pwc-archive/papers-with-abstracts huggingface dataset:
XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges
Authors: C. Liaskos, A. Tsioliaridou, K. Georgopoulos, G. Morianos, S. Ioannidis, I. Salem, D. Manessis, S. Schmid D. Tyrovolas, S. A. Tegos, P. -V. Mekikis, P. D. Diamantoulakis, A. Pitilakis, N. Kantartzis, G. K. Karagiannidis A. Tasolamprou, O. Tsilipakos, M. Kafesaki, I. F. Akyildiz, A. Pitsillides, M. Pateraki, M. Vakalellis, I. Spais
Date: 2022-09-28 00:00:00

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scalability issues faced by existing solutions. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. We leverage PWEs to i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR system whose operation is bounded in the physical layer and, hence, has the prospects for minimal end-to-end latency. Over large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES produced computer graphics.
Statistical Inference for Fisher Market Equilibrium
Authors: Luofeng Liao, Yuan Gao, Christian Kroer
Date: 2022-09-29 00:00:00
Tasks: Fairness, Management

Statistical inference under market equilibrium effects has attracted increasing attention recently. In this paper we focus on the specific case of linear Fisher markets. They have been widely use in fair resource allocation of food/blood donations and budget management in large-scale Internet ad auctions. In resource allocation, it is crucial to quantify the variability of the resource received by the agents (such as blood banks and food banks) in addition to fairness and efficiency properties of the systems. For ad auction markets, it is important to establish statistical properties of the platform's revenues in addition to their expected values. To this end, we propose a statistical framework based on the concept of infinite-dimensional Fisher markets. In our framework, we observe a market formed by a finite number of items sampled from an underlying distribution (the "observed market") and aim to infer several important equilibrium quantities of the underlying long-run market. These equilibrium quantities include individual utilities, social welfare, and pacing multipliers. Through the lens of sample average approximation (SSA), we derive a collection of statistical results and show that the observed market provides useful statistical information of the long-run market. In other words, the equilibrium quantities of the observed market converge to the true ones of the long-run market with strong statistical guarantees. These include consistency, finite sample bounds, asymptotics, and confidence. As an extension, we discuss revenue inference in quasilinear Fisher markets.
Equitable Marketplace Mechanism Design
Authors: Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch
Date: 2022-09-22 00:00:00

We consider a trading marketplace that is populated by traders with diverse trading strategies and objectives. The marketplace allows the suppliers to list their goods and facilitates matching between buyers and sellers. In return, such a marketplace typically charges fees for facilitating trade. The goal of this work is to design a dynamic fee schedule for the marketplace that is equitable and profitable to all traders while being profitable to the marketplace at the same time (from charging fees). Since the traders adapt their strategies to the fee schedule, we present a reinforcement learning framework for simultaneously learning a marketplace fee schedule and trading strategies that adapt to this fee schedule using a weighted optimization objective of profits and equitability. We illustrate the use of the proposed approach in detail on a simulated stock exchange with different types of investors, specifically market makers and consumer investors. As we vary the equitability weights across different investor classes, we see that the learnt exchange fee schedule starts favoring the class of investors with the highest weight. We further discuss the observed insights from the simulated stock exchange in light of the general framework of equitable marketplace mechanism design.
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
Authors: Ying Zhao, Luhao Ge, Huixuan Xie, Genghuai Bai, Zhao Zhang, Qiang Wei, Yun Lin, Yuchao Liu, Fangfang Zhou
Date: 2022-09-30 00:00:00
Tasks: Management, Segmentation

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices.Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.
CDBB West Cambridge Digital Twin: Lessons Learned
Authors: Justas Brazauskas, Matt Danish, Vadim Safronov, Rohit Verma, Richard Mortier, Ian Lewis
Date: 2022-09-30 00:00:00

The report describes the digital architecture developed for the West Cambridge Digital Twin, particularly focussed on real-time sensor data collection and analysis with a privacy framework allowing occupants of the buildings to be first-class participants in the system. The implementation has some notable characteristics. In particular 'push' technology is used throughout such that information streams from the incoming asynchronous individual sensor events through to the end-user web pages with the absolute minimum latency, including real-time generated simple and complex events derived from the the underlying sensor data and the updating of visualisations such as an in-building heatmap. We believe the ability of the entire system to respond in the timescale of individual sensor messages to be unique. JSON structures are used to represent all data types including sensor readings, sensor types, building objects, organisations and people, with the idea that JSON-LD may represent a more suitable way than XML/RDF for managing relations between those objects (such as the 'occupies' relationship of people to offices, or the 'type' relationship of sensors to sensor types).
The Role of Time Delay in Sim2real Transfer of Reinforcement Learning for Cyber-Physical Systems
Authors: Mohamad Chehadeh, Igor Boiko, Yahya Zweiri
Date: 2022-09-30 00:00:00
Tasks: Reinforcement Learning (RL)

This paper analyzes the simulation to reality gap in reinforcement learning (RL) cyber-physical systems with fractional delays (i.e. delays that are non-integer multiple of the sampling period). The consideration of fractional delay has important implications on the nature of the cyber-physical system considered. Systems with delays are non-Markovian, and the system state vector needs to be extended to make the system Markovian. We show that this is not possible when the delay is in the output, and the problem would always be non-Markovian. Based on this analysis, a sampling scheme is proposed that results in efficient RL training and agents that perform well in realistic multirotor unmanned aerial vehicle simulations. We demonstrate that the resultant agents do not produce excessive oscillations, which is not the case with RL agents that do not consider time delay in the model.
Towards remote fault detection by analyzing communication priorities
Authors: Alexander Gräfe, Dominik Baumann, Sebastian Trimpe
Date: 2022-09-30 00:00:00
Tasks: Fault Detection

The ability to detect faults is an important safety feature for event-based multi-agent systems. In most existing algorithms, each agent tries to detect faults by checking its own behavior. But what if one agent becomes unable to recognize misbehavior, for example due to failure in its onboard fault detection? To improve resilience and avoid propagation of individual errors to the multi-agent system, agents should check each other remotely for malfunction or misbehavior. In this paper, we build upon a recently proposed predictive triggering architecture that involves communication priorities shared throughout the network to manage limited bandwidth. We propose a fault detection method that uses these priorities to detect errors in other agents. The resulting algorithms is not only able to detect faults, but can also run on a low-power microcontroller in real-time, as we demonstrate in hardware experiments.
Multi-Antenna Configuration with Reduced Passive Self-Interference for Full-Duplex Intelligent Transportation System
Authors: Jogesh Chandra Dash, Debdeep Sarkar
Date: 2022-09-30 00:00:00

In this paper, we propose a closely spaced multi-antenna system with \textit{passive} self-interference cancellation (\textit{p}-SIC) of $\approx 90$ dB between the transmitter and receiver antenna for full-duplex application. The \textit{p}-SIC is achieved by field confinement near individual antennae using shorted metallic vias and the application of U-shaped perturbation in the ground plane. The \textit{p}-SIC technique is initially implemented in a 1-Tx and 1-Rx antenna system and explained using transmission line-based theory. Further, it is extended to 1-Tx and 2-Rx configurations. Here the proposed full-duplex antenna system is designed at $5.9$ GHz ($5.855-5.925$ GHz, IEEE 802.11p / WAVE technology) intelligent transportation system (ITS) application band using a microstrip patch configuration. The individual antenna exhibits an impedance bandwidth of $93$ MHz ($5.850-5.944$ GHz), $5.63$ dBi gain at $5.9$ GHz operating frequency and X-pol level less than $20$ dB in the broad side direction. The proposed FD configuration exhibits $|S_{ij}|$ of less than $-50$ dB over the complete operating band and $\approx -90$ dB is achieved at the operating frequency between the Tx and Rx. Similarly, $|S_{ij}|$ of less the $-30$ dB is achieved between 2-Rx antennas for a three-element FD configuration. The design procedure of the proposed FD configuration is explained and verified using fabrication and measurement. An experimental demonstration of the self-interference channel and its suppression using the proposed \textit{p}-SIC technique is also provided. Further, to study the diversity performance of the proposed multi-antenna configuration, the MIMO performance metrics such as \textit{ECC} and \textit{CCL} are evaluated using simulation and measurement.
Review of Electric Vehicle Charging Technologies, Configurations, and Architectures
Authors: Sithara S. G. Acharige, Md Enamul Haque, Mohammad Taufiqul Arif, Nasser Hosseinzadeh
Date: 2022-09-30 00:00:00

Electric Vehicles (EVs) are projected to be one of the major contributors to energy transition in the global transportation due to their rapid expansion. The EVs will play a vital role in achieving a sustainable transportation system by reducing fossil fuel dependency and greenhouse gas (GHG) emissions. However, high level of EVs integration into the distribution grid has introduced many challenges for the power grid operation, safety, and network planning due to the increase in load demand, power quality impacts and power losses. An increasing fleet of electric mobility requires the advanced charging systems to enhance charging efficiency and utility grid support. Innovative EV charging technologies are obtaining much attention in recent research studies aimed at strengthening EV adoption while providing ancillary services. Therefore, analysis of the status of EV charging technologies is significant to accelerate EV adoption with advanced control strategies to discover a remedial solution for negative grid impacts, enhance desired charging efficiency and grid support. This paper presents a comprehensive review of the current deployment of EV charging systems, international standards, charging configurations, EV battery technologies, architecture of EV charging stations, and emerging technical challenges. The charging systems require a dedicated converter topology, a control strategy and international standards for charging and grid interconnection to ensure optimum operation and enhance grid support. An overview of different charging systems in terms of onboard and off-board chargers, AC-DC and DC-DC converter topologies, and AC and DC-based charging station architectures are evaluated.
Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process
Authors: Yifei Xu, Ye Guo, Wenjun Tang, Hongbin Sun, Shiming Li, Yue Dai
Date: 2022-09-30 00:00:00
Tasks: Collaborative Filtering, State Estimation

The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution system state estimation, in which the deep Gaussian process is incorporated to infer the fast time-scale pseudo measurements and avoid anomalies. Numerical tests have demonstrated the higher estimation accuracy of the proposed method.
Construction of the Radio Map with Defective GPS Position Information
Authors: Taewon Kang, Joon Hyo Rhee
Date: 2022-09-30 00:00:00
Tasks: Position

The basic idea of RSS-based indoor positioning is to estimate the receiver location by matching the measured received signal strength indicator (RSSI) with preestablished RSSI collections with corresponding locations, known as the radio map. Hence, constructing an accurate radio map directly relates to accurate positioning performance in RSS-based indoor positioning. RSS-based indoor positioning can be easily conducted with a radio map that surveys every location, but a complete radio map cannot be constructed when the map area includes locations that are physically impossible to reach or denied access. In addition, measurement errors or device problems can occur during the survey, resulting in degradation of the radio map. We analyzed incidents that occurred in actual RSSI surveys that could disrupt the construction of the radio map and proposed methods to construct a more accurate radio map.
Geography-aware Optimal UAV 3D Placement for LOS Relaying: A Geometry Approach
Authors: Yuanshuai Zheng, Junting Chen
Date: 2022-09-30 00:00:00
Tasks: Position

Many emerging technologies for the next generation wireless network prefer line-of-sight (LOS) propagation conditions to fully release their performance advantages. This paper studies 3D unmanned aerial vehicle (UAV) placement to establish LOS links for two ground terminals in deep shadow in a dense urban environment. The challenge is that the LOS region for the feasible UAV positions can be arbitrary due to the complicated structure of the environment. While most existing works rely on simplified stochastic LOS models and problem relaxations, this paper focuses on establishing theoretical guarantees for the optimal UAV placement to ensure LOS conditions for two ground users in an actual propagation environment. It is found that it suffices to search a bounded 2D area for the globally optimal 3D UAV position. Thus, this paper develops an exploration-exploitation algorithm with a linear trajectory length and achieves above 99% global optimality over several real city environments being tested in our experiments. To further enhance the search capability in an ultra-dense environment, a dynamic multi-stage algorithm is developed and theoretically shown to find an $\epsilon$-optimal UAV position with a search length $O(1/\epsilon)$. Significant performance advantages are demonstrated in several numerical experiments for wireless communication relaying and wireless power transfer.
Continuification-based control of large multiagent systems in a ring
Authors: Gian Carlo Maffettone, Alain Boldini, Mario di Bernardo, Maurizio Porfiri
Date: 2022-09-29 00:00:00

In this paper, we propose a method to control large-scale multiagent systems swarming in a ring. Specifically, we use a continuification-based approach that transforms the microscopic, agent-level description of the system dynamics into a macroscopic, continuum-level representation, which we employ to synthesize a control action towards a desired distribution of the agents. The continuum-level control action is then discretized at the agent-level in order to practically implement it. To confirm the effectiveness and the robustness of the proposed approach, we complement theoretical derivations with a series of numerical simulations.
Predicting the power grid frequency of European islands
Authors: Thorbjørn Lund Onsaker, Heidi S. Nygård, Damià Gomila, Pere Colet, Ralf Mikut, Richard Jumar, Heiko Maass, Uwe Kühnapfel, Veit Hagenmeyer, Benjamin Schäfer
Date: 2022-09-27 00:00:00

Modelling, forecasting and overall understanding of the dynamics of the power grid and its frequency are essential for the safe operation of existing and future power grids. Much previous research was focused on large continental areas, while small systems, such as islands are less well-studied. These natural island systems are ideal testing environments for microgrid proposals and artificially islanded grid operation. In the present paper, we utilize measurements of the power grid frequency obtained in European islands: the Faroe Islands, Ireland, the Balearic Islands and Iceland and investigate how their frequency can be predicted, compared to the Nordic power system, acting as a reference. The Balearic islands are found to be particularly deterministic and easy to predict in contrast to hard-to-predict Iceland. Furthermore, we show that typically 2-4 weeks of data are needed to improve prediction performance beyond simple benchmarks.
Generalized second law of thermodynamics in the Glosten-Milgrom model
Authors: Pierre Carmier
Date: 2022-09-30 00:00:00
Tasks: Bayesian Inference

We derive an upper bound for the expected gain of informed traders in the Glosten-Milgrom model with finite horizon, fully analogous to a generalized second law of thermodynamics. This result extends that obtained by Touzo et al. a couple of years ago. The proof relies on Bayesian inference (exploiting the invariance of the problem under consecutive game sequences) and an interesting entropic inequality. We also provide numerical results both supporting the existence of a characteristic timescale in the model and illustrating the magnitude of gain fluctuations. Other possible extensions are discussed.
A Lagrangian model for drifting ecosystems reveals heterogeneity-driven enhancement of marine plankton blooms
Authors: Enrico Ser-Giacomi, Ricardo Martinez-Garcia, Stephanie Dutkiewicz, Michael J. Follows
Date: 2022-09-30 00:00:00

Marine plankton play a crucial role in carbon storage, oxygen production, global climate, and ecosystem function. Planktonic ecosystems are embedded in a Lagrangian patches of water that are continuously moving, stretching, and diluting. These processes drive inhomegeneities on a range of scales, with implications for the integrated ecosystem properties, but are hard to characterize. We present a theoretical framework which accounts for all these aspects; tracking the water patch hosting a drifting ecosystem along with its physical, environmental, and biochemical features. The model resolves patch dilution and internal physical mixing as a function of oceanic strain and diffusion. Ecological dynamics are parameterized by an idealized nutrient and phytoplankton population and we specifically capture the propagation of the biochemical spatial variances to represent within-patch heterogeneity. We find that, depending only on the physical processes to which the water patch is subjected, the plankton biomass response to a resource perturbation can vary several fold. This work indicates that we must account for these processes when interpreting and modelling marine ecosystems and provides a framework with which to do so.
Ghost translation
Authors: Wenhan Ren, Xiaoyu Nie, Tao Peng, Marlan O. Scully
Date: 2022-09-29 00:00:00
Tasks: Translation

Artificial intelligence has recently been widely used in computational imaging. The deep neural network (DNN) improves the signal-to-noise ratio of the retrieved images, whose quality is otherwise corrupted due to the low sampling ratio or noisy environments. This work proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network. The simulation database assists the network in achieving signal translation ability. The experimental single-pixel detector's signal will be `translated' into a 2D image in an end-to-end manner. High-quality images with no background noise can be retrieved at a sampling ratio as low as 2%. The illumination patterns can be either well-designed speckle patterns for sub-Nyquist imaging or random speckle patterns. Moreover, our method is robust to noise interference. This translation mechanism opens a new direction for DNN-assisted ghost imaging and can be used in various computational imaging scenarios.
DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms
Authors: Christopher Yeung, Benjamin Pham, Ryan Tsai, Katherine T. Fountaine, Aaswath P. Raman
Date: 2022-09-28 00:00:00
Tasks: All

In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given dataset's design space, an iterative optimization algorithm can further refine the solution and overcome dataset limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large datasets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pre-trained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multi-objective "all-in-one" global photonics inverse design application framework which integrates pre-trained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties - all within a single user-guided application interface. Our framework thus paves a path towards the systematic unification of ML and optimization algorithms for photonic inverse design.