pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning
Authors: Keith Anshilo Diaz, Damian Dailisan, Umang Sharaf, Carissa Santos, Qijian Gan, Francis Aldrine Uy, May T. Lim, Alexandre M. Bayen
Date: 2020-08-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven approaches for traffic control systems. To date, proposed deep RL-based traffic schemes control phase activation or duration. Yet, such approaches may bypass low volume links for several cycles in order to optimize the network-level traffic flow. Here, we propose a deep RL framework that dynamically adjusts offsets based on traffic states and preserves the planned phase timings and order derived from model-based methods. This framework allows us to improve arterial coordination while maintaining phase order and timing predictability. Using a validated and calibrated traffic model, we trained the policy of a deep RL agent that aims to reduce travel delays in the network. We evaluated the resulting policy by comparing its performance against the phase offsets deployed along a segment of Huntington Drive in the city of Arcadia. The resulting policy dynamically readjusts phase offsets in response to changes in traffic demand. Simulation results show that the proposed deep RL agent outperformed the baseline on average, effectively reducing delay time by 13.21% in the AM Scenario, 2.42% in the Noon scenario, and 6.2% in the PM scenario when offsets are adjusted in 15-minute intervals. Finally, we also show the robustness of our agent to extreme traffic conditions, such as demand surges in off-peak hours and localized traffic incidents
Distributed Nash Equilibrium Seeking Algorithm Design for Multi-Cluster Games with High-Order Players
Authors: Zhenhua Deng, Yangyang Liu
Date: 2021-06-25 00:00:00

In this paper, a multi-cluster game with high-order players is investigated. Different from the well-known multi-cluster games, the dynamics of players are taken into account in our problem. Due to the high-order dynamics of players, existing algorithms for multi-cluster games cannot solve the problem. For purpose of seeking the Nash equilibrium of the game, we design a distributed algorithm based on gradient descent and state feedback, where a distributed estimator is embedded for the players to estimate the decisions of other players. Furthermore, we analyze the exponential convergence of the algorithm via variational analysis and Lyapunov stability theory. Finally, a numerical simulation verifies the effectiveness of our method.
Coordinated Scheduling of Electric Vehicles Within Zero Carbon Emission Hybrid AC/DC Microgrids
Authors: Reza Bayani, Arash Farokhi Soofi, Saeed D. Manshadi
Date: 2021-06-24 00:00:00
Tasks: Scheduling, Sensitivity

Microgrids with AC/DC architecture benefit from advantages of both AC and DC power. In this paper, daily operation problem for a zero-carbon AC/DC microgrid in presence of electric vehicles (EVs) is considered. In this framework, EVs' batteries are mobile energy storage systems, which allow desirable operation of the microgrid during peak demand hours. This study shows in absence of storage system, EVs' batteries can be properly managed to satisfy the system requirements. In the case studies, several sensitivity analyses based on variations in battery degradation costs, solar irradiance, and inverter capacity are investigated.
Alternating active-dormitive strategy enables overtaking by disadvantaged prey through Parrondo's paradox
Authors: Tao Wen, Eugene V. Koonin, Kang Hao Cheong
Date: 2021-06-25 00:00:00

Dormancy is a costly adaptive strategy that is widespread among living organisms inhabiting diverse environments. We explore mathematical models of predator-prey systems, in order to assess the impact of prey dormancy on the competition between two types of prey, a perennially active (PA) and capable of entering dormancy (dormitive). Both the active form and the dormant form of the dormitive prey are individually at a disadvantage compared to the PA prey and would go extinct due to their low growth rate, energy waste on the production of dormant prey, and inability of the latter to grow autonomously. However, the dormitive prey can paradoxically outcompete the PA prey with superior traits and even cause its extinction by alternating between the two losing strategies. This outcome recapitulates the game-theoretic Parrondo's paradox, where two losing strategies combine to achieve a winning outcome. We observed higher fitness of the dormitive prey in rich environments because a large predator population in a rich environment cannot be supported by the prey without adopting an evasive strategy, that is, dormancy. In such environments, populations experience large-scale fluctuations, which can be survived by dormitive but not by PA prey. Dormancy of the prey appears to be a natural evolutionary response to self-destructive over-predation that stabilizes evolving predator-prey systems through Parrondo's paradox.
A Time-domain Approach to the Design of Coupled-Resonator Microstrip Filters
Authors: Andres Altieri
Date: 2021-06-25 00:00:00

Coupled-resonator microstrip filters are among the most versatile filter topologies. A known design approach uses full-wave electromagnetic simulations to determine the coupling coefficient between resonators as a function of their relative position. This could be done using time-domain simulations using a fast Fourier transform (FFT) to extract the couplings from the S parameters obtained from time-domain signals. However, this approach has a poor performance in terms of resolution and specially for weak couplings, leading to unreasonably long simulation times. To overcome this, we introduce a technique to obtain the couplings directly from time signals, without moving to the frequency domain. This procedure works for strong and weak couplings, with much shorter simulation times and a reduced simulation domain over the FFT approach. This technique is used to design coupled resonator filter efficiently from time domain simulations. We implement this procedure using the finite-difference time-domain framework using an open source solver and discuss our implementation. We show that its results are very similar to previously published ones obtained from frequency domain simulations, even in the case of very weak couplings. Finally, we design and measure a filter to show the good performance of the proposed approach.
Video-Streaming Biomedical Implants using Ultrasonic Waves for Communication
Authors: Gizem Tabak, Jae Won Choi, Rita J. Miller, Michael L. Oelze, Andrew C. Singer
Date: 2021-06-25 00:00:00
Tasks: Retrieval

The use of wireless implanted medical devices (IMDs) is growing because they facilitate continuous monitoring of patients during normal activities, simplify medical procedures required for data retrieval and reduce the likelihood of infection associated with trailing wires. However, most of the state-of-the-art IMDs are passive and offline devices. One of the key obstacles to an active and online IMD is the infeasibility of real-time, high-quality video broadcast from the IMD. Such broadcast would help develop innovative devices such as a video-streaming capsule endoscopy (CE) pill with therapeutic intervention capabilities. State-of-the-art IMDs employ radio-frequency electromagnetic waves for information transmission. However, high attenuation of RF-EM waves in tissues and federal restrictions on the transmit power and operable bandwidth lead to fundamental performance constraints for IMDs employing RF links, and prevent achieving high data rates that could accomodate video broadcast. In this work, ultrasonic waves were used for video transmission and broadcast through biological tissues. The proposed proof-of-concept system was tested on a porcine intestine ex vivo and a rabbit in vivo. It was demonstrated that using a millimeter-sized, implanted biocompatible transducer operating at 1.1-1.2 MHz, it was possible to transmit endoscopic video with high resolution (1280 pixels by 720 pixels) through porcine intestine wrapped with bacon, and to broadcast standard definition (640 pixels by 480 pixels) video near real-time through rabbit abdomen in vivo. A media repository that includes experimental demonstrations and media files accompanies this paper. The accompanying media repository can be found at this link: https://bit.ly/3wuc7tk.
Sovereign wealth funds: main activity trends
Authors: Oksana Mamina, Alexander Barannikov, Ludmila Gruzdeva
Date: 2021-06-25 00:00:00

Sovereign wealth funds are created in those countries whose budget is highly dependent on market factors, usually world commodity prices. At the same time, these funds are large institutional investors. An analysis of the nature of investments by the State Pension Fund Global of Norway showed that investments of the Fund are based on a seven-level model of diversifying its investments. This model can also be applied to the investments of the National Wealth Fund of Russia to increase its profitability.
Intergenerational risk sharing in a Defined Contribution pension system: analysis with Bayesian optimization
Authors: An Chen, Motonobu Kanagawa, Fangyuan Zhang
Date: 2021-06-25 00:00:00
Tasks: Bayesian Optimization

We study a fully funded, collective defined-contribution (DC) pension system with multiple overlapping generations. We investigate whether the welfare of participants can be improved by intergenerational risk sharing (IRS) implemented with a realistic investment strategy (e.g., no borrowing) and without an outside entity (e.g., share holders) that helps finance the pension fund. To implement IRS, the pension system uses an automatic adjustment rule for the indexation of individual accounts, which adapts to the notional funding ratio of the pension system. The pension system has two parameters that determine the investment strategy and the strength of the adjustment rule, which are optimized by expected utility maximization using Bayesian optimization. The volatility of the retirement benefits and that of the funding ratio are analyzed, and it is shown that the trade-off between them can be controlled by the optimal adjustment parameter to attain IRS. Compared with the optimal individual DC benchmark using the life-cycle strategy, the studied pension system with IRS is shown to improve the welfare of risk-averse participants, when the financial market is volatile.
Political Power and Market Power
Authors: Bo Cowgill, Andrea Prat, Tommaso Valletti
Date: 2021-06-25 00:00:00

We study the link between political influence and industrial concentration. We present a joint model of political influence and market competition: an oligopoly lobbies the government over regulation, and competes in the product market shaped by this influence. We show broad conditions for mergers to increase lobbying, both on the intensive margin and the extensive margin. We combine data on mergers with data on lobbying expenditures and campaign contributions in the US from 1999 to 2017. We document a positive association between mergers and lobbying, both by individual firms and by industry trade associations. Mergers are also associated with extensive margin changes such as the formation of in-house lobbying teams and corporate PACs. We find some evidence for a positive association between mergers and higher campaign contributions.
Relationship between Cultural Values, Sense of Community and Trust and the Effect of Trust in Workplace
Authors: Nazli Mohammad, Yvonne Stedham
Date: 2021-06-24 00:00:00

This paper provides a general overview of different perspectives and studies on trust, offers a definition of trust, and provides factors that play a substantial role in developing social trust, and shows from which perspectives it can be fostered. The results showed that trust is playing an important role in success for organizations involved in cross-national strategic partnerships. Trust can reduce transaction costs, promotes inter-organizational relationships, and improve subordinate relationships between managers.
Bibliometric Analysis Of Herding Behavior In Times Of Crisis
Authors: Fenny Marietza, Ridwan Nurazi, Fitri Santi, Saiful
Date: 2021-06-09 00:00:00
Tasks: Articles

The social and psychological concept of herding behavior provides a suitable solution to give an understanding of the behavioral biases that often occur in the capital market. The aim of this paper is to provide an overview of the broader bibliometric literature on the term and concept of herding behavior. Articles are collected through the help of software consisting of Publish or Perish (PoP), Google Scholar, Mendeley, and VOSViewer through a systematic approach, explicit and reproductive methods. In addition, the articles were scanned by Scimagojr.com (Q1, Q2, Q3, and Q4), analyzing 83 articles of 261 related articles from reputable and non-reputable journals from 1996 to 2021. Mendeley software is used to manage and resume references. To review this database, classification was performed using the VOSviewer software. Four clusters were reviewed; The words that appear most often in each group are the type of stock market, the type of crisis, and the factors that cause herding. Thus these four clusters became the main research themes on the topic of herding in times of crisis. Meanwhile, methodology and strategy are the themes for future research in the future.
Reference Dependence and Random Attention
Authors: Matthew Kovach, Elchin Suleymanov
Date: 2021-06-24 00:00:00

We explore the ways that a reference point may direct attention. Utilizing a stochastic choice framework, we provide behavioral foundations for the Reference-Dependent Random Attention Model (RD-RAM). Our characterization result shows that preferences may be uniquely identified even when the attention process depends arbitrarily on both the menu and the reference point. The RD-RAM is able to capture rich behavioral patterns, including frequency reversals among non-status quo alternatives and choice overload. We also analyze specific attention processes, characterizing reference-dependent versions of several prominent models of stochastic consideration.
Compactification of the Rigid Motions Group in Image Processing
Authors: Tamir Bendory, Ido Hadi, Nir Sharon
Date: 2021-06-25 00:00:00

Image processing problems in general, and in particular in the field of single-particle cryo-electron microscopy, often require considering images up to their rotations and translations. Such problems were tackled successfully when considering images up to rotations only, using quantities which are invariant to the action of rotations on images. Extending these methods to cases where translations are involved is more complicated. Here we present a computationally feasible and theoretically sound approximate invariant to the action of rotations and translations on images. It allows one to approximately reduce image processing problems to similar problems over the sphere, a compact domain acted on by the group of 3D rotations, a compact group. We show that this invariant is induced by a family of mappings deforming, and thereby compactifying, the group structure of rotations and translations of the plane, i.e., the group of rigid motions, into the group of 3D rotations. Furthermore, we demonstrate its viability in two image processing tasks: multi-reference alignment and classification. To our knowledge, this is the first instance of a quantity that is either exactly or approximately invariant to rotations and translations of images that both rests on a sound theoretical foundation and also applicable in practice.
A Fast Graph Kernel Based Classification Method for Wireless Link Scheduling on Riemannian Manifold
Authors: Rashed Shelim, Ahmed S. Ibrahim
Date: 2021-06-25 00:00:00
Tasks: Binary Classification, Scheduling

In this paper, we propose a novel graph kernel method for the wireless link scheduling problem in device-to-device (D2D) networks on Riemannian manifold. The link scheduling problem can be considered as a binary classification problem since each D2D pair can only hold the state active or inactive. Our goal is to learn a novel metric that facilitates the design of an efficient but less computationally demanding machine learning (ML) solution for the binary classification task of link scheduling problem that requires no channel state information (CSI) and a fewer number of training samples as opposed to other benchmark ML algorithms. To this aim, we first represent the wireless D2D network as a graph and model the features of each D2D pair, including its communication and interference links, as regularized (i.e., positively-shifted) Laplacian matrices which are symmetric positive definite (SPD) one. By doing so, we represent the feature information of each D2D pair as a point on the SPD manifold, and we analyze the topology through Riemannian geometry. We compute the Riemannian metric, e.g., Log-Euclidean metric (LEM), which are suitable distance measures between the regularized Laplacian matrices. The LEM is then utilized to define a positive definite graph kernel for the binary classification of the link scheduling decisions. Simulation results demonstrate that the proposed graph Kernel-based method is computationally less demanding and achieves a sum rate of more than 95% of benchmark algorithm FPLinQ [1] for 10 D2D pairs without using CSI and less than a hundred training network layouts.
An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors
Authors: Silvio Zanoli, Flavio Ponzina, Tomás Teijeiro, Alexandre Levisse, David Atienza
Date: 2021-06-25 00:00:00

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and storage. However, almost all state-of-the-art systems are still built upon the classical Nyquist-based periodic signal acquisition. In this work, we design and validate the Polygonal Approximation Sampler (PAS), a novel circuit to implement a general-purpose event-based sampler using a polygonal approximation algorithm as the underlying sampling trigger. The circuit can be dynamically reconfigured to produce a coarse or a detailed reconstruction of the analog input, by adjusting the error threshold of the approximation. The proposed circuit is designed at the Register Transfer Level and processes each input sample received from the ADC in a single clock cycle. The PAS has been tested with three different types of archetypal signals captured by wearable devices (electrocardiogram, accelerometer and respiration data) and compared with a standard periodic ADC. These tests show that single-channel signals, with slow variations and constant segments (like the used single-lead ECG and the respiration signals) take great advantage from the used sampling technique, reducing the amount of data used up to 99% without significant performance degradation. At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation. These results open the door to new types of wearable sensors with reduced size and higher battery lifetime.
Resonant Beam Communications with Echo Interference Elimination
Authors: Mingliang Xiong, Qingwen Liu, Gang Wang, Georgios B. Giannakis, Sihai Zhang, Jinkang Zhu, Chuan Huang
Date: 2021-06-25 00:00:00

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this paper, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 bit/s/Hz.
Pricing multi-asset contingent claims in a multi-dimensional binomial market
Authors: Jarek Kędra, Assaf Libman, Victoria Steblovskaya
Date: 2021-06-24 00:00:00

We consider an incomplete multi-asset binomial market model. We prove that for a wide class of contingent claims the extremal multi-step martingale measure is a power of the corresponding single-step extremal martingale measure. This allows for closed form formulas for the bounds of a no-arbitrage contingent claim price interval. We construct a feasible algorithm for computing those boundaries as well as for the corresponding hedging strategies. Our results apply, for example, to European basket call and put options and Asian arithmetic average options.
Fluctuating ecological networks: a synthesis of maximum-entropy approaches for pattern detection and process inference
Authors: Tancredi Caruso, Giulio Virginio Clemente, Matthias C Rillig, Diego Garlaschelli
Date: 2021-06-25 00:00:00

Ecological networks such as plant-pollinator systems and food webs vary in space and time. This variability includes fluctuations in global network properties such as total number and intensity of interactions but also in the local properties of individual nodes such as the number and intensity of species-level interactions. Fluctuations of species properties can significantly affect higher-order network features, e.g. robustness and nestedness. Local fluctuations should therefore be controlled for in applications that rely on null models, especially pattern and perturbation detection. By contrast, most randomization methods for null models used by ecologists treat node-level local properties as hard constraints that cannot fluctuate. Here, we synthesise a set of methods that resolves the limit of hard constraints and is based on statistical mechanics. We illustrate the methods with some practical examples making available open source computer codes. We clarify how this approach can be used by experimental ecologists to detect non-random network patterns with null models that not only rewire but also redistribute interaction strengths by allowing fluctuations in the null model constraints (soft constraints). Null modelling of species heterogeneity through local fluctuations around typical topological and quantitative constraints offers a statistically robust and expanded (e.g. quantitative null models) set of tools to understand the assembly and resilience of ecological networks.