pwc-archive/papers-with-abstracts huggingface dataset:
The approximately universal shapes of epidemic curves in the Susceptible-Exposed-Infectious-Recovered (SEIR) model
Date: 2020-10-31 00:00:00

Compartmental transmission models have become an invaluable tool to study the
dynamics of infectious diseases. The Susceptible-Infectious-Recovered (SIR)
model is known to have an exact semi-analytical solution. In the current study,
the approach of Harko et al. (2014) is generalised to obtain an approximate
semi-analytical solution of the Susceptible-Exposed-Infectious-Recovered (SEIR)
model. The SEIR model curves have nearly the same shapes as the SIR ones, but
with a stretch factor applied to them across time that is related to the ratio
of the incubation to infectious periods. This finding implies an approximate
characteristic timescale, scaled by this stretch factor, that is universal to
all SEIR models, which only depends on the basic reproduction number and
initial fraction of the population that is infectious.
Asset Allocation via Machine Learning and Applications to Equity Portfolio Management
Authors: Qing Yang, Zhenning Hong, Ruyan Tian, Tingting Ye, Liangliang Zhang
Date: 2020-11-01 00:00:00
Tasks: BIG-bench Machine Learning, Management, Portfolio Optimization

In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets. The methodology applies to general constrained optimization problems and overcomes many major difficulties arising in current optimization schemes. Taking mean-variance optimization as an example, we no longer need to compute the covariance matrix and its inverse, therefore the method is immune from the estimation error on this quantity. Moreover, no explicit calls of optimization routines are needed. Applications to equity portfolio management in U.S. and China equity markets are studied and we document significant excess returns to the selected benchmarks.
Distributed Precoding Using Local CSIT for MU-MIMO Heterogeneous Cellular Networks
Authors: Deokhwan Han, Namyoon Lee
Date: 2020-10-30 00:00:00

Cell densification is a key driver to increase area spectral efficiencies in multi-antenna cellular systems. As increasing the densities of base stations (BSs) and users that share the same spectrum, however, both inter-user-interference (IUI) and inter-cell interference (ICI) problems give rise to a significant loss in spectral efficiencies in such systems. To resolve this problem under the constraint of local channel state information per BS, in this paper, we present a novel noncooperative multi-user multiple-input multiple-output (MIMO) precoding technique, called signal-to-interference-pulse-leakage-pulse-noise-ratio (SILNR) maximization precoding. The key innovation of our distributed precoding method is to maximize the product of SILNRs of users per cell using local channel state information at the transmitter (CSIT). We show that our precoding technique only using local CSIT can asymptotically achieve the multi-cell cooperative bound attained by cooperative precoding using global CSIT in some cases. We also present a precoding algorithm that is robust to CSIT errors in multi-cell scenarios. By multi-cell system-level simulations, we demonstrate that our distributed precoding technique outperforms all existing noncooperative precoding methods considerably and can also achieve the multi-cell bound very tightly even with not-so-many antennas at BSs.
Duality for optimal consumption with randomly terminating income
Authors: Ashley Davey, Michael Monoyios, Harry Zheng
Date: 2020-10-30 00:00:00

We establish a rigorous duality theory, under No Unbounded Profit with Bounded Risk, for an infinite horizon problem of optimal consumption in the presence of an income stream that can terminate randomly at an exponentially distributed time, independent of the asset prices. We thus close a duality gap encountered by Vellekoop and Davis in a version of this problem in a Black-Scholes market. Many of the classical tenets of duality theory hold, with the notable exception that marginal utility at zero initial wealth is finite. We use as dual variables a class of supermartingale deflators such that deflated wealth plus cumulative deflated consumption in excess of income is a supermartingale. We show that the space of discounted local martingale deflators is dense in our dual domain, so that the dual problem can also be expressed as an infimum over the discounted local martingale deflators. We characterise the optimal wealth process, showing that optimal deflated wealth is a potential decaying to zero, while deflated wealth plus cumulative deflated consumption over income is a uniformly integrable martingale at the optimum. We apply the analysis to the Vellekoop and Davis example and give a numerical solution.
RRScell method for automated single-cell profiling of multiplexed immunofluorescence cancer tissue
Authors: Alvason Zhenhua Li, Karsten Eichholz, Anton Sholukh, Daniel Stone, Michelle A. Loprieno, Keith R. Jerome, Khamsone Phasouk, Kurt Diem, Jia Zhu, Lawrence Corey
Date: 2020-10-30 00:00:00
Tasks: Dimensionality Reduction

Multiplexed immuno-fluorescence tissue imaging, allowing simultaneous detection of molecular properties of cells, is an essential tool for characterizing the complex cellular mechanisms in translational research and clinical practice. New image analysis approaches are needed because tissue section stained with a mixture of protein, DNA and RNA biomarkers are introducing various complexities, including spurious edges due to fluorescent staining artifacts between touching or overlapping cells. We have developed the RRScell method harnessing the stochastic random-reaction-seed (RRS) algorithm and deep neural learning U-net to extract single-cell resolution profiling-map of gene expression over a million cells tissue section accurately and automatically. Furthermore, with the use of manifold learning technique UMAP for cell phenotype cluster analysis, the AI-driven RRScell has equipped with a marker-based image cytometry analysis tool (markerUMAP) in quantifying spatial distribution of cell phenotypes from tissue images with a mixture of biomarkers. The results achieved in this study suggest that RRScell provides a robust enough way for extracting cytometric single cell morphology as well as biomarker content in various tissue types, while the build-in markerUMAP tool secures the efficiency of dimension reduction, making it viable as a general tool in the spatial analysis of high dimensional tissue image.
Decentralized Algorithms for Consensus-Based Power Packet Distribution
Authors: Seongcheol Baek, Hiroyasu Ando, Takashi Hikihara
Date: 2020-10-30 00:00:00
Tasks: energy management, Management

Power packets are proposed as a transmission unit that can deliver power and information simultaneously. They are transferred using the store-and-forward method of power routers. A system that achieves power supply/demand in this manner is called a power packet network (PPN). A PPN is expected to enhance structural robustness and operational reliability in an energy storage system (ESS) with recent diverse distributed sources. However, this technology is still in its early stage, and faces numerous challenges, such as high cost of implementation and complicated energy management. In this paper, we propose a novel power control based on decentralized algorithms for a PPN. Specifically, the power supply is triggered and managed by communications between power routers. We also discuss the mechanism of the decentralized algorithm for the operation of power packets and reveal the feasibility of the given control method and application by forming biased power flows on the consensus-based distribution.
End-to-end anti-spoofing with RawNet2
Authors: Hemlata Tak, Jose Patino, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans, Anthony Larcher
Date: 2020-11-02 00:00:00
Tasks: Audio Deepfake Detection, Speaker Verification

Spoofing countermeasures aim to protect automatic speaker verification systems from attempts to manipulate their reliability with the use of spoofed speech signals. While results from the most recent ASVspoof 2019 evaluation show great potential to detect most forms of attack, some continue to evade detection. This paper reports the first application of RawNet2 to anti-spoofing. RawNet2 ingests raw audio and has potential to learn cues that are not detectable using more traditional countermeasure solutions. We describe modifications made to the original RawNet2 architecture so that it can be applied to anti-spoofing. For A17 attacks, our RawNet2 systems results are the second-best reported, while the fusion of RawNet2 and baseline countermeasures gives the second-best results reported for the full ASVspoof 2019 logical access condition. Our results are reproducible with open source software.
Time regularization as a solution to mitigate quantization induced performance degradation
Date: 2020-10-30 00:00:00
Tasks: Quantization

Reset control is known to be able to outperform PID and the like linear
controllers. However, in motion control systems, quantization can cause severe
performance degradation. This paper shows the application of time
regularization to mitigate this practical issue in reset control systems.
Numerical simulations have been conducted in order to analyze the cause of the
quantization induced performance degradation and the effectiveness of time
regularization to mitigate this degradation; with tuning guidelines for the
time regularization parameter also provided. Moreover, a robustness analysis is
performed. The solution is also tested experimentally on a high precision
motion system for validation. It is estimated by numerical simulations that
time regularization can reduce quantization induced performance degradation by
almost 10 dB. Experiments have similarly shown a reduction of several dB for
the high precision motion stage.
Higher-Order Moment-Based Anomaly Detection
Date: 2020-10-30 00:00:00
Tasks: Anomaly Detection

The identification of anomalies is a critical component of operating complex, and possibly large-scale and geo-graphically distributed cyber-physical systems. While designing anomaly detectors, it is common to assume Gaussian noise models to maintain tractability; however, this assumption can lead to the actual false alarm rate being significantly higher than expected. Here we design a distributionally robust threshold of detection using finite and fixed higher-order moments of the residual data such that it guarantees the actual false alarm rate to be upper bounded by the desired one. Further, we bound the states reachable through the action of a stealthy attack and identify the trade-off between this impact of attacks that cannot be detected and the worst-case false alarm rate. Through numerical experiments, we illustrate how knowledge of higher-order moments results in a tightened threshold, thereby restricting an attacker's potential impact.
Self-Organized Networks: Darwinian Evolution of Myosin-1
Authors: J. C. Phillips
Date: 2020-10-28 00:00:00

Cytoskeletons are self-organized networks based on polymerized proteins: actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality (self-organized criticality). The principal features of the eukaryotic evolution of the cytoskeleton motor protein myosin-1 parallel those of actin and tubulin, but also show striking differences connected to its dynamical function. Optimized (long) hydropathic waves characterize the molecular level Darwinian evolution towards optimized functionality (self-organized criticality). The N-terminal and central domains of myosin-1 have evolved in eukaryotes at different rates, with the central domain hydropathic extrema being optimally active in humans. A test shows that hydropathic scaling can yield accuracies of better than 1% near optimized functionality. Evolution towards synchronized level extrema is connected to a special function of Mys-1 in humans involving Golgi complexes.
Exploring the Synchrony Between Body Temperature and HR, RR, and Aortic Blood Pressure in Viral/Bacterial Disease Onsets with Signal Dynamics
Authors: Camille Dunning
Date: 2020-10-30 00:00:00

Signal-based early detection of illnesses has been a key topic in research and hospital settings; it reduces technological costs and paves the way for quick and effective patient-care operations. Elementary machine learning and signal processing algorithms have proven to be sufficient in classifying the onset of viral and bacterial conditions before clinical symptoms are shown. Inspired by these recent developments, this project employs signal dynamics analysis to infer changes in vital signs (temperature, respiration, and heart rate). The results demonstrate that the trends of one vital function can be predicted from that of another. In particular, it is shown that heart rate and respiration typically change shortly after body temperature, and aortic blood pressure follows. This is not an etiologically specific approach, but if advanced further, it can enable patients and wearable system users to tame these changes and prevent immediate symptoms.
Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information
Authors: Ke Ma, Dongxuan He, Hancun Sun, Zhaocheng Wang
Date: 2020-10-30 00:00:00
Tasks: Beam Prediction

Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods are hard to handle serious multipath interference and non-stationary scenarios. Inspired by the spatial similarity between low-frequency and mmWave channels in non-standalone architectures, this paper proposes to utilize prior low-frequency information to predict the optimal mmWave beam, where deep learning is adopted to enhance the prediction accuracy. Specifically, periodically estimated low-frequency channel state information (CSI) is applied to track the movement of user equipment, and timing offset indicator is proposed to indicate the instant of mmWave beam training relative to low-frequency CSI estimation. Meanwhile, long-short term memory networks based dedicated models are designed to implement the prediction. Simulation results show that our proposed scheme can achieve higher beamforming gain than the conventional methods while requiring little overhead of mmWave beam training.
Turbo-AI: Iterative Machine Learning Based Channel Estimation for 2D Massive Arrays
Authors: Yejian Chen, Jafar Mohammadi, Stefan Wesemann, Thorsten Wild
Date: 2020-10-28 00:00:00
Tasks: BIG-bench Machine Learning

Recently, Machine Learning (ML) is recognized as an effective tool for wireless communications and plays an evolutionary role to enhance Physical Layer (PHY) of 5th Generation (5G) and Beyond 5G (B5G) systems. In this paper, we focus on the ML-based channel estimation for 2- Dimensional (2D) massive antenna arrays. Due to the extremely high computational requirement for 2D arrays with Ordinary Training, we exploit 2D Kronecker covariance model to perform Subspace Training for vertical and horizontal spatial domain independently, which achieves a complexity cost saving factor $O(M^4N^4)/O(MN^4 + NM^4)$ for ML with an $M \times N$ 2D array. Furthermore, we propose an iterative training approach, referred to as Turbo-AI. Along with Subspace Training, the new approach can monotonically reduce the effective variance of additive noise of the observation, and update the Neural Network (NN) models by re-training. Furthermore, we propose a concept, named Universal Training. It allows to use one NN for a wide range of Signal-to-Noise-Ratio (SNR) operation points and spatial angles, which can greatly simplify Turbo-AI usage. Numerical results exhibit that Turbo-AI can tightly approach the genie-aided channel estimation bound, especially at low SNR.
Augmenting transferred representations for stock classification
Authors: Elizabeth Fons, Paula Dawson, Xiao-jun Zeng, John Keane, Alexandros Iosifidis
Date: 2020-10-28 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Time Series, Time Series Analysis, Transfer Learning

Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S$\&$P500 index and then transferring it to another model to directly learn a trading rule. Transferred models present more than double the risk-adjusted returns than their counterparts trained from zero. In addition, we propose the use of data augmentation on the feature space defined as the output of a pre-trained model (i.e. augmenting the aggregated time-series representation). We compare this augmentation approach with the standard one, i.e. augmenting the time-series in the input space. We show that augmentation methods on the feature space leads to $20\%$ increase in risk-adjusted return compared to a model trained with transfer learning but without augmentation.
Spectral Efficiency vs Complexity in Downlink Algorithms for Reconfigurable Intelligent Surfaces
Authors: Pooja Nuti, Brian L. Evans
Date: 2020-11-10 00:00:00

Reconfigurable Intelligent Surfaces (RIS) are an emerging technology that can be used to reconfigure the propagation environment to improve cellular communication link rates. RIS, which are thin metasurfaces composed of discrete elements, passively manipulate incident electromagnetic waves through controlled reflective phase tuning. In this paper, we investigate co-design of the multiantenna basestation beamforming vector and multielement RIS phase shifts. The downlink narrowband transmission uses sub-6 GHz frequency bands, and the user equipment has a single antenna. Subject to the non-convex constraints due to the RIS phase shifts, we maximize the spectral efficiency or equivalent channel power as a proxy. Our contributions in improving RIS-aided links include (1) design of gradient ascent codesign algorithms, and (2) comparison of seven codesign algorithms in spectral efficiency vs. computational complexity. In simulation, the best spectral efficiency vs. computational complexity tradeoffs are shown by two of our proposed gradient ascent algorithms.
A Scenario-oriented Approach for Energy-Reserve Joint Procurement and Pricing
Date: 2020-11-17 00:00:00

We propose a scenario-oriented approach for energy-reserve joint procurement
and pricing for electricity market. In this model, without the empirical
reserve requirements, reserve is procured according to all possible
contingencies and load/renewable generation fluctuations with the minimum
expected system total cost. The innovative locational marginal pricing approach
for loads, generations and reserve and the associated settlement process are
proposed. We show that payments from loads, payments to generators and
congestion rent will reach their balance in the basecase as well as in all
scenarios, so that revenue adequacy can be guaranteed for the system operator.
Pattern recognition in micro-trading behaviors before stock price jumps: A framework based on multivariate time series analysis
Authors: Ao Kong, Robert Azencott, Hongliang Zhu, Xindan Li
Date: 2020-11-10 00:00:00
Tasks: Informativeness, Time Series, Time Series Analysis

Studying the micro-trading behaviors before stock price jumps is an important problem for financial regulations and investment decisions. In this study, we provide a new framework to study pre-jump trading behaviors based on multivariate time series analysis. Different from the existing literature, our methodology takes into account the temporal information embedded in the trading-related attributes and can better evaluate and compare the abnormality levels of different attributes. Moreover, it can explore the joint informativeness of the attributes as well as select a subset of highly informative but minimally redundant attributes to analyze the homogeneous and idiosyncratic patterns in the pre-jump trades of individual stocks. In addition, our analysis involves a set of technical indicators to describe micro-trading behaviors. To illustrate the viability of the proposed methodology, an application case is conducted based on the level-2 data of 189 constituent stocks of the China Security Index 300. The individual and joint informativeness levels of the attributes in predicting price jumps are evaluated and compared. To this end, our experiment provides a set of jump indicators that can represent the pre-jump trading behaviors in the Chinese stock market and have detected some stocks with extremely abnormal pre-jump trades.
Optimal Policy Learning: From Theory to Practice
Date: 2020-11-10 00:00:00

Following in the footsteps of the literature on empirical welfare
maximization, this paper wants to contribute by stressing the policymaker
perspective via a practical illustration of an optimal policy assignment
problem. More specifically, by focusing on the class of threshold-based
policies, we first set up the theoretical underpinnings of the policymaker
selection problem, to then offer a practical solution to this problem via an
empirical illustration using the popular LaLonde (1986) training program
dataset. The paper proposes an implementation protocol for the optimal solution
that is straightforward to apply and easy to program with standard statistical
software.
Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements
Authors: Gilles Monnoyer de Galland, Thomas Feuillen, Luc Vandendorpe, Laurent Jacques
Date: 2020-11-10 00:00:00
Tasks: Quantization

In this paper we propose to bridge the gap between using extremely low resolution 1-bit measurements and estimating targets' parameters, such as their velocities, that exist in a continuum, i.e., by performing Off-the-Grid estimation. To that end, a Continuous version of Orthogonal Matching Pursuit (COMP) is modified in order to leverage the 1-bit measurements coming from a simple Doppler Radar. Using Monte-Carlo simulations, we show that although the resolution of the acquisition is dramatically reduced, velocity estimation of multiple targets can still be achieved and reaches performances beyond classic On-the-Grid methods. Furthermore, we show empirically that adding a random and uniform dithering before the quantization is necessary when estimating more than one target.
Testing and Dating Structural Changes in Copula-based Dependence Measures
Date: 2020-11-10 00:00:00
Tasks: Time Series, Time Series Analysis

This paper is concerned with testing and dating structural breaks in the
dependence structure of multivariate time series. We consider a cumulative sum
(CUSUM) type test for constant copula-based dependence measures, such as
Spearman's rank correlation and quantile dependencies. The asymptotic null
distribution is not known in closed form and critical values are estimated by
an i.i.d. bootstrap procedure. We analyze size and power properties in a
simulation study under different dependence measure settings, such as skewed
and fat-tailed distributions. To date break points and to decide whether two
estimated break locations belong to the same break event, we propose a pivot
confidence interval procedure. Finally, we apply the test to the historical
data of ten large financial firms during the last financial crisis from 2002 to
mid-2013.