pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning analysis of intracranial EEG for recognizing drug effects and mechanisms of action
Authors: Konstantin Y. Kalitin, Alexey A. Nevzorov, Denis A. Babkov, Alexander A. Spasov, Olga Y. Mukha
Date: 2020-09-28 00:00:00
Tasks: Drug Discovery, EEG, Prediction

Drug-target interaction (DTI) prediction has become a foundational task in drug repositioning, polypharmacology, drug discovery, as well as drug resistance and side-effect prediction. DTI identification using machine learning is gaining popularity in these research areas. Through the years, numerous deep learning methods have been proposed for DTI prediction. Nevertheless, prediction accuracy and efficiency remain key challenges. Pharmaco-electroencephalogram (pharmaco-EEG) is considered valuable in the development of central nervous system-active drugs. Quantitative EEG analysis demonstrates high reliability in studying the effects of drugs on the brain. Earlier preclinical pharmaco-EEG studies showed that different types of drugs can be classified according to their mechanism of action on neural activity. Here, we propose a convolutional neural network for EEG-mediated DTI prediction. This new approach can explain the mechanisms underlying complicated drug actions, as it allows the identification of similarities in the mechanisms of action and effects of psychotropic drugs.
A spatial algorithm for the analysis of transportation systems using statistical model checking
Authors: Daniël Reijsbergen, Stephen Gilmore
Date: 2020-09-28 00:00:00
Tasks: Scheduling

We present an automated methodology for using Automatic Vehicle Location measurements of public transportation vehicles to construct a probabilistic model. The model not only allows for accurate evaluation of service performance, but also makes it possible to study the effects of system modifications a priori. The methodology is almost entirely agnostic to otherwise important details of the service -- in particular its route and the location of stops. Instead, it infers this from the data using automated map generation techniques. The behaviour of vehicles in the model is analysed using computer simulation combined with statistical model checking. We present two case studies involving the Airlink service in Edinburgh and the Bellevue Express in Seattle. To demonstrate the usefulness of the approach, we analyse the impact of the scheduling strategies of bus holding and speed modification on the Airlink's performance. The data and code used to create the figures are publicly available online.
Deep Reinforcement Learning for DER Cyber-Attack Mitigation
Date: 2020-09-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The increasing penetration of DER with smart-inverter functionality is set to
transform the electrical distribution network from a passive system, with fixed
injection/consumption, to an active network with hundreds of distributed
controllers dynamically modulating their operating setpoints as a function of
system conditions. This transition is being achieved through standardization of
functionality through grid codes and/or international standards. DER, however,
are unique in that they are typically neither owned nor operated by
distribution utilities and, therefore, represent a new emerging attack vector
for cyber-physical attacks. Within this work we consider deep reinforcement
learning as a tool to learn the optimal parameters for the control logic of a
set of uncompromised DER units to actively mitigate the effects of a
cyber-attack on a subset of network DER.
Ordinal Bayesian incentive compatibility in random assignment model
Authors: Sulagna Dasgupta, Debasis Mishra
Date: 2020-09-28 00:00:00

We explore the consequences of weakening the notion of incentive compatibility from strategy-proofness to ordinal Bayesian incentive compatibility (OBIC) in the random assignment model. If the common prior of the agents is a uniform prior, then a large class of random mechanisms are OBIC with respect to this prior -- this includes the probabilistic serial mechanism. We then introduce a robust version of OBIC: a mechanism is locally robust OBIC if it is OBIC with respect all independent priors in some neighborhood of a given independent prior. We show that every locally robust OBIC mechanism satisfying a mild property called elementary monotonicity is strategy-proof. This leads to a strengthening of the impossibility result in Bogomolnaia and Moulin (2001): if there are at least four agents, there is no locally robust OBIC and ordinally efficient mechanism satisfying equal treatment of equals.
A Novel Ridge Detector for Nonstationary Multicomponent Signals: Development and Application to Robust Mode Retrieval
Authors: Nils Laurent, Sylvain Meignen
Date: 2020-09-28 00:00:00
Tasks: Retrieval

Time-frequency analysis is often used to study non stationary multicomponent signals, which can be viewed as the surperimposition of modes, associated with ridges in the TF plane. To understand such signals, it is essential to identify their constituent modes. This is often done by performing ridge detection in the time-frequency plane which is then followed by mode retrieval. Unfortunately, existing ridge detectors are often not enough robust to noise therefore hampering mode retrieval. In this paper, we therefore develop a novel approach to ridge detection and mode retrieval based on the analysis of the short-time Fourier transform of multicomponent signals in the presence of noise, which will prove to be much more robust than state-of-the-art methods based on the same time-frequency representation.
Integrated Communication and Localization in mmWave Systems
Authors: Jie Yang, Jing Xu, Xiao Li, Shi Jin, Bo Gao
Date: 2020-09-28 00:00:00

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted. Future mobile communication systems are evidently evolving towards a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication and also sense, control, and optimize wireless environments. Thus, integrating communication and localization by utilizing the highly directional transmission characteristics of millimeter-wave (mmWave) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithm. We also perform an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with extremely large antenna array and model-based neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, this paper outlines the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.
The role of behavioural plasticity in finite vs infinite populations
Authors: M. Kleshnina, K. Kaveh, K. Chatterjee
Date: 2020-09-28 00:00:00

Evolutionary game theory has proven to be an elegant framework providing many fruitful insights in population dynamics and human behaviour. Here, we focus on the aspect of behavioural plasticity and its effect on the evolution of populations. We consider games with only two strategies in both well-mixed infinite and finite populations settings. We assume that individuals might exhibit behavioural plasticity referred to as incompetence of players. We study the effect of such heterogeneity on the outcome of local interactions and, ultimately, on global competition. For instance, a strategy that was dominated before can become desirable from the selection perspective when behavioural plasticity is taken into account. Furthermore, it can ease conditions for a successful fixation in infinite populations' invasions. We demonstrate our findings on the examples of Prisoners' Dilemma and Snowdrift game, where we define conditions under which cooperation can be promoted.
Large-Scale Parameters of Spatio-Temporal Short-Range Indoor Backhaul Channels at 140 GHz
Authors: Sinh L. H. Nguyen, Katsuyuki Haneda, Jan Järveläineny, Aki Karttuneny, Jyri Putkonen
Date: 2020-09-28 00:00:00
Tasks: valid

The use of above-100 GHz radio frequencies would be one of promising approaches to enhance the fifth-generation cellular further. Any air interface and cellular network designs require channel models, for which measured evidence of largescale parameters such as pathloss, delay and angular spreads, is crucial. This paper provides the evidence from quasi-static spatiotemporal channel sounding campaigns at two indoor hotspot (InH) scenarios at 140 GHz band, assuming short-range backhaul connectivity. The measured two InH sites are shopping mall and airport check-in hall. Our estimated omni-directional large-scale parameters from the measurements are found in good match with those of the Third Generation Partnership Project (3GPP) for new radios (NR) channel model in InH scenario, despite the difference of assumed link types and radio frequency range. The 3GPP NR channel model is meant for access links and said to be valid up to 100 GHz, while our measurements cover shortrange backhaul scenarios at 140 GHz. We found more deviation between our estimated large-scale parameters and those of the 3GPP NR channel model in the airport than in the shopping mall.
Index and Composition Modulation
Authors: Ferhat Yarkin, Justin P. Coon
Date: 2020-09-28 00:00:00

In this paper, we propose a novel modulation concept which we call \emph{index and composition modulation (ICM)}. In the proposed concept, we use indices of active/deactive codeword elements and compositions of an integer to encode information. In this regard, we first determine the activated codeword elements, then we exploit energy levels of these elements to identify the compositions. We depict a practical scheme for using ICM with orthogonal frequency division multiplexing (OFDM) and show that OFDM with ICM (OFDM-ICM) can enhance the spectral efficiency (SE) and error performance of OFDM-IM. We design an efficient low-complexity detector for the proposed technique. Moreover, we analyze the error and SE performance of the OFDM-ICM technique and show that it is capable of outperforming existing OFDM benchmarks in terms of error and SE performance.
lCARE -- localizing Conditional AutoRegressive Expectiles
Authors: Xiu Xu, Andrija Mihoci, Wolfgang Karl Härdle
Date: 2020-09-28 00:00:00

We account for time-varying parameters in the conditional expectile-based value at risk (EVaR) model. The EVaR downside risk is more sensitive to the magnitude of portfolio losses compared to the quantile-based value at risk (QVaR). Rather than fitting the expectile models over ad-hoc fixed data windows, this study focuses on parameter instability of tail risk dynamics by utilising a local parametric approach. Our framework yields a data-driven optimal interval length at each time point by a sequential test. Empirical evidence at three stock markets from 2005-2016 shows that the selected lengths account for approximately 3-6 months of daily observations. This method performs favorable compared to the models with one-year fixed intervals, as well as quantile based candidates while employing a time invariant portfolio protection (TIPP) strategy for the DAX, FTSE 100 and S&P 500 portfolios. The tail risk measure implied by our model finally provides valuable insights for asset allocation and portfolio insurance.
An AI approach to measuring financial risk
Authors: Lining Yu, Wolfgang Karl Härdle, Lukas Borke, Thijs Benschop
Date: 2020-09-28 00:00:00

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (lambda) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly traded financial institutions. We demonstrate the suitability of this AI based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm.
Reference Signal-Aided Channel Estimation in Spatial Media-Based Modulation Systems
Authors: Akif Kabacı, Mehmet Başaran, Hakan Ali Çırpan
Date: 2020-09-28 00:00:00

In this letter, we study the reference signal-aided channel estimation concept which is a crucial requirement to address the realistic performance of spatial media-based modulation (SMBM) systems where the radio frequency mirrors are deployed along with the multiple transmit antennas. Accordingly, least squares and linear minimum mean square error-based channel estimation schemes are proposed for MBM-based systems for the first time in the literature where former studies mainly assume either perfect channel state information or an error model on channel coefficients. In addition, corresponding symbol detection performance is studied. To measure the efficiency of the proposed channel estimation approaches, the theoretical upper bounds on average bit error rate are derived and shown to be well overlapped with the computer simulations for the medium and high signal-to-noise ratio regions. This study is important due to the implementation of channel estimation as well as the theoretical derivation of detection bounds for MBM-based communication systems.
Liquidations: DeFi on a Knife-edge
Authors: Daniel Perez, Sam M. Werner, Jiahua Xu, Benjamin Livshits
Date: 2020-09-28 00:00:00

The trustless nature of permissionless blockchains renders overcollateralization a key safety component relied upon by decentralized finance (DeFi) protocols. Nonetheless, factors such as price volatility may undermine this mechanism. In order to protect protocols from suffering losses, undercollateralized positions can be liquidated. In this paper, we present the first in-depth empirical analysis of liquidations on protocols for loanable funds (PLFs). We examine Compound, one of the most widely used PLFs, for a period starting from its conception to September 2020. We analyze participants' behavior and risk-appetite in particular, to elucidate recent developments in the dynamics of the protocol. Furthermore, we assess how this has changed with a modification in Compound's incentive structure and show that variations of only 3% in an asset's dollar price can result in over 10m USD becoming liquidable. To further understand the implications of this, we investigate the efficiency of liquidators. We find that liquidators' efficiency has improved significantly over time, with currently over 70% of liquidable positions being immediately liquidated. Lastly, we provide a discussion on how a false sense of security fostered by a misconception of the stability of non-custodial stablecoins, increases the overall liquidation risk faced by Compound participants.
Robust MIMO Radar Waveform-Filter Design for Extended Target Detection in the Presence of Multipath
Authors: Zhou Xu, Chongyi Fan, Jian Wang, Xiaotao Huang
Date: 2020-09-28 00:00:00

The existence of multipath brings extra "looks" of targets. This paper considers the extended target detection problem with a narrow band Multiple-Input Multiple-Output(MIMO) radar in the presence of multipath from the view of waveform-filter design. The goal is to maximize the worst-case Signal-to-Interference-pulse-Noise Ratio(SINR) at the receiver against the uncertainties of the target and multipath reflection coefficients. Moreover, a Constant Modulus Constraint(CMC) is imposed on the transmit waveform to meet the actual demands of radar. Two types of uncertainty sets are taken into consideration. One is the spherical uncertainty set. In this case, the max-min waveform-filter design problem belongs to the non-convex concave minimax problems, and the inner minimization problem is converted to a maximization problem based on Lagrange duality with the strong duality property. Then the optimal waveform is optimized with Semi-Definite Relaxation(SDR) and randomization schemes. Therefore, we call the optimization algorithm Duality Maximization Semi-Definite Relaxation(DMSDR). Additionally, we further study the case of annular uncertainty set which belongs to non-convex non-concave minimax problems. In order to address it, the SDR is utilized to approximate the inner minimization problem with a convex problem, then the inner minimization problem is reformulated as a maximization problem based on Lagrange duality. We resort to a sequential optimization procedure alternating between two SDR problems to optimize the covariance matrix of transmit waveform and receive filter, so we call the algorithm Duality Maximization Double Semi-Definite Relaxation(DMDSDR). The convergences of DMDSDR are proved theoretically. Finally, numerical results highlight the effectiveness and competitiveness of the proposed algorithms as well as the optimized waveform-filter pair.
Database Assisted Nonlinear Least Squares Algorithm for Visible Light Positioning in NLOS Environments
Authors: Ahmet Faruk Saz, Sinan Gezici
Date: 2020-09-28 00:00:00
Tasks: Indoor Localization, Position

We propose an indoor localization algorithm for visible light systems by considering effects of non-line-of-sight (NLOS) propagation. The proposed algorithm, named database assisted nonlinear least squares (DA-NLS), utilizes ideas from both the classical NLS algorithm and the fingerprinting algorithm to achieve accurate and robust localization performance in NLOS environments. In particular, a database is used to learn NLOS effects, and then an NLS algorithm is employed to estimate the position. The performance of the proposed algorithm is compared against that of the fingerprinting and NLS algorithms.
Improving Radio Systems Efficiency via Employing SCRO-SOA (Squared Cosine Roll off Filter -- Semiconductor Optical Amplifier Technology) in DWDM-RoF System
Authors: Bashar J. Hamza, Wasan Kadhim Saad, Mohamed Ahmed AbdulNabi, Waheb A. Jabbar, Ibraheem Shayea
Date: 2020-09-28 00:00:00

The increasing demand on the internet trafficking to meet the demands of video streaming and mobile communication has exerted too much pressure. Accordingly, this demand will result in providing high bandwidth which in turn increase the use of cells on the network. However, the current networks do not meet the requirements of the necessary data rate. Therefore, the Dense Wavelength Division Multiplexing (DWDM) network and the Radio over Fiber (RoF) technology are the ideal solution for providing the necessary data rate needed in the current networks. The DWDM, will increase the transmission distance and will increase the data rate; nonetheless, the DWDM network will be compromised especially by the Non-linear effects. This study in intended to propose a system to find solutions for the issues of increasing the data rate and for reducing the nonlinear effects. There are number of technologies that could be adopted to fix such issues, which includes; Optical Phase Conjugation (OPC), Semiconductor Optical Amplifier (SOA),
Frequency-Domain Modelling of Reset Control Systems using an Impulsive Description
Date: 2020-09-30 00:00:00

The ever-increasing industry desire for improved performance makes linear
controller design run into fundamental limitations. Nonlinear control methods
such as Reset Control (RC) are needed to overcome these. RC is a promising
candidate since, unlike other nonlinear methods, it easily integrates into the
industry-preferred PID design framework. Thus far, RC has been analysed in the
frequency domain either through describing function analysis or by direct
closed-loop numerical computation. The former computes a simplified closed-loop
RC response by assuming a sufficient low-pass behaviour. In doing so it ignores
all harmonics, which literature has found to cause significant modelling
prediction errors. The latter gives a precise solution, but by its direct
closed-loop computation does not clearly show how open-loop RC design
translates to closed-loop performance. The main contribution of this work is
aimed at overcoming these limitations by considering an alternative approach
for modelling RC using state-dependent impulse inputs. This permits accurately
computing closed-loop RC behaviour starting from the underlying linear system,
improving system understanding. A frequency-domain description for closed-loop
RC is obtained, which is solved analytically by using several well-defined
assumptions. This analytical solution is verified using a simulated
high-precision stage, critically examining sources of modelling errors. The
accuracy of the proposed method is further substantiated using controllers
designed for various specifications.
Characterizing Inter-Numerology Interference in Mixed-Numerology OFDM Systems
Authors: Juquan Mao, Lei Zhang, Stephen McWade, Hongzhi Chen, Pei Xiao
Date: 2020-09-28 00:00:00

The advent of mixed-numerology multi-carrier (MN-MC) techniques adds flexibilities in supporting heterogeneous services in fifth generation (5G) communication systems and beyond. However, the coexistence of mixed numerologies destroys the orthogonality principle defined for single-numerology orthogonal frequency division multiplexing (SN-OFDM) systems with overlapping subcarriers of uniform subcarrier spacing. Consequently, the loss of orthogonality leads to inter-numerology interference (INI), which complicates signal generation and impedes signal isolation. In this paper, the INI in MN-OFDM systems is characterized through mathematical modeling and is shown to primarily rely on system parameters with regard to the pulse shape, the relative distance between subcarriers and the numerology scaling factor. Reduced-form formulas for the INI in continuous-time and discrete-time MN systems are derived. The derived mathematical framework facilitates the study of the effect of discretization on the INI and partial orthogonality existing in subsets of the subcarriers. The reduced-form formulas can also be used in developing interference metrics and designing mitigation techniques.
An analysis of network filtering methods to sovereign bond yields during COVID-19
Authors: Raymond Ka-Kay Pang, Oscar Granados, Harsh Chhajer, Erika Fille Legara
Date: 2020-09-28 00:00:00

In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields. We consider the temporal changes from financial correlations using network filtering methods. These methods consider a subset of links within the correlation matrix, which gives rise to a network structure. We use sovereign bond yield data from 17 European countries between the 2010 and 2020 period. We find the mean correlation to decrease across all filtering methods during the COVID-19 period. We also observe a distinctive trend between filtering methods under multiple network centrality measures. We then relate the significance of economic and health variables towards filtered networks within the COVID-19 period. Under an exponential random graph model, we are able to identify key relations between economic groups across different filtering methods.