pwc-archive/papers-with-abstracts huggingface dataset:
AoI-minimizing Scheduling in UAV-relayed IoT Networks
Authors: Biplav Choudhury, Vijay K. Shah, Aidin Ferdowsi, Jeffrey H. Reed, Y. Thomas Hou
Date: 2021-07-12 00:00:00
Tasks: Scheduling

Due to flexibility, autonomy and low operational cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as \textit{relays} to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions, i.e., error-free channels and generate-at-will traffic generation at IoT devices. On the contrary, for realistic conditions, we propose a Deep-Q-Networks (DQN) based scheduler. Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10's of IoT devices). However, it does not scale well with network size whereas MAF-MAD outperforms all other schedulers under all considered scenarios for larger networks.
T-s3ra: traffic-aware scheduling for secure slicing and resource allocation in sdn/nfv enabled 5g networks
Authors: Ali J. Ramadhan
Date: 2021-07-11 00:00:00
Tasks: Scheduling

Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high data rates and low latency. Deep learning is a potential solution for overcoming these constraints. Secure slicing avoids resource wastage; however, DDoS attackers can exploit the sliced network. Therefore, we focused on secure slicing with resource allocation under massive network traffic. Traffic-aware scheduling is proposed for secure slicing and resource allocation over SDN/NFV-enabled 5G networks. In this approach (T-S3RA), user devices are authenticated using Boolean logic with a password-based key derivation function. The traffic is scheduled in 5G access points, and secure network slicing and resource allocation are implemented using deep learning models such as SliceNet and HopFieldNet, respectively. To predict DDoS attackers, we computed the Renyi entropy for packet classification. Experiments were conducted using a network simulator with 250 nodes in the network topology. Performance was evaluated using metrics such as throughput, latency, packet transmission ratio, packet loss ratio, slice capacity, bandwidth consumption, and slice acceptance ratio. T-S3RA was implemented in three 5G use cases with different requirements including massive machine-type communication, ultrareliable low-latency communication, and enhanced mobile broadband.
Enabling microbiome research on personal devices
Authors: Igor Sfiligoi, Daniel McDonald, Rob Knight
Date: 2021-07-08 00:00:00

Microbiome studies have recently transitioned from experimental designs with a few hundred samples to designs spanning tens of thousands of samples. Modern studies such as the Earth Microbiome Project (EMP) afford the statistics crucial for untangling the many factors that influence microbial community composition. Analyzing those data used to require access to a compute cluster, making it both expensive and inconvenient. We show that recent improvements in both hardware and software now allow to compute key bioinformatics tasks on EMP-sized data in minutes using a gaming-class laptop, enabling much faster and broader microbiome science insights.
Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue Imaging
Authors: Tamás Gábor Csapó
Date: 2021-07-12 00:00:00
Tasks: Prediction, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

In this paper, we present our first experiments in text-to-articulation prediction, using ultrasound tongue image targets. We extend a traditional (vocoder-based) DNN-TTS framework with predicting PCA-compressed ultrasound images, of which the continuous tongue motion can be reconstructed in synchrony with synthesized speech. We use the data of eight speakers, train fully connected and recurrent neural networks, and show that FC-DNNs are more suitable for the prediction of sequential data than LSTMs, in case of limited training data. Objective experiments and visualized predictions show that the proposed solution is feasible and the generated ultrasound videos are close to natural tongue movement. Articulatory movement prediction from text input can be useful for audiovisual speech synthesis or computer-assisted pronunciation training.
Equivariant filter (EqF): A general filter design for systems on homogeneous spaces
Authors: Pieter van Goor, Tarek Hamel, Robert Mahony
Date: 2021-07-12 00:00:00

The kinematics of many mechanical systems encountered in robotics and other fields, such as single-bearing attitude estimation and SLAM, are naturally posed on homogeneous spaces: That is, their state lies in a smooth manifold equipped with a transitive Lie-group symmetry. This paper shows that any system posed in a homogeneous space can be extended to a larger system that is equivariant under a symmetry action. The equivariant structure of the system is exploited to propose a novel new filter, the Equivariant Filter (EqF), based on linearisation of global error dynamics derived from the symmetry action. The EqF is applied to an example of estimating the positions of stationary landmarks relative to a moving monocular camera that is intractable for previously proposed symmetry based filter design methodologies.
The Impact of Three-phase Impedances on the Stability of DER systems
Authors: Jaimie Swartz, Alexandra von Meier
Date: 2021-07-12 00:00:00

In this work we explore impedance-based interactions that arise when inverter-connected distributed energy resources (DERs) inject real and reactive power to regulate voltage and power flows on three-phase unbalanced distribution grids. We consider two inverter control frameworks that compute power setpoints: a mix of volt/var and volt/watt control, and phasor-based control. On a two-bus network we isolate how line length, R/X ratio, and mutual impedances each affects the stability of the DER system. We validate our analysis through simulation of the two-bus network, and validate that the effects found extend to the IEEE 123-node feeder. Furthermore, we find that the impedance properties make it more challenging to design a stable DER system for certain placements of DER on this feeder.
Modelling and Characterisation of Flexibility\\from Distributed Energy Resources
Authors: Shariq Riaz, Pierluigi Mancarella
Date: 2021-07-11 00:00:00

Harnessing flexibility from distributed energy resources (DER) to participate in various markets while accounting for relevant technical and commercial constraints is essential for the development of low-carbon grids. However, there is no clear definition or even description of the salient features of aggregated DER flexibility, including its steady-state and dynamic features and how these are impacted by network constraints and market requirements. This paper proposes a comprehensive DER flexibility modelling and characterisation framework that is based on the concept of nodal operating envelope (NOE). In particular, capacity, ramp, duration and cost are identified as key flexibility metrics and associated with different but consistent NOEs describing capability, feasibility, ramp, duration, economic, technical and commercial flexibility features. These NOEs, which conceptually arise from a Venn diagram, can be built via optimal power flow (OPF) analysis, visualised in the active-reactive power space, and used by different stakeholders. Results from a canonical test system and a real distribution system illustrate the value and applicability of the proposed framework to model and characterise provision of flexibility and market services from DER for different use cases.
UAV Control Optimization via Decentralized Markov Decision Processes
Authors: Md Ali Azam
Date: 2021-07-11 00:00:00

Unmanned aerial vehicle (UAV) swarm control has applications including target tracking, surveillance, terrain mapping, and precision agriculture. Decentralized control methods are particularly useful when the swarm is large, as centralized methods (a single command center controlling the UAVs) suffer from exponential computational complexity, i.e., the computing time to obtain the optimal control for the UAVs grow exponentially with the number of UAVs in the swarm in centralized approaches. Although many centralized control methods exist, literature lacks decentralized control frameworks with broad applicability. To address this knowledge gap, we present a novel decentralized UAV swarm control strategy using a decision-theoretic framework called decentralized Markov decision process (Dec-MDP). We build these control strategies in the context of two case studies: a) swarm formation control problem; b) swarm control for multitarget tracking. As most decision theoretic formulations suffer from the curse of dimensionality, we adapt an approximate dynamic programming method called nominal belief-state optimization (NBO) to solve the decentralized control problems approximately in both the case studies. In the formation control case study, the objective is to drive the swarm from a geographical region to another geographical region where the swarm must form a certain geometrical shape (e.g., selected location on the surface of a sphere). The motivation for studying such problems comes from data fusion applications with UAV swarms where the fusion performance depends on the strategic relative separation of the UAVs from each other. In the target tracking case study, the objective is the control the motion of the UAVs in a decentralized manner while maximizing the overall target tracking performance. Motivation for this case study comes from the surveillance applications using UAV swarms.
Sparse Linear Ensemble Systems and Structural Controllability
Authors: Xudong Chen
Date: 2020-04-06 00:00:00

The paper introduces and solves a structural controllability problem for continuum ensembles of linear time-invariant systems. All the individual linear systems of an ensemble are sparse, governed by the same sparsity pattern. Controllability of an ensemble system is, by convention, the capability of using a common control input to simultaneously steer every individual systems in it. A sparsity pattern is structurally controllable if it admits a controllable linear ensemble system. A main contribution of the paper is to provide a graphical condition that is necessary and sufficient for a sparsity pattern to be structurally controllable. Like other structural problems, the property of being structural controllable is monotone. We provide a complete characterization of minimal sparsity patterns as well.
Using Optimization Algorithms for Control of Multiple Output DC-DC Converters
Authors: Masoud Safarishaal, Mohammad Sarvi
Date: 2021-07-10 00:00:00

The weighted voltage mode control represents a method for control of multiple outputs DC-DC converters. Accordingly, the weighted control redistributes the error among the outputs of these converters, and the regulation error can be reduced by adjusting the weighting factors. But the problem is that most designs are performed on the trial-and-error basis, and the results were rather inconsistent. Also, in conventional mathematical approaches, this factor is designed for converters by given parameters. In this paper, three optimization algorithms namely Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are proposed for a quick and accurate estimation of the optimal weighting factors and improve the amount of regulation on outputs of multiple outputs forward DC-DC converters. Furthermore, Fuzzy Logic Controller (FLC) is utilized to minimize the total steady-state error and improve transient characteristics by controlling the duty cycle of the PWM controller. Simulations have been performed in several cases and results show that the proposed method improves the outputs cross regulations in multiple outputs forward DC-DC converters significantly. ICA based weighting factor estimator has higher speed and accuracy in comparison with two other presented algorithms (ACO and PSO) and so is more effective in comparison with them.
Autofocusing Optimal Search Algorithm for a Telescope System
Authors: Islam Helmy, Alaa Hamdy, Doaa Eid, Ahmed Shokry
Date: 2021-07-09 00:00:00
Tasks: Position

Focus accuracy affects the quality of the astronomical observations. Auto-focusing is necessary for imaging systems designed for astronomical observations. The automatic focus system searches for the best focus position by using a proposed search algorithm. The search algorithm uses the image's focus levels as its objective function in the search process. This paper aims to study the performance of several search algorithms to select a suitable one. The proper search algorithm will be used to develop an automatic focus system for Kottamia Astronomical Observatory (KAO). The optimal search algorithm is selected by applying several search algorithms into five sequences of star-clusters observations. Then, their performance is evaluated based on two criteria, which are accuracy and number of steps. The experimental results show that the Binary search is the optimal search algorithm.
Structural characteristics in network control of molecular multiplex networks
Authors: Cheng Yuan, Zu-Yu Qian, Shi-Ming Chen, Sen Nie
Date: 2021-07-12 00:00:00

Numerous real-world systems can be naturally modeled as multilayer networks, enabling an efficient way to characterize those complex systems. Much evidence in the context of system biology indicated that the collections between different molecular networks can dramatically impact the global network functions. Here, we focus on the molecular multiplex networks coupled by the transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network, exploring the controllability and energy requiring in these types of molecular multiplex networks. We find that the driver nodes tend to avoid essential or pathogen-related genes. Yet, imposing the external inputs to these essential or pathogen-related genes can remarkably reduce the energy cost, implying their crucial role in network control. Moreover, we find that lower minimal driver nodes as well as energy requiring are associated with disassortative coupling between TRN and PPI networks. Our findings in several species provide comprehensive understanding of genes' roles in biology and network control.
Inference on Individual Treatment Effects in Nonseparable Triangular Models
Authors: Jun Ma, Vadim Marmer, Zhengfei Yu
Date: 2021-07-12 00:00:00
Tasks: valid

In nonseparable triangular models with a binary endogenous treatment and a binary instrumental variable, Vuong and Xu (2017) established identification results for individual treatment effects (ITEs) under the rank invariance assumption. Using their approach, Feng, Vuong, and Xu (2019) proposed a uniformly consistent kernel estimator for the density of the ITE that utilizes estimated ITEs. In this paper, we establish the asymptotic normality of the density estimator of Feng, Vuong, and Xu (2019) and show that the ITE estimation errors have a non-negligible effect on the asymptotic distribution of the estimator. We propose asymptotically valid standard errors that account for ITEs estimation, as well as a bias correction. Furthermore, we develop uniform confidence bands for the density of the ITE using the jackknife multiplier or nonparametric bootstrap critical values.
A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters
Authors: Giacomo Bormetti, Fulvio Corsi
Date: 2021-07-12 00:00:00

We propose an observation-driven time-varying SVAR model where, in agreement with the Lucas Critique, structural shocks drive both the evolution of the macro variables and the dynamics of the VAR parameters. Contrary to existing approaches where parameters follow a stochastic process with random and exogenous shocks, our observation-driven specification allows the evolution of the parameters to be driven by realized past structural shocks, thus opening the possibility to gauge the impact of observed shocks and hypothetical policy interventions on the future evolution of the economic system.
Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response
Authors: Javier Espinosa-Brito, Christian Hennig
Date: 2021-07-11 00:00:00

The proportional odds cumulative logit model (POCLM) is a standard regression model for an ordinal response. Ordinality of predictors can be incorporated by monotonicity constraints for the corresponding parameters. It is shown that estimators defined by optimization, such as maximum likelihood estimators, for an unconstrained model and for parameters in the interior set of the parameter space of a constrained model are asymptotically equivalent. This is used in order to derive asymptotic confidence regions and tests for the constrained model, involving simple modifications for finite samples. The finite sample coverage probability of the confidence regions is investigated by simulation. Tests concern the effect of individual variables, monotonicity, and a specified monotonicity direction. The methodology is applied on real data related to the assessment of school performance.
Invidious Comparisons: Ranking and Selection as Compound Decisions
Authors: Jiaying Gu, Roger Koenker
Date: 2020-12-23 00:00:00

There is an innate human tendency, one might call it the "league table mentality," to construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are ranked even though their inherent multi-dimensionality would suggest that -- at best -- only partial orderings were possible. We consider a large class of elementary ranking problems in which we observe noisy, scalar measurements of merit for $n$ objects of potentially heterogeneous precision and are asked to select a group of the objects that are "most meritorious." The problem is naturally formulated in the compound decision framework of Robbins's (1956) empirical Bayes theory, but it also exhibits close connections to the recent literature on multiple testing. The nonparametric maximum likelihood estimator for mixture models (Kiefer and Wolfowitz (1956)) is employed to construct optimal ranking and selection rules. Performance of the rules is evaluated in simulations and an application to ranking U.S kidney dialysis centers.
Are Rents Excessive in the Central City?: A Geospatial Analysis
Authors: Scott W. Hegerty
Date: 2021-07-12 00:00:00

In many U.S. central cities, property values are relatively low, while rents are closer to those in better-off neighborhoods. This gap can lead to relatively large profits for landlords, and has been referred to as "exploitaton" for renters. While much of this gap might be explained by risk, factors such as income and race might play important roles as well. This study calculates Census tract-level measures of the rent-to-property-value (RPV) ratio for 30 large cities and their surrounding metropolitan areas. After examining the spatial distribution of this ratio and relationships with other socioeconomic variables for Milwaukee and three other cities, Z-scores and quantiles are used to identify "extreme" RPV values nationwide. "Rust Belt" cities such as Detroit, Cleveland, and Milwaukee are shown to have higher median and 95% values than do West Coast cities such as Seattle and San Francisco. A spatial lag regression estimation shows that, controlling for income, property values, and vacancy rates, racial characteristics often have the "opposite" signs from what might be expected and that there is little evidence of purely race-based "exploitation" of renters. A significantly negative coefficient for the percentage of Black residents, for example, might suggest that the RPV ratio is lower in a given tract, all else equal. While this study shows where RPV values are highest within as well as between cities, further investigation might uncover the drivers of these spatial differences more fully.
Can we improve the environmental benefits of biobased PET production through local 1 biomass value chains? A life cycle assessment perspective
Authors: Carlos Garcia-Velasquez, Yvonne van der Meer
Date: 2021-07-12 00:00:00

The transition to a low-carbon economy is one of the ambitions of the European Union for 2030. Biobased industries play an essential role in this transition. However, there has been an on-going discussion about the actual benefit of using biomass to produce biobased products, specifically the use of agricultural materials (e.g., corn and sugarcane). This paper presents the environmental impact assessment of 30% and 100% biobased PET (polyethylene terephthalate) production using EU biomass supply chains (e.g., sugar beet, wheat, and Miscanthus). An integral assessment between the life cycle assessment methodology and the global sensitivity assessment is presented as an early-stage support tool to propose and select supply chains that improve the environmental performance of biobased PET production. From the results, Miscanthus is the best option for the production of biobased PET: promoting EU local supply chains, reducing greenhouse gas (GHG) emissions (process and land-use change), and generating lower impacts in midpoint categories related to resource depletion, ecosystem quality, and human health. This tool can help improving the environmental performance of processes that could boost the shift to a low-carbon economy.
Recursive Utility with Investment Gains and Losses: Existence, Uniqueness, and Convergence
Authors: Jing Guo, Xue Dong He
Date: 2021-07-12 00:00:00

We consider a generalization of the recursive utility model by adding a new component that represents utility of investment gains and losses. We also study the utility process in this generalized model with constant elasticity of intertemporal substitution and relative risk aversion degree, and with infinite time horizon. In a specific, finite-state Markovian setting, we prove that the utility process uniquely exists when the agent derives nonnegative gain-loss utility, and that it can be non-existent or non-unique otherwise. Moreover, we prove that the utility process, when it uniquely exists, can be computed by starting from any initial guess and applying the recursive equation that defines the utility process repeatedly. We then consider a portfolio selection problem with gain-loss utility and solve it by proving that the corresponding dynamic programming equation has a unique solution. Finally, we extend certain previous results to the case in which the state space is infinite.