pwc-archive/papers-with-abstracts huggingface dataset:
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information
Authors: Hugh Christensen, Simon Godsill, Richard E Turner
Date: 2020-06-15 00:00:00
Tasks: Bayesian Inference, Model Selection

A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.
Joint Communications and Sensing: A Comprehensive Study
Authors: Husheng Li
Date: 2020-06-15 00:00:00

Joint communications and sensing (JCS) can improve the efficiency of power, bandwidth and hardware usage. The conflict between communications and sensing is analyzed in terms of bandwidth and power. It is found that the bandwidth is approximately partitioned between communication and sensing, thus making an almost zero-sum game, while the conflict in the power is marginal. The same conclusion holds when the receiver and target are separated. The feasible region and tradeoff boundary of JCS are obtaine.
Standardization of Propagation Models: 800 MHz to 100 GHz -- A Historical Perspective
Authors: Harsh Tataria, Katsuyuki Haneda, Andreas F. Molisch, Mansoor Shafi, Fredrik Tufvesson
Date: 2020-06-15 00:00:00

Propagation models constitute a fundamental building block of wireless communications research. Before we build and operate real systems, we must understand the science of radio propagation, and develop channel models that both reflect the important propagation processes and allow a fair comparison of different systems. In the past five decades, wireless systems have gone through five generations, from supporting voice applications to enhanced mobile broadband. To meet the ever increasing data rate demands of wireless systems, frequency bands covering a wide range from 800 MHz to 100 GHz have been allocated for use. The standardization of these systems started in the early/mid 1980's in Europe by the European Telecommunications Standards Institute with the advent of Global System for Mobile Communications. This motivated the development of the first standardized propagation model by the European Cooperation in Science and Technology (COST) 207 working group. These standardization activities were continued and expanded for the third, fourth, and fifth generations of COST, as well as by the Third Generation Partnership Project, and the International Telecommunication Union. This paper presents a historical overview of the standardized propagation models covering first to fifth-generation systems. In particular, we discuss the evolution and standardization of pathloss models, as well as large and small-scale fading parameters for single antenna and multiple antenna systems. Furthermore, we present insights into the progress of deterministic modelling across the five generations of systems, as well as discuss more advanced modelling components needed for the detailed simulations of millimeter-wave channels. A comprehensive bibliography at the end of the paper will aid the interested reader to dig deeper.
An Iterative Graph Spectral Subtraction Method for Speech Enhancement
Authors: Xue Yan, Zhen Yang, Tingting Wang, Haiyan Guo
Date: 2020-06-15 00:00:00
Tasks: Speech Enhancement

In this paper, we investigate the application of graph signal processing (GSP) theory in speech enhancement. We first propose a set of shift operators to construct graph speech signals, and then analyze their spectrum in the graph Fourier domain. By leveraging the differences between the spectrum of graph speech and graph noise signals, we further propose the graph spectral subtraction (GSS) method to suppress the noise interference in noisy speech. Moreover, based on GSS, we propose the iterative graph spectral subtraction (IGSS) method to further improve the speech enhancement performance. Our experimental results show that the proposed operators are suitable for graph speech signals, and the proposed methods outperform the traditional basic spectral subtraction (BSS) method and iterative basic spectral subtraction (IBSS) method in terms of both signal-to-noise ratios (SNR) and mean Perceptual Evaluation of Speech Quality (PESQ).
Is SARS-CoV-2 a new Frankenstein monster virus?
Authors: Mohammed A. El-Magd
Date: 2020-06-14 00:00:00

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel virus of beta coronavirus genus originated in Wuhan, China in December 2019, resulted in the pandemic spread of Corona Virus Disease 2019 (COVID-19) worldwide. This genus also contains SARS-CoV (originated in China 2002-2003) and MERS-CoV (found in Saudi Arabia 2012). The nucleotide sequences of SARS-CoV-2 is closer to SARS-CoV (with approximately 80% identity) than to MERS-CoV. Despite these similarities, SARS-CoV-2 has two main features over the other coronaviruses. The first is the high contagious rate and the second is the immune response evasion. The higher transmission ability makes this virus quickly spread worldwide with a high mortality rate and more economic losses. This review will provide an overview of the current knowledge on the role of some viral and host cell factors in higher transmission and contagious spread of SARS-CoV-2.
Universal Relation for Life-span Energy Consumption in Living Organisms: Insights for the origin of ageing
Authors: Andres Escala
Date: 2020-06-15 00:00:00
Tasks: Relation

Metabolic energy consumption has long been thought to play a major role in the aging process ({\it 1}). Across species, a gram of tissue on average expends about the same amount of energy during life-span ({\it 2}). Energy restriction has also been shown that increases maximum life-span ({\it 3}) and retards age-associated changes ({\it 4}). However, there are significant exceptions to a universal energy consumption during life-span, mainly coming from the inter-class comparison ({\it 5, 6}). Here we present a unique relation for life-span energy consumption, valid for $\sim$300 species representing all classes of living organisms, from unicellular ones to the largest mammals. The relation has an average scatter of only 0.3 dex, with 95\% ($\rm 2-\sigma$) of the organisms having departures less than a factor of $\pi$ from the relation, despite the $\sim$20 orders of magnitude difference in body mass, reducing any possible inter-class variation in the relation to only a geometrical factor. This result can be interpreted as supporting evidence for the existence of an approximately constant total number $\rm N_r \sim 10^8$ of respiration cycles per lifetime for all organisms, effectively predetermining the extension of life by the basic energetics of respiration, being an incentive for future studies that investigate the relation of such constant $\rm N_r$ cycles per lifetime with the production rates of free radicals and oxidants, which may give definite constraints on the origin of ageing.
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning
Date: 2020-06-15 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

Markov Decision Processes (MDPs) provide important capabilities for
facilitating the dynamic adaptation and self-optimization of cyber physical
systems at runtime. In recent years, this has primarily taken the form of
Reinforcement Learning (RL) techniques that eliminate some MDP components for
the purpose of reducing computational requirements. In this work, we show that
recent advancements in Compact MDP Models (CMMs) provide sufficient cause to
question this trend when designing wireless sensor network nodes. In this work,
a novel CMM-based approach to designing self-aware wireless sensor nodes is
presented and compared to Q-Learning, a popular RL technique. We show that a
certain class of CPS nodes is not well served by RL methods, and contrast RL
versus CMM methods in this context. Through both simulation and a prototype
implementation, we demonstrate that CMM methods can provide significantly
better runtime adaptation performance relative to Q-Learning, with comparable
resource requirements.
The Importance of Low Latency to Order Book Imbalance Trading Strategies
Authors: David Byrd, Sruthi Palaparthi, Maria Hybinette, Tucker Hybinette Balch
Date: 2020-06-15 00:00:00

There is a pervasive assumption that low latency access to an exchange is a key factor in the profitability of many high-frequency trading strategies. This belief is evidenced by the "arms race" undertaken by certain financial firms to co-locate with exchange servers. To the best of our knowledge, our study is the first to validate and quantify this assumption in a continuous double auction market with a single exchange similar to the New York Stock Exchange. It is not feasible to conduct this exploration with historical data in which trader identity and location are not reported. Accordingly, we investigate the relationship between latency of access to order book information and profitability of trading strategies exploiting that information with an agent-based interactive discrete event simulation in which thousands of agents pursue archetypal trading strategies. We introduce experimental traders pursuing a low-latency order book imbalance (OBI) strategy in a controlled manner across thousands of simulated trading days, and analyze OBI trader profit while varying distance (latency) from the exchange. Our experiments support that latency is inversely related to profit for the OBI traders, but more interestingly show that latency rank, rather than absolute magnitude, is the key factor in allocating returns among agents pursuing a similar strategy.
Neural mass modeling of slow-fast dynamics of seizure initiation and abortion
Date: 2020-06-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Epilepsy is a dynamic and complex neurological disease affecting about 1% of
the worldwide population, among which 30% of the patients are drug-resistant.
Epilepsy is characterized by recurrent episodes of paroxysmal neural discharges
(the so-called seizures), which manifest themselves through a large-amplitude
rhythmic activity observed in depth-EEG recordings, in particular in local
field potentials (LFPs). The signature characterizing the transition to
seizures involves complex oscillatory patterns, which could serve as a marker
to prevent seizure initiation by triggering appropriate therapeutic
neurostimulation methods. To investigate such protocols, neurophysiological
lumped-parameter models at the mesoscopic scale, namely neural mass models, are
powerful tools that not only mimic the LFP signals but also give insights on
the neural mechanisms related to different stages of seizures. Here, we analyze
the multiple time-scale dynamics of a neural mass model and explain the
underlying structure of the complex oscillations observed before seizure
initiation. We investigate population-specific effects of the stimulation and
the dependence of stimulation parameters on synaptic timescales. In particular,
we show that intermediate stimulation frequencies (>20 Hz) can abort seizures
if the timescale difference is pronounced. Those results have the potential in
the design of therapeutic brain stimulation protocols based on the
neurophysiological properties of tissue.
Generalized Outer Bounds on the Finite Geometric Sum of Ellipsoids
Authors: Navid Hashemi, Justin Ruths
Date: 2020-06-15 00:00:00

General results on convex bodies are reviewed and used to derive an exact closed-form parametric formula for the boundary of the geometric (Minkowski) sum of $k$ ellipsoids in $n$-dimensional Euclidean space. Previously this was done through iterative algorithms in which each new ellipsoid was added to an ellipsoid approximation of the sum of the previous ellipsoids. Here we provide one shot formulas to add $k$ ellipsoids directly with no intermediate approximations required. This allows us to observe a new degree of freedom in the family of ellipsoidal bounds on the geometric sum. We demonstrate an application of these tools to compute the reachable set of a discrete-time dynamical system.
Semi-Streaming Architecture: A New Design Paradigm for CNN Implementation on FPGAs
Date: 2020-06-15 00:00:00

The recent research advances in deep learning have led to the development of
small and powerful Convolutional Neural Network (CNN) architectures. Meanwhile
Field Programmable Gate Arrays (FPGAs) has become a popular hardware target
choice for their deployment, splitting into two main implementation categories:
streaming hardware architectures and single computation engine design
approaches. The streaming hardware architectures generally require implementing
every layer as a discrete processing unit, and are suitable for smaller
software models that could fit in their unfolded versions into
resource-constrained targets. On the other hand, single computation engines can
be scaled to fit into a device to execute CNN models of different sizes and
complexities, however, the achievable performance of one-size-fits-all
implementations may vary across CNNs with different workload attributes leading
to inefficient utilization of hardware resources. By combing the advantages of
both of the above methods, this work proposes a new design paradigm called
semi-streaming architecture, where layerspecialized configurable engines are
used for network realization. As a proof of concept this paper presents a set
of five layerspecialized configurable processing engines for implementing 8-bit
quantized MobilenevV2 CNN model. The engines are chained to partially preserve
data streaming and tuned individually to efficiently process specific types of
layers: normalized addition of residuals, depthwise, pointwise (expansion and
projection), and standard 2D convolution layers capable of delivering 5.4GOp/s,
16GOp/s, 27.2GOp/s, 27.2GOp/s and 89.6GOp/s, respectively, with the overall
energy efficiency of 5.32GOp/s/W at a 100MHz system clock, requiring total
power of 6.2W on a XCZU7EV SoC FPGA.
A User-Based Charge and Subsidy Scheme for Single O-D Network Mobility Management
Date: 2020-06-15 00:00:00
Tasks: Management

We propose a path guidance system with a user-based charge and subsidy (UBCS)
scheme for single O-D network mobility management. Users who are willing to
join the scheme (subscribers) can submit travel requests along with their VOTs
to the system before traveling. Those who are not willing to join (outsiders)
only need to submit travel requests to the system. Our system will give all
users path guidance from their origins to their destinations, and collect a
\emph{path payment} from the UBCS subscribers. Subscribers will be charged or
subsided in a way that renders the UBCS strategy-proof, revenue-neutral, and
Pareto-improving. A numerical example shows that the UBCS scheme is equitable
and progressive.
Liquidity Provider Returns in Geometric Mean Markets
Authors: Alex Evans
Date: 2020-06-15 00:00:00

Geometric mean market makers (G3Ms), such as Uniswap and Balancer, comprise a popular class of automated market makers (AMMs) defined by the following rule: the reserves of the AMM before and after each trade must have the same (weighted) geometric mean. This paper extends several results known for constant-weight G3Ms to the general case of G3Ms with time-varying and potentially stochastic weights. These results include the returns and no-arbitrage prices of liquidity pool (LP) shares that investors receive for supplying liquidity to G3Ms. Using these expressions, we show how to create G3Ms whose LP shares replicate the payoffs of financial derivatives. The resulting hedges are model-independent and exact for derivative contracts whose payoff functions satisfy an elasticity constraint. These strategies allow LP shares to replicate various trading strategies and financial contracts, including standard options. G3Ms are thus shown to be capable of recreating a variety of active trading strategies through passive positions in LP shares.
Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic
Authors: Yize Chen, Weiwei Yang, Baosen Zhang
Date: 2020-06-15 00:00:00
Tasks: Load Forecasting, Transfer Learning

The novel coronavirus (COVID-19) pandemic has posed unprecedented challenges for the utilities and grid operators around the world. In this work, we focus on the problem of load forecasting. With strict social distancing restrictions, power consumption profiles around the world have shifted both in magnitude and daily patterns. These changes have caused significant difficulties in short-term load forecasting. Typically algorithms use weather, timing information and previous consumption levels as input variables, yet they cannot capture large and sudden changes in socioeconomic behavior during the pandemic. In this paper, we introduce mobility as a measure of economic activities to complement existing building blocks of forecasting algorithms. Mobility data acts as good proxies for the population-level behaviors during the implementation and subsequent easing of social distancing measures. The major challenge with such dataset is that only limited mobility records are associated with the recent pandemic. To overcome this small data problem, we design a transfer learning scheme that enables knowledge transfer between several different geographical regions. This architecture leverages the diversity across these regions and the resulting aggregated model can boost the algorithm performance in each region's day-ahead forecast. Through simulations for regions in the US and Europe, we show our proposed algorithm can outperform conventional forecasting methods by more than three-folds. In addition, we demonstrate how the proposed model can be used to project how electricity consumption would recover based on different mobility scenarios.
Multiagent Reinforcement Learning based Energy Beamforming Control
Authors: Liping Bai, Zhongqiang Pang
Date: 2020-06-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Ultra low power devices make far-field wireless power transfer a viable option for energy delivery despite the exponential attenuation. Electromagnetic beams are constructed from the stations such that wireless energy is directionally concentrated around the ultra low power devices. Energy beamforming faces different challenges compare to information beamforming due to the lack of feedback on channel state. Various methods have been proposed such as one-bit channel feedback to enhance energy beamforming capacity, yet it still has considerable computation overhead and need to be computed centrally. Valuable resources and time is wasted on transfering control information back and forth. In this paper, we propose a novel multiagent reinforcement learning(MARL) formulation for codebook based beamforming control. It takes advantage of the inherienntly distributed structure in a wirelessly powered network and lay the ground work for fully locally computed beam control algorithms. Source code can be found at https://github.com/BaiLiping/WirelessPowerTransfer.
ECG Language Processing (ELP): a New Technique to Analyze ECG Signals
Authors: Sajad Mousavi, Fatemeh Afghah, Fatemeh Khadem, U. Rajendra Acharya
Date: 2020-06-13 00:00:00
Tasks: Sentence

A language is constructed of a finite/infinite set of sentences composing of words. Similar to natural languages, Electrocardiogram (ECG) signal, the most common noninvasive tool to study the functionality of the heart and diagnose several abnormal arrhythmias, is made up of sequences of three or four distinct waves including the P-wave, QRS complex, T-wave and U-wave. An ECG signal may contain several different varieties of each wave (e.g., the QRS complex can have various appearances). For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies. Analogous to natural language processing (NLP) which is used to help computers understand and interpret the human's natural language, it is possible to develop methods inspired by NLP to aid computers to gain a deeper understanding of Electrocardiogram signals. In this work, our goal is to propose a novel ECG analysis technique, \textit{ECG language processing (ELP)}, focusing on empowering computers to understand ECG signals in a way physicians do. We evaluated the proposed method on two tasks including the classification of heartbeats and the detection of atrial fibrillation in the ECG signals. Experimental results on three databases (i.e., PhysionNet's MIT-BIH, MIT-BIH AFIB and PhysioNet Challenge 2017 AFIB Dataset databases) reveal that the proposed method is a general idea that can be applied to a variety of biomedical applications and is able to achieve remarkable performance.
Optimisation of non-pharmaceutical measures in COVID-19 growth via neural networks
Authors: Annalisa Riccardi, Jessica Gemignani, Francisco Fernández-Navarro, Anna Heffernan
Date: 2020-06-16 00:00:00
Tasks: Time Series Analysis

On 19th March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected, and an optimiser, with a parametrisation of the government restriction time series, to understanding the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in traveling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions. It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both regions, resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.