pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Task Offloading Policy in Edge Computing Systems with Firm Deadlines
Authors: Khai Doan, Wesley Araujo, Evangelos Kranakis, Ioannis Lambadaris, Yannis Viniotis
Date: 2022-10-28 00:00:00
Tasks: Edge-computing

The recent drastic increase in mobile data traffic has pushed the mobile edge computing systems to the limit of their capacity. A promising solution to this problem is the task migration provided by unmanned aerial vehicles (UAV). Key factors to be taken into account in the design of UAV offloading schemes must include the number of tasks waiting in the system as well as their corresponding deadlines. An appropriate system cost which is used as an objective function to be minimized comprises two parts. First, an offloading cost which can be interpreted as the cost of using computational resources at the UAV. Second, a penalty cost due to potential task expiration. In order to minimize the expected (time average) cost over a time horizon, we formulate a Dynamic Programming (DP) equation and analyze it to describe properties of a candidate optimal offloading policy. The DP equation suffers from the well-known "Curse of Dimensionality" that makes computations intractable, especially when the state space is infinite. In order to reduce the computational burden, we identify three important properties of the optimal policy. Based on these properties, we show that it suffices to evaluate the DP equation on a finite subset of the state space only. We then show that the optimal task offloading decision associated with a state can be inferred from the decision taken at its "adjacent" states, further reducing the computational load. Finally, we provide numerical results to evaluate the influence of different parameters on the system performance as well as verify the theoretical results.
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters
Authors: Fengjun Yang, Fernando Gama, Somayeh Sojoudi, Nikolai Matni
Date: 2022-10-28 00:00:00

Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and information exchange structure, e.g., sparsity, delay, or spatial invariance. In this paper, we borrow ideas from graph signal processing and define and analyze a class of Graph Symmetric Systems (GSSs), which are systems that are symmetric with respect to an underlying graph topology. We show that for linear quadratic problems subject to dynamics defined by a GSS, the optimal centralized controller is given by a novel class of graph filters with transfer function valued filter taps and can be implemented via distributed message passing. We then propose several methods for approximating the optimal centralized graph filter by a distributed controller only requiring communication with a small subset of neighboring subsystems. We further provide stability and suboptimality guarantees for the resulting distributed controllers. Finally, we empirically demonstrate that our approach allows for a principled tradeoff between communication cost and performance while guaranteeing stability. Our results can be viewed as a first step towards bridging the fields of distributed optimal control and graph signal processing.
Optimal Inverter-Based Resources Placement in Low-Inertia Power Systems
Authors: Atinuke Ademola-Idowu, Baosen Zhang
Date: 2022-10-28 00:00:00
Tasks: Model Predictive Control

An increase in the integration of Inverter Based Resources (IBRs) to the electric grid, will lead to a corresponding decrease in the amount of connected synchronous generators, resulting in a decline in the available rotational inertia system-wide. This can lead to pronounced frequency deviations when there are disturbances and faults in the grid. This decline in available rotational inertia can be compensated for by fast acting IBRs participating in frequency response services, by rapidly injecting into or removing power from the grid. Currently, there are still relative small number of sizable IBRs in the grid. Therefore, the placement of the IBRs in the system, as well as the inverter configuration type and controller, will have a material impact on the frequency response of the grid. In this work, we present an optimal placement algorithm that maximizes the benefits of utilizing IBRs in providing frequency response services. That is, we minimize the overall system frequency deviation while using a minimal amount of electric power injection from the IBRs. The proposed algorithm uses the resistance distance to place the IBRs at nodes that are central to the rest of the nodes in the network thus minimizing the distance of power flow. The proposed greedy algorithm achieves a near optimal performance and relies on the supermodularity of the resistance distances. We validate the performance of the placement algorithm on three IEEE test systems of varying sizes, by comparing its performance to an exhaustive search algorithm. We further evaluate the performance of the placed IBRs on an IEEE test system, to determine their impact on frequency stability. The IBRs are configured in a grid-forming mode and equipped with a model predictive control (MPC)-based inverter power control.
On Infinite-horizon System Level Synthesis Problems
Authors: Olle Kjellqvist, Jing Yu
Date: 2022-10-28 00:00:00

System level synthesis is a promising approach that formulates structured optimal controller synthesis problems as convex problems. This work solves the distributed linear-quadratic regulator problem under communication constraints directly in infinite-dimensional space, without the finite-impulse response relaxation common in related work. Our method can also be used to construct optimal distributed Kalman filters with limited information exchange. We combine the distributed Kalman filter with state-feedback control to perform localized LQG control with communication constraints. We provide agent-level implementation details for the resulting output-feedback state-space controller.
Data-Driven Thermal Anomaly Detection in Large Battery Packs
Authors: Kiran Bhaskar, Ajith Kumar, James Bunce, Jacob Pressman, Neil Burkell, Christopher D. Rahn
Date: 2022-10-27 00:00:00
Tasks: Anomaly Detection

The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and evaluated using Principal Component Analysis. The evaluated residuals are then thresholded using a cumulative sum control chart to detect anomalies. The mild external short circuits associated with cell balancing are detected in the voltage signals and necessitate voltage retraining after balancing. Temperature residuals prove to be critical, enabling anomaly detection of module balancing events within 14 min that are unobservable from the voltage residuals. Statistical testing of the proposed approach is performed on the experimental data from a battery electric locomotive injected with model-based anomalies. The proposed anomaly detection approach has a low false-positive rate and accurately detects and traces the synthetic voltage and temperature anomalies. The performance of the proposed approach compared with direct thresholding of mean-based residuals shows a 56% faster detection time, 42% fewer false negatives, and 60% fewer missed anomalies while maintaining a comparable false-positive rate.
Emergence of sector and spiral patterns from a two-species mutualistic cross-feeding model
Authors: Jiaqi. Lin, Hui. Sun, JiaJia Dong
Date: 2022-10-28 00:00:00

The ubiquitous existence of microbial communities marks the importance of understanding how species interact within the community to coexist and their spatial organization. We study a two-species mutualistic cross-feeding model through a stochastic cellular automaton on a square lattice using kinetic Monte Carlo simulation. Our model encapsulates the essential dynamic processes such as cell growth, and nutrient excretion, diffusion and uptake. Focusing on the interplay among nutrient diffusion and individual cell division, we discover three general classes of colony morphology: co-existing sectors, co-existing spirals, and engulfment. When the cross-feeding nutrient is widely available, either through high excretion or fast diffusion, a stable circular colony with alternating species sector emerges. When the consumer cells rely on being spatially close to the producers, we observe a stable spiral. We also see one species being engulfed by the other when species interfaces merge due to stochastic fluctuation. By tuning the diffusion rate and the growth rate, we are able to gain quantitative insights into the structures of the sectors and the spirals.
Eigenvalue tests for the number of latent factors in short panels
Authors: Alain-Philippe Fortin, Patrick Gagliardini, Olivier Scaillet
Date: 2022-10-28 00:00:00

This paper studies new tests for the number of latent factors in a large cross-sectional factor model with small time dimension. These tests are based on the eigenvalues of variance-covariance matrices of (possibly weighted) asset returns, and rely on either the assumption of spherical errors, or instrumental variables for factor betas. We establish the asymptotic distributional results using expansion theorems based on perturbation theory for symmetric matrices. Our framework accommodates semi-strong factors in the systematic components. We propose a novel statistical test for weak factors against strong or semi-strong factors. We provide an empirical application to US equity data. Evidence for a different number of latent factors according to market downturns and market upturns, is statistically ambiguous in the considered subperiods. In particular, our results contradicts the common wisdom of a single factor model in bear markets.
How to sample and when to stop sampling: The generalized Wald problem and minimax policies
Authors: Karun Adusumilli
Date: 2022-10-28 00:00:00
Tasks: Experimental Design

We study sequential experiments where sampling is costly and a decision-maker aims to determine the best treatment for full scale implementation by (1) adaptively allocating units between two possible treatments, and (2) stopping the experiment when the expected welfare (inclusive of sampling costs) from implementing the chosen treatment is maximized. Working under a continuous time limit, we characterize the optimal policies under the minimax regret criterion. We show that the same policies also remain optimal under both parametric and non-parametric outcome distributions in an asymptotic regime where sampling costs approach zero. The minimax optimal sampling rule is just the Neyman allocation: it is independent of sampling costs and does not adapt to observed outcomes. The decision-maker halts sampling when the product of the average treatment difference and the number of observations surpasses a specific threshold. The results derived also apply to the so-called best-arm identification problem, where the number of observations is exogenously specified.
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach
Authors: Xiaolin Sun
Date: 2022-10-28 00:00:00
Tasks: Model Selection, valid

We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with multiple exogenous covariates and a potentially endogenous treatment variable. Our approach integrates a Robinson transformation to handle the nonparametric component, the Smooth Minimum Distance (SMD) method to leverage conditional mean independence restrictions, and a Neyman-Orthogonalized first-order condition (FOC). By employing regularized model selection techniques like the Lasso method, our estimator accommodates numerous covariates while exhibiting reduced bias, consistency, and asymptotic normality. Simulations demonstrate its robust performance with diverse instrument sets compared to traditional GMM-type estimators. Applying this method to estimate Medicaid's heterogeneous treatment effects from the Oregon Health Insurance Experiment reveals more robust and reliable results than conventional GMM approaches.
"Rust Belt" Across America: An Application of a Nationwide, Block-Group-Level Deprivation Index
Authors: Scott W Hegerty
Date: 2022-10-28 00:00:00

In the United States, large post-industrial cites such as Detroit are well-known for high levels of socioeconomic deprivation. But while Detroit is an exceptional case, similar levels of deprivation can still be found in other large cities, as well as in smaller towns and rural areas. This study calculates a standardized measure for all block groups in the lower 48 states and DC, before isolating "high-deprivation" areas that exceed Detroit's median value. These block groups are investigated and mapped for the 83 cities with populations above 250,000, as well as at the state level and for places of all sizes. Detroit is shown to indeed be unique not only for its levels of deprivation (which are higher than 95 percent of the country), but also for the dispersion of highly-deprived block groups throughout the city. Smaller, more concentrated pockets of high deprivation can be found in nearly every large city, and some cities below 20,000 residents have an even larger share of high-deprivation areas. Cities' percentages of high-deprivation areas are positively related to overall poverty, population density, and the percentage of White residents, and negatively related to the share of Black residents.
Intelligence and Global Bias in the Stock Market
Authors: Kazuo Sano
Date: 2022-10-28 00:00:00
Tasks: Management

Trade is one of the essential feature of human intelligence. The securities market is the ultimate expression of it. The fundamental indicators of stocks include information as well as the effects of noise and bias on the stock prices; however, identifying the effects of noise and bias is generally difficult. In this article, I present the true fundamentals hypothesis based on rational expectations and detect the global bias components from the actual fundamental indicators by using a log-normal distribution model based on the true fundamentals hypothesis. The analysis results show that biases generally exhibit the same characteristics, strongly supporting the true fundamentals hypothesis. Notably, the positive price-to-cash flows from the investing activities ratio is a proxy for the true fundamentals. Where do these biases come from? The answer is extremely simple: ``Cash is a fact, profit is an opinion.'' Namely, opinions of management and accounting are added to true fundamentals. As a result, Kesten process is realized and the Pareto distribution is to be obtained. This means that the market knows it and represents as a stable global bias in the stock market.
Local Media and the Shaping of Social Norms: Evidence from the Ebola outbreak
Authors: Ada Gonzalez-Torres
Date: 2022-10-28 00:00:00

Media around the world is disseminated at the national level as well as at the local level. While the capacity of media to shape preferences and behavior has been widely recognized, less is known about the differential impacts of local media. Local media may have particularly important effects on social norms due to the provision of locally relevant information that becomes common knowledge in a community. I examine this possibility in a high-stakes context: the Ebola epidemic in Guinea. I exploit quasi-random variation in access to distinct media outlets and the timing of a public-health campaign on community radio. I find that 13% of Ebola cases would have been prevented if places with access to neighboring community radio stations had instead their own. This is driven by radio stations' locality, not ethno-linguistic boundaries, and by coordination in social behaviors sanctioned locally.
The Role of Immigrants, Emigrants, and Locals in the Historical Formation of European Knowledge Agglomerations
Authors: Philipp Koch, Viktor Stojkoski, César A. Hidalgo
Date: 2022-10-28 00:00:00
Tasks: Philosophy

Did migrants make Paris a Mecca for the arts and Vienna a beacon of classical music? Or was their rise a pure consequence of local actors? Here, we use data on more than 22,000 historical individuals born between the years 1000 and 2000 to estimate the contribution of famous immigrants, emigrants, and locals to the knowledge specializations of European regions. We find that the probability that a region develops or keeps specialization in an activity (based on the birth of famous physicists, painters, etc.) grows with both, the presence of immigrants with knowledge on that activity and immigrants with knowledge in related activities. In contrast, we do not find robust evidence that the presence of locals with related knowledge explains entries and/or exits. We address some endogeneity concerns using fixed-effects models considering any location-period-activity specific factors (e.g. the presence of a new university attracting scientists).
The Unequal Incidence of Payroll Taxes with Imperfect Competition: Theory and Evidence
Authors: Felipe Lobel
Date: 2022-10-27 00:00:00

This paper provides a comprehensive examination of a Brazilian corporate tax reform targeted at the sector and product level. Difference-in-differences estimates instrumented by sector eligibility show that a 20 percentage point cut on payroll tax rates caused a 9% employment increase at the firm level, mostly driven by small firms. This expansion is not driven by formalization of existing workers, and it is explained by reduction on separations rather than additional hires. In terms of earnings, there is a significant 4% earnings increase in the long run, which is concentrated at leadership positions. The unequal pass-through worsen within-firm wage inequality. I exploit the exogenous variation on labor cost to document substantial labor market power in Brazil, where wages are marked down by 36%. Consistent with the empirical findings, I develop a model of factor demand with imperfect competition in the goods and labor market to shed light on the mechanism through which imperfect competition drives corporate tax incidence. The model is identified by the reduced form elasticities, and allows me to structurally estimate the capital-labor elasticity of substitution, which differs from the benchmark case of perfect competition.
Information Design in Allocation with Costly Verification
Authors: Yi-Chun Chen, Gaoji Hu, Xiangqian Yang
Date: 2022-10-28 00:00:00

A principal who values an object allocates it to one or more agents. Agents learn private information (signals) from an information designer about the allocation payoff to the principal. Monetary transfer is not available but the principal can costly verify agents' private signals. The information designer can influence the agents' signal distributions, based upon which the principal maximizes the allocation surplus. An agent's utility is simply the probability of obtaining the good. With a single agent, we characterize (i) the agent-optimal information, (ii) the principal-worst information, and (iii) the principal-optimal information. Even though the objectives of the principal and the agent are not directly comparable, we find that any agent-optimal information is principal-worst. Moreover, there exists a robust mechanism that achieves the principal's payoff under (ii), which is therefore an optimal robust mechanism. Many of our results extend to the multiple-agent case; if not, we provide counterexamples.
LCCM-VC: Learned Conditional Coding Modes for Video Compression
Authors: Hadi Hadizadeh, Ivan V. Bajić
Date: 2022-10-28 00:00:00
Tasks: Video Compression

End-to-end learning-based video compression has made steady progress over the last several years. However, unlike learning-based image coding, which has already surpassed its handcrafted counterparts, learning-based video coding still has some ways to go. In this paper we present learned conditional coding modes for video coding (LCCM-VC), a video coding model that achieves state-of-the-art results among learning-based video coding methods. Our model utilizes conditional coding engines from the recent conditional augmented normalizing flows (CANF) pipeline, and introduces additional coding modes to improve compression performance. The compression efficiency is especially good in the high-quality/high-bitrate range, which is important for broadcast and video-on-demand streaming applications. The implementation of LCCM-VC is available at https://github.com/hadihdz/lccm_vc
Dimensionality Reduced Antenna Array for Beamforming/steering
Authors: Shiyi Xia, Mingyang Zhao, Qian Ma, Xunnan Zhang, Ling Yang, Yazhi Pi, Hyunchul Chung, Ad Reniers, A. M. J. Koonen, Zizheng Cao
Date: 2022-10-28 00:00:00

Beamforming makes possible a focused communication method. It is extensively employed in many disciplines involving electromagnetic waves, including arrayed ultrasonic, optical, and high-speed wireless communication. Conventional beam steering often requires the addition of separate active amplitude phase control units after each radiating element. The high power consumption and complexity of large-scale phased arrays can be overcome by reducing the number of active controllers, pushing beamforming into satellite communications and deep space exploration. Here, we suggest a brand-new design for a phased array antenna with a dimension reduced cascaded angle offset (DRCAO-PAA). Furthermore, the suggested DRCAO-PAA was compressed by using the concept of singular value deposition. To pave the way for practical application the particle swarm optimization algorithm and deep neural network Transformer were adopted. Based on this theoretical framework, an experimental board was built to verify the theory. Finally, the 16/8/4 -array beam steering was demonstrated by using 4/3/2 active controllers, respectively.
Near-field Localization with Dynamic Metasurface Antennas
Authors: Qianyu Yang, Anna Guerra, Francesco Guidi, Nir Shlezinger, Haiyang Zhang, Davide Dardari, Baoyun Wang, Yonina C. Eldar
Date: 2022-10-28 00:00:00

Sixth generation (6G) cellular communications are expected to support enhanced wireless localization capabilities. The widespread deployment of large arrays and high-frequency bandwidths give rise to new considerations for localization applications. First, emerging antenna architectures, such as dynamic metasurface antennas (DMAs), are expected to be frequently utilized thanks to the achievable high angular resolution and low hardware complexity. Further, wireless localization is likely to take place in the radiating near-field (Fresnel) region, which provides new degrees of freedom, because of the adoption of arrays with large apertures. While current studies mostly focus on the use of costly fully-digital antenna arrays, in this paper we investigate how DMAs can be applied for near-field localization of a single user. We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy. Next, we propose an algorithm for configuring the DMA to optimize near-field localization, by first tuning the adjustable DMA coefficients to minimize the estimation error using postulated knowledge of the actual user position. Finally, we propose a sub-optimal iterative algorithm that does not rely on such knowledge. Simulation results show that the DMA-based near-field localization accuracy could approach that of fully-digital arrays at lower cost.