pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Interactive Two-stage Joint Retail Electricity Market for Multiple Microgrids
Authors: Chunyi Huang, Mingzhi Zhang, Chengmin Wang, Ning Xie, Jian Wang, Shi Peng
Date: 2021-07-27 00:00:00
Tasks: energy trading

To accommodate the advent of microgrids (MG) managing distributed energy resources (DER) in distribution systems, an interactive two-stage joint retail electricity market mechanism is proposed to provide an effective platform for these prosumers to proactively join in retail transactions. Day-ahead stochastic energy trading between the distribution system operator (DSO) and MGs is conducted in the first stage of a centralized retail market, where a chance-constrained uncertainty distribution locational marginal price (CC-UDLMP) containing the cost of uncertainty precautions is used to settle transactions. In the second stage, a novel intra-day peer-to-peer-based (P2P) flexibility transaction pattern is implemented between MGs in local flexibility markets under the regulation of DSO to eliminate power imbalances caused by rolling-based estimates whilst considering systematic operations. A fully distributed iterative algorithm is presented to find the equilibrium solution of this two-stage sequential game framework. Moreover, in order to enhance the versatility of this algorithm, an improved Lp-box alternating direction methods of multipliers (ADMM) algorithm is used to efficiently resolve the first-stage stochastic economic dispatch problem with a mixed-integer second-order cone structure. It is verified that the proposed market mechanism can effectively improve the overall market efficiency under uncertainties.
Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids
Authors: Hang Shuai, Xiaomeng Ai, Jiakun Fang, Wei Yao, Senior Member, Jinyu Wen, Member, IEEE
Date: 2021-07-27 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning

The uncertainties from distributed energy resources (DERs) bring significant challenges to the real-time operation of microgrids. In addition, due to the nonlinear constraints in the AC power flow equation and the nonlinearity of the battery storage model, etc., the optimization of the microgrid is a mixed-integer nonlinear programming (MINLP) problem. It is challenging to solve this kind of stochastic nonlinear optimization problem. To address the challenge, this paper proposes a deep reinforcement learning (DRL) based optimization strategy for the real-time operation of the microgrid. Specifically, we construct the detailed operation model for the microgrid and formulate the real-time optimization problem as a Markov Decision Process (MDP). Then, a double deep Q network (DDQN) based architecture is designed to solve the MINLP problem. The proposed approach can learn a near-optimal strategy only from the historical data. The effectiveness of the proposed algorithm is validated by the simulations on a 10-bus microgrid system and a modified IEEE 69-bus microgrid system. The numerical simulation results demonstrate that the proposed approach outperforms several existing methods.
Estimating high-dimensional Markov-switching VARs
Authors: Kenwin Maung
Date: 2021-07-27 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

Maximum likelihood estimation of large Markov-switching vector autoregressions (MS-VARs) can be challenging or infeasible due to parameter proliferation. To accommodate situations where dimensionality may be of comparable order to or exceeds the sample size, we adopt a sparse framework and propose two penalized maximum likelihood estimators with either the Lasso or the smoothly clipped absolute deviation (SCAD) penalty. We show that both estimators are estimation consistent, while the SCAD estimator also selects relevant parameters with probability approaching one. A modified EM-algorithm is developed for the case of Gaussian errors and simulations show that the algorithm exhibits desirable finite sample performance. In an application to short-horizon return predictability in the US, we estimate a 15 variable 2-state MS-VAR(1) and obtain the often reported counter-cyclicality in predictability. The variable selection property of our estimators helps to identify predictors that contribute strongly to predictability during economic contractions but are otherwise irrelevant in expansions. Furthermore, out-of-sample analyses indicate that large MS-VARs can significantly outperform "hard-to-beat" predictors like the historical average.
A Unifying Framework for Testing Shape Restrictions
Authors: Zheng Fang
Date: 2021-07-26 00:00:00

This paper makes the following original contributions. First, we develop a unifying framework for testing shape restrictions based on the Wald principle. The test has asymptotic uniform size control and is uniformly consistent. Second, we examine the applicability and usefulness of some prominent shape enforcing operators in implementing our framework. In particular, in stark contrast to its use in point and interval estimation, the rearrangement operator is inapplicable due to a lack of convexity. The greatest convex minorization and the least concave majorization are shown to enjoy the analytic properties required to employ our framework. Third, we show that, despite that the projection operator may not be well-defined/behaved in general parameter spaces such as those defined by uniform norms, one may nonetheless employ a powerful distance-based test by applying our framework. Monte Carlo simulations confirm that our test works well. We further showcase the empirical relevance by investigating the relationship between weekly working hours and the annual wage growth in the high-end labor market.
No free lunch for markets with multiple numéraires
Authors: Laurence Carassus
Date: 2021-07-27 00:00:00

We consider a global market constituted by several submarkets, each with its own assets and num\'eraire. We provide theoretical foundations for the existence of equivalent martingale measures and results on superreplication prices which allows to take into account difference of features between submarkets.
The Rising Entropy of English in the Attention Economy
Authors: Charlie Pilgrim, Weisi Guo, Thomas T. Hills
Date: 2021-07-27 00:00:00
Tasks: Form

We present evidence that the word entropy of American English has been rising steadily since around 1900, contrary to predictions from existing sociolinguistic theories. We also find differences in word entropy between media categories, with short-form media such as news and magazines having higher entropy than long-form media, and social media feeds having higher entropy still. To explain these results we develop an ecological model of the attention economy that combines ideas from Zipf's law and information foraging. In this model, media consumers maximize information utility rate taking into account the costs of information search, while media producers adapt to technologies that reduce search costs, driving them to generate higher entropy content in increasingly shorter formats.
Income Inequality and Intergenerational Mobility in India
Authors: Anuradha Singh
Date: 2021-07-27 00:00:00

Using three rounds of NSS datasets, the present paper attempts to understand the relationship between income inequality and intergenerational income mobility (IGIM) by segregating generations into social and income classes. The originality of the paper lies in assessing the IGIM using different approaches, which we expect to contribute to the existing literature. We conclude that the country has low-income mobility and high inequality which is no longer associated with a particular social class in India. Also, both may have a negative or positive relationship, hence needs to be studied at a regional level.
Dhaka Water-logging: Causes, Effects and Remedial Policy Options
Authors: Hossain Ahmed Taufiq
Date: 2021-07-27 00:00:00

Water-logging is a major challenge for Dhaka city, the capital of Bangladesh. The rapid, unregulated, and unplanned urbanization, as well as detrimental social, economic, infrastructural, and environmental consequences, not to mention diseases like dengue, challenge the several crash programs combating water-logging in the city. This study provides a brief contextual analysis of the Dhakas topography and natural, as well as storm water drainage systems, before concentrating on the man-made causes and effects of water-logging, ultimately exploring a few remedial measures.
They Chose to Not Tell You
Authors: Bruce Knuteson
Date: 2021-07-26 00:00:00
Tasks: Position

The world's stock markets display a strikingly suspicious, decades long pattern of overnight and intraday returns that nobody (other than us) has plausibly explained and that nobody (other than us) has clearly and persistently alerted you to. We use correspondence on this topic over the past five years to show that the silence of others on this issue does not arise from their having a good reason to believe this pattern is fine. Separately, and regardless of whether this pattern turns out to be fine, we have documented that people in a position to alert you to the presence of strikingly suspicious return patterns in the world's stock markets that nobody can innocuously explain are aware of this issue, have no good reason to believe it is not a problem, and chose to not tell you.
A Free and Fair Economy: A Game of Justice and Inclusion
Authors: Ghislain H. Demeze-Jouatsa, Roland Pongou, Jean-Baptiste Tondji
Date: 2021-07-27 00:00:00
Tasks: Ethics, Fairness

Frequent violations of fair principles in real-life settings raise the fundamental question of whether such principles can guarantee the existence of a self-enforcing equilibrium in a free economy. We show that elementary principles of distributive justice guarantee that a pure-strategy Nash equilibrium exists in a finite economy where agents freely (and non-cooperatively) choose their inputs and derive utility from their pay. Chief among these principles is that: 1) your pay should not depend on your name, and 2) a more productive agent should not earn less. When these principles are violated, an equilibrium may not exist. Moreover, we uncover an intuitive condition -- technological monotonicity -- that guarantees equilibrium uniqueness and efficiency. We generalize our findings to economies with social justice and inclusion, implemented in the form of progressive taxation and redistribution, and guaranteeing a basic income to unproductive agents. Our analysis uncovers a new class of strategic form games by incorporating normative principles into non-cooperative game theory. Our results rely on no particular assumptions, and our setup is entirely non-parametric. Illustrations of the theory include applications to exchange economies, surplus distribution in a firm, contagion and self-enforcing lockdown in a networked economy, and bias in the academic peer-review system. Keywords: Market justice; Social justice; Inclusion; Ethics; Discrimination; Self-enforcing contracts; Fairness in non-cooperative games; Pure strategy Nash equilibrium; Efficiency. JEL Codes: C72, D30, D63, J71, J38
Learning-based Prediction, Rendering and Transmission for Interactive Virtual Reality in RIS-Assisted Terahertz Networks
Authors: Xiaonan Liu, Yansha Deng, Chong Han, Marco Di Renzo
Date: 2021-07-27 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing

The quality of experience (QoE) requirements of wireless Virtual Reality (VR) can only be satisfied with high data rate, high reliability, and low VR interaction latency. This high data rate over short transmission distances may be achieved via abundant bandwidth in the terahertz (THz) band. However, THz waves suffer from severe signal attenuation, which may be compensated by the reconfigurable intelligent surface (RIS) technology with programmable reflecting elements. Meanwhile, the low VR interaction latency may be achieved with the mobile edge computing (MEC) network architecture due to its high computation capability. Motivated by these considerations, in this paper, we propose a MEC-enabled and RIS-assisted THz VR network in an indoor scenario, by taking into account the uplink viewpoint prediction and position transmission, MEC rendering, and downlink transmission. We propose two methods, which are referred to as centralized online Gated Recurrent Unit (GRU) and distributed Federated Averaging (FedAvg), to predict the viewpoints of VR users. In the uplink, an algorithm that integrates online Long-short Term Memory (LSTM) and Convolutional Neural Networks (CNN) is deployed to predict the locations and the line-of-sight and non-line-of-sight statuses of the VR users over time. In the downlink, we further develop a constrained deep reinforcement learning algorithm to select the optimal phase shifts of the RIS under latency constraints. Simulation results show that our proposed learning architecture achieves near-optimal QoE as that of the genie-aided benchmark algorithm, and about two times improvement in QoE compared to the random phase shift selection scheme.
Exploring the Interdependencies Between Transmit Waveform Ambiguity Function Shape and Off-Axis Bearing Estimation
Authors: David A. Hague, Matthew D. Tidwell
Date: 2021-07-27 00:00:00

The frequency dependent beampatterns of an active sonar projector filters the acoustic signal that is transmitted into the medium, also known as the transmit waveform. This filtering encodes information about the target's bearing relative to the main response axis. For any given projector and transmit waveform spectrum, there exists an optimal angle of operation which maximizes the Fisher Information (FI) of the target bearing estimate. Previous investigations into this phenomena show that for narrowband (i.e, high $Q$) Linear Frequency Modulated (LFM) waveforms, the angle of maximum FI is solely determined by its center frequency $f_c$. Steering the region of maximum bearing estimation precision is then achieved by appropriate selection of the LFM waveform's center frequency $f_c$. This fine bearing estimation is accomplished without steering the projector's main response axis. In addition to LFM waveforms, a wide variety of other active sonar waveform types exist that possess distinct spectral characteristics. These other waveforms possess different Ambiguity Function (AF) shapes from the LFM and are typically utilized to suite the range-Doppler resolution requirements of the active sonar system. This paper investigates the transmit waveform impact on off-axis bearing estimation performance and the spectral filtering impact on the waveform's AF shape. High $Q$ waveforms perform similarly to the LFM for off-axis bearing estimation while the transducer's spectral filtering perturbs the waveform's AF shape.
Neural Style Transfer Enhanced Training Support For Human Activity Recognition
Authors: Shelly Vishwakarma, Wenda Li, Chong Tang, Karl Woodbridge, Raviraj Adve, Kevin Chetty
Date: 2021-07-27 00:00:00
Tasks: Activity Recognition, Data Augmentation, Human Activity Recognition, Integrated sensing and communication, ISAC, Style Transfer

This work presents an application of Integrated sensing and communication (ISAC) system for monitoring human activities directly related to healthcare. Real-time monitoring of humans can assist professionals in providing healthy living enabling technologies to ensure the health, safety, and well-being of people of all age groups. To enhance the human activity recognition performance of the ISAC system, we propose to use synthetic data generated through our human micro-Doppler simulator, SimHumalator to augment our limited measurement data. We generate a more realistic micro-Doppler signature dataset using a style-transfer neural network. The proposed network extracts environmental effects such as noise, multipath, and occlusions effects directly from the measurement data and transfers these features to our clean simulated signatures. This results in more realistic-looking signatures qualitatively and quantitatively. We use these enhanced signatures to augment our measurement data and observe an improvement in the classification performance by 5% compared to no augmentation case. Further, we benchmark the data augmentation performance of the style transferred signatures with three other synthetic datasets -- clean simulated spectrograms (no environmental effects), simulated data with added AWGN noise, and simulated data with GAN generated noise. The results indicate that style transferred simulated signatures well captures environmental factors more than any other synthetic dataset.
HAPS Selection for Hybrid RF/FSO Satellite Networks
Authors: Olfa Ben Yahia, Eylem Erdogan, Gunes Karabulut Kurt, Ibrahim Altunbas, Halim Yanikomeroglu
Date: 2021-07-27 00:00:00

Non-terrestrial networks have been attracting much interest from the industry and academia. Satellites and high altitude platform station (HAPS) systems are expected to be the key enablers of next-generation wireless networks. In this paper, we introduce a novel downlink satellite communication (SatCom) model where free-space optical (FSO) communication is adopted between a satellite and a HAPS node. A hybrid FSO/radio-frequency (RF) transmission model is used between the HAPS node and ground station (GS). In the first phase of transmission, the satellite selects the HAPS node that provides the highest signal-to-noise ratio (SNR). In the second phase, the selected HAPS decodes and forwards the signal to the GS. To evaluate the performance of the proposed system, outage probability expressions are derived for exponentiated Weibull (EW) and shadowed-Rician fading models while considering the atmospheric turbulence, stratospheric attenuation, and attenuation due to scattering, path loss, and pointing errors. Additionally, asymptotic analysis is carried out and diversity gain is provided. Furthermore, the impact of aperture averaging technique, temperature, and wind speed are investigated. We also provide some important guidelines that can be helpful for the design of practical HAPS-aided SatCom. Finally, the results show that the use of HAPS improves the system performance and that the proposed model performs better than all other existing models.
Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface
Authors: Shuhang Chen, Xiang Zhang, Xiang Shen, Yifan Huang, Yiwen Wang
Date: 2021-07-27 00:00:00

Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where the brain signals change in response to external stimuli or movements, plays an important role in BMIs. When subjects purely use neural activity to brain-control a prosthesis, some neurons will actively explore a new tuning property to accomplish the movement task. The prediction of this neural tuning property can help subjects adapt more efficiently to brain control and maintain good decoding performance. Existing prediction methods track the slow change of the tuning property in the manual control, which is not suitable for the fast neural adaptation in brain control. In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework. We implement the method on real data from rats performing a two-lever discrimination task under manual control and brain control. The results show our method successfully predicts the neural modulation state and identifies the neurons that become active in brain control. Compared to existing methods, ours tracks the fast changes of the hyper preferred direction from manual control to brain control more accurately and efficiently and reconstructs the kinematics better and faster.
Event-driven Two-stage Solution to Non-intrusive Load Monitoring
Authors: Lei Yan, Wei Tian, Jiayu Han, Zuyi Li
Date: 2021-07-27 00:00:00
Tasks: Non-Intrusive Load Monitoring, Vocal Bursts Valence Prediction

Existing methods of non-intrusive load monitoring (NILM) in literatures generally suffer from high computational complexity and/or low accuracy in identifying working household appliances. This paper proposes an event-driven Factorial Hidden Markov model (eFHMM) for multiple appliances with multiple states in a household, aiming for low computational complexity and high load disaggregation accuracy. The proposed eFHMM decreases the computational complexity to be linear to the event number, which ensures online load disaggregation. Furthermore, the eFHMM is solved in two stages, where the first stage identifies state-changing appliance using transient signatures and the second stage confirms the inferred states using steady-state signatures. The combination of transient and steady-state signatures, which are extracted from transient and steady periods segmented by detected events, enhances the uniqueness of each state transition and associated appliances, which ensures accurate load disaggregation. The event-driven two-stage NILM solution, termed as eFHMM-TS, is naturally fit into an edge-cloud framework, which makes possible the real-world application of NILM. The proposed eFHMM-TS method is validated on the LIFTED and synD datasets. Results demonstrate that the eFHMM-TS method outperforms other methods and can be applied in practice.
New Candidates for Furin Inhibition as Probable Treat for COVID-19: Docking Output
Authors: Mohammad Reza Dayer
Date: 2021-07-27 00:00:00
Tasks: Drug Design

Furin is a serine protease that takes part in the processing and activation of the host cell pre-proteins. The enzyme also plays an important role in the activation of several viruses like the newly emerging SARS-CoV-2 virus that causes COVID-19 disease with a high rate of virulence and mortality. Unlike viral enzymes, furin owns a constant sequence and active site characteristics and seems to be a better target for drug design for COVID-19 treatment. Considering furin active site as receptor and some approved drugs from different classes including antiviral, antibiotics, and anti protozoa/anti parasites with suspected beneficial effects on COVID-19, as ligands we have carried out docking experiments in HEX software to pickup those capable to bind furin active site with high affinity and suggest them as probable candidates for clinical trials assessments. Our docking experiments show that saquinavir, nelfinavir, and atazanavir with cumulative inhibitory effects of 2.52, 2.16, and 2.13 respectively seem to be the best candidates for furin inhibition even in severe cases of COVID-19 as adjuvant therapy, while clarithromycin, niclosamide, and erythromycin with cumulative inhibitory indices of 1.97, 1.90, and 1.84 respectively with lower side effects than antiviral drugs could be suggested as prophylaxes for the first stage of COVID-19 as a promising treat.
Bam-readcount -- rapid generation of basepair-resolution sequence metrics
Authors: Ajay Khanna, David E. Larson, Sridhar Nonavinkere Srivatsan, Matthew Mosior, Travis E. Abbott, Susanna Kiwala, Timothy J. Ley, Eric J. Duncavage, Matthew J. Walter, Jason R. Walker, Obi L. Griffith, Malachi Griffith, Christopher A. Miller
Date: 2021-07-27 00:00:00

Bam-readcount is a utility for generating low-level information about sequencing data at specific nucleotide positions. Originally designed to help filter genomic mutation calls, the metrics it outputs are useful as input for variant detection tools and for resolving ambiguity between variant callers . In addition, it has found broad applicability in diverse fields including tumor evolution, single-cell genomics, climate change ecology, and tracking community spread of SARS-CoV-2. Here we report on the release of version 1.0 of this tool, which adds CRAM support, among other improvements. It is released under a permissive MIT license and available at https://github.com/genome/bam-readcount.