pwc-archive/papers-with-abstracts huggingface dataset:
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
Authors: Xu Yan, Xiaoliang Fan, Peizhen Yang, Zonghan Wu, Shirui Pan, Longbiao Chen, Yu Zang, Cheng Wang
Date: 2021-09-27 00:00:00
Tasks: Dynamic Node Classification, Link Prediction, Node Classification, Representation Learning

Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existing dynamic embedding methods on TIG discretely update node embeddings merely when an interaction occurs. They fail to capture the continuous dynamic evolution of embedding trajectories of nodes. In this paper, we propose a two-module framework named ConTIG, a continuous representation method that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploit three-fold factors in dynamic networks which include latest interaction, neighbor features and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes' state trajectories by learning from time-adjacent interaction patterns between node pairs using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiments results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation and dynamic node classification tasks compared with a range of state-of-the-art baselines, especially for long-interval interactions prediction.
Method to Build Equivalent Models of Microgrids for RMS Dynamic Simulation of Power Systems
Authors: Rodrigo A. Ramos, Ahda P. Grilo-Pavani, Artur B. Piardi, Tatiane C. C. Fernandes
Date: 2021-10-12 00:00:00
Tasks: parameter estimation, Sensitivity

The high penetration of distributed renewable energy resources in power systems has changed their dynamic behavior, not only at the distribution level but also at the transmission levels. For analyses performed in this new reality of interconnected systems, a suitable equivalent model is required to represent the active dynamics of distribution systems. In this context, this paper proposes the application of a gray-box method to obtain an appropriate equivalent model for active distribution networks. From data measured at the point of common coupling, a trajectory sensitivity analysis is carried out to select the most important parameters of this equivalent model, which are then estimated by an evolutionary algorithm. The results show that the application of the sensitivity analysis can improve the quality of the parameter estimation process (since it focuses only on relevant parameters), enabling an efficient tuning of an equivalent ADN model.
Auction-Based vs Continuous Clearing in Local Flexibility Markets with Block Bids
Authors: Alicia Alarcón Cobacho, Eléa Prat, Daniel Vázquez Pombo, Spyros Chatzivasileiadis
Date: 2021-10-12 00:00:00

Flexibility markets can be introduced as a tool for the distribution system operator (DSO) to avoid high costs and public opposition against new network investments. Continuous flexibility markets have the advantage of allowing more liquidity, which can be critical in the earlier stages of such markets, and can be operated closer to real-time, thereby enabling a better use of the latest forecasts; but, by design, they also result to a lower social welfare compared to auction-based markets. This paper has two main contributions. First, it introduces a continuous local flexibility market which includes both network constraints and asymmetric block bids. Second, it proposes an algorithm that can accurately determine the upper and lower bound of the social welfare loss compared with an auction-based clearing model.
Role of Externally Provided Randomness in Stochastic Teams and Zero-sum Team Games
Authors: Rahul Meshram
Date: 2021-10-12 00:00:00

Stochastic team decision problem is extensively studied in literature and the existence of optimal solution is obtained in recent literature. The value of information in statistical problem and decision theory is classical problem. Much of earlier does not qualitatively describe role of externally provided private and common randomness in stochastic team problem and team vs team zero sum game. In this paper, we study the role of extrenally provided private or common randomness in stochastic team decision. We make observation that the randomness independent of environment does not benefit either team but randomness dependent on environment benefit teams and decreases the expected cost function. We also studied LQG team game with special information structure on private or common randomness. We extend these study to problem team vs team zero sum game. We show that if a game admits saddle point solution, then private or common randomness independent of environment does not benefit either team. We also analyze the scenario when a team with having more information than other team which is dependent on environment and game has saddle point solution, then team with more information benefits. This is also illustrated numerically for LQG team vs team zero sum game. Finally, we show for discrete team vs team zero sum game that private randomness independent of environment benefits team when there is no saddle point condition. Role of common randomness is discussed for discrete game.
Global games with Poisson observations: Bio-inspired distributed coordination of multi-agent systems
Authors: Marcos M. Vasconcelos
Date: 2021-10-12 00:00:00

Global games are a class of incomplete information games where the payoffs exhibit strategic complementarity leading to an incentive for the agents to coordinate their actions. Such games have been used to model scenarios in many socioeconomic phenomena, where the private signals available to the agents are typically assumed to be Gaussian. We study an instance of a global game where the agents observe Poisson random variables, which are inspired by applications in microbiology where information signals are disseminated via discrete molecular signals rather than continuous. Although this observation model violates the essential technical assumptions present in the Gaussian case, we present preliminary results on the existence of Bayesian Nash equilibria in pure threshold policies in two variants of the underlying random state-of-the-world: an arbitrarily distributed discrete binary state and a continuous state with uniform distribution.
Development of A Load Control Algorithm to Enhance Energy Sustainability for the International Space Station
Authors: E. Herwald, G. Holliday, M. Kuzlu, U. Cali, O. Elma
Date: 2021-10-12 00:00:00

This paper presents a load control algorithm for control of energy sources and loads to enhance energy sustainability and reliability of the International Space Station (ISS), which is a large spacecraft in orbit around Earth. In this paper, the ISS electric power system was simulated in MATLAB/Simulink to be able to evaluate the performance of the developed algorithm in a simulated environment. This study also aims to emphasize the importance of load control algorithms on energy sustainability for critical systems, like ISS, having limited energy sources.
Data-Driven Strictly Positive Real System Identification with prior System Knowledge
Authors: Nikhil Potu Surya Prakash, Zhi Chen, Roberto Horowitz
Date: 2021-10-12 00:00:00

Strictly Positive Real (SPR) transfer functions arise in many areas of engineering like passivity theory in circuit analysis and adaptive control to name a few. In many physical systems, it is possible to conclude that the system is Positive Real (PR) or SPR but system identification algorithms might produce estimates which are not SPR. In this paper, an algorithm to approximate frequency response data with SPR transfer functions using Generalized Orthonormal Basis Functions (GOBFs) is presented. Prior knowledge of the system helps us to get approximate pole locations, which can then be used to construct GOBFs. Next, a convex optimization problem will be formulated to obtain an estimate of the SPR transfer function.
Adaptive Feedforward Reference Design for Active Vibration Rejection in Multi-Actuator Hard Disk Drives
Authors: Zhi Chen, Nikhil Potu Surya Prakash, Roberto Horowitz
Date: 2021-10-12 00:00:00

In December 2017, Seagate unveiled the Multi Actuator Technology to double the data performance of the future generation hard disk drives (HDD). This technology will equip drives with two dual stage actuators (DSA) each comprising of a voice coil motor (VCM) actuator and a piezoelectric micro actuator (MA) operating on the same pivot point. Each DSA is responsible for controlling half of the drive's arms. As both the DSAs operate independently on the same pivot timber, the control forces and torques generated by one can affect the operation of the other and thereby worsening the performance drastically. In this paper, a robust adaptive feedforward controller is designed as an add-on controller to an existing stabilizing feedback controller to reject the disturbances transferred through the common pivot timber by shaping the references to the VCM actuator and the total output of the dual stage system.
Hybrid Top-Down and Bottom-Up Approach for Investigating Residential Load Compositions and Load Percentages
Authors: Ahmed S. Alahmed, Muhammed M. Almuhaini
Date: 2020-04-27 00:00:00
Tasks: Management

Load points are one of the most vital parts of power systems. Due to the new load forms and programs introduced in the demand side, the load-serving entities (LSEs) no longer deal with lump loads, but rather with more dynamic, rational and price elastic loads. The high inter-temporal and behavioral variability of the load profile makes it almost impossible for utilities and system operators to expect the demand curve with the needed accuracy. A sound granularity of the load compositions and consumption percentages and patterns throughout the year is essential for avoiding energy losses, designing demand-side management programs, and ensuring proper adjustments of electricity rates. In this paper, a simplistic model that can be followed by system operators to initially understand the customers' consumption pattern and the household load structure is proposed. A top-down approach is combined and matched with a detailed bottom-up one, to extract load compositions and percentages. Real and local load profiles integrated with household statistical data such as device time of use (ToU), number of device units per house, and activities exercised in households are all included in the model. The main results of the paper show the load composition in residential demand and the percentage of such composition under seasonal-based scenarios.
Improving Stability of Low-Inertia Systems using Virtual Induction Machine Synchronization for Grid-Following Converters
Authors: Ognjen Stanojev, Uros Markovic, Petros Aristidou, Gabriela Hug
Date: 2021-10-11 00:00:00

This paper presents a novel strategy for the synchronization of grid-following Voltage Source Converters (VSCs) in power systems with low rotational inertia. The proposed synchronization unit is based on emulating the physical properties of an induction machine and capitalizes on its inherent grid-friendly properties such as self-synchronization, oscillation damping, and standalone capabilities. To this end, the mathematical model of an induction machine is analyzed and reformulated to obtain the unknown grid frequency by processing the voltage and current measurements at the converter output. This eliminates the need for a Phase-Locked Loop (PLL) unit, traditionally employed in grid-following VSC control schemes, while simultaneously preserving the system- and device-level control. Furthermore, we provide the appropriate steps for obtaining an index-1 DAE representation of the induction-machine-based synchronization unit, suitable for stability analysis. Our analysis shows that replacing the PLL unit with the virtual induction machine-based synchronization may considerably improve the stability of systems with grid-following converters and facilitate the frequency containment. Furthermore, we validate the performance of the proposed synchronization unit through simulations and provide recommendations for its tuning.
Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression
Authors: John Jackson, Luca Laurenti, Eric Frew, Morteza Lahijanian
Date: 2021-10-11 00:00:00
Tasks: regression

Autonomous systems often have complex and possibly unknown dynamics due to, e.g., black-box components. This leads to unpredictable behaviors and makes control design with performance guarantees a major challenge. This paper presents a data-driven control synthesis framework for such systems subject to linear temporal logic on finite traces (LTLf) specifications. The framework combines a baseline (offline) controller with a novel online controller and refinement procedure that improves the baseline guarantees as new data is collected. The baseline controller is computed offline on an uncertain abstraction constructed using Gaussian process (GP) regression on a given dataset. The offline controller provides a lower bound on the probability of satisfying the LTLf specification, which may be far from optimal due to both discretization and regression errors. The synergy arises from the online controller using the offline abstraction along with the current state and new data to choose the next best action. The online controller may improve the baseline guarantees since it avoids the discretization error and reduces regression error as new data is collected. The new data are also used to refine the abstraction and offline controller using local GP regression, which significantly reduces the computation overhead. Evaluations show the efficacy of the proposed offline-online framework, especially when compared against the offline controller.
Extreme exoworlds and the extremophile paradox
Authors: Ian von Hegner
Date: 2021-09-17 00:00:00

Extremophiles have gained prominence by providing an experimental approach to astrobiology. Extremophiles gain equal value by being part of a framework for high-level characterisation of the evolutionary mechanisms that must necessarily restrict or promote their emergence and presence on solar system bodies. Thus, extremophiles exist in extreme environments, and therein lies the paradox: extremophiles can only live in extreme environments but yet are not able to originate in such environments. Therefore, even though the range of extremophile capabilities in extreme environments is wider than that in mesophiles, the range of their emergence possibilities is still equally restricted. Therefore, even if one locates an extreme exoworld where terrestrial extremophiles could live here-and-now, it can be predicted that no extremophile analogues are present anyway. Furthermore, it is possible for a world to be uninhabited, yet be habitable, and therein arises the extreme environment paradox: an extreme environment can sustain chemical evolution as well as arriving non-native life, yet native life cannot be built up in that very environment. Thus, life may exist on an extraterrestrial extreme world (if imported there), and chemical evolution may be present on that world. However, it can be predicted that there is no native life anyway. This situation can be predicted to function as a chemosignature and eventually as a biosignature. However, the fact that a non-native extremopile in principle can exist in extreme environments may demonstrate that the intermediate step between chemical evolution and extremophiles can still occur in the form of a statistical deviation.
Inferring supply networks from mobile phone data to estimate the resilience of a national economy
Authors: Tobias Reisch, Georg Heiler, Christian Diem, Stefan Thurner
Date: 2021-10-11 00:00:00

National economies rest on networks of millions of customer-supplier relations. Some companies -- in the case of their default -- can trigger significant cascades of shock in the supply-chain network and are thus systemically risky. Up to now, systemic risk of individual companies was practically not quantifiable, due to the unavailability of firm-level transaction data. So far, economic shocks are typically studied in the framework of input-output analysis on the industry-level that can't relate risk to individual firms. Exact firm-level supply networks based on tax or payment data exist only for very few countries. Here we explore to what extent telecommunication data can be used as an inexpensive, easily available, and real-time alternative to reconstruct national supply networks on the firm-level. We find that the conditional probability of correctly identifying a true customer-supplier link -- given a communication link exists -- is about 90%. This quality level allows us to reliably estimate a systemic risk profile of an entire country that serves as a proxy for the resilience of its economy. In particular, we are able to identify the high systemic risk companies. We find that 65 firms have the potential to trigger large cascades of disruption in production chains that could cause severe damages in the economy. We verify that the topological features of the inter-firm communication network are highly similar to national production networks with exact firm-level interactions.
Image Compression and Classification Using Qubits and Quantum Deep Learning
Authors: Ali Mohsen, Mo Tiwari
Date: 2021-10-08 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, Image Compression, Quantum Machine Learning

Recent work suggests that quantum machine learning techniques can be used for classical image classification by encoding the images in quantum states and using a quantum neural network for inference. However, such work has been restricted to very small input images, at most 4 x 4, that are unrealistic and cannot even be accurately labeled by humans. The primary difficulties in using larger input images is that hitherto-proposed encoding schemes necessitate more qubits than are physically realizable. We propose a framework to classify larger, realistic images using quantum systems. Our approach relies on a novel encoding mechanism that embeds images in quantum states while necessitating fewer qubits than prior work. Our framework is able to classify images that are larger than previously possible, up to 16 x 16 for the MNIST dataset on a personal laptop, and obtains accuracy comparable to classical neural networks with the same number of learnable parameters. We also propose a technique for further reducing the number of qubits needed to represent images that may result in an easier physical implementation at the expense of final performance. Our work enables quantum machine learning and classification on classical datasets of dimensions that were previously intractable by physically realizable quantum computers or classical simulation
Fixed $T$ Estimation of Linear Panel Data Models with Interactive Fixed Effects
Authors: Ayden Higgins
Date: 2021-10-11 00:00:00

This paper studies the estimation of linear panel data models with interactive fixed effects, where one dimension of the panel, typically time, may be fixed. To this end, a novel transformation is introduced that reduces the model to a lower dimension, and, in doing so, relieves the model of incidental parameters in the cross-section. The central result of this paper demonstrates that transforming the model and then applying the principal component (PC) estimator of \cite{bai_panel_2009} delivers $\sqrt{n}$ consistent estimates of regression slope coefficients with $T$ fixed. Moreover, these estimates are shown to be asymptotically unbiased in the presence of cross-sectional dependence, serial dependence, and with the inclusion of dynamic regressors, in stark contrast to the usual case. The large $n$, large $T$ properties of this approach are also studied, where many of these results carry over to the case in which $n$ is growing sufficiently fast relative to $T$. Transforming the model also proves to be useful beyond estimation, a point illustrated by showing that with $T$ fixed, the eigenvalue ratio test of \cite{horenstein} provides a consistent test for the number of factors when applied to the transformed model.
Dyadic double/debiased machine learning for analyzing determinants of free trade agreements
Authors: Harold D Chiang, Yukun Ma, Joel Rodrigue, Yuya Sasaki
Date: 2021-10-08 00:00:00
Tasks: BIG-bench Machine Learning

This paper presents novel methods and theories for estimation and inference about parameters in econometric models using machine learning for nuisance parameters estimation when data are dyadic. We propose a dyadic cross fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross fitting method enables root-$n$ consistent estimation and inference robustly against dyadic dependence. We illustrate an application of our general framework to high-dimensional network link formation models. With this method applied to empirical data of international economic networks, we reexamine determinants of free trade agreements (FTA) viewed as links formed in the dyad composed of world economies. We document that standard methods may lead to misleading conclusions for numerous classic determinants of FTA formation due to biased point estimates or standard errors which are too small.
Indian urban workers' labour market transitions
Authors: Jyotirmoy Bhattacharya
Date: 2021-10-11 00:00:00

This paper studies gross labour market flows and determinants of labour market transitions for urban Indian workers using a panel dataset constructed from Indian Periodic Labour Force Survey (PLFS) data for the period 2017--18 to 2019--20. Longitudinal studies based on the PLFS have been hampered by data problems that prevent a straightforward merging of the 2017--18 and 2018--19 data releases. In this paper, we propose and validate a matching procedure based on individual and household characteristics that can successfully link almost all records across these two years. We use the constructed data set to document a number of stylised facts about gross worker flows and to estimate the effects of different individual characteristics and work histories on probabilities of job gain and loss.
Tiebout sorting in online communities
Authors: John Lynham, Philip R. Neary
Date: 2021-10-08 00:00:00

This paper proposes a stylized, dynamic model to address the issue of sorting online. There are two large homogeneous groups of individuals. Everyone must choose between two online platforms, one of which has superior amenities (akin to having superior local public goods). Each individual enjoys interacting online with those from their own group but dislikes being on the same platform as those in the other group. Unlike a Tiebout model of residential sorting, both platforms have unlimited capacity so there are no constraints on cross-platform migration. It is clear how each group would like to sort themselves but, in the presence of the other type, only the two segregated outcomes are guaranteed to be equilibria. Integration on a platform can be supported in equilibrium as long as the platform is sufficiently desirable. If online integration of the two communities is a desired social outcome then the optimal policy is clear: make the preferred platform even more desirable. Revitalizing the inferior platform will never lead to integration and even increases the likelihood the segregation. Finally, integration is more elastic in response to an increase in platform amenities than to reductions in intolerance.