pwc-archive/papers-with-abstracts huggingface dataset:
Asymptotic analysis of V-BLAST MIMO for coherent optical wireless communications in Gamma-Gamma turbulence
Authors: Yiming Li, Chao GAO, Mark S. Leeson, Xiaofeng Li
Date: 2021-07-12 00:00:00
Tasks: Diversity

This paper investigates the asymptotic BER performance of coherent optical wireless communication systems in Gamma-Gamma turbulence when applying the V-BLAST MIMO scheme. A new method is proposed to quantify the performance of the system and mathematical solutions for asymptotic BER performance are derived. Counterintuitive results are shown since the diversity gain of the V-BLAST MIMO system is equal to the number of the receivers. As a consequence, it is shown that when applying the V-BLAST MIMO scheme, the symbol rate per transmission can be equal to the number of transmitters with some cost to diversity gain. This means that we can simultaneously exploit the spatial multiplexing and diversity properties of the MIMO system to achieve a higher data rate than existing schemes in a channel that displays severe turbulence and moderate attenuation.
Board-level Code-Modulated Embedded Test and Calibration of an X-band Phased-Array Transceiver
Authors: Zhangjie Hong, Simon Schönherr, Vikas Chauhan, Brian Floyd
Date: 2021-07-12 00:00:00

We present methods for built-in test and calibration of phased arrays using code-modulated embedded test (CoMET). Our approach employs Cartesian modulation of test signals within each element using existing phase shifters, combining of these signals into an aggregate code-multiplexed response, downconversion and creation of code-modulated element-to-element "interference products" using a built-in power detector, demodulation of correlations from the digitized interference response, and extraction of amplitude and phase per element using an equation solver. Rotated-axis methodology is discussed for accurate extraction of phase near the original 0/90/180/270 degree axes. Our techniques are demonstrated at board level for both receive and transmit modes using an eight-element 8-16 GHz phased array constructed using ADAR1000 chips from ADI. At 6 GHz, CoMET-extracted gain and phase are accurate to within 0.2 dB and 3 degree compared to network-analyzer measurements. We then employ CoMET in a calibration loop to determinate optimum control settings at 6 GHz, outside the 8-16 GHz band for which the array was designed. We achieve seven-bit phase resolution with equalized gain. The root-mean squared gain and phase errors are improved from 0.8 dB and 8 degree before calibration to 0.1 dB and 1.7 degree after calibration.
Designing a Robust Carrier Frequency Offset Estimation Scheme for Meeting Target Decoding Performance in an OFDM System
Authors: Minkyeong Jeong, Sang-Won Choi, Juyeop Kim
Date: 2021-07-11 00:00:00

In a target communication system, a delicately designed frequency offset estimation scheme is required to meet certain decoding performance. In this paper, we proposed at wo-step estimation scheme, coarse and residual, with different value of an time interval parameter. A result of RF conduction test shows that the proposed method has an 1dB gain of SNR compared to coarse-only estimator. A result of the commercial test also indicates the proposed method outperforms coarse-only estimator especially in low SNR condition.
Hierarchical Learning Framework for UAV Detection and Identification
Authors: Olusiji O Medaiyese, Martins Ezuma, Adrian P Lauf, Ayodeji A Adeniran
Date: 2021-07-10 00:00:00
Tasks: Denoising

The ubiquity of unmanned aerial vehicles (UAVs) or drones is posing both security and safety risks to the public as UAVs are now used for cybercrimes. To mitigate these risks, it is important to have a system that can detect or identify the presence of an intruding UAV in a restricted environment. In this work, we propose a radio frequency (RF) based UAV detection and identification system by exploiting signals emanating from both the UAV and its flight controller, respectively. While several RF devices (i.e., Bluetooth and WiFi devices) operate in the same frequency band as UAVs, the proposed framework utilizes a semi-supervised learning approach for the detection of UAV or UAV's control signals in the presence of other wireless signals such as Bluetooth and WiFi. The semi-supervised learning approach uses stacked denoising autoencoder and local outlier factor algorithms. After the detection of UAV or UAV's control signals, the signal is decomposed by using Hilbert-Huang transform and wavelet packet transform to extract features from the time-frequency-energy domain of the signal. The extracted feature sets are used to train a three-level hierarchical classifier for identifying the type of signals (i.e., UAV or UAV control signal), UAV models, and flight mode of UAV. To demonstrate the feasibility of the proposed framework, we carried out an outdoor experiment for data collection using six UAVs, five Bluetooth devices, and two WiFi devices. The acquired data is called Cardinal RF (CardRF) dataset, and it is available for public use to foster UAV detection and identification research.
Graphene-based Distributed 3D Sensing Electrodes for Mapping Spatiotemporal Auricular Physiological Signals
Authors: Q. Huang, C. Wu, S. Hou, H. Sun, K. Yao, J. Law, M. Yang, A. L. R. Vellaisamy, X. Yu, H. Y. Chan, L. Lao, Y. Sun, W. J. Li
Date: 2021-07-10 00:00:00

Underneath the ear skin there are richly branching vascular and neural networks that ultimately connecting to our heart and brain. Hence, the three-dimensional (3D) mapping of auricular electrophysiological signals could provide a new perspective for biomedical studies such as diagnosis of cardiovascular diseases and neurological disorders. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, we report a graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes which enable full-auricle physiological monitoring. The sensing device, which incorporates programable 3D electrode thread array and personalized auricular mold, has 3D-conformable sensing interfaces with curved auricular skin, and was developed using one-step multi-material 3D-printing process. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping was demonstrated. For the first time, 3D AESR contours were generated and human subject-specific AESR distributions among a population were observed. From the data of 17 volunteers, the auricular region-specific AESR changes after cycling exercise were observed in 98% of the tests and were validated via machine learning techniques. Correlations of AESR with heart rate and blood pressure were also studied using statistical analysis. This 3D electronic platform and AESR-based new biometrical findings show promising biomedical applications.
Secure Dual-Functional Radar-Communication Transmission: Exploiting Interference for Resilience Against Target Eavesdropping
Authors: Nanchi Su, Fan Liu, Zhongxiang Wei, Ya-Feng Liu, Christos Masouros
Date: 2021-07-10 00:00:00

We study security solutions for dual-functional radar communication (DFRC) systems, which detect the radar target and communicate with downlink cellular users in millimeter-wave (mmWave) wireless networks simultaneously. Uniquely for such scenarios, the radar target is regarded as a potential eavesdropper which might surveil the information sent from the base station (BS) to communication users (CUs), that is carried by the radar probing signal. Transmit waveform and receive beamforming are jointly designed to maximize the signal-to-interference-plus-noise ratio (SINR) of the radar under the security and power budget constraints. We apply a Directional Modulation (DM) approach to exploit constructive interference (CI), where the known multiuser interference (MUI) can be exploited as a source of useful signal. Moreover, to further deteriorate the eavesdropping signal at the radar target, we utilize destructive interference (DI) by pushing the received symbols at the target towards the destructive region of the signal constellation. Our numerical results verify the effectiveness of the proposed design showing a secure transmission with enhanced performance against benchmark DFRC techniques.
UNIPOL: Unimodular sequence design via a separable iterative quartic polynomial optimization for active sensing systems
Authors: Surya Prakash Sankuru, Prabhu Babu, Mohammad Alaee-Kerahroodi
Date: 2021-07-09 00:00:00

Sequences having better autocorrelation properties play a crucial role in enhancing the performance of active sensing systems. Hence, sequences with good autocorrelation properties are very much in demand. In this paper, we addressed the problem of designing a unimodular sequence having better side-lobe levels. We formulated it as a constrained optimization problem comprising the Integrated Side-lobe Level (ISL) metric and then proposed an effective algorithm (named UNIPOL - UNImodular sequence design via a separable iterative POLynomial optimization) where we perform the polynomial optimization at every iteration. The novelty of the paper comes from deriving a quartic majorization function that is separable in the sequence variables and that can be minimized parallelly. To evaluate the performance of our proposed algorithm we perform the numerical experiments for different sequence lengths and confirm that our proposed algorithm is the fastest algorithm to attain an actual optimum minimizer of the ISL metric. Our proposed algorithm is also computationally efficient due to its ease of implementation using the FFT, IFFT operations in a parallel fashion.
A mathematical model to study the impact of intra-tumour heterogeneity on anti-tumour CD8+ T cell immune response
Authors: Emma Leschiera, Tommaso Lorenzi, Shensi Shen, Luis Almeida, Chloe Audebert
Date: 2021-07-12 00:00:00

Intra-tumour heterogeneity (ITH) has a strong impact on the efficacy of the immune response against solid tumours. The number of sub-populations of cancer cells expressing different antigens and the percentage of immunogenic cells (i.e. tumour cells that are effectively targeted by immune cells) in a tumour are both expressions of ITH. Here, we present a spatially explicit stochastic individual-based model of the interaction dynamics between tumour cells and CD8+ T cells, which makes it possible to dissect out the specific impact of these two expressions of ITH on anti-tumour immune response. The set-up of numerical simulations of the model is defined so as to mimic scenarios considered in previous experimental studies. Moreover, the ability of the model to qualitatively reproduce experimental observations of successful and unsuccessful immune surveillance is demonstrated. First, the results of numerical simulations of this model indicate that the presence of a larger number of sub-populations of tumour cells that express different antigens is associated with a reduced ability of CD8+ T cells to mount an effective anti-tumour immune response. Second, the presence of a larger percentage of tumour cells that are not effectively targeted by CD8+ T cells may reduce the effectiveness of anti-tumour immunity. Ultimately, the mathematical model presented in this paper may provide a framework to help biologists and clinicians to better understand the mechanisms that are responsible for the emergence of different outcomes of immunotherapy.
The Practice of Ensuring Repeatable and Reproducible Computational Models
Authors: Herbert M. Sauro
Date: 2021-07-07 00:00:00

Recent studies have shown that the majority of published computational models in systems biology and physiology are not repeatable or reproducible. There are a variety of reasons for this. One of the most likely reasons is that given how busy modern researchers are and the fact that no credit is given to authors for publishing repeatable work, it is inevitable that this will be the case. The situation can only be rectified when government agencies, universities and other research institutions change policies and that journals begin to insist that published work is in fact at least repeatable if not reproducible. In this chapter guidelines are described that can be used by researchers to help make sure their work is repeatable. A scoring system is suggested that authors can use to determine how well they are doing.
Nonlinear Dendritic Coincidence Detection for Supervised Learning
Authors: Fabian Schubert, Claudius Gros
Date: 2021-07-12 00:00:00
Tasks: Anatomy

Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals. In this work, we use a simple two-compartment model accounting for the nonlinear interactions between basal and apical input streams and show that standard unsupervised Hebbian learning rules in the basal compartment allow the neuron to align the feed-forward basal input with the top-down target signal received by the apical compartment. We show that this learning process, termed coincidence detection, is robust against strong distractions in the basal input space and demonstrate its effectiveness in a linear classification task.
Real time large scale $\textit{in vivo}$ observations by light-sheet microscopy reveal intrinsic synchrony, plasticity and growth cone dynamics of midline crossing axons at the ventral floor plate of the zebrafish spinal cord
Authors: Søren S. L. Andersen
Date: 2021-07-12 00:00:00

Axonal growth and guidance at the ventral floor plate is here followed $\textit{in vivo}$ in real time at high resolution by light-sheet microscopy along several hundred micrometers of the zebrafish spinal cord. The recordings show the strikingly stereotyped spatio-temporal control that governs midline crossing. Commissural axons are observed crossing the ventral floor plate midline perpendicularly at about 20 microns/h, in a manner dependent on the Robo3 receptor and with a growth rate minimum around the midline, confirming previous observations. At guidance points, commissural axons are seen to decrease their growth rate and growth cones increase in size. Commissural filopodia appear to interact with the nascent neural network, and thereby trigger immediate plastic and reversible sinusoidal-shaped bending movements of neighboring commissural shafts. Ipsilateral axons extend concurrently, but straight and without bends, at three to six times higher growth rates than commissurals, indicating they project their path on a substrate-bound surface rather than relying on diffusible guidance cues. Growing axons appeared to be under stretch, an observation that is of relevance for tension-based models of cortical morphogenesis. The \textit{in vivo} observations provide for a discussion of the current distinction between substrate-bound and diffusible guidance cues. The study applies the transparent zebrafish model that provides an experimental model system to explore further the cellular, molecular and physical mechanisms involved during axonal growth, guidance and midline crossing through a combination of $\textit{in vitro}$ and $\textit{in vivo}$ approaches.
Unifying incidence and prevalence under a time-varying general branching process
Authors: Mikko S. Pakkanen, Xenia Miscouridou, Matthew J. Penn, Charles Whittaker, Tresnia Berah, Swapnil Mishra, Thomas A. Mellan, Samir Bhatt
Date: 2021-07-12 00:00:00
Tasks: Epidemiology, Probabilistic Programming

Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump-Mode-Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We show that the equations for incidence and prevalence are consistent with the so-called back-calculation relationship. We analyse two particular cases of these integral equations, one that arises from a Bellman-Harris process and one that arises from an inhomogeneous Poisson process model of transmission. We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling. We present a numerical discretisation scheme to solve these equations, and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox.
DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models
Authors: Rıza Özçelik, Alperen Bağ, Berk Atıl, Melih Barsbey, Arzucan Özgür, Elif Özkırımlı
Date: 2021-07-04 00:00:00
Tasks: Drug Discovery, Ensemble Learning, Prediction

Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available protein-chemical interaction datasets, which may contain dataset biases that may lead the model to learn dataset-specific patterns, instead of generalizable relationships. As a result, the prediction performance of models drops for previously unseen biomolecules, $\textit{i.e.}$ the prediction models cannot generalize to biomolecules outside of the dataset. The latest approaches that aim to improve model generalizability either have limited applicability or introduce the risk of degrading prediction performance. Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models. DebiasedDTA reweights the training samples to mitigate the effect of dataset biases and is applicable to most DTA prediction models. The results suggest that models trained in the DebiasedDTA framework can achieve improved generalizability in predicting the interactions of the previously unseen biomolecules, as well as performance improvements on those previously seen. Extensive experiments with different biomolecule representations, model architectures, and datasets demonstrate that DebiasedDTA can upgrade DTA prediction models irrespective of the biomolecule representation, model architecture, and training dataset. Last but not least, we release DebiasedDTA as an open-source python library to enable other researchers to debias their own predictors and/or develop their own debiasing methods. We believe that this python library will corroborate and foster research to develop more generalizable DTA prediction models.
Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective
Authors: Cynthia Pagliaro, Dhagash Mehta, Han-Tai Shiao, Shaofei Wang, Luwei Xiong
Date: 2021-07-12 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Modeling investor behavior is crucial to identifying behavioral coaching opportunities for financial advisors. With the help of natural language processing (NLP) we analyze an unstructured (textual) dataset of financial advisors' summary notes, taken after every investor conversation, to gain first ever insights into advisor-investor interactions. These insights are used to predict investor needs during adverse market conditions; thus allowing advisors to coach investors and help avoid inappropriate financial decision-making. First, we perform topic modeling to gain insight into the emerging topics and trends. Based on this insight, we construct a supervised classification model to predict the probability that an advised investor will require behavioral coaching during volatile market periods. To the best of our knowledge, ours is the first work on exploring the advisor-investor relationship using unstructured data. This work may have far-reaching implications for both traditional and emerging financial advisory service models like robo-advising.
End-to-End Risk Budgeting Portfolio Optimization with Neural Networks
Authors: Ayse Sinem Uysal, Xiaoyue Li, John M. Mulvey
Date: 2021-07-09 00:00:00
Tasks: parameter estimation, Portfolio Optimization

Portfolio optimization has been a central problem in finance, often approached with two steps: calibrating the parameters and then solving an optimization problem. Yet, the two-step procedure sometimes encounter the "error maximization" problem where inaccuracy in parameter estimation translates to unwise allocation decisions. In this paper, we combine the prediction and optimization tasks in a single feed-forward neural network and implement an end-to-end approach, where we learn the portfolio allocation directly from the input features. Two end-to-end portfolio constructions are included: a model-free network and a model-based network. The model-free approach is seen as a black-box, whereas in the model-based approach, we learn the optimal risk contribution on the assets and solve the allocation with an implicit optimization layer embedded in the neural network. The model-based end-to-end framework provides robust performance in the out-of-sample (2017-2021) tests when maximizing Sharpe ratio is used as the training objective function, achieving a Sharpe ratio of 1.16 when nominal risk parity yields 0.79 and equal-weight fix-mix yields 0.83. Noticing that risk-based portfolios can be sensitive to the underlying asset universe, we develop an asset selection mechanism embedded in the neural network with stochastic gates, in order to prevent the portfolio being hurt by the low-volatility assets with low returns. The gated end-to-end with filter outperforms the nominal risk-parity benchmarks with naive filtering mechanism, boosting the Sharpe ratio of the out-of-sample period (2017-2021) to 1.24 in the market data.
Prequential MDL for Causal Structure Learning with Neural Networks
Authors: Jorg Bornschein, Silvia Chiappa, Alan Malek, Rosemary Nan Ke
Date: 2021-07-02 00:00:00

Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology. We show that the prequential minimum description length principle (MDL) can be used to derive a practical scoring function for Bayesian networks when flexible and overparametrized neural networks are used to model the conditional probability distributions between observed variables. MDL represents an embodiment of Occam's Razor and we obtain plausible and parsimonious graph structures without relying on sparsity inducing priors or other regularizers which must be tuned. Empirically we demonstrate competitive results on synthetic and real-world data. The score often recovers the correct structure even in the presence of strongly nonlinear relationships between variables; a scenario were prior approaches struggle and usually fail. Furthermore we discuss how the the prequential score relates to recent work that infers causal structure from the speed of adaptation when the observations come from a source undergoing distributional shift.
WVOQ at SemEval-2021 Task 6: BART for Span Detection and Classification
Authors: Cees Roele
Date: 2021-06-27 00:00:00
Proceeding: SEMEVAL 2021

A novel solution to span detection and classification is presented in which a BART EncoderDecoder model is used to transform textual input into a version with XML-like marked up spans. This markup is subsequently translated to an identification of the beginning and end of fragments and of their classes. Discussed is how pre-training methodology both explains the relative success of this method and its limitations. This paper reports on participation in task 6 of SemEval-2021: Detection of Persuasion Techniques in Texts and Images.