pwc-archive/papers-with-abstracts huggingface dataset:
Decision tree aided planning and energy balancing of planned community microgrids
Authors: Panayiotis Moutis, Spyros Skarvelis-Kazakos, Maria Brucoli
Date: 2021-05-13 00:00:00

Planned Communities (PCs) present a unique opportunity for deployment of intelligent control of demand-side distributed energy resources (DER) and storage, which may be organized in Microgrids (MGs). MGs require balancing for maintaining safe and resilient operation. This paper discusses the implications of using MG concepts for planning and control of energy systems within PCs. A novel tool is presented, based on decision trees (DT), with two potential applications: (i) planning of energy storage systems within such MGs and (ii) controlling energy resources for energy balancing within a PC MG. The energy storage planning and energy balancing methodology is validated through sensitivity case studies, demonstrating its effectiveness. A test implementation is presented, utilizing distributed controller hardware to execute the energy balancing algorithm in real-time.
Voltage Regulation Support Along a Distribution Line by a Virtual Power Plant Based on a Center of Mass Load Modeling
Authors: Panayiotis Moutis, Pavlos S. Georgilakis, Nikos D. Hatziargyriou
Date: 2021-05-13 00:00:00

A voltage regulation method for slow voltage variations at distribution level is proposed, based on a view of the loads, generators and storage along a distribution line as point weights. The "centers of mass" of the absorbed and injected currents (loads & generation, respectively) are compensated by minimizing the distance between them, through proper re-dispatching of the power of the available units and interruptible loads. The technique is recursively applied to lesser parts of the distribution line to address local phenomena and is assumed to be offered as ancillary service to the system operator by a Virtual Power Plant. The favorable results of the methodology are assessed on a distribution line of the island of Rhodes (Greece) under critical loading for numerous scenarios. Unlike previous approaches, the technique focuses specifically in the restoration of bus voltages within standard limits and may reduce the activation of on-line tap changing transformers control.
Low Complexity Secure State Estimation Design for Linear System with Non-derogatory Dynamics
Authors: Zishuo Li, Yilin Mo
Date: 2021-05-13 00:00:00
Tasks: State Estimation

We consider the problem of estimating the state of a linear Gaussian system in the presence of integrity attacks. The attacker can compromise $p$ out of $m$ sensors, the set of which is fixed and unknown to the system operator, and manipulate the measurements arbitrarily. Under the assumption that all the eigenvalues of system matrix $A$ have geometric multiplicity $1$ ($A$ is non-derogatory), we propose a secure estimation scheme that is resilient to integrity attack as long as the system is $2p$-sparse observable. In the absence of attack, the proposed estimation coincides with Kalman estimation with a certain probability that can be adjusted. Furthermore, our proposed estimator is computational efficient during the security condition checking in the designing phase and during the estimation computing in the online operating phase. A numerical example is provided to corroborate the results and illustrate the performance of the proposed estimator.
Sensor Placement with Optimal Precision for Temperature Estimation of Battery Systems
Authors: Vedang M. Deshpande, Raktim Bhattacharya, Kamesh Subbarao
Date: 2021-05-12 00:00:00
Tasks: Management

The temperature distribution in the battery significantly impacts the short-term and long-term performance of battery systems. Therefore, efficient, safe, and reliable battery system operation requires an accurate estimation of the temperature field. The current industry standard for sensors to battery cell ratio is quite frugal. Thus, the problem of sensor placement for accurate temperature estimation becomes non-trivial, especially for large-scale systems. In this paper, we explore a greedy approach for sensor placement suitable for large-scale battery systems. An observer to estimate the thermal field is designed in an $\mathcal{H}_{\infty}$ framework while simultaneously minimizing the sensor precisions, thus lowering the overall thermal management system's economic cost.
Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters
Authors: Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares
Date: 2021-05-12 00:00:00
Tasks: Bayesian Optimization

In this paper, a unified batch-online learning approach is introduced to learn a linear representation of nonlinear system dynamics using the Koopman operator. The presented system modeling approach leverages a novel incremental Koopman-based update law that retrieves a mini-batch of samples stored in a memory to not only minimizes the instantaneous Koopman operator's identification errors but also the identification errors for the batch of retrieved samples. Discontinuous modifications of gradient flows are presented for the online update law to assure finite-time convergence under easy-to-verify conditions defined on the batch of data. Therefore, this unified online-batch framework allows performing joint sample- and time-domain analysis for converging the Koopman operator's parameters. More specifically, it is shown that if the collected mini-batch of samples guarantees a rank condition, then finite-time guarantee in the time domain can be certified and the settling time depends on the quality of collected samples being reused in the update law. Moreover, the efficiency of the proposed Koopman-based update law is further analyzed by showing that the identification regret in continuous time grows sub-linearly with time. Furthermore, to avoid learning corrupted dynamics due to the selection of an inappropriate set of Koopman observables, a higher-layer meta learner employs a discrete Bayesian optimization algorithm to obtain the best library of observable functions for the operator. Since finite-time convergence of the Koopman model for each set of observable is guaranteed under a rank condition on stored data, the fitness of each set of observables can be obtained based on the identification error on the stored samples in the proposed framework and even without implementing any controller based on the learned system.
Dynamical Origin for Winner-Take-All Competition in A Biological Network of The Hippocampal Dentate Gyrus
Authors: Sang-Yoon Kim, Woochang Lim
Date: 2021-05-13 00:00:00
Tasks: All, Hippocampus, Retrieval

We consider a biological network of the hippocampal dentate gyrus (DG). The DG is a pre-processor for pattern separation which facilitates pattern storage and retrieval in the CA3 area of the hippocampus. The main encoding cells in the DG are the granule cells (GCs) which receive the input from the entorhinal cortex (EC) and send their output to the CA3. The activation degree of GCs is so low (~ 5%). This sparsity has been thought to enhance the pattern separation. We investigate the dynamical origin for winner-take-all (WTA) competition which leads to sparse activation of the GCs. The whole GCs are grouped into lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell (BC) along with excitatory (E) GCs. There are three kinds of external inputs into the GCs; the direct excitatory EC input, the indirect inhibitory EC input, mediated by the HIPP (hilar perforant path-associated) cells, and the excitatory input from the hilar mossy cells (MCs). The firing activities of the GCs are determined via competition between the external E and I inputs. The E-I conductance ratio ${\cal{R}}_{\rm E-I}^{\rm (con)*}$ (given by the time average of the external E to I conductances) may represents well the degree of such external E-I input competition. GCs become active when their $\cal{R}_{\rm E-I}^{\rm (con)*}$ is larger than a threshold ${\cal{R}}_{th}^*$, and then the mean firing rates of the active GCs are strongly correlated with $\cal{R}_{\rm E-I}^{\rm (con)*}$. In each GC cluster, the feedback inhibition of the BC may select the winner GCs. GCs with larger $\cal{R}_{\rm E-I}^{\rm (con)*}$ than the threshold ${\cal{R}}_{th}^*$ survive, and they become winners; all the other GCs with smaller $\cal{R}_{\rm E-I}^{\rm (con)*}$ become silent. The WTA competition occurs via competition between the firing activity of the GCs and the feedback inhibition from the BC in each GC cluster.
Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks
Authors: David F. Hahn, Christopher I. Bayly, Hannah E. Bruce Macdonald, John D. Chodera, Vytautas Gapsys, Antonia S. J. S. Mey, David L. Mobley, Laura Perez Benito, Christina E. M. Schindler, Gary Tresadern, Gregory L. Warren
Date: 2021-05-13 00:00:00
Tasks: Benchmarking, Drug Discovery

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark - a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields.
Safe reopening of university campuses is possible with COVID-19 vaccination
Authors: Matthew Junge, Sheng Li, Samitha Samaranayake, Matthew Zalesak
Date: 2021-05-13 00:00:00

We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester. We find that mRNA vaccine coverage above 80% makes it possible to safely reopen to in-person instruction. If vaccine coverage is 100%, then our model indicates that facemask use is not necessary. Our simulations with vaccine coverage below 70% exhibit a right-skew for total infections over the semester, which suggests that high levels of infection are not exceedingly rare with campus social connections the main transmission route. Less effective vaccines or incidence of new variants may require additional intervention such as screening testing to reopen safely.
Using Machine Learning to Create an Early Warning System for Welfare Recipients
Authors: Dario Sansone, Anna Zhu
Date: 2020-11-24 00:00:00
Tasks: BIG-bench Machine Learning

Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals are on income support in the subsequent four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at no extra cost to practitioners since the algorithms use administrative data currently available to caseworkers. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients, which can potentially provide governments with large savings in accrued welfare costs.
How Unique is Milwaukee's 53206? An Examination of Disaggregated Socioeconomic Characteristics Across the City and Beyond
Authors: Scott W. Hegerty
Date: 2021-05-13 00:00:00

Milwaukee's 53206 ZIP code, located on the city's near North Side, has drawn considerable attention for its poverty and incarceration rates, as well as for its large proportion of vacant properties. As a result, it has benefited from targeted policies at the city level. Keeping in mind that ZIP codes are often not the most effective unit of geographic analysis, this study investigates Milwaukee's socioeconomic conditions at the block group level. These smaller areas' statistics are then compared with those of their corresponding ZIP codes. The 53206 ZIP code is compared against others in Milwaukee for eight socioeconomic variables and is found to be near the extreme end of most rankings. This ZIP code would also be among Chicago's most extreme areas, but would lie near the middle of the rankings if located in Detroit. Parts of other ZIP codes, which are often adjacent, are statistically similar to 53206, however--suggesting that a focus solely on ZIP codes, while a convenient shorthand, might overlook neighborhoods that have similar need for investment. A multivariate index created for this study performs similarly to a standard multivariate index of economic deprivation if spatial correlation is taken into account, confirming that poverty and other socioeconomic stresses are clustered, both in the 53206 ZIP code and across Milwaukee.
Do City Borders Constrain Ethnic Diversity?
Authors: Scott W. Hegerty
Date: 2021-05-13 00:00:00
Tasks: Diversity

U.S. metropolitan areas, particularly in the industrial Midwest and Northeast, are well-known for high levels of racial segregation. This is especially true where core cities end and suburbs begin; often crossing the street can lead to physically similar, but much less ethnically diverse, suburban neighborhood. While these differences are often visually or "intuitively" apparent, this study seeks to quantify them using Geographic Information Systems and a variety of statistical methods. 2016 Census block group data are used to calculate an ethnic Herfindahl index for a set of two dozen large U.S. cities and their contiguous suburbs. Then, a mathematical method is developed to calculate a block-group-level "Border Disparity Index" (BDI), which is shown to vary by MSA and by specific suburbs. Its values can be compared across the sample to examine which cities are more likely to have borders that separate more-diverse block groups from less-diverse ones. The index can also be used to see which core cities are relatively more or less diverse than their suburbs, and which individual suburbs have the largest disparities vis-\`a-vis their core city. Atlanta and Detroit have particularly diverse suburbs, while Milwaukee's are not. Regression analysis shows that income differences and suburban shares of Black residents play significant roles in explaining variation across suburbs.
Learning to agree over large state spaces
Authors: Michele Crescenzi
Date: 2021-05-13 00:00:00

We study how a consensus emerges in a finite population of like-minded individuals who are asymmetrically informed about the realization of the true state of the world. Agents observe a private signal about the state and then start exchanging messages. Generalizing the classical model of rational dialogues of Geanakoplos and Polemarchakis (1982) and its subsequent extensions, we dispense with the standard assumption that the state space is a probability space and we do not put any bound on the cardinality of the state space itself or the information partitions. We show that a class of rational dialogues can be found that always lead to consensus provided that three main conditions are met. First, everybody must be able to send messages to everybody else, either directly or indirectly. Second, communication must be reciprocal. Finally, agents need to have the opportunity to engage in dialogues of transfinite length.
Manipulation Detection in Satellite Images Using Vision Transformer
Authors: János Horváth, Sriram Baireddy, Hanxiang Hao, Daniel Mas Montserrat, Edward J. Delp
Date: 2021-05-13 00:00:00
Tasks: Image Manipulation

A growing number of commercial satellite companies provide easily accessible satellite imagery. Overhead imagery is used by numerous industries including agriculture, forestry, natural disaster analysis, and meteorology. Satellite images, just as any other images, can be tampered with image manipulation tools. Manipulation detection methods created for images captured by "consumer cameras" tend to fail when used on satellite images due to the differences in image sensors, image acquisition, and processing. In this paper we propose an unsupervised technique that uses a Vision Transformer to detect spliced areas within satellite images. We introduce a new dataset which includes manipulated satellite images that contain spliced objects. We show that our proposed approach performs better than existing unsupervised splicing detection techniques.
Tencent Video Dataset (TVD): A Video Dataset for Learning-based Visual Data Compression and Analysis
Authors: Xiaozhong Xu, Shan Liu, Zeqiang Li
Date: 2021-05-12 00:00:00
Tasks: 4k, Data Compression, object-detection, Object Detection

Learning-based visual data compression and analysis have attracted great interest from both academia and industry recently. More training as well as testing datasets, especially good quality video datasets are highly desirable for related research and standardization activities. Tencent Video Dataset (TVD) is established to serve various purposes such as training neural network-based coding tools and testing machine vision tasks including object detection and tracking. TVD contains 86 video sequences with a variety of content coverage. Each video sequence consists of 65 frames at 4K (3840x2160) spatial resolution. In this paper, the details of this dataset, as well as its performance when compressed by VVC and HEVC video codecs, are introduced.
Outage Constrained Robust Secure Beamforming in Cognitive Satellite-Aerial Networks
Authors: Bai Zhao, Min Lin, Ming Cheng, Wei-Ping Zhu, Naofal Al-Dhahir
Date: 2021-05-13 00:00:00

This paper proposes a robust beamforming scheme to enhance the physical layer security (PLS) of multicast transmission in a cognitive satellite and aerial network (CSAN) operating in the millimeter wave frequency band. Based on imperfect channel state information (CSI) of both eavesdroppers (Eves) and primary users (PUs), we maximize the minimum achievable secrecy rate (ASR) of the secondary users (SUs) in the aerial network under the constraints of the interference to the PUs in the satellite network, the quality of service (QoS) requirements of the SUs and per-antenna power budget of the aerial platform. To tackle this mathematically intractable problem, we first introduce an auxiliary variable and outage constraints to simplify the complex objective function. We then convert the non-convex outage constraints into deterministic forms and adopt penalty function approach to obtain a semi-definite problem such that it can be solved in an iterative fashion. Finally, simulation results show that with the transmit power increase, the minimal ASR of SUs obtained from the proposed BF scheme well approximate the optimal value.
Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration
Authors: Gaetan Bakalli, Davide A. Cucci, Ahmed Radi, Naser El-Sheimy, Roberto Molinari, Olivier Scaillet, Stéphane Guerrier
Date: 2021-05-13 00:00:00

Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. In this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution.
Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision
Authors: Jintai Chen, Xiangshang Zheng, Hongyun Yu, Danny Z. Chen, Jian Wu
Date: 2021-05-12 00:00:00
Tasks: Self-Supervised Learning

Multi-lead electrocardiogram (ECG) provides clinical information of heartbeats from several fixed viewpoints determined by the lead positioning. However, it is often not satisfactory to visualize ECG signals in these fixed and limited views, as some clinically useful information is represented only from a few specific ECG viewpoints. For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints. To build Electrocardio Panorama, we assume that an underlying electrocardio field exists, representing locations, magnitudes, and directions of ECG signals. We present a Neural electrocardio field Network (Nef-Net), which first predicts the electrocardio field representation by using a sparse set of one or few input ECG views and then synthesizes Electrocardio Panorama based on the predicted representations. Specially, to better disentangle electrocardio field information from viewpoint biases, a new Angular Encoding is proposed to process viewpoint angles. Also, we propose a self-supervised learning approach called Standin Learning, which helps model the electrocardio field without direct supervision. Further, with very few modifications, Nef-Net can also synthesize ECG signals from scratch. Experiments verify that our Nef-Net performs well on Electrocardio Panorama synthesis, and outperforms the previous work on the auxiliary tasks (ECG view transformation and ECG synthesis from scratch). The codes and the division labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets are available at https://github.com/WhatAShot/Electrocardio-Panorama.