pwc-archive/papers-with-abstracts huggingface dataset:
Picture-word interference in language production studies: Exploring the roles of attention and processing times
Authors: Audrey Bürki, Sylvain Madec
Date: 2023-03-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The picture-word interference paradigm (participants name target pictures while ignoring distractor words) is often used to model the planning processes involved in word production. The participants' naming times are delayed in the presence of a distractor (general interference). The size of this effect depends on the relationship between the target and distractor words. Distractors of the same semantic category create more interference (semantic interference), distractors overlapping in phonology create less interference (phonological facilitation). The present study examines the relationships between these experimental effects, processing times, and attention in order to better understand the cognitive processes underlying participants' behavior in this paradigm. Participants named pictures with a superimposed line of Xs, semantically related distractors, phonologically related distractors, or unrelated distractors. General interference, semantic interference, and phonological facilitation effects are replicated. Distributional analyses reveal that general and semantic interference effects increase with naming times, while phonological facilitation decreases. The phonological facilitation and semantic interference effects are found to depend on the synchronicity in processing times between the planning of the picture's name and the processing of the distractor word. Finally, EEG power in the alpha band before stimulus onset varied with the position of the trial in the experiment and with repetition but did not predict the size of interference or facilitation effects. Taken together, these results suggest that experimental effects in the picture-word interference paradigm depend on processing times to both the target word and distractor word and that distributional patterns could party reflect this dependency.
A continuation technique for maximum likelihood estimators in biological models
Authors: Tyler Cassidy
Date: 2023-03-16 00:00:00
Tasks: Sensitivity

Estimating model parameters is a crucial step in mathematical modelling and typically involves minimizing the disagreement between model predictions and experimental data. This calibration data can change throughout a study, particularly if modelling is performed simultaneously with the calibration experiments, or during an on-going public health crisis as in the case of the COVID-19 pandemic. Consequently, the optimal parameter set, or maximal likelihood estimator (MLE), is a function of the experimental data set. Here, we develop a numerical technique to predict the evolution of the MLE as a function of the experimental data. We show that, when considering perturbations from an initial data set, our approach is significantly more computationally efficient that re-fitting model parameters while resulting in acceptable model fits to the updated data. We use the continuation technique to develop an explicit functional relationship between fit model parameters and experimental data that can be used to measure the sensitivity of the MLE to experimental data. We then leverage this inverse sensitivity analysis to select between model fits with similar information criteria, \textit{a priori} determine the experimental measurements to which the MLE is most sensitive, and suggest additional experiment measurements that can resolve parameter uncertainty.
The measurement of bovine pericardium density and its implications on leaflet stress distribution in bioprosthetic heart valves
Authors: Masod Sadipour, Ali N. Azadani
Date: 2023-03-16 00:00:00

Purpose: Bioprosthetic Heart Valves (BHVs) are currently in widespread use with promising outcomes. Computational modeling provides a framework for quantitatively describing BHVs in the preclinical phase. To obtain reliable solutions in computational modeling, it is essential to consider accurate leaflet properties such as mechanical properties and density. Bovine pericardium (BP) is widely used as BHV leaflets. Previous computational studies assume BP density to be close to the density of water or blood. However, BP leaflets undergo multiple treatments such as fixation and anti-calcification. The present study aims to measure the density of the BP used in BHVs and determine its effect on leaflet stress distribution. Methods: We determined the density of eight square BP samples laser cut from Edwards BP patches. The weight of specimens was measured using an A&D Analytical Balance, and volume was measured by high-resolution imaging. Finite element models of a BHV similar to PERIMOUNT Magna were developed in ABAQUS. Results: The average density value of the BP samples was 1410 kg/m3. In the acceleration phase of a cardiac cycle, the maximum stress value reached 1.89 MPa for a density value of 1410 kg/m3 , and 2.47 MPa for a density of 1000 kg/m3(30.7% difference). In the deceleration, the maximum stress value reached 713 and 669 kPa, respectively. Conclusion: Stress distribution and deformation of BHV leaflets are dependent upon the magnitude of density. Ascertaining an accurate value for the density of BHV leaflets is essential for computational models.
A parsimonious neural network approach to solve portfolio optimization problems without using dynamic programming
Authors: Pieter M. van Staden, Peter A. Forsyth, Yuying Li
Date: 2023-03-15 00:00:00
Tasks: Generative Adversarial Network, Portfolio Optimization

We present a parsimonious neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The number of parameters of the (potentially deep) neural network remains independent of the number of portfolio rebalancing events, and in contrast to, for example, reinforcement learning, the approach avoids the computation of high-dimensional conditional expectations. As a result, the approach remains practical even when considering large numbers of underlying assets, long investment time horizons or very frequent rebalancing events. We prove convergence of the numerical solution to the theoretical optimal solution of a large class of problems under fairly general conditions, and present ground truth analyses for a number of popular formulations, including mean-variance and mean-conditional value-at-risk problems. We also show that it is feasible to solve Sortino ratio-inspired objectives (penalizing only the variance of wealth outcomes below the mean) in dynamic trading settings with the proposed approach. Using numerical experiments, we demonstrate that if the investment objective functional is separable in the sense of dynamic programming, the correct time-consistent optimal investment strategy is recovered, otherwise we obtain the correct pre-commitment (time-inconsistent) investment strategy. The proposed approach remains agnostic as to the underlying data generating assumptions, and results are illustrated using (i) parametric models for underlying asset returns, (ii) stationary block bootstrap resampling of empirical returns, and (iii) generative adversarial network (GAN)-generated synthetic asset returns.
Stock Price Prediction Using Temporal Graph Model with Value Chain Data
Authors: Chang Liu, Sandra Paterlini
Date: 2023-03-07 00:00:00
Tasks: Stock Price Prediction

Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks. Accurate predictions of future stock prices can help traders optimize their trading strategies and maximize their profits. In this paper, we introduce a neural network-based stock return prediction method, the Long Short-Term Memory Graph Convolutional Neural Network (LSTM-GCN) model, which combines the Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) Cells. Specifically, the GCN is used to capture complex topological structures and spatial dependence from value chain data, while the LSTM captures temporal dependence and dynamic changes in stock returns data. We evaluated the LSTM-GCN model on two datasets consisting of constituents of Eurostoxx 600 and S&P 500. Our experiments demonstrate that the LSTM-GCN model can capture additional information from value chain data that are not fully reflected in price data, and the predictions outperform baseline models on both datasets.
Real Options Technique as a Tool of Strategic Risk Management
Authors: Volodymyr Savchuk
Date: 2023-03-16 00:00:00
Tasks: Management

The real options approach is now considered an effective alternative to the corporate DCF model for a feasibility study. The current paper offers a practical methodology employing binomial trees and real options techniques for evaluating investment projects. A general computation procedure is suggested for the decision tree with two active stages of real options, which correspond to additional investments. The suggested technique can be used for most real options, which are practically essential regarding enterprise strategy. The special case named Binomial-Random-Cash-Flow Real Options Model with random outcomes is developed as the next step of real options modelling. Project Value at Risk is introduced and used as a criterion of investment project feasibility under the assumption regarding random outcomes. In particular, the Gaussian probability distribution is used for modelling option outcomes uncertainty. The choice of the Gaussian distribution is caused by the desire to obtain estimates in the final analytical form. Choosing another distribution for random outcomes leads to using Monte Carlo simulation, for which a general framework is developed by demonstrating some instances. The author could avoid the computational complexity that makes these solutions feasible for business practice.
Portfolio Volatility Estimation Relative to Stock Market Cross-Sectional Intrinsic Entropy
Authors: Claudiu Vinte, Marcel Ausloos
Date: 2023-03-16 00:00:00

Selecting stock portfolios and assessing their relative volatility risk compared to the market as a whole, market indices, or other portfolios is of great importance to professional fund managers and individual investors alike. Our research uses the cross-sectional intrinsic entropy (CSIE) model to estimate the cross-sectional volatility of the stock groups that can be considered together as portfolio constituents. In our study, we benchmark portfolio volatility risks against the volatility of the entire market provided by the CSIE and the volatility of market indices computed using longitudinal data. This article introduces CSIE-based betas to characterise the relative volatility risk of the portfolio against market indices and the market as a whole. We empirically prove that, through CSIE-based betas, multiple sets of symbols that outperform the market indices in terms of rate of return while maintaining the same level of risk or even lower than the one exhibited by the market index can be discovered, for any given time interval. These sets of symbols can be used as constituent stock portfolios and, in connection with the perspective provided by the CSIE volatility estimates, to hierarchically assess their relative volatility risk within the broader context of the overall volatility of the stock market.
Improving CNN-base Stock Trading By Considering Data Heterogeneity and Burst
Authors: Keer Yang, Guanqun Zhang, Chuan Bi, Qiang Guan, Hailu Xu, Shuai Xu
Date: 2023-03-14 00:00:00

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.
Stock Trend Prediction: A Semantic Segmentation Approach
Authors: Shima Nabiee, Nader Bagherzadeh
Date: 2023-03-09 00:00:00
Tasks: Prediction, Semantic Segmentation, Stock Trend Prediction, Time Series

Market financial forecasting is a trending area in deep learning. Deep learning models are capable of tackling the classic challenges in stock market data, such as its extremely complicated dynamics as well as long-term temporal correlation. To capture the temporal relationship among these time series, recurrent neural networks are employed. However, it is difficult for recurrent models to learn to keep track of long-term information. Convolutional Neural Networks have been utilized to better capture the dynamics and extract features for both short- and long-term forecasting. However, semantic segmentation and its well-designed fully convolutional networks have never been studied for time-series dense classification. We present a novel approach to predict long-term daily stock price change trends with fully 2D-convolutional encoder-decoders. We generate input frames with daily prices for a time-frame of T days. The aim is to predict future trends by pixel-wise classification of the current price frame. We propose a hierarchical CNN structure to encode multiple price frames to multiscale latent representation in parallel using Atrous Spatial Pyramid Pooling blocks and take that temporal coarse feature stacks into account in the decoding stages. Our hierarchical structure of CNNs makes it capable of capturing both long and short-term temporal relationships effectively. The effect of increasing the input time horizon via incrementing parallel encoders has been studied with interesting and substantial changes in the output segmentation masks. We achieve overall accuracy and AUC of %78.18 and 0.88 for joint trend prediction over the next 20 days, surpassing other semantic segmentation approaches. We compared our proposed model with several deep models specifically designed for technical analysis and found that for different output horizons, our proposed models outperformed other models.
Cryptocurrency Price Prediction using Twitter Sentiment Analysis
Authors: Haritha GB, Sahana N. B
Date: 2023-03-03 00:00:00
Tasks: Prediction, Sentiment Analysis, Twitter Sentiment Analysis

The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user's following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%.
A Deep Dive into NFT Whales: A Longitudinal Study of the NFT Trading Ecosystem
Authors: Na Hyeon Park, Hanna Kim, Chanhee Lee, Changhoon Yoon, Seunghyeon Lee, Youngjin Jin, Seungwon Shin
Date: 2023-02-02 00:00:00

NFT (Non-fungible Token) has drastically increased in its size, accounting for over \$16.9B of total market capitalization. Despite the rapid growth of NFTs, this market has not been examined thoroughly from a financial perspective. In this paper, we conduct methodical analyses to identify NFT market movers who play a significant role in potentially manipulating and oscillating NFT values. We collect over 3.8M NFT transaction data from the Ethereum Blockchain from January 2021 to February 2022 to extract trading information in line with the NFT lifecycle: (i) mint, (ii) transfer/sale, and (iii) burn. Based on the size of held NFT values, we classify NFT traders into three groups (whales, dolphins, and minnows). In total, we analyze 430K traders from 91 different NFT collection sources. We find that the top 0.1\% of NFT traders (i.e., whales) drive the NFT market with consistent, high returns. We then identify and characterize the NFT whales' unique investment strategies (e.g., mint/sale patterns, wash trading) to empirically understand the whales in the NFT market for the first time.
Knowledge Discovery from Atomic Structures using Feature Importances
Authors: Joakim Linja, Joonas Hämäläinen, Antti Pihlajamäki, Paavo Nieminen, Sami Malola, Hannu Häkkinen, Tommi Kärkkäinen
Date: 2023-02-28 00:00:00
Tasks: Interpretable Machine Learning

Molecular-level understanding of the interactions between the constituents of an atomic structure is essential for designing novel materials in various applications. This need goes beyond the basic knowledge of the number and types of atoms, their chemical composition, and the character of the chemical interactions. The bigger picture takes place on the quantum level which can be addressed by using the Density-functional theory (DFT). Use of DFT, however, is a computationally taxing process, and its results do not readily provide easily interpretable insight into the atomic interactions which would be useful information in material design. An alternative way to address atomic interactions is to use an interpretable machine learning approach, where a predictive DFT surrogate is constructed and analyzed. The purpose of this paper is to propose such a procedure using a modification of the recently published interpretable distance-based regression method. Our tests with a representative benchmark set of molecules and a complex hybrid nanoparticle confirm the viability and usefulness of the proposed approach.
gr-owc: An Open Source GNURadio-Based Toolkit for Optical Wireless Communications
Authors: Arsalan Ahmed; Michael B. Rahaim
Date: 2020-03-01 00:00:00
Proceeding: https://ieeexplore.ieee.org/document/9606271 2020 3

Research in the field of optical wireless communications (OWC) has increased significantly over the past decade. As new researchers begin to explore this field, there are various hurdles related to developing a research program in OWC. This is particularly noticeable when comparing the breadth of available RF toolkits to the limited number of openly available OWC tools. Furthermore, many of the available OWC resources are standalone with limited capabilities for higher layer integration. In this work, we present gr-owc, an open source out-of-tree module for the widely used GNURadio signal processing toolkit. Within gr-owc, we have developed signal processing blocks for OWC channel simulation and common OWC modulation/demodulation techniques. We demonstrate use of this module to simulate a multi-cell/multi-user OWC network. We also describe how gr-owc signal processing blocks can be deployed in a physical implementation using Software Defined Radio (SDR) hardware, namely the universal software radio peripheral (USRP). Furthermore, methods for contribution to the project are described since the project is designed for continued development internally and with the goal of engaging the broader OWC research community.
Numerical study of halo concentrations in dark-energy cosmologies
Authors: Klaus Dolag, Matthias Bartelmann, Francesca Perrotta, Carlo Baccigalupi, Lauro Moscardini, Massimo Meneghetti, Giuseppe Tormen
Date: 2003-09-29 00:00:00

We study the concentration parameters, their mass dependence and redshift evolution, of dark-matter halos in different dark-energy cosmologies with constant and time-variable equation of state, and compare them with "standard'' Lambda-CDM and OCDM models. We find that previously proposed algorithms for predicting halo concentrations can be well adapted to dark-energy models. When centred on the analytically expected values, halo concentrations show a log-normal distribution with a uniform standard deviation of ~0.2. The dependence of averaged halo concentrations on mass and redshift permits a simple fit of the form (1+z) c=c0 (M/M0)^a, with a~-0.1 throughout. We find that the cluster concentration depends on the dark energy equation of state at the cluster formation redshift z_{coll} through the linear growth factor D_+(z_{coll}). As a simple correction accounting for dark-energy cosmologies, we propose scaling c0 from Lambda-CDM with the ratio of linear growth factors, c0 -> c0 D_+(z_{coll})/D_{+,Lambda-CDM}(z_{coll}).
DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps
Authors: Colin Decourt, Rufin VanRullen, Didier Salle, Thomas Oberlin
Date: 2022-07-19 00:00:00
Proceeding: 2022 IEEE Intelligent Vehicles Symposium (IV) 2022 7
Tasks: 2D Object Detection, Object, object-detection, Object Detection, Radar Object Detection

Due to the small number of raw data automotive radar datasets and the low resolution of such radar sensors, automotive radar object detection has been little explored with deep learning models in comparison to camera and lidar-based approaches. However, radars are low-cost sensors able to accurately sense surrounding object characteristics (e.g., distance, radial velocity, direction of arrival, radar cross-section) regardless of weather conditions (e.g., rain, snow, fog). Recent open-source datasets such as CARRADA, RADDet or CRUW have opened up research on several topics ranging from object classification to object detection and segmentation. In this paper, we present DAROD, an adaptation of Faster R-CNN object detector for automotive radar on the range-Doppler spectra. We propose a light architecture for features extraction, which shows an increased performance compare to heavier vision-based backbone architectures. Our models reach respectively an mAP@0.5 of 55.83 and 46.57 on CARRADA and RADDet datasets, outperforming competing methods.