flexible filament, lightweight UAV, capable of performing full-body perching using soft tendons, specifically landing and stabilizing on pipelines and irregular surfaces without the need for an auxiliary system. The flexibility of the UAV can be controlled during the additive manufacturing process by adjusting the infill rate distribution. However, the increase in flexibility implies difficulties in controlling the UAV, as well as structural, aerodynamic, and aeroelastic effects. This article provides insight into the dynamics of the system and validates the flyability of the vehicle for densities as low as 6%. Within this range, quasi-static arm deformations can be considered, thus the autopilot is fed back through a static arm deflection model. At lower densities, strong non-linear elastic dynamics appear, which translates to complex modeling, and it is suggested to switch to data-based approaches.
Worldwide Energy Harvesting Potential of Hybrid CPV/PV Technology
Authors: Juan F. Martínez, Marc Steiner, Maike Wiesenfarth, Henning Helmers, Gerald Siefer, Stefan W. Glunz, Frank Dimroth
Date: 2022-05-25 00:00:00

Hybridization of multi-junction concentrator photovoltaics with single-junction flat plate solar cells (CPV/PV) can deliver the highest power output per module area of any PV technology. Conversion efficiencies up to 34.2% have been published under the AM1.5g spectrum at standard test conditions for the EyeCon module which combines Fresnel lenses and III-V four-junction solar cells with bifacial c-Si. We investigate here its energy yield and compare it to conventional CPV as well as flat plate PV. The advantage of the hybrid CPV/PV module is that it converts direct sunlight with the most advanced multi-junction cell technology, while accessing diffuse, lens-scattered and back side irradiance with a Si cell that also serves as the heat distributor for the concentrator cells. This article quantifies that hybrid bifacial CPV/PV modules are expected to generate a 25 - 35% higher energy yield with respect to their closest competitor in regions with a diffuse irradiance fraction around 50%. Additionally, the relative cost of electricity generated by hybrid CPV/PV technology was calculated worldwide under certain economic assumptions. Therefore, this article gives clear guidance towards establishing competitive business cases for the technology.
Guiding Vector Fields for Following Occluded Paths
Authors: Weijia Yao, Bohuan Lin, Brian D. O. Anderson, Ming Cao
Date: 2022-05-25 00:00:00
Tasks: Collision Avoidance

Accurately following a geometric desired path in a two-dimensional space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this paper, we develop a composite guiding vector field via the use of smooth bump functions, and provide theoretical guarantees that the integral curves of the vector field can follow an arbitrary sufficiently smooth desired path and avoid collision with obstacles of arbitrary shapes. These two behaviors are reactive since path (re)-planning and global map construction are not involved. To deal with the common deadlock problem, we introduce a switching vector field, and the Zeno behavior is excluded. Simulations are conducted to support the theoretical results.
Implicit Function Theorem: Estimates on the size of the domain
Authors: Ashutosh Jindal, Debasish Chatterjee, Ravi Banavar
Date: 2022-05-25 00:00:00
Tasks: Numerical Integration, valid

In this article, we present explicit estimates of the size of the domain on which the Implicit Function Theorem and the Inverse Function Theorem are valid. For maps that are twice continuously differentiable, these estimates depend upon the magnitude of the first-order derivatives evaluated at the point of interest, and a bound on the second-order derivatives over a region of interest. One of the key contributions of this article is that the estimates presented require minimal numerical computation. In particular, these estimates are arrived at without any intermediate optimization procedures. We then present three applications in optimization and systems and control theory where the computation of such bounds turns out to be important. First, in electrical networks, the power flow operations can be written as Quadratically Constrained Quadratic Programs (QCQPs), and we utilize our bounds to compute the size of permissible power variations to ensure stable operations of the power system network. Second, the robustness margin of positive definite solutions to the Algebraic Riccati Equation (frequently encountered in control problems) subject to perturbations in the system matrices are computed with the aid of our bounds. Finally, we employ these bounds to provide quantitative estimates of the size of the domains for feedback linearization of discrete-time control systems.
Vehicle Guidance and Tracking Systems
Authors: Ryan Baker, John Garvey, Mitchell Kraft, Manoj Mathews, Kieran O Connor, Matthew Wolf
Date: 2022-05-25 00:00:00

Our application of command and control is the Aegis Combat System. Major components of this system include missile guidance and missile tracking. To look further into some of the aspects of these systems, an extremely simplified model of the Aegis Combat System will be designed. In this simplified model, a small-scale car will autonomously follow a small-scale remote-controlled car. There will be three major components of this system: the controller and the two small-scale cars. Through this model, the team can demonstrate the real-world application of certain aspects of C2 such as command, communication, and sensor data fusion. Figure 1 shows a picture of the Aegis Combat System.
Lyapunov based Stochastic Stability of a Quantum Decision System for Human-Machine Interaction
Authors: Luke Snow, Shashwat Jain, Vikram Krishnamurthy
Date: 2022-05-24 00:00:00
Tasks: Decision Making

In mathematical psychology, decision makers are modeled using the Lindbladian equations from quantum mechanics to capture important human-centric features such as order effects and violation of the sure thing principle. We consider human-machine interaction involving a quantum decision maker (human) and a controller (machine). Given a sequence of human decisions over time, how can the controller dynamically provide input messages to adapt these decisions so as to converge to a specific decision? We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically. Our methodology yields a useful mathematical framework for human-sensor decision making. The stochastic Lyapunov results are also of independent interest as they generalize recent results in the literature.
Regression Filtration with Resetting to Provide Exponential Convergence of MRAC for Plants with Jump Change of Unknown Parameters
Authors: Anton Glushchenko, Vladislav Petrov, Konstantin Lastochkin
Date: 2021-02-20 00:00:00

This paper proposes a new method to provide the exponential convergence of both the parameter and tracking errors of the composite adaptive control system without the persistent excitation (PE) requirement. Instead, the derived composite adaptive law ensures the above-mentioned properties under the strictly weaker finite excitation (FE) condition. Unlike known solutions, in addition to the PE requirement relaxation, it provides better transient response under jump change of the plant uncertainty parameters. To derive such an adaptive law, a novel scheme of uncertainty filtration with resetting is proposed, which provides the required properties of the control system. A rigorous proof of all mentioned properties of the developed adaptive law is presented. Such law is compared with the known composite ones, which also relax the PE requirement, using the wing-rock problem to conduct numerical experiments. The obtained results fully support the theoretical analysis and demonstrate the advantages of the proposed method.
Global geomagnetic perturbation forecasting using Deep Learning
Authors: Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Teo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal
Date: 2022-05-12 00:00:00
Tasks: Deep Learning

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 minutes into the future using only solar wind measurements as input. The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1-minute cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self-consistent benchmark model set. Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.
Structured foraging of soil predators unveils functional responses to bacterial defenses
Authors: Fernando W. Rossine, Gabriel Vercelli, Corina E. Tarnita, Thomas Gregor
Date: 2022-05-24 00:00:00

Predators and their foraging strategies often determine ecosystem structure and function. Yet, the role of protozoan predators in microbial soil ecosystems remains elusive despite the importance of these ecosystems to global biogeochemical cycles. In particular, amoebae -- the most abundant soil protozoan predators of bacteria -- remineralize soil nutrients and shape the bacterial community. However, their foraging strategies and their role as microbial ecosystem engineers remain unknown. Here we present a multi-scale approach, connecting microscopic single-cell analysis and macroscopic whole ecosystem dynamics, to expose a phylogenetically widespread foraging strategy, in which an amoeba population spontaneously partitions between cells with fast, polarized movement and cells with slow, unpolarized movement. Such differentiated motion gives rise to efficient colony expansion and consumption of the bacterial substrate. From these insights we construct a theoretical model that predicts how disturbances to amoeba growth rate and movement disrupt their predation efficiency. These disturbances correspond to distinct classes of bacterial defenses, which allows us to experimentally validate our predictions. All considered, our characterization of amoeba foraging identifies amoeba mobility, and not amoeba growth, as the core determinant of predation efficiency and a key target for bacterial defense systems.
Machine learning methods for Schlieren imaging of a plasma channel in tenuous atomic vapor
Authors: Gábor Bíró, Mihály Pocsai, Imre Ferenc Barna, Joshua T. Moody, Gábor Demeter
Date: 2022-05-13 00:00:00

We investigate the usage of a Schlieren imaging setup to measure the geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe light is used to image the plasma channel in a tenuous vapor and machine learning techniques are tested for extracting quantitative information from the images. By building a database of simulated signals with a range of plasma parameters for training Deep Neural Networks, we demonstrate that they can extract from the Schlieren images reliably and with high accuracy the location, the radius and the maximum ionization fraction of the plasma channel as well as the width of the transition region between the core of the plasma channel and the unionized vapor. We test several different neural network architectures with supervised learning and show that the parameter estimations supplied by the networks are resilient with respect to slight changes of the experimental parameters that may occur in the course of a measurement.
Identification of Auction Models Using Order Statistics
Authors: Yao Luo, Ruli Xiao
Date: 2022-05-25 00:00:00

Auction data often contain information on only the most competitive bids as opposed to all bids. The usual measurement error approaches to unobserved heterogeneity are inapplicable due to dependence among order statistics. We bridge this gap by providing a set of positive identification results. First, we show that symmetric auctions with discrete unobserved heterogeneity are identifiable using two consecutive order statistics and an instrument. Second, we extend the results to ascending auctions with unknown competition and unobserved heterogeneity.
American postdoctoral salaries do not account for growing disparities in cost of living
Authors: Tim Sainburg
Date: 2022-05-25 00:00:00

The National Institute of Health (NIH) sets postdoctoral (postdoc) trainee stipend levels that many American institutions and investigators use as a basis for postdoc salaries. Although salary standards are held constant across universities, the cost of living in those universities' cities and towns vary widely. Across non-postdoc jobs, more expensive cities pay workers higher wages that scale with an increased cost of living. This work investigates the extent to which postdoc wages account for cost-of-living differences. More than 27,000 postdoc salaries across all US universities are analyzed alongside measures of regional differences in cost of living. We find that postdoc salaries do not account for cost-of-living differences, in contrast with the broader labor market in the same cities and towns. Despite a modest increase in income in high cost of living areas, real (cost of living adjusted) postdoc salaries differ by 29% ($15k 2021 USD) between the least and most expensive areas. Cities that produce greater numbers of tenure-track faculty relative to students such as Boston, New York, and San Francisco are among the most impacted by this pay disparity. The postdoc pay gap is growing and is well-positioned to incur a greater financial burden on economically disadvantaged groups and contribute to faculty hiring disparities in women and racial minorities.
Synthesis of Soundfields through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks
Authors: Luca Comanducci, Fabio Antonacci, Augusto Sarti
Date: 2022-05-25 00:00:00

Most soundfield synthesis approaches deal with extensive and regular loudspeaker arrays, which are often not suitable for home audio systems, due to physical space constraints. In this article we propose a technique for soundfield synthesis through more easily deployable irregular loudspeaker arrays, i.e. where the spacing between loudspeakers is not constant, based on deep learning. The input are the driving signals obtained through a plane wave decomposition-based technique. While the considered driving signals are able to correctly reproduce the soundfield with a regular array, they show degraded performances when using irregular setups. Through a complex-valued Convolutional Neural Network (CNN) we modify the driving signals in order to compensate the errors in the reproduction of the desired soundfield. Since no ground-truth driving signals are available for the compensated ones, we train the model by calculating the loss between the desired soundfield at a number of control points and the one obtained through the driving signals estimated by the network. Numerical results show better reproduction accuracy with respect to the plane wave decomposition-based technique, pressure-matching approach and to linear optimizers for driving signal compensation.
EVM Mitigation with PAPR and ACLR Constraints in Large-Scale MIMO-OFDM Using TOP-ADMM
Authors: Shashi Kant, Mats Bengtsson, Gabor Fodor, Bo Göransson, Carlo Fischione
Date: 2022-05-25 00:00:00

Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems. Moreover, these systems must constrain the adjacent channel leakage ratio (ACLR) to comply with regulatory requirements. Several recent works have investigated the mitigation of the EVM seen at the receivers by capitalizing on the excess spatial dimensions inherent in the large-scale MIMO that assume the availability of perfect channel state information (CSI) with spatially uncorrelated wireless channels. Unfortunately, practical systems operate with erroneous CSI and spatially correlated channels. Additionally, most standards support user-specific/CSI-aware beamformed and cell-specific/non-CSI-aware broadcasting channels. Hence, we formulate a robust EVM mitigation problem under channel uncertainty with nonconvex PAPR and ACLR constraints catering to beamforming/broadcasting. To solve this formidable problem, we develop an efficient scheme using our recently proposed three-operator alternating direction method of multipliers (TOP-ADMM) algorithm and benchmark it against two three-operator algorithms previously presented for machine learning purposes. Numerical results show the efficacy of the proposed algorithm under imperfect CSI and spatially correlated channels.
On the Impact of Hardware Impairments on RIS-aided Localization
Authors: Cuneyd Ozturk, Musa Furkan Keskin, Henk Wymeersch, Sinan Gezici
Date: 2022-05-25 00:00:00

We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model. To analyze the localization performance under the mismatch between the practical model and the ideal model with unit-amplitude RIS elements, we employ the misspecified Cram\'{e}r-Rao bound (MCRB). Based on the MCRB derivation, the lower bound (LB) on the mean-squared error for estimation of UE position is evaluated and shown to converge to the MCRB at low signal-to-noise ratios (SNRs). Simulation results indicate more severe performance degradation due to the model misspecification with increasing SNR. In addition, the mismatched maximum likelihood (MML) estimator is derived and found to be tight to the LB in the high SNR regime. Finally, we observe that the model mismatch can lead to an order-of-magnitude localization performance loss at high SNRs.
A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels
Authors: Shupeng Zhang, Yibin Zhang, Xixi Zhang, Jinlong Sun, Yun Lin, Haris Gacanin, Fumiyuki Adachi, Guan Gui
Date: 2022-05-25 00:00:00

Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale datasets. However, most of these datasets are collected from 2.4G WiFi devices and through similar channel environments. Meanwhile, they only provided receiving data collected by the specific equipment. This paper utilizes software radio peripheral as a dataset generating platform. Therefore, the user can customize the parameters of the dataset, such as frequency band, modulation mode, antenna gain, and so on. In addition, the proposed dataset is generated through various and complex channel environments, which aims to better characterize the radio frequency signals in the real world. We collect the dataset at transmitters and receivers to simulate a real-world RFF dataset based on the long-term evolution (LTE). Furthermore, we verify the dataset and confirm its reliability. The dataset and reproducible code of this paper can be downloaded from GitHub link: https://github.com/njuptzsp/XSRPdataset.
Design of an embedded system with on-demand image capturing and transmission for remote agricultural monitoring
Authors: Ivan Hu, Zihuai Lin
Date: 2022-05-25 00:00:00

The ability to visually verify some element of a remotely controlled agricultural automation system through a photograph is valuable in many cases, not only in the operational phase of the system, but especially in the design and implementation phases. Owing to the remote location of many of the application sites, cellular technology is one enabling medium through which wireless transmission of the photographs could be realized. As data usage is a concern for systems using cellular technology, MQTT chosen over other protocols due to its lower message-to-header overhead. This paper outlines the hardware and firmware design of the LTE Cat-M1 enabled embedded system and the backend web development of the cloud-based web application to facilitate the receiving of the photograph. A satisfactory degree of implementation success was achieved in this project, with deployment to a production environment possible after further refinements.
Secuer: ultrafast, scalable and accurate clustering of single-cell RNA-seq data
Authors: Nana Wei, Yating Nie, Lin Liu, Xiaoqi Zheng, Hua-Jun Wu4
Date: 2022-05-25 00:00:00
Tasks: Clustering

Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage by orders of magnitude, especially for ultra-large datasets profiling over 1 or even 10 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again greatly improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.