pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical Concept Study of Cooperative Abnormality Detection and Localization in Fluidic-Medium Molecular Communication
Authors: Ladan Khaloopour, Mahtab Mirmohseni, Masoumeh Nasiri-Kenari
Date: 2021-05-16 00:00:00
Tasks: Anomaly Detection

In this paper, we propose a theoretical framework for cooperative abnormality detection and localization systems by exploiting molecular communication setup. The system consists of mobile sensors in a fluidic medium, which are injected into the medium to search the environment for abnormality. Some fusion centers (FC) are placed at specific locations in the medium, which absorb all sensors arrived at their locations, and by observing its state, each FC decides on the abnormality existence and/or its location. To reduce the effects of sensor imperfection, we propose a scheme where the sensors release some molecules (i.e., markers) into the medium after they sense an abnormality. If the goal is abnormality detection, the released molecules are used to cooperatively activate other sensors. If the goal is abnormality localization, the released molecules are used by the FCs to determine the location. In our model, both sensors' imperfection and markers background noise are taken into account. For the detection phase, we consider two sensor types based on their activation strategy by markers. To make the analysis tractable, we assume some ideal assumptions for the sensors' model. We investigate the related binary hypothesis testing problem and obtain the probabilities of false alarm and miss-detection. It is shown that using sensors with the ability of cooperatively activating each other can significantly improve the detection performance in terms of probability of error. For the localization phase, we consider two types of FCs based on their capability in reading sensors' storage levels. We study their performance and obtain the optimal and sub-optimal decision schemes and also the probability of localization error for both perfect and imperfect sensing regimes.
Cooperative 3D Beamforming for Small-Cell and Cell-Free 6G Systems
Authors: Sarath Gopi, Sheetal Kalyani, Lajos Hanzo
Date: 2021-05-16 00:00:00

Three dimensional (3D) resource reuse is an important design requirement for the prospective 6G wireless communication systems. Hence, we propose a cooperative 3D beamformer for use in 3D space. Explicitly, we harness multiple base station antennas for joint zero forcing transmit pre-coding for beaming the transmit signals in specific 3D directions. The technique advocated is judiciously configured for use in both cell-based and cell-free wireless architectures. We evaluated the performance of the proposed scheme using the novel metric of Volumetric Spectral Efficiency (VSE). We also characterized the performance of the scheme in terms of its spectral efficiency (SE) and Bit Error Rate (BER) through extensive simulation studies.
Clutter Mitigation for Joint RadCom Systems Based On Spatial Modulation
Authors: Jifa Zhang
Date: 2021-05-16 00:00:00

Joint radar and communication (RadCom) systems have been proposed to integrate radar and communication into one platform and achieve spectrum sharing in recent years. However, the joint RadCom systems cause the clutter modulation and the performance degradation of radar. Therefore, it's very essential to improve the performance of radar when designing joint RadCom systems. This paper deals with the clutter mitigation for joint RadCom systems based on spatial modulation. The communication information embedding introduces variation of transmit beampatterns in a coherent processing interval (CPI) and causes the clutter modulation and spreading of the clutter spectrum. This paper propose a reduced dimension spatial temporal adaptive processing (RD-STAP) method, i.e., we firstly perform time domain filtering and then perform spatial domain filtering on received data. As for time domain filtering, this paper mitigates the extended clutter by subspace projection (SP) and proposes a more effective eigen-decomposition algorithm based on Power method than singular value decomposition (SVD) to obtain clutter subspace basis vectors. And the matched filter is performed on the data after clutter mitigation to display the moving target. And receive beamforming is utilized for spatial domain filtering. Simulation results highlight the effectiveness of the proposed RD-STAP method and the eigen-decomposition algorithm.
RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments
Authors: Yu Tian, Shuai Yuan, Weisheng Chen, Naijin Liu
Date: 2021-05-15 00:00:00
Tasks: Position, Prediction

Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency. However, fast and reliable radio map prediction can be very challenging due to the complicated interaction between radio waves and the environment. In this paper, a novel Transformer based deep learning model termed as RadioNet is proposed for radio map prediction in urban scenarios. In addition, a novel Grid Embedding technique is proposed to substitute the original Position Embedding in Transformer to better anchor the relative position of the radiation source, destination and environment. The effectiveness of proposed method is verified on an urban radio wave propagation dataset. Compared with the SOTA model on RMP task, RadioNet reduces the validation loss by 27.3\%, improves the prediction reliability from 90.9\% to 98.9\%. The prediction speed is increased by 4 orders of magnitude, when compared with ray-tracing based method. We believe that the proposed method will be beneficial to high-efficiency wireless communication, real-time radio visualization, and even high-speed image rendering.
On Performance of Energy Harvested Cooperative NOMA Under Imperfect CSI and Imperfect SIC
Authors: Shubham Bisen, Parvez Shaik, Vimal Bhatia
Date: 2021-05-14 00:00:00

With the advent of 5G and the need for energy-efficient massively connected wireless networks, in this work, we consider an energy harvesting (EH) based multi-relay downlink cooperative non-orthogonal multiple access (NOMA) system with practical constraints. The base station serves NOMA users with the help of decode-and-forward based multiple EH relays, where relays harvest the energy from the base station's radio frequency. A relay is selected from the multiple K-relay by using a partial relay selection protocol. The system is considered to operate in half-duplex mode over a generalized independent and identical Nakagami$-m$ fading channel. The closed-form expression of outage probability and ergodic rate are derived for users, under the assumption of imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) at the receiver node. Expression of outage probability and ergodic rate for both users under the assumption of perfect CSI and perfect SIC are also presented. Further, the asymptotic expression for the outage probability is also shown. The derived analytical expressions are verified through Monte-Carlo simulations.
Actuarial strategy for pricing Asian options under a mixed fractional Brownian motion with jumps
Authors: Foad Shokrollahi, Davood Ahmadian, Luca Vincenzo Ballestra
Date: 2021-05-14 00:00:00

The mixed fractional Brownian motion ($mfBm$) has become quite popular in finance, since it allows one to model long-range dependence and self-similarity while remaining, for certain values of the Hurst parameter, arbitrage-free. In the present paper, we propose approximate closed-form solutions for pricing arithmetic Asian options on an underlying described by the $mfBm$. Specifically, we consider both arithmetic Asian options and arithmetic Asian power options, and we obtain analytical formulas for pricing them based on a convenient approximation of the strike price. Both the standard $mfBm$ and the $mfBm$ with Poisson log-normally distributed jumps are taken into account.
Rapid micro-immunohistochemistry
Authors: Robert D. Lovchik, David Taylor, Govind Kaigala
Date: 2021-05-17 00:00:00

We present a new and versatile implementation of rapid and localized immunohistochemical staining of tissue sections. Immunohistochemistry (IHC) allows to detect specific proteins on tissue sections and comprises a sequence of specific biochemical reactions. For the rapid implementation of IHC, we fabricated horizontally oriented microfluidic probes (MFP) with functionally designed apertures to enable square and circular footprints, which we employ to locally expose a tissue to time-optimized sequences of different biochemicals.
Self-organization principles of cell cycles and gene expressions in the development of cell populations
Authors: Xiaoliang Wang, Dongyun Bai
Date: 2021-05-16 00:00:00

A big challenge in current biology is to understand the exact self-organization mechanism underlying complex multi-physics coupling developmental processes. With multiscale computations of from subcellular gene expressions to cell population dynamics that is based on first principles, we show that cell cycles can self-organize into periodic stripes in the development of E. coli populations from one single cell, relying on the moving graded nutrient concentration profile, which provides directing positional information for cells to keep their cycle phases in place. Resultantly, the statistical cell cycle distribution within the population is observed to collapse to a universal function and shows a scale invariance. Depending on the radial distribution mode of genetic oscillations in cell populations, a transition between gene patterns is achieved. When an inhibitor-inhibitor gene network is subsequently activated by a gene-oscillatory network, cell populations with zebra stripes can be established, with the positioning precision of cell-fate-specific domains influenced by cells' speed of free motions. Such information may provide important implications for understanding relevant dynamic processes of multicellular systems, such as biological development.
Survival prediction of head and neck squamous cell carcinoma using machine learning models
Authors: Saurav Mandal, Akshansh Gupta, Waribam Pratibha Chanu
Date: 2021-05-16 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Survival Prediction

Head and Neck Squamous Cell Carcinoma (HNSCC) is one of cancer type that is most distressing leading to acute pain, effecting speech and primary survival functions such as swallowing and breathing. The morbidity and mortality of HNSCC patients have not significantly improved even tough there has been advancement in surgical and radiotherapy treatments. The high mortality may be attributed to the complexity and significant changes in the clinical outcomes. Therefore, it is important to increase the accuracy of predicting the outcome of cancer survival. Few cancer survival prediction models of HNSCC have been proposed so far. In this study, genomic data (whole exome sequencing) are integrated with clinical data to improve the performance of prediction model. The somatic mutations of every patient is processed using Multifractal Deterended Fluctuation Analysis (MFDFA) algorithm and the parameter values of Fractal Dimension (Dq) is included along with clinical data for cancer survival prediction. Feature ranking proves that the new engineered feature is one of the important feature in prediction model. In order to improve the performance index of models, hyperparameters were also tuned in all the classifiers considered. 10-Fold cross validation is implemented and XGBoost (98% AUROC, 94% precision, and 93% recall) proves to be best model classifier followed by Random Forest 93% AUROC, 93% precision, and 93% recall), Support Vector Machine (84% AUCROC, 79% precision, and 79% recall) and Logistic Regression (80% AUROC, 77% precision, and 76% recall).
Effective description of bistability and irreversibility in apoptosis
Authors: Sol M. Fernández Arancibia, Hernán E. Grecco, Luis G. Morelli
Date: 2021-05-14 00:00:00

Apoptosis is a mechanism of programmed cell death in which cells engage in a controlled demolition and prepare to be digested without damaging their environment. In normal conditions apoptosis is repressed, until it is irreversibly induced by an appropriate signal. In adult organisms apoptosis is a natural way to dispose of damaged cells, and its disruption or excess is associated with cancer and autoimmune diseases. Apoptosis is regulated by a complex signaling network controlled by caspases, specialized enzymes that digest essential cellular components and promote the degradation of genomic DNA. In this work we propose an effective description of the signaling network focused on caspase-3 as a readout of cell fate. We integrate intermediate network interactions into a nonlinear feedback function acting on caspase-3 and introduce the effect of pro-apoptotic stimuli and regulatory elements as a saturating activation function. We find that the theory has a robust bistable regime where two possible states coexist, representing survival and cell death fates. For a broad range of parameters, strong stimuli can induce an irreversible switch to the death fate. We use the theory to explore dynamical stimulation conditions and determine how cell fate depends on different stimuli patterns. This analysis reveals a critical relation between transient stimuli intensity and duration to trigger irreversible apoptosis.
Dynamic network analysis improves protein 3D structural classification
Authors: Khalique Newaz, Jacob Piland, Patricia L. Clark, Scott J. Emrich, Jun Li, Tijana Milenkovic
Date: 2021-05-14 00:00:00
Tasks: Classification

Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3D structural features. We recently proposed PSC approaches that model protein 3D structures as protein structure networks (PSNs) and analyze PSN-based protein features, which performed better than or comparable to state-of-the-art sequence or other 3D structure-based approaches in the task of PSC. However, existing PSN-based PSC approaches model the whole 3D structure of a protein as a static PSN. Because folding of a protein is a dynamic process, where some parts of a protein fold before others, modeling the 3D structure of a protein as a dynamic PSN might further help improve the existing PSC performance. Here, we propose for the first time a way to model 3D structures of proteins as dynamic PSNs, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on static PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches). Indeed, we confirm this on 71 datasets spanning ~44,000 protein domains from CATH and SCOPe
Edges in Brain Networks: Contributions to Models of Structure and Function
Authors: Joshua Faskowitz, Richard F. Betzel, Olaf Sporns
Date: 2021-05-14 00:00:00
Tasks: Time Series, Time Series Analysis

Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher-order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher-order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.
Efficient Least Squares Monte-Carlo Technique for PFE/EE Calculations
Authors: Yuriy Krepkiy, Asif Lakhany, Amber Zhang
Date: 2021-05-14 00:00:00
Tasks: regression

We describe a regression-based method, generally referred to as the Least Squares Monte Carlo (LSMC) method, to speed up exposure calculations of a portfolio. We assume that the portfolio contains several exotic derivatives that are priced using Monte-Carlo on each real world scenario and time step. Such a setting is often referred to as a Monte Carlo over a Monte Carlo or a Nested Monte Carlo method.
Hedging Goals
Authors: Thomas Krabichler, Marcus Wunsch
Date: 2021-05-17 00:00:00

Goal-based investing is concerned with reaching a monetary investment goal by a given finite deadline, which differs from mean-variance optimization in modern portfolio theory. In this article, we expand the close connection between goal-based investing and option hedging that was originally discovered in [Bro99b] by allowing for varying degrees of investor risk aversion using lower partial moments of different orders. Moreover, we show that maximizing the probability of reaching the goal (quantile hedging, cf. [FL99]) and minimizing the expected shortfall (efficient hedging, cf. [FL00]) yield, in fact, the same optimal investment policy. We furthermore present an innovative and model-free approach to goal-based investing using methods of reinforcement learning. To the best of our knowledge, we offer the first algorithmic approach to goal-based investing that can find optimal solutions in the presence of transaction costs.
Optimal Reinsurance and Investment under Common Shock Dependence Between Financial and Actuarial Markets
Authors: Claudia Ceci, Katia Colaneri, Alessandra Cretarola
Date: 2021-05-16 00:00:00

We study optimal proportional reinsurance and investment strategies for an insurance company which experiences both ordinary and catastrophic claims and wishes to maximize the expected exponential utility of its terminal wealth. We propose a model where the insurance framework is affected by environmental factors, and aggregate claims and stock prices are subject to common shocks, i.e. drastic events such as earthquakes, extreme weather conditions, or even pandemics, that have an immediate impact on the financial market and simultaneously induce insurance claims. Using the classical stochastic control approach based on the Hamilton-Jacobi-Bellman equation, we provide a verification result for the value function via classical solutions to two backward partial differential equations and characterize the optimal reinsurance and investment strategies. Finally, we make a comparison analysis to discuss the effect of common shock dependence.
ESG, Risk, and (Tail) Dependence
Authors: Karoline Bax, Özge Sahin, Claudia Czado, Sandra Paterlini
Date: 2021-05-15 00:00:00

While environmental, social, and governance (ESG) trading activity has been a distinctive feature of financial markets, the debate if ESG scores can also convey information regarding a company's riskiness remains open. Regulatory authorities, such as the European Banking Authority (EBA), have acknowledged that ESG factors can contribute to risk. Therefore, it is important to model such risks and quantify what part of a company's riskiness can be attributed to the ESG scores. This paper aims to question whether ESG scores can be used to provide information on (tail) riskiness. By analyzing the (tail) dependence structure of companies with a range of ESG scores, that is within an ESG rating class, using high-dimensional vine copula modelling, we are able to show that risk can also depend on and be directly associated with a specific ESG rating class. Empirical findings on real-world data show positive not negligible ESG risks determined by ESG scores, especially during the 2008 crisis.
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface
Authors: Dheeraj Rathee, Haider Raza, Sujit Roy, Girijesh Prasad
Date: 2021-04-29 00:00:00
Tasks: Brain Computer Interface, Motor Imagery

Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.
COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs
Authors: Vignav Ramesh, Blaine Rister, Daniel L. Rubin
Date: 2021-05-17 00:00:00
Tasks: Computed Tomography (CT), Lesion Segmentation

Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models. Most work to date assessing disease severity on chest imaging has focused on segmenting computed tomography (CT) images; however, given that CTs are performed much less frequently than chest X-rays for COVID-19 patients, automated lung lesion segmentation on chest X-rays could be clinically valuable. There currently exists a universal shortage of chest X-rays with ground truth COVID-19 lung lesion annotations, and manually contouring lung opacities is a tedious, labor-intensive task. To accelerate severity detection and augment the amount of publicly available chest X-ray training data for supervised deep learning (DL) models, we leverage existing annotated CT images to generate frontal projection "chest X-ray" images for training COVID-19 chest X-ray models. In this paper, we propose an automated pipeline for segmentation of COVID-19 lung lesions on chest X-rays comprised of a Mask R-CNN trained on a mixed dataset of open-source chest X-rays and coronal X-ray projections computed from annotated volumetric CTs. On a test set containing 40 chest X-rays of COVID-19 positive patients, our model achieved IoU scores of 0.81 $\pm$ 0.03 and 0.79 $\pm$ 0.03 when trained on a dataset of 60 chest X-rays and on a mixed dataset of 10 chest X-rays and 50 projections from CTs, respectively. Our model far outperforms current baselines with limited supervised training and may assist in automated COVID-19 severity quantification on chest X-rays.