pwc-archive/papers-with-abstracts huggingface dataset:
A flexible propelled arm: Mechanical considerations for the use in UAVs
Authors: Fernando Ruiz, Begoña Arrue, Aníbal Ollero
Date: 2022-04-29 00:00:00

This paper presents a soft propelled arm, 3D-printed in TPU 70A, designed to be used in flexible UAVs, an advantageous concept since it reduces risk in the event of a collision thanks to a high energy absorption. The proposed design also allows the possibility to adapt to the environment and land on pipelines. The flexibility of the arm can be controlled during the additive manufacturing process by adjusting the infill rate or internal density. In this work, a mechanical design based on simulations with an experimentally adjusted 5- parameter Mooney-Rivlin non-linear model is proposed. Thrust efficiency is also maximized through CFD simulations.
Control-Theoretic Modeling of Multi-Species Water Quality Dynamics in Drinking Water Networks: Survey, Methods, and Test Cases
Authors: Salma M. Elsherif, Shen Wang, Ahmad F. Taha, Lina Sela, Marcio H. Giacomoni, Ahmed Abokifa
Date: 2022-04-29 00:00:00

Chlorine is a widely used disinfectant and proxy for water quality (WQ) monitoring in water distribution networks (WDN). Chlorine-based WQ regulation and control aims to maintain pathogen-free water. Chlorine residual evolution within WDN is commonly modeled using the typical single-species decay and reaction dynamics that account for network-wide, spatiotemporal chlorine concentrations only. Prior studies have proposed more advanced and accurate descriptions via multi-species dynamics. This paper presents a host of novel state-space, control-theoretic representations of multi-species water quality dynamics. These representations describe decay, reaction, and transport of chlorine and a fictitious reactive substance to reflect realistic complex scenarios in WDN. Such dynamics are simulated over space- and time-discretized grids of the transport partial differential equation and the nonlinear reaction ordinary differential equation. To that end, this paper (i) provides a full description on how to formulate a high fidelity model-driven state-space representation of the multi-species water quality dynamics and (ii) investigates the applicability and performance of different Eulerian-based schemes (Lax-Wendroff, backward Euler, and Crank- Nicolson) and Lagrangian-based schemes (method of characteristics) in contrast with EPANET and its EPANET-MSX extension. Numerical case studies reveal that the Lax-Wendroff scheme and method of characteristics outperform other schemes with reliable results under reasonable assumptions and limitations.
Differentially Private Load Restoration for Microgrids with Distributed Energy Storage
Authors: Shourya Bose, Yu Zhang
Date: 2022-04-29 00:00:00

Distributed energy storage systems (ESSs) can be efficiently leveraged for load restoration (LR) for a microgrid (MG) in island mode. When the ESSs are owned by third parties rather than the MG operator (MGO), the ESS operating setpoints may be considered as private information of their respective owners. Therefore, efforts must be put forth to avoid the disclosure through adversarial analysis of load setpoints. In his paper, we consider a scenario where LR takes place in a MG by determining load and ESS power injections through the solution of an AC optimal power flow (AC-OPF) problem. Since the charge/discharge mode at any given time is assumed to be private, we develop a differentially-private mechanism which restores load while maintaining privacy of ESS mode data. The performance of the proposed mechanism is demonstrated for a 33-bus MG.
High-Fidelity Model of Stand-Alone Diesel Electric Generator with Hybrid Turbine-Governor Configuration for Microgrid Studies
Authors: Chinmay Shah, Mariko Shirazi, Richard Wies, Phylicia Cicilio, Timothy Hansen, Reinaldo Tonkoski
Date: 2022-04-29 00:00:00

Diesel electric generators are an inherent part of remote hybrid microgrids found in remote regions of the world that provide primary frequency response (PFR) to restore system frequency during load or generation changes. However, with inverter-based resources (IBR) integration into microgrids, the IBR control provides a fast frequency response (FFR) to restore the system frequency. Hence, supplementing PFR with FFR requires a sophisticated control system and a high fidelity diesel electric generator model to design these control systems. In this work, a high-fidelity model of a diesel electric generator is developed. Its parameters are tuned using a surrogate optimization algorithm by emulating its response during a load change to a 400 kVA Caterpillar C-15 diesel generator, similar to those found in remote microgrids. The diesel electric generator model consists of a synchronous machine, DC4B excitation with V/Hz limiter, and a proposed modified IEEE GGOV1 engine-governor model (GGOV1D). The performance of the GGOV1D is compared with simple, Woodward DEGOV, and a standard IEEE GGOV1 engine-governor model. Results show that error in the diesel electric generator's response to load changes using the GGOV1D model is lower with an improved frequency response during the arresting and rebound period than the other engine-governor models.
Indoor 3-Dimensional Visible Light Positioning: Error Metric and LED Layout Optimization
Authors: Jiaojiao Xu, Nuo Huang, Chen Gong
Date: 2022-04-29 00:00:00
Tasks: Position

We consider 3-dimensional (3D) visible light positioning (VLP) based on smartphone camera in an indoor scenario. Based on the positioning model in the quantized pixel-domain, we characterize the 3D normalized positioning error metric (NPEM) through the partial derivative of the positioning function, and evaluate the NPEM for horizontal and non-horizontal receiver camera positions. Moreover, under horizontal receiver terminal position, we explore the relationship between the NPEM and the light-emitting diode (LED) cell layout, approximate the relationship between the NPEM and the number of LEDs captured by the camera, and evaluate the approximation accuracy according to the simulated positioning error. Based on the approximation results, we optimize the LED transmitter cell layout to minimize NPEM assuming structured square cell layouts with certain distance parameters.
Coordinated Charging Station Search in Stochastic Environments: A Multi-Agent Approach
Authors: Marianne Guillet, Maximilian Schiffer
Date: 2022-04-26 00:00:00
Tasks: Blocking, Decision Making

Range and charge anxiety remain essential barriers to a faster electric vehicle market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an electric vehicle uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real-time availability data but struggle with data inaccuracy, e.g., due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize a driver's detour until reaching an available charging station. So far, both practical and theoretical approaches ignore driver coordination enabled by charging requests centralization or sharing of data, e.g., sharing observations of charging stations' availability or visit intentions between drivers. Against this background, we study coordinated stochastic search algorithms, which help to reduce station visit conflicts and improve the drivers' charging experience. We model a multi-agent stochastic charging station search problem as a finite-horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies. In contrast to static policies, dynamic policies account for information updates during policy planning and execution. We present a hierarchical implementation of a single-agent heuristic for decentralized decision making and a rollout algorithm for centralized decision making. Extensive numerical studies show that compared to an uncoordinated setting, a decentralized setting with visit-intentions sharing decreases the system cost by 26%, which is nearly as good as the 28% cost decrease achieved in a centralized setting, and saves up to 23% of a driver's search time while increasing her search reliability.
Controlling for Latent Confounding with Triple Proxies
Authors: Ben Deaner
Date: 2022-04-28 00:00:00

We present new results for nonparametric identification of causal effects using noisy proxies for unobserved confounders. Our approach builds on the results of \citet{Hu2008} who tackle the problem of general measurement error. We call this the `triple proxy' approach because it requires three proxies that are jointly independent conditional on unobservables. We consider three different choices for the third proxy: it may be an outcome, a vector of treatments, or a collection of auxiliary variables. We compare to an alternative identification strategy introduced by \citet{Miao2018a} in which causal effects are identified using two conditionally independent proxies. We refer to this as the `double proxy' approach. The triple proxy approach identifies objects that are not identified by the double proxy approach, including some that capture the variation in average treatment effects between strata of the unobservables. Moreover, the conditional independence assumptions in the double and triple proxy approaches are non-nested.
Auctioning Multiple Goods without Priors
Authors: Wanchang Zhang
Date: 2022-04-28 00:00:00

I consider a mechanism design problem of selling multiple goods to multiple bidders when the designer has minimal amount of information. I assume that the designer only knows the upper bounds of bidders' values for each good and has no additional distributional information. The designer takes a minimax regret approach. The expected regret from a mechanism given a joint distribution over value profiles and an equilibrium is defined as the difference between the full surplus and the expected revenue. The designer seeks a mechanism, referred to as a minimax regret mechanism, that minimizes her worst-case expected regret across all possible joint distributions over value profiles and all equilibria. I find that a separate second-price auction with random reserves is a minimax regret mechanism for general upper bounds. Under this mechanism, the designer holds a separate auction for each good; the formats of these auctions are second-price auctions with random reserves.
Complex-Valued Frequency Selective Extrapolation for Fast Image and Video Signal Extrapolation
Authors: Jürgen Seiler, André Kaup
Date: 2022-04-27 00:00:00
Tasks: Miscellaneous

Signal extrapolation tasks arise in miscellaneous manners in the field of image and video signal processing. But, due to the widespread use of low-power and mobile devices, the computational complexity of an algorithm plays a crucial role in selecting an algorithm for a given problem. Within the scope of this contribution, we introduce the complex-valued Frequency Selective Extrapolation for fast image and video signal extrapolation. This algorithm iteratively generates a generic complex-valued model of the signal to be extrapolated as weighted superposition of Fourier basis functions. We further show that this algorithm is up to 10 times faster than the existent real-valued Frequency Selective Extrapolation that takes the real-valued nature of the input signals into account during the model generation. At the same time, the quality which is achievable by the complex-valued model generation is similar to the quality of the real-valued model generation.
A Fast Algorithm for Selective Signal Extrapolation with Arbitrary Basis Functions
Authors: Jürgen Seiler, André Kaup
Date: 2022-04-27 00:00:00

Signal extrapolation is an important task in digital signal processing for extending known signals into unknown areas. The Selective Extrapolation is a very effective algorithm to achieve this. Thereby, the extrapolation is obtained by generating a model of the signal to be extrapolated as weighted superposition of basis functions. Unfortunately, this algorithm is computationally very expensive and, up to now, efficient implementations exist only for basis function sets that emanate from discrete transforms. Within the scope of this contribution, a novel efficient solution for Selective Extrapolation is presented for utilization with arbitrary basis functions. The proposed algorithm mathematically behaves identically to the original Selective Extrapolation, but is several decades faster. Furthermore, it is able to outperform existent fast transform domain algorithms which are limited to basis function sets that belong to the corresponding transform. With that, the novel algorithm allows for an efficient use of arbitrary basis functions, even if they are only numerically defined.
Automated player identification and indexing using two-stage deep learning network
Authors: Hongshan Liu, Colin Aderon, Noah Wagon, Abdul Latif Bamba, Xueshen Li, Huapu Liu, Steven MacCall, Yu Gan
Date: 2022-04-26 00:00:00
Tasks: Jersey Number Recognition, object-detection, Object Detection

American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced data for identifying players, especially jersey numbers. In this work, we propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. First, we utilize an object detection network, a detection transformer, to tackle the player detection problem in a crowded context. Second, we identify players using jersey number recognition with a secondary convolutional neural network, then synchronize it with a game clock subsystem. Finally, the system outputs a complete log in a database for play indexing. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos. The proposed system shows great potential for implementation in and analysis of football broadcast video.
Enhancing Core Image Classification Using Generative Adversarial Networks (GANs)
Authors: Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, Darkhan Kurmangaliyev, Daniyar Nurseitov
Date: 2022-04-21 00:00:00
Tasks: Classification, image-classification, Image Classification

In the thrilling world of oil exploration, drill core samples are key to unlocking geological information critical to finding lucrative oil deposits. Despite the importance of these samples, traditional core logging techniques are known to be laborious and, worse still, subjective. Thankfully, the industry has embraced an innovative solution core imaging that allows for nondestructive and noninvasive rapid characterization of large quantities of drill cores. Our preeminent research paper aims to tackle the pressing problem of core detection and classification. Using state-of-the-art techniques, we present a groundbreaking solution that will transform the industry. Our first challenge is detecting the cores and segmenting the holes in images, which we will achieve using the Faster RCNN and Mask RCNN models, respectively. Then, we will address the problem of filling the hole in the core image, utilizing the powerful Generative Adversarial Networks (GANs) and employing Contextual Residual Aggregation (CRA) to create high-frequency residuals for missing contents in images. Finally, we will apply sophisticated texture recognition models for the classification of core images, revealing crucial information to oil companies in their quest to uncover valuable oil deposits. Our research paper presents an innovative and groundbreaking approach to tackling the complex issues surrounding core detection and classification. By harnessing cutting-edge techniques and technologies, we are poised to revolutionize the industry and make significant contributions to the field of oil exploration.
Participatory Sensing for Localization of a GNSS Jammer
Authors: Glädje Karl Olsson, Erik Axell, Erik G. Larsson, Panos Papadimitratos
Date: 2022-04-29 00:00:00

GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous such incidents have been reported worldwide in the last decade. It is important to detect attacks fast and localize attackers, which can be hard if not impossible without dedicated sensing infrastructure. The notion of participatory sensing, or crowdsensing, is that a large ensemble of voluntary contributors provides the measurements, rather than relying on dedicated sensing infrastructure. This work considers embedded GNSS receivers to provide measurements for participatory jamming detection and localization. Specifically, this work proposes a novel jamming localization algorithm, based on participatory sensing, that exploits AGC and C/N_0 estimates from commercial GNSS receivers. The proposed algorithm does not require knowledge of the jamming power nor of the channels, but automatically estimates all parameters. The algorithm is shown to outperform similar state-of-the-art localization algorithms in relevant scenarios.
Size Generalization for Resource Allocation with Graph Neural Networks
Authors: Wu Jiajun, Sun Chengjian, Yang Chenyang
Date: 2022-04-29 00:00:00

Size generalization is important for learning wireless policies, which are often with dynamic sizes, say caused by time-varying number of users. Recent works of learning to optimize resource allocation empirically demonstrate that graph neural networks (GNNs) can generalize to different problem scales. However, GNNs are not guaranteed to generalize across input sizes. In this paper, we strive to analyze the size generalization mechanism of GNNs when learning permutation equivariant (PE) policies. We find that the aggregation function and activation functions of a GNN play a key role on its size generalization ability. We take the GNN with mean aggregator, called mean-GNN, as an example to demonstrate a size generalization condition, and interpret why several GNNs in the literature of wireless communications can generalize well to problem scales. To illustrate how to design GNNs with size generalizability according to our finding, we consider power and bandwidth allocation, and suggest to select or pre-train activation function in the output layer of mean-GNN for learning the PE policies. Simulation results show that the proposed GNN can generalize well to the number of users, which validate our analysis for the size generalization condition of GNNs when learning the PE policies.
Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications
Authors: Xiaoxia Xu, Yuanwei Liu, Qimei Chen, Xidong Mu, Zhiguo Ding
Date: 2022-04-28 00:00:00
Tasks: Graph Neural Network, Scheduling

A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design. The joint design problem is formulated to maximize the system sum rate while satisfying the SIC decoding requirements and users' minimum data rate requirements. To address this highly complex and coupling non-convex mixed integer nonlinear programming (MINLP), a novel distributed auto-learning graph neural network (AutoGNN) architecture is proposed to alleviate the overwhelming information exchange burdens among base stations (BSs). The proposed AutoGNN can train the GNN model weights whilst automatically optimizing the GNN architecture, namely the GNN network depth and message embedding sizes, to achieve communication-efficient distributed scheduling. Based on the proposed architecture, a bi-level AutoGNN learning algorithm is further developed to efficiently approximate the hypergradient in model training. It is theoretically proved that the proposed bi-level AutoGNN learning algorithm can converge to a stationary point. Numerical results reveal that: 1) the proposed cluster-free NOMA framework outperforms the conventional cluster-based NOMA framework in the multi-cell scenario; and 2) the proposed AutoGNN architecture significantly reduces the computation and communication overheads compared to the conventional convex optimization-based methods and the conventional GNNs with fixed architectures.
The TAP equation: evaluating combinatorial innovation in Biocosmology
Authors: Marina Cortês, Stuart A. Kauffman, Andrew R. Liddle, Lee Smolin
Date: 2022-04-24 00:00:00

We investigate solutions to the TAP equation, a phenomenological implementation of the Theory of the Adjacent Possible. Several implementations of TAP are studied, with potential applications in a range of topics including economics, social sciences, environmental change, evolutionary biological systems, and the nature of physical laws. The generic behaviour is an extended plateau followed by a sharp explosive divergence. We find accurate analytic approximations for the blow-up time that we validate against numerical simulations, and explore the properties of the equation in the vicinity of equilibrium between innovation and extinction. A particular variant, the two-scale TAP model, replaces the initial plateau with a phase of exponential growth, a widening of the TAP equation phenomenology that may enable it to be applied in a wider range of contexts.
Few-shot learning for medical text: A systematic review
Authors: Yao Ge, Yuting Guo, Yuan-Chi Yang, Mohammed Ali Al-Garadi, Abeed Sarker
Date: 2022-04-21 00:00:00
Tasks: Articles, Few-Shot Learning, Meta-Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), text-classification, Text Classification

Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for training. As many medical topics have limited annotated textual data in practical settings, FSL-based natural language processing (NLP) methods hold substantial promise. We aimed to conduct a systematic review to explore the state of FSL methods for medical NLP. Materials and Methods: We searched for articles published between January 2016 and August 2021 using PubMed/Medline, Embase, ACL Anthology, and IEEE Xplore Digital Library. To identify the latest relevant methods, we also searched other sources such as preprint servers (eg., medRxiv) via Google Scholar. We included all articles that involved FSL and any type of medical text. We abstracted articles based on data source(s), aim(s), training set size(s), primary method(s)/approach(es), and evaluation method(s). Results: 31 studies met our inclusion criteria-all published after 2018; 22 (71%) since 2020. Concept extraction/named entity recognition was the most frequently addressed task (13/31; 42%), followed by text classification (10/31; 32%). Twenty-one (68%) studies reconstructed existing datasets to create few-shot scenarios synthetically, and MIMIC-III was the most frequently used dataset (7/31; 23%). Common methods included FSL with attention mechanisms (12/31; 39%), prototypical networks (8/31; 26%), and meta-learning (6/31; 19%). Discussion: Despite the potential for FSL in biomedical NLP, progress has been limited compared to domain-independent FSL. This may be due to the paucity of standardized, public datasets, and the relative underperformance of FSL methods on biomedical topics. Creation and release of specialized datasets for biomedical FSL may aid method development by enabling comparative analyses.