pwc-archive/papers-with-abstracts huggingface dataset:
A General Control Framework for Boolean Networks
Authors: Shiyong Zhu, Jianquan Lu, Shun-ichi Azuma, Wei Xing Zheng
Date: 2020-07-01 00:00:00

This paper focuses on proposing a general control framework for large-scale Boolean networks (\texttt{BNs}). Only by the network structure, the concept of structural controllability for \texttt{BNs} is formalized. A necessary and sufficient criterion is derived for the structural controllability of \texttt{BNs}; it can be verified with $\Theta(n^2)$ time, where $n$ is the number of network nodes. An interesting conclusion is shown as that a \texttt{BN} is structurally controllable if and only if it is structurally fixed-time controllable. Afterwards, the minimum node control problem with respect to structural controllability is proved to be NP-hard for structural \texttt{BNs}. In virtue of the structurally controllable criterion, three difficult control issues can be efficiently addressed and accompanied with some advantages. In terms of the design of pinning controllers to generate a controllable \texttt{BN}, by utilizing the structurally controllable criterion, the selection procedure for the pinning node set is developed for the first time instead of just checking the controllability under the given pinning control form; the pinning controller is of distributed form, and the time complexity is $\Theta(n2^{3d^{\ast}}+2(n+m)^2)$, where $m$ and $d^\ast$ are respectively the number of generators and the maximum vertex in-degree. With regard to the control design for stabilization in probability of probabilistic \texttt{BNs} (\texttt{PBNs}), an important theorem is proved to reveal the equivalence between several types of stability. The existing difficulties on the stabilization in probability are then solved to some extent via the structurally controllable criterion.
Exploring the time-domain deep attractor network with two-stream architectures in a reverberant environment
Authors: Hangting Chen, Pengyuan Zhang
Date: 2020-07-01 00:00:00
Tasks: Speech Separation

Deep attractor networks (DANs) perform speech separation with discriminative embeddings and speaker attractors. Compared with methods based on the permutation invariant training (PIT), DANs define a deep embedding space and deliver a more elaborate representation on each time-frequency (T-F) bin. However, it has been observed that the DANs achieve limited improvement on the signal quality if directly deployed in a reverberant environment. Following the success of time-domain separation networks on the clean mixture speech, we propose a time-domain DAN (TD-DAN) with two-streams of convolutional networks, which efficiently perform both dereverberation and separation tasks under the condition of a variable number of speakers. The speaker encoding stream (SES) of the TD-DAN is trained to model the speaker information in the embedding space. The speech decoding stream (SDS) accepts speaker attractors from the SES and learns to estimate early reflections from the spectro-temporal representations. Meanwhile, additional clustering losses are used to bridge the gap between the oracle and the estimated attractors. Experiments were conducted on the Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) dataset. The early reflection was compared with the anechoic and reverberant signals and then was chosen as the learning targets. The experimental results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 9.79/7.47 dB on the reverberant 2/3-speaker evaluation set, exceeding the baseline DAN and convolutional time-domain audio separation network (Conv-TasNet) by 1.92/0.68 dB and 0.91/0.47 dB, respectively.
Robust communication on networks
Authors: Marie Laclau, Ludovic Renou, Xavier Venel
Date: 2020-07-01 00:00:00

We consider sender-receiver games, where the sender and the receiver are two distinct nodes in a communication network. Communication between the sender and the receiver is thus indirect. We ask when it is possible to robustly implement the equilibrium outcomes of the direct communication game as equilibrium outcomes of indirect communication games on the network. Robust implementation requires that: (i) the implementation is independent of the preferences of the intermediaries and (ii) the implementation is guaranteed at all histories consistent with unilateral deviations by the intermediaries. Robust implementation of direct communication is possible if and only if either the sender and receiver are directly connected or there exist two disjoint paths between the sender and the receiver.
Barriers to grid-connected battery systems: Evidence from the Spanish electricity market
Authors: Yu Hu, David Soler Soneira, María Jesús Sánchez
Date: 2020-06-28 00:00:00

Electrical energy storage is considered essential for the future energy systems. Among all the energy storage technologies, battery systems may provide flexibility to the power grid in a more distributed and decentralized way. In countries with deregulated electricity markets, grid-connected battery systems should be operated under the specific market design of the country. In this work, using the Spanish electricity market as an example, the barriers to grid-connected battery systems are investigated using utilization analysis. The concept of "potentially profitable utilization time" is proposed and introduced to identify and evaluate future potential grid applications for battery systems. The numerical and empirical analysis suggests that the high cycle cost for battery systems is still the main barrier for grid-connected battery systems. In Spain, for energy arbitrage within the day-ahead market, it is required that the battery wear cost decreases to 15 Euro/MWh to make the potentially profitable utilization rate higher than 20%. Nevertheless, the potentially profitable utilization of batteries is much higher in the applications when higher flexibility is demanded. The minimum required battery wear cost corresponding to 20% potentially profitable utilization time increases to 35 Euro/MWh for energy arbitrage within the day-ahead market and ancillary services, and 50 Euro/MWh for upward secondary reserve. The results of this study contribute to the awareness of battery storage technology and its flexibility in grid applications. The findings also have significant implications for policy makers and market operators interested in promoting grid-connected battery storage under a deregulated power market.
Performance analysis of Zero Black-Derman-Toy interest rate model in catastrophic events: COVID-19 case study
Authors: Grzegorz Krzyżanowski, Andrés Sosa
Date: 2020-07-01 00:00:00

In this paper we continue the research of our recent interest rate tree model called Zero Black-Derman-Toy (ZBDT) model, which includes the possibility of a jump at each step to a practically zero interest rate. This approach allows to better match to risk of financial slowdown caused by catastrophic events. We present how to valuate a wide range of financial derivatives for such a model. The classical Black-Derman-Toy (BDT) model and novel ZBDT model are described and analogies in their calibration methodology are established. Finally two cases of applications of the novel ZBDT model are introduced. The first of them is the hypothetical case of an S-shape term structure and decreasing volatility of yields. The second case is an application of the ZBDT model in the structure of United States sovereign bonds in the current $2020$ economic slowdown caused by the Coronavirus pandemic. The objective of this study is to understand the differences presented by the valuation in both models for different derivatives.
Generating Series for Networks of Chen-Fliess Series
Date: 2020-10-31 00:00:00
Tasks: State Space Models

Consider a set of single-input, single-output nonlinear systems whose
input-output maps are described only in terms of convergent Chen-Fliess series
without any assumption that finite dimensional state space models are
available. It is shown that any additive or multiplicative interconnection of
such systems always has a Chen-Fliess series representation that can be
computed explicitly in terms of iterated formal Lie derivatives.
Fast DTW and Fuzzy Clustering for Scenario Generation in Power System Planning Problems
Authors: Malhar Padhee, Anamitra Pal
Date: 2020-07-01 00:00:00
Tasks: Clustering, Dynamic Time Warping

Power system planning problems become computationally intractable if one accounts for all uncertain operating scenarios. Consequently, one selects a subset of scenarios that are representative of likely/extreme operating conditions, e.g. heavy summer, heavy winter, light summer, and so on. However, such an approach may not be able to accurately capture the dependencies that exist between renewable generation (RG) and system load in RG-rich power systems. This paper proposes the use of fast dynamic time warping (FDTW) and fuzzy c-means++ (FCM++) clustering to account for key statistical properties of load and RG for scenario generation for power system planning problems. Case studies using a U.S. power network, and comparison with existing scenario generation techniques demonstrate the benefits of the proposed approach.
Efficient Mining Cluster Selection for Blockchain-based Cellular V2X Communications
Authors: Furqan Jameel, Muhammad Awais Javed, Sherali Zeadally, Riku Jantti
Date: 2020-06-29 00:00:00
Tasks: Autonomous Vehicles, Fairness

Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.
Joint Passive Beamforming and User Association Optimization for IRS-assisted mmWave Systems
Authors: Dan Zhao, Hancheng Lu, Yazheng Wang, Huan Sun
Date: 2020-07-02 00:00:00

In this paper, we investigate an intelligent reflect surface (IRS) assisted multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to alleviate the blockage effect and enhance the performance of the mmWave system. Considering the impact of IRS on user association, we formulate a sum rate maximization problem by jointly optimizing the passive beamforming at IRS and user association, which is an intractable non-convex problem. Then an alternating optimization algorithm is proposed to solve the problem efficiently. In the proposed algorithm, passive beamforming at IRS is optimized by utilizing the fractional programming method and user association is solved through the network optimization based auction algorithm. We provide numerical comparisons between the proposed algorithm and different reference algorithms. Simulation results demonstrate that the proposed algorithm can achieve significant gains in the sum rate of all users.
Acoustic Source Localization with the Angular Spectrum Approach in Continuously Stratified Media
Authors: Scott Schoen Jr, Costas D. Arvanitis
Date: 2020-06-30 00:00:00
Tasks: Computational Efficiency

The angular spectrum approach (ASA)---a fast, frequency domain method for calculation of the acoustic field---enables passive source localization and modeling forward propagation in homogeneous media with high computational efficiency. Here we show that, if the medium is continuously stratified, a first-order analytical solution may be obtained for the field at arbitrary depth. Our simulations show that the stratified ASA solution enables accurate source localization as compared to the uncorrected ASA (error from 1.2$\pm$0.3 to 0.49$\pm$0.3 wavelengths) at scalings relevant to biomedical ($kL \sim$ 500, where $L$ is the length of the measurement aperture), underwater ($kL \sim$ 800), and atmospheric ($kL \sim$ 10) acoustic applications. Overall the total computation was on the order milliseconds on standard hardware (225$\pm$84 ms, compared with $78\pm63$ ms for the homogeneous ASA formulation over all cases). Collectively, the results suggest the proposed ASA phase correction enables efficient and accurate method for source localization in continuously stratified environments.
Risk Management and Return Prediction
Authors: Qingyin Ge, Yunuo Ma, Yuezhi Liao, Rongyu Li, Tianle Zhu
Date: 2020-06-28 00:00:00
Tasks: Management, Prediction, Time Series Analysis

With the good development in the financial industry, the market starts to catch people's eyes, not only by the diversified investing choices ranging from bonds and stocks to futures and options but also by the general "high-risk, high-reward" mindset prompting people to put money in the financial market. People are interested in reducing risk at a given level of return since there is no way of having both high returns and low risk. Many researchers have been studying this issue, and the most pioneering one is Harry Markowitz's Modern Portfolio Theory developed in 1952, which is the cornerstone of investment portfolio management and aims at "maximum the return at the given risk". In contrast to that, fifty years later, E. Robert Fernholz's Stochastic Portfolio Theory, as opposed to the normative assumption served as the basis of earlier modern portfolio theory, is consistent with the observable characteristics of actual portfolios and markets. In this paper, after introducing some basic theories of Markowitz's MPT and Fernholz's SPT, then we step across to the application side, trying to figure out under four basic models based on Markowitz Efficient Frontier, including Markowitz Model, Constant Correlation Model, Single Index Model, and Multi-Factor Model, which portfolios will be selected and how do these portfolios perform in the real world. Here we also involve universal Portfolio Algorithmby Thomas M. Cover to select portfolios as a comparison. Besides, each portfolio value at Risk, Expected Shortfall, and corresponding bootstrap confidence interval for risk management will be evaluated. Finally, by utilizing factor analysis and time series models, we could predict the future performance of our four models.
Improved RIP-Based Bounds for Guaranteed Performance of two Compressed Sensing Algorithms
Authors: Yun-Bin Zhao, Zhi-Quan Luo
Date: 2020-07-03 00:00:00
Tasks: 4k, compressed sensing

Iterative hard thresholding (IHT) and compressive sampling matching pursuit (CoSaMP) are two types of mainstream compressed sensing algorithms using hard thresholding operators for signal recovery and approximation. The guaranteed performance for signal recovery via these algorithms has mainly been analyzed under the condition that the restricted isometry constant of a sensing matrix, denoted by $ \delta_K$ (where $K$ is an integer number), is smaller than a certain threshold value in the interval $(0,1).$ The condition $ \delta_{K}< \delta^*$ for some constant $ \delta^* \leq 1 $ ensuring the success of signal recovery with a specific algorithm is called the restricted-isometry-property-based (RIP-based) bound for guaranteed performance of the algorithm. At the moment, the best known RIP-based bound for the guaranteed recovery of $k$-sparse signals via IHT is $\delta_{3k}< 1/\sqrt{3}\approx 0.5774,$ and the bound for guaranteed recovery via CoSaMP is $\delta_{4k} < 0.4782. $ A fundamental question in this area is whether such theoretical results can be further improved. The purpose of this paper is to affirmatively answer this question and rigorously show that the RIP-based bounds for guaranteed performance of IHT can be significantly improved to $ \delta_{3k} < (\sqrt{5}-1)/2 \approx 0.618, $ and the bound for CoSaMP can be improved and pushed to $ \delta_{4k}< 0.5102. $ These improvements are achieved through a deep property of the hard thresholding operator.
Shaping dynamics with multiple populations in low-rank recurrent networks
Date: 2020-11-17 00:00:00

An emerging paradigm proposes that neural computations can be understood at
the level of dynamical systems that govern low-dimensional trajectories of
collective neural activity. How the connectivity structure of a network
determines the emergent dynamical system however remains to be clarified. Here
we consider a novel class of models, Gaussian-mixture low-rank recurrent
networks, in which the rank of the connectivity matrix and the number of
statistically-defined populations are independent hyper-parameters. We show
that the resulting collective dynamics form a dynamical system, where the rank
sets the dimensionality and the population structure shapes the dynamics. In
particular, the collective dynamics can be described in terms of a simplified
effective circuit of interacting latent variables. While having a single,
global population strongly restricts the possible dynamics, we demonstrate that
if the number of populations is large enough, a rank-R network can approximate
any R-dimensional dynamical system.
Note on simulation pricing of $π$-options
Authors: Zbigniew Palmowski, Tomasz Serafin
Date: 2020-07-04 00:00:00

In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $\pi$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces the lower and the upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $\pi$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.
On robust fundamental theorems of asset pricing in discrete time
Authors: Huy N. Chau
Date: 2020-07-06 00:00:00

This paper is devoted to a study of robust fundamental theorems of asset pricing in discrete time and finite horizon settings. Uncertainty is modelled by a (possibly uncountable) family of price processes on the same probability space. Our technical assumption is the continuity of the price processes with respect to uncertain parameters. In this setting, we introduce a new topological framework which allows us to use the classical arguments in arbitrage pricing theory involving $L^p$ spaces, the Hahn-Banach separation theorem and other tools from functional analysis. The first result is the equivalence of a ``no robust arbitrage" condition and the existence of a new ``robust pricing system". The second result shows superhedging dualities and the existence of superhedging strategies without restrictive conditions on payoff functions, unlike other related studies. The third result discusses completeness in the present robust setting. When other options are available for static trading, we could reduce the set of robust pricing systems and hence the superhedging prices.
Forcing Seasonality of influenza-like epidemics with daily Solar resonance
Authors: F. Nicastro, G. Sironi, E. Antonello, A. Bianco, M. Biasin, J. R. Brucato, I. Ermolli, G. Pareschi, M. Salvati, P. Tozzi, D. Trabattoni, M. Clerici
Date: 2020-07-06 00:00:00

Seasonality of acute viral respiratory diseases is a well-known and yet not fully understood phenomenon. Several models have been proposed to explain the regularity of yearly recurring outbreaks and the phase-differences observed at different latitudes on Earth. Such models take into account known internal causes, primarily the periodic emergence of new virus variants that evade the host immune response. Yet, this alone, is generally unable to explain the regularity of recurrences and the observed phase-differences. Here we show that seasonality of viral respiratory diseases, as well as its distribution with latitude on Earth, can be fully explained by the virucidal properties of UV-B and A Solar photons through a daily, minute-scale, resonant forcing mechanism. Such an induced periodicity can last, virtually unperturbed, from tens to hundreds of cycles, and even in presence of internal dynamics (host's loss of immunity) much slower than seasonal will, on a long period, generate seasonal oscillations.
Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power Systems
Date: 2020-11-15 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

The integration of renewable energy sources (RES) is rapidly increasing in
electric power systems (EPS). While the inclusion of intermittent RES coupled
with the wide-scale deployment of communication and sensing devices is
important towards a fully smart grid, it has also expanded the cyber-threat
landscape, effectively making power systems vulnerable to cyberattacks. This
paper proposes a cybersecurity assessment approach designed to assess the
cyberphysical security of EPS. The work takes into consideration the
intermittent generation of RES, vulnerabilities introduced by
microprocessor-based electronic information and operational technology (IT/OT)
devices, and contingency analysis results. The proposed approach utilizes deep
reinforcement learning (DRL) and an adapted Common Vulnerability Scoring System
(CVSS) score tailored to assess vulnerabilities in EPS in order to identify the
optimal attack transition policy based on N-2 contingency results, i.e., the
simultaneous failure of two system elements. The effectiveness of the work is
validated via numerical and real-time simulation experiments performed on
literature-based power grid test cases. The results demonstrate how the
proposed method based on deep Q-network (DQN) performs closely to a
graph-search approach in terms of the number of transitions needed to find the
optimal attack policy, without the need for full observation of the system. In
addition, the experiments present the method's scalability by showcasing the
number of transitions needed to find the optimal attack transition policy in a
large system such as the Polish 2383 bus test system. The results exhibit how
the proposed approach requires one order of magnitude fewer transitions when
compared to a random transition policy.
Directional Modulation-Enabled Secure Transmission with Intelligent Reflecting Surface
Authors: Liangling Lai, Jinsong Hu, Youjia Chen, Haifeng Zheng, Nan Yang
Date: 2020-07-07 00:00:00
Tasks: Position

We propose a new secure transmission scheme which uses directional modulation (DM) with artificial noise and is aided by the intelligent reflecting surface (IRS). Specifically, the direct path and IRS-enabled reflect path carry the same confidential signal and thus can be coherently added at the desired position to maximize the total received power, while the received signals at other positions are distorted. We derive a closed-form expression for the secrecy rate achieved by the proposed scheme. Using simulation results, we show that the proposed scheme can achieve two-dimensional secure transmission at a specific position. Also, its performance advantage over the conventional DM scheme becomes more pronounced as the number of reflecting elements at the IRS increases.