pwc-archive/papers-with-abstracts huggingface dataset:
Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty
Authors: Yifu Ding, Thomas Morstyn, Malcolm D. McCulloch
Date: 2021-09-24 00:00:00
Tasks: energy management, Management

In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved jointly and each one is assigned to an optimized violation rate. To highlight, the JCC problem with the optimized violation rates has been recognized to be NP-hard and challenging to be solved. This paper proposes a novel evolutionary algorithm that successfully tackles the problem and reduces the solution conservativeness (i.e. operation cost) by around 50% comparing with the baseline Bonferroni Approximation. Considering the imperfect solar power forecast, we construct three data-driven ambiguity sets to model uncertain forecast error distributions. The solution is thus robust for any distribution in sets with the shared moment and shape assumptions. The proposed method is validated by robustness tests based on those sets and firmly secures the solution robustness.
Modeling of Low Rank Time Series
Authors: Wenqi Cao, Anders Lindquist, Giorgio Picci
Date: 2021-09-24 00:00:00
Tasks: Time Series, Time Series Analysis

Rank-deficient stationary stochastic vector processes are present in many problems in network theory and dynamic factor analysis. In this paper we study hidden dynamical relations between the components of a discrete-time stochastic vector process and investigate their properties with respect to stability and causality. More specifically, we construct transfer functions with a full-rank input process formed from selected components of the given vector process and having a vector process of the remaining components as output. An important question, which we answer in the negative, is whether it is always possible to find such a deterministic relation that is stable. If it is unstable, there must be feedback from output to input ensuring that stationarity is maintained. This leads to connections to robust control. We also show how our results could be used to investigate the structure of dynamic network models and the latent low-rank stochastic process in a dynamic factor model.
Internal Feedback in Biological Control: Locality and System Level Synthesis
Authors: Jing Shuang Li
Date: 2021-09-24 00:00:00

The presence of internal feedback pathways (IFPs) is a prevalent yet unexplained phenomenon in the brain. Motivated by experimental observations on 1) motor-related signals in visual areas, and 2) massively distributed processing in the brain, we approach this problem from a sensorimotor standpoint and make use of distributed optimal controllers to explain IFPs. We use the System Level Synthesis (SLS) controller to model neural phenomena such as signaling delay, local processing, and local reaction. Based on the SLS controller, we make qualitative predictions about IFPs that strongly align with existing experimental observations. We introduce a `mesocircuit' for optimal performance with distributed and local processing, and local disturbance rejection; this mesocircuit requires extreme amounts of IFPs and memory for proper function. This is the first theory that replicates the massive amounts of IFPs in the brain purely from a priori principles, providing a new theoretical basis upon which we can build to better understand the inner workings of the brain.
Internal Feedback in Biological Control: Diversity, Delays, and Standard Theory
Authors: Josefin Stenberg, Jing Shuang Li, Anish A. Sarma, John C. Doyle
Date: 2021-09-24 00:00:00
Tasks: Diversity

Neural architectures in organisms support efficient and robust control that is beyond the capability of engineered architectures. Unraveling the function of such architectures is challenging; their components are highly diverse and heterogeneous in their morphology, physiology, and biochemistry, and often obey severe speed-accuracy tradeoffs; they also contain many cryptic internal feedback pathways (IFPs). We claim that IFPs are crucial architectural features that strategically combine highly diverse components to give rise to optimal performance. We demonstrate this in a case study, and additionally describe how sensing and actuation delays in standard control (state feedback, full control, output feedback) give rise to independent and separable sources of IFPs. Our case study is an LQR problem with two types of sensors, one fast but sparse and one dense but slow. Controllers using only one type of sensor perform poorly, often failing even to stabilize; controllers using both types of sensors perform extremely well, demonstrating a strong diversity-enabled sweet spot (DESS). We demonstrate that IFPs are key in enabling this DESS, and additionally that with IFPs removed, controllers with delayed sensing perform poorly. The existence of strong DESS and IFP in this simple example suggest that these are fundamental architectural features in any complex system with diverse components, such as organisms and cyberphysical systems.
An operating system for extra long urban trains
Authors: Carlos F. Daganzo
Date: 2021-09-23 00:00:00

An operating system (OS) for subways and other urban railways is presented. The system uses extra long trains (XLTs) that can protrude beyond both ends of the station platforms. No added infrastructure is needed;only more rolling stock. The system's only technological requirement is that the doors in different parts of each train, e.g. its cars, can be operated independently. The system can preserve the level of service and evenly fill with passengers all the cars of an XLT so no space is wasted. With sufficiently long trains, a railway's productivity can be more than doubled. The proposed OS has a train side and a passenger side. On the train side, it includes train organization and station-stopping protocols and on the passenger side a new information system that organizes passengers at the platforms as required by the train side protocols. These protocols specify the composition of each train and what it does at each station; i.e., whether it stops or not; how it aligns its doors along the platform; whether each door opens or not; and the set of destinations advertised by each open door. A general menu of train side protocols is presented, as well as a mathematical framework for their optimization and analysis. Numerous examples illustrating key concepts are also presented.
The broad edge of synchronisation: Griffiths effects and collective phenomena in brain networks
Authors: Victor Buendía, Pablo Villegas, Raffaella Burioni, Miguel A. Muñoz
Date: 2021-09-24 00:00:00

Many of the amazing functional capabilities of the brain are collective properties stemming from the interactions of large sets of individual neurons. In particular, the most salient collective phenomena in brain activity are oscillations, which require the synchronous activation of many neurons. Here, we analyse parsimonious dynamical models of neural synchronisation running on top of synthetic networks that capture essential aspects of the actual brain anatomical connectivity such as a hierarchical-modular and core-periphery structure. These models reveal the emergence of complex collective states with intermediate and flexible levels of synchronisation, halfway in the synchronous-asynchronous spectrum. These states are best described as broad Griffiths-like phases, i.e. an extension of standard critical points that emerge in structurally heterogeneous systems. We analyse different routes (bifurcations) to synchronisation and stress the relevance of 'hybrid-type transitions' to generate rich dynamical patterns. Overall, our results illustrate the complex interplay between structure and dynamics, underlining key aspects leading to rich collective states needed to sustain brain functionality.
Retinotopic Mechanics derived using classical physics
Authors: Ifedayo-Emmanuel Adeyefa-Olasupo
Date: 2021-09-18 00:00:00
Tasks: Sensitivity

The concept of a cell$'$s receptive field is a bedrock in systems neuroscience, and the classical static description of the receptive field has had enormous success in explaining the fundamental mechanisms underlying visual processing. Borne out by the spatio-temporal dynamics of visual sensitivity to probe stimuli in primates, I build on top of this static account with the introduction of a new computational field of research, retinotopic mechanics. At its core, retinotopic mechanics assumes that during active sensing receptive fields are not static but can shift beyond their classical extent. Specifically, the canonical computations and the neural architecture that supports these computations are inherently mediated by a neurobiologically inspired force field (e.g.,$R_s\propto \sim 1 /\Delta M$). For example, when the retina is displaced because of a saccadic eye movement from one point in space to another, cells across retinotopic brain areas are tasked with discounting the retinal disruptions such active surveillance inherently introduces. This neural phenomenon is known as spatial constancy. Using retinotopic mechanics, I propose that to achieve spatial constancy or any active visually mediated task, retinotopic cells, namely their receptive fields, are constrained by eccentricity dependent elastic fields. I propose that elastic fields are self-generated by the visual system and allow receptive fields the ability to predictively shift beyond their classical extent to future post-saccadic location such that neural sensitivity which would otherwise support intermediate eccentric locations likely to contain retinal disruptions is transiently blunted.
Estimating Mean Speed-of-Sound from Sequence-Dependent Geometric Disparities
Authors: Xenia Augustin, Lin Zhang, Orcun Goksel
Date: 2021-09-24 00:00:00

In ultrasound beamforming, focusing time delays are typically computed with a spatially constant speed-of-sound (SoS) assumption. A mismatch between beamforming and true medium SoS then leads to aberration artifacts. Other imaging techniques such as spatially-resolved SoS reconstruction using tomographic techniques also rely on a good SoS estimate for initial beamforming. In this work, we exploit spatially-varying geometric disparities in the transmit and receive paths of multiple sequences for estimating a mean medium SoS. We use images from diverging waves beamformed with an assumed SoS, and propose a model fitting method for estimating the SoS offset. We demonstrate the effectiveness of our proposed method for tomographic SoS reconstruction. With corrected beamforming SoS, the reconstruction accuracy on simulated data was improved by 63% and 29%, respectively, for an initial SoS over- and under-estimation of 1.5%. We further demonstrate our proposed method on a breast phantom, indicating substantial improvement in contrast-to-noise ratio for local SoS mapping.
Linear Panel Regressions with Two-Way Unobserved Heterogeneity
Authors: Hugo Freeman, Martin Weidner
Date: 2021-09-24 00:00:00
Tasks: regression, Vocal Bursts Valence Prediction

We study linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known functional form (additive or multiplicative). In this paper, we allow for this functional form to be more general and unknown. We discuss two different estimation approaches that allow consistent estimation of the regression parameters in this setting as the number of individuals and the number of time periods grow to infinity. The first approach uses the interactive fixed effect estimator in Bai (2009), which is still applicable here, as long as the number of factors in the estimation grows asymptotically. The second approach first discretizes the two-way unobserved heterogeneity (similar to what Bonhomme, Lamadon and Manresa 2021 are doing for one-way heterogeneity) and then estimates a simple linear fixed effect model with additive two-way grouped fixed effects. For both estimation methods we obtain asymptotic convergence results, perform Monte Carlo simulations, and employ the estimators in an empirical application to UK house price data.
Treatment Effects in Market Equilibrium
Authors: Evan Munro, Xu Kuang, Stefan Wager
Date: 2021-09-23 00:00:00
Tasks: Experimental Design

Policy-relevant treatment effect estimation in a marketplace setting requires taking into account both the direct benefit of the treatment and any spillovers induced by changes to the market equilibrium. The standard way to address these challenges is to evaluate interventions via cluster-randomized experiments, where each cluster corresponds to an isolated market. This approach, however, cannot be used when we only have access to a single market (or a small number of markets). Here, we show how to identify and estimate policy-relevant treatment effects using a unit-level randomized trial run within a single large market. A standard Bernoulli-randomized trial allows consistent estimation of direct effects, and of treatment heterogeneity measures that can be used for welfare-improving targeting. Estimating spillovers - as well as providing confidence intervals for the direct effect - requires estimates of price elasticities, which we provide using an augmented experimental design. Our results rely on all spillovers being mediated via the (observed) prices of a finite number of traded goods, and the market power of any single unit decaying as the market gets large. We illustrate our results using a simulation calibrated to a conditional cash transfer experiment in the Philippines.
The Boltzmann fair division for distributive justice
Authors: Ji-Won Park, Jaeup U. Kim, Cheol-Min Ghim, Chae Un Kim
Date: 2021-09-24 00:00:00
Tasks: Fairness

Fair division is a significant, long-standing problem and is closely related to social and economic justice. The conventional division methods such as cut-and-choose are hardly applicable to realworld problems because of their complexity and unrealistic assumptions about human behaviors. Here we propose a fair division method from a completely different perspective, using the Boltzmann distribution. The Boltzmann distribution adopted from the physical sciences gives the most probable and unbiased distribution derived from a goods-centric, rather than a player-centric, division process. The mathematical model of the Boltzmann fair division was developed for both homogeneous and heterogeneous division problems, and the players' key factors (contributions, needs, and preferences) could be successfully integrated. We show that the Boltzmann fair division is a well-balanced division method maximizing the players' total utility, and it could be easily finetuned and applicable to complex real-world problems such as income/wealth redistribution or international negotiations on fighting climate change.
Absorptive capacities and economic growth in low and middle income economies
Authors: Muhammad Salar Khan
Date: 2021-09-23 00:00:00

I extend the concept of absorptive capacity, used in the analysis of firms, to a framework applicable to the national level. First, employing confirmatory factor analyses on 47 variables, I build 13 composite factors crucial to measuring six national level capacities: technological capacity, financial capacity, human capacity, infrastructural capacity, public policy capacity, and social capacity. My data cover most low- and middle-income- economies (LMICs), eligible for the World Bank's International Development Association (IDA) support between 2005 and 2019. Second, I analyze the relationship between the estimated capacity factors and economic growth while controlling for some of the incoming flows from abroad and other confounders that might influence the relationship. Lastly, I conduct K-means cluster analysis and then analyze the results alongside regression estimates to glean patterns and classifications within the LMICs. Results indicate that enhancing infrastructure (ICT, energy, trade, and transport), financial (apparatus and environment), and public policy capacities is a prerequisite for attaining economic growth. Similarly, I find improving human capital with specialized skills positively impacts economic growth. Finally, by providing a ranking of which capacity is empirically more important for economic growth, I offer suggestions to governments with limited budgets to make wise investments. Likewise, my findings inform international policy and monetary bodies on how they could better channel their funding in LMICs to achieve sustainable development goals and boost shared prosperity.
RIS-Enabled Localization Continuity Under Near-Field Conditions
Authors: Moustafa Rahal, Benoit Denis, Kamran Keykhosravi, Bernard Uguen, Henk Wymeersch
Date: 2021-09-24 00:00:00

Reconfigurable intelligent surfaces (RISs) have the potential to enable user localization in scenarios where traditional approaches fail. Building on prior work in single-antenna RIS-enabled localization, we investigate the potential to exploit wavefront curvature in geometric near-field conditions. Via a Fisher information analysis, we demonstrate that while near-field improves localization accuracy mostly at short distances when the line-of-sight (LoS) path is present, it could still provide reasonable performance when this path is blocked by relying on a single RIS reflection.
Energy-Efficient Design for RIS-assisted UAVcommunications in beyond-5G Networks
Authors: Anay Ajit Deshpande, Cristian J. Vaca-Rubio, Salman Mohebi, Dariush Salami, Elisabeth de Carvalho, Petar Popovski, Stephan Sigg, Michele Zorzi, Andrea Zanella
Date: 2021-09-24 00:00:00

The usage of Reconfigurable Intelligent Surfaces (RIS) in conjunction with Unmanned Ariel Vehicles (UAVs) is being investigated as a way to provide energy-efficient communication to ground users in dense urban areas. In this paper, we devise an optimization scenario to reduce overall energy consumption in the network while guaranteeing certain Quality of Service (QoS) to the ground users in the area. Due to the complex nature of the optimization problem, we provide a joint UAV trajectory and RIS phase decision to minimize transmission power of the UAV and Base Station (BS) that yields good performance with lower complexity. So, the proposed method uses a Successive Convex Approximation (SCA) to iteratively determine a joint optimal solution for UAV Trajectory, RIS phase and BS and UAV Transmission Power. The approach has, therefore, been analytically evaluated under different sets of criterion.
Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges
Authors: Mert Kalfa, Mehmetcan Gok, Arda Atalik, Busra Tegin, Tolga M. Duman, Orhan Arikan
Date: 2021-09-24 00:00:00

Advances in machine learning technology have enabled real-time extraction of semantic information in signals which can revolutionize signal processing techniques and improve their performance significantly for the next generation of applications. With the objective of a concrete representation and efficient processing of the semantic information, we propose and demonstrate a formal graph-based semantic language and a goal filtering method that enables goal-oriented signal processing. The proposed semantic signal processing framework can easily be tailored for specific applications and goals in a diverse range of signal processing applications. To illustrate its wide range of applicability, we investigate several use cases and provide details on how the proposed goal-oriented semantic signal processing framework can be customized. We also investigate and propose techniques for communications where sensor data is semantically processed and semantic information is exchanged across a sensor network.
Generalized autocorrelation analysis for multi-target detection
Authors: Ye'Ela Shalit, Ran Weber, Asaf Abas, Shay Kreymer, Tamir Bendory
Date: 2021-09-24 00:00:00

We study the multi-target detection problem of recovering a target signal from a noisy measurement that contains multiple copies of the signal at unknown locations. Motivated by the structure reconstruction problem in cryo-electron microscopy, we focus on the high noise regime, where noise hampers accurate detection of signal occurrences. Previous works proposed an autocorrelation analysis framework to estimate the signal directly from the measurement, without detecting signal occurrences. Specifically, autocorrelation analysis entails finding a signal that best matches the observable autocorrelations by minimizing a least squares objective. This paper extends this line of research by developing a generalized autocorrelation analysis framework that replaces the least squares by a weighted least squares. The optimal weights can be computed directly from the data and guarantee favorable statistical properties. We demonstrate signal recovery from highly noisy measurements, and show that the proposed framework outperforms autocorrelation analysis in a wide range of parameters.
A low complexity and high modularity design for continuously variable bandwidth digital filters
Authors: Sushmitha Sajeevu, V. Sakthivel
Date: 2021-09-24 00:00:00

Digital filters with variable bandwidth can be used for a variety of applications. Arbitrary change in the bandwidth of a digital Finite Impulse Response (FIR) filter can be acquired using sampling rate converters. In this paper, a sampling rate converter is proposed which is generated from Pascal structure, a fractional delay filter having low hardware complexity and high modularity. The proposed sampling rate converter requires lesser number of multipliers for implementation when compared with the sampling rate converters in the literature. A low pass filter having a single bandwidth sandwiched between two sampling rate converters can contribute multiple bandwidths in such a way that each bandwidth is an arbitrary variation of the original bandwidth. A two stage Frequency response masking approach (FRM) is used for the hardware efficient design of the original low pass filter. A low complexity and high modular novel design for a continuously varying bandwidth of a digital FIR filter is proposed in this paper using the proposed sampling rate converter. The modularity of the Pascal structure can be used to control both pass band ripple as well as stop band attenuation of the continuously variable bandwidth FIR filter design. Different communication standards in a Software defined radio (SDR) channelizer is realized using the proposed design of continuously variable bandwidth filter.