pwc-archive/papers-with-abstracts huggingface dataset:
Distributed CPU Scheduling Subject to Nonlinear Constraints
Authors: Mohammadreza Doostmohammadian, Alireza Aghasi, Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Karl H. Johansson, Themistoklis Charalambous
Date: 2022-08-30 00:00:00
Tasks: CPU, Distributed Optimization, Quantization, Scheduling

This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on the nonlinear mapping for anytime feasibility (or non-asymptotic feasibility) are addressed in this paper. For the two proposed distributed solutions, one converges over directed weight-balanced networks and the other one over undirected networks. In particular, we elaborate on uniform quantization and discuss the notion of {\epsilon}-accurate solution, which gives an estimate of how close we can get to the exact optimizer subject to different quantization levels. This work, further, handles general (possibly non-quadratic) strictly convex objective functions with application to CPU allocation among a cloud of data centers via distributed solutions. The results can be used as a coordination mechanism to optimally balance the tasks and CPU resources among a group of networked servers while addressing quantization or limited server capacity. Index Terms: multi-agent systems, sum-preserving resource allocation, distributed optimization, anytime feasibility
Comments on "Time-Varying Lyapunov Functions for Tracking Control of Mechanical Systems With and Without Frictions"
Authors: Lekan Molu
Date: 2022-08-30 00:00:00

In the article$^a$, the authors introduced a time-varying Lyapunov function for the stability analysis of nonlinear systems whose motion is governed by standard Newton-Euler equations. The authors established asymptotic stability with the choice of two symmetric positive definite matrices restricted by certain eigenvalue bounds in the control law. Exponential stability in the sense of Lyapunov using integrator backstepping and Lyapunov redesign is established in this note using just one matrix in the derived controller. We do not impose minimum eigenvalue bound requirements on the symmetric positive definite matrix introduced in our analysis to guarantee stability. Reducing the parameters needed in the control law, our analysis improves the stability and convergence rates of tracking errors reported in the article$^a$. $^a$Ren, W., Zhang, B, Li, H, and Yan L. IEEE Access. vol. 8. pp. 51510-51517. 2020.
A Comprehensive Survey on Aerial Mobile Edge Computing: Challenges, State-of-the-Art, and Future Directions
Authors: Zhengyu Song, Xintong Qin, Yuanyuan Hao, Tianwei Hou, Jun Wang, Xin Sun
Date: 2022-08-30 00:00:00
Tasks: Edge-computing, Scheduling

Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for computation resources of IoT users to support diverse applications. Mobile edge computing (MEC) has been deemed a promising solution to settle the conflict between the resource-hungry mobile applications and the resource-constrained IoT users. On the other hand, in order to provide ubiquitous and reliable connectivity in wireless networks, unmanned aerial vehicles (UAVs) can be leveraged as efficient aerial platforms by exploiting their inherent attributes, such as the on-demand deployment, high cruising altitude, and controllable maneuverability in three-dimensional (3D) space. Thus, the UAV-enabled aerial MEC is believed as a win-win solution to facilitate cost-effective and energy-saving communication and computation services in various environments. In this paper, we provide a comprehensive survey on the UAV-enabled aerial MEC. Firstly, the related advantages and research challenges for aerial MEC are discussed. Then, we provide a comprehensive review of the recent research advances, which is categorized by different domains, including the joint optimization of UAV trajectory, computation offloading and resource allocation, UAV deployment, task scheduling and load balancing, interplay between aerial MEC and other technologies, as well as the machine-learning (ML)-driven optimization. Finally, some important research directions deserved more efforts in future work are summarized.
Mathematical certification of motion planning on uncertain terrain with limited perception: a case study
Authors: Nikolaos Skouloudis, Alexandre Megretski
Date: 2022-08-30 00:00:00
Tasks: Motion Planning

We design a controller for an agent whose mission is to reach a stationary target while avoiding a family of obstacles which are not known a-priori. The agent moves in the two dimensional plane with non-trivial double integrator dynamics and receives only local information from its surroundings. Under mild assumptions on the family of obstacles (smoothness, sufficient distance from each other, bounded curvature, etc), we prove that our control algorithm yields guaranteed obstacle avoidance and convergence to the target.
Joint Optimization of Resource Allocation, Phase Shift and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems
Authors: Xintong Qin, Zhengyu Song, Tianwei Hou, Wenjuan Yu, Jun Wang, Xin Sun
Date: 2022-08-30 00:00:00
Tasks: CPU, Edge-computing

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach's method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) with the deployment of RIS, our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of task-input bits of IoT devices; 3) when the CPU cycles required for computing 1-bit of task-input data becomes larger, more task bits will be offloaded to the UAV while the energy efficiency will be decreased.
Complex-Frequency Synchronization of Converter-Based Power Systems
Authors: Xiuqiang He, Verena Häberle, Florian Dörfler
Date: 2022-08-29 00:00:00

In this paper, we study phase-amplitude multivariable dynamics in converter-based power systems from a complex-frequency perspective. Complex frequency represents the rate of change of voltage amplitude and phase angle by its real and imaginary parts, respectively. This emerging notion is of significance as it accommodates the multivariable characteristics of power networks where active and reactive power are inherently coupled with both voltage amplitude and phase. We propose the notion of complex-frequency synchronization to study the phase-amplitude multivariable stability issue in a power system with dispatchable virtual oscillator-controlled (dVOC) converters. To achieve this, we separate the system into linear fast dynamics and approximately linear slow dynamics. The linearity property makes it tractable to analyze fast complex-frequency synchronization and slower voltage stabilization. From the perspective of complex frequency and complex-frequency synchronization, we provide novel insights into the equivalence of dVOC and complex-power-frequency droop control, stability analysis methods, and stability criteria. Our study offers a practical solution to address challenging stability issues in converter-based power systems.
Direct Data-Driven Discrete-time Bilinear Biquadratic Regulator
Authors: Shanelle G. Clarke, Omanshu Thapliyal, Inseok Hwang
Date: 2022-08-29 00:00:00
Tasks: Q-Learning

We present a novel direct data-driven algorithm that learns an optimal control policy for the Bilinear Biquadratic Regulator (BBR) for an unknown bilinear system. The BBR is difficult to solve owing to the presence of the nonlinear biquadratic performance index and the bilinear cross-term in the dynamics. To address these difficulties, we apply several transformations on the state decision variables to obtain a nonlinear optimization problem with a linear performance index and affine (in the parameterized control) state-dependent equality. The adroit use of the Hamiltonian and Pontryagin's Minimum Principle allows us to derive a pair of first-order necessary conditions that, at each point in time, are easily solvable linear matrix equalities (LMEs) which give the optimal state-dependent control law. We then use the marginal sample autocorrelation of the collected data to obtain a direct data-driven equivalent of these LMEs. We demonstrate the performance of the proposed algorithm via illustrative numerical examples.
Micro-Vibration Modes Reconstruction Based on Micro-Doppler Coincidence Imaging
Authors: Shuang Liu, Chenjin Deng, Chaoran Wang, Zunwang Bo, Shensheng Han, Zihuai Lin
Date: 2022-08-30 00:00:00

Micro-vibration, a ubiquitous nature phenomenon, can be seen as a characteristic feature on the objects, these vibrations always have tiny amplitudes which are much less than the wavelengths of the sensing systems, thus these motions information can only be reflected in the phase item of echo. Normally the conventional radar system can detect these micro vibrations through the time frequency analyzing, but these vibration characteristics can only be reflected by time-frequency spectrum, the spatial distribution of these micro vibrations can not be reconstructed precisely. Ghost imaging (GI), a novel imaging method also known as Coincidence Imaging that originated in the quantum and optical fields, can reconstruct unknown images using computational methods. To reconstruct the spatial distribution of micro vibrations, this paper proposes a new method based on a coincidence imaging system. A detailed model of target micro-vibration is created first, taking into account two categories: discrete and continuous targets. We use the first-order field correlation feature to obtain objective different micro vibration distribution based on the complex target models and time-frequency analysis in this work.
Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices
Authors: Emanuel Kohlscheen
Date: 2022-08-30 00:00:00

This study analyses oil price movements through the lens of an agnostic random forest model, which is based on 1,000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in sample root mean square errors by 65% relative to a standard linear least square model that uses the same set of 11 explanatory factors. In forecasting exercises the RMSE reduction ranges between 51% and 68%, highlighting the relevance of non linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models RMSE reduction in the post 2010 sample, rising to 48% in the post 2020 sample. If Covid 19 is also considered as a risk factor, these shares become even larger.
Does robotization affect job quality? Evidence from European regional labour markets
Authors: José-Ignacio Antón, Rudolf Winter-Ebmer, Enrique Fernández-Macías
Date: 2022-08-30 00:00:00
Tasks: Survey

Whereas there are recent papers on the effect of robot adoption on employment and wages, there is no evidence on how robots affect non-monetary working conditions. We explore the impact of robot adoption on several domains of non-monetary working conditions in Europe over the period 1995-2005 combining information from the World Robotics Survey and the European Working Conditions Survey. In order to deal with the possible endogeneity of robot deployment, we employ an instrumental variables strategy, using the robot exposure by sector in other developed countries as an instrument. Our results indicate that robotization has a negative impact on the quality of work in the dimension of work intensity and no relevant impact on the domains of physical environment or skills and discretion.
Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial
Authors: Keshav Agrawal, Susan Athey, Ayush Kanodia, Emil Palikot
Date: 2022-08-30 00:00:00
Tasks: Recommendation Systems

We study the impact of personalized content recommendations on the usage of an educational app for children. In a randomized controlled trial, we show that the introduction of personalized recommendations increases the consumption of content in the personalized section of the app by approximately 60%. We further show that the overall app usage increases by 14%, compared to the baseline system where human content editors select stories for all students at a given grade level. The magnitude of individual gains from personalized content increases with the amount of data available about a student and with preferences for niche content: heavy users with long histories of content interactions who prefer niche content benefit more than infrequent, newer users who like popular content. To facilitate the move to personalized recommendation systems from a simpler system, we describe how we make important design decisions, such as comparing alternative models using offline metrics and choosing the right target audience.
Prolonged Learning and Hasty Stopping: the Wald Problem with Ambiguity
Authors: Sarah Auster, Yeon-Koo Che, Konrad Mierendorff
Date: 2022-08-30 00:00:00

This paper studies sequential information acquisition by an ambiguity-averse decision maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM's stopping rule is non-monotonic in beliefs and features randomized stopping.
Regret-free truth-telling voting rules
Authors: R. Pablo Arribillaga, Agustin G. Bonifacio, Marcelo Ariel Fernandez
Date: 2022-08-29 00:00:00

We study the ability of different classes of voting rules to induce agents to report their preferences truthfully, if agents want to avoid regret. First, we show that regret-free truth-telling is equivalent to strategy-proofness among tops-only rules. Then, we focus on three important families of (non-tops-only) voting methods: maxmin, scoring, and Condorcet consistent ones. We prove positive and negative results for both neutral and anonymous versions of maxmin and scoring rules. In several instances we provide necessary and sufficient conditions. We also show that Condorcet consistent rules that satisfy a mild monotonicity requirement are not regret-free truth-telling. Successive elimination rules fail to be regret-free truth-telling despite not satisfying the monotonicity condition. Lastly, we provide two characterizations for the case of three alternatives and two agents.
Deep Spatial and Tonal Data Optimisation for Homogeneous Diffusion Inpainting
Authors: Pascal Peter, Karl Schrader, Tobias Alt, Joachim Weickert
Date: 2022-08-30 00:00:00
Tasks: Image Compression

Diffusion-based inpainting can reconstruct missing image areas with high quality from sparse data, provided that their location and their values are well optimised. This is particularly useful for applications such as image compression, where the original image is known. Selecting the known data constitutes a challenging optimisation problem, that has so far been only investigated with model-based approaches. So far, these methods require a choice between either high quality or high speed since qualitatively convincing algorithms rely on many time-consuming inpaintings. We propose the first neural network architecture that allows fast optimisation of pixel positions and pixel values for homogeneous diffusion inpainting. During training, we combine two optimisation networks with a neural network-based surrogate solver for diffusion inpainting. This novel concept allows us to perform backpropagation based on inpainting results that approximate the solution of the inpainting equation. Without the need for a single inpainting during test time, our deep optimisation accelerates data selection by more than four orders of magnitude compared to common model-based approaches. This provides real-time performance with high quality results.
G-SemTMO: Tone Mapping with a Trainable Semantic Graph
Authors: Abhishek Goswami, Erwan Bernard, Wolf Hauser, Frederic Dufaux, Rafal Mantiuk
Date: 2022-08-30 00:00:00
Tasks: Tone Mapping

A Tone Mapping Operator (TMO) is required to render images with a High Dynamic Range (HDR) on media with limited dynamic capabilities. TMOs compress the dynamic range with the aim of preserving the visually perceptual cues of the scene. Previous literature has established the benefits of TMOs being semantic aware, understanding the content in the scene to preserve the cues better. Expert photographers analyze the semantic and the contextual information of a scene and decide tonal transformations or local luminance adjustments. This process can be considered a manual analogy to tone mapping. In this work, we draw inspiration from an expert photographer's approach and present a Graph-based Semantic-aware Tone Mapping Operator, G-SemTMO. We leverage semantic information as well as the contextual information of the scene in the form of a graph capturing the spatial arrangements of its semantic segments. Using Graph Convolutional Network (GCN), we predict intermediate parameters called Semantic Hints and use these parameters to apply tonal adjustments locally to different semantic segments in the image. In addition, we also introduce LocHDR, a dataset of 781 HDR images tone mapped manually by an expert photo-retoucher with local tonal enhancements. We conduct ablation studies to show that our approach, G-SemTMO\footnote{Code and dataset to be published with the final version of the manuscript}, can learn both global and local tonal transformations from a pair of input linear and manually retouched images by leveraging the semantic graphs and produce better results than both classical and learning based TMOs. We also conduct ablation experiments to validate the advantage of using GCN.
3D Near-Field Virtual MIMO-SAR Imaging Using FMCW Radar Systems at 77 GHz
Authors: Shahrokh Hamidi
Date: 2022-08-30 00:00:00

In this paper, we present 3D high resolution radar imaging at millimeter-Wave (mmWave) frequencies by means of a combination of virtual Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) Radars and Synthetic Aperture Radar (SAR) which results in a compact, low-cost, and high-speed 3D mmWave imagery system with low complexity.
A General Model for Pointing Error of High Frequency Directional Antennas
Authors: Mohammad Taghi Dabiri, Mazen Hasna, Nizar Zorba, Tamer Khattab, Khalid A. Qaraqe
Date: 2022-08-30 00:00:00
Tasks: valid, Vocal Bursts Intensity Prediction

This paper focuses on providing an analytical framework for the quantification and evaluation of the pointing error for a general case at high-frequency millimeter wave (mmWave) and terahertz (THz) communication links. For this aim, we first derive the the probability density function (PDF) and cumulative distribution functions (CDF) of the pointing error between an unstable transmitter (Tx) and receiver (Rx), that have different antenna patterns and for which the vibrations are not similar in the Yaw and Pitch directions. The special case where the Tx and Rx are both equipped with uniform linear array antenna is also investigated. In addition, using $\alpha-\mu$ distribution, which is a valid model for small-scale fading of mmWave/THz links, the end-to-end PDF and CDF of the considered channel is derived for all the considered cases. Finally, by employing Monte-Carlo simulations, the accuracy of the analytical expressions is verified and the performance of the system is studied.
CD and PMD Effect on Cyclostationarity-Based Timing Recovery for Optical Coherent Receivers
Authors: Dawei Wang, Meng Qiao, Kunjian Lian, Zhaohui Li
Date: 2022-08-30 00:00:00

Timing recovery is critical for synchronizing the clocks at the transmitting and receiving ends of a digital coherent communication system. The core of timing recovery is to determine reliably the current sampling error of the local digitizer so that the timing circuit may lock to a stable operation point. Conventional timing phase detectors need to adapt to the optical fiber channel so that the common effects of this channel, such as chromatic dispersion (CD) and polarization mode dispersion (PMD), on the timing phase extraction must be understood. Here we exploit the cyclostationarity of the optical signal and derive a model for studying the CD and PMD effect. We prove that the CD-adjusted cyclic correlation matrix contains full information about timing and PMD, and the determinant of the matrix is a timing phase detector immune to both CD and PMD. We also obtain other results such as a completely PMD-independent CD estimator, etc. Our analysis is supported by both simulations and experiments over a field implemented optical cable.
A review of transcranial magnetic stimulation and Alzheimer's disease
Authors: Arsalan Heidarpanah
Date: 2022-08-29 00:00:00

Since four decades ago, that the transcranial magnetic stimulation (TMS) technique was introduced, the increasing attention of neuroscience researchers and medical engineers has been focused on the development of this technique and its use to manage the treatment of a wide range of neurological conditions, including Alzheimer's disease. The ability of TMS, specifically a substantial type known as repetitive transcranial magnetic stimulation (rTMS) in changing the plasticity of the cortex, has been the most important feature that has created the hopes of controlling Alzheimer's disease with this technique more than ever.